ka66: Period-Bounded High/Low LinesIndicator: Period-Bounded High/Low Lines
There's a few similar ones on TradingView already (as expected), nothing particularly special about this, was just fun to write the logic for it, and understand how it might be used to trade.
Interestingly, I just came across the idea from watching Adam Grimes' Chartschool video, "Anticipating Intraday Action":
www.youtube.com
Thought it was pretty neat. Use the "Daily" bound (default) with intra-day interval charts to get the same effect as in the video.
Now, to watch the video for its actual purpose. ;-)
在脚本中搜索"charts"
Volatility Finite Volume Elements Backtest This version has an important enhancement to the previous one that`s
especially useful with intraday minute charts.
Due to the volatility had not been taken into account to avoid the extra
complication in the formula, the previous formula has some drawbacks:
The main drawback is that the constant cutoff coefficient will overestimate
price changes in minute charts and underestimate corresponding changes in
weekly or monthly charts.
And now the indicator uses adaptive cutoff coefficient which will adjust to
all time frames automatically.
You can change long to short in the Input Settings
Please, use it only for learning or paper trading. Do not for real trading.
Volatility Finite Volume Elements Strategy This version has an important enhancement to the previous one that`s
especially useful with intraday minute charts.
Due to the volatility had not been taken into account to avoid the extra
complication in the formula, the previous formula has some drawbacks:
The main drawback is that the constant cutoff coefficient will overestimate
price changes in minute charts and underestimate corresponding changes in
weekly or monthly charts.
And now the indicator uses adaptive cutoff coefficient which will adjust to
all time frames automatically.
WARNING:
This script to change bars colors.
DecisionPoint Price Momentum Oscillator [LazyBear]The DecisionPoint Price Momentum Oscillator (PMO) is an oscillator based on a Rate of Change calculation that is smoothed twice with custom exponential moving averages. Because the PMO is normalized, it can also be used as a relative strength tool.
PMO can be used in many ways:
- PMO can be used to determine the OB/OS state. While the +2.5 to -2.5 is the usual range for broad stock market indexes, each price index will have its own “signature” range. You may have to tune this for your instrument.
- PMO triggers buy/sell on signal crossovers and ZERO crossovers.
- Common patterns like BullKiss, BearKiss are useful to track. See the link below for more info.
- Divergences.
- Histo-only mode that can be used similar to MACD-Histo.
I have exposed all params as configurable. Have fun tuning :) Don't forget to share what you found for your instrument.
More Info:
stockcharts.com
List of my public indicators: bit.ly
List of my app-store indicators: blog.tradingview.com
Some Sample Charts:
TWTR:
MSFT:
GOOGL:
Fibline GlanceThis is a set of three indicators combined into one script. The source of the three indicators comes from the tradingview user fibline (www.tradingview.com). The lines remind me of ichimoku, because at a glance, you can tell what the stock is up to. To me, the orange line does an excellent job of showing support and resistance. I'd be happy to add more to the script if anyone has any ideas.
See one of his charts:
Kaufman Stress IndicatorStress Indicator, first proposed by Mr. Perry Kaufman, provides an easy way for trading pairs / arbs.
Kaufman's trading rules for Stress Indicator:
- Decide on a pair to trade: For ex., AAPL v QQQ
- Calculate the Stress Indicator (SI) for that pair
- Buy the stock when SI 50
- Calculate the 60-day moving average of QQQ
- If the trend of QQQ is down, hedge the stock position with QQQ equal to the risk of the stock using the 20-day ATR of each
- Exit the hedge when the stock position exits, or exit the hedge when the trend of QQQ turns up
- Do not trade stocks under $3
Explanation of all potential SI applications is beyond this post. For more info:
- ptasite.s3.amazonaws.com
- www.futuresmag.com
- kaufmansignals.com
- TASC 2014 March issue.
Though Kaufman's Stress stategy is built on top of this Stress Indicator, I suggest reading up his full strategy guidelines before applying this.
Kaufman suggests using 60SMA on the index to track the slope. I have included a custom SMA (find it in the middle pane) that can show SMA for any selected symbol. Use the guide below to import that in to your charts: drive.google.com
FVE (Volatility Modified) This is another version of FVE indicator that we have posted earlier
in this forum.
This version has an important enhancement to the previous one that`s
especially useful with intraday minute charts.
Due to the volatility had not been taken into account to avoid the extra
complication in the formula, the previous formula has some drawbacks:
The main drawback is that the constant cutoff coefficient will overestimate
price changes in minute charts and underestimate corresponding changes in
weekly or monthly charts.
And now the indicator uses adaptive cutoff coefficient which will adjust to
all time frames automatically.
Elite Cumulative Volume Delta OscillatorOverview
The Elite CVD+ is a premium-grade, session-resettable Cumulative Volume Delta indicator designed exclusively for professional futures and volume-profile traders. By focusing on the cleaner and more actionable Line-Focused mode, it transforms raw order flow data into a precise decision engine that reveals institutional buying/selling pressure, absorption, exhaustion, and high-probability reversal/continuation zones.
Unlike standard CVD tools that accumulate indefinitely or reset awkwardly, this version resets cleanly at your chosen anchor period (default daily) while pulling granular delta from lower timeframes when desired. The result: a smooth, non-repainting line that highlights real-time shifts in aggressive participation without the noise of perpetual accumulation.
Why This Indicator Is Elite-Level Useful
True Institutional Footprint
Cumulative Volume Delta measures the net aggressive buying (bid hits) vs. selling (ask hits). Sustained positive CVD = buyers in control; negative = sellers dominating. When price makes new highs on weakening CVD → classic bearish divergence signaling distribution. The session reset prevents old data from distorting current conviction, making divergences far more reliable than perpetual CVD.
Early Reversal Detection via Absorption & Extremes
Absorption highlighting flags scenarios where heavy delta pushes against price but price refuses to follow (e.g., massive selling into lows yet price holds or closes higher) — textbook trapping/retail stop-hunting.
Session CVD extremes with dynamic test zones pinpoint where aggressive flow is exhausted. Price returning to test these levels often produces high-R:R reversals.
Confluence-Rich Signals
Dual EMAs provide trend/filter context (crossovers, zero-line bounces). Dynamic coloring instantly shows momentum strength. Extreme single-bar delta highlights climax buying/selling. Built-in regular + hidden divergences align order flow with price structure.
Multi-Timeframe Consistency
Optional custom lower-TF delta fetch ensures the same granular data regardless of chart timeframe — critical for traders who switch between 1-min execution charts and 15-min/1H analysis charts.
Clean, Low-Lag Visuals
Thick CVD line with intelligent coloring, subtle backgrounds, persistent extreme lines, and optional labels keep the pane readable even during fast markets. No clutter from inferior candle representations.
How Professional Traders Use Elite CVD+ Most Successfully
Primary Setup Framework
Use on futures with reliable volume delta (ES, NQ, YM, CL, GC, etc.). Best timeframes: 3–15 minutes for intraday, 1H–4H for swing. Combine with price action structure (order blocks, fair value gaps, market profile highs/lows).
Practical Tips for Maximum Edge
Anchor Period: '1D' for regular session trading (resets at 00:00 exchange time). Use '1W' for weekly bias or '4H' for London/NY session-specific flow.
Lower Timeframe Delta: Enable custom and set to '1' or '3' for maximum granularity on indices. Leave disabled on higher charts for smoother read.
Absorption Tuning: Raise threshold to 80–90 on volatile instruments (NQ) to filter noise; lower to 70 on quieter ones (CL, GC).
Divergences: Most powerful on 15M+. Disable hidden on very low TFs if too noisy.
Alerts: Use the master “Any Event” alert for push/email/webhook notifications of zero crosses or new extremes — perfect for mobile monitoring.
Combination Tools: Pair with session VWAP, volume profile (fixed range at highs/lows), or psychological levels for triple confluence.
TuxAlgo Plus a SMC and Trap Toolkit V0.98r1 by McTogaThe “TuxAlgo Plus” script is a powerful, standalone, conceptual open-source project and self-sufficient “smart money toolkit” with automatic trap detection (SMT), liquidity grabs, FVG confluence, and complete bot setup signals for TV charts in the “H1 to H6” time frame and daily charts.
The script is used to improve SMC/trap analyses, i.e., the structure and visualization logic for TradingView charts have been expanded in the “TuxAlgo++” project in line with Smart Money Concepts (SMC) and Smart Money Traps (SMT).
