SMC BOS - Structure Breaks & Median Continuation ProjectionsThis tool shows what usually happens after a Break of Structure (BOS).
It scans past BOS events on your chart, finds the ones most similar to the latest break (using ATR to filter by volatility), and then plots the median continuation path.
Optional percentile bands (P10–P90) display the possible range of outcomes around the median.
Key features:
• Automatic detection of bullish and bearish BOS events
• Library of past BOS with adjustable size and spacing
• ATR-based similarity and recency weighting
• Median continuation projections with optional percentile bands
• Customizable colors, signals, and stats table
• Works on any market and timeframe
Use cases:
• See how price typically behaves after a BOS
• Support SMC analysis with data-driven projections
• Improve trade planning by visualizing likely continuations
• Apply across crypto, forex, stocks, and futures
Originality:
Instead of only marking BOS, this script learns from history and projects forward the median path of the most similar past cases, adjusted for volatility. It turns BOS signals into practical continuation scenarios.
Instructions:
Add the indicator to your chart. When a BOS is detected, the projection is drawn automatically.
Use the settings to adjust the library, ATR weighting, projection style, percentile bands, and the display of signals or stats.
For questions or customization, contact Julien Eche (Julien_Eche) on TradingView.
אינדיקטורים ואסטרטגיות
Dynamic Stop Loss Optimizer [BackQuant]Dynamic Stop Loss Optimizer
Overview
Stop placement decides expectancy. This tool gives you three professional-grade, adaptive stop engines, ATR, Volatility, and Hybrid. So your exits scale with current conditions instead of guessing fixed ticks. It trails intelligently, redraws as the market evolves, and annotates the chart with clean labels/lines and a compact stats table. Pick the engine that fits the trade, or switch on the fly.
What it does
Calculates three adaptive stops in real time (ATR-based, Volatility-based, and Hybrid) and keeps them trailed as price makes progress.
Shows exactly where your risk lives with on-chart levels, color-coded markers (long/short), and precise “Risk %” labels at the current bar.
Surfaces context you actually use - current ATR, daily volatility, selected method, and the live stop level—in a tidy, movable table.
Fires alerts on stop hits so you can automate exits or journal outcomes without staring at the screen.
Why it matters
Adaptive risk control: Stops expand in fast tape and tighten in quiet tape. You’re not punished for volatility; you’re aligned with it.
Consistency across assets: The same playbook works whether you’re trading indexes, FX, crypto, or equities, because the engine normalizes to each symbol’s behavior.
Cleaner decision-making: One chart shows your entry idea and its invalidation in the same breath. If price trespasses, you know it instantly.
The three methods (choose your engine)
1) ATR Based “Structure-aware” distance
This classic approach keys off Average True Range to set a stop just beyond typical bar-to-bar excursion. It adapts smoothly to changing ranges and respects swing structure.
Use when: you want a steady, intuitive buffer that tracks trend legs without hugging price.
See it in action:
2) Volatility Based “Behavior-aware” distance
This engine derives stop distance from current return volatility (annualized, then scaled back down to the session). It reacts to regime shifts quickly and normalizes risk across symbols with very different prices.
Use when: you want the stop to breathe with realized volatility and respond faster to heat-ups/cool-downs.
See it in action:
3) Hybrid “Best of both worlds”
The Hybrid blends the ATR and Volatility distances into one consensus level, then trails it intelligently. You get the structural common sense of ATR and the regime sensitivity of Vol.
Use when: you want robust, all-weather behavior without micromanaging inputs.
See it in action:
How it trails
Longs: The stop ratchets up with favorable movement and holds its ground on shallow pullbacks. If price closes back into the risk zone, the level refreshes to the newest valid distance.
Shorts: Mirror logic ratchets down with trend, resists noise, and refreshes if price reclaims the zone.
Hybrid trailing: Uses the blended distance and the same “no give-backs” principle to keep gains protected as structure builds.
Reading the chart
Markers: Circles = ATR stops, Crosses = Vol stops, Diamonds = Hybrid. Colors indicate long (red level under price) vs short (green level above price).
Lines: The latest active stop is extended with a dashed line so you can see it at a glance.
Labels: “Long SL / Short SL” shows the exact price and current risk % from the last close no math required.
Table: ATR value, Daily Vol %, your chosen Method, the Current SL, and Risk %—all in one compact block that you can pin top-left/right/center.
Quick workflow
Define the idea: Long or Short, and which engine fits the tape (ATR, Vol, or Hybrid).
Place and trail: Let the optimizer print the level; trail automatically as the move develops.
Manage outcomes: If the line is tagged, you’re out clean. If it holds, you’ve contained heat while giving the trade room to work.
Inputs you’ll actually touch
Calculation Settings
ATR Length / Multiplier: Controls the “structural” cushion.
Volatility Length / Multiplier: Controls the “behavioral” cushion.
Trading Days: 252 or 365 to keep the volatility math aligned with the asset’s trading calendar.
Stop Loss Method
ATR Based | Volatility Based | Hybrid : Switch engines instantly to fit the trade.
Position Type
Long | Short | Both : Show only what you need for the current strategy.
Visual Settings
Show ATR / Vol / Hybrid Stops: Toggle families on/off.
Show Labels: Print price + Risk % at the live stop.
Table Position: Park the metrics where you like.
Coloring
Long/Short/Hybrid colors: Set a palette that matches your theme and stands out on your background.
Practical patterns to watch
Trend-pullback continuation: The stop ratchets behind higher lows (long) or lower highs (short). If price tests the level and rejects, that’s your risk-defined continuation cue.
Break-and-run: After a clean break, the Hybrid will usually sit slightly wider than pure Vol, use it to avoid getting shaken on the first retest.
Range compression: When the ATR and Vol distances converge, the table will show small Risk %. That’s your green light to size up with the same dollar risk, or keep it conservative if you expect expansion.
Alerts
Long Stop Loss Hit : Notifies when price crosses below the live long stop.
Short Stop Loss Hit : Notifies when price crosses above the live short stop.
Why this feels “set-and-serious”
You get a single look that answers three questions in real time: “Where’s my line in the sand?”, “How much heat am I taking right now?”, and “Is this distance appropriate for current conditions?” With ATR, Vol, and Hybrid in one tool, you can run the exact same playbook across symbols and regimes while keeping your chart clean and your risk explicit.
EMA50 + SR Boxes + VP Right + ATR + SL% + Entries + SentimentThis indicator combines several pro-grade building blocks to read the market at a glance:
EMA50 as a trend filter.
Smart Support/Resistance zones (rectangles) detected where price has touched multiple times.
“U / Inverted U” markers (confirmed pivots).
Optional Buy/Sell signals: only when a U appears inside a support zone with price above the EMA50 (buy), or an inverted U inside a resistance zone with price below the EMA50 (sell).
Simplified right-side Volume Profile (with a special Forex fallback if volume isn’t usable).
ATR & SL%: displays current ATR and an SL% based on ATR(100) Daily / Close × 100, attached to the latest candle.
Advanced Trading System - [WOLONG X DBG]Advanced Multi-Timeframe Trading System
Overview
This technical analysis indicator combines multiple established methodologies to provide traders with market insights across various timeframes. The system integrates SuperTrend analysis, moving average clouds, MACD-based candle coloring, RSI analysis, and multi-timeframe trend detection to suggest potential entry and exit opportunities for both swing and day trading approaches.
