Volatility-Targeted Momentum Portfolio [BackQuant]Volatility-Targeted Momentum Portfolio
A complete momentum portfolio engine that ranks assets, targets a user-defined volatility, builds long, short, or delta-neutral books, and reports performance with metrics, attribution, Monte Carlo scenarios, allocation pie, and efficiency scatter plots. This description explains the theory and the mechanics so you can configure, validate, and deploy it with intent.
Table of contents
What the script does at a glance
Momentum, what it is, how to know if it is present
Volatility targeting, why and how it is done here
Portfolio construction modes: Long Only, Short Only, Delta Neutral
Regime filter and when the strategy goes to cash
Transaction cost modelling in this script
Backtest metrics and definitions
Performance attribution chart
Monte Carlo simulation
Scatter plot analysis modes
Asset allocation pie chart
Inputs, presets, and deployment checklist
Suggested workflow
1) What the script does at a glance
Pulls a list of up to 15 tickers, computes a simple momentum score on each over a configurable lookback, then volatility-scales their bar-to-bar return stream to a target annualized volatility.
Ranks assets by raw momentum, selects the top 3 and bottom 3, builds positions according to the chosen mode, and gates exposure with a fast regime filter.
Accumulates a portfolio equity curve with risk and performance metrics, optional benchmark buy-and-hold for comparison, and a full alert suite.
Adds visual diagnostics: performance attribution bars, Monte Carlo forward paths, an allocation pie, and scatter plots for risk-return and factor views.
2) Momentum: definition, detection, and validation
Momentum is the tendency of assets that have performed well to continue to perform well, and of underperformers to continue underperforming, over a specific horizon. You operationalize it by selecting a horizon, defining a signal, ranking assets, and trading the leaders versus laggards subject to risk constraints.
Signal choices . Common signals include cumulative return over a lookback window, regression slope on log-price, or normalized rate-of-change. This script uses cumulative return over lookback bars for ranking (variable cr = price/price - 1). It keeps the ranking simple and lets volatility targeting handle risk normalization.
How to know momentum is present .
Leaders and laggards persist across adjacent windows rather than flipping every bar.
Spread between average momentum of leaders and laggards is materially positive in sample.
Cross-sectional dispersion is non-trivial. If everything is flat or highly correlated with no separation, momentum selection will be weak.
Your validation should include a diagnostic that measures whether returns are explained by a momentum regression on the timeseries.
Recommended diagnostic tool . Before running any momentum portfolio, verify that a timeseries exhibits stable directional drift. Use this indicator as a pre-check: It fits a regression to price, exposes slope and goodness-of-fit style context, and helps confirm if there is usable momentum before you force a ranking into a flat regime.
3) Volatility targeting: purpose and implementation here
Purpose . Volatility targeting seeks a more stable risk footprint. High-vol assets get sized down, low-vol assets get sized up, so each contributes more evenly to total risk.
Computation in this script (per asset, rolling):
Return series ret = log(price/price ).
Annualized volatility estimate vol = stdev(ret, lookback) * sqrt(tradingdays).
Leverage multiplier volMult = clamp(targetVol / vol, 0.1, 5.0).
This caps sizing so extremely low-vol assets don’t explode weight and extremely high-vol assets don’t go to zero.
Scaled return stream sr = ret * volMult. This is the per-bar, risk-adjusted building block used in the portfolio combinations.
Interpretation . You are not levering your account on the exchange, you are rescaling the contribution each asset’s daily move has on the modeled equity. In live trading you would reflect this with position sizing or notional exposure.
4) Portfolio construction modes
Cross-sectional ranking . Assets are sorted by cr over the chosen lookback. Top and bottom indices are extracted without ties.
Long Only . Averages the volatility-scaled returns of the top 3 assets: avgRet = mean(sr_top1, sr_top2, sr_top3). Position table shows per-asset leverages and weights proportional to their current volMult.
Short Only . Averages the negative of the volatility-scaled returns of the bottom 3: avgRet = mean(-sr_bot1, -sr_bot2, -sr_bot3). Position table shows short legs.
Delta Neutral . Long the top 3 and short the bottom 3 in equal book sizes. Each side is sized to 50 percent notional internally, with weights within each side proportional to volMult. The return stream mixes the two sides: avgRet = mean(sr_top1,sr_top2,sr_top3, -sr_bot1,-sr_bot2,-sr_bot3).
Notes .
The selection metric is raw momentum, the execution stream is volatility-scaled returns. This separation is deliberate. It avoids letting volatility dominate ranking while still enforcing risk parity at the return contribution stage.
If everything rallies together and dispersion collapses, Long Only may behave like a single beta. Delta Neutral is designed to extract cross-sectional momentum with low net beta.
5) Regime filter
A fast EMA(12) vs EMA(21) filter gates exposure.
Long Only active when EMA12 > EMA21. Otherwise the book is set to cash.
Short Only active when EMA12 < EMA21. Otherwise cash.
Delta Neutral is always active.
This prevents taking long momentum entries during obvious local downtrends and vice versa for shorts. When the filter is false, equity is held flat for that bar.
6) Transaction cost modelling
There are two cost touchpoints in the script.
Per-bar drag . When the regime filter is active, the per-bar return is reduced by fee_rate * avgRet inside netRet = avgRet - (fee_rate * avgRet). This models proportional friction relative to traded impact on that bar.
Turnover-linked fee . The script tracks changes in membership of the top and bottom baskets (top1..top3, bot1..bot3). The intent is to charge fees when composition changes. The template counts changes and scales a fee by change count divided by 6 for the six slots.
Use case: increase fee_rate to reflect taker fees and slippage if you rebalance every bar or trade illiquid assets. Reduce it if you rebalance less often or use maker orders.
Practical advice .
If you rebalance daily, start with 5–20 bps round-trip per switch on liquid futures and adjust per venue.
For crypto perp microcaps, stress higher cost assumptions and add slippage buffers.
If you only rotate on lookback boundaries or at signals, use alert-driven rebalances and lower per-bar drag.
7) Backtest metrics and definitions
The script computes a standard set of portfolio statistics once the start date is reached.
Net Profit percent over the full test.
Max Drawdown percent, tracked from running peaks.
Annualized Mean and Stdev using the chosen trading day count.
Variance is the square of annualized stdev.
Sharpe uses daily mean adjusted by risk-free rate and annualized.
Sortino uses downside stdev only.
Omega ratio of sum of gains to sum of losses.
Gain-to-Pain total gains divided by total losses absolute.
CAGR compounded annual growth from start date to now.
Alpha, Beta versus a user-selected benchmark. Beta from covariance of daily returns, Alpha from CAPM.
Skewness of daily returns.
VaR 95 linear-interpolated 5th percentile of daily returns.
CVaR average of the worst 5 percent of daily returns.
Benchmark Buy-and-Hold equity path for comparison.
8) Performance attribution
Cumulative contribution per asset, adjusted for whether it was held long or short and for its volatility multiplier, aggregated across the backtest. You can filter to winners only or show both sides. The panel is sorted by contribution and includes percent labels.
9) Monte Carlo simulation
The panel draws forward equity paths from either a Normal model parameterized by recent mean and stdev, or non-parametric bootstrap of recent daily returns. You control the sample length, number of simulations, forecast horizon, visibility of individual paths, confidence bands, and a reproducible seed.
Normal uses Box-Muller with your seed. Good for quick, smooth envelopes.
Bootstrap resamples realized returns, preserving fat tails and volatility clustering better than a Gaussian assumption.
Bands show 10th, 25th, 75th, 90th percentiles and the path mean.
10) Scatter plot analysis
Four point-cloud modes, each plotting all assets and a star for the current portfolio position, with quadrant guides and labels.
Risk-Return Efficiency . X is risk proxy from leverage, Y is expected return from annualized momentum. The star shows the current book’s composite.
Momentum vs Volatility . Visualizes whether leaders are also high vol, a cue for turnover and cost expectations.
Beta vs Alpha . X is a beta proxy, Y is risk-adjusted excess return proxy. Useful to see if leaders are just beta.
Leverage vs Momentum . X is volMult, Y is momentum. Shows how volatility targeting is redistributing risk.
11) Asset allocation pie chart
Builds a wheel of current allocations.
Long Only, weights are proportional to each long asset’s current volMult and sum to 100 percent.
Short Only, weights show the short book as positive slices that sum to 100 percent.
Delta Neutral, 50 percent long and 50 percent short books, each side leverage-proportional.
Labels can show asset, percent, and current leverage.
12) Inputs and quick presets
Core
Portfolio Strategy . Long Only, Short Only, Delta Neutral.
Initial Capital . For equity scaling in the panel.
Trading Days/Year . 252 for stocks, 365 for crypto.
Target Volatility . Annualized, drives volMult.
Transaction Fees . Per-bar drag and composition change penalty, see the modelling notes above.
Momentum Lookback . Ranking horizon. Shorter is more reactive, longer is steadier.
Start Date . Ensure every symbol has data back to this date to avoid bias.
Benchmark . Used for alpha, beta, and B&H line.
Diagnostics
Metrics, Equity, B&H, Curve labels, Daily return line, Rolling drawdown fill.
Attribution panel. Toggle winners only to focus on what matters.
Monte Carlo mode with Normal or Bootstrap and confidence bands.
Scatter plot type and styling, labels, and portfolio star.
Pie chart and labels for current allocation.
Presets
Crypto Daily, Long Only . Lookback 25, Target Vol 50 percent, Fees 10 bps, Regime filter on, Metrics and Drawdown on. Monte Carlo Bootstrap with Recent 200 bars for bands.
Crypto Daily, Delta Neutral . Lookback 25, Target Vol 50 percent, Fees 15–25 bps, Regime filter always active for this mode. Use Scatter Risk-Return to monitor efficiency and keep the star near upper left quadrants without drifting rightward.
Equities Daily, Long Only . Lookback 60–120, Target Vol 15–20 percent, Fees 5–10 bps, Regime filter on. Use Benchmark SPX and watch Alpha and Beta to keep the book from becoming index beta.
13) Suggested workflow
Universe sanity check . Pick liquid tickers with stable data. Thin assets distort vol estimates and fees.
Check momentum existence . Run on your timeframe. If slope and fit are weak, widen lookback or avoid that asset or timeframe.
Set risk budget . Choose a target volatility that matches your drawdown tolerance. Higher target increases turnover and cost sensitivity.
