HES - SL/TP1/TP2 - 80% winrate (Helal)This script automatically identifies Fair Value Gaps (FVG) and Order Blocks (OB) across multiple timeframes, calculates bias alignment, and executes simulated trades with dynamic stop loss and two take-profit targets (TP1/TP2). It also logs each trade, displays real-time trade info and performance summary tables, and triggers alerts on new entries.
指标和策略
Nifty vs Nifty Fut Premium indicator This indicator compares Nifty Spot and Nifty Futures prices in real-time, displaying the premium (or discount) between them at the top of the pane.
Trading applications:
Arbitrage opportunities: When the premium becomes unusually high or low compared to fair value (based on cost of carry), traders can exploit the mispricing through cash-futures arbitrage
Market sentiment: A rising premium often indicates bullish sentiment as traders are willing to pay more for futures, while a declining or negative premium suggests bearish sentiment
Rollover strategy: Near expiry, monitoring the premium helps traders decide optimal timing for rolling positions from current month to next month contracts
Risk assessment: Sudden spikes in premium can signal increased demand for leveraged long positions, potentially indicating overbought conditions or strong momentum
🐬RSI_CandleRSI_Candle
Calculates the RSI based on the open, high, low, and close prices, and displays it in the form of candles.
The overbought and oversold zones are highlighted with background colors, which become darker as the RSI value approaches 100 or 0.
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RSI_Candle
RSI를 시가, 고가, 저가, 종가로 계산하여 캔들로 보여줍니다.
과매수/과매도 구간에서 배경색으로 보여주며, 100/0에 가까울수록 배경색이 짙어집니다.
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🐬Stochastic_RSIStochastic RSI
The indicator highlights the chart background for two specific signals:
- A bearish deadcross occurring above the upper band.
- A bullish goldencross occurring below the lower band.
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스토캐스틱 RSI
두가지 신호를 배경색으로 나타냅니다.
- 어퍼 밴드 위에서의 데드크로스
- 로우어 밴드 아래에서의 골든크로스
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Relative Strength Peers -> PROFABIGHI_CAPITAL🌟 Overview
This indicator evaluates relative strength among a customizable group of assets by comparing their smoothed RSI values, identifying outperformers and underperformers through a scoring matrix. It generates visual tables to rank assets based on peer performance, aiding traders in spotting momentum leaders for potential allocation or rotation strategies.
⚙️ Settings
- Adjustable number of assets for analysis, balancing depth with performance
- RSI calculation period for momentum sensitivity
- Primary moving average type and length for initial RSI smoothing
- Optional secondary moving average type and length for advanced comparison
- Toggle for dual moving average scoring versus threshold-based evaluation
- Volatility lookback for adaptive smoothing in variable market conditions
- Table customization options like text size, header visibility, and input summaries
- Highlighting preferences for trends, top performers, and visual emphasis methods
- Enable/disable switches for RSI computations, table displays, and asset inputs
📊 Data Acquisition & Preparation
- Fetches real-time closing prices from selected asset tickers using security requests
- Cleans ticker symbols by removing exchange prefixes for consistent labeling
- Limits analysis to specified asset count to optimize processing speed
- Stores prices in dedicated variables per asset for efficient relative calculations
- Validates data integrity by detecting constant or invalid sources
- Builds an array of user-defined assets, supporting up to 40 cryptocurrency pairs
- Updates prices only on confirmed bars to ensure reliable historical alignment
📈 RSI Smoothing & Scoring Logic
- Computes base RSI on asset prices normalized against each peer for relative momentum
- Applies user-selected smoothing to RSI using various moving average methods
- Supports simple averages like SMA and EMA for basic trend filtering
- Includes advanced options such as HMA for reduced lag and VIDYA for volatility adaptation
- Handles double smoothing with optional second MA for crossover-based signals
- Assigns binary scores: outperforming (1) if smoothed RSI exceeds neutral threshold or faster MA leads slower one
