Trader Otto - Christmas Tree V2.1 [Pearson Flux]Trader Otto - Pearson Flux is a high-performance statistical engine designed to visualize the true strength and direction of the market trend without the lag of traditional indicators.
Instead of cluttered lines, this system processes price action through a dual-core statistical algorithm (Pearson Correlation + Deviation Logic) to color-code candles directly based on their probability state.
**Core Intelligence:**
* **Statistical Trend Flow (Pearson R-Value):** The system continuously measures the correlation coefficient of price against time.
* **Cyan/Magenta Candles:** Indicate a statistically significant trend (High R-Value). This is the "Flux" state where momentum is strongest.
* **Grey Candles:** Indicate a low-correlation environment (Sideways/Chop). The system automatically filters out these noise periods.
* **Gold/Purple Candles:** Indicate a neutral but directional bias, useful for early trend anticipation.
* **Smart Reversion Signals:**
* **BUY/SELL Signals:** Generated only when price deviates statistically from its mean *AND* the correlation signature confirms a high-probability reversal point.
* **Zone Logic:** Automatically identifies "Overextended" price zones invisible to the naked eye.
* **Live Flux Dashboard:**
* A professional panel (position adjustable) displays the real-time R-Value, current Trend Status, and Signal State, giving you a complete statistical readout of the asset in seconds.
**Proprietary Calibration:**
The system uses a calibrated "Correlation Threshold" to filter signals.
* **Sensitivity Control:** Users can fine-tune the strictness of the trend filter via the settings menu (default 1.0). This protects the core statistical constants while allowing adaptation to different asset volatilities.
**Session Control:**
* Includes an optional "Time Filter" to restrict signals to specific trading hours (e.g., Active Market Session), perfect for filtering out low-volume noise in 24h markets.
*This script is protected to maintain the integrity of the underlying statistical engine.*
Trend Analizi
Vantage-LO1Vantage LO1 is a licensed, modular trading software system designed to support rules-based breakout trading in the futures markets. It uses a structured London session lookback to inform execution during the New York session and is built to operate consistently across both prop firm and personal trading accounts. LO1 is trading software only. It does not provide financial advice, trade recommendations, or discretionary account management.
LO1 is designed specifically for YM (Dow Jones Futures) and MYM (Micro Dow Futures). These markets were selected because they offer:
Consistent liquidity during U.S. market hours
Clear session structure
Tight spreads relative to volatility
Reliable execution across both prop firm and personal accounts
While no market is without risk, YM and MYM provide a balance of structure and movement that aligns well with LO1’s rules-based design.
For more information:
vantage-stack.com
Discord
ORB Asia London NYORB – Asia London NY in UTC time
Can adjust time settings to your own ORB strategy.
NY Open CRT FrameworkNY Open CRT Framework Indicator
The NY Open CRT Framework automatically captures the 9:30 AM New York opening candle and projects its High, Low, Midpoint, Open, and Close forward to provide objective intraday context based on acceptance and rejection.
This indicator is designed as a framework, not a signal generator — allowing traders to align with New York session structure and let price action confirm directional bias.
✂️ Overview:
The New York Open is one of the most important moments of the trading day.
This framework uses the NY Open candle as a fixed reference range and evaluates how price behaves relative to that range throughout the session.
Once the NY Open candle closes, its levels are locked in and used to determine:
- Acceptance above the range
- Acceptance below the range
- Failed breakouts and rejection
- Neutral / rotational conditions
All logic is objective, non-repainting, and session-anchored.
NQ 1m Open Candle Levels
✨ CRT Range Levels:
The following levels are plotted from the NY 9.30 AM Open candle.
- CRT High – Upper boundary of acceptance
- CRT Low – Lower boundary of acceptance
- CRT Midpoint – Equilibrium level of the range
- CRT Open – True NY session open price
- CRT Close – Close of the opening impulse
These levels form the core decision framework for the session.
📚 CRT Acceptance & Failure Logic:
After the NY Open candle closes, the framework evaluates price behavior.
Acceptance Above (Bullish)
- Two consecutive closes above the CRT High
- Indicates bullish acceptance and continuation bias
Acceptance Below (Bearish)
- Two consecutive closes below the CRT Low
- Indicates bearish acceptance and continuation bias
Failure / Rejection
- Price trades beyond the range but closes back inside
- Suggests rejection, traps, or rotational conditions
Line colors update automatically to reflect the current state.
Dynamic Color Coding:
CRT High and Low lines change color based on market behavior.
- Green – Accepted above (bullish bias)
- Red – Accepted below (bearish bias)
- Gray – Failed breakout (rejection)
- Neutral (faded) – No confirmation yet
This allows you to read bias instantly without over-analysis.
NQ 15m Open Candle Levels
💻 HUD / Status Box:
A compact on-chart HUD provides real-time clarity without clutter.
The HUD displays
- Bias – Accepted Above, Accepted Below, Failure, or Neutral
- NY Open Range Status
- FORMING → NY Open candle still building
- SET → NY Open candle closed, framework active
- Price Location
- Above Range
- Below Range
- Inside Range
This allows traders to understand session context instantly at a glance.
📈 Best Practices
- Ideal for 1m–15m timeframes
- Avoid trading inside the range without confirmation
- Use the midpoint as equilibrium, not a signal
- Let acceptance define bias — not opinions
🛠️ Settings Overview:
Inputs Tab
- Show Midpoint
- Toggle the CRT midpoint on or off.
- Extend Lines (bars)
- Controls how far all CRT levels extend forward.
- Show CRT Status HUD
- Enables or disables the on-chart HUD.
Style Tab
- Toggle individual CRT lines on or off
- Hide labels entirely for a cleaner chart
- Customize visibility based on your trading style
⚠️ Disclaimer
This indicator is for educational and informational purposes only.
It does not provide financial advice or trade signals.
Market conditions can change rapidly, and historical patterns or standard deviations do not predict future price movements. Users are responsible for their own decisions, including risk management, trade execution, and capital allocation.
The developer is not liable for any losses or damages resulting from the use of this indicator. By using this tool, you accept full responsibility for your trading actions.
Final Note:
The NY Open CRT Framework is intended to provide clear, objective session context, allowing traders to align with market behavior rather than predict it.
If you'd like access or have any questions, feel free to reach out to me directly via DM.
Legacy Lab FX Pro [Ultra]Legacy Lab FX Pro is a comprehensive trading system designed to automate the detection of institutional liquidity sweeps during the New York Session. Built on the logic that "breakouts" during specific time windows are often liquidity traps, this tool helps traders identify high-probability reversals at the 8:00 AM EST Open.
1. The Methodology: The London Sweep
The core logic of this indicator is based on time-segmented price action. It tracks the specific "Liquidity Build" phase established during the London Session (default 02:00 – 07:00 EST).
The Trap: Retail traders often place stop losses just above or below this range.
The Sweep: Institutions push price past these levels to collect liquidity before reversing.
The Logic: The indicator waits for a "Sweep" (a wick that breaks the range but closes back inside) during the Execution Window (08:00 – 12:00 EST).
2. Key Features
A. Visual Liquidity Ranges The script automatically highlights the specific Highs and Lows of the London session with an Orange Liquidity Box. This removes the need to manually draw lines or calculate time zones.
B. "Gold Candle" Confirmation When a valid sweep occurs that meets all filter criteria (ATR, Trend, Time), the signal candle is painted GOLD. This serves as a visual execution trigger.
C. Integrated Risk Management The indicator projects a fixed, rule-based risk framework onto the chart:
Stop Loss (Red): Fixed pip distance (Default: 10 pips) from the sweep level.
TP1 (Green): 0.8R (Conservative banking).
TP2 (Green): 1.6R (Standard target).
TP3 (Green): 2.4R (Extended institutional target).
D. Smart Data Dashboard The bottom-right panel provides real-time backtesting data for the current chart. It features a unique "Best Reached" logic, which calculates the maximum potential R-multiple of every signal (e.g., if price hits TP2 then reverses, it is recorded as a +1.6R win rather than a loss).
Win Rate %
Net R (Return on Risk)
Total Trade Count
3. Institutional Filters
To prevent false signals during high-volatility news events or strong trends, the script includes:
ATR Filter: Rejects signals if the candle is abnormally large (Volatility protection).
Trend Filter: Optional 200 EMA filter to only take trades in the direction of the long-term trend.
Sweep Size: Configurable minimum pip distance required for a sweep to be valid.
4. How to Use
Timezone: Ensure your chart is set to New York time (or adjust the settings to match your local time).
Wait: Allow the Orange Liquidity Box to form (2am-7am).
Signal: Wait for a Gold Candle to appear during the 8am-12pm window.
Execute: Enter at the close of the Gold Candle. Place SL at the Red Line and TPs at the Green Lines.
5. Settings Configuration
Session Timing: fully customizable start/end times for the range and the entry window.
Risk Protocol: Adjust the fixed SL pips and R-Multiples for TP1, TP2, and TP3.
Backtest Engine: Toggle between "TP1 Only" mode (conservative stats) or "Best Reached" mode (maximum potential stats).
This script is Invite-Only. It is the official tool of the Legacy Lab FX trading community. Access is granted to members to ensure proper understanding of the liquidity protocols used within the code.
TL HITL v3.3 (Pine v6) Bespoke Dynamic Trendline Alerts Indicator - you're free to take a look at it, but I'm only publishing it so I can access it from external workflows that are activated by webhook alerts
ORB Asia London NYThis script plots the highs & lows of all three market sessions Asia, London, and NY. in UTC The time frames can be adjusted to you're own ORB strategy.
The time period of opening range & the max timeframe to display it on can be adjusted from the settings.
So for eg. if want to use 15m NY ORB to trade, then set "NY time " as 14:30-14:45
All-in-one trend clarityTrendLens is a multi-layer, all-in-one overlay indicator designed to visually detect and filter market direction — not a buy/sell strategy.
It highlights early trend shifts based on candle behavior, then supports that view using Pivot High/Low structure, three customizable EMAs, and a visible daily session window to focus on active market hours.
What’s included (All inside one indicator)
Structural Trend Candles
If price closes above the highest high of the previous N bars → candle turns white (bullish structural breakout).
If price closes below the lowest low of the previous N bars → candle turns black (bearish structural breakdown).
Pivot High / Pivot Low Markers
Detects swing highs/lows using adjustable left/right bars (default 7) and plots small gray triangle markers on the chart.
Active Session Window
Highlights a fixed daily time window (default 06:00–18:00 UTC) with a transparent green background to visually mark the active trading session.
3 Customizable EMAs
EMA Fast (default 10)
EMA Mid (default 20)
EMA Long (default 100)
Each EMA supports custom length, source, color, and thickness.
How to use it
Use white/black candles as a quick trend filter and early structure shift cue.
Use EMA100 as the main trend bias reference; use EMA10/EMA20 positioning to gauge momentum.
Use Pivot High/Low to spot structure levels for potential support/resistance and risk management.
Enable the session highlight to focus analysis on high-activity hours.
Disclaimer
This indicator is a technical analysis helper, not a trading strategy.
It does not provide buy/sell recommendations. You are responsible for your own trade decisions and risk management.
Strength Relative to XXX [Hysteresis Smoothed]Strength Relative to XXX
█ OVERVIEW
This versatile indicator measures the relative strength of the current charted asset against any user-selected benchmark symbol (e.g., BTC, ETH, SP:SPX, TVC:GOLD, or any other asset). Green fill = Current asset outperforming the benchmark (bullish relative strength).
Red fill = Current asset underperforming the benchmark (bearish relative weakness). Perfect for rotation strategies across crypto, stocks, forex, and commodities — quickly identify assets gaining momentum edge over a chosen benchmark.
█ HOW IT WORKS
• Relative Ratio : Calculates current close / benchmark close for normalized comparison.
• Smoothing : Applies a Simple Moving Average (SMA) to the ratio (adjustable length).
• Oscillator : Plots deviation from the SMA, centered around zero.
• Hysteresis Enhancement : Adds a small relative threshold (~0.03% default) to prevent rapid color flips from minor noise. Color persists until a convincing cross — stable blocks without lag.
█ FEATURES & INPUTS
• Compare to : Symbol input for any benchmark (match exchange for accuracy).
• MA Length : Smoothing period (default 10).
• Relative Hysteresis Threshold : Noise filter strength (default 0.0003; tweak for responsiveness vs. stability).
█ USAGE TIPS
• Apply to ALT/BTC pairs for crypto rotations, stocks vs. SP:SPX for sector strength, or any custom comparison.
• Works on all timeframes — ideal for short-term scans on 4H/daily.
• Green zones = potential outperformance; red = caution.
• Combine with volume or momentum for confluence.
This refined relative strength oscillator delivers clean, reliable visuals in volatile markets.
Volume-Weighted RSI [VWRSI 2D Pro]A modular, volume-weighted RSI indicator built for clarity and control.
✅ Profile-based auto modes (Scalping → Macro)
✅ Toggleable Buy/Sell signals with strict mode
✅ RSI MA overlays for smoother entries
Buy Signal
RSI crosses above RSI MA
RSI > 50 (or > 55 in strict mode)
Sell Signal
RSI crosses below RSI MA
RSI < 50 (or < 45 in strict mode)
Strict mode filters out weak signals for higher conviction entries.
Volatility-Adaptive RSI Thresholds:
Traditional RSI uses static levels (70/30).
VWRSI Pro replaces these with dynamic bands:
🔹dynHigh = mean + mult × deviation
🔹 dynLow = mean − mult × deviation
Technical write-up can be found here: github.com
Adaptive Market Wave Theory - ProAdaptive Market Wave Theory
🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS
Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation.
The Wave Analysis Problem:
Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses:
1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition.
2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw.
3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management.
4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024.
5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve.
The AMWT Solution:
AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning:
• Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed.
• Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated.
• No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3."
• Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time.
• No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance.
The Core Insight:
"Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'"
🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS
Why Hidden Markov Models?
Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables.
This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations.