Pick mode . Long Only for bull regimes, Short Only for sustained downtrends, Delta Neutral for cross-sectional harvesting when index direction is unclear.
Tune lookback . If leaders rotate too often, lengthen it. If entries lag, shorten it.
Validate cost assumptions . Increase fee_rate and stress Monte Carlo. If the edge vanishes with modest friction, refine selection or lengthen rebalance cadence.
Run attribution . Confirm the strategy’s winners align with intuition and not one unstable outlier.
Use alerts . Enable position change, drawdown, volatility breach, regime, momentum shift, and crash alerts to supervise live runs.
Important implementation details mapped to code
Momentum measure . cr = price / price - 1 per symbol for ranking. Simplicity helps avoid overfitting.
Volatility targeting . vol = stdev(log returns, lookback) * sqrt(tradingdays), volMult = clamp(targetVol / vol, 0.1, 5), sr = ret * volMult.
Selection . Extract indices for top1..top3 and bot1..bot3. The arrays rets, scRets, lev_vals, and ticks_arr track momentum, scaled returns, leverage multipliers, and display tickers respectively.
Regime filter . EMA12 vs EMA21 switch determines if the strategy takes risk for Long or Short modes. Delta Neutral ignores the gate.
Equity update . Equity multiplies by 1 + netRet only when the regime was active in the prior bar. Buy-and-hold benchmark is computed separately for comparison.
Tables . Position tables show current top or bottom assets with leverage and weights. Metric table prints all risk and performance figures.
Visualization panels . Attribution, Monte Carlo, scatter, and pie use the last bars to draw overlays that update as the backtest proceeds.
Final notes
Momentum is a portfolio effect. The edge comes from cross-sectional dispersion, adequate risk normalization, and disciplined turnover control, not from a single best asset call.
Volatility targeting stabilizes path but does not fix selection. Use the momentum regression link above to confirm structure exists before you size into it.
Always test higher lag costs and slippage, then recheck metrics, attribution, and Monte Carlo envelopes. If the edge persists under stress, you have something robust.
Indikatoren und Strategien
TrendDetectorLibLibrary "TrendDetector_Lib"
method formatTF(timeframe)
Namespace types: series string, simple string, input string, const string
Parameters:
timeframe (string) : (string) The timeframe to convert (e.g., "15", "60", "240").
Returns: (string) The formatted timeframe (e.g., "15M", "1H", "4H").
f_ma(type, src, len)
Computes a Moving Average value based on type and length.
Parameters:
type (simple string) : (string) One of: "SMA", "EMA", "RMA", "WMA", "VWMA".
src (float) : (series float) Source series for MA (e.g., close).
len (simple int) : (simple int) Length of the MA.
Returns: (float) The computed MA series.
render(tbl, trendDetectorSwitch, frameColor, frameWidth, borderColor, borderWidth, textColor, ma1ShowTrendData, ma1Timeframe, ma1Value, ma2ShowTrendData, ma2Timeframe, ma2Value, ma3ShowTrendData, ma3Timeframe, ma3Value)
Fills the provided table with Trend Detector contents.
@desc This renderer does NOT plot and does NOT create tables; call from indicator after your table exists.
Parameters:
tbl (table) : (table) Existing table to render into.
trendDetectorSwitch (bool) : (bool) Master toggle to draw the table content.
frameColor (color) : (color) Table frame color.
frameWidth (int) : (int) Table frame width (0–5).
borderColor (color) : (color) Table border color.
borderWidth (int) : (int) Table border width (0–5).
textColor (color) : (color) Table text color.
ma1ShowTrendData (bool) : (bool) Show MA #1 in table.
ma1Timeframe (simple string) : (string) MA #1 timeframe.
ma1Value (float)
ma2ShowTrendData (bool) : (bool) Show MA #2 in table.
ma2Timeframe (simple string) : (string) MA #2 timeframe.
ma2Value (float)
ma3ShowTrendData (bool) : (bool) Show MA #3 in table.
ma3Timeframe (simple string) : (string) MA #3 timeframe.
ma3Value (float)
Swing High/Low Support ResistanceThis indicator detects recent swing highs and swing lows using Pine Script pivots and marks them with visible chart labels. These points highlight potential turning areas in price action and can help identify short-term support or resistance for intraday or swing trading.
How to Apply
Locate the indicator in TradingView’s “Indicators” library; search by its name or author.
Click the star icon to mark it as a favourite for quick future access.
Apply directly to your chosen chart and timeframe with a single click—no need to enter or paste code.
Adjust the input parameters from the settings panel if desired to personalize swing sensitivity.
Choose Your Timeframe:
Apply to any intraday or swing timeframe; shorter lengths show more frequent pivots.
Set Sensitivity:
Use the “Swing Detection Length” input to adjust how many bars define a pivot, making swings more or less sensitive to price action.
How to Analyze
Swing High Labels: Mark recent local peaks, suggesting resistance zones or possible reversal points.
Swing Low Labels: Highlight recent bottoms, indicating support or bounce areas.
Monitor labels for clustering or repeated appearance at similar levels, which may strengthen their importance as price reacts near those points.
Track how price behaves after forming new pivots—multiple tests can affirm the relevance of a level.
What Traders Should Watch
Price reaction at labeled areas: frequent tests may anticipate reversals or breakouts.
Transition between higher highs/higher lows (uptrend) vs. lower highs/lower lows (downtrend).
Combine the swing levels with other analysis methods, such as volume, RSI, or EMA, for better signal quality.
Features Included
Dynamic swing high and low detection via confirmed pivots.
Direct labeling on the chart for market structure clarity.
No repainting—labels show only after complete formation.
Fully automatic updates as price action unfolds.
No promotional, external, or non-compliant elements; open source and safe for public or private use.
Compliance Notes
No signals, buy/sell calls, financial advice, or performance claims.
No hidden code, advertising, or off-platform contacts.
Pure educational and analytical utility; adheres to all TradingView house rules and script publishing policies.
Disclaimer
This indicator is for informational purposes only and does not constitute advice. Always do your own research and use proper risk management.
Algorithm Predator - ML-liteAlgorithm Predator - ML-lite
This indicator combines four specialized trading agents with an adaptive multi-armed bandit selection system to identify high-probability trade setups. It is designed for swing and intraday traders who want systematic signal generation based on institutional order flow patterns , momentum exhaustion , liquidity dynamics , and statistical mean reversion .
Core Architecture
Why These Components Are Combined:
The script addresses a fundamental challenge in algorithmic trading: no single detection method works consistently across all market conditions. By deploying four independent agents and using reinforcement learning algorithms to select or blend their outputs, the system adapts to changing market regimes without manual intervention.
The Four Trading Agents
1. Spoofing Detector Agent 🎭
Detects iceberg orders through persistent volume at similar price levels over 5 bars
Identifies spoofing patterns via asymmetric wick analysis (wicks exceeding 60% of bar range with volume >1.8× average)
Monitors order clustering using simplified Hawkes process intensity tracking (exponential decay model)
Signal Logic: Contrarian—fades false breakouts caused by institutional manipulation
Best Markets: Consolidations, institutional trading windows, low-liquidity hours
2. Exhaustion Detector Agent ⚡
Calculates RSI divergence between price movement and momentum indicator over 5-bar window
Detects VWAP exhaustion (price at 2σ bands with declining volume)
Uses VPIN reversals (volume-based toxic flow dissipation) to identify momentum failure
Signal Logic: Counter-trend—enters when momentum extreme shows weakness
Best Markets: Trending markets reaching climax points, over-extended moves
3. Liquidity Void Detector Agent 💧
Measures Bollinger Band squeeze (width <60% of 50-period average)
Identifies stop hunts via 20-bar high/low penetration with immediate reversal and volume spike
Detects hidden liquidity absorption (volume >2× average with range <0.3× ATR)
Signal Logic: Breakout anticipation—enters after liquidity grab but before main move
Best Markets: Range-bound pre-breakout, volatility compression zones
4. Mean Reversion Agent 📊
Calculates price z-scores relative to 50-period SMA and standard deviation (triggers at ±2σ)
Implements Ornstein-Uhlenbeck process scoring (mean-reverting stochastic model)
Uses entropy analysis to detect algorithmic trading patterns (low entropy <0.25 = high predictability)
Signal Logic: Statistical reversion—enters when price deviates significantly from statistical equilibrium
Best Markets: Range-bound, low-volatility, algorithmically-dominated instruments
Adaptive Selection: Multi-Armed Bandit System
The script implements four reinforcement learning algorithms to dynamically select or blend agents based on performance:
Thompson Sampling (Default - Recommended):
Uses Bayesian inference with beta distributions (tracks alpha/beta parameters per agent)
Balances exploration (trying underused agents) vs. exploitation (using proven winners)
Each agent's win/loss history informs its selection probability
Lite Approximation: Uses pseudo-random sampling from price/volume noise instead of true random number generation
UCB1 (Upper Confidence Bound):
Calculates confidence intervals using: average_reward + sqrt(2 × ln(total_pulls) / agent_pulls)
Deterministic algorithm favoring agents with high uncertainty (potential upside)
More conservative than Thompson Sampling
Epsilon-Greedy:
Exploits best-performing agent (1-ε)% of the time
Explores randomly ε% of the time (default 10%, configurable 1-50%)
Simple, transparent, easily tuned via epsilon parameter
Gradient Bandit:
Uses softmax probability distribution over agent preference weights
Updates weights via gradient ascent based on rewards
Best for Blend mode where all agents contribute
Selection Modes:
Switch Mode: Uses only the selected agent's signal (clean, decisive)