- Aggregates scores across all peers into per-asset totals for overall strength ranking
- Ranks assets by descending sum, with ties preserved in top performer lists
📋 Matrix & Ranking Computation
- Constructs a comprehensive score matrix comparing each asset against every other
- Populates rows and columns with directional indicators for quick outperformance scans
- Sums row values to quantify an asset's dominance over the peer group
- Derives ranks through pairwise comparisons, prioritizing higher total scores
- Manages ties in rankings to ensure fair representation in leaderboards
- Combines matrix data into a flattened array for efficient table rendering
- Filters computations to active asset count, avoiding unnecessary overhead
📉 Visualization
- Renders a main table as a heatmap-style matrix with rocket (🚀) for outperformance and down arrow (📉) for underperformance
- Displays asset labels along axes, with diagonal blanks to avoid self-comparisons
- Includes summary columns for total scores and final ranks, with optional gradient highlighting
- Positions a compact top assets table in the upper right, listing leaders with points allocation
- Customizes appearance via text sizing, background/text emphasis, and header toggles
- Shows input parameters summary row for quick reference without menu access
- Updates visuals only on the last bar for real-time relevance without repainting
🛠 Performance & Customization
- Conditional enabling of features like RSI analysis to reduce computational load
- Modular functions for price fetching, smoothing, and scoring to enhance maintainability
- Array-based storage for scalable handling of up to 40 assets without code bloat
- Inline options for MA configurations to streamline user interface
- Tooltip guidance on each input for contextual help during setup
- Fixed table positions (bottom center for main, top right for leaders) for consistent viewing
- Handles edge cases like zero volatility or missing data with fallback logic
✅ Key Takeaways
- Delivers peer-relative momentum insights through RSI-driven scoring and visual matrices
- Flexible smoothing and dual-MA modes adapt to diverse trading styles and sensitivities
- Prioritizes top performers with ranked tables, easing asset rotation decisions
- Optimizes for performance with toggles and limits, suitable for live trading dashboards
- Combines quantitative ranks with intuitive symbols for rapid market scanning
Position Sizer Box-by ParthibPosition Sizer Box — Ultra Simple Position Size Calculator
A clean, no-nonsense tool for instantly calculating your position size based on your chosen risk amount, entry price, and stop loss.
How it works:
Enter your risk (₹), entry price, and stop price in the input fields.
The indicator uses the standard formula:
Position Size = Risk Amount ÷ |Entry − Stop|
Instantly see the number of shares/units to buy in a minimalist, semi-transparent box at the top-right of your chart.
Features:
Simple, distraction-free design—perfect for focused traders.
All values (risk, entry, SL, and total shares) are always visible.
Auto-refreshes as soon as you change your inputs—no need to recalculate or reload.
Designed for manual and systematic traders who want instant, objective answers on position size.
Works on any time frame or instrument.
Why use this?
Keeps your risk management sharp and automated.
Never waste time on manual calculator work again—just enter values and trade.
Fits any strategy that relies on fixed risk per trade.
Notes:
This indicator does NOT place trades or calculate stops for you—it's purely for position size planning.
Use the indicator’s settings to enter your numbers and see the result instantly.
You can connect with me on-
Mail- parthibtrades01@gmail.com
Instagram- trade.parthib
Session-Conditioned Regime ATRWhy this exists
Classic ATR is great—until the open. The first few bars often inherit overnight gaps and 24-hour noise that have nothing to do with the intraday regime you actually trade. That inflates early ATR, scrambles thresholds, and invites hyper-recency bias (“today is crazy!”) when it’s just the open being the open.
This tool was built to:
Separate session reality from 24h noise. Measure volatility only inside your defined session (e.g., NYSE 09:30–16:00 ET).
Judge candles against the current regime, not the last 2–3 bars. A rolling statistic from the last N completed sessions defines what “typical” means right now.
