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Jurik Moving Average with Stair-StepJurik Moving Average with Stair-Step Filter โ Precision Smoothing with Event-Driven Signal Filtering
๐ Version:
Built in Pine Script v6, leveraging the full JMA core with an added stair-step threshold filter for discrete, event-based signal generation.
๐ Overview:
This enhanced Jurik Moving Average (JMA) combines the low-lag smoothing algorithm with a custom stair-step logic layer that transforms continuous JMA output into state-based, noise-filtered movement.
While the traditional JMA provides ultra-smooth, adaptive trend detection, it still updates continuously with each price tick. The Stair-Step version introduces a quantized output โ the JMA value remains unchanged until price moves by a user-defined amount (in ticks or absolute price units). The result is a โdigitalโ trend line that updates only when meaningful change occurs, filtering out minor fluctuations and giving traders clearer, more actionable transitions.
๐ How It Works:
โ
Adaptive JMA Core: Dynamically adjusts smoothing to volatility for ultra-low lag.
โ
Stair-Step Logic: Holds the JMA value steady until the underlying line moves by a chosen threshold.
โ
Event-Driven Updates: Each โstepโ represents a statistically significant change in market direction.
โ
Tick / Price-Based Sensitivity: Tune the filter to the instrumentโs volatility, spread, or cost structure.
This dual-layer system blends JMAโs continuous adaptability with discrete regime detection โ turning a smooth line into a decision-ready trend model.
๐ How to Use:
๐น Bias Detection: Each new step indicates a potential regime shift or breakout confirmation.
๐น Noise Reduction: Ideal in choppy or range-bound markets where traditional MAs over-react.
๐น Automated Systems: Use stair transitions as clean event triggers for entries, exits, or bias flips.
๐น Scalping & Swing Trading: Thresholds can be sized by tick, ATR, or volatility to match timeframe and cost tolerance.
๐ Why This Version Is Unique:
This is not just another moving average โ itโs a stateful JMA, adding event-driven decision logic to one of the marketโs most precise filters.
๐น Discretized Trend Mapping: Flat plateaus define stability; steps define momentum bursts.
๐น Reduced Whipsaws: Only reacts when moves exceed statistical or cost thresholds.
๐น Execution-Grade Precision: Perfect for algorithmic strategies needing fewer false flips.
๐ Example Use:
Combine with VWAP, ATR, or momentum oscillators to confirm bias shifts. In automated strategies, use stair flips as โgo / stopโ states to control position changes or trade size adjustments.
๐ Summary:
The Jurik Moving Average with Stair-Step Filter preserves JMAโs hallmark smoothness while delivering a structured, event-driven representation of market movement.
Itโs precision smoothing โ now with adaptive noise gating โ designed for traders who demand clarity, stability, and algorithm-ready signal behavior.
๐ Disclaimer:
This indicator is not affiliated with or derived from any proprietary Jurik Research algorithms. Itโs an independent implementation that applies similar adaptive-smoothing principles, extended with a stair-step filtering mechanism for discrete trend transitions.
XIV Trading Strategy This simple strategy uses VVIX , the VIX of VIX , to find BUY/SELL signals for XIV. The actual return of for this strategy is actually lower than what is produced by Tradingview's backtesting engine ( 525 % vs 221 % in my testing ) . More detail available in my blog.
Cheers
Algo.
ISM Indicator As a Strategy Here's a very easy code, plotting the ISM against the SPX. In this exercise, i wanted to see if one could use the ISM indicator only to generate buy/sell signal, and what would be the performance.
What is the ISM
The ISM Manufacturing Index monitors employment, production inventories, new orders and supplier deliveries.By monitoring the ISM Manufacturing Index, investors are able to better understand national economic conditions. When this index is increasing, investors can assume that the stock markets should increase because of higher corporate profits. The opposite can be thought of the bond markets, which may decrease as the ISM Manufacturing Index increases because of sensitivity to potential inflation.
Buy/Sell Signal
ISM above 50 usually good economic condition and vice versa when below 50 . For this code I used 48.50 as my buy/sell signal line.
Results
To test this on a longer time period, I use the SPX index instead of SPY. The results are surprisingly good. 76.92% profitability with 3.03 profit factor.
Conclusion
Investors could use the ISM with other indicators to determine better entry and exit point. I will see if combining the ISM with other custom indicators , could generate better result. Feel free to share your results here.
Cheers
Algo.
AK_RSI 2 Strategy ( based on Chris Moody RSI(2) indicator )Good Morning,
Republishing this in the script section to make the code visible to everyone. This strategy is based on Chris Moody's RSi(2) indicator. Good success rate on SPY. Again, this is for educational purposes only .
cheers
Algo
AK MACD BB INDICATOR V 1.00Here's my version of the MACD _BB . This is a great indicator to capture short term trends.
yellow candles = long
aqua candles = short
This indicator can be much better. I will work on it and publish an improved version (hopefully) soon. In the mean time , go ahead and play around with the code, and please share your findings :)
Cheers
Algo
AK TREND ID v1.00Hello,
"Are we at the top yet ? "........ " Is it a good time to invest ? " ......." Should I buy or sell ? " These are the many questions I hear and get on the daily basis. 1000's of investors do not know when to go in and out of the market. Most of them rely on the opinion of "experts" on television to make their investment decisions. Bad idea.Taking a systematic approach when investing, could save you a lot of time and headache. If there was only a way to know when to get in and out of the market !! hmmmm. The good news is that there many ways to do that. The bad news is , are you disciplined enough to follow it ?
I coded the AK_TREND ID specifically to identified trends in the SPX or SPY only . How does it work ? very simply , I simply plot the spread between the 3 month and 8 month moving average on the chart.
If the spread > 0 @ month end = BUY
if the spread < 0 @ month end = SELL
The AK TREND ID is a LAGGING Indicator , so it will not get you in at the very bottom or get you out at the very top. I did a backtest on the SPX from 1984 to 7/2/2014 (yesterday), The rule was to buy only when the AK TREND ID was green. let's look at the result:
14 trades : 11 W 3 L , 78.75 % winning %
Biggest winner (%) = 108 %
Biggest loser (%) = -10.7 %
Average Return = 27 %
Total Return since 1984 = 351.3 %
You can see the result in detail here : docs.google.com
Although the backtesting results are good, the AK TREND ID is not to be used as a trading system. It is simply design to let you know when to invest and when to get out. I'm working a more accurate version of this Indicator , that will use both technical and fundamental data. In the mean time , I hope this will give some of you piece of mind, and eliminate emotions from your trading decision. Feel free to modify the code as you wish, but please share your finding with the rest of Trading View community.
All the best
Algo
Risk Recommender โ (Heatmap)๐ Risk Recommender โ Per-Trade & Annualized (Heatmap Columns)
Estimate the optimal risk percentage for any market regime.
This tool dynamically recommends how much of your account equity to risk โ either per trade or at a portfolio (annualized) level โ using volatility as the guide.
โ๏ธ How it works
Two distinct modes give you flexibility:
1๏ธโฃ Per-Trade (ATR-based)
โข Calculates the current Average True Range (ATR) compared to its long-term baseline.
โข When volatility is high (ATR โ), risk per trade decreases to maintain constant dollar risk.
โข When volatility is low (ATR โ), risk per trade increases within your defined floor and ceiling.
โข The display is normalized by stop distance (ร ATR) and smoothed to avoid noise.
2๏ธโฃ Annualized (Volatility Targeting)
โข Computes realized volatility (standard deviation of log returns) and an EWMA forecast of future volatility.
โข Blends current and forecast volatilities to estimate โeffectiveโ volatility.
โข Scales your base risk so that portfolio volatility converges toward your chosen annual target (e.g., 20%).
โข Useful for portfolio-level or systematic strategies that maintain constant volatility exposure.
๐จ Heatmap Visualization
The vertical column graph acts like a thermometer:
โข ๐ฅ Red โ โReduce riskโ (volatility high).
โข ๐ฉ Green โ โIncrease riskโ (volatility low).
โข Smoothed and bounded between your Floor and Ceiling risk levels.
โข Optional dotted guides mark those bounds.
โข Label shows the current mode, recommended risk %, and key metrics (ATR ratio or effective volatility).
๐ง Key Inputs
โข Base max risk per trade (%) โ your normal per-trade risk budget.
โข ATR length / Baseline ATR length โ control sensitivity to short- vs. long-term volatility.
โข Target annualized volatility (%) โ portfolio volatility target for quant mode.
โข ฮป (lambda) โ smoothing factor for the EWMA volatility forecast (0.90โ0.99 typical).
โข Floor & Ceiling โ clamps the output to avoid extreme sizing.
โข Smoothing & Hysteresis โ prevent rapid changes in risk recommendations.