Blend Mode: Combines all agents using exponentially weighted confidence scores controlled by temperature parameter (smooth, diversified)
Lock Agent Feature:
Optional manual override to force one specific agent
Useful after identifying which agent dominates your specific instrument
Only applies in Switch mode
Four choices: Spoofing Detector, Exhaustion Detector, Liquidity Void, Mean Reversion
Memory System
Dual-Layer Architecture:
Short-Term Memory: Stores last 20 trade outcomes per agent (configurable 10-50)
Long-Term Memory: Stores episode averages when short-term reaches transfer threshold (configurable 5-20 bars)
Memory Boost Mechanism: Recent performance modulates agent scores by up to ±20%
Episode Transfer: When an agent accumulates sufficient results, averages are condensed into long-term storage
Persistence: Manual restoration of learned parameters via input fields (alpha, beta, weights, microstructure thresholds)
How Memory Works:
Agent generates signal → outcome tracked after 8 bars (performance horizon)
Result stored in short-term memory (win = 1.0, loss = 0.0)
Short-term average influences agent's future scores (positive feedback loop)
After threshold met (default 10 results), episode averaged into long-term storage
Long-term patterns (weighted 30%) + short-term patterns (weighted 70%) = total memory boost
Market Microstructure Analysis
These advanced metrics quantify institutional order flow dynamics:
Order Flow Toxicity (Simplified VPIN):
Measures buy/sell volume imbalance over 20 bars: |buy_vol - sell_vol| / (buy_vol + sell_vol)
Detects informed trading activity (institutional players with non-public information)
Values >0.4 indicate "toxic flow" (informed traders active)
Lite Approximation: Uses simple open/close heuristic instead of tick-by-tick trade classification
Price Impact Analysis (Simplified Kyle's Lambda):
Measures market impact efficiency: |price_change_10| / sqrt(volume_sum_10)
Low values = large orders with minimal price impact ( stealth accumulation )
High values = retail-dominated moves with high slippage
Lite Approximation: Uses simplified denominator instead of regression-based signed order flow
Market Randomness (Entropy Analysis):
Counts unique price changes over 20 bars / 20
Measures market predictability
High entropy (>0.6) = human-driven, chaotic price action
Low entropy (<0.25) = algorithmic trading dominance (predictable patterns)
Lite Approximation: Simple ratio instead of true Shannon entropy H(X) = -Σ p(x)·log₂(p(x))
Order Clustering (Simplified Hawkes Process):
Tracks self-exciting event intensity (coordinated order activity)
Decays at 0.9× per bar, spikes +1.0 when volume >1.5× average
High intensity (>0.7) indicates clustering (potential spoofing/accumulation)
Lite Approximation: Simple exponential decay instead of full λ(t) = μ + Σ α·exp(-β(t-tᵢ)) with MLE
Signal Generation Process
Multi-Stage Validation:
Stage 1: Agent Scoring
Each agent calculates internal score based on its detection criteria
Scores must exceed agent-specific threshold (adjusted by sensitivity multiplier)
Agent outputs: Signal direction (+1/-1/0) and Confidence level (0.0-1.0)
Stage 2: Memory Boost
Agent scores multiplied by memory boost factor (0.8-1.2 based on recent performance)
Successful agents get amplified, failing agents get dampened
Stage 3: Bandit Selection/Blending
If Adaptive Mode ON:
Switch: Bandit selects single best agent, uses only its signal
Blend: All agents combined using softmax-weighted confidence scores
If Adaptive Mode OFF:
Traditional consensus voting with confidence-squared weighting
Signal fires when consensus exceeds threshold (default 70%)
Stage 4: Confirmation Filter
Raw signal must repeat for consecutive bars (default 3, configurable 2-4)
Minimum confidence threshold: 0.25 (25%) enforced regardless of mode
Trend alignment check: Long signals require trend_score ≥ -2, Short signals require trend_score ≤ 2
Stage 5: Cooldown Enforcement
Minimum bars between signals (default 10, configurable 5-15)
Prevents over-trading during choppy conditions
Stage 6: Performance Tracking
After 8 bars (performance horizon), signal outcome evaluated
Win = price moved in signal direction, Loss = price moved against
Results fed back into memory and bandit statistics
Trading Modes (Presets)
Pre-configured parameter sets:
Conservative: 85% consensus, 4 confirmations, 15-bar cooldown
Expected: 60-70% win rate, 3-8 signals/week
Best for: Swing trading, capital preservation, beginners
Balanced: 70% consensus, 3 confirmations, 10-bar cooldown
Expected: 55-65% win rate, 8-15 signals/week
Best for: Day trading, most traders, general use
Aggressive: 60% consensus, 2 confirmations, 5-bar cooldown
Expected: 50-58% win rate, 15-30 signals/week
Best for: Scalping, high-frequency trading, active management
Elite: 75% consensus, 3 confirmations, 12-bar cooldown
Expected: 58-68% win rate, 5-12 signals/week
Best for: Selective trading, high-conviction setups
Adaptive: 65% consensus, 2 confirmations, 8-bar cooldown
Expected: Varies based on learning
Best for: Experienced users leveraging bandit system
How to Use
1. Initial Setup (5 Minutes):
Select Trading Mode matching your style (start with Balanced)
Enable Adaptive Learning (recommended for automatic agent selection)
Choose Thompson Sampling algorithm (best all-around performance)
Keep Microstructure Metrics enabled for liquid instruments (>100k daily volume)
2. Agent Tuning (Optional):
Adjust Agent Sensitivity multipliers (0.5-2.0):
<0.8 = Highly selective (fewer signals, higher quality)
0.9-1.2 = Balanced (recommended starting point)
1.3 = Aggressive (more signals, lower individual quality)
Monitor dashboard for 20-30 signals to identify dominant agent
If one agent consistently outperforms, consider using Lock Agent feature
3. Bandit Configuration (Advanced):
Blend Temperature (0.1-2.0):
0.3 = Sharp decisions (best agent dominates)
0.5 = Balanced (default)
1.0+ = Smooth (equal weighting, democratic)
Memory Decay (0.8-0.99):
0.90 = Fast adaptation (volatile markets)
0.95 = Balanced (most instruments)
0.97+ = Long memory (stable trends)
4. Signal Interpretation:
Green triangle (▲): Long signal confirmed
Red triangle (▼): Short signal confirmed
Dashboard shows:
Active agent (highlighted row with ► marker)
Win rate per agent (green >60%, yellow 40-60%, red <40%)
Confidence bars (█████ = maximum confidence)
Memory size (short-term buffer count)
Colored zones display:
Entry level (current close)
Stop-loss (1.5× ATR)
Take-profit 1 (2.0× ATR)
Take-profit 2 (3.5× ATR)
5. Risk Management:
Never risk >1-2% per signal (use ATR-based stops)
Signals are entry triggers, not complete strategies
Combine with your own market context analysis
Consider fundamental catalysts and news events
Use "Confirming" status to prepare entries (not to enter early)
6. Memory Persistence (Optional):
After 50-100 trades, check Memory Export Panel
Record displayed alpha/beta/weight values for each agent
Record VPIN and Kyle threshold values
Enable "Restore From Memory" and input saved values to continue learning
Useful when switching timeframes or restarting indicator
Visual Components
On-Chart Elements:
Spectral Layers: EMA8 ± 0.5 ATR bands (dynamic support/resistance, colored by trend)
Energy Radiance: Multi-layer glow boxes at signal points (intensity scales with confidence, configurable 1-5 layers)
Probability Cones: Projected price paths with uncertainty wedges (15-bar projection, width = confidence × ATR)
Connection Lines: Links sequential signals (solid = same direction continuation, dotted = reversal)
Kill Zones: Risk/reward boxes showing entry, stop-loss, and dual take-profit targets
Signal Markers: Triangle up/down at validated entry points
Dashboard (Configurable Position & Size):
Regime Indicator: 4-level trend classification (Strong Bull/Bear, Weak Bull/Bear)
Mode Status: Shows active system (Adaptive Blend, Locked Agent, or Consensus)
Agent Performance Table: Real-time win%, confidence, and memory stats
Order Flow Metrics: Toxicity and impact indicators (when microstructure enabled)
Signal Status: Current state (Long/Short/Confirming/Waiting) with confirmation progress
Memory Panel (Configurable Position & Size):
Live Parameter Export: Alpha, beta, and weight values per agent
Adaptive Thresholds: Current VPIN sensitivity and Kyle threshold
Save Reminder: Visual indicator if parameters should be recorded
What Makes This Original
This script's originality lies in three key innovations:
1. Genuine Meta-Learning Framework:
Unlike traditional indicator mashups that simply display multiple signals, this implements authentic reinforcement learning (multi-armed bandits) to learn which detection method works best in current conditions. The Thompson Sampling implementation with beta distribution tracking (alpha for successes, beta for failures) is statistically rigorous and adapts continuously. This is not post-hoc optimization—it's real-time learning.
2. Episodic Memory Architecture with Transfer Learning:
The dual-layer memory system mimics human learning patterns:
Short-term memory captures recent performance (recency bias)
Long-term memory preserves historical patterns (experience)
Automatic transfer mechanism consolidates knowledge
Memory boost creates positive feedback loops (successful strategies become stronger)
This architecture allows the system to adapt without retraining , unlike static ML models that require batch updates.
3. Institutional Microstructure Integration:
Combines retail-focused technical analysis (RSI, Bollinger Bands, VWAP) with institutional-grade microstructure metrics (VPIN, Kyle's Lambda, Hawkes processes) typically found in academic finance literature and professional trading systems, not standard retail platforms. While simplified for Pine Script constraints, these metrics provide insight into informed vs. uninformed trading , a dimension entirely absent from traditional technical analysis.
Mashup Justification:
The four agents are combined specifically for risk diversification across failure modes:
Spoofing Detector: Prevents false breakout losses from manipulation
Exhaustion Detector: Prevents chasing extended trends into reversals
Liquidity Void: Exploits volatility compression (different regime than trending)
Mean Reversion: Provides mathematical anchoring when patterns fail
The bandit system ensures the optimal tool is automatically selected for each market situation, rather than requiring manual interpretation of conflicting signals.
