DEMA Double Exponential Moving Average Strategy@Moneros 2017
Based on The DEMA is a fast-acting moving average that is more responsive to market changes than a traditional moving average
en.wikipedia.org
!!!! IN ORDER TO AVOID REPAITING ISSUES !!!!
!!!! DO NOT VIEW IN LOWER RESOLUTIONS THAN res/2 PARAMETER !!!!
for example res = 120 view >= 60m res = 60 view >= 30m
the length of the DEMA sampling shouldn't be longer than a candle
Best profits tested on BTCUSD
res = 105 slowPeriod = 2 fastPeriod = 32
res = 125 slowPeriod = 3 fastPeriod = 21
res = 120 slowPeriod = 2 fastPeriod = 32
res = 130 slowPeriod = 1 fastPeriod = 24
res = 40 slowPeriod = 4 fastPeriod = 93
res = 60 slowPeriod = 1 fastPeriod = 67
BTCUSD
חפש סקריפטים עבור "市值60亿的股票"
RSI in Bull and Bear Market V2.0RSI oversold at 60/40 in bullish market
And Overbought at 40/60 in Bearish market
for more info of this Strategy
WaveTrend [MastroFran]Great indicator to show short term price movements. 5 day moving average oscillator. When green crosses red and under the 60 mark, buy with caution. when over the 60 mark and red crosses green sell immediately for highest profits.
Hersheys CoCo VolumeCoCo Volume shows you volume movement of your symbol after subtracting the movement from another symbol, preferrably the sector or market the stock belongs to.
My latest update to my CoCoVolume Indicator. It calculates today's volume percent over the 60 period average for both your symbol and index, and displays that difference. If the percent is over the max it highlights the color, showing BIG action for that stock.
The last version was calculating the percent volume difference from yesterday to today for the stock and index and displaying the difference. The prior method had large swings on low volume stocks... this one shows the independent volume action much better. The default values will suit most stocks.
You can set three variables...
- the index symbol, default is SPY
- the period for averaging, default is 60
- the max volume percent, default is 500
Good trading!
Brian Hershey
close-hl2 Price actionStill not tested, but looks very good ; it is the difference between EMA median price and EMA close in different time frame, I used 240, 60, and the current Time frame ,plus one more customed period ; can forcast the price movement , but it s not in scale, so it can not show how much higher or lower the price can goes but just the next direction. I think intraday on 5 ,15 ,60 better then high frame.If you need to try on Daily frame have to change the period to higher then Daily
Everyday 0002 _ MAC 1st Trading Hour WalkoverThis is the second strategy for my Everyday project.
Like I wrote the last time - my goal is to create a new strategy everyday
for the rest of 2016 and post it here on TradingView.
I'm a complete beginner so this is my way of learning about coding strategies.
I'll give myself between 15 minutes and 2 hours to complete each creation.
This is basically a repetition of the first strategy I wrote - a Moving Average Crossover,
but I added a tiny thing.
I read that "Statistics have proven that the daily high or low is established within the first hour of trading on more than 70% of the time."
(source: )
My first Moving Average Crossover strategy, tested on VOLVB daily, got stoped out by the volatility
and because of this missed one nice bull run and a very nice bear run.
So I added this single line: if time("60", "1000-1600") regarding when to take exits:
if time("60", "1000-1600")
strategy.exit("Close Long", "Long", profit=2000, loss=500)
strategy.exit("Close Short", "Short", profit=2000, loss=500)
Sweden is UTC+2 so I guess UTC 1000 equals 12.00 in Stockholm. Not sure if this is correct, actually.
Anyway, I hope this means the strategy will only take exits based on price action which occur in the afternoon, when there is a higher probability of a lower volatility.
When I ran the new modified strategy on the same VOLVB daily it didn't get stoped out so easily.
On the other hand I'll have to test this on various stocks .
Reading and learning about how to properly test strategies is on my todo list - all tips on youtube videos or blogs
to read on this topic is very welcome!
Like I said the last time, I'm posting these strategies hoping to learn from the community - so any feedback, advice, or corrections is very much welcome and appreciated!
/pbergden
Reduced-Lag Chande Momentum Oscillator [BOSWaves]Reduced-Lag Chande Momentum Oscillator – Adaptive Momentum Geometry with Reduced-Latency Reversion Logic
Overview
The Reduced-Lag Chande Momentum Oscillator represents a sophisticated extension of the classical Chande Momentum Oscillator, preserving the foundational measurement of net directional pressure while addressing inherent limitations in lag, noise, and signal clarity. The traditional CMO provides reliable snapshots of upward versus downward force but reacts slowly to rapid market accelerations and can obscure meaningful momentum inflections with delayed readings. This iteration integrates a dual-stage reduced-lag filter, optional advanced smoothing, and acceleration-based analytics, producing a real-time, multi-dimensional representation of market momentum.
The design reframes classical momentum using a layered curvature and gradient structure - main, midline, and shadow - to show trajectory, velocity, and intensity in one view. Instead of the usual ±70/30 extremes, it uses ±50 as a statistically grounded threshold where one side of the market begins exerting true dominance. This captures structural imbalance more reliably, exposing exhaustion and actionable inflection without amplifying noise.
This visualization gives traders a continuous, responsive read on market structure, revealing not just direction but rate of change, acceleration alignment, and curvature behavior. The oscillator becomes a momentum map, expressing both probability and intensity behind directional shifts.
Where conventional oscillators mislabel short-lived swings as signals, the Reduced-Lag CMO separates baseline shifts from high-conviction transitions, enabling cleaner, more decisive signal interpretation.
Theoretical Foundation
The classical Chande Momentum Oscillator, created by Tushar Chande, calculates the normalized net difference between consecutive upward and downward price changes over a defined window, generating readings from –100 to +100. While effective for capturing basic directional pressure, the unmodified CMO suffers from signal latency and sensitivity to abrupt market swings, which can obscure actionable inflection points.
The Reduced-Lag CMO augments this foundation with three key mechanisms:
Reduced-Lag Filtering : A dual-EMA structure eliminates inertial lag, aligning the oscillator curve closely with real-time market momentum without producing overshoot artifacts.
Smoothing Architecture : Optional SMA, EMA, or WMA smoothing is applied post-filter, balancing noise reduction with trajectory fidelity. A multi-layer line system (shadow → midline → main) communicates depth, curvature, and gradient dynamics.
Acceleration Integration : First and second derivatives of the smoothed curve quantify velocity and acceleration, allowing the indicator to identify not only momentum flips but the force behind each shift, forming the basis for the strong-signal overlay.
The combination of these mechanisms produces an oscillator that respects the original CMO framework while delivering real-time, context-sensitive intelligence. The ±50 boundaries are selected as the statistically validated pressure zones where directional dominance exceeds neutral oscillation. Crosses and rejections at these boundaries are not arbitrary overbought/oversold events, but measurable imbalances with actionable significance.
How It Works
The Reduced-Lag CMO is constructed through a multi-stage process:
Momentum Estimation Core : Raw CMO values are calculated and then passed through a reduced-lag filter to remove delay, creating a curve that closely tracks instantaneous directional pressure.
Smoothing & Layered Representation : The filtered curve can be smoothed and split into three layers - shadow, midline, and main - giving visual depth, trajectory clarity, and curvature instead of a single-line oscillator.
Gradient-Based Pressure Mapping : Color gradients encode momentum strength and polarity. Green-yellow transitions highlight increasing upward dominance, while red-yellow transitions indicate weakening downward force.
Pressure-Zone Anchoring (±50) : The system defines statistically significant pressure zones at ±50. Moves beyond these levels reflect dominant directional control, and rejections inside the zone signal potential exhaustion.
Signal Generation : Momentum events are evaluated through velocity and acceleration. Standard signals appear as triangle markers indicating validated momentum flips. Strong signals appear as triangles with diamonds when acceleration confirms a high-conviction transition.
A cooldown rule spaces signals apart to reduce clutter and emphasize structurally meaningful events.
Interpretation
The Reduced-Lag CMO reframes momentum as a dynamic equilibrium between directional force and structural pressure:
Positive Momentum Phases : Curves above zero with green-yellow gradients indicate sustained upward pressure. Shallow retracements or midline tests denote controlled pullbacks.
Negative Momentum Phases : Curves below zero with red-yellow gradients show downward dominance. Rejections from –50 highlight potential exhaustion and reversal readiness.
Pressure-Zone Dynamics (±50) : Crosses beyond ±50 confirm dominant directional force. Meanwhile, rejections and rotations inside the zone signal structural fatigue.
Velocity & Acceleration Analysis : Rising momentum with decelerating velocity suggests fading force; acceleration alignment amplifies signal strength and forms the basis of strong signals.
Signal Architecture
The Reduced-Lag CMO produces a single event type with two intensities: a validated momentum inflection.
Standard Signals - Triangles:
Triggered by momentum flips confirmed by velocity.
Represent moderate-intensity directional changes.
Appear at zero-line crosses or ±50 rejections with aligned velocity.
Strong Signals Triangles + Diamonds:
Triggered when acceleration confirms the directional change.
Represent high-intensity, high-conviction shifts.
Rare by design; indicate robust momentum inflections.
Cooldown mechanics prevent repeated signals in short succession, emphasizing structural reliability over noise.
Strategy Integration
Trend Confirmation : Align zero-line flips with higher-timeframe directional bias.
Reversal Detection : Strong signals from ±50 zones highlight potential inflection points.
Volatility Assessment : Gradient transitions reveal strengthening or weakening momentum.
Pullback Timing : Multi-layer curvature identifies controlled retracements vs trend exhaustion.
Confluence Mapping : Pair with structure-based indicators to filter signals in context.
Technical Implementation Details
Core Engine : Classical CMO with Ehlers reduced-lag extension
Lag Reduction : Dual EMA filtering
Smoothing : Optional SMA/EMA/WMA post-filter
Multi-Layer Curve : Shadow, midline, main
Signal System : Two-tier momentum-acceleration framework
Pressure Zones : ±50 statistically validated thresholds
Cooldown Logic : Bar-indexed suppression
Gradient Mapping : Encodes magnitude and direction
Alerts : Standard and strong signals
Optimal Application Parameters
Timeframes:
1 - 5 min : Intraday momentum tracking
15 - 60 min : Trend rotations & volatility transitions
4H - Daily : Macro momentum exhaustion & re-accumulation mapping
Suggested Ranges:
CMO Length : 7 - 12
Reduced-Lag Length : 5 - 15
Smoothing : 10 - 20
Cooldown Bars : 5 - 15
Performance Characteristics
High Effectiveness:
Markets with directional pulses & clean pressure transitions
Trending phases with measurable pullbacks
Instruments with stable volatility cycles
Reduced Edge:
Choppy consolidations
Ultra-low volatility environments
Disclaimer
The Reduced-Lag Chande Momentum Oscillator is a professional-grade analytical tool. It is not predictive and carries no guaranteed profitability. Effectiveness depends on asset class, volatility regime, parameter selection, and disciplined execution. Any suggested application timeframes or recommended ranges are guidance only - they are not universally optimal and will not deliver consistent accuracy on every asset or market condition. BOSWaves recommends using it in conjunction with structure, liquidity, and momentum context.
[PickMyTrade] Trendline Strategy# PickMyTrade Advanced Trend Following Strategy for Long Positions | Automated Trading Indicator
**Optimize Your Trading with PickMyTrade's Professional Trend Strategy - Auto-Execute Trades with Precision**
---
## Table of Contents
1. (#overview)
2. (#why-this-strategy-makes-money)
3. (#key-features)
4. (#how-it-works)
5. (#strategy-settings--configuration)
6. (#pickmytrade-integration)
7. (#advanced-features)
8. (#risk-management)
9. (#best-practices)
10. (#performance-optimization)
11. (#getting-started)
12. (#faq)
---
## Overview
The **PickMyTrade Advanced Trend Following Strategy** is a sophisticated, open-source Pine Script indicator designed for traders seeking consistent profits through trend-based long positions. This powerful algorithm identifies high-probability entry points by detecting valid trendlines with multiple touch confirmations, ensuring you only enter trades when the trend is strongly established.
### What Makes This Strategy Unique?
- **Multi-Trendline Detection**: Simultaneously tracks multiple downtrend breakouts for increased trading opportunities
- **Intelligent Entry Validation**: Requires multiple price touches (configurable) to confirm trendline validity
- **Flexible Take Profit Methods**: Choose from Risk/Reward Ratio, Lookback Candles, or Fibonacci-based exits
- **Automated Risk Management**: Built-in position sizing based on dollar risk per trade
- **PickMyTrade Ready**: Seamlessly integrate with PickMyTrade for fully automated trade execution
**Perfect for**: Swing traders, trend followers, futures traders, and anyone using PickMyTrade for automated trading execution.
---
## Why This Strategy Makes Money
### 1. **Breakout Trading Edge**
The strategy profits by identifying when price breaks above established downtrend resistance lines. These breakouts often signal:
- Shift in market sentiment from bearish to bullish
- Strong buying momentum entering the market
- High probability of continued upward movement
### 2. **Trend Confirmation Filter**
Unlike simple breakout strategies, this requires **multiple touches** (default: 3) on the trendline before considering it valid. This eliminates:
- False breakouts from weak trendlines
- Choppy, sideways markets with no clear trend
- Low-quality setups that lead to losses
### 3. **Dynamic Risk-Reward Optimization**
The strategy automatically calculates:
- **Optimal position sizing** based on your risk tolerance ($100 default)
- **Stop loss placement** using recent pivot lows (not arbitrary levels)
- **Take profit targets** using either R:R ratios (1.5:1 default) or Fibonacci extensions
**Expected Profitability**: With proper settings, traders typically achieve:
- Win rate: 45-60% (depending on market conditions)
- Risk/Reward: 1.5:1 to 2.5:1 (configurable)
- Monthly returns: 5-15% (varies by market and risk settings)
### 4. **Fibonacci Profit Scaling**
The advanced Fibonacci mode allows you to:
- Take partial profits at multiple levels (0.618, 1.0, 1.312, 1.618)
- Lock in gains while letting winners run
- Maximize profits during strong trending moves
---
## Key Features
### Trend Detection & Validation
✅ **Dynamic Trendline Drawing**: Automatically identifies and extends downtrend resistance lines
✅ **Touch Validation**: Configurable number of touches (1-10) to confirm trendline strength
✅ **Valid Percentage Buffer**: Allows minor price deviations (default 0.1%) for more realistic trendlines
✅ **Pivot-Based Validation**: Optional extra filter using smaller pivot points for precision
### Position Management
✅ **Multi-Position Support**: Trade up to 1000 positions simultaneously (pyramiding)
✅ **Single or Multi-Trend Mode**: Track one primary trend or multiple concurrent trends
✅ **Dollar-Based Position Sizing**: Risk fixed dollar amount per trade (not percentage of account)
✅ **Automatic Quantity Calculation**: Determines optimal contract size based on risk and stop distance
### Take Profit Methods (3 Options)
#### 1. **Risk/Reward Ratio** (Recommended for Beginners)
- Set desired R:R (default 1.5:1)
- Simple, consistent profit targets
- Works well in trending markets
#### 2. **Lookback Candles** (For Swing Traders)
- Exits when price makes new low over X candles (default 10)
- Adapts to market volatility
- Best for capturing extended moves
#### 3. **Fibonacci Extensions** (For Advanced Traders)
- Up to 4 profit targets: 61.8%, 100%, 131.2%, 161.8%
- Automatically scales out of positions
- Maximizes gains during strong trends
### Stop Loss Options
✅ **Pivot-Based Stop Loss**: Uses recent pivot lows for logical stop placement
✅ **Buffer/Offset**: Add extra distance (in ticks) below pivot for safety
✅ **Trailing Stop**: Optional feature to lock in profits as trade moves in your favor
✅ **Enable/Disable Toggle**: Full control over stop loss activation
### Session Control
✅ **Time-Based Trading**: Limit trades to specific hours (e.g., 9:00 AM - 6:00 PM)
✅ **Auto-Close at Session End**: Automatically closes all positions outside trading hours
✅ **Works on All Timeframes**: Intraday and higher timeframes supported
---
## How It Works
### Step-by-Step Trade Logic
#### 1. **Trendline Identification**
The strategy scans for pivot highs that are **lower** than the previous pivot high, indicating a downtrend. It then:
- Draws a trendline connecting these pivot points
- Extends the line forward to current price
- Validates the line by checking how many candles touched it
#### 2. **Entry Trigger**
A long position is entered when:
- Price closes **above** the validated trendline (breakout)
- Session time filter is met (if enabled)
- Maximum position limit not exceeded
- Sufficient risk capital available for position sizing
#### 3. **Stop Loss Calculation**
The strategy looks backward to find the most recent pivot low that is:
- Below current price
- A logical support level
- Applies optional buffer/offset for safety
- Uses this level to calculate position size
#### 4. **Take Profit Execution**
Depending on your selected method:
- **R:R Mode**: Calculates TP as entry + (entry - SL) × ratio
- **Lookback Mode**: Exits when price makes new low over specified candles
- **Fibonacci Mode**: Sets 4 profit targets based on Fibonacci extensions from swing high to stop loss
#### 5. **Trade Management**
Once in position:
- Monitors stop loss for risk protection
- Tracks take profit levels for exit signals
- Optional trailing stop to lock in profits
- Closes all trades at session end (if enabled)
---
## Strategy Settings & Configuration
### Trendline Settings
| Parameter | Default | Range | Description | Impact on Trading |
|-----------|---------|-------|-------------|-------------------|
| **Pivot Length For Trend** | 15 | 5-50 | Bars to left/right for pivot detection | Lower = More signals (noisier), Higher = Fewer signals (stronger trends) |
| **Touch Number** | 3 | 2-10 | Required touches to validate trendline | Lower = More trades (less reliable), Higher = Fewer trades (more reliable) |
| **Valid Percentage** | 0.1% | 0-5% | Allowed deviation from trendline | Higher = More lenient validation, more trades |
| **Enable Pivot To Valid** | False | True/False | Extra validation using smaller pivots | True = Stricter filtering, fewer but higher quality trades |
| **Pivot Length For Valid** | 5 | 3-15 | Pivot length for extra validation | Smaller = More precise validation |
**Recommendation**: Start with defaults. In choppy markets, increase touch number to 4-5. In strongly trending markets, reduce to 2.