Label “large” and “small” objectively. Bars are colored only when True Range meaningfully departs from the session regime—no gut feel, no open-bar distortion (gap inclusion optional).
Overview
Purpose: objectively identify unusually big or small candles within the active trading session, compared to the recent session regime.
Use cases: volatility filters, entry/exit confirmation, session bias detection, adaptive sizing.
This indicator replaces generic ATR with a session-conditioned, regime-aware measure. It colors candles only when their True Range (TR) is abnormally large/small versus the last N completed sessions of the same session window.
How it works
Session gating: Only bars inside the selected session are evaluated (presets for NYSE, CME RTH, FX NY; custom supported).
Per-bar TR: TR = max(high, prevRef) − min(low, prevRef).
prevRef is the prior close for in-session bars.
First bar of the session can include the overnight gap (optional; default off).
Regime statistic: For any bar in session k, aggregate all in-session TRs from the previous N completed sessions (k−N … k−1), then compute Median (default) or Mean.
Today’s anchor: Running statistic from today’s session start → current bar (for context and the on-chart ratio).
Color logic:
Big if TR ≥ bigMult × RegimeStat
Small if TR ≤ smallMult × RegimeStat
Colored states: big bull, big bear, small bull, small bear.
Non-triggering bars retain the chart’s native colors.
Panel (top-right by default)
Regime ATR (Nd): session-conditioned statistic over the past N completed sessions.
Today ATR (anchored): running statistic for the current session.
Ratio (Today/Regime): intraday volatility vs regime.
Sample size n: number of bars used in the regime calculation.
Inputs
Session Preset: NYSE (09:30–16:00 ET), CME RTH (08:30–15:00 CT), FX NY (08:00–17:00 ET), Custom (session + IANA timezone).
Regime Window: number of completed sessions (default 5).
Statistic: Median (robust) or Mean.
Include Open Gap: include overnight gap in the first in-session bar’s TR (default off).
Big/Small thresholds: multipliers relative to RegimeStat (defaults: Big=1.5×, Small=0.67×).
Colors: four independent colors for big/small × bull/bear.
Panel position & text size.
Hidden outputs: expose RegimeStat, TodayStat, Ratio, and Z-score to other scripts.
Alerts
RegimeATR: BIG bar — triggers when a bar meets the “Big” condition.
RegimeATR: SMALL bar — triggers when a bar meets the “Small” condition.
Hidden outputs (for strategies/screeners)
RegimeATR_stat, TodayATR_stat, Today_vs_Regime_Ratio, BarTR_Zscore.
Notes & limitations
No look-ahead: calculations only use information available up to that bar. Historical colors reflect what would have been known then.
Warm-up: colors begin once there are at least N completed sessions; before that, regime is undefined by design.
Changing inputs (session window, multipliers, median/mean, gap toggle) recomputes the full series using the same rolling regime logic per bar.
Designed for standard candles. Styling respects existing chart colors when no condition triggers.
Practical tips
For a broader or tighter notion of “unusual,” adjust Big/Small multipliers.
Prefer Median in markets prone to outliers; use Mean if you want Z-score alignment with the panel’s regime mean/std.
Use the Ratio readout to spot compression/expansion days quickly (e.g., <0.7× = compressed session, >1.3× = expanded).
Roadmap
More session presets:
24h continuous (crypto, index CFDs).
23h/Globex futures (CME ETH with a 60-minute maintenance break).
Regional equities (LSE, Xetra, TSE), Asia/Europe/NY overlaps for FX.
Half-day/holiday templates and dynamic calendars.
Multi-regime comparison: track multiple overlapping regimes (e.g., RTH vs ETH for futures) and show separate stats/ratios.
Robust stats options: trimmed mean, MAD/Huber alternatives; optional percentile thresholds instead of fixed multipliers.
Subpanel visuals: rolling TodayATR and Ratio plots; optional Z-score ribbon.