HMM Components:
1. Hidden States (S) : The unobservable market conditions
2. Observations (O) : What we can measure (price, volume, indicators)
3. Transition Matrix (A) : Probability of moving between states
4. Emission Matrix (B) : Probability of observations given each state
5. Initial Distribution (π) : Starting state probabilities
AMWT's Six Market States:
State 0: IMPULSE_UP
• Definition: Strong bullish momentum with high participation
• Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising
• Typical Duration: 5-20 bars depending on timeframe
• What It Means: Institutional buying pressure, trend acceleration phase
State 1: IMPULSE_DN
• Definition: Strong bearish momentum with high participation
• Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling
• Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster)
• What It Means: Institutional selling pressure, panic or distribution acceleration
State 2: CORRECTION
• Definition: Counter-trend consolidation with declining momentum
• Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing
• Typical Duration: 8-30 bars
• What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg
State 3: ACCUMULATION
• Definition: Base-building near lows where informed participants absorb supply
• Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range
• Typical Duration: 15-50 bars
• What It Means: Smart money buying from weak hands, preparing for markup phase
State 4: DISTRIBUTION
• Definition: Top-forming near highs where informed participants distribute holdings
• Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range
• Typical Duration: 15-50 bars
• What It Means: Smart money selling to late buyers, preparing for markdown phase
State 5: TRANSITION
• Definition: Regime change period with mixed signals and elevated uncertainty
• Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction
• Typical Duration: 5-15 bars
• What It Means: Market deciding next direction, dangerous for directional trades
The Transition Matrix:
The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online:
From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS
IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02
IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02
CORR 0.15 0.15 0.50 0.10 0.10 0.00
ACCUM 0.30 0.05 0.15 0.40 0.05 0.05
DIST 0.05 0.30 0.15 0.05 0.40 0.05
TRANS 0.20 0.20 0.20 0.15 0.15 0.10
Key Insights from Transition Probabilities:
• Impulse states are sticky (70% self-transition): Once trending, markets tend to continue
• Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain
• Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies
• Distribution strongly favors IMP_DN transition (30%): Topping leads to declines
The Viterbi Algorithm:
Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space.
Mathematical Formulation:
δ_t(j) = max_i × B_j(O_t)
Where:
δ_t(j) = probability of most likely path ending in state j at time t
A_ij = transition probability from state i to state j
B_j(O_t) = emission probability of observation O_t given state j
AMWT Implementation:
AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting:
• Current state estimate
• State confidence (probability of current state vs alternatives)
• State sequence for pattern detection
Online Learning (Baum-Welch Adaptation):
Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior:
f_onlineUpdateHMM(prev_state, curr_state, observation, decay) =>
// Update transition matrix
A *= decay
A += (1.0 - decay)
// Renormalize row
// Update emission matrix
B *= decay
B += (1.0 - decay)
// Renormalize row
The decay parameter (default 0.85) controls adaptation speed:
• Higher decay (0.95): Slower adaptation, more stable, better for consistent markets
• Lower decay (0.80): Faster adaptation, more reactive, better for regime changes
Why This Matters for Trading:
Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment :
"There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION."
This enables:
• Position sizing by confidence : 90% confidence = full size; 60% confidence = half size
• Risk management by transition probability : High correction probability = tighten stops
• Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in
🎰 THE 3-BANDIT CONSENSUS SYSTEM
The Multi-Agent Philosophy:
No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets.
AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions.
Agent 1: TREND AGENT
Philosophy : Markets trend. Follow the trend until it ends.
Analytical Components:
• EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish)
• MACD Histogram: Direction and rate of change
• Price Momentum: Close relative to ATR-normalized movement
• VWAP Position: Price above/below volume-weighted average price
Signal Generation:
Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP
→ Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4
Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1
→ Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3
Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP
→ Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4
Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1
→ Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3
When Trend Agent Excels:
• Trend days (IB extension >1.5x)
• Post-breakout continuation
• Institutional accumulation/distribution phases
When Trend Agent Fails:
• Range-bound markets (ADX <20)
• Chop zones after volatility spikes
• Reversal days at major levels
Agent 2: REVERSION AGENT
Philosophy: Markets revert to mean. Extreme readings reverse.
Analytical Components:
• Bollinger Band Position: Distance from bands, percent B
• RSI Extremes: Overbought (>70) and oversold (<30)
• Stochastic: %K/%D crossovers at extremes
• Band Squeeze: Bollinger Band width contraction
Signal Generation:
Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25
→ Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01
Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75
→ Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01
Squeeze Fire Bull: Band squeeze ending AND close > upper band
→ Signal: +1 (Long), Confidence: 0.65
Squeeze Fire Bear: Band squeeze ending AND close < lower band
→ Signal: -1 (Short), Confidence: 0.65
When Reversion Agent Excels:
• Rotation days (price stays within IB)
• Range-bound consolidation
• After extended moves without pullback
When Reversion Agent Fails:
• Strong trend days (RSI can stay overbought for days)
• Breakout moves
• News-driven directional moves
Agent 3: STRUCTURE AGENT
Philosophy: Market structure reveals institutional intent. Follow the smart money.
Analytical Components:
• Break of Structure (BOS): Price breaks prior swing high/low
• Change of Character (CHOCH): First break against prevailing trend
• Higher Highs/Higher Lows: Bullish structure
• Lower Highs/Lower Lows: Bearish structure
• Liquidity Sweeps: Stop runs that reverse
Signal Generation:
BOS Bull: Price breaks above prior swing high with momentum
→ Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bull: First higher low after downtrend, breaking structure
→ Signal: +1 (Long), Confidence: 0.75
BOS Bear: Price breaks below prior swing low with momentum
→ Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bear: First lower high after uptrend, breaking structure
→ Signal: -1 (Short), Confidence: 0.75
Liquidity Sweep Long: Price sweeps below swing low then reverses strongly
→ Signal: +1 (Long), Confidence: 0.80
Liquidity Sweep Short: Price sweeps above swing high then reverses strongly
→ Signal: -1 (Short), Confidence: 0.80
When Structure Agent Excels:
• After liquidity grabs (stop runs)
• At major swing points
• During institutional accumulation/distribution
When Structure Agent Fails:
• Choppy, structureless markets
• During news events (structure becomes noise)
• Very low timeframes (noise overwhelms structure)
Thompson Sampling: The Bandit Algorithm
With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives).
Thompson Sampling Solution:
Each agent maintains a Beta distribution representing its success/failure history:
Agent success rate modeled as Beta(α, β)
Where:
α = number of successful signals + 1
β = number of failed signals + 1
On Each Bar:
1. Sample from each agent's Beta distribution
2. Weight agent signals by sampled probabilities
3. Combine weighted signals into consensus
4. Update α/β based on trade outcomes
Mathematical Implementation:
// Beta sampling via Gamma ratio method
f_beta_sample(alpha, beta) =>
g1 = f_gamma_sample(alpha)
g2 = f_gamma_sample(beta)
g1 / (g1 + g2)
// Thompson Sampling selection
for each agent:
sampled_prob = f_beta_sample(agent.alpha, agent.beta)
weight = sampled_prob / sum(all_sampled_probs)
consensus += agent.signal × agent.confidence × weight
Why Thompson Sampling?
• Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution)
• Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff
• Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation
• Self-Correcting : Poor performers naturally get lower weights over time
Example Evolution:
Day 1 (Initial):
Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
After 50 Signals:
Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence)
Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming)
Structure Agent: Beta(32,19) → samples ~0.63 (outperforming)
Result: Structure Agent now receives highest weight in consensus
Consensus Requirements by Mode:
Aggressive Mode:
• Minimum 1/3 agents agreeing
• Consensus threshold: 45%
• Use case: More signals, higher risk tolerance
Balanced Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 55%
• Use case: Standard trading
Conservative Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 65%
• Use case: Higher quality, fewer signals
Institutional Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 75%
• Additional: Session quality >0.65, mode adjustment +0.10
• Use case: Highest quality signals only
🌀 INTELLIGENT CHOP DETECTION ENGINE
The Chop Problem:
Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike.
AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades.
Five-Factor Chop Analysis:
Factor 1: ADX Component (25% weight)
ADX (Average Directional Index) measures trend strength regardless of direction.
ADX < 15: Very weak trend (high chop score)
ADX 15-20: Weak trend (moderate chop score)
ADX 20-25: Developing trend (low chop score)
ADX > 25: Strong trend (minimal chop score)
adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100
Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop.
Factor 2: Choppiness Index (25% weight)
The Choppiness Index measures price efficiency using the ratio of ATR sum to price range:
CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n)
CI > 61.8: Choppy (range-bound, inefficient movement)
CI < 38.2: Trending (directional, efficient movement)
CI 38.2-61.8: Transitional
chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100
Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range).
Factor 3: Range Compression (20% weight)
Compares recent range to longer-term range, detecting volatility squeezes:
recent_range = Highest(20) - Lowest(20)
longer_range = Highest(50) - Lowest(50)
compression = 1 - (recent_range / longer_range)
compression > 0.5: Strong squeeze (potential breakout imminent)
compression < 0.2: No compression (normal volatility)
range_compression_score = compression × 100
Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet.
Factor 4: Channel Position (15% weight)
Tracks price position within the macro channel:
channel_position = (close - channel_low) / (channel_high - channel_low)
position 0.4-0.6: Center of channel (indecision zone)
position <0.2 or >0.8: Near extremes (potential reversal or breakout)
channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score
Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability.
Factor 5: Volume Quality (15% weight)
Assesses volume relative to average:
vol_ratio = volume / SMA(volume, 20)
vol_ratio < 0.7: Low volume (lack of conviction)
vol_ratio 0.7-1.3: Normal volume
vol_ratio > 1.3: High volume (conviction present)
volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0
Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall.
Combined Chop Intensity:
chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) +
(range_compression_score × 0.20) + (channel_chop × 0.15) +
(volume_chop × i_volumeChopWeight × 0.15)
Regime Classifications:
Based on chop intensity and component analysis:
• Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively
• Trending (20-35%): ADX >20, moderate directional bias, trade normally
• Transitioning (35-50%): Mixed signals, regime change possible, reduce size
• Mid-Range (50-60%): Price trapped in channel center, avoid new positions
• Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only
• Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout
• Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely
Signal Suppression:
When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification.
Chop Box Visualization:
When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions.
💧 LIQUIDITY ANCHORING SYSTEM
The Liquidity Concept:
Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing.
AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence.
Liquidity Event Types:
Type 1: Volume Spikes
Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x)
Interpretation: Sudden volume surge indicates institutional activity
• Near swing low + reversal: Likely accumulation
• Near swing high + reversal: Likely distribution
• With continuation: Institutional conviction in direction
Type 2: Stop Runs (Liquidity Sweeps)
Definition: Price briefly exceeds swing high/low then reverses within N bars
Detection:
• Price breaks above recent swing high (triggering buy stops)
• Then closes back below that high within 3 bars
• Signal: Bullish stop run complete, reversal likely
Or inverse for bearish:
• Price breaks below recent swing low (triggering sell stops)
• Then closes back above that low within 3 bars
• Signal: Bearish stop run complete, reversal likely
Type 3: Absorption Events
Definition: High volume with small candle body
Detection:
• Volume > 2x average
• Candle body < 30% of candle range
• Interpretation: Large orders being filled without moving price
• Implication: Accumulation (at lows) or distribution (at highs)
Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity)
BSL (Buy-Side Liquidity):
• Cluster of swing highs within ATR proximity
• Stop losses from shorts sit above these highs
• Breaking BSL triggers short covering (fuel for rally)
SSL (Sell-Side Liquidity):
• Cluster of swing lows within ATR proximity
• Stop losses from longs sit below these lows
• Breaking SSL triggers long liquidation (fuel for decline)
Liquidity Pool Mapping:
AMWT continuously scans for and maps liquidity pools:
// Detect swing highs/lows using pivot function
swing_high = ta.pivothigh(high, 5, 5)
swing_low = ta.pivotlow(low, 5, 5)
// Track recent swing points
if not na(swing_high)
bsl_levels.push(swing_high)
if not na(swing_low)
ssl_levels.push(swing_low)
// Display on chart with labels
Confluence Scoring Integration:
When signals fire near identified liquidity events, confluence scoring increases:
• Signal near volume spike: +10% confidence
• Signal after liquidity sweep: +15% confidence
• Signal at BSL/SSL pool: +10% confidence
• Signal aligned with absorption zone: +10% confidence
Why Liquidity Anchoring Matters:
Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst.
📊 SIGNAL GRADING SYSTEM
The Quality Problem:
Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence.
Confluence Components (100 points total):
1. Bandit Consensus Strength (25 points)
consensus_str = weighted average of agent confidences
score = consensus_str × 25
Example:
Trend Agent: +1 signal, 0.80 confidence, 0.35 weight
Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight
Structure Agent: +1 signal, 0.75 confidence, 0.40 weight
Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77
Score = 0.77 × 25 = 19.25 points
2. HMM State Confidence (15 points)
score = hmm_confidence × 15
Example:
HMM reports 82% probability of IMPULSE_UP
Score = 0.82 × 15 = 12.3 points
3. Session Quality (15 points)
Session quality varies by time:
• London/NY Overlap: 1.0 (15 points)
• New York Session: 0.95 (14.25 points)
• London Session: 0.70 (10.5 points)
• Asian Session: 0.40 (6 points)
• Off-Hours: 0.30 (4.5 points)
• Weekend: 0.10 (1.5 points)
4. Energy/Participation (10 points)
energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25
score = min(energy, 1.0) × 10
5. Volume Confirmation (10 points)
if volume > SMA(volume, 20) × 1.5:
score = 10
else if volume > SMA(volume, 20):
score = 5
else:
score = 0
6. Structure Alignment (10 points)
For long signals:
• Bullish structure (HH + HL): 10 points
• Higher low only: 6 points
• Neutral structure: 3 points
• Bearish structure: 0 points
Inverse for short signals
7. Trend Alignment (10 points)
For long signals:
• Price > EMA21 > EMA50: 10 points
• Price > EMA21: 6 points
• Neutral: 3 points
• Against trend: 0 points
8. Entry Trigger Quality (5 points)
• Strong trigger (multiple confirmations): 5 points
• Moderate trigger (single confirmation): 3 points
• Weak trigger (marginal): 1 point
Grade Scale:
Total Score → Grade
85-100 → A+ (Exceptional—all factors aligned)
70-84 → A (Strong—high probability)
55-69 → B (Acceptable—proceed with caution)
Below 55 → C (Marginal—filtered by default)
Grade-Based Signal Brightness:
Signal arrows on the chart have transparency based on grade:
• A+: Full brightness (alpha = 0)
• A: Slight fade (alpha = 15)
• B: Moderate fade (alpha = 35)
• C: Significant fade (alpha = 55)
This visual hierarchy helps traders instantly identify signal quality.
Minimum Grade Filter:
Configurable filter (default: C) sets the minimum grade for signal display:
• Set to "A" for only highest-quality signals
• Set to "B" for moderate selectivity
• Set to "C" for all signals (maximum quantity)
🕐 SESSION INTELLIGENCE
Why Sessions Matter:
Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality.
