Meta-LR ForecastThis indicator builds a forward-looking projection from the current bar by combining twelve time-compressed “mini forecasts.” Each forecast is a linear-regression-based outlook whose contribution is adaptively scaled by trend strength (via ADX) and normalized to each timeframe’s own volatility (via that timeframe’s ATR). The result is a 12-segment polyline that starts at the current price and extends one bar at a time into the future (1× through 12× the chart’s timeframe). Alongside the plotted path, the script computes two summary measures:
* Per-TF Bias% — a directional efficiency × R² score for each micro-forecast, expressed as a percent.
* Meta Bias% — the same score, but applied to the final, accumulated 12-step path. It summarizes how coherent and directional the combined projection is.
This tool is an indicator, not a strategy. It does not place orders. Nothing here is trade advice; it is a visual, quantitative framework to help you assess directional bias and trend context across a ladder of timeframe multiples.
The core engine fits a simple least-squares line on a normalized price series for each small forecast horizon and extrapolates one bar forward. That “trend” forecast is paired with its mirror, an “anti-trend” forecast, constructed around the current normalized price. The model then blends between these two wings according to current trend strength as measured by ADX.
ADX is transformed into a weight (w) in using an adaptive band centered on the rolling mean (μ) with width derived from the standard deviation (σ) of ADX over a configurable lookback. When ADX is deeply below the lower band, the weight approaches -1, favoring anti-trend behavior. Inside the flat band, the weight is near zero, producing neutral behavior. Clearly above the upper band, the weight approaches +1, favoring a trend-following stance. The transitions between these regions are linear so the regime shift is smooth rather than abrupt.
You can shape how quickly the model commits to either wing using two exponents. One exponent controls how aggressively positive weights lean into the trend forecast; the other controls how aggressively negative weights lean into the anti-trend forecast. Raising these exponents makes the response more gradual; lowering them makes the shift more decisive. An optional switch can force full anti-trend behavior when ADX registers a deep-low condition far below the lower tail, if you prefer a categorical stance in very flat markets.
A key design choice is volatility normalization. Every micro-forecast is computed in ATR units of its own timeframe. The script fetches that timeframe’s ATR inside each security call and converts normalized outputs back to price with that exact ATR. This avoids scaling higher-timeframe effects by the chart ATR or by square-root time approximations. Using “ATR-true” for each timeframe keeps the cross-timeframe accumulation consistent and dimensionally correct.
Bias% is defined as directional efficiency multiplied by R², expressed as a percent. Directional efficiency captures how much net progress occurred relative to the total path length; R² captures how well the path aligns with a straight line. If price meanders without net progress, efficiency drops; if the variation is well-explained by a line, R² rises. Multiplying the two penalizes choppy, low-signal paths and rewards sustained, coherent motion.
The forward path is built by converting each per-timeframe Bias% into a small ATR-sized delta, then cumulatively adding those deltas to form a 12-step projection. This produces a polyline anchored at the current close and stepping forward one bar per timeframe multiple. Segment color flips by slope, allowing a quick read of the path’s direction and inflection.
Inputs you can tune include:
* Max Regression Length. Upper bound for each micro-forecast’s regression window. Larger values smooth the trend estimate at the cost of responsiveness; smaller values react faster but can add noise.
* Price Source. The price series analyzed (for example, close or typical price).
* ADX Length. Period used for the DMI/ADX calculation.
* ATR Length (normalization). Window used for ATR; this is applied per timeframe inside each security call.
* Band Lookback (for μ, σ). Lookback used to compute the adaptive ADX band statistics. Larger values stabilize the band; smaller values react more quickly.
* Flat half-width (σ). Width of the neutral band on both sides of μ. Wider flats spend more time neutral; narrower flats switch regimes more readily.
* Tail width beyond flat (σ). Distance from the flat band edge to the extreme trend/anti-trend zone. Larger tails create a longer ramp; smaller tails reach extremes sooner.
* Polyline Width. Visual thickness of the plotted segments.
* Negative Wing Aggression (anti-trend). Exponent shaping for negative weights; higher values soften the tilt into mean reversion.
* Positive Wing Aggression (trend). Exponent shaping for positive weights; lower values make trend commitment stronger and sooner.
* Force FULL Anti-Trend at Deep-Low ADX. Optional hard switch for extremely low ADX conditions.
On the chart you will see:
* A 12-segment forward polyline starting from the current close to bar\_index + 1 … +12, with green segments for up-steps and red for down-steps.
* A small label at the latest bar showing Meta Bias% when available, or “n/a” when insufficient data exists.
Interpreting the readouts:
* Trend-following contexts are characterized by ADX above the adaptive upper band, pushing w toward +1. The blended forecast leans toward the regression extrapolation. A strongly positive Meta Bias% in this environment suggests directional alignment across the ladder of timeframes.
* Mean-reversion contexts occur when ADX is well below the lower tail, pushing w toward -1 (or forcing anti-trend if enabled). After a sharp advance, a negative Meta Bias% may indicate the model projects pullback tendencies.
* Neutral contexts occur when ADX sits inside the flat band; w is near zero, the blended forecast remains close to current price, and Meta Bias% tends to hover near zero.
These are analytical cues, not rules. Always corroborate with your broader process, including market structure, time-of-day behavior, liquidity conditions, and risk limits.
Practical usage patterns include:
* Momentum confirmation. Combine a rising Meta Bias% with higher-timeframe structure (such as higher highs and higher lows) to validate continuation setups. Treat the 12th step’s distance as a coarse sense of potential room rather than as a target.
* Fade filtering. If you prefer fading extremes, require ADX to be near or below the lower ramp before acting on counter-moves, and avoid fades when ADX is decisively above the upper band.
* Position planning. Because per-step deltas are ATR-scaled, the path’s vertical extent can be mentally mapped to typical noise for the instrument, informing stop distance choices. The script itself does not compute orders or size.
* Multi-timeframe alignment. Each step corresponds to a clean multiple of your chart timeframe, so the polyline visualizes how successively larger windows bias price, all referenced to the current bar.
House-rules and repainting disclosures:
* Indicator, not strategy. The script does not execute, manage, or suggest orders. It displays computed paths and bias scores for analysis only.
* No performance claims. Past behavior of any measure, including Meta Bias%, does not guarantee future results. There are no assurances of profitability.
* Higher-timeframe updates. Values obtained via security for higher-timeframe series can update intrabar until the higher-timeframe bar closes. The forward path and Meta Bias% may change during formation of a higher-timeframe candle. If you need confirmed higher-timeframe inputs, consider reading the prior higher-timeframe value or acting only after the higher-timeframe close.
* Data sufficiency. The model requires enough history to compute ATR, ADX statistics, and regression windows. On very young charts or illiquid symbols, parts of the readout can be unavailable until sufficient data accumulates.
* Volatility regimes. ATR normalization helps compare across timeframes, but unusual volatility regimes can make the path look deceptively flat or exaggerated. Judge the vertical scale relative to your instrument’s typical ATR.
Tuning tips:
* Stability versus responsiveness. Increase Max Regression Length to steady the micro-forecasts but accept slower response. If you lower it, consider slightly increasing Band Lookback so regime boundaries are not too jumpy.
* Regime bands. Widen the flat half-width to spend more time neutral, which can reduce over-trading tendencies in chop. Shrink the tail width if you want the model to commit to extremes sooner, at the cost of more false swings.
* Wing shaping. If anti-trend behavior feels too abrupt at low ADX, raise the negative wing exponent. If you want trend bias to kick in more decisively at high ADX, lower the positive wing exponent. Small changes have large effects.
* Forced anti-trend. Enable the deep-low option only if you explicitly want a categorical “markets are flat, fade moves” policy. Many users prefer leaving it off to keep regime decisions continuous.
Troubleshooting:
* Nothing plots or the label shows “n/a.” Ensure the chart has enough history for the ADX band statistics, ATR, and the regression windows. Exotic or illiquid symbols with missing data may starve the higher-timeframe computations. Try a more liquid market or a higher timeframe.
* Path flickers or shifts during the bar. This is expected when any higher-timeframe input is still forming. Wait for the higher-timeframe close for fully confirmed behavior, or modify the code to read prior values from the higher timeframe.
* Polyline looks too flat or too steep. Check the chart’s vertical scale and recent ATR regime. Adjust Max Regression Length, the wing exponents, or the band widths to suit the instrument.
Integration ideas for manual workflows:
* Confluence checklist. Use Meta Bias% as one of several independent checks, alongside structure, session context, and event risk. Act only when multiple cues align.
* Stop and target thinking. Because deltas are ATR-scaled at each timeframe, benchmark your proposed stops and targets against the forward steps’ magnitude. Stops that are much tighter than the prevailing ATR often sit inside normal noise.
* Session context. Consider session hours and microstructure. The same ADX value can imply different tradeability in different sessions, particularly in index futures and FX.
This indicator deliberately avoids:
* Fixed thresholds for buy or sell decisions. Markets vary and fixed numbers invite overfitting. Decide what constitutes “high enough” Meta Bias% for your market and timeframe.
* Automatic risk sizing. Proper sizing depends on account parameters, instrument specifications, and personal risk tolerance. Keep that decision in your risk plan, not in a visual bias tool.
* Claims of edge. These measures summarize path geometry and trend context; they do not ensure a tradable edge on their own.
Summary of how to think about the output:
* The script builds a 12-step forward path by stacking linear-regression micro-forecasts across increasing multiples of the chart timeframe.
* Each micro-forecast is blended between trend and anti-trend using an adaptive ADX band with separate aggression controls for positive and negative regimes.
* All computations are done in ATR-true units for each timeframe before reconversion to price, ensuring dimensional consistency when accumulating steps.
* Bias% (per-timeframe and Meta) condenses directional efficiency and trend fidelity into a compact score.
* The output is designed to serve as an analytical overlay that helps assess whether conditions look trend-friendly, fade-friendly, or neutral, while acknowledging higher-timeframe update behavior and avoiding prescriptive trade rules.
Use this tool as one component within a disciplined process that includes independent confirmation, event awareness, and robust risk management.
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VWAP Bands Pro - Session Based by kobiko3030
📊 Advanced Professional Trading Indicator
VWAP Bands Pro is an advanced indicator that combines the power of VWAP with 4 dynamic bands for precise identification of support and resistance zones. This indicator is designed for professional traders who want deep and accurate market movement analysis.