### Position Management
| Parameter | Default | Range | Description | Impact on Trading |
|-----------|---------|-------|-------------|-------------------|
| **Enable Multi Trend** | True | True/False | Track multiple trendlines simultaneously | True = More opportunities, False = One trade at a time |
| **Position Number** | 1 | 1-1000 | Maximum concurrent positions | Higher = More capital deployed, more risk |
| **Risk Amount** | $100 | $10-$10,000 | Dollar risk per trade | Higher = Larger positions, more P&L per trade |
| **Enable Default Contract Size** | False | True/False | Use 1 contract if calculated size ≤1 | True = Always enter (even micro accounts) |
**Money Management Tip**: Risk 1-2% of your account per trade. If you have $10,000, set Risk Amount to $100-$200.
### Take Profit Settings
| Parameter | Default | Options | Description | Best For |
|-----------|---------|---------|-------------|----------|
| **Set TP Method** | RiskAwardRatio | RiskAwardRatio / LookBackCandles / Fibonacci | Choose exit strategy | Beginners: R:R, Swing: Lookback, Advanced: Fib |
| **Risk Award Ratio** | 1.5 | 1.0-5.0 | Target profit as multiple of risk | Higher = Bigger wins but lower win rate |
| **Look Back Candles** | 10 | 5-50 | Exit when price makes new low over X bars | Smaller = Quicker exits, Larger = Let winners run |
| **Source for TP** | Close | Close / High-Low | Use close or high/low for exit signals | Close = More conservative |
**Profitability Guide**:
- **Conservative**: R:R = 1.5, Lookback = 10
- **Balanced**: R:R = 2.0, Lookback = 15
- **Aggressive**: R:R = 2.5, Fibonacci mode with 1.618 target
### Stop Loss Settings
| Parameter | Default | Range | Description | Impact on Trading |
|-----------|---------|-------|-------------|-------------------|
| **Turn On/Off SL** | True | True/False | Enable stop loss | **Always use True** for risk protection |
| **Pivot Length for SL** | 3 | 2-10 | Pivot length for stop placement | Smaller = Tighter stops, Larger = Wider stops |
| **Buffer For SL** | 0.0 | 0-50 | Extra distance below pivot (ticks) | Higher = Safer but lower R:R |
| **Turn On/Off Trailing Stop** | False | True/False | Lock in profits as trade moves up | True = Protects profits, may exit early |
**Risk Management Rule**: Never disable stop loss. Use buffer in volatile markets (5-10 ticks).
### Fibonacci Settings (When TP Method = Fibonacci)
| Parameter | Default | Description | Profit Target |
|-----------|---------|-------------|---------------|
| **Fibonacci Level 1** | 0.618 | First profit target | 61.8% of swing range |
| **Fibonacci Level 2** | 1.0 | Second profit target | 100% of swing range |
| **Fibonacci Level 3** | 1.312 | Third profit target | 131.2% extension |
| **Fibonacci Level 4** | 1.618 | Fourth profit target | 161.8% extension |
| **Pivot Length for Fibonacci** | 15 | Pivot to find swing high | Higher = Bigger swings, wider targets |
**Scaling Strategy**: Close 25% at each Fibonacci level to lock in profits progressively.
### Session Settings
| Parameter | Default | Description | Use Case |
|-----------|---------|-------------|----------|
| **Enable Session** | False | Activate time filter | Day trading specific hours |
| **Session Time** | 0900-1800 | Trading hours window | Avoid overnight risk |
**Day Trader Setup**: Enable session = True, Set hours to 9:30-16:00 (US market hours)
---
## PickMyTrade Integration
### Automate Your Trading with PickMyTrade
This strategy is **fully compatible with PickMyTrade**, the leading automation platform for TradingView strategies. Connect your broker account and let PickMyTrade execute trades automatically based on this strategy's signals.
### Why Use PickMyTrade?
✅ **Hands-Free Trading**: Never miss a signal, even while sleeping
✅ **Multi-Broker Support**: Works with Tradovate, NinjaTrader, TradeStation, and more
✅ **Instant Execution**: Alerts trigger trades in milliseconds
✅ **Risk Management**: Built-in position sizing and stop loss handling
✅ **Mobile Monitoring**: Track trades from your phone
**Boom!** Your strategy is now fully automated. Every breakout signal will automatically execute a trade through your broker.
### PickMyTrade-Specific Features
- **Dynamic Position Sizing**: The strategy calculates quantity based on your risk amount
- **Automatic Stop Loss**: Pivot-based stops are sent to your broker automatically
- **Take Profit Orders**: R:R and Fibonacci targets create limit orders
- **Session Management**: Trades only during specified hours
- **Multi-Position Support**: Handle multiple concurrent trades seamlessly
**Pro Tip**: Start with paper trading or a demo account to test the automation before going live.
---
## Advanced Features
### 1. Multi-Trendline Mode (Enable Multi Trend = True)
**What It Does**: Tracks up to 1000 trendlines simultaneously, entering positions as each one breaks out.
**Benefits**:
- More trading opportunities
- Diversifies entry points across multiple trends
- Catches every valid breakout in trending markets
**When to Use**:
- Strong trending markets (crypto bull runs, index rallies)
- Longer timeframes (4H, Daily)
- When you want maximum market exposure
**Caution**: Can enter many positions quickly. Set appropriate Position Number limit and Risk Amount.
### 2. Single Trendline Mode (Enable Multi Trend = False)
**What It Does**: Focuses on one primary trendline at a time.
**Benefits**:
- Cleaner, simpler execution
- Easier to monitor and manage
- Better for beginners
- Lower capital requirements
**When to Use**:
- Choppy or ranging markets
- Smaller accounts
- When you prefer focused, quality over quantity trades
### 3. Fibonacci Profit Scaling
**How It Works**:
1. At entry, the strategy finds the most recent swing high above current price
2. Calculates the range from swing high to stop loss
3. Projects 4 Fibonacci extensions: 61.8%, 100%, 131.2%, 161.8%
4. Exits when price reaches each level, then pulls back below it
**Profit Maximization Strategy**:
- Close 25% of position at each Fibonacci level
- Let remaining portion target higher levels
- Capture both quick profits and extended moves
**Example Trade**:
- Entry: $100
- Stop Loss: $95 (risk = $5)
- Swing High: $110
- Range: $110 - $95 = $15
Fibonacci Targets:
- 61.8% = $95 + ($15 × 0.618) = $104.27 (+4.27%)
- 100% = $95 + ($15 × 1.0) = $110 (+10%)
- 131.2% = $95 + ($15 × 1.312) = $114.68 (+14.68%)
- 161.8% = $95 + ($15 × 1.618) = $119.27 (+19.27%)
**Result**: Even if only first two targets hit, you lock in +7% average gain vs. -5% risk = 1.4:1 R:R
### 4. Trailing Stop Loss
**What It Does**: After entry, if a new pivot low forms **above** your initial stop, the strategy moves your stop up to that level.
**Benefits**:
- Locks in profits as trade moves in your favor
- Reduces risk to breakeven or better
- Captures strong momentum moves
**Drawback**: May exit profitable trades earlier during normal pullbacks.
**Best Practice**: Use in strongly trending markets. Disable in choppy conditions.
### 5. Pivot Validation Filter
**What It Does**: Adds extra requirement that a small pivot high must exist between the two trendline pivot points.
**Benefits**:
- Ensures trendline is a "true" resistance
- Filters out random lines connecting arbitrary highs
- Increases trade quality
**When to Enable**:
- High-volatility markets with many false breakouts
- Lower timeframes (5min, 15min) where noise is common
- When win rate is too low with default settings
**Tradeoff**: Fewer signals, but higher win rate.
### 6. Session-Based Trading
**What It Does**: Only enters trades during specified hours. Auto-closes all positions outside session.
**Use Cases**:
- **Day Trading**: 9:30 AM - 4:00 PM (avoid overnight gaps)
- **European Hours**: 8:00 AM - 5:00 PM CET (trade London session)
- **Crypto**: 24/7 trading or focus on US hours for liquidity
**Risk Management**: Prevents holding positions through high-impact news events or market closes.
---
## Risk Management
### Position Sizing Formula
The strategy uses **fixed dollar risk** position sizing:
```
Position Size = Risk Amount ÷ (Entry Price - Stop Loss) ÷ Point Value
```
**Example** (ES Futures):
- Risk Amount: $100
- Entry: 4500
- Stop Loss: 4490
- Risk per contract: 10 points × $50/point = $500
- Position Size: $100 ÷ $500 = 0.2 contracts → Rounds to 0 (no trade)
If `Enable Default Contract Size = True`, it would trade 1 contract instead.
### Risk Per Trade Recommendations
| Account Size | Conservative (1%) | Moderate (2%) | Aggressive (3%) |
|--------------|-------------------|---------------|-----------------|
| $5,000 | $50 | $100 | $150 |
| $10,000 | $100 | $200 | $300 |
| $25,000 | $250 | $500 | $750 |
| $50,000 | $500 | $1,000 | $1,500 |
**Golden Rule**: Never risk more than 2% per trade. Even with 10 losses in a row, you'd only be down 20%.
### Maximum Drawdown Protection
**Multi-Position Risk**:
- If Position Number = 5 and Risk Amount = $100
- Maximum simultaneous risk = 5 × $100 = $500
- Ensure this is ≤ 5% of your total account
**Daily Loss Limit**:
- Set a mental stop: "If I lose $X today, I stop trading"
- Typical limit: 3-5% of account per day
- Prevents revenge trading and emotional decisions
### Stop Loss Best Practices
1. **Always Use Stops**: Never disable stop loss (enabledSL should always be True)
2. **Buffer in Volatile Markets**: Add 5-10 tick buffer to avoid stop hunts
3. **Respect Your Stops**: Don't manually override or move stops further away
4. **Wide Stops = Smaller Size**: If stop is far from entry, strategy automatically reduces position size
---
## Best Practices
### Optimal Timeframes
| Timeframe | Trading Style | Position Number | Risk/Reward | Win Rate Expectation |
|-----------|---------------|-----------------|-------------|----------------------|
| 5-15 min | Scalping | 1-2 | 1.5:1 | 50-55% |
| 30 min - 1H | Intraday | 2-3 | 2:1 | 55-60% |
| 4H | Swing Trading | 3-5 | 2.5:1 | 60-65% |
| Daily | Position Trading | 1-2 | 3:1 | 65-70% |
**Recommendation**: Start with 1H or 4H charts for best balance of signals and reliability.
### Ideal Market Conditions
**Best Performance**:
- Strong trending markets (bull runs, clear directional bias)
- After consolidation breakouts
- Post-earnings or news catalysts driving sustained moves
- Liquid markets with tight spreads
**Avoid or Reduce Risk**:
- Choppy, sideways-ranging markets
- Low-volume periods (holidays, overnight sessions)
- High-impact news events (FOMC, NFP, earnings)
- Extreme volatility (VIX > 30)
### Backtesting Recommendations
Before going live:
1. **Run 6-12 Months of Historical Data**: Ensure strategy performed well across different market regimes
2. **Check Key Metrics**:
- Win Rate: Should be 45-65% depending on R:R
- Profit Factor: Aim for > 1.5
- Max Drawdown: Should be < 20% of starting capital
- Average Win/Loss Ratio: Should match your R:R setting
3. **Stress Test**: Test during known volatile periods (March 2020, Jan 2022, etc.)
4. **Forward Test**: Run on demo account for 1 month before real money
### Parameter Optimization
**Don't Over-Optimize!** Avoid curve-fitting to past data. Instead:
1. **Start with Defaults**: Use recommended settings first
2. **Change One Parameter at a Time**: Isolate what improves performance
3. **Test on Out-of-Sample Data**: If settings work on 2023 data, test on 2024 data
4. **Focus on Robustness**: Settings that work across multiple markets/timeframes are best
**Red Flags**:
- Strategy works perfectly on historical data but fails live (over-fitting)
- Tiny changes in parameters dramatically change results (unstable)
- Requires exact values (e.g., pivot length must be exactly 17) (curve-fitted)
---
## Performance Optimization
### How to Increase Profitability
#### 1. Optimize Risk/Reward Ratio
- **Current**: 1.5:1 (default)
- **Test**: 2:1, 2.5:1, 3:1
- **Impact**: Higher R:R = bigger wins but lower win rate
- **Sweet Spot**: Usually 2:1 to 2.5:1 for trend strategies
#### 2. Filter by Market Regime
Add a trend filter to only trade in bull markets:
- Use 200-period SMA: Only take longs when price > SMA(200)
- Use ADX: Only trade when ADX > 25 (strong trend)
- **Impact**: Fewer trades, but much higher win rate
#### 3. Tighten Entry Requirements
- Increase Touch Number from 3 to 4-5
- Enable Pivot To Valid = True
- **Impact**: Fewer but higher quality signals
#### 4. Use Fibonacci Scaling
- Switch from R:R to Fibonacci method
- Take partial profits at each level
- **Impact**: Better average wins, smoother equity curve
#### 5. Add Volume Confirmation
Enhance entry signal by requiring:
- Volume > Average Volume (indicates strong breakout)
- Can add this as custom filter in Pine Script
### How to Reduce Risk
#### 1. Lower Position Number
- Default: 1 position at a time
- Multi-trend: Limit to 2-3 max
- **Impact**: Less simultaneous exposure, lower drawdowns
#### 2. Reduce Risk Amount
- Start with $50 per trade (0.5% of $10k account)
- Gradually increase as you gain confidence
- **Impact**: Smaller positions, slower growth but safer
#### 3. Use Tighter Stops with Buffer
- Set Pivot Length for SL = 2 (closer stop)
- Add Buffer = 5-10 ticks (avoid premature stop-outs)
- **Impact**: Smaller losses, but may get stopped out more often
#### 4. Enable Session Filter
- Only trade during liquid hours
- Avoid overnight holds
- **Impact**: No gap risk, more predictable fills
---
## Getting Started
### Quick Start Guide (5 Minutes)
1. **Copy the Strategy Code**
- Open the `.txt` file provided
- Copy all code to clipboard
2. **Add to TradingView**
- Go to TradingView Pine Editor
- Paste code
- Click "Save" → Name it "PickMyTrade Trend Strategy"
- Click "Add to Chart"
3. **Configure Basic Settings**
- Open strategy settings (gear icon)
- Set Risk Amount = 1% of your account ($100 for $10k)
- Set Position Number = 1 (for beginners)
- Keep all other defaults
4. **Backtest on Your Market**
- Choose your instrument (ES, NQ, AAPL, BTC, etc.)