Session Definitions:
Asian Session (18:00-03:00 ET)
• Characteristics: Lower volatility, range-bound tendency, fewer institutional participants
• Quality Score: 0.40 (40% of peak quality)
• Strategy Implications: Fade extremes, expect ranges, smaller position sizes
• Best For: Mean-reversion setups, accumulation/distribution identification
London Session (03:00-12:00 ET)
• Characteristics: European institutional activity, volatility pickup, trend initiation
• Quality Score: 0.70 (70% of peak quality)
• Strategy Implications: Watch for trend development, breakouts more reliable
• Best For: Initial trend identification, structure breaks
New York Session (08:00-17:00 ET)
• Characteristics: Highest liquidity, US institutional activity, major moves
• Quality Score: 0.95 (95% of peak quality)
• Strategy Implications: Best environment for directional trades
• Best For: Trend continuation, momentum plays
London/NY Overlap (08:00-12:00 ET)
• Characteristics: Peak liquidity, both European and US participants active
• Quality Score: 1.0 (100%—maximum quality)
• Strategy Implications: Highest probability for successful breakouts and trends
• Best For: All signal types—this is prime time
Off-Hours
• Characteristics: Thin liquidity, erratic price action, gaps possible
• Quality Score: 0.30 (30% of peak quality)
• Strategy Implications: Avoid new positions, wider stops if holding
• Best For: Waiting
Smart Weekend Detection:
AMWT properly handles the Sunday evening futures open:
// Traditional (broken):
isWeekend = dayofweek == saturday OR dayofweek == sunday
// AMWT (correct):
anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime)
isWeekend = calendarWeekend AND NOT anySessionActive
This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend."
Session Transition Boosts:
Certain session transitions create trading opportunities:
• Asian → London transition: +15% confidence boost (volatility expansion likely)
• London → Overlap transition: +20% confidence boost (peak liquidity approaching)
• Overlap → NY-only transition: -10% confidence adjustment (liquidity declining)
• Any → Off-Hours transition: Signal suppression recommended
📈 TRADE MANAGEMENT SYSTEM
The Signal Spam Problem:
Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance.
Trade Lock Mechanism:
Once a signal fires, the system enters a "trade lock" state:
Trade Lock Duration: Configurable (default 30 bars)
Early Exit Conditions:
• TP3 hit (full target reached)
• Stop Loss hit (trade failed)
• Lock expiration (time-based exit)
During lock:
• No new signals of same type displayed
• Opposite signals can override (reversal)
• Trade status tracked in dashboard
Target Levels:
Each signal generates three profit targets based on ATR:
TP1 (Conservative Target)
• Default: 1.0 × ATR
• Purpose: Quick partial profit, reduce risk
• Action: Take 30-40% off position, move stop to breakeven
TP2 (Standard Target)
• Default: 2.5 × ATR
• Purpose: Main profit target
• Action: Take 40-50% off position, trail stop
TP3 (Extended Target)
• Default: 5.0 × ATR
• Purpose: Runner target for trend days
• Action: Close remaining position or continue trailing
Stop Loss:
• Default: 1.9 × ATR from entry
• Purpose: Define maximum risk
• Placement: Below recent swing low (longs) or above recent swing high (shorts)
Invalidation Level:
Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies:
invalidation = entry - (ATR × INVALIDATION_MULT × 1.5)
If price reaches invalidation, the current market interpretation is wrong—not just the trade.
Visual Trade Management:
During active trades, AMWT displays:
• Entry arrow with grade label (▲A+, ▼B, etc.)
• TP1, TP2, TP3 horizontal lines in green
• Stop Loss line in red
• Invalidation line in orange (dashed)
• Progress indicator in dashboard
Persistent Execution Markers:
When targets or stops are hit, permanent markers appear:
• TP hit: Green dot with "TP1"/"TP2"/"TP3" label
• SL hit: Red dot with "SL" label
These persist on the chart for review and statistics.
💰 PERFORMANCE TRACKING & STATISTICS
Tracked Metrics:
• Total Trades: Count of all signals that entered trade lock
• Winning Trades: Signals where at least TP1 was reached before SL
• Losing Trades: Signals where SL was hit before any TP
• Win Rate: Winning / Total × 100%
• Total R Profit: Sum of R-multiples from winning trades
• Total R Loss: Sum of R-multiples from losing trades
• Net R: Total R Profit - Total R Loss
Currency Conversion System:
AMWT can display P&L in multiple formats:
R-Multiple (Default)
• Shows risk-normalized returns
• "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades
• Best for comparing across different position sizes
Currency Conversion (USD/EUR/GBP/JPY/INR)
• Converts R-multiples to currency based on:
- Dollar Risk Per Trade (user input)
- Tick Value (user input)
- Selected currency
Example Configuration:
Dollar Risk Per Trade: $100
Display Currency: USD
If Net R = +4.2R
Display: Net P&L: +$420.00 | 78 trades
Ticks
• For futures traders who think in ticks
• Converts based on tick value input
Statistics Reset:
Two reset methods:
1. Toggle Reset
• Turn "Reset Statistics" toggle ON then OFF
• Clears all statistics immediately
2. Date-Based Reset
• Set "Reset After Date" (YYYY-MM-DD format)
• Only trades after this date are counted
• Useful for isolating recent performance
🎨 VISUAL FEATURES
Macro Channel:
Dynamic regression-based channel showing market boundaries:
• Upper/lower bounds calculated from swing pivot linear regression
• Adapts to current market structure
• Shows overall trend direction and potential reversal zones
Chop Boxes:
Semi-transparent overlay during high-chop periods:
• Purple/orange coloring indicates dangerous conditions
• Visual reminder to avoid new positions
Confluence Heat Zones:
Background shading indicating setup quality:
• Darker shading = higher confluence
• Lighter shading = lower confluence
• Helps identify optimal entry timing
EMA Ribbon:
Trend visualization via moving average fill:
• EMA 8/21/50 with gradient fill between
• Green fill when bullish aligned
• Red fill when bearish aligned
• Gray when neutral
Absorption Zone Boxes:
Marks potential accumulation/distribution areas:
• High volume + small body = absorption
• Boxes drawn at these levels
• Often act as support/resistance
Liquidity Pool Lines:
BSL/SSL levels with labels:
• Dashed lines at liquidity clusters
• "BSL" label above swing high clusters
• "SSL" label below swing low clusters
Six Professional Themes:
• Quantum: Deep purples and cyans (default)
• Cyberpunk: Neon pinks and blues
• Professional: Muted grays and greens
• Ocean: Blues and teals
• Matrix: Greens and blacks
• Ember: Oranges and reds
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Learning the System (Week 1)
Goal: Understand AMWT concepts and dashboard interpretation
Setup:
• Signal Mode: Balanced
• Display: All features enabled
• Grade Filter: C (see all signals)
Actions:
• Paper trade ONLY—no real money
• Observe HMM state transitions throughout the day
• Note when agents agree vs disagree
• Watch chop detection engage and disengage
• Track which grades produce winners vs losers
Key Learning Questions:
• How often do A+ signals win vs B signals? (Should see clear difference)
• Which agent tends to be right in current market? (Check dashboard)
• When does chop detection save you from bad trades?
• How do signals near liquidity events perform vs signals in vacuum?
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to your instrument and timeframe
Signal Mode Testing:
• Run 5 days on Aggressive mode (more signals)
• Run 5 days on Conservative mode (fewer signals)
• Compare: Which produces better risk-adjusted returns?
Grade Filter Testing:
• Track A+ only for 20 signals
• Track A and above for 20 signals
• Track B and above for 20 signals
• Compare win rates and expectancy
Chop Threshold Testing:
• Default (80%): Standard filtering
• Try 70%: More aggressive filtering
• Try 90%: Less filtering
• Which produces best results for your instrument?
Phase 3: Strategy Development (Weeks 3-4)
Goal: Develop personal trading rules based on system signals
Position Sizing by Grade:
• A+ grade: 100% position size
• A grade: 75% position size
• B grade: 50% position size
• C grade: 25% position size (or skip)
Session-Based Rules:
• London/NY Overlap: Take all A/A+ signals
• NY Session: Take all A+ signals, selective on A
• Asian Session: Only A+ signals with extra confirmation
• Off-Hours: No new positions
Chop Zone Rules:
• Chop >70%: Reduce position size 50%
• Chop >80%: No new positions
• Chop <50%: Full position size allowed
Phase 4: Live Micro-Sizing (Month 2)
Goal: Validate paper trading results with minimal risk
Setup:
• 10-20% of intended full position size
• Take ONLY A+ signals initially
• Follow trade management religiously
Tracking:
• Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus
• Calculate: Win rate by grade, by session, by chop level
• Compare to paper trading (should be within 15%)
Red Flags:
• Win rate diverges significantly from paper trading: Execution issues
• Consistent losses during certain sessions: Adjust session rules
• Losses cluster when specific agent dominates: Review that agent's logic
Phase 5: Scaling Up (Months 3-6)
Goal: Gradually increase to full position size
Progression:
• Month 3: 25-40% size (if micro-sizing profitable)
• Month 4: 40-60% size
• Month 5: 60-80% size
• Month 6: 80-100% size
Scale-Up Requirements:
• Minimum 30 trades at current size
• Win rate ≥50%
• Net R positive
• No revenge trading incidents
• Emotional control maintained
💡 DEVELOPMENT INSIGHTS
Why HMM Over Simple Indicators:
Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend.
The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP."
The Multi-Agent Evolution:
Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way.
The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection.
Chop Detection Revelation:
Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones.
Liquidity Anchoring Discovery:
Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals.
The Grade System Impact:
Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What AMWT Is NOT:
• NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty.
• NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment.
• NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis.
• NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate.
Core Assumptions:
1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure.
2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals.
3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns.
4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation.
Performs Best On:
• Liquid Futures : ES, NQ, MNQ, MES, CL, GC
• Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY
• Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap)
• Liquid Crypto : BTC, ETH on major exchanges
Performs Poorly On:
• Illiquid Instruments : Low volume stocks, exotic pairs
• Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal)
• Binary Event Days : Earnings, FDA approvals, court rulings
• Manipulated Markets : Penny stocks, low-cap altcoins
Known Weaknesses:
• Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable.
• Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance.
• Complexity : More parameters than simple indicators. Requires understanding to use effectively.
⚠️ RISK DISCLOSURE
Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses.
AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results.
Hidden Markov Model assumptions may not hold during:
• Major news events disrupting normal market behavior
• Flash crashes or circuit breaker events
• Low liquidity periods with erratic price action
• Algorithmic manipulation or spoofing
Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear.
Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing.
Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception.
By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes.
"Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable."
Initial Public Release
Core Engine:
• True Hidden Markov Model with online Baum-Welch learning
• Viterbi algorithm for optimal state sequence decoding
• 6-state market regime classification
Agent System:
• 3-Bandit consensus (Trend, Reversion, Structure)
• Thompson Sampling with true Beta distribution sampling
• Adaptive weight learning based on performance
Signal Generation:
• Quality-based confluence grading (A+/A/B/C)
• Four signal modes (Aggressive/Balanced/Conservative/Institutional)
• Grade-based visual brightness
Chop Detection:
• 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume)
• 7 regime classifications
• Configurable signal suppression threshold
Liquidity:
• Volume spike detection
• Stop run (liquidity sweep) identification
• BSL/SSL pool mapping
• Absorption zone detection
Trade Management:
• Trade lock with configurable duration
• TP1/TP2/TP3 targets
• ATR-based stop loss
• Persistent execution markers
Session Intelligence:
• Asian/London/NY/Overlap detection
• Smart weekend handling (Sunday futures open)
• Session quality scoring
Performance:
• Statistics tracking with reset functionality
• 7 currency display modes
• Win rate and Net R calculation
Visuals:
• Macro channel with linear regression
• Chop boxes
• EMA ribbon
• Liquidity pool lines
• 6 professional themes
Dashboards:
• Main Dashboard: Market State, Consensus, Trade Status, Statistics
• AMWT Advisor : Market Pulse, Agent Matrix, Structure, Watch For
Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
Adaptive Market Wave TheoryAdaptive Market Wave Theory
🌊 CORE INNOVATION: PROBABILISTIC PHASE DETECTION WITH MULTI-AGENT CONSENSUS
Adaptive Market Wave Theory (AMWT) represents a fundamental paradigm shift in how traders approach market phase identification. Rather than counting waves subjectively or drawing static breakout levels, AMWT treats the market as a hidden state machine —using Hidden Markov Models, multi-agent consensus systems, and reinforcement learning algorithms to quantify what traditional methods leave to interpretation.
The Wave Analysis Problem:
Traditional wave counting methodologies (Elliott Wave, harmonic patterns, ABC corrections) share fatal weaknesses that AMWT directly addresses:
1. Non-Falsifiability : Invalid wave counts can always be "recounted" or "adjusted." If your Wave 3 fails, it becomes "Wave 3 of a larger degree" or "actually Wave C." There's no objective failure condition.
2. Observer Bias : Two expert wave analysts examining the same chart routinely reach different conclusions. This isn't a feature—it's a fundamental methodology flaw.
3. No Confidence Measure : Traditional analysis says "This IS Wave 3." But with what probability? 51%? 95%? The binary nature prevents proper position sizing and risk management.
4. Static Rules : Fixed Fibonacci ratios and wave guidelines cannot adapt to changing market regimes. What worked in 2019 may fail in 2024.
5. No Accountability : Wave methodologies rarely track their own performance. There's no feedback loop to improve.
The AMWT Solution:
AMWT addresses each limitation through rigorous mathematical frameworks borrowed from speech recognition, machine learning, and reinforcement learning:
• Non-Falsifiability → Hard Invalidation : Wave hypotheses die permanently when price violates calculated invalidation levels. No recounting allowed.
• Observer Bias → Multi-Agent Consensus : Three independent analytical agents must agree. Single-methodology bias is eliminated.
• No Confidence → Probabilistic States : Every market state has a calculated probability from Hidden Markov Model inference. "72% probability of impulse state" replaces "This is Wave 3."
• Static Rules → Adaptive Learning : Thompson Sampling multi-armed bandits learn which agents perform best in current conditions. The system adapts in real-time.
• No Accountability → Performance Tracking : Comprehensive statistics track every signal's outcome. The system knows its own performance.
The Core Insight:
"Traditional wave analysis asks 'What count is this?' AMWT asks 'What is the probability we are in an impulsive state, with what confidence, confirmed by how many independent methodologies, and anchored to what liquidity event?'"
🔬 THEORETICAL FOUNDATION: HIDDEN MARKOV MODELS
Why Hidden Markov Models?
Markets exist in hidden states that we cannot directly observe—only their effects on price are visible. When the market is in an "impulse up" state, we see rising prices, expanding volume, and trending indicators. But we don't observe the state itself—we infer it from observables.
This is precisely the problem Hidden Markov Models (HMMs) solve. Originally developed for speech recognition (inferring words from sound waves), HMMs excel at estimating hidden states from noisy observations.
