Bitcoin Multibook v1.0 [Apollo Algo]Bitcoin Multibook v1.0 by Apollo Algo is an advanced market depth and order flow visualization tool that brings professional-grade multi-exchange order book analysis to TradingView. Inspired by Bookmap's multibook functionality and built upon LucF's original single "Tape" indicator concept, this tool aggregates real-time trading data from multiple Bitcoin exchanges into a unified tape display.
Credits & Attribution
This indicator is an evolution of the original "Tape" indicator created by LucF (TradingView: @LucF). The multibook enhancement and Bitcoin-specific optimizations were developed by Apollo Algo to provide traders with institutional-grade market microstructure visibility across major Bitcoin trading venues.
Purpose & Philosophy
Bitcoin leads the entire cryptocurrency market. By monitoring order flow across the primary Bitcoin exchanges simultaneously, traders gain crucial insights into:
Cross-exchange arbitrage opportunities
Institutional order flow patterns
Market maker positioning
True market sentiment beyond single-exchange data
Key Features
📊 Multi-Exchange Data Aggregation
Real-time tape from 3 major exchanges:
Binance (BTCUSDT)
Coinbase (BTCUSD)
Kraken (BTCUSD)
Customizable source inputs for any trading pair
Synchronized price and volume tracking
Exchange name identification in tape display
📈 Advanced Tape Display
Dynamic tape visualization with configurable line quantity (0-50 lines)
Directional flow indicators (＋/－ symbols for price changes)
Exchange identification for each trade
Volume precision control (0-16 decimal places)
Flexible positioning (9 screen positions available)
Real-time only operation for accurate order flow
🎯 Volume Delta Analysis
Real-time cumulative volume delta calculation
Divergence detection (price vs. volume direction)
Colored visual feedback for market sentiment
Total session delta displayed in footer
Cross-exchange delta aggregation
🚨 Smart Alert System
Marker 1: Volume Delta Bumps (⬆⬇)
Triggers on consecutive volume delta increases
Identifies momentum acceleration points
Filters out divergent movements
Marker 2: Volume Delta Thresholds (⇑⇓)
Fires when delta exceeds user-defined thresholds
Catches significant order imbalances
Excludes divergence conditions
Marker 3: Large Volume Detection (⤊⤋)
Highlights unusually large individual trades
Spots potential institutional activity
Direction-specific triggers
Configure Data Sources
Adjust exchange pairs if needed (e.g., for altcoin analysis)
Leave blank to disable specific exchanges
Use format: EXCHANGE:SYMBOL
Customize Display
Set tape line quantity based on screen size
Position the table for optimal visibility
Choose color scheme (text or background)
Adjust text size for readability
Configure Alerts
Enable desired markers (1, 2, or 3)
Set volume thresholds appropriate for your timeframe
Choose direction (Longs, Shorts, or Both)
Create TradingView alerts on marker signals
Trading Applications
Scalping (1-5 min)
Monitor tape speed for momentum shifts
Watch for cross-exchange divergences
Track large volume clusters
Use Marker 1 for quick momentum trades
Day Trading (5-60 min)
Identify accumulation/distribution phases
Spot institutional positioning
Confirm breakout validity with volume delta
Use Marker 2 for significant imbalances
Swing Trading (1H+)
Analyze volume delta trends
Detect smart money rotation
Time entries with order flow confirmation
Use Marker 3 for institutional footprints
Advanced Techniques
Cross-Exchange Arbitrage Detection
When price disparities appear between exchanges:
Immediate Opportunity: Price differences > 0.1%
Bot Activity: Rapid convergence patterns
Liquidity Vacuum: One exchange leading others
Divergence Trading Strategies
Volume delta diverging from price direction:
Absorption: Strong hands entering (price down, delta up)
Distribution: Smart money exiting (price up, delta down)
Reversal Setup: Sustained divergence over multiple bars
Institutional Footprint Recognition
Large volume characteristics:
Simultaneous Spikes: Same timestamp across exchanges
TWAP Patterns: Consistent volume over time
Iceberg Orders: Repeated same-size trades
Pine Script v6 Enhancements
Type Safety Improvements
Strict boolean type handling
Explicit type declarations
Enhanced error checking
Performance Optimizations
Improved request.security() function
Better memory management with arrays
Optimized table rendering
Modern Syntax Updates
indicator() instead of study()
Namespaced math functions (math.round())
Typed input functions (input.int(), input.float())
Performance Considerations
System Requirements
Real-time Data: Essential for tape operation
Multiple Security Calls: May impact performance
Array Operations: Memory intensive with high line counts
Table Rendering: CPU usage increases with tape size
Optimization Tips
Reduce tape lines for better performance
Increase volume filter to reduce noise
Disable unused markers
Use text-only coloring for faster rendering
"bot"に関するスクリプトを検索
Indicator ***TuYa*** V8.2 – HH/HL MTF + Peak Mid ZoneIndicator TuYa V8.0 – HH/HL MTF + Peak Mid Zone
TuYa V8.0 combines multi-timeframe market structure with a Peak Reaction midline to create clean, rule-based reversal and trend entries – designed primarily for 1-minute execution with 1-hour bias.
🧠 Core Concept
This indicator fuses three ideas:
HTF Peak Reaction Midline (1H)
Uses a Peak Reaction style logic on the higher timeframe (HTF, default: 1H).
Identifies a reaction high and reaction low, then calculates their midpoint → the Peak Mid Zone.
This midline acts as a dynamic sentiment divider (above = premium / below = discount).
Multi-Timeframe HH/HL/LH/LL Structure
HTF structure (1H): detects HH, HL, LH, LL using pivot highs/lows.
LTF structure (1m): detects HH, HL, LH, LL on the execution timeframe (chart TF, intended for 1m).
HTF → LTF Confirmation Window
After a 1H structure event (HH, HL, LL, LH), the indicator opens a confirmation window of up to N LTF candles (default: 10 x 1m bars).
Within that window, the required 1m structure event must occur to confirm an entry.
🎯 Signal Logic
All entries are generated on the LTF (e.g. 1m chart), using HTF (e.g. 1H) bias + Peak Mid Zone:
1️⃣ Price ABOVE Peak Mid (Bullish premium zone)
Reversal SELL
HTF: HH (Higher High)
Within N 1m bars: LTF HH
→ SELL signal (fading HTF strength near premium)
Trend/Bullish BUY
HTF: HL (Higher Low)
Within N 1m bars: LTF LL
→ BUY signal (buying dips in an uptrend above midline)
2️⃣ Price BELOW Peak Mid (Bearish discount zone)
Reversal BUY
HTF: LL (Lower Low)
Within N 1m bars: LTF LL
→ BUY signal (catching potential reversal from discount)
Trend/Bearish SELL
HTF: LH (Lower High)
Within N 1m bars: LTF HH
→ SELL signal (shorting strength in a downtrend below midline)
Signals are plotted as small BUY/SELL triangles on the chart and exposed via alert conditions.
🧾 Filters & Options
⏳ HTF → LTF Delay Window
Input: “Max 1m bars after HTF trigger” (default: 10)
After a 1H HH/HL/LL/LH event, the indicator waits up to N LTF candles for the matching 1m structure pattern.
If no match occurs within the window, no signal is generated.
📉 RSI No-Trade Zone (HTF)
Toggle: Use RSI no-trade zone
Inputs:
RSI Length (HTF)
No-trade lower bound (default 45)
No-trade upper bound (default 65)
If HTF RSI is inside the defined band (e.g. 45–65), signals are blocked (no-trade regime), helping to avoid noisy mid-range conditions.
You can turn this filter ON/OFF and adjust the band dynamically.
🧱 5m OB / Direction Filter (Optional)
Toggle: Use 5m OB direction filter
Timeframe: Configurable (default: 5m).
Uses a simple directional proxy on the OB timeframe:
For BUY signals → require a bullish candle on OB timeframe.
For SELL signals → require a bearish candle on OB timeframe.
When enabled, this adds an extra layer of confluence by aligning entries with the short-term directional context.
⚙️ Key Inputs (Summary)
Timeframes
HTF (Peak Reaction & Structure): default 60 (1H)
Peak Reaction
Lookback bars (HTF)
ATR multiplier for zones
Show/Hide Peak Mid line
Structure
Pivot left/right bars (for HH/HL/LH/LL swings)
Toggle structure labels (HTF & LTF)
Confirmation
Max LTF bars after HTF trigger (default 10, fully configurable)
RSI Filter
Use filter (on/off)
RSI length
No-trade range (low/high)
5m OB Filter
Use filter (on/off)
OB timeframe (default 5m)
📡 Alerts & Automation
The script includes alertconditions for both BUY and SELL signals, with JSON-formatted alert messages suitable for routing to external bridges (e.g. bots, MT5/MT4, n8n, etc.).
Each alert includes:
Symbol
Side (BUY / SELL)
Price / Entry
SL & TP placeholders (from hidden plots, ready to be wired to your own logic)
Time
Performance tag
CommentCode (for strategy/type tagging on the receiver side)
You can attach these alerts to a webhook and let your execution engine handle SL/TP and order management.
📌 How to Use
Attach the indicator to a 1-minute chart.
Set HTF timeframe to 60 (or your preferred higher timeframe).
Optionally enable:
RSI regime filter
5m OB direction filter
Watch for:
Price relative to the Peak Mid line
BUY/SELL triangles that respect HTF structure + LTF confirmation + filters.
For automation, create alerts using the built-in conditions and your preferred JSON alert template.
⚠️ Disclaimer
This tool is for educational and informational purposes only.
It is not financial advice and does not guarantee profits. Always test thoroughly in replay / paper trading before using with live funds, and trade at your own risk.
specific breakout FiFTOStrategy Description: 10:14 Breakout Only
Overview This is a time-based intraday trading strategy designed to capture momentum bursts that occur specifically after the 10:14 AM candle closes. It operates on the logic that if price breaks the high of this specific candle within a short window, a trend continuation is likely.
Core Logic & Rules
The Setup Candle (10:14 AM)
The strategy waits specifically for the minute candle at 10:14 to complete.
Once this candle closes, the strategy records its High price.
Defining the Entry Level
It calculates a trigger price by taking the 10:14 High and adding a user-defined Buffer (e.g., +1 point).
Formula: Entry Level = 10:14 High + Buffer
The "Active Window" (Expiry)
The trade setup does not remain open all day. It has a strict time limit.
By default, the setup is valid from 10:15 to 10:20.
If the price does not break the Entry Level by the expiry time (default 10:20), the setup is cancelled and no trade is taken for the day.
Entry Trigger
If a candle closes above the Entry Level while the window is open, a Long (Buy) position is opened immediately.
Exits (Risk Management)
Stop Loss: A fixed number of points below the entry price.
Target: A fixed number of points above the entry price.
Visual & Automation Features
Visual Boxes: Upon entry, the strategy draws a "Long Position" style visual on the chart. A green box highlights the profit zone, and a red box highlights the loss zone. These boxes extend automatically until the trade closes.
JSON Alerts: The strategy is pre-configured to send data-rich alerts for automation (e.g., Telegram bots).
Entry Alert: Includes Symbol, Entry Price, SL, and TP.
Exit Alerts: Specific messages for "Target Hit" or "SL Hit".
Summary of User Inputs
Entry Buffer: Extra points added to the high to filter false breaks.
Fixed Stop Loss: Risk per trade in points.
Fixed Target: Reward per trade in points.
Expiry Minute: The minute (10:xx) at which the setup becomes invalid if not triggered.
🟡 GOLD 4H HUD v8.9 — Loose ICT OB + Strong/Weak + FVG/HVN/LVNGOLD 4H HUD v8.9 is a clean, structured Smart Money Concepts (SMC)–based analysis tool designed exclusively for XAUUSD on the 4-hour timeframe.
It focuses on the three most important elements for institutional orderflow analysis:
✔ Loose ICT Order Blocks (Demand/Supply)
✔ Fair Value Gaps (FVG)
✔ Volume Profile Zones (HVN/LVN/POC)
The script builds a professional-style HUD that displays the key institutional regions and structural levels that matter most for gold traders.
📌 Key Features
1 — Market Structure Engine (HH/HL & BOS)
The indicator detects:
Minor swing Highs and Lows
Last confirmed HH / HL levels
Break of Structure (BOS) for directional bias
EMA-200 trend filter (UP / DOWN / NEUTRAL)
This gives traders a clean structural read without clutter or noise.
2 — Loose FVG Engine (Tolerance-Based ICT Gaps)
A soft-threshold FVG engine detects “loose” Fair Value Gaps using a 0.1% price tolerance.
This method ensures:
Fewer missed imbalances
Cleaner OB/FVG alignment
Higher accuracy on 4H gold displacement legs
FVGs automatically shift to the right side of the chart for clean visualization.
3 — Order Block Engine (Demand/Supply + Strong/Weak Classification)
A simplified ICT-style OB engine scans the past few candles whenever BOS is detected.
It identifies:
Demand OB during bullish BOS
Supply OB during bearish BOS
Strong OB if fully nested inside an active FVG
Weak OB otherwise
OB boxes include:
Clear color coding (strong vs. weak)
Price range labels inside each box
Automatic right-shift for visual clarity
4 — Volume Profile Engine (POC / HVN / LVN / VAH / VAL)
Based on a rolling window (default 120 bars), the script builds a lightweight volume distribution.
It displays:
POC (Point of Control)
HVN (High Volume Node)
LVN (Low Volume Node)
Value Area High / Low
HVN/LVN zones are shown as right-shifted colored boxes with price labels.
These zones help identify:
Institutional accumulation
Low-liquidity rejection points
Areas where price tends to react strongly
5 — Support / Resistance Mapping
The script automatically generates:
OB-based support/resistance
Swing-high/swing-low levels
HVN/LVN structural levels
These are displayed in the HUD for fast reference.
6 — Professional HUD Panel
A compact, easy-to-read HUD summarizes:
Trend direction
Latest HH/HL
OB ranges (Strong/Weak)
HVN/LVN price zones
POC
Multi-layer support & resistance
This turns the script into a fully functional analysis dashboard.
📌 What This Indicator Is NOT
To avoid misunderstanding:
It does not take entries or generate buy/sell signals
It does not auto-detect CHOCH, MSS, SMT, or sweeps
It is not a trading bot
This tool is designed as an institutional-style map and analysis HUD, not a strategy.
📌 Best Use Case
This indicator is ideal for traders who want to:
Read institutional structure on XAUUSD
Identify clean Demand/Supply zones
Visualize FVG/OB/HVN interactions
Track high-value liquidity levels
Build directional bias on 4H before dropping to execution timeframes
⚠ Important Note
This tool is designed exclusively for the 4H timeframe.