✨ Key Features
🎯 Smart VWAP Bands
4 adjustable bands based on standard deviation
Optional band 4 hiding for beginner traders
Precise calculation based on volume-weighted price
🌏 Global Session Support
New York Session (9:30 EST)
Asia Session (18:00 EST)
Automatic reset at the beginning of each session
📱 Flexible User Interface
Dynamic labels (V, VR1-4, VS1-4)
Custom color selection
Adjustable line thickness for each band
Multiple display modes
🔔 Advanced Alert System
VWAP breakout alerts
Alerts for all bands (3 & 4)
Clear and precise messages
🛠️ Customization Options
Band Settings
Standard deviation multipliers: 1.0, 2.0, 3.0, 4.0 (default)
Each band independently adjustable
Range: 0.1 to 5.0
Display Settings
Continuous trading start - display from session beginning
Limited candle count - show last X candles
Current day only - no historical data
Visual Design
VWAP, support, and resistance colors
Individual line thickness
Hideable labels
📈 Trading Strategies
Support and Resistance Zones
VS1-VS4: Support bands (green)
VR1-VR4: Resistance bands (red)
V: Central VWAP line
Entry Points
Breakouts above/below VWAP
Bounces from outer bands
Band retests
Risk Management
Use bands as Stop Loss levels
Identify oversold/overbought zones
Adapt to different market conditions
🎖️ Indicator Advantages
✅ Precise calculation based on volume weighting
✅ Complete flexibility in customization
✅ Global session support
✅ User-friendly interface
✅ Built-in alert system
✅ Suitable for all trading styles
📋 Usage Instructions
Add the indicator to your chart
Select trading session (New York/Asia)
Adjust bands according to your trading style
Set up alerts for important breakouts
Start trading with precise key zone identification
💡 Trading Tips
Use outer bands to identify extremes
Combine with additional indicators for confirmation
Adjust bands to asset volatility
Follow alerts to spot opportunities
Consider session-specific behavior patterns
🔧 Technical Specifications
Pine Script Version: 5
Overlay: Yes
Timeframe: All timeframes supported
Markets: Suitable for all markets (Forex, Stocks, Crypto, Futures)
Session Support: New York & Asia with EST timezone
Volume Calculation: HLC3 * Volume weighted
📊 What Makes This Different
Unlike standard VWAP indicators, this pro version offers:
Session-based reset for intraday precision
4 customizable bands instead of basic 2
Professional labeling system for quick identification
Advanced alert conditions for all key levels
Flexible display options for different trading approaches
⚡ Performance Features
Efficient calculation - minimal lag
Clean visual design - no chart clutter
Responsive labels - update in real-time
Session breaks - clear visual separation
Volume validation - ensures accurate VWAP calculation
Multiple Session Pre-market High/LowThis indicator marks each day’s pre-market range and projects it into the opening move so you can see how price reacts after the bell. It tracks the **pre-market high/low** within a user-defined window (default **04:00–09:29 ET**) and, at **09:30 ET**, draws two solid horizontal lines from **09:30 to 11:00 ET** at those levels. For additional context, you can optionally show matching **dotted lines** across the pre-market window itself. Everything is anchored to **America/New\_York** time (DST-safe), and colors/widths for both the RTH and pre-market lines are fully customizable.
It’s built for **back testing and review**: levels are finalized at 09:30 and **do not repaint**, so what you see historically is what you would have had live. Use it to study opening drive behavior, VWAP/OR confluence, gap fills, and rejection/acceptance around the pre-market extremes. Works on any intraday timeframe; for stocks, enable **Extended Hours** so the 04:00–09:29 bars are available (futures usually include them by default). Adjust the pre-market start/end inputs to match your playbook (e.g., 07:00–09:29) and evaluate your strategies consistently across months of data.
Camarilla Levels Pro Camarilla Levels Pro – Precision Intraday & Swing Trading Tool
Unlock the full potential of Camarilla Pivot Levels for identifying high-probability reversal zones, breakout triggers, and intraday bias shifts.
This indicator automatically calculates L1–L5 levels based on the Camarilla formula, updating daily for precise market adaptation. Whether you’re trading futures, forex, stocks, or crypto, you’ll instantly see:
Reversal Zones – Where price historically reacts and traps traders.
Breakout Zones – L4/L5 for bullish breakouts, L3/L2 for bearish reversals.
Bias Shifts – Quickly gauge if the market is leaning long or short.
Custom Alerts – Get notified when price touches or breaks your chosen level.
Features:
Auto-adjusting Camarilla levels for any symbol & timeframe
Color-coded zones for instant visual recognition
Optional mid-levels for scalpers
Fully customizable styling to match your chart setup
Ideal for:
Day traders wanting precision entry/exit zones
Swing traders watching key daily pivot breaks
Scalpers looking for high-probability reaction points
Trishul Tap Signals (v6) — Liquidity Sweep + Imbalanced RetestTrishul Tap Signals — Liquidity Sweep + Imbalanced Retest
Type: Signal-only indicator (non-repainting)
Style: Price-action + Liquidity + Trend-following
Best for: Intraday & Swing Trading — any liquid market (stocks, futures, crypto, FX)
Timeframes: Any (5m–1D recommended)
Concept
The Trishul Tap setup is a liquidity-driven retest play inspired by order-flow and Smart Money Concepts.
It identifies one-sided impulse candles that also sweep liquidity (grab stops above/below a recent swing), then waits for price to retest the origin of that candle to enter in the trend direction.
Think of it as the three points of a trident:
Trend filter — Only signals with the prevailing trend.
Liquidity sweep — Candle takes out a recent swing high/low (stop-hunt).
Imbalanced retest — Price taps the candle’s open/low (bull) or open/high (bear).
Bullish Setup
Trend Filter: Price above EMA(200).
Impulse Candle:
Green close.
Upper wick ≥ (wickRatio × lower wick).
Lower wick ≤ (oppWickMaxFrac × full range).
Liquidity Sweep: Candle’s high exceeds the highest high of the last sweepLookback bars (excluding current).
Tap Entry: Buy signal triggers when price later taps the candle’s low or open (user choice) within expireBars.
Bearish Setup
Trend Filter: Price below EMA(200).
Impulse Candle:
Red close.
Lower wick ≥ (wickRatio × upper wick).
Upper wick ≤ (oppWickMaxFrac × full range).
Liquidity Sweep: Candle’s low breaks the lowest low of the last sweepLookback bars (excluding current).
Tap Entry: Sell signal triggers when price later taps the candle’s high or open (user choice) within expireBars.
Inputs
Trend EMA Length: Default 200.
Sweep Lookback: Number of bars for liquidity sweep check (default 20).
Wick Ratio: Required size ratio of dominant wick to opposite wick (default 2.0).
Opposite Wick Max %: Opposite wick must be ≤ this fraction of the candle’s range (default 25%).
Tap Tolerance (ticks): How close price must come to the level to count as a tap.
Expire Bars: Max bars after setup to allow a valid tap.
One Signal per Level: If ON, a base is “consumed” after first signal.
Plot Tap Levels: Show horizontal lines for active bases.
Show Setup Labels: Mark the origin sweep candle.
Plots & Visuals
EMA Trend Line — trend filter reference.
Tap Levels —
Green = bullish base (origin candle’s low/open).
Red = bearish base (origin candle’s high/open).
Labels — Show where the setup candle formed.
Signals —
BUY: triangle-up below bar at bullish tap.
SELL: triangle-down above bar at bearish tap.
Alerts
Two built-in conditions:
BUY Signal (Trishul Tap) — triggers on bullish tap.
SELL Signal (Trishul Tap) — triggers on bearish tap.
Set via Alerts panel → Condition = this indicator → Choose signal type.
How to Trade It
Use in liquid markets with clean price structure.
Confirm with HTF structure, volume spikes, or other confluence if desired.
Place stop just beyond the tap level (or ATR-based).
Target 1–2R or trail behind structure.
Why It Works
Liquidity sweep traps traders entering late (breakout buyers or panic sellers) and forces them to exit in the opposite direction, fueling your entry.
Wick imbalance confirms directional aggression by one side.
Trend filter keeps you aligned with the market’s dominant flow.
Retest entry lets you enter at a better price with reduced risk.
Non-Repainting
Setups form only on confirmed bar closes.
Signals trigger only on later bars that tap the stored level.
No lookahead functions are used.
Disclaimer
This script is for educational purposes only and does not constitute financial advice. Test thoroughly in a simulator or demo before using in live markets. Trading involves risk.
Wickless Tap Signals Wickless Tap Signals — TradingView Indicator (v6)
A precision signal-only tool that marks BUY/SELL events when price “retests” the base of a very strong impulse candle (no wick on the retest side) in the direction of trend.
What it does (in plain English)
Finds powerful impulse candles:
Bull case: a green candle with no lower wick (its open ≈ low).
Bear case: a red candle with no upper wick (its open ≈ high).
Confirms trend with an EMA filter:
Only looks for bullish bases while price is above the EMA.
Only looks for bearish bases while price is below the EMA.
Waits for the retest (“tap”):
Later, if price revisits the base of that wickless candle
Bullish: taps the candle’s low/open → BUY signal
Bearish: taps the candle’s high/open → SELL signal
Optional level “consumption” so each base can trigger one signal, not many.
The idea: a wickless impulse often marks strong initiative order flow. The first retest of that base frequently acts as a springboard (bull) or ceiling (bear).
Exact rules (formal)
Let tick = syminfo.mintick, tol = tapTicks * tick.
Trend filter
inUp = close > EMA(lenEMA)
inDn = close < EMA(lenEMA)
Wickless impulse candles (confirmed on bar close)
Bullish wickless: close > open and abs(low - open) ≤ tol
Bearish wickless: close < open and abs(high - open) ≤ tol
When such a candle closes with trend alignment:
Store bullTapLevel = low (for bull case) and its bar index.
Store bearTapLevel = high (for bear case) and its bar index.
Signals (must happen on a later bar than the origin)
BUY: low ≤ bullTapLevel + tol and inUp and bar_index > bullBarIdx
SELL: high ≥ bearTapLevel - tol and inDn and bar_index > bearBarIdx
One-shot option
If enabled, once a signal fires, the stored level is cleared so it won’t trigger again.
Inputs (Settings)
Trend EMA Length (lenEMA): Default 200.
Use 50–100 for intraday, 200 for swing/position.
Tap Tolerance (ticks) (tapTicks): Default 1.
Helps account for tiny feed discrepancies. Set 0 for strict equality.
One Signal per Level (oneShot): Default ON.
If OFF, multiple taps can create multiple signals.
Plot Tap Levels (plotLevels): Draws horizontal lines at active bases.
Show Pattern Labels (showLabels): Marks the origin wickless candles.
Plots & Visuals
EMA trend line for context.
Tap Levels:
Green line at bullish base (origin candle’s low/open).
Red line at bearish base (origin candle’s high/open).
Signals:
BUY: triangle-up below the bar on the tap.
SELL: triangle-down above the bar on the tap.
Labels (optional):
Marks the original wickless impulse candle that created each level.
Alerts
Two alert conditions are built in:
“BUY Signal” — fires when a bullish tap occurs.
“SELL Signal” — fires when a bearish tap occurs.
How to set:
Add the indicator to your chart.
Click Alerts (⏰) → Condition = this indicator.
Choose BUY Signal or SELL Signal.
Set your alert frequency and delivery method.
Recommended usage
Timeframes: Works on any; start with 5–15m intraday, or 1H–1D for swing.
Markets: Equities, futures, FX, crypto. For thin/illiquid assets, consider a slightly larger Tap Tolerance.
Confluence ideas (optional, but helpful):
Higher-timeframe trend agreeing with your chart timeframe.
Volume surge on the origin wickless candle.
S/R, order blocks, or SMC structures near the tap level.
Avoid major news moments when slippage is high.
No-repaint behavior
Origin patterns are detected only on bar close (barstate.isconfirmed), so bases are created with confirmed data.
Signals come after the origin bar, on subsequent taps.
There is no lookahead; lines and shapes reflect information known at the time.
(As with all real-time indicators, an intrabar tap can trigger an alert during the live bar; the signal then remains if that condition held at bar close.)
