Opening Range IndicatorComplete Trading Guide: Opening Range Breakout Strategy
What Are Opening Ranges?
Opening ranges capture the high and low prices during the first few minutes of market open. These levels often act as key support and resistance throughout the trading day because:
Heavy volume occurs at market open as overnight orders execute
Institutional activity is concentrated during opening minutes
Price discovery happens as market participants react to overnight news
Psychological levels are established that traders watch all day
Understanding the Three Timeframes
OR5 (5-Minute Range: 9:30-9:35 AM)
Most sensitive - captures immediate market reaction
Quick signals but higher false breakout rate
Best for scalping and momentum trading
Use for early entry when conviction is high
OR15 (15-Minute Range: 9:30-9:45 AM)
Balanced approach - most popular among day traders
Moderate sensitivity with better reliability
Good for swing trades lasting several hours
Primary timeframe for most strategies
OR30 (30-Minute Range: 9:30-10:00 AM)
Most reliable but slower signals
Lower false breakout rate
Best for position trades and trend following
Use when looking for major moves
Core Trading Strategies
Strategy 1: Basic Breakout
Setup:
Wait for price to break above OR15 high or below OR15 low
Enter on the breakout candle close
Stop loss: Opposite side of the range
Target: 2-3x the range size
Example:
OR15 range: $100.00 - $102.00 (Range = $2.00)
Long entry: Break above $102.00
Stop loss: $99.50 (below OR15 low)
Target: $104.00+ (2x range size)
Strategy 2: Multiple Confirmation
Setup:
Wait for OR5 break first (early signal)
Confirm with OR15 break in same direction
Enter on OR15 confirmation
Stop: Below OR30 if available, or OR15 opposite level
Why it works:
Multiple timeframe confirmation reduces false signals and increases probability of sustained moves.
Strategy 3: Failed Breakout Reversal
Setup:
Price breaks OR15 level but fails to hold
Wait for re-entry into the range
Enter reversal trade toward opposite OR level
Stop: Recent breakout high/low
Target: Opposite side of range + extension
Key insight: Failed breakouts often lead to strong moves in the opposite direction.
Advanced Techniques
Range Quality Assessment
High-Quality Ranges (Trade these):
Range size: 0.5% - 2% of stock price
Clean boundaries (not choppy)
Volume spike during range formation
Clear rejection at range levels
Low-Quality Ranges (Avoid these):
Very narrow ranges (<0.3% of stock price)
Extremely wide ranges (>3% of stock price)
Choppy, overlapping candles
Low volume during formation
Volume Confirmation
For Breakouts:
Look for volume spike (2x+ average) on breakout
Declining volume often signals false breakout
Rising volume during range formation shows interest
Market Context Filters
Best Conditions:
Trending market days (SPY/QQQ with clear direction)
Earnings reactions or news-driven moves
High-volume stocks with good liquidity
Volatility above average (VIX considerations)
Avoid Trading When:
Extremely low volume days
Major economic announcements pending
Holidays or half-days
Choppy, sideways market conditions
Risk Management Rules
Position Sizing
Conservative: Risk 0.5% of account per trade
Moderate: Risk 1% of account per trade
Aggressive: Risk 2% maximum per trade
Stop Loss Placement
Inside the range: Quick exit but higher stop-out rate
Outside opposite level: More room but larger risk
ATR-based: 1.5-2x Average True Range below entry
Profit Taking
Target 1: 1x range size (take 50% off)
Target 2: 2x range size (take 25% off)
Runner: Trail remaining 25% with moving stops
Specific Entry Techniques
Breakout Entry Methods
Method 1: Immediate Entry
Enter as soon as price closes above/below range
Fastest entry but highest false signal rate
Best for strong momentum situations
Method 2: Pullback Entry
Wait for breakout, then pullback to range level
Enter when price bounces off former resistance/support
Better risk/reward but may miss some moves
Method 3: Volume Confirmation
Wait for breakout + volume spike
Enter after volume confirmation candle
Reduces false signals significantly
Multiple Timeframe Entries
Aggressive: OR5 break → immediate entry
Conservative: OR5 + OR15 + OR30 all align → enter
Balanced: OR15 break with OR30 support → enter
Common Mistakes to Avoid
1. Trading Poor-Quality Ranges
❌ Don't trade ranges that are too narrow or too wide
✅ Focus on clean, well-defined ranges with good volume
2. Ignoring Volume
❌ Don't chase breakouts without volume confirmation
✅ Always check for volume spike on breakouts
3. Over-Trading
❌ Don't force trades when ranges are unclear
✅ Wait for high-probability setups only
4. Poor Risk Management
❌ Don't risk more than planned or use tight stops in volatile conditions
✅ Stick to predetermined risk levels
5. Fighting the Trend
❌ Don't fade breakouts in strongly trending markets
✅ Align trades with overall market direction
Daily Trading Routine
Pre-Market (8:00-9:30 AM)
Check overnight news and earnings
Review major indices (SPY, QQQ, IWM)
Identify potential opening range candidates
Set alerts for range breakouts
Market Open (9:30-10:00 AM)
Watch opening range formation
Note volume and price action quality
Mark key levels on charts
Prepare for breakout signals
Trading Session (10:00 AM - 4:00 PM)
Execute breakout strategies
Manage existing positions
Trail stops as profits develop
Look for additional setups
Post-Market Review
Analyze winning and losing trades
Review range quality vs. outcomes
Identify improvement areas
Prepare for next session
Best Stocks/ETFs for Opening Range Trading
Large Cap Stocks (Best for beginners):
AAPL, MSFT, GOOGL, AMZN, TSLA
High liquidity, predictable behavior
Good range formation most days
ETFs (Consistent patterns):
SPY, QQQ, IWM, XLF, XLE
Excellent liquidity
Clear range boundaries
Mid-Cap Growth (Advanced traders):
Stocks with good volume (1M+ shares daily)
Recent news catalysts
Clean technical patterns
Performance Optimization
Track These Metrics:
Win rate by range type (OR5 vs OR15 vs OR30)
Average R/R (risk vs reward ratio)
Best performing market conditions
Time of day performance
Continuous Improvement:
Keep detailed trade journal
Review failed breakouts for patterns
Adjust position sizing based on win rate
Refine entry timing based on backtesting
Final Tips for Success
Start small - Paper trade or use tiny positions initially
Focus on quality - Better to miss trades than take bad ones
Stay disciplined - Stick to your rules even during losing streaks
Adapt to conditions - What works in trending markets may fail in choppy conditions
Keep learning - Markets evolve, so should your approach
The opening range strategy is powerful because it captures natural market behavior, but like all strategies, it requires practice, discipline, and proper risk management to be profitable long-term.
Cari dalam skrip untuk "profitable"
Candlestick Themes NYSE Pro [GPXalgo]The Critical Role of Color in Trading Performance
Professional trading environments demand visual systems that support rapid decision-making while
minimizing cognitive load and visual fatigue. The NYSE trading desk color schemes have evolved
through decades of refinement, incorporating feedback from over 10,000 active traders and
quantitative performance analysis.
Key Design Principles
1. Contrast Optimization
Minimum contrast ratio of 7:1 for critical data elements against dark backgrounds (#0A0A0A to
#1C1C1C).
2. Semantic Consistency
Universal color language across all trading platforms and instruments.
3. Fatigue Mitigation
Spectral distribution optimized for extended viewing periods without degradation in pattern
recognition.
4. Information Hierarchy
Clear visual prioritization of price action, volume, and technical indicators.
Scientific Foundation
Visual Perception in Trading Contexts
Neurological Processing
The human visual cortex processes color information 60,000 times faster than text. In trading
contexts, this translates to:
• 0.13 seconds average recognition time for color-coded signals
• 0.45 seconds for text-based information
• 72% improvement in pattern recognition with optimized color schemes
Circadian Rhythm Consideration
Trading desk colors are calibrated to minimize melatonin suppression during extended sessions:
• Blue light emission reduced by 65% compared to standard displays
• Warm-spectrum alternatives for overnight sessions
• Adaptive brightness curves aligned with natural circadian cycles
Eye Strain Metrics
Laboratory studies (n=500 traders, 6-month period) demonstrate:
• 43% reduction in reported eye strain
• 31% decrease in headache frequency• 28% improvement in focus duration
• 17% increase in profitable trade execution
Implementation Standards
Display Calibration Requirements
Monitor Specifications
Minimum 1000:1 contrast ratio
sRGB coverage ≥ 99%
Delta E < 2.0 color accuracy
Brightness: 120-150 cd/m² (dark environment)
Color temperature: 5800K ± 200K
Multi-Monitor Consistency
• Maximum ΔE variance between displays: 1.5
• Synchronized brightness across array
• Uniform color profiles (ICC v4)
Accessibility Compliance
WCAG 2.1 Level AA Standards
Normal text: 4.5:1 contrast minimum
Large text: 3:1 contrast minimum
Interactive elements: 3:1 contrast minimum
Focus indicators: 3:1 contrast minimum
Colorblind Accommodation All critical information maintains distinguishability under:
• Protanopia (red-blind)
• Deuteranopia (green-blind)
• Tritanopia (blue-blind)
Expected Value Monte CarloI created this indicator after noticing that there was no Expected Value indicator here on TradingView.
The EVMC provides statistical Expected Value to what might happen in the future regarding the asset you are analyzing.
It uses 2 quantitative methods:
Historical Backtest to ground your analysis in long-term, factual data.
Monte Carlo Simulation to project a cone of probable future outcomes based on recent market behavior.
This gives you a data-driven edge to quantify risk, and make more informed trading decisions.
The indicator includes:
Dual analysis: Combines historical probability with forward-looking simulation.
Quantified projections: Provides the Expected Value ($ and %), Win Rate, and Sharpe Ratio for both methods.
Asset-aware: Automatically adjusts its calculations for Stocks (252 trading days) and Crypto (365 days) for mathematical accuracy.
The projection cone shows the mean expected path and the +/- 1 standard deviation range of outcomes.
No repainting
Calculation:
1. Historical Expected Value:
This is a systematic backtest over thousands of bars. It calculates the return Rᵢ for N past trades (buy-and-hold). The Historical EV is the simple average of these returns, giving a baseline performance measure.
Historical EV % = (Σ Rᵢ) / N
2. Monte Carlo Projection:
This projection uses the Geometric Brownian Motion (GBM) model to simulate thousands of future price paths based on the market's recent behavior.
It first measures the drift (μ), or recent trend, and volatility (σ), or recent risk, from the Projection Lookback period. It then projects a final return for each simulation using the core GBM formula:
Projected Return = exp( (μ - σ²/2)T + σ√T * Z ) - 1
(Where T is the time horizon and Z is a random variable for the simulation.)
The purple line on the chart is the average of all simulated outcomes (the Monte Carlo EV). The cone represents one standard deviation of those outcomes.
The dashed lines represent one standard deviation (+/- 1σ) from the average, forming a cone of probable outcomes. Roughly 68% of the simulated paths ended within this cone.
This projection answers the question: "If the recent trend and volatility continue, where is the price most likely to go?"
Here's how to read the indicator
Expected Value ($/%): Is my average trade profitable?
Win Rate: How often can I expect to be right?
Sharpe Ratio: Am I being adequately compensated for the risk I'm taking?
User Guide
Max trade duration (bars): This is your analysis timeframe. Are you interested in the probable outcome over the next month (21 bars), quarter (63 bars), or year (252 bars)?
Position size ($): Set this to your typical trade size to see the Expected Value in real dollar terms.
Projection lookback (bars): This is the most important input for the Monte Carlo model. A short lookback (e.g., 50) makes the projection highly sensitive to recent momentum. Use this to identify potential recency bias. A long lookback (e.g., 252) provides a more stable, long-term projection of trend and volatility.
Historical Lookback (bars): For the historical backtest, more data is always better. Use the maximum that your TradingView plan allows for the most statistically significant results.
Use TP/SL for Historical EV: Check this box to see how the historical performance would have changed if you had used a simple Take Profit and Stop Loss, rather than just holding for the full duration.
I hope you find this indicator useful and please let me know if you have any suggestions. 😊
ORB Breakout Strategy with reversalORB 1,5,15,30,60min with reversals, its my first strategy Im not 100% sure it works well. Im not a programmer nor a profitable trader.
Max stoploss in points sets maximum fixed stoploss
Stop offset sets additional points below/above signal bar
RR Ratio is pretty self explanatory, it sets target based on stoploss
American session is time when positions can be opened
ORB SessionIs basically almost the same but when the time runs it closes all positions\
ORB candle timeframe is the time which orb is measured
Enable reverse position enables reversing positions on stoploss of first position, stoploss of reverse position is based on max stoploss and target is set by RR times max stoploss
Im sharing this to share this with my friends, discuss some things and dont have to test it manually.
I made it all myself and with help of AI
Sorry for bad english
Multipower Entry SecretMultipower Entry Secret indicator is designed to be the ultimate trading companion for traders of all skill levels—especially those who struggle with decision-making due to unclear or overwhelming signals. Unlike conventional trading systems cluttered with too many lines and confusing alerts, this indicator provides a clear, adaptive, and actionable guide for market entries and exits.
Key Points:
Clear Buy/Sell/Wait Signals:
The script dynamically analyzes price action, candle patterns, volume, trend strength, and higher time frame context. This means it gives you “Buy,” “Sell,” or “Wait” signals based on real, meaningful market information—filtering out the noise and weak trades.
Multi-Timeframe Adaptive Analysis:
It synchronizes signals between higher and current timeframes, ensuring you get the most reliable direction—reducing the risk of getting caught in fake moves or sudden reversals.
Automatic Support, Resistance & Liquidity Zones:
Key levels like support, resistance, and liquidity zones are auto-detected and displayed directly on the chart, helping you make precise decisions without manual drawing.
Real-Time Dashboard:
All relevant information, such as trend strength, market intent, volume sentiment, and the reason behind each signal, is neatly summarized in a dashboard—making monitoring effortless and intuitive.
Customizable & Beginner-Friendly:
Whether you’re a newcomer wanting straightforward guidance or a professional needing advanced customization, the indicator offers flexible options to adjust analysis depth, timeframes, sensitivity, and more.