Using it on lower timeframes will display a warning.
Obsidian Flux Matrix# Obsidian Flux Matrix | JackOfAllTrades
Made with my Senior Level AI Pine Script v6 coding bot for the community!
Narrative Overview
Obsidian Flux Matrix (OFM) is an open-source Pine Script v6 study that fuses social sentiment, higher timeframe trend bias, fair-value-gap detection, liquidity raids, VWAP gravitation, session profiling, and a diagnostic HUD. The layout keeps the obsidian palette so critical overlays stay readable without overwhelming a price chart.
Purpose & Scope
OFM focuses on actionable structure rather than marketing claims. It documents every driver that powers its confluence engine so reviewers understand what triggers each visual.
Core Analytical Pillars
1. Social Pulse Engine
Sentiment Webhook Feed: Accepts normalized scores (-1 to +1). Signals only arm when the EMA-smoothed value exceeds the `sentimentMin` input (0.35 by default).
Volume Confirmation: Requires local volume > 30-bar average × `volSpikeMult` (default 2.0) before sentiment flags.
EMA Cross Validation: Fast EMA 8 crossing above/below slow EMA 21 keeps momentum aligned with flow.
Momentum Alignment: Multi-timeframe momentum composite must agree (positive for longs, negative for shorts).
2. Peer Momentum Heatmap
Multi-Timeframe Blend: RSI + Stoch RSI fetched via request.security() on 1H/4H/1D by default.
Composite Scoring: Each timeframe votes +1/-1/0; totals are clamped between -3 and +3.
Intraday Readability: Configurable band thickness (1-5) so scalpers see context without losing space.
Dynamic Opacity: Stronger agreement boosts column opacity for quick bias checks.
3. Trend & Displacement Framework
Dual EMA Ribbon: Cyan/magenta ribbon highlights immediate posture.
HTF Bias: A higher-timeframe EMA (default 55 on 4H) sets macro direction.
Displacement Score: Body-to-ATR ratio (>1.4 default) detects impulses that seed FVGs or VWAP raids.
ATR Normalization: All thresholds float with volatility so the study adapts to assets and regimes.
4. Intelligent Fair Value Gap (FVG) System
Gap Detection: Three-candle logic (bullish: low > high ; bearish: high < low ) with ATR-sized minimums (0.15 × ATR default).
Overlap Prevention: Price-range checks stop redundant boxes.
Spacing Control: `fvgMinSpacing` (default 5) avoids stacking from the same impulse.
Storage Caps: Max three FVGs per side unless the user widens the limit.
Session Awareness: Kill zone filters keep taps focused on London/NY if desired.
Auto Cleanup: Boxes delete when price closes beyond their invalidation level.
5. VWAP Magnet + Liquidity Raid Engine
Session or Rolling VWAP: Toggle resets to match intraday or rolling preferences.
Equal High/Low Scanner: Looks back 20 bars by default for liquidity pools.
Displacement Filter: ATR multiplier ensures raids represent genuine liquidity sweeps.
Mean Reversion Focus: Signals fire when price displaces back toward VWAP following a raid.
6. Session Range Breakout System
Initial Balance Tracking: First N bars (15 default) define the session box.
Breakout Logic: Requires simultaneous liquidity spikes, nearby FVG activity, and supportive momentum.
Z-Score Volume Filter: >1.5σ by default to filter noisy moves.
7. Lifestyle Liquidity Scanner
Volume Z-Scores: 50-bar baseline highlights statistically significant spikes.
Smart Money Footprints: Bottom-of-chart squares color-code buy vs sell participation.
Panel Memory: HUD logs the last five raid timestamps, direction, and normalized size.
8. Risk Matrix & Diagnostic HUD
HUD Structure: Table in the top-right summarizes HTF bias, sentiment, momentum, range state, liquidity memory, and current risk references.
Signal Tags: Aggregates SPS, FVG, VWAP, Range, and Liquidity states into a compact string.
Risk Metrics: Swing-based stops (5-bar lookback) + ATR targets (1.5× default) keep risk transparent.
Signal Families & Alerts
Social Pulse (SPS): Volume-confirmed sentiment alignment; triangle markers with “SPS”.
Kill-Zone FVG: Session + HTF alignment + FVG tap; arrow markers plus SL/TP labels.
Local FVG: Captures local reversals when HTF bias has not flipped yet.
VWAP Raid: Equal-high/low raids that snap toward VWAP; “VWAP” label markers.
Range Breakout: Initial balance violations with liquidity and imbalance confirmation; circle markers.
Liquidity Spike: Z-score spikes ≥ threshold; square markers along the baseline.
Visual Design & Customization
Theme Palette: Primary background RGB (12,6,24). Accent shading RGB (26,10,48). Long accents RGB (88,174,255). Short accents RGB (219,109,255).
Stylized Candles: Optional overlay using theme colors.
Signal Toggles: Independently enable markers, heatmap, and diagnostics.
Label Spacing: Auto-spacing enforces ≥4-bar gaps to prevent text overlap.
Customization & Workflow Notes
Adjust ATR/FVG thresholds when volatility shifts.
Re-anchor sentiment to your webhook cadence; EMA smoothing (default 5) dampens noise.
Reposition the HUD by editing the `table.new` coordinates.
Use multiples of the chart timeframe for HTF requests to minimize load.
Session inputs accept exchange-local time; align them to your market.
Performance & Compliance
Pure Pine v6: Single-line statements, no `lookahead_on`.
Resource Safe: Arrays trimmed, boxes limited, `request.security` cached.
Repaint Awareness: Signals confirm on close; alerts mirror on-chart logic.
Runtime Safety: Arrays/loops guard against `na`.
Use Cases
Measure when social sentiment aligns with structure.
Plan ICT-style intraday rebalances around session-specific FVG taps.
Fade VWAP raids when displacement shows exhaustion.
Watch initial balance breaks backed by statistical volume.
Keep risk/target references anchored in ATR logic.
Signal Logic Snapshot
Social Pulse Long/Short: `sentimentEMA` gated by `sentimentMin`, `volSpike`, EMA 8/21 cross, and `momoComposite` sign agreement. Keeps hype tied to structural follow-through.
Kill-Zone FVG Long/Short: Requires session filter, HTF EMA bias alignment, and an active FVG tap (`bullFvgTap` / `bearFvgTap`). Labels include swing stops + ATR targets pulled from `swingLookback` and `liqTargetMultiple`.
Local FVG Long/Short: Uses `localBullish` / `localBearish` heuristics (EMA slope, displacement, sequential closes) to surface intraday reversals even when HTF bias has not flipped.
VWAP Raids: Detect equal-high/equal-low sweeps (`raidHigh`, `raidLow`) that revert toward `sessionVwap` or rolling VWAP when displacement exceeds `vwapAlertDisplace`.
Range Breakouts: Combine `rangeComplete`, breakout confirmation, liquidity spikes, and nearby FVG activity for statistically backed initial balance breaks.
Liquidity Spikes: Volume Z-score > `zScoreThreshold` logs direction, size, and timestamp for the HUD and optional review workflows.
Session Logic & VWAP Handling
Kill zone + NY session inputs use TradingView’s session strings; `f_inSession()` drives both visual shading and whether FVG taps are tradeable when `killZoneOnly` is true.
Session VWAP resets using cumulative price × volume sums that restart when the daily timestamp changes; rolling VWAP falls back to `ta.vwap(hlc3)` for instruments where daily resets are less relevant.
Initial balance box (`rangeBars` input) locks once complete, extends forward, and stays on chart to contextualize later liquidity raids or breakouts.
Parameter Reference
Trend: `emaFastLen`, `emaSlowLen`, `htfResolution`, `htfEmaLen`, `showEmaRibbon`, `showHtfBiasLine`.
Momentum: `tf1`, `tf2`, `tf3`, `rsiLen`, `stochLen`, `stochSmooth`, `heatmapHeight`.
Volume/Liquidity: `volLookback`, `volSpikeMult`, `zScoreLen`, `zScoreThreshold`, `equalLookback`.
VWAP & Sessions: `vwapMode`, `showVwapLine`, `vwapAlertDisplace`, `killSession`, `nySession`, `showSessionShade`, `rangeBars`.
FVG/Risk: `fvgMinTicks`, `fvgLookback`, `fvgMinSpacing`, `killZoneOnly`, `liqTargetMultiple`, `swingLookback`.
Visualization Toggles: `showSignalMarkers`, `showHeatmapBand`, `showInfoPanel`, `showStylizedCandles`.
Workflow Recipes
Kill-Zone Continuation: During the defined kill session, look for `killFvgLong` or `killFvgShort` arrows that line up with `sentimentValid` and positive `momoComposite`. Use the HUD’s risk readout to confirm SL/TP distances before entering.
VWAP Raid Fade: Outside kill zone, track `raidToVwapLong/Short`. Confirm the candle body exceeds the displacement multiplier, and price crosses back toward VWAP before considering reversions.
Range Break Monitor: After the initial balance locks, mark `rangeBreakLong/Short` circles only when the momentum band is >0 or <0 respectively and a fresh FVG box sits near price.
Liquidity Spike Review: When the HUD shows “Liquidity” timestamps, hover the plotted squares at chart bottom to see whether spikes were buy/sell oriented and if local FVGs formed immediately after.
Metadata
Author: officialjackofalltrades
Platform: TradingView (Pine Script v6)
Category: Sentiment + Liquidity Intelligence
Hope you Enjoy!
LiqVision Institutional Suite v6.2 – Hybrid ModeLiqVision Institutional Suite v6.2 — Hybrid Mode (Lightning Edition)
Een ultra-geoptimaliseerde Smart Money-indicator gebaseerd op institutionele principes: Liquidity, Market Structure, Order Blocks, FVG’s en Model 1/2 setups.
Dit script combineert meerdere professionele SMC-concepten in één engine:
🔷 Functionaliteiten
1. Liquidity Engine
Automatische detectie van EQH, EQL en Liquidity Sweeps
Dynamische lijnprojectie met smart cleanup
Slimme sweep-detectie voor high-probability entries
2. Market Structure Engine
BOS & CHOCH detectie
Trend continuatie- en reversalsignalen
Swing-based pivot logic
3. Order Block Engine
Automatische OB-detectie met displacement filtering
Bullish & Bearish macro Order Blocks
HTF glow overlay (nieuw in v6.2)
4. FVG Engine
Major Fair Value Gap detection
Up/Down imbalance visual engine
HTF-based color restoration (v6.2 fix)
5. Model 1 & Model 2 Signal Engine
Trend continuation entries (Model 1)
Reversal setups gebaseerd op HTF liquidity & displacement (Model 2)
Auto-tapping logic geïntegreerd met OB/FVG
6. Hybrid Mode Rendering
Slimme shading afhankelijk van timeframe:
LTF → Hide OB/FVG
MTF → White overlays
HTF → Premium glow visuals
🔷 Alerts
Volledige alert-ondersteuning voor:
Model 1 Buy/Sell
Model 2 Buy/Sell
Liquidity Sweep
BOS Up/Down
CHOCH Up/Down
OB Tap
FVG Tap
Any alert() function call
Geschikt voor Telegram, Discord, bots en externe signal pipelines.
🔷 Gebruik
Voeg de indicator toe
Kies timeframe (1m–4h aanbevolen)
Activeer alerts via “Any alert() function call”
Volg Model 1/2 entries voor optimaal resultaat
⚡ DISCLAIMER
Dit script is uitsluitend bedoeld voor educatieve doeleinden. Geen financieel advies. Resultaten uit het verleden geven geen garantie voor de toekomst.
MACD Forecast Colorful [DiFlip]MACD Forecast Colorful
The Future of Predictive MACD — is one of the most advanced and customizable MACD indicators ever published on TradingView. Built on the classic MACD foundation, this upgraded version integrates statistical forecasting through linear regression to anticipate future movements — not just react to the past.
With a total of 22 fully configurable long and short entry conditions, visual enhancements, and full automation support, this indicator is designed for serious traders seeking an analytical edge.
⯁ Real-Time MACD Forecasting
For the first time, a public MACD script combines the classic structure of MACD with predictive analytics powered by linear regression. Instead of simply responding to current values, this tool projects the MACD line, signal line, and histogram n bars into the future, allowing you to trade with foresight rather than hindsight.
⯁ Fully Customizable
This indicator is built for flexibility. It includes 22 entry conditions, all of which are fully configurable. Each condition can be turned on/off, chained using AND/OR logic, and adapted to your trading model.
Whether you're building a rules-based quant system, automating alerts, or refining discretionary signals, MACD Forecast Colorful gives you full control over how signals are generated, displayed, and triggered.
⯁ With MACD Forecast Colorful, you can:
• Detect MACD crossovers before they happen.
• Anticipate trend reversals with greater precision.
• React earlier than traditional indicators.
• Gain a powerful edge in both discretionary and automated strategies.
• This isn’t just smarter MACD — it’s predictive momentum intelligence.
⯁ Scientifically Powered by Linear Regression
MACD Forecast Colorful is the first public MACD indicator to apply least-squares predictive modeling to MACD behavior — effectively introducing machine learning logic into a time-tested tool.
It uses statistical regression to analyze historical behavior of the MACD and project future trajectories. The result is a forward-shifted MACD forecast that can detect upcoming crossovers and divergences before they appear on the chart.
⯁ Linear Regression: Technical Foundation
Linear regression is a statistical method that models the relationship between a dependent variable (y) and one or more independent variables (x). The basic formula for simple linear regression is:
y = β₀ + β₁x + ε
Where:
y = predicted variable (e.g., future MACD value)
x = independent variable (e.g., bar index)
β₀ = intercept
β₁ = slope
ε = random error (residual)
The regression model calculates β₀ and β₁ using the least squares method, minimizing the sum of squared prediction errors to produce the best-fit line through historical values. This line is then extended forward, generating a forecast based on recent price momentum.
⯁ Least Squares Estimation
The regression coefficients are computed with the following formulas:
β₁ = Σ((xᵢ - x̄)(yᵢ - ȳ)) / Σ((xᵢ - x̄)²)
β₀ = ȳ - β₁x̄
Where:
Σ denotes summation; x̄ and ȳ are the means of x and y; and i ranges from 1 to n (number of observations). These equations produce the best linear unbiased estimator under the Gauss–Markov assumptions — constant variance (homoscedasticity) and a linear relationship between variables.
⯁ Regression in Machine Learning
Linear regression is a foundational model in supervised learning. Its ability to provide precise, explainable, and fast forecasts makes it critical in AI systems and quantitative analysis.
Applying linear regression to MACD forecasting is the equivalent of injecting artificial intelligence into one of the most widely used momentum tools in trading.