Why "ML-lite"? Simplifications and Approximations
This is the "lite" version due to necessary simplifications for Pine Script execution:
1. Simplified VPIN Calculation:
Academic Implementation: True VPIN uses volume bucketing (fixed-volume bars) and tick-by-tick buy/sell classification via Lee-Ready algorithm or exchange-provided trade direction flags
This Implementation: 20-bar rolling window with simple open/close heuristic (close > open = buy volume)
Impact: May misclassify volume during ranging/choppy markets; works best in directional moves
2. Pseudo-Random Sampling:
Academic Implementation: Thompson Sampling requires true random number generation from beta distributions using inverse transform sampling or acceptance-rejection methods
This Implementation: Deterministic pseudo-randomness derived from price and volume decimal digits: (close × 100 - floor(close × 100)) + (volume % 100) / 100
Impact: Not cryptographically random; may have subtle biases in specific price ranges; provides sufficient variation for agent selection
3. Hawkes Process Approximation:
Academic Implementation: Full Hawkes process uses maximum likelihood estimation with exponential kernels: λ(t) = μ + Σ α·exp(-β(t-tᵢ)) fitted via iterative optimization
This Implementation: Simple exponential decay (0.9 multiplier) with binary event triggers (volume spike = event)
Impact: Captures self-exciting property but lacks parameter optimization; fixed decay rate may not suit all instruments
4. Kyle's Lambda Simplification:
Academic Implementation: Estimated via regression of price impact on signed order flow over multiple time intervals: Δp = λ × Δv + ε
This Implementation: Simplified ratio: price_change / sqrt(volume_sum) without proper signed order flow or regression
Impact: Provides directional indicator of impact but not true market depth measurement; no statistical confidence intervals
5. Entropy Calculation:
Academic Implementation: True Shannon entropy requires probability distribution: H(X) = -Σ p(x)·log₂(p(x)) where p(x) is probability of each price change magnitude
This Implementation: Simple ratio of unique price changes to total observations (variety measure)
Impact: Measures diversity but not true information entropy with probability weighting; less sensitive to distribution shape
6. Memory System Constraints:
Full ML Implementation: Neural networks with backpropagation, experience replay buffers (storing state-action-reward tuples), gradient descent optimization, and eligibility traces
This Implementation: Fixed-size array queues with simple averaging; no gradient-based learning, no state representation beyond raw scores
Impact: Cannot learn complex non-linear patterns; limited to linear performance tracking
7. Limited Feature Engineering:
Advanced Implementation: Dozens of engineered features, polynomial interactions (x², x³), dimensionality reduction (PCA, autoencoders), feature selection algorithms
This Implementation: Raw agent scores and basic market metrics (RSI, ATR, volume ratio); minimal transformation
Impact: May miss subtle cross-feature interactions; relies on agent-level intelligence rather than feature combinations
8. Single-Instrument Data:
Full Implementation: Multi-asset correlation analysis (sector ETFs, currency pairs, volatility indices like VIX), lead-lag relationships, risk-on/risk-off regimes
This Implementation: Only OHLCV data from displayed instrument
Impact: Cannot incorporate broader market context; vulnerable to correlated moves across assets
9. Fixed Performance Horizon:
Full Implementation: Adaptive horizon based on trade duration, volatility regime, or profit target achievement
This Implementation: Fixed 8-bar evaluation window
Impact: May evaluate too early in slow markets or too late in fast markets; one-size-fits-all approach
Performance Impact Summary:
These simplifications make the script:
✅ Faster: Executes in milliseconds vs. seconds (or minutes) for full academic implementations
✅ More Accessible: Runs on any TradingView plan without external data feeds, APIs, or compute servers
✅ More Transparent: All calculations visible in Pine Script (no black-box compiled models)
✅ Lower Resource Usage: <500 bars lookback, minimal memory footprint
⚠️ Less Precise: Approximations may reduce statistical edge by 5-15% vs. academic implementations
⚠️ Limited Scope: Cannot capture tick-level dynamics, multi-order-book interactions, or cross-asset flows
⚠️ Fixed Parameters: Some thresholds hardcoded rather than dynamically optimized
When to Upgrade to Full Implementation:
Consider professional Python/C++ versions with institutional data feeds if:
Trading with >$100K capital where precision differences materially impact returns
Operating in microsecond-competitive environments (HFT, market making)
Requiring regulatory-grade audit trails and reproducibility
Backtesting with tick-level precision for strategy validation
Need true real-time adaptation with neural network-based learning
For retail swing/day trading and position management, these approximations provide sufficient signal quality while maintaining usability, transparency, and accessibility. The core logic—multi-agent detection with adaptive selection—remains intact.
Technical Notes
All calculations use standard Pine Script built-in functions ( ta.ema, ta.atr, ta.rsi, ta.bb, ta.sma, ta.stdev, ta.vwap )
VPIN and Kyle's Lambda use simplified formulas optimized for OHLCV data (see "Lite" section above)
Thompson Sampling uses pseudo-random noise from price/volume decimal digits for beta distribution sampling
No repainting: All calculations use confirmed bar data (no forward-looking)
Maximum lookback: 500 bars (set via max_bars_back parameter)
Performance evaluation: 8-bar forward-looking window for reward calculation (clearly disclosed)
Confidence threshold: Minimum 0.25 (25%) enforced on all signals
Memory arrays: Dynamic sizing with FIFO queue management
Limitations and Disclaimers
Not Predictive: This indicator identifies patterns in historical data. It cannot predict future price movements with certainty.
Requires Human Judgment: Signals are entry triggers, not complete trading strategies. Must be confirmed with your own analysis, risk management rules, and market context.
Learning Period Required: The adaptive system requires 50-100 bars minimum to build statistically meaningful performance data for bandit algorithms.
Overfitting Risk: Restoring memory parameters from one market regime to a drastically different regime (e.g., low volatility to high volatility) may cause poor initial performance until system re-adapts.
Approximation Limitations: Simplified calculations (see "Lite" section) may underperform academic implementations by 5-15% in highly efficient markets.
No Guarantee of Profit: Past performance, whether backtested or live-traded, does not guarantee future performance. All trading involves risk of loss.
Forward-Looking Bias: Performance evaluation uses 8-bar forward window—this creates slight look-ahead for learning (though not for signals). Real-time performance may differ from indicator's internal statistics.
Single-Instrument Limitation: Does not account for correlations with related assets or broader market regime changes.
Recommended Settings
Timeframe: 15-minute to 4-hour charts (sufficient volatility for ATR-based stops; adequate bar volume for learning)
Assets: Liquid instruments with >100k daily volume (forex majors, large-cap stocks, BTC/ETH, major indices)
Not Recommended: Illiquid small-caps, penny stocks, low-volume altcoins (microstructure metrics unreliable)
Complementary Tools: Volume profile, order book depth, market breadth indicators, fundamental catalysts
Position Sizing: Risk no more than 1-2% of capital per signal using ATR-based stop-loss
Signal Filtering: Consider external confluence (support/resistance, trendlines, round numbers, session opens)
Start With: Balanced mode, Thompson Sampling, Blend mode, default agent sensitivities (1.0)
After 30+ Signals: Review agent win rates, consider increasing sensitivity of top performers or locking to dominant agent
Alert Configuration
The script includes built-in alert conditions:
Long Signal: Fires when validated long entry confirmed
Short Signal: Fires when validated short entry confirmed
Alerts fire once per bar (after confirmation requirements met)
Set alert to "Once Per Bar Close" for reliability
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Range breaking indicatorDescription
Bull/Bear Area Ratio (last N candles) helps identify potential end-of-range situations by analyzing the relative strength of bullish vs bearish candles over a rolling window of N bars.
Instead of simply counting up or down candles, this script measures the "area" of each candle — the absolute distance between open and close, optionally weighted by volume.
By summing these areas over the last N bars, it calculates the percentage of bullish and bearish energy within that period.
When both sides become balanced (near 50/50), it often signals range exhaustion or possible trend transition.
How it works
Calculates the bullish and bearish area of each candle (abs(close - open), optionally × volume).
Maintains rolling buffers of the last N bars to compute running totals.
Plots both Bullish % (green) and Bearish % (red).
Highlights possible range-ending zones when the bullish ratio nears 50% ± threshold.
Displays a label showing the current balance.
Includes an alert condition when equilibrium is detected.
Inputs
Number of candles (N) – Rolling window length.
Use volume weighting – Multiplies each candle’s area by its volume.
Balance threshold (%) – Sensitivity for detecting equilibrium (default: 10%).
Best use
Combine with volume or volatility indicators to confirm market compression or expansion.
Use on higher timeframes (H1, H4, D1) to detect early signs of accumulation or distribution.
Works across all asset types: crypto, forex, stocks, indices, etc.
Alerts
An alert is triggered when:
“The range of the last N candles is balanced (possible end of range).”
cd_sfp_CxGeneral:
This indicator is designed to assist users who trade the Swing Failure Pattern ( SFP ).
In technical literature (various definitions exist), an SFP is a situation where the price violates a previous swing level but fails to close beyond that level.
• (Liquidity Sweep)
• (Buyer or seller dominance)
• (Stop hunt)
• (Turtle Soup)
The general strategy is built upon seeking trade opportunities after an SFP is formed and conviction is established that the market direction has changed.
Components used to gather confirmation:
• Determining Bias: Periodic SAR
• Obtaining Breakout/Reversal Confirmation: Change in State Delivery (CISD)
• Defining the Buyer/Seller Block (Supply/Demand Zones): Mitg Blocks (Mitigation Blocks), FVG (Fair Value Gaps), and Standard Deviation Projection
• Key Levels: Previous HTF (Higher Time Frame) levels
• Setting Targets: Standard Deviation Projection
• Trade Management: Anchored VWAP and opposing blocks
• Time-Based Context: Session Killzone times
• Notifications: An alarm/alert system will be utilized to stay informed.
________________________________________
Details:
Swing and Swing Failure Pattern:
Swing Sweep Types (Liquidity Sweep):
1. Single
2. Consecutive (The liquidity of the entity that swept the liquidity is being swept)
Bias Determination
We need to filter out the numerous SFPs that occur across all time frames. Our first strong filter will be the Bias. We will only look for trades aligned with our bias.
We will use Periodic SAR (Stop and Reverse) to determine the bias. We compare the price with the SAR value from a Higher Time Frame than the one we are trading on.
• Price > SAR => Bullish Bias
• Price < SAR => Bearish Bias
Depending on the pair, H1 SAR may be chosen for scalp trades, and Daily/Weekly SAR for intraday and swing trades.
Key Levels
Strategies looking for trades after a liquidity grab generally state that the sweep / stop hunt movement should occur at a significant price level.
The most fundamental Key Level levels are (User can customize):
• Previous Week High & Low
• Previous Day High & Low
• Previous H4 High & Low
• Previous H1 High & Low
• Asia Killzone High & Low
• London Killzone High & Low
• New York Killzone High & Low
• Monday Range High & Low values
We will prefer SFP formations that occur when these levels are swept. When Key Levels are violated, an information label appears on the screen.
Blocks / Zones
To strengthen our hand, we will use three types of blocks/zones, either with Key Levels or separately. When an SFP structure is formed in these areas (along with bias and breakout confirmation), our expectation is for the price to continue in our desired direction. These regions are:
1. Mitigation Blocks (Mtg)
o (Details can be found in the cd_VWAP_mtg_Cx indicator)
o In short: A second candle, following a bullish candle, crosses its high but fails to close above it. We call this a sweep / SFP. When the price, which was expected to go to the low, instead makes a new high/close, an Mtg block is formed. (Buyers are dominant)
2. FVGs (Fair Value Gaps)
o We use classic FVG structures.