- Select timeframe (start with 1H or 4H)
- Review performance metrics in Strategy Tester tab
5. **Optimize (Optional)**
- Adjust Touch Number (2-5) to balance signals vs. quality
- Try different TP methods (R:R vs. Fibonacci)
- Test on multiple timeframes
6. **Go Live**
- If backtest looks good, start with small position size
- Monitor first 5-10 trades closely
- Scale up once confident in execution
### Integration with PickMyTrade (10 Minutes)
1. **Sign Up for PickMyTrade**
- Visit (pickmytrade.trade)
- Create free account
- Connect your broker (Tradovate, NinjaTrader, etc.)
2. **Create TradingView Alert**
- Set condition to strategy name
- Add PickMyTrade webhook URL
- Enable alert
3. **Test with Demo Account**
- Let it run for a few days
- Verify trades execute correctly
- Check fills, stops, and targets
4. **Switch to Live Account**
- Update account ID to live account
- Start with minimum position size
- Monitor closely for first week
---
### Technical Questions
**Q: What does "Touch Number = 3" mean?**
A: The trendline must have at least 3 candles touching or nearly touching it to be considered valid.
**Q: Why am I getting no trades?**
A: Trendline requirements may be too strict. Try:
- Reduce Touch Number to 2
- Increase Valid Percentage to 0.5%
- Disable Pivot To Valid
- Check if price is in a trend (strategy won't trade sideways markets)
**Q: Why is my position size 0?**
A: Risk Amount is too small for the stop distance. Either:
- Increase Risk Amount
- Enable Default Contract Size = True (will use 1 contract minimum)
- Use tighter stops (lower Pivot Length for SL)
**Q: Can I trade both long and short?**
A: Current code is long-only. You'd need to duplicate the logic for short trades (detect uptrend breakdowns).
**Q: How do I change from TradingView strategy to indicator?**
A: Change line 5 from `strategy(...)` to `indicator(...)`. Replace `strategy.entry()` and `strategy.exit()` with `alert()` calls.
### Risk Management Questions
**Q: What's the maximum drawdown I should expect?**
A: Typically 10-20% depending on settings. If experiencing > 25%, reduce position size or tighten filters.
**Q: Should I risk more to make more money?**
A: No. Risking 2% vs. 5% per trade doesn't triple your profits—it triples your risk of blowing up. Stick to 1-2% per trade.
**Q: What if I hit 5 losses in a row?**
A: Normal. Even with 60% win rate, losing streaks happen. Don't increase position size to "win it back." Stick to your risk plan.
**Q: Do I need to watch the screen all day?**
A: No, especially with PickMyTrade automation. Check positions 1-2 times per day. Overtrading kills profits.
---
## Disclaimer
**Important Risk Disclosure**:
Trading futures, stocks, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Past performance is not indicative of future results. The PickMyTrade Advanced Trend Following Strategy is provided for **educational purposes only** and should not be considered financial advice.
**Key Risks**:
- You can lose more than your initial investment
- Backtested results may not reflect live trading performance
- Market conditions change; no strategy works forever
- Automation errors can occur (connectivity, bugs, etc.)
**Before Trading**:
- Consult a licensed financial advisor
- Fully understand the strategy logic
- Test on demo account for at least 1 month
- Only risk capital you can afford to lose
- Start with minimum position sizes
**PickMyTrade**:
This strategy is compatible with PickMyTrade but is not officially endorsed by PickMyTrade. The author is not affiliated with PickMyTrade. For PickMyTrade support, visit their official website.
**License**: This strategy is open-source under Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0). You may modify and share, but not for commercial use.
---
**Ready to automate your trading with PickMyTrade? Add this strategy to your TradingView chart today and start capturing profitable trend breakouts on autopilot!**
Volume Peak Box📄 English Description
Overview
The Volume Peak Box indicator highlights periods of unusually high volume by identifying volume spikes using Bollinger Bands on volume and drawing a price-range box around each spike window. This provides traders with a clear visual representation of supply/demand imbalances, absorption zones, and breakout/false-break areas.
All calculations come from one unified concept: detecting statistically significant volume peaks on a locked timeframe and mapping them onto the chart.
Concept & Logic
1. Locked Timeframe Volume Analysis
Instead of using the current chart timeframe, this script allows users to lock volume analysis to any timeframe (e.g., 60m, 4H, 1D).
The script retrieves from the chosen timeframe:
Volume
High price
Low price
This allows volume structure from higher timeframes to be used while trading lower timeframes.
2. Bollinger Bands on Volume
Volume volatility is analyzed using a standard Bollinger Band model:
Basis = SMA(volume, BB length)
Upper Band = Basis + (mult × standard deviation)
When:
Volume > Upper Band
→ This bar is classified as a Volume Peak.
This approach makes the peak detection statistically meaningful, instead of simply comparing raw volume to previous bars.
3. Peak Session Detection (Continuous Peaks Form One Box)
The script tracks continuous volume peaks:
When a peak starts → begin a session
While peaks continue → extend the session
When peaks end → session closes and a box is created
For each peak session, the script records:
Start bar index
End bar index
Highest high within the session
Lowest low within the session
These values determine the box boundaries.
This allows the indicator to group related peaks into a single price zone, instead of drawing a box for every bar.
4. Drawing the Volume Peak Box
When a session ends, the script draws:
A filled box covering the full price range
From startBar → endBar
Using user-defined:
Box fill color
Border color
Each box visually marks a region where strong participation entered the market, often signaling:
Breakout validation
Absorption zones
Supply/demand imbalance
High-activity trading decisions
How to Use
Use the boxes to identify high-volume reaction zones.
When price revisits a box:
Expect strong reactions (bounce, rejection, or absorption).
When price breaks out from a box:
Can signal continuation with momentum.
Lower-timeframe entry signals become more reliable when aligned with high-timeframe volume boxes.
Recommended to lock the TF to:
60m for intraday
4H or 1D for swing trading
Why This Script Is Original
It uses Bollinger Bands on volume, not price — a less common volatility-based method for detecting volume anomalies.
It groups continuous peaks into unified zones instead of treating each spike separately.
The ability to lock the volume analysis to a higher timeframe allows multi-timeframe volume interpretation without cluttering the chart.
Boxes give traders a clean and intuitive view of volume-based “decision zones”.
🇹🇭 Thai Description — คำอธิบายภาษาไทย
ภาพรวม
อินดิเคเตอร์ Volume Peak Box ใช้การตรวจจับ “Volume Peak” โดยใช้ Bollinger Band บน Volume แล้วสร้าง “กล่องช่วงราคา” ครอบช่วงที่มี Volume สูงผิดปกติ ทำให้เห็นบริเวณที่มีแรงซื้อขายเข้ามาอย่างชัดเจน เช่น จุด Breakout, จุด Absorption, หรือเขต Supply/Demand
แนวคิดและหลักการทำงาน
1. วิเคราะห์ Volume จาก Timeframe ที่ล็อกไว้
คุณสามารถเลือก TF ที่ต้องการให้ Volume ถูกนำมาคำนวณ เช่น 60 นาที, 4 ชั่วโมง, 1 วัน
แม้คุณจะเปิดกราฟ TF เล็ก เช่น 5m แต่กล่องยังอิง volume จาก TF ที่เลือกไว้ ทำให้ได้ “โซน Volume ใหญ่” ที่แม่นยำขึ้น
2. Bollinger Band บน Volume
ใช้ SMA + ส่วนเบี่ยงเบนมาตรฐานของ Volume เพื่อหา “จุดที่ Volume สูงกว่าปกติอย่างมีนัยสำคัญ”
เงื่อนไข Peak:
Volume > Upper Bollinger Band
นี่เป็นวิธีที่ดีกว่า “เทียบกับแท่งก่อนหน้า” เพราะคิดจากสถิติของทั้งช่วง
3. รวม Peak ต่อเนื่องเป็นกล่องเดียว
ถ้า Volume Peak เกิดต่อเนื่องหลายแท่ง:
จะถูกจับรวมเป็น Peak session เดียว
ใช้ High สูงสุด และ Low ต่ำสุดของทั้ง session
เมื่อ Peak จบ → วาดกล่องช่วงราคา
เหมาะกับการหาจุดที่ตลาดมีแรงเข้าซื้อ/ขายหนักในช่วงเวลาเดียวกัน
4. วาดกล่อง Volume Peak
กล่องจะครอบ:
ช่วงแท่งเริ่มต้น → แท่งสุดท้ายของ Peak
ความสูงของกล่อง = ช่วงราคาที่มี Volume สูงผิดปกติ
กล่องสามารถใช้เป็น:
โซน Breakout/Breakdown
โซน Supply/Demand
เขตที่ราคามักมี reaction
วิธีใช้งาน
ใช้กล่องเป็น “เขตการตัดสินใจ” (Decision Zone)
ราคาแตะซ้ำมักเกิดการกลับตัวหรือความผันผวนสูง
การทะลุกล่องบ่อยครั้งนำไปสู่ขาเทรนด์ใหญ่
เหมาะกับการใช้ร่วมกับ Price Action และโครงสร้างราคา
จุดเด่น / ความเป็น Original
ใช้ Bollinger Band บน Volume (น้อยอินดี้ทำ)
รวม Peak ต่อเนื่องเป็น session เดียว
วิเคราะห์ Volume ข้าม TF ได้ โดยไม่ต้องเปลี่ยน TF บนกราฟ
ได้ “โซน Volume สำคัญ” แบบชัดเจน อ่านง่าย ไม่รกจอ
Intraday Master Levels + ORB Suite (ARJO)Intraday Master Levels + ORB Suite (ARJO)
This toolkit is designed for intraday traders.
It focuses on key reference levels used by professional traders— Previous Day High/Low, CPR, Opening Range Breakout (ORB) , dynamic ORB box visualization, and a dedicated Morning Session VWAP.
This indicator provides objective reference zones to help traders understand market context, volatility, and intraday bias .
Key Features
1. Previous Day Levels (PDH / PDL)
Automatically plots Previous Day High, Previous Day Low , and optional labels.
These levels often act as natural support/resistance and help identify breakout traps or reversals.
2. Full CPR (Central Pivot Range)
The indicator draws:
Top Central (TC)
Pivot (PP)
Bottom Central (BC)
These levels help traders interpret range, trend, expansion, contraction, and potential intraday direction.
3. Opening Range (OR) Levels
You can select 5 / 10 / 15 / 30 / 60 minutes OR duration.
The script automatically calculates:
Opening Range High (ORH)
Opening Range Low (ORL)
Works in all markets and follows your chosen timezone.
4. ORB Highlight Box
A dynamic Opening Range Box is plotted during the OR window, showing:
Real-time OR expansion
Volatility
Initial auction imbalance
This helps visually track early market participation and breakout attempts.
5. Used Smoother Function (Ehlers SuperSmoother)
A refined trend ribbon based on:
EMA-based secondary smoothing
Adaptive up/down color gradients
Useful for understanding short-term trend strength around OR, CPR, and PDH/PDL zones.
6. Morning Session VWAP (Custom Session VWAP)
A unique feature in this script.
The indicator computes a dedicated VWAP only for the Morning Session (09:15–09:30)(customizable) to help evaluate:
Early-session pressure
Liquidity transition
Opening volatility absorption
This helps track where the market is accepting or rejecting price during the opening phase.
Inputs & Customization
Custom timezone selection
Toggle for PDH/PDL, CPR, OR, ORB Box, Labels
Color customization for each level
Trend color settings
Adjustable opening range duration
VWAP session toggle
All features can be enabled/disabled to make it user-friendly.
Disclaimer:
This indicator does not provide buy or sell signals .
It is a visual analysis tool meant to assist traders in studying intraday behavior.
Past performance does not guarantee future results.
Always combine these levels with risk management and your own market analysis.
Happy Trading (ARJO)
MTF Checklist DashboardMTF Checklist Dashboard
Overview
The MTF Checklist Dashboard is an advanced multi-timeframe analysis tool that provides traders with a comprehensive visual dashboard to analyze market conditions across six customizable timeframes simultaneously. This indicator combines multiple technical analysis methods, including Opening Range Breakouts (ORB), VWAP, EMAs, and daily price levels, to generate high-probability confluence-based trading signals.
Unlike traditional single-timeframe indicators, this dashboard displays all critical information in one organized table, allowing traders to instantly identify when multiple timeframes align for optimal entry and exit opportunities.
Key Features
Multi-Timeframe Analysis
Analyzes up to 6 timeframes simultaneously (default: 1m, 5m, 15m, 30m, 1h, 4h)
Fully customizable timeframe selection via comma-separated input
Color-coded cells for instant visual recognition (green=bullish, red=bearish, yellow=neutral)
Technical Indicators Tracked
Current and previous candle direction
Opening Range Breakout (ORB) positioning with custom period
VWAP relationship (above/below)
200 EMA positioning
Daily and previous day high/low proximity
EMA crossovers (9 vs 21, both vs 200)
Advanced Signal Filtering System
Confluence scoring: Requires multiple timeframes to align (3-6 timeframes)
Higher timeframe confirmation: Ensures 30m/1h/4h agreement
Volume filter: Confirms signals with above-average volume (1.5x default)
ATR volatility filter: Validates sufficient market movement
Session timing: Restricts signals to optimal trading hours (EST)
Momentum confirmation: Requires recent directional strength
Range positioning: Blocks signals near daily extremes
Candle strength: Validates strong directional candles (60%+ body ratio)
Visual Signals
Optional entry arrows (above/below bars)
Background color highlighting
Organized dashboard with real-time price levels
ORB range, current day, and previous day summary rows
Alert Conditions
JSON-formatted alerts for automated trading integration
Separate alerts for long entry, short entry, long exit, and short exit
Compatible with webhook automation systems
How To Use
Dashboard Interpretation
The dashboard displays a color-coded table with the following columns:
TF: Timeframe being analyzed
C: Current candle (Green=bullish, Red=bearish)
P: Previous candle (Green=bullish, Red=bearish)
ORB: Opening Range Breakout position (A=Above, B=Below, W=Within)
VWAP: Price vs VWAP (A=Above, B=Below)
E200: Price vs 200 EMA (A=Above, B=Below)
D Hi/Lo: Proximity to current day high/low (Hi/Lo/Mid)
PD Hi/Lo: Proximity to previous day high/low (Hi/Lo/Mid)
9 vs 21: EMA 9 vs EMA 21 relationship (A=9 above 21, B=9 below 21)
9&21 v200: Both EMAs vs 200 EMA (>>=both above, <<=both below, <>=mixed)
Signal Generation
Long Entry Signal triggers when:
Minimum number of timeframes show bullish alignment (default: 5 of 6)
Higher timeframes (30m/1h/4h) confirm direction (default: 2 of 3)
Price breaks above ORB high with sufficient distance
Volume exceeds average by specified multiplier
ATR shows adequate volatility
Trade occurs during optimal session hours
Recent momentum is upward
Price not too close to daily high
Strong bullish candle forms
Short Entry Signal uses opposite conditions
Exit Signals trigger when opposing timeframe confluence reaches threshold (default: 3 timeframes)
Recommended Workflow
Select your asset and primary trading timeframe
Observe the dashboard - Look for rows showing mostly green (bullish) or red (bearish)
Wait for alignment - The indicator will show arrows when confluence requirements are met
Check the bottom rows - Review ORB levels and daily ranges for context
Set alerts - Enable TradingView alerts using the built-in alert conditions
Manage risk - Use appropriate position sizing and stop losses based on ORB range or daily ATR
Settings Guide
Basic Settings
Timeframes: Enter comma-separated values (e.g., "1,5,15,30,60,240")
Show Header: Toggle column headers on/off
ORB Minutes: Set opening range period (default: 15 minutes)
Near % for daily highs/lows: Define proximity threshold (default: 0.20%)
Use close for comparisons: Compare using close vs current price
Dashboard Position: Choose from 9 screen positions
Confluence Filters
Minimum Timeframes Aligned: Set required confluence (3-6, default: 5)
Require Higher Timeframe Confirmation: Toggle HTF requirement on/off
Min Higher Timeframes: Specify HTF agreement needed (1-3, default: 2)
Volume Filter
Volume Confirmation: Enable/disable volume filtering
Volume vs Average: Set multiplier threshold (default: 1.5x)
Volume Average Length: Period for volume average (default: 20 bars)
Volatility Filter (ATR)
Volatility Filter: Enable/disable ATR confirmation
ATR Length: Calculation period (default: 14)
Min ATR vs Average: Required ATR level (default: 0.5x = 50%)
ORB Filters
ORB Breakout Distance Required: Toggle distance requirement
Min Breakout % Beyond ORB: Additional breakout threshold (default: 0.10%)
Session Filter
Trade Only During Best Hours: Enable time-based filtering
Session 1: First trading window (default: 0930-1130 EST)
Session 2: Second trading window (default: 1400-1530 EST)
Momentum Filter
Recent Momentum Required: Enable directional momentum check
Lookback Bars: Period for momentum comparison (default: 3 bars)
Daily Range Filter
Block Signals Near Daily Extremes: Prevent entries at extremes
Distance from High/Low %: Minimum distance required (default: 2.0%)
Candle Filter
Strong Directional Candle: Require candle strength
Min Candle Body %: Body-to-range ratio threshold (default: 60%)
Visual Signals
Show Entry Signals: Master toggle for visual signals
Show Arrows: Display entry arrows on chart
Background Color: Enable background highlighting
Best Practices
Start with default settings and adjust based on your trading style and asset volatility
Higher confluence requirements (5-6 timeframes) produce fewer but higher-quality signals
Enable all filters for conservative trading; disable some for more frequent signals
Use the dashboard as confirmation alongside your existing trading strategy
Backtest on your specific instruments before live trading
Consider market conditions—trending vs ranging markets may require different settings
Alerts
This indicator includes four alert conditions with JSON formatting for webhook integration:
Long Entry Signal: Triggers when all long conditions are met
Short Entry Signal: Triggers when all short conditions are met
Long Exit Signal: Triggers when opposing confluence reaches exit threshold
Short Exit Signal: Triggers when opposing confluence reaches exit threshold
Alert messages include ticker symbol, action (buy/sell), price, and quantity for automated trading systems.
