Rifle UnifiedThis script is designed for use on 30-second charts of Dow Jones-related symbols (YM, MYM, US30). It provides automated buy and sell signals using a combination of price action, RSI (Relative Strength Index), and volume analysis. The script is intended for both live trading signals and backtesting, with configurable risk management and debugging features.
Core Functionality
1. Signal Generation Logic
Trigger: The algorithm looks for a sharp price move (drop or rise) of a user-defined threshold (default: 80 points) within a specified lookback window (default: 20 minutes).
Levels: It monitors for price drops below specific numerical levels ending in 23, 43, or 73 (e.g., 42223, 42273).
RSI Condition: When price falls below one of these levels and the RSI is below 30, the setup is considered active.
Buy Signal: A buy is triggered if, after setup:
Price rises back above the level,
The RSI rate of change (ROC) indicates exhaustion of the drop,
The current bar shows positive momentum.
2. Trade Management
Stop Loss & Take Profit: Configurable fixed or trailing stop loss and take profit levels are plotted and managed automatically.
Exit Signals: The script signals exit based on price action relative to these risk management levels.
3. Filters & Enhancements
Parabolic Move Filter: Prevents entries during extreme price moves.
Dead Cat Bounce Filter: Avoids false signals after sharp reversals.
Volume Filter: Optionally requires volume conditions for trade entries (especially for shorts).
Multiple Confirmation Layers : Includes checks for 5-minute RSI, momentum, and price retracement.
User Inputs & Customization
Trade Direction: Toggle between LONG and SHORT signal generation.
Trigger Settings: Adjust thresholds for price moves, lookback windows, RSI ROC, and volume requirements.
Trade Settings: Set take profit, stop loss, and trailing stop behavior.
Debug & Visualization: Enable or disable various plots, labels, and debug tables for in-depth analysis.
Backtesting: Integrated backtester with summary and detailed statistics tables.
Technical Features
Uses External Libraries: Relies on RifleShooterLib for core logic and BackTestLib for backtesting and statistics.
Multi-timeframe Analysis: Incorporates both 30-second and 5-minute RSI calculations.
Chart Annotations: Plots entry/exit points, risk levels, and debug information directly on the chart.
Alert Conditions: Built-in alert triggers for key events (initial move, stall, entry).
Intended Use
Markets: Dow Jones symbols (YM, MYM, US30, or US30 CFD).
Timeframe: 30-second chart.
Purpose: Automated signal generation for discretionary or algorithmic trading, with robust risk management and backtesting support.
Notable Customization & Extension Points
Momentum Calculation: Plans to replace the current momentum measure with "sqz momentum".
Displacement Logic: Future update to use "FVG concept" for displacement.
High-Contrast RSI: Optional visual enhancements for RSI extremes.
Time-based Stop: Consideration for adding a time-based stop mechanism.
This script is highly modular, with extensive user controls, and is suitable for both live trading and historical analysis of Dow Jones index movements
חפש סקריפטים עבור "backtest"
Price based concepts / quantifytools- Overview
Price based concepts incorporates a collection of multiple price action based concepts. Main component of the script is market structure, on top of which liquidity sweeps and deviations are built on, leaving imbalances the only standalone concept included. Each concept can be enabled/disabled separately for creating a selection of indications that one deems relevant for their purposes. Price based concepts are quantified using metrics that measure their expected behavior, such as historical likelihood of supportive price action for given market structure state and volume traded at liquidity sweeps. The concepts principally work on any chart, whether that is equities, currencies, cryptocurrencies or commodities, charts with volume data or no volume data. Essentially any asset that can be considered an ordinary speculative asset. The concepts also work on any timeframe, from second charts to monthly charts. None of the indications are repainted.
Market structure
Market structure is an analysis of support/resistance levels (pivots) and their position relative to each other. Market structure is considered to be bullish on a series of higher highs/higher lows and bearish on a series of lower highs/lower lows. Market structure shifts from bullish to bearish and vice versa on a break of the most recent pivot high/low, indicating weak ability to defend a key level from the dominating side. Supportive market structure typically provides lengthier and sustained trending environment, making it an ideal point of confluence for establishing directional bias for trades.
Liquidity sweeps
Liquidity sweeps are formed when price exceeds a pivot level that served as a provable level of demand once and is expected to display demand again when revisited. A simple way to look at liquidity sweeps is re-tests of untapped support/resistance levels.
Deviations
Deviations are formed when price exceeds a reference level (market structure shift level/liquidity sweep level) and shortly closes back in, leaving participating breakout traders in an awkward position. On further adverse movement, stuck breakout traders are forced to cover their underwater positions, creating ideal conditions for a lengthier reversal.
Imbalances
Imbalances, also known as fair value gaps or single prints, depict areas of inefficient and one sided transacting. Given inclination for markets to trade efficiently, price is naturally attracted to areas that lack proper participation, making imbalances ideal targets for entries or exits.
Key takeaways
- Price based concepts consists of market structure, liquidity sweeps, deviations and imbalances.
- Market structure shifts from bullish to bearish and vice versa on a break of the most recent pivot high/low, indicating weak ability to defend a key level from the dominating side.
- Supportive market structure tends to provide lengthier and sustained movement for the dominating side, making it an ideal foundation for establishing directional bias for trades.
- Liquidity sweeps are formed when price exceeds an untapped support/resistance level that served as a provable level of demand in the past, likely to show demand again when revisited.
- Deviations are formed when price exceeds a key level and shortly closes back in, leaving breakout traders in an awkward position. Further adverse movement compels trapped participants to cover their positions, creating ideal conditions for a reversal.
- Imbalances depict areas of inefficient and one sided transacting where price is naturally attracted to, making them ideal targets for entries or exits.
- Price based concepts are quantified using metrics that measure expected behavior, such as historical likelihood of supportive structure and volume traded at liquidity sweeps.
- For practical guide with practical examples, see last section.
Accessing script 🔑
See "Author's instructions" section, found at bottom of the script page.
Disclaimer
Price based concepts are not buy/sell signals, a standalone trading strategy or financial advice. They also do not substitute knowing how to trade. Example charts and ideas shown for use cases are textbook examples under ideal conditions, not guaranteed to repeat as they are presented. Price based concepts notify when a set of conditions are in place from a purely technical standpoint. Price based concepts should be viewed as one tool providing one kind of evidence, to be used in conjunction with other means of analysis.
Price based concepts are backtested using metrics that reasonably depict their expected behaviour, such as historical likelihood of supportive price movement on each market structure state. The metrics are not intended to be elaborate and perfect, but to serve as a general barometer for feedback created by the indications. Backtesting is done first and foremost to exclude scenarios where the concepts clearly don't work or work suboptimally, in which case they can't be considered as valid evidence. Even when the metrics indicate historical reactions of good quality, price impact can and inevitably does deviate from the expected. Past results do not guarantee future performance.
- Example charts
Chart #1 : BTCUSDT
Chart #2 : EURUSD
Chart #3 : ES futures
Chart #4 : NG futures
Chart #5 : Custom timeframes
- Concepts
Market structure
Knowing when price has truly pivoted is much harder than it might seem at first. In this script, pivots are determined using a custom formula based on volatility adjusted average price, a fundamentally different approach to the widely used highest/lowest price within X amount of bars. The script calculates average price within set period and adjusts it to volatility. Using this formula, the script determines when price has turned significantly enough and aggressively enough to constitute a relevant pivot, resulting in high accuracy while ruling out subjective decision making completely. Users can adjust length of market structure basis and sensitivity of volatility adjustment to achieve desired magnitude of pivots, reflected on the average swing metrics. Note that structure pivots are backpainted. Typical confirmation time for a pivot is within 2-3 bars after peak in price.
Market structure shifts
Generally speaking, traders consider market structure to have shifted when most recent structure high/low gets taken out, flipping underlying bias from one side over to the other (e.g. from bullish structure favoring upside to bearish structure favoring downside). However, there are many ways to approach the concept and the most popular method might not always be the best one. Users can determine their own market structure shift rules by choosing source (close, high, low, ohlc4 etc.) for determining structure shift. Users can also choose additional rules for structure shift, such as two consecutive closes above/below pivot to qualify as a valid shift.
Liquidity sweeps
Users can set maximum amount of bars liquidity levels are considered relevant from the moment of confirmed pivot. By default liquidity levels are monitored for 250 bars and then discarded. Level of tolerance can be set to anything between 100 and 1000 bars. For each liquidity sweep, relative volume (volume relative to volume moving average) is stored and added to average calculations for keeping track of typical depth of liquidity found at sweeps.
Deviations
Users can set a maximum amount of bars price has to spend above/below reference level to consider a deviation to be in place. By default set to 6 bars.
Imbalances
Users can set a desired fill point for imbalances using the following options: 100%, 75%, 50%, 25%. Users can also opt for excluding insignificant imbalances to attain better relevance in indications.
- Backtesting
Built-in backtesting is based on metrics that are considered to reasonably quantify expected behaviour of the main concept, market structure. Structure feedback is monitored using two metrics, supportive structure and structure period gain. Rest of the metrics provided are informational in nature, such as average swing and average relative volume traded at liquidity sweeps. Main purpose of the metrics is to form a general barometer for monitoring whether or not the concepts can be viewed as valid evidence. When the concepts are clearly not working optimally, one should adjust expectations accordingly or take action to improve performance. To make any valid conclusions of performance, sample size should also be significant enough to eliminate randomness effectively. If sample size on any individual chart is insufficient, one should view feedback scores on multiple correlating and comparable charts to make up for the loss.
For more elaborate backtesting, price based concepts can be used in any other script that has a source input, including fully mechanic strategies utilizing Tradingview's native backtester. Each concept and their indications (e.g. higher low on a bearish structure, lower high on a bullish structure, market structure shift up, imbalance filled etc.) can be utilized separately and used as a component in a backtesting script of your choice.
Structure feedback
Structure feedback is monitored using two metrics, likelihood of supportive price movement following a market structure shift and average structure period gain. If either of the two employed tests indicate failed reactions beyond a tolerable level, one should take action to improve feedback by adjusting the settings. If feedback metrics after adjusting the settings are still insufficient, the concepts are working suboptimally for the given chart and cannot be regarded as valid technical evidence as they are.
Metric #1 : Supportive structure
Each structure pivot is benchmarked against its respective structure shift level. Feedback is considered successful if structure pivot takes place above market structure shift level (in the case of bullish structure) or below market structure shift level (in the case of bearish structure). Structure feedback constitutes as one test indicating how often a market structure state results in price movement that can be considered supportive.
Metric #2 : Structure period gain
Each structure period is expected to present favorable appreciation, measured from one market structure shift level to another. E.g. bullish structure period gain is measured from market structure shift up level to market structure shift down level that ends the bullish structure period. Bearish structure is measured in a vice versa manner, from market structure shift down level to market structure shift up level that ends the bearish structure period. Feedback is considered successful if average structure period gain is supportive for a given structure (positive for bullish structure, negative for bearish structure).
Additional metrics
On top of structure feedback metrics, percentage gain for each swing (distance between a pivot to previous pivot) is recorded and stored to average calculations. Average swing calculations shed light on typical pivot magnitude for better understanding changes made in market structure settings. Average relative volume traded at liquidity sweep on the other hand gives a clue of depth of liquidity typically found on a sweeps.
Feedback scores
When market structure (basis for most concepts) is working optimally, quality threshold for both feedback metrics are met. By default, threshold for supportive structure is set to 66%, indicating valid feedback on 2/3 of backtesting periods on average. On top, average structure period gain needs to be positive (for bullish structures) and negative (for bearish structure) to qualify as valid feedback. When both tests are passed, a tick indicating valid feedback will appear next to feedback scores, otherwise an exclamation mark indicating suboptimal performance on either or both. If both or either test fail, market structure parameters need to be optimized for better performance or one needs to adjust expectations accordingly.
Verifying backtest calculations
Backtest metrics can be toggled on via input menu, separately for bullish and bearish structure. When toggled on, both cumulative and average counters used in backtesting will appear on "Data Window" tab. Calculation states are shown at a point in time where cursor is hovered. E.g. when hovering cursor on 4th of January 2021, backtest calculations as they were during this date will be shown.
- Alerts
Available alerts are the following.
- HH/HL/LH/LL/EQL/EQH on a bullish/bearish structure
- Bullish/bearish market structure shift
- Bullish/bearish imbalance created
- Bullish/bearish imbalance filled
- Bullish/bearish liquidity sweep
- Bullish/bearish deviation
- Visuals
Each concept can be enabled/disabled separately for creating a selection indications that one deems relevant for their purposes. On top, each concept has a stealth visual option for more discreet visuals.
Unfilled imbalances and untapped liquidity levels can be extended forward to better gauge key areas of interest.
Liquidity sweeps have an intensity option, using color and width to visualize volume traded at sweep.
Market structure states and market structure shifts can be visualized as chart color.
Metric table can be offsetted horizontally or vertically from any four corners of the chart, allowing space for tables from other scripts.
Table sizes, label sizes and colors are fully customizable via input menu.
- Practical guide
The basic idea behind market structure is that a side (bulls or bears) have shown significant weakness on a failed attempt to defend a key level (most recent pivot high/low). In the same way, a side has shown significant strength on a successful attempt to break through a key level. This successful break through a key level often leads to sustained lengthier movement for the side that provably has the upper hand, making it an ideal tool for establishing directional bias.
Multi-timeframe view of market structure provides crucial guidance for analyzing market structure states on any individual timeframe. If higher timeframe market structure is bullish, it doesn't make sense to expect contradicting lower timeframe market structure to provide significant adverse movement, but rather a normal correction within a long term trend. In the same way, if lower timeframe market structure is in agreement with higher timeframe market structure, one can expect a reliable trending environment to ensue as multiple points of confluence are in place.
Bullish structure can be considered constructive on a series of higher highs and higher lows, indicating strong interest from bulls to sustain an uptrend. Vice versa is true for bearish structure, a series of lower highs and lower lows can be considered constructive. When structure does not indicate strong interest to maintain a supportive trend (lower highs on bullish structure, higher lows on bearish structure), a structure shift and a turn in trend might be nearing.
Market structure shifts are of great interest for breakout traders who position for continuation. Structure shifts can indeed be fertile ground for executing a breakout trade, but breakouts can easily turn into fakeouts that leave participants in an awkward position. When price moves further away from the underwater participants, potential for snowball effect of covering positions and driving price further away is elevated.
Liquidity sweeps as a concept is based on the premise that pivoting price is evidence of meaningful depth of liquidity found at/around pivot. If liquidity existed at a pivot once, it is likely to exist there in the future as well. When price grinds against liquidity, it is on a path of resistance rather than path of least resistance. Pivots are also attractive placements for traders to set stop-losses, which act as fuel for price to move to the opposite direction when swept and triggered.
Behind tightly formed pivots are potentially many stop-loss orders lulled in the comfort of having many layers of levels protecting their position. Compression that leaves such clusters of unswept liquidity rarely goes unvisited.
As markets strive for efficient and proper transacting most of the time, imbalances serve as points in price where price is naturally attracted to. However, imbalances too are contextual and sometimes one sided trading is rewarded with follow through, rather than with a fill. Identifying market regimes give further clue into what to expect from imbalances. In a ranging environment, one can expect imbalances to fill relatively quick, making them ideal targets for entries and exits.
On a strongly trending environment on the other hand imbalances tend to stick for a much longer time. In such environments continuation can be expected with no fills or only partial fills. Signs of demand preventing fill attempts serve as additional clues for imminent continuation.
Market Regime# MARKET REGIME IDENTIFICATION & TRADING SYSTEM
## Complete User Guide
---
## 📋 TABLE OF CONTENTS
1. (#overview)
2. (#regimes)
3. (#indicator-usage)
4. (#entry-signals)
5. (#exit-signals)
6. (#regime-strategies)
7. (#confluence)
8. (#backtesting)
9. (#optimization)
10. (#examples)
---
## OVERVIEW
### What This System Does
This is a **complete market regime identification and trading system** that:
1. **Identifies 6 distinct market regimes** automatically
2. **Adapts trading tactics** to each regime
3. **Provides high-probability entry signals** with confluence scoring
4. **Shows optimal exit points** for each trade
5. **Can be backtested** to validate performance
### Two Components Provided
1. **Indicator** (`market_regime_indicator.pine`)
- Visual regime identification
- Entry/exit signals on chart
- Dynamic support/resistance
- Info tables with live data
- Use for manual trading
2. **Strategy** (`market_regime_strategy.pine`)
- Fully automated backtestable version
- Same logic as indicator
- Position sizing and risk management
- Performance metrics
- Use for backtesting and automation
---
## THE 6 MARKET REGIMES
### 1. 🟢 BULL TRENDING
**Characteristics:**
- Strong uptrend
- Price above SMA50 and SMA200
- ADX > 25 (strong trend)
- Higher highs and higher lows
- DI+ > DI- (bullish momentum)
**What It Means:**
- Market has clear upward direction
- Buyers in control
- Pullbacks are buying opportunities
- Strongest regime for long positions
**How to Trade:**
- ✅ **BUY dips to EMA20 or SMA20**
- ✅ Enter when RSI < 60 on pullback
- ✅ Hold through minor corrections
- ❌ Don't short against the trend
- ❌ Don't sell too early
**Expected Behavior:**
- Pullbacks are shallow (5-10%)
- Bounces are strong
- Support at moving averages holds
- Volume increases on rallies
---
### 2. 🔴 BEAR TRENDING
**Characteristics:**
- Strong downtrend
- Price below SMA50 and SMA200
- ADX > 25 (strong trend)
- Lower highs and lower lows
- DI- > DI+ (bearish momentum)
**What It Means:**
- Market has clear downward direction
- Sellers in control
- Rallies are selling opportunities
- Strongest regime for short positions
**How to Trade:**
- ✅ **SELL rallies to EMA20 or SMA20**
- ✅ Enter when RSI > 40 on bounce
- ✅ Hold through minor bounces
- ❌ Don't buy against the trend
- ❌ Don't cover shorts too early
**Expected Behavior:**
- Rallies are weak (5-10%)
- Selloffs are strong
- Resistance at moving averages holds
- Volume increases on declines
---
### 3. 🔵 BULL RANGING
**Characteristics:**
- Bullish bias but consolidating
- Price near or above SMA50
- ADX < 20 (weak trend)
- Trading in range
- Choppy price action
**What It Means:**
- Uptrend is pausing
- Accumulation phase
- Support and resistance zones clear
- Lower volatility
**How to Trade:**
- ✅ **BUY at support zone**
- ✅ Enter when RSI < 40
- ✅ Take profits at resistance
- ⚠️ Smaller position sizes
- ⚠️ Tighter stops
**Expected Behavior:**
- Range-bound oscillations
- Support bounces repeatedly
- Resistance rejections common
- Eventually breaks higher (usually)
---
### 4. 🟠 BEAR RANGING
**Characteristics:**
- Bearish bias but consolidating
- Price near or below SMA50
- ADX < 20 (weak trend)
- Trading in range
- Choppy price action
**What It Means:**
- Downtrend is pausing
- Distribution phase
- Support and resistance zones clear
- Lower volatility
**How to Trade:**
- ✅ **SELL at resistance zone**
- ✅ Enter when RSI > 60
- ✅ Take profits at support
- ⚠️ Smaller position sizes
- ⚠️ Tighter stops
**Expected Behavior:**
- Range-bound oscillations
- Resistance holds repeatedly
- Support bounces are weak
- Eventually breaks lower (usually)
---
### 5. ⚪ CONSOLIDATION
**Characteristics:**
- No clear direction
- Range compression
- Very low ADX (< 15 often)
- Price inside tight range
- Neutral sentiment
**What It Means:**
- Market is coiling
- Building energy for next move
- Indecision between buyers/sellers
- Calm before the storm
**How to Trade:**
- ✅ **WAIT for breakout direction**
- ✅ Enter on high-volume breakout
- ✅ Direction becomes clear
- ❌ Don't trade inside the range
- ❌ Avoid choppy scalping
**Expected Behavior:**
- Narrow range
- Low volume
- False breakouts possible
- Explosive move when it breaks
---
### 6. 🟣 CHAOS (High Volatility)
**Characteristics:**
- Extreme volatility
- No clear direction
- Erratic price swings
- ATR > 2x average
- Unpredictable
**What It Means:**
- Market panic or euphoria
- News-driven moves
- Emotion dominates logic
- Highest risk environment
**How to Trade:**
- ❌ **STAY OUT!**
- ❌ No positions
- ❌ Wait for stability
- ✅ Protect existing positions
- ✅ Reduce risk
**Expected Behavior:**
- Large intraday swings
- Gaps up/down
- Stop hunts
- Whipsaws
- Eventually calms down
---
## INDICATOR USAGE
### Visual Elements
#### 1. Background Colors
- **Light Green** = Bull Trending (go long)
- **Light Red** = Bear Trending (go short)
- **Light Teal** = Bull Ranging (buy dips)
- **Light Orange** = Bear Ranging (sell rallies)
- **Light Gray** = Consolidation (wait)
- **Purple** = Chaos (stay out!)
#### 2. Regime Labels
- Appear when regime changes
- Show new regime name
- Positioned at highs (bullish) or lows (bearish)
#### 3. Entry Signals
- **Green "LONG"** labels = Buy here
- **Red "SHORT"** labels = Sell here
- Number shows confluence score (X/5 signals)
- Hover for details (stop, target, RSI, etc.)
#### 4. Exit Signals
- **Orange "EXIT LONG"** = Close long position
- **Orange "EXIT SHORT"** = Close short position
- Shows exit reason in tooltip
#### 5. Support/Resistance Lines
- **Green line** = Dynamic support (buy zone)
- **Red line** = Dynamic resistance (sell zone)
- Adapts to regime automatically
#### 6. Moving Averages
- **Blue** = SMA 20 (short-term trend)
- **Orange** = SMA 50 (medium-term trend)
- **Purple** = SMA 200 (long-term trend)
### Information Tables
#### Top Right Table (Main Info)
Shows real-time market conditions:
- **Current Regime** - What regime we're in
- **Bias** - Long, Short, Breakout, or Stay Out
- **ADX** - Trend strength (>25 = strong)
- **Trend** - Strong, Moderate, or Weak
- **Volatility** - High or Normal
- **Vol Ratio** - Current vs average volatility
- **RSI** - Momentum (>70 overbought, <30 oversold)
- **vs SMA50/200** - Price position relative to MAs
- **Support/Resistance** - Exact price levels
- **Long/Short Signals** - Confluence scores (X/5)
#### Bottom Right Table (Regime Guide)
Quick reference for each regime:
- What action to take
- What strategy to use
- Color-coded for quick identification
---
## ENTRY SIGNALS EXPLAINED
### Confluence Scoring System (5 Factors)
Each entry signal is scored 0-5 based on how many factors align:
#### For LONG Entries:
1. ✅ **Regime Alignment** - In Bull Trending or Bull Ranging
2. ✅ **RSI Pullback** - RSI between 35-50 (not overbought)
3. ✅ **Near Support** - Price within 2% of dynamic support
4. ✅ **MACD Turning Up** - Momentum shifting bullish
5. ✅ **Volume Confirmation** - Above average volume
#### For SHORT Entries:
1. ✅ **Regime Alignment** - In Bear Trending or Bear Ranging
2. ✅ **RSI Rejection** - RSI between 50-65 (not oversold)
3. ✅ **Near Resistance** - Price within 2% of dynamic resistance
4. ✅ **MACD Turning Down** - Momentum shifting bearish
5. ✅ **Volume Confirmation** - Above average volume
### Confluence Requirements
**Minimum Confluence** (default = 2):
- 2/5 = Entry signal triggered
- 3/5 = Good signal
- 4/5 = Strong signal
- 5/5 = Excellent signal (rare)
**Higher confluence = Higher probability = Better trades**
### Specific Entry Patterns
#### 1. Bull Trending Entry
```
Requirements:
- Regime = Bull Trending
- Price pulls back to EMA20
- Close above EMA20 (bounce)
- Up candle (close > open)
- RSI < 60
- Confluence ≥ 2
```
#### 2. Bear Trending Entry
```
Requirements:
- Regime = Bear Trending
- Price rallies to EMA20
- Close below EMA20 (rejection)
- Down candle (close < open)
- RSI > 40
- Confluence ≥ 2
```
#### 3. Bull Ranging Entry
```
Requirements:
- Regime = Bull Ranging
- RSI < 40 (oversold)
- Price at or below support
- Up candle (reversal)
- Confluence ≥ 1 (more lenient)
```
#### 4. Bear Ranging Entry
```
Requirements:
- Regime = Bear Ranging
- RSI > 60 (overbought)
- Price at or above resistance
- Down candle (rejection)
- Confluence ≥ 1 (more lenient)
```
#### 5. Consolidation Breakout
```
Requirements:
- Regime = Consolidation
- Price breaks above/below range
- Volume > 1.5x average (explosive)
- Strong directional candle
```
---
## EXIT SIGNALS EXPLAINED
### Three Types of Exits
#### 1. Regime Change Exits (Automatic)
- **Long Exit**: Regime changes to Bear Trending or Chaos
- **Short Exit**: Regime changes to Bull Trending or Chaos
- **Reason**: Market character changed, strategy no longer valid
#### 2. Support/Resistance Break Exits
- **Long Exit**: Price breaks below support by 2%
- **Short Exit**: Price breaks above resistance by 2%
- **Reason**: Key level violated, trend may be reversing
#### 3. Momentum Exits
- **Long Exit**: RSI > 70 (overbought) AND down candle
- **Short Exit**: RSI < 30 (oversold) AND up candle
- **Reason**: Overextension, take profits
### Stop Loss & Take Profit
**Stop Loss** (Automatic in strategy):
- Placed at Entry - (ATR × 2)
- Adapts to volatility
- Protected from whipsaws
- Typically 2-4% for stocks, 5-10% for crypto
**Take Profit** (Automatic in strategy):
- Placed at Entry + (Stop Distance × R:R Ratio)
- Default 2.5:1 reward:risk
- Example: $2 risk = $5 reward target
- Allows winners to run
---
## TRADING EACH REGIME
### BULL TRENDING - Most Profitable Long Environment
**Strategy: Buy Every Dip**
**Entry Rules:**
1. Wait for pullback to EMA20 or SMA20
2. Look for RSI < 60
3. Enter when candle closes above MA
4. Confluence should be 2+
**Stop Loss:**
- Below the recent swing low
- Or 2 × ATR below entry
**Take Profit:**
- At previous high
- Or 2.5:1 R:R minimum
**Position Size:**
- Can use full size (2% risk)
- High win rate regime
**Example Trade:**
```
Price: $100, pulls back to $98 (EMA20)
Entry: $98.50 (close above EMA)
Stop: $96.50 (2 ATR)
Target: $103.50 (2.5:1)
Risk: $2, Reward: $5
```
---
### BEAR TRENDING - Most Profitable Short Environment
**Strategy: Sell Every Rally**
**Entry Rules:**
1. Wait for bounce to EMA20 or SMA20
2. Look for RSI > 40
3. Enter when candle closes below MA
4. Confluence should be 2+
**Stop Loss:**
- Above the recent swing high
- Or 2 × ATR above entry
**Take Profit:**
- At previous low
- Or 2.5:1 R:R minimum
**Position Size:**
- Can use full size (2% risk)
- High win rate regime
**Example Trade:**
```
Price: $100, rallies to $102 (EMA20)
Entry: $101.50 (close below EMA)
Stop: $103.50 (2 ATR)
Target: $96.50 (2.5:1)
Risk: $2, Reward: $5
```
---
### BULL RANGING - Buy Low, Sell High
**Strategy: Range Trading (Long Bias)**
**Entry Rules:**
1. Wait for price at support zone
2. Look for RSI < 40
3. Enter on reversal candle
4. Confluence should be 1-2+
**Stop Loss:**
- Below support zone
- Tighter than trending (1.5 ATR)
**Take Profit:**
- At resistance zone
- Don't hold through resistance
**Position Size:**
- Reduce to 1-1.5% risk
- Lower win rate than trending
**Example Trade:**
```
Range: $95-$105
Entry: $96 (at support, RSI 35)
Stop: $94 (below support)
Target: $104 (at resistance)
Risk: $2, Reward: $8 (4:1)
```
---
### BEAR RANGING - Sell High, Buy Low
**Strategy: Range Trading (Short Bias)**
**Entry Rules:**
1. Wait for price at resistance zone
2. Look for RSI > 60
3. Enter on rejection candle
4. Confluence should be 1-2+
**Stop Loss:**
- Above resistance zone
- Tighter than trending (1.5 ATR)
**Take Profit:**
- At support zone
- Don't hold through support
**Position Size:**
- Reduce to 1-1.5% risk
- Lower win rate than trending
**Example Trade:**
```
Range: $95-$105
Entry: $104 (at resistance, RSI 65)
Stop: $106 (above resistance)
Target: $96 (at support)
Risk: $2, Reward: $8 (4:1)
```
---
### CONSOLIDATION - Wait for Breakout
**Strategy: Breakout Trading**
**Entry Rules:**
1. Identify consolidation range
2. Wait for VOLUME SURGE (1.5x+ avg)
3. Enter on close outside range
4. Direction must be clear
**Stop Loss:**
- Opposite side of range
- Or 2 ATR
**Take Profit:**
- Measure range height, project it
- Example: $10 range = $10 move expected
**Position Size:**
- Reduce to 1% risk
- 50% false breakout rate
**Example Trade:**
```
Consolidation: $98-$102 (4-point range)
Breakout: $102.50 (high volume)
Entry: $103
Stop: $100 (back in range)
Target: $107 (4-point range projected)
Risk: $3, Reward: $4
```
---
### CHAOS - STAY OUT!
**Strategy: Preservation**
**What to Do:**
- ❌ NO new positions
- ✅ Close existing positions if near entry
- ✅ Tighten stops on profitable trades
- ✅ Reduce position sizes dramatically
- ✅ Wait for regime to stabilize
**Why It's Dangerous:**
- Stop hunts are common
- Whipsaws everywhere
- News-driven volatility
- No technical reliability
- Even "perfect" setups fail
**When Does It End:**
- Volatility ratio drops < 1.5
- ADX starts rising (direction appears)
- Price respects support/resistance again
- Usually 1-5 days
---
## CONFLUENCE SYSTEM
### How It Works
The system scores each potential entry on 5 factors. More factors aligning = higher probability.
### Confluence Requirements by Regime
**Trending Regimes** (strictest):
- Minimum 2/5 required
- 3/5 = Good
- 4-5/5 = Excellent
**Ranging Regimes** (moderate):
- Minimum 1-2/5 required
- 2/5 = Good
- 3+/5 = Excellent
**Consolidation** (breakout only):
- Volume is most critical
- Direction confirmation
- Less confluence needed
### Adjusting Minimum Confluence
**If too few signals:**
- Lower from 2 to 1
- More trades, lower quality
**If too many false signals:**
- Raise from 2 to 3
- Fewer trades, higher quality
**Recommendation:**
- Start at 2
- Adjust based on win rate
- Aim for 55-65% win rate
---
## STRATEGY BACKTESTING
### Loading the Strategy
1. Copy `market_regime_strategy.pine`
2. Open Pine Editor in TradingView
3. Paste and "Add to Chart"
4. Strategy Tester tab opens at bottom
### Initial Settings
```
Risk Per Trade: 2%
ATR Stop Multiplier: 2.0
Reward:Risk Ratio: 2.5
Trade Longs: ✓
Trade Shorts: ✓
Trade Trending Only: ✗ (test both)
Avoid Chaos: ✓
Minimum Confluence: 2
```
### What to Look For
**Good Results:**
- Win Rate: 50-60%
- Profit Factor: 1.8-2.5
- Net Profit: Positive
- Max Drawdown: <20%
- Consistent equity curve
**Warning Signs:**
- Win Rate: <45% (too many losses)
- Profit Factor: <1.5 (barely profitable)
- Max Drawdown: >30% (too risky)
- Erratic equity curve (unstable)
### Testing Different Regimes
**Test 1: Trending Only**
```
Trade Trending Only: ✓
Result: Higher win rate, fewer trades
```
**Test 2: All Regimes**
```
Trade Trending Only: ✗
Result: More trades, potentially lower win rate
```
**Test 3: Long Only**
```
Trade Longs: ✓
Trade Shorts: ✗
Result: Works in bull markets
```
**Test 4: Short Only**
```
Trade Longs: ✗
Trade Shorts: ✓
Result: Works in bear markets
```
---
## SETTINGS OPTIMIZATION
### Key Parameters to Adjust
#### 1. Risk Per Trade (Most Important)
- **0.5%** = Very conservative
- **1.0%** = Conservative (recommended for beginners)
- **2.0%** = Moderate (recommended)
- **3.0%** = Aggressive
- **5.0%** = Very aggressive (not recommended)
**Impact:** Higher risk = higher returns BUT bigger drawdowns
#### 2. Reward:Risk Ratio
- **2:1** = More wins needed, hit target faster
- **2.5:1** = Balanced (recommended)
- **3:1** = Fewer wins needed, hold longer
- **4:1** = Very patient, best in trending
**Impact:** Higher R:R = can have lower win rate
#### 3. Minimum Confluence
- **1** = More signals, lower quality
- **2** = Balanced (recommended)
- **3** = Fewer signals, higher quality
- **4** = Very selective
- **5** = Almost never triggers
**Impact:** Higher = fewer but better trades
#### 4. ADX Thresholds
- **Trending: 20-30** (default 25)
- Lower = detect trends earlier
- Higher = only strong trends
- **Ranging: 15-25** (default 20)
- Lower = identify ranging earlier
- Higher = only weak trends
#### 5. Trend Period (SMA)
- **20-50** = Short-term trends
- **50** = Medium-term (default, recommended)
- **100-200** = Long-term trends
**Impact:** Longer period = slower regime changes, more stable
### Optimization Workflow
**Step 1: Baseline**
- Use all default settings
- Test on 3+ years
- Record: Win Rate, PF, Drawdown
**Step 2: Risk Optimization**
- Test 1%, 1.5%, 2%, 2.5%
- Find best risk-adjusted return
- Balance profit vs drawdown
**Step 3: R:R Optimization**
- Test 2:1, 2.5:1, 3:1
- Check which maximizes profit factor
- Consider holding time
**Step 4: Confluence Optimization**
- Test 1, 2, 3
- Find sweet spot for win rate
- Aim for 55-65% win rate
**Step 5: Regime Filter**
- Test with/without trend filter
- Test with/without chaos filter
- Find what works for your asset
---
## REAL TRADING EXAMPLES
### Example 1: Bull Trending - SPY
**Setup:**
- Regime: BULL TRENDING
- Price pulls back from $450 to $445
- EMA20 at $444
- RSI drops to 45
- Confluence: 4/5
**Entry:**
- Price closes at $445.50 (above EMA20)
- LONG signal appears
- Enter at $445.50
**Risk Management:**
- Stop: $443 (2 ATR = $2.50)
- Target: $451.75 (2.5:1 = $6.25)
- Risk: $2.50 per share
- Position: 80 shares (2% of $10k = $200 risk)
**Outcome:**
- Price rallies to $452 in 3 days
- Target hit
- Profit: $6.50 × 80 = $520
- Return: 2.6 × risk (excellent)
---
### Example 2: Bear Ranging - AAPL
**Setup:**
- Regime: BEAR RANGING
- Range: $165-$175
- Price rallies to $174
- Resistance at $175
- RSI at 68
- Confluence: 3/5
**Entry:**
- Rejection candle at $174
- SHORT signal appears
- Enter at $173.50
**Risk Management:**
- Stop: $176 (above resistance)
- Target: $166 (support)
- Risk: $2.50
- Position: 80 shares
**Outcome:**
- Price drops to $167 in 2 days
- Target hit
- Profit: $6.50 × 80 = $520
- Return: 2.6 × risk
---
### Example 3: Consolidation Breakout - BTC
**Setup:**
- Regime: CONSOLIDATION
- Range: $28,000 - $30,000
- Compressed for 2 weeks
- Volume declining
**Breakout:**
- Price breaks $30,000
- Volume surges 200%
- Close at $30,500
- LONG signal
**Entry:**
- Enter at $30,500
**Risk Management:**
- Stop: $29,500 (back in range)
- Target: $32,000 (range height = $2k)
- Risk: $1,000
- Position: 0.2 BTC ($200 risk on $10k)
**Outcome:**
- Price runs to $33,000
- Target exceeded
- Profit: $2,500 × 0.2 = $500
- Return: 2.5 × risk
---
### Example 4: Avoiding Chaos - Tesla
**Setup:**
- Regime: BULL TRENDING
- LONG position from $240
- Elon tweets something crazy
- Regime changes to CHAOS
**Action:**
- EXIT signal appears
- Close position immediately
- Current price: $242 (small profit)
**Outcome:**
- Next 3 days: wild swings
- High $255, Low $230
- By staying out, avoided:
- Potential stop out
- Whipsaw losses
- Stress
**Result:**
- Small profit preserved
- Capital protected
- Re-enter when regime stabilizes
---
## ALERTS SETUP
### Available Alerts
1. **Bull Trending Regime** - Market goes bullish
2. **Bear Trending Regime** - Market goes bearish
3. **Chaos Regime** - High volatility, stay out
4. **Long Entry Signal** - Buy opportunity
5. **Short Entry Signal** - Sell opportunity
6. **Long Exit Signal** - Close long
7. **Short Exit Signal** - Close short
### How to Set Up
1. Click **⏰ (Alert)** icon in TradingView
2. Select **Condition**: Choose indicator + alert type
3. **Options**: Popup, Email, Webhook, etc.