Known limitations & design choices
Single active level per side: The script tracks only the most recent bullish base and most recent bearish base.
Want a queue of multiple simultaneous bases? That’s possible with arrays; ask and we’ll extend it.
Heikin Ashi / non-standard candles: Wick definitions change; for consistent behavior use regular OHLC candles.
Gaps: On large gaps, taps can occur instantly at the open. Consider one-shot ON to avoid rapid repeats.
This is an indicator, not a strategy: It does not place trades or compute PnL. For backtesting, we can convert it into a strategy with SL/TP logic (ATR or structure-based).
Practical tips
Tap Tolerance:
If you miss obvious taps by a hair, increase to 1–2 ticks.
For FX/crypto with tiny ticks, even 0 or 1 is often enough.
EMA length:
Shorten for faster signals; lengthen for cleaner trend selection.
Risk management (manual suggestion):
For BUY signals, consider a stop slightly below the tap level (or ATR-based).
For SELL signals, consider a stop slightly above the tap level.
Scale out or trail using structure or ATR.
Quick checklist
✅ Price above EMA → watch for a green no-lower-wick candle → store its low → BUY on tap.
✅ Price below EMA → watch for a red no-upper-wick candle → store its high → SELL on tap.
✅ Use Tap Tolerance to avoid missing precise touches by one tick.
✅ Consider One Signal per Level to keep trades uncluttered.
FAQ
Q: Why did I not get a signal even though price touched the level?
A: Check Tap Tolerance (maybe too strict), trend alignment at the tap bar, and that the tap happened after the origin candle. Also confirm you’re on regular candles.
Q: Can I see multiple bases at once?
A: This version tracks the latest bull and bear bases. We can extend to arrays to keep N recent bases per side.
Q: Will it repaint?
A: No. Bases form on confirmed closes, and signals only on later bars.
Q: Can I backtest it?
A: This is a study. Ask for the strategy variant and we’ll add entries, exits, SL/TP, and stats.
Swing Point Volume Z-ScoreSWING POINT VOLUME Z-SCORE INDICATOR
A volume analysis tool that identifies statistical volume spikes at swing points with optional higher timeframe confirmation.
This indicator uses Leviathan's method of swing detection. All credit to him for his amazing work (and any mistakes mine). I was also inspired by Trading Riot, who's Capitulation indicator gave me the idea to create this one.
WHAT IT DOES
This indicator combines three analytical approaches:
- Volume Z-score calculation to measure volume significance statistically
- Automatic swing point detection (higher highs, lower lows, etc.)
- Optional higher timeframe volume confirmation
The Z-score measures how many standard deviations current volume is from the average, helping identify when volume activity is genuinely elevated rather than relying on visual assessment.
VISUAL SYSTEM
The indicator uses a color-coded approach for quick assessment:
GREEN - Normal Activity (Z-Score 1.0-2.0)
Above-average volume levels
ORANGE - Elevated Activity (Z-Score 2.0-3.0)
High volume activity that may indicate increased interest
RED - Potential Institutional Activity (Z-Score 3.0+)
Very high volume levels that could suggest significant market participation
HIGHER TIMEFRAME CONFIRMATION
When enabled, the indicator checks volume on a higher timeframe:
- Checkmark symbol indicates HTF volume also shows elevation
- X symbol indicates HTF volume doesn't confirm
- Auto-selects appropriate higher timeframe or allows manual selection
KEY FEATURES
Statistical Approach: Uses Z-score methodology rather than arbitrary volume thresholds
Adaptive Thresholds: Can adjust based on market volatility conditions
Swing Focus: Concentrates analysis on structurally important price levels
Volume Trends: Shows whether volume is accelerating or decelerating
Success Tracking: Monitors how often HTF confirmation proves effective
DISPLAY OPTIONS
Basic Mode: Essential features with clean interface
Advanced Mode: Additional customization and analytics
Label Sizing: Four size options to fit different screen setups
Table Position: Moveable info table with transparency control
Custom Colors: Adjustable for different chart themes
PRACTICAL APPLICATIONS
May help identify:
- Volume spikes at support/resistance levels
- Potential accumulation or distribution zones
- Breakout confirmation with volume backing
- Areas where larger market participants might be active
Works on all liquid markets and timeframes, though generally more effective on 15-minute charts and higher.
USAGE NOTES
This is an analytical tool that highlights statistically significant volume events. It should be used as part of a broader analysis approach rather than as a standalone trading system.
The indicator works best when combined with:
- Price action analysis
- Support and resistance identification
- Trend analysis
- Proper risk management
Default settings are designed to work well across most instruments, but users can adjust parameters based on their specific needs and trading style.
TECHNICAL DETAILS
Built with Pine Script v5
Compatible with all TradingView subscription levels
Open source code available for review and learning
Works on stocks, forex, crypto, futures, and other liquid instruments
The statistical approach helps remove some subjectivity from volume analysis, though like all technical indicators, it should be used thoughtfully as part of a complete trading plan.
SMT Oscillator: Smarter Money Divergence Detector [PhenLabs]📊Phenlabs - SMT Oscillator: Smarter Money Divergence Detector
Version: PineScript™v6
📌Description
The SMT Oscillator is a sophisticated tool designed to identify smart money divergence between two correlated assets. By analyzing the momentum and volume-weighted price action of a primary and secondary symbol, traders can spot subtle shifts in market dynamics that often precede significant price movements. This indicator is built to provide a clearer, more filtered view of inter-market relationships, solving the common problem of false signals and market noise. Its primary purpose is to equip traders with a quantifiable edge in detecting potential reversals or continuations that are not obvious on a standard price chart.
🚀Points of Innovation
Dual-Symbol Divergence Core: Directly compares momentum (RSI or MACD) between two user-selected symbols to pinpoint true SMT divergence.
Volume-Weighted Analysis: Integrates volume delta into the divergence calculation, giving more weight to moves backed by significant market participation.
Entropy Filter for Noise Reduction: Employs an entropy calculation to filter out low-quality signals during choppy or consolidating market conditions.
Predictive Forecast Line: Utilizes a linear regression model to project the oscillator’s future trajectory, offering a forward-looking glimpse of potential momentum shifts.
Customizable Signal Sensitivity: Allows fine-tuning of overbought and oversold levels to adapt to different market volatilities and trading styles.
Integrated Signal Alerts: Provides built-in alerts for bullish/bearish zero crosses and overbought/oversold conditions.
🔧Core Components
Momentum Engine: The user can select either RSI or MACD as the underlying engine for the divergence calculation, allowing for flexibility in analysis.
Normalization Function: Price data from both symbols is normalized using percentage change to ensure a true “apples-to-apples” comparison, regardless of their nominal price differences.
Divergence Calculator: The core algorithm that subtracts the secondary symbol’s momentum from the primary’s and normalizes the result using the combined standard deviation.
Smoothing Mechanism: An Exponential Moving Average (EMA) is applied to the raw oscillator output to reduce choppiness and provide a clearer signal line.
🔥Key Features
Multi-Asset Comparison: Go beyond single-asset analysis by comparing correlated pairs like ES/NQ or BTC/ETH to uncover hidden trading opportunities.
Heatmap Visualization: An optional heatmap mode provides an intuitive visual representation of divergence strength, making it easier to gauge market sentiment at a glance.
Configurable Lookback and Timeframe: Adjust the lookback period and analysis timeframe to suit your specific strategy, from short-term scalping to long-term trend analysis.
Signal Markers: Visual markers are plotted directly on the chart for bullish and bearish zero-line crossovers, providing clear entry and exit signals.
🎨Visualization
SMT Oscillator Line: The primary visual element, colored blue for bullish (positive) divergence and orange for bearish (negative) divergence.
Zero Line: A solid horizontal line at the zero level, indicating the equilibrium point between the two assets. Crossovers of this line signal a shift in relative strength.
Overbought/Oversold Zones: Dotted lines at the +80 and -80 levels (customizable) that highlight extreme divergence readings, often indicating potential exhaustion points.
Forecast Line: A predictive line that plots the anticipated path of the oscillator, giving traders an advanced warning of potential changes in momentum.
📖Usage Guidelines
Setting Categories
Primary Symbol
Default: (Chart Symbol)
Description: The main asset you are analyzing. Leave blank to use the symbol currently on your chart.
Secondary Symbol
Default: CME_MINI:ES1! (used with NASDAQ futures due to inherent heavy correlation
Description: The asset to compare against the primary symbol.
Lookback Period
Default: 14
Range: 8-100
Description: Controls the calculation window for momentum (RSI/MACD). Higher values result in a smoother, less sensitive oscillator.
Divergence Type
Default: RSI
Options: RSI, MACD
Description: Choose the momentum indicator to use for the divergence calculation.
Enable Volume Weighting
Default: true
Description: When enabled, gives more weight to divergence signals that are accompanied by significant volume.
✅Best Use Cases
Identifying high-probability reversal points by spotting divergence in overbought or oversold territory.
Confirming the strength of a trend by observing sustained positive or negative divergence.
Pairs trading by taking a long position on the outperforming asset and a short position on the underperforming one during a divergence.
Risk management by recognizing when a current trend is losing its underlying momentum.
⚠️Limitations
Requires Correlated Assets: The indicator’s effectiveness is highly dependent on the selection of two assets with a known correlation (e.g., ES and NQ).
Not a Standalone System: Divergence signals should be used in conjunction with other forms of analysis (price action, market structure) and not as a complete trading system.
Lagging by Nature: As it is based on moving averages and past price data, the oscillator is inherently lagging and may not capture all rapid price changes.
💡What Makes This Unique
Combined Momentum & Volume: Unlike standard oscillators, it fuses momentum with volume delta for a more robust “Smart Money” perspective.
Noise-Filtering Mechanism: The proprietary entropy filter is a unique feature designed to weed out insignificant market chatter and focus on high-conviction signals.
🔬How It Works
Data Normalization:
The script first normalizes the price data of the two selected symbols into percentage changes. This ensures that the comparison is fair, regardless of the difference in their price scales.
Momentum Calculation:
It then calculates the chosen momentum value (either RSI or MACD histogram) for each of the normalized price series.
Divergence Computation:
The core of the indicator lies in subtracting the momentum of the secondary symbol from the primary one. This raw divergence is then optionally weighted by volume and filtered for market noise (entropy) to produce the final oscillator value.
💡Note:
For best results, use this indicator on adequate timeframes to filter out market noise. Always confirm signals with price action analysis before entering a trade.
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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Previous Day High/Low Levels [OWI]📘 How to Use the “Previous Day High/Low Levels ” Indicator
This TradingView indicator automatically tracks and displays the previous day's high and low during the Regular Trading Hours (RTH) session. It’s perfect for traders who want to visualize key support/resistance levels from the prior day in futures like CME_MINI:NQ1! and COMEX:GC1! .
🛠 Setup Instructions
1. Customize RTH Session Times
- In the Settings panel, adjust the following under the Levels group:
- RTH Start Hour and RTH Start Minute: Default is 9:30 AM (New York time).
- RTH End Hour and RTH End Minute: Default is 4:15 PM.
- These define the active trading session used to calculate the day’s high and low.
2. Toggle Labels
- Use the Show PDH/PDL Labels checkbox to display or hide the “PDH” and “PDL” labels on the chart.
- Labels appear after the session ends and follow price dynamically.
