WhaleCrew VisionVision is an advanced momentum oscillator that visualizes momentum strength with overbought and oversold readings.
Features
Momentum Waves
Divergence Detection (regular and hidden divergences)
Detection for momentum shifts (detects higher lows/lower highs on the oscillator)
Detection for momentum stagnation
Moneyflow
Most important: Our Custom Strategy Builder
Custom Strategy Builder
The custom strategy builder is a framework that allows you to easily create custom strategies.
1. Configure long/short conditions
Pre-defined conditions for custom timeframes, under which you're looking to potentially enter a trade.
2. Configure trigger
Select a trigger (e.g. "Wave Cross" or "Wave Lower High") to generate labels/alerts for potential entries, whenever long/short conditions are met.
3. Configure Take Profit Conditions
Potential Take Profits are triggered by momentum stagnation.
4. Backtest your strategy
By using our open-source backtester script (published on our profile).
5. Trade responsibly
Manually review each signal/alert before taking any actions.
Note: The "Strategy Backtest" input section can also help you develop your strategy.
Usage
You can use this indicator to follow the trend, detect momentum shifts or memorize patterns.
Take a systematic approach by using our strategy builder.
Access to this indicator can be obtained through our website.
스크립트에서 "wave"에 대해 찾기
Weis Wave Volume NumbersWhat is it?
This is an indicator to complement @modhelius' Weis Wave Volume Indicator.
Original code has been modified to display wave volume (cumulative) numbers above or below the latest candle of the corresponding wave on the main pane. Since we are concerned only with relative volume, VOLUME NUMBERS HAVE BEEN SCALED DOWN. (If you need actual volume numbers, uncheck "Scale Down Volume" option in Settings). Rising wave volume is denoted in green. Falling wave volume is denoted in red. Developing wave volume is postfixed with a '_'. Confirmed wave volumes won't have this.
Who is it for?
This indicator is useful if you already use Weis Waves in your analysis and could do with an additional numerical representation of the wave volume on the main pane. Can be used in conjunciton with @modhelius' Weis Wave Volume (WWV) indicator (need to be added separately) to complement the visual representation of the waves. Can be used independently as well.
Pelase note that if you use any other Weis Wave indicator (other than @modhelius'), the numbers and the waveforms might not match.
Statistical pivot wave - Average periods and drawdownsStatistical pivot wave - Average cycle periods and drawdowns (and assuming there is a trend)
How does these cycle periods and drawdowns come from?
Collecting the data from the last 70 pivot waves. Pivot waves are defined by once a new pivot low is recognized.
Explanation of variables:
Period(i) : Timespan from one pivot low to its previous pivot low.
Drawdown(i) : Max drawdown (from 22 bars lookback high + trend adjustment)
Trend(up / down): Historical linear regression
Median cycle: Median value of Period(i), based on i=1 to i=70 pivot waves data.
Median drawdown: Median value of Drawdown(i), from the trend projected high, based on i=1 to i=70 pivot waves data.
Flunki Multi Sine WaveDec 1
Herewith 6 sinewaves, including amplitude, phase, wavelength in bars, colour and fill options.
May develop to include a bunch wavelength value presets, and other waveforms, and modulation options.
Enjoy the harmonics !
Release Notes: Cosmetic minor fix for fill colour
Added centre line
Release Notes: Added global fill transparency input option
Release Notes: Added a Global option to use the period value as the amplitude value too, more fun than it sounds...
Release Notes: Last update for now
Added input source level as sine waveform period, as a plot too.
Release Notes: Set the CLOSE plot to be able to accept other source inputs (even itself :)
Release Notes: Added a global bar period multiplier, and some other bits to the price modulated sine, invert and a separate amplitude multiplier
Added individual flip phase per oscillator, tweaked some defaults
** Had to republish due to TV rule miscellany **
Wyckoff Wave"The Wyckoff Wave is a weighted index consisting of 12 stocks that are leaders in their perspective industries. It was introduced by the Stock Market Institute in 1931.
Made up of leaders in the important stock groups, the Wyckoff Wave represents the core of the American industrial complex.
The Wyckoff Wave has been a market indicator for Wyckoff students for over 50 years. While the stocks comprising the Wyckoff Wave have changed over time, it continues to be a sensitive leading market indicator. The Wyckoff Wave has consistently identified market trends.
The Wyckoff Wave is extremely helpful in predicting the stock market’s timing and the direction of the next market move.
The Wyckoff Wave is analyzed in five minute intervals and individual up and down iintra-day waves are created.
These individual waves, which include the price action and volume during those brief up and down market swings, also provide the data for other important Wyckoff Stock Market Institute indicators, including the Optimism-Pessimism volume index and the Trend Barometer.
These 12 stocks that make up the Wyckoff Wave. They are listed, along with their multipliers, below."
Wave Stock / Multiplier
AT&T / 79
Bank of America / 50
Boeing / 39
Bristol Myers / 119
Caterpillar / 35
DowDuPont / 72
Exxon Mobile / 32
IBM / 21
General Electric / 90
Ford / 25
Union Pacific / 60
WalMart / 43
In 2019, DowDuPont split into three companies: Dow, DuPont, and Corteva. Because TV limits the number of securities in a script to 40, only Dow and DuPont are factored into the Wave calculation (higher market caps than Corteva) with a multiplier of 36 each.