Methodology
The indicator employs a multi-layered analytical approach based on established technical analysis principles:
Core Signal Generation
SuperTrend Engine: Utilizes adaptive SuperTrend calculations with customizable sensitivity (1-20) combined with SMA confirmation filters to identify potential trend changes and continuations
Braid Filter System: Implements moving average filtering using multiple MA types (McGinley Dynamic, EMA, DEMA, TEMA, Hull, Jurik, FRAMA) with percentage-based strength filtering to help reduce false signals
Multi-Timeframe Analysis: Analyzes trend conditions across 10 different timeframes (1-minute to Daily) using EMA-based trend detection for broader market context
Advanced Features
MACD Candle Coloring: Applies dynamic 4-level candle coloring system based on MACD histogram momentum and signal line relationships for visual trend strength assessment
RSI Analysis: Identifies potential reversal areas using RSI oversold/overbought conditions with SuperTrend confirmation
Take Profit Analysis: Features dual-mode TP detection using statistical slope analysis and Parabolic SAR integration for exit timing analysis
Key Components
Signal Types
Primary Signals: Green ▲ for potential long entries, Red ▼ for potential short entries with trend and SMA alignment
Reversal Signals: Small circular indicators for RSI-based counter-trend possibilities
Take Profit Markers: X-cross symbols indicating statistical TP analysis zones
Pullback Signals: Purple arrows for potential trend continuation entries using Parabolic SAR
Visual Elements
8-Layer MA Cloud: Customizable moving average cloud system with 3 color themes for trend visualization
Real-Time Dashboard: Multi-timeframe trend analysis table showing bullish/bearish status across all timeframes
Dynamic Candle Colors: 4-intensity MACD-based coloring system (ranging from light to strong trend colors)
Entry/SL/TP Labels: Automatic calculation and display of suggested entry points, stop losses, and multiple take profit levels
Usage Instructions
Basic Configuration
Sensitivity Setting: Start with default value 6
Increase (7-15) for more frequent signals in volatile markets
Decrease (3-5) for higher quality signals in trending markets
MA Filter Type: McGinley Dynamic recommended for smoother signals
Filter Strength: Set to 80% for balanced filtering, adjust based on market conditions
Signal Interpretation
Long Entry: Green ▲ suggests when price crosses above SuperTrend with bullish SMA alignment
Short Entry: Red ▼ suggests when price crosses below SuperTrend with bearish SMA alignment
Reversal Opportunities: Small circles indicate RSI-based counter-trend analysis
Take Profit Zones: X-crosses mark statistical TP areas based on slope analysis
Dashboard Analysis
Green Cells: Bullish trend detected on that timeframe
Red Cells: Bearish trend detected on that timeframe
Multi-Timeframe Confluence: Look for alignment across multiple timeframes for stronger signal confirmation
Risk Management Features
Automatic Calculations
ATR-Based Stop Loss: Dynamic stop loss calculation using ATR multiplier (default 1.9x)
Multiple Take Profit Levels: Three TP targets with 1:1, 1:2, and 1:3 risk-reward ratios
Position Sizing Guidance: Entry labels display suggested price levels for order placement
Confirmation Requirements
Trend Alignment: Requires SuperTrend and SMA confirmation before signal generation
Filter Validation: Braid filter must show sufficient strength before signals activate
Multi-Timeframe Context: Dashboard provides broader market context for decision making
Optimal Settings
Timeframe Recommendations
Scalping: 1M-5M charts with sensitivity 8-12
Day Trading: 15M-1H charts with sensitivity 6-8
Swing Trading: 4H-Daily charts with sensitivity 4-6
Market Conditions
Trending Markets: Reduce sensitivity, increase filter strength
Ranging Markets: Increase sensitivity, enable reversal signals
High Volatility: Adjust ATR risk factor to 2.0-2.5
Advanced Features
Customization Options
MA Cloud Periods: 8 customizable periods for cloud layers (default: 2,6,11,18,21,24,28,34)
Color Themes: Three professional color schemes plus transparent option
Dashboard Position: 9 positioning options with 4 size settings
Signal Filtering: Individual toggle controls for each signal type
Technical Specifications
Moving Average Types: 21 different MA calculations including advanced types (Jurik, FRAMA, VIDA, CMA)
Pullback Detection: Parabolic SAR with customizable start, increment, and maximum values
Statistical Analysis: Linear regression slope calculation for trend-based TP analysis
Important Limitations
Lagging Nature: Some signals may appear after potential entry points due to confirmation requirements
Ranging Markets: May produce false signals during extended sideways price action
High Volatility: Requires parameter adjustment during news events or unusual market conditions
Computational Load: Multiple timeframe analysis may impact performance on slower devices
No Guarantee: All signals are suggestions based on technical analysis and may be incorrect
Educational Disclaimers
This indicator is designed for educational and analytical purposes only. It represents a technical analysis tool based on mathematical calculations of historical price data and should not be considered as financial advice or trading recommendations.
Risk Warning: Trading involves substantial risk of loss and is not suitable for all investors. Past performance of any trading system or methodology is not necessarily indicative of future results. The high degree of leverage can work against you as well as for you.
Important Notes:
Always conduct your own analysis before making trading decisions
Use appropriate position sizing and risk management strategies
Never risk more than you can afford to lose
Consider your investment objectives, experience level, and risk tolerance
Seek advice from qualified financial professionals when needed
Performance Disclaimer: Backtesting results do not guarantee future performance. Market conditions change constantly, and what worked in the past may not work in the future. Always paper trade new strategies before risking real capital.
DashBoard 2.3.1📌 Indicator Name:
DashBoard 2.3 – Smart Visual Market Overlay
📋 Description:
DashBoard 2.3 is a clean, efficient, and highly informative market overlay, designed to give you real-time context directly on your chart — without distractions. Whether you're swing trading or investing long-term, this tool keeps critical market data at your fingertips.
🔍 Key Features:
Symbol + Timeframe + Market Cap
Shows the current ticker and timeframe, optionally with real-time market cap.
ATR 14 with Volatility Signal
Displays ATR with color-coded risk levels:
🟢 Low
🟡 Moderate
🔴 High
⚫️ Extreme
You can choose between Daily ATR or timeframe-based ATR (auto-adjusted to chart resolution).
Adaptive Labeling
The ATR label updates to reflect the resolution:
ATR 14d (daily)
ATR 14W (weekly)
ATR 14H (hourly), etc.
Moving Average Tracker
Instantly shows whether price is above or below your selected moving average (e.g., 150 MA), with green/red indication.
Earnings Countdown
Clearly shows how many days remain until the next earnings report.
Industry & Sector Info (optional)
Useful for thematic or sector-based trading strategies.
Fully Customizable UI
Choose positioning, padding, font size, and which data to show. Designed for minimalism and clarity.
✅ Smart Logic:
Color dots appear only in relevant conditions (e.g., ATR color signals shown only on daily when enabled).
ATR display automatically reflects your time frame, if selected.
Clean chart integration – the overlay sits quietly in a corner, enhancing your analysis without intruding.
🧠 Ideal for:
Swing traders, position traders, and investors who want fast, high-impact insights directly from the chart.
Anyone looking for a compact, beautiful, and informative dashboard while they trade.
EMA & VWAP Precision Overlay📢WELCOME TO FUTURE YOU!
📈 This isn’t your grandma’s moving average script.
This is pure alpha visualization. We're talking 9, 21, 50, and 200 EMAs. Plus VWAP Session AND Anchored VWAP — all dynamically labeled so you know exactly where price is cooking.
🚀 Features:
Toggle lines like a boss
Label everything (or nothing, if you’re into minimalist flexing)
Anchored VWAP for sniper entries (you pick the start)
Labels shift forward so your candles don’t cry
Built for traders who actually care about levels and not just vibes. Whether you’re scalping dog coins or trend-riding BTC, this thing keeps your chart clean, informative, and slightly intimidating.
I use it. It works. You should probably use it too.
If it gives you psychic powers — you're welcome.
If it doesn't — still looks cool.
Super SignalWhen all lines are below the 20 line its a super signal to buy. When all trends are above the 80 line it is a super signal to sell.
Simple Pivot Zones (Error-free) — v11. Core Idea
The indicator we built is a “pivot-based zone detector with breakout signals.”
It does three things:
1. Finds important swing highs and swing lows in price (pivots).
2. Creates support and resistance zones around those pivots using volatility (ATR).
3. Watches price action to see if those zones get broken, then gives signals.
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2. What is a Pivot?
A pivot high happens when the price makes a local peak — a bar is higher than the bars around it.
A pivot low happens when the price makes a local dip — a bar is lower than the bars around it.
These are natural turning points in the market, showing where buyers or sellers had strong control temporarily. Traders often use them to draw support (pivot lows) and resistance (pivot highs).
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3. Why Use ATR for Zones?
ATR (Average True Range) measures the average volatility of a market. Instead of drawing just a flat line at the pivot, we create a zone above and below it, sized according to ATR.
Example:
• If ATR is 20 points and zone size is 0.5, then the zone extends 10 points above and below the pivot level.
This turns thin “lines” into thicker areas of interest. Real markets don’t respect razor-thin levels, but zones are more realistic.
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4. How Support & Resistance Zones Work
• Resistance zones are created at pivot highs. They mark where sellers were strong before.
• Support zones are created at pivot lows. They mark where buyers were strong before.
Over time, these zones extend forward until the price interacts with them.
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5. Breakout Detection
The indicator checks whether the price closes beyond the last pivot high or low:
• If price closes above the last pivot high, it means buyers have broken resistance.
