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.
M-oscillator
UM Nadaraya-Watson OscillatorDescription
This is a different take on the Nadaraya-Watson Estimator from both Jdhorty and LuxAlgo. Both great scripts, I encourage everyone to check them out. Think of this script as a measure of trend direction, direction change, and trend acceleration or deceleration. It is not a Moving Average, but you could think of it as loosely as an intelligent adaptive regression curve with the focus on trend direction. The Gaussian calculations prefer and add more weight to the most recent bars. The end result is the oscillator is more responsive with less lag and less prone to pure price noise.
How it Works
The indicator was added to the chart twice; once with an MA, once without. The oscillator indicates trend change by crossing up through the zero line or down through the zero line. Once the indicator turns positive, we are in a positive trend until it crosses below zero and then the trend turns negative. I implemented a Moving Average overlay for additional signal determination; if the configured MA (EMA, SMA, WMA, or Nadaraya-Watson Estimator) trends higher, it is green. When trending down, it is red. The indicator also changes the color of the price bars; when the indicator below zero and red, the price bars are red. When the indicator is above zero and green, the price bars are green.
I marked up the chart and indicator to identify LONG, SHORT, and divergences between price and oscillator.
Default Settings
The default settings are 16 for Bandwidth and a WMA with 110. This is shown in the chart example. There directional arrows, but they are off by default. The Price bars are colored green or red to match the oscillator and the bar coloring is on by default.
All settings are user-configurable including bandwidth, MA type, MA length, bar coloring, and arrows.
Suggested Settings and uses
I personally like the 30 min chart with a bandwidth of 16 and a WMA of 110. The bandwidth 8 and 8 period EMA or WMA also work well on 6 hour and daily charts. Add this to your chart arsenal and use your favorite indicators for confirmation. This indicator works well on the 30 minute chart for inverse ETFs as well (SQQQ, SOXS, TZA). Also, the oscillator is good for identifying divergences between price and and indicator. (see chart for illustration)
Experiment with settings and adapt them to your trading style.
Alerts
If you right click the indicator, and select add alert, I have configured 4 standard alerts: A bullish cross above zero, A bearish cross below zero, An MA bullish turned up to trend higher, (green), and an MA bearish turned down to trend lower (red).
Third eye • StrategyThird eye • Strategy – User Guide
1. Idea & Concept
Third eye • Strategy combines three things into one system:
Ichimoku Cloud – to define market regime and support/resistance.
Moving Average (trend filter) – to trade only in the dominant direction.
CCI (Commodity Channel Index) – to generate precise entry signals on momentum breakouts.
The script is a strategy, not an indicator: it can backtest entries, exits, SL, TP and BreakEven logic automatically.
2. Indicators Used
2.1 Ichimoku
Standard Ichimoku settings (by default 9/26/52/26) are used:
Conversion Line (Tenkan-sen)
Base Line (Kijun-sen)
Leading Span A & B (Kumo Cloud)
Lagging Span is calculated but hidden from the chart (for visual simplicity).
From the cloud we derive:
kumoTop – top of the cloud under current price.
kumoBottom – bottom of the cloud under current price.
Flags:
is_above_kumo – price above the cloud.
is_below_kumo – price below the cloud.
is_in_kumo – price inside the cloud.
These conditions are used as trend / regime filters and for stop-loss & trailing stops.
2.2 Moving Average
You can optionally display and use a trend MA:
Types: SMA, EMA, DEMA, WMA
Length: configurable (default 200)
Source: default close
Filter idea:
If MA Direction Filter is ON:
When Close > MA → strategy allows only Long signals.
When Close < MA → strategy allows only Short signals.
The MA is plotted on the chart (if enabled).
2.3 CCI & Panel
The CCI (Commodity Channel Index) is used for entry timing:
CCI length and source are configurable (default length 20, source hlc3).
Two thresholds:
CCI Upper Threshold (Long) – default +100
CCI Lower Threshold (Short) – default –100
Signals:
Long signal:
CCI crosses up through the upper threshold
cci_val < upper_threshold and cci_val > upper_threshold
Short signal:
CCI crosses down through the lower threshold
cci_val > lower_threshold and cci_val < lower_threshold
There is a panel (table) in the bottom-right corner:
Shows current CCI value.
Shows filter status as colored dots:
Green = filter enabled and passed.
Red = filter enabled and blocking trades.
Gray = filter is disabled.
Filters shown in the panel:
Ichimoku Cloud filter (Long/Short)
Ichimoku Lines filter (Conversion/Base vs Cloud)
MA Direction filter
3. Filters & Trade Direction
All filters can be turned ON/OFF independently.
3.1 Ichimoku Cloud Filter
Purpose: trade only when price is clearly above or below the Kumo.
Long Cloud Filter (Use Ichimoku Cloud Filter) – when enabled:
Long trades only if close > cloud top.
Short Cloud Filter – when enabled:
Short trades only if close < cloud bottom.
If the cloud filter is disabled, this condition is ignored.
3.2 Ichimoku Lines Above/Below Cloud
Purpose: stronger trend confirmation: Ichimoku lines should also be on the “correct” side of the cloud.
Long Lines Filter:
Long allowed only if Conversion Line and Base Line are both above the cloud.
Short Lines Filter:
Short allowed only if both lines are below the cloud.
If this filter is OFF, the conditions are not checked.
3.3 MA Direction Filter
As described above:
When ON:
Close > MA → only Longs.