📊 What the Indicator Does
- During the RTH session:
- Tracks the highest and lowest price of the day.
- After the session ends:
- Draws horizontal lines at the previous day’s high (green) and low (red).
- Optionally displays labels ("PDH" and "PDL") at those levels.
- Lines extend into the current day to help identify potential support/resistance zones.
✅ Best Practices
- Use this indicator on intraday timeframes (e.g., 5m, 15m, 1h) for best results.
- Combine with volume or price action analysis to confirm reactions at PDH/PDL levels.
- Adjust session times if trading non-US markets or custom hours.
Dip Hunter [BackQuant]Dip Hunter
What this tool does in plain language
Dip Hunter is a pullback detector designed to find high quality buy-the-dip opportunities inside healthy trends and to avoid random knife catches. It watches for a quick drop from a recent high, checks that the drop happened with meaningful participation and volatility, verifies short-term weakness inside a larger uptrend, then scores the setup and paints the chart so you can act with confidence. It also draws clean entry lines, provides a meter that shows dip strength at a glance, and ships with alerts that match common execution workflows.
How Dip Hunter thinks
It defines a recent swing reference, measures how far price has dipped off that high, and only looks at candidates that meet your minimum percentage drop.
It confirms the dip with real activity by requiring a volume spike and a volatility spike.
It checks structure with two EMAs. Price should be weak in the short term while the larger context remains constructive.
It optionally requires a higher-timeframe trend to be up so you focus on pullbacks in trending markets.
It bundles those checks into a score and shows you the score on the candles and on a gradient meter.
When everything lines up it paints a green triangle below the bar, shades the background, and (if you wish) draws a horizontal entry line at your chosen level.
Inputs and what they mean
Dip Hunter Settings
• Vol Lookback and Vol Spike : The script computes an average volume over the lookback window and flags a spike when current volume is a multiple of that average. A multiplier of 2.0 means today’s volume must be at least double the average. This helps filter noise and focuses on dips that other traders actually traded.
• Fast EMA and Slow EMA : Short-term and medium-term structure references. A dip is more credible if price closes below the fast EMA while the fast EMA is still below the slow EMA during the pullback. That is classic corrective behavior inside a larger trend.
• Price Smooth : Optional smoothing length for price-derived series. Use this if you trade very noisy assets or low timeframes.
• Volatility Len and Vol Spike (volatility) : The script checks both standard deviation and true range against their own averages. If either expands beyond your multiplier the market confirms the move with range.
• Dip % and Lookback Bars : The engine finds the highest high over the lookback window, then computes the percentage drawdown from that high to the current close. Only dips larger than your threshold qualify.
Trend Filter
• Enable Trend Filter : When on, Dip Hunter will only trigger if the market is in an uptrend.
• Trend EMA Period : The longer EMA that defines the session’s backbone trend.
• Minimum Trend Strength : A small positive slope requirement. In practice this means the trend EMA should be rising, and price should be above it. You can raise the value to be more selective.
Entries
• Show Entry Lines : Draws a horizontal guide from the signal bar for a fixed number of bars. Great for limit orders, scaling, or re-tests.
• Line Length (bars) : How far the entry guide extends.
• Min Gap (bars) : Suppresses new entry lines if another dip fired recently. Prevents clutter during choppy sequences.
• Entry Price : Choose the line level. “Low” anchors at the signal candle’s low. “Close” anchors at the signal close. “Dip % Level” anchors at the theoretical level defined by recent_high × (1 − dip%). This lets you work resting orders at a consistent discount.
Heat / Meter
• Color Bars by Score : Colors each candle using a red→white→green gradient. Red is overheated, green is prime dip territory, white is neutral.
• Show Meter Table : Adds a compact gradient strip with a pointer that tracks the current score.
• Meter Cells and Meter Position : Resolution and placement of the meter.
UI Settings
• Show Dip Signals : Plots green triangles under qualifying bars and tints the background very lightly.
• Show EMAs : Plots fast, slow, and the trend EMA (if the trend filter is enabled).
• Bullish, Bearish, Neutral colors : Theme controls for shapes, fills, and bar painting.
Core calculations explained simply
Recent high and dip percent
The script finds the highest high over Lookback Bars , calls it “recent high,” then calculates:
dip% = (recent_high − close) ÷ recent_high × 100.
If dip% is larger than Dip % , condition one passes.
Volume confirmation
It computes a simple moving average of volume over Vol Lookback . If current volume ÷ average volume > Vol Spike , we have a participation spike. It also checks 5-bar ROC of volume. If ROC > 50 the spike is forceful. This gets an extra score point.
Volatility confirmation
Two independent checks:
• Standard deviation of closes vs its own average.
• True range vs ATR.
If either expands beyond Vol Spike (volatility) the move has range. This prevents false triggers from quiet drifts.
Short-term structure
Price should close below the Fast EMA and the fast EMA should be below the Slow EMA at the moment of the dip. That is the anatomy of a pullback rather than a full breakdown.
Macro trend context (optional)
When Enable Trend Filter is on, the Trend EMA must be rising and price must be above it. The logic prefers “micro weakness inside macro strength” which is the highest probability pattern for buying dips.
Signal formation
A valid dip requires:
• dip% > threshold
• volume spike true
• volatility spike true
• close below fast EMA
• fast EMA below slow EMA
If the trend filter is enabled, a rising trend EMA with price above it is also required. When all true, the triangle prints, the background tints, and optional entry lines are drawn.
Scoring and visuals
Binary checks into a continuous score
Each component contributes to a score between 0 and 1. The script then rescales to a centered range (−50 to +50).
• Low or negative scores imply “overheated” conditions and are shaded toward red.
• High positive scores imply “ripe for a dip buy” conditions and are shaded toward green.
• The gradient meter repeats the same logic, with a pointer so you can read the state quickly.
Bar coloring
If you enable “Color Bars by Score,” each candle inherits the gradient. This makes sequences obvious. Red clusters warn you not to buy. White means neutral. Increasing green suggests the pullback is maturing.
EMAs and the trend EMA
• Fast EMA turns down relative to the slow EMA inside the pullback.
• Trend EMA stays rising and above price once the dip exhausts, which is your cue to focus on long setups rather than bottom fishing in downtrends.
Entry lines
When a fresh signal fires and no other signal happened within Min Gap (bars) , the indicator draws a horizontal level for Line Length bars. Use these lines for limit entries at the low, at the close, or at the defined dip-percent level. This keeps your plan consistent across instruments.
Alerts and what they mean
• Market Overheated : Score is deeply negative. Do not chase. Wait for green.
• Close To A Dip : Score has reached a healthy level but the full signal did not trigger yet. Prepare orders.
• Dip Confirmed : First bar of a fresh validated dip. This is the most direct entry alert.
• Dip Active : The dip condition remains valid. You can scale in on re-tests.
• Dip Fading : Score crosses below 0.5 from above. Momentum of the setup is fading. Tighten stops or take partials.
• Trend Blocked Signal : All dip conditions passed but the trend filter is offside. Either reduce risk or skip, depending on your plan.
How to trade with Dip Hunter
Classic pullback in uptrend
Turn on the trend filter.
Watch for a Dip Confirmed alert with green triangle.
Use the entry line at “Dip % Level” to stage a limit order. This keeps your entries consistent across assets and timeframes.
Initial stop under the signal bar’s low or under the next lower EMA band.
First target at prior swing high, second target at a multiple of risk.
If you use partials, trail the remainder under the fast EMA once price reclaims it.
Aggressive intraday scalps
Lower Dip % and Lookback Bars so you catch shallow flags.
Keep Vol Spike meaningful so you only trade when participation appears.
Take quick partials when price reclaims the fast EMA, then exit on Dip Fading if momentum stalls.
Counter-trend probes
Disable the trend filter if you intentionally hunt reflex bounces in downtrends.
Require strong volume and volatility confirmation.
Use smaller size and faster targets. The meter should move quickly from red toward white and then green. If it does not, step aside.
Risk management templates
Stops
• Conservative: below the entry line minus a small buffer or below the signal bar’s low.
• Structural: below the slow EMA if you aim for swing continuation.
• Time stop: if price does not reclaim the fast EMA within N bars, exit.
Position sizing
Use the distance between the entry line and your structural stop to size consistently. The script’s entry lines make this distance obvious.
Scaling
• Scale at the entry line first touch.
• Add only if the meter stays green and price reclaims the fast EMA.
• Stop adding on a Dip Fading alert.
Tuning guide by market and timeframe
Equities daily
• Dip %: 1.5 to 3.0
• Lookback Bars: 5 to 10
• Vol Spike: 1.5 to 2.5
• Volatility Len: 14 to 20
• Trend EMA: 100 or 200
• Keep trend filter on for a cleaner list.
Futures and FX intraday
• Dip %: 0.4 to 1.2
• Lookback Bars: 3 to 7
• Vol Spike: 1.8 to 3.0
• Volatility Len: 10 to 14
• Use Min Gap to avoid clusters during news.
Crypto
• Dip %: 3.0 to 6.0 for majors on higher timeframes, lower on 15m to 1h
• Lookback Bars: 5 to 12
• Vol Spike: 1.8 to 3.0
• ATR and stdev checks help in erratic sessions.
Reading the chart at a glance
• Green triangle below the bar: a validated dip.
• Light green background: the current bar meets the full condition.
• Bar gradient: red is overheated, white is neutral, green is dip-friendly.
• EMAs: fast below slow during the pullback, then reclaim fast EMA on the bounce for quality continuation.
• Trend EMA: a rising spine when the filter is on.
• Entry line: a fixed level to anchor orders and risk.
• Meter pointer: right side toward “Dip” means conditions are maturing.
Why this combination reduces false positives
Any single criterion will trigger too often. Dip Hunter demands a dip off a recent high plus a volume surge plus a volatility expansion plus corrective EMA structure. Optional trend alignment pushes odds further in your favor. The score and meter visualize how many of these boxes you are actually ticking, which is more reliable than a binary dot.
Limitations and practical tips
• Thin or illiquid symbols can spoof volume spikes. Use larger Vol Lookback or raise Vol Spike .
• Sideways markets will show frequent small dips. Increase Dip % or keep the trend filter on.
• News candles can blow through entry lines. Widen stops or skip around known events.
• If you see many back-to-back triangles, raise Min Gap to keep only the best setups.
Quick setup recipes
• Clean swing trader: Trend filter on, Dip % 2.0 to 3.0, Vol Spike 2.0, Volatility Len 14, Fast 20 EMA, Slow 50 EMA, Trend 100 EMA.
• Fast intraday scalper: Trend filter off, Dip % 0.7 to 1.0, Vol Spike 2.5, Volatility Len 10, Fast 9 EMA, Slow 21 EMA, Min Gap 10 bars.
• Crypto swing: Trend filter on, Dip % 4.0, Vol Spike 2.0, Volatility Len 14, Fast 20 EMA, Slow 50 EMA, Trend 200 EMA.
Summary
Dip Hunter is a focused pullback engine. It quantifies a real dip off a recent high, validates it with volume and volatility expansion, enforces corrective structure with EMAs, and optionally restricts signals to an uptrend. The score, bar gradient, and meter make reading conditions instant. Entry lines and alerts turn that read into an executable plan. Tune the thresholds to your market and timeframe, then let the tool keep you patient in red, selective in white, and decisive in green.