๐งฎ Interpreting the Output
โข โRecommended Risk (%)โ = suggested portion of equity to risk on the next trade (or current exposure).
โข In Per-Trade mode: reflects current ATR รท baseline ATR .
โข In Annualized mode: reflects target volatility รท effective volatility .
โข Use the color and height of the column as a quick visual cue for aggressiveness.
๐ก Typical Use Cases
โข Position-sizing overlay for discretionary traders.
โข Volatility-targeting component for algorithmic or multi-asset systems.
โข Educational tool to understand how volatility governs prudent risk management.
๐ Notes
โข This indicator provides risk suggestions only ; it does not place trades.
โข Works on any symbol or timeframe.
โข Combine with your own strategy or alerts for full automation.
โข All calculations use built-in Pine functions; no proprietary logic.
Tags:
#RiskManagement #ATR #Volatility #Quant #PositionSizing #SystematicTrading #AlgorithmicTrading #Portfolio #TradingStrategy #Heatmap #EWMA #Risk
Jurik Moving AverageJurik Moving Average (JMA) โ Precision Smoothing with Adaptive Filtering
๐ Version: This script is written in Pine Script v6, utilizing advanced array handling and dynamic filtering for improved performance.
๐ Overview:
The Jurik Moving Average (JMA) was originally developed by Mark Jurik and is widely recognized for its ability to provide smooth trend-following signals with minimal lag. Unlike traditional moving averages, which suffer from a tradeoff between responsiveness and smoothness, JMA employs an adaptive smoothing algorithm that dynamically adjusts based on market conditions, reducing false signals while maintaining trend accuracy.
This version of JMA has been implemented in Pine Script v6 with enhancements that make it even more efficient for TradingView users. By utilizing advanced array-based calculations, logarithmic scaling, and cycle-based filtering, this implementation delivers an optimized, customizable, and high-performance smoothing indicator.
๐ How It Works:
โ
Adaptive Filtering: Dynamically adjusts smoothing based on price volatility.
โ
Cycle-Based Adjustments: Uses historical price action to fine-tune lag vs. responsiveness.
โ
Advanced Phase Control: Traders can shift the moving average forward or backward to optimize signal alignment.
Unlike existing open-source JMA implementations, this version features:
๐น Enhanced Array-Based Calculations for better memory management & performance.
๐น Logarithmic and Square Root Scaling to dynamically adjust phase & smoothing.
๐น Improved Noise Reduction Techniques to minimize false breakouts.
๐ How to Use:
๐น Trend Confirmation: Use JMA to validate trend direction and avoid whipsaws.
๐น Trade Entries & Exits: Combine with price action or momentum indicators for refined entry/exit points.
๐น Scalping & Swing Trading: Ideal for short-term and long-term strategies due to its adaptability.
๐ Why This Version is Unique:
This JMA expands on standard implementations by incorporating multi-level cycle smoothing, phase correction, and adaptive noise filtering. The result? A more precise, stable, and robust trend indicator that performs better than existing open-source versions.
Murrey Math
The Murrey Math indicator is a set of horizontal price levels, calculated from an algorithm developed by stock trader T.J. Murray.
The main concept behind Murrey Math is that prices tend to react and rotate at specific price levels. These levels are calculated by dividing the price range into fixed segments called "ranges", usually using a number of 8, 16, 32, 64, 128 or 256.
Murrey Math levels are calculated as follows:
1. A particular price range is taken, for example, 128.
2. Divide the current price by the range (128 in this example).
3. The result is rounded to the nearest whole number.
4. Multiply that whole number by the original range (128).
This results in the Murrey Math level closest to the current price. More Murrey levels are calculated and drawn by adding and subtracting multiples of the range to the initially calculated level.
Traders use Murrey Math levels as areas of possible support and resistance as it is believed that prices tend to react and pivot at these levels. They are also used to identify price patterns and possible entry and exit points in trading.
The Murrey Math indicator itself simply calculates and draws these horizontal levels on the price chart, allowing traders to easily visualize them and use them in their technical analysis.
HOW TO USE THIS INDICATOR?
To use the Murrey Math indicator effectively, here are some tips:
1. Choose the appropriate Murrey Math range : The Murrey Math range input (128 by default in the provided code) determines the spacing between the levels. Common ranges used are 8, 16, 32, 64, 128, and 256. A smaller range will give you more levels, while a larger range will give you fewer levels. Choose a range that suits the volatility and trading timeframe you're working with.
2. Identify potential support and resistance levels: The horizontal lines drawn by the indicator represent potential support and resistance levels based on the Murrey Math calculation. Prices often react or reverse at these levels, so they can be used to spot areas of interest for entries and exits.
3. Look for price reactions at the levels: Watch for price action like rejections, bounces, or breakouts at the Murrey Math levels. These reactions can signal potential trend continuation or reversal setups.
4. Trail stop-loss orders: You can place stop-loss orders just below/above the nearest Murrey Math level to manage risk if the price moves against your trade.
5. Set targets at future levels: Project potential profit targets by looking at upcoming Murrey Math levels in the direction of the trend.
7. Adjust range as needed: If prices are consistently breaking through levels without reacting, try adjusting the range input to a different value to see if it provides better levels.
In which asset can this indicator perform better?
The Murrey Math indicator can potentially perform well on any liquid financial asset that exhibits some degree of mean-reversion or trading range behavior. However, it may be more suitable for certain asset classes or trading timeframes than others.
Here are some assets and scenarios where the Murrey Math indicator can potentially perform better:
1. Forex Markets: The foreign exchange market is known for its ranging and mean-reverting nature, especially on higher timeframes like the daily or weekly charts. The Murrey Math levels can help identify potential support and resistance levels within these trading ranges.
2. Futures Markets: Futures contracts, such as those for commodities (e.g., crude oil, gold, etc.) or equity indices, often exhibit trading ranges and mean-reversion trends. The Murrey Math indicator can be useful in identifying potential turning points within these ranges.
3. Stocks with Range-bound Behavior: Some stocks, particularly those of large-cap companies, can trade within well-defined ranges for extended periods. The Murrey Math levels can help identify the boundaries of these ranges and potential reversal points.
4. I ntraday Trading: The Murrey Math indicator may be more effective on lower timeframes (e.g., 1-hour, 30-minute, 15-minute) for intraday trading, as prices tend to respect support and resistance levels more closely within shorter time periods.
5. Trending Markets: While the Murrey Math indicator is primarily designed for range-bound markets, it can also be used in trending markets to identify potential pullback or continuation levels.
Trend-Following & Breakout โ Index Quant Strategy (NASDAQ)๐ Trend-Following & Breakout โ Index Quant Strategy (NASDAQ & S&P 500)
Type: Invite-only strategy
Markets: NASDAQ 100 (NAS100 / US100 / NQ), S&P 500 (US500 / SPX), and other major equity indices.
๐ง Concept: Continuous trend model combining EWMAC (trend-following) and Donchian (breakout) signals, scaled by forecast strength and portfolio risk.
โ๏ธ Execution: Rebalances only on decision-bar closes, using hysteresis and a no-trade band to reduce churn.
๐ Default bias: Long-only โ aligned with equity index drift.
๐งฉ How it works
โข EWMAC Trend: Difference between fast and slow EMAs, normalized by an EWMA of absolute returns.
โข Donchian Breakout: Distance beyond a 200-bar channel (Strict mode) or relative z-score position within it.
โข Forecast combination: Weighted sum of trend and breakout points, clamped to ยฑ capPoints.
โข Hysteresis: Prevents quick sign flips near zero forecast.
โข Risk scaling: Maps forecast strength to position size using equity ร risk budget ร ATR-based stop distance.
โข Rebalance: Executes only if the required quantity change exceeds the ฮqty threshold; can optionally block increases on Sundays (for CFDs).
โ๏ธ Default parameters
Deployed on NQ / US100 / NAS100 on Daily Timeframe
โข Decision timeframe = 360 min (other options from 1 min to 1 week).
โข Trend (EWMAC): Fast = 64, Slow = 256, Vol Norm = 32, Weight = 0.8.
โข Breakout (Donchian): Length = 200, Mode = Strict, Weight = 0.2.
โข Forecast scaling: ptsPerSigma = 1.0, capPoints = 10.
โข Risk % per rebalance = 4 % of equity.
โข ATR stop: ATR(14) ร 1.0.
โข No-trade band (ฮqty) = 4 units.
โข Hysteresis = 2 forecast points.
โข Bias = Long-only (Neutral / Long-bias 50 % optional).
โข Skip Sunday increases = false (default).
๐ Backtest properties (documented)
โข Initial capital = 100 000 USD.
โข Commission = 0.20 % per trade.
โข Pyramiding = 10.