Renko Weis Wave VolumeThis is live and non-repainting Renko Weis Wave Volume tool. The tool has it’s own engine and not using integrated function of Trading View.
Renko charts ignore time and focus solely on price changes that meet a minimum requirement. Time is not a factor on Renko chart but as you can see with this script Renko RSI created on time chart.
Renko chart provide several advantages, some of them are filtering insignificant price movements and noise, focusing on important price movements and making support/resistance levels much easier to identify.
As source Closing price or High/Low can be used.
Traditional or ATR can be used for scaling. If ATR is chosen then there is rounding algorithm according to mintick value of the security. For example if mintick value is 0.001 and brick size (ATR/Percentage) is 0.00124 then box size becomes 0.001. And also while using dynamic brick size (ATR), box size changes only when Renko closing price changed.
This tool is based on the Weis Wave described by David H. Weis (a Wyckoff specialist). The Weis Waves Indicator sums up volumes in each wave. This is how we receive a bar chart of cumulative volumes of alternating waves and The cumulative volume makes the Weis wave charts unique.
If there is no volume information for the security then this tool has an option to use “True Range” instead of volume .
Better to use this script with the following one:
Enjoy!
Point and Figure (PnF) Weis Wave VolumeThis is live and non-repainting Point and Figure Chart Weis Wave Volume tool. The script has it’s own P&F engine and not using integrated function of Trading View.
Point and Figure method is over 150 years old. It consist of columns that represent filtered price movements. Time is not a factor on P&F chart but as you can see with this script P&F chart created on time chart.
P&F chart provide several advantages, some of them are filtering insignificant price movements and noise, focusing on important price movements and making support/resistance levels much easier to identify.
This tool is based on the Weis Wave described by David H. Weis (a Wyckoff specialist). The Weis Waves Indicator sums up volumes in each wave. This is how we receive a bar chart of cumulative volumes of alternating waves and The cumulative volume makes the Weis wave charts unique.
If there is no volume information for the security then this tool has an option to use “True Range” instead of volume .
If you are new to Point & Figure Chart then you better get some information about it before using this tool. There are very good web sites and books. Please PM me if you need help about resources.
Options in the Script
Box size is one of the most important part of Point and Figure Charting. Chart price movement sensitivity is determined by the Point and Figure scale. Large box sizes see little movement across a specific price region, small box sizes see greater price movement on P&F chart. There are four different box scaling with this tool: Traditional, Percentage, Dynamic (ATR), or User-Defined
4 different methods for Box size can be used in this tool.
User Defined: The box size is set by user. A larger box size will result in more filtered price movements and fewer reversals. A smaller box size will result in less filtered price movements and more reversals.
ATR: Box size is dynamically calculated by using ATR, default period is 20.
Percentage: uses box sizes that are a fixed percentage of the stock's price. If percentage is 1 and stock’s price is $100 then box size will be $1
Traditional: uses a predefined table of price ranges to determine what the box size should be.
Price Range Box Size
Under 0.25 0.0625
0.25 to 1.00 0.125
1.00 to 5.00 0.25
5.00 to 20.00 0.50
20.00 to 100 1.0
100 to 200 2.0
200 to 500 4.0
500 to 1000 5.0
1000 to 25000 50.0
25000 and up 500.0
Default value is “ATR”, you may use one of these scaling method that suits your trading strategy.
If ATR or Percentage is chosen then there is rounding algorithm according to mintick value of the security. For example if mintick value is 0.001 and box size (ATR/Percentage) is 0.00124 then box size becomes 0.001.
And also while using dynamic box size (ATR or Percentage), box size changes only when closing price changed.
Reversal : It is the number of boxes required to change from a column of Xs to a column of Os or from a column of Os to a column of Xs. Default value is 3 (most used). For example if you choose reversal = 2 then you get the chart similar to Renko chart.
Source: Closing price or High-Low prices can be chosen as data source for P&F charting.
There is only one option for Weis Wave Volume, “Use True Range (if no Volume info)” if you select this option and volume info is not avaliable then it uses “true range”, but if volume info is available, it never use true range. Default value is set to use true range.
VolWaves‴ | Volume Waves‴What does it do?
This indicator allows you to identify possible top and bottom reversals by having a prior volume reversal identifiable by positive (top reversal) and negative (bottom reversal) waves.
How does it work?
Everytime the wave starts ending its movement by shrinking the size of the histogram bars, it might be signing that a price reversal is on its way. It is possible to adjust the wave shape by increasing/decreasing its gradient value analysis, but it's so easy to use that sometimes no reconfiguration is needed, just add it and let it guide you.
What's my filling?
I've been testing this indicator for weeks and so far with incredible reversal signals.
Swing Wave Helper by 2tmHello Everyone.
I'd inspired from Renko Chart and make to find Waves.
As you know guys, the candle make Waves and the node of Waves make 3 Points.
With those 3 Points we can find where these candle go. Up? or Down.
With This script you can find easily those 3 Points easily.
Thank you and have a nice day.
CryptoWave Pro v2CryptoWave Pro v2 delivers the same great wave algorithm as the previous version, but offers more customisation options to allow power users to fine tune every last piece of the puzzle.