• If price closes below the last pivot low, it means sellers have broken support.
These moments are significant because they often trigger trend continuation.
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6. Parameters It Uses
1. Pivot Length – how many bars to look back and forward to confirm a pivot. A higher length makes pivots less frequent but stronger.
2. ATR Length and Multiplier – defines the size of the zones (wider zones in more volatile markets).
3. Max Zones to Keep – avoids clutter by keeping only the most recent zones.
4. Colors & Styling – helps traders visually separate bullish and bearish zones.
________________________________________
7. How It Helps Traders
• Visual clarity: Instead of guessing support and resistance, the chart automatically highlights them.
• Dynamic adjustment: Zones adapt to volatility using ATR, making them useful in both calm and volatile markets.
• Breakout signals: Traders get notified when price actually breaks key levels, instead of reacting late.
• Cleaner charts: Instead of dozens of hand-drawn lines, the tool manages zones for you, deleting old ones.
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8. The Logic in One Sentence
It finds important swing highs and lows, turns them into support/resistance zones scaled by volatility, and alerts you when the market breaks through them.
________________________________________
👉 In practice, this helps traders spot where the market is likely to bounce or break, and gives a framework to plan trades — for example, buying on bullish breakouts or selling on bearish breakouts.
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Triple RSI | MisinkoMasterThe Triple RSI (TRSI) is an advanced trend-following oscillator designed to capture trend reversals with speed and smoothness, combining concepts from traditional RSI, multi-timeframe momentum analysis, and layered moving average smoothing.
By blending multiple RSI lengths and applying a unique smoothing sequence, the TRSI creates a fast, momentum-driven RSI oscillator that reduces noise without sacrificing responsiveness.
🔎 Methodology
The indicator is built in three main steps:
Multi-Length RSI Calculation
Three RSIs are calculated using different lengths derived from the user’s input n:
RSI(√n) → very fast, highly responsive.
RSI(n/2) → moderately fast.
RSI(n) → slower, more stable baseline.
Each RSI is normalized by subtracting 50, centering values around zero.
Triple RSI Formula
The three RSIs are combined into the base formula:
TRSI=RSI(√n)+RSI(n/2)−RSI(n)
TRSI=RSI(√n)+RSI(n/2)−RSI(n)
This subtracts the slower RSI from the faster ones, boosting responsiveness and making the TRSI more momentum-oriented than a standard RSI.
Layered Smoothing
The raw TRSI is smoothed in three steps:
RMA(n/2)
RMA(√n)
HMA(√n)
This sequence balances stability and speed:
RMA provides consistency and reduces false noise.
HMA adds responsiveness and precision.
The result is a smooth yet reactive oscillator, optimized for reversal detection.
📈 Trend Classification
The TRSI offers three ways to interpret trend direction:
Oscillator Values
Above 0 → Bullish (uptrend).
Below 0 → Bearish (downtrend).
Oscillator Colors
Green TRSI line → Positive momentum.
Red TRSI line → Negative momentum.
Background Colors
Green background flash → Reversal into bullish trend.
Red background flash → Reversal into bearish trend.
This makes it easy to scan past price history and quickly identify turning points.
🎨 Visualization
TRSI line plotted with dynamic coloring (green/red).
Filled area between TRSI and zero-line reflects momentum bias.
Background flashes highlight trend reversal points, adding context and clarity for visual traders.
⚡ Features
Adjustable length parameter (n).
Dynamic use of √n and n/2 for multi-speed RSI blending.
Built-in smoothing with 2× RMA + 1× HMA.
Multiple trend detection methods (value, color, background).
Works across all assets and timeframes (crypto, forex, stocks, indices).
✅ Use Cases
Reversal Detection → Catch early shifts in trend direction.
Trend Confirmation → Stay aligned with momentum.
Momentum Filter → Avoid counter-trend trades in trending markets.
Historical Analysis → Quickly scan past reversals via background coloring.
⚠️ Limitations
As with all oscillators, TRSI may give false signals in sideways/choppy markets.
Optimal sensitivity depends on asset volatility → adjust n for best results.
It is not a standalone system and should be combined with other tools (trend filters, volume, higher timeframe confluence).
Apex Edge – HTF Overlay Candles“Trade your 5m chart with the eyes of the 1H — Apex Edge brings higher-timeframe structure and liquidity sweeps directly onto your execution chart.”
Apex Edge – HTF Overlay Candles
The Apex Edge – HTF Overlay Candles indicator overlays higher-timeframe (HTF) candles directly onto your lower-timeframe chart. Instead of flipping between timeframes, you see HTF structure “breathe” live on your execution chart.
What It Does
• HTF Body Boxes → open/close zones drawn as semi-transparent rectangles.
• HTF Wick Boxes → high/low extremes projected as envelopes around each body.
• Midpoint Line → a dynamic equilibrium line that flips bias as price trades above or below.
• Sweep Arrows → one-time markers showing the first liquidity raid at HTF highs or lows.
Under the Hood
This isn’t just a visual overlay — it’s engineered for accuracy and performance in PineScript.
1. HTF Data Retrieval
• Uses request.security() to import open, high, low, close, time from any selected HTF.
• lookahead=barmerge.lookahead_off ensures OHLC values update bar by bar as the HTF
candle builds.
• When the HTF bar closes, boxes and midpoint lock to historical values — matching the
native HTF chart exactly.
2. Box Construction
• Body box: built from HTF open → close.
• Wick box: built from HTF high → low.
• Boxes extend dynamically across each HTF period, updating in real time, then freeze at
close.
3. Midpoint Logic
• (htfOpen + htfClose) / 2 calculates intrabar midpoint.
• Line drawn edge-to-edge across the active HTF body.
• Style, width, color, and opacity are user-controlled.
4. Sweep Detection
• Flags (sweepedHigh / sweepedLow) prevent clutter: only the first tap per side per HTF
candle is marked.
• Lower-timeframe price breaking the HTF high/low triggers the sweep arrow.
• Arrows are offset above/below wick envelopes for clean visuals.
5. Customisation
• Every layer (body, wick, midpoint, arrows) has independent color + opacity settings.
• Arrow size, arrow color, and transparency are adjustable.
• Default HTF = 1H (perfect for 5m/15m traders) but can be switched to 30m, 4H, Daily,
etc.
Why It’s Useful
• HTF intent + LTF execution without chart hopping.
• Liquidity mapping: see where liquidity is swept in real time.
• Bias clarity: midpoint line defines HTF equilibrium.
• Clean signals: only the first sweep prints — no spam.
What Makes It Different
Most MTF overlays just plot candles or single lines. This tool:
• Splits body vs wick zones for institutional precision.
• Updates live intrabar (no repainting).
• Highlights liquidity sweeps clearly.
• Built for readability and professional use — not another retail signal toy.
Cheat-Sheet Playbook
1️⃣ Structure Bias
• Above midpoint line = bullish intent.
• Below midpoint line = bearish intent.
• Chop around midpoint = no clear direction.
2️⃣ Liquidity Sweeps
• ▲ Green up arrow below wick box = sell-side liquidity taken → watch for longs.
• ▼ Red down arrow above wick box = buy-side liquidity taken → watch for shorts.
• First sweep is the cleanest.
3️⃣ Trade Logic
• Body box = where institutions transact.
• Wick box = liquidity traps.
• Midpoint = bias filter.
• Best setups occur when sweep + midpoint flip align.
4️⃣ Example (5m + 1H Overlay)
1. ▲ Green up arrow prints below HTF wick.
2. Price reclaims the body box.
3. Midpoint flips to support.
4. Enter long → stop below sweep → targets = midpoint first, opposite wick second.
In short:
• Boxes = structure
• Wicks = liquidity pools
• Midpoint = bias line
• Arrows = liquidity sweeps
This is your SMC edge on one chart — HTF structure and liquidity fused directly into your execution timeframe.
Buyer vs Seller Control BUYER vs SELLER CONTROL INDICATOR
Identify market dominance and potential trend shifts with wick analysis
What This Indicator Measures:
This indicator analyzes who controls the market by measuring the battle between buyers and sellers on each candle:
Buyer Control: How far the closing price is above the candle's low (bottom wick strength)
Seller Control: How far the closing price is below the candle's high (top wick strength)
What's Plotted:
Lime Line: 20-period moving average of buyer control
Fuchsia Line: 20-period moving average of seller control
Dynamic Fill: Area between lines - color shows who's winning
Histogram: Shows the difference between buyer and seller control
Control Label: Text showing current market dominance
Info Table: Real-time values and control strength percentage
How to Read the Signals:
🟢 LIME FILL = BUYERS IN CONTROL
When the lime line is above fuchsia, buyers are dominating. The brighter the fill, the stronger their control.