4. **Message**: Customize notification
5. Click **Create**
### Recommended Alert Strategy
**For Active Traders:**
- Long Entry Signal
- Short Entry Signal
- Long Exit Signal
- Short Exit Signal
**For Position Traders:**
- Bull Trending Regime (enter longs)
- Bear Trending Regime (enter shorts)
- Chaos Regime (exit all)
**For Conservative:**
- Only regime change alerts
- Manually review entries
- More selective
---
## TIPS FOR SUCCESS
### 1. Start Small
- Paper trade first
- Then 0.5% risk
- Build to 1-2% over time
### 2. Follow the Regime
- Don't fight it
- Adapt your style
- Different tactics for each
### 3. Trust the Confluence
- 4-5/5 = Best trades
- 2-3/5 = Good trades
- 1/5 = Skip unless desperate
### 4. Respect Exits
- Don't hope and hold
- Cut losses quickly
- Take profits at targets
### 5. Avoid Chaos
- Seriously, just stay out
- Protect your capital
- Wait for clarity
### 6. Keep a Journal
- Record every trade
- Note regime and confluence
- Review weekly
- Learn patterns
### 7. Backtest Thoroughly
- 3+ years minimum
- Multiple market conditions
- Different assets
- Walk-forward test
### 8. Be Patient
- Best setups are rare
- 1-3 trades per week is normal
- Quality over quantity
- Compound over time
---
## COMMON QUESTIONS
**Q: How many trades per month should I expect?**
A: Depends on timeframe and settings. Daily chart: 5-15 trades/month. 4H chart: 15-30 trades/month.
**Q: What's a good win rate?**
A: 55-65% is excellent. 50-55% is good. Below 50% needs adjustment.
**Q: Should I trade all regimes?**
A: Beginners: Only trending. Intermediate: Trending + ranging. Advanced: All except chaos.
**Q: Can I use this on any timeframe?**
A: Best on Daily and 4H. Works on 1H with more noise. Not recommended <1H.
**Q: What if I'm in a trade and regime changes?**
A: Exit immediately (if using indicator) or let strategy handle it automatically.
**Q: How do I know if I'm over-optimizing?**
A: If results are perfect on one period but fail on another. Use walk-forward testing.
**Q: Should I always take 5/5 confluence trades?**
A: Yes, but they're rare (1-2/month). Don't wait only for these.
**Q: Can I combine this with other indicators?**
A: Yes, but keep it simple. RSI, MACD already included. Maybe add volume profile.
**Q: What assets work best?**
A: Liquid stocks, major crypto, futures. Avoid forex spot (use futures), penny stocks.
**Q: How long to hold positions?**
A: Trending: Days to weeks. Ranging: Hours to days. Breakout: Days. Let the regime guide you.
---
## FINAL THOUGHTS
This system gives you:
- ✅ Clear market context (regime)
- ✅ High-probability entries (confluence)
- ✅ Defined exits (automatic signals)
- ✅ Adaptable tactics (regime-specific)
- ✅ Backtestable results (strategy version)
**Success requires:**
- 📚 Understanding each regime
- 🎯 Following the signals
- 💪 Discipline to wait
- 🧠 Emotional control
- 📊 Proper risk management
**Start your journey:**
1. Load the indicator
2. Watch for 1 week (no trading)
3. Identify regime patterns
4. Paper trade for 1 month
5. Go live with small size
6. Scale up as you gain confidence
**Remember:** The market will always be here. There's no rush. Master one regime at a time, and you'll be profitable in all conditions!
Good luck! 🚀
Kernel Market Dynamics [WFO - MAB]Kernel Market Dynamics
⚛️ CORE INNOVATION: KERNEL-BASED DISTRIBUTION ANALYSIS
The Kernel Market Dynamics system represents a fundamental departure from traditional technical indicators. Rather than measuring price levels, momentum, or oscillator extremes, KMD analyzes the statistical distribution of market returns using advanced kernel methods from machine learning theory. This allows the system to detect when market behavior has fundamentally changed—not just when price has moved, but when the underlying probability structure has shifted.
The Distribution Hypothesis:
Traditional indicators assume markets move in predictable patterns. KMD assumes something more profound: markets exist in distinct distributional regimes , and profitable trading opportunities emerge during regime transitions . When the distribution of recent returns diverges significantly from the historical baseline, the market is restructuring—and that's when edge exists.
Maximum Mean Discrepancy (MMD):
At the heart of KMD lies a sophisticated statistical metric called Maximum Mean Discrepancy. MMD measures the distance between two probability distributions by comparing their representations in a high-dimensional feature space created by a kernel function.
The Mathematics:
Given two sets of normalized returns:
• Reference period (X) : Historical baseline (default 100 bars)
• Test period (Y) : Recent behavior (default 20 bars)
MMD is calculated as:
MMD² = E + E - 2·E
Where:
• E = Expected kernel similarity within reference period
• E = Expected kernel similarity within test period
• E = Expected cross-similarity between periods
When MMD is low : Test period behaves like reference (stable regime)
When MMD is high : Test period diverges from reference (regime shift)
The final MMD value is smoothed with EMA(5) to reduce single-bar noise while maintaining responsiveness to genuine distribution changes.
The Kernel Functions:
The kernel function defines how similarity is measured. KMD offers four mathematically distinct kernels, each with different properties:
1. RBF (Radial Basis Function / Gaussian):
• Formula: k(x,y) = exp(-d² / (2·σ²·scale))
• Properties: Most sensitive to distribution changes, smooth decision boundaries
• Best for: Clean data, clear regime shifts, low-noise markets
• Sensitivity: Highest - detects subtle changes
• Use case: Stock indices, major forex pairs, trending environments
2. Laplacian:
• Formula: k(x,y) = exp(-|d| / σ)
• Properties: Medium sensitivity, robust to moderate outliers
• Best for: Standard market conditions, balanced noise/signal
• Sensitivity: Medium - filters minor fluctuations
• Use case: Commodities, standard timeframes, general trading
3. Cauchy (Default - Most Robust):
• Formula: k(x,y) = 1 / (1 + d²/σ²)
• Properties: Heavy-tailed, highly robust to outliers and spikes
• Best for: Noisy markets, choppy conditions, crypto volatility
• Sensitivity: Lower - only major distribution shifts trigger
• Use case: Cryptocurrencies, illiquid markets, volatile instruments
4. Rational Quadratic:
• Formula: k(x,y) = (1 + d²/(2·α·σ²))^(-α)
• Properties: Tunable via alpha parameter, mixture of RBF kernels
• Alpha < 1.0: Heavy tails (like Cauchy)
• Alpha > 3.0: Light tails (like RBF)
• Best for: Adaptive use, mixed market conditions
• Use case: Experimental optimization, regime-specific tuning
Bandwidth (σ) Parameter:
The bandwidth controls the "width" of the kernel, determining sensitivity to return differences:
• Low bandwidth (0.5-1.5) : Narrow kernel, very sensitive
- Treats small differences as significant
- More MMD spikes, more signals
- Use for: Scalping, fast markets
• Medium bandwidth (1.5-3.0) : Balanced sensitivity (recommended)
- Filters noise while catching real shifts
- Professional-grade signal quality
- Use for: Day/swing trading
• High bandwidth (3.0-10.0) : Wide kernel, less sensitive
- Only major distribution changes register
- Fewer, stronger signals
- Use for: Position trading, trend following
Adaptive Bandwidth:
When enabled (default ON), bandwidth automatically scales with market volatility:
Effective_BW = Base_BW × max(0.5, min(2.0, 1 / volatility_ratio))
• Low volatility → Tighter bandwidth (0.5× base) → More sensitive
• High volatility → Wider bandwidth (2.0× base) → Less sensitive
This prevents signal flooding during wild markets and avoids signal drought during calm periods.
Why Kernels Work:
Kernel methods implicitly map data to infinite-dimensional space where complex, nonlinear patterns become linearly separable. This allows MMD to detect distribution changes that simpler statistics (mean, variance) would miss. For example:
• Same mean, different shape : Traditional metrics see nothing, MMD detects shift
• Same volatility, different skew : Oscillators miss it, MMD catches it
• Regime rotation : Price unchanged, but return distribution restructured
The kernel captures the entire distributional signature —not just first and second moments.
🎰 MULTI-ARMED BANDIT FRAMEWORK: ADAPTIVE STRATEGY SELECTION
Rather than forcing one strategy on all market conditions, KMD implements a Multi-Armed Bandit (MAB) system that learns which of seven distinct strategies performs best and dynamically selects the optimal approach in real-time.
The Seven Arms (Strategies):
Each arm represents a fundamentally different trading logic:
ARM 0 - MMD Regime Shift:
• Logic: Distribution divergence with directional bias
• Triggers: MMD > threshold AND direction_bias confirmed AND velocity > 5%
• Philosophy: Trade the regime transition itself
• Best in: Volatile shifts, breakout moments, crisis periods
• Weakness: False alarms in choppy consolidation
ARM 1 - Trend Following:
• Logic: Aligned EMAs with strong ADX
• Triggers: EMA(9) > EMA(21) > EMA(50) AND ADX > 25
• Philosophy: Ride established momentum
• Best in: Strong trending regimes, directional markets
• Weakness: Late entries, whipsaws at reversals
ARM 2 - Breakout:
• Logic: Bollinger Band breakouts with volume
• Triggers: Price crosses BB outer band AND volume > 1.2× average
• Philosophy: Capture volatility expansion events
• Best in: Range breakouts, earnings, news events
• Weakness: False breakouts in ranging markets
ARM 3 - RSI Mean Reversion:
• Logic: RSI extremes with reversal confirmation
• Triggers: RSI < 30 with uptick OR RSI > 70 with downtick
• Philosophy: Fade overbought/oversold extremes
• Best in: Ranging markets, mean-reverting instruments
• Weakness: Fails in strong trends, catches falling knives
ARM 4 - Z-Score Statistical Reversion:
• Logic: Price deviation from 50-period mean
• Triggers: Z-score < -2 (oversold) OR > +2 (overbought) with reversal
• Philosophy: Statistical bounds reversion
• Best in: Stable volatility regimes, pairs trading
• Weakness: Trend continuation through extremes
ARM 5 - ADX Momentum:
• Logic: Strong directional movement with acceleration
• Triggers: ADX > 30 with DI+ or DI- strengthening
• Philosophy: Momentum begets momentum
• Best in: Trending with increasing velocity
• Weakness: Late exits, momentum exhaustion
ARM 6 - Volume Confirmation:
• Logic: OBV trend + volume spike + candle direction
• Triggers: OBV > EMA(20) AND volume > average AND bullish candle
• Philosophy: Follow institutional money flow
• Best in: Liquid markets with reliable volume
• Weakness: Manipulated volume, thin markets
Q-Learning with Rewards:
Each arm maintains a Q-value representing its expected reward. After every bar, the system calculates a reward based on the arm's signal and actual price movement:
Reward Calculation:
If arm signaled LONG:
reward = (close - close ) / close
If arm signaled SHORT:
reward = -(close - close ) / close
If arm signaled NEUTRAL:
reward = 0
Penalty multiplier: If loss > 0.5%, reward × 1.3 (punish big losses harder)
Q-Value Update (Exponential Moving Average):
Q_new = Q_old + α × (reward - Q_old)
Where α (learning rate, default 0.08) controls adaptation speed:
• Low α (0.01-0.05): Slow, stable learning
• Medium α (0.06-0.12): Balanced (recommended)
• High α (0.15-0.30): Fast, reactive learning
This gradually shifts Q-values toward arms that generate positive returns and away from losing arms.
Arm Selection Algorithms:
KMD offers four mathematically distinct selection strategies:
1. UCB1 (Upper Confidence Bound) - Recommended:
Formula: Select arm with max(Q_i + c·√(ln(t)/n_i))
Where:
• Q_i = Q-value of arm i
• c = exploration constant (default 1.5)
• t = total pulls across all arms
• n_i = pulls of arm i
Philosophy: Balance exploitation (use best arm) with exploration (try uncertain arms). The √(ln(t)/n_i) term creates an "exploration bonus" that decreases as an arm gets more pulls, ensuring all arms get sufficient testing.
Theoretical guarantee: Logarithmic regret bound - UCB1 provably converges to optimal arm selection over time.
2. UCB1-Tuned (Variance-Aware UCB):
Formula: Select arm with max(Q_i + √(ln(t)/n_i × min(0.25, V_i + √(2·ln(t)/n_i))))
Where V_i = variance of rewards for arm i
Philosophy: Incorporates reward variance into exploration. Arms with high variance (unpredictable) get less exploration bonus, focusing effort on stable performers.
Better bounds than UCB1 in practice, slightly more conservative exploration.
3. Epsilon-Greedy (Simple Random):
Algorithm:
With probability ε: Select random arm (explore)
With probability 1-ε: Select highest Q-value arm (exploit)
Default ε = 0.10 (10% exploration, 90% exploitation)
Philosophy: Simplest algorithm, easy to understand. Random exploration ensures all arms stay updated but may waste time on clearly bad arms.
4. Thompson Sampling (Bayesian):
The most sophisticated selection algorithm, using true Bayesian probability.
Each arm maintains Beta distribution parameters:
• α (alpha) = successes + 1
• β (beta) = failures + 1
Selection Process:
1. Sample θ_i ~ Beta(α_i, β_i) for each arm using Marsaglia-Tsang Gamma sampler
2. Select arm with highest sample: argmax_i(θ_i)
3. After reward, update:
- If reward > 0: α += |reward| × 100 (increment successes)
- If reward < 0: β += |reward| × 100 (increment failures)
Why Thompson Sampling Works:
The Beta distribution naturally represents uncertainty about an arm's true win rate. Early on with few trials, the distribution is wide (high uncertainty), leading to more exploration. As evidence accumulates, it narrows around the true performance, naturally shifting toward exploitation.
Unlike UCB which uses deterministic confidence bounds, Thompson Sampling is probabilistic—it samples from the posterior distribution of each arm's success rate, providing automatic exploration/exploitation balance without tuning.
Comparison:
• UCB1: Deterministic, guaranteed regret bounds, requires tuning exploration constant
• Thompson: Probabilistic, natural exploration, no tuning required, best empirical performance
• Epsilon-Greedy: Simplest, consistent exploration %, less efficient
• UCB1-Tuned: UCB1 + variance awareness, best for risk-averse
Exploration Constant (c):
For UCB algorithms, this multiplies the exploration bonus:
• Low c (0.5-1.0): Strongly prefer proven arms, rare exploration
• Medium c (1.2-1.8): Balanced (default 1.5)
• High c (2.0-3.0): Frequent exploration, diverse arm usage
Higher exploration constant in volatile/unstable markets, lower in stable trending environments.
🔬 WALK-FORWARD OPTIMIZATION: PREVENTING OVERFITTING
The single biggest problem in algorithmic trading is overfitting—strategies that look amazing in backtest but fail in live trading because they learned noise instead of signal. KMD's Walk-Forward Optimization system addresses this head-on.
How WFO Works:
The system divides time into repeating cycles:
1. Training Window (default 500 bars): Learn arm Q-values on historical data
2. Testing Window (default 100 bars): Validate on unseen "future" data
Training Phase:
• All arms accumulate rewards and update Q-values normally
• Q_train tracks in-sample performance
• System learns which arms work on historical data
Testing Phase:
• System continues using arms but tracks separate Q_test metrics
• Counts trades per arm (N_test)
• Testing performance is "out-of-sample" relative to training
Validation Requirements:
An arm is only "validated" (approved for live use) if:
1. N_test ≥ Minimum Trades (default 10): Sufficient statistical sample
2. Q_test > 0 : Positive out-of-sample performance
Arms that fail validation are blocked from generating signals, preventing the system from trading strategies that only worked on historical data.
Performance Decay:
At the end of each WFO cycle, all Q-values decay exponentially:
Q_new = Q_old × decay_rate (default 0.95)
This ensures old performance doesn't dominate forever. An arm that worked 10 cycles ago but fails recently will eventually lose influence.
Decay Math:
• 0.95 decay after 10 periods → 0.95^10 = 0.60 (40% forgotten)
• 0.90 decay after 10 periods → 0.90^10 = 0.35 (65% forgotten)
Fast decay (0.80-0.90): Quick adaptation, forgets old patterns rapidly
Slow decay (0.96-0.99): Stable, retains historical knowledge longer
WFO Efficiency Metric:
The key metric revealing overfitting:
Efficiency = (Q_test / Q_train) for each validated arm, averaged
• Efficiency > 0.8 : Excellent - strategies generalize well (LOW overfit risk)
• Efficiency 0.5-0.8 : Acceptable - moderate generalization (MODERATE risk)
• Efficiency < 0.5 : Poor - strategies curve-fitted to history (HIGH risk)
If efficiency is low, the system has learned noise. Training performance was good but testing (forward) performance is weak—classic overfitting.
The dashboard displays real-time WFO efficiency, allowing users to gauge system robustness. Low efficiency should trigger parameter review or reduced position sizing.
Why WFO Matters:
Consider two scenarios:
Scenario A - No WFO:
• Arm 3 (RSI Reversion) shows Q-value of 0.15 on all historical data
• System trades it aggressively
• Reality: It only worked during one specific ranging period
• Live trading: Fails because market has trended since backtest
Scenario B - With WFO:
• Arm 3 shows Q_train = 0.15 (good in training)
• But Q_test = -0.05 (loses in testing) with 12 test trades
• N_test ≥ 10 but Q_test < 0 → Arm BLOCKED
• System refuses to trade it despite good backtest
• Live trading: Protected from false strategy
WFO ensures only strategies that work going forward get used, not just strategies that fit the past.
Optimal Window Sizing:
Training Window:
• Too short (100-300): May learn recent noise, insufficient data
• Too long (1000-2000): May include obsolete market regimes
• Recommended: 4-6× testing window (default 500)
Testing Window:
• Too short (50-80): Insufficient validation, high variance
• Too long (300-500): Delayed adaptation to regime changes
• Recommended: 1/5 to 1/4 of training (default 100)
Minimum Trades:
• Too low (5-8): Statistical noise, lucky runs validate
• Too high (30-50): Many arms never validate, system rarely trades
• Recommended: 10-15 (default 10)
⚖️ WEIGHTED CONFLUENCE SYSTEM: MULTI-FACTOR SIGNAL QUALITY
Not all signals are created equal. KMD implements a sophisticated 100-point quality scoring system that combines eight independent factors with different importance weights.
The Scoring Framework:
Each potential signal receives a quality score from 0-100 by accumulating points from aligned factors:
CRITICAL FACTORS (20 points each):
1. Bandit Arm Alignment (20 points):
• Full points if selected arm's signal matches trade direction
• Zero points if arm disagrees
• Weight: Highest - the bandit selected this arm for a reason
2. MMD Regime Quality (20 points):
• Requires: MMD > dynamic threshold AND directional bias confirmed
• Scaled by MMD percentile (how extreme vs history)
• If MMD in top 10% of history: 100% of 20 points
• If MMD at 50th percentile: 50% of 20 points
• Weight: Highest - distribution shift is the core signal
HIGH IMPACT FACTORS (15 points each):
3. Trend Alignment (15 points):
• Full points if EMA(9) > EMA(21) > EMA(50) for longs (inverse for shorts)
• Scaled by ADX strength:
- ADX > 25: 100% (1.0× multiplier) - strong trend
- ADX 20-25: 70% (0.7× multiplier) - moderate trend
- ADX < 20: 40% (0.4× multiplier) - weak trend
• Weight: High - trend is friend, alignment increases probability
4. Volume Confirmation (15 points):
• Requires: OBV > EMA(OBV, 20) aligned with direction
• Scaled by volume ratio: vol_current / vol_average
- Volume 1.5×+ average: 100% of points (institutional participation)
- Volume 1.0-1.5× average: 67% of points (above average)
- Volume below average: 0 points (weak conviction)
• Weight: High - volume validates price moves
MODERATE FACTORS (10 points each):
5. Market Structure (10 points):
• Full points (10) if bullish structure (higher highs, higher lows) for longs
• Partial points (6) if near support level (within 1% of swing low)
• Similar logic inverted for bearish trades
• Weight: Moderate - structure context improves entries
6. RSI Positioning (10 points):
• For long signals:
- RSI < 50: 100% of points (1.0× multiplier) - room to run
- RSI 50-60: 60% of points (0.6× multiplier) - neutral
- RSI 60-70: 30% of points (0.3× multiplier) - elevated
- RSI > 70: 0 points (0× multiplier) - overbought
• Inverse for short signals
• Weight: Moderate - momentum context, not primary signal
BONUS FACTORS (10 points each):
7. Divergence (10 points):
• Full 10 points if bullish divergence detected for long (or bearish for short)
• Zero points otherwise
• Weight: Bonus - leading indicator, adds confidence when present
8. Multi-Timeframe Confirmation (10 points):
• Full 10 points if higher timeframe aligned (HTF EMA trending same direction, RSI supportive)
• Zero points if MTF disabled or HTF opposes
• Weight: Bonus - macro context filter, prevents counter-trend disasters
Total Maximum: 110 points (20+20+15+15+10+10+10+10)
Signal Quality Calculation:
Quality Score = (Accumulated_Points / Maximum_Possible) × 100
Where Maximum_Possible = 110 points if all factors active, adjusts if MTF disabled.
Example Calculation:
Long signal candidate:
• Bandit Arm: +20 (arm signals long)
• MMD Quality: +16 (MMD high, 80th percentile)
• Trend: +11 (EMAs aligned, ADX = 22 → 70% × 15)
• Volume: +10 (OBV rising, vol 1.3× avg → 67% × 15 = 10)
• Structure: +10 (higher lows forming)
• RSI: +6 (RSI = 55 → 60% × 10)
• Divergence: +0 (none present)
• MTF: +10 (HTF bullish)
Total: 83 / 110 × 100 = 75.5% quality score
This is an excellent quality signal - well above threshold (default 60%).
Quality Thresholds:
• Score 80-100 : Exceptional setup - all factors aligned
• Score 60-80 : High quality - most factors supportive (default minimum)
• Score 40-60 : Moderate - mixed confluence, proceed with caution
• Score 20-40 : Weak - minimal support, likely filtered out
• Score 0-20 : Very weak - almost certainly blocked
The minimum quality threshold (default 60) is the gatekeeper. Only signals scoring above this value can trigger trades.
Dynamic Threshold Adjustment:
The system optionally adjusts the threshold based on historical signal distribution:
If Dynamic Threshold enabled:
Recent_MMD_Mean = SMA(MMD, 50)
Recent_MMD_StdDev = StdDev(MMD, 50)
Dynamic_Threshold = max(Base_Threshold × 0.5,
min(Base_Threshold × 2.0,
MMD_Mean + MMD_StdDev × 0.5))
This auto-calibrates to market conditions:
• Quiet markets (low MMD): Threshold loosens (0.5× base)
• Active markets (high MMD): Threshold tightens (2× base)
Signal Ranking Filter:
When enabled, the system tracks the last 100 signal quality scores and only fires signals in the top percentile.
If Ranking Percentile = 75%:
• Collect last 100 signal scores in memory
• Sort ascending
• Threshold = Score at 75th percentile position
• Only signals ≥ this threshold fire
This ensures you're only taking the cream of the crop —top 25% of signals by quality, not every signal that technically qualifies.
🚦 SIGNAL GENERATION: TRANSITION LOGIC & COOLDOWNS
The confluence system determines if a signal qualifies , but the signal generation logic controls when triangles appear on the chart.
Core Qualification:
For a LONG signal to qualify:
1. Bull quality score ≥ signal threshold (default 60)
2. Selected arm signals +1 (long)
3. Cooldown satisfied (bars since last signal ≥ cooldown period)
4. Drawdown protection OK (current drawdown < pause threshold)
5. MMD ≥ 80% of dynamic threshold (slight buffer below full threshold)
For a SHORT signal to qualify:
1. Bear quality score ≥ signal threshold
2. Selected arm signals -1 (short)
3-5. Same as long
But qualification alone doesn't trigger a chart signal.
Three Signal Modes:
1. RESPONSIVE (Default - Recommended):
Signals appear on:
• Fresh qualification (wasn't qualified last bar, now is)
• Direction reversal (was qualified short, now qualified long)
• Quality improvement (already qualified, quality jumps 25%+ during EXTREME regime)
This mode shows new opportunities and significant upgrades without cluttering the chart with repeat signals.
2. TRANSITION ONLY:
Signals appear on:
• Fresh qualification only
• Direction reversal only
This is the cleanest mode - signals only when first qualifying or when flipping direction. Misses re-entries if quality improves mid-regime.
3. CONTINUOUS:
Signals appear on:
• Every bar that qualifies
Testing/debugging mode - shows all qualified bars. Very noisy but useful for understanding when system wants to trade.
Cooldown System:
Prevents signal clustering and overtrading by enforcing minimum bars between signals.
Base Cooldown: User-defined (default 5 bars)
Adaptive Cooldown (Optional):
If enabled, cooldown scales with volatility:
Effective_Cooldown = Base_Cooldown × volatility_multiplier
Where:
ATR_Pct = ATR(14) / Close × 100
Volatility_Multiplier = max(0.5, min(3.0, ATR_Pct / 2.0))
• Low volatility (ATR 1%): Multiplier ~0.5× → Cooldown = 2-3 bars (tight)
• Medium volatility (ATR 2%): Multiplier 1.0× → Cooldown = 5 bars (normal)
• High volatility (ATR 4%+): Multiplier 2.0-3.0× → Cooldown = 10-15 bars (wide)
This prevents excessive trading during wild swings while allowing more signals during calm periods.
Regime Filter:
Three modes controlling which regimes allow trading:
OFF: Trade in any regime (STABLE, TRENDING, SHIFTING, ELEVATED, EXTREME)
SMART (Recommended):
• Regime score = 1.0 for SHIFTING, ELEVATED (optimal)
• Regime score = 0.8 for TRENDING (acceptable)
• Regime score = 0.5 for EXTREME (too chaotic)
• Regime score = 0.2 for STABLE (too quiet)
Quality scores are multiplied by regime score. A 70% quality signal in STABLE regime becomes 70% × 0.2 = 14% → blocked.
STRICT:
• Regime score = 1.0 for SHIFTING, ELEVATED only
• Regime score = 0.0 for all others → hard block
Only trades during optimal distribution shift regimes.
Drawdown Protection:
If current equity drawdown exceeds pause threshold (default 8%), all signals are blocked until equity recovers.
This circuit breaker prevents compounding losses during adverse conditions or broken market structure.
🎯 RISK MANAGEMENT: ATR-BASED STOPS & TARGETS
Every signal generates volatility-normalized stop loss and target levels displayed as boxes on the chart.
Stop Loss Calculation:
Stop_Distance = ATR(14) × ATR_Multiplier (default 1.5)
For LONG: Stop = Entry - Stop_Distance
For SHORT: Stop = Entry + Stop_Distance
The stop is placed 1.5 ATRs away from entry by default, adapting automatically to instrument volatility.
Target Calculation:
Target_Distance = Stop_Distance × Risk_Reward_Ratio (default 2.0)
For LONG: Target = Entry + Target_Distance
For SHORT: Target = Entry - Target_Distance
Default 2:1 risk/reward means target is twice as far as stop.
Example:
• Price: $100
• ATR: $2
• ATR Multiplier: 1.5
• Risk/Reward: 2.0
LONG Signal:
• Entry: $100
• Stop: $100 - ($2 × 1.5) = $97.00 (-$3 risk)
• Target: $100 + ($3 × 2.0) = $106.00 (+$6 reward)
• Risk/Reward: $3 risk for $6 reward = 1:2 ratio
Target/Stop Box Lifecycle:
Boxes persist for a lifetime (default 20 bars) OR until an opposite signal fires, whichever comes first. This provides visual reference for active trade levels without permanent chart clutter.
When a new opposite-direction signal appears, all existing boxes from the previous direction are immediately deleted, ensuring only relevant levels remain visible.
Adaptive Stop/Target Sizing:
While not explicitly coded in the current version, the shadow portfolio tracking system calculates PnL based on these levels. Users can observe which ATR multipliers and risk/reward ratios produce optimal results for their instrument/timeframe via the dashboard performance metrics.
📊 COMPREHENSIVE VISUAL SYSTEM
KMD provides rich visual feedback through four distinct layers:
1. PROBABILITY CLOUD (Adaptive Volatility Bands):
Two sets of bands around price that expand/contract with MMD:
Calculation:
Std_Multiplier = 1 + MMD × 3
Upper_1σ = Close + ATR × Std_Multiplier × 0.5
Lower_1σ = Close - ATR × Std_Multiplier × 0.5
Upper_2σ = Close + ATR × Std_Multiplier
Lower_2σ = Close - ATR × Std_Multiplier
• Inner band (±0.5× adjusted ATR) : 68% probability zone (1 standard deviation equivalent)
• Outer band (±1.0× adjusted ATR) : 95% probability zone (2 standard deviation equivalent)
When MMD spikes, bands widen dramatically, showing increased uncertainty. When MMD calms, bands tighten, showing normal price action.
2. MOMENTUM FLOW VECTORS (Directional Arrows):
Dynamic arrows that visualize momentum strength and direction:
Arrow Properties:
• Length: Proportional to momentum magnitude (2-10 bars forward)
• Width: 1px (weak), 2px (medium), 3px (strong)
• Transparency: 30-100 (more opaque = stronger momentum)
• Direction: Up for bullish, down for bearish
• Placement: Below bars (bulls) or above bars (bears)
Trigger Logic:
• Always appears every 5 bars (regular sampling)
• Forced appearance if momentum strength > 50 OR regime shift OR MMD velocity > 10%
Strong momentum (>75%) gets:
• Secondary support arrow (70% length, lighter color)
• Label showing "75%" strength
Very strong momentum (>60%) gets:
• Gradient flow lines (thick vertical lines showing momentum vector)
This creates a dynamic "flow field" showing where market pressure is pushing price.
3. REGIME ZONES (Distribution Shift Highlighting):
Boxes drawn around price action during periods when MMD > threshold:
Zone Detection:
• System enters "in_regime" mode when MMD crosses above threshold
• Tracks highest high and lowest low during regime
• Exits "in_regime" when MMD crosses back below threshold
• Draws box from regime_start to current bar, spanning high to low
Zone Colors:
• EXTREME regime: Red with 90% transparency (dangerous)
• SHIFTING regime: Amber with 92% transparency (active)
• Other regimes: Teal with 95% transparency (normal)
Emphasis Boxes:
When regime_shift occurs (MMD crosses above threshold that bar), a special 4-bar wide emphasis box highlights the exact transition moment with thicker borders and lower transparency.
This visual immediately shows "the market just changed" moments.
4. SIGNAL CONNECTION LINES:
Lines connecting consecutive signals to show trade sequences:
Line Types:
• Solid line : Same direction signals (long → long, short → short)
• Dotted line : Reversal signals (long → short or short → long)
Visual Purpose:
• Identify signal clusters (multiple entries same direction)
• Spot reversal patterns (system changing bias)
• See average bars between signals
• Understand system behavior patterns
Connections are limited to signals within 100 bars of each other to avoid across-chart lines.
📈 COMPREHENSIVE DASHBOARD: REAL-TIME SYSTEM STATE
The dashboard provides complete transparency into system internals with three size modes:
MINIMAL MODE:
• Header (Regime + WFO phase)
• Signal Status (LONG READY / SHORT READY / WAITING)
• Core metrics only
COMPACT MODE (Default):
• Everything in Minimal
• Kernel info
• Active bandit arm + validation
• WFO efficiency
• Confluence scores (bull/bear)
• MMD current value
• Position status (if active)
• Performance summary
FULL MODE:
• Everything in Compact
• Signal Quality Diagnostics:
- Bull quality score vs threshold with progress bar
- Bear quality score vs threshold with progress bar
- MMD threshold check (✓/✗)
- MMD percentile (top X% of history)
- Regime fit score (how well current regime suits trading)
- WFO confidence level (validation strength)
- Adaptive cooldown status (bars remaining vs required)
• All Arms Signals:
- Shows all 7 arm signals (▲/▼/○)
- Q-value for each arm
- Indicates selected arm with ◄
• Thompson Sampling Parameters (if TS mode):
- Alpha/Beta values for selected arm
- Probability estimate (α/(α+β))
• Extended Performance:
- Expectancy per trade
- Sharpe ratio with star rating
- Individual arm performance (if enough data)
Key Dashboard Sections:
REGIME: Current market regime (STABLE/TRENDING/SHIFTING/ELEVATED/EXTREME) with color-coded background
SIGNAL STATUS:
• "▲ LONG READY" (cyan) - Long signal qualified
• "▼ SHORT READY" (red) - Short signal qualified
• "○ WAITING" (gray) - No qualified signals
• Signal Mode displayed (Responsive/Transition/Continuous)
KERNEL:
• Active kernel type (RBF/Laplacian/Cauchy/Rational Quadratic)
• Current bandwidth (effective after adaptation)
• Adaptive vs Fixed indicator
• RBF scale (if RBF) or RQ alpha (if RQ)
BANDIT:
• Selection algorithm (UCB1/UCB1-Tuned/Epsilon/Thompson)
• Active arm name (MMD Shift, Trend, Breakout, etc.)
• Validation status (✓ if validated, ? if unproven)
• Pull count (n=XXX) - how many times selected
• Q-Value (×10000 for readability)
• UCB score (exploration + exploitation)
• Train Q vs Test Q comparison
• Test trade count
WFO:
• Current period number
• Progress through period (XX%)
• Efficiency percentage (color-coded: green >80%, yellow 50-80%, red <50%)
• Overfit risk assessment (LOW/MODERATE/HIGH)
• Validated arms count (X/7)
CONFLUENCE:
• Bull score (X/7) with progress bar (███ full, ██ medium, █ low, ○ none)
• Bear score (X/7) with progress bar
• Color-coded: Green/red if ≥ minimum, gray if below
MMD:
• Current value (3 decimals)
• Threshold (2 decimals)
• Ratio (MMD/Threshold × multiplier, e.g. "1.5x" = 50% above threshold)
• Velocity (+/- percentage change) with up/down arrows
POSITION:
• Status: LONG/SHORT/FLAT
• Active indicator (● if active, ○ if flat)
• Bars since entry
• Current P&L percentage (if active)
• P&L direction (▲ profit / ▼ loss)
• R-Multiple (how many Rs: PnL / initial_risk)
PERFORMANCE:
• Total Trades
• Wins (green) / Losses (red) breakdown
• Win Rate % with visual bar and color coding
• Profit Factor (PF) with checkmark if >1.0
• Expectancy % (average profit per trade)
• Sharpe Ratio with star rating (★★★ >2, ★★ >1, ★ >0, ○ negative)
• Max DD % (maximum drawdown) with "Now: X%" showing current drawdown
🔧 KEY PARAMETERS EXPLAINED
Kernel Configuration:
• Kernel Function : RBF / Laplacian / Cauchy / Rational Quadratic
- Start with Cauchy for stability, experiment with others
• Bandwidth (σ) (0.5-10.0, default 2.0): Kernel sensitivity
- Lower: More signals, more false positives (scalping: 0.8-1.5)
- Medium: Balanced (swing: 1.5-3.0)
- Higher: Fewer signals, stronger quality (position: 3.0-8.0)
• Adaptive Bandwidth (default ON): Auto-adjust to volatility
- Keep ON for most markets
• RBF Scale (0.1-2.0, default 0.5): RBF-specific scaling
- Only matters if RBF kernel selected
- Lower = more sensitive (0.3 for scalping)
- Higher = less sensitive (1.0+ for position)
• RQ Alpha (0.5-5.0, default 2.0): Rational Quadratic tail behavior
- Only matters if RQ kernel selected
- Low (0.5-1.0): Heavy tails, robust to outliers (like Cauchy)
- High (3.0-5.0): Light tails, sensitive (like RBF)
Analysis Windows:
• Reference Period (30-500, default 100): Historical baseline
- Scalping: 50-80
- Intraday: 80-150
- Swing: 100-200
- Position: 200-500
• Test Period (5-100, default 20): Recent behavior window
- Should be 15-25% of Reference Period
- Scalping: 10-15
- Intraday: 15-25
- Swing: 20-40
- Position: 30-60
• Sample Size (10-40, default 20): Data points for MMD
- Lower: Faster, less reliable (scalping: 12-15)
- Medium: Balanced (standard: 18-25)
- Higher: Slower, more reliable (position: 25-35)
Walk-Forward Optimization:
• Enable WFO (default ON): Master overfitting protection
- Always ON for live trading
• Training Window (100-2000, default 500): Learning data
- Should be 4-6× Testing Window
- 1m-5m: 300-500
- 15m-1h: 500-800
- 4h-1D: 500-1000
- 1D-1W: 800-2000
• Testing Window (50-500, default 100): Validation data
- Should be 1/5 to 1/4 of Training
- 1m-5m: 50-100
- 15m-1h: 80-150
- 4h-1D: 100-200
- 1D-1W: 150-500
• Min Trades for Validation (5-50, default 10): Statistical threshold
- Active traders: 8-12
- Position traders: 15-30
• Performance Decay (0.8-0.99, default 0.95): Old data forgetting
- Aggressive: 0.85-0.90 (volatile markets)
- Moderate: 0.92-0.96 (most use cases)
- Conservative: 0.97-0.99 (stable markets)
Multi-Armed Bandit:
• Learning Rate (α) (0.01-0.3, default 0.08): Adaptation speed
- Low: 0.01-0.05 (position trading, stable)
- Medium: 0.06-0.12 (day/swing trading)
- High: 0.15-0.30 (scalping, fast adaptation)
• Selection Strategy : UCB1 / UCB1-Tuned / Epsilon-Greedy / Thompson
- UCB1 recommended for most (proven, reliable)
- Thompson for advanced users (best empirical performance)
• Exploration Constant (c) (0.5-3.0, default 1.5): Explore vs exploit
- Low: 0.5-1.0 (conservative, proven strategies)
- Medium: 1.2-1.8 (balanced)
- High: 2.0-3.0 (experimental, volatile markets)
• Epsilon (0.0-0.3, default 0.10): Random exploration (ε-greedy only)
- Only applies if Epsilon-Greedy selected
- Standard: 0.10 (10% random)
Signal Configuration:
• MMD Threshold (0.05-1.0, default 0.15): Distribution divergence trigger
- Low: 0.08-0.12 (scalping, sensitive)
- Medium: 0.12-0.20 (day/swing)
- High: 0.25-0.50 (position, strong signals)
- Stocks/indices: 0.12-0.18
- Forex: 0.15-0.25
- Crypto: 0.20-0.35
• Confluence Filter (default ON): Multi-factor requirement
- Keep ON for quality signals
• Minimum Confluence (1-7, default 2): Factors needed
- Very low: 1 (high frequency)
- Low: 2-3 (active trading)
- Medium: 4-5 (swing)
- High: 6-7 (rare perfect setups)
• Cooldown (1-20, default 5): Bars between signals
- Short: 1-3 (scalping, allows rapid re-entry)
- Medium: 4-7 (day/swing)
- Long: 8-20 (position, ensures development)
• Signal Mode : Responsive / Transition Only / Continuous
- Responsive: Recommended (new + upgrades)
- Transition: Cleanest (first + reversals)
- Continuous: Testing (every qualified bar)
Advanced Signal Control:
• Minimum Signal Strength (30-90, default 60): Quality floor
- Lower: More signals (scalping: 40-50)
- Medium: Balanced (standard: 55-65)
- Higher: Fewer signals (position: 70-80)
• Dynamic MMD Threshold (default ON): Auto-calibration
- Keep ON for adaptive behavior
• Signal Ranking Filter (default ON): Top percentile only
- Keep ON to trade only best signals
• Ranking Percentile (50-95, default 75): Selectivity
- 75 = top 25% of signals
- 85 = top 15% of signals
- 90 = top 10% of signals
• Adaptive Cooldown (default ON): Volatility-scaled spacing
- Keep ON for intelligent spacing
• Regime Filter : Off / Smart / Strict
- Off: Any regime (maximize frequency)
- Smart: Avoid extremes (recommended)
- Strict: Only optimal regimes (maximum quality)
Risk Parameters:
• Risk:Reward Ratio (1.0-5.0, default 2.0): Target distance multiplier
- Conservative: 1.0-1.5 (higher WR needed)
- Balanced: 2.0-2.5 (standard professional)
- Aggressive: 3.0-5.0 (lower WR acceptable)
• Stop Loss (ATR mult) (0.5-4.0, default 1.5): Stop distance
- Tight: 0.5-1.0 (scalping, low vol)
- Medium: 1.2-2.0 (day/swing)
- Wide: 2.5-4.0 (position, high vol)
• Pause After Drawdown (2-20%, default 8%): Circuit breaker
- Aggressive: 3-6% (small accounts)
- Moderate: 6-10% (most traders)
- Relaxed: 10-15% (large accounts)
Multi-Timeframe:
• MTF Confirmation (default OFF): Higher TF filter
- Turn ON for swing/position trading
- Keep OFF for scalping/day trading
• Higher Timeframe (default "60"): HTF for trend check
- Should be 3-5× chart timeframe
- 1m chart → 5m or 15m
- 5m chart → 15m or 60m
- 15m chart → 60m or 240m
- 1h chart → 240m or D
Display:
• Probability Cloud (default ON): Volatility bands
• Momentum Flow Vectors (default ON): Directional arrows
• Regime Zones (default ON): Distribution shift boxes
• Signal Connections (default ON): Lines between signals
• Dashboard (default ON): Stats table
• Dashboard Position : Top Left / Top Right / Bottom Left / Bottom Right
• Dashboard Size : Minimal / Compact / Full
• Color Scheme : Default / Monochrome / Warm / Cool
• Show MMD Debug Plot (default OFF): Overlay MMD value
- Turn ON temporarily for threshold calibration
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Parameter Calibration (Week 1)
Goal: Find optimal kernel and bandwidth for your instrument/timeframe
Setup:
• Enable "Show MMD Debug Plot"
• Start with Cauchy kernel, 2.0 bandwidth
• Run on chart with 500+ bars of history
Actions:
• Watch yellow MMD line vs red threshold line
• Count threshold crossings per 100 bars
• Adjust bandwidth to achieve desired signal frequency:
- Too many crossings (>20): Increase bandwidth (2.5-3.5)
- Too few crossings (<5): Decrease bandwidth (1.2-1.8)
• Try other kernels to see sensitivity differences
• Note: RBF most sensitive, Cauchy most robust
Target: 8-12 threshold crossings per 100 bars for day trading
Phase 2: WFO Validation (Weeks 2-3)
Goal: Verify strategies generalize out-of-sample
Requirements:
• Enable WFO with default settings (500/100)
• Let system run through 2-3 complete WFO cycles
• Accumulate 50+ total trades
Actions:
• Monitor WFO Efficiency in dashboard
• Check which arms validate (green ✓) vs unproven (yellow ?)
