Stablecoin Liquidity Delta (Aggregate Market Cap Flow)Hi All,
This indicator visualizes the bar-to-bar change in the aggregate market capitalization of major stablecoins, including USDT, USDC, DAI, and others. It serves as a proxy for monitoring on-chain liquidity and measuring capital inflows or outflows across the crypto market.
Stablecoins are the primary liquidity layer of the crypto economy. Their combined market capitalization acts as a mirror of the available fiat-denominated liquidity in digital markets:
🟩 An increase in the total stablecoin market capitalization indicates new issuance (capital entering the market).
🟥 A decrease reflects redemption or burning (liquidity exiting the system).
Tracking these flows helps anticipate macro-level liquidity trends that often lead overall market direction, providing context for broader price movements.
All values are derived from TradingView’s public CRYPTOCAP tickers, which represent the market capitalization of each stablecoin. While minor deviations can occur due to small price fluctuations around the $1 peg, these figures serve as a proxy for circulating supply and net issuance across the stablecoin ecosystem.
Statistics
Nqaba Probable High/Low{Larry Method}The Probable High/Low indicator is an advanced tool inspired by Larry R. Williams’ original projection formulas.
It calculates probable daily highs and lows based on the prior day’s open, high, low, and close, allowing traders to anticipate key intraday price levels with precision.
This version provides full control over visibility, styling, and historical analysis — making it both educational and powerful for active traders.
Continuation Probability (0–100)This indicator helps measure how likely the current candle trend will continue or reverse, giving a probability score between 0–100.
It combines multiple market factors trend, candle strength, volume, and volatility to create a single, intuitive signal.
Mouvement Moyen Journalier (ATR) IlkerAverage Daily Movement (ATR) - Daily Timeframe
Description:
This is a simple script that calculates and plots the Average True Range (ATR) fixed on the daily timeframe.
The ATR is a standard measure of market volatility. It calculates the "true range" for each period (which accounts for price gaps between days) and then smooths the result using an RMA (Relative Moving Average) based on the user-defined period.
Useful for:
* Assessing average daily price movement.
* Setting stop-loss or take-profit levels.
* Gauging market volatility.
Simple FloatFloat Display Indicator
A simple, clean indicator that displays the current float (shares outstanding float) for any stock directly in your indicator status line at the top left of the chart.
Features:
- Shows the float value with automatic K/M formatting for thousands and millions
- No chart clutter - value only appears in the status line, nothing plotted on the chart
- Works on any stock that has float data available
- Lightweight and efficient
Perfect for traders who want quick access to float information without switching between windows or cluttering their charts.
Note: Float data availability depends on TradingView's financial data for the specific ticker. Some tickers may not have this data available.
Momentum Quant Spread IlkerThis script is the opposite of a traditional mean-reversion pairs trading strategy. It is a "Cointegration Breakdown" or "Momentum Divergence" tool.
Instead of betting on a spread's Z-Score to revert to 0, this strategy is designed to identify when the statistical relationship (the "elastic band") has snapped. It then provides signals to trade with the momentum as the spread diverges.
It filters for true breakouts by waiting for a "Momentum Regime," which is confirmed only when the pair's relationship becomes statistically unstable.
## 📈 Key Features
1. The Momentum Regime (Blue Background)
This is the core of the indicator. The background turns BLUE to signal a "Momentum Regime". This is the only time you should look for a momentum trade.
The blue background activates only if TWO conditions are met simultaneously:
• 1. Relationship Instability: The pair's relationship is broken. This is confirmed when either the rolling Correlation Z-Score (purple line) breaks down OR the Volatility Ratio (orange line) becomes unstable.
• 2. Divergence Confirmation: The Half-Life calculation (from our v2.8 script) shows "N/A (Divergent)" in the dashboard. This mathematically confirms the mean-reverting force (\lambda) is gone (it has turned positive) and the spread is statistically diverging.
If the background is GRAY, the script is in a "Neutral" or "Mean-Reversion" state, and all momentum signals should be ignored.
2. Momentum Breakout Signals
This strategy inverts the Z-Score logic. The 0-line is not a profit target; it is the breakout line.
• BUY Signal (Blue Triangle ▲): Appears only if the background is BLUE and the Z-Score (blue line) crosses ABOVE 0. This is your long momentum entry.
• SELL Signal (Fuchsia Triangle ▼): Appears only if the background is BLUE and the Z-Score crosses BELOW 0. This is your short momentum entry.
3. Built-in Trade Management
• Take Profit (X Cross): Your profit target is the outer band. The script plots an 'X' when the Z-Score hits the +2.0 band (for longs) or the -2.0 band (for shorts).
• Stop Loss (X Cross): Your stop is a failure of the momentum. The script plots an 'X' if the Z-Score re-crosses the 0-line against your trade.
4. Full Quant Dashboard
All the statistical components are plotted for analysis:
• Price Z-Score (Blue Line): Your primary momentum indicator.
• Z-Score Correlation (Purple Line): Lets you visually confirm the correlation breakdown.
• Volatility Ratio (Orange Line): Lets you visually confirm the volatility spike.
• Half-Life Dashboard: Confirms the regime by showing "N/A (Divergent)".
## 🛠 How to Use (Required Setup)
IMPORTANT: This indicator is designed to run on a spread chart (e.g., M2K/MES or MGC/SIL).
1. Load your spread chart first (e.g., type M2K/MES in the ticker bar).
2. Add this indicator to the chart.
3. Go into the indicator's Settings (⚙).
4. In the "Inputs" tab, you MUST fill in the two individual tickers:
• Ticker du Symbole 1 (REQUIS): M2K
• Ticker du Symbole 2 (REQUIS): MES
5. The script uses these two inputs to calculate the Volatility and Correlation filters. The main Z-Score is calculated from the spread chart itself.