The SMT block (“Smart Money Traps”) supplements classic smart money concepts with:
1. Detection of bull traps (short setups) and bear traps (long setups)
2. Display of trap boxes in the chart (liquidity grab areas)
3. A bot setup box (table) with ready-made entry/SL/TP levels:
as well as age in bars & days and “Valid until ~X d” (remaining term)
SMT / bot setup only run on the following timeframes:
- H1, H2, H3, H4, H6 This means that traps, labels, and the bot setup box are only displayed on these timeframes
Trap definition “Bull Trap (Short Setup)”:
- Valid swing high
- Swing trend bullish
- High (Wick) pierces above the swing high (Liquidity Grab)
- Close falls back below the swing high (false breakout)
-> Result: Short setup (bull trap), marked in orange
Trap definition “Bear Trap (long setup)”:
- Valid swing low
- Swing trend bearish
- Low (wick) pierces below the swing low (liquidity grab)
- Close rises above the swing low again (false breakout)
-> Result: Long setup (bear trap), marked in blue
Entry / SL / TP calculation
A price range is taken for each trap:
Bull trap (short):
- Range =
- Entry = point within this range:
Entry = hiBT - (hiBT - loBT) * TrapEntryRatio (0..1)
-> 0.0 = at the Wick extreme, 0.5 = middle, 1.0 = at the Swing level
- SL = Wick extreme (upper edge of the trap)
- Risk = |Entry - SL|
- TP1 = Entry - R1 * Risk
- TP2 = Entry - R2 * Risk
Bear Trap (Long):
- Range =
- Entry analogous within the range according to TrapEntryRatio
- SL = wick bottom (lower edge of the trap)
- Risk = |Entry - SL|
- TP1 = Entry + R1 * Risk
- TP2 = Entry + R2 * Risk
R1 / R2 correspond to the inputs:
- botRR1Input = TP1 Risk/Reward (e.g., 1.5R)
- botRR2Input = TP2 Risk/Reward (e.g., 3.0R)
Age & Validity
Each trap stores:
- lastTrapBarIndex -> last bar of the trap
- Age in bars -> bar_index - lastTrapBarIndex
- Age in days (~d) -> AgeBars * BarDurationInDays (depending on TF)
Input: trapMaxAgeBars determines how long a trap is valid.
The bot setup box is only displayed if:
- a trap is present,
- AgeBars <= trapMaxAgeBars,
- SMT + Box + SMC timeframe are active.
Color logic (color blind friendly):
- Blue (accentBlue) = fresh traps (Age <= 1/3 MaxAge)
- Orange (accentOrange) = medium age
- Violet (accentPurple) = old, but still within MaxAge
- Gray (accentGray) = expired (> MaxAge)
The box also shows “Valid until ~X d” as the remaining term.
Day/Night Mode & Colors
- chart.bg_color is used to detect dark or light mode.
- Text/background colors adjust (light/dark).
- Accent colors (blue/orange/purple/gray) are suitable for red/green color blindness.
- Trap labels in the chart:
- Bull trap label = orange (short setup)
- Bear trap label = blue (long setup)
- Text color depends on chart mode (dark/light)
Typical workflow (example):
1. Select a suitable symbol & SMC timeframe (e.g., H4 or H6).
2. Wait for a bull trap (short) or bear trap (long).
3. Read in the bot setup box:
- Direction (long/short)
- Entry, SL, TP1, TP2
- Age & “Valid until ~X d”
4. These values can be used as a template for manual trading or external bot/order systems.
5. Position size & leverage must always be calculated separately in your own risk management
(e.g., 2% rule). This script does not calculate position sizes.
Gyspy Bot Trade Engine - V1.2B - Alerts - 12-7-25 - SignalLynxGypsy Bot Trade Engine (MK6 V1.2B) - Alerts & Visualization
Brought to you by Signal Lynx | Automation for the Night-Shift Nation 🌙
1. Executive Summary & Architecture
Gypsy Bot (MK6 V1.2B) is not merely a strategy; it is a massive, modular Trade Engine built specifically for the TradingView Pine Script V6 environment. While most tools rely on a single dominant indicator to generate signals, Gypsy Bot functions as a sophisticated Consensus Algorithm.
Note: This is the Indicator / Alerts version of the engine. It is designed for visual analysis and generating live alert signals for automation. If you wish to see Backtest data (Equity Curves, Drawdown, Profit Factors), please use the Strategy version of this script.
The engine calculates data from up to 12 distinct Technical Analysis Modules simultaneously on every bar closing. It aggregates these signals into a "Vote Count" and only fires a signal plot when a user-defined threshold of concurring signals is met. This "Voting System" acts as a noise filter, requiring multiple independent mathematical models—ranging from volume flow and momentum to cyclical harmonics and trend strength—to agree on market direction.
Beyond entries, Gypsy Bot features a proprietary Risk Management suite called the Dump Protection Team (DPT). This logic layer operates independently of the entry modules, specifically scanning for "Moon" (Parabolic) or "Nuke" (Crash) volatility events to signal forced exits, preserving capital during Black Swan events.
2. ⚠️ The Philosophy of "Curve Fitting" (Must Read)
One must be careful when applying Gypsy Bot to new pairs or charts.
To be fully transparent: Gypsy Bot is, by definition, a very advanced curve-fitting engine. Because it grants the user granular control over 12 modules, dozens of thresholds, and specific voting requirements, it is extremely easy to "over-fit" the data. You can easily toggle switches until the charts look perfect in hindsight, only to have the signals fail in live markets because they were tuned to historical noise rather than market structure.
To use this engine successfully:
Visual Verification: Do not just look for "green arrows." Look for signals that occur at logical market structure points.
Stability: Ensure signals are not flickering. This script uses closed-candle logic for key decisions to ensure that once a signal plots, it remains painted.
Regular Maintenance is Mandatory: Markets shift regimes (e.g., from Bull Trend to Crab Range). Gypsy Bot settings should be reviewed and adjusted at regular intervals to ensure the voting logic remains aligned with current market volatility.
Timeframe Recommendations:
Gypsy Bot is optimized for High Time Frame (HTF) trend following. It generally produces the most reliable results on charts ranging from 1-Hour to 12-Hours, with the 4-Hour timeframe historically serving as the "sweet spot" for most major cryptocurrency assets.
3. The Voting Mechanism: How Entries Are Generated
The heart of the Gypsy Bot engine is the ActivateOrders input (found in the "Order Signal Modifier" settings).
The engine constantly monitors the output of all enabled Modules.
Long Votes: GoLongCount
Short Votes: GoShortCount
If you have 10 Modules enabled, and you set ActivateOrders to 7:
The engine will ONLY plot a Buy Signal if 7 or more modules return a valid "Buy" signal on the same closed candle.
If only 6 modules agree, the signal is rejected.
4. Technical Deep Dive: The 12 Modules
Gypsy Bot allows you to toggle the following modules On/Off individually to suit the asset you are trading.
Module 1: Modified Slope Angle (MSA)
Logic: Calculates the geometric angle of a moving average relative to the timeline.
Function: Filters out "lazy" trends. A trend is only considered valid if the slope exceeds a specific steepness threshold.
Module 2: Correlation Trend Indicator (CTI)
Logic: Measures how closely the current price action correlates to a straight line (a perfect trend).
Function: Ensures that we are moving up with high statistical correlation, reducing fake-outs.
Module 3: Ehlers Roofing Filter
Logic: A spectral filter combining High-Pass (trend removal) and Super Smoother (noise removal).
Function: Isolates the "Roof" of price action to catch cyclical turning points before standard moving averages.
Module 4: Forecast Oscillator
Logic: Uses Linear Regression forecasting to predict where price "should" be relative to where it is.
Function: Signals when the regression trend flips. Offers "Aggressive" and "Conservative" calculation modes.
Module 5: Chandelier ATR Stop
Logic: A volatility-based trend follower that hangs a "leash" (ATR multiple) from extremes.
Function: Used as an entry filter. If price is above the Chandelier line, the trend is Bullish.
Module 6: Crypto Market Breadth (CMB)
Logic: Pulls data from multiple major tickers (BTC, ETH, and Perpetual Contracts).
Function: Calculates "Market Health." If Bitcoin is rising but the rest of the market is dumping, this module can veto a trade.
Module 7: Directional Index Convergence (DIC)
Logic: Analyzes the convergence/divergence between Fast and Slow Directional Movement indices.
Function: Identifies when trend strength is expanding.
Module 8: Market Thrust Indicator (MTI)
Logic: A volume-weighted breadth indicator using Advance/Decline and Volume data.
Function: One of the most powerful modules. Confirms that price movement is supported by actual volume flow. Recommended setting: "SSMA" (Super Smoother).
Module 9: Simple Ichimoku Cloud
Logic: Traditional Japanese trend analysis.
Function: Checks for a "Kumo Breakout." Price must be fully above/below the Cloud to confirm entry.
Module 10: Simple Harmonic Oscillator
Logic: Analyzes harmonic wave properties to detect cyclical tops and bottoms.
Function: Serves as a counter-trend or early-reversal detector.
Module 11: HSRS Compression / Super AO
Logic: Detects volatility compression (HSRS) or Momentum/Trend confluence (Super AO).
