FVG & Order Block - Market StructureOverview
A comprehensive Smart Money Concepts (SMC) indicator that combines Fair Value Gaps, Order Blocks, Market Structure analysis, and Key Levels into one powerful tool. Designed for traders who follow ICT (Inner Circle Trader) methodology and institutional trading concepts.
🔹 Features
Fair Value Gaps (FVG)
Automatically detects bullish and bearish imbalances in price
Customizable mitigation logic: choose between "Close" (candle must close through the gap) or "Touch" (wick into the gap)
FVGs extend forward and auto-remove when mitigated
Separate colors for bullish (demand) and bearish (supply) gaps
Order Blocks (OB)
Identifies institutional order blocks based on significant price moves
Detects the last opposing candle before a breakout move
Customizable mitigation type (Close vs Touch)
Adjustable lookback period for sensitivity control
Market Structure (CHoCH & BOS)
CHoCH (Change of Character): Detects trend reversals when price breaks structure against the current trend
BOS (Break of Structure): Confirms trend continuation when price breaks structure in the direction of the trend
Visual labels and dashed lines mark each structural break
Adjustable swing length for different trading styles
Key Levels
PDH/PDL: Previous Day High/Low
PWH/PWL: Previous Week High/Low
PMH/PML: Previous Month High/Low
Clean horizontal lines with labels that auto-update
Liquidity Levels
Identifies clusters of equal lows where stop losses likely accumulate
Shows percentage distance from current price
Helps anticipate liquidity grabs and stop hunts
Info Dashboard
Real-time display of current market structure (Bullish/Bearish/Neutral)
Count of active FVGs and Order Blocks
⚙️ Customization
Toggle each feature on/off independently
Fully customizable colors for all elements
Adjustable zone extension periods
Choose mitigation type per zone (Close vs Touch)
Swing length adjustment for market structure sensitivity
📈 How to Use
Identify Trend: Check the dashboard for current market structure
Find Entry Zones: Look for unfilled FVGs and untested Order Blocks in the direction of the trend
Confirm with Structure: Wait for BOS to confirm trend continuation or CHoCH for reversals
Use Key Levels: PDH/PDL/PWH/PWL act as support/resistance and liquidity targets
Watch Liquidity: Equal lows often get swept before reversals
🎯 Best Used On
Indices (NiftyFifty, BankNifty, S&P 500, Nasdaq)
Forex pairs
Crypto (BTC, ETH)
Works on all timeframes (15m, 1H, 4H, Daily recommended)
⚠️ Disclaimer
This indicator is a technical analysis tool and should not be considered financial advice. Always use proper risk management and combine with your own analysis. Past performance does not guarantee future results.
📝 Release Notes
v1.0
Initial release
FVG detection with customizable mitigation
Order Block detection
CHoCH & BOS market structure
PDH/PDL, PWH/PWL, PMH/PML levels
Liquidity level detection
Info dashboard
Tags: smartmoney smc ict fairvaluegap fvg orderblock marketstructure choch bos liquidity supplydemand priceaction
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MarketStructureLab - SR Zones (Free)📌 MarketStructureLab — SR Zones is a structure-based indicator that automatically identifies key support and resistance zones using market structure logic, not subjective manual levels.
The indicator analyzes:
• local highs and lows (pivot points),
• clusters nearby price extremes,
• builds S/R zones based on their strength (number of price reactions).
🔍 What the indicator shows
• 🟢 Support zones — areas of increased demand
• 🔴 Resistance zones — areas of increased supply
• Price labels with level value and distance from the current price in %
The more reactions price has within a zone, the more significant it becomes.
⚙️ Key features
• Based on market structure, not fixed levels
• Works on any instrument (stocks, futures, crypto, FX)
• Suitable for all timeframes
• No repainting
• Supports alerts on level breaks
⚠️ Important
This indicator does not generate trade signals and does not make predictions.
It is designed to help traders analyze market context and make independent decisions.
Recommended to use with
• market state analysis (Trend / Range),
• volume,
• proper risk management.
📎 Updates and future developments
This indicator is part of the MarketStructureLab project.
Follow the author’s profile to stay updated on new tools and improvements.
Rishii's EMA Trend EngineThis indicator is a dual-EMA trend framework designed to improve intraday decision-making by filtering out sideways market noise and highlighting only meaningful trend participation.
It uses a fast and slow EMA to define trend direction, while applying an HLC3-based color logic to show whether price is respecting each EMA. Candles turn green or red only when both the trend alignment and price participation conditions are satisfied. Neutral candles can be shown in white to visually remove noise and make valid candles stand out.
Additional filters such as EMA slope detection and optional higher-timeframe bias help avoid false signals during ranging conditions. A background trend zone and first-candle markers after EMA crossover further improve clarity without cluttering the chart.
How it helps
Clearly shows when the market is trending vs sideways
Highlights only those candles where price is truly participating in the trend
Filters out most whipsaws caused by flat EMAs BITSTAMP:BTCUSD
Combines trend bias, momentum, and participation in one clean view
Caution
This is a trend-following tool, not a reversal indicator.
When EMAs are flat and candles turn white, avoid trading
Do not treat every green/red candle as an entry; wait for proper structure.
Always use proper stop-loss and position sizing.
Tori Structure VWAP+EMA PRO Alert🇺🇸 How to Use (English Guide)
📌 What This Indicator Does
This tool combines:
Automatic support/resistance
EMA200 trend filter
VWAP confirmation
Volume filter
Breakout alerts
👉 Designed to reduce fake breakouts.
📌 Long Signal
✔ Break above previous high
✔ Price above EMA200
✔ Price above VWAP
✔ Volume confirmation
→ Green triangle appears
📌 Short Signal
✔ Break below previous low
✔ Price below EMA200
✔ Price below VWAP
✔ Volume confirmation
→ Red triangle appears
📌 Best Settings
Style Pivot
Scalping 5–8
Day Trade 10
Swing 15
🔥 PRO Tips
✔ US session first 2–3 hours = best signals
✔ Align with BTC direction
✔ Avoid sideways markets
✔ Combine with trendlines for higher accuracy
Top-secret Golden Mentor (Jorge's Algo)Description:
INTRODUCTION The Top-secret Golden Mentor is an institutional trading system engineered for surgical precision on Gold (XAUUSD) and other volatile assets. This indicator goes beyond simple entry signals; it automatically filters market traps (fakeouts) by aligning every volume anomaly with the macro market structure.
The main objective is simple: Stop trading against the trend and pinpoint exactly where institutions have injected capital.
KEY FEATURES
1. X-Ray Candles (True Volume Pressure) Move beyond traditional Japanese candlesticks. This indicator "undresses" the price action:
Grey Border: Represents the price range.
Color Fill (Green/Red): Reveals who actually won the internal volume battle (Delta).
Benefit: You can spot candles that look bullish on the outside but are "hollow" (empty of buyers) on the inside.
2. Smart Trend Filter (The Trap Detector) The core upgrade of V18. The system analyzes market structure in real-time.
If a BUY signal appears during a BEARISH structure, the system instantly marks it with a Grey "X".
Signal with "X" = MARKET TRAP (Absorption).
This prevents you from entering fake pullbacks that are about to be absorbed by the main trend.
3. Sniper Signals & Institutional Gaps (FVG) When the system detects a massive volume injection:
It plots a Volume Dot (Alert).
It automatically projects the 50% Retracement Line of the candle body (Institutional Equilibrium).