โข Calc on every tick = false.
โข Point value = 1 (for NAS100 CFD).
โข No financing or slippage modeled.
โข If using CFDs, account for overnight funding.
โข On futures (NQ / ES), carry is implicit.
๐ Typical behaviour
โข Many small scratches, a few large winners.
โข Performs best during multi-week / multi-month trends.
โข Underperforms in tight or volatile ranges.
โข Average hold โ 30 โ 90 days in historical tests.
๐ก Risk and performance guide (illustrative)
Sharpe โ 1.25
Sortino โ 1.10 โ 1.30
Max drawdown โ โ18 % to โ25 %
Annual volatility โ 24 โ 28 %
CAGR โ 50 โ 60 % (at 4 % risk)
Edge ratio โ 5 (MFE / MAE)
Historical backtests only โ past performance does not guarantee future results.
๐ Intended markets and timeframes
Optimized for NASDAQ 100 and S&P 500; also effective on similar indices (DAX, Dow Jones, FTSE).
Best on Daily or higher timeframes.
Aligns with long-term index drift โ suitable for long-bias systematic trend portfolios.
โ ๏ธ Limitations
โข Backtests exclude CFD funding costs.
โข Trend models will have losing streaks in range-bound markets.
โข Designed for experienced traders seeking systematic exposure.
๐ Requesting access
Send a private TradingView message to with the text:
โRequest access to Trend-Following & Breakout โ Index Quant Strategy.โ
Access is granted only on explicit request.
For further information, see my TradingView Signature.
๐ Release notes (v1.0)
โข Initial release (360 min TF): EWMAC 64/256 + Donchian 200 Strict.
โข Risk 4 %, ATR ร 1.0, Long-only bias, hysteresis 2 pts, ฮqty โฅ 4.
โข Developed for NASDAQ 100 and S&P 500 indices.
โข Implements continuous risk-scaled positioning and no-trade band logic.
๐งพ Originality statement
This strategy is original work built entirely from TradingView built-ins (EMA, ATR, Highest, Lowest).
It does not reuse open-source invite-only code.
Any future reuse of open scripts will be done with explicit permission and credit.
Algorithm Predator - ProAlgorithm Predator - Pro: Advanced Multi-Agent Reinforcement Learning Trading System
Algorithm Predator - Pro combines four specialized market microstructure agents with a state-of-the-art reinforcement learning framework . Unlike traditional indicator mashups, this system implements genuine machine learning to automatically discover which detection strategies work best in current market conditions and adapts continuously without manual intervention.
Core Innovation: Rather than forcing traders to interpret conflicting signals, this system uses 15 different multi-armed bandit algorithms and a full reinforcement learning stack (Q-Learning, TD(ฮป) with eligibility traces, and Policy Gradient with REINFORCE) to learn optimal agent selection policies. The result is a self-improving system that gets smarter with every trade.
Target Users: Swing traders, day traders, and algorithmic traders seeking systematic signal generation with mathematical rigor. Suitable for stocks, forex, crypto, and futures on liquid instruments (>100k daily volume).
Why These Components Are Combined
The Fundamental Problem
No single indicator works consistently across all market regimes. What works in trending markets fails in ranging conditions. Traditional solutions force traders to manually switch indicators (slow, error-prone) or interpret all signals simultaneously (cognitive overload).
This system solves the problem through automated meta-learning: Deploy multiple specialized agents designed for specific market microstructure conditions, then use reinforcement learning to discover which agent (or combination) performs best in real-time.
Why These Specific Four Agents?
The four agents provide orthogonal failure mode coverage โeach agent's weakness is another's strength:
Spoofing Detector - Optimal in consolidation/manipulation; fails in trending markets (hedged by Exhaustion Detector)
Exhaustion Detector - Optimal at trend climax; fails in range-bound markets (hedged by Liquidity Void)
Liquidity Void - Optimal pre-breakout compression; fails in established trends (hedged by Mean Reversion)
Mean Reversion - Optimal in low volatility; fails in strong trends (hedged by Spoofing Detector)
This creates complete market state coverage where at least one agent should perform well in any condition. The bandit system identifies which one without human intervention.
Why Reinforcement Learning vs. Simple Voting?
Traditional consensus systems have fatal flaws: equal weighting assumes all agents are equally reliable (false), static thresholds don't adapt, and no learning means past mistakes repeat indefinitely.
Reinforcement learning solves this through the exploration-exploitation tradeoff: Continuously test underused agents (exploration) while primarily relying on proven winners (exploitation). Over time, the system builds a probability distribution over agent quality reflecting actual market performance.
Mathematical Foundation: Multi-armed bandit problem from probability theory, where each agent is an "arm" with unknown reward distribution. The goal is to maximize cumulative reward while efficiently learning each arm's true quality.
The Four Trading Agents: Technical Explanation
Agent 1: ๐ญ Spoofing Detector (Institutional Manipulation Detection)
Theoretical Basis: Market microstructure theory on order flow toxicity and information asymmetry. Based on research by Easley, Lรณpez de Prado, and O'Hara on high-frequency trading manipulation.
What It Detects:
1. Iceberg Orders (Hidden Liquidity Absorption)
Method: Monitors volume spikes (>2.5ร 20-period average) with minimal price movement (<0.3ร ATR)
Formula: score += (close > open ? -2.5 : 2.5) when volume > vol_avg ร 2.5 AND abs(close - open) / ATR < 0.3
Interpretation: Large volume without price movement indicates institutional absorption (buying) or distribution (selling) using hidden orders
Signal Logic: Contrarianโfade false breakouts caused by institutional manipulation
2. Spoofing Patterns (Fake Liquidity via Layering)
Method: Analyzes candlestick wick-to-body ratios during volume spikes
Formula: if upper_wick > body ร 2 AND volume_spike: score += 2.0
Mechanism: Spoofing creates large wicks (orders pulled before execution) with volume evidence
Signal Logic: Wick direction indicates trapped participants; trade against the failed move
3. Post-Manipulation Reversals
Method: Tracks volume decay after manipulation events
Formula: if volume > vol_avg ร 3 AND volume / volume < 0.3: score += (close > open ? -1.5 : 1.5)
Interpretation: Sharp volume drop after manipulation indicates exhaustion of manipulative orders
Why It Works: Institutional manipulation creates detectable microstructure anomalies. While retail traders see "mysterious reversals," this agent quantifies the order flow patterns causing them.
Parameter: i_spoof (sensitivity 0.5-2.0) - Controls detection threshold
Best Markets: Consolidations before breakouts, London/NY overlap windows, stocks with institutional ownership >70%
Agent 2: โก Exhaustion Detector (Momentum Failure Analysis)
Theoretical Basis: Technical analysis divergence theory combined with VPIN reversals from market microstructure literature.
What It Detects:
1. Price-RSI Divergence (Momentum Deceleration)
Method: Compares 5-bar price ROC against RSI change
Formula: if price_roc > 5% AND rsi_current < rsi : score += 1.8
Mathematics: Second derivative detecting inflection points
Signal Logic: When price makes higher highs but momentum makes lower highs, expect mean reversion
2. Volume Exhaustion (Buying/Selling Climax)
Method: Identifies strong price moves (>5% ROC) with declining volume (<-20% volume ROC)
Formula: if price_roc > 5 AND vol_roc < -20: score += 2.5
Interpretation: Price extension without volume support indicates retail chasing while institutions exit
3. Momentum Deceleration (Acceleration Analysis)
Method: Compares recent 3-bar momentum to prior 3-bar momentum
Formula: deceleration = abs(mom1) < abs(mom2) ร 0.5 where momentum significant (> ATR)
Signal Logic: When rate of price change decelerates significantly, anticipate directional shift
Why It Works: Momentum is lagging, but momentum divergence is leading. By comparing momentum's rate of change to price, this agent detects "weakening conviction" before reversals become obvious.
Parameter: i_momentum (sensitivity 0.5-2.0)
Best Markets: Strong trends reaching climax, parabolic moves, instruments with high retail participation
Agent 3: ๐ง Liquidity Void Detector (Breakout Anticipation)
Theoretical Basis: Market liquidity theory and order book dynamics. Based on research into "liquidity holes" and volatility compression preceding expansion.