3. Standard Deviation Projection Boxes
o When we get an SFP structure + breakout confirmation (CISD), we use the Standard Deviation Projection to determine our profit-taking and take-profit levels.
o Based on the idea that the price often respects the range between -2 and -2.5 of the projection values, we box this range and use it as our area of interest. (Our expectation is for the price to reverse after reaching this target).
o Let's mark it on the chart.
Confirmation
To summarize what has been explained so far: we look for the price to form an SFP structure in levels/zones we deem important, aligned with our bias, and for the breakout to be confirmed with a CISD.
No single component is strong on its own, but the success rate increases when they occur together.
We observe the following as additional confirmation along with the CISD: a new Mtg block forming in the direction of the breakout, high-volume movement (with FVG and a large body), and respect for VWAPs, the resistance/support line, and the defense block.
Additional Confirmations with Breakouts:
• Defence block, new mtg and VWAP
• Resistance / Support Line:
Indicator Signals
The indicator marks all formed sweeps, selected key levels, blocks, the projection, and CISD confirmations on the screen. The candle where the CISD confirmation occurs is indicated by an arrow.
• Arrows with double short lines signify a CISD that follows an SFP occurring at a Key Level.
• All other CISD candle indications are shown with single-line arrows.
Trade Management
When selecting profit targets in trades (preferably), the projection, opposing blocks, and structures that have formed are taken into account. Do not neglect to look at the structures that have formed against you when entering a trade.
Menu Settings:
• For Mtg blocks, the trading timeframe or a higher timeframe can be selected.
• FVGs formed in the current timeframe are displayed when the price creates an SFP (in "Fvg" option).
• Deviation boxes are displayed when the price creates an SFP (in box).
• The SAR HTF setting (H1) for scalp trades may vary depending on the pair. Users trying trades on higher timeframes should increase the HTF setting.
o Example: If you are looking for a trade with an SFP structure on H1, the SAR HTF setting should be H4 or higher.
• VWAP lines are refreshed starting from the candle that executed the sweep when the price forms an SFP. The only setting to adjust is the source selection setting (hlc3 is selected).
• Time frames and Killzone / Special Zone settings for Key Levels can be changed/should be checked.
Alarms / Alerts:
The conditions that will trigger an alert can be selected from the menu.
• To receive an alert aligned with the bias, the "Alignment with bias" checkbox must be selected.
• The alert should be set on the timeframe where you plan to enter the trade.
• The display options do not affect the alarm conditions. (Example: FVGs are monitored even when the menu selection is "off").
• If the necessary conditions are met, the alarm is triggered on the new candle that opens after the CISD confirmation.
• The alarm will not be triggered more than once at the same Key Level.
The user can preferably select alerts:
• Bias-aligned or Bias-independent
• Sweep (without waiting for CISD)
• Sweep + CISD (without looking for other conditions)
• Sweep + Key Level + CISD (the swept level is a Key Level)
• Sweep + Mtg / Fvg / Dev. + CISD (SFP formed in any of the blocks)
• Sweep + Mtg + CISD (SFP formed in the Mtg block)
• Sweep + Fvg + CISD (SFP formed inside the FVG)
• Sweep + Deviation Box + CISD (SFP formed inside the Dev. Box)
• Sweep + Key Level + Mtg / Fvg / Dev. + CISD (SFP formed simultaneously at a Key Level and any of the blocks)
Trade Example:
• Conditions: Bias-aligned + Sweep + Mtg/Fvg/Dev (at least one) + CISD
• Extra Confirmations: Respect for the Defense Block + Respect for VWAP
• Target (TP): Projection between -2 and -2.5
I welcome your thoughts and suggestions regarding my indicator, which I believe will be successful in the long run by adhering to uncompromising risk management and a strict trading plan.
Happy Trading!
Stochastic + Bollinger Bands Multi-Timeframe StrategyThis strategy fuses the Stochastic Oscillator from the 4-hour timeframe with Bollinger Bands from the 1-hour timeframe, operating on a 10-hour chart to capture a unique volatility rhythm and temporal alignment discovered through observational alpha.
By blending momentum confirmation from the higher timeframe with short-term volatility extremes, the strategy leverages what some traders refer to as “rotating volatility” — a phenomenon where multi-timeframe oscillations sync to reveal hidden trade opportunities.
🧠 Strategy Logic
✅ Long Entry Condition:
Stochastic on the 4H timeframe:
%K crosses above %D
Both %K and %D are below 20 (oversold zone)
Bollinger Bands on the 1H timeframe:
Price crosses above the lower Bollinger Band, indicating a potential reversal
→ A long trade is opened when both momentum recovery and volatility reversion align.
✅ Long Exit Condition:
Stochastic on the 4H:
%K crosses below %D
Both %K and %D are above 80 (overbought zone)
Bollinger Bands on the 1H:
Price reaches or exceeds the upper Bollinger Band, suggesting exhaustion
→ The long trade is closed when either signal suggests a potential reversal or overextension.
🧬 Temporal Structure & Alpha
This strategy is deployed on a 10-hour chart — a non-standard timeframe that may align more effectively with multi-timeframe mean reversion dynamics.
This subtle adjustment exploits what some traders identify as “temporal drift” — the desynchronization of volatility across timeframes that creates hidden rhythm in price action.
→ For example, Stochastic on 4H (lookback 17) and Bollinger Bands on 1H (lookback 20) may periodically sync around 10H intervals, offering unique alpha windows.
📊 Indicator Components
🔹 Stochastic Oscillator (4H, Length 17)
Detects momentum reversals using %K and %D crossovers
Helps define overbought/oversold zones from a mid-term view
🔹 Bollinger Bands (1H, Length 20, ±2 StdDev)
Measures price volatility using standard deviation around a moving average
Entry occurs near lower band (support), exits near upper band (resistance)
🔹 Multi-Timeframe Logic
Uses request.security() to safely reference 4H and 1H indicators from a 10H chart
Avoids repainting by using closed higher-timeframe candles only
📈 Visualization
A plot selector input allows toggling between:
Stochastic Plot (%K & %D, with overbought/oversold levels)
Bollinger Bands Plot (Upper, Basis, Lower from 1H data)
This helps users visually confirm entry/exit triggers in real time.
🛠 Customization
Fully configurable Stochastic and BB settings
Timeframes are independently adjustable
Strategy settings like position sizing, slippage, and commission are editable
⚠️ Disclaimer
This strategy is intended for educational and informational purposes only.
It does not constitute financial advice or a recommendation to buy or sell any asset.
Market conditions vary, and past performance does not guarantee future results.
Always test any trading strategy in a simulated environment and consult a licensed financial advisor before making real-world investment decisions.
VPG – MTF PrevClose Dashboard (Horizontal 6TF, Bottom Right, VPG – MTF PrevClose Dashboard is a lightweight, real-time visual indicator that displays the current price position across six key timeframes — Weekly (W), Daily (D), 4H, 1H, 30m, and 15m.
It compares the current market price to the previous candle close and shows whether the price is:
🟢 RALLY → higher than the previous close
🔵 BASE → roughly equal (sideways / consolidation)
🔴 DROP → lower than the previous close
Designed as a clean, horizontal dashboard fixed at the bottom-right corner of your chart, it provides instant multi-timeframe insight without cluttering your workspace.
⚙️ Key Features
🔹 Real-time monitoring of six key timeframes (W, D, 4H, 1H, 30m, 15m)
🔹 Clear and intuitive color scheme: Green = RALLY, Blue = BASE, Red = DROP
🔹 Fixed bottom-right placement for consistent visibility
🔹 Horizontal layout for compact, at-a-glance analysis
🔹 Adjustable tolerance to define how “equal” prices are classified as BASE
🔹 No alerts or labels — clean, fast, and resource-light
📊 Best For
Multi-timeframe traders who need quick directional context
Scalpers, intraday, and swing traders doing top-down analysis
Dashboard lovers who want a minimalist, data-driven overview
Confirming short-term price moves against higher-timeframe trends
💡 How to Use
Add VPG – MTF PrevClose Dashboard to any chart (Forex, Crypto, Stocks, Gold, Indices, etc.).
Adjust the tolerance parameter if you want a wider or stricter “BASE” range.
Watch the table in the bottom-right corner — it updates live with every price move.
🧠 About the Author
Nizar M — Developer of VPG indicators focused on clarity, momentum visualization, and fast market interpretation for real-time decision-making.
Soothing Trades - Risk Per Contract Table (1 candle)What it does
A compact risk table for futures/derivatives that estimates adverse move risk per contract from the current bar. It uses bar OHLC and the instrument’s minimum price increment (syminfo.mintick). In this script, a “step” means one minimum price increment (not exchange tick data).
Long Risk = potential adverse move from Close → Low on the active bar.
Short Risk = potential adverse move from Close → High on the active bar.
“Live” rows update while the bar forms.
Per-step currency value defaults to syminfo.pointvalue × syminfo.mintick, or you can set a Custom Per-Step Value (e.g., $5 per 0.25 for NQ).
How to use
Add the indicator and choose where to place the table.
Set your contract quantities (four quick rows).
If the default per-step value doesn’t match your instrument, turn on Use Custom Per-Step Value and enter the correct currency value for one minimum price increment.
Read the columns: Long / Short show estimated adverse risk per row of contracts; “Live” versions update intrabar.
What this is not
It does not use or claim access to historical tick data.
TradingView doesn’t provide tick-data charts; this tool works from bar data only.
It does not place orders or tell you what to trade.
It’s a convenience calculator for sizing awareness.
Notes
Contract specs vary. Always confirm your contract’s point value and minimum price increment with your broker/exchange.
Educational use only. No financial advice.