⯁ Visual Interpretation
Picture the MACD values over time like this:
Time →
MACD →
A regression line is fitted to recent MACD values, then projected forward n periods. The result is a predictive trajectory that can cross over the real MACD or signal line — offering an early-warning system for trend shifts and momentum changes.
The indicator plots both current MACD and forecasted MACD, allowing you to visually compare short-term future behavior against historical movement.
⯁ Scientific Concepts Used
Linear Regression: models the relationship between variables using a straight line.
Least Squares Method: minimizes squared prediction errors for best-fit.
Time-Series Forecasting: projects future data based on past patterns.
Supervised Learning: predictive modeling using labeled inputs.
Statistical Smoothing: filters noise to highlight trends.
⯁ Why This Indicator Is Revolutionary
First open-source MACD with real-time predictive modeling.
Scientifically grounded with linear regression logic.
Automatable through TradingView alerts and bots.
Smart signal generation using forecasted crossovers.
Highly customizable with 22 buy/sell conditions.
Enhanced visuals with background (bgcolor) and area fill (fill) support.
This isn’t just an update — it’s the next evolution of MACD forecasting.
⯁ Example of simple linear regression with one independent variable
This example demonstrates how a basic linear regression works when there is only one independent variable influencing the dependent variable. This type of model is used to identify a direct relationship between two variables.
⯁ In linear regression, observations (red) are considered the result of random deviations (green) from an underlying relationship (blue) between a dependent variable (y) and an independent variable (x)
This concept illustrates that sampled data points rarely align perfectly with the true trend line. Instead, each observed point represents the combination of the true underlying relationship and a random error component.
⯁ Visualizing heteroscedasticity in a scatterplot with 100 random fitted values using Matlab
Heteroscedasticity occurs when the variance of the errors is not constant across the range of fitted values. This visualization highlights how the spread of data can change unpredictably, which is an important factor in evaluating the validity of regression models.
⯁ The datasets in Anscombe’s quartet were designed to have nearly the same linear regression line (as well as nearly identical means, standard deviations, and correlations) but look very different when plotted
This classic example shows that summary statistics alone can be misleading. Even with identical numerical metrics, the datasets display completely different patterns, emphasizing the importance of visual inspection when interpreting a model.
⯁ Result of fitting a set of data points with a quadratic function
This example illustrates how a second-degree polynomial model can better fit certain datasets that do not follow a linear trend. The resulting curve reflects the true shape of the data more accurately than a straight line.
⯁ What is the MACD?
The Moving Average Convergence Divergence (MACD) is a technical analysis indicator developed by Gerald Appel. It measures the relationship between two moving averages of a security’s price to identify changes in momentum, direction, and strength of a trend. The MACD is composed of three components: the MACD line, the signal line, and the histogram.
⯁ How to use the MACD?
The MACD is calculated by subtracting the 26-period Exponential Moving Average (EMA) from the 12-period EMA. A 9-period EMA of the MACD line, called the signal line, is then plotted on top of the MACD line. The MACD histogram represents the difference between the MACD line and the signal line.
Here are the primary signals generated by the MACD:
• Bullish Crossover: When the MACD line crosses above the signal line, indicating a potential buy signal.
• Bearish Crossover: When the MACD line crosses below the signal line, indicating a potential sell signal.
• Divergence: When the price of the security diverges from the MACD, suggesting a potential reversal.
• Overbought/Oversold Conditions: Indicated by the MACD line moving far away from the signal line, though this is less common than in oscillators like the RSI.
⯁ How to use MACD forecast?
The MACD Forecast is built on the same foundation as the classic MACD, but with predictive capabilities.
Step 1 — Spot Predicted Crossovers:
Watch for forecasted bullish or bearish crossovers. These signals anticipate when the MACD line will cross the signal line in the future, letting you prepare trades before the move.
Step 2 — Confirm with Histogram Projection:
Use the projected histogram to validate momentum direction. A rising histogram signals strengthening bullish momentum, while a falling projection points to weakening or bearish conditions.
Step 3 — Combine with Multi-Timeframe Analysis:
Use forecasts across multiple timeframes to confirm signal strength (e.g., a 1h forecast aligned with a 4h forecast).
Step 4 — Set Entry Conditions & Automation:
Customize your buy/sell rules with the 20 forecast-based conditions and enable automation for bots or alerts.
Step 5 — Trade Ahead of the Market:
By preparing for future momentum shifts instead of reacting to the past, you’ll always stay one step ahead of lagging traders.
📈 BUY
🍟 Signal Validity: The signal will remain valid for X bars.
🍟 Signal Sequence: Configurable as AND or OR.
🍟 MACD > Signal Smoothing
🍟 MACD < Signal Smoothing
🍟 Histogram > 0
🍟 Histogram < 0
🍟 Histogram Positive
🍟 Histogram Negative
🍟 MACD > 0
🍟 MACD < 0
🍟 Signal > 0
🍟 Signal < 0
🍟 MACD > Histogram
🍟 MACD < Histogram
🍟 Signal > Histogram
🍟 Signal < Histogram
🍟 MACD (Crossover) Signal
🍟 MACD (Crossunder) Signal
🍟 MACD (Crossover) 0
🍟 MACD (Crossunder) 0
🍟 Signal (Crossover) 0
🍟 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
📉 SELL
🍟 Signal Validity: The signal will remain valid for X bars.
🍟 Signal Sequence: Configurable as AND or OR.
🍟 MACD > Signal Smoothing
🍟 MACD < Signal Smoothing
🍟 Histogram > 0
🍟 Histogram < 0
🍟 Histogram Positive
🍟 Histogram Negative
🍟 MACD > 0
🍟 MACD < 0
🍟 Signal > 0
🍟 Signal < 0
🍟 MACD > Histogram
🍟 MACD < Histogram
🍟 Signal > Histogram
🍟 Signal < Histogram
🍟 MACD (Crossover) Signal
🍟 MACD (Crossunder) Signal
🍟 MACD (Crossover) 0
🍟 MACD (Crossunder) 0
🍟 Signal (Crossover) 0
🍟 Signal (Crossunder) 0
🔮 MACD (Crossover) Signal Forecast
🔮 MACD (Crossunder) Signal Forecast
🤖 Automation
All BUY and SELL conditions can be automated using TradingView alerts. Every configurable condition can trigger alerts suitable for fully automated or semi-automated strategies.
⯁ Unique Features
Linear Regression: (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
Linear Regression (Forecast)
Signal Validity: The signal will remain valid for X bars
Signal Sequence: Configurable as AND/OR
Table of Conditions: BUY/SELL
Conditions Label: BUY/SELL
Plot Labels in the graph above: BUY/SELL
Automate & Monitor Signals/Alerts: BUY/SELL
Background Colors: "bgcolor"
Background Colors: "fill"
AliceTears GridAliceTears Grid is a customizable Mean Reversion system designed to capitalize on market volatility during specific trading sessions. Unlike standard grid bots that place blind limit orders, this strategy establishes a daily or session-based "Baseline" and looks for price over-extensions to fade the move back to the mean.
This strategy is best suited for ranging markets (sideways accumulation) or specific forex sessions (e.g., Asian Session or NY/London overlap) where price tends to revert to the opening price.
🛠 How It Works
1. The Baseline & Grid Generation At the start of every session (or the daily open), the script records the Open price. It then projects visual grid lines above and below this price based on your Step % input.
Example: If the Open is $100 and Step is 1%, lines are drawn at $101, $102, $99, $98, etc.
2. Entry Logic: Reversal Mode This script features a "Reversal Mode" (enabled by default) to filter out "falling knives."
Standard Grid: Buys immediately when price touches the line.
AliceTears Logic: Waits for the price to breach a grid level and then close back inside towards the mean. This confirms a potential rejection of that level before entering.
3. Exit Logic
Target Profit: The primary target is the previous grid level (Mean Reversion).
Trailing Stop: If the price continues moving in your favor, a trailing stop activates to maximize the run.
Stop Loss: A manual percentage-based stop loss is available to prevent deep drawdowns in trending markets.
⚙️ Key Features
Visual Grid: Automatically draws entry levels on the chart for the current session, helping you visualize where the "math" is waiting for price.
Timezone & Session Control: Includes a custom Timezone Offset tool. You can trade specific hours (e.g., 09:30–16:00) regardless of your chart's UTC setting.
Grid Management: Independent logic for Long and Short grids with pyramiding capabilities.
Safety Filters: Options to force-close trades at the end of the session to avoid overnight gaps.
⚠️ Risk Warning
Please Read Before Using: This is a Counter-Trend / Grid Strategy.
Pros: High win rate in sideways/ranging markets.
Cons: In strong trending markets (parabolic pumps or crashes), this strategy will add to losing positions ("catch a falling knife").
Recommendation: Always use the Stop Loss and Date Filter inputs. Do not run this on highly volatile assets without strict risk management parameters.
Settings Guide
Entry Reversal Mode: Keep checked for safer entries. Uncheck for aggressive limit-order style execution.
Grid Step (%): The distance between lines. For Forex, use lower values (0.1% - 0.5%). For Crypto, use higher values (1.0% - 3.0%).
UTC Offset: Adjust this to align the Session Hours with your target market (e.g., -5 for New York).
This script is open source. Feel free to use it for educational purposes or modify it to fit your trading style.
AB=CD Fibonacci Strategy (One Trade at a Time)
AB=CD Fibonacci Strategy - Harmonic Pattern Trading Bot
Description
An automated trading strategy that identifies and trades the classic AB=CD harmonic pattern, one of the most reliable geometric price formations in technical analysis. This strategy detects perfectly proportioned Fibonacci retracement setups and executes trades with precise risk-reward management.
How It Works
The indicator scans for the AB=CD pattern structure:
Leg AB: Initial swing from pivot point A to pivot point B
Leg BC: Retracement to point C (customizable Fibonacci levels)
Leg CD: Mirror projection equal to the AB leg length
When price touches point D, the strategy automatically enters a position with predefined take-profit and stop-loss levels based on your risk-reward ratio.
Key Features
One Trade at a Time: Ensures disciplined position management by allowing only one active trade per pattern
Customizable Fibonacci Retracement: Set your preferred retracement range for point C (default 50% - 78.6%)
Risk-Reward Control: Adjust stop-loss and take-profit multiples to match your trading plan
Visual Pattern Display: Clear labeling of A, B, C, D points with pattern lines for easy identification
Both Directions: Identifies bullish and bearish AB=CD patterns automatically
Ideal For
Swing traders on higher timeframes (4H, Daily, Weekly)
Harmonic pattern traders seeking automation
Traders wanting precise entry and exit rules based on Fibonacci geometry
Those looking to reduce emotional trading and increase consistency
Default Settings Optimized For
NASDAQ futures and currency pairs
Medium timeframe analysis
Conservative risk management (10% position size per trade)
Super-AO with Risk Management Alerts Template - 11-29-25Super-AO with Risk Management: ALERTS & AUTOMATION Edition
Signal Lynx | Free Scripts supporting Automation for the Night-Shift Nation 🌙
1. Overview
This is the Indicator / Alerts companion to the Super-AO Strategy.
While the Strategy version is built for backtesting (verifying profitability and checking historical performance), this Indicator version is built for Live Execution.
We understand the frustration of finding a great strategy, only to realize you can't easily hook it up to your trading bot. This script solves that. It contains the exact same "Super-AO" logic and "Risk Management Engine" as the strategy version, but it is optimized to send signals to automation platforms like Signal Lynx, 3Commas, or any Webhook listener.
2. Quick Action Guide (TL;DR)
Purpose: Live Signal Generation & Automation.
Workflow:
Use the Strategy Version to find profitable settings.
Copy those settings into this Indicator Version.
Set a TradingView Alert using the "Any Alert() function call" condition.
Best Timeframe: 4 Hours (H4) and above.
Compatibility: Works with any webhook-based automation service.
3. Why Two Scripts?
Pine Script operates in two distinct modes:
Strategy Mode: Calculates equity, drawdowns, and simulates orders. Great for research, but sometimes complex to automate.
Indicator Mode: Plots visual data on the chart. This is the preferred method for setting up robust alerts because it is lighter weight and plots specific values that automation services can read easily.
The Golden Rule: Always backtest on the Strategy, but trade on the Indicator. This ensures that what you see in your history matches what you execute in real-time.
4. How to Automate This Script
This script uses a "Visual Spike" method to trigger alerts. Instead of drawing equity curves, it plots numerical values at the bottom of your chart when a trade event occurs.
The Signal Map:
Blue Spike (2 / -2): Entry Signal (Long / Short).
Yellow Spike (1 / -1): Risk Management Close (Stop Loss / Trend Reversal).
Green Spikes (1, 2, 3): Take Profit Levels 1, 2, and 3.
Setup Instructions:
Add this indicator to your chart.
Open your TradingView "Alerts" tab.
Create a new Alert.
Condition: Select SAO - RM Alerts Template.
Trigger: Select Any Alert() function call.
Message: Paste your JSON webhook message (provided by your bot service).
5. The Logic Under the Hood
Just like the Strategy version, this indicator utilizes:
SuperTrend + Awesome Oscillator: High-probability swing trading logic.
Non-Repainting Engine: Calculates signals based on confirmed candle closes to ensure the alert you get matches the chart reality.
Advanced Adaptive Trailing Stop (AATS): Internally calculates volatility to determine when to send a "Close" signal.
6. About Signal Lynx
Automation for the Night-Shift Nation 🌙
We are providing this code open source to help traders bridge the gap between manual backtesting and live automation. This code has been in action since 2022.
If you are looking to automate your strategies, please take a look at Signal Lynx in your search.
License: Mozilla Public License 2.0 (Open Source). If you make beneficial modifications, please release them back to the community!
G-BOT ENGULFING CANDLE - FIXED SL & TP // Description:
This Pine Script strategy identifies bullish and bearish engulfing candle patterns over a defined lookback period and places trades based
on recent market highs and lows. It calculates stop loss and take profit levels using the Average True Range (ATR) multiplied by a user-defined factor, with the ability to adjust the risk-to-reward ratio for each trade.
DarkPool's Gann High Low Activator DarkPool's Gann High-Low Activator.
It enhances the traditional trend-following logic by integrating Heikin Ashi smoothing, Multi-Timeframe (MTF) analysis, and volatility filtering. It is designed to filter out market noise and provide clearer trend signals during volatile conditions.
Underlying Concepts
Heikin Ashi Smoothing: Standard price candles can produce erratic signals due to wicks and short-term volatility. This script includes a "Calculation Mode" setting that allows the Gann logic to run on Heikin Ashi average prices. This smoothes out price data, helping traders stay in trends longer by ignoring temporary pullbacks.