Screener/strategy hooks: export boolean series for BIG/SMALL, plus a lightweight strategy template for backtesting entries/exits conditioned on regime volatility.
Performance/QOL: per-symbol presets, smarter warm-up, and finer control over sample caps for ultra-low TF charts.
Changelog
v0.9b (Beta)
Session presets (NYSE/CME RTH/FX NY/Custom) with timezone handling.
Panel enhancements: ratio + sample size n.
Four-state bar coloring (big/small × bull/bear).
Alerts for BIG/SMALL bars.
Hidden Z-score stream for downstream use.
Gap-in-TR toggle for the first in-session bar.
Disclaimer
For educational purposes only. Not investment advice. Validate thresholds and session settings across symbols/timeframes before live use.
Volatility Adjusted Relative Strength (VARS) - Histogram OptionI’ve developed a new version of VARS that includes an option to toggle it into a histogram view. I recommend using a single neutral color rather than the conventional “red below 0, green above 0” scheme — because true RS analysis shouldn’t rely on color cues. The focus should be on the immediacy and persistence of RS itself to capture that initial breakout move as the most optimal RRR entry. This also provides clearer insight and visualization into how RS functions (both traditional and VARS) since RS is a static EOD metric derived from a defined timeframe.
I want to emphasize again that VARS is useful to identify low-risk entries, with relative strength calibrated to the volatility of the reference index (in this case, AMEX:SPY ). It is not used to determine my exits — those should be governed by a strict, non-discretionary framework for partial profit-taking and final exit of a position.
Kalman Filter [DCAUT]█ Kalman Filter
📊 ORIGINALITY & INNOVATION
The Kalman Filter represents an important adaptation of aerospace signal processing technology to financial market analysis. Originally developed by Rudolf E. Kalman in 1960 for navigation and guidance systems, this implementation brings the algorithm's noise reduction capabilities to price trend analysis.
This implementation addresses a common challenge in technical analysis: the trade-off between smoothness and responsiveness. Traditional moving averages must choose between being smooth (with increased lag) or responsive (with increased noise). The Kalman Filter improves upon this limitation through its recursive estimation approach, which continuously balances historical trend information with current price data based on configurable noise parameters.
The key advancement lies in the algorithm's adaptive weighting mechanism. Rather than applying fixed weights to historical data like conventional moving averages, the Kalman Filter dynamically adjusts its trust between the predicted trend and observed prices. This allows it to provide smoother signals during stable periods while maintaining responsiveness during genuine trend changes, helping to reduce whipsaws in ranging markets while not missing significant price movements.
📐 MATHEMATICAL FOUNDATION
The Kalman Filter operates through a two-phase recursive process:
Prediction Phase:
The algorithm first predicts the next state based on the previous estimate:
State Prediction: Estimates the next value based on current trend
Error Covariance Prediction: Calculates uncertainty in the prediction
Update Phase:
Then updates the prediction based on new price observations:
Kalman Gain Calculation: Determines the weight given to new measurements
State Update: Combines prediction with observation based on calculated gain
Error Covariance Update: Adjusts uncertainty estimate for next iteration
Core Parameters:
Process Noise (Q): Represents uncertainty in the trend model itself. Higher values indicate the trend can change more rapidly, making the filter more responsive to price changes.
Measurement Noise (R): Represents uncertainty in price observations. Higher values indicate less trust in individual price points, resulting in smoother output.
Kalman Gain Formula:
The Kalman Gain determines how much weight to give new observations versus predictions:
K = P(k|k-1) / (P(k|k-1) + R)
Where:
K is the Kalman Gain (0 to 1)
P(k|k-1) is the predicted error covariance
R is the measurement noise parameter
When K approaches 1, the filter trusts new measurements more (responsive).
When K approaches 0, the filter trusts its prediction more (smooth).