Visual & Clutter-Free:
The design ensures that your chart remains clean and readable, showing only the most important information. This minimizes mental overload and allows for instant decision-making.
Who Will Benefit?
Beginners who want to learn trading logic, avoid common traps, and see the exact reason behind every signal.
Advanced traders who require adaptive multi-timeframe analytics, fast execution, and stress-free monitoring.
Anyone who wants to save screen time, reduce analysis paralysis, and have more confidence in every trade they take.
1. No Indicator Clutter
Intent:
Many traders get confused by charts filled with too many indicators and signals. This often leads to hesitation, missed trades, or taking random, risky trades.
In this Indicator:
You get a clean and clutter-free chart. Only the most important buy/sell/wait signals and relevant support/resistance/liquidity levels are shown. These update automatically, removing the “overload” and keeping your focus sharp, so your decision-making is faster and stress-free.
2. Exact Entry Guide
Intent:
Traders often struggle with entry timing, leading to FOMO (fear of missing out) or getting trapped in sudden market reversals.
In this Indicator:
The system uses powerful adaptive logic to filter out weak signals and only highlight the strongest market moves. This not only prevents you from entering late or on noise, but also helps avoid losses from false breakouts or whipsaws. You get actionable suggestions—when to enter, when to hold back—so your entries are high-conviction and disciplined.
3. HTF+LTF Logic: Multitimeframe Sync Analysis
Intent:
Most losing trades happen when you act only on the short-term chart, ignoring the bigger market trend.
In this Indicator:
Signals are based on both the current chart timeframe (LTF) and a higher (HTF, like hourly/daily) timeframe. The indicator synchronizes trend direction, momentum, and structure across both levels, quickly adapting to show you when both are aligned. This filtering results in “only trade with the bigger trend”—dramatically increasing your win rate and market confidence.
4. Auto Support/Resistance & Liquidity Zones
Intent:
Drawing support/resistance and liquidity zones manually is time-consuming and error-prone, especially for beginners.
In this Indicator:
The system automatically identifies and plots the most crucial support/resistance levels and liquidity zones on your chart. This is based on adaptive, real-time price and volume analysis. These zones highlight where major institutional activity, trap setups, or real breakouts/reversals are most likely, removing guesswork and giving you a clear reference for entries, exits, and stop placements.
5. Clear Action/Direction
Intent:
Traders need certainty—what does the market want right now? Most indicators are vague.
In this Indicator:
Your dashboard always displays in plain words (like “BUY”, “SELL”, or “WAIT”) what action makes sense in the current market phase. Whether it’s a bull trap, volume spike, wick reversal, or exhaustion—it’s interpreted and explained clearly. No more confusion—just direct, real-time advice.
6. For Everyone (Beginner to Pro)
Intent:
Most advanced indicators are overwhelming for new traders; simple ones lack depth for professionals.
In this Indicator:
It is simple enough for a beginner—just add it to the chart and instantly see what action to consider. At the same time, it includes advanced adaptive analysis, multi-timeframe logic, and customizable settings so professional traders can fine-tune it for their strategies.
7. Ideal Usage and User Benefits
Instant Decision Support:
Whenever you’re unsure about a trade, just look at the indicator’s suggestion for clarity.
Entry Learning:
Beginners get real-time “practice” by not only seeing signals, but also the reason behind them—improving your chart reading and market understanding.
Screen Time & Stress Reduction:
Clear, relevant information only; no noise, less fatigue, faster decisions.
Makes Trading Confident & Simple:
The smart dashboard splits actionable levels (HTF, LTF, action) so you never miss a move, avoid traps, and stay aligned with high-probability trades.
8. Advanced Input Settings (Smart Customization)
Explained with Examples:
Enable Wick Analysis:
Finds candles with strong upper/lower wicks (signs of rejection/buying/selling force), alerting you to hidden reversals and protecting from FOMO entries.
Enable Absorption:
Detects when heavy order flow from one side is “absorbed” by the other (shows where institutional buyers/sellers are likely active, helps spot fake breakouts).
Enable Unusual Breakout:
Highlights real breakouts—large volatility plus high volume—so you catch genuine moves and avoid random spikes.
Enable Range/Expansion:
Smartly flags sudden range expansions—when the market goes from quiet to volatile—so you can act at the start of real trends.
Trend Bar Lookback:
Adjusts how many bars/candles are used in trend calculations. Short (fast trades, more signals), long (more reliability, fewer whipsaws).
Bull/Bear Bars for Strong Trend Min:
Sets how many candles in a row must support a trend before calling it “strong”—prevents flipping signals, keeps you disciplined.
Volume MA Length:
Lets you adjust how many bars back volume is averaged—fine-tune for your asset and trading style for best volume signals.
Swing Lookback Bars:
Set how many bars to use for swing high/low detection—short (quick swing levels), long (stronger support/resistance).
HTF (Bias Window):
Decide which higher timeframe the indicator should use for big-picture market mood. Adjustable for any style (scalp, swing, position).
Adaptive Lookback (HTF):
Choose how much HTF history is used for detecting major extremes/zones. Quick adjust for more/less sensitivity.
Show Support/Resistance, Liquidity Zones, Trendlines:
Toggle them on/off instantly per your needs—keeps your chart relevant and tailored.
9. Live Dashboard Sections Explained
Intent HTF:
Shows if the bigger timeframe currently has a Bullish, Bearish, or Neutral (“Chop”) intent, based on strict volume/price body calculations. Instant clarity—no more guessing on trend bias.
HTF Bias:
Clear message about which side (buy/sell/sideways) controls the market on the higher timeframe, so you always trade with the “big money.”
Chart Action:
The central action for the current bar—Whether to Buy, Sell, or Wait—calculated from all indicator logic, not just one rule.
TrendScore Long/Short:
See how many candles in your chosen window were bullish or bearish, at a glance. Instantly gauge market momentum.
Reason (WHY):
Every time a signal appears, the “reason” cell tells you the primary logic (breakout, wick, strong trend, etc.) behind it. Full transparency and learning—never trade blindly.
Strong Trend:
Shows if the market is currently in a powerful trend or not—helping you avoid choppy, risky entries.
HTF Vol/Body:
Displays current higher timeframe volume and candle body %—helping spot when big players are active for higher probability trades.
Volume Sentiment:
A real-time analysis of market psychology (strong bullish/bearish, neutral)—making your decision-making much more confident.
10. Smart and User-Friendly Design
Multi-timeframe Adaptive:
All calculations can now be drawn from your choice of higher or current timeframe, ensuring signals are filtered by larger market context.
Flexible Table Position:
You can set the live dashboard/summary anywhere on the chart for best visibility.
Refined Zone Visualization:
Liquidity and order blocks are visually highlighted, auto-tuning for your settings and always cleaning up to stay clutter-free.
Multi-Lingual & Beginner Accessible:
With Hindi and simple English support, descriptions and settings are accessible for a wide audience—anyone can start using powerful trading logic with zero language barrier.
Efficient Labels & Clear Reasoning:
Signal labels and reasons are shown/removed dynamically so your chart stays informative, not messy.
Every detail of this indicator is designed to make trading both simpler and smarter—helping you avoid the common pitfalls, learn real price action, stay in sync with the market’s true mood, and act with discipline for higher consistency and confidence.
This indicator makes professional-grade market analysis accessible to everyone. It’s your trusted assistant for making smarter, faster, and more profitable trading decisions—providing not just signals, but also the “why” behind every action. With auto-adaptive logic, clear visuals, and strong focus on real trading needs, it lets you focus on capturing the moves that matter—every single time.
QZ Trend (Crypto Edition) v1.1a: Donchian, EMA, ATR, Liquidity/FThe "QZ Trend (Crypto Edition)" is a rules-based trend-following breakout strategy for crypto spot or perpetual contracts, focusing on following trends, prioritizing risk control, seeking small losses and big wins, and trading only when advantageous.
Key mechanisms include:
- Market filters: Screen favorable conditions via ADX (trend strength), dollar volume (liquidity), funding fee windows, session/weekend restrictions, and spot-long-only settings.
- Signals & entries: Based on price position relative to EMA and EMA trends, combined with breaking Donchian channel extremes (with ATR ratio confirmation), plus single-position rules and post-exit cooldowns.
- Position sizing: Calculate positions by fixed risk percentage; initial stop-loss is ATR-based, complying with exchange min/max lot requirements.
- Exits & risk management: Include initial stop-loss, trailing stop (tightens only), break-even rule (stop moves to entry when target floating profit is hit), time-based exit, and post-exit cooldowns.
- Pyramiding: Add positions only when profitable with favorable momentum, requiring ATR-based spacing; add size is a fraction of the base position, with layers sharing stop logic but having unique order IDs.
Charts display EMA, Donchian channels, current stop lines, and highlight low ADX, avoidable funding windows, and low-liquidity periods.
Recommend starting with 4H or 1D timeframes, with typical parameters varying by cycle. Liquidity settings differ by token; perpetuals should enable funding window filters, while spot requires "long-only" and matching fees. The strategy performs well in trends with quick stop-losses but faces whipsaws in ranges (filters mitigate but don’t eliminate noise). Share your symbol and timeframe for tailored parameters.
Composite Time ProfileComposite Time Profile Overlay (CTPO) - Market Profile Compositing Tool
Automatically composite multiple time periods to identify key areas of balance and market structure
What is the Composite Time Profile Overlay?
The Composite Time Profile Overlay (CTPO) is a Pine Script indicator that automatically composites multiple time periods to identify key areas of balance and market structure. It's designed for traders who use market profile concepts and need to quickly identify where price is likely to find support or resistance.
The indicator analyzes TPO (Time Price Opportunity) data across different timeframes and merges overlapping profiles to create composite levels that represent the most significant areas of balance. This helps you spot where institutional traders are likely to make decisions based on accumulated price action.
Why Use CTPO for Market Profile Trading?
Eliminate Manual Compositing Work
Instead of manually drawing and compositing profiles across different timeframes, CTPO does this automatically. You get instant access to composite levels without spending time analyzing each individual period.
Spot Areas of Balance Quickly
The indicator highlights the most significant areas of balance by compositing overlapping profiles. These areas often act as support and resistance levels because they represent where the most trading activity occurred across multiple time periods.
Focus on What Matters
Rather than getting lost in individual session profiles, CTPO shows you the composite levels that have been validated across multiple timeframes. This helps you focus on the levels that are most likely to hold.
How CTPO Works for Market Profile Traders
Automatic Profile Compositing
CTPO uses a proprietary algorithm that:
- Identifies period boundaries based on your selected timeframe (sessions, daily, weekly, monthly, or auto-detection)
- Calculates TPO profiles for each period using the C2M (Composite 2 Method) row sizing calculation
- Merges overlapping profiles using configurable overlap thresholds (default 50% overlap required)
- Updates composite levels as new price action develops in real-time
Key Levels for Market Profile Analysis
The indicator displays:
- Value Area High (VAH) and Value Area Low (VAL) levels calculated from composite TPO data
- Point of Control (POC) levels where most trading occurred across all composited periods
- Composite zones representing areas of balance with configurable transparency
- 1.618 Fibonacci extensions for breakout targets based on composite range
Multiple Timeframe Support
- Sessions: For intraday market profile analysis
- Daily: For swing trading with daily profiles
- Weekly: For position trading with weekly structure
- Monthly: For long-term market profile analysis
- Auto: Automatically selects timeframe based on your chart
Trading Applications for Market Profile Users
Support and Resistance Trading
Use composite levels as dynamic support and resistance zones. These levels often hold because they represent areas where significant trading decisions were made across multiple timeframes.
Breakout Trading
When composite levels break, they often lead to significant moves. The indicator calculates 1.618 Fibonacci extensions to give you clear targets for breakout trades.
Mean Reversion Strategies
Value Area levels represent the price range where most trading activity occurred. These levels often act as magnets, drawing price back when it moves too far from the mean.
Institutional Level Analysis
Composite levels represent areas where institutional traders have made significant decisions. These levels often hold more weight than traditional technical analysis levels because they're based on actual trading activity.
Key Features for Market Profile Traders
Smart Compositing Logic
- Automatic overlap detection using price range intersection algorithms
- Configurable overlap thresholds (minimum 50% overlap required for merging)
- Dead composite identification (profiles that become engulfed by newer composites)
- Real-time updates as new price action develops using barstate.islast optimization
Visual Customization
- Customizable colors for active, broken, and dead composites
- Adjustable transparency levels for each composite state
- Premium/Discount zone highlighting based on current price vs composite range
- TPO aggression coloring using TPO distribution analysis to identify buying/selling pressure
- Fibonacci level extensions with 1.618 target calculations based on composite range
Clean Chart Presentation
- Only shows the most relevant composite levels (maximum 10 active composites)
- Eliminates clutter from individual session profiles
- Focuses on areas of balance that matter most to current price action
Real-World Trading Examples
Day Trading with Session Composites
Use session-based composites to identify intraday areas of balance. The VAH and VAL levels often act as natural profit targets and stop-loss levels for scalping strategies.
Swing Trading with Daily Composites
Daily composites provide excellent swing trading levels. Look for price reactions at composite zones and use the 1.618 extensions for profit targets.
Position Trading with Weekly Composites
Weekly composites help identify major trend changes and long-term areas of balance. These levels often hold for months or even years.
Risk Management
Composite levels provide natural stop-loss levels. If a composite level breaks, it often signals a significant shift in market sentiment, making it an ideal place to exit losing positions.
Why Composite Levels Work
Composite levels work because they represent areas where significant trading decisions were made across multiple timeframes. When price returns to these levels, traders often remember the previous price action and make similar decisions, creating self-fulfilling prophecies.
The compositing process uses a proprietary algorithm that ensures only levels validated across multiple time periods are displayed. This means you're looking at levels that have proven their significance through actual market behavior, not just random technical levels.
Technical Foundation
The indicator uses TPO (Time Price Opportunity) data combined with price action analysis to identify areas of balance. The C2M row sizing method ensures accurate profile calculations, while the overlap detection algorithm (minimum 50% price range intersection) ensures only truly significant composites are displayed. The algorithm calculates row size based on ATR (Average True Range) divided by 10, then converts to tick size for precise level calculations.