In addition to the wave you'll now see the following :
Yellow line = RSI
Red and Green lines (above and below the waves dots) = RSI OB and OS
White lines + labels = Wave Divergence indicator - Showing Regular and Hidden divergences
Coloured Bar at bottom = Money Flow - Bright Red = OS, Bright Green = OB
Super clean and easy to read at a glance, CryptoWave Pro v2 is the upgrade you've been waiting for!
[djt] wv2Display volume totals of each wave of movement. With this you can generally determine when a trend is weakening by lesser relative wave volume in the trend direction whilst increasing volume in the reverse direction.
You can also identify absorption based on high volume waves with less wave distance.
Currently limited to around 50 wave counts per chart due to tradingview limits.
For best results, work through timeframes in the structure you're looking at to obtain the clearest vision of the directional waves.
Moving Average Channel and Elliott of BiznesFilosofThis indicator is based on my indicator "MAC of BiznesFilosof", but it differs in that it shows three waves. Daily, weekly and monthly wave. Based on the color of these waves, you can easily determine the trend to use the indicator in combination with oscillators.
The main idea of this indicator is ease of use. Although I made it possible to show the corridor in the settings, but I consider it more convenient when there is a minimum of heaps on the chart. The color of the moving average perfectly shows when overbought and oversold. The idea is that the asset value is slower than the price. And it helps to enter the transaction correctly.
More details will be on my channel in YouTube.
===
Этот индикатор создан на базе моего индикатора "MAC of BiznesFilosof", но он отличается тем, что показывает три волны. Волна дневная, недельная и месячная. На основании цвета этих волн можно легко определить тренд, чтобы использовать индикатор в сочетании с осциляторами.
Основная идея данного индикатора - это простота использования. Хоть я и сделал возможность в настройках показать коридор, но считаю более удобным, когда на графике минимум нагромождений. Цвет скользящей средней прекрасно показывает, когда перекупленность и перепроданность. Идея состоит в том, что ценность актива более медленная, чем цена. И это помогает правильно входить в сделку.
Больше подробностей будет на моём канале в Ютуб.
Fractal Resonance BarLazyBear's WaveTrend port has been praised for highlighting trend reversals with precision and punctuality (minimal lag). But strong "3rd Wave" trends can "embed" or saturate any oscillator flashing several premature crosses while stuck overbought/oversold. This happens when the trend stretches over a longer timescale than the oscillator's averaging window or filter time constant. Our solution: monitor many timescales. With Fractal Resonance Bar's rich color codings, strong wavefronts form across timescales and jump out like an approaching line of thunderclouds!
Fractal Resonance Bar color-codes the status of eight underlying stochastic oscillators, with each row averaging over twice the time of the row above.
Fractal Resonance Bar shifts its timescales along with your choice of main chart timescale:
1 minute chart: 1 minute through 128 minute (~2 hour) oscillators.
15 minute chart: 15 minute through 1920 minute (~32 hour) oscillators.
1 hour chart: 1 hour through 128 hour (~2 week) oscillators.
Daily chart: 1 day through 128 day (~4 month) oscillators.
The color map is configured as follows:
Hot Pink: Extreme Overbought (> 100%) rolled over to sell, but oscillators probably embedded with more upside (revert to Dark Green) possible after a pause.
Deep Red: Overbought (> 75%) crossover ripe for selling (validated when red spreads to timescales below).
Brown: Minor (< 75%) crossover sell from which could bounce back green or start a plunge toward gray/black.
Gray/Black: Mature (< -75%) sells turning full black in a plunge before the dawn.
Lime Green: Extreme Oversold (< -100%) and bouncing, though may yet bottom even lower.
Green: Oversold (< -75%) crossover ripe for buy. Green spreading to all timescales below will validate bottom is in.
Dark Green/Teal: Mature buy in overbought (> 75%) range, waiting for sell crossover to Hot Pink for a pause or correction.
White Stripes are Impulsive Trend Warning
Fractal Resonance Bar warns of oscillator embedding by showing white stripes when it detects strong, early surges in the timescale rows below.The white stripes usually accompany Hot Pink warning it's too early to go short, or Lime Green warning it's too early to go long.
Heeding these warnings will probably miss the exact top or bottom, but you're less likely to get overrun in a momentum move.
Usually the market gives us a second opportunity to short very close to the top or buy very close to the bottom after the warning white stripes have subsided.
NOTE: Recently rolled over Futures contracts may not have enough history for all oscillator calculations, in which case no bar colors will appear.
Tweakable Attributes
The default Channel Length, Stochastic Ratio Length and Lag Length work reasonably well on all timescales in our experience. Minor tweaks don't hurt but this may just overfit to a particular chart history.
We don't recommend changing the 75% Overbought and 100% Extreme Overbought default levels as these are ideal numbers relative to the underlying oscillator statistic calculations. But these settings can shift the color transition levels.
Embedded attribute controls the sensitivity/conservativeness of the white strip embedding detectors. Closer to 75 increases the warning sensitivity while closer to 100 decreases the aggressiveness of blocking white stripes.
Embed Separation also affects the white stripe sensitivity.
Row width increases each row's thickness to fill the available screen height you've afforded the bar.
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.