This tool is for traders who want to capture explosive divergence moves that happen during fundamental news or regime changes, while filtering out the "noise" of stable, mean-reverting periods.
Zscore correlation volatility Demi vie IlkerThis is an all-in-one "regime" dashboard for pairs trading. It's designed to stop you from taking bad mean-reversion trades by first identifying if the market conditions are stable.
It answers two key questions:
1. "Is this a good time to trade a mean-reversion strategy?" (The Regime Filter)
2. "If yes, how fast should I expect the trade to work?" (The Half-Life)
## 📈 Key Features
This script runs four main calculations at once:
1. The Price Z-Score (Blue Line)
This is your primary entry signal. It shows you how "cheap" (e.g., -2.0) or "expensive" (e.g., +2.0) the spread is relative to its short-term history (z_len).
2. The Regime Background (Green / Red)
This is the most important part. It acts as a "traffic light" for your trading:
• 🟢 GREEN (Stable Regime): It's safe to look for mean-reversion trades. This means both the correlation and volatility filters are stable.
• 🔴 RED (Unstable Regime): DO NOT trade mean-reversion. The relationship between the assets is broken. Any signal is likely a trap.
3. The Regime Filters (Your "Guards")
These two filters determine the background color:
• Correlation Z-Score (Purple Line): It measures the stability of the correlation. If this purple line drops below the red threshold (corr_z_threshold), it means the correlation has broken down, and the background turns RED.
• Volatility Ratio (Orange Line): It compares the volatility of the two assets. If one asset suddenly becomes much more volatile than the other (deviating from its average ratio), the background turns RED.
4. The Half-Life Dashboard (Top-Right Table)
This is your "speedometer." Based on an Ornstein-Uhlenbeck model, it calculates the average time (in bars) it takes for the spread to revert 50% of the way back to its mean.
• HL: 13.86 periods: You can expect it to take ~14 bars to go from a Z-Score of 2.0 to 1.0.
• N/A (Divergent): A critical warning. The math shows the spread is currently diverging and has no tendency to revert.
## 💡 How to Use This Indicator
Setup (Required):
1. Load a spread chart (e.g., type MES/MNQ or MGC/SIL into the TradingView search).
2. Add this indicator to the spread chart.
3. Go into the indicator's Settings (⚙).
4. In the "Inputs" tab, you must enter the two individual tickers:
• Symbol 1 Ticker: MGC
• Symbol 2 Ticker: SIL
(This is so the script can calculate the Correlation and Volatility filters).
Trading Signals
1. Mean-Reversion Signals
• BUY Signal (Green Triangle ▲): Appears only if the background is GREEN and the Price Z-Score (blue line) crosses below the -2.0 band.
• SELL Signal (Red Triangle ▼): Appears only if the background is GREEN and the Price Z-Score (blue line) crosses above the +2.0 band.
• EXIT: Your target is a reversion back to the 0 line. The Half-Life value gives you an idea of how long to wait.
2. Divergence Warning Signals
• Blue/Fuchsia Triangles (▲ / ▼): These appear at the exact moment the background turns RED. They warn you that the "stable" regime is broken and a new "divergence" or "trend" regime may be starting. This is a signal to stay out or manage any existing positions.
This tool is designed to add a layer of quantitative, risk-management logic to a standard Z-Score strategy. It helps you trade only when the statistics are in your favor.
Vandan V2Vandan V2 is an automated trend-following strategy for NASDAQ E-mini Futures (NQ1!).
It uses multi-timeframe momentum and volatility filters to identify high-probability entries.
Includes dynamic risk management and trailing logic optimized for intraday trading.
Risk Position Sizer (Entry=Close, Stop=Daily Low)This is for trading stocks/shares. Its main goal is to help you gauge how big or how small of a position you should add based on your account size.
Info Box⚙️ Purpose
Shows useful trade and event-related data such as:
% Distance from stop levels (D, DH)
Earnings countdown in bars
All displayed in a single floating info box (table) on the chart.
📋 Key Features
Customizable Display
Choose table position (Top Right, Bottom Center, etc.)
Choose table size (Auto, Large, Tiny, etc.)
Custom text and background colors
Metrics Shown
D: % Distance from stop (difference between close and low/high)
DH: % Distance from midpoint of the candle
Earnings Countdown: Number of bars until next earnings event
Conditional Styling
If earnings are within 3 bars, text color turns red as a warning.
Execution Conditions
Runs only on daily timeframe
Updates on last bar only (no historical clutter)
Output
Displays all selected metrics in one line, separated by “×”
e.g. → D: -2.1% × 5 × DH: 1.4%
🧩 Overall Function
Creates a clean, customizable “info box” showing trade distances and upcoming earnings countdown for quick decision-making directly on your TradingView chart.
Zscore COrrelation volatility OberlinThis is a complete multi-strategy dashboard for statistical arbitrage (pairs trading). It is designed to solve the biggest challenge in pairs trading: knowing when to trade mean-reversion and when to trade a regime break.
This indicator automatically analyzes the stability of the pair's relationship using two critical filters (a Volatility Ratio filter and a Correlation Z-Score filter). It then provides clear, actionable signals for two opposite strategies based on the current market "regime."
The Regime "Traffic Light" System
The indicator's background color tells you which strategy is currently active.
• 🟢 GREEN Background (Stable Regime): This is the "Mean Reversion" regime. It means both the volatility and correlation filters are stable. The pair is behaving predictably, and you can trust the Z-Score to revert to its mean.
• 🔴 RED Background (Unstable Regime): This is the "Divergence" or "Breakout" regime. It means the pair's relationship has failed (correlation has broken down OR volatility has exploded). In this regime, the Z-Score is not expected to revert and may continue to diverge.