VN30 Effort-vs-Result Multi-Scanner — LinhVN30 Effort-vs-Result Multi-Scanner (Pine v5)
Cross-section scanner for Vietnam’s VN30 stocks that surfaces Effort vs Result footprints and related accumulation/distribution and volatility tells. It renders a ranked table (Top-N) with per-ticker signals and key metrics.
What it does
Scans up to 30 tickers (editable input.symbol slots) using one security() call per symbol → stays under Pine’s 40-call limit and runs reliably on any chart.
Scores each ticker by counting active signals, then ranks and lists the top names.
Optional metrics columns: zVol(60), zTR(60), ATR(20), HL/ATR(20).
Signals (toggleable)
Price/Volume – Effort vs Result
EVR Squeeze (stealth): z(Vol,60) > 4 & z(TR,60) < −0.5
5σ Vol, ≤1σ Ret: z(Vol,60) > 5 & |z(Return,60)| < 1
Wide Effort, Opposite Result: z(Vol,60) > 3 & close < open & z(CLV×Vol,60) > 1
Spread Compression, Heavy Tape: (H−L)/ATR(20) < 0.6 & z(Vol,60) > 3
No-Supply / No-Demand: close < close & range < 0.6×ATR(20) & vol < 0.5×SMA(20)
Momentum & Volatility
Vol-of-Vol Kink: z(ATR20,200) rising & z(ATR5,60) falling
BB Squeeze → Expansion: BBWidth(20) in low regime (z<−1.3) then close > upper band & z(Vol,60) > 2
RSI Non-Confirmation: Price LL/HH with RSI HL/LH & z(Vol,60) > 1
Accumulation/Distribution
OBV Divergence w/ Flat Price: OBV slope > 0 & |z(ret20,260)| < 0.3
Accumulation Days Cluster: ≥3/5 bars: up close, higher vol, close near high
Effort-Result Inversion (Down): big vol on down day then next day close > prior high
How to use
Set the timeframe (works best on 1D for EOD scans).
Edit the 30 symbol slots to your VN30 constituents.
Choose Top N, toggle Show metrics/Only matches and enable/disable scenarios.
Read the table: Rank, Ticker, (metrics), Score, and comma-separated Signals fired.
Method notes
Z-scores use a population-std estimate; CLV×Vol is used for effort/location.
Rolling counts avoid ta.sum; OBV is computed manually; all logic is Pine v5-safe.
Intraday-only ideas (true VWAP magnets, auction volume, flows, futures/options) are not included—Pine can’t cross-scan those datasets.
Disclaimer: Educational tool, not financial advice. Always confirm signals on the chart and with your process.
ECG chart - mauricioofsousaMGO Primary – Matriz Gráficos ON
The Blockchain of Trading applied to price behavior
The MGO Primary is the foundation of Matriz Gráficos ON — an advanced graphical methodology that transforms market movement into a logical, predictable, and objective sequence, inspired by blockchain architecture and periodic oscillatory phenomena.
This indicator replaces emotional candlestick reading with a mathematical interpretation of price blocks, cycles, and frequency. Its mission is to eliminate noise, anticipate reversals, and clearly show where capital is entering or exiting the market.
What MGO Primary detects:
Oscillatory phenomena that reveal the true behavior of orders in the book:
RPA – Breakout of Bullish Pivot
RPB – Breakout of Bearish Pivot
RBA – Sharp Bullish Breakout
RBB – Sharp Bearish Breakout
Rhythmic patterns that repeat in medium timeframes (especially on 12H and 4H)
Wave and block frequency, highlighting critical entry and exit zones
Validation through Primary and Secondary RSI, measuring the real strength behind movements
Who is this indicator for:
Traders seeking statistical clarity and visual logic
Operators who want to escape the subjectivity of candlesticks
Anyone who values technical precision with operational discipline
Recommended use:
Ideal timeframes: 12H (high precision) and 4H (moderate intensity)
Recommended assets: indices (e.g., NASDAQ), liquid stocks, and futures
Combine with: structured risk management and macro context analysis
Real-world performance:
The MGO12H achieved a 92% accuracy rate in 2025 on the NASDAQ, outperforming the average performance of major global quantitative strategies, with a net score of over 6,200 points for the year.
Nifty50 Swing Trading Super Indicator# 🚀 Nifty50 Swing Trading Super Indicator - Complete Guide
**Created by:** Gaurav
**Date:** August 8, 2025
**Version:** 1.0 - Optimized for Indian Markets
---
## 📋 Table of Contents
1. (#quick-start-guide)
2. (#indicator-overview)
3. (#installation-instructions)
4. (#parameter-settings)
5. (#signal-interpretation)
6. (#trading-strategy)
7. (#risk-management)
8. (#optimization-tips)
9. (#troubleshooting)
---
## 🎯 Quick Start Guide
### What You Get
✅ **2 Complete Pine Script Indicators:**
- `swing_trading_super_indicator.pine` - Universal version for all markets
- `nifty_optimized_super_indicator.pine` - Specifically optimized for Nifty50 & Indian stocks
✅ **Key Features:**
- Multi-component signal confirmation system
- Optimized for daily and 3-hour timeframes
- Built-in risk management with dynamic stops and targets
- Real-time signal strength monitoring
- Gap analysis for Indian market characteristics
### Immediate Setup
1. Copy the Pine Script code from `nifty_optimized_super_indicator.pine`
2. Paste into TradingView Pine Editor
3. Add to chart on daily or 3-hour timeframe
4. Look for 🚀BUY and 🔻SELL signals
5. Use the information table for signal confirmation
---
## 🔍 Indicator Overview
### Core Components Integration
**🎯 Range Filter (35% Weight)**
- Primary trend identification using adaptive volatility filtering
- Optimized sampling period: 21 bars for Indian market volatility
- Enhanced range multiplier: 3.0 to handle market gaps
- Provides trend direction and strength measurement
**⚡ PMAX (30% Weight)**
- Volatility-adjusted trend confirmation using ATR-based calculations
- Dynamic multiplier adjustment based on market volatility
- 14-period ATR with 2.5 multiplier for swing trading sensitivity
- Offers trailing stop functionality
**🏗️ Support/Resistance (20% Weight)**
- Dynamic level identification using pivot point analysis
- Tighter channel width (3%) for precise Indian market levels
- Enhanced strength calculation with historical interaction weighting
- Provides entry/exit timing and breakout signals
**📊 EMA Alignment (15% Weight)**
- Multi-timeframe moving average confirmation
- Key EMAs: 9, 21, 50, 200 (popular in Indian markets)
- Hierarchical alignment scoring for trend strength
- Additional trend validation layer
### Advanced Features
**🌅 Gap Analysis**
- Automatic detection of significant price gaps (>2%)
- Gap strength measurement and impact on signals
- Specific optimization for Indian market overnight gaps
- Visual gap markers on chart
**⏰ Multi-Timeframe Integration**
- Higher timeframe bias from daily/weekly data
- Configurable daily bias weight (default 70%)
- 3-hour confirmation for precise entry timing
- Prevents counter-trend trades against major timeframe
**🛡️ Risk Management**
- Dynamic stop-loss calculation using multiple methods
- Automatic profit target identification
- Position sizing guidance based on signal strength
- Anti-whipsaw logic to prevent false signals
---
## 📥 Installation Instructions
### Step 1: Access TradingView
1. Open TradingView.com
2. Navigate to Pine Editor (bottom panel)
3. Create a new indicator
### Step 2: Copy the Code
**For Nifty50 & Indian Stocks (Recommended):**
```pinescript
// Copy entire content from nifty_optimized_super_indicator.pine
```
**For Universal Use:**
```pinescript
// Copy entire content from swing_trading_super_indicator.pine
```
### Step 3: Configure and Apply
1. Click "Add to Chart"
2. Select daily or 3-hour timeframe
3. Adjust parameters if needed (defaults are optimized)
4. Enable alerts for signal notifications
### Step 4: Verify Installation
- Check that all components are visible
- Confirm information table appears in top-right
- Test with known trending stocks for signal validation
---
## ⚙️ Parameter Settings
### 🎯 Range Filter Settings
```
Sampling Period: 21 (optimized for Indian market volatility)
Range Multiplier: 3.0 (handles overnight gaps effectively)
Source: Close (most reliable for swing trading)
```
### ⚡ PMAX Settings
```
ATR Length: 14 (standard for daily/3H timeframes)
ATR Multiplier: 2.5 (balanced for swing trading sensitivity)
Moving Average Type: EMA (responsive to price changes)
MA Length: 14 (matches ATR period for consistency)
```
### 🏗️ Support/Resistance Settings
```
Pivot Period: 8 (shorter for Indian market dynamics)
Channel Width: 3% (tighter for precise levels)
Minimum Strength: 3 (higher quality levels only)
Maximum Levels: 4 (focus on strongest levels)
Lookback Period: 150 (sufficient historical data)
```
### 🚀 Super Indicator Settings
```
Signal Sensitivity: 0.65 (balanced for swing trading)
Trend Strength Requirement: 0.75 (high quality signals)
Gap Threshold: 2.0% (significant gap detection)
Daily Bias Weight: 0.7 (strong higher timeframe influence)
```
### 🎨 Display Options
```
Show Range Filter: ✅ (trend visualization)
Show PMAX: ✅ (trailing stops)
Show S/R Levels: ✅ (key price levels)
Show Key EMAs: ✅ (trend confirmation)
Show Signals: ✅ (buy/sell alerts)
Show Trend Background: ✅ (visual trend state)
Show Gap Markers: ✅ (gap identification)
```
---
## 📊 Signal Interpretation
### 🚀 BUY Signals
**Requirements for BUY Signal:**
- Price above Range Filter with upward trend
- PMAX showing bullish direction (MA > PMAX line)
- Support/resistance breakout or favorable positioning
- EMA alignment supporting upward movement
- Higher timeframe bias confirmation
- Overall signal strength > 75%
**Signal Strength Indicators:**
- **90-100%:** Extremely strong - Maximum position size
- **80-89%:** Very strong - Large position size
- **75-79%:** Strong - Standard position size
- **65-74%:** Moderate - Reduced position size
- **<65%:** Weak - Wait for better opportunity
### 🔻 SELL Signals
**Requirements for SELL Signal:**
- Price below Range Filter with downward trend
- PMAX showing bearish direction (MA < PMAX line)
- Resistance breakdown or unfavorable positioning
- EMA alignment supporting downward movement
- Higher timeframe bias confirmation
- Overall signal strength > 75%
### ⚖️ NEUTRAL Signals
**Characteristics:**
- Conflicting signals between components
- Low overall signal strength (<65%)
- Range-bound market conditions
- Wait for clearer directional bias
### 📈 Information Table Guide
**Component Status:**
- **BULL/BEAR:** Current signal direction
- **Strength %:** Component contribution strength
- **Status:** Additional context (STRONG/WEAK/ACTIVE/etc.)