• Review Train Q vs Test Q for selected arm
• If efficiency < 0.5: System overfitting, adjust parameters
Red Flags:
• Efficiency consistently <0.4: Serious overfitting
• Zero arms validate after 2 cycles: Windows too short or thresholds too strict
• Selected arm never validates: Investigate arm logic relevance
Phase 3: Signal Quality Tuning (Week 4)
Goal: Optimize confluence and quality thresholds
Requirements:
• Switch dashboard to FULL mode
• Enable all diagnostic displays
• Track signals for 100+ bars
Actions:
• Watch Bull/Bear quality scores in real-time
• Note quality distribution of fired signals (are they all 60-70% or higher?)
• If signal ranking on, check percentile cutoff appropriateness
• Adjust "Minimum Signal Strength" to filter weak setups
• Adjust "Minimum Confluence" if too many/few signals
Optimization:
• If win rate >60%: Lower thresholds (capture more opportunities)
• If win rate <45%: Raise thresholds (improve quality)
• If Profit Factor <1.2: Increase minimum quality by 5-10 points
Phase 4: Regime Awareness (Week 5)
Goal: Understand which regimes work best
Setup:
• Track performance by regime using notes/journal
• Dashboard shows current regime constantly
Actions:
• Note signal quality and outcomes in each regime:
- STABLE: Often weak signals, low confidence
- TRENDING: Trend-following arms dominate
- SHIFTING: Highest signal quality, core opportunity
- ELEVATED: Good signals, moderate success
- EXTREME: Mixed results, high variance
• Adjust Regime Filter based on findings
• If losing in EXTREME consistently: Use "Smart" or "Strict" filter
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate forward performance with minimal capital
Requirements:
• Paper trading shows: WR >45%, PF >1.2, Efficiency >0.6
• Understand why signals fire and why they're blocked
• Comfortable with dashboard interpretation
Setup:
• 10-25% intended position size
• Focus on ML-boosted signals (if any pattern emerges)
• Keep detailed journal with screenshots
Actions:
• Execute every signal the system generates (within reason)
• Compare your P&L to shadow portfolio metrics
• Track divergence between your results and system expectations
• Review weekly: What worked? What failed? Any execution issues?
Red Flags:
• Your WR >20% below paper: Execution problems (slippage, timing)
• Your WR >20% above paper: Lucky streak or parameter mismatch
• Dashboard metrics drift significantly: Market regime changed
Phase 6: Full Scale Deployment (Month 3+)
Goal: Progressively increase to full position sizing
Requirements:
• 30+ micro live trades completed
• Live WR within 15% of paper WR
• Profit Factor >1.0 live
• Max DD <15% live
• Confidence in parameter stability
Progression:
• Months 3-4: 25-50% intended size
• Months 5-6: 50-75% intended size
• Month 7+: 75-100% intended size
Maintenance:
• Weekly dashboard review for metric drift
• Monthly WFO efficiency check (should stay >0.5)
• Quarterly parameter re-optimization if market character shifts
• Annual deep review of arm performance and kernel relevance
Stop/Reduce Rules:
• WR drops >20% from baseline: Reduce to 50%, investigate
• Consecutive losses >12: Reduce to 25%, review parameters
• Drawdown >20%: Stop trading, reassess system fit
• WFO efficiency <0.3 for 2+ periods: System broken, retune completely
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Kernel Discovery:
Early versions used simple moving average crossovers and momentum indicators—they captured obvious moves but missed subtle regime changes. The breakthrough came from reading academic papers on two-sample testing and kernel methods. Applying Maximum Mean Discrepancy to financial returns revealed distribution shifts 10-20 bars before traditional indicators signaled. This edge—knowing the market had fundamentally changed before it was obvious—became the core of KMD.
Testing showed Cauchy kernel outperformed others by 15% win rate in crypto specifically because its heavy tails ignored the massive outlier spikes (liquidation cascades, bot manipulation) that fooled RBF into false signals.
The Seven Arms Revelation:
Originally, the system had one strategy: "Trade when MMD crosses threshold." Performance was inconsistent—great in ranging markets, terrible in trends. The insight: different market structures require different strategies. Creating seven distinct arms based on different market theories (trend-following, mean-reversion, breakout, volume, momentum) and letting them compete solved the problem.
The multi-armed bandit wasn't added as a gimmick—it was the solution to "which strategy should I use right now?" The system discovers the answer automatically through reinforcement learning.
The Thompson Sampling Superiority:
UCB1 worked fine, but Thompson Sampling empirically outperformed it by 8% over 1000+ trades in backtesting. The reason: Thompson's probabilistic selection naturally hedges uncertainty. When two arms have similar Q-values, UCB1 picks one deterministically (whichever has slightly higher exploration bonus). Thompson samples from both distributions, sometimes picking the "worse" one—and often discovering it's actually better in current conditions.
Implementing true Beta distribution sampling (Box-Muller + Marsaglia-Tsang) instead of fake approximations was critical. Fake Thompson (using random with bias) underperformed UCB1. Real Thompson with proper Bayesian updating dominated.
The Walk-Forward Necessity:
Initial backtests showed 65% win rate across 5000 trades. Live trading: 38% win rate over first 100 trades. Crushing disappointment. The problem: overfitting. The training data included the test data (look-ahead bias). Implementing proper walk-forward optimization with out-of-sample validation dropped backtest win rate to 51%—but live performance matched at 49%. That's a system you can trust.
WFO efficiency metric became the North Star. If efficiency >0.7, live results track paper. If efficiency <0.5, prepare for disappointment.
The Confluence Complexity:
First signals were simple: "MMD high + arm agrees." This generated 200+ signals on 1000 bars with 42% win rate—not tradeable. Adding confluence (must have trend + volume + structure + RSI) reduced signals to 40 with 58% win rate. The math clicked: fewer, better signals outperform many mediocre signals .
The weighted system (20pt critical factors, 15pt high-impact, 10pt moderate/bonus) emerged from analyzing which factors best predicted wins. Bandit arm alignment and MMD quality were 2-3× more predictive than RSI or divergence, so they got 2× the weight. This isn't arbitrary—it's data-driven.
The Dynamic Threshold Insight:
Fixed MMD threshold failed across different market conditions. 0.15 worked perfectly on ES but fired constantly on Bitcoin. The adaptive threshold (scaling with recent MMD mean + stdev) auto-calibrated to instrument volatility. This single change made the system deployable across forex, crypto, stocks without manual tuning per instrument.
The Signal Mode Evolution:
Originally, every qualified bar showed a triangle. Charts became unusable—dozens of stacked triangles during trending regimes. "Transition Only" mode cleaned this up but missed re-entries when quality spiked mid-regime. "Responsive" mode emerged as the optimal balance: show fresh qualifications, reversals, AND significant quality improvements (25%+) during extreme regimes. This captures the signal intent ("something important just happened") without chart pollution.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : KMD doesn't forecast prices. It identifies when the current distribution differs from historical baseline, suggesting regime transition—but not direction or magnitude.
• NOT Holy Grail : Typical performance is 48-56% win rate with 1.3-1.8 avg R-multiple. This is a probabilistic edge, not certainty. Expect losing streaks of 8-12 trades.
• NOT Universal : Performs best on liquid, auction-driven markets (futures, major forex, large-cap stocks, BTC/ETH). Struggles with illiquid instruments, thin order books, heavily manipulated markets.
• NOT Hands-Off : Requires monitoring for news events, earnings, central bank announcements. MMD cannot detect "Fed meeting in 2 hours" or "CEO stepping down"—it only sees statistical patterns.
• NOT Immune to Regime Persistence : WFO helps but cannot predict black swans or fundamental market structure changes (pandemic, war, regulatory overhaul). During these events, all historical patterns may break.
Core Assumptions:
1. Return Distributions Exhibit Clustering : Markets alternate between relatively stable distributional regimes. Violation: Permanent random walk, no regime structure.
2. Distribution Changes Precede Price Moves : Statistical divergence appears before obvious technical signals. Violation: Instantaneous regime flips (gaps, news), no statistical warning.
3. Volume Reflects Real Activity : Volume-based confluence assumes genuine participation. Violation: Wash trading, spoofing, exchange manipulation (common in crypto).
4. Past Arm Performance Predicts Future Arm Performance : The bandit learns from history. Violation: Fundamental strategy regime change (e.g., market transitions from mean-reverting to trending permanently).
5. ATR-Based Stops Are Rational : Volatility-normalized risk management avoids premature exits. Violation: Flash crashes, liquidity gaps, stop hunts precisely targeting ATR multiples.
6. Kernel Similarity Maps to Economic Similarity : Mathematical similarity (via kernel) correlates with economic similarity (regime). Violation: Distributions match by chance while fundamentals differ completely.
Performs Best On:
• ES, NQ, RTY (S&P 500, Nasdaq, Russell 2000 futures)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY, AUD/USD
• Liquid commodities: CL (crude oil), GC (gold), SI (silver)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M avg daily volume)
• Major crypto on reputable exchanges: BTC, ETH (Coinbase, Kraken)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume)
• Exotic forex pairs with erratic spreads
• Illiquid crypto altcoins (manipulation, unreliable volume)
• Pre-market/after-hours (thin liquidity, gaps)
• Instruments with frequent corporate actions (splits, dividends)
• Markets with persistent one-sided intervention (central bank pegs)
Known Weaknesses:
• Lag During Instantaneous Shifts : MMD requires (test_window) bars to detect regime change. Fast-moving events (5-10 bar crashes) may bypass detection entirely.
• False Positives in Choppy Consolidation : Low-volatility range-bound markets can trigger false MMD spikes from random noise crossing threshold. Regime filter helps but doesn't eliminate.
• Parameter Sensitivity : Small bandwidth changes (2.0→2.5) can alter signal frequency by 30-50%. Requires careful calibration per instrument.
• Bandit Convergence Time : MAB needs 50-100 trades per arm to reliably learn Q-values. Early trades (first 200 bars) are essentially random exploration.
• WFO Warmup Drag : First WFO cycle has no validation data, so all arms start unvalidated. System may trade rarely or conservatively for first 500-600 bars until sufficient test data accumulates.
• Visual Overload : With all display options enabled (cloud, vectors, zones, connections), chart can become cluttered. Disable selectively for cleaner view.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Kernel Market Dynamics system, including its multi-armed bandit and walk-forward optimization components, is provided for educational purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The adaptive learning algorithms optimize based on historical data—there is no guarantee that learned strategies will remain profitable or that kernel-detected regime changes will lead to profitable trades. Market conditions change, correlations break, and distributional regimes shift in ways that historical data cannot predict. Black swan events occur.
Walk-forward optimization reduces but does not eliminate overfitting risk. WFO efficiency metrics indicate likelihood of forward performance but cannot guarantee it. A system showing high efficiency on one dataset may show low efficiency on another timeframe or instrument.
The dashboard shadow portfolio simulates trades under idealized conditions: instant fills, no slippage, no commissions, perfect execution. Real trading involves slippage (often 1-3 ticks per trade), commissions, latency, partial fills, rejected orders, requotes, and liquidity constraints that significantly reduce performance below simulated results.
Maximum Mean Discrepancy is a statistical distance metric—high MMD indicates distribution divergence but does not indicate direction, magnitude, duration, or profitability of subsequent moves. MMD can spike during sideways chop, producing signals with no directional follow-through.
Users must independently validate system performance on their specific instruments, timeframes, broker execution, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 trades) and start with micro position sizing (10-25% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (1-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Algorithmic systems do not change this fundamental reality—they systematize decision-making but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, or fitness for any particular purpose. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read and understood these risk disclosures and accept full responsibility for all trading activity and potential losses.
📁 SUGGESTED TRADINGVIEW CATEGORIES
PRIMARY CATEGORY: Statistics
The Kernel Market Dynamics system is fundamentally a statistical learning framework . At its core lies Maximum Mean Discrepancy—an advanced two-sample statistical test from the academic machine learning literature. The indicator compares probability distributions using kernel methods (RBF, Laplacian, Cauchy, Rational Quadratic) that map data to high-dimensional feature spaces for nonlinear similarity measurement.
The multi-armed bandit framework implements reinforcement learning via Q-learning with exponential moving average updates. Thompson Sampling uses true Bayesian inference with Beta posterior distributions. Walk-forward optimization performs rigorous out-of-sample statistical validation with train/test splits and efficiency metrics that detect overfitting.
The confluence system aggregates multiple statistical indicators (RSI, ADX, OBV, Z-scores, EMAs) with weighted scoring that produces a 0-100 quality metric. Signal ranking uses percentile-based filtering on historical quality distributions. The dashboard displays comprehensive statistics: win rates, profit factors, Sharpe ratios, expectancy, drawdowns—all computed from trade return distributions.
This is advanced statistical analysis applied to trading: distribution comparison, kernel methods, reinforcement learning, Bayesian inference, hypothesis testing, and performance analytics. The statistical sophistication distinguishes KMD from simple technical indicators.
SECONDARY CATEGORY: Volume
Volume analysis plays a crucial role in KMD's signal generation and validation. The confluence system includes volume confirmation as a high-impact factor (15 points): signals require above-average volume (>1.2× mean) for full points, with scaling based on volume ratio. The OBV (On-Balance Volume) trend indicator determines directional bias for Arm 6 (Volume Confirmation strategy).
Volume ratio (current / 20-period average) directly affects confluence scores—higher volume strengthens signal quality. The momentum flow vectors scale width and opacity based on volume momentum relative to average. Energy particle visualization specifically marks volume burst events (>2× average volume) as potential market-moving catalysts.
Several bandit arms explicitly incorporate volume:
• Arm 2 (Breakout): Requires volume confirmation for Bollinger Band breaks
• Arm 6 (Volume Confirmation): Primary logic based on OBV trend + volume spike
The system recognizes volume as the "conviction" behind price moves—distribution changes matter more when accompanied by significant volume, indicating genuine participant behavior rather than noise. This volume-aware filtering improves signal reliability in liquid markets.
TERTIARY CATEGORY: Volatility
Volatility measurement and adaptation permeate the KMD system. ATR (Average True Range) forms the basis for all risk management: stops are placed at ATR × multiplier, targets are scaled accordingly. The adaptive bandwidth feature scales kernel bandwidth (0.5-2.0×) inversely with volatility—tightening during calm markets, widening during volatile periods.
The probability cloud (primary visual element) directly visualizes volatility: bands expand/contract based on (1 + MMD × 3) multiplier applied to ATR. Higher MMD (distribution divergence) + higher ATR = dramatically wider uncertainty bands.
Adaptive cooldown scales minimum bars between signals based on ATR percentage: higher volatility = longer cooldown (up to 3× base), preventing overtrading during whipsaw conditions. The gamma parameter in the tensor calculation (from related indicators) and volatility ratio measurements influence MMD sensitivity.
Regime classification incorporates volatility metrics: high volatility with ranging price action produces "RANGE⚡" regime, while volatility expansion with directional movement produces trending regimes. The system adapts its behavior to volatility regimes—tighter requirements during extreme volatility, looser requirements during stable periods.
ATR-based risk management ensures position sizing and exit levels automatically adapt to instrument volatility, making the system deployable across instruments with different average volatilities (stocks vs crypto) without manual recalibration.
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CLOSING STATEMENT
══════════════════════════════════════════
Kernel Market Dynamics doesn't just measure price—it measures the probability structure underlying price. It doesn't just pick one strategy—it learns which strategies work in which conditions. It doesn't just optimize on history—it validates on the future.
This is machine learning applied correctly to trading: not curve-fitting oscillators to maximize backtest profit, but implementing genuine statistical learning algorithms (kernel methods, multi-armed bandits, Bayesian inference) that adapt to market evolution while protecting against overfitting through rigorous walk-forward testing.
The seven arms compete. The Thompson sampler selects. The kernel measures. The confluence scores. The walk-forward validates. The signals fire.
Most indicators tell you what happened. KMD tells you when the game changed.
"In the space between distributions, where the kernel measures divergence and the bandit learns from consequence—there, edge exists." — KMD-WFO-MAB v2
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
ES1! H1 Stats+ES1! H1 Stats - Detailed Prob & Excursion Indicator
Overview
ES1! H1 Stats - Detailed Prob & Excursion is a specialized statistical overlay indicator for TradingView, tailored for E-mini S&P 500 futures (ES1!) on a 1-hour framework. It provides real-time insights into the probability of price returning to the hourly open after sweeping the previous hour’s high (PHH) or previous hour’s low (PHL), based on historical data segmented by hour (0–23) and 20-minute intervals. The indicator visualizes these sweeps with lines, labels, circles, background fills, and “excursion zones” (also called “Magic Boxes”) that highlight median/mean extensions post-sweep, along with percentile lines (75th / 90th / 95th) for gauging potential “pain” or extreme moves. This tool is designed for intraday S&P 500 traders focusing on liquidity sweeps and mean-reversion behavior, helping to quantify edge using empirical probabilities and excursion statistics.
The data is hardcoded from extensive historical analysis of ES1! behavior (e.g., probabilities ranging roughly from ~7% to ~91%, with sample sizes up to 2000+ per segment), making it a backtested reference rather than a dynamic learning model. It emphasizes visual clarity during active hours, with options to filter for Regular Trading Hours (RTH: 09:00–15:59 ET) or high-probability (>70%) events only. Note: This is an educational tool for analyzing market structure; it does not predict future performance or provide trading signals/advice. Past data does not guarantee future results, and users should backtest on current conditions (as of December 2025 data availability) and use at their own risk, in compliance with TradingView’s house rules.
Key Features
• Sweep Detection & Probability Labels: Identifies when price breaks PHH (upside) or PHL (downside), displaying a centered label with probability of returning to the hourly open, sample size (N), time of sweep, and a checkmark (✅) if the open is retested post-sweep.
• Visual Lines & Markers: Draws hourly open (h.o.), PHH, and PHL lines with customizable styles/colors; adds small circles on sweep bars for quick spotting.
• Breakout→Open Background Fill: Shaded zone from sweep bar until price returns to open, visualizing extension duration and retracement.
• Excursion (Pain) Zone - “Magic Box”: Post-sweep box showing median/mean extension percentages, colored dynamically by probability (green high, orange mid, red low); includes dashed lines for 75th/90th/95th percentiles to mark statistical extremes.
• Time-Segmented Data: Probabilities and excursions vary by hour (0–23) and 20-min segments (0–19 min: _0, 20–39: _1, 40–59: _2), capturing intraday nuances (e.g., higher probs in early/late hours).
• Filters for Focus: RTH-only mode hides non-session elements; high-prob-only shows >70% events to reduce noise.
• Alerts: Triggers on PHH/PHL sweeps with messages for chart checks.
How It Works
• Data Foundation: Uses pre-computed maps for probabilities (prob_high_taken/prob_low_taken), sample sizes, and excursions (mean, median, p75/p90/p95 as percentages of open). Data is initialized on the first bar via f_init_high_data() and f_init_low_data(), covering 24 hours with 3 segments each (e.g., key "9_1" for 09:20–09:39). Probabilities represent historical likelihood of price returning to open after sweep; excursions quantify average/rare extensions (e.g., 0.156% mean = 0.156% of open price).
• Period Detection: On new 1H bars (new_period_bar), resets visuals, draws lines for open/PHH/PHL extending 1 hour forward, and labels if enabled. Uses request.security on standard ticker for real OHLC, bypassing chart transformations (e.g., Heikin Ashi).
• Sweep Logic: On each bar, checks if real high > PHH or real low < PHL. If so, fetches segment-specific data (hour + floor(minute/20)), displays probability label centered mid-hour. Skips if filtered (RTH-only or <70% prob).
• Excursion Visualization: If enabled, draws “Magic Box” from 1-min to 58-min into the hour, bounded by mean/median levels (top/bottom adjusted for high/low sweep). Adds percentile lines with labels (e.g., “75%”) at right end. Box color reflects prob strength for quick bias assessment.
• Retest Check: Monitors for open retest post-sweep (high/low cross open, or gap scenarios from prev bar). Adds ✅ to label if hit on subsequent bars (skips sweep bar to avoid false positives). Stops background fill on retest or at 58-min mark.
• Background Fill: Activates on sweep, shades until retest, using user color.
• Cleanup & Performance: Manages labels in arrays, clears on new periods; no excess drawing beyond max counts (500 lines/labels/boxes).
This setup blends statistical backtesting with real-time visualization: hardcoded data provides empirical probabilities/excursions (reducing subjectivity in breakouts), while dynamic elements (lines, fills, boxes) overlay structure on the chart. It helps ES traders assess if a sweep is “high-edge” (e.g., >70% probability of reverting) or likely to run (low probability, high excursion), pairing historical context with current price action.
Settings and Customization
Inputs are grouped for ease:
Settings:
o Show RTH Only (9:00–15:59): Restricts to main session (default: false; tooltip: for RTH-focused stats).
o Show High Prob Only (>70%): Filters low-prob sweeps visually (default: false; tooltip: highlights confidence).
Visuals:
o Show Line Labels: Toggle “h.o.” / “phh” / “phl” (default: true).
o Period Open Line Color: Gray 50% (default).
o Previous High/Low Line Colors: Gray 100% (default).
o Open Line Style/Width: Dotted/1 (default; options: Solid/Dotted/Dashed).
Breakout→Open Background:
o Show Breakout→Open Background: Toggle fill (default: true).
o Fill Color: Teal 85% (default).
Breakout Circles:
o Show Breakout Circles: Toggle (default: true).
o PHH/PHL Break Circle Colors: White 20% (default).
Info Label Style:
o Text Size: Small (default; options: Auto/Tiny/Normal/Large/Huge).
o Label Text Color: White (default).
o Low/Mid/High Probability Colors: Red 20% / Orange 20% / Green 20% (default).
Excursion (Pain) Zone:
o Show Excursion Zone: Toggle Magic Box (default: true).
o Excursion Box Color: Gray 75% (default; dynamic overrides).
o 75th/90th/95th Percentile Lines: Orange 30% / Red 30% / Dark Red 100% (default).
No additional tables/plots; all elements are lines/labels/boxes for overlay focus.
Usage Tips
• Breakout Trading: Watch for sweeps with high probability (>70%, green label) as potential fades back to open; low probability (red) may signal runs—use the excursion box for targets (e.g., exit at 90th percentile for extremes).
• Time Awareness: Probabilities often peak in key liquidity windows and drop in quieter hours; segments capture momentum shifts (e.g., _2 often lower prob).
• RTH Focus: Enable for cleaner stats during high-liquidity session hours; disable for a 24-hour view.
• Visual Filtering: Use high-prob-only in volatile conditions to reduce noise; combine with volume or other confluence tools for confirmation.
• Alerts Integration: Set TradingView alerts on sweeps; check label for probability/N before acting.
• Chart Setup: Best on 1H or lower ES1! charts; adjust text size for readability on smaller screens.
• Backtesting: Manually review historical sweeps against data maps to validate; update hardcoded values if new data emerges (as of 2025).
Limitations
• Fixed Data: Hardcoded stats may not reflect recent market changes (e.g., post-2025 regime shifts); not adaptive.
• Reactive Only: Detects sweeps after they occur; no predictive signals.
• Timeframe Specific: Locked to 1H logic; may not translate to other assets/timeframes without recoding data.
• Visual Clutter: On busy charts, labels/boxes may overlap—toggle selectively.
• No Live Stats: Sample sizes are historical; real-time N/prob not updated.
• Gaps & Extremes: Handles gaps in retest logic, but rare events (e.g., macro news) may exceed the 95th percentile.
Disclaimer
This indicator is for informational and educational purposes only. Trading involves significant risk of loss and is not suitable for all investors. The hardcoded data represents past E-mini S&P 500 futures (ES1!) performance and does not guarantee future outcomes. No claims of profitability are made—results depend on market conditions, user strategy, and risk management. Consult a financial advisor before trading, and backtest extensively. Abiding by TradingView rules, this tool provides no investment recommendations.
6B1! H1 Stats+6B1! H1 Stats - Detailed Prob & Excursion Indicator
Overview
6B1! H1 Stats - Detailed Prob & Excursion is a specialized statistical overlay indicator for TradingView, tailored for British Pound futures (6B1!) on a 1-hour framework. It provides real-time insights into the probability of price returning to the hourly open after sweeping the previous hour’s high (PHH) or previous hour’s low (PHL), based on historical data segmented by hour (0–23) and 20-minute intervals. The indicator visualizes these sweeps with lines, labels, circles, background fills, and “excursion zones” (also called “Magic Boxes”) that highlight median/mean extensions post-sweep, along with percentile lines (75th / 90th / 95th) for gauging potential “pain” or extreme moves. This tool is designed for intraday British Pound traders focusing on liquidity sweeps and mean-reversion behavior, helping to quantify edge using empirical probabilities and excursion statistics.
The data is hardcoded from extensive historical analysis of 6B1! behavior (e.g., probabilities ranging roughly from ~7% to ~91%, with sample sizes up to 2000+ per segment), making it a backtested reference rather than a dynamic learning model. It emphasizes visual clarity during active hours, with options to filter for Regular Trading Hours (RTH: 09:00–15:59 ET) or high-probability (>70%) events only. Note: This is an educational tool for analyzing market structure; it does not predict future performance or provide trading signals/advice. Past data does not guarantee future results, and users should backtest on current conditions (as of December 2025 data availability) and use at their own risk, in compliance with TradingView’s house rules.
________________________________________
Key Features
• Sweep Detection & Probability Labels: Identifies when price breaks PHH (upside) or PHL (downside), displaying a centered label with probability of returning to the hourly open, sample size (N), time of sweep, and a checkmark (✅) if the open is retested post-sweep.
• Visual Lines & Markers: Draws hourly open (h.o.), PHH, and PHL lines with customizable styles/colors; adds small circles on sweep bars for quick spotting.
• Breakout→Open Background Fill: Shaded zone from sweep bar until price returns to open, visualizing extension duration and retracement.
• Excursion (Pain) Zone - “Magic Box”: Post-sweep box showing median/mean extension percentages, colored dynamically by probability (green high, orange mid, red low); includes dashed lines for 75th/90th/95th percentiles to mark statistical extremes.
• Time-Segmented Data: Probabilities and excursions vary by hour (0–23) and 20-min segments (0–19 min: _0, 20–39: _1, 40–59: _2), capturing intraday nuances (e.g., higher probs in early/late hours).
• Filters for Focus: RTH-only mode hides non-session elements; high-prob-only shows >70% events to reduce noise.
• Alerts: Triggers on PHH/PHL sweeps with messages for chart checks.
________________________________________
How It Works
• Data Foundation: Uses pre-computed maps for probabilities (prob_high_taken/prob_low_taken), sample sizes, and excursions (mean, median, p75/p90/p95 as percentages of open). Data is initialized on the first bar via f_init_high_data() and f_init_low_data(), covering 24 hours with 3 segments each (e.g., key "9_1" for 09:20–09:39). Probabilities represent historical likelihood of price returning to open after sweep; excursions quantify average/rare extensions (e.g., 0.156% mean = 0.156% of open price).
• Period Detection: On new 1H bars (new_period_bar), resets visuals, draws lines for open/PHH/PHL extending 1 hour forward, and labels if enabled. Uses request.security on standard ticker for real OHLC, bypassing chart transformations (e.g., Heikin Ashi).
• Sweep Logic: On each bar, checks if real high > PHH or real low < PHL. If so, fetches segment-specific data (hour + floor(minute/20)), displays probability label centered mid-hour. Skips if filtered (RTH-only or <70% prob).
• Excursion Visualization: If enabled, draws “Magic Box” from 1-min to 58-min into the hour, bounded by mean/median levels (top/bottom adjusted for high/low sweep). Adds percentile lines with labels (e.g., “75%”) at right end. Box color reflects prob strength for quick bias assessment.
• Retest Check: Monitors for open retest post-sweep (high/low cross open, or gap scenarios from prev bar). Adds ✅ to label if hit on subsequent bars (skips sweep bar to avoid false positives). Stops background fill on retest or at 58-min mark.
• Background Fill: Activates on sweep, shades until retest, using user color.
• Cleanup & Performance: Manages labels in arrays, clears on new periods; no excess drawing beyond max counts (500 lines/labels/boxes).
This setup blends statistical backtesting with real-time visualization: hardcoded data provides empirical probabilities/excursions (reducing subjectivity in breakouts), while dynamic elements (lines, fills, boxes) overlay structure on the chart. It helps British Pound traders assess if a sweep is “high-edge” (e.g., >70% probability of reverting) or likely to run (low probability, high excursion), pairing historical context with current price action.
________________________________________
Settings and Customization
Inputs are grouped for ease:
1. Settings:
o Show RTH Only (9:00–15:59): Restricts to main session (default: false; tooltip: for RTH-focused stats).
o Show High Prob Only (>70%): Filters low-prob sweeps visually (default: false; tooltip: highlights confidence).
2. Visuals:
o Show Line Labels: Toggle “h.o.” / “phh” / “phl” (default: true).
o Period Open Line Color: Gray 50% (default).
o Previous High/Low Line Colors: Gray 100% (default).
o Open Line Style/Width: Dotted/1 (default; options: Solid/Dotted/Dashed).
3. Breakout→Open Background:
o Show Breakout→Open Background: Toggle fill (default: true).
o Fill Color: Teal 85% (default).
4. Breakout Circles:
o Show Breakout Circles: Toggle (default: true).
o PHH/PHL Break Circle Colors: White 20% (default).
5. Info Label Style:
o Text Size: Small (default; options: Auto/Tiny/Normal/Large/Huge).
o Label Text Color: White (default).
o Low/Mid/High Probability Colors: Red 20% / Orange 20% / Green 20% (default).
6. Excursion (Pain) Zone:
o Show Excursion Zone: Toggle Magic Box (default: true).
o Excursion Box Color: Gray 75% (default; dynamic overrides).
o 75th/90th/95th Percentile Lines: Orange 30% / Red 30% / Dark Red 100% (default).
No additional tables/plots; all elements are lines/labels/boxes for overlay focus.
________________________________________
Usage Tips
• Breakout Trading: Watch for sweeps with high probability (>70%, green label) as potential fades back to open; low probability (red) may signal runs—use the excursion box for targets (e.g., exit at 90th percentile for extremes).
• Time Awareness: Probabilities often peak in key liquidity windows and drop in quieter hours; segments capture momentum shifts (e.g., _2 often lower prob).
• RTH Focus: Enable for cleaner stats during high-liquidity session hours; disable for a 24-hour view.
• Visual Filtering: Use high-prob-only in volatile conditions to reduce noise; combine with volume or other confluence tools for confirmation.
• Alerts Integration: Set TradingView alerts on sweeps; check label for probability/N before acting.
• Chart Setup: Best on 1H or lower 6B1! charts; adjust text size for readability on smaller screens.
• Backtesting: Manually review historical sweeps against data maps to validate; update hardcoded values if new data emerges (as of 2025).
________________________________________
Limitations
• Fixed Data: Hardcoded stats may not reflect recent market changes (e.g., post-2025 regime shifts); not adaptive.
• Reactive Only: Detects sweeps after they occur; no predictive signals.
• Timeframe Specific: Locked to 1H logic; may not translate to other assets/timeframes without recoding data.
• Visual Clutter: On busy charts, labels/boxes may overlap—toggle selectively.
• No Live Stats: Sample sizes are historical; real-time N/prob not updated.
• Gaps & Extremes: Handles gaps in retest logic, but rare events (e.g., macro news) may exceed the 95th percentile.
________________________________________
Disclaimer
This indicator is for informational and educational purposes only. Trading involves significant risk of loss and is not suitable for all investors. The hardcoded data represents past British Pound futures (6B1!) performance and does not guarantee future outcomes. No claims of profitability are made—results depend on market conditions, user strategy, and risk management. Consult a financial advisor before trading, and backtest extensively. Abiding by TradingView rules, this tool provides no investment recommendations.
GC1! H1 Stats+GC1! H1 Stats - Detailed Prob & Excursion Indicator
Overview
GC1! H1 Stats - Detailed Prob & Excursion is a specialized statistical overlay indicator for TradingView, tailored for Gold futures (GC1!) on a 1-hour framework. It provides real-time insights into the probability of price returning to the hourly open after sweeping the previous hour’s high (PHH) or previous hour’s low (PHL), based on historical data segmented by hour (0–23) and 20-minute intervals. The indicator visualizes these sweeps with lines, labels, circles, background fills, and “excursion zones” (also called “Magic Boxes”) that highlight median/mean extensions post-sweep, along with percentile lines (75th / 90th / 95th) for gauging potential “pain” or extreme moves. This tool is designed for intraday Gold traders focusing on liquidity sweeps and mean-reversion behavior, helping to quantify edge using empirical probabilities and excursion statistics.
The data is hardcoded from extensive historical analysis of GC1! behavior (e.g., probabilities ranging roughly from ~7% to ~91%, with sample sizes up to 2000+ per segment), making it a backtested reference rather than a dynamic learning model. It emphasizes visual clarity during active hours, with options to filter for Regular Trading Hours (RTH: 09:00–15:59 ET) or high-probability (>70%) events only. Note: This is an educational tool for analyzing market structure; it does not predict future performance or provide trading signals/advice. Past data does not guarantee future results, and users should backtest on current conditions (as of December 2025 data availability) and use at their own risk, in compliance with TradingView’s house rules.
________________________________________
Key Features
• Sweep Detection & Probability Labels: Identifies when price breaks PHH (upside) or PHL (downside), displaying a centered label with probability of returning to the hourly open, sample size (N), time of sweep, and a checkmark (✅) if the open is retested post-sweep.
• Visual Lines & Markers: Draws hourly open (h.o.), PHH, and PHL lines with customizable styles/colors; adds small circles on sweep bars for quick spotting.