How to Use: The Two Strategies
The indicator will plot text labels on your chart for four specific signals.
📈 Strategy 1: Mean Reversion (Green Regime 🟢)
This is the classic pairs trading strategy. You only take these signals when the background is GREEN.
• LONG Signal: "ACHAT MOYENNE" (Buy Mean)
• What it means: The Z-Score (blue line) has crossed below the lower band (e.g., -2.0) while the regime is stable.
• Your Bet: The spread is statistically "too cheap" and will rise back to the 0-line.
• Action: Buy the Spread (e.g., Buy MES, Sell MNQ).
• SHORT Signal: "VENTE MOYENNE" (Sell Mean)
• What it means: The Z-Score (blue line) has crossed above the upper band (e.g., +2.0) while the regime is stable.
• Your Bet: The spread is statistically "too expensive" and will fall back to the 0-line.
• Action: Sell the Spread (e.g., Sell MES, Buy MNQ).
• Exit Target: Close your position when the Z-Score (blue line) returns to 0.
🚀 Strategy 2: Divergence / Momentum (Red Regime 🔴)
This is a momentum strategy that bets on the continuation of a regime break. These signals appear on the exact bar the background turns RED.
• LONG Signal: "ACHAT ÉCART" (Buy Divergence)
• What it means: The regime just broke (turned RED) at the same time the Z-Score was already rising.
• Your Bet: The pair's relationship is broken, and the spread will continue to "rip" higher, diverging further from the mean.
• Action: Buy the Spread (e.g., Buy MES, Sell MNQ) and hold for momentum.
• SHORT Signal: "VENTE ÉCART" (Sell Divergence)
• What it means: The regime just broke (turned RED) at the same time the Z-Score was already falling.
• Your Bet: The pair's relationship is broken, and the spread will continue to "crash" lower, diverging further from the mean.
• Action: Sell the Spread (e.g., Sell MES, Buy MNQ) and hold for momentum.
• Exit Target: This is a momentum trade, so the exit is not the 0-line. Use a trailing stop or exit when the regime becomes stable again (turns GREEN).
The 3 Indicator Panes
1. Pane 1: Main Dashboard (Signal Pane)
• Z-Score PRIX (Blue Line): Your main signal. Shows the spread's deviation.
• Regime (Background Color): Your "traffic light" (Green for Mean Reversion, Red for Divergence).
• Trade Labels: The explicit entry signals.
2. Pane 2: Volatility Ratio (Diagnostic Pane)
• This pane shows the ratio of the two assets' volatility (Orange Line) vs. its long-term average (Gray Line).
• It is one of the two filters used to decide if the regime is "stable." If the orange line moves too far from the gray line, the regime turns RED.
3. Pane 3: Correlation Z-Score (Diagnostic Pane)
• This is the most critical filter. It measures the Z-Score of the rolling correlation itself.
• If this Purple Line drops below the Red Dashed Line (the "Danger Threshold"), it means the pair's correlation has statistically broken. This is the primary trigger for the RED "Divergence" regime.
Settings
• Symbol 1 & 2 Tickers: Set the two assets for the filters (e.g., "MES1!" and "MNQ1!"). Note: You must still load the spread chart itself (e.g., MES1!-MNQ1!) for the Price Z-Score to work.
• Z-Score Settings: Adjust the lookback period and bands for the Price Z-Score.
• Volatility Filter Settings: Adjust the ATR period, the MA period, and the deviation threshold.
• Correlation Filter Settings: Adjust the lookback periods and the "danger threshold" for the Correlation Z-Score.
Disclaimer: This indicator is for educational and informational purposes only. It does not constitute financial advice. All trading involves significant risk. Past performance is not indicative of future results.
ZScore correlation volatility spread pacThis is a complete multi-strategy dashboard for statistical arbitrage (pairs trading). It is designed to solve the biggest challenge in pairs trading: knowing when to trade mean-reversion and when to trade a regime break.
This indicator automatically analyzes the stability of the pair's relationship using two critical filters (a Volatility Ratio filter and a Correlation Z-Score filter). It then provides clear, actionable signals for two opposite strategies based on the current market "regime."
The Regime "Traffic Light" System
The indicator's background color tells you which strategy is currently active.
• 🟢 GREEN Background (Stable Regime): This is the "Mean Reversion" regime. It means both the volatility and correlation filters are stable. The pair is behaving predictably, and you can trust the Z-Score to revert to its mean.
• 🔴 RED Background (Unstable Regime): This is the "Divergence" or "Breakout" regime. It means the pair's relationship has failed (correlation has broken down OR volatility has exploded). In this regime, the Z-Score is not expected to revert and may continue to diverge.
How to Use: The Two Strategies
The indicator will plot text labels on your chart for four specific signals.
📈 Strategy 1: Mean Reversion (Green Regime 🟢)
This is the classic pairs trading strategy. You only take these signals when the background is GREEN.
• LONG Signal: "ACHAT MOYENNE" (Buy Mean)
• What it means: The Z-Score (blue line) has crossed below the lower band (e.g., -2.0) while the regime is stable.
• Your Bet: The spread is statistically "too cheap" and will rise back to the 0-line.
• Action: Buy the Spread (e.g., Buy MES, Sell MNQ).
• SHORT Signal: "VENTE MOYENNE" (Sell Mean)
• What it means: The Z-Score (blue line) has crossed above the upper band (e.g., +2.0) while the regime is stable.
• Your Bet: The spread is statistically "too expensive" and will fall back to the 0-line.
• Action: Sell the Spread (e.g., Sell MES, Buy MNQ).
• Exit Target: Close your position when the Z-Score (blue line) returns to 0.
