Crypto Perp Calc v1Advanced Perpetual Position Calculator for TradingView
Description
A comprehensive position sizing and risk management tool designed specifically for perpetual futures trading. This indicator eliminates the confusion of calculating leveraged positions by providing real-time position metrics directly on your chart.
Key Features:
Interactive Price Selection: Click directly on chart to set entry, stop loss, and take profit levels
Accurate Lot Size Calculation: Instantly calculates the exact position size needed for your margin and leverage
Multiple Entry Support: DCA into positions with up to 3 entry points with customizable allocation
Multiple Take Profit Levels: Scale out of positions with up to 3 TP targets
Comprehensive Risk Metrics: Shows dollar P&L, account risk percentage, and liquidation price
Visual Risk/Reward: Color-coded boxes and lines display your trade setup clearly
Real-time Info Table: All critical position data in one organized panel
Perfect for traders using perpetual futures who need precise position sizing with leverage.
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How to Use
Quick Start (3 Clicks)
1. Add the indicator to your chart
2. Click three times when prompted:
First click: Set your entry price
Second click: Set your stop loss
Third click: Set your take profit
3. Read the TOTAL LOTS value from the info table (highlighted in yellow)
4. Use this lot size in your exchange when placing the trade
Detailed Setup
Step 1: Configure Your Account
Enter your account balance (total USDT in account)
Set your margin amount (how much USDT to risk on this trade)
Choose your leverage (1x to 125x)
Select Long or Short position
Step 2: Set Price Levels
Main levels use interactive clicking (Entry, SL, TP)
For multiple entries or TPs, use the settings panel to manually input prices and percentages
Step 3: Read the Results
The info table shows:
TOTAL LOTS - The position size to enter on your exchange
Margin Used - Your actual capital at risk
Notional - Total position value (margin × leverage)
Max Risk - Dollar amount you'll lose at stop loss
Total Profit - Dollar amount you'll gain at take profit
R:R Ratio - Risk to reward ratio
Account Risk - Percentage of account at risk
Liquidation - Price where position gets liquidated
Step 4: Advanced Features (Optional)
Multiple Entries (DCA):
Enable "Use Multiple Entries"
Set up to 3 entry prices
Allocate percentage for each (must total 100%)
See individual lot sizes for each entry
Multiple Take Profits:
Enable "Use Multiple TPs"
Set up to 3 TP levels
Allocate percentage to close at each level (must total 100%)
View profit at each target
Visual Elements
Blue lines/labels: Entry points
Red lines/labels: Stop loss
Green lines/labels: Take profit targets
Colored boxes: Visual risk (red) and reward (green) zones
Info table: Can be positioned anywhere on screen
Alerts
Set price alerts for:
Entry zones reached
Stop loss approached
Take profit levels hit
Works with TradingView's alert system
Tips for Best Results
Always verify the lot size matches your intended risk
Check the liquidation price stays far from your stop loss
Monitor the account risk percentage (recommended: keep under 2-3%)
Use the warning indicators if risk exceeds margin
For quick trades, use single entry/TP; for complex strategies, use multiple levels
Example Workflow
Find your trade setup using your analysis
Add this indicator and click to set levels
Check risk metrics in the table
Copy the TOTAL LOTS value
Enter this exact position size on your exchange
Set alerts for key levels if desired
This tool bridges the gap between TradingView charting and exchange execution, ensuring your position sizing is always accurate when trading with leverage.
Disclaimer, this was coded with help of AI, double check calculations if they are off.
Buscar en scripts para "AI技术更新"
OPTIMAL super trend tripple confirm for leverage. Ai implemented for higher r:r still a work in progresss
P/B Ratio (Per Share) vs Median + Bollinger Band- 📝 This indicator highlights potential buying opportunities by analyzing the Price-to-Book (P/B) ratio in relation to Bollinger Bands and its historical median.
- 🎯 The goal is to provide a visually intuitive signal for value-oriented entries, especially when valuation compression aligns with historical context.
- 💡 Vertical green shading is applied when the P/B ratio drops below the lower Bollinger Band, which is calculated directly from the P/B ratio itself — not price. This condition often signals the ticker may be oversold.
- 🟢 Lighter green appears when the ratio is below the lower band but above the median, suggesting a possible shorter-term entry with slightly more risk.
- 🟢 Darker green appears when the ratio is both below the lower band and below the median, pointing to a potentially stronger, longer-term value entry.
- ⚠️ This logic was tested using 1 and 2-day time frames. It may not be as helpful in longer time frames, as the financial data TradingView pulls in begins in Q4 2017.
- ⚠️ Note: This script relies on financial data availability through TradingView. It may not function properly with certain tickers — especially ETFs, IPOs, or thinly tracked assets — where P/S ratio data is missing or incomplete.
- ⚠️ This indicator will not guarantee successful results. Use in conjunction with other indicators and do your due diligence.
- 🤖 This script was iteratively refined with the help of AI to ensure clean logic, minimalist design, and actionable signal clarity.
- 📢 Idea is based on the script "Historical PE ratio vs median" by haribotagada
- 💬 Questions, feedback, or suggestions? Drop a comment — I’d love to hear how you’re using it or what you'd like to see changed.
P/E Ratio vs Median + Bollinger Band- 📝 This indicator highlights potential buying opportunities by analyzing the Price-to-Earnings (P/E) ratio in relation to Bollinger Bands and its historical median.
- 🎯 The goal is to provide a visually intuitive signal for value-oriented entries, especially when valuation compression aligns with historical context.
- 💡 Vertical green shading is applied when the P/E ratio drops below the lower Bollinger Band, which is calculated directly from the P/E ratio itself — not price. This condition often signals the ticker may be oversold.
- 🟢 Lighter green appears when the ratio is below the lower band but above the median, suggesting a possible shorter-term entry with slightly more risk.
- 🟢 Darker green appears when the ratio is both below the lower band and below the median, pointing to a potentially stronger, longer-term value entry.
- ⚠️ This logic was tested using 1 and 2-day time frames. It may not be as helpful in longer time frames, as the financial data TradingView pulls in begins in Q4 2017.
- ⚠️ Note: This script relies on financial data availability through TradingView. It may not function properly with certain tickers — especially ETFs, IPOs, or thinly tracked assets — where P/S ratio data is missing or incomplete.
- ⚠️ This indicator will not guarantee successful results. Use in conjunction with other indicators and do your due diligence.
- 🤖 This script was iteratively refined with the help of AI to ensure clean logic, minimalist design, and actionable signal clarity.
- 📢 Idea is based on the script "Historical PE ratio vs median" by haribotagada
- 💬 Questions, feedback, or suggestions? Drop a comment — I’d love to hear how you’re using it or what you'd like to see changed.
P/S Ratio vs Median + Bollinger Band- 📝 This indicator highlights potential buying opportunities by analyzing the Price-to-Sales (P/S) ratio in relation to Bollinger Bands and its historical median.
- 🎯 The goal is to provide a visually intuitive signal for value-oriented entries, especially when valuation compression aligns with historical context.
- 💡 Vertical green shading is applied when the P/S ratio drops below the lower Bollinger Band, which is calculated directly from the P/S ratio itself — not price. This condition often signals the ticker may be oversold.