HMM Components:
1. Hidden States (S) : The unobservable market conditions
2. Observations (O) : What we can measure (price, volume, indicators)
3. Transition Matrix (A) : Probability of moving between states
4. Emission Matrix (B) : Probability of observations given each state
5. Initial Distribution (π) : Starting state probabilities
AMWT's Six Market States:
State 0: IMPULSE_UP
• Definition: Strong bullish momentum with high participation
• Observable Signatures: Rising prices, expanding volume, RSI >60, price above upper Bollinger Band, MACD histogram positive and rising
• Typical Duration: 5-20 bars depending on timeframe
• What It Means: Institutional buying pressure, trend acceleration phase
State 1: IMPULSE_DN
• Definition: Strong bearish momentum with high participation
• Observable Signatures: Falling prices, expanding volume, RSI <40, price below lower Bollinger Band, MACD histogram negative and falling
• Typical Duration: 5-20 bars (often shorter than bullish impulses—markets fall faster)
• What It Means: Institutional selling pressure, panic or distribution acceleration
State 2: CORRECTION
• Definition: Counter-trend consolidation with declining momentum
• Observable Signatures: Sideways or mild counter-trend movement, contracting volume, RSI returning toward 50, Bollinger Bands narrowing
• Typical Duration: 8-30 bars
• What It Means: Profit-taking, digestion of prior move, potential accumulation for next leg
State 3: ACCUMULATION
• Definition: Base-building near lows where informed participants absorb supply
• Observable Signatures: Price near recent lows but not making new lows, volume spikes on up bars, RSI showing positive divergence, tight range
• Typical Duration: 15-50 bars
• What It Means: Smart money buying from weak hands, preparing for markup phase
State 4: DISTRIBUTION
• Definition: Top-forming near highs where informed participants distribute holdings
• Observable Signatures: Price near recent highs but struggling to advance, volume spikes on down bars, RSI showing negative divergence, widening range
• Typical Duration: 15-50 bars
• What It Means: Smart money selling to late buyers, preparing for markdown phase
State 5: TRANSITION
• Definition: Regime change period with mixed signals and elevated uncertainty
• Observable Signatures: Conflicting indicators, whipsaw price action, no clear momentum, high volatility without direction
• Typical Duration: 5-15 bars
• What It Means: Market deciding next direction, dangerous for directional trades
The Transition Matrix:
The transition matrix A captures the probability of moving from one state to another. AMWT initializes with empirically-derived values then updates online:
From/To IMP_UP IMP_DN CORR ACCUM DIST TRANS
IMP_UP 0.70 0.02 0.20 0.02 0.04 0.02
IMP_DN 0.02 0.70 0.20 0.04 0.02 0.02
CORR 0.15 0.15 0.50 0.10 0.10 0.00
ACCUM 0.30 0.05 0.15 0.40 0.05 0.05
DIST 0.05 0.30 0.15 0.05 0.40 0.05
TRANS 0.20 0.20 0.20 0.15 0.15 0.10
Key Insights from Transition Probabilities:
• Impulse states are sticky (70% self-transition): Once trending, markets tend to continue
• Corrections can transition to either impulse direction (15% each): The next move after correction is uncertain
• Accumulation strongly favors IMP_UP transition (30%): Base-building leads to rallies
• Distribution strongly favors IMP_DN transition (30%): Topping leads to declines
The Viterbi Algorithm:
Given a sequence of observations, how do we find the most likely state sequence? This is the Viterbi algorithm—dynamic programming to find the optimal path through the state space.
Mathematical Formulation:
δ_t(j) = max_i × B_j(O_t)
Where:
δ_t(j) = probability of most likely path ending in state j at time t
A_ij = transition probability from state i to state j
B_j(O_t) = emission probability of observation O_t given state j
AMWT Implementation:
AMWT runs Viterbi over a rolling window (default 50 bars), computing the most likely state sequence and extracting:
• Current state estimate
• State confidence (probability of current state vs alternatives)
• State sequence for pattern detection
Online Learning (Baum-Welch Adaptation):
Unlike static HMMs, AMWT continuously updates its transition and emission matrices based on observed market behavior:
f_onlineUpdateHMM(prev_state, curr_state, observation, decay) =>
// Update transition matrix
A *= decay
A += (1.0 - decay)
// Renormalize row
// Update emission matrix
B *= decay
B += (1.0 - decay)
// Renormalize row
The decay parameter (default 0.85) controls adaptation speed:
• Higher decay (0.95): Slower adaptation, more stable, better for consistent markets
• Lower decay (0.80): Faster adaptation, more reactive, better for regime changes
Why This Matters for Trading:
Traditional indicators give you a number (RSI = 72). AMWT gives you a probabilistic state assessment :
"There is a 78% probability we are in IMPULSE_UP state, with 15% probability of CORRECTION and 7% distributed among other states. The transition matrix suggests 70% chance of remaining in IMPULSE_UP next bar, 20% chance of transitioning to CORRECTION."
This enables:
• Position sizing by confidence : 90% confidence = full size; 60% confidence = half size
• Risk management by transition probability : High correction probability = tighten stops
• Strategy selection by state : IMPULSE = trend-follow; CORRECTION = wait; ACCUMULATION = scale in
🎰 THE 3-BANDIT CONSENSUS SYSTEM
The Multi-Agent Philosophy:
No single analytical methodology works in all market conditions. Trend-following excels in trending markets but gets chopped in ranges. Mean-reversion excels in ranges but gets crushed in trends. Structure-based analysis works when structure is clear but fails in chaotic markets.
AMWT's solution: employ three independent agents , each analyzing the market from a different perspective, then use Thompson Sampling to learn which agents perform best in current conditions.
Agent 1: TREND AGENT
Philosophy : Markets trend. Follow the trend until it ends.
Analytical Components:
• EMA Alignment: EMA8 > EMA21 > EMA50 (bullish) or inverse (bearish)
• MACD Histogram: Direction and rate of change
• Price Momentum: Close relative to ATR-normalized movement
• VWAP Position: Price above/below volume-weighted average price
Signal Generation:
Strong Bull: EMA aligned bull AND MACD histogram > 0 AND momentum > 0.3 AND close > VWAP
→ Signal: +1 (Long), Confidence: 0.75 + |momentum| × 0.4
Moderate Bull: EMA stack bull AND MACD rising AND momentum > 0.1
→ Signal: +1 (Long), Confidence: 0.65 + |momentum| × 0.3
Strong Bear: EMA aligned bear AND MACD histogram < 0 AND momentum < -0.3 AND close < VWAP
→ Signal: -1 (Short), Confidence: 0.75 + |momentum| × 0.4
Moderate Bear: EMA stack bear AND MACD falling AND momentum < -0.1
→ Signal: -1 (Short), Confidence: 0.65 + |momentum| × 0.3
When Trend Agent Excels:
• Trend days (IB extension >1.5x)
• Post-breakout continuation
• Institutional accumulation/distribution phases
When Trend Agent Fails:
• Range-bound markets (ADX <20)
• Chop zones after volatility spikes
• Reversal days at major levels
Agent 2: REVERSION AGENT
Philosophy: Markets revert to mean. Extreme readings reverse.
Analytical Components:
• Bollinger Band Position: Distance from bands, percent B
• RSI Extremes: Overbought (>70) and oversold (<30)
• Stochastic: %K/%D crossovers at extremes
• Band Squeeze: Bollinger Band width contraction
Signal Generation:
Oversold Bounce: BB %B < 0.20 AND RSI < 35 AND Stochastic < 25
→ Signal: +1 (Long), Confidence: 0.70 + (30 - RSI) × 0.01
Overbought Fade: BB %B > 0.80 AND RSI > 65 AND Stochastic > 75
→ Signal: -1 (Short), Confidence: 0.70 + (RSI - 70) × 0.01
Squeeze Fire Bull: Band squeeze ending AND close > upper band
→ Signal: +1 (Long), Confidence: 0.65
Squeeze Fire Bear: Band squeeze ending AND close < lower band
→ Signal: -1 (Short), Confidence: 0.65
When Reversion Agent Excels:
• Rotation days (price stays within IB)
• Range-bound consolidation
• After extended moves without pullback
When Reversion Agent Fails:
• Strong trend days (RSI can stay overbought for days)
• Breakout moves
• News-driven directional moves
Agent 3: STRUCTURE AGENT
Philosophy: Market structure reveals institutional intent. Follow the smart money.
Analytical Components:
• Break of Structure (BOS): Price breaks prior swing high/low
• Change of Character (CHOCH): First break against prevailing trend
• Higher Highs/Higher Lows: Bullish structure
• Lower Highs/Lower Lows: Bearish structure
• Liquidity Sweeps: Stop runs that reverse
Signal Generation:
BOS Bull: Price breaks above prior swing high with momentum
→ Signal: +1 (Long), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bull: First higher low after downtrend, breaking structure
→ Signal: +1 (Long), Confidence: 0.75
BOS Bear: Price breaks below prior swing low with momentum
→ Signal: -1 (Short), Confidence: 0.70 + structure_strength × 0.2
CHOCH Bear: First lower high after uptrend, breaking structure
→ Signal: -1 (Short), Confidence: 0.75
Liquidity Sweep Long: Price sweeps below swing low then reverses strongly
→ Signal: +1 (Long), Confidence: 0.80
Liquidity Sweep Short: Price sweeps above swing high then reverses strongly
→ Signal: -1 (Short), Confidence: 0.80
When Structure Agent Excels:
• After liquidity grabs (stop runs)
• At major swing points
• During institutional accumulation/distribution
When Structure Agent Fails:
• Choppy, structureless markets
• During news events (structure becomes noise)
• Very low timeframes (noise overwhelms structure)
Thompson Sampling: The Bandit Algorithm
With three agents giving potentially different signals, how do we decide which to trust? This is the multi-armed bandit problem —balancing exploitation (using what works) with exploration (testing alternatives).
Thompson Sampling Solution:
Each agent maintains a Beta distribution representing its success/failure history:
Agent success rate modeled as Beta(α, β)
Where:
α = number of successful signals + 1
β = number of failed signals + 1
On Each Bar:
1. Sample from each agent's Beta distribution
2. Weight agent signals by sampled probabilities
3. Combine weighted signals into consensus
4. Update α/β based on trade outcomes
Mathematical Implementation:
// Beta sampling via Gamma ratio method
f_beta_sample(alpha, beta) =>
g1 = f_gamma_sample(alpha)
g2 = f_gamma_sample(beta)
g1 / (g1 + g2)
// Thompson Sampling selection
for each agent:
sampled_prob = f_beta_sample(agent.alpha, agent.beta)
weight = sampled_prob / sum(all_sampled_probs)
consensus += agent.signal × agent.confidence × weight
Why Thompson Sampling?
• Automatic Exploration : Agents with few samples get occasional chances (high variance in Beta distribution)
• Bayesian Optimal : Mathematically proven optimal solution to exploration-exploitation tradeoff
• Uncertainty-Aware : Small sample size = more exploration; large sample size = more exploitation
• Self-Correcting : Poor performers naturally get lower weights over time
Example Evolution:
Day 1 (Initial):
Trend Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Reversion Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
Structure Agent: Beta(1,1) → samples ~0.50 (high uncertainty)
After 50 Signals:
Trend Agent: Beta(28,23) → samples ~0.55 (moderate confidence)
Reversion Agent: Beta(18,33) → samples ~0.35 (underperforming)
Structure Agent: Beta(32,19) → samples ~0.63 (outperforming)
Result: Structure Agent now receives highest weight in consensus
Consensus Requirements by Mode:
Aggressive Mode:
• Minimum 1/3 agents agreeing
• Consensus threshold: 45%
• Use case: More signals, higher risk tolerance
Balanced Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 55%
• Use case: Standard trading
Conservative Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 65%
• Use case: Higher quality, fewer signals
Institutional Mode:
• Minimum 2/3 agents agreeing
• Consensus threshold: 75%
• Additional: Session quality >0.65, mode adjustment +0.10
• Use case: Highest quality signals only
🌀 INTELLIGENT CHOP DETECTION ENGINE
The Chop Problem:
Most trading losses occur not from being wrong about direction, but from trading in conditions where direction doesn't exist . Choppy, range-bound markets generate false signals from every methodology—trend-following, mean-reversion, and structure-based alike.
AMWT's chop detection engine identifies these low-probability environments before signals fire, preventing the most damaging trades.
Five-Factor Chop Analysis:
Factor 1: ADX Component (25% weight)
ADX (Average Directional Index) measures trend strength regardless of direction.
ADX < 15: Very weak trend (high chop score)
ADX 15-20: Weak trend (moderate chop score)
ADX 20-25: Developing trend (low chop score)
ADX > 25: Strong trend (minimal chop score)
adx_chop = (i_adxThreshold - adx_val) / i_adxThreshold × 100
Why ADX Works: ADX synthesizes +DI and -DI movements. Low ADX means price is moving but not directionally—the definition of chop.
Factor 2: Choppiness Index (25% weight)
The Choppiness Index measures price efficiency using the ratio of ATR sum to price range:
CI = 100 × LOG10(SUM(ATR, n) / (Highest - Lowest)) / LOG10(n)
CI > 61.8: Choppy (range-bound, inefficient movement)
CI < 38.2: Trending (directional, efficient movement)
CI 38.2-61.8: Transitional
chop_idx_score = (ci_val - 38.2) / (61.8 - 38.2) × 100
Why Choppiness Index Works: In trending markets, price covers distance efficiently (low ATR sum relative to range). In choppy markets, price oscillates wildly but goes nowhere (high ATR sum relative to range).
Factor 3: Range Compression (20% weight)
Compares recent range to longer-term range, detecting volatility squeezes:
recent_range = Highest(20) - Lowest(20)
longer_range = Highest(50) - Lowest(50)
compression = 1 - (recent_range / longer_range)
compression > 0.5: Strong squeeze (potential breakout imminent)
compression < 0.2: No compression (normal volatility)
range_compression_score = compression × 100
Why Range Compression Matters: Compression precedes expansion. High compression = market coiling, preparing for move. Signals during compression often fail because the breakout hasn't occurred yet.
Factor 4: Channel Position (15% weight)
Tracks price position within the macro channel:
channel_position = (close - channel_low) / (channel_high - channel_low)
position 0.4-0.6: Center of channel (indecision zone)
position <0.2 or >0.8: Near extremes (potential reversal or breakout)
channel_chop = abs(0.5 - channel_position) < 0.15 ? high_score : low_score
Why Channel Position Matters: Price in the middle of a range is in "no man's land"—equally likely to go either direction. Signals in the channel center have lower probability.
Factor 5: Volume Quality (15% weight)
Assesses volume relative to average:
vol_ratio = volume / SMA(volume, 20)
vol_ratio < 0.7: Low volume (lack of conviction)
vol_ratio 0.7-1.3: Normal volume
vol_ratio > 1.3: High volume (conviction present)
volume_chop = vol_ratio < 0.8 ? (1 - vol_ratio) × 100 : 0
Why Volume Quality Matters: Low volume moves lack institutional participation. These moves are more likely to reverse or stall.