Institutional WaveTrend [Div & Confirmed] Institutional WaveTrend – Indicator Description
Institutional WaveTrend is an advanced momentum and divergence indicator based on the WaveTrend oscillator, designed to identify institutional-level turning points, trend continuation signals, and confirmed momentum shifts.
This indicator combines WaveTrend structure, divergence detection, and confirmation logic, making it suitable for both discretionary traders and systematic analysis.
🔹 Core Features
WaveTrend oscillator with dual lines (WT1 / WT2)
Adaptive coloring to visualize momentum direction
Clearly defined Overbought / Oversold zones
Built-in regular and hidden divergence detection
Confirmed signals only on closed candles (non-repainting)
🔹 WaveTrend Structure
WT1: Main momentum line
WT2: Signal / smoothing line
Color changes dynamically based on WT1 vs WT2 relationship
Zero line and ±53 levels help identify momentum regime shifts
🔹 Divergence Detection (Pivot-Based)
The indicator automatically detects divergence using pivot logic:
Bullish Divergence
Regular: Momentum makes a higher low while price makes a lower low
Hidden: Momentum makes a lower low while price makes a higher low
Bearish Divergence
Regular: Momentum makes a lower high while price makes a higher high
Hidden: Momentum makes a higher high while price makes a lower high
Visual elements:
Solid lines → Regular divergence
Dashed lines → Hidden divergence
Labels:
R = Regular divergence
H = Hidden divergence
🔹 Confirmed Signals (●)
Confirmed signals are generated only when all conditions are met:
Confirmed Buy
WT1 crosses above WT2
WT1 is in the oversold zone
Candle is fully confirmed (bar close)
Confirmed Sell
WT1 crosses below WT2
WT1 is in the overbought zone
Candle is fully confirmed (bar close)
Signals are plotted as small dots (●) directly on the oscillator for clarity and minimal chart clutter.
⚠️ These signals are confirmation-based, not predictive.
🔹 Best Use Cases
Detecting institutional accumulation / distribution zones
Momentum-based entry confirmation
Divergence-driven reversal analysis
Trend continuation validation using hidden divergence
Filtering false signals in ranging markets
🔹 Recommended Usage
Use divergences to identify context
Use confirmed dots to time execution
Combine with:
Price action
Support / resistance
Higher timeframe bias
Works effectively on:
FX
Indices
Stocks
Crypto
Gold / Commodities
⚠ Disclaimer
This indicator is for analysis and decision support only.
Always apply proper risk management and confirm signals within your own trading plan.
✅ Ultra-Short Description (1 line)
WaveTrend-based momentum and divergence indicator with confirmed institutional-style signals.
✅ Recommended Categories (TradingView)
Oscillators
Momentum
Trend Analysis
Smart Momentum Wave
Smart Momentum Wave – Market Momentum Analysis System
Smart Momentum Wave (SMW) is a sophisticated analytical tool designed for traders who value data clarity and objectivity in market assessment. The algorithm integrates price dynamics, capital activity, and volatility, providing a comprehensive situational overview in a single, transparent panel below the price chart.
Analytical Modules and Technology
1. Primary Impulse Wave
The core logic of the indicator is based on two dynamic lines reacting to the price structure.
• Dynamic Wave: Adapts its color to green during bullish phases and red during bearish phases, allowing for an immediate assessment of the dominant trend.
• Signal Base: A subtle blue reference line used to identify momentum crossover points.
2. Capital Activity
A module that analyzes the flow of market funds to verify trend strength.
• Visualization: Background columns represent the intensity of capital accumulation or distribution.
• Application: Helps distinguish genuine market moves from temporary corrections.
3. Adaptive Volatility Zones
The algorithm utilizes intelligent bands that automatically adjust to current market conditions.
• Mechanics: The ranges scale during periods of high activity and contract during consolidation.
• Application: Effectively filters low-quality signals by focusing on moments of significant statistical price deviation.
4. Divergence Identification (Type A & Type B)
SMW automatically identifies and plots divergence lines on the chart:
• Type A (Classic): Signals potential trend exhaustion points.
• Type B (Hidden): Indicates a potential continuation of the current directional move.
Extended Features and Personalization
• Fast Pulse Overlay: Displayed as a background cloud, it indicates local areas of extreme deviation, which is crucial for the early identification of reversal points.
• Trend Candles: An optional feature that projects trend logic directly onto the price chart, assisting in maintaining analytical discipline.
• Multi-Timeframe (MTF): Provides the ability to analyze trends from higher timeframes without changing the primary chart interval.
Intelligent Alert System
The indicator features a built-in, comprehensive alert system that enables full automation of market monitoring:
• Entry Signals: Notifications for confirmed bullish (Long) and bearish (Short) impulses.
• Divergences: Separate alerts for detected Type A and Type B divergences.
FAQ – Frequently Asked Questions
1. ACCESS: This tool is available exclusively to subscribers (Invite-only). To gain access, please send an email to:
SmartMoneyAlgoSystem@proton.me
Access to Smart Momentum Wave is granted manually. After purchasing a subscription, provide your TradingView username. The indicator will appear under the Indicators -> Invite-only scripts tab.
2. Which markets and instruments does SMW work on? The algorithm is universal and works on all liquid instruments available on TradingView, including Cryptocurrencies, Forex, Stocks, Indices, and Commodities. It demonstrates the highest analytical effectiveness in high-volume markets.
