GTI BGTI: RSI Suite (Standard • Stochastic • Smoothed)
A three-layer momentum and trend toolkit that combines Standard RSI, Stochastic RSI, and a Smoothed/“Macro” RSI to help you read intraday swings, trend transitions, and high-probability reversal/continuation spots.
All in one pane with intuitive coloring and optional divergence markers and alerts.
Why this works
* Stochastic RSI (K/D) visualizes fast momentum swings and timing.
* Standard RSI moves more gradually, helping confirm trend transitions that may span several Stochastic cycles.
* Smoothed RSI (Average → Macro) adds a second-pass filter and slope persistence to reveal the macro direction while suppressing noise.
Used together, Stochastic guides entries/exits around local highs/lows, while the RSI layers improve confidence when a small swing is likely part of a larger turn.
What you’ll see
* Standard RSI (yellow; pink above Bull line, aqua below Bear line).
* Stochastic RSI (K/D) with contextual colors:
* Greens when RSI is weak/oversold (bearish conditions → watch for bullish reversals/continuations).
* Reds when RSI is strong/overbought (bullish conditions → watch for bearish reversals/continuations).
* Smoothed (Macro) RSI with trend color:
* Red when macro is ascending (bullish),
* Aqua when macro is descending (bearish).
* Divergences (optional markers):
* Bearish: RSI Lower High + Price Higher High (red ⬇).
* Bullish: RSI Higher Low + Price Lower Low (green ⬆).
* No repaint: pivots confirm after the chosen right-bars window.
How to use it
* Bullish Reversal
* Macro RSI is reversing at a higher low after price has been in a overall downtrend
* Stochastic RSI is switching from green to red in an overall downtrend
* Bullish Oversold
* Macro RSI is reversing from a significantly low level after price has a short but strong dip during an overall uptrend
* Stochastic RSI is switching from green to red in an overall uptrend
* Bullish Continuation
* Macro RSI is ascending with a strong slope or forming a higher low above the 50 line
* Stochastic RSI is reaching a bottom but still painted red
* Bearish Reversal
* Macro RSI is reversing at a lower high after price has been in a overall uptrend
* Stochastic RSI is switching from red to green in an overall uptrend
* Bearish Overbought
* Macro RSI is reversing from a significantly high level after price has a short but strong jump during an overall downtrend
* Stochastic RSI is switching from red to green in an overall downtrend
* Bearish Continuation
* Macro RSI is descending with a strong slope or forming a lower high below the 50 line
* Stochastic RSI is reaching a top but still painted green
* Divergences: Use as signals of exhaustion—best when aligned with Macro RSI color/slope and key levels (e.g., Bull/Bear lines, 50 midline).
*** IMPORTANT ***
* Stack confluence, don’t single-signal trade. Look for:
* 1) Macro RSI color & slope (red = ascending/bullish, aqua = descending/bearish)
* 2) Standard RSI location (above/below Bull/Bear lines or 50)
* 3) Stoch flip + direction
* 4) Price structure (HH/HL vs LH/LL)
* 5) Divergence type (regular vs hidden) at meaningful levels
* Trade with the macro
* Prioritize longs when Macro RSI is red or just flipped up
* Prioritize shorts when Macro RSI is aqua or just flipped down
* Counter-trend setups = smaller size and faster management.
* Location > signal
* The same crossover/divergence is higher quality near Bull (~60)/Bear(~40) or extremes than in the mid-range chop around 50.
* Early vs confirmed
* Use the early pivot heads-up for anticipation, but scale in only after the confirmed pivot (right-bars complete). If early signal fails to confirm, stand down.
* Define invalidation upfront
* For divergence entries, place stops beyond the pivot extreme (LL/HH). If Macro RSI flips against your trade or RSI breaks back through 50 with slope, exit or tighten.
* Multi-timeframe alignment
* Best results come when entry timeframe (e.g., 1H) aligns with higher-TF macro (e.g., 4H/D). If they disagree, treat it as mean-reversion only.
* Avoid common traps
* Skip: isolated Stochastic flips without RSI support, divergences without price HH/LL confirmation, and serial divergences when Macro RSI slope is strong against the idea.
* Parameter guidance
* Start with defaults; then tune: confirmBars 3–7, minSlope 0.05–0.15 RSI pts/bar, pivot left/right tighter for faster but noisier signals, wider for cleaner but fewer.
* Alerts = workflow, not auto-trades
* Use Macro Flip + Divergence alerts as a checklist trigger; enter only when your confluence rules are met and risk is defined.
Key inputs (tweak to your market/timeframe)
* RSI / Stochastic lengths and K/D smoothing.
* Bull / Bear Lines (default 61.1 / 43.6).
* Average RSI Method/Length (SMA/EMA/RMA/WMA) + Macro Smooth Length.
* Trend confirmation: bars of persistence and minimum slope to reduce flip noise.
* Pivot look-back (left/right) for divergence confirmation strictness.
Alerts included
* Macro Flip Up / Down (Smoothed RSI regime change).
* RSI Bullish/Bearish Divergence (confirmed at pivot).
* Stochastic RSI continuation/divergence (optional).
Tips
* Level + Slope matter. High/low RSI level flags conditions; slope confirms impulse/continuation.
* Let Stochastic time the swing; let Macro RSI filter the trend.
* Tighten or loosen pivot windows to trade fewer/cleaner vs. more/faster signals.
Osciladores
Adaptive AI Polar Oscillator [by Oberlunar]Adaptive AI Oscillator blends trading signals with two order-flow style oscillators and a lightweight online-learning model to keep it reactive, adaptive and computationally feasible.
What it is
A lightweight Multi Layer Perceptron (neural net) updates online on every bar, so it keeps adapting as conditions change.
An adaptive collector that fuses features like Price (close, ohlc4, etc...), a selectable (but not used in the original implementation) Moving Average (EMA/SMA/WMA/RMA/HMA/DEMA/TEMA), RSI, the classic volume datafeeds, plus two “OberPolar” oscillators computed above and below the current integral area price.
What you see
White line — the model’s denormalised forecast (in price units).
Colored price line — actual price, shown aqua when forecast ≥ price (“golden” bias) and red when forecast < price (“death” bias).
Why it helps
Combines heterogeneous information (trend, momentum, participation, regional buy/sell pressure) into a single adaptive forecast.
Online learning reduces regime staleness versus fixed-parameter indicators.
The aqua/red bias offers a quick, visual state for discretionary decisions.
How it works (intuitive)
Each AI input is standardised (z-score) with optional clamping to mitigate outliers.
A rolling window of recent values feeds a 2-layer AI to predict one step ahead.
After each bar closes, the model compares forecast vs. reality and nudges its weights (SGD with momentum, L2, optional gradient clipping).
The forecast is de-standardised back to price units and plotted as the white line.
Reading guide
Crossovers between forecast and price often mark potential bias flips.
Persistent aqua → model perceives supportive/positive conditions.
Persistent red → model perceives headwinds/negative conditions.
Complex Strategy — Oscillator Trendline Break
Connect the first pivot in the fading bias with the first pivot in the new bias, then trade the break of that line in the direction of the new bias.
Idea in one line
Use the Adaptive AI Oscillator (green = bullish bias, red = bearish). When bias flips, build a line across the oscillator pivots that “span” the transition; the break of that line times the entry.
Long setup (mirror for shorts)
Bias transition : a bearish (red) regime is ongoing, then the oscillator turns bullish (green).
Anchor pivots : take the first MIN in red just before/around the flip and the first MAX in green after the flip. Draw a trendline L through these two oscillator values (time–value line).
Trigger : enter LONG on the close that breaks above L —optional confirmations: price above your MA, non-decreasing volume, no immediate supply zone overhead.
Risk : stop below the last oscillator swing low or below a retest of L; first target at 1R–1.5R or at the opposite bias zone; trail under successive oscillator higher lows.
Short setup
Bias turns from green (bullish) to red (bearish).
Connect the first MAX in green to the first MIN in red → line L.
Enter SHORT on a close below L ; stop above the last oscillator swing high; symmetric targets/trailing.
Complex Strategy #2 — Bias-Pivot Breakout with Exit on Line Failure
Connect two pivots of the same bias to build a dynamic barrier; trade the breakout in the bias direction and exit when that line later fails.
Long play (mirror for shorts)
Build the line. During a green (bullish) phase, mark the first two local MAX of the oscillator. Connect them to form the yellow resistance line L (extend it right). If a new, clearer MAX appears before a break, re-anchor using the two most recent highs.
Entry trigger. Go LONG on a close above L (the “Break and LONG” in the image). Optional filters: price above your MA, rising volume, no immediate overhead level.
Risk. Initial stop: below the last oscillator swing low or below the retest of L . Position size for 1–2R baseline.
Exit. Close the long when the oscillator later breaks back below L (the “Break and LONG exit”), or on a bias flip to red, or at a fixed target/trailing under higher lows.
Short play (symmetric)
In a red phase, connect the first two local MIN to form support line L .
Enter SHORT on a close below L ; stop above the last oscillator swing high; exit on a break back above L or on a flip to green.
Notes
Require a minimum slope/spacing between pivots to avoid flat/noisy lines.
Re-anchor the line if fresher pivots emerge before a valid break.
Use with your regime filter (MA slope, higher-timeframe bias) to reduce whipsaws.
Complex Strategy #3 — Lateral Box & Zero-Slope Breakout
An easy way to understand sideways phases and the next price direction: draw two zero-slope lines (flat upper/lower bounds) across the oscillator’s lateral area; when a strong break occurs, trade in the direction of that break.
How to use it
Identify a lateral area on the oscillator (flat, low-variance region). Place a flat upper line on tops and a flat lower line on bottoms (slope ≈ 0).
Wait for a decisive break : close outside the band with expansion (range/true range rising, or a wide candle).
• Break up → bias for LONG .
• Break down → bias for SHORT .
Why it helps
Flat lines isolate congestion; the next impulsive move is often revealed by which side is broken with force.
It filters noise inside the range and focuses attention on the transition from balance → imbalance.
Practical filters (optional)
Require minimum bar body/ATR on the breakout candle to avoid false breaks .
Confirm with your regime filter (e.g., price above/below your MA) or a quick retest that holds.
Invalidate the signal if the price immediately returns inside the band on the next bar.
General Operational notes
If new pivots form before a break, re-anchor the line with the most recent qualifying pair (keeps the structure fresh).
Ignore very shallow lines (near-flat): require a minimum slope or angle to avoid noise.
Combine with your bias filter (e.g., MA slope/regime) to reduce false starts.
Limits & good practice
Adaptive models can react to noise; treat signals as context within a risk-managed plan.
No model predicts the future—this summarises evolving conditions compactly.
— Oberlunar 👁 ★
Smart Moving Average Dynamics [ChartNation]Smart Moving Average Dynamics (SMAD) — by Chart Nation
What it does:
SMAD maps how far price deviates from a chosen moving average and normalizes that distance into a bounded oscillator (−100…+100). It detects extreme expansions and prints non-repainting dots when the move exits an extreme. Price-level rails are drawn from those events (with optional fade/expiry) to highlight likely reaction zones. The MA line is colored by bias. A slim gauge summarizes the current oscillator percentile; a compact info panel shows TF, Trend, Volume rank, and Volatility rank.
How it works (high-level, closed-source)
Core signal: diff = price – MA(type, length) where MA can be SMA/EMA/RMA/WMA/VWMA.
Normalization (choose one):
Highest Abs (N): scales diff by the highest absolute excursion over N bars (fast, adaptive).
Z-Score: scales by stdev(diff, N) and maps ±σ to ±100 via a user factor.
ATR-Scaled: scales by ATR * k, relating deviation to current volatility.
Percent Rank: ranks the magnitude of |diff| over N bars and reapplies the original sign.
All methods clamp to −100…+100 to keep visuals consistent across assets/TFs.
Extremes & confirmation: Dots print only when an extreme exits ±100 (optionally on bar close) and can be filtered by linger bars and short-term slope flip, reducing one-bar spikes.
Rails: When an extreme confirms, a rail is anchored at the corresponding price swing and can soft-fade and/or expire after X bars.
Trend color: MA color = Up (green) when oscillator > threshold and MA slope > 0; Down (magenta) for the opposite; Neutral otherwise.
Context panels:
Slim Gauge: current oscillator bucket (0–20) with the exact normalized reading.
Info Panel: TF, Trend, and 0–100 percent-ranks of Volume and ATR-based volatility grouped as Low / Medium / High.
SMAD isn’t a collection of plots; it’s a single framework that integrates:
a deviation-from-MA engine,
four interchangeable normalization models (selected per market regime),
a gated extreme detector (linger + slope + confirm-on-close), and
time-aware rails with soft fade/expiry, presented with a minimal gauge and info panel so traders can compare regimes across TFs without recalibrating thresholds.
How to use (examples, not signals)
Mean-revert plays: When price exits an extreme and prints a dot, look for reactions near the new rail. Combine with your S/R and risk model.
Trend continuation: In strong trends the oscillator will spend more time above/below zero; the colored MA helps keep you aligned and avoid fading every push.
Regime switching: Try Percent Rank or ATR-Scaled on choppy/alts; Z-Score on majors; Highest Abs (N) when you want fastest adaptation.
Risk ideas: Rails can be used as partial-take or invalidate levels. Always backtest on your pair/TF.
Key settings
Normalization: Highest Abs / Z-Score / ATR-Scaled / Percent Rank (with N & factors).
Filters: Extreme threshold, linger bars, slope lookback, confirm on close.
Rails: Expire after X bars; soft-fade step.
Panels: Slim gauge (bottom-right), Info panel (middle-right).
Notes & limits
Prints confirm after the extreme exits ±100; nothing repaints retroactively.
Normalization can change sensitivity—choose the one matching your asset’s regime.
NSR Dynamic Channel - HTF + ReversionNSR Dynamic Channel – HTF Volatility + Reversion
(Beginner-friendly, pro-grade, non-repainting)
The NSR Dynamic Channel builds an adaptive volatility envelope that compares current price action to a statistically-derived “expected” range pulled from a user-selected higher timeframe (HTF).
Is this just another keltner variation?
In short: Keltner reacts. NSR anticipates.
Keltner says “price moved a lot.”
NSR says “this move is abnormal compared to the last 2 days on a higher timeframe — and here’s the probability it snaps back.”
The channel is not a simple multiple of recent ATR or standard deviation; instead it:
Samples HTF volatility over a rolling window (default: last 2 days on the chosen HTF).
Expected Range
HTF Volatility Spread = StDev of 1-bar ATR on the HTF
Scales this HTF range to the current chart’s volatility using a compression ratio :
compRatio = SMA(High-Low over lookback) / Expected Range
This makes the channel tighten in low-vol regimes and widen in high-vol regimes .
Centers the channel on a composite mean ( AVGMEAN ) calculated from:
Smoothed Adaptive Averages of the current timeframe close
SMA of close over the user-defined lookback ( Slow )
The three means are averaged to reduce lag and noise.
Draws two layers :
HTF Expected Channel (gray fill) = PAMEAN ± expectedD
Dynamic Expected Band (inner gray) = HTF Expected Range
Adds a fast 2σ envelope around AVGMEAN using the standard deviation of close over the lookback period.
Core Calculations (Conceptual Overview)
HTF Baseline → ATR on user HTF → SMA & StDev over a defined number of days
Compression Ratio → Normalizes current range to HTF “normal” volatility
Expected Band Width → Expected Range × CompressionRatio
Bias Detection → % change of composite mean over 2 bars → “bullish” / “bearish” filter
Overextension % → Position of price within the expected band (0–100%)
How to Use It (3 Steps)
Apply to any chart – defaults work on futures (NQ/ES), stocks (SPY), crypto (BTC), forex, etc.
Price is outside both the fast 2σ envelope and the HTF-scaled expected band
Expect some sort of reversion
Enable alerts – two built-in conditions:
NSR Exit Long – bullish bias + high crosses upper expected edge
NSR Exit Short – bearish bias + low crosses lower expected edge
Optional toggles :
Show 2σ Price Range → fast overextension lines
Expected Channel → HTF-based gray fill
Mean → MEAN centerline
Why It Works
Context-aware : Uses HTF “normal” volatility as anchor
Adaptive : Shrinks in consolidation, expands in breakouts
Filtered signals : Only triggers when both statistical layers agree
Non-repainting : All calculations use confirmed bars
Happy trading!
nsrgroup
RSI + MFIRSI and MFI combined, width gradient fields if OS or OB, shows divergences separate for wicks and bodies, shows dots when mfi and rsi oversold at the same time.
RSI + Elder Bull-Bear pressure RSI + Bull/Bear (Elder-Ray enhanced RSI)
What it is
An extended RSI that overlays Elder-Ray Bull/Bear Power on the same, zero-centered scale. You get classic RSI regime cues plus a live read of buy/sell pressure, with optional smoothing, bands, and right-edge value labels.