- 🟢 Lighter green appears when the ratio is below the lower band but above the median, suggesting a possible shorter-term entry with slightly more risk.
- 🟢 Darker green appears when the ratio is both below the lower band and below the median, pointing to a potentially stronger, longer-term value entry.
- ⚠️ This logic was tested using 1 and 2-day time frames. It may not be as helpful in longer time frames, as the financial data TradingView pulls in begins in Q4 2017.
- ⚠️ Note: This script relies on financial data availability through TradingView. It may not function properly with certain tickers — especially ETFs, IPOs, or thinly tracked assets — where P/S ratio data is missing or incomplete.
- ⚠️ This indicator will not guarantee successful results. Use in conjunction with other indicators and do your due diligence.
- 🤖 This script was iteratively refined with the help of AI to ensure clean logic, minimalist design, and actionable signal clarity.
- 📢 Idea is based on the script "Historical PE ratio vs median" by @haribotagada
- 💬 Questions, feedback, or suggestions? Drop a comment — I’d love to hear how you’re using it or what you'd like to see changed.
Confluence StackPlease read the instructions below. The code was mostly written using AI so may contain errors. Happy trading all and good luck. ATB Richard
INTENDED USE
This indicator is designed for technical traders who want to move beyond simple buy/sell signals and gain a deeper understanding of the underlying market dynamics. It is ideal for trend followers, swing traders, and anyone looking to confirm the quality of a trend.
WHO IS THIS FOR?
Traders who want to differentiate between strong, sustainable trends and weak, unreliable moves.
Analysts looking to identify high-conviction setups backed by multiple factors (e.g., momentum confirmed by volume).
Discretionary traders who need a quick, visual tool to gauge market sentiment and avoid choppy conditions.
WHY USE IT?
Traditional indicators often give conflicting signals. The Confluence Stack solves this by aggregating multiple perspectives into one clear visual. It helps you answer not just "Is the market going up?" but "WHY is it going up, and how strong is the conviction?". This allows for more informed decision-making and helps filter out low-probability trades.
DISCLAIMER AND LICENSE
This script is for educational purposes only and is not a recommendation to buy or sell any financial instrument. All trading and investment decisions are the sole responsibility of the user. Trading involves significant risk.
This source code is subject to the terms of the Mozilla Public License 2.0 at www.mozilla.org
HOW TO USE THIS INDICATOR
This indicator is designed to show the 'character' of a market move by grouping signals into distinct categories. Instead of seeing many individual signals, you see the strength of the underlying forces driving the price.
1. READ THE HEIGHT (Strength of Confluence)
The total height of the stack shows the strength of agreement. A tall stack means many signals are aligned, indicating a high-conviction move. A short stack means weak agreement and a choppy, indecisive market.
2. READ THE COLOR (Character of the Move)
The colors tell you WHY the market is moving.
BLUE (Momentum): A stack of mostly blue shades indicates a trend driven by pure momentum. This is the 'speed' of the market.
RSI (Relative Strength Index): Measures the magnitude of recent price gains versus losses. A smooth measure of trend strength.
Stochastic Oscillator: Measures the current closing price's position within the recent high-low range. More sensitive to immediate price action.
CCI (Commodity Channel Index): Measures the price's deviation from its moving average. Excels at identifying cyclical turns.
MACD (Moving Average Convergence Divergence): A trend-following momentum indicator showing the relationship between two moving averages. Excellent for identifying the start and end of trends.
YELLOW (Volume): The appearance of yellow shades confirms the move is supported by high market participation. This is the 'fuel' for the trend.
Volume Ratio: A custom signal that triggers when buy or sell volume is unusually high compared to its recent average.
CRV (Candle Range Volume): A custom signal that looks for candles with significant price range and volume.
OBV (On-Balance Volume): A cumulative indicator that adds volume on up days and subtracts it on down days. It shows the long-term flow of money.
FUCHSIA (Volatility): A fuchsia block signals a volatility breakout. This adds a sense of urgency and confirms the price is moving with exceptional force.
Bollinger Bands: A signal triggers when the price closes outside of the upper or lower standard deviation bands.
ORANGE (Price Action): An orange block is a pure price structure signal. It's a raw statement of intent from the market.
Price Gap: A signal that triggers when there's a gap up or gap down between candles.
3. READ THE TRANSITION (Shift in Sentiment)
The most important signal from the stacks is the flip from one side of the zero line to the other.
Flipping from Negative to Positive: A bearish stack disappears and is replaced by a bullish stack. This indicates market sentiment is shifting from bearish to bullish.
Flipping from Positive to Negative: A bullish stack disappears and is replaced by a bearish stack. This warns of a potential top or the start of a new downtrend.
4. FILTER FOR NOISE (Plot Threshold)
In choppy markets, the stack can flicker with low signal counts (e.g., +1 or -1). To focus only on high-conviction moves, go to the indicator settings and increase the "Plot Threshold". A setting of 2 or 3 will hide all stacks that don't have at least 2 or 3 agreeing signals, effectively filtering out market noise and keeping your chart clean.
5. CUSTOMIZE YOUR SIGNALS (Enable/Disable)
This indicator is fully customizable. In the settings, you can enable or disable each of the 9 indicators individually. For example, if you are a pure momentum trader, you could disable all Volume, Volatility, and Price Action signals to focus only on the blue stacks. Tailor it to fit your specific trading style.
EXAMPLE INTERPRETATIONS
Strong, Confirmed Trend: A tall stack of mostly blue (Momentum) and yellow (Volume) indicates a high-quality trend backed by both speed and market participation.
Momentum-Only Trend: A tall stack of only blue is a strong momentum move, but the lack of yellow (Volume) is a warning that the move may lack the "fuel" to be sustained.
Choppy/Indecisive Market: A short, mixed-color stack flickering around the zero line means the market is choppy with no clear conviction. It's often best to stay out.
Volatility Breakout: A new stack that appears suddenly with a fuchsia (Bollinger Bands) block on its first bar suggests a volatility-driven breakout is initiating.
Exhaustion Move: An orange (Price Gap) block appearing at the peak of a tall, long-standing stack can signal an exhaustion gap, potentially marking the end of the trend.
Weakening Conviction (Divergence): If price makes a new high but the positive stack is visibly shorter than the stack at the previous price high, it suggests underlying conviction is weakening.
PDT AI✅ Features
Multi-indicator fusion: RSI + MACD + EMA + higher timeframe RSI
Signal strength (%): Each signal gets a confidence score (0–100)
Dynamic ATR-based targets and stops
Alerts: Buy/Sell triggers for real-time notifications
Fully customizable inputs
Trend Strength Confidence Gauge LiteMost traders don’t fail from bad charts — they fail from bad timing. Jumping in too early, bailing too soon, or freezing when the move finally comes.
The Trend Strength Confidence Meter strips away the noise and highlights the three factors that matter most:
Trend → The confirmed direction of the market
Confidence → Concise tool clarity providing quick entries
Strength → Strength Score shows the underlying battle between buyers and sellers
How to Use It:
Watch the Moving Average Ribbon (Hull MA) for a flip: green = uptrend, red = downtrend.
Act only when ribbon color matches the Confidence thumbs-up.
Confirm with Strength 3+ before entry.