Close < MA → only Shorts.
4. Anti-Re-Entry Logic (Cloud Touch Reset)
The strategy uses internal flags to avoid continuous re-entries in the same direction without a reset.
Two flags:
allowLong
allowShort
After a Long entry, allowLong is set to false, allowShort to true.
After a Short entry, allowShort is set to false, allowLong to true.
Flags are reset when price touches the Kumo:
If Low goes into the cloud → allowLong = true
If High goes into the cloud → allowShort = true
If Close is inside the cloud → both allowLong and allowShort are set to true
There is a key option:
Wait Position Close Before Flag Reset
If ON: cloud touch will reset flags only when there is no open position.
If OFF: flags can be reset even while a trade is open.
This gives a kind of regime-based re-entry control: after a trend leg, you wait for a “cloud interaction” to allow new signals.
5. Risk Management
All risk management is handled inside the strategy.
5.1 Position Sizing
Order Size % of Equity – default 10%
The strategy calculates:
position_value = equity * (Order Size % / 100)
position_qty = position_value / close
So position size automatically adapts to your current equity.
5.2 Take Profit Modes
You can choose one of two TP modes:
Percent
Fibonacci
5.2.1 Percent Mode
Single Take Profit at X% from entry (default 2%).
For Long:
TP = entry_price * (1 + tp_pct / 100)
For Short:
TP = entry_price * (1 - tp_pct / 100)
One strategy.exit per side is used: "Long TP/SL" and "Short TP/SL".
5.2.2 Fibonacci Mode (2 partial TPs)
In this mode, TP levels are based on a virtual Fib-style extension between entry and stop-loss.
Inputs:
Fib TP1 Level (default 1.618)
Fib TP2 Level (default 2.5)
TP1 Share % (Fib) (default 50%)
TP2 share is automatically 100% - TP1 share.
Process for Long:
Compute a reference Stop (see SL section below) → sl_for_fib.
Compute distance: dist = entry_price - sl_for_fib.
TP levels:
TP1 = entry_price + dist * (Fib TP1 Level - 1)
TP2 = entry_price + dist * (Fib TP2 Level - 1)
For Short, the logic is mirrored.
Two exits are used:
TP1 – closes TP1 share % of position.
TP2 – closes remaining TP2 share %.
Same stop is used for both partial exits.
5.3 Stop-Loss Modes
You can choose one of three Stop Loss modes:
Stable – fixed % from entry.
Ichimoku – fixed level derived from the Kumo.
Ichimoku Trailing – dynamic SL following the cloud.
5.3.1 Stable SL
For Long:
SL = entry_price * (1 - Stable SL % / 100)
For Short:
SL = entry_price * (1 + Stable SL % / 100)
Used both for Percent TP mode and as reference for Fib TP if Kumo is not available.
5.3.2 Ichimoku SL (fixed, non-trailing)
At the time of a new trade:
For Long:
Base SL = cloud bottom minus small offset (%)
For Short:
Base SL = cloud top plus small offset (%)
The offset is configurable: Ichimoku SL Offset %.
Once computed, that SL level is fixed for this trade.
5.3.3 Ichimoku Trailing SL
Similar to Ichimoku SL, but recomputed each bar:
For Long:
SL = cloud bottom – offset
For Short:
SL = cloud top + offset
A red trailing SL line is drawn on the chart to visualize current stop level.
This trailing SL is also used as reference for BreakEven and for Fib TP distance.
6. BreakEven Logic (with BE Lines)
BreakEven is optional and supports two modes:
Percent
Fibonacci
Inputs:
Percent mode:
BE Trigger % (from entry) – move SL to BE when price goes this % in profit.
BE Offset % from entry – SL will be set to entry ± this offset.
Fibonacci mode:
BE Fib Level – Fib level at which BE will be activated (default 1.618, same style as TP).
BE Offset % from entry – how far from entry to place BE stop.
The logic:
Before BE is triggered, SL follows its normal mode (Stable/Ichimoku/Ichimoku Trailing).
When BE triggers:
For Long:
New SL = max(current SL, BE SL).
For Short:
New SL = min(current SL, BE SL).
This means BE will never loosen the stop – only tighten it.
When BE is activated, the strategy draws a violet horizontal line at the BreakEven level (once per trade).
BE state is cleared when the position is closed or when a new position is opened.
7. Entry & Exit Logic (Summary)
7.1 Long Entry
Conditions for a Long:
CCI signal:
CCI crosses up through the upper threshold.
Ichimoku Cloud Filter (optional):
If enabled → price must be above the Kumo.
Ichimoku Lines Filter (optional):
If enabled → Conversion Line and Base Line must be above the Kumo.
MA Direction Filter (optional):
If enabled → Close must be above the chosen MA.
Anti-re-entry flag:
allowLong must be true (cloud-based reset).
Position check:
Long entries are allowed when current position size ≤ 0 (so it can also reverse from short to long).
If all these conditions are true, the strategy sends:
strategy.entry("Long", strategy.long, qty = calculated_qty)
After entry:
allowLong = false
allowShort = true
7.2 Short Entry
Same structure, mirrored:
CCI signal:
CCI crosses down through the lower threshold.
Cloud filter: price must be below cloud (if enabled).
Lines filter: conversion & base must be below cloud (if enabled).
MA filter: Close must be below MA (if enabled).
allowShort must be true.
Position check: position size ≥ 0 (allows reversal from long to short).