What It Detects:
1. Bollinger Band Squeeze (Volatility Compression)
Method: Monitors Bollinger Band width relative to 50-period average
Formula: bb_width = (upper_band - lower_band) / middle_band; triggers when < 0.6ร average
Mathematical Foundation: Regression to the meanโlow volatility precedes high volatility
Signal Logic: When volatility compresses AND cumulative delta shows directional bias, anticipate breakout
2. Volume Profile Gaps (Thin Liquidity Zones)
Method: Identifies sharp volume transitions indicating few limit orders
Formula: if volume < vol_avg ร 0.5 AND volume < vol_avg ร 0.5 AND volume > vol_avg ร 1.5
Interpretation: Sudden volume drop after spike indicates price moved through order book to low-opposition area
Signal Logic: Price accelerates through low-liquidity zones
3. Stop Hunts (Liquidity Grabs Before Reversals)
Method: Detects new 20-bar highs/lows with immediate reversal and rejection wick
Formula: if new_high AND close < high - (high - low) ร 0.6: score += 3.0
Mechanism: Market makers push price to trigger stop-loss clusters, then reverse
Signal Logic: Enter reversal after stop-hunt completes
Why It Works: Order book theory shows price moves fastest through zones with minimal liquidity. By identifying these zones before major moves, this agent provides early entry for high-reward breakouts.
Parameter: i_liquidity (sensitivity 0.5-2.0)
Best Markets: Range-bound pre-breakout setups, volatility compression zones, instruments prone to gap moves
Agent 4: ๐ Mean Reversion (Statistical Arbitrage Engine)
Theoretical Basis: Statistical arbitrage theory, Ornstein-Uhlenbeck mean-reverting processes, and pairs trading methodology applied to single instruments.
What It Detects:
1. Z-Score Extremes (Standard Deviation Analysis)
Method: Calculates price distance from 20-period and 50-period SMAs in standard deviation units
Formula: zscore_20 = (close - SMA20) / StdDev(50)
Statistical Interpretation: Z-score >2.0 means price is 2 standard deviations above mean (97.5th percentile)
Trigger Logic: if abs(zscore_20) > 2.0: score += zscore_20 > 0 ? -1.5 : 1.5 (fade extremes)
2. Ornstein-Uhlenbeck Process (Mean-Reverting Stochastic Model)
Method: Models price as mean-reverting stochastic process: dx = ฮธ(ฮผ - x)dt + ฯdW
Implementation: Calculates spread = close - SMA20, then z-score of spread vs. spread distribution
Formula: ou_signal = (spread - spread_mean) / spread_std
Interpretation: Measures "tension" pulling price back to equilibrium
3. Correlation Breakdown (Regime Change Detection)
Method: Compares 50-period price-volume correlation to 10-period correlation
Formula: corr_breakdown = abs(typical_corr - recent_corr) > 0.5
Enhancement: if corr_breakdown AND abs(zscore_20) > 1.0: score += zscore_20 > 0 ? -1.2 : 1.2
Why It Works: Mean reversion is the oldest quantitative strategy (1970s pairs trading at Morgan Stanley). While simple, it remains effective because markets exhibit periodic equilibrium-seeking behavior. This agent applies rigorous statistical testing to identify when mean reversion probability is highest.
Parameter: i_statarb (sensitivity 0.5-2.0)
Best Markets: Range-bound instruments, low-volatility periods (VIX <15), algo-dominated markets (forex majors, index futures)
Multi-Armed Bandit System: 15 Algorithms Explained
What Is a Multi-Armed Bandit Problem?
Origin: Named after slot machines ("one-armed bandits"). Imagine facing multiple slot machines, each with unknown payout rates. How do you maximize winnings?
Formal Definition: K arms (agents), each with unknown reward distribution with mean ฮผแตข. Goal: Maximize cumulative reward over T trials. Challenge: Balance exploration (trying uncertain arms to learn quality) vs. exploitation (using known-best arm for immediate reward).
Trading Application: Each agent is an "arm." After each trade, receive reward (P&L). Must decide which agent to trust for next signal.
Algorithm Categories
Bayesian Approaches (probabilistic, optimal for stationary environments):
Thompson Sampling
Bootstrapped Thompson Sampling
Discounted Thompson Sampling
Frequentist Approaches (confidence intervals, deterministic):
UCB1
UCB1-Tuned
KL-UCB
SW-UCB (Sliding Window)
D-UCB (Discounted)
Adversarial Approaches (robust to non-stationary environments):
EXP3-IX
Hedge
FPL-Gumbel
Reinforcement Learning Approaches (leverage learned state-action values):
Q-Values (from Q-Learning)
Policy Network (from Policy Gradient)
Simple Baseline:
Epsilon-Greedy
Softmax
Key Algorithm Details
Thompson Sampling (DEFAULT - RECOMMENDED)
Theoretical Foundation: Bayesian decision theory with conjugate priors. Published by Thompson (1933), rediscovered for bandits by Chapelle & Li (2011).
How It Works:
Model each agent's reward distribution as Beta(ฮฑ, ฮฒ) where ฮฑ = wins, ฮฒ = losses
Each step, sample from each agent's beta distribution: ฮธแตข ~ Beta(ฮฑแตข, ฮฒแตข)
Select agent with highest sample: argmaxแตข ฮธแตข
Update winner's distribution after observing outcome
Mathematical Properties:
Optimality: Achieves logarithmic regret O(K log T) (proven optimal)
Bayesian: Maintains probability distribution over true arm means
Automatic Balance: High uncertainty โ more exploration; high certainty โ exploitation
โ ๏ธ CRITICAL APPROXIMATION: This is a pseudo-random approximation of true Thompson Sampling. True implementation requires random number generation from beta distributions, which Pine Script doesn't provide. This version uses Box-Muller transform with market data (price/volume decimal digits) as entropy source. While not mathematically pure, it maintains core exploration-exploitation balance and learns agent preferences effectively.
When To Use: Best all-around choice. Handles non-stationary markets reasonably well, balances exploration naturally, highly sample-efficient.
UCB1 (Upper Confidence Bound)
Formula: UCB_i = reward_mean_i + sqrt(2 ร ln(total_pulls) / pulls_i)
Interpretation: First term (exploitation) + second term (exploration bonus for less-tested arms)
Mathematical Properties:
Deterministic : Always selects same arm given same state
Regret Bound: O(K log T) โ same optimality as Thompson Sampling
Interpretable: Can visualize confidence intervals
When To Use: Prefer deterministic behavior, want to visualize uncertainty, stable markets
EXP3-IX (Exponential Weights - Adversarial)
Theoretical Foundation: Adversarial bandit algorithm. Assumes environment may be actively hostile (worst-case analysis).
How It Works:
Maintain exponential weights: w_i = exp(ฮท ร cumulative_reward_i)
Select agent with probability proportional to weights: p_i = (1-ฮณ)w_i/ฮฃw_j + ฮณ/K
After outcome, update with importance weighting: estimated_reward = observed_reward / p_i
Mathematical Properties:
Adversarial Regret: O(sqrt(TK log K)) even if environment is adversarial
No Assumptions: Doesn't assume stationary or stochastic reward distributions
Robust: Works even when optimal arm changes continuously
When To Use: Extreme non-stationarity, don't trust reward distribution assumptions, want robustness over efficiency
KL-UCB (Kullback-Leibler Upper Confidence Bound)
Theoretical Foundation: Uses KL-divergence instead of Hoeffding bounds. Tighter confidence intervals.
Formula (conceptual): Find largest q such that: n ร KL(p||q) โค ln(t) + 3รln(ln(t))
Mathematical Properties:
Tighter Bounds: KL-divergence adapts to reward distribution shape
Asymptotically Optimal: Better constant factors than UCB1
Computationally Intensive: Requires iterative binary search (15 iterations)
When To Use: Maximum sample efficiency needed, willing to pay computational cost, long-term trading (>500 bars)
Q-Values & Policy Network (RL-Based Selection)
Unique Feature: Instead of treating agents as black boxes with scalar rewards, these algorithms leverage the full RL state representation .
Q-Values Selection:
Uses learned Q-values: Q(state, agent_i) from Q-Learning
Selects agent via softmax over Q-values for current market state
Advantage: Selects based on state-conditional quality (which agent works best in THIS market state)
Policy Network Selection:
Uses neural network policy: ฯ(agent | state, ฮธ) from Policy Gradient
Direct policy over agents given market features
Advantage: Can learn non-linear relationships between market features and agent quality
When To Use: After 200+ RL updates (Q-Values) or 500+ updates (Policy Network) when models converged
Machine Learning & Reinforcement Learning Stack
Why Both Bandits AND Reinforcement Learning?
Critical Distinction:
Bandits treat agents as contextless black boxes: "Agent 2 has 60% win rate"
Reinforcement Learning adds state context: "Agent 2 has 60% win rate WHEN trend_score > 2 and RSI < 40"
Power of Combination: Bandits provide fast initial learning with minimal assumptions. RL provides state-dependent policies for superior long-term performance.
Component 1: Q-Learning (Value-Based RL)
Algorithm: Temporal Difference Learning with Bellman equation.