🚀 Strategy 2: Divergence / Momentum (Red Regime 🔴)
This is a momentum strategy that bets on the continuation of a regime break. These signals appear on the exact bar the background turns RED.
• LONG Signal: "ACHAT ÉCART" (Buy Divergence)
• What it means: The regime just broke (turned RED) at the same time the Z-Score was already rising.
• Your Bet: The pair's relationship is broken, and the spread will continue to "rip" higher, diverging further from the mean.
• Action: Buy the Spread (e.g., Buy MES, Sell MNQ) and hold for momentum.
• SHORT Signal: "VENTE ÉCART" (Sell Divergence)
• What it means: The regime just broke (turned RED) at the same time the Z-Score was already falling.
• Your Bet: The pair's relationship is broken, and the spread will continue to "crash" lower, diverging further from the mean.
• Action: Sell the Spread (e.g., Sell MES, Buy MNQ) and hold for momentum.
• Exit Target: This is a momentum trade, so the exit is not the 0-line. Use a trailing stop or exit when the regime becomes stable again (turns GREEN).
The 3 Indicator Panes
1. Pane 1: Main Dashboard (Signal Pane)
• Z-Score PRIX (Blue Line): Your main signal. Shows the spread's deviation.
• Regime (Background Color): Your "traffic light" (Green for Mean Reversion, Red for Divergence).
• Trade Labels: The explicit entry signals.
2. Pane 2: Volatility Ratio (Diagnostic Pane)
• This pane shows the ratio of the two assets' volatility (Orange Line) vs. its long-term average (Gray Line).
• It is one of the two filters used to decide if the regime is "stable." If the orange line moves too far from the gray line, the regime turns RED.
3. Pane 3: Correlation Z-Score (Diagnostic Pane)
• This is the most critical filter. It measures the Z-Score of the rolling correlation itself.
• If this Purple Line drops below the Red Dashed Line (the "Danger Threshold"), it means the pair's correlation has statistically broken. This is the primary trigger for the RED "Divergence" regime.
Settings
• Symbol 1 & 2 Tickers: Set the two assets for the filters (e.g., "MES1!" and "MNQ1!"). Note: You must still load the spread chart itself (e.g., MES1!-MNQ1!) for the Price Z-Score to work.
• Z-Score Settings: Adjust the lookback period and bands for the Price Z-Score.
• Volatility Filter Settings: Adjust the ATR period, the MA period, and the deviation threshold.
• Correlation Filter Settings: Adjust the lookback periods and the "danger threshold" for the Correlation Z-Score.
Disclaimer: This indicator is for educational and informational purposes only. It does not constitute financial advice. All trading involves significant risk. Past performance is not indicative of future results.
Nqaba Goldminer StrategyThis indicator plots the New York session key timing levels used in institutional intraday models.
It automatically marks the 03:00 AM, 10:00 AM, and 2:00 PM (14:00) New York times each day:
Vertical lines show exactly when those time windows open — allowing traders to identify major global liquidity shifts between London, New York, and U.S. session overlaps.
Horizontal lines mark the opening price of the 5-minute candle that begins at each of those key times, providing precision reference levels for potential reversals, continuation setups, and intraday bias shifts.
Users can customize each line’s color, style (solid/dashed/dotted), width, and horizontal-line length.
A history toggle lets you display all past occurrences or just today’s key levels for a cleaner chart.
These reference levels form the foundation for strategies such as:
London Breakout to New York Reversal models
Opening Range / Session Open bias confirmation
Institutional volume transfer windows (London → NY → Asia)
The tool provides a simple visual structure for traders to frame intraday decision-making around recurring institutional time events.
Percentile Rank Oscillator (Price + VWMA)A statistical oscillator designed to identify potential market turning points using percentile-based price analytics and volume-weighted confirmation.
What is PRO?
Percentile Rank Oscillator measures how extreme current price behavior is relative to its own recent history. It calculates a rolling percentile rank of price midpoints and VWMA deviation (volume-weighted price drift). When price reaches historically rare levels – high or low percentiles – it may signal exhaustion and potential reversal conditions.
How it works
Takes midpoint of each candle ((H+L)/2)
Ranks the current value vs previous N bars using rolling percentile rank
Maps percentile to a normalized oscillator scale (-1..+1 or 0–100)
Optionally evaluates VWMA deviation percentile for volume-confirmed signals
Highlights extreme conditions and confluence zones
Why percentile rank?
Median-based percentiles ignore outliers and read the market statistically – not by fixed thresholds. Instead of guessing “overbought/oversold” values, the indicator adapts to current volatility and structure.
Key features
Rolling percentile rank of price action
Optional VWMA-based percentile confirmation
Adaptive, noise-robust structure
User-selectable thresholds (default 95/5)
Confluence highlighting for price + VWMA extremes
Optional smoothing (RMA)
Visual extreme zone fills for rapid signal recognition
How to use
High percentile values –> statistically extreme upward deviation (potential top)
Low percentile values –> statistically extreme downward deviation (potential bottom)
Price + VWMA confluence strengthens reversal context
Best used as part of a broader trading framework (market structure, order flow, etc.)
Tip: Look for percentile spikes at key HTF levels, after extended moves, or where liquidity sweeps occur. Strong moves into rare percentile territory may precede mean reversion.
Suggested settings
Default length: 100 bars
Thresholds: 95 / 5
Smoothing: 1–3 (optional)
Important note
This tool does not predict direction or guarantee outcomes. It provides statistical context for price extremes to help traders frame probability and timing. Always combine with sound risk management and other tools.
Multi-Mode Seasonality Map [BackQuant]Multi-Mode Seasonality Map
A fast, visual way to expose repeatable calendar patterns in returns, volatility, volume, and range across multiple granularities (Day of Week, Day of Month, Hour of Day, Week of Month). Built for idea generation, regime context, and execution timing.