Then:
strategy.entry("Short", strategy.short, qty = calculated_qty)
Flags update:
allowShort = false
allowLong = true
7.3 Exits
While in a position:
The strategy continuously recalculates SL (depending on chosen mode) and, in Percent mode, TP.
In Fib mode, fixed TP levels are computed at entry.
BreakEven may raise/tighten the SL if its conditions are met.
Exits are executed via strategy.exit:
Percent mode: one TP+SL exit per side.
Fib mode: two partial exits (TP1 and TP2) sharing the same SL.
At position open, the script also draws visual lines:
White line — entry price.
Green line(s) — TP level(s).
Red line — SL (if not using Ichimoku Trailing; with trailing, the red line is updated dynamically).
Maximum of 30 lines are kept to avoid clutter.
8. How to Use the Strategy
Choose market & timeframe
Works well on trending instruments. Try crypto, FX or indices on H1–H4, or intraday if you prefer more trades.
Adjust Ichimoku settings
Keep defaults (9/26/52/26) or adapt to your timeframe.
Configure Moving Average
Typical: EMA 200 as a trend filter.
Turn MA Direction Filter ON if you want to trade only with the main trend.
Set CCI thresholds
Default ±100 is classic.
Lower thresholds → more signals, higher noise.
Higher thresholds → fewer but stronger signals.
Enable/disable filters
Turn on Ichimoku Cloud and Ichimoku Lines if you want only “clean” trend trades.
Use Wait Position Close Before Flag Reset to control how often re-entries are allowed.
Choose TP & SL mode
Percent mode is simpler and easier to understand.
Fibonacci mode is more advanced: it aligns TP levels with the distance to stop, giving asymmetric RR setups (two partial TPs).
Choose Stable SL for fixed-risk trades, or Ichimoku / Ichimoku Trailing to tie stops to the cloud structure.
Set BreakEven
Enable BE if you want to lock in risk-free trades after a certain move.
Percent mode is straightforward; Fib mode keeps BreakEven in harmony with your Fib TP setup.
Run Backtest & Optimize
Press “Add to chart” → go to Strategy Tester.
Adjust parameters to your market and timeframe.
Look at equity curve, PF, drawdown, average trade, etc.
Live / Paper Trading
After you’re satisfied with backtest results, use the strategy to generate signals.
You can mirror entries/exits manually or connect them to alerts (if you build an alert-based execution layer).
🤖 DarkPool's Omni-MA APEX v3 🤖DarkPool's Omni-MA APEX v3 is an all-encompassing technical analysis suite designed to replace multiple indicators with a single, highly optimized tool. At its core, it features five independently customizable "Omni-MAs" capable of running various calculation models (SMA, EMA, HMA, LSMA, etc.) across multiple timeframes.
Beyond standard trend lines, the APEX v3 integrates a sophisticated "Market Structure Engine" that automatically plots Support & Resistance zones based on pivot points and volatility (ATR). It also features a "Trend Cloud" to visualize macro sentiment and a professional-grade Dashboard that aggregates data from over 10 different sources (RSI, MACD, OBV, Volume, etc.) to provide a real-time health check of the asset.
Key Features
5-Layer Omni-MA System: Five distinct moving averages with "Smart Coloring" that detects trends, consolidations (flat markets), and reversals.
Auto Support & Resistance: A dynamic algorithm that draws, updates, and prunes liquidity zones on the chart automatically.
Macro Trend Cloud: A visual background fill comparing Daily and Weekly momentum to keep you aligned with the higher timeframe.
Data Dashboard: A customizable panel displaying real-time metrics for Momentum, Volume, RSI, Divergences, and VWAP status.
Signal Generator: Alerts for MA crossovers, S/R breakouts, and trend shifts.
How to Use
1. The Omni-MAs (The Lines) The indicator plots up to five lines, color-coded for instant trend recognition:
Green/Blue: Price is above the previous value (Uptrend).
Red/Maroon: Price is below the previous value (Downtrend).
Gray: The line is flat (Consolidation/Chop).
MA 1-2 (Fast): Use these for entry triggers and scalping.
MA 3 (Medium): The "Anchor" line, often used as dynamic support.
MA 4-5 (Slow): The macro trend filters. If price is below MA 5, looking for longs is risky.
2. The Trend Cloud
Background Fill: This visualizes the difference between the Daily EMA and Weekly EMA.
Green Cloud: The Daily trend is above the Weekly trend (Strong Bullish Market).
Red Cloud: The Daily trend is below the Weekly trend (Strong Bearish Market).
3. Support & Resistance Zones
The Boxes: The script identifies pivot points and projects them forward as boxes.
Strategy: Watch for price to react at these zones. If a candle closes through a zone, it signals a Breakout (Green triangle) or Breakdown (Red triangle).
4. The Dashboard Located in the corner of your chart, this table provides a "Cockpit View" of the market:
Momentum Score: A composite score (-100 to +100) derived from RSI, MACD, and Stochastic.
Vol Ratio: Compares current volume to the average. A green bar indicates volume is higher than usual.
Market State: Classifies the market into regimes like "Volatile Bull," "Quiet Bear," or "Ranging."
Configuration Settings
Dashboard UI
Compact Mode: Reduces the table to show only the final Buy/Sell signal.
Active Widgets: Toggle individual data points (e.g., turn off "OBV" or "ADX" if you don't use them) to save screen space.
Global Analysis (Strategy Engine)
ATR Filter: Filters out "Weak" trends. If the price movement is too small (low volatility), signals are suppressed.