🔴 FUCHSIA FILL = SELLERS IN CONTROL
When the fuchsia line is above lime, sellers are dominating. The brighter the fill, the stronger their control.
Trading Applications:
Trend Confirmation: Strong buyer control confirms uptrends, strong seller control confirms downtrends
Reversal Signals: Watch for control shifts - when lines cross, momentum may be changing
Entry Timing: Enter long when buyer control strengthens, short when seller control strengthens
Market Structure: Persistent control by one side suggests strong directional bias
Key Features:
Works on any timeframe
Customizable moving average period (default: 20)
Optional info table display
Dynamic transparency shows control strength
Clean visual design for both dark and light themes
Pro Tip: Use this with your favorite trend or momentum indicators for confluence. Strong buyer/seller control often precedes significant price moves!
// Based on wick analysis and moving averages
// Green = Buyers dominating market
// Red = Sellers dominating market
// Fill intensity = Control strength
Bollinger Adaptive Trend Navigator [QuantAlgo]🟢 Overview
The Bollinger Adaptive Trend Navigator synthesizes volatility channel analysis with variable smoothing mechanics to generate trend identification signals. It uses price positioning within Bollinger Band structures to modify moving average responsiveness, while incorporating ATR calculations to establish trend line boundaries that constrain movement during volatile periods. The adaptive nature makes this indicator particularly valuable for traders and investors working across various asset classes including stocks, forex, commodities, and cryptocurrencies, with effectiveness spanning multiple timeframes from intraday scalping to longer-term position analysis.
🟢 How It Works
The core mechanism calculates price position within Bollinger Bands and uses this positioning to create an adaptive smoothing factor:
bbPosition = bbUpper != bbLower ? (source - bbLower) / (bbUpper - bbLower) : 0.5
adaptiveFactor = (bbPosition - 0.5) * 2 * adaptiveMultiplier * bandWidthRatio
alpha = math.max(0.01, math.min(0.5, 2.0 / (bbPeriod + 1) * (1 + math.abs(adaptiveFactor))))
This adaptive coefficient drives an exponential moving average that responds more aggressively when price approaches Bollinger Band extremes:
var float adaptiveTrend = source
adaptiveTrend := alpha * source + (1 - alpha) * nz(adaptiveTrend , source)
finalTrend = 0.7 * adaptiveTrend + 0.3 * smoothedCenter
ATR-based volatility boundaries constrain the final trend line to prevent excessive movement during volatile periods:
volatility = ta.atr(volatilityPeriod)
upperBound = bollingerTrendValue + (volatility * volatilityMultiplier)
lowerBound = bollingerTrendValue - (volatility * volatilityMultiplier)
The trend line direction determines bullish or bearish states through simple slope comparison, with the final output displaying color-coded signals based on the synthesis of Bollinger positioning, adaptive smoothing, and volatility constraints (green = long/buy, red = short/sell).
🟢 Signal Interpretation
Rising Trend Line (Green): Indicates upward direction based on Bollinger positioning and adaptive smoothing = Potential long/buy opportunity
Falling Trend Line (Red): Indicates downward direction based on Bollinger positioning and adaptive smoothing = Potential short/sell opportunity
Built-in Alert System: Automated notifications trigger when bullish or bearish states change, allowing you to act on significant development without constantly monitoring the charts
Candle Coloring: Optional feature applies trend colors to price bars for visual consistency
Configuration Presets: Three parameter sets available - Default (standard settings), Scalping (faster response), and Swing Trading (slower response)
Entry Signals (Long/Short)The indicator visualizes precise entry signals for long and short setups directly on the price chart. Long is marked with a green triangle-up, short with a red triangle-down. To contextualize trend structure, the Fast EMA (5) is plotted in black and the Slow EMA (20) in blue (line width 1). Signals print only at bar close for reproducible execution. Applicable across all timeframes—ideal for top-down analysis from the 195-minute chart through daily to weekly.
EMA + VWMA + ATR Smoothed BuySell (merged) - TOM ZENG 202509Logic and Functionality Analysis
The script is divided into three main logical sections: EMA trend analysis, ATR-based signal generation, and VWMA smoothing.
1. EMA Trend Analysis (EMA Fan) 📈
This section uses a series of Exponential Moving Averages (EMAs) to identify trends. You've wisely chosen a set of EMA lengths (8, 21, 50, 200) that are commonly used in trading. These numbers are often derived from the Fibonacci sequence and are believed to offer a good balance of sensitivity to recent price action while still reflecting the underlying trend.
Purpose: The EMAs serve as dynamic support and resistance levels. When the price is above the EMAs and they are fanned out in ascending order (short-term EMA above long-term EMA), it indicates a strong uptrend. Conversely, a descending order indicates a downtrend.
Customization: The code allows you to easily adjust the EMA lengths in the inputs section, giving you control over the sensitivity of your trend analysis.
2. ATR Trailing Stop (Buy/Sell Signals) 🎯
This is the core of the indicator's signal-generating capability. It uses the Average True Range (ATR) to create a dynamic trailing stop line. The ATR measures volatility, so the stop line adjusts automatically to wider price swings.
Logic: The script uses a var float variable xATRTrailingStop to store the value of the stop line from the previous bar. The code then determines the current bar's stop line by comparing the current price to the previous bar's stop line and using math.max and math.min to smoothly move the line along with the trend.
Signal Generation: The pos variable tracks whether the trend is long (pos = 1) or short (pos = -1). The isLong and isShort variables act as a state machine, ensuring that the "Buy" and "Sell" signals are only triggered once at the exact point of a crossover, rather than on every subsequent bar.
Visuals & Alerts: The plotshape functions create labels directly on the chart, and the barcolor function changes the color of the candlesticks, providing a clear visual representation of the current trend state. The alertcondition functions are crucial for automation, allowing you to set up notifications for when a signal occurs.
3. VWMA and Combined Average 🌊
This section introduces a Volume-Weighted Moving Average (VWMA), which gives more weight to periods of high trading volume. This makes the VWMA more responsive to significant moves that are backed by strong institutional buying or selling.
Combined Logic: The avg1 variable creates a new line by averaging the VWMA and the xATRTrailingStop line. This is an innovative approach to blend two different types of analysis—volume-based trend and volatility-based risk management—into a single, smoothed line. It can act as an additional filter or a unique trading signal on its own.
Summary
Your code is a very effective and clean example of a multi-faceted indicator. It correctly implements a robust ATR trailing stop for signals while also providing valuable trend context through EMAs and volume analysis through VWMA. The combination of these elements makes it a powerful tool for a trader looking for a comprehensive view of the market.
Trinity Multi-Timeframe MA TrendOriginal script can be found here: {Multi-Timeframe Trend Analysis } www.tradingview.com
1. all credit the original author www.tradingview.com
2. why change this script:
- added full transparency function to each EMA
- changed to up and down arrows
- change the dashboard to be able to resize and reposition
How to Use This Indicator
This indicator, "Trinity Multi-Timeframe MA Trend," is designed for TradingView and helps visualize Exponential Moving Average (EMA) trends across multiple timeframes. It plots EMAs on your chart, fills areas between them with directional colors (up or down), shows crossover/crossunder labels, and displays a dashboard table summarizing EMA directions (bullish ↑ or bearish ↓) for selected timeframes. It's useful for multi-timeframe analysis in trading strategies, like confirming trends before entries.
Configure Settings (via the Gear Icon on the Indicator Title):
Timeframes Group: Set up to 5 custom timeframes (e.g., "5" for 5 minutes, "60" for 1 hour). These determine the multi-timeframe analysis in the dashboard. Defaults: 5m, 15m, 1h, 4h, 5h.
EMA Group: Adjust the lengths of the 5 EMAs (defaults: 5, 10, 20, 50, 200). These are the moving averages plotted on the chart.
Colors (Inline "c"): Choose uptrend color (default: lime/green) and downtrend color (default: purple). These apply to plots, fills, labels, and dashboard cells.
Transparencies Group: Set transparency levels (0-100) for each EMA's plot and fill (0 = opaque, 100 = fully transparent). Defaults decrease from EMA1 (80) to EMA5 (0) for a gradient effect.