Function: Great for catching explosive moves resulting from consolidation.
Module 12: Fisher Transform (MTF)
Logic: Converts price data into a Gaussian normal distribution.
Function: Identifies extreme price deviations. Uses Multi-Timeframe (MTF) logic to ensure you aren't trading against the major trend.
5. Global Inhibitors (The Veto Power)
Even if 12 out of 12 modules vote "Buy," Gypsy Bot performs a final safety check using Global Inhibitors.
Bitcoin Halving Logic: Prevents trading during chaotic weeks surrounding Halving events (dates projected through 2040).
Miner Capitulation: Uses Hash Rate Ribbons to identify bearish regimes when miners are shutting down.
ADX Filter: Prevents trading in "Flat/Choppy" markets (Low ADX).
CryptoCap Trend: Checks the total Crypto Market Cap chart for broad market alignment.
6. Risk Management & The Dump Protection Team (DPT)
Even in this Indicator version, the RM logic runs to generate Exit Signals.
Dump Protection Team (DPT): Detects "Nuke" (Crash) or "Moon" (Pump) volatility signatures. If triggered, it plots an immediate Exit Signal (Yellow Plot).
Advanced Adaptive Trailing Stop (AATS): Dynamically tightens stops in low volatility ("Dungeon") and loosens them in high volatility ("Penthouse").
Staged Take Profits: Plots TP1, TP2, and TP3 events on the chart for visual confirmation or partial exit alerts.
7. Recommended Setup Guide
When applying Gypsy Bot to a new chart, follow this sequence:
Set Timeframe: 4 Hours (4H).
Tune DPT: Adjust "Dump/Moon Protection" inputs first. These filter out bad signals during high volatility.
Tune Module 8 (MTI): Experiment with the MA Type (SSMA is recommended).
Select Modules: Enable/Disable modules based on the asset's personality (Trending vs. Ranging).
Voting Threshold: Adjust ActivateOrders to filter out noise.
Alert Setup: Once visually satisfied, use the "Any Alert Function Call" option when creating an alert in TradingView to capture all Buy/Sell/Close events generated by the engine.
8. Technical Specs
Engine Version: Pine Script V6
Repainting: This indicator uses Closed Candle data for all Risk Management and Entry decisions. This ensures that signals do not vanish after the candle closes.
Visuals:
Blue Plot: Buy/Sell Signal.
Yellow Plot: Risk Management (RM) / DPT Close Signal.
Green/Lime/Olive Plots: Take Profit hits.
Disclaimer:
This script is a complex algorithmic tool for market analysis. Past performance is not indicative of future results. Cryptocurrency trading involves substantial risk of loss. Use this tool to assist your own decision-making, not to replace it.
9. About Signal Lynx
Automation for the Night-Shift Nation 🌙
Signal Lynx focuses on helping traders and developers bridge the gap between indicator logic and real-world automation. The same RM engine you see here powers multiple internal systems and templates, including other public scripts like the Super-AO Strategy with Advanced Risk Management.
We provide this code open source under the Mozilla Public License 2.0 (MPL-2.0) to:
Demonstrate how Adaptive Logic and structured Risk Management can outperform static, one-layer indicators
Give Pine Script users a battle-tested RM backbone they can reuse, remix, and extend
If you are looking to automate your TradingView strategies, route signals to exchanges, or simply want safer, smarter strategy structures, please keep Signal Lynx in your search.
License: Mozilla Public License 2.0 (Open Source).
If you make beneficial modifications, please consider releasing them back to the community so everyone can benefit.
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.
Forex Session TrackerForex Session Tracker - Professional Trading Session Indicator
The Forex Session Tracker is a comprehensive and visually intuitive indicator designed specifically for forex traders who need precise tracking of major global trading sessions. This powerful tool helps traders identify active market sessions, monitor session-specific price ranges, and capitalize on volatility patterns unique to each trading period.
Understanding when major financial centers are active is crucial for forex trading success. This indicator provides real-time visualization of the Tokyo, London, New York, and Sydney trading sessions, allowing traders to align their strategies with peak liquidity periods and avoid low-volatility trading windows.
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Key Features
📊 Four Major Global Trading Sessions
The indicator tracks all four primary forex trading sessions with precision:
- Tokyo Session (Asian Market) - Captures the Asian trading hours, ideal for JPY, AUD, and NZD pairs
- London Session (European Market) - Monitors the most liquid trading period, perfect for EUR, GBP pairs
- New York Session (American Market) - Tracks US market hours, essential for USD-based currency pairs
- Sydney Session (Pacific Market) - Identifies the opening of the trading week and AUD/NZD activity
Each session is fully customizable with individual color schemes, making it easy to distinguish between different market periods at a glance.
🎯 Session Range Visualization
For each active trading session, the indicator automatically:
- Draws rectangular boxes that highlight the session's time period
- Tracks and displays session HIGH and LOW price levels in real-time
- Creates horizontal lines at session extremes for easy reference
- Positions session labels at the center of each trading period
- Updates dynamically as new highs or lows are formed within the session
This visual approach helps traders quickly identify:
- Session breakout opportunities
- Support and resistance zones formed during specific sessions
- Range-bound vs. trending session behavior
- Key price levels that institutional traders are watching
📱 Live Information Dashboard
A sleek, professional information panel displays:
- Real-time session status - Instantly see which sessions are currently active
- Color-coded indicators - Green dots for active sessions, gray for closed sessions
- Timezone information - Confirms your current timezone settings
- Customizable positioning - Place the dashboard anywhere on your chart (Top Left, Top Right, Bottom Left, Bottom Right)
- Adjustable size - Choose from Tiny, Small, Normal, or Large text sizes for optimal visibility
The dashboard provides at-a-glance awareness of market conditions without cluttering your chart analysis.
⚙️ Extensive Customization Options
Every aspect of the indicator can be tailored to your trading preferences:
Session-Specific Controls:
- Enable/disable individual sessions
- Customize colors for each trading period
- Adjust session times to match your broker's server time
- Toggle background highlighting on/off
- Show/hide session high/low lines independently
General Settings:
- UTC Offset Control - Adjust timezone from UTC-12 to UTC+14
- Exchange Timezone Option - Automatically use your chart's exchange timezone
- Background Transparency - Fine-tune the opacity of session highlighting (0-100%)
- Session Labels - Show or hide session name labels
- Information Panel - Toggle the live status dashboard on/off
Style Settings:
- Turn session backgrounds ON/OFF directly from the Style tab
- Maintain clean charts while keeping all analytical features active
🔔 Built-in Alert System
Stay informed about session openings with customizable alerts:
- Tokyo Session Started
- London Session Started
- New York Session Started
- Sydney Session Started
Set up notifications to never miss important market opening periods, even when you're away from your charts.
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How to Use This Indicator
For Day Traders:
1. Identify High-Volatility Periods - Focus your trading during London and New York session overlaps for maximum liquidity
2. Monitor Session Breakouts - Watch for price breaks above/below session highs and lows
3. Avoid Low-Volume Periods - Recognize when major sessions are closed to avoid false signals
For Swing Traders:
1. Mark Key Levels - Use session highs and lows as support/resistance zones
2. Track Multi-Session Patterns - Observe how price behaves across different trading sessions
3. Plan Entry/Exit Points - Time your trades around session openings for better execution
For Currency-Specific Traders:
1. JPY Pairs - Focus on Tokyo session movements
2. EUR/GBP Pairs - Monitor London session activity
3. USD Pairs - Track New York session volatility
4. AUD/NZD Pairs - Watch Sydney and Tokyo sessions
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Technical Specifications
- Pine Script Version: 5
- Overlay Indicator: Yes (displays directly on price chart)
- Maximum Bars Back: 500
- Drawing Objects: Up to 500 lines, boxes, and labels
- Performance: Optimized for real-time data processing
- Compatibility: Works on all timeframes (recommended: 5m to 1H for session tracking)
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Installation & Setup
1. Add to Chart - Click "Add to Chart" after copying the script to Pine Editor
2. Configure Timezone - Set your UTC offset or enable "Use Exchange Timezone"
3. Customize Colors - Choose your preferred color scheme for each session
4. Adjust Display - Enable/disable features based on your trading style
5. Set Alerts - Create alert notifications for session starts
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Best Practices
✅ Combine with Price Action - Use session ranges alongside candlestick patterns for confirmation
✅ Watch Session Overlaps - The London-New York overlap (1300-1600 UTC) typically shows highest volatility
✅ Respect Session Highs/Lows - These levels often act as intraday support and resistance
✅ Adjust for Your Broker - Verify session times match your broker's server clock
✅ Use Multiple Timeframes - View sessions on both lower (15m) and higher (1H) timeframes for context
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Why Choose Forex Session Tracker Pro?