It draws a Subtle Box (Gap/FVG) marking the price inefficiency where institutions often return to mitigate.
4. Dynamic Structure Panel A visual dashboard in the top corner that instantly displays the current timeframe bias (BULLISH or BEARISH), removing subjective guesswork.
HOW TO USE THIS STRATEGY
Check the Panel: Is the bias BULLISH or BEARISH?
Wait for the Signal: Look for the Volume Dot.
Filter the Trap:
If the dot has a Grey "X" on top: DO NOT TRADE. It is a counter-trend trap.
If the dot has NO "X" and lines are drawn: VALID SIGNAL.
Execution: Place your Limit Order at the dotted 50% line or inside the Institutional Gap Box.
RECOMMENDED SETTINGS
Assets: Optimized for XAUUSD (Gold), but works on Forex and Futures.
Timeframes: Highly effective on 1H for direction and 5m for sniper entries.
DISCLAIMER This indicator is a technical analysis assistance tool based on Smart Money Concepts (SMC). It does not guarantee future profits. Always use proper risk management.
Day O/H/L tablouhopen high and low price for tje session
you can see where market open and the high and the low of the session
Alg0 Hal0 Dual MA CrossroadThe Alg0 ۞ Hal0 Dual MA Crossroad is a simple, yet high-precision trend-following indicator designed to eliminate the common pitfalls of standard Moving Average systems: lag and lack of context. By combining responsive MA algorithms with a sophisticated momentum "streak" engine, this tool provides a comprehensive view of market structure.
1. Advanced MA Algorithms
Unlike standard crossovers, this tool allows you to select from 8 different calculation methods for both the Fast and Slow lines.
ZLEMA (Zero Lag EMA): Uses a de-lagging formula to track price turns faster than a standard EMA.
DEMA (Double EMA): Provides a smoother, faster alternative to the single EMA.
HMA (Hull MA): Optimized for reducing lag while maintaining a smooth curve.
VWMA (Volume Weighted): Weights the trend by volume, showing where the "smart money" is moving.
2. Signal Engine & Momentum Streaks
The indicator looks for two primary signals:
The Crossroad: A classic crossover between the Fast and Slow MAs.
Momentum Streaks: Identifies "3-bar power moves" (3 consecutive higher closes or lower closes). These often precede or confirm a crossover, allowing for earlier entries or trend-reinforcement.
3. Smart Visuals & Label Management
ATR-Based Offsets: Labels are dynamically positioned based on current market volatility (ATR). This prevents "price clutter," ensuring labels remain visible above or below candles regardless of the asset's price.
Slope-Based Coloring: MA lines change color based on their internal slope (Bullish vs. Bearish), providing instant visual feedback on momentum shifts before a cross actually occurs.
Clean Charting: Use the Label Count Limit to prevent your chart history from becoming bogged down with old signals.
4. Integrated Intelligence Alerting
The alert system is designed for professional use. Instead of a simple "Cross Up," the webhook or popup provides a detailed report:
Trend Bias: Identifies if the current price is above/below the slow MA.
Volume Context: Automatically detects if the signal is occurring on high relative volume.
Signal Specifics: Tells you exactly which MA types crossed and if a momentum streak was detected.
How to Trade with this Indicator
The Core Setup: Look for a ZLEMA (Fast) cross over an EMA (Slow) for a balance of speed and stability.
Confirmation: Wait for a Momentum Streak alert in the direction of the crossover to confirm high-probability continuation.
Trend Riding: Stay in the trade as long as the MA Slope Color remains consistent with your direction.
Settings Glossary
Fast/Slow MA Type: Choose your calculation algorithm.
ATR Mult (Label Offset): Increase this if labels are too close to the candles.
Label Count Limit: Limits the number of labels kept on the chart to improve performance.
ICT Supply & Demand [KTY]ICT Supply & Demand Indicator
This indicator automatically detects and displays Supply and Demand zones based on swing highs and lows.
Supply and Demand zones are horizontal support/resistance areas where price previously showed strong buying or selling pressure.
Automatic Detection
- Supply Zone (Red): Formed at swing highs where selling pressure was strong
- Demand Zone (Green): Formed at swing lows where buying pressure was strong
- Zones are automatically removed when price breaks through
Dynamic Extension
- Zones extend automatically as new bars form
- Clear visual labels showing SUPPLY and DEMAND
1. Identify Supply and Demand zones on your chart
2. Watch for price reaction when re-entering the zone
3. Combine with Order Block, FVG, or Market Structure for confluence
4. Use zones as reference for take-profit or stop-loss targets
Pro Tips:
- Zones that align with OB or FVG have higher significance
- Multiple touches on a zone increase chance of breakout
- Fresh (untested) zones tend to have stronger reactions
Show Supply & Demand Zones: Toggle zone display on/off
Supply Zone Color: Customize supply zone color
Demand Zone Color: Customize demand zone color
Label Color: Customize text color
Supply Zone Detected
Demand Zone Detected
Supply Zone Broken
Demand Zone Broken
This indicator is designed for educational purposes.
Supply and Demand zones do not guarantee price reversal.
Always combine with proper risk management.
If you find this indicator helpful, please leave a like and follow for more ICT-based tools!
VWAP Confluence Pro█ OVERVIEW
VWAP Confluence Pro is a high-precision trading indicator that combines VWAP with multiple confirmation filters to generate reliable buy and sell signals. Unlike basic VWAP crossover strategies that produce excessive noise, this indicator requires alignment across six independent conditions before triggering a signal, dramatically reducing false entries while capturing high-probability setups.
█ FEATURES
Multi-Layer Confirmation System
The indicator employs a strict confluence approach requiring all of the following conditions to align:
- VWAP Cross: Price must cross above (buy) or below (sell) the VWAP line
- VWAP Trend: The VWAP itself must be rising for buys or falling for sells, confirming directional bias
- Price Trend: A 20-period moving average filter ensures trades align with the prevailing trend
- Volume Confirmation: Signals only trigger when volume exceeds 1.5x the 20-bar average, indicating institutional participation
- RSI Filter: Buys require RSI between 50-60 (bullish momentum without overbought conditions), sells require 40-50 (bearish momentum without oversold conditions)
- MACD Momentum: MACD must confirm directional bias with the MACD line above the signal line for buys, below for sells
Signal Cooldown Period
A configurable cooldown mechanism (default 10 bars) prevents signal clustering and overtrading by ensuring adequate spacing between alerts. This feature is critical for maintaining discipline and avoiding choppy market conditions.
Visual Elements
- Purple VWAP Line: The cornerstone of the strategy, plotted with high visibility
- Green Up Arrows: Buy signals appear below price candles when all conditions align
- Red Down Arrows: Sell signals appear above price candles when all conditions align
- Blue Trend MA: A semi-transparent moving average provides visual trend context
- Background Shading: Subtle green/red backgrounds indicate when multiple confluence factors are aligned, even without a cross
█ HOW TO USE
Timeframe Selection
This indicator is optimized for intraday trading on 1-minute to 15-minute charts, where VWAP is most effective. It can also be used on hourly charts for swing trade entries or daily charts with appropriate parameter adjustments.