Dashboard Settings Group (newly added):
Dashboard Position: Select where the table appears (Top Right, Top Left, Bottom Right, Bottom Left).
Dashboard Size: Choose text size (Tiny, Small, Normal, Large, Huge) to scale the table for better visibility on crowded charts.
Understanding the Visuals:
EMA Plots: Five colored lines on the chart (EMA1 shortest, EMA5 longest). Color changes based on direction: uptrend (your selected up color) if rising, downtrend (down color) if falling.
Fills Between EMAs: Shaded areas between consecutive EMAs, colored and transparent based on the faster EMA's direction and your transparency settings.
Crossover Labels: Arrow labels (↑ for crossover/uptrend start, ↓ for crossunder/downtrend start) appear on the chart at EMA direction changes, with tooltips like "EMA1".
Dashboard Table (top-right by default):
Rows: EMA1 to EMA5 (with lengths shown).
Columns: Selected timeframes (converted to readable format, e.g., "5m", "1h").
Cells: ↑ (bullish/up) or ↓ (bearish/down) arrows, colored green/lime or purple based on trend, with fading transparency for visual hierarchy.
Use this to quickly check alignment across timeframes (e.g., all ↑ in multiple TFs might signal a strong uptrend).
Trading Tips:
Trend Confirmation: Look for alignment where most EMAs in higher timeframes are ↑ (bullish) or ↓ (bearish).
Entries/Exits: Use crossovers on the chart EMAs as signals, confirmed by the dashboard (e.g., enter long if lower TF EMA crosses up and higher TFs are aligned).
Customization: On lower timeframe charts, set dashboard timeframes to higher ones for top-down analysis. Adjust transparencies to avoid chart clutter.
Limitations: This is a trend-following tool; combine with volume, support/resistance, or other indicators. Backtest on historical data before live use.
Performance: Works best on trending markets; may whipsaw in sideways conditions.
Futures Playbook: VWAP + OR + Cross-Asset TellsFutures Playbook: VWAP + OR + Cross-Asset Tells (with Trade Messages + Coach Panel)
This all-in-one futures trading toolkit combines Opening Range (OR) levels, VWAP, and cross-asset signals to help traders quickly read intraday structure, manage execution, and filter noise.
Core Features
• Opening Range (OR):
• Customizable OR window with High/Low and Midpoint.
• Automatic shading of the OR zone.
• VWAP & Bands:
• Built-in or session-anchored VWAP.
• Optional standard deviation bands for context.
• Cross-Asset Tells:
• Live reads on US 10Y yield, DXY, Crude, and Gold.
• Regime detection: rates risk, USD strength, energy softness, and real-rate easing.
• Confirmations:
• Volume vs. moving average filter.
• Cumulative delta with smoothing.
• ATR-based chop filter to avoid low-quality trends.
Trade Messages + Coach Panel
• Trade Messages (labels): Automatic on-chart prompts for OR completion, VWAP reclaim/loss, long/short setups, and EU close flows.
• Coach Panel (table): Real-time dashboard with regime context, directional bias, execution notes, risk reminders, and key levels (ORH, ORL, VWAP).
Alerts
• OR breakout (long/short with confirmations).
• VWAP reclaim or loss.
• 10Y yield crossing risk threshold.
Use Case
Designed for futures traders and scalpers who rely on VWAP + OR dynamics and need cross-asset confirmation before committing to trades. Great for structuring entries, managing risk, and filtering market noise throughout the session.
Small Business Economic Conditions - Statistical Analysis ModelThe Small Business Economic Conditions Statistical Analysis Model (SBO-SAM) represents an econometric approach to measuring and analyzing the economic health of small business enterprises through multi-dimensional factor analysis and statistical methodologies. This indicator synthesizes eight fundamental economic components into a composite index that provides real-time assessment of small business operating conditions with statistical rigor. The model employs Z-score standardization, variance-weighted aggregation, higher-order moment analysis, and regime-switching detection to deliver comprehensive insights into small business economic conditions with statistical confidence intervals and multi-language accessibility.
1. Introduction and Theoretical Foundation
The development of quantitative models for assessing small business economic conditions has gained significant importance in contemporary financial analysis, particularly given the critical role small enterprises play in economic development and employment generation. Small businesses, typically defined as enterprises with fewer than 500 employees according to the U.S. Small Business Administration, constitute approximately 99.9% of all businesses in the United States and employ nearly half of the private workforce (U.S. Small Business Administration, 2024).
The theoretical framework underlying the SBO-SAM model draws extensively from established academic research in small business economics and quantitative finance. The foundational understanding of key drivers affecting small business performance builds upon the seminal work of Dunkelberg and Wade (2023) in their analysis of small business economic trends through the National Federation of Independent Business (NFIB) Small Business Economic Trends survey. Their research established the critical importance of optimism, hiring plans, capital expenditure intentions, and credit availability as primary determinants of small business performance.
The model incorporates insights from Federal Reserve Board research, particularly the Senior Loan Officer Opinion Survey (Federal Reserve Board, 2024), which demonstrates the critical importance of credit market conditions in small business operations. This research consistently shows that small businesses face disproportionate challenges during periods of credit tightening, as they typically lack access to capital markets and rely heavily on bank financing.
The statistical methodology employed in this model follows the econometric principles established by Hamilton (1989) in his work on regime-switching models and time series analysis. Hamilton's framework provides the theoretical foundation for identifying different economic regimes and understanding how economic relationships may vary across different market conditions. The variance-weighted aggregation technique draws from modern portfolio theory as developed by Markowitz (1952) and later refined by Sharpe (1964), applying these concepts to economic indicator construction rather than traditional asset allocation.
Additional theoretical support comes from the work of Engle and Granger (1987) on cointegration analysis, which provides the statistical framework for combining multiple time series while maintaining long-term equilibrium relationships. The model also incorporates insights from behavioral economics research by Kahneman and Tversky (1979) on prospect theory, recognizing that small business decision-making may exhibit systematic biases that affect economic outcomes.
2. Model Architecture and Component Structure
The SBO-SAM model employs eight orthogonalized economic factors that collectively capture the multifaceted nature of small business operating conditions. Each component is normalized using Z-score standardization with a rolling 252-day window, representing approximately one business year of trading data. This approach ensures statistical consistency across different market regimes and economic cycles, following the methodology established by Tsay (2010) in his treatment of financial time series analysis.
2.1 Small Cap Relative Performance Component
The first component measures the performance of the Russell 2000 index relative to the S&P 500, capturing the market-based assessment of small business equity valuations. This component reflects investor sentiment toward smaller enterprises and provides a forward-looking perspective on small business prospects. The theoretical justification for this component stems from the efficient market hypothesis as formulated by Fama (1970), which suggests that stock prices incorporate all available information about future prospects.
The calculation employs a 20-day rate of change with exponential smoothing to reduce noise while preserving signal integrity. The mathematical formulation is:
Small_Cap_Performance = (Russell_2000_t / S&P_500_t) / (Russell_2000_{t-20} / S&P_500_{t-20}) - 1
This relative performance measure eliminates market-wide effects and isolates the specific performance differential between small and large capitalization stocks, providing a pure measure of small business market sentiment.
2.2 Credit Market Conditions Component
Credit Market Conditions constitute the second component, incorporating commercial lending volumes and credit spread dynamics. This factor recognizes that small businesses are particularly sensitive to credit availability and borrowing costs, as established in numerous Federal Reserve studies (Bernanke and Gertler, 1995). Small businesses typically face higher borrowing costs and more stringent lending standards compared to larger enterprises, making credit conditions a critical determinant of their operating environment.
The model calculates credit spreads using high-yield bond ETFs relative to Treasury securities, providing a market-based measure of credit risk premiums that directly affect small business borrowing costs. The component also incorporates commercial and industrial loan growth data from the Federal Reserve's H.8 statistical release, which provides direct evidence of lending activity to businesses.
The mathematical specification combines these elements as:
Credit_Conditions = α₁ × (HYG_t / TLT_t) + α₂ × C&I_Loan_Growth_t
where HYG represents high-yield corporate bond ETF prices, TLT represents long-term Treasury ETF prices, and C&I_Loan_Growth represents the rate of change in commercial and industrial loans outstanding.
2.3 Labor Market Dynamics Component
The Labor Market Dynamics component captures employment cost pressures and labor availability metrics through the relationship between job openings and unemployment claims. This factor acknowledges that labor market tightness significantly impacts small business operations, as these enterprises typically have less flexibility in wage negotiations and face greater challenges in attracting and retaining talent during periods of low unemployment.