Key features
RSI with bands – default bands 30 / 50 / 70 (editable).
Bull/Bear Power (Elder) – ATR-normalized; optional EMA/SMA/RMA/HMA smoothing.
One-pane overlay – RSI and Bull/Bear share a common midline (RSI-50 ↔ panel 0).
Right-edge labels – always visible at the chart’s right margin with adjustable offsets.
How to read it
Cyan line = RSI (normalized)
Above the mid band = bullish regime; below = bearish regime.
Green = Bull Power, Red = Bear Power
Columns/lines above 0 show buy pressure; below 0 show sell pressure.
Smoothing reduces noise; zero-line remains your key reference.
Trade logic (simple playbook)
Entry
BUY (primary):
RSI crosses up through 50 (regime turns bullish), and
Bull (green) crosses up through 0 (buy pressure confirms).
SELL (primary):
RSI crosses down through 50, and
Bear (red) crosses down through 0 (sell pressure confirms).
Alternative momentum entries
Aggressive BUY: Bull (green) pushes above RSI-80 band (strong upside impulse).
Aggressive SELL: Bear (red) pushes below RSI-30 band (strong downside impulse).
Exits / trade management
In a long: consider exiting or tightening stops if Bear (red) dips below the 0 line (rising sell pressure) or RSI loses 50.
In a short: consider exiting or tightening if Bull (green) rises above 0 or RSI reclaims 50.
Tip: “0” on the panel is your pressure zero-line (maps to RSI-50). Most whipsaws happen near this line; smoothing (e.g., EMA 21) helps.
Defaults (on first load)
RSI bands: 30 / 50 / 70 with subtle fills.
Labels: tiny, pushed far right (large offsets).
Bull/Bear smoothing: EMA(21), smoothed line plot mode.
RSI plotted normalized so it overlaps the pressure lines cleanly.
Tighten or loosen the Bull/Bear thresholds (e.g., Bull ≥ +0.5 ATR, Bear ≤ −0.5 ATR) to demand stronger confirmation.
Settings that matter
Smoothing length/type – balances responsiveness vs. noise.
Power/RSI Gain – visual scaling only (doesn’t change logic).
Band placement – keep raw 30/50/80 or switch to “distance from 50” if you prefer symmetric spacing.
Label offsets – move values clear of the last bar/scale clutter.
Good practices
Combine with structure/ATR stops (e.g., 1–1.5× ATR, swing high/low).
In trends, hold while RSI stays above/below 50 and the opposite pressure line doesn’t dominate.
In ranges, favor signals occurring near the mid band and take profits at the opposite band.
Disclaimer: This is a research/visual tool, not financial advice at any kind. Test your rules on multiple markets/timeframes and size positions responsibly.
Dynamic Fractal Flow [Alpha Extract]An advanced momentum oscillator that combines fractal market structure analysis with adaptive volatility weighting and multi-derivative calculus to identify high-probability trend reversals and continuation patterns. Utilizing sophisticated noise filtering through choppiness indexing and efficiency ratio analysis, this indicator delivers entries that adapt to changing market regimes while reducing false signals during consolidation via multi-layer confirmation centered on acceleration analysis, statistical band context, and dynamic omega weighting—without any divergence detection.
🔶 Fractal-Based Market Structure Detection
Employs Williams Fractal methodology to identify pivotal market highs and lows, calculating normalized price position within the established fractal range to generate oscillator signals based on structural positioning. The system tracks fractal points dynamically and computes relative positioning with ATR fallback protection, ensuring continuous signal generation even during extended trending periods without fractal formation.
🔶 Dynamic Omega Weighting System
Implements an adaptive weighting algorithm that adjusts signal emphasis based on real-time volatility conditions and volume strength, calculating dynamic omega coefficients ranging from 0.3 to 0.9. The system applies heavier weighting to recent price action during high-conviction moves while reducing sensitivity during low-volume environments, mitigating lag inherent in fixed-period calculations through volatility normalization and volume-strength integration.
🔶 Cascading Robustness Filtering
Features up to five stages of progressive EMA smoothing with user-adjustable robustness steps, each layer systematically filtering microstructure noise while preserving essential trend information. Smoothing periods scale with the chosen fractal length and robustness steps using a fixed smoothing multiplier for consistent, predictable behavior.
🔶 Adaptive Noise Suppression Engine
Integrates dual-component noise filtering combining Choppiness Index calculation with Kaufman’s Efficiency Ratio to detect ranging versus trending market conditions. The system applies dynamic damping that maintains full signal strength during trending environments while suppressing signals during choppy consolidation, aligning output with the prevailing regime.
🔶 Acceleration and Jerk Analysis Framework
Calculates second-derivative acceleration and third-derivative jerk to identify explosive momentum shifts before they fully materialize on traditional indicators. Detects bullish acceleration when both acceleration and jerk turn positive in negative oscillator territory, and bearish acceleration when both turn negative in positive territory, providing early entry signals for high-velocity trend initiation phases.
🔶 Multi-Layer Signal Generation Architecture
Combines three primary signal types with hierarchical validation: acceleration signals, band crossover entries, and threshold momentum signals. Each signal category includes momentum confirmation, trend-state validation, and statistical band context; signals are further conditioned by band squeeze detection to avoid low-probability entries during compression phases. Divergence is intentionally excluded for a purely structure- and momentum-driven approach.
🔶 Dynamic Statistical Band System
Utilizes Bollinger-style standard deviation bands with configurable multiplier and length to create adaptive threshold zones that expand during volatile periods and contract during consolidation. Includes band squeeze detection to identify compression phases that typically precede expansion, with signal suppression during squeezes to prevent premature entries.
🔶 Gradient Color Visualization System
Features color gradient mapping that dynamically adjusts line intensity based on signal strength, transitioning from neutral gray to progressively intense bullish or bearish colors as conviction increases. Includes gradient fills between the signal line and zero with transparency scaling based on oscillator intensity for immediate visual confirmation of trend strength and directional bias.
All analysis provided by Alpha Extract is for educational and informational purposes only. The information and publications are not meant to be, and do not constitute, financial, investment, trading, or other types of advice or recommendations.
Maxtra Reversal Range Breakout StrategyReversal Range Breakout Strategy
This strategy uses the first candle as a directional filter. If the first candle is green, it anticipates a potential reversal and takes sell trades only. If the first candle is red, it looks for buy opportunities. The logic is to trade against the initial move, expecting a reversal after the early breakout or momentum spike.
True Range(TR) + Average True Range (ATR) COMBINEDThis indicator combines True Range (TR) and Average True Range (ATR) into a single panel for a clearer understanding of price volatility.
True Range (TR) measures the absolute price movement between highs, lows, and previous closes — showing raw, unsmoothed volatility.
Average True Range (ATR) is a moving average of the True Range, providing a smoother, more stable volatility signal.
📊 Usage Tips:
High TR/ATR values indicate strong price movement or volatility expansion.
Low values suggest compression or a potential volatility breakout zone.
Can be used for stop-loss placement, volatility filters, or trend strength confirmation.
⚙️ Features:
Multiple smoothing methods: RMA, SMA, EMA, WMA.
Adjustable ATR length.
Separate colored plots for TR (yellow) and ATR (red).
Works across all timeframes and instruments.
APXTradez - Intraday RSI (8)🔹 APXTradez Intraday RSI (8)
Purpose:
A fast-reacting momentum and bias indicator built for intraday options and scalping setups. This version of RSI (8) identifies immediate shifts in strength, momentum slope, and trend bias—allowing traders to spot reversals, momentum builds, or choppy zones within seconds.
What It Shows
RSI (8) → ultra-responsive short-term strength indicator.
Bias Zones:
- Bull Bias (Green) – RSI rising above 55 with slope up → intraday long setups favored.
- Bear Bias (Red) – RSI falling below 45 with slope down → short setups favored.
- Chop (Gray) – Neutral area between 45–55 → reduced edge, wait for direction.
- Background Color: Highlights current bias (green/red/gray) for quick visual confirmation.
- Dynamic Label: Displays live bias text on chart (Bull, Bear, or Chop).
How to Use
Apply on 1m–15m charts for day trading or scalping options.
Trade in bias direction:
- Enter long when RSI crosses + slopes above 55 (bull bias).
- Enter short when RSI crosses + slopes below 45 (bear bias).
- Avoid chop zones (RSI between 45–55 or flat). Wait for a slope confirmation.
Combine with APX Intraday VWAP + EMA overlay, APX TTM Squeeze, and/or the APX MACD to align direction with trend and volume pressure.
Overbought/Oversold: Above 70 or below 30 still mark exhaustion zones — use for exits, not entries.
Best Use Case
Intraday confirmation of trend bias and momentum strength — helping you stay on the right side of fast-moving setups and avoid low-edge chop.
Composite Buy/Sell Score [-100 to +100] by LMComposite Buy/Sell Score (Stabilized + Sensitivity) by LM
Description:
This indicator calculates a composite trend strength score ranging from -100 to +100 by combining multiple popular technical indicators into a single, smoothed metric. It is designed to give traders a clear view of bullish and bearish trends, while filtering out short-term noise.
The score incorporates signals from:
PPO (Percentage Price Oscillator) – measures momentum via the difference between fast and slow EMAs.
ADX (Average Directional Index) – detects trend strength.
RSI (Relative Strength Index) – identifies short-term momentum swings.
Stochastic RSI – measures RSI momentum and speed of change.
MACD (Moving Average Convergence Divergence) – detects momentum shifts using EMA crossovers.
Williams %R – highlights overbought/oversold conditions.
Each component is weighted, smoothed, and optionally confirmed across a configurable number of bars, producing a stabilized composite score that reacts more reliably to significant trend changes.
Key Features:
Smoothed Composite Score
The final score is smoothed using an EMA to reduce volatility and emphasize meaningful trends.
A Sensitivity Multiplier allows traders to exaggerate the score for stronger trend signals or dampen it for quieter markets.
Customizable Inputs
You can adjust each indicator’s parameters, smoothing lengths, and confirm bars to suit your preferred timeframe and trading style.
The sensitivity multiplier allows fine-tuning the responsiveness of the trend line without changing underlying indicator calculations.
Visual Representation
Score Line: Green for positive (bullish) trends, red for negative (bearish) trends, gray near neutral.
Reference Lines:
0 = neutral
+100 = maximum bullish
-100 = maximum bearish
Adaptive Background: Optionally highlights the background intensity proportional to trend strength. Strong green for bullish trends, strong red for bearish trends.
Multi-Indicator Integration
Combines momentum, trend, and overbought/oversold signals into a single metric.
Helps identify clear entry/exit trends while avoiding whipsaw noise common in individual indicators.
Recommended Use:
Trend Identification: Look for sustained movement above 0 for bullish trends and below 0 for bearish trends.
Exaggerated Trends: Use the Sensitivity Multiplier to emphasize strong trends.
Filtering Noise: The smoothed score and confirmBars settings help reduce false signals from minor price fluctuations.
Inputs Overview:
Input Purpose
PPO Fast EMA / Slow EMA / Signal Controls PPO momentum sensitivity
ADX Length / Threshold Detects trend strength
RSI Length / Overbought / Oversold Measures short-term momentum
Stoch RSI Length / %K / %D Measures speed of RSI changes
MACD Fast / Slow / Signal Measures momentum crossover
Williams %R Length Detects overbought/oversold conditions
Final Score Smoothing Length EMA smoothing for final composite score
Confirm Bars for Each Signal Number of bars used to confirm individual indicator signals
Sensitivity Multiplier Scales the final composite score for exaggerated trend response
Highlight Background by Trend Strength Enables adaptive background coloring
This indicator is suitable for traders looking for a single, clear trend metric derived from multiple indicators. It can be applied to any timeframe and can help identify both strong and emerging trends in the market.
APXTradez - MACD🔹 APXTradez MACD — Summary & Usage Guide
Purpose
- The APXTradez MACD measures momentum shifts and trend reversals using exponential moving averages (EMAs).
- It helps you see when momentum changes direction before price fully reacts — perfect for catching early entries or confirming strength behind a move.
Core Components
- MACD Line (Aqua / Orange)
- Calculated as the difference between the 12 EMA and 26 EMA.
- Shows short-term vs. long-term momentum.
- Aqua: Fast EMA above slow EMA → bullish momentum building.
- Orange: Fast EMA below slow EMA → bearish momentum dominating.
- Signal Line (Blue)
- A 9-EMA of the MACD line that smooths the momentum swings.
When the MACD Line crosses above it → bullish crossover.
When the MACD Line crosses below it → bearish crossover.
-Histogram (Green/Red Columns)
Represents the distance between the MACD Line and Signal Line.
Lime = Bullish momentum expanding.
Faded Lime = Bullish momentum fading.
Red = Bearish momentum expanding.
Faded Red = Bearish momentum fading.
The histogram visually shows when momentum is increasing or losing strength.
-Zero Line
Center baseline separating bullish (above) and bearish (below) momentum.
Crosses through zero often mark early trend reversals.
How to Use It
1️⃣ Identify Trend Direction
- MACD Line above Signal Line & Histogram above Zero:
→ Bullish momentum. Confirms uptrend or call-side bias.
- MACD Line below Signal Line & Histogram below Zero:
→ Bearish momentum. Confirms downtrend or put-side bias.
2️⃣ Spot Momentum Shifts (Crossovers)
- Bullish Crossover:
MACD (Aqua) crosses above Signal (Blue).
→ Potential start of new upward leg or reversal.
- Bearish Crossover:
MACD (Orange) crosses below Signal (Blue).
→ Possible start of new downward leg.
These are your “momentum pivot” moments — strongest when aligned with a squeeze fire or RSI midline cross.
3️⃣ Read Momentum Strength (Histogram)
- Growing Lime bars → momentum accelerating upward.
- Shrinking Lime bars → bullish side fading.
- Growing Red bars → bearish momentum accelerating.
- Shrinking Red bars → bearish side weakening.
When you see histogram bars shrink or color fade, it often signals a pause or pullback before a potential flip.
4️⃣ Combine with APX Squeeze & RSI
- MACD crosses up + TTM Squeeze fires + RSI > 50 → powerful bullish swing signal.
- MACD crosses down + Squeeze fires down + RSI < 50 → strong bearish setup.
This triple-stack alignment confirms momentum, volatility, and sentiment all pointing in one direction.
- The APXTradez MACD is your momentum confirmation tool.
- It helps you visually see when momentum flips, builds, or fades, giving you precise entry timing inside larger swing or intraday setups.
- Used with the APX RSI and APX TTM Squeeze, it forms one of the three core pillars of the APX Momentum Framework — confirming when volatility, strength, and direction all align.
APXTradez - Swing RSI🔹 APXTradez Swing RSI — Summary & Usage Guide
Purpose
- The APXTradez Swing RSI measures momentum strength and exhaustion in a stock’s price movement.
- It tells you when price is overextended, balanced, or reversing, helping you time entries, exits, and confirmation on swing trades.
This version is tuned for clean visual momentum tracking during 2-to-5-day swings or multi-week trends.
Core Components
-RSI (14-period)
- Uses a standard 14-bar calculation on the closing price (or whichever source you choose).
- Ranges between 0 and 100, showing how strong the recent price push has been.
- Smooth enough for swing trading; reactive enough to catch early turns.
- Color-Coded RSI Line
White = Neutral zone (healthy price action).
Lime = Oversold (< 30) → buying opportunity or short-covering zone.
Red = Overbought (> 70) → profit-taking or potential pullback zone.
Color shifts automatically as RSI crosses key thresholds, so you see sentiment flips instantly.
Horizontal Levels
70 = Overbought (red dashed line)
30 = Oversold (lime dashed line)
50 = Midline (gray dotted line, optional toggle)
These levels frame the RSI’s “zones of strength.”
How to Use It
1️⃣ Identify Momentum Regime
-Above 50 → Bullish Bias
Momentum favors buyers. Use this to confirm call or long positions.
- Below 50 → Bearish Bias
Momentum favors sellers. Confirms put or short setups.
- The 50 midline is your “momentum compass.”
Crosses above or below often precede trend reversals.
2️⃣ Spot Extremes (30 / 70 Zones)
RSI > 70 = Overbought
Price is stretched; risk of pullback or short-term cooling.
→ Good area to trim longs or watch for reversal candles.
RSI < 30 = Oversold
Price is washed out; potential bounce forming.
→ Good area to look for bullish reversal + volume confirmation.
3️⃣ Combine with APX TTM Squeeze and Overlay
- When RSI > 50 and rising + TTM histogram bright teal + Squeeze fires black dot up → high-probability long swing.
- When RSI < 50 and falling + TTM histogram bright yellow + Squeeze fires down → high-probability short swing.