What is “seasonality” in markets?
Seasonality refers to statistically repeatable patterns tied to the calendar or clock, rather than to price levels. Examples include specific weekdays tending to be stronger, certain hours showing higher realized volatility, or month-end flow boosting volumes. This tool measures those effects directly on your charted symbol.
Why seasonality matters
It’s orthogonal alpha: timing edges independent of price structure that can complement trend, mean reversion, or flow-based setups.
It frames expectations: when a session typically runs hot or cold, you size and pace risk accordingly.
It improves execution: entering during historically favorable windows, avoiding historically noisy windows.
It clarifies context: separating normal “calendar noise” from true anomaly helps avoid overreacting to routine moves.
How traders use seasonality in practice
Timing entries/exits : If Tuesday morning is historically weak for this asset, a mean-reversion buyer may wait for that drift to complete before entering.
Sizing & stops : If 13:00–15:00 shows elevated volatility, widen stops or reduce size to maintain constant risk.
Session playbooks : Build repeatable routines around the hours/days that consistently drive PnL.
Portfolio rotation : Compare seasonal edges across assets to schedule focus and deploy attention where the calendar favors you.
Why Day-of-Week (DOW) can be especially helpful
Flows cluster by weekday (ETF creations/redemptions, options hedging cadence, futures roll patterns, macro data releases), so DOW often encodes a stable micro-structure signal.
Desk behavior and liquidity provision differ by weekday, impacting realized range and slippage.
DOW is simple to operationalize: easy rules like “fade Monday afternoon chop” or “press Thursday trend extension” can be tested and enforced.
What this indicator does
Multi-mode heatmaps : Switch between Day of Week, Day of Month, Hour of Day, Week of Month .
Metric selection : Analyze Returns , Volatility ((high-low)/open), Volume (vs 20-bar average), or Range (vs 20-bar average).
Confidence intervals : Per cell, compute mean, standard deviation, and a z-based CI at your chosen confidence level.
Sample guards : Enforce a minimum sample size so thin data doesn’t mislead.
Readable map : Color palettes, value labels, sample size, and an optional legend for fast interpretation.
Scoreboard : Optional table highlights best/worst DOW and today’s seasonality with CI and a simple “edge” tag.
How it’s calculated (under the hood)
Per bar, compute the chosen metric (return, vol, volume %, or range %) over your lookback window.
Bucket that metric into the active calendar bin (e.g., Tuesday, the 15th, 10:00 hour, or Week-2 of month).
For each bin, accumulate sum , sum of squares , and count , then at render compute mean , std dev , and confidence interval .
Color scale normalizes to the observed min/max of eligible bins (those meeting the minimum sample size).
How to read the heatmap
Color : Greener/warmer typically implies higher mean value for the chosen metric; cooler implies lower.
Value label : The center number is the bin’s mean (e.g., average % return for Tuesdays).
Confidence bracket : Optional “ ” shows the CI for the mean, helping you gauge stability.
n = sample size : More samples = more reliability. Treat small-n bins with skepticism.
Suggested workflows
Pick the lens : Start with Analysis Type = Returns , Heatmap View = Day of Week , lookback ≈ 252 trading days . Note the best/worst weekdays and their CI width.
Sanity-check volatility : Switch to Volatility to see which bins carry the most realized range. Use that to plan stop width and trade pacing.
Check liquidity proxy : Flip to Volume , identify thin vs thick windows. Execute risk in thicker windows to reduce slippage.
Drill to intraday : Use Hour of Day to reveal opening bursts, lunchtime lulls, and closing ramps. Combine with your main strategy to schedule entries.
Calendar nuance : Inspect Week of Month and Day of Month for end-of-month, options-cycle, or data-release effects.
Codify rules : Translate stable edges into rules like “no fresh risk during bottom-quartile hours” or “scale entries during top-quartile hours.”
Parameter guidance
Analysis Period (Days) : 252 for a one-year view. Shorten (100–150) to emphasize the current regime; lengthen (500+) for long-memory effects.
Heatmap View : Start with DOW for robustness, then refine with Hour-of-Day for your execution window.
Confidence Level : 95% is standard; use 90% if you want wider coverage with fewer false “insufficient data” bins.
Min Sample Size : 10–20 helps filter noise. For Hour-of-Day on higher timeframes, consider lowering if your dataset is small.
Color Scheme : Choose a palette with good mid-tone contrast (e.g., Red-Green or Viridis) for quick thresholding.
Interpreting common patterns
Return-positive but low-vol bins : Favorable drift windows for passive adds or tight-stop trend continuation.
Return-flat but high-vol bins : Opportunity for mean reversion or breakout scalping, but manage risk accordingly.
High-volume bins : Better expected execution quality; schedule size here if slippage matters.
Wide CI : Edge is unstable or sample is thin; treat as exploratory until more data accumulates.
Best practices
Revalidate after regime shifts (new macro cycle, liquidity regime change, major exchange microstructure updates).
Use multiple lenses: DOW to find the day, then Hour-of-Day to refine the entry window.
Combine with your core setup signals; treat seasonality as a filter or weight, not a standalone trigger.
Test across assets/timeframes—edges are instrument-specific and may not transfer 1:1.
Limitations & notes
History-dependent: short histories or sparse intraday data reduce reliability.
Not causal: a hot Tuesday doesn’t guarantee future Tuesday strength; treat as probabilistic bias.
Aggregation bias: changing session hours or symbol migrations can distort older samples.
CI is z-approximate: good for fast triage, not a substitute for full hypothesis testing.
Quick setup
Use Returns + Day of Week + 252d to get a clean yearly map of weekday edge.
Flip to Hour of Day on intraday charts to schedule precise entries/exits.