Combined Chop Intensity:
chopIntensity = (adx_chop × 0.25) + (chop_idx_score × 0.25) +
(range_compression_score × 0.20) + (channel_chop × 0.15) +
(volume_chop × i_volumeChopWeight × 0.15)
Regime Classifications:
Based on chop intensity and component analysis:
• Strong Trend (0-20%): ADX >30, clear directional momentum, trade aggressively
• Trending (20-35%): ADX >20, moderate directional bias, trade normally
• Transitioning (35-50%): Mixed signals, regime change possible, reduce size
• Mid-Range (50-60%): Price trapped in channel center, avoid new positions
• Ranging (60-70%): Low ADX, price oscillating within bounds, fade extremes only
• Compression (70-80%): Volatility squeeze, expansion imminent, wait for breakout
• Strong Chop (80-100%): Multiple chop factors aligned, avoid trading entirely
Signal Suppression:
When chop intensity exceeds the configurable threshold (default 80%), signals are suppressed entirely. The dashboard displays "⚠️ CHOP ZONE" with the current regime classification.
Chop Box Visualization:
When chop is detected, AMWT draws a semi-transparent box on the chart showing the chop zone. This visual reminder helps traders avoid entering positions during unfavorable conditions.
💧 LIQUIDITY ANCHORING SYSTEM
The Liquidity Concept:
Markets move from liquidity pool to liquidity pool. Stop losses cluster at predictable locations—below swing lows (buy stops become sell orders when triggered) and above swing highs (sell stops become buy orders when triggered). Institutions know where these clusters are and often engineer moves to trigger them before reversing.
AMWT identifies and tracks these liquidity events, using them as anchors for signal confidence.
Liquidity Event Types:
Type 1: Volume Spikes
Definition: Volume > SMA(volume, 20) × i_volThreshold (default 2.8x)
Interpretation: Sudden volume surge indicates institutional activity
• Near swing low + reversal: Likely accumulation
• Near swing high + reversal: Likely distribution
• With continuation: Institutional conviction in direction
Type 2: Stop Runs (Liquidity Sweeps)
Definition: Price briefly exceeds swing high/low then reverses within N bars
Detection:
• Price breaks above recent swing high (triggering buy stops)
• Then closes back below that high within 3 bars
• Signal: Bullish stop run complete, reversal likely
Or inverse for bearish:
• Price breaks below recent swing low (triggering sell stops)
• Then closes back above that low within 3 bars
• Signal: Bearish stop run complete, reversal likely
Type 3: Absorption Events
Definition: High volume with small candle body
Detection:
• Volume > 2x average
• Candle body < 30% of candle range
• Interpretation: Large orders being filled without moving price
• Implication: Accumulation (at lows) or distribution (at highs)
Type 4: BSL/SSL Pools (Buy-Side/Sell-Side Liquidity)
BSL (Buy-Side Liquidity):
• Cluster of swing highs within ATR proximity
• Stop losses from shorts sit above these highs
• Breaking BSL triggers short covering (fuel for rally)
SSL (Sell-Side Liquidity):
• Cluster of swing lows within ATR proximity
• Stop losses from longs sit below these lows
• Breaking SSL triggers long liquidation (fuel for decline)
Liquidity Pool Mapping:
AMWT continuously scans for and maps liquidity pools:
// Detect swing highs/lows using pivot function
swing_high = ta.pivothigh(high, 5, 5)
swing_low = ta.pivotlow(low, 5, 5)
// Track recent swing points
if not na(swing_high)
bsl_levels.push(swing_high)
if not na(swing_low)
ssl_levels.push(swing_low)
// Display on chart with labels
Confluence Scoring Integration:
When signals fire near identified liquidity events, confluence scoring increases:
• Signal near volume spike: +10% confidence
• Signal after liquidity sweep: +15% confidence
• Signal at BSL/SSL pool: +10% confidence
• Signal aligned with absorption zone: +10% confidence
Why Liquidity Anchoring Matters:
Signals "in a vacuum" have lower probability than signals anchored to institutional activity. A long signal after a liquidity sweep below swing lows has trapped shorts providing fuel. A long signal in the middle of nowhere has no such catalyst.
📊 SIGNAL GRADING SYSTEM
The Quality Problem:
Not all signals are created equal. A signal with 6/6 factors aligned is fundamentally different from a signal with 3/6 factors aligned. Traditional indicators treat them the same. AMWT grades every signal based on confluence.
Confluence Components (100 points total):
1. Bandit Consensus Strength (25 points)
consensus_str = weighted average of agent confidences
score = consensus_str × 25
Example:
Trend Agent: +1 signal, 0.80 confidence, 0.35 weight
Reversion Agent: 0 signal, 0.50 confidence, 0.25 weight
Structure Agent: +1 signal, 0.75 confidence, 0.40 weight
Weighted consensus = (0.80×0.35 + 0×0.25 + 0.75×0.40) / (0.35 + 0.40) = 0.77
Score = 0.77 × 25 = 19.25 points
2. HMM State Confidence (15 points)
score = hmm_confidence × 15
Example:
HMM reports 82% probability of IMPULSE_UP
Score = 0.82 × 15 = 12.3 points
3. Session Quality (15 points)
Session quality varies by time:
• London/NY Overlap: 1.0 (15 points)
• New York Session: 0.95 (14.25 points)
• London Session: 0.70 (10.5 points)
• Asian Session: 0.40 (6 points)
• Off-Hours: 0.30 (4.5 points)
• Weekend: 0.10 (1.5 points)
4. Energy/Participation (10 points)
energy = (realized_vol / avg_vol) × 0.4 + (range / ATR) × 0.35 + (volume / avg_volume) × 0.25
score = min(energy, 1.0) × 10
5. Volume Confirmation (10 points)
if volume > SMA(volume, 20) × 1.5:
score = 10
else if volume > SMA(volume, 20):
score = 5
else:
score = 0
6. Structure Alignment (10 points)
For long signals:
• Bullish structure (HH + HL): 10 points
• Higher low only: 6 points
• Neutral structure: 3 points
• Bearish structure: 0 points
Inverse for short signals
7. Trend Alignment (10 points)
For long signals:
• Price > EMA21 > EMA50: 10 points
• Price > EMA21: 6 points
• Neutral: 3 points
• Against trend: 0 points
8. Entry Trigger Quality (5 points)
• Strong trigger (multiple confirmations): 5 points
• Moderate trigger (single confirmation): 3 points
• Weak trigger (marginal): 1 point
Grade Scale:
Total Score → Grade
85-100 → A+ (Exceptional—all factors aligned)
70-84 → A (Strong—high probability)
55-69 → B (Acceptable—proceed with caution)
Below 55 → C (Marginal—filtered by default)
Grade-Based Signal Brightness:
Signal arrows on the chart have transparency based on grade:
• A+: Full brightness (alpha = 0)
• A: Slight fade (alpha = 15)
• B: Moderate fade (alpha = 35)
• C: Significant fade (alpha = 55)
This visual hierarchy helps traders instantly identify signal quality.
Minimum Grade Filter:
Configurable filter (default: C) sets the minimum grade for signal display:
• Set to "A" for only highest-quality signals
• Set to "B" for moderate selectivity
• Set to "C" for all signals (maximum quantity)
🕐 SESSION INTELLIGENCE
Why Sessions Matter:
Markets behave differently at different times. The London open is fundamentally different from the Asian lunch hour. AMWT incorporates session-aware logic to optimize signal quality.
Session Definitions:
Asian Session (18:00-03:00 ET)
• Characteristics: Lower volatility, range-bound tendency, fewer institutional participants
• Quality Score: 0.40 (40% of peak quality)
• Strategy Implications: Fade extremes, expect ranges, smaller position sizes
• Best For: Mean-reversion setups, accumulation/distribution identification
London Session (03:00-12:00 ET)
• Characteristics: European institutional activity, volatility pickup, trend initiation
• Quality Score: 0.70 (70% of peak quality)
• Strategy Implications: Watch for trend development, breakouts more reliable
• Best For: Initial trend identification, structure breaks
New York Session (08:00-17:00 ET)
• Characteristics: Highest liquidity, US institutional activity, major moves
• Quality Score: 0.95 (95% of peak quality)
• Strategy Implications: Best environment for directional trades
• Best For: Trend continuation, momentum plays
London/NY Overlap (08:00-12:00 ET)
• Characteristics: Peak liquidity, both European and US participants active
• Quality Score: 1.0 (100%—maximum quality)
• Strategy Implications: Highest probability for successful breakouts and trends
• Best For: All signal types—this is prime time
Off-Hours
• Characteristics: Thin liquidity, erratic price action, gaps possible
• Quality Score: 0.30 (30% of peak quality)
• Strategy Implications: Avoid new positions, wider stops if holding
• Best For: Waiting
Smart Weekend Detection:
AMWT properly handles the Sunday evening futures open:
// Traditional (broken):
isWeekend = dayofweek == saturday OR dayofweek == sunday
// AMWT (correct):
anySessionActive = not na(asianTime) or not na(londonTime) or not na(nyTime)
isWeekend = calendarWeekend AND NOT anySessionActive
This ensures Sunday 6pm ET (when futures open) correctly shows "Asian Session" rather than "Weekend."
Session Transition Boosts:
Certain session transitions create trading opportunities:
• Asian → London transition: +15% confidence boost (volatility expansion likely)
• London → Overlap transition: +20% confidence boost (peak liquidity approaching)
• Overlap → NY-only transition: -10% confidence adjustment (liquidity declining)
• Any → Off-Hours transition: Signal suppression recommended
📈 TRADE MANAGEMENT SYSTEM
The Signal Spam Problem:
Many indicators generate signal after signal, creating confusion and overtrading. AMWT implements a complete trade lifecycle management system that prevents signal spam and tracks performance.
Trade Lock Mechanism:
Once a signal fires, the system enters a "trade lock" state:
Trade Lock Duration: Configurable (default 30 bars)
Early Exit Conditions:
• TP3 hit (full target reached)
• Stop Loss hit (trade failed)
• Lock expiration (time-based exit)
During lock:
• No new signals of same type displayed
• Opposite signals can override (reversal)
• Trade status tracked in dashboard
Target Levels:
Each signal generates three profit targets based on ATR:
TP1 (Conservative Target)
• Default: 1.0 × ATR
• Purpose: Quick partial profit, reduce risk
• Action: Take 30-40% off position, move stop to breakeven
TP2 (Standard Target)
• Default: 2.5 × ATR
• Purpose: Main profit target
• Action: Take 40-50% off position, trail stop
TP3 (Extended Target)
• Default: 5.0 × ATR
• Purpose: Runner target for trend days
• Action: Close remaining position or continue trailing
Stop Loss:
• Default: 1.9 × ATR from entry
• Purpose: Define maximum risk
• Placement: Below recent swing low (longs) or above recent swing high (shorts)
Invalidation Level:
Beyond stop loss, AMWT calculates an "invalidation" level where the wave hypothesis dies:
invalidation = entry - (ATR × INVALIDATION_MULT × 1.5)
If price reaches invalidation, the current market interpretation is wrong—not just the trade.
Visual Trade Management:
During active trades, AMWT displays:
• Entry arrow with grade label (▲A+, ▼B, etc.)
• TP1, TP2, TP3 horizontal lines in green
• Stop Loss line in red
• Invalidation line in orange (dashed)
• Progress indicator in dashboard
Persistent Execution Markers:
When targets or stops are hit, permanent markers appear:
• TP hit: Green dot with "TP1"/"TP2"/"TP3" label
• SL hit: Red dot with "SL" label
These persist on the chart for review and statistics.
💰 PERFORMANCE TRACKING & STATISTICS
Tracked Metrics:
• Total Trades: Count of all signals that entered trade lock
• Winning Trades: Signals where at least TP1 was reached before SL
• Losing Trades: Signals where SL was hit before any TP
• Win Rate: Winning / Total × 100%
• Total R Profit: Sum of R-multiples from winning trades
• Total R Loss: Sum of R-multiples from losing trades
• Net R: Total R Profit - Total R Loss
Currency Conversion System:
AMWT can display P&L in multiple formats:
R-Multiple (Default)
• Shows risk-normalized returns
• "Net P&L: +4.2R | 78 trades" means 4.2 times initial risk gained over 78 trades
• Best for comparing across different position sizes
Currency Conversion (USD/EUR/GBP/JPY/INR)
• Converts R-multiples to currency based on:
- Dollar Risk Per Trade (user input)
- Tick Value (user input)
- Selected currency
Example Configuration:
Dollar Risk Per Trade: $100
Display Currency: USD
If Net R = +4.2R
Display: Net P&L: +$420.00 | 78 trades
Ticks
• For futures traders who think in ticks
• Converts based on tick value input
Statistics Reset:
Two reset methods:
1. Toggle Reset
• Turn "Reset Statistics" toggle ON then OFF
• Clears all statistics immediately
2. Date-Based Reset
• Set "Reset After Date" (YYYY-MM-DD format)
• Only trades after this date are counted
• Useful for isolating recent performance
🎨 VISUAL FEATURES
Macro Channel:
Dynamic regression-based channel showing market boundaries:
• Upper/lower bounds calculated from swing pivot linear regression
• Adapts to current market structure
• Shows overall trend direction and potential reversal zones
Chop Boxes:
Semi-transparent overlay during high-chop periods:
• Purple/orange coloring indicates dangerous conditions
• Visual reminder to avoid new positions
Confluence Heat Zones:
Background shading indicating setup quality:
• Darker shading = higher confluence
• Lighter shading = lower confluence
• Helps identify optimal entry timing
EMA Ribbon:
Trend visualization via moving average fill:
• EMA 8/21/50 with gradient fill between
• Green fill when bullish aligned
• Red fill when bearish aligned
• Gray when neutral
Absorption Zone Boxes:
Marks potential accumulation/distribution areas:
• High volume + small body = absorption
• Boxes drawn at these levels
• Often act as support/resistance
Liquidity Pool Lines:
BSL/SSL levels with labels:
• Dashed lines at liquidity clusters
• "BSL" label above swing high clusters
• "SSL" label below swing low clusters
Six Professional Themes:
• Quantum: Deep purples and cyans (default)
• Cyberpunk: Neon pinks and blues
• Professional: Muted grays and greens
• Ocean: Blues and teals
• Matrix: Greens and blacks
• Ember: Oranges and reds
🎓 PROFESSIONAL USAGE PROTOCOL
Phase 1: Learning the System (Week 1)
Goal: Understand AMWT concepts and dashboard interpretation
Setup:
• Signal Mode: Balanced
• Display: All features enabled
• Grade Filter: C (see all signals)
Actions:
• Paper trade ONLY—no real money
• Observe HMM state transitions throughout the day
• Note when agents agree vs disagree
• Watch chop detection engage and disengage
• Track which grades produce winners vs losers
Key Learning Questions:
• How often do A+ signals win vs B signals? (Should see clear difference)
• Which agent tends to be right in current market? (Check dashboard)
• When does chop detection save you from bad trades?
• How do signals near liquidity events perform vs signals in vacuum?
Phase 2: Parameter Optimization (Week 2)
Goal: Tune system to your instrument and timeframe
Signal Mode Testing:
• Run 5 days on Aggressive mode (more signals)
• Run 5 days on Conservative mode (fewer signals)
• Compare: Which produces better risk-adjusted returns?