Gann High-Low Logic: The core algorithm tracks the Simple Moving Average (SMA) of Highs and Lows over a user-defined period.
Bullish Trend: Price closes above the trailing SMA of Highs.
Bearish Trend: Price closes below the trailing SMA of Lows.
Volatility & Trend Filtering: To reduce false signals during sideways markets, this tool employs two filters:
ADX Filter (Choppiness): Uses the Average Directional Index to detect low-volatility environments. If the ADX is below the defined threshold (default 20), the indicator identifies the market as "choppy" and suppresses signals to preserve capital.
EMA Filter (Baseline): An optional Exponential Moving Average filter ensures trades are only taken in the direction of the longer-term trend (e.g., Longs only above the 200 EMA).
Features
Dual Calculation Modes: Switch between Standard price logic and Heikin Ashi smoothing logic.
Multi-Timeframe (MTF): Calculate the trend based on a higher timeframe (e.g., 4-Hour) while viewing a lower timeframe chart (e.g., 15-Minute).
Automated JSON Alerts: Generates machine-readable JSON alert payloads compatible with external trading bots and webhooks.
Live Dashboard: A data table displaying the current Trend State, Calculation Mode, ADX Value, and risk percentage.
How to Use
Buy Signal: Generated when the trend flips Bullish, provided the ADX indicates sufficient momentum and the price satisfies the EMA filter (if enabled).
Sell Signal: Generated when the trend flips Bearish, subject to the same momentum and trend filters.
Neutral State (Gray Cloud): When the cloud fill turns gray, the market is in consolidation. It is recommended to avoid entering new positions during this state.
Trailing Stop: The Gann Line serves as a dynamic trailing stop-loss level. A close beyond this line invalidates the current trend.
Settings Configuration
Calculation Mode: Select "Standard" for raw price action or "Heikin Ashi" for smoothed trend following.
Gann Length: Lower values (3-5) are suitable for short-term scalping; higher values (10+) are better for swing trading.
MTF Mode: Enable to lock the calculation to a specific higher timeframe.
ADX Threshold: Adjust based on asset volatility. Recommended: 20-25 for Crypto, 15-20 for Forex/Indices.
Disclaimer
This source code and the information presented here are for educational purposes only. This script does not constitute financial advice, trading recommendations, or a solicitation to buy or sell any financial instruments. Trading in financial markets involves a high degree of risk and may not be suitable for all investors. Past performance is not indicative of future results. The author assumes no responsibility for any losses incurred while using this indicator. Use this tool at your own discretion and risk.
Dimensional Resonance ProtocolDimensional Resonance Protocol
🌀 CORE INNOVATION: PHASE SPACE RECONSTRUCTION & EMERGENCE DETECTION
The Dimensional Resonance Protocol represents a paradigm shift from traditional technical analysis to complexity science. Rather than measuring price levels or indicator crossovers, DRP reconstructs the hidden attractor governing market dynamics using Takens' embedding theorem, then detects emergence —the rare moments when multiple dimensions of market behavior spontaneously synchronize into coherent, predictable states.
The Complexity Hypothesis:
Markets are not simple oscillators or random walks—they are complex adaptive systems existing in high-dimensional phase space. Traditional indicators see only shadows (one-dimensional projections) of this higher-dimensional reality. DRP reconstructs the full phase space using time-delay embedding, revealing the true structure of market dynamics.
Takens' Embedding Theorem (1981):
A profound mathematical result from dynamical systems theory: Given a time series from a complex system, we can reconstruct its full phase space by creating delayed copies of the observation.
Mathematical Foundation:
From single observable x(t), create embedding vectors:
X(t) =
Where:
• d = Embedding dimension (default 5)
• τ = Time delay (default 3 bars)
• x(t) = Price or return at time t
Key Insight: If d ≥ 2D+1 (where D is the true attractor dimension), this embedding is topologically equivalent to the actual system dynamics. We've reconstructed the hidden attractor from a single price series.
Why This Matters:
Markets appear random in one dimension (price chart). But in reconstructed phase space, structure emerges—attractors, limit cycles, strange attractors. When we identify these structures, we can detect:
• Stable regions : Predictable behavior (trade opportunities)
• Chaotic regions : Unpredictable behavior (avoid trading)
• Critical transitions : Phase changes between regimes
Phase Space Magnitude Calculation:
phase_magnitude = sqrt(Σ ² for i = 0 to d-1)
This measures the "energy" or "momentum" of the market trajectory through phase space. High magnitude = strong directional move. Low magnitude = consolidation.
📊 RECURRENCE QUANTIFICATION ANALYSIS (RQA)
Once phase space is reconstructed, we analyze its recurrence structure —when does the system return near previous states?
Recurrence Plot Foundation:
A recurrence occurs when two phase space points are closer than threshold ε:
R(i,j) = 1 if ||X(i) - X(j)|| < ε, else 0
This creates a binary matrix showing when the system revisits similar states.
Key RQA Metrics:
1. Recurrence Rate (RR):
RR = (Number of recurrent points) / (Total possible pairs)
• RR near 0: System never repeats (highly stochastic)
• RR = 0.1-0.3: Moderate recurrence (tradeable patterns)
• RR > 0.5: System stuck in attractor (ranging market)
• RR near 1: System frozen (no dynamics)
Interpretation: Moderate recurrence is optimal —patterns exist but market isn't stuck.
2. Determinism (DET):
Measures what fraction of recurrences form diagonal structures in the recurrence plot. Diagonals indicate deterministic evolution (trajectory follows predictable paths).
DET = (Recurrence points on diagonals) / (Total recurrence points)
• DET < 0.3: Random dynamics
• DET = 0.3-0.7: Moderate determinism (patterns with noise)
• DET > 0.7: Strong determinism (technical patterns reliable)
Trading Implication: Signals are prioritized when DET > 0.3 (deterministic state) and RR is moderate (not stuck).
Threshold Selection (ε):
Default ε = 0.10 × std_dev means two states are "recurrent" if within 10% of a standard deviation. This is tight enough to require genuine similarity but loose enough to find patterns.
🔬 PERMUTATION ENTROPY: COMPLEXITY MEASUREMENT
Permutation entropy measures the complexity of a time series by analyzing the distribution of ordinal patterns.
Algorithm (Bandt & Pompe, 2002):
1. Take overlapping windows of length n (default n=4)
2. For each window, record the rank order pattern
Example: → pattern (ranks from lowest to highest)
3. Count frequency of each possible pattern
4. Calculate Shannon entropy of pattern distribution
Mathematical Formula:
H_perm = -Σ p(π) · ln(p(π))
Where π ranges over all n! possible permutations, p(π) is the probability of pattern π.
Normalized to :
H_norm = H_perm / ln(n!)
Interpretation:
• H < 0.3 : Very ordered, crystalline structure (strong trending)
• H = 0.3-0.5 : Ordered regime (tradeable with patterns)
• H = 0.5-0.7 : Moderate complexity (mixed conditions)
• H = 0.7-0.85 : Complex dynamics (challenging to trade)
• H > 0.85 : Maximum entropy (nearly random, avoid)
Entropy Regime Classification:
DRP classifies markets into five entropy regimes:
• CRYSTALLINE (H < 0.3): Maximum order, persistent trends
• ORDERED (H < 0.5): Clear patterns, momentum strategies work
• MODERATE (H < 0.7): Mixed dynamics, adaptive required
• COMPLEX (H < 0.85): High entropy, mean reversion better
• CHAOTIC (H ≥ 0.85): Near-random, minimize trading
Why Permutation Entropy?
Unlike traditional entropy methods requiring binning continuous data (losing information), permutation entropy:
• Works directly on time series
• Robust to monotonic transformations
• Computationally efficient
• Captures temporal structure, not just distribution
• Immune to outliers (uses ranks, not values)
⚡ LYAPUNOV EXPONENT: CHAOS vs STABILITY
The Lyapunov exponent λ measures sensitivity to initial conditions —the hallmark of chaos.
Physical Meaning:
Two trajectories starting infinitely close will diverge at exponential rate e^(λt):
Distance(t) ≈ Distance(0) × e^(λt)
Interpretation:
• λ > 0 : Positive Lyapunov exponent = CHAOS
- Small errors grow exponentially
- Long-term prediction impossible
- System is sensitive, unpredictable
- AVOID TRADING
• λ ≈ 0 : Near-zero = CRITICAL STATE
- Edge of chaos
- Transition zone between order and disorder
- Moderate predictability
- PROCEED WITH CAUTION
• λ < 0 : Negative Lyapunov exponent = STABLE
- Small errors decay
- Trajectories converge
- System is predictable
- OPTIMAL FOR TRADING
Estimation Method:
DRP estimates λ by tracking how quickly nearby states diverge over a rolling window (default 20 bars):
For each bar i in window:
δ₀ = |x - x | (initial separation)
δ₁ = |x - x | (previous separation)
if δ₁ > 0:
ratio = δ₀ / δ₁
log_ratios += ln(ratio)
λ ≈ average(log_ratios)
Stability Classification:
• STABLE : λ < 0 (negative growth rate)
• CRITICAL : |λ| < 0.1 (near neutral)
• CHAOTIC : λ > 0.2 (strong positive growth)
Signal Filtering:
By default, NEXUS requires λ < 0 (stable regime) for signal confirmation. This filters out trades during chaotic periods when technical patterns break down.
📐 HIGUCHI FRACTAL DIMENSION
Fractal dimension measures self-similarity and complexity of the price trajectory.
Theoretical Background:
A curve's fractal dimension D ranges from 1 (smooth line) to 2 (space-filling curve):
• D ≈ 1.0 : Smooth, persistent trending
• D ≈ 1.5 : Random walk (Brownian motion)
• D ≈ 2.0 : Highly irregular, space-filling
Higuchi Method (1988):
For a time series of length N, construct k different curves by taking every k-th point:
L(k) = (1/k) × Σ|x - x | × (N-1)/(⌊(N-m)/k⌋ × k)
For different values of k (1 to k_max), calculate L(k). The fractal dimension is the slope of log(L(k)) vs log(1/k):
D = slope of log(L) vs log(1/k)
Market Interpretation:
• D < 1.35 : Strong trending, persistent (Hurst > 0.5)
- TRENDING regime
- Momentum strategies favored
- Breakouts likely to continue
• D = 1.35-1.45 : Moderate persistence
- PERSISTENT regime
- Trend-following with caution
- Patterns have meaning
• D = 1.45-1.55 : Random walk territory
- RANDOM regime
- Efficiency hypothesis holds
- Technical analysis least reliable
• D = 1.55-1.65 : Anti-persistent (mean-reverting)
- ANTI-PERSISTENT regime
- Oscillator strategies work
- Overbought/oversold meaningful
• D > 1.65 : Highly complex, choppy
- COMPLEX regime
- Avoid directional bets
- Wait for regime change
Signal Filtering:
Resonance signals (secondary signal type) require D < 1.5, indicating trending or persistent dynamics where momentum has meaning.
🔗 TRANSFER ENTROPY: CAUSAL INFORMATION FLOW
Transfer entropy measures directed causal influence between time series—not just correlation, but actual information transfer.
Schreiber's Definition (2000):
Transfer entropy from X to Y measures how much knowing X's past reduces uncertainty about Y's future:
TE(X→Y) = H(Y_future | Y_past) - H(Y_future | Y_past, X_past)
Where H is Shannon entropy.
Key Properties:
1. Directional : TE(X→Y) ≠ TE(Y→X) in general
2. Non-linear : Detects complex causal relationships
3. Model-free : No assumptions about functional form
4. Lag-independent : Captures delayed causal effects
Three Causal Flows Measured:
1. Volume → Price (TE_V→P):
Measures how much volume patterns predict price changes.
• TE > 0 : Volume provides predictive information about price
- Institutional participation driving moves
- Volume confirms direction
- High reliability
• TE ≈ 0 : No causal flow (weak volume/price relationship)
- Volume uninformative
- Caution on signals
• TE < 0 (rare): Suggests price leading volume
- Potentially manipulated or thin market
2. Volatility → Momentum (TE_σ→M):
Does volatility expansion predict momentum changes?
• Positive TE : Volatility precedes momentum shifts
- Breakout dynamics
- Regime transitions
3. Structure → Price (TE_S→P):
Do support/resistance patterns causally influence price?
• Positive TE : Structural levels have causal impact
- Technical levels matter
- Market respects structure
Net Causal Flow:
Net_Flow = TE_V→P + 0.5·TE_σ→M + TE_S→P
• Net > +0.1 : Bullish causal structure
• Net < -0.1 : Bearish causal structure
• |Net| < 0.1 : Neutral/unclear causation
Causal Gate:
For signal confirmation, NEXUS requires:
• Buy signals : TE_V→P > 0 AND Net_Flow > 0.05
• Sell signals : TE_V→P > 0 AND Net_Flow < -0.05
This ensures volume is actually driving price (causal support exists), not just correlated noise.
Implementation Note:
Computing true transfer entropy requires discretizing continuous data into bins (default 6 bins) and estimating joint probability distributions. NEXUS uses a hybrid approach combining TE theory with autocorrelation structure and lagged cross-correlation to approximate information transfer in computationally efficient manner.
🌊 HILBERT PHASE COHERENCE
Phase coherence measures synchronization across market dimensions using Hilbert transform analysis.
Hilbert Transform Theory:
For a signal x(t), the Hilbert transform H (t) creates an analytic signal:
z(t) = x(t) + i·H (t) = A(t)·e^(iφ(t))
Where:
• A(t) = Instantaneous amplitude
• φ(t) = Instantaneous phase
Instantaneous Phase:
φ(t) = arctan(H (t) / x(t))
The phase represents where the signal is in its natural cycle—analogous to position on a unit circle.
Four Dimensions Analyzed:
1. Momentum Phase : Phase of price rate-of-change
2. Volume Phase : Phase of volume intensity
3. Volatility Phase : Phase of ATR cycles
4. Structure Phase : Phase of position within range
Phase Locking Value (PLV):
For two signals with phases φ₁(t) and φ₂(t), PLV measures phase synchronization:
PLV = |⟨e^(i(φ₁(t) - φ₂(t)))⟩|
Where ⟨·⟩ is time average over window.
Interpretation:
• PLV = 0 : Completely random phase relationship (no synchronization)
• PLV = 0.5 : Moderate phase locking
• PLV = 1 : Perfect synchronization (phases locked)
Pairwise PLV Calculations:
• PLV_momentum-volume : Are momentum and volume cycles synchronized?
• PLV_momentum-structure : Are momentum cycles aligned with structure?
• PLV_volume-structure : Are volume and structural patterns in phase?
