Noro's Trend MAs Strategy v1.6Trade strategy which uses only 2 MA.
The slow MA (blue) is used for definition of a trend
The fast MA (red) is used for an entrance to the transaction
For:
- For H1
- For crypto/fiat
Recomended:
Long = true (if it is profitable as a result of backtests)
Short = true (if it is profitable as a result of backtests)
Type of slow MA = 7 (only for Crypto/Fiat)
Source of slow MA = close or OHLC4
Use Fast MA = true
Fast MA Period = 5
Slow MA Period = 20
Bars Q = (2 for "BitCoin/Fiat" or 1 for "Fork/Fiat")
In the new version 1.5
+ Profit became more
+ Losses became less
+ Alerts
+ Background (lime = uptrend, red = downtrend)
Types of slow MA:
1 = SMA = Simple Moving Average
2 = EMA = Exponential Moving Average
3 = VWMA = Volume-Weighted Moving Average
4 = DEMA = Double Exponential Moving Average
5 = TEMA = Triple Exponential Moving Average
6 = KAMA = Kaufman's Adaptive Moving Average
7 = Price Channel
Cari dalam skrip untuk "profitable"
Noro's Trend MAs Strategy 1.5Trade strategy which uses only 2 MA .
The slow MA (blue) is used for definition of a trend
The fast MA (red) is used for an entrance to the transaction
For:
- For H1
- For crypto/fiat
Recomended:
Long = true (if it is profitable as a result of backtests)
Short = true (if it is profitable as a result of backtests)
Type of slow MA = 7 (only for Crypto/Fiat)
Source of slow MA = clole or OHLC4
Use Fast MA = true
Fast MA Period = 5
Slow MA Period = 20
Bars Q = (2 for "BitCoin/Fiat" or 1 for "Fork/Fiat")
In the new version 1.5
+ Source
+ Types of slow MA
Types of slow MA:
1 = SMA = Simple Moving Average
2 = EMA = Exponential Moving Average
3 = VWMA = Volume-Weighted Moving Average
4 = DEMA = Double Exponential Moving Average
5 = TEMA = Triple Exponential Moving Average
6 = KAMA = Kaufman's Adaptive Moving Average
7 = Price Channel
PS: 100000000%, because of use of a piramiding have turned out
Noro's Trend SMA Strategy v1.4Trade strategy which uses only 2 SMA .
The slow SMA (blue) is used for definition of a trend
The fast SMA (red) is used for an entrance to the transaction
Recomended:
For H1
For crypto/fiat
Long = true (if it is profitable as a result of backtests)
Short = true (if it is profitable as a result of backtests)
Use Fast SMA = true
Fast SMA Period = 5
Slow SMA Period = 20
Bars = (2 for "BitCoin/Fiat" or 1 for "Fork/Fiat")
In the new version 1.4
- Parameters are added
Russian:
Перевожу на понятный. В новой версии 1.4 ничего не поменялось в логике, работает так же. Но добавлены новые параметры, можно поэкспериментировать с настройками, убедиться как что выгоднее.
Есть галка лонг и шорт. По умолчанию обе включены. Если убрать галку лонг, то исчезнут лонги вообще, если убрать шорт, то соответственно исчезнут шорты. По идее галку надо будет снимать если стратегия создает убыточные шорты, то их можно отключить. Смотреть в сводке показателей тестера стратегий профитны они или нет. По идее почти на всех парах крипто/фиат лучше ставить обе галки. Или убирайте галку шорт если не хотите шортить из религиозных соображений.
Добавлена галка отключающая быструю SMA. То есть если галку убрать то стратегия будет её игнорировать. Таким образом, параметр Fast SMA (который 5) перестанет влиять на результаты. Однако, скорее всего без этой галки станет только хуже. Но можете проверить. Позволяет убедиться что входить в сделку по быстрой SMA в среднем немного профитнее, чем входить где попало.
"Bars" - количество свечек одного цвета после после которых будет открываться сделка. По умолчанию 2. Можно от 0 до 3 ставить. Если 0 то цвет свечек игнорируется. Если 3, значит будет ждать 3 красных свечи подряд чтобы открыть лонг. Так же и с зелеными свечами для шорта. 2 - оптимально для пар типа биткойн/фиат. А для пар типа форк/фиат лучше ставить 1 свеча в параметре "Bars".
TZv420simplified version of TZ original. With Alert function
Transient Zones (v420)
I drew the trades on the arrow signals so you can see, its not all win, but with good money management and other ways of finding Target points (MA's or Pivots or Fib or Structure etc)
It is profitable. No repaint, No offset
CCI Level Zero Strategy (by Marcoweb) v1.0Hi guys,
My strategy is ready :)
Finally the zero level of the CCI gives the start and stop to my positions. As you could notice, setting up the CCI length to 340 area on 1 minute chart will let the profit factor go up to 20% from an already wonderful 16%. This is a great result cause will let profitable trades run while stopping the wrong ones with a very limited loss. What makes our profit are not several small little positions that are clearly unrepitable in real trade but few and very profitable positions in which jumping in will be easier due to their length (71 bars average).
Please share with me your impressions and suggestions.
Have a nice trade :)
I_Heikin Ashi CandleWhen apply a strategy to Heikin Ashi Candle chart (HA candle), the strategy will use the open/close/high/low values of the Heikin Ashi candle to calculate the Profit and Loss, hence also affecting the Percent Profitable, Profit Factor, etc., often resulting a unrealistic high Percent Profitable and Profit Factor, which is misleading. But if you want to use the HA candle's values to calculate your indicator / strategy, but pass the normal candle's open/close/high/low values to the strategy to calculate the Profit / Loss, you can do this:
1) set up the main chart to be a normal candle chart
2) use this indicator script to plot a secondary window with indicator looks exactly like a HA-chart
3) to use the HA-candle's open/close/high/low value to calculate whatever indicator you want (you may need to create a separate script if you want to plot this indicator in a separate indicator window)
MACDouble + RSI (rec. 15min-2hr intrv) Uses two sets of MACD plus an RSI to either long or short. All three indicators trigger buy/sell as one (ie it's not 'IF MACD1 OR MACD2 OR RSI > 1 = buy", its more like "IF 1 AND 2 AND RSI=buy", all 3 match required for trigger)
The MACD inputs should be tweaked depending on timeframe and what you are trading. If you are doing 1, 3, 5 min or real frequent trading then 21/44/20 and 32/66/29 or other high value MACDs should be considered. If you are doing longer intervals like 2, 3, 4hr then consider 9/19/9 and 21/44/20 for MACDs (experiment! I picked these example #s randomly).
Ideal usage for the MACD sets is to have MACD2 inputs at around 1.5x, 2x, or 3x MACD1's inputs.
Other settings to consider: try having fastlength1=macdlength1 and then (fastlength2 = macdlength2 - 2). Like 10/26/10 and 23/48/20. This seems to increase net profit since it is more likely to trigger before major price moves, but may decrease profitable trade %. Conversely, consider FL1=MCDL1 and FL2 = MCDL2 + (FL2 * 0.5). Example: 10/26/10 and 22/48/30 this can increase profitable trade %, though may cost some net profit.
Feel free to message me with suggestions or questions.