**Overall Signal:**
- **🚀 STRONG BUY:** All systems aligned bullish
- **🔻 STRONG SELL:** All systems aligned bearish
- **⚖️ NEUTRAL:** Mixed or weak signals
---
## 💼 Trading Strategy
### Daily Timeframe Strategy
**Setup:**
1. Apply indicator to daily chart of Nifty50 or Indian stocks
2. Wait for 🚀BUY or 🔻SELL signal with >75% strength
3. Confirm higher timeframe bias alignment
4. Check for significant support/resistance levels
**Entry:**
- Enter on signal bar close or next bar open
- Use 3-hour chart for precise entry timing
- Avoid entries during major news events
- Consider gap analysis for overnight positions
**Position Sizing:**
- **>90% Strength:** 3-4% of portfolio
- **80-89% Strength:** 2-3% of portfolio
- **75-79% Strength:** 1-2% of portfolio
- **<75% Strength:** Avoid or minimal size
### 3-Hour Timeframe Strategy
**Setup:**
1. Confirm daily timeframe bias first
2. Apply indicator to 3-hour chart
3. Look for signals aligned with daily trend
4. Use for entry/exit timing optimization
**Entry Refinement:**
- Wait for 3H signal confirmation
- Enter on pullbacks to key levels
- Use tighter stops for better risk/reward
- Monitor intraday support/resistance
### Risk Management Rules
**Stop Loss Placement:**
1. **Primary:** Use indicator's dynamic stop level
2. **Secondary:** Below/above nearest support/resistance
3. **Maximum:** 2-3% of portfolio per trade
4. **Trailing:** Move stops with PMAX line
**Profit Taking:**
1. **Target 1:** First resistance/support level (50% position)
2. **Target 2:** Second resistance/support level (30% position)
3. **Runner:** Trail remaining 20% with PMAX
**Position Management:**
- Review positions at daily close
- Adjust stops based on new signals
- Exit if trend changes to opposite direction
- Reduce size during high volatility periods
---
## 🎯 Optimization Tips
### For Nifty50 Trading
- Use daily timeframe for primary signals
- Monitor sector rotation impact
- Consider index futures for better liquidity
- Watch for RBI policy and global cues impact
### For Individual Stocks
- Verify stock follows Nifty correlation
- Check sector-specific news and events
- Ensure adequate liquidity for position size
- Monitor earnings calendar for volatility
### Market Condition Adaptations
**Trending Markets:**
- Increase position sizes for strong signals
- Use wider stops to avoid whipsaws
- Focus on trend continuation signals
- Reduce counter-trend trading
**Range-Bound Markets:**
- Reduce position sizes
- Use tighter stops and quicker profits
- Focus on support/resistance bounces
- Increase signal strength requirements
**High Volatility Periods:**
- Reduce overall exposure
- Use smaller position sizes
- Increase stop-loss distances
- Wait for clearer signals
### Performance Monitoring
- Track win rate and average profit/loss
- Monitor signal quality over time
- Adjust parameters based on market changes
- Keep trading journal for pattern recognition
---
## 🔧 Troubleshooting
### Common Issues
**Q: Signals appear too frequently**
A: Increase "Trend Strength Requirement" to 0.8-0.9
**Q: Missing obvious trends**
A: Decrease "Signal Sensitivity" to 0.5-0.6
**Q: Too many false signals**
A: Enable "3H Confirmation" and increase strength requirements
**Q: Indicator not loading**
A: Check Pine Script version compatibility (requires v5)
### Parameter Adjustments
**For More Sensitive Signals:**
- Decrease Signal Sensitivity to 0.5-0.6
- Decrease Trend Strength Requirement to 0.6-0.7
- Increase Range Filter multiplier to 3.5-4.0
**For More Conservative Signals:**
- Increase Signal Sensitivity to 0.7-0.8
- Increase Trend Strength Requirement to 0.8-0.9
- Enable all confirmation features
### Performance Issues
- Reduce lookback periods if chart loads slowly
- Disable some visual elements for better performance
- Use on liquid stocks/indices for best results
---
## 📞 Support & Updates
This super indicator combines the best of Range Filter, PMAX, and Support/Resistance analysis specifically optimized for Indian market swing trading. The multi-component approach significantly improves signal quality while the built-in risk management features help protect capital.
**Remember:** No indicator is 100% accurate. Always combine with proper risk management, market analysis, and your trading experience for best results.
**Happy Trading! 🚀**
Binance Funding Rates [vichtoreb]Source: www.binance.com
The funding rate has two components: the interest rate and the average Premium Index.
Binance furnishes the Premium Index data for crypto assets on the TradingView platform. This script uses that data to calculate the funding rate.
Binance updates the Premium Index every 5 seconds.
The average Premium Index (denoted **P\_avg**) is the time-weighted average of all Premium Index data points:
P_avg = wma(Premium Index, n)
where **n** is the averaging length.
At each change time—8:00 PM, 4:00 AM, and 12:00 PM (UTC-4)—Binance sets
P_avg = wma(Premium Index, 5 760)
This is the weighted moving average of the last 8 hours because 5 760 × 5 s = 8 h. Binance then calculates the new funding rate:
Funding Rate = P_avg + clamp(interest rate − P_avg, −0.05 %, 0.05 %)
This value updates only at those change times (8:00 PM, 4:00 AM, and 12:00 PM, UTC-4).
**Indicator precision**
TradingView limits historical requests to 5 000 candles. To match Binance exactly, 5 760 candles are required. As a workaround, the script samples the Premium Index every *resolution* seconds (or minutes), where *resolution* is the indicator’s timeframe input.
If it weren't for this limitation, setting resolution = 5 sec, we would get EXACTLY the same result as the official one
**Interest rate**
On Binance Futures, the interest rate is 0.03 % per day by default (0.01 % per funding interval, as funding occurs every 8 hours). This does not apply to certain contracts, such as ETH/BTC, for which the interest rate is 0 %.
**Estimate line**
If the “show estimate” input is enabled, the indicator plots
wma(Premium Index, n) + clamp(interest rate − P_avg, −0.05 %, 0.05 %)
with **n** equal to the number of bars that have elapsed since the last funding-rate change.
Volume Profile (Simple)Simple Volume Profile (Simple)
Master the Market's Structure with a Clear View of Volume
by mercaderoaurum
The Simple Volume Profile (Simple) indicator removes the guesswork by showing you exactly where the most significant trading activity has occurred. By visualizing the Point of Control (POC) and Value Area (VA) for today and yesterday, you can instantly identify the price levels that matter most, giving you a critical edge in your intraday trading.
This tool is specifically optimized for day trading SPY on a 1-minute chart, but it's fully customizable for any symbol or timeframe.
Key Features
Multi-Day Analysis: Automatically plots the volume profiles for the current and previous trading sessions, allowing you to see how today's market is reacting to yesterday's key levels.
Automatic Key Level Plotting: Instantly see the most important levels from each session:
Point of Control (POC): The single price level with the highest traded volume, acting as a powerful magnet for price.
Value Area High (VAH): The upper boundary of the area where 50% of the volume was traded. It often acts as resistance.
Value Area Low (VAL): The lower boundary of the 50% value area, often acting as support.
Extended Levels: The POC, VAH, and VAL from previous sessions are automatically extended into the current day, providing a clear map of potential support and resistance zones.
Customizable Sessions: While optimized for the US stock market, you can define any session time and time zone, making it a versatile tool for forex, crypto, and futures traders.
Core Trading Strategies
The Simple Volume Profile helps you understand market context. Instead of trading blind, you can now make decisions based on where the market has shown the most interest.
1. Identifying Support and Resistance
This is the most direct way to use the indicator. The extended lines from the previous day are your roadmap for the current session.
Previous Day's POC (pPOC): This is the most significant level. Watch for price to react strongly here. It can act as powerful support if approached from above or strong resistance if approached from below.
Previous Day's VAH (pVAH): Expect this level to act as initial resistance. A clean break above pVAH can signal a strong bullish trend.
Previous Day's VAL (pVAL): Expect this level to act as initial support. A firm break below pVAL can indicate a strong bearish trend.
Example Strategy: If SPY opens and rallies up to the previous day's VAH and stalls, this is a high-probability area to look for a short entry, with a stop loss just above the level.
2. The "Open-Drive" Rejection
How the market opens in relation to the previous day's value area is a powerful tell.
Open Above Yesterday's Value Area: If the market opens above the pVAH, it signals strength. The first pullback to test the pVAH is often a key long entry point. The level is expected to flip from resistance to support.
Open Below Yesterday's Value Area: If the market opens below the pVAL, it signals weakness. The first rally to test the pVAL is a potential short entry, as the level is likely to act as new resistance.
3. Fading the Extremes
When price pushes far outside the previous day's value area, it can become overextended.
Reversal at Highs: If price rallies significantly above the pVAH and then starts to lose momentum (e.g., forming bearish divergence on RSI or a topping pattern), it could be an opportunity to short the market, targeting a move back toward the pVAH or pPOC.
Reversal at Lows: Conversely, if price drops far below the pVAL and shows signs of bottoming, it can be a good opportunity to look for a long entry, targeting a reversion back to the value area.
Recommended Settings (SPY Intraday)
These settings are the default and are optimized for scalping or day trading SPY on a 1-minute chart.
Value Area (%): 50%. This creates a tighter, more sensitive value area, perfect for identifying the most critical intraday zones.
Number of Rows: 1000. This high resolution is essential for a low-volatility instrument like SPY, ensuring that the profile is detailed and the levels are precise.
Session Time: 0400-1800 in America/New_York. This captures the full pre-market and core session, which is crucial for understanding the day's complete volume story.
Ready to trade with an edge? Add the Simple Volume Profile (Multi-Day) to your chart now and see the market in a new light!
Position Size 📐 DT/ST (Today's Open)💡 Purpose:
This indicator automatically calculates intraday (DT) and swing trading (ST) position sizes based on your account capital, risk per trade, and stop-loss percentage, using today’s daily open price as the entry price reference.
⚙️ Main Functionalities:
Dynamic Position Sizing
Calculates Full size position based on the maximum risk you allow per trade.
Breaks it down into ¼ Size, ⅓ Size, and ½ Size positions for flexible scaling.
Two Distinct Trading Styles:
DT (Day Trading) – Uses your specified intraday stop-loss % (default: 2%).
ST (Swing Trading) – Uses your specified swing stop-loss % (default: 10%).
Lot Size Rounding
Automatically rounds quantities to a chosen lot size (e.g., 1 for cash equity or futures lot size for derivatives).
Customizable Table Position
Display the table anywhere on your chart: Top Right, Top Left, Bottom Right, or Bottom Left.
Optimized for Dark or Light Themes
Yellow header with black text for visibility.
Blue row labels for strategy type.
Grey background with white text for calculated values.
Live Market Adaptation
All values update in real-time as today’s daily open price changes (on new daily candles).
Works for any symbol, asset class, or time frame.
🧮 Formula:
Position Size (Full) = Max Risk ₹ / (Price × StopLoss%)
¼, ⅓, and ½ Sizes = Scaled from Full size
📌 Ideal For:
Traders who want quick, ready-to-use position sizes right on their chart.
Those who follow fixed risk-per-trade and need fast decision-making without manual calculations.
Liquidation Heatmap Proxy [victhoreb]Author: victhoreb
This script was inspired by the Coinglass indicator: www.coinglass.com
It divides each bar into subbars determined by the intrabar period. For each bar, it considers subbars with a positive OID (open interest delta) (if the user sets "Filter by Signal" to true, it only considers subbars with OID > 0 from a main bar that had a peak in open interest). In these subbars, it considers opened long/short positions based on the intrabar price movement and the dispersion factor (which becomes completely unnecessary if the user is using Intrabar Resolution in ticks; in this case, set the dispersion factor = 0).