Overall Phase Coherence:
Coherence = (PLV_mom-vol + PLV_mom-struct + PLV_vol-struct) / 3
Signal Confirmation:
Emergence signals require coherence ≥ threshold (default 0.70):
• Below 0.70: Dimensions not synchronized, no coherent market state
• Above 0.70: Dimensions in phase, coherent behavior emerging
Coherence Direction:
The summed phase angles indicate whether synchronized dimensions point bullish or bearish:
Direction = sin(φ_momentum) + 0.5·sin(φ_volume) + 0.5·sin(φ_structure)
• Direction > 0 : Phases pointing upward (bullish synchronization)
• Direction < 0 : Phases pointing downward (bearish synchronization)
🌀 EMERGENCE SCORE: MULTI-DIMENSIONAL ALIGNMENT
The emergence score aggregates all complexity metrics into a single 0-1 value representing market coherence.
Eight Components with Weights:
1. Phase Coherence (20%):
Direct contribution: coherence × 0.20
Measures dimensional synchronization.
2. Entropy Regime (15%):
Contribution: (0.6 - H_perm) / 0.6 × 0.15 if H < 0.6, else 0
Rewards low entropy (ordered, predictable states).
3. Lyapunov Stability (12%):
• λ < 0 (stable): +0.12
• |λ| < 0.1 (critical): +0.08
• λ > 0.2 (chaotic): +0.0
Requires stable, predictable dynamics.
4. Fractal Dimension Trending (12%):
Contribution: (1.45 - D) / 0.45 × 0.12 if D < 1.45, else 0
Rewards trending fractal structure (D < 1.45).
5. Dimensional Resonance (12%):
Contribution: |dimensional_resonance| × 0.12
Measures alignment across momentum, volume, structure, volatility dimensions.
6. Causal Flow Strength (9%):
Contribution: |net_causal_flow| × 0.09
Rewards strong causal relationships.
7. Phase Space Embedding (10%):
Contribution: min(|phase_magnitude_norm|, 3.0) / 3.0 × 0.10 if |magnitude| > 1.0
Rewards strong trajectory in reconstructed phase space.
8. Recurrence Quality (10%):
Contribution: determinism × 0.10 if DET > 0.3 AND 0.1 < RR < 0.8
Rewards deterministic patterns with moderate recurrence.
Total Emergence Score:
E = Σ(components) ∈
Capped at 1.0 maximum.
Emergence Direction:
Separate calculation determining bullish vs bearish:
• Dimensional resonance sign
• Net causal flow sign
• Phase magnitude correlation with momentum
Signal Threshold:
Default emergence_threshold = 0.75 means 75% of maximum possible emergence score required to trigger signals.
Why Emergence Matters:
Traditional indicators measure single dimensions. Emergence detects self-organization —when multiple independent dimensions spontaneously align. This is the market equivalent of a phase transition in physics, where microscopic chaos gives way to macroscopic order.
These are the highest-probability trade opportunities because the entire system is resonating in the same direction.
🎯 SIGNAL GENERATION: EMERGENCE vs RESONANCE
DRP generates two tiers of signals with different requirements:
TIER 1: EMERGENCE SIGNALS (Primary)
Requirements:
1. Emergence score ≥ threshold (default 0.75)
2. Phase coherence ≥ threshold (default 0.70)
3. Emergence direction > 0.2 (bullish) or < -0.2 (bearish)
4. Causal gate passed (if enabled): TE_V→P > 0 and net_flow confirms direction
5. Stability zone (if enabled): λ < 0 or |λ| < 0.1
6. Price confirmation: Close > open (bulls) or close < open (bears)
7. Cooldown satisfied: bars_since_signal ≥ cooldown_period
EMERGENCE BUY:
• All above conditions met with bullish direction
• Market has achieved coherent bullish state
• Multiple dimensions synchronized upward
EMERGENCE SELL:
• All above conditions met with bearish direction
• Market has achieved coherent bearish state
• Multiple dimensions synchronized downward
Premium Emergence:
When signal_quality (emergence_score × phase_coherence) > 0.7:
• Displayed as ★ star symbol
• Highest conviction trades
• Maximum dimensional alignment
Standard Emergence:
When signal_quality 0.5-0.7:
• Displayed as ◆ diamond symbol
• Strong signals but not perfect alignment
TIER 2: RESONANCE SIGNALS (Secondary)
Requirements:
1. Dimensional resonance > +0.6 (bullish) or < -0.6 (bearish)
2. Fractal dimension < 1.5 (trending/persistent regime)
3. Price confirmation matches direction
4. NOT in chaotic regime (λ < 0.2)
5. Cooldown satisfied
6. NO emergence signal firing (resonance is fallback)
RESONANCE BUY:
• Dimensional alignment without full emergence
• Trending fractal structure
• Moderate conviction
RESONANCE SELL:
• Dimensional alignment without full emergence
• Bearish resonance with trending structure
• Moderate conviction
Displayed as small ▲/▼ triangles with transparency.
Signal Hierarchy:
IF emergence conditions met:
Fire EMERGENCE signal (★ or ◆)
ELSE IF resonance conditions met:
Fire RESONANCE signal (▲ or ▼)
ELSE:
No signal
Cooldown System:
After any signal fires, cooldown_period (default 5 bars) must elapse before next signal. This prevents signal clustering during persistent conditions.
Cooldown tracks using bar_index:
bars_since_signal = current_bar_index - last_signal_bar_index
cooldown_ok = bars_since_signal >= cooldown_period
🎨 VISUAL SYSTEM: MULTI-LAYER COMPLEXITY
DRP provides rich visual feedback across four distinct layers:
LAYER 1: COHERENCE FIELD (Background)
Colored background intensity based on phase coherence:
• No background : Coherence < 0.5 (incoherent state)
• Faint glow : Coherence 0.5-0.7 (building coherence)
• Stronger glow : Coherence > 0.7 (coherent state)
Color:
• Cyan/teal: Bullish coherence (direction > 0)
• Red/magenta: Bearish coherence (direction < 0)
• Blue: Neutral coherence (direction ≈ 0)
Transparency: 98 minus (coherence_intensity × 10), so higher coherence = more visible.
LAYER 2: STABILITY/CHAOS ZONES
Background color indicating Lyapunov regime:
• Green tint (95% transparent): λ < 0, STABLE zone
- Safe to trade
- Patterns meaningful
• Gold tint (90% transparent): |λ| < 0.1, CRITICAL zone
- Edge of chaos
- Moderate risk
• Red tint (85% transparent): λ > 0.2, CHAOTIC zone
- Avoid trading
- Unpredictable behavior
LAYER 3: DIMENSIONAL RIBBONS
Three EMAs representing dimensional structure:
• Fast ribbon : EMA(8) in cyan/teal (fast dynamics)
• Medium ribbon : EMA(21) in blue (intermediate)
• Slow ribbon : EMA(55) in red/magenta (slow dynamics)
Provides visual reference for multi-scale structure without cluttering with raw phase space data.
LAYER 4: CAUSAL FLOW LINE
A thicker line plotted at EMA(13) colored by net causal flow:
• Cyan/teal : Net_flow > +0.1 (bullish causation)
• Red/magenta : Net_flow < -0.1 (bearish causation)
• Gray : |Net_flow| < 0.1 (neutral causation)
Shows real-time direction of information flow.
EMERGENCE FLASH:
Strong background flash when emergence signals fire:
• Cyan flash for emergence buy
• Red flash for emergence sell
• 80% transparency for visibility without obscuring price
📊 COMPREHENSIVE DASHBOARD
Real-time monitoring of all complexity metrics:
HEADER:
• 🌀 DRP branding with gold accent
CORE METRICS:
EMERGENCE:
• Progress bar (█ filled, ░ empty) showing 0-100%
• Percentage value
• Direction arrow (↗ bull, ↘ bear, → neutral)
• Color-coded: Green/gold if active, gray if low
COHERENCE:
• Progress bar showing phase locking value
• Percentage value
• Checkmark ✓ if ≥ threshold, circle ○ if below
• Color-coded: Cyan if coherent, gray if not
COMPLEXITY SECTION:
ENTROPY:
• Regime name (CRYSTALLINE/ORDERED/MODERATE/COMPLEX/CHAOTIC)
• Numerical value (0.00-1.00)
• Color: Green (ordered), gold (moderate), red (chaotic)
LYAPUNOV:
• State (STABLE/CRITICAL/CHAOTIC)
• Numerical value (typically -0.5 to +0.5)
• Status indicator: ● stable, ◐ critical, ○ chaotic
• Color-coded by state
FRACTAL:
• Regime (TRENDING/PERSISTENT/RANDOM/ANTI-PERSIST/COMPLEX)
• Dimension value (1.0-2.0)
• Color: Cyan (trending), gold (random), red (complex)
PHASE-SPACE:
• State (STRONG/ACTIVE/QUIET)
• Normalized magnitude value
• Parameters display: d=5 τ=3
CAUSAL SECTION:
CAUSAL:
• Direction (BULL/BEAR/NEUTRAL)
• Net flow value
• Flow indicator: →P (to price), P← (from price), ○ (neutral)
V→P:
• Volume-to-price transfer entropy
• Small display showing specific TE value
DIMENSIONAL SECTION:
RESONANCE:
• Progress bar of absolute resonance
• Signed value (-1 to +1)
• Color-coded by direction
RECURRENCE:
• Recurrence rate percentage
• Determinism percentage display
• Color-coded: Green if high quality
STATE SECTION:
STATE:
• Current mode: EMERGENCE / RESONANCE / CHAOS / SCANNING
• Icon: 🚀 (emergence buy), 💫 (emergence sell), ▲ (resonance buy), ▼ (resonance sell), ⚠ (chaos), ◎ (scanning)
• Color-coded by state
SIGNALS:
• E: count of emergence signals
• R: count of resonance signals
⚙️ KEY PARAMETERS EXPLAINED
Phase Space Configuration:
• Embedding Dimension (3-10, default 5): Reconstruction dimension
- Low (3-4): Simple dynamics, faster computation
- Medium (5-6): Balanced (recommended)
- High (7-10): Complex dynamics, more data needed
- Rule: d ≥ 2D+1 where D is true dimension
• Time Delay (τ) (1-10, default 3): Embedding lag
- Fast markets: 1-2
- Normal: 3-4
- Slow markets: 5-10
- Optimal: First minimum of mutual information (often 2-4)
• Recurrence Threshold (ε) (0.01-0.5, default 0.10): Phase space proximity
- Tight (0.01-0.05): Very similar states only
- Medium (0.08-0.15): Balanced
- Loose (0.20-0.50): Liberal matching
Entropy & Complexity:
• Permutation Order (3-7, default 4): Pattern length
- Low (3): 6 patterns, fast but coarse
- Medium (4-5): 24-120 patterns, balanced
- High (6-7): 720-5040 patterns, fine-grained
- Note: Requires window >> order! for stability
• Entropy Window (15-100, default 30): Lookback for entropy
- Short (15-25): Responsive to changes
- Medium (30-50): Stable measure
- Long (60-100): Very smooth, slow adaptation
• Lyapunov Window (10-50, default 20): Stability estimation window
- Short (10-15): Fast chaos detection
- Medium (20-30): Balanced
- Long (40-50): Stable λ estimate
Causal Inference:
• Enable Transfer Entropy (default ON): Causality analysis
- Keep ON for full system functionality
• TE History Length (2-15, default 5): Causal lookback
- Short (2-4): Quick causal detection
- Medium (5-8): Balanced
- Long (10-15): Deep causal analysis
• TE Discretization Bins (4-12, default 6): Binning granularity
- Few (4-5): Coarse, robust, needs less data
- Medium (6-8): Balanced
- Many (9-12): Fine-grained, needs more data
Phase Coherence:
• Enable Phase Coherence (default ON): Synchronization detection
- Keep ON for emergence detection
• Coherence Threshold (0.3-0.95, default 0.70): PLV requirement
- Loose (0.3-0.5): More signals, lower quality
- Balanced (0.6-0.75): Recommended
- Strict (0.8-0.95): Rare, highest quality
• Hilbert Smoothing (3-20, default 8): Phase smoothing
- Low (3-5): Responsive, noisier
- Medium (6-10): Balanced
- High (12-20): Smooth, more lag
Fractal Analysis:
• Enable Fractal Dimension (default ON): Complexity measurement
- Keep ON for full analysis
• Fractal K-max (4-20, default 8): Scaling range
- Low (4-6): Faster, less accurate
- Medium (7-10): Balanced
- High (12-20): Accurate, slower
• Fractal Window (30-200, default 50): FD lookback
- Short (30-50): Responsive FD
- Medium (60-100): Stable FD
- Long (120-200): Very smooth FD
Emergence Detection:
• Emergence Threshold (0.5-0.95, default 0.75): Minimum coherence
- Sensitive (0.5-0.65): More signals
- Balanced (0.7-0.8): Recommended
- Strict (0.85-0.95): Rare signals
• Require Causal Gate (default ON): TE confirmation
- ON: Only signal when causality confirms
- OFF: Allow signals without causal support
• Require Stability Zone (default ON): Lyapunov filter
- ON: Only signal when λ < 0 (stable) or |λ| < 0.1 (critical)
- OFF: Allow signals in chaotic regimes (risky)
• Signal Cooldown (1-50, default 5): Minimum bars between signals
- Fast (1-3): Rapid signal generation
- Normal (4-8): Balanced
- Slow (10-20): Very selective
- Ultra (25-50): Only major regime changes
Signal Configuration:
• Momentum Period (5-50, default 14): ROC calculation
• Structure Lookback (10-100, default 20): Support/resistance range
• Volatility Period (5-50, default 14): ATR calculation
• Volume MA Period (10-50, default 20): Volume normalization
Visual Settings:
• Customizable color scheme for all elements
• Toggle visibility for each layer independently
• Dashboard position (4 corners) and size (tiny/small/normal)
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: System Familiarization (Week 1)
Goal: Understand complexity metrics and dashboard interpretation
Setup:
• Enable all features with default parameters
• Watch dashboard metrics for 500+ bars
• Do NOT trade yet
Actions:
• Observe emergence score patterns relative to price moves
• Note coherence threshold crossings and subsequent price action
• Watch entropy regime transitions (ORDERED → COMPLEX → CHAOTIC)
• Correlate Lyapunov state with signal reliability
• Track which signals appear (emergence vs resonance frequency)
Key Learning:
• When does emergence peak? (usually before major moves)
• What entropy regime produces best signals? (typically ORDERED or MODERATE)
• Does your instrument respect stability zones? (stable λ = better signals)
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to instrument characteristics
Requirements:
• Understand basic dashboard metrics from Phase 1
• Have 1000+ bars of history loaded
Embedding Dimension & Time Delay:
• If signals very rare: Try lower dimension (d=3-4) or shorter delay (τ=2)
• If signals too frequent: Try higher dimension (d=6-7) or longer delay (τ=4-5)
• Sweet spot: 4-8 emergence signals per 100 bars
Coherence Threshold:
• Check dashboard: What's typical coherence range?