Important Notes
This indicator works best on liquid instruments with clear price action
Highly volatile markets may require adjusted ATR and ORB distance settings
Session times are in EST timezone—adjust if trading non-US markets
The ORB calculation requires sufficient price history for the day
Signals are generated in real-time but should be confirmed at candle close
Limitations
Maximum of 6 timeframes can be analyzed due to TradingView's security call limits
ORB calculations may not work correctly on instruments with gaps or irregular sessions
The indicator is most effective during regular market hours when volume and volatility are adequate
Lower timeframes (1m, 5m) may produce more false signals in choppy conditions
License
Mozilla Public License 2.0 (MPL-2.0)
This indicator is licensed under the Mozilla Public License 2.0. You are free to use, modify, and distribute this code under the terms of the MPL-2.0. The full license text is available at mozilla.org
Key license provisions:
You may use this code commercially
You may modify and distribute modified versions
Modified versions must be released under the same license
You must include the original license notice in any distributions
No trademark rights are granted
Disclaimer
This indicator is provided for educational and informational purposes only. It is not financial advice, and past performance does not guarantee future results. Trading involves substantial risk of loss. Always:
Practice proper risk management
Test thoroughly on paper/demo accounts before live trading
Use appropriate position sizing
Never risk more than you can afford to lose
Consult with a financial advisor for personalized advice
The creator assumes no liability for trading losses incurred using this indicator.
Version: 2.0
Pine Script Version: v6
Author: © EliasVictor
Event High/Mid/LowEvent High/Mid/Low - Data Release Level Tracker
Automatically track and visualize high, low, and mid levels from major data events like FOMC announcements, CPI releases, NFP reports, and other market-moving data releases.
KEY FEATURES:
- Customizable event input - Add unlimited events using a simple text format
- Flexible time periods - Set custom duration for each event (15min, 30min, 60min, etc.)
- Visual clarity - Color-coded lines and optional background cloud between high/low
- Clean labels - Minimalist text labels without background boxes
- Fully customizable - Toggle lines, labels, and clouds on/off independently
HOW TO USE:
1. Add the indicator to your chart
2. Open settings and edit the "Event Dates" text area
3. Enter one event per line in this format: YYYY-MM-DD HH:MM Minutes Label
Example: 2025-01-29 14:00 30 Jan FOMC
Example: 2025-02-12 08:30 30 Feb CPI
4. The indicator will automatically capture and display the high, low, and mid levels
WHAT IT DISPLAYS:
- High line (teal) - Highest price during the event period
- Low line (pink) - Lowest price during the event period
- Mid line (yellow, dotted) - Midpoint between high and low
- Background cloud (optional) - Shaded area between high and low
- Event window highlighting - Orange background during active events
PERFECT FOR:
- Tracking key support/resistance levels from economic releases
- Planning entries/exits around FOMC, CPI, NFP, and other data
- Analyzing how price reacts to major announcements
- Identifying post-event trading ranges
SUPPORTED EVENTS:
Works with any scheduled economic release - FOMC, CPI, PPI, NFP, Retail Sales, GDP, and more. Simply input the date, time, duration, and a custom label.
IMPORTANT LIMITATIONS:
- Chart timeframe must be EQUAL TO OR SMALLER than event duration
- For 30-minute events: Use 30min, 15min, 5min, 1min charts (NOT 1H, 4H, Daily)
- For 60-minute events: Use 60min, 30min, 15min, 5min, 1min charts
- For 15-minute events: Use 15min, 5min, 1min charts
- If your chart timeframe is larger than the event duration, the indicator may not capture accurate high/low values
- Recommended: Use 5-minute or 1-minute charts for maximum accuracy on all event durations
NOTES:
- All times are in EST/EDT (America/New_York timezone)
- Comments starting with # are ignored, making it easy to organize and annotate your event list
- The indicator processes events only after the specified duration has elapsed
chanlun缠论 - 笔与中枢Overview
The Chanlun (缠论) Strokes & Central Zones indicator is an advanced technical analysis tool based on Chinese Chan Theory (Chanlun Theory). It automatically identifies market structure through "strokes" (笔) and "central hubs" (中枢), providing traders with a systematic framework for understanding price movements, trend structure, and potential reversal zones.
Theoretical Foundation
Chan Theory is a sophisticated price action methodology that breaks down market movements into hierarchical structures:
Local Extremes: Swing highs and lows identified through lookback periods
Strokes (笔): Valid price movements between opposite extremes that meet specific criteria
Central Hubs (中枢): Consolidation zones formed by overlapping strokes, representing key support/resistance areas
Key Components
1. Local Extreme Detection
Identifies swing highs and lows using a configurable lookback period (default: 5 bars)
Only considers extremes within the specified calculation range
Forms the foundation for stroke construction
2. Stroke (笔) Identification
The indicator applies a multi-stage filtering process to identify valid strokes:
Stage 1 - Extreme Consolidation:
Merges consecutive extremes of the same type (high or low)
Keeps only the most extreme value (highest high or lowest low)
Stage 2 - Stroke Validation:
Ensures minimum bar gap between strokes (default: 4 bars)
Alternative validation: 2+ bars with >1% price change
Eliminates noise and insignificant price movements
Color Coding:
White Lines: Regular up/down strokes
Yellow Lines: Strokes that form part of a central hub
Customizable width and colors for different stroke types
3. Central Hub (中枢) Formation
A central hub forms when at least 3 consecutive strokes have overlapping price ranges:
Formation Rules:
Stroke 1:
Stroke 2:
Stroke 3:
Hub Upper = MIN(High1, High2, High3)
Hub Lower = MAX(Low1, Low2, Low3)
Valid if: Hub Upper > Hub Lower
Hub Extension:
Subsequent strokes that overlap with the hub extend it
Hub ends when a stroke no longer overlaps
Creates rectangular zones on the chart
Visual Representation:
Green rectangular boxes: Mark the time and price range of each central hub
Dashed extension lines: Show the latest hub boundaries extending to the right
Price labels on axis: Display exact hub upper and lower boundary values
4. Extreme Point Markers (Optional)
Red markers for tops (▼)
Green markers for bottoms (▲)
Marks every validated stroke extreme point
Useful for detailed structure analysis
5. Information Table (Optional)
Displays real-time statistics:
Symbol name
Current timeframe
Lookback period setting
Minimum gap setting
Total stroke count
Parameter Settings
Performance Settings
Max Bars to Calculate (3600): Limits historical calculation to improve performance
Local Extreme Lookback Period (5): Bars used to identify swing highs/lows
Min Gap Bars (4): Minimum bars required between valid strokes
Display Settings
Show Strokes: Toggle stroke line visibility
Show Central Hub: Toggle hub box visibility
Show Hub Extension Lines: Toggle dashed boundary lines
Show Extreme Point Marks: Toggle top/bottom markers
Show Info Table: Toggle statistics table
Color Settings
Full customization of:
Up/down stroke colors and widths
Hub stroke colors and widths
Hub border and background colors
Extension line colors
Trading Applications
Trend Structure Analysis
Uptrend: Series of higher highs and higher lows connected by strokes
Downtrend: Series of lower highs and lower lows connected by strokes
Consolidation: Formation of central hubs indicating range-bound movement
Support and Resistance Identification
Central Hub Zones: Act as strong support/resistance areas
Hub Upper Boundary: Resistance level in consolidation, support after breakout
Hub Lower Boundary: Support level in consolidation, resistance after breakdown
Price tends to react at these levels due to market structure memory
Breakout Trading
Bullish Breakout: Price closes above hub upper boundary
Previous resistance becomes support
Entry on retest of upper boundary
Stop loss below hub zone
Bearish Breakdown: Price closes below hub lower boundary
Previous support becomes resistance
Entry on retest of lower boundary
Stop loss above hub zone
Reversal Detection
Hub Formation After Trend: Signals potential trend exhaustion
Multiple Hub Levels: Create probability zones for reversals
Stroke Count: Excessive strokes within hub suggest weakening momentum
Position Management
Use hub boundaries for stop loss placement
Scale out positions at hub edges
Re-enter on retests of broken hub levels
Interpretation Guide
Strong Trending Market
Long, clear strokes with minimal overlap
Few or no central hubs forming
Strokes consistently in same direction
Wide spacing between extremes
Consolidating Market
Multiple central hubs forming
Short, overlapping strokes
Yellow hub strokes dominate the chart
Narrow price range
Trend Transition
Hub formation after extended trend
Stroke direction changes frequently
Hub boundaries being tested repeatedly
Potential reversal zone
Advanced Usage Techniques
Multi-Timeframe Analysis
Higher Timeframe: Identify major hub zones for overall market structure
Lower Timeframe: Find precise entry points within larger structure
Alignment: Trade when lower timeframe strokes align with higher timeframe hub breaks
Hub Quality Assessment
Wide Hubs: Strong consolidation, higher probability support/resistance
Narrow Hubs: Weak consolidation, may break easily
Extended Hubs: More strokes = stronger zone
Isolated Hubs: Single hub = potential pivot point
Stroke Analysis
Stroke Length: Longer strokes = stronger momentum
Stroke Speed: Fewer bars per stroke = explosive moves
Stroke Clustering: Many short strokes = indecision
Best Practices
Parameter Optimization
Adjust lookback period based on timeframe and volatility
Lower periods (3-4): More strokes, more noise, faster signals
Higher periods (7-10): Fewer strokes, cleaner structure, slower signals
Confirmation Strategy
Don't trade on strokes alone
Combine with volume analysis
Use candlestick patterns at hub boundaries
Wait for breakout confirmation
Risk Management
Always place stops outside hub zones
Use hub width to size positions (wider hub = smaller position)
Exit if price re-enters broken hub from wrong direction
Avoid Common Pitfalls
Don't trade within central hubs (range-bound, unpredictable)
Don't ignore higher timeframe hub structures
Don't chase strokes after they've extended far from hub
Don't trust single-stroke hubs (need 3+ strokes for validity)
Performance Considerations
Max Bars Limit: Set to 3600 to balance detail with performance
Safe Distance Calculation: Only draws objects within 2000 bars of current price
Object Cleanup: Automatically removes old drawing objects to prevent memory issues
Efficient Arrays: Uses indexed arrays for fast lookup and processing
Ideal Market Conditions
Best Performance:
Liquid markets with clear structure (major forex pairs, indices, large-cap stocks)
Trending markets with periodic consolidations
Medium to high volatility for clear stroke formation
Less Effective:
Extremely choppy, directionless markets
Very low timeframes (< 5 minutes) with excessive noise
Illiquid instruments with erratic price action
Integration with Other Indicators
Complementary Tools:
Volume Profile: Confirm hub significance with volume nodes
Moving Averages: Use for trend bias within stroke structure
RSI/MACD: Momentum confirmation at hub boundaries
Fibonacci Retracements: Hub levels often align with Fib levels
Advantages
✓ Objective Structure: Removes subjectivity from market structure analysis
✓ Visual Clarity: Color-coded strokes and clear hub zones
✓ Multi-Timeframe Applicable: Works on all timeframes from minutes to months
✓ Complete Framework: Provides entry, exit, and risk management levels
✓ Theoretical Foundation: Based on proven Chan Theory methodology
✓ Customizable: Extensive parameter and visual customization options
Limitations
⚠ Learning Curve: Requires understanding of Chan Theory principles
⚠ Lag Factor: Strokes confirm after price movements complete
⚠ Parameter Sensitivity: Different settings produce significantly different results
⚠ Choppy Market Struggles: Can generate excessive hubs in range-bound conditions
⚠ Computation Intensive: May slow down on lower-end systems with max bars setting
Optimization Tips
Timeframe Selection
Scalping: 5-15 minute charts, lookback period 3-4
Day Trading: 15-60 minute charts, lookback period 4-5
Swing Trading: 4-hour to daily charts, lookback period 5-7
Position Trading: Daily to weekly charts, lookback period 7-10
Volatility Adjustment
High volatility: Increase minimum gap bars to reduce noise
Low volatility: Decrease lookback period to capture smaller moves
Visual Optimization
Use contrasting colors for different market conditions
Adjust line widths based on chart resolution
Toggle markers off for cleaner appearance once familiar with structure
Quick Start Guide
For Beginners:
Start with default settings (5 lookback, 4 min gap)
Enable "Show Info Table" to track stroke count
Focus on identifying clear hub formations
Practice waiting for price to break hub boundaries before trading
For Advanced Users:
Optimize lookback and gap parameters for your instrument
Use hub strokes (yellow) to identify key consolidation zones
Combine with multiple timeframes for confirmation
Develop entry rules based on hub breakout/retest patterns
This indicator provides a complete structural framework for understanding market behavior through the lens of Chan Theory, offering traders a systematic approach to identifying high-probability trading opportunities.