SPY Master v1.0This is a simple swing trading algorithm that uses a fast RSI-EMA to trigger buy/cover signals and a slow RSI-EMA to trigger sell/short signals for SPY, an xchange-traded fund for the S&P 500.
The idea behind this strategy follows the premise that most profitable momentum trades usually occur during periods when price is trending up or down. Periods of flat price actions are usually where most unprofitable trades occur. Because we cannot predict exactly when trending periods will occur, the algorithm basically bets money on all trade opportunities during all market conditions. Despite an accuracy rate of only 40%, the algorithm's asymmetric risk/reward profile allows the average winner to be 2x the average loser. The end result is a positive (profitable) net payout.
TRADING RULES:
Buy/Cover = EMA3(RSI2) cross> 50
Sell/Short = EMA5(RSI2) cross< 50
BACKTEST SETTINGS:
- Period = March 2011 - Present
- Initial capital = $10,000
- Dividends excluded
- Trading costs excluded
PERFORMANCE COMPARISON:
There are 657 trades, which means 1,314 orders. Assuming each order costs $2 (what I pay for at Interactive Brokers), total trading costs should be $2,628.
-SPY (buy & hold) = 132.73 ---> 193.22 = +45.57% (dividends excluded)
-SPY Master v1.0 = $12,649 - $2,628 = $10,021 = +100.21%
DISCLAIMER: None of my ideas and posts are investment advice. Past performance is not an indication of future results. This strategy was constructed with the benefit of hindsight and its future performance cannot be guaranteed.
Ichimoku EMA BandsSome find Ichimoku Clouds bit complicated. This simplified version is combined with EMA Bands may be profitable. Give a try!. I recommend hourly timeframe for good results. Aye! :D
yuthavithi volatility based force trade scalper strategyI have converted my volatility based force scalper into strategy. Nice to see it is so profitable. Work best with Heikin Ashi bar.
BACKTEST SCRIPT 0.999 ALPHATRADINGVIEW BACKTEST SCRIPT by Lionshare (c) 2015
THS IS A REAL ALTERNATIVE FOR LONG AWAITED TV NATIVE BACKTEST ENGINE.
READY FOR USE JUST RIGHT NOW.
For user provided trading strategy, executes the trades on pricedata history and continues to make it over live datafeed.
Calculates and (plots on premise) the next performance statistics:
profit - i.e. gross profit/loss.
profit_max - maximum value of gross profit/loss.
profit_per_trade - each trade's profit/loss.
profit_per_stop_trade - profit/loss per "stop order" trade.
profit_stop - gross profit/loss caused by stop orders.
profit_stop_p - percentage of "stop orders" profit/loss in gross profit/loss.
security_if_bought_back - size of security portfolio if bought back.
trades_count_conseq_profit - consecutive gain from profitable series.
trades_count_conseq_profit_max - maxmimum gain from consecutive profitable series achieved.
trades_count_conseq_loss - same as for profit, but for loss.
trades_count_conseq_loss_max - same as for profit, but for loss.
trades_count_conseq_won - number of trades, that were won consecutively.
trades_count_conseq_won_max - maximum number of trades, won consecutively.
trades_count_conseq_lost - same as for won trades, but for lost.
trades_count_conseq_lost_max - same as for won trades, but for lost.
drawdown - difference between local equity highs and lows.
profit_factor - profit-t-loss ratio.
profit_factor_r - profit(without biggest winning trade)-to-loss ratio.
recovery_factor - equity-to-drawdown ratio.
expected_value - median gain value of all wins and loss.
zscore - shows how much your seriality of consecutive wins/loss diverges from the one of normal distributed process. valued in sigmas. zscore of +3 or -3 sigmas means nonrandom realitonship of wins series-to-loss series.
confidence_limit - the limit of confidence in zscore result. values under 0.95 are considered inconclusive.
sharpe - sharpe ratio - shows the level of strategy stability. basically it is how the profit/loss is deviated around the expected value.
sortino - the same as sharpe, but is calculated over the negative gains.
k - Kelly criterion value, means the percentage of your portfolio, you can trade the scripted strategy for optimal risk management.
k_margin - Kelly criterion recalculated to be meant as optimal margin value.
DISCLAIMER :
The SCRIPT is in ALPHA stage. So there could be some hidden bugs.
Though the basic functionality seems to work fine.
Initial documentation is not detailed. There could be english grammar mistakes also.
NOW Working hard on optimizing the script. Seems, some heavier strategies (especially those using the multiple SECURITY functions) call TV processing power limitation errors.
Docs are here:
docs.google.com
CM Stochastic POP Method 1 - Jake Bernstein_V1A good friend ucsgears recently published a Stochastic Pop Indicator designed by Jake Bernstein with a modified version he found.
I spoke to Jake this morning and asked if he had any updates to his Stochastic POP Trading Method. Attached is a PDF Jake published a while back (Please read for basic rules, which also Includes a New Method). I will release the Additional Method Tomorrow.
Jake asked me to share that he has Updated this Method Recently. Now across all symbols he has found the Stochastic Values of 60 and 30 to be the most profitable. NOTE - This can be Significantly Optimized for certain Symbols/Markets.
Jake Bernstein will be a contributor on TradingView when Backtesting/Strategies are released. Jake is one of the Top Trading System Developers in the world with 45+ years experience and he is going to teach how to create Trading Systems and how to Optimize the correct way.
Below are a few Strategy Results....Soon You Will Be Able To Find Results Like This Yourself on TradingView.com
BackTesting Results Example: EUR-USD Daily Chart Since 01/01/2005
Strategy 1:
Go Long When Stochastic Crosses Above 60. Go Short When Stochastic Crosses Below 30. Exit Long/Short When Stochastic has a Reverse Cross of Entry Value.
Results:
Total Trades = 164
Profit = 50, 126 Pips
Win% = 38.4%
Profit Factor = 1.35
Avg Trade = 306 Pips Profit
***Most Consecutive Wins = 3 ... Most Consecutive Losses = 6
Strategy 2:
Rules - Proprietary Optimization Jake Will Teach. Only Added 1 Additional Exit Rule.
Results:
Total Trades = 164
Profit = 62, 876 Pips!!!
Win% = 38.4%
Profit Factor = 1.44
Avg Trade = 383 Pips Profit
***Most Consecutive Wins = 3 ... Most Consecutive Losses = 6
Strategy 3:
Rules - Proprietary Optimization Jake Will Teach. Only added 1 Additional Exit Rule.
Results:
Winning Percent Increases to 72.6%!!! , Same Amount of Trades.
***Most Consecutive Wins = 21 ...Most Consecutive Losses = 4
Indicator Includes:
-Ability to Color Candles (CheckBox In Inputs Tab)
Green = Long Trade
Blue = No Trade
Red = Short Trade
-Color Coded Stochastic Line based on being Above/Below or In Between Entry Lines.
Link To Jakes PDF with Rules
dl.dropboxusercontent.com
Vervoort Heiken Ashi Candlestick OscillatorHeiken-Ashi Candlestick Oscillator (HACO), by Sylvian Vervoort, is a digital oscillator version of the colored candlesticks.
Explanation from Vervoort:
"HACO is not meant to be an automatic trading system, so when there is a buy or sell signal from HACO, make sure it is confirmed by other TA techniques. HACO will certainly aid in signaling buy/sell opportunities and help you hold on to a trade, making it more profitable. The behavior of HACO is closely related to the level and speed of price change. It can be used on charts of any time frame ranging from intraday to monthly."