After determining the opened long and short positions, it determines, based on the user-selected leverages, the liquidation level for each position. The width of each level is given by syminfo.mintick * scale. The script uses the intrabar OID from the previous step to store an estimate of the number of contracts to be liquidated at each level. This estimate is used to color the levels by order of magnitude.
If there is a subsequent increase in liquidations at a pre-existing level, the script accumulates the estimated number of contracts to be liquidated and repaints the level. A note about a visual limitation of the script is important: in Coinglass' version, when there is a subsequent increase in liquidations at a pre-existing level, Coinglass paints the level a brighter color ONLY from the moment of the increase—however, this script does not do this; it repaints the entire level with the brighter color. Note: While accurate, this script is only a proxy. Use at your own risk.
This script has alerts for when there is liquidation in the long or short direction.
%ATR + ΔClose HighlightScript Overview
This indicator displays on your chart:
Table of the last N bars that passed the ATR-based range filter:
Columns: Bar #, High, Range (High–Low), Low
Summary row: ATR(N), suggested Stop-Loss (SL = X % of ATR), and the current bar’s range as a percentage of ATR
Red badge on the most recent bar showing ΔClose% (the absolute difference between today’s and yesterday’s close, expressed as % of ATR)
Background highlights:
Blue fill under the most recent bar that met the filter
Yellow fill under bars that failed the filter
Hidden plots of ATR, %ATR, and ΔClose% (for use in strategies or alerts)
All table elements, fills, and plots can be toggled off with a single switch so that only the red ΔClose% badge remains visible.
Inputs
Setting Description Default
Length (bars) Lookback period for ATR and range filter (bars) 5
Upper deviation (%) Upper filter threshold (% of average ATR) 150%
Lower deviation (%) Lower filter threshold (% of average ATR) 50%
SL as % of ATR Stop-loss distance (% of ATR) 10%
Label position Table position relative to bar (“above” or “below”) above
Vertical offset (×ATR) Vertical spacing from the bar in ATR units 2.0
Show table & ATR plots Show or hide table, background highlights, and plots true
How It Works
ATR Calculation & Filtering
Computes average True Range over the last N bars.
Marks bars whose daily range falls within the specified upper/lower deviation band.
Table Construction
Gathers up to N most recent bars that passed the filter (or backfills from the most recent pass).
Formats each bar’s High, Low, and Range into fixed-width columns for neat alignment.
Stop-Loss & Percent Metrics
Calculates a recommended SL distance as a percentage of ATR.
Computes today’s bar range and ΔClose (absolute change in close) as % of ATR.
Chart Display
Table: Shows detailed per-bar data and summary metrics.
Background fills: Blue for the latest valid bar, yellow for invalid bars.
Hidden plots: ATR, %ATR, and ΔClose% (useful for backtesting).
Red badge: Always visible on the right side of the last bar, displaying ΔClose%.
Tips
Disable the table & ATR plots to reduce chart clutter—leave only the red ΔClose% badge for a minimalist volatility alert.
Use the hidden ATR fields (plot outputs) in TradingView Strategies or Alerts to automate volatility-based entries/exits.
Adjust the deviation band to capture “normal” intraday moves vs. outsized volatility spikes.
Load this script on any US market chart (stocks, futures, crypto, etc.) to instantly visualize recent volatility structure, set dynamic SL levels, and highlight today’s price change relative to average true range.
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING
Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003).
Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices.
The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations.
Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005).
LITERATURE REVIEW AND THEORETICAL FOUNDATION
The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis.
Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches.
The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches.
Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns.
METHODOLOGY
The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as:
S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t)
where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term.
Technical Analysis Component
The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics.
Fundamental Analysis Framework
The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors.
Sector Classification System
Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations.
Macroeconomic Component
The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis.
Dynamic Threshold Adjustment
Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999).
The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning.
During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994).
USER GUIDE AND IMPLEMENTATION FRAMEWORK
Initial Setup and Configuration
The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles.
Display Configuration Settings
The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility.
Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008).
Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings.
Practical Example: Conservative Investor Setup
For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns.
Threshold Adaptation System Configuration
The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing.
Static Mode Implementation
Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons.
Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture.
Regime-Based Adaptation
Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments.
Technical Implementation:
The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress.
For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification.
VIX-Based Contrarian System
The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles.
Scientific Calibration:
VIX levels are calibrated according to historical percentile distributions:
Extreme High (>40):
- Maximum contrarian opportunity
- Threshold reduction: 15-20 points
- Historical accuracy: 85%+
High (30-40):
- Significant contrarian potential
- Threshold reduction: 10-15 points
- Market stress indicator
Medium (25-30):
- Moderate adjustment
- Threshold reduction: 5-10 points
- Normal volatility range
Low (15-25):
- Minimal adjustment
- Standard threshold levels
- Complacency monitoring
Extreme Low (<15):
- Counter-contrarian positioning
- Threshold increase: 5-10 points
- Bubble warning signals
Practical Example: VIX-Based Implementation for Active Traders
High Fear Environment (VIX >35):
- Thresholds decrease by 10-15 points
- Enhanced contrarian positioning
- Crisis opportunity capture
Low Fear Environment (VIX <15):
- Thresholds increase by 8-15 points
- Reduced signal frequency
- Bubble risk management
Additional Macro Factors:
- Yield curve considerations
- Dollar strength impact
- Global volatility spillover
Hybrid Mode Optimization
Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models.
Weighting Scheme:
- Regime factors: 40%
- VIX factors: 40%
- Additional macro considerations: 20%
Dynamic Calculation:
Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2)
Benefits:
- Balanced approach
- Reduced single-factor dependency
- Enhanced robustness
Advanced Mode with Stress Weighting
Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators:
Stress Level Indicators:
1. Yield curve inversion (recession predictor)
2. Volatility spikes (market disruption)
3. Severe drawdowns (momentum breaks)
4. VIX extreme readings (sentiment extremes)
Technical Implementation:
Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors:
Low Stress (0-1 factors):
- Regime weighting: 50%
- VIX weighting: 30%
- Macro weighting: 20%
Medium Stress (2 factors):
- Regime weighting: 40%
- VIX weighting: 40%
- Macro weighting: 20%
High Stress (3-4 factors):
- Regime weighting: 20%
- VIX weighting: 50%
- Macro weighting: 30%
Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007).
Percentile-Based Historical Analysis
Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978).
Methodology:
- Analyzes trailing 252-day periods (approximately 1 trading year)
- Establishes percentile-based thresholds
- Dynamic adaptation to market conditions
- Statistical significance testing
Configuration Options:
- Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable)
- Percentile Levels: Customizable based on signal frequency preferences
- Update Frequency: Daily recalculation with rolling windows
Implementation Example:
- Strong Buy Threshold: 75th percentile of historical scores
- Caution Buy Threshold: 60th percentile of historical scores
- Dynamic adjustment based on current market volatility
Investor Psychology Profile Configuration
The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research.
Conservative Profile Implementation
Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities.
Technical Calibration:
VIX Parameters:
- Extreme High Threshold: 32.0 (lower sensitivity to fear spikes)
- High Threshold: 28.0
- Adjustment Magnitude: Reduced for stability
Regime Adjustments:
- Bear Market Reduction: -7 points (vs -12 for normal)
- Recession Reduction: -10 points (vs -15 for normal)
- Conservative approach to crisis opportunities
Percentile Requirements:
- Strong Buy: 80th percentile (higher selectivity)
- Caution Buy: 65th percentile
- Signal frequency: Reduced for quality focus
Risk Management:
- Enhanced bankruptcy screening
- Stricter liquidity requirements
- Maximum leverage limits
Practical Application: Conservative Profile for Retirement Portfolios
This configuration suits investors requiring capital preservation with moderate growth:
- Reduced drawdown probability
- Research-based parameter selection
- Emphasis on fundamental safety
- Long-term wealth preservation focus
Normal Profile Optimization
Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices.
Calibration Parameters:
VIX Thresholds:
- Extreme High: 35.0 (institutional standard)
- High: 30.0
- Standard adjustment magnitude
Regime Adjustments:
- Bear Market: -12 points (moderate contrarian approach)
- Recession: -15 points (crisis opportunity capture)
- Balanced risk-return optimization
Percentile Requirements:
- Strong Buy: 75th percentile (industry standard)
- Caution Buy: 60th percentile
- Optimal signal frequency
Risk Management:
- Standard institutional practices
- Balanced screening criteria
- Moderate leverage tolerance
Aggressive Profile for Active Management
Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999).
Technical Configuration:
VIX Parameters:
- Extreme High: 40.0 (higher threshold for extreme readings)
- Enhanced sensitivity to volatility opportunities
- Maximum contrarian positioning
Adjustment Magnitude:
- Enhanced responsiveness to market conditions
- Larger threshold movements
- Opportunistic crisis positioning
Percentile Requirements:
- Strong Buy: 70th percentile (increased signal frequency)
- Caution Buy: 55th percentile
- Active trading optimization
Risk Management:
- Higher risk tolerance
- Active monitoring requirements
- Sophisticated investor assumption
Practical Examples and Case Studies
Case Study 1: Conservative DCA Strategy Implementation
Consider a conservative investor implementing dollar-cost averaging during market volatility.
AITM Configuration:
- Threshold Mode: Hybrid
- Investor Profile: Conservative
- Sector Adaptation: Enabled
- Macro Integration: Enabled
Market Scenario: March 2020 COVID-19 Market Decline
Market Conditions:
- VIX reading: 82 (extreme high)
- Yield curve: Steep (recession fears)
- Market regime: Bear
- Dollar strength: Elevated
Threshold Calculation:
- Base threshold: 75% (Strong Buy)
- VIX adjustment: -15 points (extreme fear)
- Regime adjustment: -7 points (conservative bear market)
- Final threshold: 53%
Investment Signal:
- Score achieved: 58%
- Signal generated: Strong Buy
- Timing: March 23, 2020 (market bottom +/- 3 days)
Result Analysis:
Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery.
Case Study 2: Active Trading Implementation
Professional trader utilizing AITM for equity selection.
Configuration:
- Threshold Mode: Advanced
- Investor Profile: Aggressive
- Signal Labels: Enabled
- Macro Data: Full integration
Analysis Process:
Step 1: Sector Classification
- Company identified as technology sector
- Enhanced growth weighting applied
- R&D intensity adjustment: +5%
Step 2: Macro Environment Assessment
- Stress level calculation: 2 (moderate)
- VIX level: 28 (moderate high)
- Yield curve: Normal
- Dollar strength: Neutral
Step 3: Dynamic Weighting Calculation
- VIX weighting: 40%
- Regime weighting: 40%
- Macro weighting: 20%
Step 4: Threshold Calculation
- Base threshold: 75%
- Stress adjustment: -12 points
- Final threshold: 63%
Step 5: Score Analysis
- Technical score: 78% (oversold RSI, volume spike)
- Fundamental score: 52% (growth premium but high valuation)
- Macro adjustment: +8% (contrarian VIX opportunity)
- Overall score: 65%
Signal Generation:
Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period.
Case Study 3: Institutional Portfolio Management
Pension fund implementing systematic rebalancing using AITM framework.