State Space: 54 discrete states formed from:
trend_state = {0: bearish, 1: neutral, 2: bullish} (3 values)
volatility_state = {0: low, 1: normal, 2: high} (3 values)
RSI_state = {0: oversold, 1: neutral, 2: overbought} (3 values)
volume_state = {0: low, 1: high} (2 values)
Total states: 3 ร 3 ร 3 ร 2 = 54 states
Action Space: 5 actions (No trade, Agent 1, Agent 2, Agent 3, Agent 4)
Total state-action pairs: 54 ร 5 = 270 Q-values
Bellman Equation:
Q(s,a) โ Q(s,a) + ฮฑ ร
Parameters:
ฮฑ (learning rate): 0.01-0.50, default 0.10 - Controls step size for updates
ฮณ (discount factor): 0.80-0.99, default 0.95 - Values future rewards
ฮต (exploration): 0.01-0.30, default 0.10 - Probability of random action
Update Mechanism:
Position opens with state s, action a (selected agent)
Every bar position is open: Calculate floating P&L โ scale to reward
Perform online TD update
When position closes: Perform terminal update with final reward
Gradient Clipping: TD errors clipped to ; Q-values clipped to for stability.
Why It Works: Q-Learning learns "quality" of each agent in each market state through trial and error. Over time, builds complete state-action value function enabling optimal state-dependent agent selection.
Component 2: TD(ฮป) Learning (Temporal Difference with Eligibility Traces)
Enhancement Over Basic Q-Learning: Credit assignment across multiple time steps.
The Problem TD(ฮป) Solves:
Position opens at t=0
Market moves favorably at t=3
Position closes at t=8
Question: Which earlier decisions contributed to success?
Basic Q-Learning: Only updates Q(sโ, aโ) โ reward
TD(ฮป): Updates ALL visited state-action pairs with decayed credit
Eligibility Trace Formula:
e(s,a) โ ฮณ ร ฮป ร e(s,a) for all s,a (decay all traces)
e(s_current, a_current) โ 1 (reset current trace)
Q(s,a) โ Q(s,a) + ฮฑ ร TD_error ร e(s,a) (update all with trace weight)
Lambda Parameter (ฮป): 0.5-0.99, default 0.90
ฮป=0: Pure 1-step TD (only immediate next state)
ฮป=1: Full Monte Carlo (entire episode)
ฮป=0.9: Balance (recommended)
Why Superior: Dramatically faster learning for multi-step tasks. Q-Learning requires many episodes to propagate rewards backwards; TD(ฮป) does it in one.
Component 3: Policy Gradient (REINFORCE with Baseline)
Paradigm Shift: Instead of learning value function Q(s,a), directly learn policy ฯ(a|s).
Policy Network Architecture:
Input: 12 market features
Hidden: None (linear policy)
Output: 5 actions (softmax distribution)
Total parameters: 12 features ร 5 actions + 5 biases = 65 parameters
Feature Set (12 Features):
Price Z-score (close - SMA20) / ATR
Volume ratio (volume / vol_avg - 1)
RSI deviation (RSI - 50) / 50
Bollinger width ratio
Trend score / 4 (normalized)
VWAP deviation
5-bar price ROC
5-bar volume ROC
Range/ATR ratio - 1
Price-volume correlation (20-period)
Volatility ratio (ATR / ATR_avg - 1)
EMA50 deviation
REINFORCE Update Rule:
ฮธ โ ฮธ + ฮฑ ร โlog ฯ(a|s) ร advantage
where advantage = reward - baseline (variance reduction)
Why Baseline? Raw rewards have high variance. Subtracting baseline (running average) centers rewards around zero, reducing gradient variance by 50-70%.
Learning Rate: 0.001-0.100, default 0.010 (much lower than Q-Learning because policy gradients have high variance)
Why Policy Gradient?
Handles 12 continuous features directly (Q-Learning requires discretization)
Naturally maintains exploration through probability distribution
Can converge to stochastic optimal policy
Component 4: Ensemble Meta-Learner (Stacking)
Architecture: Level-1 meta-learner combines Level-0 base learners (Q-Learning, TD(ฮป), Policy Gradient).
Three Meta-Learning Algorithms:
1. Simple Average (Baseline)
Final_prediction = (Q_prediction + TD_prediction + Policy_prediction) / 3
2. Weighted Vote (Reward-Based)
weight_i โ 0.95 ร weight_i + 0.05 ร (reward_i + 1)
3. Adaptive Weighting (Gradient-Based) โ RECOMMENDED
Loss Function: L = (y_true - ลท_ensemble)ยฒ
Gradient: โL/โweight_i = -2 ร (y_true - ลท_ensemble) ร agent_contribution_i
Updates weights via gradient descent with clipping and normalization
Why It Works: Unlike simple averaging, meta-learner discovers which base learner is most reliable in current regime. If Policy Gradient excels in trending markets while Q-Learning excels in ranging, meta-learner learns these patterns and weights accordingly.
Feature Importance Tracking
Purpose: Identify which of 12 features contribute most to successful predictions.
Update Rule: importance_i โ 0.95 ร importance_i + 0.05 ร |feature_i ร reward|
Use Cases:
Feature selection: Drop low-importance features
Market regime detection: Importance shifts reveal regime changes
Agent tuning: If VWAP deviation has high importance, consider boosting agents using VWAP
RL Position Tracking System
Critical Innovation: Proper reinforcement learning requires tracking which decisions led to outcomes.
State Tracking (When Signal Validates):
active_rl_state โ current_market_state (0-53)
active_rl_action โ selected_agent (1-4)
active_rl_entry โ entry_price
active_rl_direction โ 1 (long) or -1 (short)
active_rl_bar โ current_bar_index
Online Updates (Every Bar Position Open):
floating_pnl = (close - entry) / entry ร direction
reward = floating_pnl ร 10 (scale to meaningful range)
reward = clip(reward, -5.0, 5.0)
Update Q-Learning, TD(ฮป), and Policy Gradient
Terminal Update (Position Close):
Final Q-Learning update (no next Q-value, terminal state)
Update meta-learner with final result
Update agent memory
Clear position tracking
Exit Conditions:
Time-based: โฅ3 bars held (minimum hold period)
Stop-loss: 1.5% adverse move
Take-profit: 2.0% favorable move
Market Microstructure Filters
Why Microstructure Matters
Traditional technical analysis assumes fair, efficient markets. Reality: Markets have friction, manipulation, and information asymmetry. Microstructure filters detect when market structure indicates adverse conditions.
Filter 1: VPIN (Volume-Synchronized Probability of Informed Trading)
Theoretical Foundation: Easley, Lรณpez de Prado, & O'Hara (2012). "Flow Toxicity and Liquidity in a High-Frequency World."
What It Measures: Probability that current order flow is "toxic" (informed traders with private information).
Calculation:
Classify volume as buy or sell (close > close = buy volume)
Calculate imbalance over 20 bars: VPIN = |ฮฃ buy_volume - ฮฃ sell_volume| / ฮฃ total_volume
Compare to moving average: toxic = VPIN > VPIN_MA(20) ร sensitivity
Interpretation:
VPIN < 0.3: Normal flow (uninformed retail)
VPIN 0.3-0.4: Elevated (smart money active)
VPIN > 0.4: Toxic flow (informed institutions dominant)
Filter Logic:
Block LONG when: VPIN toxic AND price rising (don't buy into institutional distribution)
Block SHORT when: VPIN toxic AND price falling (don't sell into institutional accumulation)
Adaptive Threshold: If VPIN toxic frequently, relax threshold; if rarely toxic, tighten threshold. Bounded .
Filter 2: Toxicity (Kyle's Lambda Approximation)
Theoretical Foundation: Kyle (1985). "Continuous Auctions and Insider Trading."
What It Measures: Price impact per unit volume โ market depth and informed trading.
Calculation:
price_impact = (close - close ) / sqrt(ฮฃ volume over 10 bars)
impact_zscore = (price_impact - impact_mean) / impact_std
toxicity = abs(impact_zscore)
Interpretation:
Low toxicity (<1.0): Deep liquid market, large orders absorbed easily
High toxicity (>2.0): Thin market or informed trading
Filter Logic: Block ALL SIGNALS when toxicity > threshold. Most dangerous when price breaks from VWAP with high toxicity.
Filter 3: Regime Filter (Counter-Trend Protection)
Purpose: Prevent counter-trend trades during strong trends.
Trend Scoring:
trend_score = 0
trend_score += close > EMA8 ? +1 : -1
trend_score += EMA8 > EMA21 ? +1 : -1
trend_score += EMA21 > EMA50 ? +1 : -1
trend_score += close > EMA200 ? +1 : -1
Range:
Regime Classification:
Strong Bull: trend_score โฅ +3 โ Block all SHORT signals
Strong Bear: trend_score โค -3 โ Block all LONG signals
Neutral: -2 โค trend_score โค +2 โ Allow both directions
Filter 4: Liquidity Boost (Signal Enhancer)
Unique: Unlike other filters (which block), this amplifies signals during low liquidity.