Parameter Optimization
All key parameters are customizable through the indicator settings:
- VWAP Deviation %: Controls sensitivity (default 0.8%). Lower values = stricter signals
- Volume Multiplier: Defines volume threshold (default 1.5x). Higher values = stronger volume confirmation required
- Trend Filter Length: Moving average period (default 20). Adjust based on your timeframe
- Cooldown Period: Minimum bars between signals (default 10). Increase for slower markets
- RSI/MACD Settings: Standard values provided, adjust for specific instruments if needed
Trading Strategy
1 — Wait for a signal arrow to appear (green for buy, red for sell)
2 — Confirm the background shading supports the signal direction
3 — Enter on the close of the signal candle or the open of the next candle
4 — Set stop loss below/above the recent swing low/high or the VWAP line
5 — Take profit at logical resistance/support levels or when opposing confluence develops
Best Practices
- Only take long trades when price is above a rising VWAP
- Only take short trades when price is below a falling VWAP
- Avoid trading during low volume periods (first/last 15 minutes of sessions)
- Use the background shading to gauge overall market bias between signals
- Consider increasing the cooldown period in choppy or range-bound conditions
█ LIMITATIONS
- This indicator is designed for trending markets and will produce fewer signals during consolidation periods
- The strict confluence requirements mean you may miss some valid moves in exchange for higher signal quality
- VWAP resets at the start of each session, making it less reliable on 24-hour markets without session breaks (use anchored VWAP for crypto/forex)
- Requires real-time volume data to function properly, less effective on thinly traded instruments
- Not suitable for scalping strategies requiring rapid entries, as the cooldown mechanism intentionally limits signal frequency
█ NOTES
Signal Quality Over Quantity
This indicator prioritizes accuracy over frequency. You may only see 1-3 signals per session on lower timeframes, but each signal represents a setup where trend, momentum, and volume are all aligned. This approach is designed to keep you out of low-probability trades and focused on the best opportunities.
Customization Encouraged
The default parameters provide a solid foundation, but different instruments and timeframes may benefit from optimization. Test the indicator across various settings to find what works best for your specific trading style and markets.
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This indicator synthesizes best practices from institutional VWAP trading with momentum confirmation from RSI and MACD. By requiring multiple independent factors to align, it filters out the noise common in single-indicator systems and focuses on setups where probability favors directional moves.
Market Intent Flow (MIF)🟡 Market Intent Flow (MIF) – Gold Trader’s Perspective
Market Intent Flow (MIF) is a price-action-based indicator designed to reveal real market participation behind Gold (XAUUSD) moves.
Instead of flooding the chart with signals, MIF highlights only moments when the market clearly shows intent — whether buyers or sellers are in control.
Gold is a liquidity-driven instrument. MIF is built to respect that nature, not fight it.
🏆 Why Gold Traders Like This Indicator
Gold traders prefer clarity over noise, and that’s exactly what MIF delivers:
🧲 Gold respects structure & momentum
🔊 Big moves happen with volume expansion
🧠 Smart money shows intent before continuation
⏳ Fewer signals = higher quality setups
🎯 Works well on H1, H4, and M15
This makes MIF ideal for intraday, swing, and positional Gold traders.
🧠 Detection Logic
Simple • Effective • Battle-Tested
MIF does not rely on lagging indicators.
It confirms intent using three proven market elements:
📈 Structure Shift – Price must break recent highs or lows
🕯 Candle Strength – Strong body dominance, not weak wicks
🔊 Volume Expansion – Participation confirms conviction
Only when all conditions align, a signal is printed.
💥 Displacement Filter
Power Move Confirmation
Gold often creates fake breakouts.
MIF avoids them using a displacement filter:
🚀 Signals appear only during impulsive candles
❌ Weak, slow, or choppy candles are ignored
📊 Confirms real institutional movement
🔥 Ideal for catching continuation after liquidity events
This keeps the indicator clean, disciplined, and professional.
📌 How to Use It Best
🟢 Green Signal → Bullish intent confirmed
🔴 Red Signal → Bearish intent confirmed
🔵 EMA Line → Market bias & trend filter
⚠️ Important Note
This indicator is a confirmation tool, not a prediction engine.
It is designed to help traders trade with the market, not against it.
Bollinger Bands + B%Overview
This script replicates the robust functionality and visual style of the Bollinger Band studies found in Sierra Chart, adapted for the TradingView environment. It is designed as an "All-in-One" suite that calculates the Bollinger Bands for the main price chart while simultaneously offering advanced oscillator studies (like %B and Bandwidth) in the pane below.
A unique feature of this script is the high degree of customization, particularly the ability to choose different Moving Average types for the Bollinger Band basis, and a dynamic coloration system for the %B indicator.
Key Features
Main Chart Overlay: Draws the Bollinger Bands (Upper, Lower, and Basis) directly on the price chart, even though the script runs in a separate pane.
Advanced %B Indicator: A visually enhanced version of Bollinger Bands %B. It features dynamic coloring based on a midline (default 0.5) and intensifies the colors when the value exceeds the high or low thresholds (simulating a band breakout).
Bollinger Bandwidth: Optional display of the bandwidth to measure volatility (Squeeze detection).
Customizable Calculation: Choose from 6 different Moving Average types to calculate the bands.
Moving Average Types Explained
The standard Bollinger Band uses a Simple Moving Average (SMA). This script allows you to change the mathematical basis of the bands to fit your specific trading strategy:
Simple (SMA): The standard calculation. Every price in the period has equal weight. Best for general use.
Exponential (EMA): Gives more weight to recent prices. Reacts faster to price changes than the SMA.
Weighted (WMA): Assigns a linear weighting factor. Recent data is more important, but the drop-off is smoother than EMA.
Linear Regression (LSMA): Calculates a linear regression line for each point. This is excellent for identifying the prevailing trend direction and "fitting" the price action.
Wilders (RMA): The smoothing method used in the RSI indicator. It reacts very slowly and smooths out significant noise.
Smoothed (SMMA): A blend that takes a broad view of price history. It is very stable and filters out minor market fluctuations effectively.
Settings & Parameters
1. Bollinger Bands (Price-Chart)
Show BB in Main Chart: Toggles the visibility of the bands on the price candles.
Length: The lookback period for the bands (Default: 20).
Standard Deviation: The multiplier for the width of the bands (Default: 2.0).
Moving Average Type: Select the algorithm for the center line (Basis).
2. Study: Bollinger Bands %B
Show %B: Toggles the %B oscillator.
High/Low Threshold: Sets the levels for the "Breakout" warnings (Default: 1.0 and 0.0).
Midline: The center point for the color switch (Default: 0.5).
Green: Value > Midline.
Red: Value < Midline.
Bright Green/Red: Value crosses the High/Low Thresholds.
3. Study: Bollinger Bandwidth
Show Bandwidth: Toggles the volatility measurement line.
Usage Tip:
Since %B (0.0 - 1.0) and Bandwidth (variable scale) use different y-axis scales, it is recommended to only enable one sub-study at a time via the checkboxes to maintain a clean chart view.
Disclaimer : This script is for educational and analytical purposes only. It is a code conversion based on public documentation of Sierra Chart Study ID 14 & 136.
Sierra Chart, best trading software, EVER!
With the best datafeet. Denali Exchange Data Feed.
alerts scriptThis script helps traders identify important institutional price zones and receive BUY / SELL alerts automatically when the market reaches those zones, instead of watching charts manually.
The entire system is designed to:
- Reduce manual chart monitoring
- Provide real-time actionable alerts
Gann Market Cycle Alerts (Long-Term)according to gann time cycle move and buy and sell and side ways
Bubble Risk ModelThe question of whether markets can be objectively assessed for overextension has occupied financial researchers for decades. Charles Kindleberger, in his seminal work "Manias, Panics, and Crashes" (1978), documented that speculative bubbles follow remarkably consistent patterns across centuries and asset classes. Yet identifying these patterns in real time remains notoriously difficult. The Bubble Risk Model attempts to address this challenge not by predicting crashes, but by systematically measuring the statistical characteristics that historically precede fragile market conditions.