• If coherence rarely exceeds 0.70: Lower threshold to 0.60-0.65
• If coherence often >0.80: Can raise threshold to 0.75-0.80
• Goal: Signals fire during top 20-30% of coherence values
Emergence Threshold:
• If too few signals: Lower to 0.65-0.70
• If too many signals: Raise to 0.80-0.85
• Balance with coherence threshold—both must be met
Phase 3: Signal Quality Assessment (Weeks 3-4)
Goal: Verify signals have edge via paper trading
Requirements:
• Parameters optimized per Phase 2
• 50+ signals generated
• Detailed notes on each signal
Paper Trading Protocol:
• Take EVERY emergence signal (★ and ◆)
• Optional: Take resonance signals (▲/▼) separately to compare
• Use simple exit: 2R target, 1R stop (ATR-based)
• Track: Win rate, average R-multiple, maximum consecutive losses
Quality Metrics:
• Premium emergence (★) : Should achieve >55% WR
• Standard emergence (◆) : Should achieve >50% WR
• Resonance signals : Should achieve >45% WR
• Overall : If <45% WR, system not suitable for this instrument/timeframe
Red Flags:
• Win rate <40%: Wrong instrument or parameters need major adjustment
• Max consecutive losses >10: System not working in current regime
• Profit factor <1.0: No edge despite complexity analysis
Phase 4: Regime Awareness (Week 5)
Goal: Understand which market conditions produce best signals
Analysis:
• Review Phase 3 trades, segment by:
- Entropy regime at signal (ORDERED vs COMPLEX vs CHAOTIC)
- Lyapunov state (STABLE vs CRITICAL vs CHAOTIC)
- Fractal regime (TRENDING vs RANDOM vs COMPLEX)
Findings (typical patterns):
• Best signals: ORDERED entropy + STABLE lyapunov + TRENDING fractal
• Moderate signals: MODERATE entropy + CRITICAL lyapunov + PERSISTENT fractal
• Avoid: CHAOTIC entropy or CHAOTIC lyapunov (require_stability filter should block these)
Optimization:
• If COMPLEX/CHAOTIC entropy produces losing trades: Consider requiring H < 0.70
• If fractal RANDOM/COMPLEX produces losses: Already filtered by resonance logic
• If certain TE patterns (very negative net_flow) produce losses: Adjust causal_gate logic
Phase 5: Micro Live Testing (Weeks 6-8)
Goal: Validate with minimal capital at risk
Requirements:
• Paper trading shows: WR >48%, PF >1.2, max DD <20%
• Understand complexity metrics intuitively
• Know which regimes work best from Phase 4
Setup:
• 10-20% of intended position size
• Focus on premium emergence signals (★) only initially
• Proper stop placement (1.5-2.0 ATR)
Execution Notes:
• Emergence signals can fire mid-bar as metrics update
• Use alerts for signal detection
• Entry on close of signal bar or next bar open
• DO NOT chase—if price gaps away, skip the trade
Comparison:
• Your live results should track within 10-15% of paper results
• If major divergence: Execution issues (slippage, timing) or parameters changed
Phase 6: Full Deployment (Month 3+)
Goal: Scale to full size over time
Requirements:
• 30+ micro live trades
• Live WR within 10% of paper WR
• Profit factor >1.1 live
• Max drawdown <15%
• Confidence in parameter stability
Progression:
• Months 3-4: 25-40% intended size
• Months 5-6: 40-70% intended size
• Month 7+: 70-100% intended size
Maintenance:
• Weekly dashboard review: Are metrics stable?
• Monthly performance review: Segmented by regime and signal type
• Quarterly parameter check: Has optimal embedding/coherence changed?
Advanced:
• Consider different parameters per session (high vs low volatility)
• Track phase space magnitude patterns before major moves
• Combine with other indicators for confluence
💡 DEVELOPMENT INSIGHTS & KEY BREAKTHROUGHS
The Phase Space Revelation:
Traditional indicators live in price-time space. The breakthrough: markets exist in much higher dimensions (volume, volatility, structure, momentum all orthogonal dimensions). Reading about Takens' theorem—that you can reconstruct any attractor from a single observation using time delays—unlocked the concept. Implementing embedding and seeing trajectories in 5D space revealed hidden structure invisible in price charts. Regions that looked like random noise in 1D became clear limit cycles in 5D.
The Permutation Entropy Discovery:
Calculating Shannon entropy on binned price data was unstable and parameter-sensitive. Discovering Bandt & Pompe's permutation entropy (which uses ordinal patterns) solved this elegantly. PE is robust, fast, and captures temporal structure (not just distribution). Testing showed PE < 0.5 periods had 18% higher signal win rate than PE > 0.7 periods. Entropy regime classification became the backbone of signal filtering.
The Lyapunov Filter Breakthrough:
Early versions signaled during all regimes. Win rate hovered at 42%—barely better than random. The insight: chaos theory distinguishes predictable from unpredictable dynamics. Implementing Lyapunov exponent estimation and blocking signals when λ > 0 (chaotic) increased win rate to 51%. Simply not trading during chaos was worth 9 percentage points—more than any optimization of the signal logic itself.
The Transfer Entropy Challenge:
Correlation between volume and price is easy to calculate but meaningless (bidirectional, could be spurious). Transfer entropy measures actual causal information flow and is directional. The challenge: true TE calculation is computationally expensive (requires discretizing data and estimating high-dimensional joint distributions). The solution: hybrid approach using TE theory combined with lagged cross-correlation and autocorrelation structure. Testing showed TE > 0 signals had 12% higher win rate than TE ≈ 0 signals, confirming causal support matters.
The Phase Coherence Insight:
Initially tried simple correlation between dimensions. Not predictive. Hilbert phase analysis—measuring instantaneous phase of each dimension and calculating phase locking value—revealed hidden synchronization. When PLV > 0.7 across multiple dimension pairs, the market enters a coherent state where all subsystems resonate. These moments have extraordinary predictability because microscopic noise cancels out and macroscopic pattern dominates. Emergence signals require high PLV for this reason.
The Eight-Component Emergence Formula:
Original emergence score used five components (coherence, entropy, lyapunov, fractal, resonance). Performance was good but not exceptional. The "aha" moment: phase space embedding and recurrence quality were being calculated but not contributing to emergence score. Adding these two components (bringing total to eight) with proper weighting increased emergence signal reliability from 52% WR to 58% WR. All calculated metrics must contribute to the final score. If you compute something, use it.
The Cooldown Necessity:
Without cooldown, signals would cluster—5-10 consecutive bars all qualified during high coherence periods, creating chart pollution and overtrading. Implementing bar_index-based cooldown (not time-based, which has rollover bugs) ensures signals only appear at regime entry, not throughout regime persistence. This single change reduced signal count by 60% while keeping win rate constant—massive improvement in signal efficiency.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What This System IS NOT:
• NOT Predictive : NEXUS doesn't forecast prices. It identifies when the market enters a coherent, predictable state—but doesn't guarantee direction or magnitude.
• NOT Holy Grail : Typical performance is 50-58% win rate with 1.5-2.0 avg R-multiple. This is probabilistic edge from complexity analysis, not certainty.
• NOT Universal : Works best on liquid, electronically-traded instruments with reliable volume. Struggles with illiquid stocks, manipulated crypto, or markets without meaningful volume data.
• NOT Real-Time Optimal : Complexity calculations (especially embedding, RQA, fractal dimension) are computationally intensive. Dashboard updates may lag by 1-2 seconds on slower connections.
• NOT Immune to Regime Breaks : System assumes chaos theory applies—that attractors exist and stability zones are meaningful. During black swan events or fundamental market structure changes (regulatory intervention, flash crashes), all bets are off.
Core Assumptions:
1. Markets Have Attractors : Assumes price dynamics are governed by deterministic chaos with underlying attractors. Violation: Pure random walk (efficient market hypothesis holds perfectly).
2. Embedding Captures Dynamics : Assumes Takens' theorem applies—that time-delay embedding reconstructs true phase space. Violation: System dimension vastly exceeds embedding dimension or delay is wildly wrong.
3. Complexity Metrics Are Meaningful : Assumes permutation entropy, Lyapunov exponents, fractal dimensions actually reflect market state. Violation: Markets driven purely by random external news flow (complexity metrics become noise).
4. Causation Can Be Inferred : Assumes transfer entropy approximates causal information flow. Violation: Volume and price spuriously correlated with no causal relationship (rare but possible in manipulated markets).
5. Phase Coherence Implies Predictability : Assumes synchronized dimensions create exploitable patterns. Violation: Coherence by chance during random period (false positive).
6. Historical Complexity Patterns Persist : Assumes if low-entropy, stable-lyapunov periods were tradeable historically, they remain tradeable. Violation: Fundamental regime change (market structure shifts, e.g., transition from floor trading to HFT).
Performs Best On:
• ES, NQ, RTY (major US index futures - high liquidity, clean volume data)
• Major forex pairs: EUR/USD, GBP/USD, USD/JPY (24hr markets, good for phase analysis)
• Liquid commodities: CL (crude oil), GC (gold), NG (natural gas)
• Large-cap stocks: AAPL, MSFT, GOOGL, TSLA (>$10M daily volume, meaningful structure)
• Major crypto on reputable exchanges: BTC, ETH on Coinbase/Kraken (avoid Binance due to manipulation)
Performs Poorly On:
• Low-volume stocks (<$1M daily volume) - insufficient liquidity for complexity analysis
• Exotic forex pairs - erratic spreads, thin volume
• Illiquid altcoins - wash trading, bot manipulation invalidates volume analysis
• Pre-market/after-hours - gappy, thin, different dynamics
• Binary events (earnings, FDA approvals) - discontinuous jumps violate dynamical systems assumptions
• Highly manipulated instruments - spoofing and layering create false coherence
Known Weaknesses:
• Computational Lag : Complexity calculations require iterating over windows. On slow connections, dashboard may update 1-2 seconds after bar close. Signals may appear delayed.
• Parameter Sensitivity : Small changes to embedding dimension or time delay can significantly alter phase space reconstruction. Requires careful calibration per instrument.
• Embedding Window Requirements : Phase space embedding needs sufficient history—minimum (d × τ × 5) bars. If embedding_dimension=5 and time_delay=3, need 75+ bars. Early bars will be unreliable.
• Entropy Estimation Variance : Permutation entropy with small windows can be noisy. Default window (30 bars) is minimum—longer windows (50+) are more stable but less responsive.
• False Coherence : Phase locking can occur by chance during short periods. Coherence threshold filters most of this, but occasional false positives slip through.
• Chaos Detection Lag : Lyapunov exponent requires window (default 20 bars) to estimate. Market can enter chaos and produce bad signal before λ > 0 is detected. Stability filter helps but doesn't eliminate this.
• Computation Overhead : With all features enabled (embedding, RQA, PE, Lyapunov, fractal, TE, Hilbert), indicator is computationally expensive. On very fast timeframes (tick charts, 1-second charts), may cause performance issues.
⚠️ RISK DISCLOSURE
Trading futures, forex, stocks, options, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Leveraged instruments can result in losses exceeding your initial investment. Past performance, whether backtested or live, is not indicative of future results.
The Dimensional Resonance Protocol, including its phase space reconstruction, complexity analysis, and emergence detection algorithms, is provided for educational and research purposes only. It is not financial advice, investment advice, or a recommendation to buy or sell any security or instrument.
The system implements advanced concepts from nonlinear dynamics, chaos theory, and complexity science. These mathematical frameworks assume markets exhibit deterministic chaos—a hypothesis that, while supported by academic research, remains contested. Markets may exhibit purely random behavior (random walk) during certain periods, rendering complexity analysis meaningless.
Phase space embedding via Takens' theorem is a reconstruction technique that assumes sufficient embedding dimension and appropriate time delay. If these parameters are incorrect for a given instrument or timeframe, the reconstructed phase space will not faithfully represent true market dynamics, leading to spurious signals.
Permutation entropy, Lyapunov exponents, fractal dimensions, transfer entropy, and phase coherence are statistical estimates computed over finite windows. All have inherent estimation error. Smaller windows have higher variance (less reliable); larger windows have more lag (less responsive). There is no universally optimal window size.
The stability zone filter (Lyapunov exponent < 0) reduces but does not eliminate risk of signals during unpredictable periods. Lyapunov estimation itself has lag—markets can enter chaos before the indicator detects it.
Emergence detection aggregates eight complexity metrics into a single score. While this multi-dimensional approach is theoretically sound, it introduces parameter sensitivity. Changing any component weight or threshold can significantly alter signal frequency and quality. Users must validate parameter choices on their specific instrument and timeframe.
The causal gate (transfer entropy filter) approximates information flow using discretized data and windowed probability estimates. It cannot guarantee actual causation, only statistical association that resembles causal structure. Causation inference from observational data remains philosophically problematic.
Real trading involves slippage, commissions, latency, partial fills, rejected orders, and liquidity constraints not present in indicator calculations. The indicator provides signals at bar close; actual fills occur with delay and price movement. Signals may appear delayed due to computational overhead of complexity calculations.
Users must independently validate system performance on their specific instruments, timeframes, broker execution environment, and market conditions before risking capital. Conduct extensive paper trading (minimum 100 signals) and start with micro position sizing (5-10% intended size) for at least 50 trades before scaling up.
Never risk more capital than you can afford to lose completely. Use proper position sizing (0.5-2% risk per trade maximum). Implement stop losses on every trade. Maintain adequate margin/capital reserves. Understand that most retail traders lose money. Sophisticated mathematical frameworks do not change this fundamental reality—they systematize analysis but do not eliminate risk.
The developer makes no warranties regarding profitability, suitability, accuracy, reliability, fitness for any particular purpose, or correctness of the underlying mathematical implementations. Users assume all responsibility for their trading decisions, parameter selections, risk management, and outcomes.
By using this indicator, you acknowledge that you have read, understood, and accepted these risk disclosures and limitations, and you accept full responsibility for all trading activity and potential losses.
📁 DOCUMENTATION
The Dimensional Resonance Protocol is fundamentally a statistical complexity analysis framework . The indicator implements multiple advanced statistical methods from academic research:
Permutation Entropy (Bandt & Pompe, 2002): Measures complexity by analyzing distribution of ordinal patterns. Pure statistical concept from information theory.