✨ Professional Grade Tool - Built with clean, efficient code following TradingView best practices
✨ Beginner Friendly - Intuitive design with clear visual cues
✨ Highly Customizable - Adapt every feature to match your trading style
✨ Performance Optimized - Lightweight code that won't slow down your charts
✨ Actively Maintained - Regular updates and improvements
✨ No Repainting - All visual elements are fixed once the session completes
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Support & Updates
This indicator is designed to provide reliable, accurate session tracking for forex traders of all experience levels. Whether you're a scalper looking for high-volatility windows or a position trader marking key institutional levels, the Forex Session Tracker Pro delivers the insights you need to make informed trading decisions.
Happy Trading! 📈
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Disclaimer
This indicator is a tool for technical analysis and should be used as part of a comprehensive trading strategy. Past performance does not guarantee future results. Always practice proper risk management and never risk more than you can afford to lose. Trading forex carries a high level of risk and may not be suitable for all investors.
Daily MA — Higher-Timeframe Daily Moving Average OverlayThis indicator plots a clean, higher-timeframe daily moving average directly on any chart, so you can always see where price sits relative to the daily trend — even while trading on lower timeframes (1m, 5m, etc.).
It’s designed to be:
Simple – a single, configurable daily MA line
Consistent – always anchored to the 1D timeframe
Flexible – choose EMA or SMA and customize line width/color
⸻
What This Indicator Does
Pulls the 1-Day (1D) moving average of the current symbol, regardless of your chart timeframe.
Lets you choose between EMA (Exponential Moving Average) or SMA (Simple Moving Average).
Plots that daily MA as a smooth overlay on your current chart.
Keeps the line visually clean and continuous, making it easy to see daily trend and dynamic support/resistance.
This is not a signals/strategy script. It doesn’t generate buy/sell arrows or backtest logic. It’s a context tool for visualizing the daily trend while you execute your own strategy.
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Why a Daily MA Overlay Is Useful
Traders commonly use a daily moving average to:
Anchor intraday trades to the higher-timeframe trend
Longs when price is holding above the Daily MA
Shorts or caution when price is rejecting from the Daily MA
Identify dynamic support/resistance
Price often reacts around well-watched daily MAs (e.g., 50, 100, 200)
Filter setups
Only take long setups when price is above the daily trend line
Avoid counter-trend trades when price is extended far from the Daily MA
Because this script forces the MA to always be computed on 1D, you don’t have to switch back and forth between intraday and daily charts to keep track of the bigger picture.
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Inputs & Settings
MA Length
Default: 200
Any positive integer (min 1)
Common examples: 50, 100, 200 for trend structure
MA Type
EMA – reacts faster to recent price (default)
SMA – smoother, slower, more “classic” feel
Line Width
Default: 2
Range: 1 to 10
Increase if you want the Daily MA to stand out clearly against other indicators
Color
Default: Purple tone
Fully customizable – pick any color that works with your chart theme
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How to Use It in Your Workflow
Intraday traders (scalpers/day-traders):
Apply the indicator to your 1m/5m/15m charts.
Use the Daily MA as a trend filter :
Only look for long scalps when price is above the Daily MA.
Be more cautious with longs or consider shorts when price is below it.
Swing traders :
Use it on 1H/4H charts to see where price sits relative to a longer-term daily trend.
Watch for:
Pullbacks to the Daily MA in an uptrend as potential demand zones.
Rejections at the Daily MA in a downtrend as potential supply zones.
Risk management & context :
Avoid chasing extended moves far from the Daily MA.
Mark confluence with other tools (support/resistance, volume profile, etc.) around the Daily MA.
⸻
Notes & Limitations
The moving average itself is calculated from daily candles , then displayed on your current timeframe.
This is a visual aid only . It does not guarantee future performance or provide financial advice.
Always combine this indicator with your own analysis, risk management, and trading plan.
⸻
Disclaimer :
This script is provided for educational and informational purposes only. It is not financial advice and does not constitute a recommendation to buy or sell any financial instrument. Always do your own research and trade at your own risk.
Momentum Breakout Filter + ATR ZonesMomentum Breakout Filter + ATR Zones - User Guide
What This Indicator Does
This indicator helps you with your MACD + volume momentum strategy by:
Filtering out fake breakouts - Shows ⚠️ warnings when breakouts lack confirmation
Showing clear entry signals - 🚀 LONG and 🔻 SHORT labels when all conditions align
Automatic stop loss & profit targets - Based on ATR (Average True Range)
Visual trend confirmation - Background color + EMA alignment
Signal Types
🚀 LONG Entry Signal (Green Label)
Appears when ALL conditions met:
✅ MACD crosses above signal line
✅ Volume > 1.5× average
✅ Price > EMA 9 > EMA 21 > EMA 200 (bullish trend)
✅ Price closes above recent 20-bar high
🔻 SHORT Entry Signal (Red Label)
Appears when ALL conditions met:
✅ MACD crosses below signal line
✅ Volume > 1.5× average
✅ Price < EMA 9 < EMA 21 < EMA 200 (bearish trend)
✅ Price closes below recent 20-bar low
⚠️ FAKE Breakout Warning (Orange Label)
Appears when price breaks high/low BUT lacks confirmation:
❌ Low volume (below 1.5× average), OR
❌ Wick break only (didn't close through level), OR
❌ MACD not aligned with direction
Hover over the warning label to see what's missing!
ATR Stop Loss & Targets
When you get a signal, colored lines automatically appear:
Long Position
Red solid line = Stop Loss (Entry - 1.5×ATR)
Green dashed lines = Profit Targets:
Target 1: Entry + 2×ATR
Target 2: Entry + 3×ATR
Target 3: Entry + 4×ATR
Short Position
Red solid line = Stop Loss (Entry + 1.5×ATR)
Green dashed lines = Profit Targets:
Target 1: Entry - 2×ATR
Target 2: Entry - 3×ATR
Target 3: Entry - 4×ATR
The lines move with each bar until you exit the position.
Chart Elements
Moving Averages
Blue line = EMA 9 (fast)
Orange line = EMA 21 (medium)
White line = EMA 200 (trend filter)
Volume
Yellow bars = High volume (above threshold)
Gray bars = Normal volume
Background Color
Light green = Bullish trend (all EMAs aligned up)
Light red = Bearish trend (all EMAs aligned down)
No color = Neutral/mixed
MACD (Bottom Pane)
Green/Red columns = MACD Histogram
Blue line = MACD Line
Orange line = Signal Line
Info Dashboard (Bottom Right)
ItemWhat It ShowsVolumeCurrent volume vs average (✓ HIGH or ✗ Low)MACDDirection (BULLISH or BEARISH)TrendEMA alignment (BULL, BEAR, or NEUTRAL)ATRCurrent ATR value in dollarsPositionCurrent position (LONG, SHORT, or NONE)R:RRisk-to-Reward ratio (shows when in position)
How To Use It
Basic Workflow
Wait for setup
Watch for MACD to approach signal line
Volume should be building
Price should be near EMA structure
Get confirmation
Wait for 🚀 LONG or 🔻 SHORT label
Check dashboard shows "✓ HIGH" volume
Verify trend is aligned (green or red background)
Enter the trade
Enter when signal appears
Note your stop loss (red line)
Note your targets (green dashed lines)
Manage the trade
Exit at first target for partial profit
Move stop to breakeven
Trail remaining position
What To Avoid
❌ Don't trade when you see:
⚠️ FAKE labels (wait for confirmation)
Neutral background (no clear trend)
"✗ Low" volume in dashboard
MACD and Trend not aligned
Settings You Can Adjust
Volume Sensitivity
High Volume Threshold: Default 1.5×
Increase to 2.0× for cleaner signals (fewer trades)
Decrease to 1.2× for more signals (more trades)
Fake Breakout Filters
You can toggle these ON/OFF:
Volume Confirmation: Requires high volume
Close Through: Requires candle close, not just wick
MACD Alignment: Requires MACD direction match
Tip: Turn all three ON for highest quality signals
ATR Stop/Target Multipliers
Default settings (conservative):
Stop Loss: 1.5×ATR
Target 1: 2×ATR (1.33:1 R:R)
Target 2: 3×ATR (2:1 R:R)
Target 3: 4×ATR (2.67:1 R:R)
Aggressive traders might use:
Stop Loss: 1.0×ATR
Target 1: 2×ATR (2:1 R:R)
Target 2: 4×ATR (4:1 R:R)
Conservative traders might use:
Stop Loss: 2.0×ATR
Target 1: 3×ATR (1.5:1 R:R)
Target 2: 5×ATR (2.5:1 R:R)
Example Trade Scenarios
Scenario 1: Perfect Long Setup ✅
Stock consolidating near EMA 21
MACD curling up toward signal line
Volume bar turns yellow (high volume)
🚀 LONG label appears
Red stop line and green target lines appear
Result: High probability trade
Scenario 2: Fake Breakout Avoided ✅
Price breaks above resistance
Volume is normal (gray bar)
⚠️ FAKE label appears (hover shows "Low volume")
No entry signal
Price falls back below breakout level
Result: Avoided losing trade
Scenario 3: Premature Entry ❌
MACD crosses up
Volume is high
BUT trend is NEUTRAL (no background color)
No signal appears (trend filter blocks it)
Result: Avoided choppy/sideways market
Quick Reference
Entry Checklist
🚀 or 🔻 label on chart
Dashboard shows "✓ HIGH" volume
Dashboard shows aligned MACD + Trend
Colored background (green or red)
ATR lines visible
No ⚠️ FAKE warning
Exit Strategy
Target 1 (2×ATR): Take 50% profit, move stop to breakeven
Target 2 (3×ATR): Take 25% profit, trail stop
Target 3 (4×ATR): Take remaining profit or trail aggressively
Stop Loss: Exit entire position if hit
Alerts
Set up these alerts:
Long Entry: Fires when 🚀 LONG signal appears
Short Entry: Fires when 🔻 SHORT signal appears
Fake Breakout Warning: Fires when ⚠️ appears (optional)
Tips for Success
Use on 5-minute charts for day trading momentum plays
Only trade high volume stocks ($5-20 range works best)
Wait for full confirmation - don't jump early
Respect the stop loss - it's calculated based on volatility
Scale out at targets - don't hold for home runs
Avoid trading first 15 minutes - let market settle
Best during 10am-11am and 2pm-3pm - peak momentum times
Common Questions
Q: Why didn't I get a signal even though MACD crossed?