Keep Show Values and Confidence Intervals on while you calibrate; hide later for a clean visual.
The Multi-Mode Seasonality Map helps you convert the calendar from an afterthought into a quantitative edge, surfacing when an asset tends to move, expand, or stay quiet—so you can plan, size, and execute with intent.
Adaptive Trend Kernel📘 Adaptive Trend Kernel — Smoothed Regression Trend with Dynamic Bands
The Adaptive Trend Kernel is a regression-based trend indicator that dynamically adapts to market volatility.
It combines linear regression with standard deviation bands to identify directional bias, momentum shifts, and potential entry zones with high precision.
This tool helps traders visualize the underlying trend and filter out market noise by plotting a smooth regression line (the "kernel") surrounded by upper and lower deviation bands.
The indicator also provides crossover-based buy and sell signals when price crosses above or below the adaptive regression line.
🔍 How It Works
Regression Line: A linear regression line smooths the closing price to represent the dominant market direction.
Volatility Bands: The upper and lower bands (based on standard deviation) expand or contract according to market volatility.
Signal Triggers:
• Long Signal (Green Triangle): When price crosses above the regression line.
• Short Signal (Red Triangle): When price crosses below the regression line.
Trend Labels: Optional “Up” and “Down” labels appear on crossover points for better visual clarity.
💡 Trading Strategy
Trend Following: Enter long when a green “Up” signal appears, confirming an upward crossover.
Exit when the price touches the upper band or when a red “Down” signal appears.
Reversal Catching: In ranging markets, watch for quick crossovers near the bands — these often precede short-term pullbacks.
Volatility Filter: When the bands widen, volatility is high — consider using smaller position sizes or waiting for confirmation.
Narrowing bands often indicate consolidation and upcoming breakout potential.
⚙️ Features
Adjustable Regression Length and Band Multiplier
Customizable colors, transparency, and label visibility
Lightweight and repaint-free by design
Works well on all timeframes and asset classes (Forex, Crypto, Stocks, Gold)
🧭 Recommended Use
Use this indicator as a trend confirmation tool or entry filter in combination with momentum or volume indicators.
Best results occur when aligned with higher timeframe trend direction.
Market SessionsMarket Sessions (Asian, London, NY, Pacific)
Summary
This indicator plots the main global market sessions (Asian, European, American, Pacific) as boxes on your chart, complete with dynamic high/low tracking.
It's an essential tool for intraday traders to track session-based volatility patterns and visualize key support/resistance levels (like the Asian Range) that often define price action for the rest of the day.
Who it’s for
Intraday traders, scalpers, and day traders who need to visualize market hours and session-based ranges. If your strategy depends on the London open, the New York close, or the Asian range, this script will map it out for you.
What it shows
Customizable Session Boxes: Four fully configurable boxes for the Asian, European (London), American (New York), and Pacific (Sydney) sessions.
Session High & Low: The script tracks and boxes the highest high and lowest low of each session, dynamically updating as the session progresses.
Session Labels: Clear labels (e.g., "AS", "EU") mark each session, anchored to the start time.
Key Features
Powerful Timezone Control: This is the core feature.
Use Exchange Timezone (Default): Simply enter session times (e.g., 8:00 for London) relative to the exchange's timezone (e.g., "NASDAQ" or "BINANCE").
Use UTC Offset: Uncheck the box and enter a UTC offset (e.g., +3 or -5). Now, all session times you enter are relative to that specific UTC offset. This gives you full control regardless of the chart you're on.
Fully Customizable: Toggle any session on/off.
Style Control: Change the fill color, border color, transparency, border width, and line style (Solid, Dashed, Dotted) for each session individually.
Smart Labels: Labels stay anchored to the start of the session (no "sliding") and float just above the session high.
Why this helps
Track Volatility & Market Behavior: Visually identify the "personality" of each session. Some sessions might consistently produce powerful pumps or dumps, while others are prone to sideways "chop" or accumulation. This indicator helps you see these repeating patterns.
Find Key Support/Resistance Levels: The High and Low of a session (e.g., the Asian Range) often become critical support and resistance levels for the next session (e.g., London). This script makes it easy to spot these "session-to-session" S/R flips and reactions.
Aid Statistical Analysis: The script provides the core visual data for your statistical research. You can easily track how often the London session breaks the Asian high, or which session is most likely to reverse the trend, helping you build a robust trading plan.
Context is King: Instantly see which market is active, which are overlapping (like the high-volume London-NY overlap), and which have closed.
Quick setup
Go to Timezone Settings.
Decide how you want to enter times:
Easy (Default): Leave Use Exchange Timezone checked. Enter session times based on the chart's native exchange (e.g., for BTC/USDT on Binance, use UTC+0 times).
Manual (Pro): Uncheck Use Exchange Timezone. Enter your UTC Offset (e.g., +2 for Berlin). Now, enter all session times as they appear on the clock in Berlin.
Go to each session tab (Asian, European...) to enable/disable it and set the correct start/end hours and minutes.
Style the colors to match your chart theme.
Disclaimer
For educational/informational purposes only; not financial advice. Trading involves risk—manage it responsibly.