The theoretical foundation for this component draws from search and matching theory as developed by Mortensen and Pissarides (1994), which explains how labor market frictions affect employment dynamics. Small businesses often face higher search costs and longer hiring processes, making them particularly sensitive to labor market conditions.
The component is calculated as:
Labor_Tightness = Job_Openings_t / (Unemployment_Claims_t × 52)
This ratio provides a measure of labor market tightness, with higher values indicating greater difficulty in finding workers and potential wage pressures.
2.4 Consumer Demand Strength Component
Consumer Demand Strength represents the fourth component, combining consumer sentiment data with retail sales growth rates. Small businesses are disproportionately affected by consumer spending patterns, making this component crucial for assessing their operating environment. The theoretical justification comes from the permanent income hypothesis developed by Friedman (1957), which explains how consumer spending responds to both current conditions and future expectations.
The model weights consumer confidence and actual spending data to provide both forward-looking sentiment and contemporaneous demand indicators. The specification is:
Demand_Strength = β₁ × Consumer_Sentiment_t + β₂ × Retail_Sales_Growth_t
where β₁ and β₂ are determined through principal component analysis to maximize the explanatory power of the combined measure.
2.5 Input Cost Pressures Component
Input Cost Pressures form the fifth component, utilizing producer price index data to capture inflationary pressures on small business operations. This component is inversely weighted, recognizing that rising input costs negatively impact small business profitability and operating conditions. Small businesses typically have limited pricing power and face challenges in passing through cost increases to customers, making them particularly vulnerable to input cost inflation.
The theoretical foundation draws from cost-push inflation theory as described by Gordon (1988), which explains how supply-side price pressures affect business operations. The model employs a 90-day rate of change to capture medium-term cost trends while filtering out short-term volatility:
Cost_Pressure = -1 × (PPI_t / PPI_{t-90} - 1)
The negative weighting reflects the inverse relationship between input costs and business conditions.
2.6 Monetary Policy Impact Component
Monetary Policy Impact represents the sixth component, incorporating federal funds rates and yield curve dynamics. Small businesses are particularly sensitive to interest rate changes due to their higher reliance on variable-rate financing and limited access to capital markets. The theoretical foundation comes from monetary transmission mechanism theory as developed by Bernanke and Blinder (1992), which explains how monetary policy affects different segments of the economy.
The model calculates the absolute deviation of federal funds rates from a neutral 2% level, recognizing that both extremely low and high rates can create operational challenges for small enterprises. The yield curve component captures the shape of the term structure, which affects both borrowing costs and economic expectations:
Monetary_Impact = γ₁ × |Fed_Funds_Rate_t - 2.0| + γ₂ × (10Y_Yield_t - 2Y_Yield_t)
2.7 Currency Valuation Effects Component
Currency Valuation Effects constitute the seventh component, measuring the impact of US Dollar strength on small business competitiveness. A stronger dollar can benefit businesses with significant import components while disadvantaging exporters. The model employs Dollar Index volatility as a proxy for currency-related uncertainty that affects small business planning and operations.
The theoretical foundation draws from international trade theory and the work of Krugman (1987) on exchange rate effects on different business segments. Small businesses often lack hedging capabilities, making them more vulnerable to currency fluctuations:
Currency_Impact = -1 × DXY_Volatility_t
2.8 Regional Banking Health Component
The eighth and final component, Regional Banking Health, assesses the relative performance of regional banks compared to large financial institutions. Regional banks traditionally serve as primary lenders to small businesses, making their health a critical factor in small business credit availability and overall operating conditions.
This component draws from the literature on relationship banking as developed by Boot (2000), which demonstrates the importance of bank-borrower relationships, particularly for small enterprises. The calculation compares regional bank performance to large financial institutions:
Banking_Health = (Regional_Banks_Index_t / Large_Banks_Index_t) - 1
3. Statistical Methodology and Advanced Analytics
The model employs statistical techniques to ensure robustness and reliability. Z-score normalization is applied to each component using rolling 252-day windows, providing standardized measures that remain consistent across different time periods and market conditions. This approach follows the methodology established by Engle and Granger (1987) in their cointegration analysis framework.
3.1 Variance-Weighted Aggregation
The composite index calculation utilizes variance-weighted aggregation, where component weights are determined by the inverse of their historical variance. This approach, derived from modern portfolio theory, ensures that more stable components receive higher weights while reducing the impact of highly volatile factors. The mathematical formulation follows the principle that optimal weights are inversely proportional to variance, maximizing the signal-to-noise ratio of the composite indicator.
The weight for component i is calculated as:
w_i = (1/σᵢ²) / Σⱼ(1/σⱼ²)
where σᵢ² represents the variance of component i over the lookback period.
3.2 Higher-Order Moment Analysis
Higher-order moment analysis extends beyond traditional mean and variance calculations to include skewness and kurtosis measurements. Skewness provides insight into the asymmetry of the sentiment distribution, while kurtosis measures the tail behavior and potential for extreme events. These metrics offer valuable information about the underlying distribution characteristics and potential regime changes.
Skewness is calculated as:
Skewness = E / σ³
Kurtosis is calculated as:
Kurtosis = E / σ⁴ - 3
where μ represents the mean and σ represents the standard deviation of the distribution.
3.3 Regime-Switching Detection
The model incorporates regime-switching detection capabilities based on the Hamilton (1989) framework. This allows for identification of different economic regimes characterized by distinct statistical properties. The regime classification employs percentile-based thresholds:
- Regime 3 (Very High): Percentile rank > 80
- Regime 2 (High): Percentile rank 60-80
- Regime 1 (Moderate High): Percentile rank 50-60
- Regime 0 (Neutral): Percentile rank 40-50
- Regime -1 (Moderate Low): Percentile rank 30-40
- Regime -2 (Low): Percentile rank 20-30
- Regime -3 (Very Low): Percentile rank < 20
3.4 Information Theory Applications
The model incorporates information theory concepts, specifically Shannon entropy measurement, to assess the information content of the sentiment distribution. Shannon entropy, as developed by Shannon (1948), provides a measure of the uncertainty or information content in a probability distribution:
H(X) = -Σᵢ p(xᵢ) log₂ p(xᵢ)
Higher entropy values indicate greater unpredictability and information content in the sentiment series.
3.5 Long-Term Memory Analysis
The Hurst exponent calculation provides insight into the long-term memory characteristics of the sentiment series. Originally developed by Hurst (1951) for analyzing Nile River flow patterns, this measure has found extensive application in financial time series analysis. The Hurst exponent H is calculated using the rescaled range statistic:
H = log(R/S) / log(T)
where R/S represents the rescaled range and T represents the time period. Values of H > 0.5 indicate long-term positive autocorrelation (persistence), while H < 0.5 indicates mean-reverting behavior.
3.6 Structural Break Detection
The model employs Chow test approximation for structural break detection, based on the methodology developed by Chow (1960). This technique identifies potential structural changes in the underlying relationships by comparing the stability of regression parameters across different time periods:
Chow_Statistic = (RSS_restricted - RSS_unrestricted) / RSS_unrestricted × (n-2k)/k
where RSS represents residual sum of squares, n represents sample size, and k represents the number of parameters.
4. Implementation Parameters and Configuration
4.1 Language Selection Parameters
The model provides comprehensive multi-language support across five languages: English, German (Deutsch), Spanish (Español), French (Français), and Japanese (日本語). This feature enhances accessibility for international users and ensures cultural appropriateness in terminology usage. The language selection affects all internal displays, statistical classifications, and alert messages while maintaining consistency in underlying calculations.
4.2 Model Configuration Parameters
Calculation Method: Users can select from four aggregation methodologies:
- Equal-Weighted: All components receive identical weights
- Variance-Weighted: Components weighted inversely to their historical variance
- Principal Component: Weights determined through principal component analysis
- Dynamic: Adaptive weighting based on recent performance
Sector Specification: The model allows for sector-specific calibration:
- General: Broad-based small business assessment
- Retail: Emphasis on consumer demand and seasonal factors
- Manufacturing: Enhanced weighting of input costs and currency effects
- Services: Focus on labor market dynamics and consumer demand
- Construction: Emphasis on credit conditions and monetary policy
Lookback Period: Statistical analysis window ranging from 126 to 504 trading days, with 252 days (one business year) as the optimal default based on academic research.
Smoothing Period: Exponential moving average period from 1 to 21 days, with 5 days providing optimal noise reduction while preserving signal integrity.