A: All conditions must be met - check dashboard for what's missing (likely volume or trend alignment)
Q: Can I use this on any timeframe?
A: Yes, but it's designed for 5-15 minute charts. On daily charts, adjust ATR multipliers higher.
Q: The stop loss seems too tight, can I widen it?
A: Yes, increase "Stop Loss (×ATR)" from 1.5 to 2.0 or 2.5 in settings.
Q: I keep seeing FAKE warnings but price keeps going - what gives?
A: The filter is conservative. You can disable some filters in settings, but expect more false signals.
Q: Can I use this for swing trading?
A: Yes, but use larger timeframes (1H or 4H) and adjust ATR multipliers up (3× for stops, 6-9× for targets).
Grok's xAI Signal (GXS) Indicator for BTC V6Grok's xAI Signal (GXS) Indicator: A Simple Guide
Imagine trying to decide if Bitcoin is a "buy," "sell," or "wait" without staring at 10 different charts. The GXS Indicator does that for you—it's like a smart dashboard for BTC traders, overlaying signals right on your price chart. It boils down complex market clues into one easy score (from -1 "super bearish" to +1 "super bullish") and flashes green/red arrows or shaded zones when action's needed. No fancy math overload; just clear visuals like tiny triangles for trades, colored clouds for trends, and a bottom "mood bar" (green=up vibe, red=down, gray=meh).
At its core, GXS mixes three big-picture checks:
Price Momentum (50% weight): Quick scans of RSI (overbought/oversold vibes), MACD (speed of ups/downs), EMAs (is price riding the trend wave?), and Bollinger Bands (is the market squeezing for a breakout?). This catches short-term "hot or not" energy.
Network Health (30% weight): A simple "NVT" hack using trading volume vs. price to spot if BTC feels undervalued (buy hint) or overhyped (sell warning). It's like checking if the crowd's too excited or chill.
Trend Strength (20% weight): ADX filter ensures signals only fire in "trending" markets (not choppy sideways noise), plus a MACD boost for extra momentum nudge.
Why this approach? BTC's wild—pure price charts give false alarms in flat times, while ignoring volume/network ignores the "why" behind moves. GXS blends old-school TA (reliable for patterns) with on-chain smarts (crypto-specific "under the hood" data) and a trend gate (skips 70% of bad trades). It's conservative: Signals need the score to cross ±0.08 and a strong trend, reducing noise for swing/position traders. Result? Fewer emotional guesses, more "wait for confirmation" patience—perfect for volatile assets like BTC where hype kills.
Quick Tips to Tweak for Better Results
Start with defaults, then experiment on historical charts (backtest via TradingView's strategy tester if pairing with one):
Fewer False Signals: Bump thresholds to ±0.15 (buy/sell)—trades only on stronger conviction, cutting whipsaws by 20-30% in choppy markets. Or raise ADX thresh to 28 for "only big trends."
Faster/Slower Response: Shorten EMAs (e.g., 5/21) or RSI (10) for quicker scalps; lengthen (12/50) for swing holds. Test on 4H/daily BTC.
Volume Sensitivity: If NVT flips too often, extend its length to 20—smooths on-chain noise in bull runs.
Visual Polish: Crank cloud opacity to 80% for subtler fills; toggle off EMAs if they clutter. Enable table for score breakdowns during live trades.
Risk Tip: Always pair with stops (e.g., 2-3% below signals). On BTC, tweak in bull markets (looser thresh) vs. bears (tighter).
In short, GXS is your BTC "sixth sense"—balanced, not black-box. Tweak small, track win rate, and let trends lead. Happy trading!
Adaptive Trend 1m ### Overview
The "Adaptive Trend Impulse Parallel SL/TP 1m Realistic" strategy is a sophisticated trading system designed specifically for high-volatility markets like cryptocurrencies on 1-minute timeframes. It combines trend-following with momentum filters and adaptive parameters to dynamically adjust to market conditions, ensuring more reliable entries and risk management. This strategy uses SuperTrend for primary trend detection, enhanced by MACD, RSI, Bollinger Bands, and optional volume spikes. It incorporates parallel stop-loss (SL) and multiple take-profit (TP) levels based on ATR, with options for breakeven and trailing stops after the first TP. Optimized for realistic backtesting on short timeframes, it avoids over-optimization by adapting indicators to market speed and efficiency.
### Principles of Operation
The strategy operates on the principle of adaptive impulse trading, where entry signals are generated only when multiple conditions align to confirm a strong trend reversal or continuation:
1. **Trend Detection (SuperTrend)**: The core signal comes from an adaptive SuperTrend indicator. It calculates upper and lower bands using ATR (Average True Range) with dynamic periods and multipliers. A buy signal occurs when the price crosses above the lower band (from a downtrend), and a sell signal when it crosses below the upper band (from an uptrend). Adaptation is based on Rate of Change (ROC) to measure market speed, shortening periods in fast markets for quicker responses.
2. **Momentum and Trend Filters**:
- **MACD**: Uses adaptive fast and slow lengths. In "Trend Filter" mode (default when "Use MACD Cross" is false), it checks if the MACD line is above/below the signal for long/short. In cross mode, it requires a crossover/crossunder.
- **RSI**: Adaptive period RSI must be above 50 for longs and below 50 for shorts, confirming overbought/oversold conditions dynamically.
- **Bollinger Bands (BB)**: Depending on the mode ("Midline" by default), it requires the price to be above/below the BB midline for longs/shorts, or a breakout in "Breakout" mode. Deviation adapts to market efficiency.
- **Volume Spike Filter** (optional): Entries require volume to exceed an adaptive multiple of its SMA, signaling strong impulse.
3. **Volatility Filter**: Entries are only allowed if current ATR percentage exceeds a historical minimum (adaptive), preventing trades in low-volatility ranges.
4. **Risk Management (Parallel SL/TP)**:
- **Stop-Loss**: Set at an adaptive ATR multiple below/above entry for long/short.
- **Take-Profits**: Three levels at adaptive ATR multiples, with partial position closures (e.g., 51% at TP1, 25% at TP2, remainder at TP3).
- **Post-TP1 Features**: Optional breakeven moves SL to entry after TP1. Trailing SL uses BB midline as a dynamic trail.
- All levels are calculated per trade using the ATR at entry, making them "realistic" for 1m charts by widening SL and tightening initial TPs.
The strategy enters long on buy signals with all filters met, and short on sell signals. It uses pyramid margin (100% long/short) for full position sizing.
Adaptation is driven by:
- **Market Speed (normSpeed)**: Based on ROC, tightens multipliers in volatile periods.
- **Efficiency Ratio (ER)**: Measures trend strength, adjusting periods for trending vs. ranging markets.
This ensures the strategy "adapts" without manual tweaks, reducing false signals in varying conditions.
### Main Advantages
- **Adaptability**: Unlike static strategies, parameters dynamically adjust to market volatility and trend strength, improving performance across ranging and trending phases without over-optimization.
- **Realistic Risk Management for 1m**: Wider SL and tiered TPs prevent premature stops in noisy short-term charts, while partial profits lock in gains early. Breakeven/trailing options protect profits in extended moves.
- **Multi-Filter Confirmation**: Combines trend, momentum, and volume for high-probability entries, reducing whipsaws. The volatility filter avoids flat markets.