Smart Dollar Cost Averaging DashboardThis closed-source TradingView indicator implements a comprehensive Dollar Cost Averaging (DCA) savings plan simulation designed to automate systematic investments. The script allows users to set a fixed investment amount and choose a customizable interval—weekly, monthly, or quarterly—at which purchases are simulated against historical or live price data. The core functionality calculates the average buy-in price dynamically by tracking cumulative invested capital and total acquired shares, providing a true average cost basis rather than simple price signals. This average price is visualized as a persistent, non-draggable horizontal line on the chart, enabling traders to intuitively compare the market price to their average entry point. A movable and toggleable dashboard accompanies the indicator, delivering real-time metrics including total investment, number of purchases, portfolio value, profit/loss both in absolute and percentage terms, and the price gap relative to the computed average buy-in. This transparency helps users understand their position’s health and supports disciplined long-term investment strategies. This script stands unique by combining flexible periodic investment scheduling with real capital calculations and detailed, easy-to-read visual feedback that is rarely bundled so intuitively in similar scripts. Unlike many open-source trend-following or scalping tools, this indicator focuses on systematic investment and passive portfolio growth, ideal for investors pursuing dollar cost averaging. Unlike standard buy/sell signal creators or simplistic moving average crossovers, this script models actual cash flow deployment and quantifies performance in real-time with a clean, professional UI. Its originality lies in marrying realistic capital flow simulation with intuitive visualization and multi-interval flexibility.
How It Works:
Tracks virtual investments of fixed cash amounts at user-defined intervals Converts invested amounts into shares based on closing prices, accumulating holding size Recalculates weighted average purchase price after each simulated buy Continuously displays the average buy-in as a stable graphic element on any price chart Offers detailed investment metrics through an interactive dashboard overlay Supports weekly, monthly, and quarterly investment cadences with user-selectable investment days Use Cases: Ideal for investors employing systematic savings plans to build long-term positions Fits cryptocurrency, stock, ETF, and index investments on TradingView Supports financial education by illustrating dollar cost averaging principles visually Facilitates performance tracking for passive investors who prioritize consistent buying over timing The script is an advanced tool meeting a distinct trading niche: systematic, cash-based, passive investment modeling with transparency and user control. This originality and usefulness justify the closed-source mode to protect intellectual property.
NFCI National Financial Conditions IndexChicago Fed National Financial Conditions Index (NFCI)
This indicator plots the Chicago Fed’s National Financial Conditions Index (NFCI).
The NFCI updates weekly, and its latest value is displayed across all chart intervals.
The NFCI measures how tight or loose overall U.S. financial conditions are. It combines over 100 weekly indicators from the money, bond, and equity markets—along with credit and leverage data—into a single composite index.
The NFCI has three key subcomponents, each of which can be independently selected within the indicator:
Risk: Captures volatility, credit spreads, and overall market stress.
Credit: Tracks how easy or difficult it is to borrow across households and businesses.
Leverage: Reflects the level of debt and balance-sheet strength in the financial system.
When the NFCI rises, financial conditions are tightening — liquidity is contracting, borrowing costs are climbing, and investors tend to reduce risk.
When the NFCI falls, conditions are loosening — liquidity expands, credit flows more freely, and markets generally become more risk-seeking.
Traders often use the NFCI as a macro backdrop for risk appetite: rising values signal growing stress and defensive positioning, while falling values indicate improving liquidity and a more supportive market environment.
Rolling Correlation vs Another Symbol (SPY Default)This indicator visualizes the rolling correlation between the current chart symbol and another selected asset, helping traders understand how closely the two move together over time.
It calculates the Pearson correlation coefficient over a user-defined period (default 22 bars) and plots it as a color-coded line:
• Green line → positive correlation (move in the same direction)
• Red line → negative correlation (move in opposite directions)
• A gray dashed line marks the zero level (no correlation).
The background highlights periods of strong relationship:
• Light green when correlation > +0.7 (strong positive)
• Light red when correlation < –0.7 (strong negative)
Use this tool to quickly spot diversification opportunities, confirm hedges, or understand how assets interact during different market regimes.
Standardization (Z-score)Standardization, often referred to as Z-score normalization, is a data preprocessing technique that rescales data to have a mean of 0 and a standard deviation of 1. The resulting values, known as Z-scores, indicate how many standard deviations an individual data point is from the mean of the dataset (or a rolling sample of it).
This indicator calculates and plots the Z-score for a given input series over a specified lookback period. It is a fundamental tool for statistical analysis, outlier detection, and preparing data for certain machine learning algorithms.
## Core Concepts
* **Standardization:** The process of transforming data to fit a standard normal distribution (or more generally, to have a mean of 0 and standard deviation of 1).
* **Z-score (Standard Score):** A dimensionless quantity that represents the number of standard deviations by which a data point deviates from the mean of its sample.
The formula for a Z-score is:
`Z = (x - μ) / σ`
Where:
* `x` is the individual data point (e.g., current value of the source series).
* `μ` (mu) is the mean of the sample (calculated over the lookback period).
* `σ` (sigma) is the standard deviation of the sample (calculated over the lookback period).
* **Mean (μ):** The average value of the data points in the sample.
* **Standard Deviation (σ):** A measure of the amount of variation or dispersion of a set of values. A low standard deviation indicates that the values tend to be close to the mean, while a high standard deviation indicates that the values are spread out over a wider range.
## Common Settings and Parameters
| Parameter | Type | Default | Function | When to Adjust |
| :-------------- | :----------- | :------ | :------------------------------------------------------------------------------------------------------ | :-------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| Source | series float | close | The input data series (e.g., price, volume, indicator values). | Choose the series you want to standardize. |
| Lookback Period | int | 20 | The number of bars (sample size) used for calculating the mean (μ) and standard deviation (σ). Min 2. | A larger period provides more stable estimates of μ and σ but will be less responsive to recent changes. A shorter period is more reactive. `minval` is 2 because `ta.stdev` requires it. |
**Pro Tip:** Z-scores are excellent for identifying anomalies or extreme values. For instance, applying Standardization to trading volume can help quickly spot days with unusually high or low activity relative to the recent norm (e.g., Z-score > 2 or < -2).
## Calculation and Mathematical Foundation
The Z-score is calculated for each bar as follows, using a rolling window defined by the `Lookback Period`:
1. **Calculate Mean (μ):** The simple moving average (`ta.sma`) of the `Source` data over the specified `Lookback Period` is calculated. This serves as the sample mean `μ`.