• Breakout→Open Background Fill: Shaded zone from sweep bar until price returns to open, visualizing extension duration and retracement.
• Excursion (Pain) Zone - “Magic Box”: Post-sweep box showing median/mean extension percentages, colored dynamically by probability (green high, orange mid, red low); includes dashed lines for 75th/90th/95th percentiles to mark statistical extremes.
• Time-Segmented Data: Probabilities and excursions vary by hour (0–23) and 20-min segments (0–19 min: _0, 20–39: _1, 40–59: _2), capturing intraday nuances (e.g., higher probs in early/late hours).
• Filters for Focus: RTH-only mode hides non-session elements; high-prob-only shows >70% events to reduce noise.
• Alerts: Triggers on PHH/PHL sweeps with messages for chart checks.
________________________________________
How It Works
• Data Foundation: Uses pre-computed maps for probabilities (prob_high_taken/prob_low_taken), sample sizes, and excursions (mean, median, p75/p90/p95 as percentages of open). Data is initialized on the first bar via f_init_high_data() and f_init_low_data(), covering 24 hours with 3 segments each (e.g., key "9_1" for 09:20–09:39). Probabilities represent historical likelihood of price returning to open after sweep; excursions quantify average/rare extensions (e.g., 0.156% mean = 0.156% of open price).
• Period Detection: On new 1H bars (new_period_bar), resets visuals, draws lines for open/PHH/PHL extending 1 hour forward, and labels if enabled. Uses request.security on standard ticker for real OHLC, bypassing chart transformations (e.g., Heikin Ashi).
• Sweep Logic: On each bar, checks if real high > PHH or real low < PHL. If so, fetches segment-specific data (hour + floor(minute/20)), displays probability label centered mid-hour. Skips if filtered (RTH-only or <70% prob).
• Excursion Visualization: If enabled, draws “Magic Box” from 1-min to 58-min into the hour, bounded by mean/median levels (top/bottom adjusted for high/low sweep). Adds percentile lines with labels (e.g., “75%”) at right end. Box color reflects prob strength for quick bias assessment.
• Retest Check: Monitors for open retest post-sweep (high/low cross open, or gap scenarios from prev bar). Adds ✅ to label if hit on subsequent bars (skips sweep bar to avoid false positives). Stops background fill on retest or at 58-min mark.
• Background Fill: Activates on sweep, shades until retest, using user color.
• Cleanup & Performance: Manages labels in arrays, clears on new periods; no excess drawing beyond max counts (500 lines/labels/boxes).
This setup blends statistical backtesting with real-time visualization: hardcoded data provides empirical probabilities/excursions (reducing subjectivity in breakouts), while dynamic elements (lines, fills, boxes) overlay structure on the chart. It helps Gold traders assess if a sweep is “high-edge” (e.g., >70% probability of reverting) or likely to run (low probability, high excursion), pairing historical context with current price action.
________________________________________
Settings and Customization
Inputs are grouped for ease:
1. Settings:
o Show RTH Only (9:00–15:59): Restricts to main session (default: false; tooltip: for RTH-focused stats).
o Show High Prob Only (>70%): Filters low-prob sweeps visually (default: false; tooltip: highlights confidence).
2. Visuals:
o Show Line Labels: Toggle “h.o.” / “phh” / “phl” (default: true).
o Period Open Line Color: Gray 50% (default).
o Previous High/Low Line Colors: Gray 100% (default).
o Open Line Style/Width: Dotted/1 (default; options: Solid/Dotted/Dashed).
3. Breakout→Open Background:
o Show Breakout→Open Background: Toggle fill (default: true).
o Fill Color: Teal 85% (default).
4. Breakout Circles:
o Show Breakout Circles: Toggle (default: true).
o PHH/PHL Break Circle Colors: White 20% (default).
5. Info Label Style:
o Text Size: Small (default; options: Auto/Tiny/Normal/Large/Huge).
o Label Text Color: White (default).
o Low/Mid/High Probability Colors: Red 20% / Orange 20% / Green 20% (default).
6. Excursion (Pain) Zone:
o Show Excursion Zone: Toggle Magic Box (default: true).
o Excursion Box Color: Gray 75% (default; dynamic overrides).
o 75th/90th/95th Percentile Lines: Orange 30% / Red 30% / Dark Red 100% (default).
No additional tables/plots; all elements are lines/labels/boxes for overlay focus.
________________________________________
Usage Tips
• Breakout Trading: Watch for sweeps with high probability (>70%, green label) as potential fades back to open; low probability (red) may signal runs—use the excursion box for targets (e.g., exit at 90th percentile for extremes).
• Time Awareness: Probabilities often peak in key liquidity windows and drop in quieter hours; segments capture momentum shifts (e.g., _2 often lower prob).
• RTH Focus: Enable for cleaner stats during high-liquidity session hours; disable for a 24-hour view.
• Visual Filtering: Use high-prob-only in volatile conditions to reduce noise; combine with volume or other confluence tools for confirmation.
• Alerts Integration: Set TradingView alerts on sweeps; check label for probability/N before acting.
• Chart Setup: Best on 1H or lower GC1! charts; adjust text size for readability on smaller screens.
• Backtesting: Manually review historical sweeps against data maps to validate; update hardcoded values if new data emerges (as of 2025).
________________________________________
Limitations
• Fixed Data: Hardcoded stats may not reflect recent market changes (e.g., post-2025 regime shifts); not adaptive.
• Reactive Only: Detects sweeps after they occur; no predictive signals.
• Timeframe Specific: Locked to 1H logic; may not translate to other assets/timeframes without recoding data.
• Visual Clutter: On busy charts, labels/boxes may overlap—toggle selectively.
• No Live Stats: Sample sizes are historical; real-time N/prob not updated.
• Gaps & Extremes: Handles gaps in retest logic, but rare events (e.g., macro news) may exceed the 95th percentile.
________________________________________
Disclaimer
This indicator is for informational and educational purposes only. Trading involves significant risk of loss and is not suitable for all investors. The hardcoded data represents past Gold futures (GC1!) performance and does not guarantee future outcomes. No claims of profitability are made—results depend on market conditions, user strategy, and risk management. Consult a financial advisor before trading, and backtest extensively. Abiding by TradingView rules, this tool provides no investment recommendations.
NQ H1 Stats+NQ H1 Stats - Detailed Prob & Excursion Indicator
Overview
NQ H1 Stats - Detailed Prob & Excursion is a specialized statistical overlay indicator for TradingView, tailored for the Nasdaq futures (NQ) on a 1-hour timeframe. It provides real-time insights into the probability of price returning to the hourly open after sweeping the previous hour's high (PHH) or low (PHL), based on historical data segmented by hour and 20-minute intervals. The indicator visualizes these sweeps with lines, labels, circles, background fills, and "excursion zones" (also called "Magic Boxes") that highlight median/mean extensions post-sweep, along with percentile lines (75th, 90th, 95th) for gauging potential "pain" or extreme moves. This tool is designed for intraday traders focusing on liquidity sweeps, or mean-reversion setups, helping to quantify edge based on empirical probabilities and volatility excursions.
The data is hardcoded from extensive historical analysis of NQ behavior (e.g., probabilities range from ~7% to ~91%, with sample sizes up to 2000+ per segment), making it a backtested reference rather than dynamic learning. It emphasizes visual clarity during active hours, with options to filter for Regular Trading Hours (RTH: 09:00–15:59 ET) or high-probability (>70%) events only. Note: This is an educational tool for analyzing market structure; it does not predict future performance or provide trading signals/advice. Past data does not guarantee future results, and users should backtest on current conditions (as of December 2025 data availability) and use at their own risk, in compliance with TradingView's house rules.
Key Features
• Sweep Detection & Probability Labels: Identifies when price breaks PHH (upside) or PHL (downside), displaying a centered label with probability of returning to the hourly open, sample size (N), time of sweep, and a checkmark (✅) if the open is retested post-sweep.
• Visual Lines & Markers: Draws hourly open (h.o.), PHH, and PHL lines with customizable styles/colors; adds small circles on sweep bars for quick spotting.
• Breakout→Open Background Fill: Shaded zone from sweep bar until price returns to open, visualizing extension duration and retracement.
• Excursion (Pain) Zone - "Magic Box": Post-sweep box showing median/mean extension percentages, colored dynamically by probability (green high, orange mid, red low); includes dashed lines for 75th/90th/95th percentiles to mark statistical extremes.
• Time-Segmented Data: Probabilities and excursions vary by hour (0-23) and 20-min segments (0-19 min: _0, 20-39: _1, 40-59: _2), capturing intraday nuances (e.g., higher probs in early/late hours).
• Filters for Focus: RTH-only mode hides non-session elements; high-prob-only shows >70% events to reduce noise.
• Alerts: Triggers on PHH/PHL sweeps with messages for chart checks.
How It Works
• Data Foundation: Uses pre-computed maps for probabilities (prob_high_taken/prob_low_taken), sample sizes, and excursions (mean, median, p75/p90/p95 as percentages of open). Data is initialized on the first bar via f_init_high_data() and f_init_low_data(), covering 24 hours with 3 segments each (e.g., key "9_1" for 09:20-09:39). Probabilities represent historical likelihood of price returning to open after sweep; excursions quantify average/rare extensions (e.g., 0.156% mean = 0.156% of open price).
• Period Detection: On new 1H bars (new_period_bar), resets visuals, draws lines for open/PHH/PHL extending 1 hour forward, and labels if enabled. Uses request.security on standard ticker for real OHLC, bypassing chart transformations (e.g., Heikin Ashi).
• Sweep Logic: On each bar, checks if real high > PHH or real low < PHL. If so, fetches segment-specific data (hour + floor(minute/20)), displays probability label centered mid-hour. Skips if filtered (RTH-only or <70% prob).
• Excursion Visualization: If enabled, draws "Magic Box" from 1-min to 58-min into the hour, bounded by mean/median levels (top/bottom adjusted for high/low sweep). Adds percentile lines with labels (e.g., "75%") at right end. Box color reflects prob strength for quick bias assessment.
• Retest Check: Monitors for open retest post-sweep (high/low cross open, or gap scenarios from prev bar). Adds ✅ to label if hit on subsequent bars (skips sweep bar to avoid false positives). Stops background fill on retest or at 58-min mark.
• Background Fill: Activates on sweep, shades until retest, using user color.
• Cleanup & Performance: Manages labels in arrays, clears on new periods; no excess drawing beyond max counts (500 lines/labels/boxes).
This setup "meshes" statistical backtesting with real-time visualization: Hardcoded data provides empirical probabilities/excursions (reducing subjectivity in breakouts), while dynamic elements (lines, fills, boxes) overlay structure on the chart. It helps traders assess if a sweep is "high-edge" (e.g., >70% prob of revert) or likely to run (low prob, high excursion), blending historical context with current price action for informed decisions.
Settings and Customization
Inputs are grouped for ease:
1. Settings:
o Show RTH Only (9:00-15:59): Restricts to main session (default: false; tooltip: for RTH-focused stats).
o Show High Prob Only (>70%): Filters low-prob sweeps visually (default: false; tooltip: highlights confidence).
2. Visuals:
o Show Line Labels: Toggle "h.o."/ "phh"/ "phl" (default: true).
o Period Open Line Color: Gray 50% (default).
o Previous High/Low Line Colors: Gray 100% (default).
o Open Line Style/Width: Dotted/1 (default; options: Solid/Dotted/Dashed).
3. Breakout→Open Background:
o Show Breakout→Open Background: Toggle fill (default: true).
o Fill Color: Teal 85% (default).
4. Breakout Circles:
o Show Breakout Circles: Toggle (default: true).
o PHH/PHL Break Circle Colors: White 20% (default).
5. Info Label Style:
o Text Size: Small (default; options: Auto/Tiny/Normal/Large/Huge).
o Label Text Color: White (default).
o Low/Mid/High Probability Colors: Red 20%/Orange 20%/Green 20% (default).
6. Excursion (Pain) Zone:
o Show Excursion Zone: Toggle Magic Box (default: true).
o Excursion Box Color: Gray 75% (default; dynamic overrides).
o 75th/90th/95th Percentile Lines: Orange 30%/Red 30%/Dark Red 100% (default).
No additional tables/plots; all elements are lines/labels/boxes for overlay focus.
Usage Tips
• Breakout Trading: Watch for sweeps with high prob (>70%, green label) as potential fades back to open; low prob (red) may signal runs—use excursion box for targets (e.g., exit at 90th percentile for extremes).
• Time Awareness: Probabilities peak in open hours (e.g., 09:00 ~90%+ for initial sweeps) and drop in off-hours; segments capture momentum shifts (e.g., _2 often lower prob).
• RTH Focus: Enable for cleaner stats during high-liquidity sessions; disable for 24/7 view.
• Visual Filtering: Use high-prob-only in volatile conditions to avoid noise; combine with volume or other indicators for confirmation.
• Alerts Integration: Set TradingView alerts on sweeps; check label for prob/N before acting.
• Chart Setup: Best on 1H or lower NQ charts; adjust text size for readability on mobiles.
• Backtesting: Manually review historical sweeps against data maps to validate; update hardcoded values if new data emerges (as of 2025).
Limitations
• Fixed Data: Hardcoded stats may not reflect recent market changes (e.g., post-2025 volatility shifts); not adaptive.
• Reactive Only: Detects sweeps after they occur; no predictive signals.
• Timeframe Specific: Locked to 1H logic; may not translate to other assets/TFs without recoding data.
• Visual Clutter: On busy charts, labels/boxes may overlap—toggle off selectively.
• No Live Stats: Sample sizes are historical; real-time N/prob not updated.
• Gaps & Extremes: Handles gaps in retest logic, but rare events (e.g., news) may exceed 95th percentile.
Disclaimer
This indicator is for informational and educational purposes only. Trading involves significant risk of loss and is not suitable for all investors. The hardcoded data represents past NQ performance and does not guarantee future outcomes. No claims of profitability are made—results depend on market conditions, user strategy, and risk management. Consult a financial advisor before trading, and backtest extensively. Abiding by TradingView rules, this tool provides no investment recommendations.
Luxy Super-Duper SuperTrend Predictor Engine and Buy/Sell signalA professional trend-following grading system that analyzes historical trend
patterns to provide statistical duration estimates using advanced similarity
matching and k-nearest neighbors analysis. Combines adaptive Supertrend with
intelligent duration statistics, multi-timeframe confluence, volume confirmation,
and quality scoring to identify high-probability setups with data-driven
target ranges across all timeframes.
Note: All duration estimates are statistical calculations based on historical data, not guarantees of future performance.
WHAT MAKES THIS DIFFERENT
Unlike traditional SuperTrend indicators that only tell you trend direction, this system answers the critical question: "What is the typical duration for trends like this?"
The Statistical Analysis Engine:
• Analyzes your chart's last 15+ completed SuperTrend trends (bullish and bearish separately)
• Uses k-nearest neighbors similarity matching to find historically similar setups
• Calculates statistical duration estimates based on current market conditions
• Learns from estimation errors and adapts over time (Advanced mode)
• Displays visual duration analysis box showing median, average, and range estimates
• Tracks Statistical accuracy with backtest statistics
Complete Trading System:
• Statistical trend duration analysis with three intelligence levels
• Adaptive Supertrend with dynamic ATR-based bands
• Multi-timeframe confluence analysis (6 timeframes: 5M to 1W)
• Volume confirmation with spike detection and momentum tracking
• Quality scoring system (0-70 points) rating each setup
• One-click preset optimization for all trading styles
• Anti-repaint guarantee on all signals and duration estimates
METHODOLOGY CREDITS
This indicator's approach is inspired by proven trading methodologies from respected market educators:
• Mark Minervini - Volatility Contraction Pattern (VCP) and pullback entry techniques
• William O'Neil - Volume confirmation principles and institutional buying patterns (CANSLIM methodology)
• Dan Zanger - Volatility expansion entries and momentum breakout strategies
Important: These are educational references only. This indicator does not guarantee any specific trading results. Always conduct your own analysis and risk management.
KEY FEATURES
1. TREND DURATION ANALYSIS SYSTEM - The Core Innovation
The statistical analysis engine is what sets this indicator apart from standard SuperTrend systems. It doesn't just identify trend changes - it provides statistical analysis of potential duration.
How It Works:
Step 1: Historical Tracking
• Automatically records every completed SuperTrend trend (duration in bars)
• Maintains separate databases for bullish trends and bearish trends
• Stores up to 15 most recent trends of each type
• Captures market conditions at each trend flip: volume ratio, ATR ratio, quality score, price distance from SuperTrend, proximity to support/resistance
Step 2: Similarity Matching (k-Nearest Neighbors)
• When new trend begins, system compares current conditions to ALL historical flips
• Calculates similarity score based on:
- Volume similarity (30% weight) - Is volume behaving similarly?
- Volatility similarity (30% weight) - Is ATR/volatility similar?
- Quality similarity (20% weight) - Is setup strength comparable?
- Distance similarity (10% weight) - Is price distance from ST similar?
- Support/Resistance proximity (10% weight) - Similar structural context?
• Selects the 15 MOST SIMILAR historical trends (not just all trends)
• This is like asking: "When conditions looked like this before, how long did trends last?"
Step 3: Statistical Analysis
• Calculates median duration (most common outcome)
• Calculates average duration (mean of similar trends)
• Determines realistic range (min to max of similar trends)
• Applies exponential weighting (recent trends weighted more heavily)
• Outputs confidence-weighted statistical estimate
Step 4: Advanced Intelligence (Advanced Mode Only)
The Advanced mode applies five sophisticated multipliers to refine estimates:
A) Market Structure Multiplier (±30%):
• Detects nearby support/resistance levels using pivot detection
• If flip occurs NEAR a key level: Estimate adjusted -30% (expect bounce/rejection)
• If flip occurs in open space: Estimate adjusted +30% (clear path for continuation)
• Uses configurable lookback period and ATR-based proximity threshold
B) Asset Type Multiplier (±40%):
• Adjusts duration estimates based on asset volatility characteristics
• Small Cap / Biotech: +40% (explosive, extended moves)
• Tech Growth: +20% (momentum-driven, longer trends)
• Blue Chip / Large Cap: 0% (baseline, steady trends)
• Dividend / Value: -20% (slower, grinding trends)
• Cyclical: Variable based on macro regime
• Crypto / High Volatility: +30% (parabolic potential)
C) Flip Strength Multiplier (±20%):
• Analyzes the QUALITY of the trend flip itself
• Strong flip (high volume + expanding ATR + quality score 60+): +20%
• Weak flip (low volume + contracting ATR + quality score under 40): -20%
• Logic: Historical data shows that powerful flips tend to be followed by longer trends
D) Error Learning Multiplier (±15%):
• Tracks Statistical accuracy over last 10 completed trends
• Calculates error ratio: (estimated duration / Actual Duration)
• If system consistently over-estimates: Apply -15% correction
• If system consistently under-estimates: Apply +15% correction
• Learns and adapts to current market regime
E) Regime Detection Multiplier (±20%):
• Analyzes last 3 trends of SAME TYPE (bull-to-bull or bear-to-bear)
• Compares recent trend durations to historical average
• If recent trends 20%+ longer than average: +20% adjustment (trending regime detected)
• If recent trends 20%+ shorter than average: -20% adjustment (choppy regime detected)
• Detects whether market is in trending or mean-reversion mode
Three analysis modes:
SIMPLE MODE - Basic Statistics
• Uses raw median of similar trends only
• No multipliers, no adjustments
• Best for: Beginners, clean trending markets
• Fastest calculations, minimal complexity
STANDARD MODE - Full Statistical Analysis
• Similarity matching with k-nearest neighbors
• Exponential weighting of recent trends
• Median, average, and range calculations
• Best for: Most traders, general market conditions
• Balance of accuracy and simplicity
ADVANCED MODE - Statistics + Intelligence
• Everything in Standard mode PLUS
• All 5 advanced multipliers (structure, asset type, flip strength, learning, regime)
• Highest Statistical accuracy in testing
• Best for: Experienced traders, volatile/complex markets
• Maximum intelligence, most adaptive
Visual Duration Analysis Box:
When a new trend begins (SuperTrend flip), a box appears on your chart showing:
• Analysis Mode (Simple / Standard / Advanced)
• Number of historical trends analyzed
• Median expected duration (most likely outcome)
• Average expected duration (mean of similar trends)
• Range (minimum to maximum from similar trends)
• Advanced multipliers breakdown (Advanced mode only)
• Backtest accuracy statistics (if available)
The box extends from the flip bar to the estimated endpoint based on historical data, giving you a visual target for trend duration. Box updates in real-time as trend progresses.
Backtest & Accuracy Tracking:
• System backtests its own duration estimates using historical data
• Shows accuracy metrics: how well duration estimates matched actual durations
• Tracks last 10 completed duration estimates separately
• Displays statistics in dashboard and duration analysis boxes
• Helps you understand statistical reliability on your specific symbol/timeframe
Anti-Repaint Guarantee:
• duration analysis boxes only appear AFTER bar close (barstate.isconfirmed)
• Historical duration estimates never disappear or change
• What you see in history is exactly what you would have seen real-time
• No future data leakage, no lookahead bias
2. INTELLIGENT PRESET CONFIGURATIONS - One-Click Optimization
Unlike indicators that require tedious parameter tweaking, this system includes professionally optimized presets for every trading style. Select your approach from the dropdown and ALL parameters auto-configure.
"AUTO (DETECT FROM TF)" - RECOMMENDED
The smartest option: automatically selects optimal settings based on your chart timeframe.
• 1m-5m charts → Scalping preset (ATR: 7, Mult: 2.0)
• 15m-1h charts → Day Trading preset (ATR: 10, Mult: 2.5)
• 2h-4h-D charts → Swing Trading preset (ATR: 14, Mult: 3.0)
• W-M charts → Position Trading preset (ATR: 21, Mult: 4.0)
Benefits:
• Zero configuration - works immediately
• Always matched to your timeframe
• Switch timeframe = automatic adjustment
• Perfect for traders who use multiple timeframes
"SCALPING (1-5M)" - Ultra-Fast Signals
Optimized for: 1-5 minute charts, high-frequency trading, quick profits
Target holding period: Minutes to 1-2 hours maximum
Best markets: High-volume stocks, major crypto pairs, active futures
Parameter Configuration:
• Supertrend: ATR 7, Multiplier 2.0 (very sensitive)
• Volume: MA 10, High 1.8x, Spike 3.0x (catches quick surges)
• Volume Momentum: AUTO-DISABLED (too restrictive for fast scalping)
• Quality minimum: 40 points (accepts more setups)
• Duration Analysis: Uses last 15 trends with heavy recent weighting
Trading Logic:
Speed over precision. Short ATR period and low multiplier create highly responsive SuperTrend. Volume momentum filter disabled to avoid missing fast moves. Quality threshold relaxed to catch more opportunities in rapid market conditions.
Signals per session: 5-15 typically
Hold time: Minutes to couple hours
Best for: Active traders with fast execution
"DAY TRADING (15M-1H)" - Balanced Approach
Optimized for: 15-minute to 1-hour charts, intraday moves, session-based trading
Target holding period: 30 minutes to 8 hours (within trading day)
Best markets: Large-cap stocks, major indices, established crypto
Parameter Configuration:
• Supertrend: ATR 10, Multiplier 2.5 (balanced)
• Volume: MA 20, High 1.5x, Spike 2.5x (standard detection)
• Volume Momentum: 5/20 periods (confirms intraday strength)
• Quality minimum: 50 points (good setups preferred)
• Duration Analysis: Balanced weighting of recent vs historical
Trading Logic:
The most balanced configuration. ATR 10 with multiplier 2.5 provides steady trend following that avoids noise while catching meaningful moves. Volume momentum confirms institutional participation without being overly restrictive.
Signals per session: 2-5 typically
Hold time: 30 minutes to full day
Best for: Part-time and full-time active traders
"SWING TRADING (4H-D)" - Trend Stability
Optimized for: 4-hour to Daily charts, multi-day holds, trend continuation
Target holding period: 2-15 days typically
Best markets: Growth stocks, sector ETFs, trending crypto, commodity futures
Parameter Configuration:
• Supertrend: ATR 14, Multiplier 3.0 (stable)
• Volume: MA 30, High 1.3x, Spike 2.2x (accumulation focus)
• Volume Momentum: 10/30 periods (trend stability)
• Quality minimum: 60 points (high-quality setups only)
• Duration Analysis: Favors consistent historical patterns
Trading Logic:
Designed for substantial trend moves while filtering short-term noise. Higher ATR period and multiplier create stable SuperTrend that won't flip on minor corrections. Stricter quality requirements ensure only strongest setups generate signals.
Signals per week: 2-5 typically
Hold time: Days to couple weeks
Best for: Part-time traders, swing style
"POSITION TRADING (D-W)" - Long-Term Trends
Optimized for: Daily to Weekly charts, major trend changes, portfolio allocation
Target holding period: Weeks to months
Best markets: Blue-chip stocks, major indices, established cryptocurrencies
Parameter Configuration:
• Supertrend: ATR 21, Multiplier 4.0 (very stable)
• Volume: MA 50, High 1.2x, Spike 2.0x (long-term accumulation)
• Volume Momentum: 20/50 periods (major trend confirmation)
• Quality minimum: 70 points (excellent setups only)
• Duration Analysis: Heavy emphasis on multi-year historical data
Trading Logic:
Conservative approach focusing on major trend changes. Extended ATR period and high multiplier create SuperTrend that only flips on significant reversals. Very strict quality filters ensure signals represent genuine long-term opportunities.
Signals per month: 1-2 typically
Hold time: Weeks to months
Best for: Long-term investors, set-and-forget approach
"CUSTOM" - Advanced Configuration
Purpose: Complete manual control for experienced traders
Use when: You understand the parameters and want specific optimization
Best for: Testing new approaches, unusual market conditions, specific instruments
Full control over:
• All SuperTrend parameters
• Volume thresholds and momentum periods
• Quality scoring weights
• analysis mode and multipliers
• Advanced features tuning
Preset Comparison Quick Reference:
Chart Timeframe: Scalping (1M-5M) | Day Trading (15M-1H) | Swing (4H-D) | Position (D-W)
Signals Frequency: Very High | High | Medium | Low
Hold Duration: Minutes | Hours | Days | Weeks-Months
Quality Threshold: 40 pts | 50 pts | 60 pts | 70 pts
ATR Sensitivity: Highest | Medium | Lower | Lowest
Time Investment: Highest | High | Medium | Lowest
Experience Level: Expert | Advanced | Intermediate | Beginner+
3. QUALITY SCORING SYSTEM (0-70 Points)
Every signal is rated in real-time across three dimensions:
Volume Confirmation (0-30 points):
• Volume Spike (2.5x+ average): 30 points
• High Volume (1.5x+ average): 20 points
• Above Average (1.0x+ average): 10 points
• Below Average: 0 points
Volatility Assessment (0-30 points):
• Expanding ATR (1.2x+ average): 30 points
• Rising ATR (1.0-1.2x average): 15 points
• Contracting/Stable ATR: 0 points
Volume Momentum (0-10 points):
• Strong Momentum (1.2x+ ratio): 10 points
• Rising Momentum (1.0-1.2x ratio): 5 points
• Weak/Neutral Momentum: 0 points
Score Interpretation:
60-70 points - EXCELLENT:
• All factors aligned
• High conviction setup
• Maximum position size (within risk limits)
• Primary trading opportunities
45-59 points - STRONG:
• Multiple confirmations present
• Above-average setup quality
• Standard position size
• Good trading opportunities
30-44 points - GOOD:
• Basic confirmations met
• Acceptable setup quality
• Reduced position size
• Wait for additional confirmation or trade smaller
Below 30 points - WEAK:
• Minimal confirmations
• Low probability setup
• Consider passing
• Only for aggressive traders in strong trends
Only signals meeting your minimum quality threshold (configurable per preset) generate alerts and labels.
4. MULTI-TIMEFRAME CONFLUENCE ANALYSIS
The system can simultaneously analyze trend alignment across 6 timeframes (optional feature):
Timeframes analyzed:
• 5-minute (scalping context)
• 15-minute (intraday momentum)
• 1-hour (day trading bias)
• 4-hour (swing context)
• Daily (primary trend)
• Weekly (macro trend)
Confluence Interpretation:
• 5-6/6 aligned - Very strong multi-timeframe agreement (highest confidence)
• 3-4/6 aligned - Moderate agreement (standard setup)
• 1-2/6 aligned - Weak agreement (caution advised)
Dashboard shows real-time alignment count with color-coding. Higher confluence typically correlates with longer, stronger trends.
5. VOLUME MOMENTUM FILTER - Institutional Money Flow
Unlike traditional volume indicators that just measure size, Volume Momentum tracks the RATE OF CHANGE in volume:
How it works:
• Compares short-term volume average (fast period) to long-term average (slow period)
• Ratio above 1.0 = Volume accelerating (money flowing IN)
• Ratio above 1.2 = Strong acceleration (institutional participation likely)
• Ratio below 0.8 = Volume decelerating (money flowing OUT)
Why it matters:
• Confirms trend with actual money flow, not just price
• Leading indicator (volume often leads price)
• Catches accumulation/distribution before breakouts
• More intuitive than complex mathematical filters
Integration with signals:
• Optional filter - can be enabled/disabled per preset
• When enabled: Only signals with rising volume momentum fire
• AUTO-DISABLED in Scalping mode (too restrictive for fast trading)
• Configurable fast/slow periods per trading style
6. ADAPTIVE SUPERTREND MULTIPLIER
Traditional SuperTrend uses fixed ATR multiplier. This system dynamically adjusts the multiplier (0.8x to 1.2x base) based on:
• Trend Strength: Price correlation over lookback period
• Volume Weight: Current volume relative to average
Benefits:
• Tighter bands in calm markets (less premature exits)
• Wider bands in volatile conditions (avoids whipsaws)
• Better adaptation to biotech, small-cap, and crypto volatility
• Optional - can be disabled for classic constant multiplier
7. VISUAL GRADIENT RIBBON
26-layer exponential gradient fill between price and SuperTrend line provides instant visual trend strength assessment:
Color System:
• Green shades - Bullish trend + volume confirmation (strongest)
• Blue shades - Bullish trend, normal volume
• Orange shades - Bearish trend + volume confirmation
• Red shades - Bearish trend (weakest)
Opacity varies based on:
• Distance from SuperTrend (farther = more opaque)
• Volume intensity (higher volume = stronger color)
The ribbon provides at-a-glance trend strength without cluttering your chart. Can be toggled on/off.
8. INTELLIGENT ALERT SYSTEM
Two-tier alert architecture for flexibility:
Automatic Alerts:
• Fire automatically on BUY and SELL signals
• Include full context: quality score, volume state, volume momentum
• One alert per bar close (alert.freq_once_per_bar_close)
• Message format: "BUY: Supertrend bullish + Quality: 65/70 | Volume: HIGH | Vol Momentum: STRONG (1.35x)"
Customizable Alert Conditions:
• Appear in TradingView's "Create Alert" dialog
• Three options: BUY Signal Only, SELL Signal Only, ANY Signal (BUY or SELL)
• Use TradingView placeholders: {{ticker}}, {{interval}}, {{close}}, {{time}}
• Fully customizable message templates
All alerts use barstate.isconfirmed - Zero repaint guarantee.
9. ANTI-REPAINT ARCHITECTURE
Every component guaranteed non-repainting:
• Entry signals: Only appear after bar close
• duration analysis boxes: Created only on confirmed SuperTrend flips
• Informative labels: Wait for bar confirmation
• Alerts: Fire once per closed bar
• Multi-timeframe data: Uses lookahead=barmerge.lookahead_off
What you see in history is exactly what you would have seen in real-time. No disappearing signals, no changed duration estimates.
HOW TO USE THE INDICATOR
QUICK START - 3 Steps to Trading:
Step 1: Select Your Trading Style
Open indicator settings → "Quick Setup" section → Trading Style Preset dropdown
Options:
• Auto (Detect from TF) - RECOMMENDED: Automatically configures based on your chart timeframe
• Scalping (1-5m) - For 1-5 minute charts, ultra-fast signals
• Day Trading (15m-1h) - For 15m-1h charts, balanced approach
• Swing Trading (4h-D) - For 4h-Daily charts, trend stability
• Position Trading (D-W) - For Daily-Weekly charts, long-term trends
• Custom - Manual configuration (advanced users only)
Choose "Auto" and you're done - all parameters optimize automatically.
Step 2: Understand the Signals
BUY Signal (Green Triangle Below Price):
• SuperTrend flipped bullish
• Quality score meets minimum threshold (varies by preset)
• Volume confirmation present (if filter enabled)
• Volume momentum rising (if filter enabled)
• duration analysis box shows expected trend duration
SELL Signal (Red Triangle Above Price):
• SuperTrend flipped bearish
• Quality score meets minimum threshold
• Volume confirmation present (if filter enabled)
• Volume momentum rising (if filter enabled)
• duration analysis box shows expected trend duration
Duration Analysis Box:
• Appears at SuperTrend flip (start of new trend)
• Shows median, average, and range duration estimates
• Extends to estimated endpoint based on historical data visually
• Updates mode-specific intelligence (Simple/Standard/Advanced)
Step 3: Use the Dashboard for Context
Dashboard (top-right corner) shows real-time metrics:
• Row 1 - Quality Score: Current setup rating (0-70)
• Row 2 - SuperTrend: Direction and current level
• Row 3 - Volume: Status (Spike/High/Normal/Low) with color
• Row 4 - Volatility: State (Expanding/Rising/Stable/Contracting)
• Row 5 - Volume Momentum: Ratio and trend
• Row 6 - Duration Statistics: Accuracy metrics and track record
Every cell has detailed tooltip - hover for full explanations.
SIGNAL INTERPRETATION BY QUALITY SCORE:
Excellent Setup (60-70 points):
• Quality Score: 60-70
• Volume: Spike or High
• Volatility: Expanding
• Volume Momentum: Strong (1.2x+)
• MTF Confluence (if enabled): 5-6/6
• Action: Primary trade - maximum position size (within risk limits)
• Statistical reliability: Highest - duration estimates most accurate
Strong Setup (45-59 points):
• Quality Score: 45-59
• Volume: High or Above Average
• Volatility: Rising
• Volume Momentum: Rising (1.0-1.2x)
• MTF Confluence (if enabled): 3-4/6
• Action: Standard trade - normal position size
• Statistical reliability: Good - duration estimates reliable
Good Setup (30-44 points):
• Quality Score: 30-44
• Volume: Above Average
• Volatility: Stable or Rising
• Volume Momentum: Neutral to Rising
• MTF Confluence (if enabled): 3-4/6
• Action: Cautious trade - reduced position size, wait for additional confirmation
• Statistical reliability: Moderate - duration estimates less certain
Weak Setup (Below 30 points):
• Quality Score: Below 30
• Volume: Low or Normal
• Volatility: Contracting or Stable
• Volume Momentum: Weak
• MTF Confluence (if enabled): 1-2/6
• Action: Pass or wait for improvement
• Statistical reliability: Low - duration estimates unreliable
USING duration analysis boxES FOR TRADE MANAGEMENT:
Entry Timing:
• Enter on SuperTrend flip (signal bar close)
• duration analysis box appears simultaneously
• Note the median duration - this is your expected hold time
Profit Targets:
• Conservative: Use MEDIAN duration as profit target (50% probability)
• Moderate: Use AVERAGE duration (mean of similar trends)
• Aggressive: Aim for MAX duration from range (best historical outcome)
Position Management:
• Scale out at median duration (take partial profits)
• Trail stop as trend extends beyond median
• Full exit at average duration or SuperTrend flip (whichever comes first)
• Re-evaluate if trend exceeds estimated range
analysis mode Selection:
• Simple: Clean trending markets, beginners, minimal complexity
• Standard: Most markets, most traders (recommended default)
• Advanced: Volatile markets, complex instruments, experienced traders seeking highest accuracy
Asset Type Configuration (Advanced Mode):
If using Advanced analysis mode, configure Asset Type for optimal accuracy:
• Small Cap: Stocks under $2B market cap, low liquidity
• Biotech / Speculative: Clinical-stage pharma, penny stocks, high-risk
• Blue Chip / Large Cap: S&P 500, mega-cap tech, stable large companies
• Tech Growth: High-growth tech (TSLA, NVDA, growth SaaS)
• Dividend / Value: Dividend aristocrats, value stocks, utilities
• Cyclical: Energy, materials, industrials (macro-driven)
• Crypto / High Volatility: Bitcoin, altcoins, highly volatile assets
Correct asset type selection improves Statistical accuracy by 15-20%.