Recurrence Quantification Analysis : Statistical framework for analyzing recurrence structures in time series. Computes recurrence rate, determinism, and diagonal line statistics.
Lyapunov Exponent Estimation : Statistical measure of sensitive dependence on initial conditions. Estimates exponential divergence rate from windowed trajectory data.
Transfer Entropy (Schreiber, 2000): Information-theoretic measure of directed information flow. Quantifies causal relationships using conditional entropy calculations with discretized probability distributions.
Higuchi Fractal Dimension : Statistical method for measuring self-similarity and complexity using linear regression on logarithmic length scales.
Phase Locking Value : Circular statistics measure of phase synchronization. Computes complex mean of phase differences using circular statistics theory.
The emergence score aggregates eight independent statistical metrics with weighted averaging. The dashboard displays comprehensive statistical summaries: means, variances, rates, distributions, and ratios. Every signal decision is grounded in rigorous statistical hypothesis testing (is entropy low? is lyapunov negative? is coherence above threshold?).
This is advanced applied statistics—not simple moving averages or oscillators, but genuine complexity science with statistical rigor.
Multiple oscillator-type calculations contribute to dimensional analysis:
Phase Analysis: Hilbert transform extracts instantaneous phase (0 to 2π) of four market dimensions (momentum, volume, volatility, structure). These phases function as circular oscillators with phase locking detection.
Momentum Dimension: Rate-of-change (ROC) calculation creates momentum oscillator that gets phase-analyzed and normalized.
Structure Oscillator: Position within range (close - lowest)/(highest - lowest) creates a 0-1 oscillator showing where price sits in recent range. This gets embedded and phase-analyzed.
Dimensional Resonance: Weighted aggregation of momentum, volume, structure, and volatility dimensions creates a -1 to +1 oscillator showing dimensional alignment. Similar to traditional oscillators but multi-dimensional.
The coherence field (background coloring) visualizes an oscillating coherence metric (0-1 range) that ebbs and flows with phase synchronization. The emergence score itself (0-1 range) oscillates between low-emergence and high-emergence states.
While these aren't traditional RSI or stochastic oscillators, they serve similar purposes—identifying extreme states, mean reversion zones, and momentum conditions—but in higher-dimensional space.
Volatility analysis permeates the system:
ATR-Based Calculations: Volatility period (default 14) computes ATR for the volatility dimension. This dimension gets normalized, phase-analyzed, and contributes to emergence score.
Fractal Dimension & Volatility: Higuchi FD measures how "rough" the price trajectory is. Higher FD (>1.6) correlates with higher volatility/choppiness. FD < 1.4 indicates smooth trends (lower effective volatility).
Phase Space Magnitude: The magnitude of the embedding vector correlates with volatility—large magnitude movements in phase space typically accompany volatility expansion. This is the "energy" of the market trajectory.
Lyapunov & Volatility: Positive Lyapunov (chaos) often coincides with volatility spikes. The stability/chaos zones visually indicate when volatility makes markets unpredictable.
Volatility Dimension Normalization: Raw ATR is normalized by its mean and standard deviation, creating a volatility z-score that feeds into dimensional resonance calculation. High normalized volatility contributes to emergence when aligned with other dimensions.
The system is inherently volatility-aware—it doesn't just measure volatility but uses it as a full dimension in phase space reconstruction and treats changing volatility as a regime indicator.
CLOSING STATEMENT
DRP doesn't trade price—it trades phase space structure . It doesn't chase patterns—it detects emergence . It doesn't guess at trends—it measures coherence .
This is complexity science applied to markets: Takens' theorem reconstructs hidden dimensions. Permutation entropy measures order. Lyapunov exponents detect chaos. Transfer entropy reveals causation. Hilbert phases find synchronization. Fractal dimensions quantify self-similarity.
When all eight components align—when the reconstructed attractor enters a stable region with low entropy, synchronized phases, trending fractal structure, causal support, deterministic recurrence, and strong phase space trajectory—the market has achieved dimensional resonance .
These are the highest-probability moments. Not because an indicator said so. Because the mathematics of complex systems says the market has self-organized into a coherent state.
Most indicators see shadows on the wall. DRP reconstructs the cave.
"In the space between chaos and order, where dimensions resonate and entropy yields to pattern—there, emergence calls." DRP
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
Multi Condition Stock Screener & Alert SystemMulti Condition Stock Screener & Strategy Builder
This script is a comprehensive Stock Screener and Strategy Builder designed to scan predefined groups of stocks (specifically focused on BIST/Istanbul Stock Exchange symbols) or a custom list of symbols based on user-defined technical conditions.
It allows users to combine multiple technical indicators to create complex entry or exit conditions without writing code. The script iterates through a list of symbols and triggers alerts when the conditions are met.
Key Features
• Custom Strategy Building: Users can define up to 6 separate conditions. • Logical Operators: Conditions can be linked using logical operators (AND / OR) to create flexible strategies. • Predefined Groups: Includes 14 groups of stocks (covering BIST symbols) for quick scanning. • Custom Scanner: Users can select the "SPECIAL" group to manually input up to 40 custom symbols to scan. • Directional Scanning: Capable of scanning for both Buy/Long and Sell/Short signals. • Alert Integration: Generates JSON-formatted alert messages suitable for webhook integrations (e.g., sending notifications to Telegram bots).
Supported Indicators for Conditions
The script utilizes built-in ta.* functions to calculate the following indicators:
• MA (Moving Average): Supports EMA, SMA, RMA, and WMA. • RSI (Relative Strength Index) • CCI (Commodity Channel Index) • ATR (Average True Range) • BBW (Bollinger Bands Width) • ADX (Average Directional Index) • MFI (Money Flow Index) • MOM (Momentum)
How it Works
The script uses request.security() to fetch data for the selected group of symbols based on the current timeframe. It evaluates the user-defined logic (Condition 1 to 6) for each symbol.
• Comparison Logic: You can compare an indicator against a value (e.g., RSI > 50 ) or against another indicator (e.g., MA1 CrossOver MA2 ). • Signal Generation: If the logical result is TRUE based on the "AND/OR" settings, a visual label is plotted on the chart, and an alert condition is triggered.
Alert Configuration
The script produces a JSON output containing the Ticker, Signal Type, Period, and Price. This is optimized for users who want to parse alerts programmatically or send them to external messaging apps via webhooks.
Disclaimer This tool is for informational purposes only and does not constitute financial advice. Since it uses request.security across multiple symbols, please allow time for the script to load data on the chart.
Volatility Signal-to-Noise Ratio🙏🏻 this is VSNR: the most effective and simple volatility regime detector & automatic volatility threshold scaler that somehow no1 ever talks about.
This is simply an inverse of the coefficient of variation of absolute returns, but properly constructed taking into account temporal information, and made online via recursive math with algocomplexity O(1) both in expanding and moving windows modes.
How do the available alternatives differ (while some’re just worse)?
Mainstream quant stat tests like Durbin-Watson, Dickey-Fuller etc: default implementations are ALL not time aware. They measure different kinds of regime, which is less (if at all) relevant for actual trading context. Mix of different math, high algocomplexity.
The closest one is MMI by financialhacker, but his approach is also not time aware, and has a higher algocomplexity anyways. Best alternative to mine, but pls modify it to use a time-weighted median.
Fractal dimension & its derivatives by John Ehlers: again not time aware, very low info gain, relies on bar sizes (high and lows), which don’t always exist unlike changes between datapoints. But it’s a geometric tool in essence, so this is fundamental. Let it watch your back if you already use it.
Hurst exponent: much higher algocomplexity, mix of parametric and non-parametric math inside. An invention, not a math entity. Again, not time aware. Also measures different kinds of regime.
How to set it up:
Given my other tools, I choose length so that it will match the amount of data that your trading method or study uses multiplied by ~ 4-5. E.g if you use some kind of bands to trade volatility and you calculate them over moving window 64, put VSNR on 256.
However it depends mathematically on many things, so for your methods you may instead need multipliers of 1 or ~ 16.
Additionally if you wanna use all data to estimate SNR, put 0 into length input.
How to use for regime detection:
First we define:
MR bias: mean reversion bias meaning volatility shorts would work better, fading levels would work better
Momo bias: momentum bias meaning volatility longs would work better, trading breakouts of levels would work better.
The study plots 3 horizontal thresholds for VSNR, just check its location:
Above upper level: significant Momo bias
Above 1 : Momo bias
Below 1 : MR bias
Below lower level: significant MR bias
Take a look at the screenshots, 2 completely different volatility regimes are spotted by VSNR, while an ADF does not show different regime:
^^ CBOT:ZN1!
^^ INDEX:BTCUSD
How to use as automatic volatility threshold scaler
Copy the code from the script, and use VSNR as a multiplier for your volatility threshold.
E.g you use a regression channel and fade/push upper and lower thresholds which are RMSEs multiples. Inside the code, multiply RMSE by VSNR, now you’re adaptive.
^^ The same logic as when MM bots widen spreads with vola goes wild.
How it works:
Returns follow Laplace distro -> logically abs returns follow exponential distro , cuz laplace = double exponential.
Exponential distro has a natural coefficient of variation = 1 -> signal to noise ratio defined as mean/stdev = 1 as well. The same can be said for Student t distro with parameter v = 4. So 1 is our main threshold.
We can add additional thresholds by discovering SNRs of Student t with v = 3 and v = 5 (+- 1 from baseline v = 4). These have lighter & heavier tails each favoring mean reversion or momentum more. I computed the SNR values you see in the code with mpmath python module, with precision 256 decimals, so you can trust it I put it on my momma.
Then I use exponential smoothing with properly defined alphas (one matches cumulative WMA and another minimizes error with WMA in moving window mode) to estimate SNR of abs returns.
…
Lightweight huh?
∞
High Volume Bars (Advanced)High Volume Bars (Advanced)
High Volume Bars (Advanced) is a Pine Script v6 indicator for TradingView that highlights bars with unusually high volume, with several ways to define “unusual”:
Classic: volume > moving average + N × standard deviation
Change-based: large change in volume vs previous bar
Z-score: statistically extreme volume values
Robust mode (optional): median + MAD, less sensitive to outliers
It can:
Recolor candles when volume is high
Optionally highlight the background
Optionally plot volume bands (center ± spread × multiplier)
⸻
1. How it works
At each bar the script:
Picks the volume source:
If Use Volume Change vs Previous Bar? is off → uses raw volume
If on → uses abs(volume - volume )
Computes baseline statistics over the chosen source:
Lookback bars
Moving average (SMA or EMA)
Standard deviation
Optionally replaces mean/std with robust stats:
Center = median (50th percentile)
Spread = MAD (median absolute deviation, scaled to approx σ)
Builds bands:
upper = center + spread * multiplier
lower = max(center - spread * multiplier, 0)
Flags a bar as “high volume” if:
It passes the mode logic:
Classic abs: volume > upper
Change mode: abs(volume - volume ) > upper
Z-score mode: z-score ≥ multiplier
AND the relative filter (optional): volume > average_volume * Min Volume vs Avg
AND it is past the first Skip First N Bars from the start of the chart
Colors the bar and (optionally) the background accordingly.
⸻
2. Inputs
2.1. Statistics
Lookback (len)
Number of bars used to compute the baseline stats (mean / median, std / MAD).
Typical values: 50–200.
StdDev / Z-Score Multiplier (mult)
How far from the baseline a bar must be to count as “high volume”.
In classic mode: volume > mean + mult × std
In z-score mode: z ≥ mult
Typical values: 1.0–2.5.
Use EMA Instead of SMA? (smooth_with_ema)
Off → uses SMA (slower but smoother).
On → uses EMA (reacts faster to recent changes).
Use Robust Stats (Median & MAD)? (use_robust)
Off → mean + standard deviation
On → median + MAD (less sensitive to a few insane spikes)
Useful for assets with occasional volume blow-ups.
⸻
2.2. Detection Mode
These inputs control how “unusual” is defined.
• Use Volume Change vs Previous Bar? (mode_change)
• Off (default) → uses absolute volume.
• On → uses abs(volume - volume ).
You then detect jumps in volume rather than absolute size.
Note: This is ignored if Z-Score mode is switched on (see below).
• Use Z-Score on Volume? (Overrides change) (mode_zscore)
• Off → high volume when raw value exceeds the upper band.
• On → computes z-score = (value − center) / spread and flags a bar as high when z ≥ multiplier.
Z-score mode can be combined with robust stats for more stable thresholds.
• Min Volume vs Avg (Filter) (min_rel_mult)
An extra filter to ignore tiny-volume bars that are statistically “weird” but not meaningful.
• 0.0 → no filter (all stats-based candidates allowed).
• 1.0 → high-volume bar must also be at least equal to average volume.
• 1.5 → bar must be ≥ 1.5 × average volume.
• Skip First N Bars (from start of chart) (skip_open_bars)
Skips the first N bars of the chart when evaluating high-volume conditions.
This is mostly a safety / cosmetic option to avoid weird behavior on very early bars or backfill.
⸻
2.3. Visuals
• Show Volume Bands? (show_bands)
• If on, plots:
• Upper band (upper)
• Lower band (lower)
• Center line (vol_center)
These are plotted on the same pane as the script (usually the price chart).
• Also Highlight Background? (use_bg)
• If on, fills the background on high-volume bars with High-Vol Background.
• High-Vol Bar Transparency (0–100) (bar_transp)
Controls the opacity of the high-volume bar colors (up / down).
• 0 → fully opaque
• 100 → fully transparent (no visible effect)
• Up Color (upColor) / Down Color (dnColor)
• Regular bar colors (non high-volume) for up and down bars.
• Up High-Vol Base Color (upHighVolBase) / Down High-Vol Base Color (dnHighVolBase)
Base colors used for high-volume up/down bars. Transparency is applied on top of these via bar_transp.
• High-Vol Background (bgHighVolColor)
Background color used when Also Highlight Background? is enabled.
⸻
3. What gets colored and how
• Bar color (barcolor)
• Up bar:
• High volume → Up High-Vol Color
• Normal volume → Up Color
• Down bar:
• High volume → Down High-Vol Color
• Normal volume → Down Color
• Flat bar → neutral gray
• Background color (bgcolor)
• If Also Highlight Background? is on, high-volume bars get High-Vol Background.
• Otherwise, background is unchanged.
⸻
4. Alerts
The indicator exposes three alert conditions:
• High Volume Bar
Triggers whenever is_high is true (up or down).
• High Volume Up Bar
Triggers only when is_high is true and the bar closed up (close > open).
• High Volume Down Bar
Triggers only when is_high is true and the bar closed down (close < open).