When trend, confidence, and strength align, you reduce risk and step in at tighter entry points — giving clarity for entries and conviction to hold through stronger moves.
Advanced Indicators Made Simple — Provided by The AI Trading Desk
Altcoin Market Share vs ETH/BTCIdea from x.com on X
Each colored line represents the percentage share of different altcoin baskets (excluding stablecoins) or ETH relative to either the ETH or BTC market cap (can add more, e.g. SOL or create different dashboards with Memes, AI, DeFi, you name it)
I know: At first glance, this may seem noisy and complex, but it all depends on the questions you want to answer. Once you define those, much of the noise becomes irrelevant, allowing you to simplify the analysis and focus only on what matters to you. What I’ve done here is provide a few initial insights that I found useful (will isolate a couple of them in future).
This analysis doesn’t tell you which specific coins to buy, but rather provides a broad market overview as a foundation. It helps guide you toward areas of relative strength or weakness.
I’ve included a lot of information here, but the key is to extract the signal from the noise by asking the right questions, for example: At what point do altcoins become overvalued or undervalued against Ethereum? However, when asking these questions, it's important to remember that an overvaluation or undervaluation of Ethereum relative to altcoins tells you little about its valuation against Bitcoin or USD. These are separate questions further down the process.
Long Elite Squeeze (LES) — H.H 22 Lindsay (AI)LES (Long Elite Squeeze)
LES (Long Elite Squeeze) is a trading framework designed to capture the highest-probability long setups. It’s not just another signal script — it’s a structured system built to filter noise, manage risk, and keep you aligned with real momentum.
🔹 Core Logic
Breakout Confirmation – Ensures moves have structure, not just random spikes.
Relative Volume (RVOL) – Confirms participation and fuel behind the move.
RSI Alignment – Avoids overextended traps and fakeouts.
Squeeze Momentum – The backbone of LES. Signals fire only after a defined squeeze pattern shift (6+ dark green bars followed by a light green bar).
🔹 Trade Management Built In
Automated Sell Signals – Trigger on either:
2 consecutive dark green bars on Squeeze Momentum
WaveTrend cross down
(only valid after a Buy signal — no random shorts)
HUD Entry Checklist – Live conditions shown on chart.
Status Tracker HUD – Flips between “Waiting for Entry” and “In Trade” for clear context.
🔹 Flexibility
3 switchable squeeze versions (V1, V2, V3) for different market conditions.
Customizable EMA & ATR settings (with color options).
Session-aware logic — filter signals to prime trading hours.
🔹 Blueprint & Credits
LES is a fusion of proven concepts, standing on the shoulders of respected creators:
-Squeeze Momentum – LazyBear
-WaveTrend Oscillator – LazyBear
-Relative Volume – LonesomeTheBlue
Breakout/structural logic – refined from classic frameworks
Their work laid the foundation — LES expands and integrates them into a complete trading system.
⚡ Why LES Stands Out
LES wasn’t coded overnight. It’s the result of countless hours of live testing, rebuilding, and refining. Every feature earned its place by proving value in real trading, not theory.
LES is more than an indicator. It’s a disciplined framework — crafted to turn chaos into structure, randomness into probability, and noise into clarity.
⚠️ Disclaimer: This is a trading framework, not financial advice. Performance depends on trader discipline, risk management, and market conditions.
Alexmoku Genesis v0.2.6 — Runtime BulletproofAlexmoku Genesis v0.2.6 – Reinventing Ichimoku with Precision and Intelligence
Built by Alex • Engineered for traders who demand more than lagging clouds
🔬 Overview
Alexmoku Genesis is a ground-up reimagining of the traditional Ichimoku system — retaining its philosophical foundations of harmony, balance, and structure, while infusing it with modern logic, advanced volatility modeling, and AI-aligned signal classification.
This is not your grandfather’s Ichimoku. Genesis transforms it into a forward-sensing, volatility-aware system that adapts in real time and guides discretionary and algorithmic traders alike with smarter signals, predictive structure, and built-in trade intelligence.
⚙️ Core Enhancements
📐 Volatility-Responsive Smoothing
Tenkan-sen and Kijun-sen lengths are dynamically adjusted based on recent price volatility (ATR-based).
Prevents overreaction in choppy markets and improves responsiveness during strong trends.
🌀 Adaptive Cloud Projection
Span A and Span B are still projected 52 periods forward — but cloud width adapts to market rhythm, not just static values.
🕵️ Enhanced Chikou Span (CKS+)
Chikou logic is upgraded to emphasize structure interaction, not just simple price overlay.
Useful for identifying support/resistance echoes and divergence validation.
🌐 Multi-Timeframe Cloud Awareness (Genesis Core)
Internal alignment checks between higher and lower timeframe Kumo positioning.
Future module support for overlaying MTF clouds.
🔖 Signal Classifier Engine (Experimental)
Each major structure event is tagged with signal intent:
📦 prep
🚀 launch
🔁 reversion
🪤 trap
Enables faster discretionary reads or future automation.
🧠 Trust Score Logic (Coming soon)
Future versions will assign numerical trust scores (1–10) to trade setups based on confluence.
🧰 Settings
🔧 Volatility Scaling Factor – Adjusts how reactive the indicator is to volatility. Default: 300.
🌫 Displacement (Kumo) – Cloud projection forward. Default (and required): 52.
⛅ Cloud Display Toggle – Show/hide Kumo visuals without affecting logic.
⚡️ Future expansion modules already scaffolded (MTF overlays, color-coded signal classifiers, Trust Score engine, etc.)
🧘 Philosophy Behind the System
"This isn’t just about catching runners — it’s about becoming the kind of person who can trade with clarity, peace, and precision."
Alexmoku Genesis reflects the W.E.A.L.T.H. System's holistic philosophy:
Wealth
Engineered
Algorithmic
Leveraged
Trading
Holistically
Trading isn't just a strategy. It's a mirror. Genesis is designed to train both your execution and your awareness.
⚠️ Disclaimer
This tool is designed for experienced technical traders and is still in active development. Use judgment and proper risk management. This is not financial advice.
Comet C/2025 N1 (ATLAS) Ephemeris☄️ Ephemeris How-To: Plot JPL Horizons Data on TradingView (Educational)
Overview
This open-source Pine Script™ v6 indicator demonstrates how to bring external astronomical ephemeris into TradingView and plot it on a daily chart. Using Comet C/2025 N1 (ATLAS) as an example dataset, it shows the mechanics of structuring arrays, indexing by date, and drawing past and forward ( future projections ) values—strictly as an educational visualization of celestial motion.
Why This Approach
Data is generated from NASA JPL Horizons, a mission-grade, publicly available ephemeris service ( (ssd.jpl.nasa.gov)). On the daily timeframe, Horizons provides high-precision positions you can regenerate whenever solutions update—useful for educational accuracy in exploring orbital data.
What’s Plotted
- Geocentric ecliptic longitude (Earth-view)
- Heliocentric ecliptic longitude (Sun-centered)
- Declination (deg from celestial equator)
Features
- Simple arrays + date indexing (no per-row timestamps)
- Circles for historical/current bars; polylines to connect forward points, emphasizing future projections
- Toggle any series on/off via inputs
- Daily timeframe enforced (runtime error if not 1D)
- Optional table with zodiac conversion (AstroLib by BarefootJoey)
Data & Updates
The example arrays span 2025-07-01 (discovery date) → 2026-01-01. You can refresh them anytime from JPL Horizons (Observer: Geocentric; daily step; include ecliptic lon/lat and declination) and paste the new values into the script.