4.3 Statistical Threshold Parameters
Upper Statistical Boundary: Configurable threshold between 60-80 (default 70) representing the upper significance level for regime classification.
Lower Statistical Boundary: Configurable threshold between 20-40 (default 30) representing the lower significance level for regime classification.
Statistical Significance Level (α): Alpha level for statistical tests, configurable between 0.01-0.10 with 0.05 as the standard academic default.
4.4 Display and Visualization Parameters
Color Theme Selection: Eight professional color schemes optimized for different user preferences and accessibility requirements:
- Gold: Traditional financial industry colors
- EdgeTools: Professional blue-gray scheme
- Behavioral: Psychology-based color mapping
- Quant: Value-based quantitative color scheme
- Ocean: Blue-green maritime theme
- Fire: Warm red-orange theme
- Matrix: Green-black technology theme
- Arctic: Cool blue-white theme
Dark Mode Optimization: Automatic color adjustment for dark chart backgrounds, ensuring optimal readability across different viewing conditions.
Line Width Configuration: Main index line thickness adjustable from 1-5 pixels for optimal visibility.
Background Intensity: Transparency control for statistical regime backgrounds, adjustable from 90-99% for subtle visual enhancement without distraction.
4.5 Alert System Configuration
Alert Frequency Options: Three frequency settings to match different trading styles:
- Once Per Bar: Single alert per bar formation
- Once Per Bar Close: Alert only on confirmed bar close
- All: Continuous alerts for real-time monitoring
Statistical Extreme Alerts: Notifications when the index reaches 99% confidence levels (Z-score > 2.576 or < -2.576).
Regime Transition Alerts: Notifications when statistical boundaries are crossed, indicating potential regime changes.
5. Practical Application and Interpretation Guidelines
5.1 Index Interpretation Framework
The SBO-SAM index operates on a 0-100 scale with statistical normalization ensuring consistent interpretation across different time periods and market conditions. Values above 70 indicate statistically elevated small business conditions, suggesting favorable operating environment with potential for expansion and growth. Values below 30 indicate statistically reduced conditions, suggesting challenging operating environment with potential constraints on business activity.
The median reference line at 50 represents the long-term equilibrium level, with deviations providing insight into cyclical conditions relative to historical norms. The statistical confidence bands at 95% levels (approximately ±2 standard deviations) help identify when conditions reach statistically significant extremes.
5.2 Regime Classification System
The model employs a seven-level regime classification system based on percentile rankings:
Very High Regime (P80+): Exceptional small business conditions, typically associated with strong economic growth, easy credit availability, and favorable regulatory environment. Historical analysis suggests these periods often precede economic peaks and may warrant caution regarding sustainability.
High Regime (P60-80): Above-average conditions supporting business expansion and investment. These periods typically feature moderate growth, stable credit conditions, and positive consumer sentiment.
Moderate High Regime (P50-60): Slightly above-normal conditions with mixed signals. Careful monitoring of individual components helps identify emerging trends.
Neutral Regime (P40-50): Balanced conditions near long-term equilibrium. These periods often represent transition phases between different economic cycles.
Moderate Low Regime (P30-40): Slightly below-normal conditions with emerging headwinds. Early warning signals may appear in credit conditions or consumer demand.
Low Regime (P20-30): Below-average conditions suggesting challenging operating environment. Businesses may face constraints on growth and expansion.
Very Low Regime (P0-20): Severely constrained conditions, typically associated with economic recessions or financial crises. These periods often present opportunities for contrarian positioning.
5.3 Component Analysis and Diagnostics
Individual component analysis provides valuable diagnostic information about the underlying drivers of overall conditions. Divergences between components can signal emerging trends or structural changes in the economy.
Credit-Labor Divergence: When credit conditions improve while labor markets tighten, this may indicate early-stage economic acceleration with potential wage pressures.
Demand-Cost Divergence: Strong consumer demand coupled with rising input costs suggests inflationary pressures that may constrain small business margins.
Market-Fundamental Divergence: Disconnection between small-cap equity performance and fundamental conditions may indicate market inefficiencies or changing investor sentiment.
5.4 Temporal Analysis and Trend Identification
The model provides multiple temporal perspectives through momentum analysis, rate of change calculations, and trend decomposition. The 20-day momentum indicator helps identify short-term directional changes, while the Hodrick-Prescott filter approximation separates cyclical components from long-term trends.
Acceleration analysis through second-order momentum calculations provides early warning signals for potential trend reversals. Positive acceleration during declining conditions may indicate approaching inflection points, while negative acceleration during improving conditions may suggest momentum loss.
5.5 Statistical Confidence and Uncertainty Quantification
The model provides comprehensive uncertainty quantification through confidence intervals, volatility measures, and regime stability analysis. The 95% confidence bands help users understand the statistical significance of current readings and identify when conditions reach historically extreme levels.
Volatility analysis provides insight into the stability of current conditions, with higher volatility indicating greater uncertainty and potential for rapid changes. The regime stability measure, calculated as the inverse of volatility, helps assess the sustainability of current conditions.
6. Risk Management and Limitations
6.1 Model Limitations and Assumptions
The SBO-SAM model operates under several important assumptions that users must understand for proper interpretation. The model assumes that historical relationships between economic variables remain stable over time, though the regime-switching framework helps accommodate some structural changes. The 252-day lookback period provides reasonable statistical power while maintaining sensitivity to changing conditions, but may not capture longer-term structural shifts.
The model's reliance on publicly available economic data introduces inherent lags in some components, particularly those based on government statistics. Users should consider these timing differences when interpreting real-time conditions. Additionally, the model's focus on quantitative factors may not fully capture qualitative factors such as regulatory changes, geopolitical events, or technological disruptions that could significantly impact small business conditions.
The model's timeframe restrictions ensure statistical validity by preventing application to intraday periods where the underlying economic relationships may be distorted by market microstructure effects, trading noise, and temporal misalignment with the fundamental data sources. Users must utilize daily or longer timeframes to ensure the model's statistical foundations remain valid and interpretable.
6.2 Data Quality and Reliability Considerations
The model's accuracy depends heavily on the quality and availability of underlying economic data. Market-based components such as equity indices and bond prices provide real-time information but may be subject to short-term volatility unrelated to fundamental conditions. Economic statistics provide more stable fundamental information but may be subject to revisions and reporting delays.
Users should be aware that extreme market conditions may temporarily distort some components, particularly those based on financial market data. The model's statistical normalization helps mitigate these effects, but users should exercise additional caution during periods of market stress or unusual volatility.
6.3 Interpretation Caveats and Best Practices
The SBO-SAM model provides statistical analysis and should not be interpreted as investment advice or predictive forecasting. The model's output represents an assessment of current conditions based on historical relationships and may not accurately predict future outcomes. Users should combine the model's insights with other analytical tools and fundamental analysis for comprehensive decision-making.
The model's regime classifications are based on historical percentile rankings and may not fully capture the unique characteristics of current economic conditions. Users should consider the broader economic context and potential structural changes when interpreting regime classifications.
7. Academic References and Bibliography
Bernanke, B. S., & Blinder, A. S. (1992). The Federal Funds Rate and the Channels of Monetary Transmission. American Economic Review, 82(4), 901-921.
Bernanke, B. S., & Gertler, M. (1995). Inside the Black Box: The Credit Channel of Monetary Policy Transmission. Journal of Economic Perspectives, 9(4), 27-48.
Boot, A. W. A. (2000). Relationship Banking: What Do We Know? Journal of Financial Intermediation, 9(1), 7-25.
Chow, G. C. (1960). Tests of Equality Between Sets of Coefficients in Two Linear Regressions. Econometrica, 28(3), 591-605.
Dunkelberg, W. C., & Wade, H. (2023). NFIB Small Business Economic Trends. National Federation of Independent Business Research Foundation, Washington, D.C.
Engle, R. F., & Granger, C. W. J. (1987). Co-integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55(2), 251-276.
Fama, E. F. (1970). Efficient Capital Markets: A Review of Theory and Empirical Work. Journal of Finance, 25(2), 383-417.
Federal Reserve Board. (2024). Senior Loan Officer Opinion Survey on Bank Lending Practices. Board of Governors of the Federal Reserve System, Washington, D.C.
Friedman, M. (1957). A Theory of the Consumption Function. Princeton University Press, Princeton, NJ.
Gordon, R. J. (1988). The Role of Wages in the Inflation Process. American Economic Review, 78(2), 276-283.