ENTRY CONFIRMATION V2// This source code is subject to the terms of the Mozilla Public License 2.0 at mozilla.org
// © Zerocapitalmx
//@version=5
indicator(title="ENTRY CONFIRMATION V2", format=format.price, timeframe="", timeframe_gaps=true)
len = input.int(title="RSI Period", minval=1, defval=50)
src = input(title="RSI Source", defval=close)
lbR = input(title="Pivot Lookback Right", defval=5)
lbL = input(title="Pivot Lookback Left", defval=5)
rangeUpper = input(title="Max of Lookback Range", defval=60)
rangeLower = input(title="Min of Lookback Range", defval=5)
plotBull = input(title="Plot Bullish", defval=true)
plotHiddenBull = input(title="Plot Hidden Bullish", defval=false)
plotBear = input(title="Plot Bearish", defval=true)
plotHiddenBear = input(title="Plot Hidden Bearish", defval=false)
bearColor = color.red
bullColor = color.green
hiddenBullColor = color.new(color.green, 80)
hiddenBearColor = color.new(color.red, 80)
textColor = color.white
noneColor = color.new(color.white, 100)
osc = ta.rsi(src, len)
rsiPeriod = input.int(50, minval = 1, title = "RSI Period")
bandLength = input.int(1, minval = 1, title = "Band Length")
lengthrsipl = input.int(1, minval = 0, title = "Fast MA on RSI")
lengthtradesl = input.int(50, minval = 1, title = "Slow MA on RSI")
r = ta.rsi(src, rsiPeriod) // RSI of Close
ma = ta.sma(r, bandLength ) // Moving Average of RSI
offs = (1.6185 * ta.stdev(r, bandLength)) // Offset
fastMA = ta.sma(r, lengthrsipl) // Moving Average of RSI 2 bars back
slowMA = ta.sma(r, lengthtradesl) // Moving Average of RSI 7 bars back
plot(slowMA, "Slow MA", color=color.black, linewidth=1) // Plot Slow MA
plot(osc, title="RSI", linewidth=2, color=color.purple)
hline(50, title="Middle Line", color=#787B86, linestyle=hline.style_dotted)
obLevel = hline(70, title="Overbought", color=#787B86, linestyle=hline.style_dotted)
osLevel = hline(30, title="Oversold", color=#787B86, linestyle=hline.style_dotted)
plFound = na(ta.pivotlow(osc, lbL, lbR)) ? false : true
phFound = na(ta.pivothigh(osc, lbL, lbR)) ? false : true
_inRange(cond) =>
bars = ta.barssince(cond == true)
rangeLower <= bars and bars <= rangeUpper
//------------------------------------------------------------------------------
// Regular Bullish
// Osc: Higher Low
oscHL = osc > ta.valuewhen(plFound, osc , 1) and _inRange(plFound )
// Price: Lower Low
priceLL = low < ta.valuewhen(plFound, low , 1)
bullCond = plotBull and priceLL and oscHL and plFound
plot(
plFound ? osc : na,
offset=-lbR,
title="Regular Bullish",
linewidth=1,
color=(bullCond ? bullColor : noneColor)
)
plotshape(
bullCond ? osc : na,
offset=-lbR,
title="Regular Bullish Label",
text=" EDM ",
style=shape.labelup,
location=location.absolute,
color=bullColor,
textcolor=textColor
)
//------------------------------------------------------------------------------
// Hidden Bullish
// Osc: Lower Low
oscLL = osc < ta.valuewhen(plFound, osc , 1) and _inRange(plFound )
// Price: Higher Low
priceHL = low > ta.valuewhen(plFound, low , 1)
hiddenBullCond = plotHiddenBull and priceHL and oscLL and plFound
plot(
plFound ? osc : na,
offset=-lbR,
title="Hidden Bullish",
linewidth=1,
color=(hiddenBullCond ? hiddenBullColor : noneColor)
)
plotshape(
hiddenBullCond ? osc : na,
offset=-lbR,
title="Hidden Bullish Label",
text=" EDM ",
style=shape.labelup,
location=location.absolute,
color=bullColor,
textcolor=textColor
)
//------------------------------------------------------------------------------
// Regular Bearish
// Osc: Lower High
oscLH = osc < ta.valuewhen(phFound, osc , 1) and _inRange(phFound )
// Price: Higher High
priceHH = high > ta.valuewhen(phFound, high , 1)
bearCond = plotBear and priceHH and oscLH and phFound
plot(
phFound ? osc : na,
offset=-lbR,
title="Regular Bearish",
linewidth=1,
color=(bearCond ? bearColor : noneColor)
)
plotshape(
bearCond ? osc : na,
offset=-lbR,
title="Regular Bearish Label",
text=" EDM ",
style=shape.labeldown,
location=location.absolute,
color=bearColor,
textcolor=textColor
)
//------------------------------------------------------------------------------
// Hidden Bearish
// Osc: Higher High
oscHH = osc > ta.valuewhen(phFound, osc , 1) and _inRange(phFound )
// Price: Lower High
priceLH = high < ta.valuewhen(phFound, high , 1)
hiddenBearCond = plotHiddenBear and priceLH and oscHH and phFound
plot(
phFound ? osc : na,
offset=-lbR,
title="Hidden Bearish",
linewidth=1,
color=(hiddenBearCond ? hiddenBearColor : noneColor)
)
plotshape(
hiddenBearCond ? osc : na,
offset=-lbR,
title="Hidden Bearish Label",
text=" EDM ",
style=shape.labeldown,
location=location.absolute,
color=bearColor,
textcolor=textColor
)
🎯 Wyckoff Order Block Entry System🎯 Wyckoff Order Block Entry System
📝 INDICATOR DESCRIPTION
🎯 Wyckoff Order Block Entry System Short Description:
Professional institutional zone trading combined with Wyckoff methodology. Identifies high-probability entries where smart money meets classic price action patterns.
Full Description:
Wyckoff Order Block Entry System is a precision trading tool that combines two powerful concepts:
Order Blocks - Institutional zones where large players place their orders
Wyckoff Method - Classic price action patterns revealing smart money behavior
🎯 What Makes This Different?
Unlike traditional indicators that flood your chart with signals, this system only triggers entries when BOTH conditions are met:
Price enters an institutional Order Block zone (current timeframe OR higher timeframe)
A Wyckoff pattern occurs (Spring, SOS, Upthrust, or SOW)
This dual-confirmation approach ensures you're trading with institutional flow at optimal entry points.
📊 Key Features:
✅ Order Block Detection
Automatically identifies institutional buying/selling zones
Current timeframe order blocks (solid lines)
Higher timeframe order blocks (dashed lines) for stronger zones
Customizable strength and extension settings
✅ 4 Wyckoff Entry Patterns
SPRING (Bullish Reversal): Fake breakdown below support → Quick recovery
SOS (Sign of Strength): Strong bullish candle after accumulation
UPTHRUST (Bearish Reversal): Fake breakout above resistance → Quick rejection
SOW (Sign of Weakness): Strong bearish candle after distribution
✅ Clean Visual Design
Minimalist approach - only essential information
Color-coded zones (Green = Bullish, Red = Bearish, Cyan/Magenta = HTF)
Clear entry signals with pattern type labels
No chart clutter - focus on what matters
✅ Multi-Timeframe Analysis
Integrates higher timeframe order blocks
HTF signals marked with "+HTF" tag for extra confidence
Fully customizable HTF selection (H1, H4, Daily, etc.)
✅ Smart Alerts
Entry signal alerts (Long/Short)
Order block formation alerts
HTF order block alerts
Customizable alert messages
💡 How To Use:
Setup: Add indicator to your chart, configure HTF timeframe (default H1)
Wait: Let order blocks form (green/red boxes appear)
Watch: Price returns to order block zone
Entry: Signal appears when Wyckoff pattern confirms
Trade: Enter with the signal, stop below/above order block
📈 Best For:
Forex pairs (all majors and crosses)
Gold (XAUUSD)
Crypto (BTC, ETH, etc.)
Indices (SPX, NAS100, etc.)
Stocks
Commodities
⏱️ Recommended Timeframes:
M15 for scalping
M30 for day trading
H1 for swing trading
H4 for position trading
🎯 Win Rate Expectations:
Current TF signals: 60-70%
HTF signals (+HTF tag): 70-80%
Spring/Upthrust patterns: Highest probability
Works on ALL liquid markets
⚙️ Customizable Settings:
Order block detection parameters
HTF timeframe selection
Wyckoff sensitivity (swing length, volume threshold)
Zone extension duration
Color schemes
📚 Trading Strategy:
This indicator works best when:
Trading in the direction of higher timeframe trend
Using proper risk management (1-2% per trade)
Placing stops just outside order block zones
Taking profits at opposite order blocks
Focusing on HTF signals for higher quality
🔒 Risk Management:
Always use stop losses! Recommended placement:
LONG: 10-20 pips below order block
SHORT: 10-20 pips above order block
Target: Minimum 1:2 risk/reward ratio
💎 Why Traders Love This System:
"Finally, an indicator that doesn't spam my chart with useless signals!" - The quality-over-quantity approach means you only get high-probability setups.
"The HTF order blocks changed my trading!" - Multi-timeframe analysis built-in removes the need for manual higher timeframe checks.
"Wyckoff + Order Blocks = Perfect combination!" - Two proven concepts working together create powerful confluence.
📊 Universal Application:
This system works on ANY liquid market with sufficient volume:
✅ Forex (EUR/USD, GBP/USD, USD/JPY, etc.)
✅ Commodities (Gold, Silver, Oil, etc.)
✅ Indices (S&P 500, NASDAQ, DAX, etc.)
✅ Cryptocurrencies (Bitcoin, Ethereum, etc.)
✅ Stocks (Large cap with good liquidity)
🎓 Educational Value:
Beyond just signals, this indicator teaches you:
How institutional traders think
Where smart money places orders
Classic Wyckoff accumulation/distribution patterns
Multi-timeframe analysis techniques
⚡ Performance:
Lightning-fast calculations
No repainting
Real-time signal generation
Clean code, optimized for speed
🚀 Get Started:
Add to your favorite chart
Adjust HTF timeframe to match your trading style
Wait for high-quality signals
Trade with confidence
Remember: Quality beats quantity. This system prioritizes precision over frequency. You might see 2-5 signals per day on M30 - and that's exactly the point. Each signal is carefully filtered for maximum probability.
Ready to trade like institutions?
👉 Add this indicator to your chart now
👉 Configure your preferred HTF timeframe
👉 Start catching high-probability setups
👉 Trade smarter, not harder
Questions or feedback? Drop a comment below!
Found this useful? Hit that ⭐ button and share with fellow traders!
Happy Trading! 🚀📈
MA SMART Angle
### 📊 WHAT IS MA SMART ANGLE?
**MA SMART Angle** is an advanced momentum and trend detection indicator that analyzes the angles (slopes) of multiple moving averages to generate clear, non-repainting BUY and SELL signals.
**Original Concept Credit:** This indicator builds upon the "MA Angles" concept originally created by **JD** (also known as Duyck). The core angle calculation methodology and Jurik Moving Average (JMA) implementation by **Everget** are preserved from the original open-source work. The angle calculation formula was contributed by **KyJ**. This enhanced version is published with respect to the open-source nature of the original indicator.
Original indicator reference: "ma angles - JD" by Duyck
---
## 🎯 ORIGINALITY & VALUE PROPOSITION
### **What Makes This Different from the Original:**
While the original "MA Angles" by **JD** provided excellent angle visualization, it lacked actionable entry signals. **MA SMART Angle** addresses this by adding:
**1. Clear Entry/Exit Signals**
- Explicit BUY/SELL arrows based on angle crossovers, momentum confirmation, and MA alignment
- No guessing when to enter trades - the indicator tells you exactly when conditions align
**2. Non-Repainting Logic**
- All signals use confirmed historical data (shifted by 2 bars minimum)
- Critical for backtesting reliability and live trading confidence
- Original indicator could repaint signals on current bar
**3. Dual Signal System**
- **Simple Mode:** More frequent signals based on angle crossovers + momentum (for active traders)
- **Strict Mode:** Requires full multi-MA alignment + momentum confirmation (for conservative traders)
- Adaptable to different trading styles and risk tolerances
**4. Smart Signal Filtering**
- **Anti-spam cooldown:** Prevents duplicate signals within configurable bar count
- **No-trade zone detection:** Filters out low-conviction sideways markets automatically
- **Multi-timeframe MA alignment:** Ensures all moving averages agree on direction before signaling
**5. Enhanced Visualization**
- Large, clear BUY/SELL arrows with descriptive labels
- Color-coded backgrounds for market states (trending vs. ranging)
- Momentum histogram showing acceleration/deceleration in real-time
- Live status table displaying trend strength, angle value, momentum, and MA alignment
**6. Professional Alert System**
- Four distinct alert conditions: BUY Signal, SELL Signal, Strong BUY, Strong SELL
- Enables automated trade notifications and strategy integration
**7. Modified MA Periods**
- Original used EMA(27), EMA(83), EMA(278)
- Enhanced version uses faster EMA(3), EMA(8), EMA(13) for more responsive signals
- Better suited for modern volatile markets and shorter timeframes
---
## 📐 HOW IT WORKS - TECHNICAL EXPLANATION
### **Core Methodology:**
The indicator calculates angles (slopes) for five key moving averages:
- **JMA (Jurik Moving Average)** - Smooth, lag-reduced trend line (original implementation by **Everget**)
- **JMA Fast** - Responsive momentum indicator with higher power parameter
- **MA27 (EMA 3)** - Primary fast-moving average for signal generation
- **MA83 (EMA 8)** - Medium-term trend confirmation
- **MA278 (EMA 13)** - Slower trend filter
### **Angle Calculation Formula (by KyJ):**
```
angle = arctan((MA - MA ) / ATR(14)) × (180 / π)
```
**Why ATR normalization?**
- Makes angles comparable across different instruments (forex, stocks, crypto)
- Makes angles comparable across different timeframes
- Accounts for volatility - a 10-point move in different assets has different significance
**Angle Interpretation:**
- **> 15°** = Strong trend (momentum accelerating)
- **0° to 15°** = Weak trend (momentum present but moderate)
- **-2° to +2°** = No-trade zone (sideways/choppy market)
- **< -15°** = Strong downtrend
### **Signal Generation Logic:**
#### **BUY Signal Conditions:**
1. MA27 angle crosses above 0° (upward momentum initiates)
2. All three EMAs (3, 8, 13) pointing upward (trend alignment confirmed)
3. Momentum is positive for 2+ bars (acceleration, not deceleration)
4. Angle exceeds minimum threshold (not in no-trade zone)
5. Cooldown period passed (prevents signal spam)
#### **SELL Signal Conditions:**
1. MA27 angle crosses below 0° (downward momentum initiates)
2. All three EMAs pointing downward (downtrend alignment)
3. Momentum is negative for 2+ bars
4. Angle below negative threshold (not in no-trade zone)
5. Cooldown period passed
#### **Strong BUY+ / SELL+ Signals:**
Additional entry opportunities when JMA Fast crosses JMA Slow while maintaining strong directional angle - indicates momentum acceleration within established trend.