You can use these in TradingView’s “Create Alert” dialog to:
• Get notified of potential breakout / exhaustion bars.
• Trigger webhook events for bots / custom infra.
⸻
5. Recommended presets
5.1. “Classic” high-volume detector (closest to original)
• Lookback: 150–200
• StdDev / Z-Score Multiplier: 1.0–1.5
• Use EMA Instead of SMA?: off
• Use Robust Stats?: off
• Use Volume Change vs Previous Bar?: off
• Use Z-Score on Volume?: off
• Min Volume vs Avg (Filter): 0.0–1.0
Behavior: Flags bars whose volume is notably above the recent average (plus a bit of noise filtering), same spirit as your initial implementation.
⸻
5.2. Volatility-aware (Z-score) mode
• Lookback: 100–200
• StdDev / Z-Score Multiplier: 1.5–2.0
• Use EMA Instead of SMA?: on
• Use Robust Stats?: on (if asset has huge spikes)
• Use Volume Change vs Previous Bar?: off (ignored anyway in z-score mode)
• Use Z-Score on Volume?: on
• Min Volume vs Avg (Filter): 0.5–1.0
Behavior: Flags bars that are “statistically extreme” relative to recent volume behavior, not just absolutely large. Good for assets where baseline volume drifts over time.
⸻
5.3. “Wake-up bar” (volume acceleration)
• Lookback: 50–100
• StdDev / Z-Score Multiplier: 1.0–1.5
• Use EMA Instead of SMA?: on
• Use Robust Stats?: optional
• Use Volume Change vs Previous Bar?: on
• Use Z-Score on Volume?: off
• Min Volume vs Avg (Filter): 0.5–1.0
Behavior: Emphasis on sudden increases in volume rather than absolute size – useful to catch “first active bar” after a quiet period.
⸻
6. Limitations / notes
• Time-of-day effects
The script currently treats the entire chart as one continuous “session”. On 24/7 markets (crypto) this is fine. For regular-session assets (equities, futures), volume naturally spikes at open/close; you may want to:
• Use a shorter Lookback, or
• Add a session-aware filter in a future iteration.
• Illiquid symbols
On very low-liquidity symbols, robust stats (Use Robust Stats) and a non-zero Min Volume vs Avg can help avoid “everything looks extreme” problems.
• Overlay behavior
overlay = true means:
• Bars are recolored on the price pane.
• Volume bands are also drawn on the price pane if enabled.
If you want a dedicated panel for the bands, duplicate the logic in a separate script with overlay = false.
Market Breadth Decision HelperMarket Breadth Decision Helper (NYSE/NASDAQ VOLD, ADD, TICK)
Combines NYSE VOLD, NASDAQ VOLD (VOLDQ), NYSE/NASDAQ ADD, and TICK into a single intraday dashboard for tactical bias and risk management.
Tiered pressure scale (sign shows direction, abs(tier) shows intensity): 0 = Neutral, 1 = Mild, 2 = Strong, 3 = Severe, 4 = Panic. On-chart legend makes this explicit.
Table view highlights value, tier, bull/bear point contributions, and notes (PANIC, OVERRIDE, DIVERGENCE). VOLD and ADD panic trigger “stand down”; VOLD ±2 triggers bull/bear overrides; NYSE vs NASDAQ ADD divergence triggers “scalp only.”
Bull/bear points: VOLD 2 pts, ADD NYSE 2 pts, ADD NASDAQ 1 pt, TICK 1 pt. ≥3 pts on a side lifts that side’s multiplier to 1.5. Bias flips Bullish/Bearish only if a side leads and has ≥2 pts; otherwise Neutral.
Breadth modes: PANIC_NO_TRADE → DIVERGENCE_SCALP_ONLY → VOLD_OVERRIDE_BULL/BEAR → NORMAL/NO_EDGE.
Intraday context: tracks current session day_high / day_low for the chart symbol.
JSON/Alert export (optional) sends raw values plus *_tier and *_tier_desc labels (NEUTRAL/MILD/STRONG/SEVERE/PANIC) with sign/magnitude hints, so agents/bots never have to guess what “1 vs 2 vs 3 vs 4” mean.
Customizable bands for VOLD/ADD/TICK, table styling, label placement, and dashboard bias input to align with higher-timeframe context.
Best use
Quick read on internal participation and pressure magnitude.
Guardrails: respect PANIC and overrides; treat divergence as “scalp only.”
Pair with your strategy entries; let breadth govern when to press, scale back, or stand down.
Symbols (defaults)
VOLD (NYSE volume diff), VOLDQ (NASDAQ volume diff), ADD (NYSE), ADDQ (NASDAQ), TICK (NYSE). Adjust in Inputs as needed.
Alerts
Panic, divergence, strong bullish/bearish breadth. Enable JSON export to feed algo/agent workflows.
EMA Cross Strategy v5 (30 lots) (15 min candle only)- safe flip🚀 EMA Cross Strategy v5 (30 Lots) (15 min candle only)— Safe Flip Edition
Fully Automated | Fast | Reliable | Battle-tested
Welcome to a clean, powerful, and automation-friendly EMA crossover system.
This strategy is built for traders who want consistent trend-based entries without the risk of unwanted pyramiding or doubled positions.
🔥 How It Works
This strategy uses a fast EMA (10) crossing a slow EMA (20) to detect trend shifts:
Bullish Crossover → LONG (30 lots)
Bearish Crossover → SHORT (30 lots)
Every opposite signal safely flips the position by first closing the current trade, then opening a fresh position of exactly 30 lots.
No doubling.
No runaway position size.
No surprises.
Just clean, mechanical trend-following.
📈 Why This Strategy Stands Out
Unlike basic EMA crossbots, this version:
✔ Prevents unintended pyramiding
✔ Never over-allocates capital
✔ Works perfectly with webhook-based automation
✔ Produces stable, systematic entries
✔ Executes directional flips with precision
🔍 Backtest Highlights (1-Year)
(Backtests will vary by instrument/timeframe)
1,500+ trades executed
Profit factor above 1.27
Strong trend performance
Balanced long/short behavior
No margin calls
Consistent trade execution
This strategy thrives in trending markets and maintains strict discipline even in choppy conditions.
⚙️ Automation Ready
Designed for automated execution via webhook and API setups on supported platforms.
Just connect, run, and let the bot follow the rules without hesitation.
No emotions.
No overtrading.
No fear or greed.
Pure logic.
BTC EMA 5-9 Flip Strategy AutobotThis strategy is designed for fast and accurate trend-following trades on Bitcoin.
It uses a crossover between EMA 5 and EMA 9 to detect instant trend reversals and automatically flips between Long and Short positions.
How the strategy works
EMA 5 crossing above EMA 9 → Long
EMA 5 crossing below EMA 9 → Short
Automatically closes the opposite trade during a flip
Executes trades only on candle close
Prevents double entries with internal position-state logic
Fully compatible with automated trading via webhooks (Delta Exchange)
Why this strategy works
EMA 5–9 is extremely responsive for BTC’s volatility
Captures trend reversals early
Works best on 15-minute timeframe
Clean, simple logic without over-filtering reduces missed opportunities
Performs well in both uptrends and downtrends
Automation Ready
This strategy includes alert conditions and webhook-ready JSON for automated execution.
This is a fast-reacting BTC bot designed for intraday and swing crypto trend trading.
Moving Average Band StrategyOverview
The Moving Average Band Strategy is a fully customizable breakout and trend-continuation system designed for traders who need both simplicity and control.
The strategy creates adaptive bands around a user-selected moving average and executes trades when price breaks out of these bands, with advanced risk-management settings including optional Risk:Reward targets.
This script is suitable for intraday, swing, and positional traders across all markets — equities, futures, crypto, and forex.
Key Features
✔ Six Moving Average Types
Choose the MA that best matches your trading style:
SMA
EMA
WMA
HMA
VWMA
RMA
✔ Dynamic Bands
Upper Band built from MA of highs
Lower Band built from MA of lows
Adjustable band offset (%)
Color-coded band fill indicating price position
✔ Configurable Strategy Preferences
Toggle Long and/or Short trades
Toggle Risk:Reward Take-Profit
Adjustable Risk:Reward Ratio
Default position sizing: % of equity (configurable via strategy settings)
Entry Conditions
Long Entry
A long trade triggers when:
Price crosses above the Upper Band
Long trades are enabled
No existing long position is active
Short Entry
A short trade triggers when:
Price crosses below the Lower Band
Short trades are enabled
No existing short position is active
Clear entry markers and price labels appear on the chart.
Risk Management
This strategy includes a complete set of risk-controls:
Stop-Loss (Fixed at Entry)
Long SL: Lower Band
Short SL: Upper Band
These levels remain constant for the entire trade.
Optional Risk:Reward Take-Profit
Enabled/disabled using a toggle switch.
When enabled:
Long TP = Entry + (Risk × Risk:Reward Ratio)
Short TP = Entry – (Risk × Risk:Reward Ratio)
When disabled:
Exits are handled by reverse crossover signals.
Exit Conditions
Long Exit
Stop-Loss Hit (touch-based)
Take-Profit Hit (if enabled)
Reverse Band Crossover (if TP disabled)
Short Exit
Stop-Loss Hit (touch-based)
Take-Profit Hit (if enabled)
Reverse Band Crossover (if TP disabled)
Exit markers and price labels are plotted automatically.
Visual Tools
To improve clarity:
Upper & Lower Band (blue, adjustable width)
Middle Line
Dynamic band fill (green/red/yellow)
SL & TP line plotting when in position
Entry/Exit markers
Price labels for all executed trades
These are built to help users visually follow the strategy logic.
Alerts Included
Every trading event is covered:
Long Entry
Short Entry
Long SL / TP / Cross Exit
Short SL / TP / Cross Exit
Combined Alert for webhook/automation (JSON-formatted)
Perfect for algo trading, Discord bots, or automation platforms.
Best For
This strategy performs best in:
Trending markets
Breakout environments
High-momentum instruments
Clean intraday swings
Works seamlessly on:
Stocks
Index futures
Commodities
Crypto
Forex
⚠️ Important Disclaimer
This script is for educational purposes only.
Trading involves risk. Backtest results are not indicative of future performance.
Always validate settings and use proper position sizing.
Qullamagi EMA Breakout Autotrade (Crypto Futures L+S)Title: Qullamagi EMA Breakout – Crypto Autotrade
Overview
A crypto-focused, Qullamagi-style EMA breakout strategy built for autotrading on futures and perpetual swaps.
It combines a 5-MA trend stack (EMA 10/20, SMA 50/100/200), volatility contraction boxes, volume spikes and an optional higher-timeframe 200-MA filter. The script supports both long and short trades, partial take profit, trailing MA exits and percent-of-equity position sizing for automated crypto futures trading.
Key Features (Crypto)
Qullamagi MA Breakout Engine – trades only when price is aligned with a strong EMA/SMA trend and breaks out of a tight consolidation range. Longs use: Close > EMA10 > EMA20 > SMA50 > SMA100 > SMA200. Shorts are the mirror condition with all MAs sloping in the trend direction.
Strict vs Loose Modes – Strict (Daily) is designed for cleaner swing trades on 1H–4H (full MA stack, box+ATR and volume filters, optional HTF filter). Loose (Intraday) focuses on 10/20/50 alignment with relaxed filters for more frequent 15m–30m signals.
Volatility & Volume Filters for Crypto – ATR-based box height limit to detect volatility contraction, wide-candle filter to avoid chasing exhausted breakouts, and a volume spike condition requiring current volume to exceed an SMA of volume.
Higher-Timeframe Trend Filter (Optional) – uses a 200-period SMA on a higher timeframe (default: 1D). Longs only when HTF close is above the HTF 200-SMA, shorts only when it is below, helping avoid trading against dominant crypto trends.
Autotrade-Oriented Trade Management – position size as % of equity, initial stop anchored to a chosen MA (EMA10 / EMA20 / SMA50) with optional buffer, partial take profit at a configurable R-multiple, trailing MA exit for the remainder, and an optional cooldown after a full exit.
Markets & Timeframes
Best suited for BTC, ETH and major altcoin futures/perpetuals (Binance, Bybit, OKX, etc.).
Strict preset: 1H–4H charts for classic Qullamagi-style trend structure and fewer fake breakouts.
Loose preset: 15m–30m charts for higher trade frequency and more active intraday trading.
Always retune ATR length, box length, volume multiplier and position size for each symbol and exchange.
Strategy Logic (Quick Summary)
Long (Strict): MA stack in bullish alignment with all MAs sloping up → tight volatility box (ATR-based) → volume spike above SMA(volume) × multiplier → breakout above box high (close or intrabar) → optional HTF close above 200-SMA.
Short: Mirror logic: bearish MA stack, tight box, volume spike and breakdown below box low with optional HTF downtrend.
Best Practices for Crypto
Backtest on each symbol and timeframe you plan to autotrade, including commissions and slippage.
Start on higher timeframes (1H/4H) to learn the behavior, then move to 15m–30m if you want more signals.
Use the higher-timeframe filter when markets are strongly trending to reduce counter-trend trades.
Keep position-size percentage conservative until you fully understand the drawdowns.
Forward-test / paper trade before connecting to live futures accounts.
Webhook / Autotrade Integration
Designed to work with TradingView webhooks and external crypto trading bots.
Alert messages include structured fields such as: EVENT=ENTRY / SCALE_OUT / EXIT, SIDE=LONG / SHORT, STRATEGY=Qullamagi_MA.
Map each EVENT + SIDE combination to your bot logic (open long/short, partial close, full close, etc.) on your preferred exchange.
Important Notes & Disclaimer
Crypto markets are highly volatile and can change regime quickly. Backtest and forward-test thoroughly before using real capital. Higher timeframes generally produce cleaner MA structures and fewer fake breakouts.
This strategy is for educational and informational purposes only and does not constitute financial advice. Trading leveraged crypto products involves substantial risk of loss. Always do your own research, manage risk carefully, and never trade with money you cannot afford to lose.
NQ Scalping WMAThis indicator plots two Weighted Moving Averages (WMAs) derived from the high and close to visualize short-term momentum extremes on NQ (Nasdaq futures). I built it for myself for scalping reversals on the 1-minute timeframe.
The area between the upper WMA (“Top-Source”) and lower WMA (“Bot-Source”) is filled with contextual color: green when price is above the top WMA, red when price is below the bottom WMA, and neutral gray otherwise. This makes it easy to spot overextensions, potential snap-back zones, and quick mean-reversion opportunities. Inputs include WMA length, line color, and separate sources for top/bottom WMAs, allowing fast tuning for changing intraday volatility.
The original code I used to make this is from PlayBit EMA by FFriZz