- **Debug Visualization**: Built-in plots for signals, levels, and component checks (when "Show Debug" is enabled) help users verify logic on charts.
- **Efficiency**: Low computational load, suitable for real-time trading on TradingView with alerts.
Backtesting shows robust results on volatile assets, with a focus on sustainable risk (e.g., SL at 3x ATR avoids excessive drawdowns).
### Uniqueness
What sets this strategy apart is its **fully adaptive framework** integrating multiple indicators with real-time market metrics (ROC for speed, ER for efficiency). Most trend strategies use fixed parameters, leading to poor adaptation; here, every key input (periods, multipliers, deviations) scales dynamically within bounds, creating a "self-tuning" system. The "parallel SL/TP with 1m realism" adds custom handling for micro-timeframes: tightened initial TPs for quick wins and adaptive min-ATR filter to skip low-vol bars. Unlike generic mashups, it justifies the combination—SuperTrend for trend, MACD/RSI/BB for impulse confirmation, volume for conviction—working synergistically to capture "trend impulses" while filtering noise. The post-TP1 breakeven/trailing tied to BB adds a unique profit-locking mechanism not common in open-source scripts.
### Recommended Settings
These settings are optimized and recommended for trading ASTER/USDT on Bybit, with 1-minute chart, x10 leverage, and cross margin mode. They provide a balanced risk-reward for this volatile pair:
- **Base Inputs**:
- Base ATR Period: 10
- Base SuperTrend ATR Multiplier: 2.5
- Base MACD Fast: 8
- Base MACD Slow: 17
- Base MACD Signal: 6
- Base RSI Period: 9
- Base Bollinger Period: 12
- Bollinger Deviation: 1.8
- Base Volume SMA Period: 19
- Base Volume Spike Multiplier: 1.8
- Adaptation Window: 54
- ROC Length: 10
- **TP/SL Settings**:
- Use Stop Loss: True
- Base SL Multiplier (ATR): 3
- Use Take Profits: True
- Base TP1 Multiplier (ATR): 5.5
- Base TP2 Multiplier (ATR): 10.5
- Base TP3 Multiplier (ATR): 19
- TP1 % Position: 51
- TP2 % Position: 25
- Breakeven after TP1: False
- Trailing SL after TP1: False
- Base Min ATR Filter: 0.001
- Use Volume Spike Filter: True
- BB Condition: Midline
- Use MACD Cross (false=Trend Filter): True
- Show Debug: True
For backtesting, use initial capital of 30 USD, base currency USDT, order size 100 USDT, pyramiding 1, commission 0.1%, slippage 0 ticks, long/short margin 0%.
Always backtest on your platform and use risk management—risk no more than 1-2% per trade. This is not financial advice; trade at your own risk.
ICT Killzones & MacrosICT Killzones & Macros (v1.1.5) — configurable ICT session windows + refined “macro” windows with live High/Low levels, optional extensions, next-window previews, and lightweight opening-price lines. Built to be clock-robust, timezone-aware, and performant on intraday charts.
Tip: All times are interpreted in your chosen IANA timezone (default: America/New_York) and auto-handle DST. You can rename, recolor, enable/disable, and retime every window.
What it plots
- Killzones (5) : Asia (19:00–02:00), London (02:00–05:00), NY AM (07:00–09:30), London Close (10:00–12:00), NY PM (13:30–16:00) — full-height boxes with optional header.
- Macros (8) (defaults tailored for common ICT “refined” windows): Asia-1 (18:00–21:00), Asia-2 (21:00–00:00), London-1 (01:00–04:00), AM-1 (09:45–10:15), AM-2 (10:45–11:15), Lunch (12:00–13:00), PM-1 (13:30–14:30), Power Hour (15:10–16:00).
- Live High/Low lines for the current Macro/Killzone window.
- Optional HL extension to the right until price crosses or the trading day rolls (style selectable).
- “Next” previews : earliest upcoming Macro and Killzone header; optional next-window background band.
- Opening Prices (3 lightweight time lines) : defaults 00:00, 08:30, 09:30 with right-edge labels, scoped to a session you choose (auto-cleans at session end).
- Key inputs & styling
- General : Timezone (IANA), “Sessions to show” (per window) to keep only the last N completed windows.
- Header : height (ticks), gap (ticks), fill opacity, border width/style, text size/color, toggle “Next Macro/Killzone” headers.
- Boxes : global fill opacity, global border width/style (used by both Macros & Killzones).
- High/Low : show HL, HL line style, extend on/off + extension style, optional extension labels.
- Opening Prices : enable Time 1/2/3, set HH:MM for each, session window, per-line colors, style (dotted/dashed/solid), width.
- Per-window controls : each Macro/Killzone has Enable, Session (HHMM-HHMM), Label, Fill color.
How to use (quick start)
- Set Timezone to your preference (default America/New_York).
- Toggle on the Macros and Killzones you trade. Adjust session times if needed.
- (Optional) Turn on Extend High/Low to project levels until crossed/day-roll.
- (Optional) Enable Next… headers to see the next upcoming window at a glance.
- (Optional) Configure Opening Prices (00:00 / 08:30 / 09:30 by default) and the session over which they appear.
Behavior & notes
- Time windows are computed by clock, not by guessing bar timestamps, making them robust across brokers and timeframes.
- With HL extension on, the current window’s levels extend until crossed or the end of the trading day (in your timezone). With it off, completed windows keep static HL markers (limited by “Sessions to show”).
- “Sessions to show” applies per Macro/Killzone to automatically prune older windows and keep charts snappy.
- Opening-price lines exist only within the chosen “Opening Prices Session” and are removed when it ends (keeps charts clean).
Defaults (color cues)
Killzones: Asia (blue), London (purple), NY AM (green), London Close (yellow), NY PM (orange).
Macros: neutral greys with Lunch and PM accents out of the box (all customizable).
Performance tips
- Reduce “Sessions to show” if you scroll far back in history.
- Disable “Next…” previews and/or extension labels on very slow machines.
- Narrow the “Opening Prices Session” window to exactly when you need those lines.
Changelog highlights
- v1.1.5 : Internal refinements and stability.
- v1.1.3 : Live High/Low lines for current windows + optional extension.
- v1.1.2 : Added “next Killzone” preview (to match “next Macro”).
- v1.1.0 : Defaults updated (5 KZ, 8 Macros). Removed “snap-to-killzone” behavior.
- v1.0.0 : Independent Macro vs. Killzone rendering; cleaner header logic.
- Known limitations
If your chart warns about drawings, trim “Sessions to show”.
If your broker session times differ from NY hours, adjust the sessions or change the indicator timezone.
Credits & intent
Inspired by ICT timing concepts; provided for education/mark-up, not financial advice.
Built to be flexible so you can mirror your personal playbook and journaling workflow.
Inside Bar Highlighter by nkChartsOverview:
The Inside Candle Highlighter is a simple yet powerful TradingView indicator designed to identify inside bars (inside candles) on your chart. An inside candle is defined as a candle whose high is lower than the previous candle's high and low is higher than the previous candle's low, meaning it forms entirely within the range of the preceding candle.
Inside candles are commonly interpreted by traders as periods of market consolidation or indecision and often precede breakouts or significant price moves. This indicator highlights these candles directly on your chart, making them easy to spot at a glance.
Features
Detects Inside Candles: Automatically identifies bars that are fully contained within the previous bar’s high-low range.
Confirmed Bar Coloring: Colors the candle after it closes, ensuring no repainting occurs during formation.
Style Tab Customization: Users can adjust the candle color directly from the Style tab, allowing seamless integration with your chart theme.
Clean & Minimal: Only inside candles are highlighted, keeping charts uncluttered.
How Traders Can Use It
Identify Consolidation Zones: Quickly spot periods where the market is contracting.
Prepare for Breakouts: Inside candles often signal an upcoming directional move; traders can plan entry or exit points based on breakouts from the inside candle range.
Combine With Other Indicators: Use alongside trend indicators, volume tools, or support/resistance levels to enhance trade confirmation.
Recommended Use
Works on all timeframes — from intraday charts to daily or weekly charts.
Particularly useful in price action trading, swing trading, and trend-following strategies.
Ideal for traders who want a visual cue for consolidation and potential breakout areas without adding complexity to the chart.
Note: This indicator only highlights inside candles. Interpretation and trading decisions are left to the user.
TGFA Flexible Alerts Multi-MA CrossoversTGFA Flexible Alerts, Multi-MA Crossovers
Description
Flexible MA crossovers with BUY/SELL alerts, customizable candle colors, and an info box for ATR/volatility insights. Supports EMA/SMA/HMA/VWAP on any chart.