Volume MA: Sets the lookback period for calculating relative volume.
Support & Resistance
Pivot Sensitivity: Lower numbers find more zones (more noise); higher numbers find fewer, stronger zones.
Zone Width: Multiplies the ATR to determine how thick the S/R boxes should be.
MA Settings (1-5)
Type: Choose from SMA, EMA, WMA, HMA (Hull), VWMA, LSMA, ALMA, and more.
Timeframe: You can set MA 5 to "D" (Daily) while trading on a 15-minute chart to see the daily trend line overlaid.
Disclaimer This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a guarantee of future results.
Advanced VolumeAdvanced Volume is a modular volume analysis suite designed to uncover hidden buying and selling pressure that standard volume bars often miss. Instead of cluttering your chart with multiple oscillators, this tool combines five institutional metrics—Chaikin Money Flow (CMF), Money Flow Index (MFI), Volume RSI, Relative Volume (RVOL), and Volume Oscillator—into a single, adaptive pane.
The script features a "Hero Metric" system, allowing you to choose which oscillator is visualized on the chart, while an intelligent dashboard monitors the remaining metrics in the background to generate a composite "Total" market bias score.
Key Features
5-in-1 Architecture: Switch the main view between CMF, MFI, Volume RSI, RVOL, or Volume Oscillator without removing the indicator.
Divergence Engine: Automatically detects and labels bullish and bearish discrepancies between price action and the selected volume metric.
Hidden Liquidity Detection: Identifies "Churn" candles—specifically Dojis with unusually high relative volume—often signaling absorption or a potential reversal.
Live Dashboard: A real-time table displaying the status of all five metrics simultaneously, culminating in an aggregate Bullish/Bearish/Neutral score.
How to Use
1. The Hero Metric (Main Oscillator) By default, the indicator displays Chaikin Money Flow (CMF).
Green Bars: Buying pressure (Inflow).
Red Bars: Selling pressure (Outflow).
Customization: You can change this in the settings to MFI, Volume RSI, or RVOL depending on your trading style.
2. Divergence Signals The script looks for pivot points where price and volume disagree.
Bullish Div (Label Up): Price makes a lower low, but volume momentum makes a higher low. This suggests selling exhaustion.
Bearish Div (Label Down): Price makes a higher high, but volume momentum makes a lower high. This suggests weak buying conviction.
3. Hidden Liquidity (Yellow Diamonds) Small yellow diamond markers appear above candles when a specific condition is met: The candle is a Doji (indecision) BUT Relative Volume is extremely high (default > 2.0x average). This indicates a battle between buyers and sellers where significant volume was exchanged with zero net price movement—often a sign of a localized top or bottom.
4. The Dashboard Located on the chart, this panel provides a summary of the broader market health:
Money Flow: Tracks CMF status.
MFI: Checks for Overbought (>80) or Oversold (<20) conditions.
Rel Vol: Displays the current volume multiplier (e.g., 2.5x).
Vs VWAP: Checks if the price is above or below the Volume Weighted Average Price.
TOTAL: Sums up the signals from all metrics to give a final trend bias (Bullish, Bearish, or Neutral).
Disclaimer This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a guarantee of future results.
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.
Elastic Volume-Weighted Momentum 🔥Elastic Volume-Weighted Momentum (EVWM) is a hybrid oscillator that measures the "force" of a price move by combining distance from the mean (elasticity) with relative trading volume. Unlike standard momentum indicators that only look at price speed, EVWM assumes that volume is the fuel for sustainable trends.
The indicator calculates a baseline (default: Hull Moving Average) and measures how far price stretches away from it, normalized by market volatility (ATR). This "elasticity" is then multiplied by a Volume Factor. The result is a histogram and signal line that distinguishes between high-conviction moves (high volume) and weak speculation (low volume).
How to Use
The EVWM is designed to filter false breakouts and identify mean-reversion opportunities through three distinct signal types:
1. Ignition Signals (Triangles) These occur when the momentum breaks outside the standard deviation bands with high volume.
Signal: Yellow Triangles.
Interpretation: This represents a valid breakout. The market has stretched away from the average with significant participation. This is often a signal to enter in the direction of the breakout.
2. Zombie Signals (X Marks) These occur when the momentum breaks outside the bands with low volume.
Signal: Grey "X".
Interpretation: This is a "fakeout" or a trap. The price moved, but there is no volume supporting it. Traders should exercise caution or consider fading the move, as it lacks the energy to sustain the trend.
3. Snap-Back Signals (Circles) These occur when the momentum line returns inside the bands after being overextended.
Signal: Red/Green Circles.
Interpretation: The "rubber band" is snapping back. This is a classic mean-reversion signal, often used to take profits on an existing position or to enter a counter-trend trade targeting the baseline.
4. Divergences The indicator includes an optional feature to detect discrepancies between price action and momentum.
Bearish Divergence: Price makes a higher high, but EVWM makes a lower high.
Bullish Divergence: Price makes a lower low, but EVWM makes a higher low. These patterns often precede a trend reversal.
Configuration Guide
Lookback Length: Controls the speed of the indicator. Use lower values (21) for scalping and higher values (50+) for swing trading.
Baseline Type: Selects the moving average used as the center of gravity. "Hull MA" is the default for its responsiveness, while "SMA" offers a smoother, slower baseline.