- When RSI flat around 50 → trendless; wait for compression + momentum confirmation.
4️⃣ Divergences (Advanced Use)
- Bullish Divergence: Price makes a lower low, RSI makes a higher low → momentum turning up before price.
- Bearish Divergence: Price makes a higher high, RSI makes a lower high → momentum weakening.
- These signals are strongest when combined with a Squeeze firing or EMA crossover from your Swing Overlay.
Best Timeframes
Daily / 4-Hour: Ideal for swing entries and momentum tracking.
1-Hour: For fine-tuning entries inside larger setups.
- The APXTradez Swing RSI gives you a clean, visual read on who controls momentum and when that strength is fading or reversing.
- It’s not meant to predict exact tops or bottoms — it’s a confirmation and rhythm tool:
Use the 50-line for trend bias.
Use 30/70 for exhaustion.
Use color shifts for quick sentiment reads.
Combine with your APX Squeeze and Overlay for the complete swing-timing system.
RSI Trendline Pro - Multi Confirmation
Overview
RSI Trendline Pro is an advanced Pine Script indicator that automatically draws trendlines on the RSI (Relative Strength Index) to detect support and resistance breakouts. It generates high-quality trading signals through a multi-confirmation system.
Key Features
Auto Trendlines: Detects pivot points on RSI to create intelligent support and resistance lines
Multi-Confirmation System: Combines Volume, Stochastic RSI, ADX, and Divergence filters to reduce false signals
RSI Divergence Detection: Automatically identifies bullish/bearish divergences between price and RSI
Live Dashboard: Displays RSI value, active trendlines, ADX strength, and last signal info on a visual panel
Smart Breakout Detection: Identifies trendline breaks and generates LONG/SHORT signals
How to Use
Add to TradingView: Paste code into Pine Editor and add to chart
Configure Parameters:
RSI Length: RSI period (default: 14)
Pivot Strength: Trendline sensitivity (lower = more lines)
Filters: Enable/disable Volume, Divergence, Stoch RSI, and ADX confirmations
Follow Signals:
LONG (Green): When RSI breaks resistance upward
SHORT (Red): When RSI breaks support downward
Divergence: "D" markers indicate potential trend reversals
Alert Setup
Script offers 4 alert types:
LONG Breakout: Resistance break
SHORT Breakout: Support break
Bullish/Bearish Divergence: Divergence detection
Any Signal: Combined alert for all signals
Best Practices
Prioritize high-volume breakouts (Volume Filter enabled)
Trends are stronger when ADX > 25
Confirm divergence signals with price action
Trade when 2-3 confirmations align
Cora Combined Suite v1 [JopAlgo]Cora Combined Suite v1 (CCSV1)
This is an 2 in 1 indicator (Overlay & Oscillator) the Cora Combined Suite v1 .
CCSV1 combines a price-pane Overlay for structure/trend with a compact Oscillator for timing/pressure. It’s designed to be clear, beginner-friendly, and largely automatic: you pick a profile (Scalp / Intraday / Swing), choose whether to run as Overlay or Oscillator, and CCSV1 tunes itself in the background.
What’s inside — at a glance
1) Overlay (price pane)
CoRa Wave: a smooth trend line based on a compound-ratio WMA (CRWMA).
Green when the slope rises (bull bias), Red when it falls (bear bias).
Asymmetric ATR Cloud around the CoRa Wave
Width expands more up when buyer pressure dominates and more down when seller pressure dominates.
Fill is intentionally light, so candlesticks remain readable.
Chop Guard (Range-Lock Gate)
When the cloud stays very narrow versus ATR (classic “dead water”), pullback alerts are muted to avoid noise.
Visuals don’t change—only the alerting logic goes quiet.
Typical Overlay reads
Trend: Follow the CoRa color; green favors long setups, red favors shorts.
Value: Pullbacks into/through the cloud in trend direction are higher-quality than chasing breaks far outside it.
Dominance: A visibly asymmetric cloud hints which side is funding the move (buyers vs sellers).
2) Oscillator (subpane or inline preview)
Stretch-Z (columns): how far price is from the CoRa mean (mean-reversion context), clipped to ±clip.
Near 0 = equilibrium; > +2 / < −2 = stretched/extended.
Slope-Z (line): z-score of CoRa’s slope (momentum of the trend line).
Crossing 0 upward = potential bullish impulse; downward = potential bearish impulse.
VPO (stepline): a normalized Volume-Pressure read (positive = buyers funding, negative = sellers).
Rendered as a clean stepline to emphasize state changes.
Event Bands ±2 (subpane): thin reference lines to spot extension/exhaustion zones fast.
Floor/Ceiling lines (optional): quiet boundaries so the panel doesn’t feel “bottomless.”
Inline vs Subpane
Inline (overlay): the oscillator auto-anchors and scales beneath price, so it never crushes the price scale.
Subpane (raw): move to a new pane for the classic ±clip view (with ±2 bands). Recommended for systematic use.
Why traders like it
Two in one: Structure on the chart, timing in the panel—built to complement each other.
Retail-first automation: Choose Scalp / Intraday / Swing and let CCSV1 auto-tune lengths, clips, and pressure windows.
Robust statistics: On fast, spiky markets/timeframes, it prefers outlier-resistant math automatically for steadier signals.
Optional HTF gate: You can require higher-timeframe agreement for oscillator alerts without changing visuals.
Quick start (simple playbook)
Run As
Overlay for structure: assess trend direction, where value is (the cloud), and whether chop guard is active.
Oscillator for timing: move to a subpane to see Stretch-Z, Slope-Z, VPO, and ±2 bands clearly.
Profile
Scalp (1–5m), Intraday (15–60m), or Swing (4H–1D). CCSV1 adjusts length/clip/pressure windows accordingly.
Overlay entries
Trade with CoRa color.
Prefer pullbacks into/through the cloud (trend direction).
If chop guard is active, wait; let the market “breathe” before engaging.
Oscillator timing
Look for Funded Flips: Slope-Z crossing 0 in the direction of VPO (i.e., momentum + funded pressure).
Use ±2 bands to manage risk: stretched conditions can stall or revert—better to scale or wait for a clean reset.
Optional HTF gate
Enable to green-light only those oscillator alerts that align with your chosen higher timeframe.
What each signal means (plain language)
CoRa turns green/red (Overlay): trend bias shift on your chart.
Cloud width tilts asymmetrically: one side (buyers/sellers) is dominating; extensions on that side are more likely.
Stretch-Z near 0: fair value around CoRa; pullback timing zone.
Stretch-Z > +2 / < −2: extended; watch for slowing momentum or scale decisions.
Slope-Z cross up/down: new impulse starting; combine with VPO sign to avoid unfunded crosses.
VPO positive/negative: net buying/selling pressure funding the move.
Alerts included
Overlay
Pullback Long OK
Pullback Short OK
Oscillator
Funded Flip Up / Funded Flip Down (Slope-Z crosses 0 with VPO agreement)
Pullback Long Ready / Pullback Short Ready (near equilibrium with aligned momentum and pressure)
Exhaustion Risk (Long/Short) (Stretch-Z beyond ±2 with weakening momentum or pressure)
Tip: Keep chart alerts concise and use strategy rules (TP/SL/filters) in your trade plan.
Best practices
One glance workflow
Read Overlay for direction + value.
Use Oscillator for trigger + confirmation.
Pairing
Combine with S/R or your preferred execution framework (e.g., your JopAlgo setups).
The suite is neutral: it won’t force trades; it highlights context and quality.
Markets
Works on crypto, indices, FX, and commodities.
Where real volume is available, VPO is strongest; on synthetic volume, treat VPO as a soft filter.
Timeframes
Use the Profile preset closest to your style; feel free to fine-tune later.
For multi-TF trading, enable the HTF gate on the oscillator alerts only.
Inputs you’ll actually use (the rest can stay on Auto)
Run As: Overlay or Oscillator.
Profile: Scalp / Intraday / Swing.
Oscillator Render: “Subpane (raw)” for a classic panel; “Inline (overlay)” only for a quick preview.
HTF gate (optional): require higher-timeframe Slope-Z agreement for oscillator alerts.
Everything else ships with sensible defaults and auto-logic.
Limitations & tips
Not a strategy: CCSV1 is a decision support tool; you still need your entry/exit rules and risk management.
Non-repainting design: Signals finalize on bar close; intrabar graphics can adjust during the bar (Pine standard).
Very flat sessions: If price and volume are extremely quiet, expect fewer alerts; that restraint is intentional.
Who is this for?
Beginners who want one clean overlay for structure and one simple oscillator for timing—without wrestling settings.
Intermediates seeking a coherent trend/pressure framework with HTF confirmation.
Advanced users who appreciate robust stats and clean engineering behind the visuals.
Disclaimer: Educational purposes only. Not financial advice. Trading involves risk. Use at your own discretion.
MTF K-Means Price Regimes [matteovesperi] ⚠️ The preview uses a custom example to identify support/resistance zones. due to the fact that this identifier clusterizes, this is possible. this example was set up "in a hurry", therefore it has a possible inaccuracy. When setting up the indicator, it is extremely important to select the correct parameters and double-check them on the selected history.
📊 OVERVIEW
Purpose
MTF K-Means Price Regimes is a TradingView indicator that automatically identifies and classifies the current market regime based on the K-Means machine learning algorithm. The indicator uses data from a higher timeframe (Multi-TimeFrame, MTF) to build stable classification and applies it to the working timeframe in real-time.
Key Features
✅ Automatic market regime detection — the algorithm finds clusters of similar market conditions
✅ Multi-timeframe (MTF) — clustering on higher TF, application on lower TF
✅ Adaptive — model recalculates when a new HTF bar appears with a rolling window
✅ Non-Repainting — classification is performed only on closed bars
✅ Visualization — bar coloring + information panel with cluster characteristics
✅ Flexible settings — from 2 to 10 clusters, customizable feature periods, HTF selection
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🔬 TECHNICAL DETAILS
K-Means Clustering Algorithm
What is K-Means?
K-Means is one of the most popular clustering algorithms (unsupervised machine learning). It divides a dataset into K groups (clusters) so that similar elements are within each cluster, and different elements are between clusters.
Algorithm objective:
Minimize within-cluster variance (sum of squared distances from points to their cluster center).
How Does K-Means Work in Our Indicator?
Step 1: Data Collection
The indicator accumulates history from the higher timeframe (HTF):
RSI (Relative Strength Index) — overbought/oversold indicator
ATR% (Average True Range as % of price) — volatility indicator
ΔP% (Price Change in %) — trend strength and direction indicator
By default, 200 HTF bars are accumulated (clusterLookback parameter).
Step 2: Creating Feature Vectors
Each HTF bar is described by a three-dimensional vector:
Vector =
Step 3: Normalization (Z-Score)
All features are normalized to bring them to a common scale:
Normalized_Value = (Value - Mean) / StdDev
This is critically important, as RSI is in the range 0-100, while ATR% and ΔP% have different scales. Without normalization, one feature would dominate over others.
Step 4: K-Means++ Centroid Initialization
Instead of random selection of K initial centers, an improved K-Means++ method is used:
First centroid is randomly selected from the data
Each subsequent centroid is selected with probability proportional to the square of the distance to the nearest already selected centroid
This ensures better initial centroid distribution and faster convergence
Step 5: Iterative Optimization (Lloyd's Algorithm)
Repeat until convergence (or maxIterations):
1. Assignment step:
For each point find the nearest centroid and assign it to this cluster
2. Update step:
Recalculate centroids as the average of all points in each cluster
3. Convergence check:
If centroids shifted less than 0.001 → STOP
Euclidean distance in 3D space is used:
Distance = sqrt((RSI1 - RSI2)² + (ATR1 - ATR2)² + (ΔP1 - ΔP2)²)
Step 6: Adaptive Update
With each new HTF bar:
The oldest bar is removed from history (rolling window method)
New bar is added to history
K-Means algorithm is executed again on updated data
Model remains relevant for current market conditions
Real-Time Classification
After building the model (clusters + centroids), the indicator works in classification mode:
On each closed bar of the current timeframe, RSI, ATR%, ΔP% are calculated
Feature vector is normalized using HTF statistics (Mean/StdDev)
Distance to all K centroids is calculated
Bar is assigned to the cluster with minimum distance
Bar is colored with the corresponding cluster color
Important: Classification occurs only on a closed bar (barstate.isconfirmed), which guarantees no repainting .
Data Architecture
Persistent variables (var):
├── featureVectors - Normalized HTF feature vectors
├── centroids - Cluster center coordinates (K * 3 values)
├── assignments - Assignment of each HTF bar to a cluster
├── htfRsiHistory - History of RSI values from HTF
├── htfAtrHistory - History of ATR values from HTF
├── htfPcHistory - History of price changes from HTF
├── htfCloseHistory - History of close prices from HTF
├── htfRsiMean, htfRsiStd - Statistics for RSI normalization
├── htfAtrMean, htfAtrStd - Statistics for ATR normalization
├── htfPcMean, htfPcStd - Statistics for Price Change normalization
├── isCalculated - Model readiness flag
└── currentCluster - Current active cluster
All arrays are synchronized and updated atomically when a new HTF bar appears.
Computational Complexity
Data collection: O(1) per bar
K-Means (one pass):
- Assignment: O(N * K) where N = number of points, K = number of clusters
- Update: O(N * K)
- Total: O(N * K * I) where I = number of iterations (usually 5-20)
Example: With N=200 HTF bars, K=5 clusters, I=20 iterations:
200 * 5 * 20 = 20,000 operations (executes quickly)
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📖 USER GUIDE
Quick Start
1. Adding the Indicator
TradingView → Indicators → Favorites → MTF K-Means Price Regimes
Or copy the code from mtf_kmeans_price_regimes.pine into Pine Editor.
2. First Launch
When adding the indicator to the chart, you'll see a table in the upper right corner:
┌─────────────────────────┐
│ Status │ Collecting HTF │
├─────────────────────────┤
│ Collected│ 15 / 50 │
└─────────────────────────┘
This means the indicator is accumulating history from the higher timeframe. Wait until the counter reaches the minimum (default 50 bars for K=5).
3. Active Operation
After data collection is complete, the main table with cluster information will appear:
┌────┬──────┬──────┬──────┬──────────────┬────────┐
│ ID │ RSI │ ATR% │ ΔP% │ Description │Current │
├────┼──────┼──────┼──────┼──────────────┼────────┤
│ 1 │ 68.5 │ 2.15 │ 1.2 │ High Vol,Bull│ │
│ 2 │ 52.3 │ 0.85 │ 0.1 │ Low Vol,Flat │ ► │
│ 3 │ 35.2 │ 1.95 │ -1.5 │ High Vol,Bear│ │
└────┴──────┴──────┴──────┴──────────────┴────────┘
The arrow ► indicates the current active regime. Chart bars are colored with the corresponding cluster color.
Customizing for Your Strategy
Choosing Higher Timeframe (HTF)
Rule: HTF should be at least 4 times higher than the working timeframe.
| Working TF | Recommended HTF |
|------------|-----------------|
| 1 min | 15 min - 1H |
| 5 min | 1H - 4H |
| 15 min | 4H - D |
| 1H | D - W |
| 4H | D - W |
| D | W - M |
HTF Selection Effect:
Lower HTF (closer to working TF): More sensitive, frequently changing classification
Higher HTF (much larger than working TF): More stable, long-term regime assessment
Number of Clusters (K)
K = 2-3: Rough division (e.g., "uptrend", "downtrend", "flat")
K = 4-5: Optimal for most cases (DEFAULT: 5)
K = 6-8: Detailed segmentation (requires more data)
K = 9-10: Very fine division (only for long-term analysis with large windows)
Important constraint:
clusterLookback ≥ numClusters * 10
I.e., for K=5 you need at least 50 HTF bars, for K=10 — at least 100 bars.
Clustering Depth (clusterLookback)
This is the rolling window size for building the model.
50-100 HTF bars: Fast adaptation to market changes
200 HTF bars: Optimal balance (DEFAULT)
500-1000 HTF bars: Long-term, stable model
If you get an "Insufficient data" error:
Decrease clusterLookback
Or select a lower HTF (e.g., "4H" instead of "D")
Or decrease numClusters
Color Scheme
Default 10 colors:
Red → Often: strong bearish, high volatility
Orange → Transition, medium volatility
Yellow → Neutral, decreasing activity
Green → Often: strong bullish, high volatility
Blue → Medium bullish, medium volatility
Purple → Oversold, possible reversal
Fuchsia → Overbought, possible reversal
Lime → Strong upward momentum
Aqua → Consolidation, low volatility
White → Undefined regime (rare)
Important: Cluster colors are assigned randomly at each model recalculation! Don't rely on "red = bearish". Instead, look at the description in the table (RSI, ATR%, ΔP%).