How we pulled the ephemeris from JPL Horizons (quick guide):
0) Open ssd.jpl.nasa.gov System
1. Ephemeris Type: Observer Table
2. Target Body: C/2025 N1 (ATLAS) (or any object you want)
3. Observer Location: Geocentric
4. Time Specification: set Start, Stop, Step = 1 day
5. Table Settings → Quantities:
* Astrometric RA & Dec
* Heliocentric ecliptic longitude & latitude
* Observer (geocentric) ecliptic longitude & latitude
6. Additional Table Settings:
* Calendar format: Gregorian
* Date/Time: calendar (UTC), Hours & Minutes (HH:MM)
* Angle format: Decimal degrees
* Refraction model: No refraction / airless
* Range units: Astronomical units (au)
7. Generate → Download results (CSV or text).
8. Use AI or a small script to parse columns (e.g., Obs ecliptic lon, Helio ecliptic lon, Declination) into arrays, then paste them into your Pine script.
Educational Note
This indicator’s goal is to show how to prepare and plot ephemeris—so you can adapt the method for other comets or celestial bodies, or swap in data from existing astro libraries, for learning about astronomical projections using JPL daily data.
Credits & License
- Ephemeris: Solar System Dynamics Group, Horizons On-Line Ephemeris System, 4800 Oak Grove Drive, Jet Propulsion Laboratory, Pasadena, CA 91109, USA.
- Zodiac conversion: AstroLib by BarefootJoey
- License: MIT
- For educational use only.
Machine Learning BBPct [BackQuant]Machine Learning BBPct
What this is (in one line)
A Bollinger Band %B oscillator enhanced with a simplified K-Nearest Neighbors (KNN) pattern matcher. The model compares today’s context (volatility, momentum, volume, and position inside the bands) to similar situations in recent history and blends that historical consensus back into the raw %B to reduce noise and improve context awareness. It is informational and diagnostic—designed to describe market state, not to sell a trading system.
Background: %B in plain terms
Bollinger %B measures where price sits inside its dynamic envelope: 0 at the lower band, 1 at the upper band, ~ 0.5 near the basis (the moving average). Readings toward 1 indicate pressure near the envelope’s upper edge (often strength or stretch), while readings toward 0 indicate pressure near the lower edge (often weakness or stretch). Because bands adapt to volatility, %B is naturally comparable across regimes.
Why add (simplified) KNN?
Classic %B is reactive and can be whippy in fast regimes. The simplified KNN layer builds a “nearest-neighbor memory” of recent market states and asks: “When the market looked like this before, where did %B tend to be next bar?” It then blends that estimate with the current %B. Key ideas:
• Feature vector . Each bar is summarized by up to five normalized features:
– %B itself (normalized)
– Band width (volatility proxy)
– Price momentum (ROC)
– Volume momentum (ROC of volume)
– Price position within the bands
• Distance metric . Euclidean distance ranks the most similar recent bars.
• Prediction . Average the neighbors’ prior %B (lagged to avoid lookahead), inverse-weighted by distance.
• Blend . Linearly combine raw %B and KNN-predicted %B with a configurable weight; optional filtering then adapts to confidence.
This remains “simplified” KNN: no training/validation split, no KD-trees, no scaling beyond windowed min-max, and no probabilistic calibration.
How the script is organized (by input groups)
1) BBPct Settings
• Price Source – Which price to evaluate (%B is computed from this).
• Calculation Period – Lookback for SMA basis and standard deviation.
• Multiplier – Standard deviation width (e.g., 2.0).
• Apply Smoothing / Type / Length – Optional smoothing of the %B stream before ML (EMA, RMA, DEMA, TEMA, LINREG, HMA, etc.). Turning this off gives you the raw %B.
2) Thresholds
• Overbought/Oversold – Default 0.8 / 0.2 (inside ).
• Extreme OB/OS – Stricter zones (e.g., 0.95 / 0.05) to flag stretch conditions.
3) KNN Machine Learning
• Enable KNN – Switch between pure %B and hybrid.
• K (neighbors) – How many historical analogs to blend (default 8).
• Historical Period – Size of the search window for neighbors.
• ML Weight – Blend between raw %B and KNN estimate.
• Number of Features – Use 2–5 features; higher counts add context but raise the risk of overfitting in short windows.
4) Filtering
• Method – None, Adaptive, Kalman-style (first-order),
or Hull smoothing.
• Strength – How aggressively to smooth. “Adaptive” uses model confidence to modulate its alpha: higher confidence → stronger reliance on the ML estimate.
5) Performance Tracking
• Win-rate Period – Simple running score of past signal outcomes based on target/stop/time-out logic (informational, not a robust backtest).
• Early Entry Lookback – Horizon for forecasting a potential threshold cross.
• Profit Target / Stop Loss – Used only by the internal win-rate heuristic.
6) Self-Optimization
• Enable Self-Optimization – Lightweight, rolling comparison of a few canned settings (K = 8/14/21 via simple rules on %B extremes).
• Optimization Window & Stability Threshold – Governs how quickly preferred K changes and how sensitive the overfitting alarm is.
• Adaptive Thresholds – Adjust the OB/OS lines with volatility regime (ATR ratio), widening in calm markets and tightening in turbulent ones (bounded 0.7–0.9 and 0.1–0.3).
7) UI Settings
• Show Table / Zones / ML Prediction / Early Signals – Toggle informational overlays.
• Signal Line Width, Candle Painting, Colors – Visual preferences.
Step-by-step logic
A) Compute %B
Basis = SMA(source, len); dev = stdev(source, len) × multiplier; Upper/Lower = Basis ± dev.
%B = (price − Lower) / (Upper − Lower). Optional smoothing yields standardBB .
B) Build the feature vector
All features are min-max normalized over the KNN window so distances are in comparable units. Features include normalized %B, normalized band width, normalized price ROC, normalized volume ROC, and normalized position within bands. You can limit to the first N features (2–5).
C) Find nearest neighbors
For each bar inside the lookback window, compute the Euclidean distance between current features and that bar’s features. Sort by distance, keep the top K .
D) Predict and blend
Use inverse-distance weights (with a strong cap for near-zero distances) to average neighbors’ prior %B (lagged by one bar). This becomes the KNN estimate. Blend it with raw %B via the ML weight. A variance of neighbor %B around the prediction becomes an uncertainty proxy ; combined with a stability score (how long parameters remain unchanged), it forms mlConfidence ∈ . The Adaptive filter optionally transforms that confidence into a smoothing coefficient.
E) Adaptive thresholds
Volatility regime (ATR(14) divided by its 50-bar SMA) nudges OB/OS thresholds wider or narrower within fixed bounds. The aim: comparable extremeness across regimes.
F) Early entry heuristic
A tiny two-step slope/acceleration probe extrapolates finalBB forward a few bars. If it is on track to cross OB/OS soon (and slope/acceleration agree), it flags an EARLY_BUY/SELL candidate with an internal confidence score. This is explicitly a heuristic—use as an attention cue, not a signal by itself.