Trend Filter: A safety mechanism that checks a higher timeframe (e.g., 4-hour or Daily). If enabled, the indicator will block "Buy" signals if the higher timeframe trend is bearish, helping traders stay on the right side of the market.
Volume Threshold: Adjusts what defines "High Volume." Increasing this value makes "Ignition" signals rarer but potentially more reliable.
Disclaimer: This indicator is provided for educational purposes only. Past performance does not guarantee future results.
Money Flow Matrix This comprehensive indicator is a multi-faceted momentum and volume oscillator designed to identify trend strength, potential reversals, and market confluence. It combines a volume-weighted RSI (Money Flow) with a double-smoothed momentum oscillator (Hyper Wave) to filter out noise and provide high-probability signals.
Core Components
1. Money Flow (The Columns) This is the backbone of the indicator. It calculates a normalized RSI and weights it by relative volume.
Green Columns: Positive money flow (Buying pressure).
Red Columns: Negative money flow (Selling pressure).
Neon Colors (Overflow): When the columns turn bright Neon Green or Neon Red, the Money Flow has breached the dynamic Bollinger Band thresholds. This indicates an extreme overbought or oversold condition, suggesting a potential climax in the current move.
2. Hyper Wave (The Line) This is a double-smoothed Exponential Moving Average (EMA) derived from price changes. It acts as the "signal line" for the system. It is smoother than standard RSI or MACD, reducing false signals during choppy markets.
Green Line: Momentum is increasing.
Red Line: Momentum is decreasing.
3. Confluence Zones (Background) The background color changes based on the agreement between Money Flow and Hyper Wave.
Green Background: Both Money Flow and Hyper Wave are bullish. This represents a high-probability long environment.
Red Background: Both Money Flow and Hyper Wave are bearish. This represents a high-probability short environment.
Signal Guide
The Matrix provides three tiers of signals, ranging from early warnings to confirmation entries.
1. Warning Dots (Circles) These appear when the Hyper Wave crosses specific internal levels (-30/30).
Green Dot: Early warning of a bullish rotation.
Red Dot: Early warning of a bearish rotation.
Usage: These are not immediate entry signals but warnings to tighten stop-losses or prepare for a reversal.
2. Major Crosses (Triangles) These occur when Money Flow crosses the zero line, confirmed by momentum direction.
Green Triangle Up: Major Buy Signal (Money Flow crosses above 0).
Red Triangle Down: Major Sell Signal (Money Flow crosses below 0).
Usage: These are the primary trend-following entry signals.
3. Divergences (Labels "R" and "H") The script automatically detects discrepancies between Price action and the Hyper Wave oscillator.
"R" (Regular Divergence): Indicates a potential Reversal.
Bullish R: Price makes a lower low, but Oscillator makes a higher low.
Bearish R: Price makes a higher high, but Oscillator makes a lower high.
"H" (Hidden Divergence): Indicates a potential Trend Continuation.
Bullish H: Price makes a higher low, but Oscillator makes a lower low.
Bearish H: Price makes a lower high, but Oscillator makes a higher high.
Dashboard (Confluence Meter)
Located in the bottom right of the chart, the dashboard provides a snapshot of the current candle's status. It calculates a score based on three factors:
Is Money Flow positive?
Is Hyper Wave positive?
Is Hyper Wave trending up?
Readings:
STRONG BUY: All metrics are bullish.
WEAK BUY: Mixed metrics, but leaning bullish.
NEUTRAL: Metrics are conflicting.
WEAK/STRONG SELL: Bearish equivalents of the buy signals.
Trading Strategies
Strategy A: The Trend Rider
Entry: Wait for a Green Triangle (Major Buy).
Confirmation: Ensure the Background is highlighted Green (Confluence).
Exit: Exit when the background turns off or a Red Warning Dot appears.
Strategy B: The Reversal Catch
Setup: Look for a Neon Red Column (Overflow/Oversold).
Trigger: Wait for a Green "R" Label (Regular Bullish Divergence) or a Green Warning Dot.
Confirmation: Wait for the Hyper Wave line to turn green.
Strategy C: The Pullback (Continuation)
Context: The market is in a strong trend (Green Background).
Trigger: Price pulls back, but a Green "H" Label (Hidden Bullish Divergence) appears.
Action: Enter in the direction of the original trend.
Settings Configuration
The code includes tooltips for all inputs to assist with configuration.
Money Flow Length: Adjusts the sensitivity of the volume calculation. Lower numbers are faster but noisier; higher numbers are smoother.
Threshold Multiplier: Controls the "Neon" overflow bars. Increasing this (e.g., to 2.5 or 3.0) will result in fewer, more extreme signals.
Divergence Lookback: Determines how many candles back the script looks to identify pivots. Increase this number to find larger, macro divergences.
Disclaimer
This source code and the accompanying documentation are for educational and informational purposes only. They do not constitute financial, investment, or trading advice.
DarkPool's RSi DarkPool's RSi is an enhanced momentum oscillator designed to automatically detect structural discrepancies between price action and the Relative Strength Index. While retaining the standard RSI visualization, this script overlays advanced divergence recognition logic to identify potential trend reversals.
The tool identifies pivot points in real-time and compares recent peaks and valleys against historical data. When the momentum of the RSI contradicts the direction of price action, the indicator highlights these events using dynamic trendlines, shape markers, and background coloring. A built-in dashboard table provides an immediate status check of active divergence signals.
Key Features
Automated Divergence Detection: Automatically spots both Regular Bullish and Regular Bearish divergences based on pivot lookback settings.