You can customize colors in the "Colors" settings section.
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⚙️ INDICATOR PARAMETERS
Main Parameters
Higher Timeframe (htf)
Type: Timeframe selection
Default: "D" (daily)
Description: Timeframe on which the clustering model is built
Recommendation: At least 4 times larger than your working TF
Clustering Depth (clusterLookback)
Type: Integer
Range: 50 - 2000
Default: 200
Description: Number of HTF bars for building the model (rolling window size)
Recommendation:
- Increase for more stable long-term model
- Decrease for fast adaptation or if there's insufficient historical data
Number of Clusters (K) (numClusters)
Type: Integer
Range: 2 - 10
Default: 5
Description: Number of market regimes the algorithm will identify
Recommendation:
- K=3-4 for simple strategies (trending/ranging)
- K=5-6 for universal strategies
- K=7-10 only when clusterLookback ≥ 100*K
Max K-Means Iterations (maxIterations)
Type: Integer
Range: 5 - 50
Default: 20
Description: Maximum number of algorithm iterations
Recommendation:
- 10-20 is sufficient for most cases
- Increase to 30-50 if using K > 7
Feature Parameters
RSI Period (rsiLength)
Type: Integer
Default: 14
Description: Period for RSI calculation (overbought/oversold feature)
Recommendation:
- 14 — standard
- 7-10 — more sensitive
- 20-25 — more smoothed
ATR Period (atrLength)
Type: Integer
Default: 14
Description: Period for ATR calculation (volatility feature)
Recommendation: Usually kept equal to rsiLength
Price Change Period (pcLength)
Type: Integer
Default: 5
Description: Period for percentage price change calculation (trend feature)
Recommendation:
- 3-5 — short-term trend
- 10-20 — medium-term trend
Visualization
Show Info Panel (showDashboard)
Type: Checkbox
Default: true
Description: Enables/disables the information table on the chart
Cluster Color 1-10
Type: Color selection
Description: Customize colors for visual cluster distinction
Recommendation: Use contrasting colors for better readability
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📊 INTERPRETING RESULTS
Reading the Information Table
┌────┬──────┬──────┬──────┬──────────────┬────────┐
│ ID │ RSI │ ATR% │ ΔP% │ Description │Current │
├────┼──────┼──────┼──────┼──────────────┼────────┤
│ 1 │ 68.5 │ 2.15 │ 1.2 │ High Vol,Bull│ │
│ 2 │ 52.3 │ 0.85 │ 0.1 │ Low Vol,Flat │ ► │
│ 3 │ 35.2 │ 1.95 │ -1.5 │ High Vol,Bear│ │
│ 4 │ 45.0 │ 1.20 │ -0.3 │ Low Vol,Bear │ │
│ 5 │ 72.1 │ 3.05 │ 2.8 │ High Vol,Bull│ │
└────┴──────┴──────┴──────┴──────────────┴────────┘
"ID" Column
Cluster number (1-K). Order doesn't matter.
"RSI" Column
Average RSI value in the cluster (0-100):
< 30: Oversold zone
30-45: Bearish sentiment
45-55: Neutral zone
55-70: Bullish sentiment
> 70: Overbought zone
"ATR%" Column
Average volatility in the cluster (as % of price):
< 1%: Low volatility (consolidation, narrow range)
1-2%: Normal volatility
2-3%: Elevated volatility
> 3%: High volatility (strong movements, impulses)
Compared to the average volatility across all clusters to determine "High Vol" or "Low Vol".
"ΔP%" Column
Average price change in the cluster (in % over pcLength period):
> +0.05%: Bullish regime
-0.05% ... +0.05%: Flat (sideways movement)
< -0.05%: Bearish regime
"Description" Column
Automatic interpretation:
"High Vol, Bull" → Strong upward momentum, high activity
"Low Vol, Flat" → Consolidation, narrow range, uncertainty
"High Vol, Bear" → Strong decline, panic, high activity
"Low Vol, Bull" → Slow growth, low activity
"Low Vol, Bear" → Slow decline, low activity
"Current" Column
Arrow ► shows which cluster the last closed bar of your working timeframe is in.
Typical Cluster Patterns
Example 1: Trend/Flat Division (K=3)
Cluster 1: RSI=65, ATR%=2.5, ΔP%=+1.5 → Bullish trend
Cluster 2: RSI=50, ATR%=0.8, ΔP%=0.0 → Flat/Consolidation
Cluster 3: RSI=35, ATR%=2.3, ΔP%=-1.4 → Bearish trend
Strategy: Open positions when regime changes Flat → Trend, avoid flat.
Example 2: Volatility Breakdown (K=5)
Cluster 1: RSI=72, ATR%=3.5, ΔP%=+2.5 → Strong bullish impulse (high risk)
Cluster 2: RSI=60, ATR%=1.5, ΔP%=+0.8 → Moderate bullish (optimal entry point)
Cluster 3: RSI=50, ATR%=0.7, ΔP%=0.0 → Flat
Cluster 4: RSI=40, ATR%=1.4, ΔP%=-0.7 → Moderate bearish
Cluster 5: RSI=28, ATR%=3.2, ΔP%=-2.3 → Strong bearish impulse (panic)
Strategy: Enter in Cluster 2 or 4, avoid extremes (1, 5).
Example 3: Mixed Regimes (K=7+)
With large K, clusters can represent condition combinations:
High RSI + Low volatility → "Quiet overbought"
Neutral RSI + High volatility → "Uncertainty with high activity"
Etc.
Requires individual analysis of each cluster.
Regime Changes
Important signal: Transition from one cluster to another!
Trading situation examples:
Flat → Bullish trend → Buy signal
Bullish trend → Flat → Take profit, close longs
Flat → Bearish trend → Sell signal
Bearish trend → Flat → Close shorts, wait
You can build a trading system based on the current active cluster and transitions between them.
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💡 USAGE EXAMPLES
Example 1: Scalping with HTF Filter
Task: Scalping on 5-minute charts, but only enter in the direction of the daily regime.
Settings:
Working TF: 5 min
HTF: D (daily)
K: 3 (simple division)
clusterLookback: 100
Logic:
IF current cluster = "Bullish" (ΔP% > 0.5)
→ Look for long entry points on 5M
IF current cluster = "Bearish" (ΔP% < -0.5)
→ Look for short entry points on 5M
IF current cluster = "Flat"
→ Don't trade / reduce risk
Example 2: Swing Trading with Volatility Filtering
Task: Swing trading on 4H, enter only in regimes with medium volatility.
Settings:
Working TF: 4H
HTF: D (daily)
K: 5
clusterLookback: 200
Logic:
Allowed clusters for entry:
- ATR% from 1.5% to 2.5% (not too quiet, not too chaotic)
- ΔP% with clear direction (|ΔP%| > 0.5)
Prohibited clusters:
- ATR% > 3% → Too risky (possible gaps, sharp reversals)
- ATR% < 1% → Too quiet (small movements, commissions eat profit)
Example 3: Portfolio Rotation
Task: Managing a portfolio of multiple assets, allocate capital depending on regimes.
Settings:
Working TF: D (daily)
HTF: W (weekly)
K: 4
clusterLookback: 100
Logic:
For each asset in portfolio:
IF regime = "Strong trend + Low volatility"
→ Increase asset weight in portfolio (40-50%)
IF regime = "Medium trend + Medium volatility"
→ Standard weight (20-30%)
IF regime = "Flat" or "High volatility without trend"
→ Minimum weight or exclude (0-10%)
Example 4: Combining with Other Indicators
MTF K-Means as a filter:
Main strategy: MA Crossover
Filter: MTF K-Means on higher TF
Rule:
IF MA_fast > MA_slow AND Cluster = "Bullish regime"
→ LONG
IF MA_fast < MA_slow AND Cluster = "Bearish regime"
→ SHORT
ELSE
→ Don't trade (regime doesn't confirm signal)
This dramatically reduces false signals in unsuitable market conditions.
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📈 OPTIMIZATION RECOMMENDATIONS
Optimal Settings for Different Styles
Day Trading
Working TF: 5M - 15M
HTF: 1H - 4H
numClusters: 4-5
clusterLookback: 100-150
Swing Trading
Working TF: 1H - 4H
HTF: D
numClusters: 5-6
clusterLookback: 150-250
Position Trading
Working TF: D
HTF: W - M
numClusters: 4-5
clusterLookback: 100-200
Scalping
Working TF: 1M - 5M
HTF: 15M - 1H
numClusters: 3-4
clusterLookback: 50-100
Backtesting
To evaluate effectiveness:
Load historical data (minimum 2x clusterLookback HTF bars)
Apply the indicator with your settings
Study cluster change history:
- Do changes coincide with actual trend transitions?
- How often do false signals occur?
Optimize parameters:
- If too much noise → increase HTF or clusterLookback
- If reaction too slow → decrease HTF or increase numClusters
Combining with Other Techniques
Regime-Based Approach:
MTF K-Means (regime identification)
↓
+---+---+---+
| | | |
v v v v
Trend Flat High_Vol Low_Vol
↓ ↓ ↓ ↓
Strategy_A Strategy_B Don't_trade
Examples:
Trend: Use trend-following strategies (MA crossover, Breakout)
Flat: Use mean-reversion strategies (RSI, Bollinger Bands)
High volatility: Reduce position sizes, widen stops
Low volatility: Expect breakout, don't open positions inside range
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📞 SUPPORT
Report an Issue
If you found a bug or have a suggestion for improvement:
Describe the problem in as much detail as possible
Specify your indicator settings
Attach a screenshot (if possible)
Specify the asset and timeframe where the problem is observed
Fakeout Kavach by Pooja v10📘 Description – Fakeout Kavach by Pooja
Fakeout Kavach by Pooja is a precision-built technical analysis tool designed for structured momentum and divergence evaluation within the RSI pane.
It helps visualize potential exhaustion zones using RSI divergence, ADX trend confirmation, and an integrated VAD (Volume + ATR + Delta) module — ensuring clarity and confirmation-based plotting.
⚙️ Core Functional Modules
1️⃣ RSI & Moving Average Module
Adaptive RSI with real-time color gradients
Optional RSI moving average (yellow) for momentum tracking
Dynamic fill zones showing overbought / oversold areas
Background fill for quick zone visualization
2️⃣ RSI Divergence Detection (Bull / Bear)
Auto-detects pivot-based bullish and bearish divergences
Non-repainting logic confirmed post-pivot formation
Smart line management with automatic cleanup
Visual divergence lines and clear on-chart markers
3️⃣ ADX Trend Confirmation
Adjustable comparison: “Higher than N bars ago” or “Higher than highest of last N”
Confirms directional strength before SB / SS signals are displayed
4️⃣ SB / SS Signal Module
“Signal Bull / Signal Sell” markers confirmed post candle closure
Integrated session-block feature to exclude specific intraday periods
Non-repainting, bar-confirmed signal plotting
5️⃣ VAD (Volume + ATR + Delta) Divergence Engine
Highlights hidden momentum shifts via volatility + volume flow logic
Bullish (B-DV) / Bearish (S-DV) divergence markers plotted at pivot bars
Customizable label or symbol-style visualization
🧩 Built-in Features
Non-repainting structure using barstate confirmation
Optimized for all timeframes and chart types
Lightweight execution with flexible styling options
Modular input control for easy customization
⚠️ Disclaimer
This indicator is for technical analysis and educational purposes only.
It does not provide financial advice, does not predict price direction, and does not guarantee profits or performance.
All trading decisions are the sole responsibility of the user. Always test thoroughly before applying to live markets.
Automated Z-scoring - [JTCAPITAL]Automated Z-Scoring - is a modified way to use statistical normalization through Z-Scores for analyzing price deviations, volatility extremes, and mean reversion opportunities in financial markets.
The indicator works by calculating in the following steps:
Source Selection
The indicator begins by selecting a user-defined price source (default is the Close price). Traders can modify this to use any indicator that is deployed on the chart, for accurate and fast Z-scoring.
Mean Calculation
A Simple Moving Average (SMA) is calculated over the selected length period (default 3000). This represents the long-term equilibrium price level or the “statistical mean” of the dataset. It provides the baseline around which all price deviations are measured.
Standard Deviation Measurement
The script computes the Standard Deviation of the price series over the same period. This value quantifies how far current prices tend to stray from the mean — effectively measuring market volatility. The larger the standard deviation, the more volatile the market environment.
Z-Score Normalization
The Z-Score is calculated as:
(Current Price − Mean) ÷ Standard Deviation .
This normalization expresses how many standard deviations the current price is away from its long-term average. A Z-Score above 0 means the price is above average, while a negative score indicates it is below average.
Visual Representation
The Z-Score is plotted dynamically, with color-coding for clarity:
Bullish readings (Z > 0) are showing positive deviation from the mean.
Bearish readings (Z < 0) are showing negative deviation from the mean.
Make sure to select the correct source for what you exactly want to Z-score.
Buy and Sell Conditions:
While the indicator itself is designed as a statistical framework rather than a direct buy/sell signal generator, traders can derive actionable strategies from its behavior:
Trend Following: When the Z-Score crosses above zero after a prolonged negative period, it suggests a return to or above the mean — a possible bullish reversal or trend continuation signal.
Mean Reversion: When the Z-score is below for example -1.5 it indicates a good time for a DCA buying opportunity.
Trend Following: When the Z-Score crosses below zero after being positive, it may indicate a momentum slowdown or bearish shift.
Mean Reversion: When the Z-score is above for example 1.5 it indicates a good time for a DCA sell opportunity
Features and Parameters:
Length – Defines the period for both SMA and Standard Deviation. A longer length smooths the Z-Score and captures broader market context, while a shorter length increases responsiveness.
Source – Allows the user to choose which price data is analyzed (Close, Open, High, Low, etc.).
Fill Visualization – Highlights the magnitude of deviation between the Z-Score and the zero baseline, enhancing readability of volatility extremes.
Specifications:
Mean (Simple Moving Average)
The SMA calculates the average of the selected source over the defined length. It provides a central value to which the price tends to revert. In this indicator, the mean acts as the equilibrium point — the “zero” reference for all deviations.
Standard Deviation
Standard Deviation measures the dispersion of data points from their mean. In trading, it quantifies volatility. A high standard deviation indicates that prices are spread out (volatile), while a low value means they are clustered near the average (stable). The indicator uses this to scale deviations consistently across different market conditions.
Z-Score
The Z-Score converts raw price data into a standardized value measured in units of standard deviation.
A Z-Score of 0 = Price equals its mean.
A Z-Score of +1 = Price is one standard deviation above the mean.
A Z-Score of −1 = Price is one standard deviation below the mean.
This allows comparison of deviation magnitudes across instruments or timeframes, independent of price level.
Length Parameter
A long lookback period (e.g., 3000 bars) smooths temporary volatility and reveals long-term mean deviations — ideal for macro trend identification. Shorter lengths (e.g., 100–500) capture quicker oscillations and are useful for short-term mean reversion trades.
Statistical Interpretation
From a probabilistic perspective, if the distribution of prices is roughly normal:
About 68% of price observations lie within ±1 standard deviation (Z between −1 and +1).
About 95% lie within ±2 standard deviations.
Therefore, when the Z-Score moves beyond ±2, it statistically represents a rare event — often corresponding to price extremes or potential reversal zones.
Practical Benefit of Z-Scoring in Trading
Z-Scoring transforms raw price into a normalized volatility-adjusted metric. This allows traders to:
Compare instruments on a common statistical scale.
Identify mean-reversion setups more objectively.
Spot volatility expansions or contractions early.
Detect when price action significantly diverges from long-term equilibrium.
By automating this process, Automated Z-Scoring - provides traders with a powerful analytical lens to measure how “stretched” the market truly is — turning abstract statistics into a visually intuitive and actionable form.
Enjoy!
SigmaKernel - AdaptiveSigmaKernel - Adaptive Self-Optimizing Multi-Factor Trading System
SigmaKernel - Adaptive is a self-learning algorithmic trading strategy that combines four distinct analytical dimensions—momentum, market structure, volume flow, and reversal patterns—within a machine-learning-inspired framework that continuously adjusts its own parameters based on realized trading performance. Unlike traditional fixed-parameter strategies that maintain static weightings regardless of market conditions or results, this system implements a feedback loop that tracks which signal types, directional biases, and market conditions produce profitable outcomes, then mathematically adjusts component weightings, minimum score thresholds, position sizing multipliers, and trade spacing requirements to optimize future performance.
The strategy is designed for futures traders operating on prop firm accounts or live capital, incorporating realistic execution mechanics including configurable entry modes (stop breakout orders, limit pullback entries, or market-on-open), commission structures calibrated to retail futures contracts ($0.62 per contract default), one-tick slippage modeling, and professional risk controls including trailing drawdown guards, daily loss limits, and weekly profit targets. The system features universal futures compatibility—it automatically detects and adapts to any futures contract by reading the instrument's tick size and point value directly from the chart, eliminating the need for manual configuration across different markets.