Logic: if volume < vol_avg ร 0.7: agent_scores ร 1.2
Why It Works: Low liquidity often precedes explosive moves (breakouts). By increasing agent sensitivity during compression, system catches pre-breakout signals earlier.
Technical Implementation & Approximations
โ ๏ธ Critical Approximations Required by Pine Script
1. Thompson Sampling: Pseudo-Random Beta Distribution
Academic Standard: True random sampling from beta distributions using cryptographic RNG
This Implementation: Box-Muller transform for normal distribution using market data (price/volume decimal digits) as entropy source, then scale to beta distribution mean/variance
Impact: Not cryptographically random, may have subtle biases in specific price ranges, but maintains correct mean and approximate variance. Sufficient for bandit agent selection.
2. VPIN: Simplified Volume Classification
Academic Standard: Lee-Ready algorithm or exchange-provided aggressor flags with tick-by-tick data
This Implementation: Bar-based classification: if close > close : buy_volume += volume
Impact: 10-15% precision loss. Works well in directional markets, misclassifies in choppy conditions. Still captures order flow imbalance signal.
3. Policy Gradient: Simplified Per-Action Updates
Academic Standard: Full softmax gradient updating all actions (selected action UP, others DOWN proportionally)
This Implementation: Only updates selected action's weights
Impact: Valid approximation for small action spaces (5 actions). Slower convergence than full softmax but still learns optimal policy.
4. Kyle's Lambda: Simplified Price Impact
Academic Standard: Regression over multiple time scales with signed order flow
This Implementation: price_impact = ฮprice_10 / sqrt(ฮฃvolume_10); z_score calculation
Impact: 15-20% precision loss. No proper signed order flow. Still detects informed trading signals at extremes (>2ฯ).
5. Other Simplifications:
Hawkes Process: Fixed exponential decay (0.9) not MLE-optimized
Entropy: Ratio approximation not true Shannon entropy H(X) = -ฮฃ p(x)ยทlogโ(p(x))
Feature Engineering: 12 features vs. potential 100+ with polynomial interactions
RL Hybrid Updates: Both online and terminal (non-standard but empirically effective)
Overall Precision Loss Estimate: 10-15% compared to academic implementations with institutional data feeds.
Practical Trade-off: For retail trading with OHLCV data, these approximations provide 90%+ of the edge while maintaining full transparency, zero latency, no external dependencies, and runs on any TradingView plan.
How to Use: Practical Guide
Initial Setup (5 Minutes)
Select Trading Mode: Start with "Balanced" for most users
Enable ML/RL System: Toggle to TRUE, select "Full Stack" ML Mode
Bandit Configuration: Algorithm: "Thompson Sampling", Mode: "Switch" or "Blend"
Microstructure Filters: Enable all four filters, enable "Adaptive Microstructure Thresholds"
Visual Settings: Enable dashboard (Top Right), enable all chart visuals
Learning Phase (First 50-100 Signals)
What To Monitor:
Agent Performance Table: Watch win rates develop (target >55%)
Bandit Weights: Should diverge from uniform (0.25 each) after 20-30 signals
RL Core Metrics: "RL Updates" should increase when position open
Filter Status: "Blocked" count indicates filter activity
Optimization Tips:
Too few signals: Lower min_confidence to 0.25, increase agent sensitivities to 1.1-1.2
Too many signals: Raise min_confidence to 0.35-0.40, decrease agent sensitivities to 0.8-0.9
One agent dominates (>70%): Consider "Lock Agent" feature
Signal Interpretation
Dashboard Signal Status:
โช WAITING FOR SIGNAL: No agent signaling
โณ ANALYZING...: Agent signaling but not confirmed
๐ก CONFIRMING 2/3: Building confirmation (2 of 3 bars)
๐ข LONG ACTIVE : Validated long entry
๐ด SHORT ACTIVE : Validated short entry
Kill Zone Boxes: Entry price (triangle marker), Take Profit (Entry + 2.5ร ATR), Stop Loss (Entry - 1.5ร ATR). Risk:Reward = 1:1.67
Risk Management
Position Sizing:
Risk per trade = 1-2% of capital
Position size = (Capital ร Risk%) / (Entry - StopLoss)
Stop-Loss Placement:
Initial: Entry ยฑ 1.5ร ATR (shown in kill zone)
Trailing: After 1:1 R:R achieved, move stop to breakeven
Take-Profit Strategy:
TP1 (2.5ร ATR): Take 50% off
TP2 (Runner): Trail stop at 1ร ATR or use opposite signal as exit
Memory Persistence
Why Save Memory: Every chart reload resets the system. Saving learned parameters preserves weeks of learning.
When To Save: After 200+ signals when agent weights stabilize
What To Save: From Memory Export panel, copy all alpha/beta/weight values and adaptive thresholds
How To Restore: Enable "Restore From Saved State", input all values into corresponding fields
What Makes This Original
Innovation 1: Genuine Multi-Armed Bandit Framework
This implements 15 mathematically rigorous bandit algorithms from academic literature (Thompson Sampling from Chapelle & Li 2011, UCB family from Auer et al. 2002, EXP3 from Auer et al. 2002, KL-UCB from Garivier & Cappรฉ 2011). Each algorithm maintains proper state, updates according to proven theory, and converges to optimal behavior. This is real learning, not superficial parameter changes.
Innovation 2: Full Reinforcement Learning Stack
Beyond bandits learning which agent works best globally, RL learns which agent works best in each market state. After 500+ positions, system builds 54-state ร 5-action value function (270 learned parameters) capturing context-dependent agent quality.
Innovation 3: Market Microstructure Integration
Combines retail technical analysis with institutional-grade microstructure metrics: VPIN from Easley, Lรณpez de Prado, O'Hara (2012), Kyle's Lambda from Kyle (1985), Hawkes Processes from Hawkes (1971). These detect informed trading, manipulation, and liquidity dynamics invisible to technical analysis.
Innovation 4: Adaptive Threshold System
Dynamic quantile-based thresholds: Maintains histogram of each agent's score distribution (24 bins, exponentially decayed), calculates 80th percentile threshold from histogram. Agent triggers only when score exceeds its own learned quantile. Proper non-parametric density estimation automatically adapts to instrument volatility, agent behavior shifts, and market regime changes.
Innovation 5: Episodic Memory with Transfer Learning
Dual-layer architecture: Short-term memory (last 20 trades, fast adaptation) + Long-term memory (condensed episodes, historical patterns). Transfer mechanism consolidates knowledge when STM reaches threshold. Mimics hippocampus โ neocortex consolidation in human memory.
Limitations & Disclaimers
General Limitations
No Predictive Guarantee: Pattern recognition โ prediction. Past performance โ future results.
Learning Period Required: Minimum 50-100 bars for reliable statistics. Initial performance may be suboptimal.
Overfitting Risk: System learns patterns in historical data. May not generalize to unprecedented conditions.
Approximation Limitations: See technical implementation section (10-15% precision loss vs. academic standards)
Single-Instrument Limitation: No multi-asset correlation, sector context, or VIX integration.
Forward-Looking Bias Disclaimer
CRITICAL TRANSPARENCY: The RL system uses an 8-bar forward-looking window for reward calculation.
What This Means: System learns from rewards incorporating future price information (bars 101-108 relative to entry at bar 100).
Why Acceptable:
โ
Signals do NOT look ahead: Entry decisions use only data โค entry bar
โ
Learning only: Forward data used for optimization, not signal generation
โ
Real-time mirrors backtest: In live trading, system learns identically
โ ๏ธ Implication: Dashboard "Agent Win%" reflects this 8-bar evaluation. Real-time performance may differ slightly if positions held longer, slippage/fees not captured, or market microstructure changes.