This dynamic adjustment mechanism allows the filter to adapt to changing market conditions automatically, providing an advantage over fixed-weight moving averages.
📊 COMPREHENSIVE SIGNAL ANALYSIS
Visual Trend Indication:
The Kalman Filter line provides color-coded trend information:
Green Line: Indicates the filter value is rising, suggesting upward price momentum
Red Line: Indicates the filter value is falling, suggesting downward price momentum
Gray Line: Indicates sideways movement with no clear directional bias
Crossover Signals:
Price-filter crossovers generate trading signals:
Golden Cross: Price crosses above the Kalman Filter line, suggests potential bullish momentum development, may indicate a favorable environment for long positions, filter will naturally turn green as it adapts to price moving higher
Death Cross: Price crosses below the Kalman Filter line, suggests potential bearish momentum development, may indicate consideration for position reduction or shorts, filter will naturally turn red as it adapts to price moving lower
Trend Confirmation:
The filter serves as a dynamic trend baseline:
Price Consistently Above Filter: Confirms established uptrend
Price Consistently Below Filter: Confirms established downtrend
Frequent Crossovers: Suggests ranging or choppy market conditions
Signal Reliability Factors:
Signal quality varies based on market conditions:
Higher reliability in trending markets with sustained directional moves
Lower reliability in choppy, range-bound conditions with frequent reversals
Parameter adjustment can help adapt to different market volatility levels
🎯 STRATEGIC APPLICATIONS
Trend Following Strategy:
Use the Kalman Filter as a dynamic trend baseline:
Enter long positions when price crosses above the filter
Enter short positions when price crosses below the filter
Exit when price crosses back through the filter in the opposite direction
Monitor filter slope (color) for trend strength confirmation
Dynamic Support/Resistance:
The filter can act as a moving support or resistance level:
In uptrends: Filter often provides dynamic support for pullbacks
In downtrends: Filter often provides dynamic resistance for bounces
Price rejections from the filter can offer entry opportunities in trend direction
Filter breaches may signal potential trend reversals
Multi-Timeframe Analysis:
Combine Kalman Filters across different timeframes:
Higher timeframe filter identifies primary trend direction
Lower timeframe filter provides precise entry and exit timing
Trade only in direction of higher timeframe trend for better probability
Use lower timeframe crossovers for position entry/exit within major trend
Volatility-Adjusted Configuration:
Adapt parameters to match market conditions:
Low Volatility Markets (Forex majors, stable stocks): Use lower process noise for stability, use lower measurement noise for sensitivity
Medium Volatility Markets (Most equities): Process noise default (0.05) provides balanced performance, measurement noise default (1.0) for general-purpose filtering
High Volatility Markets (Cryptocurrencies, volatile stocks): Use higher process noise for responsiveness, use higher measurement noise for noise reduction
Risk Management Integration:
Use filter as a trailing stop-loss level in trending markets
Tighten stops when price moves significantly away from filter (overextension)
Wider stops in early trend formation when filter is just establishing direction
Consider position sizing based on distance between price and filter
📋 DETAILED PARAMETER CONFIGURATION
Source Selection:
Determines which price data feeds the algorithm:
OHLC4 (default): Uses average of open, high, low, close for balanced representation
Close: Focuses purely on closing prices for end-of-period analysis
HL2: Uses midpoint of high and low for range-based analysis
HLC3: Typical price, gives more weight to closing price
HLCC4: Weighted close price, emphasizes closing values
Process Noise (Q) - Adaptation Speed Control:
This parameter controls how quickly the filter adapts to changes:
Technical Meaning:
Represents uncertainty in the underlying trend model
Higher values allow the estimated trend to change more rapidly
Lower values assume the trend is more stable and slow-changing
Practical Impact:
Lower Values: Produces very smooth output with minimal noise, slower to respond to genuine trend changes, best for long-term trend identification, reduces false signals in choppy markets
Medium Values: Balanced responsiveness and smoothness, suitable for swing trading applications, default (0.05) works well for most markets