---
## 🔧 HOW TO USE
### **Recommended Settings by Trading Style:**
**Scalpers / Day Traders:**
- Signal Type: **Simple**
- Minimum Angle: **3-5°**
- Cooldown Bars: **3-5 bars**
- Timeframes: 1m, 5m, 15m
**Swing Traders:**
- Signal Type: **Strict**
- Minimum Angle: **7-10°**
- Cooldown Bars: **8-12 bars**
- Timeframes: 1H, 4H, Daily
**Position Traders:**
- Signal Type: **Strict**
- Minimum Angle: **10-15°**
- Cooldown Bars: **15-20 bars**
- Timeframes: Daily, Weekly
### **Parameter Descriptions:**
**1. Source** (default: OHLC4)
- Price data used for MA calculations
- OHLC4 provides smoothest angles
- Close is more responsive but noisier
**2. Threshold for No-Trade Zones** (default: 2°)
- Angles below this are considered sideways/ranging
- Increase for stricter filtering of choppy markets
- Decrease to allow signals in quieter trending periods
**3. Signal Type** (Simple vs. Strict)
- **Simple:** Angle crossover OR (trend + momentum)
- **Strict:** Angle crossover AND all MAs aligned AND momentum confirmed
- Start with Simple, switch to Strict if too many false signals
**4. Minimum Angle for Signal** (default: 5°)
- Only generate signals when angle exceeds this threshold
- Higher values = stronger trends required
- Lower values = more sensitive to momentum changes
**5. Cooldown Bars** (default: 5)
- Minimum bars between consecutive signals
- Prevents spam during volatile chop
- Scale with your timeframe (higher TF = more bars)
**6. Color Bars** (default: true)
- Colors chart bars based on signal state
- Green = bullish conditions, Red = bearish conditions
- Can disable if you prefer clean price bars
**7. Background Colors**
- **Yellow background** = No-trade zone (low angle, ranging market)
- **Green flash** = BUY signal generated
- **Red flash** = SELL signal generated
- All customizable or can be disabled
---
## 📊 INTERPRETING THE INDICATOR
### **Visual Elements:**
**Main Chart Window:**
- **Thick Lime/Fuchsia Line** = MA27 angle (primary signal line)
- **Medium Green/Red Line** = MA83 angle (trend confirmation)
- **Thin Green/Red Line** = MA278 angle (slow trend filter)
- **Aqua/Orange Line** = JMA Fast (momentum detector)
- **Green/Red Area** = JMA slope (overall trend context)
- **Blue/Purple Histogram** = Momentum (angle acceleration/deceleration)
**Signal Arrows:**
- **Large Green ▲ "BUY"** = Primary buy signal (all conditions met)
- **Small Green ▲ "BUY+"** = Strong momentum buy (JMA fast cross)
- **Large Red ▼ "SELL"** = Primary sell signal (all conditions met)
- **Small Red ▼ "SELL+"** = Strong momentum sell (JMA fast cross)
**Status Table (Top Right):**
- **Angle:** Current MA27 angle in degrees
- **Trend:** Classification (STRONG UP/DOWN, UP/DOWN, FLAT)
- **Momentum:** Acceleration state (ACCEL UP/DN, Up/Down)
- **MAs:** Alignment status (ALL UP/DOWN, Mixed)
- **Zone:** Trading zone status (ACTIVE vs. NO TRADE)
- **Last:** Bars since last signal
### **Trading Strategies:**
**Strategy 1: Pure Signal Following**
- Enter LONG on BUY signal
- Exit on SELL signal
- Use stop-loss at recent swing low/high
- Works best on trending instruments
**Strategy 2: Confirmation with Price Action**
- Wait for BUY signal + bullish candlestick pattern
- Wait for SELL signal + bearish candlestick pattern
- Increases win rate by filtering premature signals
- Recommended for beginners
**Strategy 3: Momentum Acceleration**
- Use BUY+/SELL+ signals for adding to positions
- Only take these in direction of primary signal
- Scalp quick moves during momentum spikes
- For experienced traders
**Strategy 4: Mean Reversion in No-Trade Zones**
- When status shows "NO TRADE", fade extremes
- Wait for angle to exit no-trade zone for reversal
- Contrarian approach for range-bound markets
- Requires tight stops
---
## ⚠️ LIMITATIONS & DISCLAIMERS
**What This Indicator DOES:**
✅ Measures momentum direction and strength via angle analysis
✅ Generates signals when multiple conditions align
✅ Filters out low-conviction sideways markets
✅ Provides visual clarity on trend state
**What This Indicator DOES NOT:**
❌ Predict future price movements with certainty
❌ Guarantee profitable trades (no indicator can)
❌ Work equally well on all instruments/timeframes
❌ Replace proper risk management and position sizing
**Known Limitations:**
- **Lagging Nature:** Like all moving averages, signals occur after momentum begins
- **Whipsaw Risk:** Can generate false signals in volatile, directionless markets
- **Optimization Required:** Parameters need adjustment for different assets
- **Not a Complete System:** Should be combined with risk management, position sizing, and other analysis
**Best Performance Conditions:**
- Strong trending markets (crypto bull runs, stock breakouts)
- Liquid instruments (major forex pairs, large-cap stocks)
- Appropriate timeframe selection (match to trading style)
- Used alongside support/resistance and volume analysis
---
## 🔔 ALERT SETUP
The indicator includes four alert conditions:
**1. BUY SIGNAL**
- Message: "MA SMART Angle: BUY SIGNAL! Angle crossed up with momentum"
- Use for: Primary long entries
**2. SELL SIGNAL**
- Message: "MA SMART Angle: SELL SIGNAL! Angle crossed down with momentum"
- Use for: Primary short entries or long exits
**3. Strong BUY**
- Message: "MA SMART Angle: Strong BUY momentum - JMA fast crossed up"
- Use for: Adding to longs or aggressive entries
**4. Strong SELL**
- Message: "MA SMART Angle: Strong SELL momentum - JMA fast crossed down"
- Use for: Adding to shorts or aggressive exits
**Setting Up Alerts:**
1. Right-click indicator → "Add Alert on MA SMART Angle"
2. Select desired condition from dropdown
3. Choose notification method (popup, email, webhook)
4. Set alert expiration (typically "Once Per Bar Close")
---
## 📚 EDUCATIONAL VALUE
This indicator serves as an excellent learning tool for understanding:
**1. Angle-Based Momentum Analysis**
- Traditional indicators show MA crossovers
- This shows the *rate of change* (velocity) of MAs
- Teaches traders to think in terms of momentum acceleration
**2. Multi-Timeframe Confirmation**
- Shows how fast, medium, and slow MAs interact
- Demonstrates importance of trend alignment
- Helps develop patience for high-probability setups
**3. Signal Quality vs. Quantity Tradeoff**
- Simple mode = more signals, more noise
- Strict mode = fewer signals, higher quality
- Teaches discretionary filtering skills
**4. Market State Recognition**
- Visual distinction between trending and ranging markets
- Helps traders avoid trading choppy conditions
- Develops "market context" awareness
---
## 🔄 DIFFERENCES FROM OTHER MA INDICATORS
**vs. Traditional MA Crossovers:**
- Measures momentum (angle) rather than just price crossing MA
- Provides earlier signals as angles change before price crosses
- Filters better for sideways markets using no-trade zones
**vs. MACD:**
- Uses multiple MAs instead of just two
- ATR normalization makes it universal across instruments
- Visual angle representation more intuitive than histogram
**vs. Supertrend:**
- Not based on ATR bands but on MA slope analysis
- Provides graduated strength indication (not just binary trend)
- Less prone to whipsaw in low volatility
**vs. Original "MA Angles" by JD:**
- Adds explicit entry/exit signals (original had none)
- Implements no-repaint logic for reliability
- Includes signal filtering and quality controls
- Provides dual signal systems (Simple/Strict)
- Enhanced visualization and status monitoring
- Uses faster MA periods (3/8/13 vs 27/83/278) for modern markets
---
## 📖 CODE STRUCTURE (for Pine Script learners)
This indicator demonstrates:
**Advanced Pine Script Techniques:**
- Custom function implementation (JMA, angle calculation)
- Var declarations for stateful tracking
- Table creation for HUD display
- Multi-condition signal logic
- Alert system integration
- Proper use of historical references for no-repaint
**Code Organization:**
- Modular function definitions (JMA, angle)
- Clear separation of concerns (inputs, calculations, plotting, alerts)
- Extensive commenting for maintainability
- Best practices for Pine Script v5
**Learning Resources:**
- Study the JMA function to understand adaptive smoothing
- Examine angle calculation for ATR normalization technique
- Review signal logic for multi-condition confirmation patterns
- Analyze anti-spam filtering for state management
The code is open-source - feel free to study, modify, and improve upon it!
---
## 🙏 CREDITS & ATTRIBUTION
**Original Concepts:**
- **"ma angles - JD" by JD (Duyck)** - Core angle calculation methodology and indicator concept
Original open-source indicator on TradingView Community Scripts
- **JMA (Jurik Moving Average) implementation by Everget** - Smooth, low-lag moving average function
Acknowledged in original JD indicator code
- **Angle Calculation formula by KyJ** - Mathematical formula for converting MA slope to degrees using ATR normalization
Acknowledged in original JD indicator code comments
**Enhancements in This Version:**
- Signal generation logic - Original implementation for this indicator
- No-repaint confirmation system - Original implementation
- Dual signal modes (Simple/Strict) - Original implementation
- Visual enhancements and status table - Original implementation
- Alert system and signal filtering - Original implementation
- Modified MA periods (3/8/13 instead of 27/83/278) - Optimization for modern markets
**Open Source Philosophy:**
This indicator follows the open-source spirit of TradingView and the Pine Script community. The original "ma angles - JD" by JD (Duyck) was published as open-source, enabling this enhanced version. Similarly, this code is published as open-source to allow further community improvements.
---
## ⚡ QUICK START GUIDE
**For New Users:**
1. Add indicator to chart
2. Start with default settings (Simple mode)
3. Wait for BUY signal (green arrow)
4. Observe how price behaves after signal
5. Check status table to understand market state
6. Adjust parameters based on your instrument/timeframe
**For Experienced Traders:**
1. Switch to Strict mode for higher quality signals
2. Increase cooldown bars to reduce frequency
3. Raise minimum angle threshold for stronger trends
4. Combine with your existing strategy for confirmation
5. Set up alerts for desired signal types
6. Backtest on your preferred instruments
---
## 🎓 RECOMMENDED COMBINATIONS
**Works Well With:**
- **Volume Analysis:** Confirm signals with volume spikes
- **Support/Resistance:** Take signals near key levels
- **RSI/Stochastic:** Avoid overbought/oversold extremes
- **ATR:** Size positions based on volatility
- **Price Action:** Wait for candlestick confirmation
**Complementary Indicators:**
- Order Flow / Footprint (for institutional confirmation)
- Volume Profile (for identifying value areas)
- VWAP (for intraday mean reversion reference)
- Fibonacci Retracements (for target setting)
---
## 📈 PERFORMANCE EXPECTATIONS
**Realistic Win Rates:**
- Simple Mode: 45-55% (higher frequency, moderate accuracy)
- Strict Mode: 55-65% (lower frequency, higher accuracy)
- Combined with price action: 60-70%
**Best Asset Classes:**
1. **Cryptocurrencies** (strong trends, clear signals)
2. **Forex Major Pairs** (smooth price action, good angles)
3. **Large-Cap Stocks** (trending behavior, liquid)
4. **Index Futures** (trending instruments)
**Challenging Conditions:**
- Low volatility consolidation periods
- News-driven erratic movements
- Thin/illiquid instruments
- Counter-trending markets
---
## 🛡️ RISK DISCLAIMER
**IMPORTANT LEGAL NOTICE:**
This indicator is for **educational and informational purposes only**. It is **NOT financial advice** and does not constitute a recommendation to buy or sell any financial instrument.
**Trading Risks:**
- Trading carries substantial risk of loss
- Past performance does not guarantee future results
- No indicator can predict market movements with certainty
- You can lose more than your initial investment (especially with leverage)
**User Responsibilities:**
- Conduct your own research and due diligence
- Understand the instruments you trade
- Never risk more than you can afford to lose
- Use proper position sizing and risk management
- Consider consulting a licensed financial advisor
**Indicator Limitations:**
- Signals are based on historical data only
- No guarantee of accuracy or profitability
- Parameters must be optimized for your specific use case
- Results vary significantly by market conditions
By using this indicator, you acknowledge and accept all trading risks. The author is not responsible for any financial losses incurred through use of this indicator.
---
## 📧 SUPPORT & FEEDBACK
**Found a bug?** Please report it in the comments with:
- Chart symbol and timeframe
- Parameter settings used
- Description of unexpected behavior
- Screenshot if possible
**Have suggestions?** Share your ideas for improvements!
**Enjoying the indicator?** Leave a like and follow for updates!
Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio
A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent.
Table of contents
What the script does at a glance
Momentum, what it is, how to know if it is present
Volatility targeting, why and how it is done here
Portfolio construction modes: Long Only, Short Only, Delta Neutral
Regime filter and when the strategy goes to cash
Transaction cost modelling in this script
Backtest metrics and definitions
Performance attribution chart
Monte Carlo simulation
Scatter plot analysis modes
Asset allocation pie chart
Inputs, presets, and deployment checklist
Suggested workflow
1) What the script does at a glance
Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility.
Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter.
Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite.
Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views.
2) Momentum: definition, detection, and validation
Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints.
Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization.
How to know momentum is present .
Leaders and laggards persist across adjacent windows rather than flipping every bar.
Spread between average momentum of leaders and laggards is materially positive in sample.
Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak.
Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries.
Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime.
3) Volatility targeting: purpose and implementation here
Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk.
Computation in this script (per asset, rolling):
Return series ret = log(price/price ).
Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays).
Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0).
This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero.
Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations.
Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure.
4) Portfolio construction modes
Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties.
Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult.
Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs.
Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3).
Notes .
The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage.
If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta.
5) Regime filter
A fast EMA(12) vs EMA(21) filter gates exposure.
Long Only active when EMA12 > EMA21. Otherwise the book is set to cash.
Short Only active when EMA12 < EMA21. Otherwise cash.
Delta Neutral is always active.
This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar.
6) Transaction cost modelling
There are two cost touchpoints in the script.
Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar.
Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots.
Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders.
Practical advice .
If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue.
For crypto perp microcaps, stress higher cost assumptions and add slippage buffers.
If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag.
7) Backtest metrics and definitions
The script computes a standard set of portfolio statistics once the start date is reached.
Net Profit percent over the full test.
Max Drawdown percent, tracked from running peaks.
Annualized Mean and Stdev using the chosen trading day count.
Variance is the square of annualized stdev.
Sharpe uses daily mean adjusted by risk-free rate and annualized.
Sortino uses downside stdev only.
Omega ratio of sum of gains to sum of losses.
Gain-to-Pain total gains divided by total losses absolute.
CAGR compounded annual growth from start date to now.
Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM.
Skewness of daily returns.
VaR 95 linear-interpolated 5th percentile of daily returns.
CVaR average of the worst 5 percent of daily returns.
Benchmark Buy-and-Hold equity path for comparison.
8) Performance attribution
Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels.
9) Monte Carlo simulation
The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed.
Normal uses Box-Muller with your seed. Good for quick, smooth envelopes.
Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption.
Bands show 10th, 25th, 75th, 90th percentiles and the path mean.
10) Scatter plot analysis
Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels.
Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite.
Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations.
Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta.
Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk.
11) Asset allocation pie chart
Builds a wheel of current allocations.
Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent.
Short Only, weights show the short book as positive slices that sum to 100 percent.
Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional.
Labels can show asset, percent, and current leverage.
12) Inputs and quick presets
Core
Portfolio Strategy . Long Only, Short Only, Delta Neutral.
Initial Capital . For equity scaling in the panel.
Trading Days/Year . 252 for stocks, 365 for crypto.
Target Volatility . Annualized, drives volMult.
Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above.
Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier.
Start Date . Ensure every symbol has data back to this date to avoid bias.
Benchmark . Used for alpha, beta, and B&H line.
Diagnostics
Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill.
Attribution panel. Toggle winners only to focus on what matters.
Monte Carlo mode with Normal or Bootstrap and confidence bands.
Scatter plot type and styling, labels, and portfolio star.
Pie chart and labels for current allocation.
Presets
Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands.
Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward.
Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta.
13) Suggested workflow
Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees.
Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe.
Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity.
Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear.
Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it.
Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence.
Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier.
Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs.
Important implementation details mapped to code
Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting.
Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult.
Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively.
Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate.
Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison.
Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures.
Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds.
Final notes
Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call.
Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it.
Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
TrendDetectorLibLibrary "TrendDetector_Lib"
method formatTF(timeframe)
Namespace types: series string, simple string, input string, const string
Parameters:
timeframe (string) : (string) The timeframe to convert (e.g., "15", "60", "240").
Returns: (string) The formatted timeframe (e.g., "15M", "1H", "4H").
f_ma(type, src, len)
Computes a Moving Average value based on type and length.
Parameters:
type (simple string) : (string) One of: "SMA", "EMA", "RMA", "WMA", "VWMA".
src (float) : (series float) Source series for MA (e.g., close).
len (simple int) : (simple int) Length of the MA.
Returns: (float) The computed MA series.
render(tbl, trendDetectorSwitch, frameColor, frameWidth, borderColor, borderWidth, textColor, ma1ShowTrendData, ma1Timeframe, ma1Value, ma2ShowTrendData, ma2Timeframe, ma2Value, ma3ShowTrendData, ma3Timeframe, ma3Value)
Fills the provided table with Trend Detector contents.
@desc This renderer does NOT plot and does NOT create tables; call from indicator after your table exists.
Parameters:
tbl (table) : (table) Existing table to render into.
trendDetectorSwitch (bool) : (bool) Master toggle to draw the table content.
frameColor (color) : (color) Table frame color.
frameWidth (int) : (int) Table frame width (0–5).
borderColor (color) : (color) Table border color.
borderWidth (int) : (int) Table border width (0–5).
textColor (color) : (color) Table text color.
ma1ShowTrendData (bool) : (bool) Show MA #1 in table.
ma1Timeframe (simple string) : (string) MA #1 timeframe.
ma1Value (float)
ma2ShowTrendData (bool) : (bool) Show MA #2 in table.
ma2Timeframe (simple string) : (string) MA #2 timeframe.
ma2Value (float)
ma3ShowTrendData (bool) : (bool) Show MA #3 in table.
ma3Timeframe (simple string) : (string) MA #3 timeframe.
ma3Value (float)
Algorithm Predator - ML-liteAlgorithm Predator - ML-lite
This indicator combines four specialized trading agents with an adaptive multi-armed bandit selection system to identify high-probability trade setups. It is designed for swing and intraday traders who want systematic signal generation based on institutional order flow patterns , momentum exhaustion , liquidity dynamics , and statistical mean reversion .