Risk Warnings
No Guarantee of Profit: All trading involves risk of loss
System Failures: Bugs possible despite extensive testing
Market Conditions: Optimized for liquid markets (>100k daily volume). Performance degrades in illiquid instruments, major news events, flash crashes
Broker-Specific Issues: Execution slippage, commission/fees, overnight financing costs
Appropriate Use
This Indicator Is:
โ
Entry trigger system
โ
Risk management framework (stop/target)
โ
Adaptive agent selection engine
โ
Learning system that improves over time
This Indicator Is NOT:
โ Complete trading strategy (requires position sizing, portfolio management)
โ Replacement for fundamental analysis
โ Guaranteed profit generator
โ Suitable for complete beginners without training
Recommended Complementary Analysis: Market context (support/resistance), volume profile, fundamental catalysts, correlation with related instruments, broader market regime
Recommended Settings by Instrument
Stocks (Large Cap, >$1B):
Mode: Balanced | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling, Switch
Agent Sensitivity: Spoofing 1.0-1.2, Exhaustion 0.9-1.1, Liquidity 0.8-1.0, StatArb 1.1-1.3
Microstructure: All enabled, VPIN 1.2, Toxicity 1.5 | Timeframe: 15min-1H
Forex Majors (EURUSD, GBPUSD):
Mode: Balanced to Conservative | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling, Blend
Agent Sensitivity: Spoofing 0.8-1.0, Exhaustion 0.9-1.1, Liquidity 0.7-0.9, StatArb 1.2-1.5
Microstructure: All enabled, VPIN 1.0-1.1, Toxicity 1.3-1.5 | Timeframe: 5min-30min
Crypto (BTC, ETH):
Mode: Aggressive to Balanced | ML/RL: Enabled, Full Stack | Bandit: Thompson Sampling OR EXP3-IX
Agent Sensitivity: Spoofing 1.2-1.5, Exhaustion 1.1-1.3, Liquidity 1.2-1.5, StatArb 0.7-0.9
Microstructure: All enabled, VPIN 1.4-1.6, Toxicity 1.8-2.2 | Timeframe: 15min-4H
Futures (ES, NQ, CL):
Mode: Balanced | ML/RL: Enabled, Full Stack | Bandit: UCB1 or Thompson Sampling
Agent Sensitivity: All 1.0-1.2 (balanced)
Microstructure: All enabled, VPIN 1.1-1.3, Toxicity 1.4-1.6 | Timeframe: 5min-30min
Conclusion
Algorithm Predator - Pro synthesizes academic research from market microstructure theory, reinforcement learning, and multi-armed bandit algorithms. Unlike typical indicator mashups, this system implements 15 mathematically rigorous bandit algorithms, deploys a complete RL stack (Q-Learning, TD(ฮป), Policy Gradient), integrates institutional microstructure metrics (VPIN, Kyle's Lambda), adapts continuously through dual-layer memory and meta-learning, and provides full transparency on approximations and limitations.
The system is designed for serious algorithmic traders who understand that no indicator is perfect, but through proper machine learning, we can build systems that improve over time and adapt to changing markets without manual intervention.
Use responsibly. Risk disclosure applies. Past performance โ future results.
Taking you to school. โ Dskyz, Trade with insight. Trade with anticipation.
Algorithm Predator - ML-liteAlgorithm Predator - ML-lite
This indicator combines four specialized trading agents with an adaptive multi-armed bandit selection system to identify high-probability trade setups. It is designed for swing and intraday traders who want systematic signal generation based on institutional order flow patterns , momentum exhaustion , liquidity dynamics , and statistical mean reversion .
Core Architecture
Why These Components Are Combined:
The script addresses a fundamental challenge in algorithmic trading: no single detection method works consistently across all market conditions. By deploying four independent agents and using reinforcement learning algorithms to select or blend their outputs, the system adapts to changing market regimes without manual intervention.
The Four Trading Agents
1. Spoofing Detector Agent ๐ญ
Detects iceberg orders through persistent volume at similar price levels over 5 bars
Identifies spoofing patterns via asymmetric wick analysis (wicks exceeding 60% of bar range with volume >1.8ร average)
Monitors order clustering using simplified Hawkes process intensity tracking (exponential decay model)
Signal Logic: Contrarianโfades false breakouts caused by institutional manipulation
Best Markets: Consolidations, institutional trading windows, low-liquidity hours
2. Exhaustion Detector Agent โก
Calculates RSI divergence between price movement and momentum indicator over 5-bar window
Detects VWAP exhaustion (price at 2ฯ bands with declining volume)
Uses VPIN reversals (volume-based toxic flow dissipation) to identify momentum failure
Signal Logic: Counter-trendโenters when momentum extreme shows weakness
Best Markets: Trending markets reaching climax points, over-extended moves
3. Liquidity Void Detector Agent ๐ง
Measures Bollinger Band squeeze (width <60% of 50-period average)
Identifies stop hunts via 20-bar high/low penetration with immediate reversal and volume spike
Detects hidden liquidity absorption (volume >2ร average with range <0.3ร ATR)
Signal Logic: Breakout anticipationโenters after liquidity grab but before main move
Best Markets: Range-bound pre-breakout, volatility compression zones
4. Mean Reversion Agent ๐
Calculates price z-scores relative to 50-period SMA and standard deviation (triggers at ยฑ2ฯ)
Implements Ornstein-Uhlenbeck process scoring (mean-reverting stochastic model)
Uses entropy analysis to detect algorithmic trading patterns (low entropy <0.25 = high predictability)
Signal Logic: Statistical reversionโenters when price deviates significantly from statistical equilibrium
Best Markets: Range-bound, low-volatility, algorithmically-dominated instruments
Adaptive Selection: Multi-Armed Bandit System
The script implements four reinforcement learning algorithms to dynamically select or blend agents based on performance:
Thompson Sampling (Default - Recommended):
Uses Bayesian inference with beta distributions (tracks alpha/beta parameters per agent)
Balances exploration (trying underused agents) vs. exploitation (using proven winners)
Each agent's win/loss history informs its selection probability
Lite Approximation: Uses pseudo-random sampling from price/volume noise instead of true random number generation
UCB1 (Upper Confidence Bound):
Calculates confidence intervals using: average_reward + sqrt(2 ร ln(total_pulls) / agent_pulls)
Deterministic algorithm favoring agents with high uncertainty (potential upside)
More conservative than Thompson Sampling
Epsilon-Greedy:
Exploits best-performing agent (1-ฮต)% of the time
Explores randomly ฮต% of the time (default 10%, configurable 1-50%)
Simple, transparent, easily tuned via epsilon parameter
Gradient Bandit:
Uses softmax probability distribution over agent preference weights
Updates weights via gradient ascent based on rewards
Best for Blend mode where all agents contribute
Selection Modes:
Switch Mode: Uses only the selected agent's signal (clean, decisive)
Blend Mode: Combines all agents using exponentially weighted confidence scores controlled by temperature parameter (smooth, diversified)
Lock Agent Feature:
Optional manual override to force one specific agent
Useful after identifying which agent dominates your specific instrument
Only applies in Switch mode
Four choices: Spoofing Detector, Exhaustion Detector, Liquidity Void, Mean Reversion
Memory System
Dual-Layer Architecture:
Short-Term Memory: Stores last 20 trade outcomes per agent (configurable 10-50)
Long-Term Memory: Stores episode averages when short-term reaches transfer threshold (configurable 5-20 bars)
Memory Boost Mechanism: Recent performance modulates agent scores by up to ยฑ20%
Episode Transfer: When an agent accumulates sufficient results, averages are condensed into long-term storage
Persistence: Manual restoration of learned parameters via input fields (alpha, beta, weights, microstructure thresholds)
How Memory Works:
Agent generates signal โ outcome tracked after 8 bars (performance horizon)
Result stored in short-term memory (win = 1.0, loss = 0.0)
Short-term average influences agent's future scores (positive feedback loop)
After threshold met (default 10 results), episode averaged into long-term storage
Long-term patterns (weighted 30%) + short-term patterns (weighted 70%) = total memory boost
Market Microstructure Analysis
These advanced metrics quantify institutional order flow dynamics:
Order Flow Toxicity (Simplified VPIN):
Measures buy/sell volume imbalance over 20 bars: |buy_vol - sell_vol| / (buy_vol + sell_vol)
Detects informed trading activity (institutional players with non-public information)
Values >0.4 indicate "toxic flow" (informed traders active)
Lite Approximation: Uses simple open/close heuristic instead of tick-by-tick trade classification
Price Impact Analysis (Simplified Kyle's Lambda):
Measures market impact efficiency: |price_change_10| / sqrt(volume_sum_10)
Low values = large orders with minimal price impact ( stealth accumulation )
High values = retail-dominated moves with high slippage
Lite Approximation: Uses simplified denominator instead of regression-based signed order flow
Market Randomness (Entropy Analysis):
Counts unique price changes over 20 bars / 20
Measures market predictability
High entropy (>0.6) = human-driven, chaotic price action
Low entropy (<0.25) = algorithmic trading dominance (predictable patterns)