Overview
TGFA Flexible Alerts is a versatile Pine Script indicator for traders seeking customizable moving average (MA) crossovers, visual signals, and quick-reference metrics. It overlays crossover lines (e.g., fast EMA over slow SMA), generates BUY/SELL labels and alerts, colors candles based on themes, and includes an optional info box with ATR bands, support/resistance, and trend projections. Built for any symbol and timeframe (optimized for 1H intraday), it auto-detects Heikin Ashi charts and handles mixed MA types like responsive HMA with lagging EMAs. All logic uses built-in TA functions for reliability—no repainting on confirmed bars.
Key Features
MA Crossover Engine: Configurable lines (EMA, SMA, HMA, VWAP) with dynamic colors (HMA tints green/red based on slope). Enable/disable via inputs.
Invert Signals Toggle: Flips BUY/SELL logic for mixed MA setups (e.g., HMA as fast line over EMA).
Reasoning: Traditional crossovers assume a fast line (low lag) crossing above a slow line (high lag) for buys. HMA's hull design makes it ultra-responsive, so it may "lead" too aggressively—causing premature signals. Inverting aligns it with user intuition (e.g., HMA dipping below then recovering signals strength), reducing false positives in trending markets. Test on your pairs!
Visual Alerts: BUY/SELL labels at crossover price (with optional price display and offset adjustment).
Single MA Overlays: Independent plots for EMA/SMA/HMA/VWAP (length 0 to hide).
Info Box: Real-time table with current price, ±1/2 ATR bands, median price (over lookback), trend (SMA50 slope), volatility % (ATR normalized), support/resistance (recent highs/lows), and reversal projections (tied to SMA50 pivot for up/down bias).
Candle Coloring: 20+ themes (dark/light canvases) for bull/bear/reversal/low-volume bars—e.g., Emerald Blaze greens uptrends, dims on low vol. Toggle off for no changes.
Chart Source Flexibility: Auto-switches to Heikin Ashi if detected; manual override for Regular/HA.
Alerts fire on crossovers/crossunders (custom messages with ticker/interval). Open-source for forking.
How to Use
Add to Chart: Search in TradingView's public library, apply to any symbol (e.g., stocks, forex). Best on 1H for intraday, but works on daily/weekly too.
Setup Crossovers: Choose Line 1/2 types/lengths (e.g., HMA 9 over SMA 20). Enable "Invert Signals" if using HMA—prevents lag mismatches in volatile assets.
Alerts & Labels: Toggle labels for visuals; set TradingView alerts on "Buy"/"Sell" conditions. Use offset for crowded charts.
Info Box Insights: Enable for quick scans—e.g., enter long near support if trend is bullish and price > median. Adjust ATR length (default 14) for sensitivity.
Candle Themes: Pick a scheme (e.g., Neon Pulse for dark mode); it overrides bar colors without altering data.
Customization Tip: For HMA-heavy setups, invert + short lengths (5-9) catch turns early; pair with volume filter in alerts.
Limitations & Disclaimers - Designed for overlay on price charts; may overlap in tight ranges—adjust transparency via styles.
HMA can repaint intra-bar; signals confirm on close. Not back tested for all assets—validate with strategy tester.
Info box projections use SMA(50) as a trend pivot (same for up/down as reference); customize via code for advanced calcs. Candle colors are cosmetic only.
This is an analysis tool, not advice. Trading involves risk; combine with fundamentals/news. Past performance isn't indicative of future results. No liability for losses.
I'm still a newbie, so feedback encouraged!
Thank you!!
ThisGirl
LA - Opening Price based Previous day Range PivotThis "LA - Opening Price based Previous day Range Pivot" indicator is a custom technical analysis tool designed for Trading View charts. It plots support and resistance levels (often referred to as pivots or ranges) based on the current opening price combined with the previous period's trading range. The "previous period" can be daily, weekly, or monthly, making it a multi-timeframe tool. These levels are projected using Fibonacci-inspired multipliers to create potential breakout or reversal zones.
The core idea is inspired by concepts like the Opening Range Breakout (ORB) strategy or Fibonacci pivots, but it's customized here to use a dynamic range calculation (the maximum of several absolute price differences) rather than a simple high-low range. This makes it more robust for volatile markets. Levels are symmetric above (resistance) and below (support) the opening price, helping traders identify potential entry/exit points, stop-losses, or targets. This will be useful when there is a gap-up/down as in Nifty/Sensex .
Purpose of the Indicator:
To visualize potential support/resistance zones for the current trading session based on the opening price and historical range data. This helps traders anticipate price movements, such as breakouts above resistance or bounces off support
Use Cases:
Intraday Trading: On lower timeframes (e.g., 5-min or 15-min charts), it shows daily levels for short-term trades.
Swing Trading: On higher timeframes (e.g., hourly or daily), it displays weekly/monthly levels for longer holds.
Range Identification: The filled bands highlight "zones" where price might consolidate or reverse.
Conditional Display: Levels only appear on appropriate timeframes (e.g., daily levels on intraday charts <60min), preventing clutter.
Theoretical Basis: It builds on pivot point theory, where the opening price acts as a central pivot. Multipliers (e.g., 0.618 for Fibonacci golden ratio) project levels, assuming price often respects these ratios due to market psychology.
How Calculations Work
Let's dive into the math with examples. Assume a stock with:
Current daily open (cdo) = $100
Previous daily high (pdh) = $105, low (pdl) = $95, close (pdc) = $102, close 2 days ago (pdc2) = $98
Step 1: Dynamic Range Calculation (var_d2):
This is the max of:
|pdh - pdc2| = |105 - 98| = 7
|pdl - pdc2| = |95 - 98| = 3
|pdh - pdl| = |105 - 95| = 10 (previous day range)
|pdh - cdo| = |105 - 100| = 5
|pdl - cdo| = |95 - 100| = 5
|pdc - cdo| = |102 - 100| = 2
|pdc2 - cdo| = |98 - 100| = 2
Max = 10 (so range = 10). This ensures the range accounts for gaps and extended moves, not just high-low.
Step 2: Level Projections:
Resistance (above open): Open + (Range * Multiplier)
dre6 = 100 + (10 * 1.5) = 115
dre5 = 100 + (10 * 1.27) ≈ 112.7
... down to dre0 = 100 + (10 * 0.1) = 101
dre50 = 100 + (10 * 0.5) = 105 (midpoint)
Support (below open): Open - (Range * Multiplier)
dsu0 = 100 - (10 * 0.1) = 99
... up to dsu6 = 100 - (10 * 1.5) = 85
Without Indicator
With Indicator
Pros and Cons
Pros:
Multi-Timeframe Flexibility: Seamlessly integrates daily, weekly, and monthly levels, useful for aligning short-term trades with longer trends (e.g., intraday breakout confirmed by weekly support).
Dynamic Range Calculation: Unlike standard pivots (just (H+L+C)/3), it uses max of multiple diffs, capturing gaps/volatility better—great for stocks with overnight moves.
Customizable via Inputs: Users can toggle levels, adjust multipliers, or change timeframes without editing code. Inline inputs keep the UI clean.
Visual Aids: Filled bands make zones obvious; conditional colors highlight "tight" vs. "wide" ranges (e.g., for volatility assessment).
Fibonacci Integration: Levels based on proven ratios, appealing to technical traders. Symmetric supports/resistances simplify strategy building (e.g., buy at support, sell at resistance).
No Repainting: Uses historical data with lookahead, so levels are fixed once calculated—reliable for back-testing.
Cons:
Chart Clutter: With all toggles on, 50+ plots/fills can overwhelm the chart, especially on mobile or small screens. Requires manual disabling.
Complexity for Beginners: Many inputs and calculations; without understanding fib ratios or range logic, it might confuse new users.
Performance Overhead: On low timeframes (e.g., 1-min), fetching higher TF data multiple times could lag, especially with many symbols or back-tests.
Assumes Volatility Persistence: Relies on previous range projecting future moves; in low-vol markets (e.g., sideways trends), levels may be irrelevant or too wide/narrow.
No Alerts or Signals: Purely visual; no built-in buy/sell alerts or crossover conditions—users must add separately.
Hardcoded Styles/Colors: Limited customization without code edits (e.g., can't change line styles via inputs).
Also, not optimized for non-stock assets (e.g., forex with 24/7 trading).
In summary, this is a versatile pivot tool for range-based trading based on Opening price, excelling in volatile markets but requiring some setup. If you're using it, start with defaults on a daily chart and toggle off unnecessary levels.