VWAP CATS background flipped 4.0VWAP CATS Background Flipped 4.0 is a sophisticated Pine Script v5 indicator for TradingView that combines a configurable moving average (MA) with dynamic Gann Square of 9 levels to create a multi-layered background shading system for price action analysis. It visualizes support/resistance zones around a central MA (often VWAP or RVWAP) using incremental offsets (either % or absolute points), generating symmetrical bands that resemble a "CATS" (Concentric Adaptive Tiered System) — hence the name.The background is "flipped" in the sense that shading intensity and structure emphasize higher-tier zones, and labels are placed to the right of the chart for future projection.Key FeaturesFeature
Description
Multi-MA Engine
Supports 20+ MA types: EMA, DEMA, TEMA, SMA, VWAP, RVWAP, HMA, ALMA, custom volume blends (CVB1–4)
RVWAP Mode
Rolling VWAP with adaptive or fixed time window (days/hours/minutes)
Gann Square of 9 Logic
Generates 80+ symmetric levels (0.25x to 17x increment) above/below the MA
Dual Increment Mode
Choose Percent or Points for spacing
Background Fills
Tiered transparency fills between Gann levels (darker = stronger zones)
Visual MA Offset
Shift MA line left/right without breaking fill alignment
Smart Labels
Projected labels on last bar: "FV", "normal", "high", "3/4" at key levels
Performance Optimized
Hidden plots + label cleanup to prevent lag
Primary Use Cases
1. Institutional VWAP Anchoring
Use RVWAP (1-day fixed) as maRaw
Set Increment = 0.5 points or 0.05%
Watch price interaction with "normal" (2x), "high" (4x), "3/4" (6x) zones
Ideal for intraday scalping on indices (ES, NQ) or forex
2. Swing Trading with Gann Projections
Use 400-period SMA/EMA on daily chart
Increment in Percent mode (~1.22%)
Identify confluence when price rejects at 2x, 4x, or 6x bands
Labels project future targets to the right
3. Volume-Weighted Mean Reversion
Select CVB1–CVB4 for heavy volume smoothing
Use Points mode for stocks with stable tick sizes (e.g. $0.50 increments)
Trade mean reversion between ±1x and ±2x bands
4. Risk Management & Stop Placement
Place stops beyond 2x or 4x bands
Take profits at next major tier (e.g. 4x → 6x)
Pro Tips
Enable "Use Fixed Time Period" for RVWAP to avoid session reset issues
Increase i_label_offset on lower timeframes to avoid overlap
Combine with volume profile or order flow for confluence
The "FV" label marks the Fair Value MA — core anchor
Summary"VWAP CATS Background Flipped 4.0" turns any moving average into a dynamic Gann-based pricing grid with intelligent background shading and forward-projected labels — perfect for institutional-style mean reversion, swing targeting, and risk-defined trading."
Market Profile Dominance Analyzer# Market Profile Dominance Analyzer
## 📊 OVERVIEW
**Market Profile Dominance Analyzer** is an advanced multi-factor indicator that combines Market Profile methodology with composite dominance scoring to identify buyer and seller strength across higher timeframes. Unlike traditional volume profile indicators that only show volume distribution, or simple buyer/seller indicators that only compare candle colors, this script integrates six distinct analytical components into a unified dominance measurement system.
This indicator helps traders understand **WHO controls the market** by analyzing price position relative to Market Profile key levels (POC, Value Area) combined with volume distribution, momentum, and trend characteristics.
## 🎯 WHAT MAKES THIS ORIGINAL
### **Hybrid Analytical Approach**
This indicator uniquely combines two separate methodologies that are typically analyzed independently:
1. **Market Profile Analysis** - Calculates Point of Control (POC) and Value Area (VA) using volume distribution across price channels on higher timeframes
2. **Multi-Factor Dominance Scoring** - Weights six independent factors to produce a composite dominance index
### **Six-Factor Composite Analysis**
The dominance score integrates:
- Price position relative to POC (equilibrium assessment)
- Price position relative to Value Area boundaries (acceptance/rejection zones)
- Volume imbalance within Value Area (institutional bias detection)
- Price momentum (directional strength)
- Volume trend comparison (participation analysis)
- Normalized Value Area position (precise location within fair value zone)
### **Adaptive Higher Timeframe Integration**
The script features an intelligent auto-selection system that automatically chooses appropriate higher timeframes based on the current chart period, ensuring optimal Market Profile structure regardless of the trading timeframe being analyzed.
## 💡 HOW IT WORKS
### **Market Profile Construction**
The indicator builds a Market Profile structure on a higher timeframe by:
1. **Session Identification** - Detects new higher timeframe sessions using `request.security()` to ensure accurate period boundaries
2. **Data Accumulation** - Stores high, low, and volume data for all bars within the current higher timeframe session
3. **Channel Distribution** - Divides the session's price range into configurable channels (default: 20 rows)
4. **Volume Mapping** - Distributes each bar's volume proportionally across all price channels it touched
### **Key Level Calculation**
**Point of Control (POC)**
- Identifies the price channel with the highest accumulated volume
- Represents the price level where the most trading activity occurred
- Serves as a magnetic level where price often returns
**Value Area (VA)**
- Starts at POC and expands both upward and downward
- Includes channels until reaching the specified percentage of total volume (default: 70%)
- Expansion algorithm compares adjacent volumes and prioritizes the direction with higher activity
- Defines the "fair value" zone where most market participants agreed to trade
### **Dominance Score Formula**
```
Dominance Score = (price_vs_poc × 10) +
(price_vs_va × 5) +
(volume_imbalance × 0.5) +
(price_momentum × 100) +
(volume_trend × 5) +
(va_position × 15)
```
**Component Breakdown:**
- **price_vs_poc**: +1 if above POC, -1 if below (shows which side of equilibrium)
- **price_vs_va**: +2 if above VAH, -2 if below VAL, 0 if inside VA
- **volume_imbalance**: Percentage difference between upper and lower VA volumes
- **price_momentum**: 5-period SMA of price change (directional acceleration)
- **volume_trend**: Compares 5-period vs 20-period volume averages
- **va_position**: Normalized position within Value Area (-1 to +1)
The composite score is then smoothed using EMA with configurable sensitivity to reduce noise while maintaining responsiveness.
### **Market State Determination**
- **BUYERS Dominant**: Smooth dominance > +10 (bullish control)
- **SELLERS Dominant**: Smooth dominance < -10 (bearish control)
- **NEUTRAL**: Between -10 and +10 (balanced market)
## 📈 HOW TO USE THIS INDICATOR
### **Trend Identification**
- **Green background** indicates buyers are in control - look for long opportunities
- **Red background** indicates sellers are in control - look for short opportunities
- **Gray background** indicates neutral market - consider range-bound strategies
### **Signal Interpretation**
**Buy Signals** (green triangle) appear when:
- Dominance crosses above -10 from oversold conditions
- Previous state was not already bullish
- Suggests shift from seller to buyer control
**Sell Signals** (red triangle) appear when:
- Dominance crosses below +10 from overbought conditions
- Previous state was not already bearish
- Suggests shift from buyer to seller control
### **Value Area Context**
Monitor the information table (top-right) to understand market structure:
- **Price vs POC**: Shows if trading above/below equilibrium
- **Volume Imbalance**: Positive values favor buyers, negative favors sellers
- **Market State**: Current dominant force (BUYERS/SELLERS/NEUTRAL)
### **Multi-Timeframe Strategy**
The auto-timeframe feature analyzes higher timeframe structure:
- On 1-minute charts → analyzes 2-hour structure
- On 5-minute charts → analyzes Daily structure
- On 15-minute charts → analyzes Weekly structure
- On Daily charts → analyzes Yearly structure
This higher timeframe context helps avoid counter-trend trades against the dominant force.
### **Confluence Trading**
Strongest signals occur when multiple factors align:
1. Price above VAH + positive volume imbalance + buyers dominant = Strong bullish setup
2. Price below VAL + negative volume imbalance + sellers dominant = Strong bearish setup
3. Price at POC + neutral state = Potential breakout/breakdown pivot
## ⚙️ INPUT PARAMETERS
- **Higher Time Frame**: Select specific HTF or use 'Auto' for intelligent selection
- **Value Area %**: Percentage of volume contained in VA (default: 70%)
- **Show Buy/Sell Signals**: Toggle signal triangles visibility
- **Show Dominance Histogram**: Toggle histogram display
- **Signal Sensitivity**: EMA period for dominance smoothing (1-20, default: 5)
- **Number of Channels**: Market Profile resolution (10-50, default: 20)
- **Color Settings**: Customize buyer, seller, and neutral colors
## 🎨 VISUAL ELEMENTS
- **Histogram**: Shows smoothed dominance score (green = buyers, red = sellers)
- **Zero Line**: Neutral equilibrium reference
- **Overbought/Oversold Lines**: ±50 levels marking extreme dominance
- **Background Color**: Highlights current market state
- **Information Table**: Displays key metrics (state, dominance, POC relationship, volume imbalance, timeframe, bars in session, total volume)
- **Signal Shapes**: Triangle markers for buy/sell signals
## 🔔 ALERTS
The indicator includes three alert conditions:
1. **Buyers Dominate** - Fires on buy signal crossovers
2. **Sellers Dominate** - Fires on sell signal crossovers
3. **Dominance Shift** - Fires when dominance crosses zero line
## 📊 BEST PRACTICES
### **Timeframe Selection**
- **Scalping (1-5min)**: Focus on 2H-4H dominance shifts
- **Day Trading (15-60min)**: Monitor Daily and Weekly structure
- **Swing Trading (4H-Daily)**: Track Weekly and Monthly dominance
### **Confirmation Strategies**
1. **Trend Following**: Enter in direction of dominance above/below ±20
2. **Reversal Trading**: Fade extreme readings beyond ±50 when diverging with price
3. **Breakout Trading**: Look for dominance expansion beyond ±30 with increasing volume
### **Risk Management**
- Avoid trading during NEUTRAL states (dominance between -10 and +10)
- Use POC levels as logical stop-loss placement
- Consider VAH/VAL as profit targets for mean reversion
## ⚠️ LIMITATIONS & WARNINGS
**Data Requirements**
- Requires sufficient historical data on current chart (minimum 100 bars recommended)
- Lower timeframes may show fewer bars per HTF session initially
- More accurate results after several complete HTF sessions have formed
**Not a Standalone System**
- This indicator analyzes market structure and participant control
- Should be combined with price action, support/resistance, and risk management
- Does not guarantee profitable trades - past dominance does not predict future results
**Repainting Characteristics**
- Higher timeframe levels (POC, VAH, VAL) update as new bars form within the session
- Dominance score recalculates with each new bar
- Historical signals remain fixed, but current session data is developing
**Volume Limitations**
- Uses exchange-provided volume data which varies by instrument type
- Forex and some CFDs use tick volume (not actual transaction volume)
- Most accurate on instruments with reliable volume data (stocks, futures, crypto)
## 🔍 TECHNICAL NOTES
**Performance Optimization**
- Uses `max_bars_back=5000` for extended historical analysis
- Efficient array management prevents memory issues
- Automatic cleanup of session data on new period
**Calculation Method**
- Market Profile uses actual volume distribution, not TPO (Time Price Opportunity)
- Value Area expansion follows traditional Market Profile auction theory
- All calculations occur on the chart's current symbol and timeframe
## 📚 EDUCATIONAL VALUE
This indicator helps traders understand:
- How institutional traders use Market Profile to identify fair value
- The relationship between price, volume, and market acceptance
- Multi-factor analysis techniques for assessing market conditions
- The importance of higher timeframe structure in trade planning
## 🎓 RECOMMENDED READING
To better understand the concepts behind this indicator:
- "Mind Over Markets" by James Dalton (Market Profile foundations)
- "Markets in Profile" by James Dalton (Value Area analysis)
- Volume Profile analysis in institutional trading
## 💬 USAGE TERMS
This indicator is provided as an educational and analytical tool. It does not constitute financial advice, investment recommendations, or trading signals. Users are responsible for their own trading decisions and should conduct their own research and due diligence.