HACO has 2 configurable length parameters - "UP TEMA length" and "Down TEMA length". Vervoort suggests having them the same value.
I have also added an option to color the bars (overlay mode).
More info:
Trading with the Heiken-Ashi Candlestick Oscillator - Sylvian Vervoort
List of my other indicators:
- GDoc: docs.google.com
- Chart:
DEnvelope [Better Bollinger Bands]*** ***
Bollinger Bands (BB) usually expand quickly after a volatility increase but contract more slowly as volatility declines. This extended time it takes for BB to contract after a volatility drop can make trading some instruments using BB alone difficult or less profitable.
In the October 1998 issue of "Futures" there is an article written by Dennis McNicholl called "Better Bollinger Bands", in which the author recommends improving BB by modifying:
- the center line formula &
- different equations for calculating the bands.
These bands, called "DEnvelope", follow price more closely and respond faster to changes in volatility with these modifications.
Fore more indicators, check out my "Master Index of indicators" (Also check my published charts page for new ones I haven't added to that list):
More scripts related to DEnvelope:
------------------------------------------------
- DEnvelope Bandwidth: pastebin.com
- DEnvelope %B : pastebin.com
Sample chart with above indicators: www.tradingview.com
The Strat - Multi-Timeframe Combo Analyzer## 📊 The Strat - Multi-Timeframe Combo Analyzer
This open-source indicator implements **The Strat** methodology, a universal price action framework developed by Rob Smith (@RobInTheBlack).
---
### 🎯 What is The Strat?
The Strat categorizes every candle into one of three scenarios based on its relationship to the previous bar:
| Type | Name | Definition |
|------|------|------------|
| **1** | Inside Bar | High < Previous High AND Low > Previous Low |
| **2** | Directional | Breaks only one side (2↑ = broke high, 2↓ = broke low) |
| **3** | Outside Bar | Breaks BOTH previous high AND low |
By tracking these bar types across timeframes, traders can identify actionable setups with defined entry triggers and target levels.
---
### ✨ Features
**Daily Timeframe Analysis:**
- Real-time 3-bar combo detection (2-1-2, 3-1-2, 1-2-2, etc.)
- Pattern classification: Bullish/Bearish Continuation or Reversal
- Entry and Target levels based on Strat rules
- Pattern status: ACTIONABLE, IN-FORCE, TRIGGERED, or WATCHING
**ATR Context:**
- Range % used (how much of daily ATR has been consumed)
- Entry quality assessment (Excellent → Exhausted)
- Day type classification (Quiet → Trend Day)
- Remaining range estimation
**15-Minute Analysis:**
- Separate combo tracking for intraday precision
- Pattern detection on lower timeframe
**Visuals:**
- Customizable info tables
- Entry/Target horizontal lines
- Signal labels on chart
- Alert conditions
---
### 🔧 How to Use
1. Look for **ACTIONABLE** patterns - these are setups waiting for a trigger
2. Entry triggers when price breaks the designated level
3. Target is the next logical Strat level (typically prior bar's high/low)
4. Use **Range%** to assess if there's room left in the daily range
5. Combine Daily and 15-Min combos for trade confluence
---
### ⚠️ Disclaimer
This indicator is for **educational purposes only**. It does not constitute financial advice or guarantee profitable trades. Trading involves substantial risk of loss. Past performance is not indicative of future results. Always conduct your own research and trade responsibly.
---
### 🙏 Credits
**The Strat** methodology was created by Rob Smith (@RobInTheBlack).
This implementation is open-source. Feel free to study, modify, and improve the code!
SMA BUY/SELL SignalsStrategy using SMA to identify BUY/SELL Signals which is the most Powerful, accurate , and highly profitable trading strategy.
IV Rank & Percentile Suite V1.0What This Indicator Does
The IV Rank & Percentile Suite provides the volatility context options traders need to time entries. It calculates two complementary metrics—IV Rank and IV Percentile—using historical volatility as a proxy, then displays clear visual zones to identify favorable conditions for premium selling strategies.
Stop guessing if volatility is "high" or "low." This indicator tells you exactly where current volatility sits relative to recent history.
The Two Metrics Explained
IV Rank (0-100) Measures where current volatility sits within its 52-week high-low range.
IV Rank = (Current HV - 52w Low) / (52w High - 52w Low) × 100
70 means current volatility is 70% of the way between the yearly low and high
Sensitive to extreme spikes (a single high reading affects the range)
IV Percentile (0-100) Measures what percentage of days in the lookback period had lower volatility than today.
IV Percentile = (Days with lower HV / Total days) × 100
70 means volatility was lower than today on 70% of days in the past year
More stable, less affected by outlier spikes
Why Both?
IV Rank reacts faster to volatility changes. IV Percentile is more stable and statistically robust. When both agree (e.g., both above 50), you have stronger confirmation. Divergence between them can signal transitional periods.
Zone System
The indicator divides readings into three zones:
Zone ------- Default Range ---- Meaning ------------------ Premium Selling
🟢 High ≥ 50 Elevated volatility Favorable
🟡 Neutral 25-50 Normal volatility Selective
🔴 Low ≤ 25 Compressed volatility Avoid
An additional Extreme threshold (default 75) highlights prime conditions when volatility is significantly elevated.
Zone thresholds are fully customizable in settings.
How to Use It
For Premium Sellers (Iron Condors, Credit Spreads, Strangles)
Wait for IV Rank to enter the green zone (≥50)
Confirm IV Percentile agrees (also elevated)
Enter premium selling positions when both metrics align
Avoid initiating new positions when in the red zone
For Premium Buyers (Long Options, Debit Spreads)
Low IV Rank/Percentile means cheaper options
Red zone can favor directional debit strategies
Avoid buying premium when both metrics are in the green zone
General Principle:
Sell premium when volatility is high (it tends to revert to mean). Buy premium when volatility is low (if you have a directional thesis).
Inputs
Volatility Calculation
HV Period — Lookback for historical volatility calculation (default: 20)
Trading Days/Year — 252 for stocks, 365 for crypto
Lookback Periods
IV Rank Lookback — Period for high/low range (default: 252 = 1 year)
IV Percentile Lookback — Period for percentile calculation (default: 252)
Zone Thresholds
High IV Zone — Readings above this are highlighted green (default: 50)
Low IV Zone — Readings below this are highlighted red (default: 25)
Extreme High — Threshold for "prime" conditions alert (default: 75)
Display Options
Toggle IV Rank, IV Percentile, and raw HV display
Show/hide zone backgrounds
Show/hide info panel
Panel position selection
Info Panel
The panel displays:
Field ------- Description
IV Rank ------- Current reading with color coding
IV Pctl ------- Current percentile with color coding
HV 20d ------- Raw historical volatility percentage
52w Range ------- Lowest to highest HV in lookback period
Zone ------- Current zone status
Premium ------- Signal quality for premium selling
Lookback ------- Days used for calculations
R/P Spread ------- Difference between Rank and Percentile
Alerts
Six alerts are available:
Zone Transitions
IV Entered High Zone — Favorable for premium selling
IV Reached Extreme Levels — Prime conditions
IV Dropped to Low Zone — Caution for premium sellers
Threshold Crosses
IV Rank Crossed Above High Threshold
IV Rank Crossed Below Low Threshold
IV Percentile Above 75
IV Percentile Below 25
Set up alerts to get notified when conditions change without watching charts.