G) Informational win-rate
The script keeps a rolling array of trade outcomes derived from signal transitions + rudimentary exits (target/stop/time). The percentage shown is a rough diagnostic , not a validated backtest.
Outputs and visual language
• ML Bollinger %B (finalBB) – The main line after KNN blending and optional filtering.
• Gradient fill – Greenish tones above 0.5, reddish below, with intensity following distance from the midline.
• Adaptive zones – Overbought/oversold and extreme bands; shaded backgrounds appear at extremes.
• ML Prediction (dots) – The KNN estimate plotted as faint circles; becomes bright white when confidence > 0.7.
• Early arrows – Optional small triangles for approaching OB/OS.
• Candle painting – Light green above the midline, light red below (optional).
• Info panel – Current value, signal classification, ML confidence, optimized K, stability, volatility regime, adaptive thresholds, overfitting flag, early-entry status, and total signals processed.
Signal classification (informational)
The indicator does not fire trade commands; it labels state:
• STRONG_BUY / STRONG_SELL – finalBB beyond extreme OS/OB thresholds.
• BUY / SELL – finalBB beyond adaptive OS/OB.
• EARLY_BUY / EARLY_SELL – forecast suggests a near-term cross with decent internal confidence.
• NEUTRAL – between adaptive bands.
Alerts (what you can automate)
• Entering adaptive OB/OS and extreme OB/OS.
• Midline cross (0.5).
• Overfitting detected (frequent parameter flipping).
• Early signals when early confidence > 0.7.
These are purely descriptive triggers around the indicator’s state.
Practical interpretation
• Mean-reversion context – In range markets, adaptive OS/OB with ML smoothing can reduce whipsaws relative to raw %B.
• Trend context – In persistent trends, the KNN blend can keep finalBB nearer the mid/upper region during healthy pullbacks if history supports similar contexts.
• Regime awareness – Watch the volatility regime and adaptive thresholds. If thresholds compress (high vol), “OB/OS” comes sooner; if thresholds widen (calm), it takes more stretch to flag.
• Confidence as a weight – High mlConfidence implies neighbors agree; you may rely more on the ML curve. Low confidence argues for de-emphasizing ML and leaning on raw %B or other tools.
• Stability score – Rising stability indicates consistent parameter selection and fewer flips; dropping stability hints at a shifting backdrop.
Methodological notes
• Normalization uses rolling min-max over the KNN window. This is simple and scale-agnostic but sensitive to outliers; the distance metric will reflect that.
• Distance is unweighted Euclidean. If you raise featureCount, you increase dimensionality; consider keeping K larger and lookback ample to avoid sparse-neighbor artifacts.
• Lag handling intentionally uses neighbors’ previous %B for prediction to avoid lookahead bias.
• Self-optimization is deliberately modest: it only compares a few canned K/threshold choices using simple “did an extreme anticipate movement?” scoring, then enforces a stability regime and an overfitting guard. It is not a grid search or GA.
• Kalman option is a first-order recursive filter (fixed gain), not a full state-space estimator.
• Hull option derives a dynamic length from 1/strength; it is a convenience smoothing alternative.
Limitations and cautions
• Non-stationarity – Nearest neighbors from the recent window may not represent the future under structural breaks (policy shifts, liquidity shocks).
• Curse of dimensionality – Adding features without sufficient lookback can make genuine neighbors rare.
• Overfitting risk – The script includes a crude overfitting detector (frequent parameter flips) and will fall back to defaults when triggered, but this is only a guardrail.
• Win-rate display – The internal score is illustrative; it does not constitute a tradable backtest.
• Latency vs. smoothness – Smoothing and ML blending reduce noise but add lag; tune to your timeframe and objectives.
Tuning guide
• Short-term scalping – Lower len (10–14), slightly lower multiplier (1.8–2.0), small K (5–8), featureCount 3–4, Adaptive filter ON, moderate strength.
• Swing trading – len (20–30), multiplier ~2.0, K (8–14), featureCount 4–5, Adaptive thresholds ON, filter modest.
• Strong trends – Consider higher adaptive_upper/lower bounds (or let volatility regime do it), keep ML weight moderate so raw %B still reflects surges.
• Chop – Higher ML weight and stronger Adaptive filtering; accept lag in exchange for fewer false extremes.
How to use it responsibly
Treat this as a state descriptor and context filter. Pair it with your execution signals (structure breaks, volume footprints, higher-timeframe bias) and risk management. If mlConfidence is low or stability is falling, lean less on the ML line and more on raw %B or external confirmation.
Summary
Machine Learning BBPct augments a familiar oscillator with a transparent, simplified KNN memory of recent conditions. By blending neighbors’ behavior into %B and adapting thresholds to volatility regime—while exposing confidence, stability, and a plain early-entry heuristic—it provides an informational, probability-minded view of stretch and reversion that you can interpret alongside your own process.
New RSI📌 New RSI
The New RSI is a modern, enhanced version of the classic RSI created in 1978 — redesigned for today’s fast-moving markets, where algorithmic trading and AI dominate price action.
This indicator combines:
Adaptive RSI: Adjusts its calculation length in real time based on market volatility, making it more responsive during high volatility and smoother during calm periods.
Dynamic Bands: Upper and lower bands calculated from historical RSI volatility, helping you spot overbought/oversold conditions with greater accuracy.
Trend & Regime Filters: EMA and ADX-based detection to confirm signals only in favorable market conditions.
Volume Confirmation: Signals appear only when high trading volume supports the move — green volume for bullish setups and red volume for bearish setups — filtering out weak and unreliable trades.
💡 How it works:
A LONG signal appears when RSI crosses above the lower band and the volume is high with a bullish candle.
A SHORT signal appears when RSI crosses below the upper band and the volume is high with a bearish candle.
Trend and higher timeframe filters (optional) can help improve precision and adapt to different trading styles.
✅ Best Use Cases:
Identify high-probability reversals or pullbacks with strong momentum confirmation.
Avoid false signals by trading only when volume validates the move.
Combine with your own support/resistance or price action strategy for even higher accuracy.
⚙️ Fully Customizable:
Adjustable RSI settings (length, volatility adaptation, smoothing)
Dynamic band sensitivity
Volume threshold multiplier
Higher timeframe RSI filter
Color-coded background for market regime visualization
This is not just another RSI — it’s a complete, next-gen momentum tool designed for traders who want accuracy, adaptability, and confirmation in every signal.
Prime NumbersPrime Numbers highlights prime numbers (no surprise there 😅), tokens and the recent "active" feature in "input".
🔸 CONCEPTS
🔹 What are Prime Numbers?
A prime number (or a prime) is a natural number greater than 1 that is not a product of two smaller natural numbers.
Wikipedia: Prime number
🔹 Prime Factorization
The fundamental theorem of arithmetic states that every integer larger than 1 can be written as a product of one or more primes. More strongly, this product is unique in the sense that any two prime factorizations of the same number will have the same number of copies of the same primes, although their ordering may differ. So, although there are many different ways of finding a factorization using an integer factorization algorithm, they all must produce the same result. Primes can thus be considered the "basic building blocks" of the natural numbers.