Higher Values: More responsive to price changes, may produce more false signals in ranging markets, better for short-term trading and day trading, captures trend changes earlier, adjust freely based on market characteristics
Measurement Noise (R) - Smoothing Control:
This parameter controls how much the filter trusts individual price observations:
Technical Meaning:
Represents uncertainty in price measurements
Higher values indicate less trust in individual price points
Lower values make each price observation more influential
Practical Impact:
Lower Values: More reactive to each price change, less smoothing with more noise in output, may produce choppy signals
Medium Values: Balanced smoothing and responsiveness, default (1.0) provides general-purpose filtering
Higher Values: Heavy smoothing for very noisy markets, reduces whipsaws significantly but increases lag in trend change detection, best for cryptocurrency and highly volatile assets, can use larger values for extreme smoothing
Parameter Interaction:
The ratio between Process Noise and Measurement Noise determines overall behavior:
High Q / Low R: Very responsive, minimal smoothing
Low Q / High R: Very smooth, maximum lag reduction
Balanced Q and R: Middle ground for most applications
Optimization Guidelines:
Start with default values (Q=0.05, R=1.0)
If too many false signals: Increase R or decrease Q
If missing trend changes: Decrease R or increase Q
Test across different market conditions before live use
Consider different settings for different timeframes
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Comparison with Traditional Moving Averages:
Versus Simple Moving Average (SMA):
The Kalman Filter typically responds faster to genuine trend changes
Produces smoother output than SMA of comparable length
Better noise reduction in ranging markets
More configurable for different market conditions
Versus Exponential Moving Average (EMA):
Similar responsiveness but with better noise filtering
Less prone to whipsaws in choppy conditions
More adaptable through dual parameter control (Q and R)
Can be tuned to match or exceed EMA responsiveness while maintaining smoothness
Versus Hull Moving Average (HMA):
Different noise reduction approach (recursive estimation vs. weighted calculation)
Kalman Filter offers more intuitive parameter adjustment
Both reduce lag effectively, but through different mechanisms
Kalman Filter may handle sudden volatility changes more gracefully
Response Characteristics:
Lag Time: Moderate and configurable through parameter adjustment
Noise Reduction: Good to excellent, particularly in volatile conditions
Trend Detection: Effective across multiple timeframes
False Signal Rate: Typically lower than simple moving averages in ranging markets
Computational Efficiency: Efficient recursive calculation suitable for real-time use
Optimal Use Cases:
Markets with mixed trending and ranging periods
Assets with moderate to high volatility requiring noise filtering
Multi-timeframe analysis requiring consistent methodology
Systematic trading strategies needing reliable trend identification
Situations requiring balance between responsiveness and smoothness
Known Limitations:
Parameters require adjustment for different market volatility levels
May still produce false signals during extreme choppy conditions
No single parameter set works optimally for all market conditions
Requires complementary indicators for comprehensive analysis
Historical performance characteristics may not persist in changing market conditions
USAGE NOTES
This indicator is designed for technical analysis and educational purposes. The Kalman Filter's effectiveness varies with market conditions, tending to perform better in markets with clear trending phases interrupted by consolidation. Like all technical indicators, it has limitations and should not be used as the sole basis for trading decisions, but rather as part of a comprehensive trading approach.
Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Always test thoroughly with different parameter settings across various market conditions before using in live trading. No technical indicator can predict future price movements with certainty, and all trading involves risk of loss.
RAF@SSET POWER-7 MA SuiteWhat it is
A clean, lightweight pack of seven moving averages (1m, 5m, 15m, 1H, 4H, 1D, 1W).
HTF lines are confirmed-only (no intra-bar wiggle), so what you see is what closed—no repaint on HTFs. Use it to anchor scalps to higher-timeframe structure without clutter.
Why you’ll like it
1m→1W in one look – see alignment from scalp to swing.