Grade Filter Testing:
• Track A+ only for 20 signals
• Track A and above for 20 signals
• Track B and above for 20 signals
• Compare win rates and expectancy
Chop Threshold Testing:
• Default (80%): Standard filtering
• Try 70%: More aggressive filtering
• Try 90%: Less filtering
• Which produces best results for your instrument?
Phase 3: Strategy Development (Weeks 3-4)
Goal: Develop personal trading rules based on system signals
Position Sizing by Grade:
• A+ grade: 100% position size
• A grade: 75% position size
• B grade: 50% position size
• C grade: 25% position size (or skip)
Session-Based Rules:
• London/NY Overlap: Take all A/A+ signals
• NY Session: Take all A+ signals, selective on A
• Asian Session: Only A+ signals with extra confirmation
• Off-Hours: No new positions
Chop Zone Rules:
• Chop >70%: Reduce position size 50%
• Chop >80%: No new positions
• Chop <50%: Full position size allowed
Phase 4: Live Micro-Sizing (Month 2)
Goal: Validate paper trading results with minimal risk
Setup:
• 10-20% of intended full position size
• Take ONLY A+ signals initially
• Follow trade management religiously
Tracking:
• Log every trade: Entry, Exit, Grade, HMM State, Chop Level, Agent Consensus
• Calculate: Win rate by grade, by session, by chop level
• Compare to paper trading (should be within 15%)
Red Flags:
• Win rate diverges significantly from paper trading: Execution issues
• Consistent losses during certain sessions: Adjust session rules
• Losses cluster when specific agent dominates: Review that agent's logic
Phase 5: Scaling Up (Months 3-6)
Goal: Gradually increase to full position size
Progression:
• Month 3: 25-40% size (if micro-sizing profitable)
• Month 4: 40-60% size
• Month 5: 60-80% size
• Month 6: 80-100% size
Scale-Up Requirements:
• Minimum 30 trades at current size
• Win rate ≥50%
• Net R positive
• No revenge trading incidents
• Emotional control maintained
💡 DEVELOPMENT INSIGHTS
Why HMM Over Simple Indicators:
Early versions used standard indicators (RSI >70 = overbought, etc.). Win rates hovered at 52-55%. The problem: indicators don't capture state. RSI can stay "overbought" for weeks in a strong trend.
The insight: markets exist in states, and state persistence matters more than indicator levels. Implementing HMM with state transition probabilities increased signal quality significantly. The system now knows not just "RSI is high" but "we're in IMPULSE_UP state with 70% probability of staying in IMPULSE_UP."
The Multi-Agent Evolution:
Original version used a single analytical methodology—trend-following. Performance was inconsistent: great in trends, destroyed in ranges. Added mean-reversion agent: now it was inconsistent the other way.
The breakthrough: use multiple agents and let the system learn which works . Thompson Sampling wasn't the first attempt—tried simple averaging, voting, even hard-coded regime switching. Thompson Sampling won because it's mathematically optimal and automatically adapts without manual regime detection.
Chop Detection Revelation:
Chop detection was added almost as an afterthought. "Let's filter out obviously bad conditions." Testing revealed it was the most impactful single feature. Filtering chop zones reduced losing trades by 35% while only reducing total signals by 20%. The insight: avoiding bad trades matters more than finding good ones.
Liquidity Anchoring Discovery:
Watched hundreds of trades. Noticed pattern: signals that fired after liquidity events (stop runs, volume spikes) had significantly higher win rates than signals in quiet markets. Implemented liquidity detection and anchoring. Win rate on liquidity-anchored signals: 68% vs 52% on non-anchored signals.
The Grade System Impact:
Early system had binary signals (fire or don't fire). Adding grading transformed it. Traders could finally match position size to signal quality. A+ signals deserved full size; C signals deserved caution. Just implementing grade-based sizing improved portfolio Sharpe ratio by 0.3.
🚨 LIMITATIONS & CRITICAL ASSUMPTIONS
What AMWT Is NOT:
• NOT a Holy Grail : No system wins every trade. AMWT improves probability, not certainty.
• NOT Fully Automated : AMWT provides signals and analysis; execution requires human judgment.
• NOT News-Proof : Exogenous shocks (FOMC surprises, geopolitical events) invalidate all technical analysis.
• NOT for Scalping : HMM state estimation needs time to develop. Sub-minute timeframes are not appropriate.
Core Assumptions:
1. Markets Have States : Assumes markets transition between identifiable regimes. Violation: Random walk markets with no regime structure.
2. States Are Inferable : Assumes observable indicators reveal hidden states. Violation: Market manipulation creating false signals.
3. History Informs Future : Assumes past agent performance predicts future performance. Violation: Regime changes that invalidate historical patterns.
4. Liquidity Events Matter : Assumes institutional activity creates predictable patterns. Violation: Markets with no institutional participation.
Performs Best On:
• Liquid Futures : ES, NQ, MNQ, MES, CL, GC
• Major Forex Pairs : EUR/USD, GBP/USD, USD/JPY
• Large-Cap Stocks : AAPL, MSFT, TSLA, NVDA (>$5B market cap)
• Liquid Crypto : BTC, ETH on major exchanges
Performs Poorly On:
• Illiquid Instruments : Low volume stocks, exotic pairs
• Very Low Timeframes : Sub-5-minute charts (noise overwhelms signal)
• Binary Event Days : Earnings, FDA approvals, court rulings
• Manipulated Markets : Penny stocks, low-cap altcoins
Known Weaknesses:
• Warmup Period : HMM needs ~50 bars to initialize properly. Early signals may be unreliable.
• Regime Change Lag : Thompson Sampling adapts over time, not instantly. Sudden regime changes may cause short-term underperformance.
• Complexity : More parameters than simple indicators. Requires understanding to use effectively.
⚠️ RISK DISCLOSURE
Trading futures, stocks, options, forex, and cryptocurrencies involves substantial risk of loss and is not suitable for all investors. Adaptive Market Wave Theory, while based on rigorous mathematical frameworks including Hidden Markov Models and multi-armed bandit algorithms, does not guarantee profits and can result in significant losses.
AMWT's methodologies—HMM state estimation, Thompson Sampling agent selection, and confluence-based grading—have theoretical foundations but past performance is not indicative of future results.
Hidden Markov Model assumptions may not hold during:
• Major news events disrupting normal market behavior
• Flash crashes or circuit breaker events
• Low liquidity periods with erratic price action
• Algorithmic manipulation or spoofing
Multi-agent consensus assumes independent analytical perspectives provide edge. Market conditions change. Edges that existed historically can diminish or disappear.
Users must independently validate system performance on their specific instruments, timeframes, and broker execution environment. Paper trade extensively before risking capital. Start with micro position sizing.
Never risk more than you can afford to lose completely. Use proper position sizing. Implement stop losses without exception.
By using this indicator, you acknowledge these risks and accept full responsibility for all trading decisions and outcomes.
"Elliott Wave was a first-order approximation of market phase behavior. AMWT is the second—probabilistic, adaptive, and accountable."
Initial Public Release
Core Engine:
• True Hidden Markov Model with online Baum-Welch learning
• Viterbi algorithm for optimal state sequence decoding
• 6-state market regime classification
Agent System:
• 3-Bandit consensus (Trend, Reversion, Structure)
• Thompson Sampling with true Beta distribution sampling
• Adaptive weight learning based on performance
Signal Generation:
• Quality-based confluence grading (A+/A/B/C)
• Four signal modes (Aggressive/Balanced/Conservative/Institutional)
• Grade-based visual brightness
Chop Detection:
• 5-factor analysis (ADX, Choppiness Index, Range Compression, Channel Position, Volume)
• 7 regime classifications
• Configurable signal suppression threshold
Liquidity:
• Volume spike detection
• Stop run (liquidity sweep) identification
• BSL/SSL pool mapping
• Absorption zone detection
Trade Management:
• Trade lock with configurable duration
• TP1/TP2/TP3 targets
• ATR-based stop loss
• Persistent execution markers
Session Intelligence:
• Asian/London/NY/Overlap detection
• Smart weekend handling (Sunday futures open)
• Session quality scoring
Performance:
• Statistics tracking with reset functionality
• 7 currency display modes
• Win rate and Net R calculation
Visuals:
• Macro channel with linear regression
• Chop boxes
• EMA ribbon
• Liquidity pool lines
• 6 professional themes
Dashboards:
• Main Dashboard: Market State, Consensus, Trade Status, Statistics
📋 AMWT vs AMWT-PRO:
This version includes all core AMWT functionality:
✓ Full Hidden Markov Model state estimation
✓ 3-Bandit Thompson Sampling consensus system
✓ Complete 5-factor chop detection engine
✓ All four signal modes
✓ Full trade management with TP/SL tracking
✓ Main dashboard with complete statistics
✓ All visual features (channels, zones, pools)
✓ Identical signal generation to PRO
✓ Six professional themes
✓ Full alert system
The PRO version adds the AMWT Advisor panel—a secondary dashboard providing:
• Real-time Market Pulse situation assessment
• Agent Matrix visualization (individual agent votes)
• Structure analysis breakdown
• "Watch For" upcoming setups
• Action Command coaching
Both versions generate identical signals . The Advisor provides additional guidance for interpreting those signals.
Taking you to school. - Dskyz, Trade with probability. Trade with consensus. Trade with AMWT.
Trend Harmony🚀 Trend Harmony: Multi-Timeframe Momentum & Trend Dashboard
Trend Harmony is a sophisticated multi-timeframe (MTF) analysis tool designed to help traders identify high-probability setups by spotting "Market Harmony." Instead of flipping through charts, this indicator synthesizes RSI momentum and EMA trend structures from four different time horizons into a single, intuitive dashboard.
🔍 How It Works
The core philosophy of this indicator is that the most powerful moves happen when short-term momentum aligns with long-term trend structure. The script tracks four user-defined timeframes simultaneously.
1. The Trend Scoring Engine
The indicator evaluates the relationship between a Fast EMA (default 20) and a Slow EMA (default 50) across all active timeframes.
Bullish Alignment: Fast EMA > Slow EMA.
Bearish Alignment: Fast EMA < Slow EMA.
2. The Harmony Summary
At the bottom of the dashboard, the "Summary" status calculates the total "Harmony" of the market:
🚀 FULL BULL HARMONY: All selected timeframes are in a bullish trend.
📉 FULL BEAR HARMONY: All selected timeframes are in a bearish trend.
⚠️ CAUTION (Overbought/Oversold): Triggered when the market is in "Full Harmony" but RSI levels suggest the price is overextended (>70 or <30). This warns you not to "chase" the trade.
Neutral/Mixed: Timeframes are in conflict (e.g., 15m is bullish but Daily is bearish).
🛠 Key Features
Unified RSI Pane: View four RSI lines on one chart to spot divergences or "clusters" where all timeframes bottom out at once.
Dynamic Table: Real-time tracking of:
Price vs EMA: Instant visual (▲/▼) showing if price is above/below your key averages.
Smart RSI Coloring: RSI values turn Green during "Power Zones" (0–30 or 50–70) and Red otherwise.
Full Customization: Change timeframes (1m, 5m, 1H, D, etc.), EMA lengths, and RSI parameters to fit your strategy.
📈 Trading Strategy Tips
Wait for the Sync: The "Full Harmony" status is your signal that the "tide" is moving in one direction. Look for long entries when the status is Green and short entries when it is Red.
The Pullback Entry: When the summary says "Caution (Overbought)," wait for the RSI lines to cool down toward the 50 level before entering the trend again.
RSI Clustering: When all four RSI lines converge at extreme levels (30 or 70), a massive volatility expansion is usually imminent.
TrintityTrendIntroducing TrinityTrend
A multi-signal indicator combining:
Candle TrendStrength
SuperTrend logic
TTM Squeeze detection
Built for clarity, momentum, and volatility awareness—across any timeframe.
TrendStrength Mode
Candle coloring reflects directional conviction.
Strong uptrend
Strong downtrend
Neutral or indecisive
Helps traders stay with momentum and avoid chop.
SuperTrend Overlay
SuperTrend Logic Dynamic trailing stop based on volatility.
🟩 Price above = bullish bias
🟥 Price below = bearish bias
Great for swing entries and exits.
TTM Squeeze Detection
TTM Squeeze Mode Detects compression zones before breakout.
Squeeze on = buildup (You can change the color of this)
Pairs well with TrendStrength for timing entries.
Multi-Timeframe Versatility
Multi-Timeframe Ready:
Intraday scalping
Daily swing setups
Weekly macro bias
Toggle modes to match your strategy
Vortex Trend Matrix [JOAT]Vortex Trend Matrix - Multi-Factor Trend Confluence System
Introduction and Purpose
Vortex Trend Matrix is an open-source overlay indicator that combines Ichimoku-style equilibrium analysis with the Vortex Indicator to create a comprehensive trend confluence system. The core problem this indicator solves is that single trend indicators often give conflicting signals. Price might be above a moving average but momentum might be weakening.
This indicator addresses that by combining five different trend factors into a single composite score, making it easy to identify when multiple factors align for high-probability trend trades.
Why These Components Work Together
Each component measures trend from a different perspective:
1. Cloud Position - Price above/below the equilibrium cloud indicates overall trend bias. The cloud acts as dynamic support/resistance.
2. TK Relationship - Conversion line vs Base line (like Tenkan/Kijun in Ichimoku). Conversion above Base = bullish momentum.
3. Lagging Span - Current price compared to price N bars ago. Confirms whether current move has follow-through.
4. Vortex Indicator - VI+ vs VI- measures directional movement strength. Provides momentum confirmation.
5. Base Direction - Whether the base line is rising or falling. Indicates medium-term trend direction.
How the Trend Score Works
float trendScore = 0.0
// Cloud position (+2/-2)
trendScore += aboveCloud ? 2.0 : belowCloud ? -2.0 : 0.0
// TK relationship (+1/-1)
trendScore += conversionLine > baseLine ? 1.0 : conversionLine < baseLine ? -1.0 : 0.0
// Lagging span (+1/-1)
trendScore += laggingBull ? 1.0 : laggingBear ? -1.0 : 0.0
// Vortex (+1.5/-1.5)
trendScore += vortexBull ? 1.5 : vortexBear ? -1.5 : 0.0
// Base direction (+0.5/-0.5)
trendScore += baseDirection * 0.5
Score ranges from approximately -6 to +6:
- +4 or higher = STRONG BULL
- +2 to +4 = BULL
- -2 to +2 = NEUTRAL
- -4 to -2 = BEAR
- -4 or lower = STRONG BEAR
Signal Types
TK Cross Up/Down - Conversion line crosses Base line (momentum shift)
Base Direction Change - Base line changes direction (medium-term shift)
Strong Bull/Bear Trend - Score reaches +4/-4 (high confluence)
Dashboard Information
Trend - Overall status with composite score
Cloud - Price position (ABOVE/BELOW/INSIDE)
TK Cross - Conversion vs Base relationship
Lagging - Lagging span bias
Vortex - VI+/VI- relationship
VI+/VI- - Individual vortex values
How to Use This Indicator
For Trend Following:
1. Enter long when trend score reaches +4 or higher (STRONG BULL)
2. Enter short when trend score reaches -4 or lower (STRONG BEAR)
3. Use cloud as dynamic support/resistance for entries
For Momentum Timing:
1. Watch for TK Cross signals for entry timing
2. Base direction changes indicate medium-term shifts
3. Vortex confirmation adds conviction
For Risk Management:
1. Exit when trend score drops to neutral
2. Use cloud edges as stop-loss references
3. Reduce position when score weakens
Input Parameters
Conversion Period (9) - Fast equilibrium line
Base Period (26) - Slow equilibrium line
Lead Span Period (52) - Cloud projection period
Displacement (26) - Cloud and lagging span offset
Vortex Period (14) - Period for vortex calculation
VI+ Strength (1.10) - Threshold for strong bullish vortex
VI- Strength (0.90) - Threshold for strong bearish vortex
Timeframe Recommendations
4H-Daily: Best for equilibrium-based analysis
1H: Good for intraday trend following
Lower timeframes may require adjusted periods
Limitations
Equilibrium calculations have inherent lag
Cloud displacement means signals are delayed
Works best in trending markets
May whipsaw in ranging conditions
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes.