Wikipedia: Fundamental theorem of arithmetic
Math Is Fun: Prime Factorization
We divide a given number by Prime Numbers until only Primes remain.
Example:
24 / 2 = 12 | 24 / 3 = 8
12 / 3 = 4 | 8 / 2 = 4
4 / 2 = 2 | 4 / 2 = 2
|
24 = 2 x 3 x 2 | 24 = 3 x 2 x 2
or | or
24 = 2² x 3 | 24 = 2² x 3
In other words, every natural/integer number above 1 has a unique representation as a product of prime numbers, no matter how the number is divided. Only the order can change, but the factors (the basic elements) are always the same.
🔸 USAGE
The Prime Numbers publication contains two use cases:
Prime Factorization: performed on "close" prices, or a manual chosen number.
List Prime Numbers: shows a list of Prime Numbers.
The other two options are discussed in the DETAILS chapter:
Prime Factorization Without Arrays
Find Prime Numbers
🔹 Prime Factorization
Users can choose to perform Prime Factorization on close prices or a manually given number.
❗️ Note that this option only applies to close prices above 1, which are also rounded since Prime Factorization can only be performed on natural (integer) numbers above 1.
In the image below, the left example shows Prime Factorization performed on each close price for the latest 50 bars (which is set with "Run script only on 'Last x Bars'" -> 50).
The right example shows Prime Factorization performed on a manually given number, in this case "1,340,011". This is done only on the last bar.
When the "Source" option "close price" is chosen, one can toggle "Also current price", where both the historical and the latest current price are factored. If disabled, only historical prices are factored.
Note that, depending on the chosen options, only applicable settings are available, due to a recent feature, namely the parameter "active" in settings.
Setting the "Source" option to "Manual - Limited" will factorize any given number between 1 and 1,340,011, the latter being the highest value in the available arrays with primes.
Setting to "Manual - Not Limited" enables the user to enter a higher number. If all factors of the manual entered number are in the 1 - 1,340,011 range, these factors will be shown; however, if a factor is higher than 1,340,011, the calculation will stop, after which a warning is shown:
The calculated factors are displayed as a label where identical factors are simplified with an exponent notation in superscript.
For example 2 x 2 x 2 x 5 x 7 x 7 will be noted as 2³ x 5 x 7²
🔹 List Prime Numbers
The "List Prime Numbers" option enables users to enter a number, where the first found Prime Number is shown, together with the next x Prime Numbers ("Amount", max. 200)
The highest shown Prime Number is 1,340,011.
One can set the number of shown columns to customize the displayed numbers ("Max. columns", max. 20).
🔸 DETAILS
The Prime Numbers publication consists out of 4 parts:
Prime Factorization Without Arrays
Prime Factorization
List Prime Numbers
Find Prime Numbers
The usage of "Prime Factorization" and "List Prime Numbers" is explained above.
🔹 Prime Factorization Without Arrays
This option is only there to highlight a hurdle while performing Prime Factorization.
The basic method of Prime Factorization is to divide the base number by 2, 3, ... until the result is an integer number. Continue until the remaining number and its factors are all primes.
The division should be done by primes, but then you need to know which one is a prime.
In practice, one performs a loop from 2 to the base number.
Example:
Base_number = input.int(24)
arr = array.new()
n = Base_number
go = true
while go
for i = 2 to n
if n % i == 0
if n / i == 1
go := false
arr.push(i)
label.new(bar_index, high, str.tostring(arr))
else
arr.push(i)
n /= i
break
Small numbers won't cause issues, but when performing the calculations on, for example, 124,001 and a timeframe of, for example, 1 hour, the script will struggle and finally give a runtime error.
How to solve this?
If we use an array with only primes, we need fewer calculations since if we divide by a non-prime number, we have to divide further until all factors are primes.
I've filled arrays with prime numbers and made libraries of them. (see chapter "Find Prime Numbers" to know how these primes were found).
🔹 Tokens
A hurdle was to fill the libraries with as many prime numbers as possible.
Initially, the maximum token limit of a library was 80K.
Very recently, that limit was lifted to 100K. Kudos to the TradingView developers!
What are tokens?
Tokens are the smallest elements of a program that are meaningful to the compiler. They are also known as the fundamental building blocks of the program.
I have included a code block below the publication code (// - - - Educational (2) - - - ) which, if copied and made to a library, will contain exactly 100K tokens.
Adding more exported functions will throw a "too many tokens" error when saving the library. Subtracting 100K from the shown amount of tokens gives you the amount of used tokens for that particular function.
In that way, one can experiment with the impact of each code addition in terms of tokens.
For example adding the following code in the library:
export a() => a = array.from(1) will result in a 100,041 tokens error, in other words (100,041 - 100,000) that functions contains 41 tokens.
Some more examples, some are straightforward, others are not )
// adding these lines in one of the arrays results in x tokens
, 1 // 2 tokens
, 111, 111, 111 // 12 tokens
, 1111 // 5 tokens
, 111111111 // 10 tokens
, 1111111111111111111 // 20 tokens
, 1234567890123456789 // 20 tokens
, 1111111111111111111 + 1 // 20 tokens
, 1111111111111111111 + 8 // 20 tokens
, 1111111111111111111 + 9 // 20 tokens
, 1111111111111111111 * 1 // 20 tokens
, 1111111111111111111 * 9 // 21 tokens
, 9999999999999999999 // 21 tokens
, 1111111111111111111 * 10 // 21 tokens
, 11111111111111111110 // 21 tokens
//adding these functions to the library results in x tokens
export f() => 1 // 4 tokens
export f() => v = 1 // 4 tokens
export f() => var v = 1 // 4 tokens
export f() => var v = 1, v // 4 tokens
//adding these functions to the library results in x tokens
export a() => const arraya = array.from(1) // 42 tokens
export a() => arraya = array.from(1) // 42 tokens
export a() => a = array.from(1) // 41 tokens
export a() => array.from(1) // 32 tokens
export a() => a = array.new() // 44 tokens
export a() => a = array.new(), a.push(1) // 56 tokens
What if we could lower the amount of tokens, so we can export more Prime Numbers?
Look at this example:
829111, 829121, 829123, 829151, 829159, 829177, 829187, 829193
Eight numbers contain the same number 8291.
If we make a function that removes recurrent values, we get fewer tokens!
829111, 829121, 829123, 829151, 829159, 829177, 829187, 829193
//is transformed to:
829111, 21, 23, 51, 59, 77, 87, 93
The code block below the publication code (// - - - Educational (1) - - - ) shows how these values were reduced. With each step of 100, only the first Prime Number is shown fully.
This function could be enhanced even more to reduce recurrent thousands, tens of thousands, etc.
Using this technique enables us to export more Prime Numbers. The number of necessary libraries was reduced to half or less.
The reduced Prime Numbers are restored using the restoreValues() function, found in the library fikira/Primes_4.
🔹 Find Prime Numbers
This function is merely added to show how I filled arrays with Prime Numbers, which were, in turn, added to libraries (after reduction of recurrent values).
To know whether a number is a Prime Number, we divide the given number by values of the Primes array (Primes 2 -> max. 1,340,011). Once the division results in an integer, where the divisor is smaller than the dividend, the calculation stops since the given number is not a Prime.