Dynamic Visuals: Draws physical lines connecting RSI peaks or troughs to visualize the divergence angle, alongside triangle markers indicating the signal direction.
Active Status Dashboard: A data table located on the chart monitors the current state of the market, flagging signals as "Active" when detected.
Standard RSI Overlay: Includes standard Overbought (70) and Oversold (30) reference lines for traditional momentum trading.
How to Use
1. Reading the Standard RSI The black line represents the Relative Strength Index.
Overbought (Above 70): Suggests the asset may be overvalued and due for a pullback.
Oversold (Below 30): Suggests the asset may be undervalued and due for a bounce.
Midline (50): Acts as a trend filter; values above 50 indicate bullish momentum, while values below 50 indicate bearish momentum.
2. Trading Divergences The primary function of this tool is to identify reversal setups.
Bullish Divergence (Green Triangle/Line): Occurs when Price makes a Lower Low, but the RSI makes a Higher Low. This indicates that selling momentum is exhausting and a price increase may follow.
Bearish Divergence (Red Triangle/Line): Occurs when Price makes a Higher High, but the RSI makes a Lower High. This indicates that buying momentum is fading and a price decrease may follow.
3. Visual Aids
Lines: The script draws solid lines directly on the RSI pane connecting the relevant pivot points to confirm the divergence slope.
Background Color: When a divergence is detected, the background of the indicator pane will highlight briefly (Green for Bullish, Red for Bearish) to draw attention to the new signal.
4. The Dashboard A small table in the bottom right corner tracks the status of the signals.
Status: ACTIVE: A divergence has been detected within the last 10 bars.
Status: None: No recent divergence patterns have been identified.
Disclaimer This indicator is provided for educational and informational purposes only. It does not constitute financial advice, investment recommendations, or a guarantee of future results. Trading cryptocurrencies and financial markets involves a high level of risk. Always perform your own due diligence before making any trading decisions.
Fat Tony Composite Histogram Dual SettingsThis is an adaptation of Rob Booker's Fat Tony Composite Histogram which allows you to put two levels for signals.
Dual MACD📘 Dual MACD — Synopsis
The Dual MACD indicator displays two separate MACD systems inside the same pane, allowing traders to compare fast and slow momentum behavior simultaneously.
What It Includes
Two fully adjustable MACDs
MACD 1 default: 12 / 12 / 9
MACD 2 default: 8 / 20 / 6
Show/Hide Toggles so each MACD can be viewed independently or together.
MACD Lines, Signal Lines, and Histograms for both systems.
Clean layout with a compact panel title: “MACD x2”
What It Helps You See
Short-term vs. longer-term momentum shifts
Faster MACD reacting to quick trend changes
Slower MACD confirming or filtering signals
Trend strength, momentum acceleration, and crossover behavior in a single pane
Why It’s Useful
The Dual MACD gives you momentum confirmation, fakeout filtering, and multi-speed trend insight—making it valuable for scalpers, intraday traders, and swing traders who want to reduce noise and improve signal quality.
Adaptive MACD PROAdaptive MACD PRO
Highlights structural momentum changes using dynamic normalization of MACD and Signal.
Phase Momentum Core
Adds directional confirmation based on short-term phase behavior.
Visual Output
• MACD & Signal lines with trend-based coloring
• Adaptive histogram reflecting momentum strength
• Fixed-position Buy/Sell dots at predefined levels
• AutoCalib dots on MACD_z threshold crossings
• Optional HUD panel displaying calibration levels and MACD_z
Features
• Selectable MA types (EMA, SMA, KAMA)
• Z-score normalization
• ATR-based volatility weighting
• Higher timeframe alignment
• Auto-calibration with SAFE / AGGRESSIVE modes
• Unified long/short triggers
• Full bar-coloring control
• Works on all assets and timeframes
The full source code is visible and may be modified or extended.
This script is intended for technical analysis and research only.
This indicator is published as a free, open-source script with full visible code.
Smart ATR ProSmart ATR Pro - Adaptive Volatility & Smart Money Indicator
Advanced oscillator combining Adaptive ATR filtering with Smart Money detection. Features:
🎯 Smart Signals
BUY/SELL alerts with star rating system (1-5 stars)
STRONG signals for high-probability entries
ATR color status (Green/Yellow/Red) for volatility conditions
📊 Multi-Timeframe Analysis
MFI with overbought/oversold zones
Cumulative Delta volume analysis
Smart Money Power histogram
Price-action divergences detection
⚡ Adaptive Technology
Auto-adjusts ATR ranges based on market conditions
Smart Money strength calculation (0-6 points)
Volume spike detection
🎨 Professional UI
Centered table with adjustable opacity
Color-coded indicators for quick reading
Clean oscillator display with multiple plots
Perfect for swing traders and day traders seeking confirmed entries with volatility filtering and smart money confirmation.
*Settings: ATR Period 14, MFI Period 12, 100-bar analysis*
LiquidityPulse RSI Candle Strength MomentumLiquidity-Pulse RSI Candle Strength Momentum is a multifunctional and original candle-analysis tool designed to highlight the potential internal strength of each candle using a combination of body size and volume.
To view the candle-strength scores clearly: right-click on the chart, go to Settings, and in the Symbol tab untick Body, Borders and Wicks.