This indicator does not constitute financial advice. Trend analysis does not guarantee profitable trades. Always use proper risk management.
- Made with passion by officialjackofalltrades
QSS v18.3 Institutional Quant EngineQSS v18.3 | Institutional Quant Engine
💎 The Ultimate Algorithmic Trading Suite
QSS (Quantitative Signal System) v18.3 is a complete institutional-grade trading engine designed to replace the noise of multiple indicators with a single, high-probability decision model.
This is not just a "Buy/Sell" indicator. It is a "Fortress" of filters, combining Trend, Momentum, Volume, Volatility, Market Cycles, and Support/Resistance into one unified system.
🚀 What is New in v18.3 ?
1. Dynamic Support & Resistance Engine: The system now automatically detects and draws Support (Green) and Resistance (Red) lines based on pivot points.
The "Fortress" Filter: QSS intelligently scans these lines. If a trade setup appears right below a resistance wall or right above a support floor, the system BLOCKS the signal to prevent buying the top or selling the bottom.
Breakout Logic: The system is smart enough to detect a true Breakout. If a candle closes through a resistance line, the filter is overridden, allowing you to catch the explosion.
2. Visual Clarity:
S/R Labels: Horizontal lines are now clearly labelled "RES" (Resistance) and "SUP" (Support).
Entry/Exit Dots: Stop Loss lines now use distinct dotted styling for better visibility.
⚔️ Core Features
1. Dual-Core Signal Engine
Choose the engine that fits your asset class:
Engine A: OTT (Optimized Trend Tracker): Default. A next-gen algorithm based on VIDYA (Variable Index Dynamic Average). It is volatility-adaptive and reacts faster than traditional tools. Perfect for Crypto & Scalping.
Engine B: SuperTrend (Classic): The industry standard for trend following. Slower, steadier, and reliable. Best for Stocks & Swing Trading.
2. Institutional Intelligence (Smart Money)
Koncorde Logic (The Sharks): Analyzing NVI (Negative Volume Index) vs. PVI (Positive Volume Index). Logic: It tracks "Sharks" (Institutions) vs. "Minnows" (Retail). Signals are BLOCKED if Sharks are selling into a pump.
Order Block Filter (SMC): Automatically detects Supply (Red Zones) and Demand (Green Zones). Logic: The system will refuse to Buy if price is hitting a Bearish Order Block (Resistance).
3. The "Cycle Hunter" Filter
Schaff Trend Cycle (STC): A cyclical momentum filter that replaces MACD/Stochastic. Logic: Signals are only valid if the market cycle is turning in your direction. This prevents getting trapped in "fake outs" where price moves slightly but the cycle is actually exhausted.
4. Advanced Noise Cancellation
ADX Trend Strength: Blocks trades during "dead" markets (Chopping/Sideways) where trend followers usually lose money.
Candle Stability Index: Ignores "wicky" candles and dojis. If the market is undecided, QSS waits for a solid candle before firing.
Trend Ribbon: A dual-SMA cloud (21/34) acting as a macro-trend governor.
5. Pro Dashboard & Risk Management
Dynamic Panel: Displays Trend Status, Smart Money Flow (Accumulation/Distribution), Cycle State, and Volatility %.
Live Trade Monitor: When a signal fires, the dashboard expands to show your exact Entry Price, Stop Loss, and TP1/TP2/TP3 targets.
Status Monitor: When waiting for a trade, the dashboard tells you exactly what it is waiting for (e.g., "Wait: Money Flow" or "Wait: S/R Wall").
🛠️ Settings Guide
🔥 SIGNAL ENGINE
Strategy Engine: Choose between OTT (Fast/Crypto) or SuperTrend (Safe/Stocks).
🏰 SUPPORT & RESISTANCE
Filter: S/R Proximity: Enables the "Fortress" logic. Blocks trades too close to walls unless they are breakouts.
Show S/R Lines: Toggles the visible horizontal lines.
🧱 ORDER BLOCK FILTER
Respect Order Blocks: Highly recommended. Prevents buying into institutional supply zones.
📊 QUANT FILTERS
Smart Money (Sharks): Essential for avoiding retail traps. Read more info below on how the filter protects you
ADX Threshold: Default 20. Increase to 25 for safer entries.
Candle Stability: Filters out high-volatility wicks.
🛡️ RISK MANAGEMENT
SL Multiplier: Distance of Stop Loss (Default 2.0x ATR).
TP Multipliers: Adjust your Risk:Reward ratios. (Default is 1:1, 1:2, and 1:3).
💡 How to Trade with QSS v18
The Signal:
Wait for a "BUY" or "SELL" label. Note if it says "OTT" or "ST".
The Confluence:
Check the Ribbon: Is it the right color?
Check the Dashboard: Is Smart Money "ACCUM" (for buys)? Is the Cycle "BULL"?
The Execution:
Enter at the Entry Price displayed on the chart/dashboard.
Set Stop Loss at the Red Dotted Line.
Take Profit at the Blue Dotted Lines.
Troubleshooting:
I see the trend change colour, but no Buy/Sell label?
Turn on "🔧 Debug Mode" in the settings. Grey labels will appear on the chart explaining exactly why the trade was rejected (e.g., "⛔ REJECTED: Hitting S/R Wall" or "⛔ REJECTED: Low ADX").
The Smart Money (Sharks) Filter Explained
The "Smart Money" filter in QSS v16.0 is based on the famous "Koncorde" indicator logic developed by Blai5. It is designed to reveal what "Institutional Investors (Sharks)" are doing, as opposed to "Retail Traders (Minnows)".
It solves the classic problem: "Why is the price going up, but the volume looks weak?"
---
1. How it Works (The Logic)
The filter uses volume analysis to separate money flow into two categories:
The Sharks (Institutions): Calculated using the NVI (Negative Volume Index). Theory: Institutions accumulate positions quietly on low volume days to avoid spiking the price and alerting the market.
The Minnows (Retail): Calculated using the PVI (Positive Volume Index). Theory: Retail traders tend to chase price on high volume days (FOMO/Panic).
The Calculation in QSS:
The system takes the NVI, smooths it with an EMA, and normalizes it into a specific range.
Blue Area (Sharks) > 0: Institutions are BUYING (Accumulation).
Blue Area (Sharks) < 0: Institutions are SELLING (Distribution).
---
2. How the Filter Protects You
When you enable **"Filter: Smart Money (Sharks)"** in the settings, the system enforces the following strict rules:
Buying Rule: QSS will BLOCK a Buy signal if the Sharks are selling (Value < 0). Scenario: Price is pumping due to retail FOMO, but institutions are selling into the pump. This is a "Bull Trap." QSS saves you from buying the top.
Selling Rule: QSS will BLOCK a Sell signal if the Sharks are buying (Value > 0). Scenario: Price is dropping due to retail panic, but institutions are scooping up cheap coins. This is a "Bear Trap." QSS saves you from selling the bottom.
---
3. How to Set It Up
This filter is Pre-Calibrated inside the code, so you do not need to adjust the math. You only need to toggle it.
In the Indicator Settings:
1. Scroll down to the **"Quant Filters"** section.
2. Look for the checkbox: **"Filter: Smart Money (Sharks)"**.
✅ Checked (Default): The safety guard is ON. You will receive fewer signals, but they will have institutional backing.
⬜ Unchecked: The safety guard is OFF. You will get more signals, catching raw price moves, but you risk getting trapped by fake-outs.
In the Dashboard:
Look at the row labelled "Smart Money".
ACCUM (Green): Sharks are buying. Safe to Long.
DIST (Red): Sharks are selling. Safe to Short.
Summary
Think of this filter as an "Institutional Background Check." Before the system lets you enter a trade, it asks: "Are the whales coming with us?" If the answer is No, the trade is rejected.
Disclaimer: Trading involves high risk. This indicator is a tool for analysis and confirmation, not financial advice. No algorithm is 100% accurate; always use stop losses.
Credits:
OTT Logic adapted from Anıl Özekşi.
Koncorde Logic adapted from Blai5.
STC Logic adapted from Doug Schaff.
SMC logic adapted from FluxCharts concepts.
MA 50/200This MA 50/200 indicator is a classic TradingView overlay that plots the 50-period Simple Moving Average (SMA) in solid green and the 200-period SMA in solid red directly on the price chart.
It highlights major trend-reversal signals:
A Golden Cross occurs when the faster 50-period SMA crosses above the slower 200-period SMA, often interpreted as a bullish signal suggesting potential upward momentum. This is marked by a prominent green cross symbol (linewidth 5) plotted at the level of the 200 SMA on the bar where the crossover happens.
A Death Cross occurs when the 50-period SMA crosses below the 200-period SMA, often seen as a bearish signal indicating potential downward momentum. This is marked by a red cross symbol (slightly translucent for subtlety) at the 200 SMA level on the crossover bar.
The layout keeps the chart clean and focused: continuous thick lines for the moving averages with clear, oversized cross markers only at crossover points to make Golden and Death Cross events instantly visible without clutter.
Adaptive Score Pro - Trade Intelligence ⚠️ IMPORTANT RISK DISCLAIMER
This script, "Adaptive Score Pro - Trade Intelligence" is published for informational and educational purposes only. It is a technical analysis tool that automates calculations based on market data.
1. Not Investment Advice
The information generated by this script does not constitute financial, investment, or trading advice of any kind . It is not a recommendation to buy, sell, or hold any security or other financial product. You should not rely on this script as a substitute for independent research or professional financial advice .
2. Trading is High Risk
Trading financial instruments carries a high degree of risk. You can lose more than your initial investment, especially when using leverage . Past performance, including any hypothetical or backtested results shown, is not indicative of future results.
3. No Warranties or Liability
The script and its outputs are provided "as is" without any guarantees of accuracy, reliability, or profitability . By using this script, you agree that the author and TradingView, Inc. are not liable for any trading decisions you make, or for any losses or damages that may arise from your use of or reliance on this script .
4. Your Responsibility
You are solely and fully responsible for evaluating the risks of any trade and for your own trading decisions . Ensure you understand the risks involved and only trade with capital you can afford to lose .
By using this script, you acknowledge that you have read, understood, and agree to this disclaimer.
📈 Indicator Overview & Purpose
"Adaptive Score Pro - Trade Intelligence MFE_MAE4" is a comprehensive, all-in-one trading system designed for systematic traders. It goes beyond basic signal generation by integrating real-time trade management, detailed performance analytics, and advanced market regime detection into a single, visually intuitive dashboard.
The core philosophy is to provide trade intelligence—not just entry signals. It analyzes market conditions, assigns a quality grade to every signal, and actively monitors open positions to suggest management adjustments, helping traders move from guesswork to data-driven decisions.
🎯 Unique Selling Points & Key Features
Your script's depth sets it apart. Here are its standout features presented in a table for clarity:
Feature Category What It Does Why It's Valuable
Open Trade Intelligence Monitor Replaces simple signal panels with a dynamic dashboard for each open trade. It compares entry vs. current conditions (score, grade, confidence, regime) and suggests specific actions (HOLD, ADD, DEF, EXIT, TP) and adjustments to stops/targets. Provides actionable, real-time management suggestions, turning a static indicator into an active trading assistant.
Expanded MFE/MAE & High/Low Analysis Tracks the Maximum Favorable and Adverse Excursion in ATR terms and records price highs/lows at strategic intervals (Bars 1, 2, 3, 5, 7, 10, 15) after entry for both long and short trades. Offers deep, standardized post-trade analytics to evaluate entry quality and trade behavior, helping refine future strategy.
Adaptive Signal Engine Generates a composite score from trend, momentum, and volume components, with weights that adapt to market regime (e.g., STRONG TREND, ACTIVE RANGE). Each signal receives a quality grade (A+ to F) and confidence score. Filters out low-probability setups, ensuring you focus on high-quality, regime-appropriate trades.
Holistic Performance Suite Displays complete trade history and detailed statistics (Win Rate, Profit Factor, Sharpe/Sortino Ratios, Max Drawdown). Includes a Threshold Analysis table showing historical win rates for specific score bands. Enables continuous strategy validation and optimization based on your actual historical performance.
Integrated Risk & Portfolio Management Features multi-position risk control, position sizing (with optional Kelly Criterion), and tracks total portfolio equity and drawdown. It enforces maximum open positions and position-size limits. Manages portfolio-level risk, a critical feature often missing from standalone indicators.
🛠️ How to Use the Indicator
Signal Generation: The indicator plots signals on the chart. Focus on trades with a B- grade or higher and high confidence for best results.
Trade Management: Once in a trade, the Open Trade Monitor (top of the table) becomes your control center. Use the suggested "ACT" (action) and adjust stops/targets based on the "NewS" and "NewT" columns.
Post-Trade Analysis: Review the "Recent Trades" section and "Performance" statistics to understand what's working. Use the "Threshold Analysis" to see which score ranges are most profitable for your asset.
💡 Ideal User & Best Practices
This script is ideal for traders who employ a systematic, discretionary approach and want a unified tool for signal filtering, trade management, and performance tracking.
Recommended Timeframes: 1-hour charts and above for reliable regime detection and to avoid excessive noise.
Initial Setup: Start with the default settings. After 20-30 trades, use the Threshold Analysis to see if adjusting the buy/sell thresholds (default 56/44) could improve your edge.
Core Philosophy: Use the A/B-grade signals as high-probatility alerts, not automated orders. The intelligence is in the context it provides—always apply your own final judgment.
⚠️ Important Considerations
Learning Curve: The indicator is feature-rich and may be overwhelming for complete beginners.
Not a "Set-and-Forget" System: It is a sophisticated decision-support tool. Successful use requires understanding its outputs and integrating them into a complete trading plan.