Trading involves substantial risk of loss. Past performance does not guarantee future results. Always use proper risk management and never risk more than you can afford to lose.
Gold vs. Dollar Sentiment Map [SB1]🟡 Gold vs Dollar Sentiment Map
The Gold vs Dollar Sentiment Map reveals the direct inverse relationship between Gold Futures (GC) and the U.S. Dollar Index (DXY) — one of the most reliable global risk-sentiment gauges.
It helps traders instantly identify whether capital is flowing into safety (Gold) or into the Dollar (risk assets) during any session or timeframe.
🔍 Core Logic
Risk-Off (Bearish background = Red): DXY ↓ and Gold ↑ → investors seeking safety, rising fear or falling yields.
Risk-On (Bullish background = Green): DXY ↑ and Gold ↓ → investors rotating into risk assets, stronger USD demand.
Neutral (Gray): Mixed signals – no dominant macro driver.
📊 Dashboard
A compact on-chart table displays real-time trend bias for:
Gold (GC) – Bullish / Bearish / Neutral
U.S. Dollar Index (DXY) – Bullish / Bearish / Neutral
Color shading reflects each asset’s intrabar momentum.
⚙️ Visual Features
Adaptive background colors to show sentiment shifts.
Strong candle markers highlighting momentum bars near range extremes.
Alerts for clear Risk-On / Risk-Off alignment.
🧭 How to Use
Red background (Risk-Off): Gold strength + Dollar weakness → favorable environment for long gold setups.
Green background (Risk-On): Dollar strength + Gold weakness → bias toward short gold or avoid long exposure.
Gray background: Stay patient; look for confirmation or wait for alignment.
💡 Ideal For
Gold and Forex traders monitoring macro rotation.
Sentiment confirmation alongside order-flow, VWAP, or volume-delta tools.
Overlaying on intraday or higher-timeframe charts to frame trade bias.
Alpha-Weighted RSIDescription:
The Alpha-Weighted RSI is a next-generation momentum oscillator that redefines the classic RSI by incorporating the mathematical principles of Lévy Flight. This advanced adaptation applies non-linear weighting to price changes, making the indicator more sensitive to significant market moves and less reactive to minor noise. It is designed for traders seeking a clearer, more powerful view of momentum and potential reversal zones.
🔍 Key Features & Innovations:
Lévy Flight Alpha Weighting: At the core of this indicator is the Alpha parameter (1.0-2.0), which controls the sensitivity to price changes.
Lower Alpha (e.g., 1.2): Makes the indicator highly responsive to recent price movements, ideal for capturing early trend shifts.
Higher Alpha (e.g., 1.8): Creates a smoother, more conservative output that filters out noise, focusing on stronger momentum.
Customizable Smoothing: The raw Lévy-RSI is smoothed by a user-selectable moving average (8 MA types supported: SMA, EMA, SMMA, etc.), allowing for further customization of responsiveness.
Intuitive Centered Oscillator: The RSI is centered around a zero line, providing a clean visual separation between bullish and bearish territory.
Dynamic Gradient Zones: Subtle, colour coded gradient fills in the overbought (>+25) and oversold (<-25) regions enhance visual clarity without cluttering the chart.
Modern Histogram Display: Momentum is plotted as a sleek histogram that changes color between bright cyan (bullish) and magenta (bearish) based on its position relative to the zero line.
🎯 How to Use & Interpret:
Zero-Line Crossovers: The most basic signals. A crossover above the zero line indicates building bullish momentum, while a crossover below suggests growing bearish momentum.
Overbought/Oversold Levels: Use the +25/-25 and +35/-35 levels as dynamic zones. A reading above +25 suggests strong bullish momentum (overbought), while a reading below -25 indicates strong bearish momentum (oversold).
Divergence Detection: Look for divergences between the Alpha-Weighted RSI and price action. For example, if price makes a new low but the RSI forms a higher low, it can signal a potential bullish reversal.
Alpha Tuning: Adjust the Alpha parameter to match market volatility. In choppy markets, increase alpha to reduce noise. In trending markets, decrease alpha to become more responsive.
⚙️ Input Parameters:
RSI Settings: Standard RSI inputs for Length and Calculation Source.
Lévy Flight Settings: The crucial Alpha factor for response control.
MA Settings: MA Type and MA Length for smoothing the final output.
By applying Lévy Flight dynamics, this indicator offers a nuanced perspective on momentum, helping you stay ahead of the curve. Feedback is always welcome!
Adaptive EMA CrossoverIndicator Name: Adaptive EMA Crossover
Description:
The Adaptive EMA Crossover is a sleek, visual tool designed to help traders identify trend direction and potential entry/exit points with clarity. By employing two Exponential Moving Averages (EMAs) with dynamic coloring, it cuts through the noise of the chart, allowing you to focus on high-probability signals.
🔍 Key Features:
Dual EMA System: Utilizes a fast and a slow EMA to gauge market momentum. The default settings are 12 (fast) and 21 (slow) periods, which can be fully customized.
Adaptive Visuals: Both EMAs change color simultaneously to reflect the dominant trend.
🟢 Bright Turquoise: Indicates an Uptrend (Fast EMA >= Slow EMA).
🔴 Bright Pink: Indicates a Downtrend (Fast EMA < Slow EMA).
Clear Crossover Signals: Prominent dots directly on the chart mark the exact moment a crossover occurs.
Turquoise Dot: A Bullish Crossover signal (Fast EMA crosses above Slow EMA).
Pink Dot: A Bearish Crossover signal (Fast EMA crosses below Slow EMA).
Integrated Alerts: Never miss a trading opportunity! Built-in alert conditions notify you instantly for both bullish and bearish crossovers.
🎯 How to Use:
Trend Identification: The primary colors of the EMAs give an immediate sense of the trend. Trade in the direction of the trend for higher-probability setups.
Signal Confirmation: Use the crossover dots as potential triggers for entry or exit. A turquoise dot in a rising market can signal a buy opportunity, while a pink dot in a falling market can signal a sell or short opportunity.
Combination with Other Tools: For best results, combine this indicator with other forms of analysis like support/resistance levels or volume confirmation to filter out false signals.
⚙️ Inputs:
EMA Small: Period for the faster-moving average (default: 12).
EMA Big: Period for the slower-moving average (default: 21).
This is my first published indicator. I welcome all feedback and suggestions for improvement! Happy Trading!
ICT Sessions Ranges [SwissAlgo]ICT Session Ranges - ICT Liquidity Zones & Market Structure
OVERVIEW
This indicator identifies and visualizes key intraday trading sessions and liquidity zones based on Inner Circle Trader (ICT) methodology (AM, NY Lunch Raid, PM Session, London Raid). It tracks 'higher high' and 'lower low' price levels during specific time periods that may represent areas where market participants have placed orders (liquidity).
PURPOSE
The indicator helps traders observe:
Session-based price ranges during different market hours
Opening range gaps between market close and next day's open
Potential areas where liquidity may be concentrated and trigger price action
SESSIONS TRACKED
1. London Session (02:00-05:00 ET): Tracks price range during early London trading hours
2. AM Session (09:30-12:00 ET): Tracks price range during the morning New York session
3. NY Lunch Session (12:00-13:30 ET): Tracks price range during typical low-volume lunch period
4. PM Session (13:30-16:00 ET): Tracks price range during the afternoon New York session
CALCULATIONS
Session High/Low: The highest high and lowest low recorded during each active session period
Opening Range Gap: Calculated as the difference between the previous day's 16:00 close and the current day's 09:30 open
Gap Mitigation: A gap is considered mitigated when the price reaches 50% of the gap range
All times are based on America/New_York timezone (ET)
BACKGROUND INDICATORS
NY Trading Hours (09:30-16:00 ET): Optional gray background overlay
Asian Session (20:00-23:59 ET): Optional purple background overlay
VISUAL ELEMENTS
Horizontal lines mark session highs and lows
Subtle background boxes highlight each session range
Labels identify each session type
Orange shaded boxes indicate unmitigated opening range gaps
Dotted line at 50% gap level shows mitigation threshold
FEATURES
Toggle visibility for each session independently
Customizable colors for each session type
Automatic removal of mitigated gaps
All drawing objects use transparent backgrounds for chart clarity
ICT CONCEPTS
This tool relates to concepts discussed by Inner Circle Trader regarding liquidity pools, session-based analysis, and gap theory. The indicator assumes that session highs and lows may represent areas where liquidity is concentrated, and that opening range gaps may attract price until mitigated.
USAGE NOTES
Best used on intraday timeframes (1-15 minute charts)
All sessions are calculated based on actual price movement during specified time periods
Historical session data is preserved as new sessions develop
Gap detection only triggers at 09:30 ET market open
DISCLAIMER
This indicator is for educational and informational purposes only. It displays historical price levels and time-based calculations. Past performance of price levels is not indicative of future results. The identification of "liquidity zones" is a theoretical concept and does not guarantee that orders exist at these levels or that prices will react to them. Trading involves substantial risk of loss. Users should conduct their own analysis and risk assessment before making any trading decisions.