Candle Strength Scores
The indicator calculates the average body size and average volume over a user-defined lookback period. Each candle is then compared to these averages, and the indicator combines relative body expansion and relative volume expansion with a square-root calculation to create a (normalised) candle-strength score from 1 to 10.
10 – exceptionally strong compared to the lookback average (large body size and volume)
1 – very weak compared to the lookback average (small body size and volume)
Bullish and bearish candles are evaluated independently, producing separate bull-strength and bear-strength scores.
Optional ATR and volume floors can be enabled to restrict strength scoring to candles that exceed a minimum volatility or participation threshold. This helps users who prefer to filter out low-impact candles during quiet market periods. This option can be enabled or adjusted in the settings but is turned off by default.
Candle Colours
This tool also shows candles coloured based on the candle-strength scores (10 colours in each theme), which makes it easier to visualise the scores and see whether the candle score was high or not. There are several options in the 'colour theme' dropdown menu in the settings. Users can also customise all colours manually.
RSI Candle Strength Arrows
The Relative Strength Index is a long-established momentum tool that calculates the ratio of average upward moves to average downward moves over a defined period, allowing traders to identify potential overbought and oversold market conditions where momentum may be stretched. As well as this, strong early momentum and participation are often associated with more sustained moves.
This indicator combines this methodology and provides optional arrows that appear only when candle strength and RSI conditions align:
– A candle meets or exceeds a chosen strength threshold
– RSI has recently reached an overbought or oversold level
– The candle direction matches the expected momentum shift
For example, if price has reached an oversold RSI level and a strong bullish candle forms (high candle-strength number), an upside arrow may plot.
Users can customise the RSI oversold and overbought thresholds, the minimum candle-strength threshold, and how many bars back the RSI condition must have occurred in the settings.
These arrows are not buy or sell signals but instead highlight rare moments where strong candle behaviour aligns with meaningful RSI extremes. This is useful to users because it allows the candle-strength logic to be applied only when momentum is genuinely stretched, filtering out noise and focusing attention on the most statistically significant market moves.
This indicator brings together a quantitative candle-strength model and a momentum-based RSI filter to give users a clearer view of how individual candles behave relative to their recent environment, while also highlighting when those movements occur during meaningful shifts in market momentum. By combining both forms of analysis, the tool helps traders distinguish ordinary price changes from potentially significant structural behaviour.
How traders can use this indicator
– Stronger candle scores in the trend direction can confirm continuation pressure.
– Powerful opposing candles appearing at RSI extremes may signal potential reversals or exhaustion points.
– If breakouts occur with high candle scores, price may be more likely to follow through.
– Weak candles with low scores help traders avoid false signals or low-quality setups.
– Candle-strength scoring helps users quickly interpret both volume and candle-body behaviour without manual analysis.
Open source, if anyone has any ideas on how to make the script better or have any questions please let me know :)
Disclaimer
This indicator is provided for educational and analytical purposes only and should not be interpreted as financial advice or a recommendation to buy or sell any asset. The candle-strength values displayed by this tool are not literal or definitive measures of market strength; they are derived from a custom mathematical model designed to highlight relative differences in candle behaviour. These values should be viewed as a simplified representation of candle dynamics, not as an objective or universal measure of strength.
Users should be aware that this calculation does not replace the importance of analysing real traded volume, order flow, liquidity conditions, or broader market context. As with any technical tool, results should be considered alongside other forms of analysis, and past performance does not guarantee future outcomes. Use at your own discretion and risk.
DarkPool's Squeeze Momentum @author LazyBearDarkPool's Squeeze Momentum Pro is a comprehensive overhaul of the classic volatility indicator, designed for the modern trader who requires deeper market insight. While staying true to the core logic of the original TTM Squeeze, this version introduces advanced features like automatic divergence detection, dynamic moving average selection, and main-chart integration to help you time entries and exits with precision.
Credit: This script is built upon the foundational "Squeeze Momentum Indicator" originally developed by LazyBear. This version expands on that legacy with enhanced visualization, alert systems, and divergence logic.
Key Features
1. Advanced Divergence Detection
The indicator automatically scans for Regular Bullish and Regular Bearish divergences between price action and momentum.
Bullish Divergence (Green "BULL" Label): Occurs when Price makes a Lower Low, but Momentum makes a Higher Low. This often precedes a bullish reversal.
Bearish Divergence (Red "BEAR" Label): Occurs when Price makes a Higher High, but Momentum makes a Lower High. This often precedes a bearish reversal.
2. Multi-Mode Squeeze Detection
The central dots on the zero line tell you the state of market volatility:
Red Dot (Squeeze ON): Volatility is compressed. The Bollinger Bands are inside the Keltner Channels. The market is "coiling" and preparing for an explosive move. Do not trade yet—wait for the fire.
Grey Dot (Squeeze OFF): The squeeze has "fired." Volatility is expanding, and price is moving.
Blue Dot (Wide Bands): Volatility is extremely high. The bands are exceptionally wide, often indicating the end of a trend or a period of high risk.
3. "Ghost" Histogram & Visual Depth
The momentum histogram features a "Ghost" fill (transparent background) to help visualize the volume of momentum without cluttering the screen.
Bright Green: Strong Bullish Momentum (Rising).
Dark Green: Weakening Bullish Momentum (Fading).
Bright Red: Strong Bearish Momentum (Falling).
Dark Red: Weakening Bearish Momentum (Recovering).
4. Dynamic Candle Coloring
Enabled by default, this feature colors the candles on your main chart to match the momentum histogram. This allows you to instantly gauge the trend strength without looking down at the oscillator pane.