Measured Move Volume XIndicator Description
The "Measured Move Volume X" indicator, developed for TradingView using Pine Script version 6, projects potential price targets based on the measured move concept, where the magnitude of a prior price leg (Leg A) is used to forecast a subsequent move. It overlays translucent boxes on the chart to visualize bullish (green) or bearish (red) price projections, extending them to the right for a user-specified number of bars. The indicator integrates volume analysis (relative to a simple moving average), RSI for momentum, and VWAP for price-volume weighting, combining these into a confidence score to filter entry signals, displayed as triangles on breakouts. Horizontal key level lines (large, medium, small) are drawn at significant price points derived from the measured moves, with customizable thresholds, colors, and styles. Exhaustion hints, shown as orange labels near box extremes, indicate potential reversal points. Anomalous candles, marked with diamond shapes, are identified based on volume spikes and body-to-range ratios. Optional higher timeframe candle coloring enhances context. The indicator is fully customizable through input groups for lookback periods, transparency, and signal weights, making it adaptable to various assets and timeframes.
Adjustment Tips for Optimization
To optimize the "Measured Move Volume X" indicator for specific assets or timeframes, adjust the following input parameters:
Leg A Lookback (default: 14 bars): Increase to 20-30 for volatile markets (e.g., cryptocurrencies) to capture larger price swings; decrease to 5-10 for intraday charts (e.g., stocks) for faster signals.
Extend Box to the Right (default: 30 bars): Extend to 50+ for daily or weekly charts to project further targets; shorten to 10-20 for lower timeframes to reduce clutter.
Volume SMA Length (default: 20) and Relative Volume Threshold (default: 1.5): Lower the threshold to 1.2-1.3 for low-volume assets (e.g., commodities) to detect subtler spikes; raise to 2.0+ for high-volume equities to filter noise. Match SMA length to RSI length for consistency.
RSI Parameters (default: length 14, overbought 70, oversold 30): Set overbought to 80 and oversold to 20 in trending markets to reduce premature exit signals; shorten length to 7-10 for scalping.
Key Level Thresholds (default: large 10%, medium 5%, small 5%): Increase thresholds (e.g., large to 15%) for volatile assets to focus on significant moves; disable medium or small lines to declutter charts.
Confidence Score Weights (default: volume 0.5, VWAP 0.3, RSI 0.2): Increase volume weight (e.g., 0.7) for volume-driven markets like futures; emphasize RSI (e.g., 0.4) for momentum-focused strategies.
Anomaly Detection (default: volume multiplier 1.5, small body ratio 0.2, large body ratio 0.75): Adjust the volume multiplier higher for stricter anomaly detection in noisy markets; fine-tune body-to-range ratios based on asset-specific candle patterns.
Use TradingView’s replay feature to test adjustments on historical data, ensuring settings suit the chosen market and timeframe.
Tips for Using the Indicator
Interpreting Signals: Green upward triangles indicate bullish breakout entries when price exceeds the prior high with a confidence score ≥40; red downward triangles signal bearish breakouts. Use these to identify potential entry points aligned with the projected box targets.
Box Projections: Bullish boxes project upward targets (top of box) equal to the prior leg’s height added to the breakout price; bearish boxes project downward. Monitor price action near box edges for target completion or reversal.
Exhaustion Hints: Orange labels near box tops (bullish) or bottoms (bearish) suggest potential exhaustion when price deviates within the set percentage (default: 5%) and RSI or volume conditions are met. Use these as cues to watch for reversals.
Key Level Lines: Large, medium, and small lines mark significant price levels from box tops/bottoms. Use these as potential support/resistance zones, especially when drawn with high volume (colored differently).
Anomaly Candles: Orange diamonds highlight candles with unusual volume/body characteristics, indicating potential reversals or pauses. Combine with box levels for context.
Higher Timeframe Coloring: Enable to color bars based on higher timeframe candle closures (e.g., 1, 2, 5, or 15 minutes) for added trend context.
Customization: Toggle "Only Show Bullish Moves" to focus on bullish setups. Adjust transparency and line styles for visual clarity. Test settings to balance signal frequency and chart readability.
Inputs: Organized into groups (e.g., "Measured Move Settings") using input.int, input.float, input.color, and input.bool for user customization, with tooltips for clarity.
Calculations: Computes relative volume (ta.sma(volume, volLookback)), VWAP (ta.vwap(hlc3)), RSI (ta.rsi(close, rsiLength)), and prior leg extremes (ta.highest/lowest) using prior bar data ( ) to prevent repainting.
Boxes and Lines: Creates boxes (box.new) for bullish/bearish projections and lines (line.new) for key levels. The f_addLine function manages line arrays (array.new_line), capping at maxLinesCount to avoid clutter.
Confidence Score: Combines volume, VWAP distance, and RSI into a weighted score (confScore), filtering entries (≥40). Rounded for display.
Exhaustion Hints: Functions like f_plotBullExitHint assess price deviation, RSI, and volume decrease, using label.new for dynamic orange labels.
Entry Signals and Plots: plotshape displays triangles for breakouts; plot and hline show VWAP and RSI levels; request.security handles higher timeframe coloring.
Anomaly Detection: Identifies candles with small-body high-volume or large-body average-volume patterns via ratios, plotted as diamonds.
Algo + Trendlines :: Medium PeriodThis indicator helps me to avoid overlooking Trendlines / Algolines. So far it doesn't search explicitly for Algolines (I don't consider volume at all), but it's definitely now already not horribly bad.
These are meant to be used on logarithmic charts btw! The lines would be displayed wrong on linear charts.
The biggest challenge is that there are some technical restrictions in TradingView, f. e. a script stops executing if a for-loop would take longer than 0.5 sec.
So in order to circumvent this and still be able to consider as many candles from the past as possible, I've created multiple versions for different purposes that I use like this:
Algo + Trendlines :: Medium Period : This script looks for "temporary highs / lows" (meaning the bar before and after has lower highs / lows) on the daily chart, connects them and shows the 5 ones that are the closest to the current price (=most relevant). This one is good to find trendlines more thoroughly, but only up to 4 years ago.
Algo + Trendlines :: Long Period : This version looks instead at the weekly charts for "temporary highs / lows" and finds out which days caused these highs / lows and connects them, Taking data from the weekly chart means fewer data points to check whether a trendline is broken, which allows to detect trendlines from up to 12 years ago! Therefore it misses some trendlines. Personally I prefer this one with "Only Confirmed" set to true to really show only the most relevant lines. This means at least 3 candle highs / lows touched the line. These are more likely stronger resistance / support lines compared to those that have been touched only twice.
Very important: sometimes you might see dotted lines that suddenly stop after a few months (after 100 bars to be precise). This indicates you need to zoom further out for TradingView to be able to load the full line. Unfortunately TradingView doesn't render lines if the starting point was too long ago, so this is my workaround. This is also the script's biggest advantage: showing you lines that you might have missed otherwise since the starting bars were outside of the screen, and required you to scroll f. e back to 2015..
One more thing to know:
Weak colored line = only 2 "collision" points with candle highs/lows (= not confirmed)
Usual colored line = 3+ "collision" points (= confirmed)
Make sure to move this indicator above the ticker in the Object Tree, so that it is drawn on top of the ticker's candles!
More infos: www.reddit.com
Algo + Trendlines :: Long PeriodThis indicator helps me to avoid overlooking Trendlines / Algolines. So far it doesn't search explicitly for Algolines (I don't consider volume at all), but it's definitely now already not horribly bad.
These are meant to be used on logarithmic charts btw! The lines would be displayed wrong on linear charts.
The biggest challenge is that there are some technical restrictions in TradingView, f. e. a script stops executing if a for-loop would take longer than 0.5 sec.
So in order to circumvent this and still be able to consider as many candles from the past as possible, I've created multiple versions for different purposes that I use like this:
Algo + Trendlines :: Medium Period : This script looks for "temporary highs / lows" (meaning the bar before and after has lower highs / lows) on the daily chart, connects them and shows the 5 ones that are the closest to the current price (=most relevant). This one is good to find trendlines more thoroughly, but only up to 4 years ago.
Algo + Trendlines :: Long Period : This version looks instead at the weekly charts for "temporary highs / lows" and finds out which days caused these highs / lows and connects them, Taking data from the weekly chart means fewer data points to check whether a trendline is broken, which allows to detect trendlines from up to 12 years ago! Therefore it misses some trendlines. Personally I prefer this one with "Only Confirmed" set to true to really show only the most relevant lines. This means at least 3 candle highs / lows touched the line. These are more likely stronger resistance / support lines compared to those that have been touched only twice.
Very important: sometimes you might see dotted lines that suddenly stop after a few months (after 100 bars to be precise). This indicates you need to zoom further out for TradingView to be able to load the full line. Unfortunately TradingView doesn't render lines if the starting point was too long ago, so this is my workaround. This is also the script's biggest advantage: showing you lines that you might have missed otherwise since the starting bars were outside of the screen, and required you to scroll f. e back to 2015..
One more thing to know:
Weak colored line = only 2 "collision" points with candle highs/lows (= not confirmed)
Usual colored line = 3+ "collision" points (= confirmed)
Make sure to move this indicator above the ticker in the Object Tree, so that it is drawn on top of the ticker's candles!
More infos: www.reddit.com