What Makes This Approach Different
Adaptive Weight Optimization System
The core differentiation is the adaptive learning architecture. The strategy maintains four independent scoring components: momentum analysis (using RSI multi-timeframe, MACD histogram, and DMI/ADX), market structure detection (breakout identification via pivot-based support/resistance and moving average positioning), volume flow analysis (Volume Price Trend indicator with standard deviation confirmation), and reversal pattern recognition (oversold/overbought conditions combined with structural levels).
Each component generates a directional score that is multiplied by its current weight. After every closed trade, the system performs a retrospective analysis on the last N trades (configurable Learning Period, default 15 trades) to calculate win rates for each signal type independently. For example, if momentum-driven trades won 65% of the time while reversal trades won only 35%, the adaptive algorithm increases the momentum weight and decreases the reversal weight proportionally. The adjustment formula is:
New_Weight = Current_Weight + (Component_Win_Rate - Average_Win_Rate) × Adaptation_Speed
This creates a self-correcting mechanism where successful signal generators receive more influence in future composite scores, while underperforming components are de-emphasized. The system separately tracks long versus short win rates and applies directional bias corrections—if shorts consistently outperform longs, the strategy applies a 10% reduction to bullish signals to prevent fighting the prevailing market character.
Dynamic Parameter Adjustment
Beyond component weightings, three critical strategy parameters self-adjust based on performance:
Minimum Signal Score: The threshold required to trigger a trade. If overall win rate falls below 45%, the system increments this threshold by 0.10 per adjustment cycle, making the strategy more selective. If win rate exceeds 60%, the threshold decreases to allow more opportunities. This prevents the strategy from overtrading during unfavorable conditions and capitalizes on high-probability environments.
Risk Multiplier: Controls position sizing aggression. When drawdown exceeds 5%, risk per trade reduces by 10% per cycle. When drawdown falls below 2%, risk increases by 5% per cycle. This implements the professional risk management principle of "bet small when losing, bet bigger when winning" algorithmically.
Bars Between Trades: Spacing filter to prevent overtrading. Base value (default 9 bars) multiplies by drawdown factor and losing streak factor. During drawdown or consecutive losses, spacing expands up to 2x to allow market conditions to change before re-entering.
All adaptation operates during live forward-testing or real trading—there is no in-sample optimization applied to historical data. The system learns solely from its own realized trades.
Universal Futures Compatibility
The strategy implements universal futures instrument detection that automatically adapts to any futures contract without requiring manual configuration. Instead of hardcoding specific contract specifications, the system reads three critical values directly from TradingView's symbol information:
Tick Size Detection: Uses `syminfo.mintick` to obtain the minimum price increment for the current instrument. This value varies widely across markets—ES trades in 0.25 ticks, crude oil (CL) in 0.01 ticks, gold (GC) in 0.10 ticks, and treasury futures (ZB) in increments of 1/32nds. The strategy adapts all entry buffer calculations and stop placement logic to the detected tick size.
Point Value Detection: Uses `syminfo.pointvalue` to determine the dollar value per full point of price movement. For ES, one point equals $50; for crude oil, one point equals $1,000; for gold, one point equals $100. This automatic detection ensures accurate P&L calculations and risk-per-contract measurements across all instruments.
Tick Value Calculation: Combines tick size and point value to compute dollar value per tick: Tick_Value = Tick_Size × Point_Value. This derived value drives all position sizing calculations, ensuring the risk management system correctly accounts for each instrument's economic characteristics.
This universal approach means the strategy functions identically on emini indices (ES, MES, NQ, MNQ), micro indices, energy contracts (CL, NG, RB), metals (GC, SI, HG), agricultural futures (ZC, ZS, ZW), treasury futures (ZB, ZN, ZF), currency futures (6E, 6J, 6B), and any other futures contract available on TradingView. No parameter adjustments or instrument-specific branches exist in the code—the adaptation happens automatically through symbol information queries.
Stop-Out Rate Monitoring System
The strategy includes an intelligent stop-out rate tracking system that monitors the percentage of your last 20 trades (or available trades if fewer than 20) that were stopped out. This metric appears in the dashboard's Performance section with color-coded guidance:
Green (<30% stop-out rate): Very few trades are being stopped out. This suggests either your stops are too loose (giving back profits on reversals) or you're in an exceptional trending market. Consider tightening your Stop Loss ATR multiplier to lock in profits more efficiently.
Orange (30-65% stop-out rate): Healthy range. Your stop placement is appropriately sized for current market conditions and the strategy's risk-reward profile. No adjustment needed.
Red (>65% stop-out rate): Too many trades are being stopped out prematurely. Your stops are likely too tight for the current volatility regime. Consider widening your Stop Loss ATR multiplier to give trades more room to develop.
Critical Design Philosophy: Unlike some systems that automatically adjust stops based on performance statistics, this strategy intentionally keeps stop-loss control in the user's hands. Automatic stop adjustment creates dangerous feedback loops—widening stops increases risk per contract, which forces position size reduction, which distorts performance metrics, leading to incorrect adaptations. Instead, the dashboard provides visibility into stop performance, empowering you to make informed manual adjustments when warranted. This preserves the integrity of the adaptive system while giving you the critical data needed for stop optimization.
Execution Kernel Architecture
The entry system offers three distinct execution modes to match trader preference and market character:
StopBreakout Mode: Places buy-stop orders above the prior bar's high (for longs) or sell-stop orders below the prior bar's low (for shorts), plus a 2-tick buffer. This ensures entries only occur when price confirms directional momentum by breaking recent structure. Ideal for trending and momentum-driven markets.
LimitPullback Mode: Places limit orders at a pullback price calculated as: Entry_Price = Close - (ATR × Pullback_Multiplier) for longs, or Close + (ATR × Pullback_Multiplier) for shorts. Default multiplier is 0.5 ATR. This waits for mean-reversion before entering in the signal direction, capturing better prices in volatile or oscillating markets.
MarketNextOpen Mode: Executes at market on the bar immediately following signal generation. This provides fastest execution but sacrifices the filtering effect of requiring price confirmation.
All pending entry orders include a configurable Time-To-Live (TTL, default 6 bars). If an order is not filled within the TTL period, it cancels automatically to prevent stale signals from executing in changed market conditions.
Professional Exit Management
The exit system implements a three-stage progression: initial stop loss, breakeven adjustment, and dynamic trailing stop.
Initial Stop Loss: Calculated as entry price ± (ATR × User_Stop_Multiplier × Volatility_Adjustment). Users have direct control via the Stop Loss ATR multiplier (default 1.25). The system then applies volatility regime adjustments: ×1.2 in high-volatility environments (stops automatically widen), ×0.8 in low volatility (stops tighten), ×1.0 in normal conditions. This ensures stops adapt to market character while maintaining user control over baseline risk tolerance.
Breakeven Trigger: When profit reaches a configurable multiple of initial risk (default 1.0R), the stop loss automatically moves to breakeven (entry price). This locks in zero-loss status once the trade demonstrates favorable movement.
Trailing Stop Activation: When profit reaches the Trail_Trigger_R multiple (default 1.2R), the system cancels the fixed stop and activates a dynamic trailing stop. The trail uses Step and Offset parameters defined in R-multiples. For example, with Trail_Offset_R = 1.0 and Trail_Step_R = 1.5, the stop trails 1.0R behind price and moves in 1.5R increments. This captures extended moves while protecting accumulated profit.
Additional failsafes include maximum time-in-trade (exits after N bars if specified) and end-of-session flatten (automatically closes all positions X minutes before session end to avoid overnight exposure).
Core Calculation Methodology
Signal Component Scoring
Momentum Component:
- Calculates 14-period DMI (Directional Movement Index) with ADX strength filter (trending when ADX > 25)
- Computes three RSI timeframes: fast (7-period), medium (14-period), slow (21-period)
- Analyzes MACD (12/26/9) histogram for directional acceleration
- Bullish momentum: uptrend (DI+ > DI- with ADX > 25) + MACD histogram rising above zero + RSI fast between 50-80 = +1.6 score
- Bearish momentum: downtrend (DI- > DI+ with ADX > 25) + MACD histogram falling below zero + RSI fast between 20-50 = -1.6 score
- Score multiplies by volatility adjustment factor: ×0.8 in high volatility (momentum less reliable), ×1.2 in low volatility (momentum more persistent)
Structure Component:
- Identifies swing highs and lows using 10-bar pivot lookback on both sides
- Maintains most recent swing high as dynamic resistance, most recent swing low as dynamic support
- Detects breakouts: bullish when close crosses above resistance with prior bar below; bearish when close crosses below support with prior bar above
- Breakout score: ±1.0 for confirmed break
- Moving average alignment: +0.5 when price > SMA20 > SMA50 (bullish structure); -0.5 when price < SMA20 < SMA50 (bearish structure)
- Total structure range: -1.5 to +1.5
Volume Component:
- Calculates Volume Price Trend: VPT = Σ [(Close - Close ) / Close × Volume]
- Compares VPT to its 10-period EMA as signal line (similar to MACD logic)
- Computes 20-period volume moving average and standard deviation
- High volume event: current volume > (volume_average + 1× std_dev)
- Bullish volume: VPT > VPT_signal AND high_volume = +1.0
- Bearish volume: VPT < VPT_signal AND high_volume = -1.0
- No score if volume is not elevated (filters out low-conviction moves)
Reversal Component:
- Identifies extreme RSI conditions: RSI slow < 30 (oversold) or > 70 (overbought)
- Requires structural confluence: price at or below support level for bullish reversal; at or above resistance for bearish reversal
- Requires momentum shift: RSI fast must be rising (for bull) or falling (for bear) to confirm reversal in progress
- Bullish reversal: RSI < 30 AND price ≤ support AND RSI rising = +1.0
- Bearish reversal: RSI > 70 AND price ≥ resistance AND RSI falling = -1.0
Composite Score Calculation
Final_Score = (Momentum × Weight_M) + (Structure × Weight_S) + (Volume × Weight_V) + (Reversal × Weight_R)
Initial weights: Momentum = 1.0, Structure = 1.2, Volume = 0.8, Reversal = 0.6
These weights adapt after each trade based on component-specific performance as described above.
The system also applies directional bias adjustment: if recent long trades have significantly lower win rate than shorts, bullish scores multiply by 0.9 to reduce aggressive long entries. Vice versa for underperforming shorts.
Position Sizing Algorithm
The position sizing calculation incorporates multiple confidence factors and automatically scales to any futures contract:
1. Base risk amount = Account_Size × Base_Risk_Percent × Adaptive_Risk_Multiplier
2. Stop distance in price units = ATR × User_Stop_Multiplier × Volatility_Regime_Multiplier × Entry_Buffer
3. Risk per contract = Stop_Distance × Dollar_Per_Point (automatically detected from instrument)
4. Raw position size = Risk_Amount / Risk_Per_Contract
Then applies confidence scaling:
- Signal confidence = min(|Weighted_Score| / Min_Score_Threshold, 2.0) — higher scores receive larger size, capped at 2×
- Direction confidence = Long_Win_Rate (for bulls) or Short_Win_Rate (for bears)
- Type confidence = Win_Rate of dominant signal type (momentum/structure/volume/reversal)
- Total confidence = (Signal_Confidence + Direction_Confidence + Type_Confidence) / 3
Adjusted size = Raw_Size × Total_Confidence × Losing_Streak_Reduction
Losing streak reduction = 0.5 if losing_streak ≥ 5, otherwise 1.0
Universal Maximum Position Calculation: Instead of hardcoded limits per instrument, the system calculates maximum position size as: Max_Contracts = Account_Size / 25000, clamped between 1 and 10 contracts. This means a $50,000 account allows up to 2 contracts, a $100,000 account allows up to 4 contracts, regardless of which futures contract is being traded. This universal approach maintains consistent risk exposure across different instruments while preventing overleveraging.
Final size is rounded to integer and bounded by the calculated maximum.
Session and Risk Management System
Timezone-Aware Session Control
The strategy implements timezone-correct session filtering. Users specify session start hour, end hour, and timezone from 12 supported zones (New York, Chicago, Los Angeles, London, Frankfurt, Moscow, Tokyo, Hong Kong, Shanghai, Singapore, Sydney, UTC). The system converts bar timestamps to the selected timezone before applying session logic.
For split sessions (e.g., Asian session 18:00-02:00), the logic correctly handles time wraparound. Weekend trading can be optionally disabled (default: disabled) to avoid low-liquidity weekend price action.
Multi-Layer Risk Controls
Daily Loss Limit: Strategy ceases all new entries when daily P&L reaches negative threshold (default $2,000). This prevents catastrophic drawdown days. Resets at timezone-corrected day boundary.
Weekly Profit Target: Strategy ceases trading when weekly profit reaches target (default $10,000). This implements the professional principle of "take the win and stop pushing luck." Resets on timezone-corrected Monday.
Maximum Daily Trades: Hard cap on entries per day (default 20) to prevent overtrading during volatile conditions when many signals may generate.
Trailing Drawdown Guard: Optional prop-firm-style trailing stop on account equity. When enabled, if equity drops below (Peak_Equity - Trailing_DD_Amount), all trading halts. This simulates the common prop firm rule where exceeding trailing drawdown results in account termination.
All limits display status in the real-time dashboard, showing "MAX LOSS HIT", "WEEKLY TARGET MET", or "ACTIVE" depending on current state.
How To Use This Strategy
Initial Setup
1. Apply the strategy to your desired futures chart (tested on 5-minute through daily timeframes)
2. The strategy will automatically detect your instrument's specifications—no manual configuration needed for different contracts
3. Configure your account size and risk parameters in the Core Settings section
4. Set your trading session hours and timezone to match your availability
5. Adjust the Stop Loss ATR multiplier based on your risk tolerance (0.8-1.2 for tighter stops, 1.5-2.5 for wider stops)
6. Select your preferred entry execution mode (recommend StopBreakout for beginners)
7. Enable adaptation (recommended) or disable for fixed-parameter operation
8. Review the strategy's Properties in the Strategy Tester settings and verify commission/slippage match your broker's actual costs
The universal futures detection means you can switch between ES, NQ, CL, GC, ZB, or any other futures contract without changing any strategy parameters—the system will automatically adapt its calculations to each instrument's unique specifications.
Dashboard Interpretation
The strategy displays a comprehensive real-time dashboard in the top-right corner showing:
Market State Section:
- Trend: Shows UPTREND/DOWNTREND/CONSOLIDATING/NEUTRAL based on ADX and DMI analysis
- ADX Value: Current trend strength (>25 = strong trend, <20 = consolidating)
- Momentum: BULL/BEAR/NEUTRAL classification with current momentum score
- Volatility: HIGH/LOW/NORMAL regime with ATR percentage of price
Volume Profile Section (Large dashboard only):
- VPT Flow: Directional bias from volume analysis
- Volume Status: HIGH/LOW/NORMAL with relative volume multiplier
Performance Section:
- Daily P&L: Current day's profit/loss with color coding
- Daily Trades: Number of completed trades today
- Weekly P&L: Current week's profit/loss
- Target %: Progress toward weekly profit target
- Stop-Out Rate: Percentage of last 20 trades (or available trades if <20) that were stopped out. Includes all stop types: initial stops, breakeven stops, trailing stops, timeout exits, and EOD flattens. Color coded with actionable guidance:
- Green (<30%): Shows "TIGHTEN" guidance. Very few stop-outs suggests stops may be too loose or exceptional market conditions. Consider reducing Stop Loss ATR multiplier.
- Orange (30-65%): Shows "OK" guidance. Healthy stop-out rate indicating appropriate stop placement for current conditions.
- Red (>65%): Shows "WIDEN" guidance. Too many premature stop-outs. Consider increasing Stop Loss ATR multiplier to give trades more room.
- Status: Overall trading status (ACTIVE/MAX LOSS HIT/WEEKLY TARGET MET/FILTERS ACTIVE)
Adaptive Engine Section:
- Min Score: Current minimum threshold for trade entry (higher = more selective)
- Risk Mult: Current position sizing multiplier (adjusts with performance)
- Bars BTW: Current minimum bars required between trades
- Drawdown: Current drawdown percentage from equity peak
- Weights: M/S/V/R showing current component weightings
Win Rates Section:
- Type: Win rates for Momentum, Structure, Volume, Reversal signal types
- Direction: Win rates for Long vs Short trades
Color coding shows green for >50% win rate, red for <50%
Session Info Section:
- Session Hours: Active trading window with timezone
- Weekend Trading: ENABLED/DISABLED status
- Session Status: ACTIVE/INACTIVE based on current time
Signal Generation and Entry
The strategy generates entries when the weighted composite score exceeds the adaptive minimum threshold (initial value configurable, typically 1.5 to 2.5). Entries display as layered triangle markers on the chart:
- Long Signal: Three green upward triangles below the entry bar
- Short Signal: Three red downward triangles above the entry bar
Triangle tooltip shows the signal score and dominant signal type (MOMENTUM/STRUCTURE/VOLUME/REVERSAL).