Technical Notes
Volatility Calculation Method
This indicator uses close-to-close historical volatility as an IV proxy:
Calculate log returns: ln(Close / Previous Close)
Take standard deviation over HV Period
Annualize: multiply by √(Trading Days)
This method correlates well with implied volatility for most liquid instruments. On highly liquid options underlyings (SPY, QQQ, major stocks), HV and IV tend to move together, making this a reliable proxy for IV Rank analysis.
Non-Repainting
All calculations use confirmed bar data. Values are fixed once a bar closes.
Lookback Requirement
The indicator needs sufficient history to calculate accurately. For a 252-day lookback, ensure your chart has at least 300+ bars of data.
Best Used On
ETFs: SPY, QQQ, IWM, DIA
Indices: SPX, NDX
High-volume stocks: AAPL, TSLA, NVDA, AMD, META
Timeframe: Daily (recommended), Weekly for longer-term view
The indicator works on any instrument but is most meaningful on underlyings with active options markets.
Important Notes
⚠️ This indicator uses historical volatility as a proxy for implied volatility. While HV and IV are correlated, they are not identical. For precise IV data, consult your options broker's platform.
⚠️ High IV Rank does not guarantee profitable premium selling. It indicates favorable conditions, not guaranteed outcomes. Position sizing and risk management remain essential.
⚠️ Past volatility patterns do not guarantee future behavior. Volatility regimes can shift, and historical ranges may not predict future ranges.
Suggested Workflow
Add to daily chart of your preferred underlying
Set up alert for "IV Entered High Zone"
When alerted, check both IV Rank and IV Percentile
If both elevated, evaluate premium selling opportunities
Use your broker's actual IV data for final entry decisions
Questions? Leave a comment below.
RED BULL WINGS [JOAT]RED BULL WINGS - Bullish-Only Institutional Overlay
Introduction and Purpose
RED BULL WINGS is an open-source overlay indicator that combines five distinct bullish detection methods into a single composite scoring system. The core problem this indicator solves is that individual bullish signals (patterns, volume, zones, trendlines) often disagree or fire in isolation. A bullish engulfing pattern means little if volume is weak and price is far from support. Traders need confluence across multiple dimensions to identify high-probability setups.
This indicator addresses that by scoring each bullish component separately, then combining them into a weighted WINGS score (0-100) that reflects overall bullish conviction. When multiple components align, the score rises; when they disagree, the score stays low.
Why These Five Modules Work Together
Each module measures a different aspect of bullish market structure:
1. Module A - Bullish Candlestick Engine - Detects classic reversal patterns (engulfing, marubozu, hammer, 3-bar cluster). These patterns identify WHERE buyers are stepping in.
2. Module B - PVSRA Volume Climax - Measures spread x volume to detect institutional participation. This tells you WHETHER smart money is involved.
3. Module C - Demand Zone Detection - Identifies and tracks order block zones where buyers previously overwhelmed sellers. This shows you WHERE institutional support exists.
4. Module D - Trendline Channel - Builds dynamic support/resistance from pivot points. This reveals the STRUCTURE of the current trend.
5. Module E - Ichimoku Assist - Optional filter using Tenkan/Kijun cross, cloud position, and Chikou confirmation. This provides TREND PERMISSION context.
The combination works because:
Patterns alone can fail without volume confirmation
Volume alone means nothing without price structure context
Zones alone are static without pattern/volume triggers
Trendlines alone miss the micro-level entry timing
When 3+ modules agree, the probability of a valid bullish setup increases significantly
How the Calculations Work
Module A - Pattern Detection:
Bullish Engulfing - Current bullish bar completely engulfs prior bearish bar:
bool engulfingCond = isBullish() and
isBearish() and
open <= close and
close >= open and
bodySize() > bodySize()
Marubozu - Strong body with minimal wicks (body >= 1.8x average, wick ratio < 20%):
float wickRatio = candleRange() > 0 ? (upperWick() + lowerWick()) / candleRange() : 0
bool marubozuCond = isBullish() and
bodySize() >= bodySizeAvg * i_maruMult and
wickRatio < i_wickRatioMax
Hammer - Long lower wick (>= 2.5x body), close in upper third, volume confirmation:
bool hammerWick = lowerWick() >= i_hammerWickMult * bodySize()
bool hammerClose = close >= low + (candleRange() * 0.66)
bool hammerVol = volume >= i_pvsraRisingMult * volAvg
3-Bar Cluster - Three consecutive bullish closes with increasing prices and volume spike:
bool threeBarBullish = isBullish() and isBullish() and isBullish()
bool increasingCloses = close > close and close > close
bool volSpike3Bar = volume >= i_pvsraRisingMult * volAvg or
volume >= i_pvsraRisingMult * volAvg
Module B - PVSRA Volume Analysis:
Uses spread x volume to detect climax conditions:
float spreadVol = candleRange() * volume
float maxSpreadVol = ta.highest(spreadVol, ADJ_PVSRA_LOOKBACK)
bool volClimax = volume >= i_pvsraClimaxMult * volAvg or spreadVol >= maxSpreadVol
bool volRising = volume >= i_pvsraRisingMult * volAvg and volume < i_pvsraClimaxMult * volAvg
Volume only scores when the candle is bullish, preventing false signals on bearish volume spikes.
Module C - Demand Zone Detection:
Identifies zones using a two-candle structure:
// Small bearish candle A followed by larger bullish candle B
bool candleA_bearish = isBearish()
bool candleB_bullish = isBullish()
bool newZoneCond = candleA_bearish and candleB_bullish and
candleB_size >= i_zoneSizeMult * candleA_size
Zones are drawn as rectangles and tracked for retests. Score increases when price is near or inside an active zone, with bonus points for rejection candles.
Module D - Trendline Channel:
Builds dynamic channel from confirmed pivot points:
float ph = ta.pivothigh(high, i_pivotLeft, i_pivotRight)
float pl = ta.pivotlow(low, i_pivotLeft, i_pivotRight)
Pivots are stored and connected to form upper/lower channel lines. The indicator detects breakouts when price closes beyond the channel with volume confirmation.
Module E - Ichimoku Assist:
Standard Ichimoku calculations with bullish scoring:
float tenkan = (ta.highest(high, i_tenkanLen) + ta.lowest(low, i_tenkanLen)) / 2
float kijun = (ta.highest(high, i_kijunLen) + ta.lowest(low, i_kijunLen)) / 2
bool tkCross = ta.crossover(tenkan, kijun)
bool priceAboveCloud = close > cloudTop
bool chikouAbovePrice = chikou > close
Module F - WINGS Composite Score:
All module scores are combined using adjustable weights:
float WINGS_score = 100 * (nW_pattern * S_pattern +
nW_volume * S_vol +
nW_zone * S_zone +
nW_trend * S_trend +
nW_ichi * S_ichi)
Default weights: Pattern 30%, Volume 25%, Zone 20%, Trend 15%, Ichimoku 10%.