When we perform these calculations in a loop, we can check whether a series of numbers is a Prime or not. Each time a number is proven not to be a Prime, the loop starts again with a higher number. Once all Primes of the array are used without the result being an integer, we have found a new Prime Number, which is added to the array.
Doing such calculations on one bar will result in a runtime error.
To solve this, the findPrimeNumbers() function remembers the index of the array. Once a limit has been reached on 1 bar (for example, the number of iterations), calculations will stop on that bar and restart on the next bar.
This spreads the workload over several bars, making it possible to continue these calculations without a runtime error.
The result is placed in log.info() , which can be copied and pasted into a hardcoded array of Prime Number values.
These settings adjust the amount of workload per bar:
Max Size: maximum size of Primes array.
Max Bars Runtime: maximum amount of bars where the function is called.
Max Numbers To Process Per Bar: maximum numbers to check on each bar, whether they are Prime Numbers.
Max Iterations Per Bar: maximum loop calculations per bar.
🔹 The End
❗️ The code and description is written without the help of an LLM, I've only used Grammarly to improve my description (without AI :) )
BTC Correlation PercentagePurpose
This indicator displays the correlation percentage between the current trading instrument and Bitcoin (BTC/USDT) as a text label on the chart. It helps traders quickly assess how closely an asset's price movements align with Bitcoin's fluctuations.
Key Features
Precise Calculation: Shows correlation as a percentage with one decimal place (e.g., 25.6%).
Customizable Appearance: Allows adjustment of colors, position, and calculation period.
Clean & Simple: Displays only essential information without cluttering the chart.
Universal Compatibility: Works on any timeframe and with any trading pair.
Input Settings
Core Parameters:
BTC Symbol – Ticker for Bitcoin (default: BINANCE:BTCUSDT).
Correlation Period – Number of bars used for calculation (default: 50 candles).
Show Correlation Label – Toggle visibility of the correlation label.
Visual Customization:
Text Color – Label text color (default: white).
Background Color – Label background color (default: semi-transparent blue).
Border Color – Border color around the label (default: gray).
Label Position – Where the label appears on the chart (default: top-right).
Interpreting Correlation Values
70% to 100% → Strong positive correlation (asset moves in sync with BTC).
30% to 70% → Moderate positive correlation.
-30% to 30% → Weak or no correlation.
-70% to -30% → Moderate negative correlation (asset moves opposite to BTC).
-100% to -70% → Strong negative correlation.
Practical Use Cases
For Altcoins: A correlation above 50% suggests high dependence on Bitcoin’s price action.
For Futures Trading: Helps assess systemic risks tied to BTC movements.
During High Volatility: Determines whether an asset’s price change is driven by its own factors or broader market trends.
How It Works
The indicator recalculates automatically with each new candle. For the most reliable results, it is recommended for use on daily or higher timeframes.
This tool provides traders with a quick, visual way to gauge Bitcoin’s influence on other assets, improving decision-making in crypto markets. 🚀
This response is AI-generated, for reference only.
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BB & RSI Trailing Stop StrategySimple BB & RSI generated using AI, gets 60% on S&P 500 with the right settings
Monthly High/Low & % Move By Ai Trade LabYou can have idea How much it moved Monthly. Just Change it to Monthly
Multi-Timeframe SFP + SMTImportant: Please Read First
This indicator is not a "one size fits all" solution. It is a professional and complex tool that requires you to learn how to use it, in addition to backtesting different settings to discover what works best for your specific trading style and the assets you trade. The default settings provided are my personal preferences for trading higher-timeframe setups, but you are encouraged to experiment and find your own optimal configuration.
Please note that while this initial version is solid, it may still contain small errors or bugs. I will be actively working on improving the indicator over time. Also, be aware that the script is not written for maximum efficiency and may be resource-intensive, but this should not pose a problem for most users.
The source code for this indicator is open. If you truly want to understand precisely how all the logic works, you can copy and paste the code into an AI assistant like Gemini or ChatGPT and ask it to explain any part of the script to you.
Author's Preferred Settings (Guideline)
As a starting point, here are the settings I personally use for my trading:
SFP Timeframe: 4-Hour (Strength: 5-5)
Max Lookback: 35 Bars
Raid Expiration: 1 Bar
SFP Lines Limit: 1
SMT Timeframe 1: 30-Minute (Strength: 2-2) with 3-Minute LTF Detection.
SMT Timeframe 2: 15-Minute (Strength: 3-3) with 3-Minute LTF Detection.
SMT Timeframe 3: 1-Hour (Strength: 1-1) with 3-Minute LTF Detection.
SMT Timeframe 4: 15-Minute (Strength: 1-1) with 3-Minute LTF Detection.
Multi-Timeframe SMT: An Overview
This indicator is a powerful tool designed to identify high-probability trading setups by combining two key institutional concepts: Swing Failure Patterns (SFP) on a higher timeframe and Smart Money Technique (SMT) divergences on a lower timeframe. A key feature is the ability to configure and run up to four independent SMT analyses simultaneously, allowing you to monitor for divergences across multiple timeframes (e.g., 15m, 1H, 4H) from a single indicator.
Its primary purpose is to generate automated signals through TradingView's alert system. By setting up alerts, the script runs server-side, monitoring the market for you. When a setup presents itself, it will send a push notification to your device, allowing you to personally evaluate the trade without being tied to your screen.
The Strategy: HTF Liquidity Sweeps into LTF SMT
The core strategy is built on a classic institutional trading model:
Wait for a liquidity sweep on a significant high timeframe (e.g., 4-hour, Daily).
Once liquidity is taken, look for a confirmation of a shift in market structure on a lower timeframe.
This indicator uses an SMT divergence as that confirmation signal, indicating that smart money may be stepping in to reverse the price.
How It Works: The Two-Step Process
The indicator's logic follows a precise two-step process to generate a signal:
Step 1: The Swing Failure Pattern (SFP)
First, the indicator identifies a high-timeframe liquidity sweep. This is configured in the "Swing Failure Pattern (SFP) Timeframe" settings.
It looks for a candle that wicks above a previous high (or below a previous low) but then closes back within the range of that pivot. This action is known as a "raid" or a "swing failure," suggesting the move failed to find genuine momentum.
Step 2: The SMT Divergence
The moment a valid SFP is confirmed, the indicator's multiple SMT engines activate.
Each engine begins monitoring the specific SMT timeframe you have configured (e.g., "SMT Timeframe 1," "SMT Timeframe 2," etc.) for a Smart Money Technique (SMT) divergence.
An SMT divergence occurs when two closely correlated assets fail to move in sync. For example, after a raid on a high, Asset A makes a new high, but Asset B fails to do so. This disagreement suggests weakness and a potential reversal.
When the script finds this divergence, it plots the SMT line and triggers an alert.
The Power of Alerts
The true strength of this indicator lies in its alert capabilities. You can create alerts for both unconfirmed and confirmed SMTs.
Enable Alerts LTF Detection: These alerts trigger when an unconfirmed, potential SMT is spotted on the lower "LTF Detection" timeframe. While not yet confirmed, these early alerts can notify you of a potential move before it fully happens, allowing you to be ahead of the curve and find the best possible trade entries.