Position Management and Stop Optimization
Once entered, the strategy automatically manages the position through its three-stage exit system. Monitor the Stop-Out Rate metric in the dashboard to optimize your stop placement:
If Stop-Out Rate is Green (<30%): You're rarely being stopped out. This could mean:
- Your stops are too loose, allowing trades to give back too much profit on reversals
- You're in an exceptional trending market where tight stops would work better
- Action: Consider reducing your Stop Loss ATR multiplier by 0.1-0.2 to tighten stops and lock in profits more efficiently
If Stop-Out Rate is Orange (30-65%): Optimal range. Your stops are appropriately sized for the strategy's risk-reward profile and current market volatility. No adjustment needed.
If Stop-Out Rate is Red (>65%): You're being stopped out too frequently. This means:
- Your stops are too tight for current market volatility
- Trades need more room to develop before reaching profit targets
- Action: Increase your Stop Loss ATR multiplier by 0.1-0.3 to give trades more breathing room
Remember: The stop-out rate calculation includes all exit types (initial stops, breakeven stops, trailing stops, timeouts, EOD flattens). A trade that reaches breakeven and gets stopped out at entry price counts as a stop-out, even though it didn't lose money. This is intentional—it indicates the stop placement didn't allow the trade to develop into profit.
Optimization Workflow
For traders wanting to customize the strategy for their specific instrument and timeframe:
Week 1-2: Run with defaults, adaptation enabled
Allow the system to execute at least 30-50 trades (the Learning Period plus additional buffer). Monitor which session periods, signal types, and market conditions produce the best results. Observe your stop-out rate—if it's consistently red or green, plan to adjust Stop Loss ATR multiplier after the learning period. Do not adjust parameters yet—let the adaptive system establish baseline performance data.
Week 3-4: Analyze adaptation behavior and optimize stops
Review the dashboard's adaptive weights and win rates. If certain signal types consistently show <40% win rate, consider slightly reducing their base weight. If a particular entry mode produces better fill quality and win rate, switch to that mode. If you notice the minimum score threshold has climbed very high (>3.0), market conditions may not suit the strategy's logic—consider switching instruments or timeframes.
Based on your Stop-Out Rate observations:
- Consistently <30%: Reduce Stop Loss ATR multiplier by 0.2-0.3
- Consistently >65%: Increase Stop Loss ATR multiplier by 0.2-0.4
- Oscillating between zones: Leave stops at default and let volatility regime adjustments handle it
Ongoing: Fine-tune risk and execution
Adjust the following based on your risk tolerance and account type:
- Base Risk Per Trade: 0.5% for conservative, 0.75% for moderate, 1.0% for aggressive
- Stop Loss ATR Multiplier: 0.8-1.2 for tight stops (scalping), 1.5-2.5 for wide stops (swing trading)
- Bars Between Trades: Lower (5-7) for more opportunities, higher (12-20) for more selective
- Entry Mode: Experiment between modes to find best fit for current market character
- Session Hours: Narrow to specific high-performance session windows if certain hours consistently underperform
Never adjust: Do not manually modify the adaptive weights, minimum score, or risk multiplier after the system has begun learning. These parameters are self-optimizing and manual interference defeats the adaptive mechanism.
Parameter Descriptions and Optimization Guidelines
Adaptive Intelligence Group
Enable Self-Optimization (default: true): Master switch for the adaptive learning system. When enabled, component weights, minimum score, risk multiplier, and trade spacing adjust based on realized performance. Disable to run the strategy with fixed parameters (useful for comparing adaptive vs non-adaptive performance).
Learning Period (default: 15 trades): Number of most recent trades to analyze for performance calculations. Shorter values (10-12) adapt more quickly to recent conditions but may overreact to variance. Longer values (20-30) produce more stable adaptations but respond slower to regime changes. For volatile markets, use shorter periods. For stable trends, use longer periods.
Adaptation Speed (default: 0.25): Controls the magnitude of parameter adjustments per learning cycle. Lower values (0.05-0.15) make gradual, conservative changes. Higher values (0.35-0.50) make aggressive adjustments. Faster adaptation helps in rapidly changing markets but increases parameter instability. Start with default and increase only if you observe the system failing to adapt quickly enough to obvious performance patterns.
Performance Memory (default: 100 trades): Maximum number of historical trades stored for analysis. This array size does not affect learning (which uses only Learning Period trades) but provides data for future analytics features including stop-out rate tracking. Higher values consume more memory but provide richer historical dataset. Typical users should not need to modify this.
Core Settings Group
Account Size (default: $50,000): Starting capital for position sizing calculations. This should match your actual account size for accurate risk per trade. The strategy uses this value to calculate dollar risk amounts and determine maximum position size (1 contract per $25,000).
Weekly Profit Target (default: $10,000): When weekly P&L reaches this value, the strategy stops taking new trades for the remainder of the week. This implements a "quit while ahead" rule common in professional trading. Set to a realistic weekly goal—20% of account size per week ($10K on $50K) is very aggressive; 5-10% is more sustainable.
Max Daily Loss (default: $2,000): When daily P&L reaches this negative threshold, strategy stops all new entries for the day. This is your maximum acceptable daily loss. Professional traders typically set this at 2-4% of account size. A $2,000 loss on a $50,000 account = 4%.
Base Risk Per Trade % (default: 0.5%): Initial percentage of account to risk on each trade before adaptive multiplier and confidence scaling. 0.5% is conservative, 0.75% is moderate, 1.0-1.5% is aggressive. Remember that actual risk per trade = Base Risk × Adaptive Risk Multiplier × Confidence Factors, so the realized risk will vary.
Trade Filters Group
Base Minimum Signal Score (default: 1.5): Initial threshold that composite weighted score must exceed to generate a signal. Lower values (1.0-1.5) produce more trades with lower average quality. Higher values (2.0-3.0) produce fewer, higher-quality setups. This value adapts automatically when adaptive mode is enabled, but the base sets the starting point. For trending markets, lower values work well. For choppy markets, use higher values.
Base Bars Between Trades (default: 9): Minimum bars that must elapse after an entry before another signal can trigger. This prevents overtrading and allows previous trades time to develop. Lower values (3-6) suit scalping on lower timeframes. Higher values (15-30) suit swing trading on higher timeframes. This value also adapts based on drawdown and losing streaks.
Max Daily Trades (default: 20): Hard limit on total trades per day regardless of signal quality. This prevents runaway trading during extremely volatile days when many signals may generate. For 5-minute charts, 20 trades/day is reasonable. For 1-hour charts, 5-10 trades/day is more typical.
Session Group
Session Start Hour (default: 5): Hour (0-23 format) when trading is allowed to begin, in the timezone specified. For US futures trading in Chicago time, session typically starts at 5:00 or 6:00 PM (17:00 or 18:00) Sunday evening.
Session End Hour (default: 17): Hour when trading stops and no new entries are allowed. For US equity index futures, regular session ends at 4:00 PM (16:00) Central Time.
Allow Weekend Trading (default: false): Whether strategy can trade on Saturday/Sunday. Most futures have low volume on weekends; keeping this disabled is recommended unless you specifically trade Sunday evening open.
Session Timezone (default: America/Chicago): Timezone for session hour interpretation. Select your local timezone or the timezone of your instrument's primary exchange. This ensures session logic aligns with your intended trading hours.
Prop Guards Group
Trailing Drawdown Guard (default: false): Enables prop-firm-style trailing maximum drawdown. When enabled, if equity drops below (Peak Equity - Trailing DD Amount), all trading halts for the remainder of the backtest/live session. This simulates rules used by funded trader programs where exceeding trailing drawdown terminates the account.
Trailing DD Amount (default: $2,500): Dollar amount of drawdown allowed from equity peak. If your equity reaches $55,000, the trailing stop sets at $52,500. If equity then drops to $52,499, the guard triggers and trading ceases.
Execution Kernel Group
Entry Mode (default: StopBreakout):
- StopBreakout: Places stop orders above/below signal bar requiring price confirmation
- LimitPullback: Places limit orders at pullback prices seeking better fills
- MarketNextOpen: Executes immediately at market on next bar
Limit Offset (default: 0.5x ATR): For LimitPullback mode, how far below/above current price to place the limit order. Smaller values (0.3-0.5) seek minor pullbacks. Larger values (0.8-1.2) wait for deeper retracements but may miss trades.
Entry TTL (default: 6 bars, 0=off): Bars an entry order remains pending before cancelling. Shorter values (3-4) keep signals fresh. Longer values (8-12) allow more time for fills but risk executing stale signals. Set to 0 to disable TTL (orders remain active indefinitely until filled or opposite signal).
Exits Group
Stop Loss (default: 1.25x ATR): Base stop distance as a multiple of the 14-period ATR. This is your primary risk control parameter and directly impacts your stop-out rate. Lower values (0.8-1.0) create tighter stops that reduce risk per trade but may get stopped out prematurely in volatile conditions—expect stop-out rates above 65% (red zone). Higher values (1.5-2.5) give trades more room to breathe but increase risk per contract—expect stop-out rates below 30% (green zone). The system applies additional volatility regime adjustments on top of this base: ×1.2 in high volatility environments (stops widen automatically), ×0.8 in low volatility (stops tighten), ×1.0 in normal conditions. For scalping on lower timeframes, use 0.8-1.2. For swing trading on higher timeframes, use 1.5-2.5. Monitor the Stop-Out Rate metric in the dashboard and adjust this parameter to keep it in the healthy 30-65% orange zone.
Move to Breakeven at (default: 1.0R): When profit reaches this multiple of initial risk, stop moves to breakeven. 1.0R means after price moves in your favor by the distance you risked, you're protected at entry price. Lower values (0.5-0.8R) lock in breakeven faster. Higher values (1.5-2.0R) allow more room before protection.
Start Trailing at (default: 1.2R): When profit reaches this multiple, the fixed stop transitions to a dynamic trailing stop. This should be greater than the BE trigger. Values typically range 1.0-2.0R depending on how much profit you want secured before trailing activates.
Trail Offset (default: 1.0R): How far behind price the trailing stop follows. Tighter offsets (0.5-0.8R) protect profit more aggressively but may exit prematurely. Wider offsets (1.5-2.5R) allow more room for profit to run but risk giving back more on reversals.
Trail Step (default: 1.5R): How far price must move in profitable direction before the stop advances. Smaller steps (0.5-1.0R) move the stop more frequently, tightening protection continuously. Larger steps (2.0-3.0R) move the stop less often, giving trades more breathing room.
Max Bars In Trade (default: 0=off): Maximum bars allowed in a position before forced exit. This prevents trades from "going stale" during periods of no meaningful price action. For 5-minute charts, 50-100 bars (4-8 hours) is reasonable. For daily charts, 5-10 bars (1-2 weeks) is typical. Set to 0 to disable.
Flatten near Session End (default: true): Whether to automatically close all positions as session end approaches. Recommended to avoid carrying positions into off-hours with low liquidity.
Minutes before end (default: 5): How many minutes before session end to flatten. 5-15 minutes provides buffer for order execution before the session boundary.
Visual Effects Configuration Group
Dashboard Size (default: Normal): Controls information density in the dashboard. Small shows only critical metrics (excludes stop-out rate). Normal shows comprehensive data including stop-out rate. Large shows all available metrics including weights, session info, and volume analysis. Larger sizes consume more screen space but provide complete visibility.
Show Quantum Field (default: true): Displays animated grid pattern on the chart indicating market state. Disable if you prefer cleaner charts or experience performance issues on lower-end hardware.
Show Wick Pressure Lines (default: true): Draws dynamic lines from bars with extreme wicks, indicating potential support/resistance or liquidity absorption zones. Disable for simpler visualization.
Show Morphism Energy Beams (default: true): Displays directional beams showing momentum energy flow. Beams intensify during strong trends. Disable if you find this visually distracting.
Show Order Flow Clouds (default: true): Draws translucent boxes representing volume flow bullish/bearish bias. Disable for cleaner price action visibility.
Show Fractal Grid (default: true): Displays multi-timeframe support/resistance levels based on fractal price structure at 10/20/30/40/50 bar periods. Disable if you only want to see primary pivot levels.
Glow Intensity (default: 4): Controls the brightness and thickness of visual effects. Lower values (1-2) for subtle visualization. Higher values (7-10) for maximum visibility but potentially cluttered charts.
Color Theme (default: Cyber): Visual color scheme. Cyber uses cyan/magenta futuristic colors. Quantum uses aqua/purple. Matrix uses green/red terminal style. Aurora uses pastel pink/purple gradient. Choose based on personal preference and monitor calibration.
Show Watermark (default: true): Displays animated watermark at bottom of chart with creator credit and current P&L. Disable if you want completely clean charts or need screen space.
Performance Characteristics and Best Use Cases
Optimal Conditions
This strategy performs best in markets exhibiting:
Trending phases with periodic pullbacks: The combination of momentum and structure components excels when price establishes directional bias but provides retracement opportunities for entries. Markets with 60-70% trending bars and 30-40% consolidation produce the highest win rates.
Medium to high volatility: The ATR-based stop sizing and dynamic risk adjustment require sufficient price movement to generate meaningful profit relative to risk. Instruments with 2-4% daily ATR relative to price work well. Extremely low volatility (<1% daily ATR) generates too many scratch trades.
Clear volume patterns: The VPT volume component adds significant edge when volume expansions align with directional moves. Instruments and timeframes where volume data reflects actual transaction flow (versus tick volume proxies) perform better.
Regular session structure: Futures markets with defined opening and closing hours, consistent liquidity throughout the session, and clear overnight/day session separation allow the session controls and time-based failsafes to function optimally.
Sufficient liquidity for stop execution: The stop breakout entry mode requires that stop orders can fill without significant slippage. Highly liquid contracts work better than illiquid instruments where stop orders may face adverse fills.
Suboptimal Conditions
The strategy may struggle with:
Extreme chop with no directional persistence: When ADX remains below 15 for extended periods and price oscillates rapidly without establishing trends, the momentum component generates conflicting signals. Win rate typically drops below 40% in these conditions, triggering the adaptive system to increase minimum score thresholds until conditions improve. Stop-out rates may also spike into the red zone.
Gap-heavy instruments: Markets with frequent overnight gaps disrupt the continuous price assumptions underlying ATR stops and EMA-based structure analysis. Gaps can also cause stop orders to fill at prices far from intended levels, distorting stop-out rate metrics.
Very low timeframes with excessive noise: On 1-minute or tick charts, the signal components react to micro-structure noise rather than meaningful price swings. The strategy works best on 5-minute through daily timeframes where price movements reflect actual order flow shifts.
Extended low-volatility compression: During historically low volatility periods, profit targets become difficult to reach before mean-reversion occurs. The trail offset, even when set to minimum, may be too wide for the compressed price environment. Stop-out rates may drop to green zone indicating stops should be tightened.
Parabolic moves or climactic exhaustion: Vertical price advances or selloffs where price moves multiple ATRs in single bars can trigger momentum signals at exhaustion points. The structure and reversal components attempt to filter these, but extreme moves may override normal logic.
The adaptive learning system naturally reduces signal frequency and position sizing during unfavorable conditions. If you observe multiple consecutive days with zero trades and "FILTERS ACTIVE" status, this indicates the strategy has self-adjusted to avoid poor conditions rather than forcing trades.
Instrument Recommendations
Emini Index Futures (ES, MES, NQ, MNQ, YM, RTY): Excellent fit. High liquidity, clear volatility patterns, strong volume signals, defined session structure. These instruments have been extensively tested and the universal detection handles all contract specifications automatically.
Micro Index Futures (MES, MNQ, M2K, MYM): Excellent fit for smaller accounts. Same market characteristics as the standard eminis but with reduced contract sizes allowing proper risk management on accounts below $50,000.
Energy Futures (CL, NG, RB, HO): Good to mixed fit. Crude oil (CL) works well due to strong trends and reasonable volatility. Natural gas (NG) can be extremely volatile—consider reducing Base Risk to 0.3-0.4% and increasing Stop Loss ATR multiplier to 1.8-2.2 for NG. The strategy automatically detects the $10/tick value for CL and adjusts position sizing accordingly.
Metal Futures (GC, SI, HG, PL): Good fit. Gold (GC) and silver (SI) exhibit clear trending behavior and work well with the momentum/structure components. The strategy automatically handles the different point values ($100/point for gold, $5,000/point for silver).