Confirmed HTFs – uses request.security() with lookahead_off and only plots closed values.
Zero fluff – just MAs, fixed colors, ultra-fast.
Your presets – default to my “Power-7” lengths (e.g., 233) or set your own.
SMA/EMA switch – pick your poison globally.
Inputs
Show/hide: 1m, 5m, 15m, 1H, 4H, 1D, 1W
Length per TF (defaults 233)
MA type: SMA / EMA
Color per TF
How it works (short)
Current-TF MA updates live.
Higher TF MAs (5m, 15m, 1H, 4H/“240”, 1D, 1W) only update when their candle closes. That removes “wiggle” and surprise shifts.
Tips
For scalping: trade off LTF, bias from 1H/4H/1D.
For swing: let 1D/1W set bias; use 1H/4H for timing.
Your current chart TF MA is live (by design). If you want it confirmed too, set your chart to the HTF you care about.
Built from my RAF@SSET workflow. Shoutout to everyone who keeps indicators simple and readable.
v1.0: First public release (Pine v6). Seven MAs (1m→1W), confirmed HTFs, fixed colors, SMA/EMA toggle.
MACD (Buy & Sell signals)This file uses the original code of the MACD and adds a Buy Sell signal when the MACD cuts the signal
30分钟事件合约开仓指标（Q群956383880）This indicator is applicable to the Binance ETHUSDT spot 1-minute candlestick chart, and the order size can be adjusted based on the security level. Theoretically, the higher the security level, the smaller the order size and the higher the win rate.
本指标适用于币安ETHUSDT现货1分钟k线图，可以通过安全等级自行调节单量。理论上，安全等级越高，单量越少，胜率越高。
Bob Volman + EMA + TP/SL Smart (EZSignals V1)A smart scalping system inspired by Bob Volman’s price action methodology.
Combines EMA trend filtering with automatic TP/SL management and precise entry logic.
Designed for intraday traders who seek clean, efficient scalps with minimal noise.
🔹 Features:
EMA-based trend confirmation
Smart TP/SL auto calculation
Dynamic signal filtering (avoid false breakouts)
Visual entry & exit markers for better clarity
📈 Perfect for scalpers who want Volman-style precision with modern risk control.
Custom zone (duy)Custom Zone (duy)
This indicator highlights custom time zones and automatically detects Fair Value Gaps (FVG) directly on your chart. It’s designed for Price Action / ICT-style traders who want to identify liquidity zones, reaction ranges, and session-based volatility.
Custom Zone:
Define any time window (e.g. 09:30–09:35 UTC-4), and the indicator will automatically draw the high–low range, label the zone, and optionally show its pip size. Perfect for marking areas like New York Open, London Killzone, or personalized setup ranges.
Auto Pip & FVG Detection:
Automatically detects the instrument type (Forex, Gold, Indices, Crypto) to calculate accurate pip values. It also identifies and displays Fair Value Gaps (FVGs) with customizable colors and a clear Central Equilibrium (CE) line.
Use cases:
Highlight session open ranges for NY or London.
Observe price reactions around liquidity zones.
Combine with FVG setups to refine trade entries.
Reversal Probability Meter PRO [optimized for Xau/Usd m5]🎯 Reversal Probability Meter PRO
A powerful multi-factor reversal probability detector that calculates the likelihood of bullish or bearish reversals using RSI, EMA bias, ATR spikes, candle patterns, volume spikes, and higher timeframe (HTF) trend alignment.
🧩 MAIN FEATURES
1. Reversal Probability (Bullish & Bearish)
Displays two key metrics:
Bull % — probability of bullish reversal
Bear % — probability of bearish reversal
These are computed using RSI, EMAs, ATR, demand/supply zones, candle confirmations, and volume spikes.