`μ = ta.sma(Source, Lookback Period)`
2. **Calculate Standard Deviation (σ):** The standard deviation (`ta.stdev`) of the `Source` data over the same `Lookback Period` is calculated. This serves as the sample standard deviation `σ`.
`σ = ta.stdev(Source, Lookback Period)`
3. **Calculate Z-score:**
* If `σ > 0`: The Z-score is calculated using the formula:
`Z = (Current Source Value - μ) / σ`
* If `σ = 0`: This implies all values in the lookback window are identical (and equal to the mean). In this case, the Z-score is defined as 0, as the current source value is also equal to the mean.
* If `σ` is `na` (e.g., insufficient data in the lookback period), the Z-score is `na`.
> 🔍 **Technical Note:**
> * The `Lookback Period` must be at least 2 for `ta.stdev` to compute a valid standard deviation.
> * The Z-score calculation uses the sample mean and sample standard deviation from the rolling lookback window.
## Interpreting the Z-score
* **Magnitude and Sign:**
* A Z-score of **0** means the data point is identical to the sample mean.
* A **positive Z-score** indicates the data point is above the sample mean. For example, Z = 1 means the point is 1 standard deviation above the mean.
* A **negative Z-score** indicates the data point is below the sample mean. For example, Z = -1 means the point is 1 standard deviation below the mean.
* **Typical Range:** For data that is approximately normally distributed (bell-shaped curve):
* About 68% of Z-scores fall between -1 and +1.
* About 95% of Z-scores fall between -2 and +2.
* About 99.7% of Z-scores fall between -3 and +3.
* **Outlier Detection:** Z-scores significantly outside the -2 to +2 range, and especially outside -3 to +3, are often considered outliers or extreme values relative to the recent historical data in the lookback window.
* **Volatility Indication:** When applied to price, large absolute Z-scores can indicate moments of high volatility or significant deviation from the recent price trend.
The indicator plots horizontal lines at ±1, ±2, and ±3 standard deviations to help visualize these common thresholds.
## Common Applications
1. **Outlier Detection:** Identifying data points that are unusual or extreme compared to the rest of the sample. This is a primary use in financial markets for spotting abnormal price moves, volume spikes, etc.
2. **Comparative Analysis:** Allows for comparison of scores from different distributions that might have different means and standard deviations. For example, comparing the Z-score of returns for two different assets.
3. **Feature Scaling in Machine Learning:** Standardizing features to have a mean of 0 and standard deviation of 1 is a common preprocessing step for many machine learning algorithms (e.g., SVMs, logistic regression, neural networks) to improve performance and convergence.
4. **Creating Normalized Oscillators:** The Z-score itself can be used as a bounded (though not strictly between -1 and +1) oscillator, indicating how far the current price has deviated from its moving average in terms of standard deviations.
5. **Statistical Process Control:** Used in quality control charts to monitor if a process is within expected statistical limits.
## Limitations and Considerations
* **Assumption of Normality for Probabilistic Interpretation:** While Z-scores can always be calculated, the probabilistic interpretations (e.g., "68% of data within ±1σ") strictly apply to normally distributed data. Financial data is often not perfectly normal (e.g., it can have fat tails).
* **Sensitivity of Mean and Standard Deviation to Outliers:** The sample mean (μ) and standard deviation (σ) used in the Z-score calculation can themselves be influenced by extreme outliers within the lookback period. This can sometimes mask or exaggerate the Z-score of other points.
* **Choice of Lookback Period:** The Z-score is highly dependent on the `Lookback Period`. A short period makes it very sensitive to recent fluctuations, while a long period makes it smoother and less responsive. The appropriate period depends on the analytical goal.
* **Stationarity:** For time series data, Z-scores are calculated based on a rolling window. This implicitly assumes some level of local stationarity (i.e., the mean and standard deviation are relatively stable within the window).
Multi-Session Viewer and AnalyzerFully customizable multi-session viewer that takes session analysis to the next level. It allows you to fully customize each session to your liking. Includes a feature that highlights certain periods of time on the chart and a Time Range Marker.
It helps you analyze the instrument that you trade and pinpoint which times are more volatile than others. It also helps you choose the best time to trade your instrument and align your life schedule with the market.
NZDUSD Example:
- 3 major sessions displayed.
- Although this is NZDUSD, Sydney is not the best time to trade this pair. Volatility picks up at Tokyo open.
- I have time to trade in the evening from 18:00 to 22:00 PST. I live in a different time zone, whereas market is based on EST. How does the pair behave during the time I am available to trade based on my time zone? Time Range Marker feature allows you to see this clearly on the chart (black lines).
- I have some time in the morning to trade during New York session, but there is no way I am waking up at 05:00 PST. 06:30 PST seems doable. Blue highlighted area is good time to trade during New York session based on what Bob said. It seem like this aligns with when I am available and when I am able to trade. Volatility is also at its peak.
- I am also available to trade between London close and Tokyo open on some days of the week, but... based on what I see, green highlighted area is clearly showing that I probably don't want to waste my time trading this pair from London close and until Tokyo open. I will use this time for something else rather than be stuck in a range.
SPX Bull Market, Bear market and Corrections Since 1929 This script show visually with labels all the BULL & BEAR Market since 1929 with intermediary corrections.
Bear Market = Price drop of >=20% (based on closing price not intra day low)
Corrections = Price drop of >=10% and < 20% (based on closing price not intra day low, in intraday price it may go beyond 20% but closes in less than 20% )
The script doesn't update as we move forward , I need to manually update during every correction/bull/bear phases.
It is a good visual to study the past bull and bear market to gain some key insights!