The theoretical foundation draws from two distinct research traditions. The first is the work on regime-switching models pioneered by James Hamilton (1989), who demonstrated that economic time series often exhibit discrete shifts between different behavioral states. The second is the literature on tail risk and market fragility, most notably articulated by Nassim Taleb in "The Black Swan" (2007), which emphasizes that extreme events carry disproportionate importance and that traditional risk measures systematically underestimate their probability.
Rather than attempting to build a probabilistic model requiring assumptions about underlying distributions, the Bubble Risk Model operates as a deterministic state-inference system. This distinction matters. Lawrence Rabiner's foundational tutorial on Hidden Markov Models (1989) established the mathematical framework for inferring hidden states from observable data through Bayesian updating. The present model borrows the conceptual architecture of states and transitions but replaces probabilistic inference with rule-based logic. States are not computed through forward-backward algorithms but inferred through deterministic thresholds. This trade-off sacrifices theoretical elegance for practical robustness and interpretability.
The measurement framework rests on four empirically grounded components. The first captures trailing twelve-month returns, reflecting the well-documented momentum effect identified by Jegadeesh and Titman (1993), who found that securities with strong past performance tend to continue outperforming over intermediate horizons. The second component measures trend persistence as the proportion of positive daily returns over a quarterly window, drawing on the research by Campbell and Shiller (1988) showing that price trends exhibit serial correlation that deviates from random walk assumptions. The third normalizes the distance between current prices and their long-term moving average by volatility, addressing the cross-sectional comparability problem noted by Fama and French (1992) when analyzing assets with different variance characteristics. The fourth component calculates return efficiency as the ratio of returns to realized volatility, a concept related to the Sharpe ratio but stripped of distributional assumptions that often fail in practice.
The aggregation methodology deliberately prioritizes worst-case scenarios. Rather than averaging component scores, the model uses quantile-based aggregation with an explicit tail penalty. This design choice reflects the asymmetric error costs in bubble detection: failing to identify fragility carries greater consequences than occasional false positives. The approach aligns with the precautionary principle advocated by Taleb and colleagues in their work on fragility and antifragility (2012), which argues that systems exposed to tail risks require conservative assessment frameworks.
Normalization presents a particular challenge. Raw metrics like year-over-year returns are not directly comparable across asset classes with different volatility profiles. The model addresses this through percentile ranking over multiple historical windows, typically two and five years. This dual-window approach provides regime stability, preventing the normalization from adapting too quickly during extended bull markets where elevated readings become statistically normal. The methodology draws on the concept of lookback bias documented by Lo and MacKinlay (1990), who demonstrated that single-window statistical measures can produce misleading results when market regimes shift.
The state machine introduces controlled inertia into the system. Once the model enters a particular state, transitions become progressively more difficult as the state matures. This transition resistance mechanism prevents rapid oscillation near threshold boundaries, a problem that plagues many indicator-based systems. The concept parallels the hysteresis effects described in economic literature by Dixit (1989), where systems exhibit path dependence and resist returning to previous states even when underlying conditions change.
Volatility regime detection adds contextual interpretation. Research by Engle (1982) on autoregressive conditional heteroskedasticity established that volatility clusters, with periods of high volatility tending to follow other high-volatility periods. The model scales its maturity thresholds inversely with volatility: in calm markets, states mature slowly and persist longer; in turbulent markets, information decays faster and states become more transient. This adaptive behavior reflects the empirical observation that low-volatility environments often precede significant market dislocations, as documented by Brunnermeier and Pedersen (2009) in their work on liquidity spirals.
The confidence metric addresses internal model consistency. When individual components diverge substantially, the overall score becomes less reliable regardless of its absolute level. This approach draws on ensemble methods in machine learning, where disagreement among predictors signals increased uncertainty. Dietterich (2000) provides theoretical justification for this principle, demonstrating that ensemble disagreement correlates with prediction error.
Distribution drift detection monitors whether the model's calibration remains valid. By comparing recent score distributions to longer historical baselines, the model can identify when market structure has shifted sufficiently to potentially invalidate its historical percentile rankings. This self-diagnostic capability reflects the concern raised by Andrews (1993) about parameter instability in time series models, where structural breaks can render previously estimated relationships unreliable.
The cross-asset analysis extends the framework beyond individual securities. By calculating scores for multiple asset classes simultaneously and measuring their correlation, the model distinguishes between idiosyncratic overextension affecting a single asset and systemic conditions affecting markets broadly. This differentiation matters for portfolio construction, as documented by Longin and Solnik (2001), who found that correlations between international equity markets increase significantly during periods of market stress.
Several limitations deserve explicit acknowledgment. The model cannot identify timing. Overextended conditions can persist far longer than rational analysis might suggest, a phenomenon documented by Shiller (2000) in his analysis of speculative episodes. The model provides no mechanism for determining when fragile conditions will resolve. Additionally, the cross-asset analysis lacks lead-lag detection, meaning it cannot distinguish whether assets became overextended simultaneously or sequentially. Finally, the rule-based nature of state inference means the model cannot express graduated probability assessments; states are discrete rather than continuous.
The philosophical stance underlying the model is one of epistemic humility. It does not claim to identify bubbles definitively or predict their collapse. Instead, it provides a systematic framework for measuring characteristics that have historically been associated with fragile market conditions. The distinction between information and action remains the user's responsibility. States describe current conditions; how to respond to those conditions requires judgment that no quantitative model can provide.
Practical guide for traders
This section translates the model's outputs into actionable intelligence for both retail traders managing personal portfolios and professional traders operating within institutional frameworks. The interpretation differs not in kind but in scale and consequence.
Understanding the score
The primary output is a continuous score ranging from zero to one. Lower scores indicate elevated bubble risk; higher scores suggest more sustainable market conditions. This inverse relationship may seem counterintuitive but reflects the model's construction: it measures how extreme current conditions are relative to historical norms, with extremity mapping to fragility.
A score above 0.50 generally indicates normal market conditions where standard investment approaches remain appropriate. Scores between 0.30 and 0.50 represent an elevated zone where caution is warranted but not alarm. Scores below 0.30 enter the extreme territory where historical precedent suggests increased fragility. These thresholds are not magical boundaries but represent statistical rarity: a score below 0.30 indicates conditions that occur in roughly the bottom quintile of historical observations.
For retail traders, a score in the normal range means continuing with established strategies without modification. In the elevated range, this might mean pausing new position additions while maintaining existing holdings. In the extreme range, retail traders should consider whether their portfolio could withstand a significant drawdown and whether their time horizon permits waiting for recovery. For professional traders, the score integrates into broader risk frameworks: normal conditions permit full risk budgets, elevated conditions might trigger reduced position sizing or tighter stop losses, and extreme conditions could warrant defensive positioning or increased hedging activity.
Reading the states
The model classifies conditions into three discrete states: Normal, Elevated, and Extreme. These states differ from the continuous score by incorporating persistence and transition resistance. A market can have a score temporarily dipping below 0.30 without triggering an Extreme state if the condition proves transient.
The Normal state indicates business as usual. Market conditions fall within historical norms across all measured dimensions. For retail traders, this means standard portfolio management applies. For professional traders, full strategy deployment remains appropriate with normal risk parameters.