📊 Interpretation:
Bull % > 70% → Buying pressure building up
Bull % > 85% → Strong bullish reversal confirmed
Bear % > 70% → Selling pressure building up
Bear % > 85% → Strong bearish reversal confirmed
2. Alert Probability Threshold
Adjustable via alertThreshold (default = 85%).
Alerts trigger only when probability ≥ threshold, and confirmed by zone + volume spike + candle pattern.
🔔 Alerts Available:
✅ Bullish Smart Reversal
🔻 Bearish Smart Reversal
To activate: Right-click chart → “Add alert” → choose the alert condition from the indicator.
3. Demand / Supply Zone Detection
The script determines the price position within the last zoneLook (default 30) bars:
🟢 DEMAND → Lower 35% of range (potential bounce zone)
🔴 SUPPLY → Upper 35% of range (potential rejection zone)
⚪ MID → Neutral area
📘 Purpose: Validates reversals based on context:
Bullish only valid in Demand zones
Bearish only valid in Supply zones
4. Higher Timeframe (HTF) Trend Alignment
Reads EMA bias from a higher timeframe (default = 15m) for trend confirmation.
Reversals against HTF trend are automatically weighted down prevents false countertrend signals.
📈 Example:
M5 chart under M15 downtrend → Bullish probability is reduced.
5. Candle Confirmation Patterns
Two key price action confirmations:
Bullish: Engulfing or Pin Bar
Bearish: Engulfing or Pin Bar
A valid reversal requires both a candle confirmation and a volume spike.
6. Volume & ATR Spike Filters
Volume Spike: volume > SMA(20) × 1.3
ATR Spike: ATR > SMA(ATR, 50) × volMult
🎯 Ensures that only strong market moves with real energy are considered valid reversals.
7. Reversal Momentum Histogram
A color-gradient oscillator showing the momentum difference:
Green = bullish dominance
Red = bearish dominance
Flat near 0 = neutral
Controlled by showOscillator toggle.
8. Smart Info Panel
A compact dashboard displayed on the top-right with 4 rows:
Row Info Description
1 Bull % Bullish reversal probability
2 Bear % Bearish reversal probability
3 Zone Market context (DEMAND / SUPPLY / MID)
4 Signal Strength Current signal intensity (probability %)
Dynamic Colors:
90% → Bright (strong signal)
75–90% → Yellow/Orange (medium)
<75% → Gray (weak)
9. Sensitivity Mode
Fine-tunes indicator reactivity:
🟥 Aggressive: Detects reversals early (more signals, less accurate)
🟨 Normal: Balanced, default mode
🟩 Conservative: Filters only strongest reversals (fewer but more reliable)
10. Custom Color Options
Customize bullish and bearish colors via bullBaseColor and bearBaseColor inputs for your preferred chart theme.
⚙️ HOW TO USE
Add to Chart
→ Paste the script into Pine Editor → “Add to chart”.
Select Timeframe
→ Best for M5–M30 (scalping/intraday).
→ H1–H4 for swing trading.
Monitor the Info Panel:
Bull % ≥ 85% + Zone = Demand → Strong bullish reversal signal
Bear % ≥ 85% + Zone = Supply → Strong bearish reversal signal
Watch the Histogram:
Rising green bars = bullish momentum gaining
Deep red bars = bearish momentum gaining
Enable Alerts:
Right-click chart → “Add alert”
Choose Bullish Smart Reversal or Bearish Smart Reversal
🧠 TRADING TIPS
Use Conservative mode for noisy lower timeframes (M5–M15).
Use Aggressive mode for higher timeframes (H1–H4).
Combine with manual support/resistance or zone boxes for precision entries. Personally i use Order Block.
Best reversal setups occur when all align:
Bull % > 85%
Zone = DEMAND
Volume spike present
Candle = Bullish engulfing
HTF trend supportive
MACD Cross Above Zero Alert (Any Timeframe)For use on a large list to spot MACD cross overs in a bullish phase or bearish phase