How the Code Actually Works
1. Period Detection and ATR Calculation
The code first determines the appropriate timeframe based on your chart:
- 1m-5m charts: Session-based profiles
- 15m-2h charts: Daily profiles
- 4h charts: Weekly profiles
- 1D charts: Monthly profiles
For each period type, it calculates the number of bars needed for ATR calculation:
- Sessions: 540 minutes divided by chart timeframe
- Daily: 1440 minutes divided by chart timeframe
- Weekly: 7 days worth of minutes divided by chart timeframe
- Monthly: 30 days worth of minutes divided by chart timeframe
2. C2M Row Size Calculation
The code calculates True Range for each bar in the determined period:
- True Range = max(high-low, |high-prevClose|, |low-prevClose|)
- Averages all True Range values to get ATR
- Row Size = (ATR / 10) converted to tick size
- This ensures each TPO row represents a meaningful price movement
3. TPO Profile Generation
For each period, the code:
- Creates price levels from lowest to highest price in the range
- Each level is separated by the calculated row size
- Counts how many bars touch each price level (TPO count)
- Finds the level with highest count = Point of Control (POC)
- Calculates Value Area by expanding from POC until 68.27% of total TPO blocks are included
4. Overlap Detection Algorithm
When a new profile is created, the code checks if it overlaps with existing composites:
- Calculates overlap range = min(currentVAH, prevVAH) - max(currentVAL, prevVAL)
- Calculates current profile range = currentVAH - currentVAL
- Overlap percentage = (overlap range / current profile range) * 100
- If overlap >= 50%, profiles are merged into a composite
5. Composite Merging Logic
When profiles overlap, the code creates a new composite by:
- Taking the earliest start bar and latest end bar
- Using the wider VAH/VAL range (max of both profiles)
- Keeping the POC from the profile with more TPO blocks
- Marking the composite as "active" until price breaks through
6. Real-Time Updates
The code uses barstate.islast to optimize performance:
- Only recalculates on the last bar of each period
- Updates active composite with live price action if enabled
- Cleans up old composites to prevent memory issues
- Redraws all visual elements from scratch each bar
7. Visual Rendering System
The code uses arrays to manage drawing objects:
- Clears all lines/boxes arrays on every bar
- Iterates through composites array to redraw everything
- Uses different colors for active, broken, and dead composites
- Calculates 1.618 Fibonacci extensions for broken composites
Getting Started with CTPO
Step 1: Choose Your Timeframe
Select the period type that matches your trading style:
- Use "Sessions" for day trading
- Use "Daily" for swing trading
- Use "Weekly" for position trading
- Use "Auto" to let the indicator choose based on your chart timeframe
Step 2: Customize the Display
Adjust colors, transparency, and display options to match your charting preferences. The indicator offers extensive customization options to ensure it fits seamlessly into your existing analysis.
Step 3: Identify Key Levels
Look for:
- Composite zones (blue boxes) - major areas of balance
- VAH/VAL lines - value area boundaries
- POC lines - areas of highest trading activity
- 1.618 extension lines - breakout targets
Step 4: Develop Your Strategy
Use these levels to:
- Set entry points near composite zones
- Place stop losses beyond composite levels
- Take profits at 1.618 extension levels
- Identify trend changes when major composites break
Perfect for Market Profile Traders
If you're already using market profile concepts in your trading, CTPO eliminates the manual work of compositing profiles across different timeframes. Instead of spending time analyzing each individual period, you get instant access to the composite levels that matter most.
The indicator's automated compositing process ensures you're always looking at the most relevant areas of balance, while its real-time updates keep you informed of changes as they happen. Whether you're a day trader looking for intraday levels or a position trader analyzing long-term structure, CTPO provides the market profile intelligence you need to succeed.
Streamline Your Market Profile Analysis
Stop wasting time on manual compositing. Let CTPO do the heavy lifting while you focus on executing profitable trades based on areas of balance that actually matter.
Ready to Streamline Your Market Profile Trading?
Add the Composite Time Profile Overlay to your charts today and experience the difference that automated profile compositing can make in your trading performance.
BUY & SELL Probability (M5..D1) - MTFMTF Probability Indicator (M5 to D1)
Indicator — Dual Histogram with Buy/Sell Labels
This indicator is designed to provide a probabilistic bias for bullish or bearish conditions by combining three different analytical components across multiple timeframes. The goal is to reduce noise from single-indicator signals and instead highlight confluence where trend, momentum, and strength agree.
Why this combination is useful
- EMA(200) Trend Filter: Identifies whether price is trading above or below a widely used long-term moving average.
- MACD Momentum: Detects short-term directional momentum through line crossovers.
- ADX Strength: Measures how strong the trend is, preventing signals in weak or flat markets.
By combining these, the indicator avoids situations where one tool signals a trade but others do not, helping to filter out low-probability setups.
How it works
- Each timeframe (M5, M15, H1, H4, D1) generates its own trend, momentum, and strength score.
- Scores are weighted according to user-defined importance and then aggregated into a single probability.
- Proximity to recent support and resistance levels can adjust the final score, accounting for nearby barriers.
- The final probability is displayed as:
- Histogram (subwindow): Green bars for bullish probability >50%, red bars for bearish <50%.
- On-chart labels: Showing exact buy/sell percentages on the last bar for quick reference.
Inputs
- EMA length (default 200), MACD settings, ADX period.
- Weights for each timeframe and component (trend, momentum, strength).
- Optional boost for the chart’s current timeframe.
- Smoothing length for probability values.
- Lookback period for support/resistance adjustment.
How to use it
- A green histogram above zero indicates bullish probability >50%.
- A red histogram below zero indicates bearish probability >50%.
- Neutral readings near 50% show low confluence and may be best avoided.
- Users can adjust weights to emphasize higher or lower timeframes, depending on their trading style.
Notes
- This script does not guarantee profitable trades.
- Best used together with price action, volume, or additional confirmation tools.
- Signals are calculated only on closed bars to avoid repainting.
- For testing and learning purposes — not financial advice.
US30 ORB 5m / 1m StrategyThis is Open range breakout strategy, its tested with Us30 only. You cant optimize or try. it seems profitable
Retail Sentiment Indicator - Multi-Asset CFD & Fear/Greed IndexRetail Sentiment Indicator - Multi-Asset CFD & Fear/Greed Index
Overview
The Retail Sentiment Indicator provides real-time sentiment data for major financial instruments including stocks, forex, commodities, and cryptocurrencies. This indicator displays retail trader positioning and market sentiment using CFD data and fear/greed indices.
Methodology and Scale Calculation
This indicator operates on a **-50 to +50 scale** with zero representing perfect market equilibrium.
Scale Interpretation:
- **Zero (0)**: Market balance - exactly 50% of investors buying, 50% selling
- **Positive values**: Majority buying pressure
- Example: If 63% of investors are buying, the indicator shows +13 (63 - 50 = +13)
- **Negative values**: Majority selling pressure
- Example: If 92% of investors are selling, the indicator shows -42 (50 - 92 = -42)
BTC Fear & Greed Index Scaling:
The original `BTC FEAR&GREED` index is natively scaled from 0-100 by its creator. In our indicator, this data has been rescaled to also fit the -50 to +50 range for consistency with other sentiment data sources.
This unified scaling approach allows for direct comparison across all instruments and data sources within the indicator.
-Important Data Source Selection-
Bitcoin (BTC) Data Sources
When viewing Bitcoin charts, the indicator offers **two different data sources**:
1. **Default Auto-Mode**: `BTCUSD Retail CFD` - Retail CFD traders sentiment data (automatically loaded).
2. **Manual Selection**: `BTC FEAR&GREED` - Fear & Greed Index from website: alternative dot me
**To access BTC Fear & Greed Index**: Input settings -> disable checkbox "Auto-load Sentiment Data" -> manually select "BTC FEAR&GREED" from the dropdown menu.
US Stock Market Data Sources
For US stocks and indices (S&P 500, NASDAQ, Dow Jones), there are **two data source options**:
1. **Default Auto-Mode**: Individual retail CFD sentiment data for each instrument
2. **Manual Selection**: `SNN FEAR&GREED` - SNN's Fear & Greed Index covering the overall US market sentiment. SNN was used as the name to avoid any potential trademark infringement.
**To access SNN Fear & Greed Index**: When viewing US market charts, disable in input settings checkbox "Auto-load Sentiment Data" and manually select "SNN FEAR&GREED" from the dropdown menu.
This distinction allows traders to choose between **instrument-specific retail sentiment** (auto-mode) or **broader market sentiment indices** (manual selection).
Features
- **Auto-Detection**: Automatically loads sentiment data based on the current chart symbol
- **Manual Selection**: Choose from 40+ supported instruments when auto-detection is unavailable
- **Multiple Data Sources**: Combines retail CFD sentiment with Fear & Greed indices
- **Visual Zones**: Clear greed/fear zones with color-coded backgrounds
- **Real-time Updates**: Live sentiment data from merged data sources
Supported Instruments
Major Indices
- S&P 500, NASDAQ, Dow Jones 30, DAX
Forex Pairs
- Major pairs: EURUSD, GBPUSD, USDJPY, USDCHF, USDCAD
- Cross pairs: EURJPY, GBPJPY, AUDUSD, NZDUSD, and 20+ others
Commodities
- Precious metals: Gold (XAUUSD), Silver (XAGUSD)
- Energy: WTI Oil
- Agricultural: Wheat, Coffee
- Industrial: Copper
Cryptocurrencies
- Bitcoin (BTC) sentiment data
- BTC & SNN Fear & Greed indices
How to Use
1. **Auto Mode** (Default): Enable "Auto-load Sentiment Data" to automatically display sentiment for the current chart symbol
2. **Manual Mode**: Disable auto-load and select from the dropdown menu for specific instruments
3. **Interpretation**:
- Values above 0 (green) indicate retail greed/bullish sentiment
- Values below 0 (red) indicate retail fear/bearish sentiment
- Fear & Greed indices use 0-100 scale (50 is neutral)
Data Sources
This indicator uses curated sentiment data from retail CFD providers and established fear/greed indices. Data is updated regularly and sourced from reputable financial data providers.
Trading Strategy & Market Philosophy
Contrarian Trading Approach
The primary purpose of this indicator is based on the fundamental market principle that **the majority of retail investors are often wrong**, and markets typically move opposite to the positions held by the majority of market participants.
Key Strategy Guidelines:
- **Contrarian Signal**: When the majority of users are positioned on one side of the market, there is statistically greater market advantage in taking positions in the opposite direction
- **Trend Exhaustion Signal**: An interesting observed phenomenon occurs when, during a long-lasting trend where the majority of investors have consistently been on the wrong side, the Sentiment indicator suddenly shows that the majority has flipped and opened positions in the direction of that long-running trend. This is often a signal of fuel exhaustion for further movement in that direction
Interpretation Examples
- High greed readings (majority bullish) → Consider bearish opportunities
- High fear readings (majority bearish) → Consider bullish opportunities
- Sudden sentiment flip during established trends → Potential trend reversal signal
Technical Notes
- Built with PineScript v6
- Dynamic symbol detection with fallback options
- Optimized for performance with minimal resource usage
- Color-coded visualization with customizable zones
Data Sources & Expansion
Acknowledgments
We extend our gratitude to **TradingView** for enabling the use of custom data feeds based on GitHub repositories, making this comprehensive sentiment analysis possible.
Data Expansion Opportunities
As the operator of this indicator, I am **open to suggestions for new data sources** that could be integrated and published. If you have ideas for additional instruments or sentiment data:
How to Submit Suggestions:
1. Send a **private message** with your proposal
2. Include: **instrument/data type**, **source**, and **brief description**
3. If technically feasible, we will work to import and publish the data
Data Infrastructure Status
Current Data Upload Process:
Please note that sentiment data uploads may occasionally experience minor interruptions. However, this should not pose significant issues as sentiment data typically changes gradually rather than rapidly.
Infrastructure Development:
We are actively working on establishing permanent cloud-based infrastructure to ensure continuous, automated data collection and upload processes. This will provide more reliable and consistent data availability in the future.
Disclaimer
This indicator is for educational and informational purposes only. Sentiment data should be used as part of a comprehensive trading strategy and not as the sole basis for trading decisions. Past performance does not guarantee future results. The contrarian approach described is a market theory and may not always produce profitable results.
Turtle Trading System (Not Financial Advise)I am testing this script, not financial advise, but this is how it works, i hope;
How the Script Works
The script is a TradingView strategy that backtests and automates the Turtle rules. It uses two systems, a short-term S1 (20-day) and a long-term S2 (55-day), which you can select to test.
The strategy follows these core principles:
Trend Identification: The script uses Donchian Channels to identify breakouts. A new trend is signaled when the price breaks above the highest high or below the lowest low of a specified period.
Position Sizing: The script automatically calculates the size of each trade unit to ensure you risk a consistent percentage of your capital on every trade. This is based on the Average True Range (ATR), which measures volatility.
Pyramiding: When a trade moves in a profitable direction, the script will add more units at specific intervals to increase your position and ride the trend. This is how the Turtles made their big profits.
Win/Loss Condition:
A crucial rule is implemented for System 1 (S1): you only enter a trade if the previous S1 trade was a loss. If the last S1 trade was a win, you must wait for a signal from System 2 (S2).
How to Enter and Exit a Long Position
The script will automatically enter a long position when the price breaks above the highest high of the last 20 days (S1) or 55 days (S2), depending on which system you've selected.
Stop-Loss (SL): Your stop-loss is automatically placed at a distance of 2 * N (ATR) below the entry price. . This is a crucial risk management rule that limits your potential loss. The stop-loss is also plotted visually on the chart.
Exit Long: The script will close the long position automatically under two conditions:
Reversal: The price drops and closes below the lowest low of the last 10 days (S1) or 20 days (S2).
Stop-Loss Hit: The price hits your initial stop-loss, limiting your loss.
How to Enter and Exit a Short Position
Enter Short: The script will automatically enter a short position when the price breaks below the lowest low of the last 20 days (S1) or 55 days (S2).
Stop-Loss (SL): Your stop-loss is automatically placed at a distance of 2 * N (ATR) above the entry price. . This prevents large losses if the price moves against you.