The Elevated state signals heightened attention. At least one dimension of market behavior has moved outside normal ranges, though not to extreme levels. Retail traders should review portfolio concentration and ensure diversification remains intact. Professional traders might reduce leverage slightly, tighten risk limits, or increase monitoring frequency.
The Extreme state represents statistically rare conditions. Multiple dimensions show readings that historically occur infrequently. Retail traders should seriously evaluate whether they can tolerate potential drawdowns and consider reducing exposure to volatile assets. Professional traders should implement defensive protocols, potentially reducing gross exposure, increasing cash allocations, or adding protective positions.
Interpreting transitions
State transitions carry more information than states themselves. The model tracks whether conditions are entering, persisting in, or exiting particular states.
An Entry into Extreme represents the most important signal. It indicates a regime shift from normal or elevated conditions into territory associated with historical fragility. For retail traders, this warrants immediate portfolio review. For professional traders, this typically triggers predefined defensive protocols.
Persistence in a state indicates stability. Whether Normal or Extreme, persistence suggests the current regime has become established. For retail traders, persistence in Extreme over extended periods actually reduces immediate concern; the dangerous moment was the entry, not the continuation. For professional traders, persistent Extreme states require maintained vigilance but do not necessarily demand additional action beyond what the initial entry triggered.
An Exit from Extreme suggests improving conditions. For retail traders, this might warrant cautious return to normal positioning over time. For professional traders, exits permit gradual normalization of risk budgets, though institutional memory typically counsels slower reentry than the mathematical signal might suggest.
Duration and its meaning
The model distinguishes between Tactical, Accelerating, and Structural durations in critical zones.
Tactical duration (10-39 bars in critical territory) represents short-term overextension. Many Tactical episodes resolve without significant market disruption. Retail traders should note the condition but need not take dramatic action. Professional traders might implement modest hedges or reduce marginal positions.
Accelerating indicates Tactical duration combined with actively deteriorating scores. This combination historically precedes more significant corrections. Retail traders should consider lightening positions in their most volatile holdings. Professional traders typically implement more substantial hedges.
Structural duration (40+ bars in critical territory) indicates persistent overextension that has become a market feature rather than a temporary condition. Paradoxically, Structural conditions are both more concerning and less immediately actionable than Accelerating conditions. The market has demonstrated ability to sustain extreme readings. Retail traders should maintain heightened awareness but recognize that timing remains impossible. Professional traders often find Structural conditions require strategy adaptation rather than simple defensive positioning.
Confidence and what it tells you
The Confidence reading indicates internal model consistency. High confidence means all four underlying components agree in their assessment. Low confidence means components diverge significantly.
High confidence combined with Extreme state represents the clearest signal. The model is both indicating fragility and agreeing with itself about that assessment. Retail and professional traders alike should treat this combination with maximum seriousness.
Low confidence in any state reduces signal reliability. For retail traders, low confidence suggests waiting for clearer conditions before making significant portfolio changes. For professional traders, low confidence warrants increased skepticism about the score and potentially reduced position sizing in either direction.
Alignment and model health
The Alignment indicator monitors whether the model's calibration remains valid relative to recent market behavior.
Good alignment means recent score distributions match longer-term historical patterns. The model's percentile rankings remain meaningful. Both retail and professional traders can interpret scores at face value.
Degraded alignment indicates that recent market behavior has shifted somewhat from historical norms. Scores remain interpretable but with reduced precision. Retail traders should apply wider uncertainty bands to their interpretation. Professional traders might reduce position sizing slightly or require additional confirmation before acting.
Poor alignment signals significant distribution shift. The model may be comparing current conditions to an increasingly irrelevant historical baseline. Retail traders should rely more heavily on other information sources during Poor alignment periods. Professional traders typically reduce model weight in their decision frameworks until alignment recovers.
Volatility regime context
The volatility regime provides essential context for score interpretation.
Low volatility combined with Extreme state creates maximum concern. Research consistently shows that low-volatility environments can precede significant market dislocations. The market's apparent calm masks underlying fragility. Retail traders should recognize that low volatility does not mean low risk; it often means compressed risk premiums that will eventually normalize, potentially violently. Professional traders typically maintain or increase defensive positioning despite the market's calm appearance.
High volatility combined with Extreme state is actually less immediately concerning than low volatility. The market has already acknowledged stress; risk premiums have expanded; potential sellers may have already sold. Retail traders should resist the urge to panic sell during high-volatility extremes, as much of the adjustment may have already occurred. Professional traders recognize that high-volatility extremes often represent better entry points than low-volatility extremes.
Normal volatility requires no regime adjustment to interpretation. Scores mean what they appear to mean.
Cross-asset analysis
When enabled, the model calculates scores for multiple asset classes simultaneously, enabling systemic versus idiosyncratic risk assessment.
Systemic risk (multiple assets in Extreme with high correlation) indicates market-wide fragility. Diversification benefits are reduced precisely when most needed. Retail traders should recognize that their portfolio's apparent diversification may not protect them during systemic events. Professional traders implement cross-asset hedges and consider tail-risk protection.
Broad risk (multiple assets in Extreme with low correlation) suggests widespread but potentially unrelated overextension. Diversification may still provide some protection. Retail traders can take modest comfort in genuine diversification. Professional traders analyze which assets might offer relative value.
Isolated risk (single asset in Extreme while others remain Normal) indicates asset-specific rather than market-wide conditions. Retail traders holding the affected asset should evaluate their position specifically. Professional traders may find relative value opportunities going long unaffected assets against the extended one.
Scattered risk represents a few assets showing elevation without clear pattern. This typically warrants monitoring rather than action for both retail and professional traders.
Parameter guidance
The Short Percentile parameter (default 504 bars, approximately two years) controls the shorter normalization window. Increasing this value makes the model more conservative, requiring more extreme readings to flag concern. Retail traders should generally leave this at default. Professional traders might increase it for assets with shorter reliable history.
The Long Percentile parameter (default 1260 bars, approximately five years) controls the longer normalization window. This provides regime stability. Again, default settings suit most applications.
The Critical Threshold (default 0.30) determines where the Extreme state boundary lies. Lowering this value makes the model less sensitive, flagging fewer Extreme conditions. Raising it increases sensitivity. Retail traders seeking fewer false alarms might lower this to 0.25. Professional traders seeking earlier warning might raise it to 0.35.
The Structural Duration parameter (default 40 bars) determines when Tactical conditions become Structural. Shorter values provide earlier Structural classification. Longer values require more persistence before reclassification.
The State Maturity and Transition Resistance parameters control how readily the model changes states. Higher values create more stable states with fewer transitions. Lower values create more responsive but potentially noisier state changes. Default settings balance responsiveness against stability.
The Adaptive Smoothing parameters control how the model filters noise. In extreme zones, longer smoothing periods reduce whipsaws but increase lag. In normal zones, shorter periods maintain responsiveness. Most traders should leave these at defaults.
What the model cannot do
The model cannot predict when overextended conditions will resolve. Markets can remain irrational longer than any trader can remain solvent, as the saying goes. Extended Extreme readings may persist for months or even years before any correction materializes.
The model cannot distinguish between healthy bull markets and dangerous bubbles in their early stages. Both initially appear as strong returns and positive momentum. The model begins flagging concern only when statistical extremity develops, which may occur well into an advance.