Enable Alerts Confirmed SMT: These alerts trigger only when a permanent, confirmed SMT line is plotted on your chosen SMT timeframe. These signals are more reliable but occur later than the early detection alerts.
Key Concepts Explained
What is Pivot Strength?
Pivot Strength determines how significant a high or low needs to be to qualify as a valid structural point. A setting of 5-5, for example, means that for a candle's high to be considered a valid pivot high, its high must be higher than the highs of the 5 candles to its left and the 5 candles to its right.
Higher Strength (e.g., 5-5, 8-8): Creates fewer, but more significant, pivots. This is ideal for identifying major structural highs and lows on higher timeframes.
Lower Strength (e.g., 2-2, 3-3): Creates more pivots, making it suitable for identifying the smaller shifts in momentum on lower timeframes.
Raid Expiration & Validity
An SFP signal is not valid forever. The "Raid Expiration" setting determines how many SFP timeframe bars can pass after a raid before that signal is considered "stale" and can no longer be used to validate an SMT. This ensures your SMT divergences are always in response to recent liquidity sweeps.
Why You Must Be on the Right Chart Timeframe to See SMT Lines
Pine Script™ has a fundamental rule: an indicator running on a chart can only "see" the bars of that chart's timeframe or higher.
When the SMT logic is set to the 15-minute timeframe, it calculates its pivots based on 15-minute data. To accurately plot lines connecting these pivots, you must be on a 15-minute chart or lower (e.g., 5-minute, 1-minute).
If you are on a higher timeframe chart, like the 1-hour, the 15-minute bars do not exist on that chart, so the indicator has no bars to draw the lines on.
This is precisely why the alert system is so powerful. You can set your alert to run on the 15-minute timeframe, and TradingView's servers will monitor that timeframe for you, sending a notification regardless of what chart you are currently viewing.
Lorentzian Key Support and Resistance Level Detector [mishy]🧮 Lorentzian Key S/R Levels Detector
Advanced Support & Resistance Detection Using Mathematical Clustering
The Problem
Traditional S/R indicators fail because they're either subjective (manual lines), rigid (fixed pivots), or break when price spikes occur. Most importantly, they don't tell you where prices actually spend time, just where they touched briefly.
The Solution: Lorentzian Distance Clustering
This indicator introduces a novel approach by using Lorentzian distance instead of traditional Euclidean distance for clustering. This is groundbreaking for financial data analysis.
Data Points Clustering:
🔬 Why Euclidean Distance Fails in Trading
Traditional K-means uses Euclidean distance:
• Formula: distance = (price_A - price_B)²
• Problem: Squaring amplifies differences exponentially
• Real impact: One 5% price spike has 25x more influence than a 1% move
• Result: Clusters get pulled toward outliers, missing real support/resistance zones
Example scenario:
Prices: ← flash spike
Euclidean: Centroid gets dragged toward 150
Actual S/R zone: Around 100 (where prices actually trade)
⚡ Lorentzian Distance: The Game Changer
Our approach uses Lorentzian distance:
• Formula: distance = log(1 + (price_difference)² / σ²)
• Breakthrough: Logarithmic compression keeps outliers in check
• Real impact: Large moves still matter, but don't dominate
• Result: Clusters focus on where prices actually spend time
Same example with Lorentzian:
Prices: ← flash spike
Lorentzian: Centroid stays near 100 (real trading zone)
Outlier (150): Acknowledged but not dominant
🧠 Adaptive Intelligence
The σ parameter isn't fixed,it's calculated from market disturbance/entropy:
• High volatility: σ increases, making algorithm more tolerant of large moves
• Low volatility: σ decreases, making algorithm more sensitive to small changes
• Self-calibrating: Adapts to any instrument or market condition automatically
Why this matters: Traditional methods treat a 2% move the same whether it's in a calm or volatile market. Lorentzian adapts the sensitivity based on current market behavior.
🎯 Automatic K-Selection (Elbow Method)
Instead of guessing how many S/R levels to draw, the indicator:
• Tests 2-6 clusters and calculates WCSS (tightness measure)
• Finds the "elbow" - where adding more clusters stops helping much
• Uses sharpness calculation to pick the optimal number automatically
Result: Perfect balance between detail and clarity.
How It Works
1. Collect recent closing prices
2. Calculate entropy to adapt to current market volatility
3. Cluster prices using Lorentzian K-means algorithm
4. Auto-select optimal cluster count via statistical analysis
5. Draw levels at cluster centers with deviation bands
📊 Manual K-Selection Guide (Using WCSS & Sharpness Analysis)
When you disable auto-selection, use both WCSS and Sharpness metrics from the analysis table to choose manually:
What WCSS tells you:
• Lower WCSS = tighter clusters = better S/R levels
• Higher WCSS = scattered clusters = weaker levels
What Sharpness tells you:
• Higher positive values = optimal elbow point = best K choice
• Lower/negative values = poor elbow definition = avoid this K
• Measures the "sharpness" of the WCSS curve drop-off
Decision strategy using both metrics:
K=2: WCSS = 150.42 | Sharpness = - | Selected =
K=3: WCSS = 89.15 | Sharpness = 22.04 | Selected = ✓ ← Best choice
K=4: WCSS = 76.23 | Sharpness = 1.89 | Selected =
K=5: WCSS = 73.91 | Sharpness = 1.43 | Selected =
Quick decision rules:
• Pick K with highest positive Sharpness (indicates optimal elbow)
• Confirm with significant WCSS drop (30%+ reduction is good)
• Avoid K values with negative or very low Sharpness (<1.0)
• K=3 above shows: Big WCSS drop (41%) + High Sharpness (22.04) = Perfect choice
Why this works:
The algorithm finds the "elbow" where adding more clusters stops being useful. High Sharpness pinpoints this elbow mathematically, while WCSS confirms the clustering quality.
Elbow Method Visualization:
Traditional clustering problems:
❌ Price spikes distort results
❌ Fixed parameters don't adapt
❌ Manual tuning is subjective
❌ No way to validate choices
Lorentzian solution:
☑️ Outlier-resistant distance metric
☑️ Entropy-based adaptation to volatility
☑️ Automatic optimal K selection
☑️ Statistical validation via WCSS & Sharpness
Features
Visual:
• Color-coded levels (red=highest resistance, green=lowest support)
• Optional deviation bands showing cluster spread
• Strength scores on labels: Each cluster shows a reliability score.
• Higher scores (0.8+) = very strong S/R levels with tight price clustering
• Lower scores (0.6-0.7) = weaker levels, use with caution
• Based on cluster tightness and data point density
• Clean line extensions and labels
Analytics:
• WCSS analysis table showing why K was chosen
• Cluster metrics and statistics
• Real-time entropy monitoring
Control:
• Auto/manual K selection toggle
• Customizable sample size (20-500 bars)
• Show/hide bands and metrics tables
The Result
You get mathematically validated S/R levels that focus on where prices actually cluster, not where they randomly spiked. The algorithm adapts to market conditions and removes guesswork from level selection.
Best for: Traders who want objective, data-driven S/R levels without manual chart analysis.
Credits: This script is for educational purposes and is inspired by the work of @ThinkLogicAI and an amazing mentor @DskyzInvestments . It demonstrates how Lorentzian geometrical concepts can be applied not only in ML classification but also quite elegantly in clustering.