Exit Short: The script will close the short position automatically under two conditions:
Reversal: The price rallies and closes above the highest high of the last 10 days (S1) or 20 days (S2).
Stop-Loss Hit: The price hits your initial stop-loss, limiting your loss.
CM Indicator About Indicator:-
1) This is best Indicator for trend identification.
2) This is based on 42 EMA with Upper Band and Lower bands for trend identification.
3) This should be used for Line Bar chart only.
4) Line bar chart should be used at 1 hour for 15 line breaks.
How to Use:-
1) To go with trend is best use of this indicator.
2) This is for stocks and options not for index. Indicator used for Stocks at one hour and options for 10-15 minutes line break.
3) There will be 5% profitability defined for each entry, 3 entries with profit are best posible in same continuous trend 4 and 5th entry is in riskier zone in continuous trend.
4) Loss will only happen if there is trend reversal.
5) Loss could only be one trade of profit out of three profitable trades.
6) Back tested on 200 stocks and 100 options.
AltCoin & MemeCoin Index Correlation [Eddie_Bitcoin]🧠 Philosophy of the Strategy
The AltCoin & MemeCoin Index Correlation Strategy by Eddie_Bitcoin is a carefully engineered trend-following system built specifically for the highly volatile and sentiment-driven world of altcoins and memecoins.
This strategy recognizes that crypto markets—especially niche sectors like memecoins—are not only influenced by individual price action but also by the relative strength or weakness of their broader sector. Hence, it attempts to improve the reliability of trading signals by requiring alignment between a specific coin’s trend and its sector-wide index trend.
Rather than treating each crypto asset in isolation, this strategy dynamically incorporates real-time dominance metrics from custom indices (OTHERS.D and MEME.D) and combines them with local price action through dual exponential moving average (EMA) crossovers. Only when both the asset and its sector are moving in the same direction does it allow for trade entries—making it a confluence-based system rather than a single-signal strategy.
It supports risk-aware capital allocation, partial exits, configurable stop loss and take profit levels, and a scalable equity-compounding model.
✅ Why did I choose OTHERS.D and MEME.D as reference indices?
I selected OTHERS.D and MEME.D because they offer a sector-focused view of crypto market dynamics, especially relevant when trading altcoins and memecoins.
🔹 OTHERS.D tracks the market dominance of all cryptocurrencies outside the top 10 by market cap.
This excludes not only BTC and ETH, but also major stablecoins like USDT and USDC, making it a cleaner indicator of risk appetite across true altcoins.
🔹 This is particularly useful for detecting "Altcoin Season"—periods where capital rotates away from Bitcoin and flows into smaller-cap coins.
A rising OTHERS.D often signals the start of broader altcoin rallies.
🔹 MEME.D, on the other hand, captures the speculative behavior of memecoin segments, which are often driven by retail hype and social media activity.
It's perfect for timing momentum shifts in high-risk, high-reward tokens.
By using these indices, the strategy aligns entries with broader sector trends, filtering out noise and increasing the probability of catching true directional moves, especially in phases of capital rotation and altcoin risk-on behavior.
📐 How It Works — Core Logic and Execution Model
At its heart, this strategy employs dual EMA crossover detection—one pair for the asset being traded and one pair for the selected market index.
A trade is only executed when both EMA crossovers agree on the direction. For example:
Long Entry: Coin's fast EMA > slow EMA and Index's fast EMA > slow EMA
Short Entry: Coin's fast EMA < slow EMA and Index's fast EMA < slow EMA
You can disable the index filter and trade solely based on the asset’s trend just to make a comparison and see if improves a classic EMA crossover strategy.
Additionally, the strategy includes:
- Adaptive position sizing, based on fixed capital or current equity (compound mode)
- Take Profit and Stop Loss in percentage terms
- Smart partial exits when trend momentum fades
- Date filtering for precise backtesting over specific timeframes
- Real-time performance stats, equity tracking, and visual cues on chart
⚙️ Parameters & Customization
🔁 EMA Settings
Each EMA pair is customizable:
Coin Fast EMA: Default = 47
Coin Slow EMA: Default = 50
Index Fast EMA: Default = 47
Index Slow EMA: Default = 50
These control the sensitivity of the trend detection. A wider spread gives smoother, slower entries; a narrower spread makes it more responsive.
🧭 Index Reference
The correlation mechanism uses CryptoCap sector dominance indexes:
OTHERS.D: Dominance of all coins EXCLUDING Top 10 ones
MEME.D: Dominance of all Meme coins
These are dynamically calculated using:
OTHERS_D = OTHERS_cap / TOTAL_cap * 100
MEME_D = MEME_cap / TOTAL_cap * 100
You can select:
Reference Index: OTHERS.D or MEME.D
Or disable the index reference completely (Don't Use Index Reference)
💰 Position Sizing & Risk Management
Two capital allocation models are supported:
- Fixed % of initial capital (default)
- Compound profits, which scales positions as equity grows
Settings:
- Compound profits?: true/false
- % of equity: Between 1% and 200% (default = 10%)
This is critical for users who want to balance growth with risk.
🎯 Take Profit / Stop Loss
Customizable thresholds determine automatic exits:
- TakeProfit: Default = 99999 (disabled)
- StopLoss: Default = 5 (%)
These exits are percentage-based and operate off the entry price vs. current close.
📉 Trend Weakening Exit (Scale Out)
If the position is in profit but the trend weakens (e.g., EMA color signals trend loss), the strategy can partially close a configurable portion of the position:
- Scale Position on Weak Trend?: true/false
- Scaled Percentage: % to close (default = 65%)
This feature is useful for preserving profits without exiting completely.
📆 Date Filter
Useful for segmenting performance over specific timeframes (e.g., bull vs bear markets):
- Filter Date Range of Backtest: ON/OFF
- Start Date and End Date: Custom time range
OTHER PARAMETERS EXPLANATION (Strategy "Properties" Tab):
- Initial Capital is set to 100 USD
- Commission is set to 0.055% (The ones I have on Bybit)
- Slippage is set to 3 ticks
- Margin (short and long) are set to 0.001% to avoid "overspending" your initial capital allocation
📊 Visual Feedback and Debug Tools
📈 EMA Trend Visualization
The slow EMA line is dynamically color-coded to visually display the alignment between the asset trend and the index trend:
Lime: Coin and index both bullish
Teal: Only coin bullish
Maroon: Only index bullish
Red: Both bearish
This allows for immediate visual confirmation of current trend strength.
💬 Real-Time PnL Labels
When a trade closes, a label shows:
Previous trade return in % (first value is the effective PL)
Green background for profit, Red for losses.
📑 Summary Table Overlay
This table appears in a corner of the chart (user-defined) and shows live performance data including:
Trade direction (yellow long, purple short)
Emojis: 💚 for current profit, 😡 for current loss
Total number of trades
Win rate
Max drawdown
Duration in days
Current trade profit/loss (absolute and %)
Cumulative PnL (absolute and %)
APR (Annualized Percentage Return)
Each metric is color-coded:
Green for strong results
Yellow/orange for average
Red/maroon for poor performance
You can select where this appears:
Top Left
Top Right
Bottom Left
Bottom Right (default)
📚 Interpretation of Key Metrics
Equity Multiplier: How many times initial capital has grown (e.g., “1.75x”)
Net Profit: Total gains including open positions
Max Drawdown: Largest peak-to-valley drop in strategy equity
APR: Annualized return calculated based on equity growth and days elapsed
Win Rate: % of profitable trades
PnL %: Percentage profit on the most recent trade
🧠 Advanced Logic & Safety Features
🛑 “Don’t Re-Enter” Filter
If a trade is closed due to StopLoss without a confirmed reversal, the strategy avoids re-entering in that same direction until conditions improve. This prevents false reversals and repetitive losses in sideways markets.
🧷 Equity Protection
No new trades are initiated if equity falls below initial_capital / 30. This avoids overleveraging or continuing to trade when capital preservation is critical.
Keep in mind that past results in no way guarantee future performance.
Eddie Bitcoin
HMK-2 | PCA-1 + Rejim + Chebyshev + VWAP (Input'lu, v6)📌 HMK-2 | PCA-1 + Regime + Chebyshev + VWAP Strategy
1️⃣ Core Structure
Instead of relying on a single indicator, this system uses the Z-Score normalized average of three oscillators (RSI, MFI, ROC).
Signal (PCA-1):
RSI(14), MFI(14), ROC(5) → each is converted into a z-score.
Their average becomes the “composite signal,” our PCA-1 value.
Trend direction: If the Z-score EMA is rising → trend UP. If falling → trend DOWN.
2️⃣ Side Filters
Regime Filter (ADX + EMA)
ADX is calculated manually.
If ADX > 20 → trend exists → a 50-period EMA of this value smooths it.
This turns “trend regime” into a probability between 0–1.
Chebyshev Filter
A return series is checked against mean ± k*sigma bands.
If the return is within this band → valid signal. Extreme moves are filtered out.
VWAP Filter
Long trades: price must be above VWAP.
Short trades: price must be below VWAP.
Trades are only taken on the correct side of institutional cost averages.
3️⃣ Entry Conditions
Long:
PCA-1 signal crosses above threshold.
Trend Up + Regime OK + Chebyshev OK + Above VWAP.
Short:
PCA-1 signal crosses below threshold.
Trend Down + Regime OK + Chebyshev OK + Below VWAP.
4️⃣ Exit Mechanism
Main Exit: ATR-based stop/target.
Stop = entry price – ATR × (SL factor).
Take profit = entry price + ATR × (TP factor).
Additional Exit:
If price crosses to the opposite side of VWAP.
If PCA-1 signal crosses zero.
👉 Prevents trades from being locked, makes exits adaptive.
5️⃣ Labels / Visualization
AL / SHORT → entry points.
SAT / COVER → exit points.
VWAP line plotted in blue.
🧩 Strategy Features
Optimizable parameters:
Z-window (zWin)
Threshold
Chebyshev factor
ATR stop/target multipliers
This system works with:
Disciplined core (PCA-1 signal)
Triple protection (Regime + Chebyshev + VWAP)
Adaptive exits (ATR + VWAP/signal cross)
👉 Not a “single-indicator robot,” but a multi-filtered trade direction engine.
💡 Final Note
This is a base model of the system — open for further development.
I’ve shared the logic to give you a roadmap.
If you spot errors, fix them → that’s how you’ll improve it.
Don’t waste time asking me questions — refine and build it better yourselves.
Wishing you profitable trades. Stay well 🙏
Trapped Traders [ScorsoneEnterprises]This indicator identifies and visualizes trapped traders - market participants caught on the wrong side of price movements with significant volume imbalances. By analyzing volume delta at specific price levels, it reveals where traders are likely experiencing unrealized losses and may be forced to exit their positions.
The point of this tool is to identify where the liquidity in a trend may be.
var lowerTimeframe = switch
useCustomTimeframeInput => lowerTimeframeInput
timeframe.isseconds => "1S"
timeframe.isintraday => "1"
timeframe.isdaily => "5"
=> "60"
= ta.requestVolumeDelta(lowerTimeframe)
price_quantity = map.new()
is_red_candle = close < open
is_green_candle = close > open
for i=0 to lkb-1 by 1
current_vol = price_quantity.get(close)
new_vol = na(current_vol) ? lastVolume : current_vol + lastVolume
price_quantity.put(close, new_vol)
if is_green_candle and new_vol < 0
price_quantity.put(close, new_vol)
else if is_red_candle and new_vol > 0
price_quantity.put(close, new_vol)
We see in this snippet, the lastVolume variable is the most recent volume delta we can receive from the lower timeframe, we keep updating the price level we're keeping track of with that lastVolume from the lower timeframe.
This is the bulk of the concept as this level and size gives us the idea of how many traders were on the wrong side of the trend, and acting as liquidity for the profitable entries. The more, the stronger.
There are 3 ways to visualize this. A basic label, that will display the size and if positive or negative next to the bar, a gradient line that goes 10 bars to the future to be used as a support or resistance line that includes the quantity, and a bubble chart with the quantity. The larger the quantity, the bigger the bubble.
We see in this example on NYMEX:CL1! that there are lines plotted throughout this price action that price interacts with in meaningful way. There are consistently many levels for us.
Here on CME_MINI:ES1! we see the labels on the chart, and the size set to large. It is the same concept just another way to view it.
This chart of CME_MINI:RTY1! shows the bubble chart visualization. It is a way to view it that is pretty non invasive on the chart.
Every timeframe is supported including daily, weekly, and monthly.
The included settings are the display style, like mentioned above. If the user would like to see the volume numbers on the chart. The text size along with the transparency percentage. Following that is the settings for which lower timeframe to calculate the volume delta on. Finally, if you would like to see your inputs in the status line.
No indicator is 100% accurate, use "Trapped Traders" along with your own discretion.
FlowStateTrader FlowState Trader - Advanced Time-Filtered Strategy
## Overview
FlowState Trader is a sophisticated algorithmic trading strategy that combines precision entry signals with intelligent time-based filtering and adaptive risk management. Built for traders seeking to achieve their optimal performance state, FlowState identifies high-probability trading opportunities within user-defined time windows while employing dynamic trailing stops and partial position management.
## Core Strategy Philosophy
FlowState Trader operates on the principle that peak trading performance occurs when three elements align: **Focus** (precise entry signals), **Flow** (optimal time windows), and **State** (intelligent position management). This strategy excels at finding reversal opportunities at key support and resistance levels while filtering out suboptimal trading periods to keep traders in their optimal flow state.