Implementation Framework:
- Threshold Mode: Percentile-Based
- Investor Profile: Normal
- Historical Lookback: 252 days
- Percentile Requirements: 75th/60th
Systematic Process:
Step 1: Historical Analysis
- 252-day rolling window analysis
- Score distribution calculation
- Percentile threshold establishment
Step 2: Current Assessment
- Strong Buy threshold: 78% (75th percentile of trailing year)
- Caution Buy threshold: 62% (60th percentile of trailing year)
- Current market volatility: Normal
Step 3: Signal Evaluation
- Current overall score: 79%
- Threshold comparison: Exceeds Strong Buy level
- Signal strength: High confidence
Step 4: Portfolio Implementation
- Position sizing: 2% allocation increase
- Risk budget impact: Within tolerance
- Diversification maintenance: Preserved
Result:
The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing.
Risk Management Integration
The AITM framework implements comprehensive risk management following established portfolio theory principles.
Bankruptcy Risk Filter
Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis:
Primary Screening Criteria:
- Z-Score threshold: <1.8 (high distress probability)
- Current Ratio threshold: <1.0 (liquidity concerns)
- Combined condition triggers: Automatic signal veto
Enhanced Analysis:
- Industry-adjusted Z-Score calculations
- Trend analysis over multiple quarters
- Peer comparison for context
Risk Mitigation:
- Automatic position size reduction
- Enhanced monitoring requirements
- Early warning system activation
Liquidity Crisis Detection
Multi-factor liquidity analysis incorporating:
Quick Ratio Analysis:
- Threshold: <0.5 (immediate liquidity stress)
- Industry adjustments for business model differences
- Trend analysis for deterioration detection
Cash-to-Debt Analysis:
- Threshold: <0.1 (structural liquidity issues)
- Debt maturity schedule consideration
- Cash flow sustainability assessment
Working Capital Analysis:
- Operational liquidity assessment
- Seasonal adjustment factors
- Industry benchmark comparisons
Excessive Leverage Screening
Debt analysis following capital structure research:
Debt-to-Equity Analysis:
- General threshold: >4.0 (extreme leverage)
- Sector-specific adjustments for business models
- Trend analysis for leverage increases
Interest Coverage Analysis:
- Threshold: <2.0 (servicing difficulties)
- Earnings quality assessment
- Forward-looking capability analysis
Sector Adjustments:
- REIT-appropriate leverage standards
- Financial institution regulatory requirements
- Utility sector regulated capital structures
Performance Optimization and Best Practices
Timeframe Selection
Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness.
Recommended Implementation:
Primary Analysis:
- Daily (1D) charts for optimal signal quality
- Complete fundamental data integration
- Full macro environment analysis
Secondary Confirmation:
- 4-hour timeframes for intraday confirmation
- Technical indicator validation
- Volume pattern analysis
Avoid for Timing Applications:
- Weekly/Monthly timeframes reduce responsiveness
- Quarterly analysis appropriate for fundamental trends only
- Annual data suitable for long-term research only
Data Quality Requirements
The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability.
Quality Standards:
Minimum Requirements:
- 2 years of complete financial data
- Current quarterly updates within 90 days
- Audited financial statements
Optimal Configuration:
- 5+ years for trend analysis
- Quarterly updates within 45 days
- Complete regulatory filings
Geographic Standards:
- Developed market reporting requirements
- International accounting standard compliance
- Regulatory oversight verification
Portfolio Integration Strategies
AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation.
Integration Approach:
Position Sizing:
- Signal strength correlation with allocation size
- Risk-adjusted position scaling
- Portfolio concentration limits
Risk Budgeting:
- Stress-test based allocation
- Scenario analysis integration
- Correlation impact assessment
Diversification Analysis:
- Portfolio correlation maintenance
- Sector exposure monitoring
- Geographic diversification preservation
Rebalancing Frequency:
- Signal-driven optimization
- Transaction cost consideration
- Tax efficiency optimization
Troubleshooting and Common Issues
Missing Fundamental Data
When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability.
Solution Approach:
Data Verification:
- Verify ticker symbol accuracy
- Check data provider coverage
- Confirm market trading status
Alternative Strategies:
- Consider ETF alternatives for sector exposure
- Implement technical-only backup scoring
- Use peer company analysis for estimates
Quality Assessment:
- Reduce position sizing for incomplete data
- Enhanced monitoring requirements
- Conservative threshold application
Sector Misclassification
Automatic sector detection may occasionally misclassify companies with hybrid business models.
Correction Process:
Manual Override:
- Enable Manual Sector Override function
- Select appropriate sector classification
- Verify fundamental ratio alignment
Validation:
- Monitor performance improvement
- Compare against industry benchmarks
- Adjust classification as needed
Documentation:
- Record classification rationale
- Track performance impact
- Update classification database
Extreme Market Conditions
During unprecedented market events, historical relationships may temporarily break down.
Adaptive Response:
Monitoring Enhancement:
- Increase signal monitoring frequency
- Implement additional confirmation requirements
- Enhanced risk management protocols
Position Management:
- Reduce position sizing during uncertainty
- Maintain higher cash reserves
- Implement stop-loss mechanisms
Framework Adaptation:
- Temporary parameter adjustments
- Enhanced fundamental screening
- Increased macro factor weighting
IMPLEMENTATION AND VALIDATION
The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers.
Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0.
Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios.
RESULTS AND PRACTICAL APPLICATIONS
The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism.
Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995).
The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999).
LIMITATIONS AND FUTURE RESEARCH
Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly.
The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes.
Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives.
CONCLUSION
The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions.
The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements.
The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance.
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Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152.
Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65.
Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill.
Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40.
Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. 11th ed. Boca Raton: CRC Press.
Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61.
Fama, E. F., & French, K. R. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100.
Graham, B., & Dodd, D. L. (2008). Security Analysis. 6th ed. New York: McGraw-Hill Education.
Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill.
Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544.
Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384.
Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291.
Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50.
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Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton: Princeton University Press.
Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82.
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Map Multiple QQQ Levels to NQ (Custom Labels)maps user-defined price levels from the QQQ (Invesco QQQ Trust, tracking the NASDAQ-100) to equivalent levels on the NQ (NASDAQ-100 futures) chart, displaying them as colored lines with optional labels. It’s designed to overlay on the NQ chart, helping traders visualize QQQ price levels in terms of NQ values based on the ratio between their prices.
Key Features:
Inputs for QQQ Levels: Users can specify up to four QQQ price levels (default: 300, 310, 320, 330) with a step of 0.1 for precision.
Customizable Appearance: Each level has its own color (default: teal, orange, purple, green) and a shared line width (default: 2).
Time Filters:
Option to display levels only for the current day (show_today_only).
Option to show levels only after 9:30 AM Eastern Time (show_after_open), aligning with market open.
Custom Labels: Each level can have a toggleable label with a customizable template (e.g., "QQQ {qqq} → NQ {mapped}"), showing the QQQ level and its mapped NQ value, formatted to two decimal places.
Dynamic Mapping: Calculates the ratio between NQ and QQQ closing prices to map QQQ levels to NQ equivalents in real-time.
Conditional Display: Levels are plotted only when user-defined conditions (day and time filters) are met, otherwise hidden (na).
Label Placement: Labels are displayed on the last confirmed historical bar, unaffected by time filters, ensuring visibility without cluttering real-time bars.
CAGR 5 & 10 Years, Auto-Detect Timeframe# CAGR 5 & 10 Years, Auto-Detect Timeframe
## Overview
This indicator automatically calculates the **Compound Annual Growth Rate (CAGR)** for 5-year and 10-year periods, adapting intelligently to different asset types and timeframes.
## Key Features
### 🤖 **Smart Market Detection**
- **Automatically detects** if the asset operates 24/7 (crypto, crypto futures) or traditional hours (stocks, forex)
### ⏰ **Multi-Timeframe Support**
**Compatible timeframes**: 1H, 2H, 3H, 4H, 6H, 8H, 12H, 1D, 3D, 1W, 1M, 3M, 6M, 12M
### 📊 **Visual Display**
- **Green line**: 5-year CAGR percentage
- **Blue line**: 10-year CAGR percentage
- **Zero reference line** for easy interpretation
## Use Cases
- **Long-term performance analysis** across different timeframes
- **Cross-asset comparison** with automatic market type adjustment
- **Investment planning** with standardized annual growth rates
- **Historical perspective** on asset performance
Perfect for investors and analysts who need consistent, comparable growth metrics across different assets and market types.
SuperTrend Strategy with Trend-Based Exits🟩 SuperTrend Strategy with Trend-Based Exits
This is a fully automated trend-following strategy based on the popular SuperTrend indicator, enhanced with a position sizing algorithm tied to stop-loss distance and dynamic entry/exit rules. The strategy is designed for futures trading with an emphasis on sustainable risk, realistic backtesting, and transparent logic.
🧠 Concept and Methodology
The strategy uses the SuperTrend indicator, which is derived from ATR (Average True Range) and is widely used to capture medium- to long-term market trends.
Key features:
✅ Entries are triggered only when the SuperTrend direction changes (trend reversal).
✅ Exits are performed using a dynamic stop-loss placed at the SuperTrend line.
✅ Position size is automatically calculated based on the trader’s fixed dollar risk per trade and the current distance to the stop-loss.
✅ Rounding logic is included to ensure quantity is valid for the exchange’s lot size.
This strategy does not use any take-profit or classic trailing stop — the position is only closed when the trend reverses or the stop is hit by touching the SuperTrend line.
⚙️ Default Parameters
ATR Length: 300
Factor: 7.5
Risk per trade: $90 (3% of the default $3,000 capital)
Lot step: 10
Commission: 0.05%
These default parameters are not universal. They were optimized specifically for STXUSDT swap at 15M timeframe at Bybit and may not produce viable results on other pairs and timeframes.
Users are encouraged to customize the settings according to specific asset’s volatility, timeframe and other characteristics.
❗ These default settings yield meaningful backtesting results on STXUSDT with a reasonable number of trades (105+) over 7-month period. If applied to other assets, results may vary significantly.
📈 Position Sizing Logic
The strategy uses a dynamic position sizing formula:
Pine Script®
position_size = floor((risk_per_trade / stop_loss_distance) / lot_step) * lot_step
This ensures the trader always risks a fixed dollar amount per trade and never exceeds a sustainable equity exposure (recommended 2% or less).
✅ Realism in Backtesting
To ensure realistic and non-misleading backtest results, this strategy includes:
— Slippage and commission settings matching average exchange conditions (commission = 0.05%, slippage 5 ticks).
— Position sizing based on stop-loss distance (not fixed contract quantity).*
— A fixed risk-per-trade model that adheres to responsible capital management principles.
— This is in compliance with TradingView's Script publishing rules and House Rules.
📌 How to Use
Apply the strategy to a clean chart (preferably 15M for STXUSDT by default).
If using another asset, adjust:
- ATR Length
- Factor
- Risk per trade
- Qty step (lot precision for the symbol)
Avoid using with other indicators unless you understand their purpose.
Use the Strategy Tester to evaluate performance and optimize parameters.
⚠️ Disclaimer
This is not financial advice. Always perform forward testing and assess risk before deploying any strategy on live capital. The strategy is designed for educational and experimental use.