RISK MANAGEMENT GUIDELINES:
1. Stop Loss Placement:
Long positions:
• Place stop below recent swing low OR
• Place stop below SuperTrend level (whichever is tighter)
• Use 1-2 ATR distance as guideline
• Recommended: SuperTrend level (built-in volatility adjustment)
Short positions:
• Place stop above recent swing high OR
• Place stop above SuperTrend level (whichever is tighter)
• Use 1-2 ATR distance as guideline
• Recommended: SuperTrend level
2. Position Sizing by Quality Score:
• Excellent (60-70): Maximum position size (2% risk per trade)
• Strong (45-59): Standard position size (1.5% risk per trade)
• Good (30-44): Reduced position size (1% risk per trade)
• Weak (Below 30): Pass or micro position (0.5% risk - learning trades only)
3. Exit Strategy Options:
Option A - Statistical Duration-Based Exit:
• Exit at median estimated duration (conservative)
• Exit at average estimated duration (moderate)
• Trail stop beyond average duration (aggressive)
Option B - Signal-Based Exit:
• Exit on opposite signal (SELL after BUY, or vice versa)
• Exit on SuperTrend flip (trend reversal)
• Exit if quality score drops below 30 mid-trend
Option C - Hybrid (Recommended):
• Take 50% profit at median estimated duration
• Trail stop on remaining 50% using SuperTrend as trailing level
• Full exit on SuperTrend flip or quality collapse
4. Trade Filtering:
For higher win-rate (fewer trades, better quality):
• Increase minimum quality score (try 60 for swing, 50 for day trading)
• Enable volume momentum filter (ensure institutional participation)
• Require higher MTF confluence (5-6/6 alignment)
• Use Advanced analysis mode with appropriate asset type
For more opportunities (more trades, lower quality threshold):
• Decrease minimum quality score (40 for day trading, 35 for scalping)
• Disable volume momentum filter
• Lower MTF confluence requirement
• Use Simple or Standard analysis mode
SETTINGS OVERVIEW
Quick Setup Section:
• Trading Style Preset: Auto / Scalping / Day Trading / Swing / Position / Custom
Dashboard & Display:
• Show Dashboard (ON/OFF)
• Dashboard Position (9 options: Top/Middle/Bottom + Left/Center/Right)
• Text Size (Auto/Tiny/Small/Normal/Large/Huge)
• Show Ribbon Fill (ON/OFF)
• Show SuperTrend Line (ON/OFF)
• Bullish Color (default: Green)
• Bearish Color (default: Red)
• Show Entry Labels - BUY/SELL signals (ON/OFF)
• Show Info Labels - Volume events (ON/OFF)
• Label Size (Auto/Tiny/Small/Normal/Large/Huge)
Supertrend Configuration:
• ATR Length (default varies by preset: 7-21)
• ATR Multiplier Base (default varies by preset: 2.0-4.0)
• Use Adaptive Multiplier (ON/OFF) - Dynamic 0.8x-1.2x adjustment
• Smoothing Factor (0.0-0.5) - EMA smoothing applied to bands
• Neutral Bars After Flip (0-10) - Hide ST immediately after flip
Volume Momentum:
• Enable Volume Momentum Filter (ON/OFF)
• Fast Period (default varies by preset: 3-20)
• Slow Period (default varies by preset: 10-50)
Volume Analysis:
• Volume MA Length (default varies by preset: 10-50)
• High Volume Threshold (default: 1.5x)
• Spike Threshold (default: 2.5x)
• Low Volume Threshold (default: 0.7x)
Quality Filters:
• Minimum Quality Score (0-70, varies by preset)
• Require Volume Confirmation (ON/OFF)
Trend Duration Analysis:
• Show Duration Analysis (ON/OFF) - Display duration analysis boxes
• analysis mode - Simple / Standard / Advanced
• Asset Type - 7 options (Small Cap, Biotech, Blue Chip, Tech Growth, Dividend, Cyclical, Crypto)
• Use Exponential Weighting (ON/OFF) - Recent trends weighted more
• Decay Factor (0.5-0.99) - How much more recent trends matter
• Structure Lookback (3-30) - Pivot detection period for support/resistance
• Proximity Threshold (xATR) - How close to level qualifies as "near"
• Enable Error Learning (ON/OFF) - System learns from estimation errors
• Memory Depth (3-20) - How many past errors to remember
Box Visual Settings:
• duration analysis box Border Color
• duration analysis box Background Color
• duration analysis box Text Color
• duration analysis box Border Width
• duration analysis box Transparency
Multi-Timeframe (Optional Feature):
• Enable MTF Confluence (ON/OFF)
• Minimum Alignment Required (0-6)
• Individual timeframe enable/disable toggles
• Custom timeframe selection options
All preset configurations override manual inputs except when "Custom" is selected.
ADVANCED FEATURES
1. Scalpel Mode (Optional)
Advanced pullback entry system that waits for healthy retracements within established trends before signaling entry:
• Monitors price distance from SuperTrend levels
• Requires pullback to configurable range (default: 30-50%)
• Ensures trend remains intact before entry signal
• Reduces whipsaw and false breakouts
• Inspired by Mark Minervini's VCP pullback entries
Best for: Swing traders and day traders seeking precision entries
Scalpers: Consider disabling for faster entries
2. Error Learning System (Advanced analysis mode Only)
The system learns from its own estimation errors:
• Tracks last 10-20 completed duration estimates (configurable memory depth)
• Calculates error ratio for each: estimated duration / Actual Duration
• If system consistently over-estimates: Applies negative correction (-15%)
• If system consistently under-estimates: Applies positive correction (+15%)
• Adapts to current market regime automatically
This self-correction mechanism improves accuracy over time as the system gathers more data on your specific symbol and timeframe.
3. Regime Detection (Advanced analysis mode Only)
Automatically detects whether market is in trending or choppy regime:
• Compares last 3 trends to historical average
• Recent trends 20%+ longer → Trending regime (+20% to estimates)
• Recent trends 20%+ shorter → Choppy regime (-20% to estimates)
• Applied separately to bullish and bearish trends
Helps duration estimates adapt to changing market conditions without manual intervention.
4. Exponential Weighting
Option to weight recent trends more heavily than distant history:
• Default decay factor: 0.9
• Recent trends get higher weight in statistical calculations
• Older trends gradually decay in importance
• Rationale: Recent market behavior more relevant than old data
• Can be disabled for equal weighting
5. Backtest Statistics
System backtests its own duration estimates using historical data:
• Walks through past trends chronologically
• Calculates what duration estimate WOULD have been at each flip
• Compares to actual duration that occurred
• Displays accuracy metrics in duration analysis boxes and dashboard
• Helps assess statistical reliability on your specific chart
Note: Backtest uses only data available AT THE TIME of each historical flip (no lookahead bias).
TECHNICAL SPECIFICATIONS
• Pine Script Version: v6
• Indicator Type: Overlay (draws on price chart)
• Max Boxes: 500 (for duration analysis box storage)
• Max Bars Back: 5000 (for comprehensive historical analysis)
• Security Calls: 1 (for MTF if enabled - optimized)
• Repainting: NO - All signals and duration estimates confirmed on bar close
• Lookahead Bias: NO - All HTF data properly offset, all duration estimates use only historical data
• Real-time Updates: YES - Dashboard and quality scores update live
• Alert Capable: YES - Both automatic alerts and customizable alert conditions
• Multi-Symbol: Works on stocks, crypto, forex, futures, indices
Performance Optimization:
• Conditional calculations (duration analysis can be disabled to reduce load)
• Efficient array management (circular buffers for trend storage)
• Streamlined gradient rendering (26 layers, can be toggled off)
• Smart label cooldown system (prevents label spam)
• Optimized similarity matching (analyzes only relevant trends)
Data Requirements:
• Minimum 50-100 bars for initial duration analysis (builds historical database)
• Optimal: 500+ bars for robust statistical analysis
• Longer history = more accurate duration estimates
• Works on any timeframe from 1 minute to monthly
KNOWN LIMITATIONS
• Trending Markets Only: Performs best in clear trends. May generate false signals in choppy/sideways markets (use quality score filtering and regime detection to mitigate)
• Lagging Nature: Like all trend-following systems, signals occur AFTER trend establishment, not at exact tops/bottoms. Use duration analysis boxes to set realistic profit targets.
• Initial Learning Period: Duration analysis system requires 10-15 completed trends to build reliable historical database. Early duration estimates less accurate (first few weeks on new symbol/timeframe).
• Visual Load: 26-layer gradient ribbon may slow performance on older devices. Disable ribbon if experiencing lag.
• Statistical accuracy Variables: Duration estimates are statistical estimates, not guarantees. Accuracy varies by:
- Market regime (trending vs choppy)
- Asset volatility characteristics
- Quality of historical pattern matches
- Timeframe traded (higher TF = more reliable)
• Not Best Suitable For:
- Ultra-short-term scalping (sub-1-minute charts)
- Mean-reversion strategies (designed for trend-following)
- Range-bound trading (requires trending conditions)
- News-driven spikes (estimates based on technical patterns, not fundamentals)
FREQUENTLY ASKED QUESTIONS
Q: Does this indicator repaint?
A: Absolutely not. All signals, duration analysis boxes, labels, and alerts use barstate.isconfirmed checks. They only appear after the bar closes. What you see in history is exactly what you would have seen in real-time. Zero repaint guarantee.
Q: How accurate are the trend duration estimates?
A: Accuracy varies by mode, market conditions, and historical data quality:
• Simple mode: 60-70% accuracy (within ±20% of actual duration)
• Standard mode: 70-80% accuracy (within ±20% of actual duration)
• Advanced mode: 75-85% accuracy (within ±20% of actual duration)
Best accuracy achieved on:
• Higher timeframes (4H, Daily, Weekly)
• Trending markets (not choppy/sideways)
• Assets with consistent behavior (Blue Chip, Large Cap)
• After 20+ historical trends analyzed (builds robust database)
Remember: All duration estimates are statistical calculations based on historical patterns, not guarantees.
Q: Which analysis mode should I use?
A:
• Simple: Beginners, clean trending markets, want minimal complexity
• Standard: Most traders, general market conditions (RECOMMENDED DEFAULT)
• Advanced: Experienced traders, volatile/complex markets (biotech, small-cap, crypto), seeking maximum accuracy
Advanced mode requires correct Asset Type configuration for optimal results.
Q: What's the difference between the trading style presets?
A: Each preset optimizes ALL parameters for a specific trading approach:
• Scalping: Ultra-sensitive (ATR 7, Mult 2.0), more signals, shorter holds
• Day Trading: Balanced (ATR 10, Mult 2.5), moderate signals, intraday holds
• Swing Trading: Stable (ATR 14, Mult 3.0), fewer signals, multi-day holds
• Position Trading: Very stable (ATR 21, Mult 4.0), rare signals, week/month holds
Auto mode automatically selects based on your chart timeframe.
Q: Should I use Auto mode or manually select a preset?
A: Auto mode is recommended for most traders. It automatically matches settings to your timeframe and re-optimizes if you switch charts. Only use manual preset selection if:
• You want scalping settings on a 15m chart (overriding auto-detection)
• You want swing settings on a 1h chart (more conservative than auto would give)
• You're testing different approaches on same timeframe
Q: Can I use this for scalping and day trading?
A: Absolutely! The preset system is specifically designed for all trading styles:
• Select "Scalping (1-5m)" for 1-5 minute charts
• Select "Day Trading (15m-1h)" for 15m-1h charts
• Or use "Auto" mode and it configures automatically
Volume momentum filter is auto-disabled in Scalping mode for faster signals.
Q: What is Volume Momentum and why does it matter?
A: Volume Momentum compares short-term volume (fast MA) to long-term volume (slow MA). It answers: "Is money flowing into this asset faster now than historically?"
Why it matters:
• Volume often leads price (early warning system)
• Confirms institutional participation (smart money)
• No lag like price-based indicators
• More intuitive than complex mathematical filters
When the ratio is above 1.2, you have strong evidence that institutions are accumulating (bullish) or distributing (bearish).
Q: How do I set up alerts?
A: Two options:
Option 1 - Automatic Alerts:
1. Right-click on chart → Add Alert
2. Condition: Select this indicator
3. Choose "Any alert() function call"
4. Configure notification method (app, email, webhook)
5. You'll receive detailed alerts on every BUY and SELL signal
Option 2 - Customizable Alert Conditions:
1. Right-click on chart → Add Alert
2. Condition: Select this indicator
3. You'll see three options in dropdown:
- "BUY Signal" (long signals only)
- "SELL Signal" (short signals only)
- "ANY Signal" (both BUY and SELL)
4. Choose desired option and customize message template
5. Uses TradingView placeholders: {{ticker}}, {{close}}, {{time}}, etc.
All alerts fire only on confirmed bar close (no repaint).
Q: What is Scalpel Mode and should I use it?
A: Scalpel Mode waits for healthy pullbacks within established trends before signaling entry. It reduces whipsaws and improves entry timing.
Recommended ON for:
• Swing traders (want precision entries on pullbacks)
• Day traders (willing to wait for better prices)
• Risk-averse traders (prefer fewer but higher-quality entries)
Recommended OFF for:
• Scalpers (need immediate entries, can't wait for pullbacks)
• Momentum traders (want to enter on breakout, not pullback)
• Aggressive traders (prefer more opportunities over precision)
Q: Why do some duration estimates show wider ranges than others?
A: Range width reflects historical trend variability:
• Narrow range: Similar historical trends had consistent durations (high confidence)
• Wide range: Similar historical trends had varying durations (lower confidence)
Wide ranges often occur:
• Early in analysis (fewer historical trends to learn from)
• In volatile/choppy markets (inconsistent trend behavior)
• On lower timeframes (more noise, less consistency)
The median and average still provide useful targets even when range is wide.
Q: Can I customize the dashboard position and appearance?
A: Yes! Dashboard settings include:
• Position: 9 options (Top/Middle/Bottom + Left/Center/Right)
• Text Size: Auto, Tiny, Small, Normal, Large, Huge
• Show/Hide: Toggle entire dashboard on/off
Choose position that doesn't overlap important price action on your specific chart.
Q: Which timeframe should I trade on?
A: Depends on your trading style and time availability:
• 1-5 minute: Active scalping, requires constant monitoring
• 15m-1h: Day trading, check few times per session
• 4h-Daily: Swing trading, check once or twice daily
• Daily-Weekly: Position trading, check weekly
General principle: Higher timeframes produce:
• Fewer signals (less frequent)
• Higher quality setups (stronger confirmations)
• More reliable duration estimates (better statistical data)
• Less noise (clearer trends)
Start with Daily chart if new to trading. Move to lower timeframes as you gain experience.
Q: Does this work on all markets (stocks, crypto, forex)?
A: Yes, it works on all markets with trending characteristics:
Excellent for:
• Stocks (especially growth and momentum names)
• Crypto (BTC, ETH, major altcoins)
• Futures (indices, commodities)
• Forex majors (EUR/USD, GBP/USD, etc.)
Best results on:
• Trending markets (not range-bound)
• Liquid instruments (tight spreads, good fills)
• Volatile assets (clear trend development)
Less effective on:
• Range-bound/sideways markets
• Ultra-low volatility instruments
• Illiquid small-caps (use caution)
Configure Asset Type (in Advanced analysis mode) to match your instrument for best accuracy.
Q: How many signals should I expect per day/week?
A: Highly variable based on:
By Timeframe:
• 1-5 minute: 5-15 signals per session
• 15m-1h: 2-5 signals per day
• 4h-Daily: 2-5 signals per week
• Daily-Weekly: 1-2 signals per month
By Market Volatility:
• High volatility = more SuperTrend flips = more signals
• Low volatility = fewer flips = fewer signals
By Quality Filter:
• Higher threshold (60-70) = fewer but better signals
• Lower threshold (30-40) = more signals, lower quality
By Volume Momentum Filter:
• Enabled = Fewer signals (only volume-confirmed)
• Disabled = More signals (all SuperTrend flips)
Adjust quality threshold and filters to match your desired signal frequency.
Q: What's the difference between entry labels and info labels?
A:
Entry Labels (BUY/SELL):
• Your primary trading signals
• Based on SuperTrend flip + all confirmations (quality, volume, momentum)
• Include quality score and confirmation icons
• These are actionable entry points
Info Labels (Volume Spike):
• Additional market context
• Show volume events that may support or contradict trend
• 8-bar cooldown to prevent spam
• NOT necessarily entry points - contextual information only
Control separately: Can show entry labels without info labels (recommended for clean charts).
Q: Can I combine this with other indicators?
A: Absolutely! This works well with:
• RSI: For divergences and overbought/oversold conditions
• Support/Resistance: Confluence with key levels
• Fibonacci Retracements: Pullback targets in Scalpel Mode
• Price Action Patterns: Flags, pennants, cup-and-handle
• MACD: Additional momentum confirmation
• Bollinger Bands: Volatility context
This indicator provides trend direction and duration estimates - complement with other tools for entry refinement and additional confluence.
Q: Why did I get a low-quality signal? Can I filter them out?
A: Yes! Increase the Minimum Quality Score in settings.
If you're seeing signals with quality below your preference:
• Day Trading: Set minimum to 50
• Swing Trading: Set minimum to 60
• Position Trading: Set minimum to 70
Only signals meeting the threshold will appear. This reduces frequency but improves win-rate.
Q: How do I interpret the MTF Confluence count?
A: Shows how many of 6 timeframes agree with current trend:
• 6/6 aligned: Perfect agreement (extremely rare, highest confidence)
• 5/6 aligned: Very strong alignment (high confidence)
• 4/6 aligned: Good alignment (standard quality setup)
• 3/6 aligned: Moderate alignment (acceptable)
• 2/6 aligned: Weak alignment (caution)
• 1/6 aligned: Very weak (likely counter-trend)
Higher confluence typically correlates with longer, stronger trends. However, MTF analysis is optional - you can disable it and rely solely on quality scoring.
Q: Is this suitable for beginners?
A: Yes, but requires foundational knowledge:
You should understand:
• Basic trend-following concepts (higher highs, higher lows)
• Risk management principles (position sizing, stop losses)
• How to read candlestick charts
• What volume and volatility mean
Beginner-friendly features:
• Auto preset mode (zero configuration)
• Quality scoring (tells you signal strength)
• Dashboard tooltips (hover for explanations)
• duration analysis boxes (visual profit targets)
Recommended for beginners:
1. Start with "Auto" or "Swing Trading" preset on Daily chart
2. Use Standard Analysis Mode (not Advanced)
3. Set minimum quality to 60 (fewer but better signals)
4. Paper trade first for 2-4 weeks
5. Study methodology references (Minervini, O'Neil, Zanger)
Q: What is the Asset Type setting and why does it matter?
A: Asset Type (in Advanced analysis mode) adjusts duration estimates based on volatility characteristics:
• Small Cap: Explosive moves, extended trends (+30-40%)
• Biotech / Speculative: Parabolic potential, news-driven (+40%)
• Blue Chip / Large Cap: Baseline, steady trends (0% adjustment)
• Tech Growth: Momentum-driven, longer trends (+20%)
• Dividend / Value: Slower, grinding trends (-20%)
• Cyclical: Macro-driven, variable (±10%)
• Crypto / High Volatility: Parabolic potential (+30%)
Correct configuration improves Statistical accuracy by 15-20%. Using Blue Chip settings on a biotech stock may underestimate trend length (you'll exit too early).
Q: Can I backtest this indicator?
A: Yes! TradingView's Strategy Tester works with this indicator's signals.
To backtest:
1. Note the entry conditions (SuperTrend flip + quality threshold + filters)
2. Create a strategy script using same logic
3. Run Strategy Tester on historical data
Additionally, the indicator includes BUILT-IN duration estimate validation:
• System backtests its own duration estimates
• Shows accuracy metrics in dashboard and duration analysis boxes
• Helps assess reliability on your specific symbol/timeframe
Q: Why does Volume Momentum auto-disable in Scalping mode?
A: Scalping requires ultra-fast entries to catch quick moves. Volume Momentum filter adds friction by requiring volume confirmation before signaling, which can cause missed opportunities in rapid scalping.
Scalping preset is optimized for speed and frequency - the filter is counterproductive for that style. It remains enabled for Day Trading, Swing Trading, and Position Trading presets where patience improves results.
You can manually enable it in Custom mode if desired.
Q: How much historical data do I need for accurate duration estimates?
A:
Minimum: 50-100 bars (indicator will function but duration estimates less reliable)
Recommended: 500+ bars (robust statistical database)
Optimal: 1000+ bars (maximum Statistical accuracy)
More history = more completed trends = better pattern matching = more accurate duration estimates.
New symbols or newly-switched timeframes will have lower Statistical accuracy initially. Allow 2-4 weeks for the system to build historical database.
IMPORTANT DISCLAIMERS
No Guarantee of Profit:
This indicator is an educational tool and does not guarantee any specific trading results. All trading involves substantial risk of loss. Duration estimates are statistical calculations based on historical patterns and are not guarantees of future performance.
Past Performance:
Historical backtest results and Statistical accuracy statistics do not guarantee future performance. Market conditions change constantly. What worked historically may not work in current or future markets.
Not Financial Advice:
This indicator provides technical analysis signals and statistical duration estimates only. It is not financial, investment, or trading advice. Always consult with a qualified financial advisor before making investment decisions.
Risk Warning:
Trading stocks, options, futures, forex, and cryptocurrencies involves significant risk. You can lose all of your invested capital. Never trade with money you cannot afford to lose. Only risk capital you can lose without affecting your lifestyle.
Testing Required:
Always test this indicator on a demo account or with paper trading before risking real capital. Understand how it works in different market conditions. Verify Statistical accuracy on your specific instruments and timeframes before trusting it with real money.
User Responsibility:
You are solely responsible for your trading decisions. The developer assumes no liability for trading losses, incorrect duration estimates, software errors, or any other damages incurred while using this indicator.
Statistical Estimation Limitations:
Trend Duration estimates are statistical estimates based on historical pattern matching. They are NOT guarantees. Actual trend durations may differ significantly from duration estimates due to unforeseen news events, market regime changes, or lack of historical precedent for current conditions.
CREDITS & ACKNOWLEDGMENTS
Methodology Inspiration:
• Mark Minervini - Volatility Contraction Pattern (VCP) concepts and pullback entry techniques
• William O'Neil - Volume analysis principles and CANSLIM institutional buying patterns
• Dan Zanger - Momentum breakout strategies and volatility expansion entries
Technical Components:
• SuperTrend calculation - Classic ATR-based trend indicator (public domain)
• Statistical analysis - Standard median, average, range calculations
• k-Nearest Neighbors - Classic machine learning similarity matching concept
• Multi-timeframe analysis - Standard request.security implementation in Pine Script
For questions, feedback, or support, please comment below or send a private message.
Happy Trading!
[Sextan] Your Indicator Source PINE v5 MTFLevel: 1
NOTE1: As requested, this is a multiple time frame(MTF) version of input signal source, which enable you to backtest any indicator/strategy MTF with "{Sextan} PINEv4 Sextans Backtest Framework". Courtesy of cheatcountry for his request.security() wrapper in PINE v5 to avoid repainting caused by request.security() function.
NOTE2: Many request this indicator template to support PINE v5. Now, here it is .This is ONLY an PINE v5 EXAMPLE on HOW-TO produce a customized "{Sextan} PINEv4 Sextans Backtest Framework" (for bactest framework it does not need to be written by PINE v5)intput signal source, you can define your own indicator in the highlighted area in compliance with the uniform format, which guarantee when you use "Indicator on Indicator" function, it would not produce any error.
I use two simple moving average crossings to produce long and short entry signal with SMA3 and SMA8 in the example.
Background
Backtesting of technical indicators and strategies is the most common way to understand a quantitative strategy. However, the complicated configuration and adaptation work of backtesting many quantitative tools makes many traders who do not understand the code daunted. Moreover, although I have written a lot of strategies, I am still not very satisfied with the backtest configuration and writing efficiency. Therefore, I have been thinking about how to build a backtesting framework that can quickly and easily evaluate the backtesting performance of any indicator with a "long/short entry" indicator, that is, a "simple backtesting tool for dummies". The performance requirements should be stable, and the operation should be simple and convenient. It is best to "copy", "paste", and "a few mouse clicks" to complete the quick backtest and evaluation of a new indicator.
Luckily, I recently realized that TradingView provides an "Indicator on Indicator" feature, which is the perfect foundation for doing "hot swap" backtesting. My basic idea is to use a two-layer design. The first layer is the technical indicator signal source that needs to be embedded, which is only used to provide buy and sell signals of custom strategies; the second layer is the trading system, which is used to receive the output signals of the first layer, and filter the signals according to the agreed specifications. , Take Profit, Stop Loss, draw buy and sell signals and cost lines, define and send custom buy and sell alert messages to mobile phones, social software or trading interfaces. In general, this two-layer design is a flexible combination of "death and alive", which can meet the needs of most traders to quickly evaluate the performance of a certain technical indicator. The first layer here is flexible. Users can insert their own strategy codes according to my template, and they can draw buy and sell signals and output them to the second layer. The second layer is fixed, and the overall framework is solidified to ensure the stability and unity of the trading system. It is convenient to compare different or similar strategies under the same conditions. Finally, all trading signals are drawn on the chart, and the output strategy returns. test report.
The main function:
The first layer: "{Sextan} Your Indicator Source", the script provides a template for personalized strategy input, and the signal and definition interfaces ensure full compatibility with the second layer. Backtesting is performed stably in the backtesting framework of the layer. The first layer of this script is also relatively simple: enter your script in the highlighted custom script area, and after ensuring the final buy and sell signals long = bool condition, short = bool condition, the design of the first layer is considered complete. Input it into the PINE script editor of TradingView, save it and add it to the chart, you can see the pulse sequence in yellow (buy) and purple (sell) on the sub-picture, corresponding to the main picture, you can subjectively judge that the quality of the trading point of the strategy is good Bad.
The second layer: "{Sextan} PINEv4 Sextans Backtest Framework". This script is the standardized trading system strategy execution and alarm, used to generate the final report of the strategy backtest and some key indicators that I have customized that I find useful, such as: winning rate , Odds, Winning Surface, Kelly Ratio, Take Profit and Stop Loss Thresholds, Trading Frequency, etc. are evaluated according to the Kelly formula. To use the second layer, first load it into the TrainingView chart, no markers will appear on the chart, since you have not specified any strategy source signals, click on the gear-shaped setting next to the "{Sextan} PINEv4 Sextans BTFW" header button, you can open the backtest settings, the first item is to select your custom strategy source. Because we have added the strategy source to the chart in the previous step, you can easily find an option "{Sextan} Your Indicator Source: Signal" at the bottom of the list, this is the strategy source input we need, select and confirm , you can see various markers on the main graph, and quickly generate a backtesting profit graph and a list of backtesting reports. You can generate files and download the backtesting reports locally. You can also click the gear on the backtest chart interface to customize some conditions of the backtest, including: initial capital amount, currency type, percentage of each order placed, amount of pyramid additions, commission fees, slippage, etc. configuration. Note: The configuration in the interface dialog overrides the same configuration implemented by the code in the backtest script.
How to output charts:
The first layer: "{Sextan} Your Indicator Source", the output of this script is the pulse value of yellow and purple, yellow +1 means buy, purple -1 means sell.
The second layer: PINEv4 Sextans Backtest Framework". The output of this script is a bit complicated. After all, it is the entire trading system with a lot of information:
1. Blue and red arrows. The blue upward arrow indicates long position, the red downward arrow indicates short position, and the horizontal bar at the end of the purple arrow indicates take profit or stop loss exit.
2. Red and green lines. This is the holding cost line of the strategy, green represents the cost of holding a long position, and red represents the cost of holding a short position. The cost line is a continuous solid line and the price action is relatively close.
3. Green and yellow long take profit and stop loss area and green and yellow long take profit and stop loss fork. Once a long position is held, there is a conditional order for take profit and stop loss. The green horizontal line is the long take profit ratio line, and the yellow is the long stop loss ratio line; the green cross indicates the long take profit price, and the yellow cross indicates the long position. Stop loss price. It's worth noting that the prongs and wires don't necessarily go together. Because of the optimization of the algorithm, for a strong market, the take profit will occur after breaking the take profit line, and the profit will not be taken until the price falls.
4. The purple and red short take profit and stop loss area and the purple red short stop loss fork. Once a short position is held, there will be a take profit and stop loss conditional order, the red is the short take profit ratio line, and the purple is the short stop loss ratio line; the red cross indicates the short take profit price, and the purple cross indicates the short stop loss price.
5. In addition to the above signs, there are also text and numbers indicating the profit and loss values of long and short positions. "L" means long; "S" means short; "XL" means close long; "XS" means close short.
TradingView Strategy Tester Panel:
The overview graph is an intuitive graph that plots the blue (gain) and red (loss) curves of all backtest periods together, and notes: the absolute value and percentage of net profit, the number of all closed positions, the winning percentage, the profit factor, The maximum trading loss, the absolute value and ratio of the average trading profit and loss, and the average number of K-lines held in all trades.
Another is the performance summary. This is to display all long and short statistical indicators of backtesting in the form of a list, such as: net profit, gross profit, Sharpe ratio, maximum position, commission, times of profit and loss, etc.
Finally, the transaction list is a table indexed by the transaction serial number, showing the signal direction, date and time, price, profit and loss, accumulated profit and loss, maximum transaction profit, transaction loss and other values.
Remarks
Finally, I will explain that this is just the beginning of this model. I will continue to optimize the trading system of the second layer. Various optimization feedback and suggestions are welcome. For valuable feedback, I am willing to provide some L4/L5 technical indicators as rewards for free subscription rights.
[Sextan] Your Indicator Source PINE v4 MTFLevel: 1
NOTE1: As requested, this is a multiple time frame(MTF) version of input signal source, which enable you to backtest any indicator/strategy MTF with "{Sextan} PINEv4 Sextans Backtest Framework". Courtesy of cheatcountry for his security() wrapper to avoid repainting caused by security() function.
NOTE2: This is ONLY an EXAMPLE on HOW-TO produce a customized "{Sextan} PINEv4 Sextans Backtest Framework" intput signal source, you can define your own indicator in the highlighted area in compliance with the uniform format, which guarantee when you use "Indicator on Indicator" function, it would not produce any error.
I use two simple moving average crossings to produce long and short entry signal with SMA3 and SMA8 in the example.
Background
Backtesting of technical indicators and strategies is the most common way to understand a quantitative strategy. However, the complicated configuration and adaptation work of backtesting many quantitative tools makes many traders who do not understand the code daunted. Moreover, although I have written a lot of strategies, I am still not very satisfied with the backtest configuration and writing efficiency. Therefore, I have been thinking about how to build a backtesting framework that can quickly and easily evaluate the backtesting performance of any indicator with a "long/short entry" indicator, that is, a "simple backtesting tool for dummies". The performance requirements should be stable, and the operation should be simple and convenient. It is best to "copy", "paste", and "a few mouse clicks" to complete the quick backtest and evaluation of a new indicator.
Luckily, I recently realized that TradingView provides an "Indicator on Indicator" feature, which is the perfect foundation for doing "hot swap" backtesting. My basic idea is to use a two-layer design. The first layer is the technical indicator signal source that needs to be embedded, which is only used to provide buy and sell signals of custom strategies; the second layer is the trading system, which is used to receive the output signals of the first layer, and filter the signals according to the agreed specifications. , Take Profit, Stop Loss, draw buy and sell signals and cost lines, define and send custom buy and sell alert messages to mobile phones, social software or trading interfaces. In general, this two-layer design is a flexible combination of "death and alive", which can meet the needs of most traders to quickly evaluate the performance of a certain technical indicator. The first layer here is flexible. Users can insert their own strategy codes according to my template, and they can draw buy and sell signals and output them to the second layer. The second layer is fixed, and the overall framework is solidified to ensure the stability and unity of the trading system. It is convenient to compare different or similar strategies under the same conditions. Finally, all trading signals are drawn on the chart, and the output strategy returns. test report.
The main function:
The first layer: "{Sextan} Your Indicator Source", the script provides a template for personalized strategy input, and the signal and definition interfaces ensure full compatibility with the second layer. Backtesting is performed stably in the backtesting framework of the layer. The first layer of this script is also relatively simple: enter your script in the highlighted custom script area, and after ensuring the final buy and sell signals long = bool condition, short = bool condition, the design of the first layer is considered complete. Input it into the PINE script editor of TradingView, save it and add it to the chart, you can see the pulse sequence in yellow (buy) and purple (sell) on the sub-picture, corresponding to the main picture, you can subjectively judge that the quality of the trading point of the strategy is good Bad.
The second layer: "{Sextan} PINEv4 Sextans Backtest Framework". This script is the standardized trading system strategy execution and alarm, used to generate the final report of the strategy backtest and some key indicators that I have customized that I find useful, such as: winning rate , Odds, Winning Surface, Kelly Ratio, Take Profit and Stop Loss Thresholds, Trading Frequency, etc. are evaluated according to the Kelly formula. To use the second layer, first load it into the TrainingView chart, no markers will appear on the chart, since you have not specified any strategy source signals, click on the gear-shaped setting next to the "{Sextan} PINEv4 Sextans BTFW" header button, you can open the backtest settings, the first item is to select your custom strategy source. Because we have added the strategy source to the chart in the previous step, you can easily find an option "{Sextan} Your Indicator Source: Signal" at the bottom of the list, this is the strategy source input we need, select and confirm , you can see various markers on the main graph, and quickly generate a backtesting profit graph and a list of backtesting reports. You can generate files and download the backtesting reports locally. You can also click the gear on the backtest chart interface to customize some conditions of the backtest, including: initial capital amount, currency type, percentage of each order placed, amount of pyramid additions, commission fees, slippage, etc. configuration. Note: The configuration in the interface dialog overrides the same configuration implemented by the code in the backtest script.
How to output charts:
The first layer: "{Sextan} Your Indicator Source", the output of this script is the pulse value of yellow and purple, yellow +1 means buy, purple -1 means sell.
The second layer: PINEv4 Sextans Backtest Framework". The output of this script is a bit complicated. After all, it is the entire trading system with a lot of information:
1. Blue and red arrows. The blue upward arrow indicates long position, the red downward arrow indicates short position, and the horizontal bar at the end of the purple arrow indicates take profit or stop loss exit.
2. Red and green lines. This is the holding cost line of the strategy, green represents the cost of holding a long position, and red represents the cost of holding a short position. The cost line is a continuous solid line and the price action is relatively close.
3. Green and yellow long take profit and stop loss area and green and yellow long take profit and stop loss fork. Once a long position is held, there is a conditional order for take profit and stop loss. The green horizontal line is the long take profit ratio line, and the yellow is the long stop loss ratio line; the green cross indicates the long take profit price, and the yellow cross indicates the long position. Stop loss price. It's worth noting that the prongs and wires don't necessarily go together. Because of the optimization of the algorithm, for a strong market, the take profit will occur after breaking the take profit line, and the profit will not be taken until the price falls.
4. The purple and red short take profit and stop loss area and the purple red short stop loss fork. Once a short position is held, there will be a take profit and stop loss conditional order, the red is the short take profit ratio line, and the purple is the short stop loss ratio line; the red cross indicates the short take profit price, and the purple cross indicates the short stop loss price.
5. In addition to the above signs, there are also text and numbers indicating the profit and loss values of long and short positions. "L" means long; "S" means short; "XL" means close long; "XS" means close short.
TradingView Strategy Tester Panel:
The overview graph is an intuitive graph that plots the blue (gain) and red (loss) curves of all backtest periods together, and notes: the absolute value and percentage of net profit, the number of all closed positions, the winning percentage, the profit factor, The maximum trading loss, the absolute value and ratio of the average trading profit and loss, and the average number of K-lines held in all trades.
Another is the performance summary. This is to display all long and short statistical indicators of backtesting in the form of a list, such as: net profit, gross profit, Sharpe ratio, maximum position, commission, times of profit and loss, etc.
Finally, the transaction list is a table indexed by the transaction serial number, showing the signal direction, date and time, price, profit and loss, accumulated profit and loss, maximum transaction profit, transaction loss and other values.
Remarks
Finally, I will explain that this is just the beginning of this model. I will continue to optimize the trading system of the second layer. Various optimization feedback and suggestions are welcome. For valuable feedback, I am willing to provide some L4/L5 technical indicators as rewards for free subscription rights.
[Sextan] Your Indicator Source for PINE v5Level: 1
NOTE: Many request this indicator template to support PINE v5. Now, here it is .This is ONLY an PINE v5 EXAMPLE on HOW-TO produce a customized "{Sextan} PINEv4 Sextans Backtest Framework" (for bactest framework it does not need to be written by PINE v5)intput signal source, you can define your own indicator in the highlighted area in compliance with the uniform format, which guarantee when you use "Indicator on Indicator" function, it would not produce any error.
I use two simple moving average crossings to produce long and short entry signal with SMA3 and SMA8 in the example.
Background
Backtesting of technical indicators and strategies is the most common way to understand a quantitative strategy. However, the complicated configuration and adaptation work of backtesting many quantitative tools makes many traders who do not understand the code daunted. Moreover, although I have written a lot of strategies, I am still not very satisfied with the backtest configuration and writing efficiency. Therefore, I have been thinking about how to build a backtesting framework that can quickly and easily evaluate the backtesting performance of any indicator with a "long/short entry" indicator, that is, a "simple backtesting tool for dummies". The performance requirements should be stable, and the operation should be simple and convenient. It is best to "copy", "paste", and "a few mouse clicks" to complete the quick backtest and evaluation of a new indicator.
Luckily, I recently realized that TradingView provides an "Indicator on Indicator" feature, which is the perfect foundation for doing "hot swap" backtesting. My basic idea is to use a two-layer design. The first layer is the technical indicator signal source that needs to be embedded, which is only used to provide buy and sell signals of custom strategies; the second layer is the trading system, which is used to receive the output signals of the first layer, and filter the signals according to the agreed specifications. , Take Profit, Stop Loss, draw buy and sell signals and cost lines, define and send custom buy and sell alert messages to mobile phones, social software or trading interfaces. In general, this two-layer design is a flexible combination of "death and alive", which can meet the needs of most traders to quickly evaluate the performance of a certain technical indicator. The first layer here is flexible. Users can insert their own strategy codes according to my template, and they can draw buy and sell signals and output them to the second layer. The second layer is fixed, and the overall framework is solidified to ensure the stability and unity of the trading system. It is convenient to compare different or similar strategies under the same conditions. Finally, all trading signals are drawn on the chart, and the output strategy returns. test report.
The main function:
The first layer: "{Sextan} Your Indicator Source", the script provides a template for personalized strategy input, and the signal and definition interfaces ensure full compatibility with the second layer. Backtesting is performed stably in the backtesting framework of the layer. The first layer of this script is also relatively simple: enter your script in the highlighted custom script area, and after ensuring the final buy and sell signals long = bool condition, short = bool condition, the design of the first layer is considered complete. Input it into the PINE script editor of TradingView, save it and add it to the chart, you can see the pulse sequence in yellow (buy) and purple (sell) on the sub-picture, corresponding to the main picture, you can subjectively judge that the quality of the trading point of the strategy is good Bad.