## Key Features
### 🎯 Focus Entry System
**Support/Resistance Zone Trading**:
- Dynamic identification of key price levels using configurable lookback periods
- Entry signals triggered when price interacts with these critical zones
- Volume confirmation ensures genuine breakout/reversal momentum
- Trend filter alignment prevents counter-trend disasters
**Entry Conditions**:
- **Long Signals**: Price closes above support buffer, touches support level, with above-average volume
- **Short Signals**: Price closes below resistance buffer, touches resistance level, with above-average volume
- Optional trend filter using EMA or SMA for directional bias confirmation
### ⏰ FlowState Time Filtering System
**Comprehensive Time Controls**:
- **12-Hour Format Trading Windows**: User-friendly AM/PM time selection
- **Multi-Timezone Support**: UTC, EST, PST, CST with automatic conversion
- **Day-of-Week Filtering**: Trade only weekdays, weekends, or both
- **Lunch Hour Avoidance**: Automatically skips low-volume lunch periods (12-1 PM)
- **Visual Time Indicators**: Background coloring shows active/inactive trading periods
**Smart Time Features**:
- Handles overnight trading sessions seamlessly
- Prevents trades during historically poor performance periods
- Customizable trading hours for different market sessions
- Real-time trading window status in dashboard
### 🛡️ Adaptive Risk Management
**Multi-Level Take Profit System**:
- **TP1**: First profit target with optional partial position closure
- **TP2**: Final profit target for remaining position
- **Flexible Scaling**: Choose number of contracts to close at each level
**Dynamic Trailing Stop Technology**:
- **Three Operating Modes**:
- **Conservative**: Earlier activation, tighter trailing (protect profits)
- **Balanced**: Optimal risk/reward balance (recommended)
- **Aggressive**: Later activation, wider trailing (let winners run)
- **ATR-Based Calculations**: Adapts to current market volatility
- **Automatic Activation**: Engages when position reaches profitability threshold
### 📊 Intelligent Position Sizing
**Contract-Based Management**:
- Configurable entry quantity (1-1000 contracts)
- Partial close quantities for profit-taking
- Clear position tracking and P&L monitoring
- Real-time position status updates
### 🎨 Professional Visualization
**Enhanced Chart Elements**:
- **Entry Zone Highlighting**: Clear visual identification of trading opportunities
- **Dynamic Risk/Reward Lines**: Real-time TP and SL levels with price labels
- **Trailing Stop Visualization**: Live tracking of adaptive stop levels
- **Support/Resistance Lines**: Key level identification
- **Time Window Background**: Visual confirmation of active trading periods
**Dual Dashboard System**:
- **Strategy Dashboard**: Real-time position info, settings status, and current levels
- **Performance Scorecard**: Live P&L tracking, win rates, and trade statistics
- **Customizable Sizing**: Small, Medium, or Large display options
### ⚙️ Comprehensive Customization
**Core Strategy Settings**:
- **Lookback Period**: Support/resistance calculation period (5-100 bars)
- **ATR Configuration**: Period and multipliers for stops/targets
- **Reward-to-Risk Ratios**: Customizable profit target calculations
- **Trend Filter Options**: EMA/SMA selection with adjustable periods
**Time Filter Controls**:
- **Trading Hours**: Start/end times in 12-hour format
- **Timezone Selection**: Four major timezone options
- **Day Restrictions**: Weekend-only, weekday-only, or unrestricted
- **Session Management**: Lunch hour avoidance and custom periods
**Risk Management Options**:
- **Trailing Stop Modes**: Conservative/Balanced/Aggressive presets
- **Partial Close Settings**: Enable/disable with custom quantities
- **Alert System**: Comprehensive notifications for all trade events
### 📈 Performance Tracking
**Real-Time Metrics**:
- Net profit/loss calculation
- Win rate percentage
- Profit factor analysis
- Maximum drawdown tracking
- Total trade count and breakdown
- Current position P&L
**Trade Analytics**:
- Winner/loser ratio tracking
- Real-time performance scorecard
- Strategy effectiveness monitoring
- Risk-adjusted return metrics
### 🔔 Alert System
**Comprehensive Notifications**:
- Entry signal alerts with price and quantity
- Take profit level hits (TP1 and TP2)
- Stop loss activations
- Trailing stop engagements
- Position closure notifications
## Strategy Logic Deep Dive
### Entry Signal Generation
The strategy identifies high-probability reversal points by combining multiple confirmation factors:
1. **Price Action**: Looks for price interaction with key support/resistance levels
2. **Volume Confirmation**: Ensures sufficient market interest and liquidity
3. **Trend Alignment**: Optional filter prevents counter-trend positions
4. **Time Validation**: Only trades during user-defined optimal periods
5. **Zone Analysis**: Entry occurs within calculated buffer zones around key levels
### Risk Management Philosophy
FlowState Trader employs a three-tier risk management approach:
1. **Initial Protection**: ATR-based stop losses set at strategy entry
2. **Profit Preservation**: Trailing stops activate once position becomes profitable
3. **Scaled Exit**: Partial profit-taking allows for both security and potential
### Time-Based Edge
The time filtering system recognizes that not all trading hours are equal:
- Avoids low-volume, high-spread periods
- Focuses on optimal liquidity windows
- Prevents trading during news events (lunch hours)
- Allows customization for different market sessions
## Best Practices and Optimization
### Recommended Settings
**For Scalping (1-5 minute charts)**:
- Lookback Period: 10-20
- ATR Period: 14
- Trailing Stop: Conservative mode
- Time Filter: Major session hours only
**For Day Trading (15-60 minute charts)**:
- Lookback Period: 20-30
- ATR Period: 14-21
- Trailing Stop: Balanced mode
- Time Filter: Extended trading hours
**For Swing Trading (4H+ charts)**:
- Lookback Period: 30-50
- ATR Period: 21+
- Trailing Stop: Aggressive mode
- Time Filter: Disabled or very broad
### Market Compatibility
- **Forex**: Excellent for major pairs during active sessions
- **Stocks**: Ideal for liquid stocks during market hours
- **Futures**: Perfect for index and commodity futures
- **Crypto**: Effective on major cryptocurrencies (24/7 capability)
### Risk Considerations
- **Market Conditions**: Performance varies with volatility regimes
- **Timeframe Selection**: Lower timeframes require tighter risk management
- **Position Sizing**: Never risk more than 1-2% of account per trade
- **Backtesting**: Always test on historical data before live implementation
## Educational Value
FlowState serves as an excellent learning tool for:
- Understanding support/resistance trading
- Learning proper time-based filtering
- Mastering trailing stop techniques
- Developing systematic trading approaches
- Risk management best practices
## Disclaimer
This strategy is for educational and informational purposes only. Past performance does not guarantee future results. Trading involves substantial risk of loss and is not suitable for all investors. Users should thoroughly backtest the strategy and understand all risks before live trading. Always use proper position sizing and never risk more than you can afford to lose.
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*FlowState Trader represents the evolution of systematic trading - combining classical technical analysis with modern risk management and intelligent time filtering to help traders achieve their optimal performance state through systematic, disciplined execution.*
Imbalance RSI Divergence Strategy# Imbalance RSI Divergence Strategy - User Guide
## What is This Strategy?
This strategy identifies **imbalance** zones in the market and combines them with **RSI divergence** to generate trading signals. It aims to capitalize on price gaps left by institutional investors and large volume movements.
### Main Settings
- **RSI Period (14)**: Period used for RSI calculation. Lower values = more sensitive, higher values = more stable signals.
- **ATR Period (10)**: Period for volatility measurement using Average True Range.
- **ATR Stop Loss Multiplier (2.0)**: How many ATR units to use for stop loss calculation.
- **Risk:Reward Ratio (4.0)**: Risk-reward ratio. 2.0 = 2 units of reward for 1 unit of risk.
- **Use RSI Divergence Filter (true)**: Enables/disables the RSI divergence filter.
### Imbalance Filters
- **Minimum Imbalance Size (ATR) (0.3)**: Minimum imbalance size in ATR units to filter out small imbalances.
- **Enable Lookback Limit (false)**: Activates historical lookback limitations.
- **Maximum Lookback Bars (300)**: Maximum number of bars to look back.
### Visual Settings
- **Show Imbalance Size**: Displays imbalance size in ATR units.
- **Show RSI Divergence Lines**: Shows/hides divergence lines.
- **Divergence Line Colors**: Colors for bullish/bearish divergence lines.
### Volatility-Based Adjustments
- **Low volatility markets**:
- Minimum Imbalance Size: 0.2-0.4 ATR
- ATR Stop Loss Multiplier: 1.5-2.0
- **High volatility markets**:
- Minimum Imbalance Size: 0.5-1.0 ATR
- ATR Stop Loss Multiplier: 2.5-3.5
### Risk Tolerance
- **Conservative approach**:
- Risk:Reward Ratio: 2.0-3.0
- RSI Divergence Filter: Enabled
- Minimum Imbalance Size: Higher (0.5+ ATR)
- **Aggressive approach**:
- Risk:Reward Ratio: 4.0-6.0
- Minimum Imbalance Size: Lower (0.2-0.3 ATR)
###Market Conditions
- **Trending markets**: Higher RSI Period (21-28)
- **Sideways markets**: Lower RSI Period (10-14)
- **Volatile markets**: Higher ATR Multiplier
## Recommended Testing Procedure
1. **Start with default settings** and backtest on 3-6 months of historical data
2. **Adjust RSI Period** to see which value produces better results
3. **Optimize ATR Multiplier** for stop loss levels
4. **Test different Risk:Reward ratios** comparatively
5. **Fine-tune Minimum Imbalance Size** to improve signal quality
## Important Considerations
- **False positive signals**: Imbalances may be less reliable during low volatility periods
- **Market openings**: First hours often produce more imbalances but can be riskier
- **News events**: Consider disabling strategy during major news releases
- **Backtesting**: Test across different market conditions (trending, sideways, volatile)
## Recommended Settings for Beginners
**Safe settings for new users:**
- RSI Period: 14
- ATR Period: 14
- ATR Stop Loss Multiplier: 2.5
- Risk:Reward Ratio: 3.0
- Minimum Imbalance Size: 0.5 ATR
- RSI Divergence Filter: Enabled
## Advanced Tips
### Signal Quality Improvement
- **Combine with market structure**: Look for imbalances near key support/resistance levels
- **Volume confirmation**: Higher volume during imbalance formation increases reliability
- **Multiple timeframe analysis**: Confirm signals on higher timeframes
### Risk Management
- **Position sizing**: Never risk more than 1-2% of account per trade
- **Maximum drawdown**: Set overall stop loss for the strategy
- **Market hours**: Consider avoiding low liquidity periods
### Performance Monitoring
- **Win rate**: Track percentage of profitable trades
- **Average R:R**: Monitor actual risk-reward achieved vs. target
- **Maximum consecutive losses**: Set alerts for strategy review
This strategy works best when combined with proper risk management and market analysis. Always backtest thoroughly before using real money and adjust parameters based on your specific market and trading style.
Order Blocks + Order-Flow ProxiesOrder Blocks + Order-Flow Proxies
This indicator combines structural analysis of order blocks with lightweight order-flow style proxies, providing a tool for chart annotation and contextual study. It is designed to help users visualize where significant structural shifts occur and how simple volume-based signals behave around those areas. The script does not guarantee profitable outcomes, nor does it issue financial advice. It is intended purely for research, learning, and discretionary use.
Conceptual Background
Order Blocks
An “order block” is a term often used to describe a zone on the chart where price left behind a significant reversal or imbalance before continuing strongly in the opposite direction. In practice, this can mean the last bullish or bearish candle before a strong breakout. Traders sometimes study these regions because they believe that unfilled resting orders may exist there, or simply because they mark important pivots in price structure. This indicator detects such moments by scanning for breaks of structure (BOS). When price pushes above or below recent swing levels with sufficient displacement, the script identifies the prior opposite candle as the potential order block.
Break of Structure
A break of structure in this context is defined when the closing price moves beyond the highest high or lowest low of a short lookback window. The script compares the magnitude of this break to an ATR-based displacement filter. This helps ensure that only meaningful moves are marked rather than small, random fluctuations.
Order-Flow Proxies
Traditional order flow analysis may use bid/ask data, footprint charts, or volume profiles. Because TradingView scripts cannot access true order-book data, this indicator instead uses proxy signals derived from standard chart data:
Delta (proxy): Estimated imbalance of buying vs. selling pressure, approximated using bar direction and volume.
Imbalance ratio: Normalizes delta by total volume, ranging between -1 and +1 in theory.
Cumulative Delta (CVD): Running sum of delta over time.
Effort vs. Result (EvR): A comparison between volume and actual bar movement, highlighting cases where large effort produced little result (or vice versa).
These are not real order-flow measurements, but rather simple mathematical constructs that mimic some of its logic.
How the Script Works
Detecting Break of Structure
The user specifies a swing length. When price closes above the recent high (for bullish BOS) or below the recent low (for bearish BOS), a potential shift is recorded.
To qualify, the breakout must exceed a displacement filter proportional to the ATR. This helps filter out weak moves.
Locating the Order Block Candle
Once a BOS is confirmed, the script looks back within a short window to find the last opposite-colored candle.
The high/low or open/close of that candle (depending on user settings) is marked as the potential order block zone.
Drawing and Maintaining Zones
Each order block is represented as a colored rectangle extending forward in time.
Bullish zones are teal by default, bearish zones are red.
Zones extend until invalidated (price closing or wicking beyond them, depending on user preference) or until a user-defined lifespan expires.
A pruning mechanism ensures that only the most recent set number of zones remain, preventing chart overload.
Monitoring Touches
The script checks whether the current bar’s range overlaps any existing order block.
If so, the “closest” zone is considered touched, and a label may appear on the chart.
Confirmation Filters
Touches can optionally be confirmed by order-flow proxies.
For a bullish confirmation, the following must align:
Imbalance ratio above threshold,
Delta EMA positive,
Effort vs. Result positive.
For a bearish confirmation, the opposite holds true.
Optionally, a higher-timeframe EMA slope filter can gate these confirmations. For example, a bullish confirmation may only be accepted if the higher-timeframe EMA is sloping upward.
Alerts
Users may create alerts based on conditions such as “bullish touch confirmed” or “bearish touch confirmed.”
Alerts can be gated to only fire after bar close, reducing intrabar noise.
Standard alertcondition calls are provided, and optional inline alert() calls can be enabled.
Inputs and Customization
Structure & OB
Swing length: Defines how many bars back to check for BOS.
ATR length & displacement factor: Adjust sensitivity for structural breaks.
Body vs. wick reference: Choose whether zones are based on candle bodies or full ranges.
Invalidation rule: Pick between wick breach or close beyond the level.
Lifespan (bars): Limit how long a zone remains active.
Max keep: Cap the number of zones stored to reduce clutter.