Signal Thresholds
WATCH (30-49) - Interesting bullish context forming, not yet actionable
MOMENTUM (50-74) - Strong bullish conditions, multiple modules agreeing
LIFT-OFF (75+) - High-confidence bullish confluence across most modules
WINGS Badge (Dashboard)
The right-side panel displays:
WINGS Score - Current composite score (0-100)
Pattern - Active pattern name and strength, or neutral placeholder
Volume - Normal / Rising / CLIMAX status
Zone - ACTIVE if price is near a demand zone
Trend - Channel position or BREAK status
Ichimoku - OFF / Weak / Bullish / STRONG
Status - Overall signal level (Neutral / WATCH / MOMENTUM / LIFT-OFF)
Input Parameters
Module Toggles:
Enable Bullish Patterns (true) - Toggle pattern detection
Enable PVSRA Volume (true) - Toggle volume analysis
Enable Order Blocks (true) - Toggle demand zone detection
Enable Trendlines (true) - Toggle pivot channel
Enable Ichimoku Assist (false) - Toggle Ichimoku filter (off by default for performance)
Enable Visual Effects (false) - Toggle labels, trails, and visual elements
LIVE MODE (false) - Enable intrabar signals (WARNING: signals may repaint)
Pattern Engine:
Pattern Lookback (5) - Bars for body size averaging
Marubozu Body Multiplier (1.8) - Minimum body size vs average
Hammer Wick Multiplier (2.5) - Minimum lower wick vs body
Max Wick Ratio (0.2) - Maximum wick percentage for marubozu
Volume / PVSRA:
PVSRA Lookback (10) - Period for volume averaging
Climax Multiplier (2.0) - Volume threshold for climax detection
Rising Volume Multiplier (1.5) - Volume threshold for rising detection
Order Blocks:
Zone Size Multiplier (2.0) - Minimum bullish candle size vs bearish
Zone Extend Bars (200) - How far zones project forward
Max Zones (12) - Maximum active zones displayed
Remove Zone on Close Below (true) - Delete broken zones
Trendlines:
Pivot Left/Right Bars (3/3) - Pivot detection sensitivity
Min Slope % (0.25) - Minimum trendline angle
Max Trendlines (5) - Maximum pivot points stored
Trendline Projection Bars (60) - Forward projection distance
Ichimoku:
Tenkan Length (9) - Conversion line period
Kijun Length (26) - Base line period
Senkou B Length (52) - Leading span B period
Displacement (26) - Cloud displacement
WINGS Score:
Weight: Pattern (0.30) - Pattern contribution to score
Weight: Volume (0.25) - Volume contribution to score
Weight: Zone (0.20) - Zone contribution to score
Weight: Trend (0.15) - Trendline contribution to score
Weight: Ichimoku (0.10) - Ichimoku contribution to score
Lift-Off Threshold (75) - Score required for LIFT-OFF signal
Momentum Watch Threshold (50) - Score required for MOMENTUM signal
Visuals:
Signal Cooldown (8) - Minimum bars between labels
Show WINGS Score Badge (true) - Toggle dashboard
Show Wing Combos (true) - Show DOUBLE/MEGA WINGS streaks
Red Background Wash (true) - Tint chart background
Show Lift-Off Trails (false) - Toggle golden trail visuals
How to Use This Indicator
For Bullish Entry Identification:
1. Monitor the WINGS badge for score changes
2. Wait for MOMENTUM (50+) or LIFT-OFF (75+) signals
3. Check which modules are contributing (Pattern + Volume + Zone = stronger)
4. Use demand zones and trendlines as structural reference for entries
For Confluence Confirmation:
1. Use alongside your existing analysis
2. LIFT-OFF signals indicate multiple bullish factors aligning
3. Low scores (< 30) suggest weak bullish context even if one factor looks good
For Zone-Based Trading:
1. Watch for price approaching active demand zones
2. Look for pattern + volume confirmation at zone retests
3. Zone score increases with successful retests
For Trendline Analysis:
1. Monitor the pivot-based channel for trend structure
2. Breakouts with volume confirmation trigger TREND BREAK alerts
3. Price inside channel with bullish patterns = trend continuation setup
1M and lower timeframes:
Alerts Available
LIFT-OFF - High-confidence bullish confluence
MOMENTUM - Strong bullish conditions
Zone Retest - Bullish rejection from demand zone
Trendline Break - Breakout with volume confirmation
Individual patterns (Engulfing, Marubozu, Hammer, 3-Bar Cluster)
Volume Climax - Institutional volume spike
DOUBLE WINGS / MEGA WINGS - Consecutive lift-off signals
Repainting Behavior
By default, the indicator uses confirmed bars only (barstate.isconfirmed), meaning signals appear after the bar closes and do not repaint. However:
LIVE MODE - When enabled, signals can appear intrabar but may disappear if conditions change before bar close. A warning label displays when LIVE MODE is active.
Trendlines - Pivot detection requires lookback bars, so the most recent trendline segments may adjust as new pivots confirm. This is inherent to pivot-based analysis.
Demand Zones - Zones are created on confirmed bars and do not repaint, but they can be removed if price closes below the zone bottom (configurable).
Live Mode with 'Enable Visual Effect' turned off in settings:
Limitations
This is a bullish-only indicator. It does not detect bearish setups or provide short signals.
The WINGS score is a confluence measure, not a prediction. High scores indicate favorable conditions, not guaranteed outcomes.
Pattern detection uses simplified logic. Not all candlestick nuances are captured.
Volume analysis requires reliable volume data. Results may vary on instruments with inconsistent volume reporting.
Ichimoku calculations add processing overhead. Disable if not needed.
Demand zones are based on a specific two-candle structure. Other valid zones may not be detected.
Trendlines use linear regression between pivots. Curved or complex channels are not supported.
Timeframe Recommendations
15m-1H: More frequent signals, useful for intraday analysis. Higher noise.
4H-Daily: Best balance of signal quality and frequency for swing trading.
Weekly: Fewer but more significant signals for position trading.
Adjust lookback periods and thresholds based on your timeframe. Shorter timeframes may benefit from shorter lookbacks.
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes. The source code is fully visible and can be studied to understand how each module works.
This indicator does not constitute financial advice. The WINGS score and signals do not guarantee profitable trades. Past performance does not guarantee future results. Always use proper risk management, position sizing, and stop-losses. Test thoroughly on your preferred instruments and timeframes before using in live trading.
- Made with passion by officialjackofalltrades
WoAlgo Premium v3.0
WoAlgo Premium v3.0 - Smart Money Analysis
Overview
** WoAlgo Premium v3.0 ** is an advanced technical analysis indicator designed for educational purposes. This tool combines Smart Money Concepts with multi-factor confluence analysis to help traders identify potential market opportunities across multiple timeframes.
The indicator integrates market structure analysis, order flow concepts, and technical momentum indicators into a comprehensive dashboard system. It is designed to assist traders in understanding institutional trading patterns and market dynamics through visual analysis tools.