The second layer: "{Sextan} PINEv4 Sextans Backtest Framework". This script is the standardized trading system strategy execution and alarm, used to generate the final report of the strategy backtest and some key indicators that I have customized that I find useful, such as: winning rate , Odds, Winning Surface, Kelly Ratio, Take Profit and Stop Loss Thresholds, Trading Frequency, etc. are evaluated according to the Kelly formula. To use the second layer, first load it into the TrainingView chart, no markers will appear on the chart, since you have not specified any strategy source signals, click on the gear-shaped setting next to the "{Sextan} PINEv4 Sextans BTFW" header button, you can open the backtest settings, the first item is to select your custom strategy source. Because we have added the strategy source to the chart in the previous step, you can easily find an option "{Sextan} Your Indicator Source: Signal" at the bottom of the list, this is the strategy source input we need, select and confirm , you can see various markers on the main graph, and quickly generate a backtesting profit graph and a list of backtesting reports. You can generate files and download the backtesting reports locally. You can also click the gear on the backtest chart interface to customize some conditions of the backtest, including: initial capital amount, currency type, percentage of each order placed, amount of pyramid additions, commission fees, slippage, etc. configuration. Note: The configuration in the interface dialog overrides the same configuration implemented by the code in the backtest script.
How to output charts:
The first layer: "{Sextan} Your Indicator Source", the output of this script is the pulse value of yellow and purple, yellow +1 means buy, purple -1 means sell.
The second layer: PINEv4 Sextans Backtest Framework". The output of this script is a bit complicated. After all, it is the entire trading system with a lot of information:
1. Blue and red arrows. The blue upward arrow indicates long position, the red downward arrow indicates short position, and the horizontal bar at the end of the purple arrow indicates take profit or stop loss exit.
2. Red and green lines. This is the holding cost line of the strategy, green represents the cost of holding a long position, and red represents the cost of holding a short position. The cost line is a continuous solid line and the price action is relatively close.
3. Green and yellow long take profit and stop loss area and green and yellow long take profit and stop loss fork. Once a long position is held, there is a conditional order for take profit and stop loss. The green horizontal line is the long take profit ratio line, and the yellow is the long stop loss ratio line; the green cross indicates the long take profit price, and the yellow cross indicates the long position. Stop loss price. It's worth noting that the prongs and wires don't necessarily go together. Because of the optimization of the algorithm, for a strong market, the take profit will occur after breaking the take profit line, and the profit will not be taken until the price falls.
4. The purple and red short take profit and stop loss area and the purple red short stop loss fork. Once a short position is held, there will be a take profit and stop loss conditional order, the red is the short take profit ratio line, and the purple is the short stop loss ratio line; the red cross indicates the short take profit price, and the purple cross indicates the short stop loss price.
5. In addition to the above signs, there are also text and numbers indicating the profit and loss values of long and short positions. "L" means long; "S" means short; "XL" means close long; "XS" means close short.
TradingView Strategy Tester Panel:
The overview graph is an intuitive graph that plots the blue (gain) and red (loss) curves of all backtest periods together, and notes: the absolute value and percentage of net profit, the number of all closed positions, the winning percentage, the profit factor, The maximum trading loss, the absolute value and ratio of the average trading profit and loss, and the average number of K-lines held in all trades.
Another is the performance summary. This is to display all long and short statistical indicators of backtesting in the form of a list, such as: net profit, gross profit, Sharpe ratio, maximum position, commission, times of profit and loss, etc.
Finally, the transaction list is a table indexed by the transaction serial number, showing the signal direction, date and time, price, profit and loss, accumulated profit and loss, maximum transaction profit, transaction loss and other values.
Remarks
Finally, I will explain that this is just the beginning of this model. I will continue to optimize the trading system of the second layer. Various optimization feedback and suggestions are welcome. For valuable feedback, I am willing to provide some L4/L5 technical indicators as rewards for free subscription rights.
Intraday Session Levels: Pre-Mkt, 5m, 15m (Replay/Toggle/Labels)Intraday Session Levels: Pre-Mkt, 5m, 15m (Replay/Toggle/Labels)
Version v1.0
Live session levels for every trader!
This indicator automatically tracks and draws the most actionable intraday levels as they develop—live in real-time and fully compatible with TradingView’s bar replay and backtesting.
How it works:
Pre-Market High & Low:
Levels appear and update live as soon as the pre-market session starts (4:00am ET), then “freeze” at the official open (9:30am ET) and remain visible for the rest of the day.
First 5-Minute Candle High/Low:
Drawn instantly after the first 5-minute candle (9:30–9:35am ET) completes.
First 15-Minute Candle High/Low:
Drawn right after the first 15-minute candle (9:30–9:45am ET) completes.
Labels on every line
Each level is clearly labeled on your chart (“PreMkt High”, “5m Low”, “15m High”, etc).
Perfect for backtesting:
All levels display exactly as they would have appeared in real time, making this indicator fully bar replay and historical test compatible.
Flexible ON/OFF toggles:
Instantly show or hide Pre-Mkt, 5m, and 15m levels via the settings panel.
Why use it?
Identify support/resistance and key reaction zones intraday
Fade or break the opening range with confidence
Backtest your strategies with accurate historical context
Reduce chart clutter with customizable, minimal visuals
Whether you’re a scalper, day trader, or backtest enthusiast, this tool keeps your charts focused and your edge sharp.
Developed by
3Commas DCA Bot Indicator v1.0Introduction
The indicator attempts to implement a majority all of the settings found on the 3Commas DCA Bot setting page that can be implemented within a TradingView indicator. Specifically, it will implement the "Open new trade ASAP" and "Trading View Custom Signal" deal start conditions. In this way, you can leverage the power of TradingView's backtester to model your settings and analysis past performance as well as leverage TradingView's alert notification subsystem to send Deal Start and Close signals to 3Commas using almost any of the thousands of scripts found in the Public Library (see "What does it do and how does it do it?" section below for more information on how to do this using the RSI indicator as an example).
How is it original and useful?
This indicator is unique in that it's the only one (as of the initial publications) that can handle 28 out of 32 bot settings (88% coverage) that are found on the 3Commas bot settings page. In addition, because it is a TradingView script, you will benefit from the following:
Model your bot's configuration using the backtester on any timeframe and evaluate its performance using the chart's history.
View a table of detailed statistics pertaining to deals started and completed, win/loss rates, daily P&L, true max drawdown, and detailed statistics on bars in deal along with safety orders filled.
View a table of "Used amount for each step" as presented in 3Commas as well.
Ability to link to almost any TradingView chart indicator that provides a single data point for buy/sell signals.
Make use of the 3Commas TA Start Conditions indicator to emulate the 3Commas built-in indicators for further performance evaluation with the backtester.
How does it compare to other scripts in the Public Library?
This indicator offers a very detailed, comprehensive settings and maybe the only one (as of the initial publication) that can handle 28 out of the 32 bot settings that are found on the 3Commas bot settings page (that can be handled within TradingView, because some cannot be modeled due to chart and language constraints). Specifically, the following 3Commas bot settings and inputs are addressed with asterisks denoting specific limitation due to TradingView's platform architecture and/or constraints:
Main settings
✅ Name
❌ Exchange* (Not handled since it is inherited from chart)
⚠ Bot type* (Only Single-pair because you simply apply the indicator to as many pairs you desire in separate charts to simulate a Multi-pair setting)
Pairs
❌ Pairs* (Not handled since it is inheriting the symbol from the chart)
Strategy
✅ Strategy
❌ Profit currency* (Not handled since there is no equivalence in TradingView)
✅ Base order size / unit
✅ Safety order size / unit
✅ Start order type
Deal start condition
⚠ TradingView* (You can modify the "Technical Rating" indicator by TradingView to output a signal yourself like @TheTradingParrot has done in the TV signal for DCA backtest script)
⚠ QFL* (See Quickfingers Luc's Base Breaking Strategy v2.5 - 3Commas Edition for an alternative)
❌ Manually/API* (Not handled since there is no equivalence in TradingView)
✅ Trading View custom signal
✅ Open new trade asap
Take profit
✅ Take profit (%)
✅ Take profit type
✅ Trailing
⚠ Trailing deviation* (modified to handle TradingView's broker emulator properly)
Stop loss
✅ Stop Loss (%)
✅ Stop Loss action
⚠ Stop Loss timeout* (modified to use "bars" as the unit of measure)
Safety orders
✅ Max safety trades count (a total of 100 safety orders are supported!)
⚠ Max active safety trades count* (Not handled since there is no equivalence in TradingView)
✅ Price deviation to open safety orders (% from initial order)
✅ Safety order volume scale
✅ Safety order step scale
Advanced settings
✅ Don't start deal(s) if the daily volume is less than
✅ Minimum price to open deal
✅ Maximum price to open deal
⚠ Deal start delay (QFL and standard TV signals only)* (modified to use "bars" as the unit of measure)
✅ Cooldown between deals
✅ Open deals & stop
Close deal after timeout
⚠ The time after which, deal will be closed automatically* (modified to use "bars" as the unit of measure)
In addition to the above inputs, the script will also support the same output data points that are found on the bot settings page, which are:
Assistant
✅ Balance
✅ Max amount for bot usage (Based on current rate)
✅ Max safety order price deviation
✅ % of available balance to be used by the bot
Table (Used amount for each step)
✅ Order No.
✅ Deviation %
✅ Order Size (Base currency)
✅ Order Volume (Quote currency)
✅ Price
✅ Average price
✅ Required price
✅ Required change
✅ Total Size (Base currency)
✅ Total Volume (Quote currency)
What does it do and how does it do it?
When applied to the chart for the first time, the default settings will work to execute the "Open new trade asap" deal start condition using a take profit of 4%, a base order size of 1.335% of equity (initial capital + net profit), a safety order size of 2.003% of equity (initial capital + net profit), a safety order volume scale of 1.5, a safety order step scale of 1.21, and a max safety trades count of 8. With these settings, as you scroll through the chart's history you should see price action crossing the DCA layers (denoted with blue triangles), and a green take-profit price line will render (with green triangle denoting the crossings). Lastly, in the upper-right corner of the chart the table of DCA statistics will render showing you all the gathered data that took place since the chart's starting history until the current time.
Using the "TV Custom Signal" section will allow you to "link" other chart indicators to supply "Start" and "Close" signals for the script. For example, here's how you can use the standard RSI indicator to generate an entry signal:
1) Add the stock RSI indicator and configure it to your desire.
2) Select the "RSI" indicator in the "Chart/Indicator Data Point" input.
3) Select "Chart/Indicator Data Point" in the "Start Deal When" input.
4) Select "Less Than" in the "Is" input.
5) Select "Custom Value(s)" in the "The Value Of" input.
6) Enter "25" in the "Custom Value 1 (Single or Lower Bound)" input.
7) Select "Trading View Custom Signal" in the "Open new trade" input (in the DEAL START CONDITION section).
8) Lastly, check the checkbox with a horizontal straight line above the "Start Deal When" input.
This means that the indicator will start a deal when the "RSI indicator" is "Less Than" the value of "25".
Strategy Results
The default settings are designed to ensure that the indicator will render chart elements when first loaded as well as to allow the backtester to gather order executions and display performance summary. The chart above is is using $10,000 initial capital, a commission rate of 0.1% for both entries and exits, and a 1 tick slippage setting. It is also using 3.22581% of the equity for the Base Order Size, 6.45161% of equity for the first Safety Order Size. The Maximum Safety Trade Count is 4 with a Safety Order Volume Scale of 2, a Safety Order Step Scale of 1.53, Price Deviation To Option Safety Orders at 5.3, and a take profit of 4.5% with Trailing turned on at a deviation of 1.5%. All other settings are defaults.
It is recommended that the indicator be "tuned" for your specific market in order to best implement the strategy and obtain better desirable results. You do so by using the backtester and statistics table and observe the output values and learn how the indicator is performing in the chart history. Using this information, you can adjust the settings accordingly until you find the settings acceptable to your trading goals and risk tolerance.
Always keep in mind that past performance may not be indicative of future results. Settings that seem favorable for one market may be found to be disastrous in another. Therefore, do take the time needed to understand how the settings will behave with the given chart symbol.
Enjoy! 😊👍
How to obtain access to the script?
You have two choices:
Use the "Website" link below to obtain access to this indicator, or
Send us a private message (PM) in TradingView itself.
Liq Levels [KoTa]Liq Levels User Guide
Overview
Liquidation Levels visualizes precomputed long & short liquidation price levels relative to the current market price.
For each enabled leverage level (5×, 10×, 20× …), it draws two horizontal lines and small labels:
Short liquidation line (above price) = price × short_multiplier
Long liquidation line (below price) = price × long_multiplier
You can choose which leverage levels to display, control label formatting, choose whether to use the previous bar’s close (no-repaint), change line style & extension, and toggle label size.
Inputs / Settings (what they do)
Use Previous Close (No Repaint) (useConfirmed)
true: the indicator uses close (previous bar close) as reference. Prevents intrabar repainting — recommended for backtesting and stable signals.
false: uses close (current price) — updates intrabar and will repaint during the bar.
Show Price in Label (showValues)
true shows the numeric price next to the x label (e.g., 5x : 42,952.78), false shows only 5x.
Line Style (styleType) — "dot", "line", "dashed".
Extend Lines (extendType) — "none", "left", "right", "both".
Label Size (labelSize) — "normal", "small", "tiny".
Show 5x / 10x / 20x ... 200x (show_5x, etc.) — which multipliers are drawn.
Other technical details in code:
barOffset = 4: label & short segment are placed 4 bars to the right of current bar (so label appears to the right of the bar).
Numbers are formatted according to syminfo.mintick so labels display the appropriate decimal precision.
The script cleans up previously drawn lines & labels on every bar (deletes old objects, draws fresh ones) — so the chart does not accumulate stale objects.
What the lines represent (interpretation)
Each multiplier is precomputed and represents a liquidation price factor used to estimate where positions would be forcibly closed for a given leverage (based on some margin model).
Short multipliers > 1 → short liquidation prices sit above the reference price.
Long multipliers < 1 → long liquidation prices sit below the reference price.
Important: These multipliers are instrument- and margin-model-dependent. The indicator uses the hard-coded multipliers present in the script. Validate these against your exchange / contract type before relying on them for live position sizing.
Why use this indicator?
Use cases:
Risk awareness — see where concentrated liquidation levels sit relative to price; helps avoid taking positions dangerously close to likely liquidation clusters.
Liquidity / cascade detection — when price approaches a large cluster of liquidation levels, sharp moves and cascades can occur; indicator highlights such zones.
Order placement & risk management — place stops or reduce leverage when price nears your liquidation zone.
Trade context — helps decide whether to scale into or out of a trade if the current price is close to many leverage-level liquidation points.
Quick start — how to use (step-by-step)
Load the indicator on the chart.
Choose Use Previous Close = true if you want non-repainting historical levels; false if you prefer intrabar updating. (Recommended: true for backtesting and strategy creation.)
Enable the leverage levels you care about (e.g., 5×, 10×). Keep the number of enabled levels modest (3–4) to avoid clutter.
Choose line style & extension. If you want persistent lines visible across the chart, use extend = left or both. If you only want ephemeral current-level markers, use none.
Interpretation:
If price is approaching the long liquidation line (below price), it’s a sign long positions could be liquidated if price drops further. Consider tightening stops or reducing leverage for long exposure.
If price is approaching the short liquidation line (above price), short positions risk forced closure; similar risk management applies for shorts.
Example strategy (practical, step-by-step)
This is a risk-aware trend-following example that uses the indicator to avoid entering trades too close to liquidation clusters.
Rules
Timeframe: 15-minute or higher for clarity.
Confirm trend with a 50 EMA:
trendUp = price > EMA50
trendDown = price < EMA50
Entry (Long):
trendUp is true.
Price breaks above a short-term resistance or candle close above EMA20 (confirmation).
Distance requirement: current price must be at least X% (example 3%) above the nearest long liquidation line (i.e., price / nearest_long_liquidation >= 1.03).
Enter with defined stop loss: set SL below the nearest long liquidation line OR at a separate level (whichever is more conservative).
Position sizing: choose leverage & size so distance to liquidation gives you at least Y% equity buffer (e.g., 3–5%).
Exit / Take Profit: use risk/reward rule (e.g., 1:2 R:R), or trail stop using EMA or ATR.
Concrete numeric example (worked):
Suppose Use Previous Close = true and the indicator calculates 5× long liquidation at 95.618967 and 5× short at 110.668360 (example base price = 100).
Computation (for clarity):
5× short: 1.10668360333397 × 100 = 110.668360333397 → label shows ~110.668360
20× long: 0.956189674354271 × 100 = 95.61896743542711 → ~95.618967
Entry rule: if price crosses above EMA20 and price / nearest_long_liquidation >= 1.03 (i.e., price ≥ 95.618967 × 1.03 = 98.487536), then entry allowed. If price = 101, condition satisfied (101 / 95.618967 ≈ 1.056).
Why this helps: only enter when you have a buffer above your potential liquidation line; avoid entering directly on top of people’s liquidation levels.
Advantages
Immediate visual risk map — quickly see where liquidations are concentrated (both long & short).
Configurable & non-repainting option — Use Previous Close reduces intrabar repainting for robust backtesting.
Compact & readable — tiny labels and optional price display minimize chart clutter.
Performance-friendly — script deletes and recreates objects each bar, keeping object counts stable and within limits.
Precision formatting via syminfo.mintick so label decimals match the instrument.
Disadvantages & risks / limitations
Multipliers are fixed in the script — they may not reflect the exact margin/liquidation formula of every exchange / contract. Verify with exchange docs before relying on them for trade sizing.
Repainting risk if Use Previous Close = false (intrabar updates). For backtests and alerts you should set it true.
Not a predictor — liquidation levels are potential pressure zones, not guarantees of price movement. Many other market factors affect price action.
Instrument-specific differences — inverse perpetuals, cross margin vs isolated margin, funding rates and insurance funds may change actual liquidation mechanics — the multipliers may be inaccurate for those.
Chart object limits — TradingView has object limits. Although your script deletes and recreates objects each bar and uses max_* _count, using too many levels + large extend combinations on very low timeframes could impact platform performance.
No automatic per-position calculation — the indicator shows levels relative to current price, not your entry; if you need per-trade liquidation price, you must compute using your entry price and actual margin/leverage settings.
Visualizes common long/short liquidation price levels for several leverage multiples. Use the “Use Previous Close” option for stable, non-repainting levels. Verify multipliers vs your exchange before trading.
Long description to paste (publish page content): include the “Why use”, “How to use”, and “Strategy example” sections above plus a short disclaimer (see below).
Include a safety/legal disclaimer in the description:
This indicator is educational and does not constitute financial advice. Multipliers are precomputed and may not precisely match the liquidation mechanics of every exchange or contract. Backtest and verify on your instruments before trading live.
Final notes & suggestions for improvement
If you want tighter integration with your position data (entry price, leverage, margin type), I can add per-trade liquidation calculation inputs (entry price, leverage, maintenance margin) and draw that liquidation line relative to the instrument.
Squeeze Momentum Strategy Signals [Bitduke]Description :
Signals based on transformed Momentum Indicator (leading indicator measuring a security's rate-of-change) with risk management system and ability and custom backtesting.
Usage:
Strategy flips long / short depending of indicator signals. There can be multiple signals of the same type, for example, 2 longs in a row, only the first of them must be taken into account.
Features :
Possible to change source: ohlc4, hl2 , hlc3, close
Enter your stop loss and take profit in %, NOT ticks
Working and robust even without take profit / stop loss
Yearly drawdown lower than 20%
Backtesting:
Backtested on BTCPERP ( FTX ).
It shows much better results on 1h timeframe (about 200% yearly, 55% in 2020) and relatively low drawdown to date.
Strategy doesn't repaint.
Absorption DetectorABSORPTION DETECTOR -
The Absorption Detector identifies institutional order flow by detecting "absorption" patterns where smart money quietly accumulates or distributes positions by absorbing retail order flow. This creates high-probability support and resistance zones for trading. This is an approximation only and does not read any footprint data.
WHAT IS ABSORPTION?
Absorption occurs when institutions take the opposite side of retail trades, creating specific candlestick patterns with high volume and significant wicks. The indicator identifies two main patterns:
SELLING ABSORPTION (P-Pattern): Red zones above candles where institutions sell into retail buying pressure, creating resistance levels. Look for high volume candles with large upper wicks that close in the lower half.
BUYING ABSORPTION (B-Pattern): Green zones below candles where institutions buy from retail selling pressure, creating support levels. Look for high volume candles with large lower wicks that close in the upper half.
KEY FEATURES
- Automatic detection of institutional absorption patterns
- Dynamic support and resistance zone creation
- Customizable styling for all visual elements
- Historic zone display for backtesting analysis
- Strength-based filtering to show only high-probability setups
- Real-time alerts for new absorption patterns
- Professional info panel with key statistics
- Multi-timeframe compatibility
MAIN SETTINGS
Volume Threshold (1.2): Minimum volume surge required compared to average. Higher values = fewer but stronger signals.
Minimum Volume (2500): Absolute volume floor to prevent signals during low-volume periods.
Min Wick Size (0.2): Minimum wick size as ATR multiple. Ensures significant rejection occurred.
Minimum Strength (1.5): Combined volume and wick strength filter. Higher values = higher quality signals.
Show Historic Zones (OFF): Enable to see all historical zones for backtesting. Disable for better performance.
Zone Extension (20): How many bars to project zones forward for anticipating future reactions.
TRADING APPROACH
ZONE REACTION STRATEGY: Wait for price to approach absorption zones and trade the bounce or rejection. Use the zones as dynamic support and resistance levels.
BREAKOUT STRATEGY: Trade decisive breaks of strong absorption zones with proper risk management. Failed zones often lead to strong moves.
CONFLUENCE TRADING: Combine absorption zones with other technical analysis for highest probability setups. Look for alignment with trend lines, Fibonacci levels, and key support/resistance.
RISK MANAGEMENT: Always use stop losses beyond the absorption zones. Target minimum 1:2 risk-reward ratios. Position size appropriately based on zone strength.
OPTIMIZATION GUIDE
For Conservative Trading (fewer, higher quality signals):
- Volume Threshold: 1.5
- Minimum Strength: 2.0
- Min Wick Size: 0.3
For Aggressive Trading (more signals, requires careful filtering):
- Volume Threshold: 1.1
- Minimum Strength: 1.0
- Min Wick Size: 0.15
BEST PRACTICES
Markets: Works best on liquid instruments with good volume - major forex pairs, popular stocks, liquid futures, and established cryptocurrencies.
Timeframes: Effective on all timeframes from 1-minute scalping to daily swing trading. Adjust settings based on your timeframe and trading style.
Confirmation: Never trade absorption signals in isolation. Always combine with trend analysis, market structure, and proper risk management.
Session Timing: Be aware of market sessions and avoid trading during low liquidity periods or major news events.
Backtesting: Use the historic zones feature to validate performance on your chosen market and timeframe before live trading.
CUSTOMIZATION
The indicator offers complete visual customization including zone colors, border styles, label appearances, and info panel positioning. All colors can be adapted to match your chart theme and personal preferences.
Alert system provides both basic and custom message alerts for real-time notifications of new absorption patterns.
PERFORMANCE NOTES
Default settings are optimized for most markets and timeframes. For best performance on older charts, keep "Show Historic Zones" disabled unless specifically backtesting.
The indicator maintains excellent performance even with extensive historical analysis enabled, handling up to 500 zones and 100 labels for comprehensive backtesting.
MultiTimeFrame Trends and Candle Bias (by MC) v1This MultiTimeFrame Trends and Candle Bias provides the trader a quick glance on how each timeframe is trending and what the current candle bias is in each timeframe.
Interpreting Candle Bias : Green points to a bullish bias while red, a bearish bias for a given specific timeframe. For instance, if the current 1 hour candle bias is red, it means that the last hour, the bias has been bearish. If the Daily candle bias is red, it means that the day in question has been a bearish for this selected symbol.
Interpreting MTF Trends: Trends for each time frame follows the simple moving average of the closing prices for the X number of candles you enter in the input section. So for example, if you decide to enter 6 for the 1-hour time frame, the trend for the last 6 hours will be shown and tracked; if on the Daily time frame, you enter 7, the trend for the last 7 days or 1 week will be shown and tracked. I have provided below (as well as on tooltips in the input section of this indicator) recommendations of what numbers to use depending on what kind of trader you are.
What is a best setup for MultiTimeFrame Trends?
Considerations Across All Timeframes:
- Trading Style : Scalpers and very short-term intraday traders may prefer fewer candles (like 12 to 20), which allow them to react quickly to price changes. Swing traders or those holding positions for a few hours to a couple of days might prefer more candles (like 50 to 120) to identify more stable trends.
- Market Conditions : In volatile markets, using more candles helps smooth out price fluctuations and provides a clearer trend signal. In trending markets, fewer candles might be sufficient to capture the trend.
- Session-Based Adjustments : Traders may adjust their settings depending on the time of day or session they are trading. For example, during high-volatility periods like market open or close, using fewer candles can help capture quick moves.
The number of preceding candles to use for estimating the recent trend can depend on various factors, including the type of market, the asset being traded, the timeframe, and the specific goals of your analysis. However, here are some general guidelines to help you decide:
### 1. **Short-Term Trends (Fast Moving Averages):**
- **5 to 20 Candles**: If you want to capture a short-term trend, typically in day trading or scalping strategies, you might use 5 to 20 candles. This is common for fast-moving averages like the 9-period or 15-period moving averages. It reacts quickly to price changes, but it can also give more false signals due to market noise.
### 2. **Medium-Term Trends (Moderate Moving Averages):**
- **20 to 50 Candles**: For a more balanced approach that reduces the impact of short-term volatility while still being responsive to trend changes, 20 to 50 candles are commonly used. This range is popular for swing trading strategies, where the goal is to capture trends that last several days to weeks.
### 3. **Long-Term Trends (Slow Moving Averages):**
- **50 to 200 Candles**: To identify long-term trends, such as those seen in position trading or for confirming major trend directions, you might use 50 to 200 candles. The 50-period and 200-period moving averages are particularly well-known and are often used by traders to identify significant trend reversals or confirmations.
### 4. **Adaptive Approach:**
- **Market Conditions**: In trending markets, fewer candles might be needed to identify a trend, while in choppy or range-bound markets, using more candles can help filter out noise.
- **Volatility**: In highly volatile markets, more candles might be necessary to smooth out price action and avoid false signals.
### **Experiment and Backtesting:**
The optimal number of candles can vary significantly based on the asset and strategy. It's often a good idea to backtest different periods to see which provides the best balance between responsiveness and reliability in identifying trends. You can use tools like the strategy tester in TradingView or other backtesting software to compare the performance of different settings.
### **General Recommendation:**
- **For Shorter Timeframes** (e.g., 5m, 15m): 10-20 candles might be effective.
- **For Medium Timeframes** (e.g., 1h, 4h): 20-50 candles are often a good starting point.
- **For Longer Timeframes** (e.g., Daily, Weekly): 50-200 candles help capture major trends.
If you're unsure, a common starting point for many traders is the 20-period moving average, which provides a balance between sensitivity and reliability.
Guidelines for 1-Minute Timeframe:
For the 1-minute (1M) timeframe, trend analysis typically focuses on very short-term price movements, which is crucial for scalping and ultra-short-term trading strategies. Here’s a breakdown of the number of preceding candles you might use:
1. **Very Short-Term Trend:**
- **10 to 20 Candles (10 to 20 Minutes):** Using 10 to 20 candles captures about 10 to 20 minutes of price action. This range is suitable for scalpers who need to identify very short-term trends and make quick trading decisions.
2. **Short-Term Trend:**
- **30 to 60 Candles (30 to 60 Minutes):** This period covers 30 to 60 minutes of trading, making it useful for traders looking to understand the trend over a full trading hour. It helps capture price movements and trends that develop within a single hour.
3. **Intraday Trend:**
- **120 Candles (2 Hours):** Using 120 candles provides a view of the trend over approximately 2 hours. This is useful for traders who want to see how the market is trending throughout a larger portion of the trading day.
4. **Extended Intraday Trend:**
- **240 to 480 Candles (4 to 8 Hours):** This longer period gives a broader view of the intraday trend, covering 4 to 8 hours. It’s helpful for identifying trends that span a significant portion of the trading day, which can be useful for traders looking to align with the broader intraday movement.
**Considerations:**
- **High Sensitivity:** The 1-minute timeframe is highly sensitive to market movements, so shorter periods (10 to 20 candles) can capture rapid price changes but may also generate noise.
- **Market Volatility:** In highly volatile markets, using more candles (like 30 to 60 or more) helps smooth out the noise and provides a clearer trend signal.
- **Trading Style:** Scalpers will typically use shorter periods to make very quick decisions. Traders holding positions for a bit longer, even within the same day, may use more candles to get a clearer picture of the trend.
**Common Approaches:**
- **5-Period Moving Average:** The 5-period moving average on a 1-minute chart can be used for extremely short-term trend signals, reacting quickly to price changes.
- **20-Period Moving Average:** The 20-period moving average is a good choice for capturing short-term trends and can help filter out some of the noise while still being responsive.
- **50-Period Moving Average:** The 50-period moving average provides a broader view of the trend and can help smooth out price movements over a longer intraday period.
**Recommendation:**
- **Start with 10 to 20 Candles:** For the most immediate and actionable signals, especially useful for scalping or very short-term trading.
- **Use 30 to 60 Candles:** For a clearer view of trends that develop over an hour, suitable for those looking to trade within a single trading hour.
- **Consider 120 Candles:** For observing broader intraday trends over 2 hours, helping align trades with more significant intraday movements.
- **Explore 240 to 480 Candles:** For a longer intraday perspective, covering up to 8 hours, which can be useful for strategies that span a larger portion of the trading day.
**Practical Example:**
- **Scalpers:** If you’re executing trades every few minutes, start with 10 to 20 candles to get rapid trend signals.
- **Short-Term Traders:** For trends that last an hour or so, 30 to 60 candles will provide a better sense of direction while still being responsive.
- **Intraday Traders:** For broader trends that span several hours, 120 candles will help you see the overall intraday movement.
Experimentation and backtesting with these settings on historical data will help you fine-tune your approach to the 1-minute timeframe for your specific trading strategy and asset.
Guidelines for 5, 15 and 30 min Timeframes:
For shorter timeframes like 5, 15, and 30 minutes, the number of preceding candles you use will depend on how quickly you want to react to changes in the trend and the specific trading style you’re employing. Here's a breakdown for each:
**5-Minute Timeframe:**
1. **Very Short-Term (Micro Trend):**
- **12 to 20 Candles (60 to 100 Minutes):** Using 12 to 20 candles on a 5-minute chart captures 1 to 1.5 hours of price action. This is ideal for very short-term trades, such as scalping, where quick entries and exits are key.
2. **Short-Term Trend:**
- **30 to 60 Candles (150 to 300 Minutes):** This period covers 2.5 to 5 hours, making it useful for intraday traders who want to identify the trend within a trading session. It helps capture the direction of the market during the most active parts of the day.
3. **Intra-Day Trend:**
- **120 Candles (10 Hours):** Using 120 candles gives you a broad view of the trend over two trading sessions. This is useful for traders who want to understand the trend throughout the entire trading day.
**15-Minute Timeframe:**
1. **Very Short-Term:**
- **12 to 20 Candles (3 to 5 Hours):** On a 15-minute chart, this period covers 3 to 5 hours, making it useful for capturing the morning or afternoon trend within a trading day. It’s often used by intraday traders who need to make quick decisions.
2. **Short-Term Trend:**
- **30 to 60 Candles (7.5 to 15 Hours):** This covers almost a full trading day to a day and a half. It’s popular among day traders who want to align their trades with the trend of the day or the previous trading session.
3. **Intra-Week Trend:**
- **120 Candles (30 Hours):** This period spans about two trading days and is useful for traders looking to capture trends that may extend beyond a single trading day but not necessarily for an entire week.
**30-Minute Timeframe:**
1. **Short-Term Trend:**
- **12 to 20 Candles (6 to 10 Hours):** This period captures the trend over a single trading session. It's useful for day traders who want to understand the market’s direction throughout the day.
2. **Medium-Term Trend:**
- **30 to 50 Candles (15 to 25 Hours):** This period covers about two trading days and is useful for short-term swing traders or intraday traders who are looking for trends that might last a couple of days.
3. **Intra-Week Trend:**
- **100 to 120 Candles (50 to 60 Hours):** This longer period captures about 4 to 5 trading days, making it useful for traders who want to understand the broader trend over the course of the week.
**Summary Recommendations:**
- **5-Minute Chart:**
- **12 to 20 candles** for very short-term trades.
- **30 to 60 candles** for intraday trends within a single session.
- **120 candles** for a broader view of the day’s trend.
- **15-Minute Chart:**
- **12 to 20 candles** for short-term trades within a few hours.
- **30 to 60 candles** for trends lasting a full day or more.
- **120 candles** for trends extending over a couple of days.
- **30-Minute Chart:**
- **12 to 20 candles** for understanding the daily trend.
- **30 to 50 candles** for trends over a couple of days.
- **100 to 120 candles** for an intra-week trend view.
Experimenting with these settings and backtesting on historical data will help you find the optimal number of candles for your specific trading style and the assets you trade.
Guidelines for 1H Timeframes:
When analyzing trends on a 1-hour (1H) timeframe, you're focusing on short to medium-term trends, often used by day traders and short-term swing traders. Here’s how you can approach selecting the number of preceding candles:
1. **Short-Term Trend:**
- **14 to 21 Candles (14 to 21 Hours):** Using 14 to 21 candles on a 1-hour chart captures roughly half a day to a full day of trading activity. This range is ideal for day traders who want to identify short-term momentum and trend changes within a single trading day.
2. **Medium-Term Trend:**
- **50 Candles (2 Days):** A 50-period moving average on a 1-hour chart covers about two days of trading. This period is popular for identifying trends that may last a couple of days, making it useful for short-term swing traders.
3. **Longer-Term Trend:**
- **100 Candles (4 Days):** Using 100 candles gives you a broader view of the trend over about four days of trading. This is helpful for traders who want to align their trades with a more sustained trend that spans the entire week.
4. **Very Short-Term (Micro Trend):**
- **7 to 10 Candles (7 to 10 Hours):** For traders looking to capture micro trends or very short-term price movements, using 7 to 10 candles can provide a quick look at recent price action. This is often used for scalping or very short-term intraday strategies.
**Considerations:**
- **Market Volatility:** In highly volatile markets, using more candles (like 50 or 100) helps smooth out noise and provides a clearer trend signal. In less volatile conditions, fewer candles may suffice to capture trends.
- **Trading Style:** If you are a day trader looking for quick moves, shorter periods (like 7 to 21 candles) might be more suitable. For those who hold positions for a day or two, longer periods (like 50 or 100 candles) can provide better trend confirmation.
- **Asset Class:** The optimal number of candles can vary depending on the asset
Guidelines for 4H Timeframes:
When analyzing trends on a 4-hour (4H) timeframe, you’re generally looking to capture short to medium-term trends. This timeframe is popular among swing traders and intraday traders who want to balance between catching more significant market moves and not being too sensitive to noise. Here's how you can approach selecting the number of preceding candles:
1. **Short-Term Trend:**
- **14 to 21 Candles (2 to 3 Days):** Using 14 to 21 candles on a 4-hour chart covers roughly 2 to 3 days of trading activity. This range is ideal for traders looking to capture short-term momentum, especially in markets where price action can move quickly within a few days.
2. **Medium-Term Trend:**
- **50 Candles (8 to 10 Days):** A 50-period moving average on a 4-hour chart represents approximately 8 to 10 days of trading (considering 6 trading periods per day). This period is popular among swing traders for identifying trends that develop over the course of one to two weeks.
3. **Longer-Term Trend:**
- **100 Candles (16 to 20 Days):** Using 100 candles gives you a broader view of the trend over about 3 to 4 weeks. This is useful for traders who want to align their trades with the more sustained market direction while still remaining responsive to recent changes.
**Considerations:**
- **Market Conditions:** In a trending market, fewer candles (like 14 or 21) may be enough to identify the trend, allowing for quicker responses to price movements. In a more volatile or range-bound market, using more candles (like 50 or 100) can help smooth out noise and avoid false signals.
- **Trading Style:** If you are an intraday trader, shorter periods (14 to 21 candles) may be preferable, as they allow for quick entries and exits. Swing traders might lean towards the 50 to 100 candle range to capture trends that last several days to a few weeks.
- **Volatility:** The higher the volatility of the asset, the more candles you might want to use to ensure that the trend signal is not too erratic.
**Common Approaches:**
- **20-Period Moving Average:** A 20-period moving average on a 4-hour chart is often used by traders to capture short-term trends that align with momentum over the past few days.
- **50-Period Moving Average:** The 50-period moving average is widely used on the 4-hour chart to track medium-term trends. It provides a good balance between reacting to new trends and avoiding too many whipsaws.
- **100-Period Moving Average:** The 100-period moving average offers insight into the longer-term trend on the 4-hour chart, helping to filter out short-term noise and confirm the overall market direction.
**Recommendation:**
- **Start with 20 Candles for Short-Term Trends:** This period is useful for capturing quick movements and short-term trends over a couple of days.
- **Use 50 Candles for Medium-Term Trends:** This is a standard setting that provides a balanced view of the market over about 1 to 2 weeks.
- **Consider 100 Candles for Longer-Term Trends:** This helps to identify more significant trends that have persisted for a few weeks.
**Practical Example:**
- **Intraday Traders:** If you’re focused on shorter-term trades and need to react quickly, using 14 to 21 candles will help you capture the most recent momentum.
- **Swing Traders:** If you’re looking to hold positions for several days to a few weeks, starting with 50 candles will give you a clearer picture of the trend over that period.
- **Position Traders:** For those holding positions for a longer duration within a month, using 100 candles helps to align with the broader trend while still being responsive enough for 4-hour price movements.
Backtesting these settings on your chosen asset and strategy will help refine the optimal number of candles for your specific needs.
Guidelines for Daily Timeframes:
When analyzing trends on a daily timeframe, you're typically focusing on short to medium-term trends. Here’s how you can determine the optimal number of preceding candles:
1. **Short-Term Trend:**
- **10 to 20 Candles (2 to 4 Weeks):** Using 10 to 20 daily candles captures about 2 to 4 weeks of price action. This is commonly used for identifying short-term trends, ideal for swing traders or those looking for quick entries and exits within a month.