3. Does the indicator "repaint" signals? No. All signals (Buy/Sell dots) and wave crossovers are based on closed candle data. Divergences appear after a pivot point is confirmed, which is the standard in reliable technical analysis.
4. Which timeframes are optimal for SMW? The indicator is designed to work on any timeframe – from scalping (1m, 5m) to long-term trading (1D, 1W). With the Multi-Timeframe (MTF) function, users can filter lower-interval signals through the lens of a higher-order trend.
5. Are the signals generated by SMW infallible? No analytical tool guarantees 100% accuracy. SMW provides a statistical advantage by identifying moments with a high probability of momentum change. The key to success is combining the algorithm's indications with your own risk management system and exit strategy.
6. Do I need to change the default settings? The factory settings are optimized to provide a balance between sensitivity and signal stability across most markets. However, advanced users can personalize parameters such as wave length or divergence sensitivity to suit the specific dynamics of a given asset.
LEGAL DISCLAIMER
The Smart Momentum Wave indicator is a tool supporting technical analysis and is intended for educational and informational purposes only.
Please note the following:
1. No Recommendation: This tool does not constitute investment advice or a recommendation within the meaning of the Regulation of the Minister of Finance of October 19, 2005.
2. Market Risk: Trading financial instruments involves a high risk of loss. Past performance does not guarantee similar future results.
3. Liability: The author of the indicator is not responsible for the investment decisions of users or their financial consequences.
4. Decision Making: Every transaction is undertaken by the user independently, based on their own strategy and risk assessment.
ChromaFlows Momentum Index - Consensus Engine V1.2ChromaFlows Momentum Index — Conceptual Description
Overview
ChromaFlows Momentum Index is a momentum-analysis tool designed to evaluate trend quality and directional agreement by combining multiple oscillators into a single consensus-based system.
Rather than displaying independent signals from separate indicators, this script produces output only when all internal engines align, filtering out conflicting or low-quality momentum conditions.
The goal is not to generate standalone trading signals, but to provide a clear visual representation of momentum consensus and regime strength.
Conceptual Architecture
The indicator is built around three momentum engines, each assigned a distinct functional role:
Slow Stochastic — acts as the primary momentum baseline, defining the broader overbought/oversold context.
Fast Stochastic — functions as a short-term acceleration filter, detecting rapid changes in momentum relative to the baseline.
RSI — serves as a regime validator, confirming whether momentum conditions are stable enough to be considered directional.
These components are not averaged or displayed independently.
Each engine is conditionally dependent on the others.
Interaction & Consensus Logic
ChromaFlows uses a strict consensus model:
A directional state is produced only when all momentum engines agree on direction.
If even one engine diverges, the system suppresses directional output and enters a neutral state.
This logic prevents partial or conflicting momentum signals from being displayed and reduces noise commonly produced by single-indicator oscillators.
The resulting output represents agreement quality, not raw oscillator values.
Visual Output & Interpretation
The main oscillator wave represents the current momentum state derived from the consensus logic:
Bullish Consensus — all engines aligned to the upside
Bearish Consensus — all engines aligned to the downside
Neutral State — disagreement or low-quality momentum
Additional visual elements (signal markers and trend filters) are derived from the same internal state, providing contextual confirmation rather than independent signals.
These visuals are intended to help users interpret momentum context, not to automate execution.
Originality & Purpose
This script is not a visual mashup of existing indicators.
Its output cannot be replicated by observing the individual components separately, as the system’s behavior depends on conditional interaction and suppression logic between engines.
By requiring full agreement before displaying momentum states, ChromaFlows emphasizes momentum clarity over signal frequency, making it suitable as a contextual analysis layer within broader trading frameworks.
Usage Notes
ChromaFlows Momentum Index is a visual analysis tool designed to assist with market interpretation.
It does not provide investment advice or guarantee outcomes and should be used in conjunction with other forms of analysis and risk management.
Version Notes (V1.2)
• Expanded divergence detection logic added for SMI line for improved momentum context
• Minor internal optimizations and code refinements
Harmonic Liquidity Waves [JOAT]Harmonic Liquidity Waves
Overview
Harmonic Liquidity Waves is an open-source oscillator indicator that combines multiple volume-based analysis techniques into a unified liquidity flow framework. It integrates VWAP calculations, Chaikin Money Flow (CMF), Money Flow Index (MFI), and Klinger Volume Oscillator (KVO) with custom harmonic wave calculations to provide a comprehensive view of volume dynamics and money flow.