Core Architecture
Why These Components Are Combined:
The script addresses a fundamental challenge in algorithmic trading: no single detection method works consistently across all market conditions. By deploying four independent agents and using reinforcement learning algorithms to select or blend their outputs, the system adapts to changing market regimes without manual intervention.
The Four Trading Agents
1. Spoofing Detector Agent 🎭
Detects iceberg orders through persistent volume at similar price levels over 5 bars
Identifies spoofing patterns via asymmetric wick analysis (wicks exceeding 60% of bar range with volume >1.8× average)
Monitors order clustering using simplified Hawkes process intensity tracking (exponential decay model)
Signal Logic: Contrarian—fades false breakouts caused by institutional manipulation
Best Markets: Consolidations, institutional trading windows, low-liquidity hours
2. Exhaustion Detector Agent ⚡
Calculates RSI divergence between price movement and momentum indicator over 5-bar window
Detects VWAP exhaustion (price at 2σ bands with declining volume)
Uses VPIN reversals (volume-based toxic flow dissipation) to identify momentum failure
Signal Logic: Counter-trend—enters when momentum extreme shows weakness
Best Markets: Trending markets reaching climax points, over-extended moves
3. Liquidity Void Detector Agent 💧
Measures Bollinger Band squeeze (width <60% of 50-period average)
Identifies stop hunts via 20-bar high/low penetration with immediate reversal and volume spike
Detects hidden liquidity absorption (volume >2× average with range <0.3× ATR)
Signal Logic: Breakout anticipation—enters after liquidity grab but before main move
Best Markets: Range-bound pre-breakout, volatility compression zones
4. Mean Reversion Agent 📊
Calculates price z-scores relative to 50-period SMA and standard deviation (triggers at ±2σ)
Implements Ornstein-Uhlenbeck process scoring (mean-reverting stochastic model)
Uses entropy analysis to detect algorithmic trading patterns (low entropy <0.25 = high predictability)
Signal Logic: Statistical reversion—enters when price deviates significantly from statistical equilibrium
Best Markets: Range-bound, low-volatility, algorithmically-dominated instruments
Adaptive Selection: Multi-Armed Bandit System
The script implements four reinforcement learning algorithms to dynamically select or blend agents based on performance:
Thompson Sampling (Default - Recommended):
Uses Bayesian inference with beta distributions (tracks alpha/beta parameters per agent)
Balances exploration (trying underused agents) vs. exploitation (using proven winners)
Each agent's win/loss history informs its selection probability
Lite Approximation: Uses pseudo-random sampling from price/volume noise instead of true random number generation
UCB1 (Upper Confidence Bound):
Calculates confidence intervals using: average_reward + sqrt(2 × ln(total_pulls) / agent_pulls)
Deterministic algorithm favoring agents with high uncertainty (potential upside)
More conservative than Thompson Sampling
Epsilon-Greedy:
Exploits best-performing agent (1-ε)% of the time
Explores randomly ε% of the time (default 10%, configurable 1-50%)
Simple, transparent, easily tuned via epsilon parameter
Gradient Bandit:
Uses softmax probability distribution over agent preference weights
Updates weights via gradient ascent based on rewards
Best for Blend mode where all agents contribute
Selection Modes:
Switch Mode: Uses only the selected agent's signal (clean, decisive)
Blend Mode: Combines all agents using exponentially weighted confidence scores controlled by temperature parameter (smooth, diversified)
Lock Agent Feature:
Optional manual override to force one specific agent
Useful after identifying which agent dominates your specific instrument
Only applies in Switch mode
Four choices: Spoofing Detector, Exhaustion Detector, Liquidity Void, Mean Reversion
Memory System
Dual-Layer Architecture:
Short-Term Memory: Stores last 20 trade outcomes per agent (configurable 10-50)
Long-Term Memory: Stores episode averages when short-term reaches transfer threshold (configurable 5-20 bars)
Memory Boost Mechanism: Recent performance modulates agent scores by up to ±20%
Episode Transfer: When an agent accumulates sufficient results, averages are condensed into long-term storage
Persistence: Manual restoration of learned parameters via input fields (alpha, beta, weights, microstructure thresholds)
How Memory Works:
Agent generates signal → outcome tracked after 8 bars (performance horizon)
Result stored in short-term memory (win = 1.0, loss = 0.0)
Short-term average influences agent's future scores (positive feedback loop)
After threshold met (default 10 results), episode averaged into long-term storage
Long-term patterns (weighted 30%) + short-term patterns (weighted 70%) = total memory boost
Market Microstructure Analysis
These advanced metrics quantify institutional order flow dynamics:
Order Flow Toxicity (Simplified VPIN):
Measures buy/sell volume imbalance over 20 bars: |buy_vol - sell_vol| / (buy_vol + sell_vol)
Detects informed trading activity (institutional players with non-public information)
Values >0.4 indicate "toxic flow" (informed traders active)
Lite Approximation: Uses simple open/close heuristic instead of tick-by-tick trade classification
Price Impact Analysis (Simplified Kyle's Lambda):
Measures market impact efficiency: |price_change_10| / sqrt(volume_sum_10)
Low values = large orders with minimal price impact ( stealth accumulation )
High values = retail-dominated moves with high slippage
Lite Approximation: Uses simplified denominator instead of regression-based signed order flow
Market Randomness (Entropy Analysis):
Counts unique price changes over 20 bars / 20
Measures market predictability
High entropy (>0.6) = human-driven, chaotic price action
Low entropy (<0.25) = algorithmic trading dominance (predictable patterns)
Lite Approximation: Simple ratio instead of true Shannon entropy H(X) = -Σ p(x)·log₂(p(x))
Order Clustering (Simplified Hawkes Process):
Tracks self-exciting event intensity (coordinated order activity)
Decays at 0.9× per bar, spikes +1.0 when volume >1.5× average
High intensity (>0.7) indicates clustering (potential spoofing/accumulation)
Lite Approximation: Simple exponential decay instead of full λ(t) = μ + Σ α·exp(-β(t-tᵢ)) with MLE
Signal Generation Process
Multi-Stage Validation:
Stage 1: Agent Scoring
Each agent calculates internal score based on its detection criteria
Scores must exceed agent-specific threshold (adjusted by sensitivity multiplier)
Agent outputs: Signal direction (+1/-1/0) and Confidence level (0.0-1.0)
Stage 2: Memory Boost
Agent scores multiplied by memory boost factor (0.8-1.2 based on recent performance)
Successful agents get amplified, failing agents get dampened
Stage 3: Bandit Selection/Blending
If Adaptive Mode ON:
Switch: Bandit selects single best agent, uses only its signal
Blend: All agents combined using softmax-weighted confidence scores
If Adaptive Mode OFF:
Traditional consensus voting with confidence-squared weighting
Signal fires when consensus exceeds threshold (default 70%)
Stage 4: Confirmation Filter
Raw signal must repeat for consecutive bars (default 3, configurable 2-4)
Minimum confidence threshold: 0.25 (25%) enforced regardless of mode
Trend alignment check: Long signals require trend_score ≥ -2, Short signals require trend_score ≤ 2
Stage 5: Cooldown Enforcement
Minimum bars between signals (default 10, configurable 5-15)
Prevents over-trading during choppy conditions
Stage 6: Performance Tracking
After 8 bars (performance horizon), signal outcome evaluated
Win = price moved in signal direction, Loss = price moved against
Results fed back into memory and bandit statistics
Trading Modes (Presets)
Pre-configured parameter sets:
Conservative: 85% consensus, 4 confirmations, 15-bar cooldown
Expected: 60-70% win rate, 3-8 signals/week
Best for: Swing trading, capital preservation, beginners
Balanced: 70% consensus, 3 confirmations, 10-bar cooldown
Expected: 55-65% win rate, 8-15 signals/week
Best for: Day trading, most traders, general use
Aggressive: 60% consensus, 2 confirmations, 5-bar cooldown
Expected: 50-58% win rate, 15-30 signals/week
Best for: Scalping, high-frequency trading, active management
Elite: 75% consensus, 3 confirmations, 12-bar cooldown
Expected: 58-68% win rate, 5-12 signals/week
Best for: Selective trading, high-conviction setups
Adaptive: 65% consensus, 2 confirmations, 8-bar cooldown
Expected: Varies based on learning
Best for: Experienced users leveraging bandit system
How to Use
1. Initial Setup (5 Minutes):
Select Trading Mode matching your style (start with Balanced)
Enable Adaptive Learning (recommended for automatic agent selection)
Choose Thompson Sampling algorithm (best all-around performance)
Keep Microstructure Metrics enabled for liquid instruments (>100k daily volume)
2. Agent Tuning (Optional):
Adjust Agent Sensitivity multipliers (0.5-2.0):
<0.8 = Highly selective (fewer signals, higher quality)
0.9-1.2 = Balanced (recommended starting point)
1.3 = Aggressive (more signals, lower individual quality)
Monitor dashboard for 20-30 signals to identify dominant agent
If one agent consistently outperforms, consider using Lock Agent feature
3. Bandit Configuration (Advanced):
Blend Temperature (0.1-2.0):
0.3 = Sharp decisions (best agent dominates)
0.5 = Balanced (default)
1.0+ = Smooth (equal weighting, democratic)
Memory Decay (0.8-0.99):
0.90 = Fast adaptation (volatile markets)
0.95 = Balanced (most instruments)
0.97+ = Long memory (stable trends)
4. Signal Interpretation:
Green triangle (▲): Long signal confirmed
Red triangle (▼): Short signal confirmed
Dashboard shows:
Active agent (highlighted row with ► marker)
Win rate per agent (green >60%, yellow 40-60%, red <40%)
Confidence bars (█████ = maximum confidence)
Memory size (short-term buffer count)
Colored zones display:
Entry level (current close)
Stop-loss (1.5× ATR)
Take-profit 1 (2.0× ATR)
Take-profit 2 (3.5× ATR)
5. Risk Management:
Never risk >1-2% per signal (use ATR-based stops)
Signals are entry triggers, not complete strategies
Combine with your own market context analysis
Consider fundamental catalysts and news events
Use "Confirming" status to prepare entries (not to enter early)
6. Memory Persistence (Optional):
After 50-100 trades, check Memory Export Panel
Record displayed alpha/beta/weight values for each agent
Record VPIN and Kyle threshold values
Enable "Restore From Memory" and input saved values to continue learning
Useful when switching timeframes or restarting indicator
Visual Components
On-Chart Elements:
Spectral Layers: EMA8 ± 0.5 ATR bands (dynamic support/resistance, colored by trend)
Energy Radiance: Multi-layer glow boxes at signal points (intensity scales with confidence, configurable 1-5 layers)
Probability Cones: Projected price paths with uncertainty wedges (15-bar projection, width = confidence × ATR)
Connection Lines: Links sequential signals (solid = same direction continuation, dotted = reversal)
Kill Zones: Risk/reward boxes showing entry, stop-loss, and dual take-profit targets
Signal Markers: Triangle up/down at validated entry points
Dashboard (Configurable Position & Size):
Regime Indicator: 4-level trend classification (Strong Bull/Bear, Weak Bull/Bear)
Mode Status: Shows active system (Adaptive Blend, Locked Agent, or Consensus)
Agent Performance Table: Real-time win%, confidence, and memory stats
Order Flow Metrics: Toxicity and impact indicators (when microstructure enabled)
Signal Status: Current state (Long/Short/Confirming/Waiting) with confirmation progress
Memory Panel (Configurable Position & Size):
Live Parameter Export: Alpha, beta, and weight values per agent
Adaptive Thresholds: Current VPIN sensitivity and Kyle threshold
Save Reminder: Visual indicator if parameters should be recorded
What Makes This Original
This script's originality lies in three key innovations:
1. Genuine Meta-Learning Framework:
Unlike traditional indicator mashups that simply display multiple signals, this implements authentic reinforcement learning (multi-armed bandits) to learn which detection method works best in current conditions. The Thompson Sampling implementation with beta distribution tracking (alpha for successes, beta for failures) is statistically rigorous and adapts continuously. This is not post-hoc optimization—it's real-time learning.
2. Episodic Memory Architecture with Transfer Learning:
The dual-layer memory system mimics human learning patterns:
Short-term memory captures recent performance (recency bias)
Long-term memory preserves historical patterns (experience)
Automatic transfer mechanism consolidates knowledge
Memory boost creates positive feedback loops (successful strategies become stronger)
This architecture allows the system to adapt without retraining , unlike static ML models that require batch updates.
3. Institutional Microstructure Integration:
Combines retail-focused technical analysis (RSI, Bollinger Bands, VWAP) with institutional-grade microstructure metrics (VPIN, Kyle's Lambda, Hawkes processes) typically found in academic finance literature and professional trading systems, not standard retail platforms. While simplified for Pine Script constraints, these metrics provide insight into informed vs. uninformed trading , a dimension entirely absent from traditional technical analysis.
Mashup Justification:
The four agents are combined specifically for risk diversification across failure modes:
Spoofing Detector: Prevents false breakout losses from manipulation
Exhaustion Detector: Prevents chasing extended trends into reversals
Liquidity Void: Exploits volatility compression (different regime than trending)
Mean Reversion: Provides mathematical anchoring when patterns fail
The bandit system ensures the optimal tool is automatically selected for each market situation, rather than requiring manual interpretation of conflicting signals.
Why "ML-lite"? Simplifications and Approximations
This is the "lite" version due to necessary simplifications for Pine Script execution:
1. Simplified VPIN Calculation:
Academic Implementation: True VPIN uses volume bucketing (fixed-volume bars) and tick-by-tick buy/sell classification via Lee-Ready algorithm or exchange-provided trade direction flags
This Implementation: 20-bar rolling window with simple open/close heuristic (close > open = buy volume)
Impact: May misclassify volume during ranging/choppy markets; works best in directional moves
2. Pseudo-Random Sampling:
Academic Implementation: Thompson Sampling requires true random number generation from beta distributions using inverse transform sampling or acceptance-rejection methods
This Implementation: Deterministic pseudo-randomness derived from price and volume decimal digits: (close × 100 - floor(close × 100)) + (volume % 100) / 100
Impact: Not cryptographically random; may have subtle biases in specific price ranges; provides sufficient variation for agent selection
3. Hawkes Process Approximation:
Academic Implementation: Full Hawkes process uses maximum likelihood estimation with exponential kernels: λ(t) = μ + Σ α·exp(-β(t-tᵢ)) fitted via iterative optimization
This Implementation: Simple exponential decay (0.9 multiplier) with binary event triggers (volume spike = event)
Impact: Captures self-exciting property but lacks parameter optimization; fixed decay rate may not suit all instruments
4. Kyle's Lambda Simplification:
Academic Implementation: Estimated via regression of price impact on signed order flow over multiple time intervals: Δp = λ × Δv + ε
This Implementation: Simplified ratio: price_change / sqrt(volume_sum) without proper signed order flow or regression
Impact: Provides directional indicator of impact but not true market depth measurement; no statistical confidence intervals
5. Entropy Calculation:
Academic Implementation: True Shannon entropy requires probability distribution: H(X) = -Σ p(x)·log₂(p(x)) where p(x) is probability of each price change magnitude
This Implementation: Simple ratio of unique price changes to total observations (variety measure)
Impact: Measures diversity but not true information entropy with probability weighting; less sensitive to distribution shape
6. Memory System Constraints:
Full ML Implementation: Neural networks with backpropagation, experience replay buffers (storing state-action-reward tuples), gradient descent optimization, and eligibility traces
This Implementation: Fixed-size array queues with simple averaging; no gradient-based learning, no state representation beyond raw scores
Impact: Cannot learn complex non-linear patterns; limited to linear performance tracking
7. Limited Feature Engineering:
Advanced Implementation: Dozens of engineered features, polynomial interactions (x², x³), dimensionality reduction (PCA, autoencoders), feature selection algorithms
This Implementation: Raw agent scores and basic market metrics (RSI, ATR, volume ratio); minimal transformation
Impact: May miss subtle cross-feature interactions; relies on agent-level intelligence rather than feature combinations
8. Single-Instrument Data:
Full Implementation: Multi-asset correlation analysis (sector ETFs, currency pairs, volatility indices like VIX), lead-lag relationships, risk-on/risk-off regimes
This Implementation: Only OHLCV data from displayed instrument
Impact: Cannot incorporate broader market context; vulnerable to correlated moves across assets
9. Fixed Performance Horizon:
Full Implementation: Adaptive horizon based on trade duration, volatility regime, or profit target achievement
This Implementation: Fixed 8-bar evaluation window
Impact: May evaluate too early in slow markets or too late in fast markets; one-size-fits-all approach
Performance Impact Summary:
These simplifications make the script:
✅ Faster: Executes in milliseconds vs. seconds (or minutes) for full academic implementations
✅ More Accessible: Runs on any TradingView plan without external data feeds, APIs, or compute servers
✅ More Transparent: All calculations visible in Pine Script (no black-box compiled models)
✅ Lower Resource Usage: <500 bars lookback, minimal memory footprint
⚠️ Less Precise: Approximations may reduce statistical edge by 5-15% vs. academic implementations
⚠️ Limited Scope: Cannot capture tick-level dynamics, multi-order-book interactions, or cross-asset flows
⚠️ Fixed Parameters: Some thresholds hardcoded rather than dynamically optimized
When to Upgrade to Full Implementation:
Consider professional Python/C++ versions with institutional data feeds if:
Trading with >$100K capital where precision differences materially impact returns
Operating in microsecond-competitive environments (HFT, market making)
Requiring regulatory-grade audit trails and reproducibility
Backtesting with tick-level precision for strategy validation
Need true real-time adaptation with neural network-based learning
For retail swing/day trading and position management, these approximations provide sufficient signal quality while maintaining usability, transparency, and accessibility. The core logic—multi-agent detection with adaptive selection—remains intact.