### What It Does
This indicator provides:
**1. Smart Money Concepts Analysis**
- Market structure identification (Break of Structure and Change of Character patterns)
- Order block detection with volume confirmation
- Fair value gap recognition
- Liquidity zone mapping (equal highs and lows)
- Premium and discount zone calculations
**2. Multi-Factor Confluence Scoring**
The indicator calculates a proprietary confluence score (0-100) based on five key components:
- Price action analysis (30% weight)
- Volume confirmation (20% weight)
- Momentum indicators (25% weight)
- Trend strength measurement (15% weight)
- Money flow analysis (10% weight)
**3. Multi-Timeframe Analysis**
- Scans 5 different timeframes (5M, 15M, 1H, 4H, Daily)
- Calculates alignment percentage across timeframes
- Displays trend and structure status for each period
**4. Visual Dashboard System**
- Comprehensive main dashboard with 13 metrics
- Real-time screener table with 10 data columns
- Multi-timeframe scanner
- Performance tracking panel
### How It Works
**Market Structure Detection**
The indicator identifies key structural changes in price action:
- **BOS (Break of Structure)**: Indicates trend continuation when price breaks previous swing points
- **CHoCH (Change of Character)**: Signals potential trend reversal when market structure shifts
**Order Block Identification**
Order blocks are detected when:
- Significant volume appears at swing points
- Price shows strong directional movement from these levels
- Enhanced detection with extreme volume confirmation (OB++ markers)
**Fair Value Gap Recognition**
Gaps between candles are identified when:
- Price leaves inefficiencies in the market
- Three consecutive candles create a gap pattern
- Gap size exceeds minimum threshold based on ATR
**Confluence Calculation**
The system evaluates multiple technical factors:
1. **Price Position**: Relative to moving averages (EMA 20, 50, 200)
2. **Volume Analysis**: Standard deviation-based volume spikes
3. **Momentum**: RSI, MACD, Stochastic indicators
4. **Trend Strength**: ADX measurements
5. **Money Flow**: MFI indicator readings
Each factor contributes weighted points to create an overall confluence score that helps assess signal strength.
### Signal Types
**Confirmation Signals (▲ / ▼)**
Generated when:
- EMA crossovers occur (20/50 cross)
- Volume confirmation is present
- RSI is in appropriate zone
- Confluence score exceeds 50%
**Strong Signals (▲+ / ▼+)**
Higher-confidence signals requiring:
- Confluence score above 70%
- Extreme volume confirmation
- Alignment with 200 EMA trend
- MACD confirmation
- Bullish or bearish market structure
**Contrarian Signals (⚡)**
Reversal indicators appearing when:
- RSI reaches extreme levels (<30 or >70)
- Stochastic shows oversold/overbought conditions
- Price touches Bollinger Band extremes
- Potential divergence patterns emerge
**Reversal Zones**
Visual boxes highlighting areas where:
- Market structure conflicts with momentum
- High probability of directional change
- Key support/resistance levels interact
**Smart Trail**
Dynamic stop-loss indicator that:
- Adjusts based on ATR (Average True Range)
- Follows trend direction
- Updates automatically as price moves
- Provides risk management reference points
### Dashboard Components
**Main Dashboard (13 Metrics)**
1. **Confluence Score**: Current bull/bear percentage (0-100)
2. **Market Regime**: Trend classification (Strong Up/Down, Range, Squeeze)
3. **Signal Status**: Active buy/sell signal indication
4. **Structure State**: Current market structure (Bullish/Bearish/Neutral)
5. **Trend Strength**: ADX-based measurement
6. **RSI Level**: Momentum indicator with overbought/oversold zones
7. **MACD Direction**: Trend momentum confirmation
8. **Money Flow Index**: Smart money sentiment
9. **Volume Status**: Current volume relative to average
10. **Volatility Rating**: ATR percentage measurement
11. **ATR Value**: Average true range for position sizing
12. **MTF Alignment**: Multi-timeframe agreement percentage
**Screener Table (10 Columns)**
- Current symbol and timeframe
- Real-time price and percentage change
- Quality rating (star system)
- Active signal type
- Smart trail status
- Market structure state
- MACD direction
- Trend strength percentage
- Bollinger Band squeeze detection
**MTF Scanner (5 Timeframes)**
Displays for each timeframe:
- Trend direction indicator
- Market structure classification
- Visual confirmation with color coding
**Performance Metrics**
- Win rate percentage (simplified calculation)
- Total signals generated
- Current confluence score
- MTF alignment status
- Volatility level
### Settings and Customization
**Preset Styles**
Choose from predefined configurations:
- **Conservative**: Fewer, higher-quality signals
- **Moderate**: Balanced approach (recommended)
- **Aggressive**: More frequent signals
- **Scalper**: Short-term focused
- **Swing**: Longer-term oriented
- **Custom**: Full manual control
**Smart Money Concepts Controls**
- Toggle each feature independently
- Adjust swing length (3-50 periods)
- Enable/disable internal structure
- Control order block display
- Manage breaker block visibility
- Show/hide fair value gaps
- Display liquidity zones
- Premium/discount zone visualization
**Signal Configuration**
- Enable/disable confirmation signals
- Toggle strong signal markers
- Control contrarian signal display
- Show/hide reversal zones
- Smart trail activation
- Sensitivity adjustment (5-50)
**Visual Customization**
- Moving average display options
- MA period adjustments (Fast: 20, Slow: 50, Trend: 200)
- Support/resistance line toggle
- Dynamic S/R lookback period
- Candle coloring based on trend
- Color scheme customization
- Dashboard size options (Small/Normal/Large)
- Position placement (4 corners)
### How to Use
**Step 1: Initial Setup**
1. Add indicator to chart
2. Select appropriate preset or use Custom
3. Adjust timeframe to match trading style
4. Configure dashboard visibility preferences
**Step 2: Analysis Workflow**
1. Check MTF Scanner for timeframe alignment
2. Review Main Dashboard confluence score
3. Observe Market Regime classification
4. Identify active signals on chart
5. Confirm with Smart Money Concepts (order blocks, FVG, structure)
**Step 3: Trade Consideration**
Strong signals (▲+ / ▼+) require:
- Confluence score >70%
- MTF alignment >60%
- Confirmation from multiple dashboard metrics
- Support from Smart Money Concepts
- Appropriate volume levels
**Step 4: Risk Management**
- Use Smart Trail as dynamic stop-loss reference
- Consider ATR for position sizing
- Monitor volatility rating
- Respect support/resistance levels
- Combine with personal risk parameters
### Best Practices
**For Scalping (1M-5M timeframes)**
- Use Scalper preset
- Reduce swing length to 5-7
- Focus on strong signals only
- Monitor MTF alignment closely
- Quick entries near order blocks
**For Intraday Trading (15M-1H timeframes)**
- Use Moderate preset (recommended)
- Default swing length (10)
- Combine confirmation and strong signals
- Check MTF scanner before entry
- Use fair value gaps for entries
**For Swing Trading (4H-D timeframes)**
- Use Swing preset
- Increase swing length to 15-20
- Focus on strong signals
- Require high MTF alignment
- Patient approach with major structure levels
### Technical Specifications
**Indicators Used**
- Exponential Moving Averages (20, 50, 200)
- Hull Moving Average
- Relative Strength Index (14)
- MACD (12, 26, 9)
- Money Flow Index (14)
- Stochastic Oscillator (14, 3)
- ADX / DMI (14)
- Bollinger Bands (20, 2)
- ATR (14)
- Volume Analysis (SMA 20 with standard deviation)
**Calculation Methods**
- Swing detection using pivot high/low functions
- Volume confirmation via statistical analysis
- Multi-factor scoring with weighted components
- Dynamic support/resistance using highest/lowest functions
- Real-time MTF data via security() function
### Limitations and Considerations
**Important Notes**
1. This indicator is designed for educational and analytical purposes only
2. Historical performance does not guarantee future results
3. Signals should be confirmed with additional analysis
4. Market conditions vary and affect indicator performance
5. Not all signals will be profitable
6. Risk management is essential for all trading
**Known Limitations**
- Confluence scoring is algorithmic and not predictive
- MTF analysis requires sufficient historical data
- Effectiveness varies across different market conditions
- Sideways markets may produce conflicting signals
- High volatility can affect signal reliability
- Backtesting results shown are simplified calculations
**Not Suitable For**
- Automated trading without human oversight
- Sole basis for trading decisions
- Guaranteed profit expectations
- Inexperienced traders without proper education
- Trading without risk management plans
### Market Applicability
**Effective On**
- Trending markets (any direction)
- Clear structure formation periods
- Liquid instruments with consistent volume
- Multiple asset classes (forex, stocks, crypto, commodities)
- Various timeframes with appropriate settings
**Less Effective During**
- Extended ranging/choppy conditions
- Extremely low volume periods
- Major news events causing gaps
- Early market open with high spread
- Illiquid instruments with erratic price action
### Risk Disclaimer
**⚠️ IMPORTANT NOTICE**
This indicator is provided for **educational and informational purposes only**. It does not constitute financial advice, investment recommendations, or trading signals.