TIME ZONE
Set your timezone to: America/New_York (UTC-5)
Lot Size CalculatorLot Size Calculator for Gold (XAU)
This indicator helps traders calculate the proper lot size for Gold (XAU) based on their entry, stop loss, and risk amount in USD.
You can set your entry and stop levels directly on the chart, and adjust your dollar risk from the settings panel.
The indicator measures the distance between entry and stop to calculate the position size that matches your selected risk.
A clean, customizable table displays key values such as Risk, Entry, Stop, Target, Lots, and Pips.
You can easily hide specific rows, change colors, and adjust layout options to fit your chart style.
Designed specifically for Gold traders, this tool provides a simple and visual way to manage risk directly on the chart.
Smarter Money Volume Rejection Blocks [PhenLabs]📊 Smarter Money Volume Rejection Blocks – Institutional Rejection Zone Detection
The Smarter Money Volume Rejection Blocks indicator combines high-volume analysis with statistical confidence intervals to identify where institutional traders are actively defending price levels through volume spikes and rejection patterns.
🔥 Core Methodology
Volume Spike Detection analyzes when current volume exceeds moving average by configurable multipliers (1.0-5.0x) to identify institutional activity
Rejection Candle Analysis uses dual-ratio system measuring wick percentage (30-90%) and maximum body ratio (10-60%) to confirm genuine rejections
Statistical Confidence Channels create three-level zones (upper, center, lower) based on ATR or Standard Deviation calculations
Smart Invalidation Logic automatically clears zones when price significantly breaches confidence levels to maintain relevance
Dynamic Channel Projection extends confidence intervals forward up to 200 bars with customizable length
Support Zone Identification detects bullish rejections where smart money absorbs selling pressure with high volume and strong lower wicks
Resistance Zone Mapping identifies bearish rejections where institutions defend price levels with volume spikes and pronounced upper wicks
Visual Information Dashboard displays real-time status table showing volume spike conditions and active support/resistance zones
⚙️ Technical Configuration
Dual Confidence Interval Methods: Choose between ATR-Based for trend-following environments or StdDev-Based for range-bound statistical precision
Volume Moving Average: Configurable period (default 20) for baseline volume comparison calculations
Volume Spike Multiplier: Adjustable threshold from 1.0 to 5.0 times average volume to filter institutional activity
Rejection Wick Percentage: Set minimum wick size from 30% to 90% of candle range for valid rejection detection
Maximum Body Ratio: Configure body-to-range ratio from 10% to 60% to ensure genuine rejection structures
Confidence Multiplier: Statistical multiplier (default 1.96) for 95% confidence interval calculations
Channel Projection Length: Extend confidence zones forward from 10 to 200 bars for anticipatory analysis
ATR Period: Customize Average True Range lookback from 5 to 50 bars for volatility-based calculations
StdDev Period: Adjust Standard Deviation period from 10 to 100 bars for statistical precision
🎯 Real-World Trading Applications
Identify high-probability support zones where institutional buyers have historically defended price with significant volume
Map resistance levels where smart money sellers consistently reject higher prices with volume confirmation
Combine with price action analysis to confirm breakout validity when price approaches confidence channel boundaries
Use invalidation signals to exit positions when smart money zones are definitively breached
Monitor the real-time dashboard to quickly assess current market structure and active rejection zones
Adapt strategy based on calculation method: ATR for trending markets, StdDev for ranging conditions
Set alerts on confidence level breaches to catch potential trend reversals or continuation patterns
📈 Visual Interpretation Guide
Green Zones indicate bullish rejection blocks where buyers defended with high volume and lower wicks
Red Zones indicate bearish rejection blocks where sellers defended with high volume and upper wicks
Solid Center Lines represent the core rejection price level where maximum volume activity occurred
Dashed Confidence Boundaries show upper and lower statistical limits based on volatility calculations
Zone Opacity decreases as channels extend forward to indicate decreasing confidence over time
Dashboard Color Coding provides instant visual feedback on active volume spike and zone conditions
⚠️ Important Considerations
Volume-based indicators identify historical rejection zones but cannot predict future price action with certainty
Market conditions change rapidly and institutional activity patterns evolve continuously
High volume does not guarantee level defense as market structure can shift without warning
Confidence intervals represent statistical probabilities, not guaranteed price boundaries
Average Dollar Volume by Mashrab
Standard Mode: By default, it shows a 20-period SMA of the Dollar Volume. This is great for swing trading to see if money flow is increasing over days.
Day Trading Mode: Go to the indicator settings (User Input) and check "Reset Average Daily".
The line will now represent the Cumulative Average for today only.
Example: If it's 10:00 AM, the line shows the average dollar volume per bar since the market opened at 9:30 AM. This helps you spot if the current 5-minute bar is truly igniting compared to the rest of the morning.
How to Use for Day Trading
Add the script to your 1-minute chart.
Ensure "Reset Average Daily" is checked in the settings (I made it default to true for you).
Look at the Table in the top right:
Avg Dollar Vol: This tells you the average money flowing into the stock per minute today.
1% Threshold: This gives you the exact number your friend likely uses to gauge "minimum viable liquidity" or specific risk calculations.
Relative Performance Binary FilterDescription:
This indicator monitors the relative performance of 30 selected crypto assets and generates a binary signal for each: 1 if the asset’s price has increased above a user-defined threshold over a specified lookback period, 0 otherwise. The script produces a JSON-formatted output suitable for webhooks, allowing you to send the signals to external applications like Google Sheets.
Key Features:
Configurable lookback period, price source, and performance threshold.
Supports confirmed or real-time bar data.
Monitors 30 crypto assets simultaneously.
Produces a one-line JSON output with batch grouping for easy webhook integration.
Includes an optional visual sum plot showing how many assets are above the threshold at any time.
Use Cases:
Automate performance tracking across multiple crypto assets.
Feed binary signals into external dashboards, trading bots, or Google Sheets.
Quickly identify which assets are outperforming a set threshold.
BTC 1h StratUses LuxAlgo-style Support/Resistance levels (pivot-based, with volume break labels).
Adds momentum confirmation (RSI + MACD) to filter fakeouts.Keeps your swing breakout logic (close above swing high / below swing low).
Includes liquidity and TP/SL risk management.
Auto Fibonacci Retracement (Labeled Swings, Rounded Prices)This tool automatically detects the latest confirmed swing high and swing low on your chart, using a user-settable pivot length. It then plots standard Fibonacci retracement levels between these confirmed pivots, labeling each retracement line with its percentage and rounded price for instant reference. All levels update only on swing confirmation, ensuring strict non-repainting logic and transparency.
How it works
Swing Detection:
Uses Pine Script’s native ta.pivothigh and ta.pivotlow functions to locate swing pivots after full confirmation, reducing noise and false signals.
Fibonacci Calculation:
Once two confirmed swings are found, the script draws standard Fibonacci retracement levels (0%, 23.6%, 38.2%, 50%, 61.8%, 78.6%, 100%) between these anchors. The levels adapt to both uptrends and downtrends, based on swing position.
Customization and Clarity:
Users can choose which retracement levels to display and adjust colors, line thickness, styles, and label sizes for chart clarity. All price labels are rounded for improved visibility.
Non-Repainting:
All levels are plotted only after a swing is confirmed by the market; nothing redraws retroactively.
How To Use It
Add the indicator to any chart and timeframe.
Select your preferred pivot length:
Smaller values yield more frequent swings, larger values wait for major structure.
Toggle each Fibonacci level you wish to see in the settings.
Adjust line and label appearance to fit your style.
Interpret retracement levels as potential support/resistance zones, awareness for pullbacks, and context for trend direction.
Combine the indicator with your technical, price action, or volume analysis to plan entries, stops, and targets.
What Traders Should Look For
Visual retracement map between confirmed swings:
Fib lines auto-update as new swings are confirmed, keeping your chart relevant.
Price reaction at Fib levels:
Watch for reversals, consolidations, or continuations near labeled percentages and prices.
Trend assessment:
Quickly spot whether market structure is showing shallow or deep retracements by the distance between levels.
Confluence:
Use retracement levels along with other indicators or market structure for more robust trade setups.
Key Features
Strict non-repainting logic (confirmed swings only)
Configurable retracement levels: Enable/disable each Fib line.
Rounded price & percentage labels
Visual customization: Colors, thickness, line style, label size
Automatic detection of direction (uptrend/downtrend pivots)
Disclaimer
This indicator is a technical analysis and educational tool. It does not provide buy/sell signals, nor guarantee future price movements. Please use in conjunction with your trading plan and risk management.
Luminous Glide Momentum Indicator [wjdtks255]This indicator, named "Customized SuperSmoother MA Oscillator," applies a smoothing filter to price data using a SuperSmoother technique to reduce noise and enhance signal clarity. It calculates two moving averages on the smoothed data—a fast and a slow—whose difference forms the oscillator line. A signal line is derived by smoothing the oscillator with another moving average. The histogram visualizes the divergence between the oscillator and signal lines, indicating momentum strength and direction.
How it works
SuperSmoother Filter: Reduces price noise to provide smoother and more reliable signals than raw data.
Fast and Slow Moving Averages: The fast MA reacts quicker to price changes, while the slow MA indicates longer trends.
Oscillator: The difference between the fast and slow MAs signals shifts in momentum.
Signal Line: A smoothed version of the oscillator used to generate crossovers.
Histogram: Displays the distance between the oscillator and signal line, with color changes indicating bullish or bearish momentum.
Trading Strategy
Buy Signal: When the oscillator crosses above the signal line, it suggests increasing upward momentum, signaling a potential buy opportunity.
Sell Signal: When the oscillator crosses below the signal line, it suggests increasing downward momentum, signaling a potential sell opportunity.
Histogram Size and Color: Larger green bars indicate stronger bullish momentum; larger red bars indicate stronger bearish momentum.
Usage Tips
Combine this oscillator with other indicators or price action analysis to confirm trading signals.
Adjust smoothing and moving average lengths according to your trading timeframe and the asset volatility.
Use proper risk management to filter out potential false signals common in oscillators.
Kronos Scalper (RAVINDRA KHADSE) 11.11.2025Nice — below is a ready-to-copy TradingView Pine Script (v5) strategy named “Kronos Scalper”.
It’s a self-contained scalping strategy (non-repainting) with configurable EMAs trend filter, RSI entry filter, ATR-based stop loss / take profit, optional trailing stop, plots and entry/exit labels. Drop it into TradingView’s Pine editor and Add to Chart → Strategy Tester.