Order-Flow Proxies
Delta mode: Choose between “Close vs Previous Close” or “Body” for delta calculation.
EMA length: Smooths the delta/imbalance series.
Z-score lookback: Defines the averaging window for EvR.
Confirmation thresholds: Adjust the imbalance levels required for long/short confirmation.
Higher Timeframe Filter
Enable HTF gate: Optional filter requiring higher-timeframe EMA slope alignment.
HTF timeframe & EMA length: Configurable for context alignment.
Style
Colors and transparency for bullish and bearish zones.
Border color customization.
Alerts
Enable inline alerts: Optional direct calls to alert().
Alerts on bar close only: Helps avoid multiple firings during bar formation.
Practical Use
This tool is best seen as a way to annotate charts and to study how simple volume-derived signals behave near important structural levels. Some users may:
Observe whether order blocks line up with later price reactions.
Study how imbalance or cumulative delta conditions align with these zones.
Use it in a discretionary workflow to highlight areas of interest for deeper analysis.
Because the proxies are based only on candle OHLCV data, they are approximations. They cannot replace true depth-of-market analysis. Similarly, order block detection here is one specific algorithmic interpretation; other traders may define order blocks differently.
Limitations and Disclaimers
This indicator does not predict future price movement.
It does not access real order book or tick-by-tick data. All signals are derived from bar OHLCV.
Past performance of signals or zones does not guarantee future results.
The script is for educational and informational purposes only. It is not financial advice.
Users should test thoroughly, adjust parameters to their own instruments and timeframes, and use it in combination with broader analysis.
Summary
The Order Blocks + Order-Flow Proxies script is an experimental study tool that:
Detects potential order blocks using a displacement-filtered break of structure.
Marks these zones as boxes that persist until invalidation or expiry.
Provides lightweight order-flow-style proxies such as delta, imbalance, CVD, and effort vs. result.
Allows confirmation of zone touches through these proxies and optional higher-timeframe context.
Offers flexible customization, alerting, and chart-style options.
It is not a trading system by itself but rather a framework for studying price/volume behavior around structurally significant areas. With careful exploration, it can give users new ways to visualize market structure and to understand how simple flow-like measures behave in those contexts.
Extended CANSLIM Indicator❖ Extended CANSLIM Indicator.
The Extended CANSLIM indicator is an indicator that concentrates all the tools usually used by CANSLIM traders.
It shows a table where all the stock fundamental information is shown at once first for the last quarter and then up to 5 years back.
The fundamental data is checked against well known CANSLIM validation criteria and is shown over 4 state levels.
1. Good = Value is CANSLIM Compliant.
2. Acceptable = Value is not CANSLIM compliant but still good. value is shown with a lighter background color.
3. Warning = Value deserves special attention. Value is shown over orange background color.
3. Stop = Value is non CANSLIM compliant or indicates a stop trading condition. Value is shown over red background color.
The indicator has also a set of technical tools calculated on price or index and shown directly on the chart.
❖ Fundamental data shown in the table.
The table is arranged in 4 sets of data:
1. Table Header, showing Indicator and Company data.
2. CANSLIM.
3. 3Rs: RS Rating, Revenue and ROE.
4. Extra Data: Piotroski score, ATR, Trend Days, D to E, Avg Vol and Vol today.
Sets 3 and 4 can be hidden from the table.
❖ Indicator and Compay Data.
The table header shows, Indicator name and version.
It then displays Company Name, sector and industry, human size and its capitalization.
❖ CANSLIM Data.
Displays either genuine CANSLIM data from TradinView or custom data as best effort when that data cannot be obtained in TV.
C = EPS diluted growth, Quarterly YoY.
>= 25% = Good, >= 0% = Acceptable, < 0% = Stop
A = EPS diluted growth, Annual YoY.
>= 25% = Good, >= 0% = Acceptable, < 0% = Stop
N = New High as best effort (Cust).
Always Good
S = Float shares as best effort.
Always Good
L = One year performance relative to S&P 500 (Cust),
Positive : 0% .. 50% = Neutral, 50%+ = Leader, 80%+ = Leader+, 100%+ = Leader++
Negative : 0% .. -10% = Laggard, -10% .. -30% = Laggard+, -30%+ = Laggard++
>= 50% = Good, >= 0% = Acceptable, >= -10% Warning, < -10% = Stop
I = Accumulation/Distribution days over last 25 days as a clue for institutional support (Cust).
A delta is calculated by subtracting Distribution to Accumulation days.
> 0 = Good, = 0 = Acceptable, < 0 = Warning, < -5 = Stop
M = Market direction and exposure measured on S&500 closing between averages (Cust).
Varies from 0% Full Bear to 100% Full Bull
>= 80% = Good, >= 60% = Acceptable, >= 40% = Warning, < 40% = Stop
❖ Extra non CANSLIM Data.
RS = RS Rating.
>= 90 = Good, >= 80 = Accept, >= 50 = Warning, < 50 = Stop
Rev. = Revenue Growth Quarterly YoY.
>= 0% = Good, <0% = Stop
ROE = Return on Equity, Quarterly YoY.
>= 17% = Good, >= 0% = Acceptable, < 0% = Stop
Piotr. = Piotroski Score, www.investopedia.com (TV)
>= 7 = Good, >= 4 = Acceptable, < 4 = Stop
ATR = Average True Range over the last 20 days (Cust).
0% - 2% = Acceptable, 2% - 4% = Ideal, 4% - 6% = Warning, 5%+ = Stop.
Trend Days = Days since EMA150 is over EMA200 (Cust).
Always Good
D. to E. = Days left before Earnings. Maybe not a good idea buying just before earnings (Cust).
>= 28 = Good, >= 21 = Acceptable, >= 14 = Warning, < 14 = Stop
Avg Vol. = 50d Average Volume (Cust).
>= 100K = Good, < 100K = Acceptable
Vol. Today = Today's percentage volume compared to 50d average (Cust).
Always Good.
❖ Historical Data.
Optionally selectable historical data can be displayed for C, A, Revenue and ROE up to 20 quarters if available.
Quarterly numbers can also be displayed for A, C and Revenue.
Information can be shown in Chronological or Reverse Chronological order (default).
Increasing growth quarters are shown in white, while diminuing ones are shown in Yellow.
Transition from Losing to Profitable quarters are shown with an exclamation mark ‘!’
Finally, losing quarters are shown between parenthesis.
❖ MAs on chart.
Displays 200, 100, 50 and 20 days MAs on chart.
The MAs are also automatically scaled in the 1W time frame.
❖ New 52 Week High on chart.
A sun is shown on the chart the first time that a new 52 week high is reached.
The N cell shows a filled sun when a 52 week high is no older than a month, an lighter sun when it’s no older than a quarter or a moon otherwise.
❖ Pocket Pivots on chart.
Small triangles below the price are signaling pocket pivots.
❖ Bases on chart, formerly Darvas Boxes.
Draw bases as defined by Darvas boxes, both top or bottom of bases can be selected to be shown in order to only show resistance or support.
❖ Market exposure/direction indicator.
When charting S&P500 (SPX), Nasdaq 100 Index (NDX), Nasdaq composite (IXIC) or Dow Jownes Index (DJIA), the indicator switches to Market Exposure indicator, showing also Accumulation/Distribution days when volume information is available. This indication which varies from 0% to 100% is what is shown under the M letter in the CANSLIM table which is calculated on the S&P500.
❖ Follow Through Days indicator.
If you are an adept of the Low-cheat entry, then you will be highly interested by the Follow Through days indicator as measured in the S&P 500 and shown as diamonds on the chart.
The follow-through days are calculated on S&P500 but shown in current stock chart so you don’t need to chart the S&P 500 to know that a follow through day occurred.
Follow Through days show correctly on Daily time frame and most are also shown on the Weekly time frame as well.
They are also classified according to the market zone in which they occur:
0%-5% from peak = Pullback : FT day is not shown.
5%-10% from peak = Minor Correction : Minor FT days is shown.
10%-20% from peak = Correction : Intermediate FT days us shown
20+% from peak = Bear Market : Makor FT days is shown
❖ RS Line and Rating indicator.
A RS Line and Rating indicator can be added to the chart.
Relative Strength Rating Accuracy.
Please note that the RS Rating is not 100% accurate when compared to IBD values.
❖ Earning Line indicator.
An Earning Line indicator can be added to the chart.
❖ ATR Bands and ATR Trade calculator.
The motivation for this calculator came from my own need to enter trades on volatile stocks where the simple 7% Stop Loss rule doest not work.
It simply calculates the number of shares you can buy at any moment based on current stock price and using the lower ATR band as a stop loss.
A few words about the ATR Bands.
On this indicator the ATR bands are not drawn as a classical channel that follows the price.
The lower band is drawn as a support until it’s broken on a closing basis. It can’t be in a down trend.
The upper band is drawn as a resistance until it’s broken on a closing basis. It can’t be in an up trend.
The idea is that when price starts to fall down from a peak, it should not violate its lower band ATR and that means that we can use that level as a Stop Loss.
You must look back for the stock volatility and find out which ATR multiplier works well meaning that the ATR bands are not violated on normal pullbacks. By default, the indicator uses 5x multiplier.
❖ Extra things, visual features and default settings.
The first square cell of current quarter displays a check mark ‘V’ if the CANSLIM criteria is OK or acceptable or a cross ‘X’ otherwise.
The first square cell of historical C and Rev show respectively the count of last consecutive positive quarters.
There are different color themes from “Forest” to “Space” you can chose from to best fit your eyes.
You also have different table sizes going from “Micro” to “Huge” for better adjustment to the size of your display.
The default settings view show: Pocket Pivots, FT Days, MA50, RS Line and ATR Bands.
That's all, Enjoy!
Indicator: Profitability by Day & Hour (stacked, non-overlay)What it does
This tool performs a simple seasonality study on the selected symbol. It measures historical returns and summarizes them in two horizontal heatmaps:
Hours table (top) — Columns 00–23 show the average return of each clock hour, plus sample size, win rate, volatility (SD), and a t-score.
Days table (middle) — Columns 1–7 correspond to Mon–Sun with the same metrics.
Summary (bottom) — Shows the most profitable day and hour in the history loaded on your chart.
Green cells indicate higher average returns; red cells indicate lower/negative averages. The layout is centered on the screen, with the hours table above the days table for quick scanning.
How it works (methodology)
Returns: by default the indicator uses log returns ln(Ct/Ct-1) (you can switch to simple % if you prefer).
Daily aggregation (no look-ahead): day statistics are computed from completed daily closes via a higher timeframe request. Yesterday’s daily close vs. the prior day is added to the appropriate weekday bucket, preventing repaint/forward bias.
Hourly aggregation (intraday only): hour statistics are computed bar-to-bar on the current intraday timeframe and accumulated by clock hour (00–23) of the symbol’s exchange timezone.
Metrics per bucket:
Mean: average return in that bucket.
n: number of observations.
Win%: share of positive returns.
SD: standard deviation of returns (volatility proxy).
t-score: mean / SD * sqrt(n) — a quick stability signal (not a hypothesis test).
The indicator does not rely on future data and does not repaint past values.
Reading the tables
Start with the Mean row in each table: it’s color-mapped (red → yellow → green).
Check n (sample size). A bright green cell with very low n is less meaningful than a mild green cell with large n.
Use Win% and SD to judge consistency and noise.
t-score is a compact “signal-to-noise × sample size” measure; higher absolute values suggest more stable effects.
Typical observations traders look for (purely illustrative): for some equity indices, the first hour after the cash open can dominate; for FX/crypto, certain late-US or early-Asia hours sometimes stand out. Always verify on your symbol and timeframe.
Market Clarity Pro Market Clarity Pro — See Key Zones, Trend & Volume Signals
Spot yesterday’s High (Supply) and Low (Demand) instantly — and know exactly where big buyers and sellers are likely waiting.
Red zones = strong selling pressure.
Green zones = strong buying pressure.
Plus, a built-in trend line keeps you trading in the right direction and away from sudden reversals.
You’ll also see:
🔴 Red arrow — not a sell signal, but a sign of heavy sellers stepping in, with volume confirmation and a candle breaking the previous one.
🔵 Blue arrow — not a buy signal, but a sign of strong buyers stepping in, with volume confirmation and a candle breaking the previous one.
These arrows highlight potential volume spikes and breakouts for confirmation only — you still confirm with the higher time frame for more market clarity.
Break above supply. Possible uptrend.
Break below demand. Possible downtrend.
📌 Before using this tool, watch the tutorial video to learn exactly how to apply it and how to spot profitable trades with confidence.
Kelly Position Size CalculatorThis position sizing calculator implements the Kelly Criterion, developed by John L. Kelly Jr. at Bell Laboratories in 1956, to determine mathematically optimal position sizes for maximizing long-term wealth growth. Unlike arbitrary position sizing methods, this tool provides a scientifically solution based on your strategy's actual performance statistics and incorporates modern refinements from over six decades of academic research.
The Kelly Criterion addresses a fundamental question in capital allocation: "What fraction of capital should be allocated to each opportunity to maximize growth while avoiding ruin?" This question has profound implications for financial markets, where traders and investors constantly face decisions about optimal capital allocation (Van Tharp, 2007).
Theoretical Foundation
The Kelly Criterion for binary outcomes is expressed as f* = (bp - q) / b, where f* represents the optimal fraction of capital to allocate, b denotes the risk-reward ratio, p indicates the probability of success, and q represents the probability of loss (Kelly, 1956). This formula maximizes the expected logarithm of wealth, ensuring maximum long-term growth rate while avoiding the risk of ruin.
The mathematical elegance of Kelly's approach lies in its derivation from information theory. Kelly's original work was motivated by Claude Shannon's information theory (Shannon, 1948), recognizing that maximizing the logarithm of wealth is equivalent to maximizing the rate of information transmission. This connection between information theory and wealth accumulation provides a deep theoretical foundation for optimal position sizing.
The logarithmic utility function underlying the Kelly Criterion naturally embodies several desirable properties for capital management. It exhibits decreasing marginal utility, penalizes large losses more severely than it rewards equivalent gains, and focuses on geometric rather than arithmetic mean returns, which is appropriate for compounding scenarios (Thorp, 2006).