2. **Medium-Term Trend:**
- **50 Candles (2 to 3 Months):** The 50-day moving average is a classic choice for capturing medium-term trends. This period covers about 2 to 3 months of trading days and is often used by swing traders and investors to identify the trend over a quarter or a season.
3. **Long-Term Trend:**
- **100 to 200 Candles (4 to 9 Months):** For longer-term trend analysis, using 100 to 200 daily candles gives you a broader perspective, covering approximately 4 to 9 months of price action. The 200-day moving average, in particular, is widely used by investors to determine the overall long-term trend and to assess market health.
**Considerations:**
- **Market Volatility:** In more volatile markets, using a larger number of candles (e.g., 50 or 200) helps smooth out noise and provides a more reliable trend signal. In less volatile markets, fewer candles might be sufficient to capture trends effectively.
- **Trading Style:** Day traders might prefer shorter periods (like 10 or 20 candles) for quicker signals, while position traders and longer-term swing traders might opt for 50 to 200 candles to focus on more sustained trends.
- **Asset Class:** The optimal number of candles can also depend on the asset class. For example, equities might have different optimal settings compared to forex or cryptocurrencies due to different volatility characteristics.
**Common Approaches:**
- **20-Period Moving Average:** The 20-day moving average is a popular choice for short-term trend analysis. It’s widely used by traders to identify the short-term direction and to make quick trading decisions.
- **50-Period Moving Average:** The 50-day moving average is a staple for medium-term trend analysis, often used as a key indicator for both entry and exit points in swing trading.
- **200-Period Moving Average:** The 200-day moving average is crucial for long-term trend identification. It's commonly used by investors and is often seen as a major support or resistance level. When the price is above the 200-day moving average, the market is generally considered to be in a long-term uptrend, and vice versa.
**Recommendation:**
- **Start with 20 Candles for Short-Term Trends:** This period is commonly used for identifying recent trends within the last few weeks.
- **Use 50 Candles for Medium-Term Trends:** This provides a good balance between responsiveness and stability, making it a good fit for most swing trading strategies.
- **Use 200 Candles for Long-Term Trends:** This period is ideal for long-term analysis and is particularly useful for investors looking at the overall market trend.
**Practical Example:**
- If you’re trading equities and want to catch short-term trends, start with 20 candles to identify trends that have developed over the past month.
- If you’re more focused on medium to long-term trends, consider using 50 or 200 candles to ensure you’re aligned with the broader market direction.
Experimenting with these periods and backtesting on historical data will help you determine the best setting for your particular strategy and the asset you're analyzing.
Guidelines for Weekly Timeframes:
When analyzing trends on a weekly timeframe, you're typically looking at intermediate to long-term trends. Here's how you might approach selecting the number of preceding candles:
1. **Intermediate-Term Trend:**
- **13 to 26 Candles (3 to 6 Months):** Using 13 to 26 weekly candles corresponds to a period of 3 to 6 months. This range is effective for identifying intermediate-term trends, which is suitable for swing traders or those looking to hold positions for several weeks to a few months.
2. **Medium-Term Trend:**
- **26 to 52 Candles (6 Months to 1 Year):** For a broader view, you might use 26 to 52 weekly candles. This represents 6 months to 1 year of price data, which is helpful for understanding the market’s behavior over a medium-term period. This range is commonly used by swing traders and position traders who are interested in capturing trends lasting several months.
3. **Long-Term Trend:**
- **104 Candles (2 Years):** Using 104 weekly candles gives you a 2-year perspective. This can be useful for long-term trend analysis, particularly for investors or those looking to identify major trend reversals or continuations over a more extended period.
**Considerations:**
- **Market Type:** In trending markets, fewer candles (like 13 or 26) may work well, capturing the trend more quickly. In choppier or range-bound markets, using more candles can help reduce noise and avoid false signals.
- **Asset Class:** The optimal number of candles can vary depending on the asset class. For example, equities might benefit from a slightly shorter lookback period compared to more volatile assets like commodities or cryptocurrencies.
- **Volatility:** If the market or asset you're analyzing is highly volatile, using a higher number of candles (like 52 or 104) can help smooth out price fluctuations and provide a more stable trend signal.
**Common Approaches:**
- **20-Period Moving Average:** A 20-week moving average is popular among traders for identifying the intermediate trend. It’s responsive enough to capture significant trend changes while filtering out short-term noise.
- **50-Period Moving Average:** The 50-week moving average is often used to identify longer-term trends and is commonly referenced in both technical analysis and by longer-term traders.
- **200-Period Moving Average:** Although less common on weekly charts compared to daily charts, a 200-week moving average can be used to identify very long-term trends, such as multi-year market cycles.
**Recommendation:**
- **Start with 26 Candles:** This gives you a half-year perspective and is a good starting point for most analyses on a weekly timeframe. It balances sensitivity to recent trends with the ability to capture more significant, sustained movements.
- **Adjust Based on Backtesting:** You can increase the number of candles to 52 if you find that you need more stability in the trend signal, or decrease to 13 if you're looking for a more responsive signal.
Experimenting with different periods and backtesting on historical data can help determine the best setting for your specific strategy and asset class.
Guidelines for Monthly Timeframes:
For analyzing trends on monthly timeframes, you would generally be looking at much longer periods to capture the broader, long-term trend. Here's how you can approach it:
1. **Long-Term Trend (Primary Trend):**
- **12 to 24 Candles (1 to 2 Years):** Using 12 to 24 monthly candles corresponds to a period of 1 to 2 years. This is typically sufficient to identify long-term trends and is commonly used by long-term investors or position traders who are interested in the overall direction of the market or asset over multiple years.
2. **Very Long-Term Trend (Secular Trend):**
- **36 to 60 Candles (3 to 5 Years):** To capture very long-term secular trends, you might use 36 to 60 monthly candles. This would represent a time frame of 3 to 5 years and is often used for understanding macroeconomic trends or very long-term investment strategies.
3. **Ultra Long-Term Trend:**
- **120 Candles (10 Years):** In some cases, especially for assets like indices or commodities that are analyzed over decades, using 120 monthly candles can help in identifying ultra long-term trends. This would be appropriate for strategic investors or those looking at generational market cycles.
**Considerations:**
- **Volatility and Stability:** Monthly timeframes generally smooth out short-term volatility, but they can also be slow to react to changes. Using a larger number of candles (e.g., 24 or more) can help ensure that the trend signal is robust and not prone to frequent whipsaws.
- **Asset Class:** The choice of period might also depend on the asset class. For instance, equities might require fewer candles compared to commodities or currencies, which can exhibit different trend dynamics.
- **Market Phases:** In different market phases (bullish, bearish, or sideways), the number of candles might need to be adjusted. For instance, in a strongly trending market, fewer candles might still provide a reliable trend indication, whereas in a more volatile or ranging market, more candles might be needed to smooth out the data.
**Common Approaches:**
- **50-Period Moving Average:** A 50-month moving average is popular among long-term traders and investors for identifying the primary trend. It offers a balance between capturing the overall trend and being responsive enough to significant changes.
- **200-Period Moving Average:** Although rarely used on a monthly chart due to the long timeframe it represents (over 16 years), it can be useful for identifying very long-term secular trends, especially for broad market indices or in macroeconomic analysis.
**Recommendation:**
- **Start with 24 Candles:** This gives you a 2-year perspective on the trend and is a good starting point for most long-term analyses on monthly charts. Adjust upwards if you need a broader trend view, depending on the stability and nature of the asset you're analyzing.
Experimentation and backtesting with your specific asset and strategy can help fine-tune the exact number of candles that work best for your analysis on a monthly timeframe.
Copeland Dynamic Dominance Matrix System | GForgeCopeland Dynamic Dominance Matrix System | GForge - v1
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📊 COMPREHENSIVE SYSTEM OVERVIEW
The GForge Dynamic BB% TrendSync System represents a revolutionary approach to algorithmic portfolio management, combining cutting-edge statistical analysis, momentum detection, and regime identification into a unified framework. This system processes up to 39 different cryptocurrency assets simultaneously, using advanced mathematical models to determine optimal capital allocation across dynamic market conditions.
Core Innovation: Multi-Dimensional Analysis
Unlike traditional single-asset indicators, this system operates on multiple analytical dimensions:
Momentum Analysis: Dual Bollinger Band Modified Deviation (DBBMD) calculations
Relative Strength: Comprehensive dominance matrix with head-to-head comparisons
Fundamental Screening: Alpha and Beta statistical filtering
Market Regime Detection: Five-component statistical testing framework
Portfolio Optimization: Dynamic weighting and allocation algorithms
Risk Management: Multi-layered protection and regime-based positioning
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🔧 DETAILED COMPONENT BREAKDOWN
1. Dynamic Bollinger Band % Modified Deviation Engine (DBBMD)
The foundation of this system is an advanced oscillator that combines two independent Bollinger Band systems with asymmetric parameters to create unique momentum readings.
Technical Implementation:
[
// BB System 1: Fast-reacting with extended standard deviation
primary_bb1_ma_len = 40 // Shorter MA for responsiveness
primary_bb1_sd_len = 65 // Longer SD for stability
primary_bb1_mult = 1.0 // Standard deviation multiplier
// BB System 2: Complementary asymmetric design
primary_bb2_ma_len = 8 // Longer MA for trend following
primary_bb2_sd_len = 66 // Shorter SD for volatility sensitivity
primary_bb2_mult = 1.7 // Wider bands for reduced noise
Key Features:
Asymmetric Design: The intentional mismatch between MA and Standard Deviation periods creates unique oscillation characteristics that traditional Bollinger Bands cannot achieve
Percentage Scale: All readings are normalized to 0-100% scale for consistent interpretation across assets
Multiple Combination Modes:
BB1 Only: Fast/reactive system
BB2 Only: Smooth/stable system
Average: Balanced blend (recommended)
Both Required: Conservative (both must agree)
Either One: Aggressive (either can trigger)
Mean Deviation Filter: Additional volatility-based layer that measures the standard deviation of the DBBMD% itself, creating dynamic trigger bands
Signal Generation Logic:
// Primary thresholds
primary_long_threshold = 71 // DBBMD% level for bullish signals
primary_short_threshold = 33 // DBBMD% level for bearish signals
// Mean Deviation creates dynamic bands around these thresholds
upper_md_band = combined_bb + (md_mult * bb_std)
lower_md_band = combined_bb - (md_mult * bb_std)
// Signal triggers when DBBMD crosses these dynamic bands
long_signal = lower_md_band > long_threshold
short_signal = upper_md_band < short_threshold
For more information on this BB% indicator, find it here:
2. Revolutionary Dominance Matrix System
This is the system's most sophisticated innovation - a comprehensive framework that compares every asset against every other asset to determine relative strength hierarchies.
Mathematical Foundation:
The system constructs a mathematical matrix where each cell represents whether asset i dominates asset j:
// Core dominance matrix (39x39 for maximum assets)
var matrix dominance_matrix = matrix.new(39, 39, 0)
// For each qualifying asset pair (i,j):
for i = 0 to active_count - 1
for j = 0 to active_count - 1
if i != j
// Calculate price ratio BB% TrendSync for asset_i/asset_j
ratio_array = calculate_price_ratios(asset_i, asset_j)
ratio_dbbmd = calculate_dbbmd(ratio_array)
// Asset i dominates j if ratio is in uptrend
if ratio_dbbmd_state == 1
matrix.set(dominance_matrix, i, j, 1)
Copeland Scoring Algorithm:
Each asset receives a dominance score calculated as:
Dominance Score = Total Wins - Total Losses
// Calculate net dominance for each asset
for i = 0 to active_count - 1
wins = 0
losses = 0
for j = 0 to active_count - 1
if i != j
if matrix.get(dominance_matrix, i, j) == 1
wins += 1
else
losses += 1
copeland_score = wins - losses
array.set(dominance_scores, i, copeland_score)
Head-to-Head Analysis Process:
Ratio Construction: For each asset pair, calculate price_asset_A / price_asset_B
DBBMD Application: Apply the same DBBMD analysis to these ratios
Trend Determination: If ratio DBBMD shows uptrend, Asset A dominates Asset B
Matrix Population: Store dominance relationships in mathematical matrix
Score Calculation: Sum wins minus losses for final ranking
This creates a tournament-style ranking where each asset's strength is measured against all others, not just against a benchmark.
3. Advanced Alpha & Beta Filtering System
The system incorporates fundamental analysis through Capital Asset Pricing Model (CAPM) calculations to filter assets based on risk-adjusted performance.
Alpha Calculation (Excess Return Analysis):
// CAPM Alpha calculation
f_calc_alpha(asset_prices, benchmark_prices, alpha_length, beta_length, risk_free_rate) =>
// Calculate asset and benchmark returns
asset_returns = calculate_returns(asset_prices, alpha_length)
benchmark_returns = calculate_returns(benchmark_prices, alpha_length)
// Get beta for expected return calculation
beta = f_calc_beta(asset_prices, benchmark_prices, beta_length)
// Average returns over period
avg_asset_return = array_average(asset_returns) * 100
avg_benchmark_return = array_average(benchmark_returns) * 100
// Expected return using CAPM: E(R) = Beta * Market_Return + Risk_Free_Rate
expected_return = beta * avg_benchmark_return + risk_free_rate
// Alpha = Actual Return - Expected Return
alpha = avg_asset_return - expected_return
Beta Calculation (Volatility Relationship):
// Beta measures how much an asset moves relative to benchmark
f_calc_beta(asset_prices, benchmark_prices, length) =>
// Calculate return series for both assets
asset_returns =
benchmark_returns =
// Populate return arrays
for i = 0 to length - 1
asset_return = (current_price - previous_price) / previous_price
benchmark_return = (current_bench - previous_bench) / previous_bench
// Calculate covariance and variance
covariance = calculate_covariance(asset_returns, benchmark_returns)
benchmark_variance = calculate_variance(benchmark_returns)
// Beta = Covariance(Asset, Market) / Variance(Market)
beta = covariance / benchmark_variance
Filtering Applications:
Alpha Filter: Only includes assets with alpha above specified threshold (e.g., >0.5% monthly excess return)
Beta Filter: Screens for desired volatility characteristics (e.g., beta >1.0 for aggressive assets)
Combined Screening: Both filters must pass for asset qualification
Dynamic Thresholds: User-configurable parameters for different market conditions
4. Intelligent Tie-Breaking Resolution System
When multiple assets have identical dominance scores, the system employs sophisticated methods to determine final rankings.
Standard Tie-Breaking Hierarchy:
// Primary tie-breaking logic
if score_i == score_j // Tied dominance scores
// Level 1: Compare Beta values (higher beta wins)
beta_i = array.get(beta_values, i)
beta_j = array.get(beta_values, j)
if beta_j > beta_i
swap_positions(i, j)
else if beta_j == beta_i
// Level 2: Compare Alpha values (higher alpha wins)
alpha_i = array.get(alpha_values, i)
alpha_j = array.get(alpha_values, j)
if alpha_j > alpha_i
swap_positions(i, j)
Advanced Tie-Breaking (Head-to-Head Analysis):
For the top 3 performers, an enhanced tie-breaking mechanism analyzes direct head-to-head price ratio performance:
// Advanced tie-breaker for top performers
f_advanced_tiebreaker(asset1_idx, asset2_idx, lookback_period) =>
// Calculate price ratio over lookback period
ratio_history =
for k = 0 to lookback_period - 1
price_ratio = price_asset1 / price_asset2
array.push(ratio_history, price_ratio)
// Apply simplified trend analysis to ratio
current_ratio = array.get(ratio_history, 0)
average_ratio = calculate_average(ratio_history)
// Asset 1 wins if current ratio > average (trending up)
if current_ratio > average_ratio
return 1 // Asset 1 dominates
else
return -1 // Asset 2 dominates
5. Five-Component Aggregate Market Regime Filter
This sophisticated framework combines multiple statistical tests to determine whether market conditions favor trending strategies or require defensive positioning.
Component 1: Augmented Dickey-Fuller (ADF) Test
Tests for unit root presence to distinguish between trending and mean-reverting price series.
// Simplified ADF implementation
calculate_adf_statistic(price_series, lookback) =>
// Calculate first differences
differences =
for i = 0 to lookback - 2
diff = price_series - price_series
array.push(differences, diff)
// Statistical analysis of differences
mean_diff = calculate_mean(differences)
std_diff = calculate_standard_deviation(differences)
// ADF statistic approximation
adf_stat = mean_diff / std_diff
// Compare against threshold for trend determination
is_trending = adf_stat <= adf_threshold
Component 2: Directional Movement Index (DMI)
Classic Wilder indicator measuring trend strength through directional movement analysis.
// DMI calculation for trend strength
calculate_dmi_signal(high_data, low_data, close_data, period) =>
// Calculate directional movements
plus_dm_sum = 0.0
minus_dm_sum = 0.0
true_range_sum = 0.0
for i = 1 to period
// Directional movements
up_move = high_data - high_data
down_move = low_data - low_data
// Accumulate positive/negative movements
if up_move > down_move and up_move > 0
plus_dm_sum += up_move
if down_move > up_move and down_move > 0
minus_dm_sum += down_move
// True range calculation
true_range_sum += calculate_true_range(i)
// Calculate directional indicators
di_plus = 100 * plus_dm_sum / true_range_sum
di_minus = 100 * minus_dm_sum / true_range_sum
// ADX calculation
dx = 100 * math.abs(di_plus - di_minus) / (di_plus + di_minus)
adx = dx // Simplified for demonstration
// Trending if ADX above threshold
is_trending = adx > dmi_threshold
Component 3: KPSS Stationarity Test
Complementary test to ADF that examines stationarity around trend components.
// KPSS test implementation
calculate_kpss_statistic(price_series, lookback, significance_level) =>
// Calculate mean and variance
series_mean = calculate_mean(price_series, lookback)
series_variance = calculate_variance(price_series, lookback)
// Cumulative sum of deviations
cumulative_sum = 0.0
cumsum_squared_sum = 0.0
for i = 0 to lookback - 1
deviation = price_series - series_mean
cumulative_sum += deviation
cumsum_squared_sum += math.pow(cumulative_sum, 2)
// KPSS statistic
kpss_stat = cumsum_squared_sum / (lookback * lookback * series_variance)
// Compare against critical values
critical_value = significance_level == 0.01 ? 0.739 :
significance_level == 0.05 ? 0.463 : 0.347
is_trending = kpss_stat >= critical_value
Component 4: Choppiness Index
Measures market directionality using fractal dimension analysis of price movement.
// Choppiness Index calculation
calculate_choppiness(price_data, period) =>
// Find highest and lowest over period
highest = price_data
lowest = price_data
true_range_sum = 0.0
for i = 0 to period - 1
if price_data > highest
highest := price_data
if price_data < lowest
lowest := price_data
// Accumulate true range
if i > 0
true_range = calculate_true_range(price_data, i)
true_range_sum += true_range
// Choppiness calculation
range_high_low = highest - lowest
choppiness = 100 * math.log10(true_range_sum / range_high_low) / math.log10(period)
// Trending if choppiness below threshold (typically 61.8)
is_trending = choppiness < 61.8
Component 5: Hilbert Transform Analysis
Phase-based cycle detection and trend identification using mathematical signal processing.
// Hilbert Transform trend detection
calculate_hilbert_signal(price_data, smoothing_period, filter_period) =>
// Smooth the price data
smoothed_price = calculate_moving_average(price_data, smoothing_period)
// Calculate instantaneous phase components
// Simplified implementation for demonstration
instant_phase = smoothed_price
delayed_phase = calculate_moving_average(price_data, filter_period)
// Compare instantaneous vs delayed signals
phase_difference = instant_phase - delayed_phase
// Trending if instantaneous leads delayed
is_trending = phase_difference > 0
Aggregate Regime Determination:
// Combine all five components
regime_calculation() =>
trending_count = 0
total_components = 0
// Test each enabled component
if enable_adf and adf_signal == 1
trending_count += 1
if enable_adf
total_components += 1
// Repeat for all five components...
// Calculate trending proportion
trending_proportion = trending_count / total_components
// Market is trending if proportion above threshold
regime_allows_trading = trending_proportion >= regime_threshold
The system only allows asset positions when the specified percentage of components indicate trending conditions. During choppy or mean-reverting periods, the system automatically positions in USD to preserve capital.
6. Dynamic Portfolio Weighting Framework
Six sophisticated allocation methodologies provide flexibility for different market conditions and risk preferences.
Weighting Method Implementations:
1. Equal Weight Distribution:
// Simple equal allocation
if weighting_mode == "Equal Weight"
weight_per_asset = 1.0 / selection_count
for i = 0 to selection_count - 1
array.push(weights, weight_per_asset)
2. Linear Dominance Scaling:
// Linear scaling based on dominance scores
if weighting_mode == "Linear Dominance"
// Normalize scores to 0-1 range
min_score = array.min(dominance_scores)
max_score = array.max(dominance_scores)
score_range = max_score - min_score
total_weight = 0.0
for i = 0 to selection_count - 1
score = array.get(dominance_scores, i)
normalized = (score - min_score) / score_range
weight = 1.0 + normalized * concentration_factor
array.push(weights, weight)
total_weight += weight
// Normalize to sum to 1.0
for i = 0 to selection_count - 1
current_weight = array.get(weights, i)
array.set(weights, i, current_weight / total_weight)
3. Conviction Score (Exponential):
// Exponential scaling for high conviction
if weighting_mode == "Conviction Score"
// Combine dominance score with DBBMD strength
conviction_scores =
for i = 0 to selection_count - 1
dominance = array.get(dominance_scores, i)
dbbmd_strength = array.get(dbbmd_values, i)
conviction = dominance + (dbbmd_strength - 50) / 25
array.push(conviction_scores, conviction)
// Exponential weighting
total_weight = 0.0
for i = 0 to selection_count - 1
conviction = array.get(conviction_scores, i)
normalized = normalize_score(conviction)
weight = math.pow(1 + normalized, concentration_factor)
array.push(weights, weight)
total_weight += weight
// Final normalization
normalize_weights(weights, total_weight)
Advanced Features:
Minimum Position Constraint: Prevents dust allocations below specified threshold
Concentration Factor: Adjustable parameter controlling weight distribution aggressiveness
Dominance Boost: Extra weight for assets exceeding specified dominance thresholds
Dynamic Rebalancing: Automatic weight recalculation on portfolio changes
7. Intelligent USD Management System
The system treats USD as a competing asset with its own dominance score, enabling sophisticated cash management.
USD Scoring Methodologies:
Smart Competition Mode (Recommended):
f_calculate_smart_usd_dominance() =>
usd_wins = 0
// USD beats assets in downtrends or weak uptrends
for i = 0 to active_count - 1
asset_state = get_asset_state(i)
asset_dbbmd = get_asset_dbbmd(i)
// USD dominates shorts and weak longs
if asset_state == -1 or (asset_state == 1 and asset_dbbmd < long_threshold)
usd_wins += 1
// Calculate Copeland-style score
base_score = usd_wins - (active_count - usd_wins)
// Boost during weak market conditions
qualified_assets = count_qualified_long_assets()
if qualified_assets <= active_count * 0.2
base_score := math.round(base_score * usd_boost_factor)
base_score
Auto Short Count Mode:
// USD dominance based on number of bearish assets
usd_dominance = count_assets_in_short_state()
// Apply boost during low activity
if qualified_long_count <= active_count * 0.2
usd_dominance := usd_dominance * usd_boost_factor
Regime-Based USD Positioning:
When the five-component regime filter indicates unfavorable conditions, the system automatically overrides all asset signals and positions 100% in USD, protecting capital during choppy markets.
8. Multi-Asset Infrastructure & Data Management
The system maintains comprehensive data structures for up to 39 assets simultaneously.
Data Collection Framework:
// Full OHLC data matrices (200 bars depth for performance)
var matrix open_data = matrix.new(39, 200, na)
var matrix high_data = matrix.new(39, 200, na)
var matrix low_data = matrix.new(39, 200, na)
var matrix close_data = matrix.new(39, 200, na)
// Real-time data collection
if barstate.isconfirmed
for i = 0 to active_count - 1
ticker = array.get(assets, i)
= request.security(ticker, timeframe.period,
[open , high , low , close ],
lookahead=barmerge.lookahead_off)
// Store in matrices with proper shifting
matrix.set(open_data, i, 0, nz(o, 0))
matrix.set(high_data, i, 0, nz(h, 0))
matrix.set(low_data, i, 0, nz(l, 0))
matrix.set(close_data, i, 0, nz(c, 0))
Asset Configuration:
The system comes pre-configured with 39 major cryptocurrency pairs across multiple exchanges:
Major Pairs: BTC, ETH, XRP, SOL, DOGE, ADA, etc.
Exchange Coverage: Binance, KuCoin, MEXC for optimal liquidity
Configurable Count: Users can activate 2-39 assets based on preferences
Custom Tickers: All asset selections are user-modifiable
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⚙️ COMPREHENSIVE CONFIGURATION GUIDE
Portfolio Management Settings
Maximum Portfolio Size (1-10):
Conservative (1-2): High concentration, captures strong trends
Balanced (3-5): Moderate diversification with trend focus
Diversified (6-10): Lower concentration, broader market exposure
Dominance Clarity Threshold (0.1-1.0):
Low (0.1-0.4): Prefers diversification, holds multiple assets frequently
Medium (0.5-0.7): Balanced approach, context-dependent allocation
High (0.8-1.0): Concentration-focused, single asset preference
Signal Generation Parameters
DBBMD Thresholds:
// Standard configuration
primary_long_threshold = 71 // Conservative: 75+, Aggressive: 65-70
primary_short_threshold = 33 // Conservative: 25-30, Aggressive: 35-40
// BB System parameters
bb1_ma_len = 40 // Fast system: 20-50
bb1_sd_len = 65 // Stability: 50-80
bb2_ma_len = 8 // Trend: 60-100
bb2_sd_len = 66 // Sensitivity: 10-20
Risk Management Configuration
Alpha/Beta Filters:
Alpha Threshold: 0.0-2.0% (higher = more selective)
Beta Threshold: 0.5-2.0 (1.0+ for aggressive assets)
Calculation Periods: 20-50 bars (longer = more stable)
Regime Filter Settings:
Trending Threshold: 0.3-0.8 (higher = stricter trend requirements)
Component Lookbacks: 30-100 bars (balance responsiveness vs stability)
Enable/Disable: Individual component control for customization
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📊 PERFORMANCE TRACKING & VISUALIZATION
Real-Time Dashboard Features
The compact dashboard provides essential information:
Current Holdings: Asset names and allocation percentages
Dominance Score: Current position's relative strength ranking
Active Assets: Qualified long signals vs total asset count
Returns: Total portfolio performance percentage
Maximum Drawdown: Peak-to-trough decline measurement
Trade Count: Total portfolio transitions executed
Regime Status: Current market condition assessment
Comprehensive Ranking Table
The left-side table displays detailed asset analysis:
Ranking Position: Numerical order by dominance score
Asset Symbol: Clean ticker identification with color coding
Dominance Score: Net wins minus losses in head-to-head comparisons
Win-Loss Record: Detailed breakdown of dominance relationships
DBBMD Reading: Current momentum percentage with threshold highlighting
Alpha/Beta Values: Fundamental analysis metrics when filters enabled
Portfolio Weight: Current allocation percentage in signal portfolio
Execution Status: Visual indicator of actual holdings vs signals
Visual Enhancement Features
Color-Coded Assets: 39 distinct colors for easy identification
Regime Background: Red tinting during unfavorable market conditions
Dynamic Equity Curve: Portfolio value plotted with position-based coloring
Status Indicators: Symbols showing execution vs signal states
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🔍 ADVANCED TECHNICAL FEATURES
State Persistence System
The system maintains asset states across bars to prevent excessive switching:
// State tracking for each asset and ratio combination
var array asset_states = array.new(1560, 0) // 39 * 40 ratios
// State changes only occur on confirmed threshold breaks
if long_crossover and current_state != 1
current_state := 1
array.set(asset_states, asset_index, 1)
else if short_crossover and current_state != -1
current_state := -1
array.set(asset_states, asset_index, -1)
Transaction Cost Integration
Realistic modeling of trading expenses:
// Transaction cost calculation
transaction_fee = 0.4 // Default 0.4% (fees + slippage)
// Applied on portfolio transitions
if should_execute_transition
was_holding_assets = check_current_holdings()
will_hold_assets = check_new_signals()
// Charge fees for meaningful transitions
if transaction_fee > 0 and (was_holding_assets or will_hold_assets)
fee_amount = equity * (transaction_fee / 100)
equity -= fee_amount
total_fees += fee_amount
Dynamic Memory Management
Optimized data structures for performance:
200-Bar History: Sufficient for calculations while maintaining speed
Matrix Operations: Efficient storage and retrieval of multi-asset data
Array Recycling: Memory-conscious data handling for long-running backtests
Conditional Calculations: Skip unnecessary computations during initialization
12H 30 assets portfolio
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🚨 SYSTEM LIMITATIONS & TESTING STATUS
CURRENT DEVELOPMENT PHASE: ACTIVE TESTING & OPTIMIZATION
This system represents cutting-edge algorithmic trading technology but remains in continuous development. Key considerations:
Known Limitations:
Requires significant computational resources for 39-asset analysis
Performance varies significantly across different market conditions
Complex parameter interactions may require extensive optimization
Slippage and liquidity constraints not fully modeled for all assets
No consideration for market impact in large position sizes
Areas Under Active Development:
Enhanced regime detection algorithms
Improved transaction cost modeling
Additional portfolio weighting methodologies
Machine learning integration for parameter optimization
Cross-timeframe analysis capabilities
---
🔒 ANTI-REPAINTING ARCHITECTURE & LIVE TRADING READINESS
One of the most critical aspects of any trading system is ensuring that signals and calculations are based on confirmed, historical data rather than current bar information that can change throughout the trading session. This system implements comprehensive anti-repainting measures to ensure 100% reliability for live trading .
The Repainting Problem in Trading Systems
Repainting occurs when an indicator uses current, unconfirmed bar data in its calculations, causing:
False Historical Signals: Backtests appear better than reality because calculations change as bars develop
Live Trading Failures: Signals that looked profitable in testing fail when deployed in real markets
Inconsistent Results: Different results when running the same indicator at different times during a trading session
Misleading Performance: Inflated win rates and returns that cannot be replicated in practice
GForge Anti-Repainting Implementation
This system eliminates repainting through multiple technical safeguards:
1. Historical Data Usage for All Calculations
// CRITICAL: All calculations use PREVIOUS bar data (note the offset)
= request.security(ticker, timeframe.period,
[open , high , low , close , close],
lookahead=barmerge.lookahead_off)
// Store confirmed previous bar OHLC for calculations
matrix.set(open_data, i, 0, nz(o1, 0)) // Previous bar open
matrix.set(high_data, i, 0, nz(h1, 0)) // Previous bar high
matrix.set(low_data, i, 0, nz(l1, 0)) // Previous bar low
matrix.set(close_data, i, 0, nz(c1, 0)) // Previous bar close
// Current bar close only for visualization
matrix.set(current_prices, i, 0, nz(c0, 0)) // Live price display
2. Confirmed Bar State Processing
// Only process data when bars are confirmed and closed
if barstate.isconfirmed
// All signal generation and portfolio decisions occur here
// using only historical, unchanging data
// Shift historical data arrays
for i = 0 to active_count - 1
for bar = math.min(data_bars, 199) to 1
// Move confirmed data through historical matrices
old_data = matrix.get(close_data, i, bar - 1)
matrix.set(close_data, i, bar, old_data)
// Process new confirmed bar data
calculate_all_signals_and_dominance()
3. Lookahead Prevention
// Explicit lookahead prevention in all security calls
request.security(ticker, timeframe.period, expression,
lookahead=barmerge.lookahead_off)
// This ensures no future data can influence current calculations
// Essential for maintaining signal integrity across all timeframes
4. State Persistence with Historical Validation
// Asset states only change based on confirmed threshold breaks
// using historical data that cannot change
var array asset_states = array.new(1560, 0)
// State changes use only confirmed, previous bar calculations
if barstate.isconfirmed
=
f_calculate_enhanced_dbbmd(confirmed_price_array, ...)
// Only update states after bar confirmation
if long_crossover_confirmed and current_state != 1
current_state := 1
array.set(asset_states, asset_index, 1)
Live Trading vs. Backtesting Consistency
The system's architecture ensures identical behavior in both environments:
Backtesting Mode:
Uses historical offset data for all calculations
Processes confirmed bars with `barstate.isconfirmed`
Maintains identical signal generation logic
No access to future information
Live Trading Mode:
Uses same historical offset data structure
Waits for bar confirmation before signal updates
Identical mathematical calculations and thresholds
Real-time price display without affecting signals
Technical Implementation Details
Data Collection Timing
// Example of proper data collection timing
if barstate.isconfirmed // Wait for bar to close
// Collect PREVIOUS bar's confirmed OHLC data
for i = 0 to active_count - 1
ticker = array.get(assets, i)
// Get confirmed previous bar data (note offset)
=
request.security(ticker, timeframe.period,
[open , high , low , close , close],
lookahead=barmerge.lookahead_off)
// ALL calculations use prev_* values
// current_close only for real-time display
portfolio_calculations_use_previous_bar_data()
Signal Generation Process
// Signal generation workflow (simplified)
if barstate.isconfirmed and data_bars >= minimum_required_bars
// Step 1: Calculate DBBMD using historical price arrays
for i = 0 to active_count - 1
historical_prices = get_confirmed_price_history(i) // Uses offset data
= calculate_dbbmd(historical_prices)
update_asset_state(i, state)
// Step 2: Build dominance matrix using confirmed data
calculate_dominance_relationships() // All historical data
// Step 3: Generate portfolio signals
new_portfolio = generate_target_portfolio() // Based on confirmed calculations
// Step 4: Compare with previous signals for changes
if portfolio_signals_changed()
execute_portfolio_transition()
Verification Methods for Users
Users can verify the anti-repainting behavior through several methods:
1. Historical Replay Test
Run the indicator on historical data
Note signal timing and portfolio changes
Replay the same period - signals should be identical
No retroactive changes in historical signals
2. Intraday Consistency Check
Load indicator during active trading session
Observe that previous day's signals remain unchanged
Only current day's final bar should show potential signal changes
Refresh indicator - historical signals should be identical
Live Trading Deployment Considerations
Data Quality Assurance
Exchange Connectivity: Ensure reliable data feeds for all 39 assets
Missing Data Handling: System includes safeguards for data gaps
Price Validation: Automatic filtering of obvious price errors
Timeframe Synchronization: All assets synchronized to same bar timing
Performance Impact of Anti-Repainting Measures
The robust anti-repainting implementation requires additional computational resources:
Memory Usage: 200-bar historical data storage for 39 assets
Processing Delay: Signals update only after bar confirmation
Calculation Overhead: Multiple historical data validations
Alert Timing: Slight delay compared to current-bar indicators
However, these trade-offs are essential for reliable live trading performance and accurate backtesting results.
Critical: Equity Curve Anti-Repainting Architecture
The most sophisticated aspect of this system's anti-repainting design is the temporal separation between signal generation and performance calculation . This creates a realistic trading simulation that perfectly matches live trading execution.
The Timing Sequence
// STEP 1: Store what we HELD during the current bar (for performance calc)
if barstate.isconfirmed
// Record positions that were active during this bar
array.clear(held_portfolio)
array.clear(held_weights)
for i = 0 to array.size(execution_portfolio) - 1
array.push(held_portfolio, array.get(execution_portfolio, i))
array.push(held_weights, array.get(execution_weights, i))
// STEP 2: Calculate performance based on what we HELD
portfolio_return = 0.0
for i = 0 to array.size(held_portfolio) - 1
held_asset = array.get(held_portfolio, i)
held_weight = array.get(held_weights, i)
// Performance from current_price vs reference_price
// This is what we ACTUALLY earned during this bar
if held_asset != "USD"
current_price = get_current_price(held_asset) // End of bar
reference_price = get_reference_price(held_asset) // Start of bar
asset_return = (current_price - reference_price) / reference_price
portfolio_return += asset_return * held_weight
// STEP 3: Apply return to equity (realistic timing)
equity := equity * (1 + portfolio_return)
// STEP 4: Generate NEW signals for NEXT period (using confirmed data)
= f_generate_target_portfolio()
// STEP 5: Execute transitions if signals changed
if signal_changed
// Update execution_portfolio for NEXT bar
array.clear(execution_portfolio)
array.clear(execution_weights)
for i = 0 to array.size(new_signal_portfolio) - 1
array.push(execution_portfolio, array.get(new_signal_portfolio, i))
array.push(execution_weights, array.get(new_signal_weights, i))
Why This Prevents Equity Curve Repainting
Performance Attribution: Returns are calculated based on positions that were **actually held** during each bar, not future signals
Signal Timing: New signals are generated **after** performance calculation, affecting only **future** bars
Realistic Execution: Mimics real trading where you earn returns on current positions while planning future moves
No Retroactive Changes: Once a bar closes, its performance contribution to equity is permanent and unchangeable
The One-Bar Offset Mechanism
This system implements a critical one-bar timing offset:
// Bar N: Performance Calculation
// ================================
// 1. Calculate returns on positions held during Bar N
// 2. Update equity based on actual holdings during Bar N
// 3. Plot equity point for Bar N (based on what we HELD)
// Bar N: Signal Generation
// ========================
// 4. Generate signals for Bar N+1 (using confirmed Bar N data)
// 5. Send alerts for what will be held during Bar N+1
// 6. Update execution_portfolio for Bar N+1
// Bar N+1: The Cycle Continues
// =============================
// 1. Performance calculated on positions from Bar N signals
// 2. New signals generated for Bar N+2
Alert System Timing
The alert system reflects this sophisticated timing:
Transaction Cost Realism
Even transaction costs follow realistic timing:
// Fees applied when transitioning between different portfolios
if should_execute_transition
// Charge fees BEFORE taking new positions (realistic timing)
if transaction_fee > 0
fee_amount = equity * (transaction_fee / 100)
equity -= fee_amount // Immediate cost impact
total_fees += fee_amount
// THEN update to new portfolio
update_execution_portfolio(new_signals)
transitions += 1
// Fees reduce equity immediately, affecting all future calculations
// This matches real trading where fees are deducted upon execution
LIVE TRADING CERTIFICATION:
This system has been specifically designed and tested for live trading deployment. The comprehensive anti-repainting measures ensure that:
Backtesting results accurately represent real trading potential
Signals are generated using only confirmed, historical data
No retroactive changes can occur to previously generated signals
Portfolio transitions are based on reliable, unchanging calculations
Performance metrics reflect realistic trading outcomes including proper timing
Users can deploy this system with confidence that live trading results will closely match backtesting performance, subject to normal market execution factors such as slippage and liquidity.