What This Indicator Does
The indicator calculates and displays:
Liquidity Flow - Volume-weighted price movement accumulated over a lookback period
Harmonic Wave - Multi-depth smoothed oscillator derived from liquidity flow
Chaikin Money Flow (CMF) - Classic accumulation/distribution indicator
Money Flow Index (MFI) - Volume-weighted RSI showing buying/selling pressure
Klinger Volume Oscillator (KVO) - Trend-volume relationship indicator
Wave Interference - Combined constructive/destructive wave patterns
Volume Profile POC - Point of Control from simplified volume distribution
How It Works
The core liquidity flow calculation tracks volume-weighted price changes:
calculateLiquidityFlow(series float vol, series float price, simple int period) =>
float priceChange = ta.change(price)
float volumeFlow = vol * math.sign(priceChange)
// Accumulated over period using buffer array
float avgFlow = flowSum / period
avgFlow
The harmonic oscillator applies multi-depth smoothing:
harmonicOscillator(series float flow, simple int depth, simple int period) =>
float harmonic = 0.0
for i = 1 to depth
float wave = ta.ema(flow, period * i) / i
harmonic += wave
harmonic / depth
CMF measures accumulation/distribution using the Money Flow Multiplier:
float mfm = ((close - low) - (high - close)) / (high - low)
float mfv = mfm * vol
float cmf = ta.sum(mfv, period) / ta.sum(vol, period) * 100
Signal Generation
Liquidity shift signals occur when:
Bullish Shift: Smoothed wave crosses above signal line
Bearish Shift: Smoothed wave crosses below signal line
Strong signals require volume indicator confirmation:
Strong Bull: Bullish shift + CMF > 0 + MFI > 50 + KVO > 0
Strong Bear: Bearish shift + CMF < 0 + MFI < 50 + KVO < 0
Divergence detection compares price pivots with liquidity wave pivots to identify potential reversals.
Dashboard Panel (Bottom-Right)
Wave Strength - Normalized wave magnitude
Volume Pressure - Current volume vs average percentage
Flow Direction - BUYING or SELLING based on wave sign
Histogram - Wave minus signal line value
CMF - Chaikin Money Flow reading
MFI - Money Flow Index value (0-100)
KVO - Klinger oscillator value
Vol Confluence - Combined volume indicator score
Signal - Current actionable status
Visual Elements
Liquidity Wave - Main oscillator line
Wave Signal - Smoothed signal line for crossover detection
Wave Histogram - Difference between wave and signal
Wave Interference - Area plot showing combined wave patterns
CMF/KVO/MFI Lines - Individual volume indicator plots
Divergence Labels - BULL DIV / BEAR DIV markers
Shift Markers - Triangles for basic shifts, labels for strong shifts
Input Parameters
Wave Period (default: 21) - Base period for liquidity calculations
Volume Weight (default: 1.5) - Multiplier for volume emphasis
Harmonic Depth (default: 3) - Number of smoothing layers
Smoothing (default: 3) - Final wave smoothing period
Suggested Use Cases
Identify accumulation/distribution phases using CMF and wave direction
Confirm momentum with MFI overbought/oversold readings
Watch for divergences between price and liquidity flow
Use strong signals when multiple volume indicators align
Timeframe Recommendations
Best on 15m to Daily charts. Volume-based indicators require sufficient trading activity for meaningful readings.
Limitations
Volume data quality varies by exchange and instrument
Divergence detection uses pivot-based lookback and may lag
Volume Profile POC is simplified and not a full profile analysis
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. It does not constitute financial advice. Past performance does not guarantee future results. Always use proper risk management.
- Made with passion by officialjackofalltrades
Shock Wave: EMA9 Slope / ATR (Normalized) for SPYShock Wave – EMA9 Slope Normalized by ATR (Fragility Gauge)
This indicator measures trend fragility, not direction.
Instead of relying on visual trendline angles (which change with zoom and chart scaling), this tool normalizes the slope of the 9-EMA by ATR, producing a scale-independent steepness metric that remains consistent across timeframes and zoom levels.
The goal is to identify late-stage acceleration and liquidity vulnerability — conditions where price is advancing faster than inventory can rebalance and the market becomes sensitive to forced liquidation.
What this indicator shows
Normalized EMA9 slope (ATR per bar)
An angle-like degree value derived from the normalized slope (for intuition only)
Background shading to highlight trend maturity / fragility
A compact table showing live readings on the chart
How to interpret
Green / low values (< ~0.30 ATR/bar): Healthy, sustainable trend
Orange / mid values (~0.30–0.40 ATR/bar): Late-stage acceleration
Red / high values (≥ ~0.45 ATR/bar): Fragile / liquidation-prone conditions
These thresholds are empirically derived from historical index behavior (e.g., SPY prior to 2018, 2020, 2022 volatility events).
Important notes
This is not a buy or sell signal
Red does not mean “short”
The indicator highlights risk asymmetry, not timing
Best used on higher timeframes (weekly) in conjunction with liquidity, inducement, and higher-timeframe structure analysis
Why use this
Markets often fail after strong trends, not because they are weak, but because they are crowded. This tool helps quantify when a trend has become structurally vulnerable, providing context for liquidity-based frameworks and macro risk management.
Elliott Wave Principle Pro - Frost & Prechter [abusuhil]الوصف العربي اسفل الوصف الإنجليزي .
✅ Professional Description (English)
Elliott Wave Principle Pro – Frost & Prechter Edition
A complete, professional-grade Elliott Wave detection and trading system designed for traders who want to identify market structure with precision and execute trades based on confirmed wave completion signals — without repainting.
This indicator combines the classical Elliott Wave rules from Frost & Prechter’s “Elliott Wave Principle” with modern algorithmic detection, Fibonacci validation, ZigZag pivot systems, and fully automated entry/exit levels.