Historical Data: The statistical models and threshold analysis become more reliable as more trade history is accumulated.
In summary, "Adaptive Score Pro" packages the tools of a professional trading desk—from signal generation and quality grading to active trade management and detailed analytics—into a single TradingView indicator. Its standout Open Trade Intelligence Monitor and expanded trade analytics make it a powerful platform for traders aiming to systematize and improve their process.
Adjusted RSI - [JTCAPITAL]Adjusted RSI – is a modified and enhanced way to use the Relative Strength Index (RSI) combined with double normalization, adaptive exponential smoothing, and range compression to create a smoother, more readable, and more structurally consistent momentum oscillator for Trend-Following and momentum analysis.
This indicator is designed to solve several common RSI issues at once:
Excessive noise in raw RSI values
Inconsistent scaling across different market conditions
Difficulty identifying true momentum shifts versus random fluctuations
By re-centering, compressing, normalizing, and smoothing RSI data twice , this script produces a highly refined momentum curve that reacts smoothly while still respecting directional changes.
The indicator works by calculating in the following steps:
Raw RSI Calculation
The script begins by calculating a standard RSI using the selected RSI Length . This RSI is based on the closing price and measures relative strength by comparing average gains and losses over the defined period.
RSI Re-Centering
After the RSI is calculated, the script subtracts 50 from the RSI value.
This converts the RSI from its native scale into a centered oscillator ranging around 0 , making positive values bullish momentum and negative values bearish momentum.
Initial RSI Smoothing
The re-centered RSI is then smoothed using a Simple Moving Average (SMA) over the defined RSI Smoothing Length .
This step removes high-frequency noise and stabilizes short-term RSI fluctuations before further processing.
Range Compression (Clipping)
To prevent extreme outliers from dominating future calculations, the RSI values are clipped:
Values below -10 are forced to -10
Values above +10 are forced to +10
This creates a controlled and consistent RSI range, ensuring later normalization behaves reliably.
First Normalization (Min-Max Scaling)
The clipped RSI values are normalized over the selected Smoothing Length :
The lowest RSI value in the window is detected
The highest RSI value in the window is detected
Current RSI is scaled to a 0–100 range based on this dynamic range
This allows the indicator to adapt automatically to changing volatility and momentum environments.
First Adaptive Smoothing
The normalized RSI is then smoothed using a custom exponential smoothing formula controlled by the Smoothing Factor .
This smoothing behaves similarly to an EMA but allows explicit control over responsiveness.
Second Normalization
The smoothed values undergo a second min-max normalization over the same length.
This further stabilizes the oscillator and ensures consistent amplitude and structure, regardless of market regime.
Second Adaptive Smoothing
A second exponential smoothing pass is applied to the normalized data, further refining the curve and reducing residual noise.
Final Re-Centering
Finally, the indicator subtracts 50 from the smoothed normalized values, re-centering the oscillator around zero .
This produces the final Adjusted RSI line used for visualization and analysis.
Common interpretations for use include:
Bullish Momentum :
When the Adjusted RSI is above zero and rising, indicating strengthening bullish pressure.
Bearish Momentum :
When the Adjusted RSI is below zero and falling, indicating strengthening bearish pressure.
Momentum Shifts :
A change in slope (from falling to rising or vice versa) often signals an early momentum transition.
Divergences :
Differences between price direction and Adjusted RSI direction can highlight potential reversals.
Because the indicator is normalized and smoothed, it pairs exceptionally well with:
Trend filters (moving averages, trend lines)
Volatility filters
Higher-timeframe confirmation
Features and Parameters:
RSI Length
Defines the lookback period for the initial RSI calculation.
RSI Smoothing Length
Controls the SMA smoothing applied directly to the re-centered RSI.
Smoothing Length
Determines the lookback window used for both normalization passes.
Smoothing Factor
Controls the responsiveness of the adaptive exponential smoothing.
Lower values = smoother, slower reaction
Higher values = faster, more responsive reaction
Specifications:
Relative Strength Index (RSI)
RSI is a momentum oscillator that measures the speed and magnitude of recent price changes. By re-centering RSI around zero, the script converts it into a directional momentum oscillator that is easier to interpret for trend-following.
Simple Moving Average (SMA)
The SMA reduces short-term fluctuations in RSI, ensuring that only meaningful momentum changes proceed to later calculations.
Range Clipping
By limiting RSI values to a defined range, extreme spikes are prevented from skewing normalization. This keeps the indicator stable across different assets and timeframes.
Min-Max Normalization
Normalization rescales values into a fixed range (0–100), allowing momentum behavior to remain consistent regardless of volatility conditions.
Adaptive Exponential Smoothing
This smoothing technique gradually adjusts values toward new data based on the smoothing factor. It allows the indicator to remain smooth while still reacting to genuine momentum shifts.
Double Normalization and Double Smoothing
Applying normalization and smoothing twice significantly improves structural stability. The result is a refined oscillator that filters noise without sacrificing trend awareness.
Why This Combination Works
By combining RSI with controlled compression, adaptive smoothing, and dynamic normalization, this indicator transforms raw momentum data into a highly structured and trend-aligned oscillator. The result is an RSI-based tool that:
Reduces noise
Adapts to volatility
Maintains consistent scaling
Highlights true momentum direction
This makes the Adjusted RSI particularly effective for swing trading, trend confirmation, and momentum-based strategies across all markets and timeframes.
Enjoy!
TRADERREVIEW GENERIC 200SMA 50/45 + ADX Filtersector trend trading based on % of stocks above their50 day moving average in the S&P500. Can speed up (more signals) or slow down (less signals) with ADX and hard stop on SMA.
Specifically designed to GET YOU IN WINNERS AND LET THEM RUN.
[devtool] Dynamic MTF Supertrend This script provides a robust, non-repainting implementation of the classic Supertrend indicator, enhanced with Multi-Timeframe (MTF) capabilities and dynamic adjustment features. It is designed for traders who require stable trend signals and reliable support/resistance levels across various market conditions.
Concept and Inspiration
First and foremost, huge respect is due to Kivanc Ozbilgic, the pioneer who popularized the Supertrend concept and made it a staple in technical analysis. This script builds upon that foundational logic but adapts it for modern charting needs, specifically addressing the issue of repainting in higher timeframe data.
Key Features
Non-Repainting Logic: The core of this indicator is built to ensure that once a candle closes, the signal and the trend line remain fixed. This is crucial for backtesting accuracy and real-time execution.
Dynamic Multi-Timeframe (MTF): You can view higher timeframe Supertrend levels on your current chart without the lag or repainting issues often associated with standard request.security calls.
Returning Levels: As seen in the examples below, the indicator clearly delineates "returning levels." These steps in the Supertrend line often act as significant support or resistance zones where price action may retest.
How to Use
Trend Identification: The primary use is trend following. Green indicates a bullish trend, while red indicates a bearish trend.
Support & Resistance: The horizontal "steps" of the Supertrend line serve as trailing stop-loss levels or dynamic support/resistance.
Multi-Timeframe Analysis: Use the settings to overlay a higher timeframe trend on a lower timeframe chart (e.g., viewing a Daily Supertrend on a 1-hour chart) to align your entries with the dominant trend.
Visual Examples
Below are examples of the indicator applied to various assets and timeframes, demonstrating its stability and the relevance of the returning levels.
Bitcoin (Daily): Capturing the major trend shifts with clear trailing levels.
Dow Jones (Daily): Demonstrating long-term trend stability.
EUR/USD (1-Hour): Intraday application showing reaction to dynamic levels.
ETH/USDT (Daily): Handling high volatility crypto markets.
Gold (15-Minute): Scalping perspective with tight trend adherence.
For Developers and Backtesters
This script is published as "Protected" to ensure the integrity of the logic while allowing users to utilize it freely. Professional developers and algorithmic traders will appreciate the non-repainting nature of this script, as it allows for reliable backtesting results that match live performance. If you are building automated systems, ensuring your source data does not repaint is the first step toward a viable strategy.
Disclaimer
This tool is for informational purposes only. Past performance DOES NOT guarantee future results. Always manage your risk appropriately.
Gann Square of Nine: Planetary Degrees█ Gann Square of Nine: Planetary Degrees maps planetary positions onto Gann's Square of Nine grid, tracking where pivot highs and lows accumulate by planetary degree. Use this indicator to identify recurring degree patterns on the So9, determine whether pivots cluster around cardinal, diagonal, or other significant angles, and project when the planet will return to those degrees.
Powered by the open-source BlueprintResearch Planetary Ephemeris library , which implements truncated VSOP87 (planets) and ELP2000 (Moon) series for high-accuracy celestial calculations entirely within Pine Script.
█ FEATURES
• Anchor Point System — Select any significant price pivot (high or low) as your reference point; all subsequent pivot tracking begins from this timestamp
• All 10 celestial bodies — Sun, Moon, Mercury, Venus, Mars, Jupiter, Saturn, Uranus, Neptune, and Pluto
• Geocentric or Heliocentric views — Toggle between Earth-centered (traditional) and Sun-centered perspectives
• Interactive Square of Nine table — Visual grid displaying the Gann spiral pattern with highlighted pivot degrees
• Automatic pivot detection — Configurable bar sensitivity to identify price pivots (symmetric left/right)
• Pivot degree labeling — Each detected pivot displays the planet's ecliptic longitude (0-360°) at that moment
• Target degree alerts — Define specific So9 degrees to watch; triggers alerts when the planet crosses them
• Preset So9 angles — Quick selection of degrees along major So9 lines (0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°)
• Custom degree input — Enter any degrees as comma-separated or newline-separated values
• Future degree projections — Scans up to 500 bars ahead and shows when the planet will reach each target degree
• Retrograde indicator — Shows ℞ symbol with red text when planets are in apparent retrograde motion
• So9 overlay tools — Plot 90° and 45° angle relationships from any entered degree
█ HOW IT WORKS
The Square of Nine Concept:
Gann's Square of Nine is a spiral grid where numbers flow outward from the center (1) in a square spiral pattern. Key angle relationships (0°, 45°, 90°, etc.) align along specific diagonals and cardinal lines. When planetary degrees land on the same So9 position as significant price pivots, it suggests potential support/resistance levels.
This Indicator:
1. User selects an "anchor" timestamp at a significant price pivot
2. The indicator calculates the selected planet's ecliptic longitude (0-360°) at each bar
3. Price pivots detected after the anchor are labeled with their planetary degrees
4. These degrees accumulate on the So9 grid, revealing patterns
5. Target degrees can be set to receive alerts when crossed
6. Future projections show when the planet will reach those target degrees
█ HOW TO USE
1. Click on the anchor timestamp input and select a significant high or low pivot on your chart
2. Choose "High" or "Low" pivot type based on your anchor point
3. Select your planet from the dropdown
4. Choose Geocentric (traditional) or Heliocentric view
5. The So9 table appears showing accumulated pivot degrees highlighted
6. Set target degrees using presets or custom input to receive crossing alerts
7. Future projections appear as vertical lines with date/time labels
8. Use the So9 overlay tools to visualize angle relationships from specific degrees
█ VISUAL GUIDE
So9 Table Colors:
• Anchor degree: White (⚓ symbol)
• Current planet position: Planet's assigned color with symbol
• Pivot Highs: Green background
• Pivot Lows: Red background
• Equal (both high and low): Orange background
• Diagonal crosses: Blue background
• Cardinal crosses: Red background
• Target degrees: Yellow highlight
Chart Labels:
• Pivot High labels appear above the price with the degree
• Pivot Low labels appear below the price with the degree
• Future projection lines: Yellow (upcoming) or Gray (already crossed since anchor)
█ SETTINGS OVERVIEW
1. Anchor Point — Set the starting pivot timestamp and type (High/Low)
2. Planet Selection — Choose celestial body and coordinate system
3. Target Degree Alerts — Configure which degrees to watch and receive alerts
4. Pivot Detection — Set bar sensitivity for pivot high/low detection and degree rounding precision
5. Visual Style — Customize colors and label sizes
6. So9 Grid Overlay — Enter a degree to visualize its angular relationships
7. So9 Table — Position, sizing, and color options for the grid
8. So9 Diagonals — Toggle and color the diagonal/cardinal cross highlights
█ LIMITATIONS & ACCURACY
This indicator uses optimized VSOP87 and ELP2000 series tailored for Pine Script performance. It delivers excellent accuracy for trading and analytical purposes.
Expected Accuracy:
• Sun, Moon, Mercury, Venus, Mars: Within 1-10 arcseconds
• Jupiter, Saturn: Within 10-30 arcseconds
• Uranus, Neptune: Within 1-2 arcminutes
• Pluto: Simplified Meeus method (valid 1900-2100)
Degree Resolution:
The So9 grid uses integer degrees (1-361). Planetary positions are rounded to the nearest whole degree for grid placement. Precise decimal degrees are retained for crossing calculations and alerts.
Crossing Detection:
Future projection lines and background highlights both point to the confirmation bar—the first bar where the crossing can be verified. Alerts also trigger on this bar. This ensures all visual elements align consistently: when the chart reaches a future projection line, that bar closes with the crossing confirmed and highlighted.
█ CREDITS
• Square of Nine grid visualization adapted from ThiagoSchmitz's "Gann Square of 9" (Feb 2023)
• Ephemeris calculations via BlueprintResearch/lib_ephemeris open-source library
3 BAR BUDDY3 Bar Buddy — Multi-Timeframe Engulfing Level Tracker
3 Bar Buddy scans the selected timeframe for valid 3-bar engulfing structures and builds an interactive queue of target levels above and below price. The table displays the last 10 active levels in order from closest to furthest.
For each engulfing candle, the indicator records both the high and low price of that structure and identifies whether the stored price comes from the High or the Low of that candle.
Levels dynamically update in real-time. When price reaches a level, it is marked as hit and a new level is generated further out in the queue — allowing the tool to continuously track evolving upside and downside targets.
No candle close is required — levels and hit-status react instantly as price moves above or below the engulfing structure.
Table Columns
Closest → Furthest
Order of levels relative to current price
Below / Above
Stored target price level
H / L
Indicates whether the level is derived from:
• High = Engulfing candle high
• Low = Engulfing candle low
Key Features
• Multi-timeframe selectable scan source
• Tracks most recent 10 active engulfing levels
• Separate Above-price & Below-price queues
• Real-time ✓ hit detection (no close required)
• Hit levels roll off & new targets are added
• Displays whether price came from candle high or low
• Visual levels extend on chart for confluence
Intended Use
Designed to help traders:
• Track structural reaction levels created by engulfing candles
• Monitor liquidity sweep / wick-through behavior
• Identify retests & continuation zones
• Build confluence with support-resistance or FVG areas
• Visually map upside & downside expansion targets
Notes
• Levels are ordered relative to current price (closest first)
• Levels remain active until price interacts with them
• Once hit, the level is cleared and a new level is added to the list