5. Adaptive Calculation Engines
Unlike standard versions fixed to SMA, you can now select the moving average algorithm that drives the Bollinger Bands and Keltner Channels:
SMA: Standard, stable signals.
EMA: More reactive to recent price action.
WMA/RMA: Weighted options for specific strategies.
🛠 How to Operate
The "Squeeze & Fire" Strategy
Identify the Squeeze: Look for a series of Red Dots on the zero line. This indicates the market is resting and building energy.
The Trigger: Wait for the dot to turn Gray AND for the histogram to expand clearly in one direction.
Long Signal: Squeeze fires (Red -> Gray) + Histogram turns Green.
Short Signal: Squeeze fires (Red -> Gray) + Histogram turns Red.
The "Divergence Reversal" Strategy
Watch for "BULL" or "BEAR" labels appearing near the peaks or valleys of the histogram.
Confirmation: A divergence is a warning. Wait for the histogram color to change (e.g., from Bright Red to Dark Red) before entering a reversal trade.
⚙️ Settings Guide
Basis MA Type: Choose between SMA, EMA, WMA, or RMA to tune the sensitivity of the squeeze.
BB/KC Settings: Fully customizable Length and Multipliers to adapt to different assets (Crypto, Forex, or Stocks).
Pivot Lookback: Controls how strict the divergence detection is. Higher numbers = fewer, more significant signals.
Colour Main Chart Candles: Toggle this OFF if you prefer your standard candle colours.
Disclaimer
Trading involves a high level of risk and is not suitable for all investors. This indicator is a tool for technical analysis and does not constitute financial advice. Past performance is not indicative of future results. Always use proper risk management and do not trade based solely on a single indicator.
MA Crossover Scalper [4H]//@version=5
indicator("MA Crossover Scalper ", overlay=false)
// Market Cap Filter (Volume as proxy)
volumeValid = volume >= 500000 and volume <= 4000000
// MA Crossover System
ma9 = ta.sma(close, 9)
ma21 = ta.sma(close, 21)
bullishCross = ta.crossover(ma9, ma21) and close > ma21
bearishCross = ta.crossunder(ma9, ma21) and close < ma21
// Volume Confirmation
volumeSpike = volume > ta.sma(volume, 20) * 1.3
// Final Signals
bullSignal = bullishCross and volumeSpike and volumeValid
bearSignal = bearishCross and volumeSpike and volumeValid
// Output for Screener
plot(bullSignal ? 1 : 0, "Bull MA Cross", color=color.green)
plot(bearSignal ? 1 : 0, "Bear MA Cross", color=color.red)
Divergence Scanner
Scanner and Indication (Divergence Scanner & Signal)An advanced experimental indicator designed to detect instances of Divergence between price action and key oscillator metrics (e.g., RSI or MACD).The primary function of this script is for Screener use. It plots a numerical value (a value greater than zero) on the chart when a confirmed bullish or bearish divergence signal appears."
50 & 200 SMA + RSI Average Strategy (Long Only, Single Trade)It works better in trending markets. It delivers its best performance in the 4-hour to 1-day timeframes.
RSI + Psy + ADXRSI + Psychological Line + ADX (with RCI-replacement logic)
This custom TradingView indicator combines three major technical analysis tools—RSI, Psychological Line (Psy), and ADX—to help traders identify trend strength, market momentum, and overbought/oversold conditions with improved clarity.
1. Multi-Period RSI
The indicator calculates three RSI values:
Short-term RSI (9)
Mid-term RSI (26)
Long-term RSI (52)
These help users observe short-, mid-, and long-term momentum simultaneously.
Threshold lines are drawn at 70, 50, and 30 for standard RSI overbought/oversold analysis.
2. Psychological Line (Psy) with Dynamic Column Display
The Psy indicator counts how many closes within the selected period (default: 12) were higher than the previous close.
Values above 75 indicate overbought markets.
Values below 25 indicate oversold markets.
When Psy crosses these thresholds, it is displayed as a column chart centered at 50, visually expanding upward (overbought) or downward (oversold).
3. ADX Trend Strength with Color Coding
ADX is calculated from DI+ and DI− values (using true range and directional movement).
The ADX line changes color based on trend strength:
Blue: Weak trend (below 20)
Yellow: Moderate trend (20–30)
Red: Strong trend (above 30)
This helps traders easily recognize when the market transitions from low-volatility to strong-trend conditions.
Delta Force Index - DFI [TCMaster]This indicator provides a proxy measurement of hidden buying and selling pressure inside each candle by combining tick volume with candle direction. It calculates a simulated delta volume (buy vs. sell imbalance), applies customizable scaling factors, and displays three components:
Delta Columns (green/red): Show estimated hidden buy or sell pressure per candle.
Delta Moving Average (orange line): Smooths delta values to highlight underlying momentum.
Cumulative Delta (blue line): Tracks the long-term accumulation of hidden order flow.
How to use:
Rising green columns with a positive Delta MA and upward Cumulative Delta suggest strong hidden buying pressure.
Falling red columns with a negative Delta MA and downward Cumulative Delta suggest strong hidden selling pressure.
Scaling parameters allow you to adjust the visual balance between columns and lines for different timeframes.
Note: This tool uses tick volume and candle direction as a proxy for order flow. It does not display actual bid/ask data or Level II market depth. For professional order flow analysis, footprint charts or DOM data are required.
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