⭐ Core Features
1. Automatic Elliott Wave Detection
Detects Impulse Waves (5-3-5-3-5)
Detects Corrective Waves (ABC) including:
• Zigzag
• Flat
• Expanded Flat
Supports multiple wave degrees (Cycle → Minuette)
2. Strict Elliott Rule Engine
All major EW rules are applied:
Wave 2 never retraces beyond Wave 1
Wave 4 must not overlap Wave 1
Wave 3 is never the shortest
Wave relationships validated using Fibonacci ratios
You can choose Strict / Standard / Flexible rule modes.
⭐ 3. Non-Repainting Confirmation System
Waves are confirmed only after pivot completion
Signals never change once displayed
Historical signals remain stable
Fully resistant to repainting
⭐ 4. Automated Trading Signals
Every completed structure triggers:
BUY Signals
End of Wave C
End of bearish Impulse (Wave 5)
SELL Signals
End of Wave 5 in bullish impulse
End of bullish ABC correction
Each signal includes:
Entry Line
Stop Loss (3 methods: Wave / ATR / Fixed)
TP1 – TP2 – TP3 (Fibonacci-based or Wave Projected)
Optional PRZ (Potential Reversal Zone)
You may show only the latest signal for clarity.
⭐ 5. Advanced Visual Tools
Wave numbers (1–5 / A–B–C)
Wave lines
Channels
Projection levels
Degree colors
Customizable labels and signal shapes (Box / Arrow / No Text)
A clean Simple Mode is available to hide all waves and show signals only.
⭐ 6. Informational Table (Optional)
Displays:
Last detected structure
Direction (Bullish / Bearish)
Active signal status (Buy / Sell / Wait)
⭐ How Traders Benefit
This tool helps traders:
Understand the full Elliott Wave context instantly
Know exactly when a wave cycle has completed
Enter trades with predefined, optimized levels
Avoid emotional decisions and subjective wave counting
Rely on a non-repainting analytical engine
Identify high-probability reversal zones
Improve trade timing and risk management
Perfect for swing trading, intraday trading, and wave practitioners.
🇸🇦 الوصف الاحترافي (العربية)
Elliott Wave Principle Pro – نسخة فروسـت وبريشتـر
مؤشر احترافي متكامل لتحليل موجات إليوت واكتشاف البُنى السعريّة بشكل آلي ودقيق، مع إعطاء إشارات تداول مؤكدة عند اكتمال الموجات — بدون إعادة رسم (Non-Repainting).
يجمع هذا المؤشر بين قواعد مدرسة إليوت الكلاسيكية من كتاب “Elliott Wave Principle” وبين خوارزميات حديثة تعتمد على الـ ZigZag، والفيبوناتشي، والتحقق الرياضي من صحة الموجة.
⭐ أهم المزايا
1. اكتشاف آلي كامل لموجات إليوت
اكتشاف الموجات الدافعة Impulse 5-3-5-3-5
اكتشاف الموجات التصحيحية ABC بما يشمل:
• Zigzag
• Flat
• Expanded Flat
دعم جميع درجات الموجة من Cycle حتى Minuette
⭐ 2. محرك قواعد إليوت الاحترافي
يطبق المؤشر جميع القواعد الأساسية لموجات إليوت، مثل:
الموجة 2 لا تتجاوز بداية الموجة 1
الموجة 4 يجب ألا تتداخل مع الموجة 1
الموجة 3 ليست الأقصر
تأكيد العلاقات باستخدام نسب فيبوناتشي
مع إمكانية اختيار نمط القواعد: صارم / قياسي / مرن.
⭐ 3. نظام تأكيد بدون إعادة رسم
لا يتم تأكيد الموجة إلا بعد اكتمالها فعليًا
لا يتم حذف أي إشارة بعد ظهورها
جميع النتائج ثابتة وغير قابلة للتغيير
مقاوم لإعادة الرسم 100%
⭐ 4. إشارات تداول تلقائية
يصدر المؤشر إشارات شراء وبيع عند اكتمال التركيبات التالية:
إشارات BUY
نهاية موجة C
نهاية موجة 5 الهابطة (انعكاس صاعد)
إشارات SELL
نهاية موجة 5 الصاعدة
نهاية تصحيح ABC الصاعد
وتتضمن الإشارة:
مستوى الدخول
وقف الخسارة (Wave / ATR / نسبة ثابتة)
الأهداف TP1 – TP2 – TP3
منطقة انعكاس محتملة PRZ (اختيارية)
ويمكن عرض آخر إشارة فقط لسهولة القراءة.
⭐ 5. أدوات بصرية متقدمة
ترقيم الموجات 1–5 و A–B–C
خطوط الموجات
قنوات Elliott
مستويات الإسقاط
ألوان الدرجات
تخصيص شكل الإشارة (مربع / سهم / بدون نص)
كما يمكن تفعيل الوضع البسيط لإظهار الإشارات فقط.