Agricultural Futures (ZC, ZS, ZW, ZL): Good fit. Grain futures often trend strongly during seasonal periods. The strategy handles the unique tick sizes (1/4 cent increments) and point values ($50/point for corn/wheat, $60/point for soybeans) automatically.
Treasury Futures (ZB, ZN, ZF, ZT): Good fit for trending rates environments. The strategy automatically handles the fractional tick sizing (32nds for ZB/ZN, halves of 32nds for ZF/ZT) through the universal detection system.
Currency Futures (6E, 6J, 6B, 6A, 6C): Good fit. Major currency pairs exhibit smooth trending behavior. The strategy automatically detects point values which vary significantly ($12.50/tick for 6E, $12.50/tick for 6J, $6.25/tick for 6B).
Cryptocurrency Futures (BTC, ETH, MBT, MET): Mixed fit. These markets have extreme volatility requiring parameter adjustment. Increase Base Risk to 0.8-1.2% and Stop Loss ATR multiplier to 2.0-3.0 to account for wider stop distances. Enable 24-hour trading and weekend trading as these markets have no traditional sessions.
The universal futures compatibility means you can apply this strategy to any of these markets without code modification—simply open the chart of your desired contract and the strategy will automatically configure itself to that instrument's specifications.
Important Disclaimers and Realistic Expectations
This is a sophisticated trading strategy that combines multiple analytical methods within an adaptive framework designed for active traders who will monitor performance and market conditions. It is not a "set and forget" fully automated system, nor should it be treated as a guaranteed profit generator.
Backtesting Realism and Limitations
The strategy includes realistic trading costs and execution assumptions:
- Commission: $0.62 per contract per side (accurate for many retail futures brokers)
- Slippage: 1 tick per entry and exit (conservative estimate for liquid futures)
- Position sizing: Realistic risk percentages and maximum contract limits based on account size
- No repainting: All calculations use confirmed bar data only—signals do not change retroactively
However, backtesting cannot fully capture live trading reality:
- Order fill delays: In live trading, stop and limit orders may not fill instantly at the exact tick shown in backtest
- Volatile periods: During high volatility or low liquidity (news events, rollover days, pre-holidays), slippage may exceed the 1-tick assumption significantly
- Gap risk: The backtest assumes stops fill at stop price, but gaps can cause fills far beyond intended exit levels
- Psychological factors: Seeing actual capital at risk creates emotional pressures not present in backtesting, potentially leading to premature manual intervention
The strategy's backtest results should be viewed as best-case scenarios. Real trading will typically produce 10-30% lower returns than backtest due to the above factors.
Risk Warnings
All trading involves substantial risk of loss. The adaptive learning system can improve parameter selection over time, but it cannot predict future price movements or guarantee profitable performance. Past wins do not ensure future wins.
Losing streaks are inevitable. Even with a 60% win rate, you will encounter sequences of 5, 6, or more consecutive losses due to normal probability distributions. The strategy includes losing streak detection and automatic risk reduction, but you must have sufficient capital to survive these drawdowns.
Market regime changes can invalidate learned patterns. If the strategy learns from 50 trades during a trending regime, then the market shifts to a ranging regime, the adapted parameters may initially be misaligned with the new environment. The system will re-adapt, but this transition period may produce suboptimal results.
Prop firm traders: understand your specific rules. Every prop firm has different rules regarding maximum drawdown, daily loss limits, consistency requirements, and prohibited trading behaviors. While this strategy includes common prop guardrails, you must verify it complies with your specific firm's rules and adjust parameters accordingly.
Never risk capital you cannot afford to lose. This strategy can produce substantial drawdowns, especially during learning periods or market regime shifts. Only trade with speculative capital that, if lost, would not impact your financial stability.
Recommended Usage
Paper trade first: Run the strategy on a simulated account for at least 50 trades or 1 month before committing real capital. Observe how the adaptive system behaves, identify any patterns in losing trades, monitor your stop-out rate trends, and verify your understanding of the entry/exit mechanics.
Start with minimum position sizing: When transitioning to live trading, reduce the Base Risk parameter to 0.3-0.4% initially (vs 0.5-1.0% in testing) to reduce early impact while the system learns your live broker's execution characteristics.
Monitor daily, but do not micromanage: Check the dashboard daily to ensure the strategy is operating normally and risk controls have not triggered unexpectedly. Pay special attention to the Stop-Out Rate metric—if it remains in the red or green zones for multiple days, adjust your Stop Loss ATR multiplier accordingly. However, resist the urge to manually adjust adaptive weights or disable trades based on short-term performance. Allow the adaptive system at least 30 trades to establish patterns before making manual changes.
Combine with other analysis: While this strategy can operate standalone, professional traders typically use systematic strategies as one component of a broader approach. Consider using the strategy for trade execution while applying your own higher-timeframe analysis or fundamental view for trade filtering or sizing adjustments.
Keep a trading journal: Document each week's results, note market conditions (trending vs ranging, high vs low volatility), record stop-out rates and any Stop Loss ATR adjustments you made, and document any manual interventions. Over time, this journal will help you identify conditions where the strategy excels versus struggles, allowing you to selectively enable or disable trading during certain environments.
Technical Implementation Notes
All calculations execute on closed bars only (`calc_on_every_tick=false`) ensuring that signals and values do not repaint. Once a bar closes and a signal generates, that signal is permanent in the history.
The strategy uses fixed-quantity position sizing (`default_qty_type=strategy.fixed, default_qty_value=1`) with the actual contract quantity determined by the position sizing function and passed to the entry commands. This approach provides maximum control over risk allocation.
Order management uses Pine Script's native `strategy.entry()` and `strategy.exit()` functions with appropriate parameters for stops, limits, and trailing stops. All orders include explicit from_entry references to ensure they apply to the correct position.
The adaptive learning arrays (trade_returns, trade_directions, trade_types, trade_hours, trade_was_stopped) are maintained as circular buffers capped at PERFORMANCE_MEMORY size (default 100 trades). When a new trade closes, its data is added to the beginning of the array using `array.unshift()`, and the oldest trade is removed using `array.pop()` if capacity is exceeded. The stop-out tracking system analyzes the trade_was_stopped array to calculate the rolling percentage displayed in the dashboard.
Dashboard rendering occurs only on the confirmed bar (`barstate.isconfirmed`) to minimize computational overhead. The table is pre-created with sufficient rows for the selected dashboard size and cells are populated with current values each update.
Visual effects (fractal grid, wick pressure, morphism beams, order flow clouds, quantum field) recalculate on each bar for real-time chart updates. These are computationally intensive—if you experience chart lag, disable these visual components. The core strategy logic continues to function identically regardless of visual settings.
Timezone conversions use Pine Script's built-in timezone parameter on the `hour()`, `minute()`, and `dayofweek()` functions. This ensures session logic and daily/weekly resets occur at correct boundaries regardless of the chart's default timezone or the server's timezone.
The universal futures detection queries `syminfo.mintick` and `syminfo.pointvalue` on each strategy initialization to obtain the current instrument's specifications. These values remain constant throughout the strategy's execution on a given chart but automatically update when the strategy is applied to a different instrument.
The strategy has been tested on TradingView across timeframes from 5-minute through daily and across multiple futures instrument types including equity indices, energy, metals, agriculture, treasuries, and currencies. It functions identically on all instruments due to the percentage-based risk model and ATR-relative calculations which adapt automatically to price scale and volatility, combined with the universal futures detection system that handles contract-specific specifications.
Ultimate Oscillator (ULTOSC)The Ultimate Oscillator (ULTOSC) is a technical momentum indicator developed by Larry Williams that combines three different time periods to reduce the volatility and false signals common in single-period oscillators. By using a weighted average of three Stochastic-like calculations across short, medium, and long-term periods, the Ultimate Oscillator provides a more comprehensive view of market momentum while maintaining sensitivity to price changes.
The indicator addresses the common problem of oscillators being either too sensitive (generating many false signals) or too slow (missing opportunities). By incorporating multiple timeframes with decreasing weights for longer periods, ULTOSC attempts to capture both short-term momentum shifts and longer-term trend strength, making it particularly valuable for identifying divergences and potential reversal points.
## Core Concepts
* **Multi-timeframe analysis:** Combines three different periods (typically 7, 14, 28) to capture various momentum cycles
* **Weighted averaging:** Assigns higher weights to shorter periods for responsiveness while including longer periods for stability
* **Buying pressure focus:** Measures the relationship between closing price and the true range rather than just high-low range
* **Divergence detection:** Particularly effective at identifying momentum divergences that precede price reversals
* **Normalized scale:** Oscillates between 0 and 100, with clear overbought/oversold levels
## Common Settings and Parameters
| Parameter | Default | Function | When to Adjust |
|-----------|---------|----------|---------------|
| Fast Period | 7 | Short-term momentum calculation | Lower (5-6) for more sensitivity, higher (9-12) for smoother signals |
| Medium Period | 14 | Medium-term momentum calculation | Adjust based on typical swing duration in the market |
| Slow Period | 28 | Long-term momentum calculation | Higher values (35-42) for longer-term position trading |
| Fast Weight | 4.0 | Weight applied to fast period | Higher weight increases short-term sensitivity |
| Medium Weight | 2.0 | Weight applied to medium period | Adjust to balance medium-term influence |
| Slow Weight | 1.0 | Weight applied to slow period | Usually kept at 1.0 as the baseline weight |
**Pro Tip:** The classic 7/14/28 periods with 4/2/1 weights work well for most markets, but consider using 5/10/20 with adjusted weights for faster markets or 14/28/56 for longer-term analysis.
## Calculation and Mathematical Foundation
**Simplified explanation:**
The Ultimate Oscillator calculates three separate "buying pressure" ratios using different time periods, then combines them using weighted averaging. Buying pressure is defined as the close minus the true low, divided by the true range.
**Technical formula:**
```
BP = Close - Min(Low, Previous Close)
TR = Max(High, Previous Close) - Min(Low, Previous Close)
BP_Sum_Fast = Sum(BP, Fast Period)
TR_Sum_Fast = Sum(TR, Fast Period)
Raw_Fast = 100 × (BP_Sum_Fast / TR_Sum_Fast)
BP_Sum_Medium = Sum(BP, Medium Period)
TR_Sum_Medium = Sum(TR, Medium Period)
Raw_Medium = 100 × (BP_Sum_Medium / TR_Sum_Medium)
BP_Sum_Slow = Sum(BP, Slow Period)
TR_Sum_Slow = Sum(TR, Slow Period)
Raw_Slow = 100 × (BP_Sum_Slow / TR_Sum_Slow)
ULTOSC = 100 × / (Fast_Weight + Medium_Weight + Slow_Weight)
```
Where:
- BP = Buying Pressure
- TR = True Range
- Fast Period = 7, Medium Period = 14, Slow Period = 28 (defaults)
- Fast Weight = 4, Medium Weight = 2, Slow Weight = 1 (defaults)
> 🔍 **Technical Note:** The implementation uses efficient circular buffers for all three period calculations, maintaining O(1) time complexity per bar. The algorithm properly handles true range calculations including gaps and ensures accurate buying pressure measurements across all timeframes.
## Interpretation Details
ULTOSC provides several analytical perspectives:
* **Overbought/Oversold conditions:** Values above 70 suggest overbought conditions, below 30 suggest oversold conditions
* **Momentum direction:** Rising ULTOSC indicates increasing buying pressure, falling indicates increasing selling pressure
* **Divergence analysis:** Divergences between ULTOSC and price often precede significant reversals
* **Trend confirmation:** ULTOSC direction can confirm or question the prevailing price trend
* **Signal quality:** Extreme readings (>80 or <20) indicate strong momentum that may be unsustainable
* **Multiple timeframe consensus:** When all three underlying periods agree, signals are typically more reliable
## Trading Applications
**Primary Uses:**
- **Divergence trading:** Identify when momentum diverges from price for reversal signals
- **Overbought/oversold identification:** Find potential entry/exit points at extreme levels
- **Trend confirmation:** Validate breakouts and trend continuations
- **Momentum analysis:** Assess the strength of current price movements
**Advanced Strategies:**
- **Multi-divergence confirmation:** Look for divergences across multiple timeframes
- **Momentum breakouts:** Trade when ULTOSC breaks above/below key levels with volume
- **Swing trading entries:** Use oversold/overbought levels for swing position entries
- **Trend strength assessment:** Evaluate trend quality using momentum consistency
## Signal Combinations
**Strong Bullish Signals:**
- ULTOSC rises from oversold territory (<30) with positive price divergence
- ULTOSC breaks above 50 after forming a base near 30
- All three underlying periods show increasing buying pressure
**Strong Bearish Signals:**
- ULTOSC falls from overbought territory (>70) with negative price divergence
- ULTOSC breaks below 50 after forming a top near 70
- All three underlying periods show decreasing buying pressure
**Divergence Signals:**
- **Bullish divergence:** Price makes lower lows while ULTOSC makes higher lows
- **Bearish divergence:** Price makes higher highs while ULTOSC makes lower highs
- **Hidden bullish divergence:** Price makes higher lows while ULTOSC makes lower lows (trend continuation)
- **Hidden bearish divergence:** Price makes lower highs while ULTOSC makes higher highs (trend continuation)
## Comparison with Related Oscillators
| Indicator | Periods | Focus | Best Use Case |
|-----------|---------|-------|---------------|
| **Ultimate Oscillator** | 3 periods | Buying pressure | Divergence detection |
| **Stochastic** | 1-2 periods | Price position | Overbought/oversold |
| **RSI** | 1 period | Price momentum | Momentum analysis |
| **Williams %R** | 1 period | Price position | Short-term signals |
## Advanced Configurations
**Fast Trading Setup:**
- Fast: 5, Medium: 10, Slow: 20
- Weights: 4/2/1, Thresholds: 75/25
**Standard Setup:**
- Fast: 7, Medium: 14, Slow: 28
- Weights: 4/2/1, Thresholds: 70/30
**Conservative Setup:**
- Fast: 14, Medium: 28, Slow: 56
- Weights: 3/2/1, Thresholds: 65/35
**Divergence Focused:**
- Fast: 7, Medium: 14, Slow: 28
- Weights: 2/2/2, Thresholds: 70/30
## Market-Specific Adjustments
**Volatile Markets:**
- Use longer periods (10/20/40) to reduce noise
- Consider higher threshold levels (75/25)
- Focus on extreme readings for signal quality
**Trending Markets:**
- Emphasize divergence analysis over absolute levels
- Look for momentum confirmation rather than reversal signals
- Use hidden divergences for trend continuation
**Range-Bound Markets:**
- Standard overbought/oversold levels work well
- Trade reversals from extreme levels
- Combine with support/resistance analysis
## Limitations and Considerations
* **Lagging component:** Contains inherent lag due to multiple moving average calculations
* **Complex calculation:** More computationally intensive than single-period oscillators
* **Parameter sensitivity:** Performance varies significantly with different period/weight combinations
* **Market dependency:** Most effective in trending markets with clear momentum patterns
* **False divergences:** Not all divergences lead to significant price reversals
* **Whipsaw potential:** Can generate conflicting signals in choppy markets
## Best Practices
**Effective Usage:**
- Focus on divergences rather than absolute overbought/oversold levels
- Combine with trend analysis for context
- Use multiple timeframe analysis for confirmation
- Pay attention to the speed of momentum changes
**Common Mistakes:**
- Over-relying on overbought/oversold levels in strong trends
- Ignoring the underlying trend direction
- Using inappropriate period settings for the market being analyzed
- Trading every divergence without additional confirmation
**Signal Enhancement:**
- Combine with volume analysis for confirmation
- Use price action context (support/resistance levels)
- Consider market volatility when setting thresholds
- Look for convergence across multiple momentum indicators
## Historical Context and Development
The Ultimate Oscillator was developed by Larry Williams and introduced in his 1985 article "The Ultimate Oscillator" in Technical Analysis of Stocks and Commodities magazine. Williams designed it to address the limitations of single-period oscillators by:
- Reducing false signals through multi-timeframe analysis
- Maintaining sensitivity to short-term momentum changes
- Providing more reliable divergence signals
- Creating a more robust momentum measurement tool
The indicator has become a standard tool in technical analysis, particularly valued for its divergence detection capabilities and its balanced approach to momentum measurement.
## References
* Williams, L. R. (1985). The Ultimate Oscillator. Technical Analysis of Stocks and Commodities, 3(4).
* Williams, L. R. (1999). Long-Term Secrets to Short-Term Trading. Wiley Trading.
Buying/Selling PressureBuying/Selling Pressure - Volume-Based Market Sentiment
Buying/Selling Pressure identifies market dominance by separating volume into buying and selling components. The indicator uses Volume ATR normalization to create a universal pressure oscillator that works consistently across all markets and timeframes.