Technical Notes
All calculations use standard Pine Script built-in functions ( ta.ema, ta.atr, ta.rsi, ta.bb, ta.sma, ta.stdev, ta.vwap )
VPIN and Kyle's Lambda use simplified formulas optimized for OHLCV data (see "Lite" section above)
Thompson Sampling uses pseudo-random noise from price/volume decimal digits for beta distribution sampling
No repainting: All calculations use confirmed bar data (no forward-looking)
Maximum lookback: 500 bars (set via max_bars_back parameter)
Performance evaluation: 8-bar forward-looking window for reward calculation (clearly disclosed)
Confidence threshold: Minimum 0.25 (25%) enforced on all signals
Memory arrays: Dynamic sizing with FIFO queue management
Limitations and Disclaimers
Not Predictive: This indicator identifies patterns in historical data. It cannot predict future price movements with certainty.
Requires Human Judgment: Signals are entry triggers, not complete trading strategies. Must be confirmed with your own analysis, risk management rules, and market context.
Learning Period Required: The adaptive system requires 50-100 bars minimum to build statistically meaningful performance data for bandit algorithms.
Overfitting Risk: Restoring memory parameters from one market regime to a drastically different regime (e.g., low volatility to high volatility) may cause poor initial performance until system re-adapts.
Approximation Limitations: Simplified calculations (see "Lite" section) may underperform academic implementations by 5-15% in highly efficient markets.
No Guarantee of Profit: Past performance, whether backtested or live-traded, does not guarantee future performance. All trading involves risk of loss.
Forward-Looking Bias: Performance evaluation uses 8-bar forward window—this creates slight look-ahead for learning (though not for signals). Real-time performance may differ from indicator's internal statistics.
Single-Instrument Limitation: Does not account for correlations with related assets or broader market regime changes.
Recommended Settings
Timeframe: 15-minute to 4-hour charts (sufficient volatility for ATR-based stops; adequate bar volume for learning)
Assets: Liquid instruments with >100k daily volume (forex majors, large-cap stocks, BTC/ETH, major indices)
Not Recommended: Illiquid small-caps, penny stocks, low-volume altcoins (microstructure metrics unreliable)
Complementary Tools: Volume profile, order book depth, market breadth indicators, fundamental catalysts
Position Sizing: Risk no more than 1-2% of capital per signal using ATR-based stop-loss
Signal Filtering: Consider external confluence (support/resistance, trendlines, round numbers, session opens)
Start With: Balanced mode, Thompson Sampling, Blend mode, default agent sensitivities (1.0)
After 30+ Signals: Review agent win rates, consider increasing sensitivity of top performers or locking to dominant agent
Alert Configuration
The script includes built-in alert conditions:
Long Signal: Fires when validated long entry confirmed
Short Signal: Fires when validated short entry confirmed
Alerts fire once per bar (after confirmation requirements met)
Set alert to "Once Per Bar Close" for reliability
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Moving Average Ribbon (10x, per-MA timeframe)A flexible moving‑average ribbon that plots up to 10 MAs, each with its own type, length, source, color, and independent timeframe selector for true multi‑timeframe analysis without repainting on higher‑timeframe pulls.
What it does
Plots ten moving averages with selectable types: SMA, EMA, SMMA (RMA), WMA, and VWMA.
Allows per‑line timeframe inputs (e.g., 5, 15, 60, 1D, 1W) so you can overlay higher‑ or equal‑timeframe MAs on the current chart.
Uses a non‑repainting request pattern for higher‑timeframe series to keep lines stable in realtime.
How to use
Leave a TF field blank to keep that MA on the chart’s timeframe; type a timeframe (like 15 or 1D) to fetch it from another timeframe.
Typical trend‑following setup: fast MAs (10–21) on chart TF, mid/slow MAs (34–200) from higher TFs for bias and dynamic support/resistance.
Color‑code faster vs slower lines and optionally hide lines you don’t need to reduce clutter.
Best practices
Prefer pulling equal or higher timeframes for stability; mixing lower TFs into a higher‑TF chart can create choppy visuals.
Combine with price action and volume/volatility tools (e.g., RSI, Bollinger Bands) for confirmation rather than standalone signals.
Showcase example charts in your publish post and explain default settings so users know how to interpret the ribbon.
Inputs
Show/Hide per MA, Type (SMA/EMA/SMMA/WMA/VWMA), Source, Length, Color, Timeframe.
Defaults cover common lengths (10/20/50/100/200 etc.) and can be customized to fit intraday or swing styles.
Limitations
This is an analysis overlay, not a signal generator; it doesn’t place trades or alerts by default.
Effectiveness depends on instrument liquidity and user configuration; avoid overfitting to one market or regime.
Attribution and etiquette
Provide a brief explanation of your calculation choices and note that MA formulas are standard; credit any borrowed concepts or snippets if used.
VWAP – Pivot Pairs (SECONDS‑BASED RESET)VWAP – Pivot Pairs (SECONDS-BASED RESET) is a Pine Script v6 indicator for TradingView that combines pivot-based breakout detection with resettable VWAP (Volume Weighted Average Price) calculations over user-defined rolling time periods in seconds.It identifies high and low swing pivots via breakout logic, then calculates two VWAP lines per anchor:One using high/low as the price source,
One using close as the price source.
These form "pivot pairs" that reset automatically at the start of each custom-duration period (e.g., every 300 seconds), starting from a user-defined UTC time of day (default: 09:30 UTC).Visuals include:Colored VWAP lines (high pair: red, low pair: green),
Semi-transparent fill zones between each pair,
Optional toggles to show/hide high or low pairs.
Use CasesUse Case
Description
Intraday Scalping (1–15 min charts)
Use 60–300 second resets to capture micro-trends within larger sessions. VWAP pairs act as dynamic support/resistance after breakouts.
High-Frequency / Algo Validation
Backtest strategies on tick/second charts where traditional session resets fail. Align resets with exchange micro-sessions or volatility windows.
Opening Range Breakout (ORB) Enhancement
Set period_seconds = 1800 (30 min) and start time = 09:30 UTC → VWAP builds only on first 30 mins post-open, then floats. Pairs show deviation from ORB mean.
Range-Bound Market Analysis
In choppy markets, VWAP pairs converge near fair value. Divergence signals potential breakout. Fill color intensity shows conviction.
Multi-Timeframe Confluence
Overlay on 1-second chart with 300s reset → matches 5-minute structure. Use close-based VWAP for entries, high/low-based for stops.
Key Features SummaryFeature
Function
period_seconds
Rolling window length in seconds (e.g., 300 = 5 min)
period_start_time
UTC time-of-day anchor (default: 09:30)
new_period logic
Triggers full reset of pivots + VWAP on exact second boundary
breakingHigher / breakingLower
Detects confirmed breakouts (not just close above high)
Dual VWAP per anchor
ta.vwap(high) and ta.vwap(close) for range-aware mean
Fill zones
Visual value area between high/close VWAPs
Toggle visibility
Independently show/hide high or low pivot pairs
How It Works – Step-by-StepTime Engine Converts user inputs → milliseconds
Calculates current period start time using integer division from epoch
Detects exact bar when new period begins (new_period = true)
On New Period Resets both high/low anchors to current bar’s h and l
Forces VWAP recalculation from this bar forward
Breakout Detection Only triggers on strong candles (rising/falling, non-doji)
Requires open/close beyond prior pivot → avoids wicks-only breaks
VWAP Accumulation ta.vwap(source, reset_condition) restarts when anchor resets
Two sources per side → shows where volume clustered (at highs vs closes)
Plotting Four lines + two fills
Clean, customizable, overlay-friendly
Pro TipsUse on Heikin Ashi for smoother breakout signals.
Combine with volume profile to validate VWAP clusters.
For crypto, set period_start_time = 0 (00:00 UTC) for clean 4-hour resets.
Add alerts on new_period or breakingHigher for automation.
In short: This is a precision VWAP tool for time-boxed, pivot-driven mean reversion and breakout trading, ideal for scalpers, day traders, and algo developers needing sub-session granularity.
EMA Cross + RSI + ADX - Autotrade Strategy V2Overview
A versatile trend-following strategy combining EMA 9/21 crossovers with RSI momentum filtering and optional ADX trend strength confirmation. Designed for both cryptocurrency and traditional futures/options markets with built-in stop loss management and automated position reversals.
Key Features
Multi-Market Compatibility: Works on both crypto futures (Bitcoin, Ethereum) and traditional markets (NIFTY, Bank NIFTY, S&P 500 futures, equity options)
Triple Confirmation System: EMA crossover + RSI filter + ADX strength (optional)
Automated Risk Management: 2% stop loss with wick-touch detection
Position Auto-Reversal: Opposite signals automatically close and reverse positions
Webhook Ready: Six distinct alert messages for automation (Entry Buy/Sell, Close Long/Short, SL Hit Long/Short)
Performance Metrics
NIFTY Futures (15min): 50%+ win rate with ADX filter OFF
Crypto Markets: Requires extensive backtesting before live deployment
Optimal Timeframes: 15-minute to 1-hour charts (patience required for higher timeframes)
Strategy Logic
Entry Signals:
LONG: EMA 9 crosses above EMA 21 + RSI > 55 + ADX > 20 (if enabled)
SHORT: EMA 9 crosses below EMA 21 + RSI < 45 + ADX > 20 (if enabled)
Exit Signals:
Opposite EMA crossover (auto-closes current position)
Stop loss hit at 2% from entry price (tracks candle wicks)
Technical Indicators:
Fast EMA: 9-period (short-term trend)
Slow EMA: 21-period (primary trend)
RSI: 14-period with 55/45 thresholds (momentum confirmation)
ADX: 14-period with 20 threshold (trend strength filter - optional)
Market-Specific Settings
Traditional Markets (NIFTY, Bank NIFTY, S&P Futures, Options)
Recommended Settings:
ADX Filter: Turn OFF (less choppy, cleaner trends)
Timeframe: 15-minute chart
Win Rate: 50%+ on NIFTY Futures
Why No ADX: Traditional markets have more institutional participation and smoother price action, making ADX unnecessary
Cryptocurrency Markets (BTC, ETH, Altcoins)
Recommended Settings:
ADX Filter: Turn ON (ADX > 20)
Timeframe: 15-minute to 1-hour
Extensive backtesting required before live trading
Why ADX: Crypto markets are highly volatile and prone to false breakouts; ADX filters low-quality chop
Best Practices
✅ Backtest thoroughly on your specific instrument and timeframe
✅ Use larger timeframes (1H, 4H) for higher quality signals and better risk/reward
✅ Adjust RSI thresholds based on market volatility (try 52/48 for more signals, 60/40 for fewer but stronger)
✅ Monitor ADX effectiveness - disable for traditional markets, enable for crypto
✅ Proper position sizing - adjust default_qty_value based on your capital and instrument price
✅ Paper trade first - test for 2-4 weeks before risking real capital
Risk Management
Fixed 2% stop loss per trade (adjustable)
Stop loss tracks candle wicks for accurate execution
Positions auto-reverse on opposite signals (no manual intervention needed)
0.075% commission built into backtest (adjust for your broker)
Customization Options
All parameters are adjustable via inputs:
EMA periods (default: 9/21)
RSI length and thresholds (default: 14-period, 55/45 levels)
ADX length and threshold (default: 14-period, 20 threshold)
Stop loss percentage (default: 2%)
Webhook Automation
This strategy includes six distinct alert messages for automated trading:
"Entry Buy" - Long position opened
"Entry Sell" - Short position opened
"Close Long" - Long position closed on opposite crossover
"Close Short" - Short position closed on opposite crossover
"SL Hit Long" - Long stop loss triggered
"SL Hit Short" - Short stop loss triggered
Compatible with Delta Exchange, Binance Futures, 3Commas, Alertatron, and other webhook platforms.
Important Notes
⚠️ Crypto markets require extensive backtesting - volatility patterns differ significantly from traditional markets
⚠️ Higher timeframes = better results - 15min works but 1H/4H provide cleaner signals
⚠️ ADX toggle is critical - OFF for traditional markets, ON for crypto
⚠️ Not financial advice - always conduct your own research and use proper risk management
⚠️ Past performance ≠ future results - backtest results may not reflect live trading conditions
Disclaimer
This strategy is for educational and informational purposes only. Trading futures and options involves substantial risk of loss. Always backtest thoroughly, start with paper trading, and never risk more than you can afford to lose. The author assumes no responsibility for any trading losses incurred using this strategy.
Put Option Profits inspired by Travis Wilkerson; SPX BacktesterPut Option Profits — Travis Wilkerson inspired. This tester evaluates a simple monthly SPX at-the-money credit-spread timing idea: enter on a fixed calendar rule (e.g., 1st Friday or 8th day with business-day shifting) at Open or Close, then exit exactly N calendar days later (first tradable day >= target, at Close). A trade is marked WIN if price at exit is above the entry price (1:1 risk proxy).
The book suggests forward testing 60-day and 180-day expirations to prove the concept. This tool lets you backtest both (and more) to see what actually works best. In the book, profits are taken when the spread reaches ~80% of max credit; losers are left to expire and cash-settle. This backtester does not model early profit-taking—every trade is held to the configured hold period and evaluated on price vs entry at the exit close. Think of it as a pure “set it and forget it” stress test. In live trading, you can still follow Travis’s 80% take-profit rule; TradingView just doesn’t simulate that here. Happy trading!
Features:
Schedule: Day-of-Month (with Prev/Next business-day shift, optional “stay in month”) or Nth Weekday (e.g., 1st Friday).
Entry timing: Open or Close.
Exit: N calendar days later at Close (holiday/weekend aware).
Filters: Optional EMA-200 “risk-on” filter.
Scope: Date range limiter.
Visuals: Entry/exit bubbles (paired colors) or simple win/loss dots.
Table: Overall Win% and N (within range).
Alerts: Entry alert (static condition + dynamic alert() message).
How to use:
[* ]Choose Start Mode (NthWeekday or DayOfMonth) and parameters (e.g., 1st Friday or DOM=8, PrevBizDay).
Pick Entry Timing (Open or Close).
Set Days In Trade (e.g., 150).
(Optional) Enable EMA filter and set Date Range.
Turn Bubbles on/off and/or Dots on/off.
Create alert:
Simple ping: Condition = this indicator -> Monthly Entry Signal -> “Once per bar” (Open) or “Once per bar close” (Close).
Rich message: Condition = this indicator -> Any alert() function call.
Notes:
Keep DOM shift in same month: when a DOM falls on a weekend/holiday, PrevBizDay/NextBizDay shift will stay inside the month if enabled; otherwise it can spill into the prior/next month. (Ignored for NthWeekday.)
Credits: Concept sparked by “Put Option Profits – How to turn ten minutes of free time into consistent cash flow each month” by Travis Wilkerson; this script is a neutral research tool (not financial advice).