The model cannot account for fundamental changes in market structure. If a new paradigm genuinely justifies higher valuations (rare but not impossible), the model will continue flagging extremity against historical norms that may no longer apply. The Alignment indicator provides partial protection against this failure mode but cannot eliminate it.
The model cannot replace judgment. It provides systematic measurement of conditions that have historically preceded fragility. Whether and how to act on that measurement remains entirely the trader's responsibility. Retail traders must still evaluate their personal circumstances, time horizons, and risk tolerance. Professional traders must still integrate model output with fundamental analysis, portfolio constraints, and client mandates.
References
Andrews, D.W.K. (1993). Tests for Parameter Instability and Structural Change with Unknown Change Point. Econometrica, 61(4).
Brunnermeier, M.K., & Pedersen, L.H. (2009). Market Liquidity and Funding Liquidity. Review of Financial Studies, 22(6).
Campbell, J.Y., & Shiller, R.J. (1988). Stock Prices, Earnings, and Expected Dividends. Journal of Finance, 43(3).
Dietterich, T.G. (2000). Ensemble Methods in Machine Learning. Multiple Classifier Systems.
Dixit, A. (1989). Entry and Exit Decisions under Uncertainty. Journal of Political Economy, 97(3).
Engle, R.F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4).
Fama, E.F., & French, K.R. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance, 47(2).
Hamilton, J.D. (1989). A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle. Econometrica, 57(2).
Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1).
Kindleberger, C.P. (1978). Manias, Panics, and Crashes: A History of Financial Crises. Basic Books.
Lo, A.W., & MacKinlay, A.C. (1990). Data-Snooping Biases in Tests of Financial Asset Pricing Models. Review of Financial Studies, 3(3).
Longin, F., & Solnik, B. (2001). Extreme Correlation of International Equity Markets. Journal of Finance, 56(2).
Rabiner, L.R. (1989). A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition. Proceedings of the IEEE, 77(2).
Shiller, R.J. (2000). Irrational Exuberance. Princeton University Press.
Taleb, N.N. (2007). The Black Swan: The Impact of the Highly Improbable. Random House.
Taleb, N.N., & Douady, R. (2012). Mathematical Definition, Mapping, and Detection of (Anti)Fragility. Quantitative Finance, 13(11).
SAS 4H Positional ScreenerSAS 4H Positional Screener is a structure-based trend filter designed for 4-hour positional trading in Indian large-cap stocks.
It identifies high-probability bullish setups by combining trend alignment, price acceptance, and institutional market structure.
This screener is not a buy/sell strategy.
It is a professional pre-trade filter used to shortlist stocks that are ready or near-ready for LONG trades.
Multi-Indicator Scoring System# Multi-Indicator Scoring System
## Overview
This indicator combines five technical analysis tools (RSI, MACD, EMA trends, and Volume) into a single unified scoring system that generates clear BUY and SELL signals. Instead of analyzing multiple indicators separately and dealing with conflicting signals, this script calculates one comprehensive 0-100% score that shows current market strength at a glance.
## Purpose and Originality
**Problem it solves:**
Traders using multiple indicators individually often face contradictory signals. For example, RSI might show oversold conditions while MACD indicates bearish momentum, or price is above EMA but volume is weak. This creates confusion and leads to poor trading decisions or missed opportunities.
**Solution:**
This script uses a weighted scoring algorithm that only generates signals when multiple technical components mathematically agree. Each indicator contributes weighted points based on its reliability in crypto markets, and the combined score filters out noise by requiring multi-indicator confirmation before triggering a signal.
**What makes it original:**
Unlike simple indicator overlays that just display multiple tools side-by-side, this script:
- Uses a mathematically weighted scoring system where each component has justified importance
- Requires conditional alignment—signals only appear when components agree, not just individual crossovers
- Normalizes complex multi-indicator data into one intuitive percentage
- Includes built-in volume confirmation to filter low-conviction setups
This approach mirrors professional algorithmic trading systems that use multi-factor quantitative models.
## How Components Work Together
The script analyzes five technical components and assigns weighted points to each:
### 1. RSI (Relative Strength Index) - Weight: 25 points
- **Period:** 14
- **Function:** Identifies overbought and oversold conditions
- **Scoring logic:**
- RSI < 30 (oversold) → +25 points (bullish reversal signal)
- RSI > 70 (overbought) → -25 points (bearish reversal signal)
- RSI between 30-70 → 0 points (neutral)
- **Why 25 points:** RSI is highly reliable for detecting potential reversal zones in cryptocurrency markets
### 2. MACD (Moving Average Convergence Divergence) - Weight: 25 points
- **Parameters:** Fast=12, Slow=26, Signal=9
- **Function:** Detects momentum shifts and trend changes
- **Scoring logic:**
- MACD line > Signal line → +25 points (bullish momentum)
- MACD line < Signal line → -25 points (bearish momentum)
- **Why 25 points:** MACD is the gold standard for momentum confirmation across timeframes
### 3. EMA Short-Term Trend (21 vs 50) - Weight: 25 points
- **Function:** Confirms immediate trend direction
- **Calculation:** Compares EMA 21 to EMA 50, plus price position relative to EMA 21
- **Scoring logic:**
- EMA 21 > EMA 50 AND Price > EMA 21 → +25 points (strong uptrend)
- EMA 21 < EMA 50 AND Price < EMA 21 → -25 points (strong downtrend)
- Mixed conditions → 0 points (no clear trend)
- **Why 25 points:** Short-term trend alignment is critical for accurate entry timing
### 4. EMA Long-Term Context (200) - Weight: 15 points
- **Function:** Validates overall market structure
- **Calculation:** Price position relative to 200-period EMA
- **Scoring logic:**
- Price > EMA 200 → +15 points (bull market context)
- Price < EMA 200 → -15 points (bear market context)
- **Why 15 points:** Lower weight because long-term trend changes more slowly
### 5. Volume Confirmation - Weight: 10 points (Bonus)
- **Function:** Confirms genuine market interest versus noise
- **Calculation:** Current volume compared to 20-period SMA
- **Scoring logic:**
- Volume > 1.5× average → +10 bonus points
- Volume ≤ 1.5× average → 0 bonus points
- **Why 10 points:** Volume adds conviction but shouldn't override technical setup
### Score Aggregation Formula
**Why these thresholds?**
Backtesting on BTC/ETH showed optimal risk/reward at 65/35 levels. Lower thresholds (50%) produce too many false signals, while higher thresholds (80%) miss opportunities. The 65/35 balance provides good sensitivity with acceptable accuracy.
## How to Use This Indicator
### Visual Components
**On Chart:**
- **Green triangle (▲) below candle** = BUY signal (score crossed above 65%)
- **Red triangle (▼) above candle** = SELL signal (score crossed below 35%)
- Clean display with no background colors or extra lines
**Dashboard Table (top-right corner):**
- **Header:** "CRYPTO SIGNAL"
- **SCORE:** Current percentage (0-100%)
- Green color = Bullish zone (65%+)
- Red color = Bearish zone (35%-)
- Orange color = Neutral zone (36-64%)
- **SIGNAL:** Current status (BUY/SELL/WAIT)
### Interpreting the Score
- **70-100% (Strong Bullish):** All or most indicators agree market is going up. Consider long positions.
- **65-69% (BUY Signal Zone):** Enough confirmation for entry. BUY signals trigger here.
- **36-64% (Neutral Zone):** No clear direction. Wait for clearer setup or maintain existing positions.
- **31-35% (SELL Signal Zone):** Enough confirmation for exit. SELL signals trigger here.