What is Buying/Selling Pressure?
This indicator answers a fundamental question: Are buyers or sellers in control? By analyzing how volume distributes within each bar, it calculates cumulative buying and selling pressure, then normalizes the result using Volume ATR for cross-market comparability.
Formula: × 100
Where Delta = Buying Volume - Selling Volume
Calculation Methods
Money Flow (Recommended):
Volume weighted by close position in bar range. Close near high = buying pressure, close near low = selling pressure.
Formula: / (high - low)
Simple Delta:
Basic approach where bullish bars = 100% buying, bearish bars = 100% selling.
Weighted Delta:
Volume weighted by body size relative to total range, focusing on candle strength.
Key Features
Volume ATR Normalization: Adapts to volume volatility for consistent readings across assets
Cumulative Delta: Tracks net buying/selling pressure over time (similar to OBV)
Signal Line: EMA smoothing for trend identification and crossover signals
Zero Line: Clear visual separation between buyer and seller dominance
Color-Coded Display: Green area = buyers control, red area = sellers control
Interpretation
Above Zero: Buyers dominating - cumulative buying pressure exceeds selling
Below Zero: Sellers dominating - cumulative selling pressure exceeds buying
Cross Signal Line: Momentum shift - pressure trend changing direction
Increasing Magnitude: Strengthening pressure in current direction
Decreasing Magnitude: Weakening pressure, potential reversal
Volume vs Pressure
High volume with low pressure indicates balanced battle between buyers and sellers. High pressure with high volume confirms strong directional conviction. This separation provides insights beyond traditional volume analysis.
Best Practices
Use with price action for confirmation
Divergences signal potential reversals (price makes new high/low but pressure doesn't)
Large volume with near-zero pressure = indecision, breakout preparation
Signal line crossovers provide momentum change signals
Extreme readings suggest potential exhaustion
Settings
Calculation Method: Choose Money Flow, Simple Delta, or Weighted Delta
EMA Length: Period for cumulative delta smoothing (default: 21)
Signal Line: Optional EMA of oscillator for crossover signals (default: 9)
Buying/Selling Pressure transforms volume analysis into actionable market sentiment, revealing whether buyers or sellers control price action beneath surface volatility.
This indicator is designed for educational and analytical purposes. Past performance does not guarantee future results. Always conduct thorough research and consider consulting with financial professionals before making investment decisions.
Wave Conflict DetectorWave Conflict Detector
Wave Conflict Detector: Identifying Pivot Conditions Through Wave Interference Analysis
Wave Conflict Detector applies wave interference principles from physics to dual-EMA analysis, identifying potential pivot conditions by measuring phase relationships and amplitude states between two moving average waves. Unlike traditional EMA crossover systems that signal on wave intersection, this indicator measures the directional alignment (phase) and interaction strength (interference amplitude) between wave states to identify conditions where wave mechanics suggest potential reversal zones.
The indicator combines two analytical components: velocity-based phase difference calculation that measures whether waves are moving in the same or opposite directions, and normalized interference amplitude that quantifies the degree of wave reinforcement or cancellation. This creates a regime-classification system with visual feedback showing when waves are aligned (constructive state) versus opposed (destructive state).
What Makes This Approach Different
Phase Relationship Measurement
The core analytical method is extracting phase alignment from wave velocities rather than simply measuring EMA separation. The system calculates the first derivative (bar-to-bar change) of each EMA, creating velocity measurements: v₁ = ψ₁ - ψ₁ and v₂ = ψ₂ - ψ₂ . These velocities are combined through normalized correlation: Φ = (v₁ × v₂) / |v|², producing an alignment value ranging from -1 (perfect opposition) to +1 (perfect alignment).
This alignment value is smoothed using EMA and converted to angular degrees: Δφ = (1 - Φ) × 90°, creating a phase difference measurement from 0° to 180°. This quantifies how much the waves are "fighting" each other directionally, independent of their separation distance. Two EMAs can be far apart yet moving in harmony (low phase difference), or close together yet moving in opposition (high phase difference).
This directional correlation approach differs from standard dual-EMA analysis by focusing on velocity alignment rather than positional crossovers.
Interference Amplitude Calculation
The interference formula implements wave superposition principles: I = (|ψ₁ + ψ₂|² - |ψ₁ - ψ₂|²) × Gain, which mathematically simplifies to I = 4 × ψ₁ × ψ₂ × Gain. This measures the product of both waves—when both are positive and large, interference is maximally constructive; when they have opposite signs or differing magnitudes, interference weakens.
The raw interference value is then normalized using adaptive statistical bounds calculated over a rolling window (default 100 bars). The system computes mean (μ) and standard deviation (σ) of raw interference, then applies bounds of μ ± 2σ, and normalizes to a 0-1 range. This creates a scale-invariant measurement that adapts automatically to different instruments and volatility regimes without requiring manual recalibration.
The combination of phase measurement and normalized amplitude creates a two-dimensional state space for classifying market conditions.
Dual-Mode Detection Architecture
The system offers two detection approaches that can be selected based on market conditions:
Interference Mode: Detects pivot conditions when normalized interference amplitude forms local peaks or troughs (current bar is higher/lower than both adjacent bars) AND exceeds the configured threshold. This identifies extremes in wave interaction strength.
Phase Mode: Detects pivot conditions when phase alignment reverses (crosses from positive to negative or vice versa) AND absolute phase difference exceeds the threshold. This identifies directional relationship changes between waves.
Both modes require price structure confirmation (traditional pivot high/low patterns) and minimum bar spacing to prevent over-signaling. This architecture allows traders to match detection sensitivity to market character—interference mode for amplitude-driven markets, phase mode for directional trend shifts.
Multi-Layer Visual System
The visualization approach uses hierarchical layers to display wave state information:
Foundation Layer: The two EMA waves (ψ₁ and ψ₂) plotted directly on the price chart, showing the underlying wave states being analyzed.
Background Layer: Color-coded zones showing regime state—green tint when phase alignment is positive (constructive interference), red tint when phase alignment is negative below -0.3 (destructive interference).
Dynamic Ribbon: A band centered on the wave average with width proportional to |ψ₁ - ψ₂| × (0.5 + interference_norm). This creates an adaptive channel that expands with interference strength and contracts during low-energy states.
Phase Field: Multi-frequency harmonic oscillations generated using three phase accumulators driven by interference amplitude, phase alignment, and accumulated phase rotation. Multiple sine-wave layers create visual texture that becomes erratic during wave conflict conditions and smooth during aligned states.
Particle System: Floating symbols whose density is proportional to interference amplitude, creating a visual intensity indicator.
Each visual component displays non-redundant information about the wave state system.
Core Calculation Methodology
Wave State Generation
Two exponential moving averages are calculated using configurable lengths (default 8 and 21 bars):
- ψ₁ = EMA(close, fastLen) — fast wave component
- ψ₂ = EMA(close, slowLen) — slow wave component
These serve as the base wave functions for all subsequent analysis.
Velocity Extraction
First derivatives are computed as simple bar-to-bar differences:
- psi1_velocity = ψ₁ - ψ₁
- psi2_velocity = ψ₂ - ψ₂
These represent the "motion" of each wave through price-time space.
Phase Alignment Calculation
The velocity product and magnitude are calculated:
- velocity_product = v₁ × v₂
- velocity_magnitude = √(v₁² + v₂²)
Phase alignment is computed as:
- phase_alignment = velocity_product / (velocity_magnitude²)
This is smoothed using EMA of configurable length (default 5) and converted to degrees:
- phase_degrees = (1 - phase_alignment_smooth) × 90
Interference Amplitude Processing
Raw interference is calculated:
- interference_raw = (constructive_amplitude - destructive_amplitude) × gain
- where constructive_amplitude = (ψ₁ + ψ₂)²
- and destructive_amplitude = (ψ₁ - ψ₂)²
Statistical normalization is applied:
- interference_mean = SMA(interference_raw, normalizationLen)
- interference_std = StdDev(interference_raw, normalizationLen)
- upper_bound = mean + 2 × std
- lower_bound = mean - 2 × std
- interference_norm = (interference_raw - lower_bound) / (upper_bound - lower_bound), clamped to
State Classification
Three regime states are identified:
- Constructive: phase_alignment_smooth > 0 (waves moving in same direction)
- Destructive: phase_alignment_smooth < -0.3 (waves moving in opposite directions)
- Neutral: phase_alignment between -0.3 and 0 (weak directional correlation)
Pivot Detection Logic
In Interference Mode:
- High pivots: interference_norm > interference_norm AND interference_norm > interference_norm AND interference_norm > threshold AND price forms pivot high AND spacing requirement met
- Low pivots: interference_norm shows local trough using opposite conditions
In Phase Mode:
- Pivots: phase alignment reverses sign AND absolute phase_degrees > threshold AND price forms pivot high/low AND spacing requirement met
All conditions must be true for a signal to generate.
Dashboard Metrics System
The dashboard displays real-time calculations:
- I (Interference): Normalized amplitude shown as bar gauge and percentage
- Δφ (Phase): Phase difference shown as bar gauge and degrees
- ψ₁ and ψ₂: Current wave values in price units
- Wave Separation: |ψ₁ - ψ₂| with directional indicator
- STATE: Current regime classification (CONSTRUCTIVE/DESTRUCTIVE/NEUTRAL)
- PIVOT Probability: Composite score calculated as interference_norm × (phase_degrees/180) × 100
The interference matrix shows historical heatmap data across four metrics (interference amplitude, phase difference, constructive flags, destructive flags) over the configurable number of bars.
How to Use This Indicator
Initial Configuration
Apply the indicator to your chart with default settings. The fast wave length (default 8) should be adjusted to match short-term price swings for your instrument and timeframe. The slow wave length (default 21) should be 2-4 times the fast length to create adequate wave separation. Enable the dashboard (recommended position: top right) to monitor regime state and metrics in real-time.
Signal Interpretation
High Pivot Marker (▼ Red Triangle): Appears above price bars when a bearish pivot condition is detected. This indicates that price formed a swing high, the selected detection criteria were met (interference peak or phase reversal depending on mode), threshold requirements were satisfied, and the minimum spacing filter passed. This represents a potential reversal zone where wave mechanics suggest downward directional change conditions.
Low Pivot Marker (▲ Green Triangle): Appears below price bars when a bullish pivot condition is detected. This indicates that price formed a swing low and all detection criteria aligned. This represents a potential reversal zone where wave mechanics suggest upward directional change conditions.
Dashboard STATE Reading
The STATE field shows current wave relationship:
- "🟢 CONSTRUCTIVE": Waves are moving in the same direction (phase alignment positive). This suggests trend continuation conditions where waves are reinforcing each other.
- "🔴 DESTRUCTIVE": Waves are moving in opposite directions (phase alignment below -0.3). This suggests reversal-prone conditions where waves are conflicting.
- "🟡 NEUTRAL": Weak directional correlation between waves. This suggests ranging or transitional conditions.
Use STATE for regime awareness rather than specific entry signals.
Interference and Phase Metrics
Monitor the I (Interference) percentage:
- Above 70%: High amplitude state, significant wave interaction
- 40-70%: Moderate amplitude state
- Below 40%: Low amplitude state, weak interaction
Monitor the Δφ (Phase) degrees:
- Above 120°: Significant wave opposition (destructive conditions)
- 60-120°: Transitional phase relationship
- Below 60°: Wave alignment (constructive conditions)
The PIVOT probability metric combines both: high values (>70%) indicate conditions where both amplitude and phase suggest elevated pivot formation potential.
Trading Workflow Example
Step 1 - Regime Check: Observe dashboard STATE to understand current wave relationship. CONSTRUCTIVE states favor trend-following approaches, DESTRUCTIVE states suggest reversal-prone conditions.
Step 2 - Metric Monitoring: Watch I% and Δφ values. Rising interference with high phase difference indicates building wave conflict.
Step 3 - Visual Confirmation: Observe amplitude ribbon width (expanding = active state) and phase field texture (chaotic = conflict conditions, smooth = aligned conditions).
Step 4 - Signal Wait: Wait for confirmed pivot marker (▼ or ▲) rather than anticipating based on metrics alone. The marker indicates all detection criteria have aligned.
Step 5 - Entry Decision: Use pivot markers as potential reversal zones. Combine with other analysis methods such as support/resistance levels, volume confirmation, and higher timeframe bias for entry decisions.
Step 6 - Risk Management: Place stops beyond recent swing structure or ribbon edges. Monitor dashboard STATE—if it flips to CONSTRUCTIVE in trade direction, the reversal may be confirmed; if PIVOT% drops significantly, conditions may be weakening.
Step 7 - Exit Criteria: Consider exits when opposite pivot marker appears, STATE changes unfavorably, or standard technical targets are reached.
Parameter Optimization Guidelines
Fast Wave Length: Adjust to match short-term swing frequency. Shorter values (5-8) for active trading on lower timeframes, longer values (13-20) for swing trading on higher timeframes.
Slow Wave Length: Should maintain 2-4x ratio with fast length. Shorter values create more interference cycles, longer values create more stable baseline.
Phase Detection Length: Smoothing for phase alignment. Lower values (3-5) for responsive detection, higher values (8-12) for stable readings with less sensitivity.
Interference Gain: Amplification multiplier. Lower values (0.5-1.0) for conservative detection, higher values (1.5-2.5) for more sensitive detection.
Normalization Period: Rolling window for statistical bounds. Shorter periods (50-100) adapt quickly to volatility changes, longer periods (150-300) provide more stable normalization.
Interference Threshold: Minimum amplitude to trigger signals. Lower values (0.50-0.60) generate more signals, higher values (0.70-0.85) are more selective.
Phase Threshold: Minimum phase difference in degrees. Lower values (90-110) are more permissive, higher values (140-170) require stronger opposition.
Min Pivot Spacing: Bars between signals. Match to average swing duration on your timeframe—tighter spacing (3-8 bars) for scalping, wider spacing (15-30 bars) for swing trading.
Best Performance Conditions
This approach works better in markets with:
- Clear swing structure where EMA-based wave analysis is meaningful
- Sufficient volatility for wave separation to develop
- Periodic oscillation between trending and ranging states
- Liquid instruments where EMAs reflect true price flow
This approach may be less effective in:
- Extremely choppy conditions with no directional persistence
- Very low volatility environments where wave separation is minimal
- Gap-heavy instruments where price discontinuities disrupt wave continuity
- Parabolic moves where waves cannot keep pace with price velocity
The system adapts by reducing signal frequency in poor conditions—when interference stays below threshold or phase alignment remains neutral, pivot markers will not appear.
Visual Performance Optimization
The phase field and particle systems are computationally intensive. If experiencing chart lag:
- Reduce Phase Field Layers from 5 to 2-3 (significant performance improvement)
- Lower Particle Density from 3 to 1 (reduces label creation overhead)
- Disable Phase Field entirely (removes most intensive calculations)
- Decrease Matrix History Bars to 15-20 (reduces table computation load)
The core wave analysis and pivot detection continue to function with all visual elements disabled.
Important Disclaimers
This indicator is an analytical tool that measures phase relationships and interference amplitude between two exponential moving averages. It identifies conditions where these wave mechanics suggest potential pivot zones based on historical price data analysis. It should not be used as a standalone trading system.
The phase and interference calculations are deterministic mathematical formulas applied to EMA values. These measurements describe current and historical wave relationships but do not predict future price movements. Past wave patterns and pivot markers do not guarantee future market behavior will follow similar patterns.
All trading involves risk. The pivot markers represent analytical conditions where wave mechanics align with specific thresholds, not certainty of directional change. Use appropriate risk management, position sizing, and combine with additional confirmation methods such as support/resistance analysis, volume patterns, and multi-timeframe alignment. No indicator can eliminate false signals or guarantee profitable trades.
The spacing filter and threshold requirements are designed to reduce noise and over-signaling, but market conditions can change rapidly and render any analytical signal invalid. Always use stop losses and never risk capital you cannot afford to lose.
Technical Implementation Notes
All calculations execute on closed bars only—there is no repainting of signals or values. The normalization system requires approximately 100 bars of historical data to establish stable statistical bounds; values in the first 50-100 bars may be unstable as the rolling statistics converge.
Phase field arrays are fixed-size based on the complexity setting. Particle labels are capped at 80 total to prevent excessive memory usage. Dashboard and matrix tables update only on the last bar to minimize computational overhead. Particle generation is throttled to every 2 bars for performance. Phase accumulators use modulo arithmetic (% 2π) to prevent numerical overflow during extended operation.
The indicator has been tested across multiple timeframes (5-minute through daily) and multiple asset classes (forex, stocks, crypto, indices). It functions identically across all instruments due to the adaptive normalization approach.