⭐ 6. جدول معلومات الاختياري
يعرض:
نوع آخر موجة مكتشفة
اتجاهها (صاعد / هابط)
حالة الإشارة الحالية (شراء / بيع / انتظار)
⭐ فوائد استخدام المؤشر للمتداول
هذا المؤشر يساعدك على:
فهم بنية موجات إليوت دون قراءة الشارت يدويًا
اكتشاف نقاط الانعكاس القوية قبل حدوثها
الدخول في صفقات محسوبة مسبقًا (Entry + SL + TP)
تقليل التشتت والتقدير الشخصي في العدّ
تحسين إدارة المخاطر
تعزيز دقة التوقيت في بداية الاتجاهات الجديدة
دراسة السوق بطريقة احترافية تعتمد على قاعدة علمية واضحة
مثالي للمضارب اليومي، المتداول المتأرجح، ولممارسي مدرسة إليوت.
Classic Wave: The Easy WayClassic Wave is a simple strategy with few rules and no over-optimization. Despite its simplicity, it is backed by a nearly century-long historical track record, delivering excellent returns on the weekly chart of the SPX (TVC).
I also recommend observing its strong performance on the SPY (weekly), which is the perfect instrument for executing this strategy with futures in the future.
Strategy Rules and Parameters
When a bullish candle closes above the 20-period EMA, we place the stop-loss below the low of that candle and target a risk-reward ratio of 1:1.
A second, more profitable variant is to change the risk-reward ratio in the code to 2:1.
-Total capital: $10,000
-We use 10% of the total capital per trade.
-Commissions: 0.1% per trade.
The code construction is simple and very well detailed within the script itself.
Risk-Reward Ratio 2:1
Using a 2:1 risk-reward ratio reduces the win rate but significantly increases profitability.
Across the full historical data of the SPX index (weekly), the system would have generated 236 trades, with a win rate of 51.27% and a profit factor of 2.53.
From January 1, 2023, to November 28, 2025, the system would have generated 5 trades, with an 80% win rate and a profit factor of 9.244.
What makes this system so good?
-It takes advantage of the long-term bullish bias of U.S. stock indices and traditional markets.
-It filters out a lot of noise thanks to the weekly timeframe.
-It uses simple parameters with no over-optimization.
Final Notes:
This strategy has consistently outperformed the returns offered by most traditional funds over time, with fewer drawdowns and significantly less stress. I hope you like it.
Filter Wave1. Indicator Name
Filter Wave
2. One-line Introduction
A visually enhanced trend strength indicator that uses linear regression scoring to render smoothed, color-shifting waves synced to price action.
3. General Overview
Filter Wave+ is a trend analysis tool designed to provide an intuitive and visually dynamic representation of market momentum.
It uses a pairwise comparison algorithm on linear regression values over a lookback period to determine whether price action is consistently moving upward or downward.
The result is a trend score, which is normalized and translated into a color-coded wave that floats above or below the current price. The wave's opacity increases with trend strength, giving a visual cue for confidence in the trend.
The wave itself is not a raw line—it goes through a three-stage smoothing process, producing a natural, flowing curve that is aesthetically aligned with price movement.
This makes it ideal for traders who need a quick visual context before acting on signals from other tools.
While Filter Wave+ does not generate buy/sell signals directly, its secure and efficient design allows it to serve as a high-confidence trend filter in any trading system.
4. Key Advantages
🌊 Smooth, Dynamic Wave Output
3-stage smoothed curves give clean, flowing visual feedback on market conditions.
🎨 Trend Strength Visualized by Color Intensity
Stronger trends appear with more solid coloring, while weak/neutral trends fade visually.
🔍 Quantitative Trend Detection
Linear regression ordering delivers precise, math-based trend scoring for confidence assessment.
📊 Price-Synced Floating Wave
Wave is dynamically positioned based on ATR and price to align naturally with market structure.
🧩 Compatible with Any Strategy
No conflicting signals—Filter Wave+ serves as a directional overlay that enhances clarity.
🔒 Secure Core Logic
Core algorithm is lightweight and secure, with minimal code exposure and strong encapsulation.
📘 Indicator User Guide
📌 Basic Concept
Filter Wave+ calculates trend direction and intensity using linear regression alignment over time.
The resulting wave is rendered as a smoothed curve, colored based on trend direction (green for up, red for down, gray for neutral), and adjusted in transparency to reflect trend strength.
This allows for fast trend interpretation without overwhelming the chart with signals.
⚙️ Settings Explained
Lookback Period: Number of bars used for pairwise regression comparisons (higher = smoother detection)
Range Tolerance (%): Threshold to qualify as an up/down trend (lower = more sensitive)
Regression Source: The price input used in regression calculation (default: close)
Linear Regression Length: The period used for the core regression line
Bull/Bear Color: Customize the color for bullish and bearish waves
📈 Timing Example
Wave color changes to green and becomes more visible (less transparent)
Wave floats above price and aligns with an uptrend
Use as trend confirmation when other signals are present
📉 Timing Example
Wave shifts to red and darkens, floating below the price
Regression direction down; price continues beneath the wave
Acts as bearish confirmation for short trades or risk-off positioning
🧪 Recommended Use Cases
Use as a trend confidence overlay on your existing strategies
Especially useful in swing trading for detecting and confirming dominant market direction
Combine with RSI, MACD, or price action for high-accuracy setups
🔒 Precautions
This is not a signal generator—intended as a trend filter or directional guide
May respond slightly slower in volatile reversals; pair with responsive indicators
Wave position is influenced by ATR and price but does not represent exact entry/exit levels
Parameter optimization is recommended based on asset class and timeframe