- **0-30% (Strong Bearish):** All or most indicators agree market is going down. Avoid longs or consider shorts.
### Step-by-Step Usage
1. **Add to chart:** Click "Add to favorites" then add from your indicators list
2. **Check the score:** Look at the dashboard table in the top-right corner
3. **Wait for signals:**
- Green triangle appears = Consider buying
- Red triangle appears = Consider selling
- No triangle = Wait patiently for clearer setup
4. **Confirm with price action:** Best results when signals appear at support/resistance levels
5. **Use risk management:** Always set stop losses (3-5% below entry for longs)
6. **Set alerts (optional):** Right-click indicator → "Add alert" → Choose "BUY Signal" or "SELL Signal"
### Best Practices
**Recommended Timeframes:**
- **4-Hour (4H):** Best for swing trading, optimal signal frequency (3-7 per month), lowest false signal rate
- **Daily (1D):** Best for position trading, very high reliability, ideal for patient traders
- **1-Hour (1H):** More signals but noisier, only for experienced traders
- **Below 15 minutes:** Not recommended, too many false signals
**Recommended Markets:**
- Bitcoin (BTCUSDT, BTCUSD) - Most reliable
- Ethereum (ETHUSDT, ETHUSD) - Excellent results
- Major altcoins (SOL, XRP, ADA, etc.) - Works well on top 20 by market cap
**Risk Management:**
- Position size: Risk only 1-2% of account per trade
- Stop loss: Place 3-5% below entry (BUY) or above entry (SELL)
- Take profit: Target 2-3× your risk distance
- Trail stops: Move to breakeven after 1:1 profit achieved
**Advanced Tips:**
- Combine signals with support/resistance levels for higher probability setups
- Check multiple timeframes: if 4H and 1D both show BUY, signal is stronger
- Wait for candle close before acting on signals
- Ignore signals against the higher timeframe trend direction
- Only trade signals accompanied by volume spikes (check dashboard)
## Default Settings
The indicator uses pre-optimized parameters based on backtesting:
- RSI Period: 14
- MACD: 12, 26, 9
- EMA Short-term: 21, 50
- EMA Long-term: 200
- Volume threshold: 1.5× average
- Signal thresholds: BUY ≥65%, SELL ≤35%
These settings are designed for cryptocurrency markets on 4H and 1D timeframes and do not require adjustment for most users.
## Limitations and Disclaimers
**What this indicator CANNOT do:**
- Predict black swan events (exchange hacks, major regulations, etc.)
- Work effectively during extreme market manipulation
- Replace proper risk management and stop losses
- Guarantee profits (no indicator can)
- Account for fundamental news (Fed decisions, major announcements)
**When signals may be less reliable:**
- Low volume periods (weekends, holidays)
- High-impact news events
- Extreme volatility (>10% daily price moves)
- Prolonged sideways/ranging markets
**Important warnings:**
- This is a technical analysis tool, not financial advice
- Past performance does not guarantee future results
- Always use stop losses to protect capital
- Test the indicator with small positions first
- Do your own research before trading
## Technical Specifications
- **Pine Script Version:** v5
- **Type:** Overlay indicator
- **Signals:** Non-repainting (confirmed at candle close only)
- **Calculation frequency:** Every bar recalculates based on current values
- **Alerts:** Available for BUY and SELL threshold crossings
- **Resource usage:** Optimized for efficient runtime performance
## Additional Notes
- Signals appear only once when threshold is crossed (no repeated signals during same trend)
- Volume filter helps eliminate low-conviction signals
- Works on any cryptocurrency pair with sufficient liquidity
- Can be combined with other indicators for additional confirmation
- Suitable for both beginners (simple visual signals) and experienced traders (customizable for deeper analysis)
---
**This indicator provides educational value by demonstrating how multi-indicator confirmation systems work and how weighted scoring can reduce false signals compared to using individual indicators alone.**
All Candlestick Patterns [theEccentricTrader]█ OVERVIEW
This indicator automatically draws and sends alerts for all of the candlestick patterns in my public library as they occur. Patterns included in this script are listed below, with their conventional classifications (in brackets) for reference only:
Doji (Neutral)
Bullish Marubozu (Bullish Continuation)
Bearish Marubozu (Bearish Continuation)
Spinning Top (Neutral)
Bullish Belt-Hold Line (Bullish Reversal)
Bearish Belt-Hold Line (Bearish Reversal)
Bullish Breakaway (Bullish Reversal)
Bearish Breakaway (Bearish Reversal)
Concealing Baby Swallow (Bullish Reversal)
Bullish Counterattack (Bullish Reversal)
Bearish Counterattack (Bearish Reversal)
Dark Cloud Cover (Bearish Reversal)
Long-Legged Doji (Neutral)
Southern Doji (Bullish Reversal)
Northern Doji (Bearish Reversal)
Dumpling Top (Bearish Reversal)
Bullish Engulfing (Bullish Reversal)
Bearish Engulfing (Bearish Reversal)
Frypan Bottom (Bullish Reversal)
Hammer (Bullish Reversal)
Hanging Man (Bearish Reversal)
Bullish Harami (Bullish Reversal)
Bearish Harami (Bearish Reversal)
Bullish Harami Cross (Bullish Reversal)
Bearish Harami Cross (Bearish Reversal)
High-Wave (Neutral)
Bullish Hikkake (Bullish Reversal)
Bearish Hikkake (Bearish Reversal)
Homing Pigeon (Bullish Reversal)
In-Neck (Bullish Reversal)
Bullish Kicking (Bullish Reversal)
Bearish Kicking (Bearish Reversal)
Matching Low (Bullish Reversal)
On-Neck (Bullish Reversal)
Piercing (Bullish Reversal)
Bullish Separating Lines (Bullish Continuation)
Bearish Separating Lines (Bearish Continuation)
Upgap Side-by-Side White Lines (Bullish Continuation)
Downgap Side-by-Side White Lines (Bearish Continuation)
Stalled Pattern (Neutral)
Bullish Star (Bullish Reversal)
Bearish Star (Bearish Reversal)
Bullish Doji Star (Bullish Reversal)
Bearish Doji Star (Bearish Reversal)
Morning Star (Bullish Reversal)
Evening Star (Bearish Reversal)
Morning Doji Star (Bullish Reversal)
Evening Doji Star (Bearish Reversal)
Abandoned Baby Bottom (Bullish Reversal)
Abandoned Baby Top (Bearish Reversal)
Inverted Hammer (Bullish Reversal)
Shooting Star (Bearish Reversal)
Dragonfly Doji (Bullish Reversal)
Gravestone Doji (Bearish Reversal)
Stick Sandwich (Bullish Reversal)
Upward Gapping Tasuki (Bullish Continuation)
Downward Gapping Tasuki (Bearish Continuation)
Three Black Crows (Bearish Reversal)
Advance Block (Neutral)
Three Advancing White Soldiers (Bullish Reversal)
Bullish Three-Line Strike (Bullish Continuation)
Bearish Three-Line Strike (Bearish Continuation)
Rising Three Methods (Bullish Continuation)
Falling Three Methods (Bearish Continuation)
Three Stars in the South (Bullish Reversal)
Thrusting (Bullish Reversal)
Tower Bottom (Bullish Reversal)
Tower Top (Bearish Reversal)
Tri-Star Bottom (Bullish Reversal)
Tri-Star Top (Bearish Reversal)
Tweezer Bottom (Bullish Reversal)
Tweezer Top (Bearish Reversal)
Upside-Gap Two Crows (Bearish Reversal)
█ CONCEPTS
Candlestick Patterns
Candlestick charts originated in Japan and were developed as a way of recording and interpreting price movement in actively traded markets. Rather than focusing only on where price closed, candlesticks preserve information about the range of trading during a given period, showing where prices opened, how far they moved, where they were rejected and where they ultimately settled. In this sense, each candlestick is a compact record of the interaction between buyers and sellers over time.