**Key Risk Factors:**
- Trading financial instruments involves substantial risk of loss
- Past performance does not indicate future results
- No indicator can predict market movements with certainty
- Users should conduct independent research and analysis
- Professional financial advice should be sought when appropriate
- Risk management and position sizing are critical to successful trading
- Users are solely responsible for their trading decisions
**Responsible Usage:**
- Combine with comprehensive market analysis
- Use appropriate stop-loss orders
- Never risk more than you can afford to lose
- Maintain realistic expectations
- Continue education on technical analysis principles
- Test thoroughly on demo accounts before live trading
- Understand all indicator features before using
### Educational Resources
**Understanding Smart Money Concepts**
Smart Money Concepts analyze how institutional traders and large market participants operate. Key principles include:
- Institutional order flow patterns
- Market structure changes
- Liquidity manipulation
- Supply and demand imbalances
- Order block formations
**Multi-Timeframe Analysis Theory**
Analyzing multiple timeframes helps:
- Identify overall market direction
- Improve entry timing
- Confirm trend strength
- Recognize consolidation periods
- Reduce conflicting signals
**Confluence Trading Approach**
Using multiple confirming factors:
- Increases signal reliability
- Reduces false signals
- Provides conviction for trades
- Helps with position sizing
- Improves risk-reward ratios
### Version History
**v3.0 (Current)**
- Multi-factor confluence scoring system
- Complete Smart Money Concepts implementation
- Real-time multi-timeframe analysis
- Four professional dashboard panels
- Enhanced order block detection
- Breaker block identification
- Premium/discount zone calculations
- Smart trail stop-loss system
- Customizable preset configurations
- Performance tracking metrics
**Development Philosophy**
This indicator was developed with focus on:
- Educational value for traders
- Transparent methodology
- Comprehensive feature set
- User-friendly interface
- Flexible customization options
### Technical Support
**For Questions About:**
- Indicator functionality
- Parameter optimization
- Signal interpretation
- Dashboard metrics
- Best practice recommendations
Please use TradingView's comment section below. The developer monitors comments and provides assistance to users learning to use the indicator effectively.
### Acknowledgments
This indicator implements concepts from:
- Smart Money Concepts trading methodology
- Multi-timeframe analysis techniques
- Technical indicator theory
- Market structure analysis principles
- Institutional order flow concepts
All implementations are original code and calculations based on established technical analysis principles.
---
## ADDITIONAL INFORMATION SECTION
**Category**: Indicators
**Type**: Market Structure / Multi-Timeframe Analysis
**Complexity**: Intermediate to Advanced
**Open Source**: Code visible for transparency and education
**Pine Script Version**: v6
**Chart Overlay**: Yes
**Maximum Objects**: 500 boxes, 500 lines, 500 labels
Zenith MACD Evolution [JOAT]
Zenith MACD Evolution - Volatility-Normalized Momentum Oscillator
Introduction and Purpose
Zenith MACD Evolution is an open-source oscillator indicator that takes the classic MACD and normalizes it by ATR (Average True Range) to create consistent overbought/oversold levels across different market conditions. The core problem this indicator solves is that traditional MACD values are incomparable across different volatility regimes. A MACD reading of 50 might be extreme in a quiet market but normal in a volatile one.
This indicator addresses that by dividing MACD by ATR and scaling to a consistent range, allowing traders to use fixed overbought/oversold levels that work across all market conditions.
Why ATR Normalization Works
Traditional MACD problems:
- Values vary wildly based on price and volatility
- No consistent overbought/oversold levels
- Hard to compare across different instruments
- Extreme readings in one period may be normal in another
ATR-normalized MACD (Zenith) solves these:
- Values scaled to consistent range
- Fixed overbought/oversold levels work across all conditions
- Comparable across different instruments
- Extreme readings are truly extreme regardless of volatility
How the Normalization Works
// Classic MACD
= ta.macd(close, fastLength, slowLength, signalLength)
// ATR for normalization
float atrValue = ta.atr(atrNormLength)
// Volatility-Normalized MACD
float zenithMACD = atrValue != 0 ? (histLine / atrValue) * 100 : 0
float zenithSignal = ta.ema(zenithMACD, signalLength)
The result is a MACD that typically ranges from -200 to +200, with consistent levels:
- Above +150 = Overbought
- Below -150 = Oversold
- Above +200 = Extreme overbought
- Below -200 = Extreme oversold
Signal Types
Zero Cross Up/Down - Zenith crosses zero line (trend change)
Overbought/Oversold Entry - Zenith enters extreme zones
Overbought/Oversold Exit - Zenith leaves extreme zones (potential reversal)
Momentum Shift - Histogram direction changes (early warning)
Divergence - Price makes new high/low but Zenith does not
Histogram Coloring
The histogram uses four colors to show momentum state:
- Strong Bull (Teal) - Positive and rising
- Weak Bull (Light Teal) - Positive but falling
- Strong Bear (Red) - Negative and falling
- Weak Bear (Light Red) - Negative but rising
This helps identify momentum shifts before crossovers occur.