---
⚡ ALERT SYSTEM & AUTOMATION
The system provides comprehensive alerting for automation and monitoring:
Available Alert Conditions
Portfolio Signal Change: Triggered when new portfolio composition is generated
Regime Override Active: Alerts when market regime forces USD positioning
Individual Asset Signals: Can be configured for specific asset transitions
Performance Thresholds: Drawdown or return-based notifications
---
📈 BACKTESTING & PERFORMANCE ANALYSIS
8 Comprehensive Metrics Tracking
The system maintains detailed performance statistics:
Equity Curve: Real-time portfolio value progression
Returns Calculation: Total and annualized performance metrics
Drawdown Analysis: Peak-to-trough decline measurements
Transaction Counting: Portfolio transition frequency
Fee Tracking: Cumulative transaction cost impact
Win Rate Analysis: Success rate of position changes
Backtesting Configuration
// Backtesting parameters
initial_capital = 10000.0 // Starting capital
use_custom_start = true // Enable specific start date
custom_start = timestamp("2023-09-01") // Backtest beginning
transaction_fee = 0.4 // Combined fees and slippage %
// Performance calculation
total_return = (equity - initial_capital) / initial_capital * 100
current_drawdown = (peak_equity - equity) / peak_equity * 100
---
🔧 TROUBLESHOOTING & OPTIMIZATION
Common Configuration Issues
Insufficient Data: Ensure 100+ bars available before start date
[*} Not Compiling: Go on an asset's price chart with 2 or 3 years of data to
make the system compile or just simply reapply the indicator again
Too Many Assets: Reduce active count if experiencing timeouts
Regime Filter Too Strict: Lower trending threshold if always in USD
Excessive Switching: Increase MD multiplier or adjust thresholds
---
💡 USER FEEDBACK & ENHANCEMENT REQUESTS
The continuous evolution of this system depends heavily on user experience and community feedback. Your insights will help motivate me for new improvements and new feature developments.
---
⚖️ FINAL COMPREHENSIVE RISK DISCLAIMER
TRADING INVOLVES SUBSTANTIAL RISK OF LOSS
This indicator is a sophisticated analytical tool designed for educational and research purposes. Important warnings and considerations:
System Limitations:
No algorithmic system can guarantee profitable outcomes
Complex systems may fail in unexpected ways during extreme market events
Historical backtesting does not account for all real-world trading challenges
Slippage, liquidity constraints, and market impact can significantly affect results
System parameters require careful optimization and ongoing monitoring
The creator and distributor of this indicator assume no liability for any financial losses, system failures, or adverse outcomes resulting from its use. This tool is provided "as is" without any warranties, express or implied.
By using this indicator, you acknowledge that you have read, understood, and agreed to assume all risks associated with algorithmic trading and cryptocurrency investments.
VP + Game Theory Integrated Strategy9s için DüşünceVP + Game Theory Integrated Strategy Indicator: Explanation and Working Logic
Hello! You can upload the provided Pine Script code to TradingView. This is compatible with Pine Script Editor (v6 version) – simply copy-paste it directly. The indicator's name is "VP + Game Theory Integrated Strategy," and since overlay=true, it will display on top of the price chart (over the candlesticks). Maximum boxes, lines, and labels are set to 500, so it handles dense charts without performance issues.
Below, I'll provide a detailed explanation of the indicator, its working logic, main components, and usage tips step by step. This indicator integrates Volume Profile (VP), Game Theory, and Wick (Candle Wick) Patterns to generate buy/sell signals. It aims to detect high-probability reversal points by analyzing market liquidity, herd behavior, and institutional movements. It's suitable for crypto, forex, or stock markets, but always backtest before using in live trading.
1. General Description
Purpose: This indicator combines volume-based analysis (Volume Profile), game theory elements (herd behavior, Nash equilibrium, contrarian strategies), and candle wick patterns. It identifies strong resistance/support levels (POC, VAH/VAL, liquidity zones) and generates "Power" signals based on them. Signals are shown with labels, lines, and alerts for buy (green) or sell (red).
Key Features:
Volume Profile (VP): Calculates high-volume areas (POC: Point of Control, the highest volume level; VAH/VAL: Value Area High/Low) and displays them on the chart.
Game Theory (GT): Models the market as "players" (retail herd, institutions). Detects herd buying/selling panics and generates contrarian signals.
Wick-Based Signals: Captures reversals with large wicks. Applies strict criteria for "Power" and "Ultra Power" levels.
Market Maker (MM) Elements: Monitors liquidity traps and institutional volume spikes.
Visualization: Nash bands, liquidity boxes, info table (top-right), background colors, and alerts.
Signal Types: Normal, Power, Ultra Power, GT-confirmed. Signals are limited (max 1-5 per zone) with a minimum wait time (40 bars).
Input Parameters: Grouped into 3 sections (GT, Wick, VP, MM). Default values are balanced, but customizable (e.g., strictMode=true makes it more selective).
Warning: This is an indicator, not a full strategy. It includes alerts, but add stop-loss/take-profit for risk management. Use TradingView's Strategy Tester for backtesting.
2. Working Logic (Step by Step)
The indicator processes each bar (candle) as follows:
a. Basic Calculations
ATR (Average True Range): Measures volatility (20 periods). Candle size (high-low) must be at least ATR x 2.5 for signals to be valid.
Candle Components: Calculates candle body (close-open), upper/lower wick.
Volume Analysis: Average volume (SMA 20), detects spikes (based on threshold).
Trend Filter: EMAs (20/50/200) determine up/downtrend. In strict mode, it's stricter (strong uptrend: EMA20 > EMA50 > EMA200 and close > EMA20).
b. Game Theory (GT) Component
Herd Behavior: RSI (14) overbought/oversold (70/30) + volume spike + momentum detects it. Herd buying: Overbuying frenzy (red background). Herd selling: Selling panic (green background).
Institutional Flow: Volume > average x 2.5 + Accumulation/Distribution (AD) indicator. Accumulation: Institutions buying (strengthens buy signals). Distribution: Selling (strengthens sell).
Liquidity Traps: In the last 50 bars, if a new high/low is broken but close pulls back + volume spike = Trap (up/down).
Smart Money: Intra-candle movement (close-open)/(high-low) x volume. Positive = Smart money inflow.
Nash Equilibrium: Price mean (SMA 100) ± deviation (stdev x 0.02). In equilibrium: Normal. Above: Sell potential. Below: Buy. Bands are optionally shown.
GT Signals:
Contrarian: Herd selling + accumulation = Buy.
Momentum: Below Nash + positive smart money = Buy (opposite for sell).
Nash Reversion: Below Nash + rising close + volume = Buy.
Power Signal: At least 3 GT signals (min_signals_for_power=3) + volume confirmation = Power GT buy/sell. Can show only GT-confirmed signals (show_gt_only_signals=true).
c. Volume Profile (VP) Component
Calculation: For the last 100 bars (vpPeriod), divides the price range (high-low) into vpRows (24) rows. Distributes volume across rows.
POC (Point of Control): Highest volume level (orange line). Threshold 80% (pocThreshold).
Value Area (VA): 70% of total volume (valueAreaPercent). VAH (upper bound, blue dotted), VAL (lower bound).
High-Volume Area: Price near POC or volume > POC x 80% = Strong zone.
Visualization: Histogram boxes on the right (blue/orange). POC/VAH/VAL lines and labels.
d. Wick (Candle Wick) and Power Signals
Main Wick Criteria: Large candle (ATR x 2.5), small body (<8%), wick 8x body length (anaFitilCarpan) and 80% of candle (anaFitilYuzde). High volume + trend filter (downtrend for upper wick).
Signal Wick: More flexible for triggers (5x length, 70%).
Power/Ultra Power:
Power Sell: Main upper wick + near POC/VAH + MM volume (2.5x) + GT contrarian/momentum.
Power Buy: Similar for lower wick.
Super Wick: Power + institutional volume + strong momentum.
Ultra Power: Super + GT power (3/3) + distribution/accumulation + Nash deviation + liquidity trap. Rarest and strongest (fuchsia/lime color).
Signal Management: Detected wick level (high/low) is saved. Wait min 40 bars, max 1-5 signals per zone. When trigger candle arrives (price reaches level + long wick + close in opposite direction) = BUY/SELL plotshape.
e. Market Maker (MM) and Liquidity
MM Volume: Average x 2.5 + wick bonus (1.3x).
Liquidity Zones: Saves last 20 high-volume highs/lows. Shown as boxes on chart (red/green, lasting 200 bars).
Traps: Integrated with GT, strengthens power signals.
f. Visualization and Alerts
Background: Ultra Power (fuchsia/lime), Power GT (red/green), Herd (red/green).
Lines: Active resistance/support (dashed, colored).
Table (Top-Right): Resistance/support levels, remaining signals, POC/VAH/VAL, GT status (herd, institutional, Nash, signal strength), volume/liquidity.
Alerts: For Ultra Power, GT Power, Super Wick, normal signals. Messages include level/price.
g. Filters and Options
Strict Mode: Stricter (higher volume 1.5x, strong trend, RSI filter).
Require Volume Confirmation: Mandatory volume check.
Only Show Power Signals: Display only power/ultra.
Require Ultra Power: Strictest, only ultra.
3. Usage Tips
Chart Timeframe: H1-D1 for medium-long term. Shorter frames (M1-M5) may produce too many signals.
Settings:
StrictMode=true: Fewer but higher-quality signals.
Use_game_theory=false: Use only VP + Wicks.
ShowVP=false: Hide histogram to reduce clutter.
Strategy Integration: Filter BUY/SELL with EMAs. Stop-loss: ATR x 1-2, Take-profit: POC/VAH levels.
Backtesting: Convert to strategy in TradingView (use alertconditions). Test on historical data.
Risk: Designed for market manipulation (MM traps), but no indicator is 100% accurate. Apply capital management.
Troubleshooting: If errors (e.g., vpInitialized=false), increase period or refresh chart.
This indicator is complex but powerful – blending VP for volume zones with GT for psychology. If you have questions or need setting changes, let me know!
Machine Learning | Adaptive Trend Signals [Bitwardex]⚙️🧠Machine Learning | Adaptive Trend Signals
🔷Overview
Machine Learning | Adaptive Trend Signals is a Pine Script™ v6 indicator designed to visualize market trends and generate signals through a combination of volatility clustering, Gaussian smoothing, and adaptive trend calculations. Built as an overlay indicator, it integrates advanced techniques inspired by machine learning concepts, such as K-Means clustering, to adapt to changing market conditions. The script is highly customizable, includes a backtesting module, and supports alert conditions, making it suitable for traders exploring trend-based strategies and developers studying volatility-driven indicator design.
🔷Functionality
The indicator performs the following core functions:
• Volatility Clustering: Uses K-Means clustering to categorize market volatility into high, medium, and low states, adjusting trend sensitivity accordingly.
• Trend Calculation: Computes adaptive trend lines (SmartTrend) based on volatility-adjusted standard deviation, smoothed RSI, and ADX filters.
• Signal Generation: Identifies potential buy and sell points through trend line crossovers and directional confirmation.
• Backtesting Module: Tracks trade outcomes based on the SmartTrend3 value, displaying win rate and total trades.
• Visualization: Plots trend lines with gradient colors and optional signal markers (bullish 🐮 and bearish 🐻).
• Alerts: Provides configurable alerts for trend shifts and volatility state changes.
🔷Technical Methodology
Volatility Clustering with K-Means
The indicator employs a K-Means clustering algorithm to classify market volatility, measured via the Average True Range (ATR), into three distinct clusters:
• Data Collection: Gathers ATR values over a user-defined training period (default: 100 bars).
• Centroid Initialization: Sets initial centroids at the highest, lowest, and midpoint ATR values within the training period.
• Iterative Clustering: Assigns ATR data points to the nearest centroid, recalculates centroid means, and repeats until convergence.
• Dynamic Adjustment: Assigns a volatility state (high, medium, or low) based on the closest centroid, adjusting the trend factor (e.g., tighter for high volatility, wider for low volatility).
This approach allows the indicator to adapt its sensitivity to varying market conditions, providing a data-driven foundation for trend calculations.
🔷Gaussian Smoothing
To enhance signal clarity and reduce noise, the indicator applies Gaussian kernel smoothing to:
• RSI: Smooths the Relative Strength Index (calculated from OHLC4) to filter short-term fluctuations.
• SmartTrend: Smooths the primary trend line for a more stable output.
The Gaussian kernel uses a sigma value derived from the user-defined smoothing length, ensuring mathematically consistent noise reduction.
🔷SmartTrend Calculation
The pineSmartTrend function is the core of the indicator, producing three trend lines:
• SmartTrend: The primary trend line, calculated using a volatility-adjusted standard deviation, smoothed RSI, and ADX conditions.
• SmartTrend2: A secondary trend line with a wider factor (base factor * 1.382) for signal confirmation.
SmartTrend3: The average of SmartTrend and SmartTrend2, used for plotting and backtesting.
Key components of the calculation include:
• Dynamic Standard Deviation: Scales based on ATR relative to its 50-period smoothed average, with multipliers (1.0 to 1.4) applied according to volatility thresholds.
• RSI and ADX Filters: Requires RSI > 50 for bullish trends or < 50 for bearish trends, alongside ADX > 15 and rising to confirm trend strength.
Volatility-Adjusted Bands: Constructs upper and lower bands around price action, adjusted by the volatility cluster’s dynamic factor.
🔷Signal Generation
The generate_signals function generates signals as follows:
• Buy Signal: Triggered when SmartTrend crosses above SmartTrend2 and the price is above SmartTrend, with directional confirmation.
• Sell Signal: Triggered when SmartTrend crosses below SmartTrend2 and the price is below SmartTrend, with directional confirmation.
Directional Logic: Tracks trend direction to filter out conflicting signals, ensuring alignment with the broader market context.
Signals are visualized as small circles with bullish (🐮) or bearish (🐻) emojis, with an option to toggle visibility.
🔷Backtesting
The get_backtest function evaluates signal outcomes using the SmartTrend3 value (rather than closing prices) to align with the trend-based methodology.
It tracks:
• Total Trades: Counts completed long and short trades.
• Win Rate: Calculates the percentage of trades where SmartTrend3 moves favorably (higher for longs, lower for shorts).
Position Management: Closes opposite positions before opening new ones, simulating a single-position trading system.
Results are displayed in a table at the top-right of the chart, showing win rate and total trades. Note that backtest results reflect the indicator’s internal logic and should not be interpreted as predictive of real-world performance.
🔷Visualization and Alerts
• Trend Lines: SmartTrend3 is plotted with gradient colors reflecting trend direction and volatility cluster, accompanied by a secondary line for visual clarity.
• Signal Markers: Optional buy/sell signals are plotted as small circles with customizable colors.
• Alerts: Supports alerts for:
• Bullish and bearish trend shifts (confirmed on bar close).
Transitions to high, medium, or low volatility states.
🔷Input Parameters
• ATR Length (default: 14): Period for ATR calculation, used in volatility clustering.
• Period (default: 21): Common period for RSI, ADX, and standard deviation calculations.
• Base SmartTrend Factor (default: 2.0): Base multiplier for volatility-adjusted bands.
• SmartTrend Smoothing Length (default: 10): Length for Gaussian smoothing of the trend line.
• Show Buy/Sell Signals? (default: true): Enables/disables signal markers.
• Bullish/Bearish Color: Customizable colors for trend lines and signals.
🔷Usage Instructions
• Apply to Chart: Add the indicator to any TradingView chart.
• Configure Inputs: Adjust parameters to align with your trading style or market conditions (e.g., shorter ATR length for faster markets).
• Interpret Output:
• Trend Lines: Use SmartTrend3’s direction and color to gauge market bias.
• Signals: Monitor bullish (🐮) and bearish (🐻) markers for potential entry/exit points.
• Backtest Table: Review win rate and total trades to understand the indicator’s behavior in historical data.
• Set Alerts: Configure alerts for trend shifts or volatility changes to support manual or automated trading workflows.
• Combine with Analysis: Use the indicator alongside other tools or market context, as it is designed to complement, not replace, comprehensive analysis.
🔷Technical Notes
• Data Requirements: Requires at least 100 bars for accurate volatility clustering. Ensure sufficient historical data is loaded.
• Market Suitability: The indicator is designed for trend detection and may perform differently in ranging or volatile markets due to its reliance on RSI and ADX filters.
• Backtesting Scope: The backtest module uses SmartTrend3 values, which may differ from price-based outcomes. Results are for informational purposes only.
• Computational Intensity: The K-Means clustering and Gaussian smoothing may increase processing time on lower timeframes or with large datasets.
🔷For Developers
The script is modular, well-commented, encouraging reuse and modification with proper attribution.
Key functions include:
• gaussianSmooth: Applies Gaussian kernel smoothing to any data series.
• pineSmartTrend: Computes adaptive trend lines with volatility and momentum filters.
• getDynamicFactor: Adjusts trend sensitivity based on volatility clusters.
• get_backtest: Evaluates signal performance using SmartTrend3.
Developers can extend these functions for custom indicators or strategies, leveraging the volatility clustering and smoothing methodologies. The K-Means implementation is particularly useful for adaptive volatility analysis.
🔷Limitations
• The indicator is not predictive and should be used as part of a broader trading strategy.
• Performance varies by market, timeframe, and parameter settings, requiring user experimentation.
• Backtest results are based on historical data and internal logic, not real-world trading conditions.
• Volatility clustering assumes sufficient historical data; incomplete data may affect accuracy.
🔷Acknowledgments
Developed by Bitwardex, inspired by machine learning concepts and adaptive trading methodologies. Community feedback is welcome via TradingView’s platform.
🔷 Risk Disclaimer
Trading involves significant risks, and most traders may incur losses. Bitwardex AI Algo is provided for informational and educational purposes only and does not constitute financial advice or a recommendation to buy or sell any financial instrument . The signals, metrics, and features are tools for analysis and do not guarantee profits or specific outcomes. Past performance is not indicative of future results. Always conduct your own due diligence and consult a financial advisor before making trading decisions.
Relative Strength Index Custom [BRTLab]RSI Custom — Strategy-Oriented RSI with Multi-Timeframe Precision
The Relative Strength Index Custom is designed with a focus on developing robust trading strategies. This powerful indicator leverages the logic of calculating RSI on higher timeframes (HTFs) while allowing traders to execute trades on lower timeframes (LTFs). Its unique ability to extract accurate RSI data from higher timeframes without waiting for those candles to close provides a real-time advantage, eliminating the "look-ahead" bias that often
distorts backtest results.
Key Features
Multi-Timeframe RSI for Strategy Development
This indicator stands out by allowing you to calculate RSI on higher timeframes, even while operating on lower timeframe charts. This means you can, for example, calculate RSI on the 1-hour or daily chart and execute trades on a 1-minute chart without needing to wait for the higher timeframe candle to close. This feature is crucial for strategy-building as it eliminates backtesting issues where data from the future is inadvertently used, providing more reliable backtest results.
Example: On a 15-minute chart, you can use the 1-hour RSI to open positions based on higher timeframe momentum, but you get this signal in real-time, improving timing and accuracy.
Accurate Data Extraction from Higher Timeframes
The indicator's custom logic ensures that accurate RSI data is retrieved from higher timeframes, providing an edge by delivering timely information for lower timeframe decisions. This prevents delayed signals often encountered when waiting for higher timeframe candles to close, which is crucial for high-frequency and intraday traders looking for precise entries based on multi-timeframe data.
Customizable RSI Settings for Strategy Tuning
The script offers full customization of the RSI, including length and source price (close, open, high, or low), allowing traders to tailor the RSI to fit specific trading strategies. These settings are housed in the "RSI Settings" section, enabling precise adjustments that align with your overall strategy.
No Future-Looking in Backtests
Traditional backtests often suffer from "future-looking" bias, where calculations unintentionally use data from candles that haven’t yet closed. This indicator is specifically designed to prevent such issues by calculating RSI values in real-time. This is particularly important when creating and testing strategies, as it ensures that the conditions under which trades would have been made are accurately represented in historical tests.
RSI-Based Moving Average for Additional Filtering
The built-in moving average (MA) based on RSI values helps filter out noise, making it easier to identify genuine trend shifts. This is particularly useful in strategies where moving average crossovers act as additional confirmation for trade entries and exits.
Overbought and Oversold Zone Detection
Visual gradient fills on the RSI chart help traders identify overbought and oversold zones (above 70 and below 30, respectively). These zones are crucial for timing reversal trades or confirming momentum-based strategies.
How This Indicator Enhances Your Strategy
Increased Accuracy for Intraday Strategies
For traders who operate on lower timeframes, using higher timeframe RSI data gives a broader perspective of market momentum while still maintaining precision for short-term trade entries. The real-time data extraction means you don't need to wait for HTF candles to close, which can dramatically improve your entry timing.
Strategic Edge in Backtesting
One of the greatest challenges in backtesting strategies is avoiding future-looking bias. This indicator is built to overcome this by using real-time multi-timeframe data, ensuring the accuracy and reliability of historical strategy testing, which provides confidence in your strategies when applied to live markets.
Advanced Filtering for Trend Strategies
By combining the RSI values with a customizable moving average (MA) and visualizing key momentum zones with overbought/oversold fills, the indicator allows for more refined trade filters. This ensures that signals generated by your strategy are based on solid momentum data and not short-term price fluctuations.
GKD-C Variety Filters w/ Dynamic Zones [Loxx]Giga Kaleidoscope GKD-C Variety Filters w/ Dynamic Zones is a Confirmation module included in Loxx's "Giga Kaleidoscope Modularized Trading System".
█ Giga Kaleidoscope Modularized Trading System
What is Loxx's "Giga Kaleidoscope Modularized Trading System"?
The Giga Kaleidoscope Modularized Trading System is a trading system built on the philosophy of the NNFX (No Nonsense Forex) algorithmic trading.
What is the NNFX algorithmic trading strategy?
The NNFX (No-Nonsense Forex) trading system is a comprehensive approach to Forex trading that is designed to simplify the process and remove the confusion and complexity that often surrounds trading. The system was developed by a Forex trader who goes by the pseudonym "VP" and has gained a significant following in the Forex community.
The NNFX trading system is based on a set of rules and guidelines that help traders make objective and informed decisions. These rules cover all aspects of trading, including market analysis, trade entry, stop loss placement, and trade management.
Here are the main components of the NNFX trading system:
1. Trading Philosophy: The NNFX trading system is based on the idea that successful trading requires a comprehensive understanding of the market, objective analysis, and strict risk management. The system aims to remove subjective elements from trading and focuses on objective rules and guidelines.
2. Technical Analysis: The NNFX trading system relies heavily on technical analysis and uses a range of indicators to identify high-probability trading opportunities. The system uses a combination of trend-following and mean-reverting strategies to identify trades.
3. Market Structure: The NNFX trading system emphasizes the importance of understanding the market structure, including price action, support and resistance levels, and market cycles. The system uses a range of tools to identify the market structure, including trend lines, channels, and moving averages.
4. Trade Entry: The NNFX trading system has strict rules for trade entry. The system uses a combination of technical indicators to identify high-probability trades, and traders must meet specific criteria to enter a trade.
5. Stop Loss Placement: The NNFX trading system places a significant emphasis on risk management and requires traders to place a stop loss order on every trade. The system uses a combination of technical analysis and market structure to determine the appropriate stop loss level.
6. Trade Management: The NNFX trading system has specific rules for managing open trades. The system aims to minimize risk and maximize profit by using a combination of trailing stops, take profit levels, and position sizing.
Overall, the NNFX trading system is designed to be a straightforward and easy-to-follow approach to Forex trading that can be applied by traders of all skill levels.
Core components of an NNFX algorithmic trading strategy
The NNFX algorithm is built on the principles of trend, momentum, and volatility. There are six core components in the NNFX trading algorithm:
1. Volatility - price volatility; e.g., Average True Range, True Range Double, Close-to-Close, etc.
2. Baseline - a moving average to identify price trend
3. Confirmation 1 - a technical indicator used to identify trends
4. Confirmation 2 - a technical indicator used to identify trends
5. Continuation - a technical indicator used to identify trends
6. Volatility/Volume - a technical indicator used to identify volatility/volume breakouts/breakdown
7. Exit - a technical indicator used to determine when a trend is exhausted
What is Volatility in the NNFX trading system?
In the NNFX (No Nonsense Forex) trading system, ATR (Average True Range) is typically used to measure the volatility of an asset. It is used as a part of the system to help determine the appropriate stop loss and take profit levels for a trade. ATR is calculated by taking the average of the true range values over a specified period.
True range is calculated as the maximum of the following values:
-Current high minus the current low
-Absolute value of the current high minus the previous close
-Absolute value of the current low minus the previous close
ATR is a dynamic indicator that changes with changes in volatility. As volatility increases, the value of ATR increases, and as volatility decreases, the value of ATR decreases. By using ATR in NNFX system, traders can adjust their stop loss and take profit levels according to the volatility of the asset being traded. This helps to ensure that the trade is given enough room to move, while also minimizing potential losses.
Other types of volatility include True Range Double (TRD), Close-to-Close, and Garman-Klass
What is a Baseline indicator?
The baseline is essentially a moving average, and is used to determine the overall direction of the market.
The baseline in the NNFX system is used to filter out trades that are not in line with the long-term trend of the market. The baseline is plotted on the chart along with other indicators, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR).
Trades are only taken when the price is in the same direction as the baseline. For example, if the baseline is sloping upwards, only long trades are taken, and if the baseline is sloping downwards, only short trades are taken. This approach helps to ensure that trades are in line with the overall trend of the market, and reduces the risk of entering trades that are likely to fail.
By using a baseline in the NNFX system, traders can have a clear reference point for determining the overall trend of the market, and can make more informed trading decisions. The baseline helps to filter out noise and false signals, and ensures that trades are taken in the direction of the long-term trend.
What is a Confirmation indicator?
Confirmation indicators are technical indicators that are used to confirm the signals generated by primary indicators. Primary indicators are the core indicators used in the NNFX system, such as the Average True Range (ATR), the Moving Average (MA), and the Relative Strength Index (RSI).
The purpose of the confirmation indicators is to reduce false signals and improve the accuracy of the trading system. They are designed to confirm the signals generated by the primary indicators by providing additional information about the strength and direction of the trend.
Some examples of confirmation indicators that may be used in the NNFX system include the Bollinger Bands, the MACD (Moving Average Convergence Divergence), and the MACD Oscillator. These indicators can provide information about the volatility, momentum, and trend strength of the market, and can be used to confirm the signals generated by the primary indicators.
In the NNFX system, confirmation indicators are used in combination with primary indicators and other filters to create a trading system that is robust and reliable. By using multiple indicators to confirm trading signals, the system aims to reduce the risk of false signals and improve the overall profitability of the trades.
What is a Continuation indicator?
In the NNFX (No Nonsense Forex) trading system, a continuation indicator is a technical indicator that is used to confirm a current trend and predict that the trend is likely to continue in the same direction. A continuation indicator is typically used in conjunction with other indicators in the system, such as a baseline indicator, to provide a comprehensive trading strategy.
What is a Volatility/Volume indicator?
Volume indicators, such as the On Balance Volume (OBV), the Chaikin Money Flow (CMF), or the Volume Price Trend (VPT), are used to measure the amount of buying and selling activity in a market. They are based on the trading volume of the market, and can provide information about the strength of the trend. In the NNFX system, volume indicators are used to confirm trading signals generated by the Moving Average and the Relative Strength Index. Volatility indicators include Average Direction Index, Waddah Attar, and Volatility Ratio. In the NNFX trading system, volatility is a proxy for volume and vice versa.
By using volume indicators as confirmation tools, the NNFX trading system aims to reduce the risk of false signals and improve the overall profitability of trades. These indicators can provide additional information about the market that is not captured by the primary indicators, and can help traders to make more informed trading decisions. In addition, volume indicators can be used to identify potential changes in market trends and to confirm the strength of price movements.
What is an Exit indicator?
The exit indicator is used in conjunction with other indicators in the system, such as the Moving Average (MA), the Relative Strength Index (RSI), and the Average True Range (ATR), to provide a comprehensive trading strategy.
The exit indicator in the NNFX system can be any technical indicator that is deemed effective at identifying optimal exit points. Examples of exit indicators that are commonly used include the Parabolic SAR, the Average Directional Index (ADX), and the Chandelier Exit.
The purpose of the exit indicator is to identify when a trend is likely to reverse or when the market conditions have changed, signaling the need to exit a trade. By using an exit indicator, traders can manage their risk and prevent significant losses.
In the NNFX system, the exit indicator is used in conjunction with a stop loss and a take profit order to maximize profits and minimize losses. The stop loss order is used to limit the amount of loss that can be incurred if the trade goes against the trader, while the take profit order is used to lock in profits when the trade is moving in the trader's favor.
Overall, the use of an exit indicator in the NNFX trading system is an important component of a comprehensive trading strategy. It allows traders to manage their risk effectively and improve the profitability of their trades by exiting at the right time.
How does Loxx's GKD (Giga Kaleidoscope Modularized Trading System) implement the NNFX algorithm outlined above?
Loxx's GKD v1.0 system has five types of modules (indicators/strategies). These modules are:
1. GKD-BT - Backtesting module (Volatility, Number 1 in the NNFX algorithm)
2. GKD-B - Baseline module (Baseline and Volatility/Volume, Numbers 1 and 2 in the NNFX algorithm)
3. GKD-C - Confirmation 1/2 and Continuation module (Confirmation 1/2 and Continuation, Numbers 3, 4, and 5 in the NNFX algorithm)
4. GKD-V - Volatility/Volume module (Confirmation 1/2, Number 6 in the NNFX algorithm)
5. GKD-E - Exit module (Exit, Number 7 in the NNFX algorithm)
(additional module types will added in future releases)
Each module interacts with every module by passing data between modules. Data is passed between each module as described below:
GKD-B => GKD-V => GKD-C(1) => GKD-C(2) => GKD-C(Continuation) => GKD-E => GKD-BT
That is, the Baseline indicator passes its data to Volatility/Volume. The Volatility/Volume indicator passes its values to the Confirmation 1 indicator. The Confirmation 1 indicator passes its values to the Confirmation 2 indicator. The Confirmation 2 indicator passes its values to the Continuation indicator. The Continuation indicator passes its values to the Exit indicator, and finally, the Exit indicator passes its values to the Backtest strategy.
This chaining of indicators requires that each module conform to Loxx's GKD protocol, therefore allowing for the testing of every possible combination of technical indicators that make up the six components of the NNFX algorithm.
What does the application of the GKD trading system look like?
Example trading system:
Backtest: Strategy with 1-3 take profits, trailing stop loss, multiple types of PnL volatility, and 2 backtesting styles
Baseline: Hull Moving Average
Volatility/Volume: Hurst Exponent
Confirmation 1: Variety Filters w/ Dynamic Zones as shown on the chart above
Confirmation 2: Williams Percent Range
Continuation: Fisher Transform
Exit: Rex Oscillator
Each GKD indicator is denoted with a module identifier of either: GKD-BT, GKD-B, GKD-C, GKD-V, or GKD-E. This allows traders to understand to which module each indicator belongs and where each indicator fits into the GKD protocol chain.
Giga Kaleidoscope Modularized Trading System Signals (based on the NNFX algorithm)
Standard Entry
1. GKD-C Confirmation 1 Signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 1 signal was less than 7 candles prior
Continuation Entry
1. Standard Entry, Baseline Entry, or Pullback; entry triggered previously
2. GKD-B Baseline hasn't crossed since entry signal trigger
3. GKD-C Confirmation Continuation Indicator signals
4. GKD-C Confirmation 1 agrees
5. GKD-B Baseline agrees
6. GKD-C Confirmation 2 agrees
1-Candle Rule Standard Entry
1. GKD-C Confirmation 1 signal
2. GKD-B Baseline agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume agrees
1-Candle Rule Baseline Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close )
2. GKD-B Baseline agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-V Volatility/Volume Agrees
1-Candle Rule Volatility/Volume Entry
1. GKD-V Volatility/Volume signal
2. GKD-C Confirmation 1 agrees
3. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
4. GKD-C Confirmation 1 signal was less than 7 candles prior
Next Candle:
1. Price retraced (Long: close < close or Short: close > close)
2. GKD-B Volatility/Volume agrees
3. GKD-C Confirmation 1 agrees
4. GKD-C Confirmation 2 agrees
5. GKD-B Baseline agrees
PullBack Entry
1. GKD-B Baseline signal
2. GKD-C Confirmation 1 agrees
3. Price is beyond 1.0x Volatility of Baseline
Next Candle:
1. Price is within a range of 0.2x Volatility and 1.0x Volatility of the Goldie Locks Mean
2. GKD-C Confirmation 1 agrees
3. GKD-C Confirmation 2 agrees
4. GKD-V Volatility/Volume Agrees
█ GKD-C Variety Filters w/ Dynamic Zones
What are Dynamic Zones?
As explained in "Stocks & Commodities V15:7 (306-310): Dynamic Zones by Leo Zamansky, Ph .D., and David Stendahl"
Most indicators use a fixed zone for buy and sell signals. Here’ s a concept based on zones that are responsive to past levels of the indicator.
One approach to active investing employs the use of oscillators to exploit tradable market trends. This investing style follows a very simple form of logic: Enter the market only when an oscillator has moved far above or below traditional trading levels. However, these oscillator- driven systems lack the ability to evolve with the market because they use fixed buy and sell zones. Traders typically use one set of buy and sell zones for a bull market and substantially different zones for a bear market. And therein lies the problem.
Once traders begin introducing their market opinions into trading equations, by changing the zones, they negate the system’s mechanical nature. The objective is to have a system automatically define its own buy and sell zones and thereby profitably trade in any market — bull or bear. Dynamic zones offer a solution to the problem of fixed buy and sell zones for any oscillator-driven system.
An indicator’s extreme levels can be quantified using statistical methods. These extreme levels are calculated for a certain period and serve as the buy and sell zones for a trading system. The repetition of this statistical process for every value of the indicator creates values that become the dynamic zones. The zones are calculated in such a way that the probability of the indicator value rising above, or falling below, the dynamic zones is equal to a given probability input set by the trader.
To better understand dynamic zones, let's first describe them mathematically and then explain their use. The dynamic zones definition:
Find V such that:
For dynamic zone buy: P{X <= V}=P1
For dynamic zone sell: P{X >= V}=P2
where P1 and P2 are the probabilities set by the trader, X is the value of the indicator for the selected period and V represents the value of the dynamic zone.
The probability input P1 and P2 can be adjusted by the trader to encompass as much or as little data as the trader would like. The smaller the probability, the fewer data values above and below the dynamic zones. This translates into a wider range between the buy and sell zones. If a 10% probability is used for P1 and P2, only those data values that make up the top 10% and bottom 10% for an indicator are used in the construction of the zones. Of the values, 80% will fall between the two extreme levels. Because dynamic zone levels are penetrated so infrequently, when this happens, traders know that the market has truly moved into overbought or oversold territory.
Calculating the Dynamic Zones
The algorithm for the dynamic zones is a series of steps. First, decide the value of the lookback period t. Next, decide the value of the probability Pbuy for buy zone and value of the probability Psell for the sell zone.
For i=1, to the last lookback period, build the distribution f(x) of the price during the lookback period i. Then find the value Vi1 such that the probability of the price less than or equal to Vi1 during the lookback period i is equal to Pbuy. Find the value Vi2 such that the probability of the price greater or equal to Vi2 during the lookback period i is equal to Psell. The sequence of Vi1 for all periods gives the buy zone. The sequence of Vi2 for all periods gives the sell zone.
In the algorithm description, we have: Build the distribution f(x) of the price during the lookback period i. The distribution here is empirical namely, how many times a given value of x appeared during the lookback period. The problem is to find such x that the probability of a price being greater or equal to x will be equal to a probability selected by the user. Probability is the area under the distribution curve. The task is to find such value of x that the area under the distribution curve to the right of x will be equal to the probability selected by the user. That x is the dynamic zone.
What is Variety Filters w/ Dynamic Zones?
This indicator first smooths price with one of 65+ moving averages and then injects that output into the Dynamic Zones algorithm to create levels of significances. These levels are used to generate trading signals.
Requirements
Inputs
Confirmation 1: GKD-V Volatility / Volume indicator
Confirmation 2: GKD-C Confirmation indicator
Continuation: GKD-C Confirmation indicator
Solo Confirmation Simple: GKD-B Baseline
Solo Confirmation Complex: GKD-V Volatility / Volume indicator
Solo Confirmation Super Complex: GKD-V Volatility / Volume indicator
Stacked 1: None
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 1
Outputs
Confirmation 1: GKD-C Confirmation 2 indicator
Confirmation 2: GKD-C Continuation indicator
Continuation: GKD-E Exit indicator
Solo Confirmation Simple: GKD-BT Backtest
Solo Confirmation Complex: GKD-BT Backtest or GKD-E Exit indicator
Solo Confirmation Super Complex: GKD-C Continuation indicator
Stacked 1: GKD-C, GKD-V, or GKD-B Stacked 2+
Stacked 2+: GKD-C, GKD-V, or GKD-B Stacked 2+ or GKD-BT Backtest
Additional features will be added in future releases.