At a basic level, markets move through a sequence of swing highs and swing lows as supply and demand fluctuates. Candlesticks are the smallest visible components of this process. The size of the candle body reflects the degree of control exercised by buyers or sellers, while the presence and length of wicks reflect rejection, hesitation or absorption of opposing orders. When similar behaviours repeat in similar locations, recognisable patterns emerge.
Candlestick patterns therefore do not represent fixed signals, but recurring expressions of market psychology. They capture moments where initiative changes hands, where momentum pauses or where one side fails to follow through. A pattern that appears during a strong trend may simply reflect temporary consolidation, while the same pattern forming at an extreme or after prolonged movement may indicate exhaustion or transition. Context is always decisive.
Over time, traders have assigned names and classifications to many of these recurring formations. However, these classifications are not universal or permanent. Patterns that were historically described as reversals are sometimes better understood as continuation structures and patterns labelled as bullish or bearish can have very different implications depending on market, timeframe, volatility and surrounding structure. As with all forms of price analysis, interpretation matters more than memorisation.
Candlestick patterns should therefore be viewed as descriptive tools rather than predictive rules. They provide insight into how price reached its current state and how supply and demand interacted along the way. Their usefulness lies in how they are combined with broader market structure, risk management and independent testing, not in the assumption that any single pattern guarantees a particular outcome.
█ INPUTS
Change label colours and size.
Set alerts for individual patterns.
█ SOURCES
Homma, M. (c. 1755) The Fountain of Gold: The Three Monkey Record of Money. Attributed Japanese trading manuscript. Modern English translation (Apple Books).
Nison, S. (2001) Japanese Candlestick Charting Techniques (2nd edn). New York: New York Institute of Finance.
Bulkowski, T. N. (2008) Encyclopedia of Candlestick Charts. Hoboken, New Jersey: John Wiley & Sons.
Peak Rejection LevelsPeak Rejection Levels is a price-action–based indicator designed to automatically identify strong rejection levels at swing highs and swing lows.
It highlights areas where price attempted to move further but was firmly rejected, often acting as key support or resistance zones.
The indicator is especially useful for :
Intraday and swing trading
Identifying high-probability rejection zones
Support/resistance mapping based on pure price action
Confluence with trend, structure, or indicator-based strategies
📈 What Is a “Peak Rejection”?
A peak rejection is defined using strict price-action rules:
🔺 Swing High Rejection (Resistance)
A swing high is marked as a rejection when:
The candle is a confirmed swing high
The candle has an upper wick
The upper wick is larger than the candle body
The wick represents the highest price of the swing
This indicates strong selling pressure and rejection from higher prices
🔻 Swing Low Rejection (Support)
A swing low is marked as a rejection when:
The candle is a confirmed swing low
The candle has a lower wick
The lower wick is larger than the candle body
The wick represents the lowest price of the swing
This indicates strong buying pressure and rejection from lower prices
When these conditions are met, the indicator draws a horizontal level at the rejection wick.
🧠 Key Features
✅ Works on any timeframe
✅ Non-repainting (uses confirmed swings)
✅ Automatically removes broken levels
✅ Automatically removes old levels based on time
✅ Clean and uncluttered chart output
✅ Pure price-action logic (no indicators, no lag)
Relative Strength Leadership Engine v2.0Relative Strength Leadership Engine v2.0OverviewThe Relative Strength Leadership Engine v2.0 is a context-first diagnostic tool designed to identify true market leadership. Instead of simple ratio lines, this script employs a multi-layered scoring model to determine if a symbol is truly outperforming its benchmark (e.g., SPY) or simply riding market beta.The Problem It SolvesMany relative strength indicators fail to distinguish between idiosyncratic leadership and market correlation. A stock might look strong simply because it is a high-beta names moving in lockstep with a rising index. This engine uses Pearson Correlation Filtering and Volatility Normalization to decouple these factors.How It Works (The Math)To ensure full transparency for the TradingView community, the "Leadership Score" (0–100) is calculated based on four proprietary technical pillars:Baseline Alignment (30 pts): Measures if the $Price / Benchmark$ ratio is above its 21-period EMA.Volatility-Normalized Momentum (25 pts): We calculate a Z-score of the RS slope and divide it by the asset's ATR % of price. This ensures momentum is measured by "clean" price action rather than high-beta volatility spikes.Beta-Decoupling (20 pts): Using ta.correlation, the script penalizes "Market Huggers." Points are awarded when a stock shows strength independent of the benchmark's immediate fluctuations.Freshness & Highs (25 pts): Points are awarded for proximity to 252-day relative strength highs, identifying stocks entering a "Power Zone" of leadership.Interpreting the StatesThe dashboard in the bottom-right identifies three distinct permission states:ENGAGE (Score 80+): Full leadership permission. The asset is outperforming with idiosyncratic strength and clean momentum (See FDX example in the gallery).OBSERVE (Score 50–79): Leadership is present but aging or overly correlated to the market (See MU example in the gallery).STAND DOWN (Score <50): Leadership is broken; the asset is a relative laggard (See CBLL example in the gallery).Technical FeaturesMulti-Timeframe Validation: Optional Weekly/Monthly RS confirmation to filter out "noise."Benchmark Timing Filter: A built-in gate that checks if the broader market (Benchmark) is in a "Risk-Off" regime.Non-Repainting: All security calls use lookahead=barmerge.lookahead_off to ensure historical accuracy.Customizable UI: Toggle the dashboard on/off via the "Style" menu for a cleaner workspace.DisclaimerThis script is an informational diagnostic tool and does not generate trade signals, entries, or exits. Educational use only.
JOWY LA VERDADERA ESTRUCTURABasically it is an indicator that perfectly represents the typical BoS Market structure in the fastest way. It is advisable to study several temporalities at the same time and not focus on just one.
Beta Coefficient & RSI Table (Midcaps vs Majors)Beta Coefficient & RSI Table (Midcaps vs Majors)
This script builds a comprehensive beta comparison framework between midcap assets and majors for benchmarks, enhanced with a simple RSI midline strategy for clean entry and exit signaling.
In addition to beta-based relative analysis, the script:
Computes raw RSI values on midcap assets for standalone trend qualification
Evaluates every midcap/major ratio combination using the same RSI-based regime logic
Produces binary (0 / 1) signals suitable for systematic filtering and automation
Designed with automation in mind, this script is perfect for daily alerts that can send webhooks externally, and is fully compatible to reliably daily close updates for:
Ratio beta comparisons (midcaps vs majors)
Binary RSI crossover signals on each ratio
Base midcap trend state (RSI > 45 indicating an active uptrend) - 45 made for a slightly faster entry signal if used as a preliminary filter
This makes the table ideal for automated system building, signal aggregates, and hands-off portfolio logic.
Full credits to @MarktQuant and @NianiaFrania🐸 for the original script source.
Asia Range + Killzones (London/NY) + Liquidity Sweep AlertsGPT Asia Range + Killzones (London/NY) + Liquidity Sweep Alerts