Dashboard Information
Zenith - Current normalized MACD value with signal line
Zone - Current zone (EXTREME OB/OVERBOUGHT/NORMAL/OVERSOLD/EXTREME OS)
Momentum - Direction (RISING/FALLING/FLAT)
Histogram - Current histogram value
ATR Norm - Current ATR value used for normalization
Classic - Traditional MACD value for reference
How to Use This Indicator
For Mean-Reversion:
1. Wait for Zenith to reach extreme zones (+200/-200)
2. Look for momentum shift (histogram color change)
3. Enter counter-trend when exiting extreme zone
For Trend Following:
1. Enter long on zero cross up
2. Enter short on zero cross down
3. Use histogram color to gauge momentum strength
For Divergence Trading:
1. Watch for DIV labels (price vs Zenith divergence)
2. Bullish divergence at support = potential long
3. Bearish divergence at resistance = potential short
Input Parameters
Fast/Slow/Signal Length (12/26/9) - Standard MACD parameters
ATR Normalization Period (26) - Period for ATR calculation
Overbought/Oversold Zone (150/-150) - Zone thresholds
Extreme Level (200) - Extreme threshold
Show Classic MACD Lines (false) - Toggle traditional lines
Show Divergence Detection (true) - Toggle divergence signals
Divergence Lookback (14) - Bars to scan for divergence
Timeframe Recommendations
All timeframes work due to normalization
Higher timeframes provide smoother signals
Normalization makes cross-timeframe comparison meaningful
Limitations
ATR normalization adds slight lag
Divergence detection is simplified
Extreme zones can persist in strong trends
Works best when combined with price action analysis
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes.
This indicator does not constitute financial advice. Momentum analysis does not guarantee profitable trades. Always use proper risk management.
- Made with passion by officialjackofalltrades
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.
Vortex Trend Matrix [JOAT]Vortex Trend Matrix - Multi-Factor Trend Confluence System
Introduction and Purpose
Vortex Trend Matrix is an open-source overlay indicator that combines Ichimoku-style equilibrium analysis with the Vortex Indicator to create a comprehensive trend confluence system. The core problem this indicator solves is that single trend indicators often give conflicting signals. Price might be above a moving average but momentum might be weakening.
This indicator addresses that by combining five different trend factors into a single composite score, making it easy to identify when multiple factors align for high-probability trend trades.
Why These Components Work Together
Each component measures trend from a different perspective:
1. Cloud Position - Price above/below the equilibrium cloud indicates overall trend bias. The cloud acts as dynamic support/resistance.
2. TK Relationship - Conversion line vs Base line (like Tenkan/Kijun in Ichimoku). Conversion above Base = bullish momentum.
3. Lagging Span - Current price compared to price N bars ago. Confirms whether current move has follow-through.
4. Vortex Indicator - VI+ vs VI- measures directional movement strength. Provides momentum confirmation.
5. Base Direction - Whether the base line is rising or falling. Indicates medium-term trend direction.
How the Trend Score Works
float trendScore = 0.0
// Cloud position (+2/-2)
trendScore += aboveCloud ? 2.0 : belowCloud ? -2.0 : 0.0
// TK relationship (+1/-1)
trendScore += conversionLine > baseLine ? 1.0 : conversionLine < baseLine ? -1.0 : 0.0
// Lagging span (+1/-1)
trendScore += laggingBull ? 1.0 : laggingBear ? -1.0 : 0.0
// Vortex (+1.5/-1.5)
trendScore += vortexBull ? 1.5 : vortexBear ? -1.5 : 0.0
// Base direction (+0.5/-0.5)
trendScore += baseDirection * 0.5
Score ranges from approximately -6 to +6:
- +4 or higher = STRONG BULL
- +2 to +4 = BULL
- -2 to +2 = NEUTRAL
- -4 to -2 = BEAR
- -4 or lower = STRONG BEAR
Signal Types
TK Cross Up/Down - Conversion line crosses Base line (momentum shift)
Base Direction Change - Base line changes direction (medium-term shift)
Strong Bull/Bear Trend - Score reaches +4/-4 (high confluence)
Dashboard Information
Trend - Overall status with composite score
Cloud - Price position (ABOVE/BELOW/INSIDE)
TK Cross - Conversion vs Base relationship
Lagging - Lagging span bias
Vortex - VI+/VI- relationship
VI+/VI- - Individual vortex values
How to Use This Indicator
For Trend Following:
1. Enter long when trend score reaches +4 or higher (STRONG BULL)
2. Enter short when trend score reaches -4 or lower (STRONG BEAR)
3. Use cloud as dynamic support/resistance for entries
For Momentum Timing:
1. Watch for TK Cross signals for entry timing
2. Base direction changes indicate medium-term shifts
3. Vortex confirmation adds conviction
For Risk Management:
1. Exit when trend score drops to neutral
2. Use cloud edges as stop-loss references
3. Reduce position when score weakens
Input Parameters
Conversion Period (9) - Fast equilibrium line
Base Period (26) - Slow equilibrium line
Lead Span Period (52) - Cloud projection period
Displacement (26) - Cloud and lagging span offset
Vortex Period (14) - Period for vortex calculation
VI+ Strength (1.10) - Threshold for strong bullish vortex
VI- Strength (0.90) - Threshold for strong bearish vortex
Timeframe Recommendations
4H-Daily: Best for equilibrium-based analysis
1H: Good for intraday trend following
Lower timeframes may require adjusted periods
Limitations
Equilibrium calculations have inherent lag
Cloud displacement means signals are delayed
Works best in trending markets
May whipsaw in ranging conditions
Open-Source and Disclaimer
This script is published as open-source under the Mozilla Public License 2.0 for educational purposes.
This indicator does not constitute financial advice. Trend analysis does not guarantee profitable trades. Always use proper risk management.
- Made with passion by officialjackofalltrades
Vector Volume Delta Candles [Capitalize Labs]Vector Volume Delta Candles is a visual market analysis indicator designed to highlight relative volume activity directly on price candles. The indicator classifies candles based on volume intensity and price range expansion compared to recent historical data and applies color coding for visual context only.
This indicator functions strictly as a candle-coloring overlay. It does not generate trade signals, entries, exits, alerts, forecasts, or predictions. No automated trading decisions are made or implied.
How it works
Evaluates current candle volume relative to a moving average of recent volume
Optionally incorporates a volume × price range comparison to identify unusually active candles
Classifies candles as:
Climactic when volume activity is significantly above recent norms
Elevated when volume is above average but not climactic
Applies configurable colors to candles based on classification and candle direction
Includes optional color customization and the ability to revert candle coloring
Uses historical data only and does not repaint or reference future bars
Intended use
This indicator is intended for educational and analytical purposes only. It may be used as a visual reference alongside other tools or discretionary analysis methods. All interpretations are subjective and must be evaluated independently by the user.
No assumptions are made regarding market direction, probability, or outcome.
Disclaimer and Risk Notice
This indicator is provided strictly for educational and informational purposes. It is not intended to constitute financial advice, investment recommendations, or an offer or solicitation to buy or sell any financial instrument or security.
Financial markets involve substantial risk, and trading decisions can result in losses that exceed initial expectations. Market conditions can change rapidly due to volatility, liquidity constraints, economic events, or other external factors. No representation is made that the use of this indicator will result in profitable outcomes or that any interpretation of its output will be accurate or complete in all market conditions.
This indicator does not take into account individual financial circumstances, objectives, or risk tolerance. Users are solely responsible for evaluating the suitability of any analysis or methodology derived from this tool and for managing their own risk, position sizing, and execution decisions.
All calculations are based on historical price and volume data. Historical or simulated behavior should not be interpreted as a guarantee or prediction of future performance. The absence of repainting or lookahead logic does not imply predictive capability.
By using this indicator, the user acknowledges that all trading decisions are made at their own discretion and risk, and that the creator assumes no responsibility or liability for any losses, damages, or outcomes arising from its use.