Scientific Implementation
This calculator extends beyond basic Kelly implementation by incorporating state of the art refinements from academic research:
Parameter Uncertainty Adjustment: Following Michaud (1989), the implementation applies Bayesian shrinkage to account for parameter estimation error inherent in small sample sizes. The adjustment formula f_adjusted = f_kelly × confidence_factor + f_conservative × (1 - confidence_factor) addresses the overconfidence bias documented by Baker and McHale (2012), where the confidence factor increases with sample size and the conservative estimate equals 0.25 (quarter Kelly).
Sample Size Confidence: The reliability of Kelly calculations depends critically on sample size. Research by Browne and Whitt (1996) provides theoretical guidance on minimum sample requirements, suggesting that at least 30 independent observations are necessary for meaningful parameter estimates, with 100 or more trades providing reliable estimates for most trading strategies.
Universal Asset Compatibility: The calculator employs intelligent asset detection using TradingView's built-in symbol information, automatically adapting calculations for different asset classes without manual configuration.
ASSET SPECIFIC IMPLEMENTATION
Equity Markets: For stocks and ETFs, position sizing follows the calculation Shares = floor(Kelly Fraction × Account Size / Share Price). This straightforward approach reflects whole share constraints while accommodating fractional share trading capabilities.
Foreign Exchange Markets: Forex markets require lot-based calculations following Lot Size = Kelly Fraction × Account Size / (100,000 × Base Currency Value). The calculator automatically handles major currency pairs with appropriate pip value calculations, following industry standards described by Archer (2010).
Futures Markets: Futures position sizing accounts for leverage and margin requirements through Contracts = floor(Kelly Fraction × Account Size / Margin Requirement). The calculator estimates margin requirements as a percentage of contract notional value, with specific adjustments for micro-futures contracts that have smaller sizes and reduced margin requirements (Kaufman, 2013).
Index and Commodity Markets: These markets combine characteristics of both equity and futures markets. The calculator automatically detects whether instruments are cash-settled or futures-based, applying appropriate sizing methodologies with correct point value calculations.
Risk Management Integration
The calculator integrates sophisticated risk assessment through two primary modes:
Stop Loss Integration: When fixed stop-loss levels are defined, risk calculation follows Risk per Trade = Position Size × Stop Loss Distance. This ensures that the Kelly fraction accounts for actual risk exposure rather than theoretical maximum loss, with stop-loss distance measured in appropriate units for each asset class.
Strategy Drawdown Assessment: For discretionary exit strategies, risk estimation uses maximum historical drawdown through Risk per Trade = Position Value × (Maximum Drawdown / 100). This approach assumes that individual trade losses will not exceed the strategy's historical maximum drawdown, providing a reasonable estimate for strategies with well-defined risk characteristics.
Fractional Kelly Approaches
Pure Kelly sizing can produce substantial volatility, leading many practitioners to adopt fractional Kelly approaches. MacLean, Sanegre, Zhao, and Ziemba (2004) analyze the trade-offs between growth rate and volatility, demonstrating that half-Kelly typically reduces volatility by approximately 75% while sacrificing only 25% of the growth rate.
The calculator provides three primary Kelly modes to accommodate different risk preferences and experience levels. Full Kelly maximizes growth rate while accepting higher volatility, making it suitable for experienced practitioners with strong risk tolerance and robust capital bases. Half Kelly offers a balanced approach popular among professional traders, providing optimal risk-return balance by reducing volatility significantly while maintaining substantial growth potential. Quarter Kelly implements a conservative approach with low volatility, recommended for risk-averse traders or those new to Kelly methodology who prefer gradual introduction to optimal position sizing principles.
Empirical Validation and Performance
Extensive academic research supports the theoretical advantages of Kelly sizing. Hakansson and Ziemba (1995) provide a comprehensive review of Kelly applications in finance, documenting superior long-term performance across various market conditions and asset classes. Estrada (2008) analyzes Kelly performance in international equity markets, finding that Kelly-based strategies consistently outperform fixed position sizing approaches over extended periods across 19 developed markets over a 30-year period.
Several prominent investment firms have successfully implemented Kelly-based position sizing. Pabrai (2007) documents the application of Kelly principles at Berkshire Hathaway, noting Warren Buffett's concentrated portfolio approach aligns closely with Kelly optimal sizing for high-conviction investments. Quantitative hedge funds, including Renaissance Technologies and AQR, have incorporated Kelly-based risk management into their systematic trading strategies.
Practical Implementation Guidelines
Successful Kelly implementation requires systematic application with attention to several critical factors:
Parameter Estimation: Accurate parameter estimation represents the greatest challenge in practical Kelly implementation. Brown (1976) notes that small errors in probability estimates can lead to significant deviations from optimal performance. The calculator addresses this through Bayesian adjustments and confidence measures.
Sample Size Requirements: Users should begin with conservative fractional Kelly approaches until achieving sufficient historical data. Strategies with fewer than 30 trades may produce unreliable Kelly estimates, regardless of adjustments. Full confidence typically requires 100 or more independent trade observations.
Market Regime Considerations: Parameters that accurately describe historical performance may not reflect future market conditions. Ziemba (2003) recommends regular parameter updates and conservative adjustments when market conditions change significantly.
Professional Features and Customization
The calculator provides comprehensive customization options for professional applications:
Multiple Color Schemes: Eight professional color themes (Gold, EdgeTools, Behavioral, Quant, Ocean, Fire, Matrix, Arctic) with dark and light theme compatibility ensure optimal visibility across different trading environments.
Flexible Display Options: Adjustable table size and position accommodate various chart layouts and user preferences, while maintaining analytical depth and clarity.
Comprehensive Results: The results table presents essential information including asset specifications, strategy statistics, Kelly calculations, sample confidence measures, position values, risk assessments, and final position sizes in appropriate units for each asset class.
Limitations and Considerations
Like any analytical tool, the Kelly Criterion has important limitations that users must understand:
Stationarity Assumption: The Kelly Criterion assumes that historical strategy statistics represent future performance characteristics. Non-stationary market conditions may invalidate this assumption, as noted by Lo and MacKinlay (1999).
Independence Requirement: Each trade should be independent to avoid correlation effects. Many trading strategies exhibit serial correlation in returns, which can affect optimal position sizing and may require adjustments for portfolio applications.
Parameter Sensitivity: Kelly calculations are sensitive to parameter accuracy. Regular calibration and conservative approaches are essential when parameter uncertainty is high.
Transaction Costs: The implementation incorporates user-defined transaction costs but assumes these remain constant across different position sizes and market conditions, following Ziemba (2003).
Advanced Applications and Extensions
Multi-Asset Portfolio Considerations: While this calculator optimizes individual position sizes, portfolio-level applications require additional considerations for correlation effects and aggregate risk management. Simplified portfolio approaches include treating positions independently with correlation adjustments.
Behavioral Factors: Behavioral finance research reveals systematic biases that can interfere with Kelly implementation. Kahneman and Tversky (1979) document loss aversion, overconfidence, and other cognitive biases that lead traders to deviate from optimal strategies. Successful implementation requires disciplined adherence to calculated recommendations.
Time-Varying Parameters: Advanced implementations may incorporate time-varying parameter models that adjust Kelly recommendations based on changing market conditions, though these require sophisticated econometric techniques and substantial computational resources.
Comprehensive Usage Instructions and Practical Examples
Implementation begins with loading the calculator on your desired trading instrument's chart. The system automatically detects asset type across stocks, forex, futures, and cryptocurrency markets while extracting current price information. Navigation to the indicator settings allows input of your specific strategy parameters.
Strategy statistics configuration requires careful attention to several key metrics. The win rate should be calculated from your backtest results using the formula of winning trades divided by total trades multiplied by 100. Average win represents the sum of all profitable trades divided by the number of winning trades, while average loss calculates the sum of all losing trades divided by the number of losing trades, entered as a positive number. The total historical trades parameter requires the complete number of trades in your backtest, with a minimum of 30 trades recommended for basic functionality and 100 or more trades optimal for statistical reliability. Account size should reflect your available trading capital, specifically the risk capital allocated for trading rather than total net worth.
Risk management configuration adapts to your specific trading approach. The stop loss setting should be enabled if you employ fixed stop-loss exits, with the stop loss distance specified in appropriate units depending on the asset class. For stocks, this distance is measured in dollars, for forex in pips, and for futures in ticks. When stop losses are not used, the maximum strategy drawdown percentage from your backtest provides the risk assessment baseline. Kelly mode selection offers three primary approaches: Full Kelly for aggressive growth with higher volatility suitable for experienced practitioners, Half Kelly for balanced risk-return optimization popular among professional traders, and Quarter Kelly for conservative approaches with reduced volatility.
Display customization ensures optimal integration with your trading environment. Eight professional color themes provide optimization for different chart backgrounds and personal preferences. Table position selection allows optimal placement within your chart layout, while table size adjustment ensures readability across different screen resolutions and viewing preferences.
Detailed Practical Examples
Example 1: SPY Swing Trading Strategy
Consider a professionally developed swing trading strategy for SPY (S&P 500 ETF) with backtesting results spanning 166 total trades. The strategy achieved 110 winning trades, representing a 66.3% win rate, with an average winning trade of $2,200 and average losing trade of $862. The maximum drawdown reached 31.4% during the testing period, and the available trading capital amounts to $25,000. This strategy employs discretionary exits without fixed stop losses.
Implementation requires loading the calculator on the SPY daily chart and configuring the parameters accordingly. The win rate input receives 66.3, while average win and loss inputs receive 2200 and 862 respectively. Total historical trades input requires 166, with account size set to 25000. The stop loss function remains disabled due to the discretionary exit approach, with maximum strategy drawdown set to 31.4%. Half Kelly mode provides the optimal balance between growth and risk management for this application.
The calculator generates several key outputs for this scenario. The risk-reward ratio calculates automatically to 2.55, while the Kelly fraction reaches approximately 53% before scientific adjustments. Sample confidence achieves 100% given the 166 trades providing high statistical confidence. The recommended position settles at approximately 27% after Half Kelly and Bayesian adjustment factors. Position value reaches approximately $6,750, translating to 16 shares at a $420 SPY price. Risk per trade amounts to approximately $2,110, representing 31.4% of position value, with expected value per trade reaching approximately $1,466. This recommendation represents the mathematically optimal balance between growth potential and risk management for this specific strategy profile.
Example 2: EURUSD Day Trading with Stop Losses
A high-frequency EURUSD day trading strategy demonstrates different parameter requirements compared to swing trading approaches. This strategy encompasses 89 total trades with a 58% win rate, generating an average winning trade of $180 and average losing trade of $95. The maximum drawdown reached 12% during testing, with available capital of $10,000. The strategy employs fixed stop losses at 25 pips and take profit targets at 45 pips, providing clear risk-reward parameters.
Implementation begins with loading the calculator on the EURUSD 1-hour chart for appropriate timeframe alignment. Parameter configuration includes win rate at 58, average win at 180, and average loss at 95. Total historical trades input receives 89, with account size set to 10000. The stop loss function is enabled with distance set to 25 pips, reflecting the fixed exit strategy. Quarter Kelly mode provides conservative positioning due to the smaller sample size compared to the previous example.
Results demonstrate the impact of smaller sample sizes on Kelly calculations. The risk-reward ratio calculates to 1.89, while the Kelly fraction reaches approximately 32% before adjustments. Sample confidence achieves 89%, providing moderate statistical confidence given the 89 trades. The recommended position settles at approximately 7% after Quarter Kelly application and Bayesian shrinkage adjustment for the smaller sample. Position value amounts to approximately $700, translating to 0.07 standard lots. Risk per trade reaches approximately $175, calculated as 25 pips multiplied by lot size and pip value, with expected value per trade at approximately $49. This conservative position sizing reflects the smaller sample size, with position sizes expected to increase as trade count surpasses 100 and statistical confidence improves.
Example 3: ES1! Futures Systematic Strategy
Systematic futures trading presents unique considerations for Kelly criterion application, as demonstrated by an E-mini S&P 500 futures strategy encompassing 234 total trades. This systematic approach achieved a 45% win rate with an average winning trade of $1,850 and average losing trade of $720. The maximum drawdown reached 18% during the testing period, with available capital of $50,000. The strategy employs 15-tick stop losses with contract specifications of $50 per tick, providing precise risk control mechanisms.
Implementation involves loading the calculator on the ES1! 15-minute chart to align with the systematic trading timeframe. Parameter configuration includes win rate at 45, average win at 1850, and average loss at 720. Total historical trades receives 234, providing robust statistical foundation, with account size set to 50000. The stop loss function is enabled with distance set to 15 ticks, reflecting the systematic exit methodology. Half Kelly mode balances growth potential with appropriate risk management for futures trading.
Results illustrate how favorable risk-reward ratios can support meaningful position sizing despite lower win rates. The risk-reward ratio calculates to 2.57, while the Kelly fraction reaches approximately 16%, lower than previous examples due to the sub-50% win rate. Sample confidence achieves 100% given the 234 trades providing high statistical confidence. The recommended position settles at approximately 8% after Half Kelly adjustment. Estimated margin per contract amounts to approximately $2,500, resulting in a single contract allocation. Position value reaches approximately $2,500, with risk per trade at $750, calculated as 15 ticks multiplied by $50 per tick. Expected value per trade amounts to approximately $508. Despite the lower win rate, the favorable risk-reward ratio supports meaningful position sizing, with single contract allocation reflecting appropriate leverage management for futures trading.
Example 4: MES1! Micro-Futures for Smaller Accounts
Micro-futures contracts provide enhanced accessibility for smaller trading accounts while maintaining identical strategy characteristics. Using the same systematic strategy statistics from the previous example but with available capital of $15,000 and micro-futures specifications of $5 per tick with reduced margin requirements, the implementation demonstrates improved position sizing granularity.
Kelly calculations remain identical to the full-sized contract example, maintaining the same risk-reward dynamics and statistical foundations. However, estimated margin per contract reduces to approximately $250 for micro-contracts, enabling allocation of 4-5 micro-contracts. Position value reaches approximately $1,200, while risk per trade calculates to $75, derived from 15 ticks multiplied by $5 per tick. This granularity advantage provides better position size precision for smaller accounts, enabling more accurate Kelly implementation without requiring large capital commitments.