Hamilton, J. D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2), 357-384.
Hurst, H. E. (1951). Long-term Storage Capacity of Reservoirs. Transactions of the American Society of Civil Engineers, 116(1), 770-799.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
Krugman, P. (1987). Pricing to Market When the Exchange Rate Changes. In S. W. Arndt & J. D. Richardson (Eds.), Real-Financial Linkages among Open Economies (pp. 49-70). MIT Press, Cambridge, MA.
Markowitz, H. (1952). Portfolio Selection. Journal of Finance, 7(1), 77-91.
Mortensen, D. T., & Pissarides, C. A. (1994). Job Creation and Job Destruction in the Theory of Unemployment. Review of Economic Studies, 61(3), 397-415.
Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379-423.
Sharpe, W. F. (1964). Capital Asset Prices: A Theory of Market Equilibrium under Conditions of Risk. Journal of Finance, 19(3), 425-442.
Tsay, R. S. (2010). Analysis of Financial Time Series (3rd ed.). John Wiley & Sons, Hoboken, NJ.
U.S. Small Business Administration. (2024). Small Business Profile. Office of Advocacy, Washington, D.C.
8. Technical Implementation Notes
The SBO-SAM model is implemented in Pine Script version 6 for the TradingView platform, ensuring compatibility with modern charting and analysis tools. The implementation follows best practices for financial indicator development, including proper error handling, data validation, and performance optimization.
The model includes comprehensive timeframe validation to ensure statistical accuracy and reliability. The indicator operates exclusively on daily (1D) timeframes or higher, including weekly (1W), monthly (1M), and longer periods. This restriction ensures that the statistical analysis maintains appropriate temporal resolution for the underlying economic data sources, which are primarily reported on daily or longer intervals.
When users attempt to apply the model to intraday timeframes (such as 1-minute, 5-minute, 15-minute, 30-minute, 1-hour, 2-hour, 4-hour, 6-hour, 8-hour, or 12-hour charts), the system displays a comprehensive error message in the user's selected language and prevents execution. This safeguard protects users from potentially misleading results that could occur when applying daily-based economic analysis to shorter timeframes where the underlying data relationships may not hold.
The model's statistical calculations are performed using vectorized operations where possible to ensure computational efficiency. The multi-language support system employs Unicode character encoding to ensure proper display of international characters across different platforms and devices.
The alert system utilizes TradingView's native alert functionality, providing users with flexible notification options including email, SMS, and webhook integrations. The alert messages include comprehensive statistical information to support informed decision-making.
The model's visualization system employs professional color schemes designed for optimal readability across different chart backgrounds and display devices. The system includes dynamic color transitions based on momentum and volatility, professional glow effects for enhanced line visibility, and transparency controls that allow users to customize the visual intensity to match their preferences and analytical requirements. The clean confidence band implementation provides clear statistical boundaries without visual distractions, maintaining focus on the analytical content.
Long Multi-TimeframeTo be used on a 30 minute time frame with Market Bias changing from red to light red or green, 4 or more consecutive red dots on the 15 minute and 30 minute frames inside the market bias, and a red to green Bx-Trender, backed up with good flow (real-time plus green net cumulative flow).
Pro AI Trading - Month Week OpenThis is a indicator that primarily marks monthly 1 hour initial balances, while highlighting every yearly half/quarter. Additionally has 9 different types of MA bands + D/W/M vertical separators. Marks custom % pivot points for easier zone marking. Possibility of generating signals based on mid line candle crosses.
$ - HTF Sweeps & PO3HTF Sweeps & PO3 Indicator
The HTF Sweeps & PO3 indicator is a powerful tool designed for traders to visualise higher timeframe (HTF) candles, identify liquidity sweeps, and track key price levels on a lower timeframe (LTF) chart. Built for TradingView using Pine Script v6, it overlays HTF candle data and highlights significant price movements, such as sweeps of previous highs or lows, to help traders identify potential liquidity sweep and reversal points. The indicator is highly customisable, offering a range of visual and alert options to suit various trading strategies.
Features
Higher Timeframe (HTF) Candle Visualisation:
- Displays up to three user-defined HTF candles (e.g., 15m, 1H, 4H) overlaid on the LTF chart.
- Customisable candle appearance with adjustable size (Tiny to Huge), offset, spacing, and colours for bullish/bearish candles and wicks.
- Option to show timeframe labels above or below HTF candles with configurable size and position.
Liquidity Sweep Detection:
- Identifies bullish and bearish sweeps when price moves beyond the high or low of a previous HTF candle and meets specific conditions.
- Displays sweeps on both LTF and HTF with customisable line styles (Solid, Dashed, Dotted), widths, and colours.
- Option to show only the most recent sweep per candle to reduce chart clutter.
Invalidated Sweep Tracking:
- Detects and visualises invalidated sweeps (when price moves past a sweep level in the opposite direction).
- Configurable display for invalidated sweeps on LTF and HTF with distinct line styles and colours.
Previous High/Low Lines:
- Plots horizontal lines at the high and low of the previous HTF candle, extending on both LTF and HTF.
- Customisable line style, width, and color for easy identification of key levels.
- Real-Time Sweep Detection:
-Optional real-time sweep visualisation for active candles, enabling traders to monitor developing price action.
Alert System:
- Triggers alerts for sweep formation (when a new sweep is detected).
- Triggers alerts for sweep invalidation (when a sweep is no longer valid).
- Alerts include details such as timeframe, ticker, and price level for precise notifications.
Performance Optimisation:
- Efficiently manages resources with configurable limits for lines, labels, boxes, and bars (up to 500 each).
- Cleans up outdated visual elements to maintain chart clarity.
Flexible Configuration:
- Supports multiple timeframes for HTF candles with user-defined settings for visibility and number of candles displayed (1–60).
- Toggle visibility for HTF candles, sweeps, invalidated sweeps, and high/low lines independently for LTF and HTF.
This indicator is ideal for traders focusing on liquidity hunting, order block analysis, or price action strategies, providing clear visual cues and alerts to enhance decision-making.
Momentum Moving Averages | MisinkoMasterThe Momentum Moving Averages (MMA) indicator blends multiple moving averages into a single momentum-scoring framework, helping traders identify whether market conditions are favoring upside momentum or downside momentum.
By comparing faster, more adaptive moving averages (DEMA, TEMA, ALMA, HMA) against a baseline EMA, the MMA produces a cumulative score that reflects the prevailing strength and direction of the trend.
🔎 Methodology
Moving Averages Used
EMA (Exponential Moving Average) → Baseline reference.
DEMA (Double Exponential Moving Average) → Reacts faster than EMA.
TEMA (Triple Exponential Moving Average) → Even faster, reduces lag further.
ALMA (Arnaud Legoux Moving Average) → Smooth but adaptive, with adjustable σ and offset.
HMA (Hull Moving Average) → Very responsive, reduces lag, ideal for momentum shifts.
Scoring System
Each comparison is made against the EMA baseline:
If another MA is above EMA → +1 point.
If another MA is below EMA → -1 point.
The total score reflects overall momentum:
Positive score → Bullish bias.
Negative score → Bearish bias.
Trend Logic
Bullish Signal → When the score crosses above 0.1.
Bearish Signal → When the score crosses below -0.1.
Neutral or sideways trends are identified when the score remains between thresholds.
📈 Visualization
All five moving averages are plotted on the chart.
Colors adapt to the current score:
Cyan (Bullish bias) → Positive momentum.
Magenta (Bearish bias) → Negative momentum.
Overlapping fills between MAs highlight zones of convergence/divergence, making momentum shifts visually clear.
⚡ Features
Adjustable length parameter for all MAs.
Adjustable ALMA parameters (sigma and offset).
Cumulative momentum score system to filter false signals.
Works across all markets (crypto, forex, stocks, indices).
Overlay design for direct chart integration.
✅ Use Cases
Trend Confirmation → Ensure alignment with market momentum.
Momentum Shifts → Spot when faster MAs consistently outperform the baseline EMA.
Entry & Exit Filter → Avoid trades when the score is neutral or indecisive.
Divergence Visualizer → Filled zones make it easier to see when MAs begin separating or converging.
Low History Required → Unlike most For Loops, this script does not require that much history, making it less lagging and more responsive
⚠️ Limitations
Works best in trending conditions; performance decreases in sideways/choppy ranges.
Sensitivity of signals depends on chosen length and ALMA settings.
Should not be used as a standalone buy/sell system—combine with volume, structure, or higher timeframe analysis.