Example 5: Bitcoin Swing Trading
Cryptocurrency markets present unique challenges requiring modified Kelly application approaches. A Bitcoin swing trading strategy on BTCUSD encompasses 67 total trades with a 71% win rate, generating average winning trades of $3,200 and average losing trades of $1,400. Maximum drawdown reached 28% during testing, with available capital of $30,000. The strategy employs technical analysis for exits without fixed stop losses, relying on price action and momentum indicators.
Implementation requires conservative approaches due to cryptocurrency volatility characteristics. Quarter Kelly mode is recommended despite the high win rate to account for crypto market unpredictability. Expected position sizing remains reduced due to the limited sample size of 67 trades, requiring additional caution until statistical confidence improves. Regular parameter updates are strongly recommended due to cryptocurrency market evolution and changing volatility patterns that can significantly impact strategy performance characteristics.
Advanced Usage Scenarios
Portfolio position sizing requires sophisticated consideration when running multiple strategies simultaneously. Each strategy should have its Kelly fraction calculated independently to maintain mathematical integrity. However, correlation adjustments become necessary when strategies exhibit related performance patterns. Moderately correlated strategies should receive individual position size reductions of 10-20% to account for overlapping risk exposure. Aggregate portfolio risk monitoring ensures total exposure remains within acceptable limits across all active strategies. Professional practitioners often consider using lower fractional Kelly approaches, such as Quarter Kelly, when running multiple strategies simultaneously to provide additional safety margins.
Parameter sensitivity analysis forms a critical component of professional Kelly implementation. Regular validation procedures should include monthly parameter updates using rolling 100-trade windows to capture evolving market conditions while maintaining statistical relevance. Sensitivity testing involves varying win rates by ±5% and average win/loss ratios by ±10% to assess recommendation stability under different parameter assumptions. Out-of-sample validation reserves 20% of historical data for parameter verification, ensuring that optimization doesn't create curve-fitted results. Regime change detection monitors actual performance against expected metrics, triggering parameter reassessment when significant deviations occur.
Risk management integration requires professional overlay considerations beyond pure Kelly calculations. Daily loss limits should cease trading when daily losses exceed twice the calculated risk per trade, preventing emotional decision-making during adverse periods. Maximum position limits should never exceed 25% of account value in any single position regardless of Kelly recommendations, maintaining diversification principles. Correlation monitoring reduces position sizes when holding multiple correlated positions that move together during market stress. Volatility adjustments consider reducing position sizes during periods of elevated VIX above 25 for equity strategies, adapting to changing market conditions.
Troubleshooting and Optimization
Professional implementation often encounters specific challenges requiring systematic troubleshooting approaches. Zero position size displays typically result from insufficient capital for minimum position sizes, negative expected values, or extremely conservative Kelly calculations. Solutions include increasing account size, verifying strategy statistics for accuracy, considering Quarter Kelly mode for conservative approaches, or reassessing overall strategy viability when fundamental issues exist.
Extremely high Kelly fractions exceeding 50% usually indicate underlying problems with parameter estimation. Common causes include unrealistic win rates, inflated risk-reward ratios, or curve-fitted backtest results that don't reflect genuine trading conditions. Solutions require verifying backtest methodology, including all transaction costs in calculations, testing strategies on out-of-sample data, and using conservative fractional Kelly approaches until parameter reliability improves.
Low sample confidence below 50% reflects insufficient historical trades for reliable parameter estimation. This situation demands gathering additional trading data, using Quarter Kelly approaches until reaching 100 or more trades, applying extra conservatism in position sizing, and considering paper trading to build statistical foundations without capital risk.
Inconsistent results across similar strategies often stem from parameter estimation differences, market regime changes, or strategy degradation over time. Professional solutions include standardizing backtest methodology across all strategies, updating parameters regularly to reflect current conditions, and monitoring live performance against expectations to identify deteriorating strategies.
Position sizes that appear inappropriately large or small require careful validation against traditional risk management principles. Professional standards recommend never risking more than 2-3% per trade regardless of Kelly calculations. Calibration should begin with Quarter Kelly approaches, gradually increasing as comfort and confidence develop. Most institutional traders utilize 25-50% of full Kelly recommendations to balance growth with prudent risk management.
Market condition adjustments require dynamic approaches to Kelly implementation. Trending markets may support full Kelly recommendations when directional momentum provides favorable conditions. Ranging or volatile markets typically warrant reducing to Half or Quarter Kelly to account for increased uncertainty. High correlation periods demand reducing individual position sizes when multiple positions move together, concentrating risk exposure. News and event periods often justify temporary position size reductions during high-impact releases that can create unpredictable market movements.
Performance monitoring requires systematic protocols to ensure Kelly implementation remains effective over time. Weekly reviews should compare actual versus expected win rates and average win/loss ratios to identify parameter drift or strategy degradation. Position size efficiency and execution quality monitoring ensures that calculated recommendations translate effectively into actual trading results. Tracking correlation between calculated and realized risk helps identify discrepancies between theoretical and practical risk exposure.
Monthly calibration provides more comprehensive parameter assessment using the most recent 100 trades to maintain statistical relevance while capturing current market conditions. Kelly mode appropriateness requires reassessment based on recent market volatility and performance characteristics, potentially shifting between Full, Half, and Quarter Kelly approaches as conditions change. Transaction cost evaluation ensures that commission structures, spreads, and slippage estimates remain accurate and current.
Quarterly strategic reviews encompass comprehensive strategy performance analysis comparing long-term results against expectations and identifying trends in effectiveness. Market regime assessment evaluates parameter stability across different market conditions, determining whether strategy characteristics remain consistent or require fundamental adjustments. Strategic modifications to position sizing methodology may become necessary as markets evolve or trading approaches mature, ensuring that Kelly implementation continues supporting optimal capital allocation objectives.
Professional Applications
This calculator serves diverse professional applications across the financial industry. Quantitative hedge funds utilize the implementation for systematic position sizing within algorithmic trading frameworks, where mathematical precision and consistent application prove essential for institutional capital management. Professional discretionary traders benefit from optimized position management that removes emotional bias while maintaining flexibility for market-specific adjustments. Portfolio managers employ the calculator for developing risk-adjusted allocation strategies that enhance returns while maintaining prudent risk controls across diverse asset classes and investment strategies.
Individual traders seeking mathematical optimization of capital allocation find the calculator provides institutional-grade methodology previously available only to professional money managers. The Kelly Criterion establishes theoretical foundation for optimal capital allocation across both single strategies and multiple trading systems, offering significant advantages over arbitrary position sizing methods that rely on intuition or fixed percentage approaches. Professional implementation ensures consistent application of mathematically sound principles while adapting to changing market conditions and strategy performance characteristics.
Conclusion
The Kelly Criterion represents one of the few mathematically optimal solutions to fundamental investment problems. When properly understood and carefully implemented, it provides significant competitive advantage in financial markets. This calculator implements modern refinements to Kelly's original formula while maintaining accessibility for practical trading applications.
Success with Kelly requires ongoing learning, systematic application, and continuous refinement based on market feedback and evolving research. Users who master Kelly principles and implement them systematically can expect superior risk-adjusted returns and more consistent capital growth over extended periods.
The extensive academic literature provides rich resources for deeper study, while practical experience builds the intuition necessary for effective implementation. Regular parameter updates, conservative approaches with limited data, and disciplined adherence to calculated recommendations are essential for optimal results.
References
Archer, M. D. (2010). Getting Started in Currency Trading: Winning in Today's Forex Market (3rd ed.). John Wiley & Sons.
Baker, R. D., & McHale, I. G. (2012). An empirical Bayes approach to optimising betting strategies. Journal of the Royal Statistical Society: Series D (The Statistician), 61(1), 75-92.
Breiman, L. (1961). Optimal gambling systems for favorable games. In J. Neyman (Ed.), Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability (pp. 65-78). University of California Press.
Brown, D. B. (1976). Optimal portfolio growth: Logarithmic utility and the Kelly criterion. In W. T. Ziemba & R. G. Vickson (Eds.), Stochastic Optimization Models in Finance (pp. 1-23). Academic Press.
Browne, S., & Whitt, W. (1996). Portfolio choice and the Bayesian Kelly criterion. Advances in Applied Probability, 28(4), 1145-1176.
Estrada, J. (2008). Geometric mean maximization: An overlooked portfolio approach? The Journal of Investing, 17(4), 134-147.
Hakansson, N. H., & Ziemba, W. T. (1995). Capital growth theory. In R. A. Jarrow, V. Maksimovic, & W. T. Ziemba (Eds.), Handbooks in Operations Research and Management Science (Vol. 9, pp. 65-86). Elsevier.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Kaufman, P. J. (2013). Trading Systems and Methods (5th ed.). John Wiley & Sons.
Kelly Jr, J. L. (1956). A new interpretation of information rate. Bell System Technical Journal, 35(4), 917-926.
Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton University Press.
MacLean, L. C., Sanegre, E. O., Zhao, Y., & Ziemba, W. T. (2004). Capital growth with security. Journal of Economic Dynamics and Control, 28(4), 937-954.
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Michaud, R. O. (1989). The Markowitz optimization enigma: Is 'optimized' optimal? Financial Analysts Journal, 45(1), 31-42.
Pabrai, M. (2007). The Dhandho Investor: The Low-Risk Value Method to High Returns. John Wiley & Sons.
Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27(3), 379-423.
Tharp, V. K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill.
Thorp, E. O. (2006). The Kelly criterion in blackjack sports betting, and the stock market. In L. C. MacLean, E. O. Thorp, & W. T. Ziemba (Eds.), The Kelly Capital Growth Investment Criterion: Theory and Practice (pp. 789-832). World Scientific.
Van Tharp, K. (2007). Trade Your Way to Financial Freedom (2nd ed.). McGraw-Hill Education.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Vince, R., & Zhu, H. (2015). Optimal betting under parameter uncertainty. Journal of Statistical Planning and Inference, 161, 19-31.
Ziemba, W. T. (2003). The Stochastic Programming Approach to Asset, Liability, and Wealth Management. The Research Foundation of AIMR.
Further Reading
For comprehensive understanding of Kelly Criterion applications and advanced implementations:
MacLean, L. C., Thorp, E. O., & Ziemba, W. T. (2011). The Kelly Capital Growth Investment Criterion: Theory and Practice. World Scientific.
Vince, R. (1992). The Mathematics of Money Management: Risk Analysis Techniques for Traders. John Wiley & Sons.
Thorp, E. O. (2017). A Man for All Markets: From Las Vegas to Wall Street. Random House.
Cover, T. M., & Thomas, J. A. (2006). Elements of Information Theory (2nd ed.). John Wiley & Sons.
Ziemba, W. T., & Vickson, R. G. (Eds.). (2006). Stochastic Optimization Models in Finance. World Scientific.
Clean Multi-Indicator Alignment System
Overview
A sophisticated multi-indicator alignment system designed for 24/7 trading across all markets, with pure signal-based exits and no time restrictions. Perfect for futures, forex, and crypto markets that operate around the clock.
Key Features
🎯 Multi-Indicator Confluence System
EMA Cross Strategy: Fast EMA (5) and Slow EMA (10) for precise trend direction
VWAP Integration: Institution-level price positioning analysis
RSI Momentum: 7-period RSI for momentum confirmation and reversal detection
MACD Signals: Optimized 8/17/5 configuration for scalping responsiveness
Volume Confirmation: Customizable volume multiplier (default 1.6x) for signal validation
🚀 Advanced Entry Logic
Initial Full Alignment: Requires all 5 indicators + volume confirmation
Smart Continuation Entries: EMA9 pullback entries when trend momentum remains intact
Flexible Time Controls: Optional session filtering or 24/7 operation
🎪 Pure Signal-Based Exits
No Forced Closes: Positions exit only on technical signal reversals
Dual Exit Conditions: EMA9 breakdown + RSI flip OR MACD cross + EMA20 breakdown
Trend Following: Allows profitable trends to run their full course
Perfect for Swing Scalping: Ideal for multi-session position holding
📊 Visual Interface
Real-Time Status Dashboard: Live alignment monitoring for all indicators
Color-Coded Candles: Instant visual confirmation of entry/exit signals
Clean Chart Display: Toggle-able EMAs and VWAP with professional styling
Signal Differentiation: Clear labels for entries, X-crosses for exits
🔔 Alert System
Entry Notifications: Separate alerts for buy/sell signals
Exit Warnings: Technical breakdown alerts for position management
Mobile Ready: Push notifications to TradingView mobile app
Market Applications
Perfect For:
Gold Futures (GC): 24-hour precious metals trading
NASDAQ Futures (NQ): High-volatility index scalping
Forex Markets: Currency pairs with continuous operation
Crypto Trading: 24/7 cryptocurrency momentum plays
Energy Futures: Oil, gas, and commodity swing trades
Optimal Timeframes:
1-5 Minutes: Ultra-fast scalping during high volatility
5-15 Minutes: Balanced approach for most markets
15-30 Minutes: Swing scalping for trend following
🧠 Smart Position Management
Tracks implied position direction
Prevents conflicting signals
Allows trend continuation entries
State-aware exit logic
⚡ Scalping Optimized
Fast-reacting indicators with shorter periods
Volume-based confirmation reduces false signals
Clean entry/exit visualization
Minimal lag for time-sensitive trades
Configuration Options
All parameters fully customizable:
EMA Lengths: Adjustable from 1-30 periods
RSI Period: 1-14 range for different market conditions
MACD Settings: Fast (1-15), Slow (1-30), Signal (1-10)
Volume Confirmation: 0.5-5.0x multiplier range
Visual Preferences: Colors, displays, and table options
Risk Management Features
Clear visual exit signals prevent emotion-based decisions
Volume confirmation reduces false breakouts
Multi-indicator confluence improves signal quality
Optional time filtering for session-specific strategies
Best Use Cases
Futures Scalping: NQ, ES, GC during active sessions
Forex Swing Trading: Major pairs during overlap periods
Crypto Momentum: Bitcoin, Ethereum trend following
24/7 Automated Systems: Algorithmic trading implementation
Multi-Market Scanning: Portfolio-wide signal monitoring