Lite Approximation: Simple ratio instead of true Shannon entropy H(X) = -ฮฃ p(x)ยทlogโ(p(x))
Order Clustering (Simplified Hawkes Process):
Tracks self-exciting event intensity (coordinated order activity)
Decays at 0.9ร per bar, spikes +1.0 when volume >1.5ร average
High intensity (>0.7) indicates clustering (potential spoofing/accumulation)
Lite Approximation: Simple exponential decay instead of full ฮป(t) = ฮผ + ฮฃ ฮฑยทexp(-ฮฒ(t-tแตข)) with MLE
Signal Generation Process
Multi-Stage Validation:
Stage 1: Agent Scoring
Each agent calculates internal score based on its detection criteria
Scores must exceed agent-specific threshold (adjusted by sensitivity multiplier)
Agent outputs: Signal direction (+1/-1/0) and Confidence level (0.0-1.0)
Stage 2: Memory Boost
Agent scores multiplied by memory boost factor (0.8-1.2 based on recent performance)
Successful agents get amplified, failing agents get dampened
Stage 3: Bandit Selection/Blending
If Adaptive Mode ON:
Switch: Bandit selects single best agent, uses only its signal
Blend: All agents combined using softmax-weighted confidence scores
If Adaptive Mode OFF:
Traditional consensus voting with confidence-squared weighting
Signal fires when consensus exceeds threshold (default 70%)
Stage 4: Confirmation Filter
Raw signal must repeat for consecutive bars (default 3, configurable 2-4)
Minimum confidence threshold: 0.25 (25%) enforced regardless of mode
Trend alignment check: Long signals require trend_score โฅ -2, Short signals require trend_score โค 2
Stage 5: Cooldown Enforcement
Minimum bars between signals (default 10, configurable 5-15)
Prevents over-trading during choppy conditions
Stage 6: Performance Tracking
After 8 bars (performance horizon), signal outcome evaluated
Win = price moved in signal direction, Loss = price moved against
Results fed back into memory and bandit statistics
Trading Modes (Presets)
Pre-configured parameter sets:
Conservative: 85% consensus, 4 confirmations, 15-bar cooldown
Expected: 60-70% win rate, 3-8 signals/week
Best for: Swing trading, capital preservation, beginners
Balanced: 70% consensus, 3 confirmations, 10-bar cooldown
Expected: 55-65% win rate, 8-15 signals/week
Best for: Day trading, most traders, general use
Aggressive: 60% consensus, 2 confirmations, 5-bar cooldown
Expected: 50-58% win rate, 15-30 signals/week
Best for: Scalping, high-frequency trading, active management
Elite: 75% consensus, 3 confirmations, 12-bar cooldown
Expected: 58-68% win rate, 5-12 signals/week
Best for: Selective trading, high-conviction setups
Adaptive: 65% consensus, 2 confirmations, 8-bar cooldown
Expected: Varies based on learning
Best for: Experienced users leveraging bandit system
How to Use
1. Initial Setup (5 Minutes):
Select Trading Mode matching your style (start with Balanced)
Enable Adaptive Learning (recommended for automatic agent selection)
Choose Thompson Sampling algorithm (best all-around performance)
Keep Microstructure Metrics enabled for liquid instruments (>100k daily volume)
2. Agent Tuning (Optional):
Adjust Agent Sensitivity multipliers (0.5-2.0):
<0.8 = Highly selective (fewer signals, higher quality)
0.9-1.2 = Balanced (recommended starting point)
1.3 = Aggressive (more signals, lower individual quality)
Monitor dashboard for 20-30 signals to identify dominant agent
If one agent consistently outperforms, consider using Lock Agent feature
3. Bandit Configuration (Advanced):
Blend Temperature (0.1-2.0):
0.3 = Sharp decisions (best agent dominates)
0.5 = Balanced (default)
1.0+ = Smooth (equal weighting, democratic)
Memory Decay (0.8-0.99):
0.90 = Fast adaptation (volatile markets)
0.95 = Balanced (most instruments)
0.97+ = Long memory (stable trends)
4. Signal Interpretation:
Green triangle (โฒ): Long signal confirmed
Red triangle (โผ): Short signal confirmed
Dashboard shows:
Active agent (highlighted row with โบ marker)
Win rate per agent (green >60%, yellow 40-60%, red <40%)
Confidence bars (โโโโโ = maximum confidence)
Memory size (short-term buffer count)
Colored zones display:
Entry level (current close)
Stop-loss (1.5ร ATR)
Take-profit 1 (2.0ร ATR)
Take-profit 2 (3.5ร ATR)
5. Risk Management:
Never risk >1-2% per signal (use ATR-based stops)
Signals are entry triggers, not complete strategies
Combine with your own market context analysis
Consider fundamental catalysts and news events
Use "Confirming" status to prepare entries (not to enter early)
6. Memory Persistence (Optional):
After 50-100 trades, check Memory Export Panel
Record displayed alpha/beta/weight values for each agent
Record VPIN and Kyle threshold values
Enable "Restore From Memory" and input saved values to continue learning
Useful when switching timeframes or restarting indicator
Visual Components
On-Chart Elements:
Spectral Layers: EMA8 ยฑ 0.5 ATR bands (dynamic support/resistance, colored by trend)
Energy Radiance: Multi-layer glow boxes at signal points (intensity scales with confidence, configurable 1-5 layers)
Probability Cones: Projected price paths with uncertainty wedges (15-bar projection, width = confidence ร ATR)
Connection Lines: Links sequential signals (solid = same direction continuation, dotted = reversal)
Kill Zones: Risk/reward boxes showing entry, stop-loss, and dual take-profit targets
Signal Markers: Triangle up/down at validated entry points
Dashboard (Configurable Position & Size):
Regime Indicator: 4-level trend classification (Strong Bull/Bear, Weak Bull/Bear)
Mode Status: Shows active system (Adaptive Blend, Locked Agent, or Consensus)
Agent Performance Table: Real-time win%, confidence, and memory stats
Order Flow Metrics: Toxicity and impact indicators (when microstructure enabled)
Signal Status: Current state (Long/Short/Confirming/Waiting) with confirmation progress
Memory Panel (Configurable Position & Size):
Live Parameter Export: Alpha, beta, and weight values per agent
Adaptive Thresholds: Current VPIN sensitivity and Kyle threshold
Save Reminder: Visual indicator if parameters should be recorded
What Makes This Original
This script's originality lies in three key innovations:
1. Genuine Meta-Learning Framework:
Unlike traditional indicator mashups that simply display multiple signals, this implements authentic reinforcement learning (multi-armed bandits) to learn which detection method works best in current conditions. The Thompson Sampling implementation with beta distribution tracking (alpha for successes, beta for failures) is statistically rigorous and adapts continuously. This is not post-hoc optimizationโit's real-time learning.
2. Episodic Memory Architecture with Transfer Learning:
The dual-layer memory system mimics human learning patterns:
Short-term memory captures recent performance (recency bias)
Long-term memory preserves historical patterns (experience)
Automatic transfer mechanism consolidates knowledge
Memory boost creates positive feedback loops (successful strategies become stronger)
This architecture allows the system to adapt without retraining , unlike static ML models that require batch updates.
3. Institutional Microstructure Integration:
Combines retail-focused technical analysis (RSI, Bollinger Bands, VWAP) with institutional-grade microstructure metrics (VPIN, Kyle's Lambda, Hawkes processes) typically found in academic finance literature and professional trading systems, not standard retail platforms. While simplified for Pine Script constraints, these metrics provide insight into informed vs. uninformed trading , a dimension entirely absent from traditional technical analysis.
Mashup Justification:
The four agents are combined specifically for risk diversification across failure modes:
Spoofing Detector: Prevents false breakout losses from manipulation
Exhaustion Detector: Prevents chasing extended trends into reversals
Liquidity Void: Exploits volatility compression (different regime than trending)
Mean Reversion: Provides mathematical anchoring when patterns fail
The bandit system ensures the optimal tool is automatically selected for each market situation, rather than requiring manual interpretation of conflicting signals.
Why "ML-lite"? Simplifications and Approximations
This is the "lite" version due to necessary simplifications for Pine Script execution:
1. Simplified VPIN Calculation:
Academic Implementation: True VPIN uses volume bucketing (fixed-volume bars) and tick-by-tick buy/sell classification via Lee-Ready algorithm or exchange-provided trade direction flags
This Implementation: 20-bar rolling window with simple open/close heuristic (close > open = buy volume)
Impact: May misclassify volume during ranging/choppy markets; works best in directional moves
2. Pseudo-Random Sampling:
Academic Implementation: Thompson Sampling requires true random number generation from beta distributions using inverse transform sampling or acceptance-rejection methods
This Implementation: Deterministic pseudo-randomness derived from price and volume decimal digits: (close ร 100 - floor(close ร 100)) + (volume % 100) / 100
Impact: Not cryptographically random; may have subtle biases in specific price ranges; provides sufficient variation for agent selection
3. Hawkes Process Approximation:
Academic Implementation: Full Hawkes process uses maximum likelihood estimation with exponential kernels: ฮป(t) = ฮผ + ฮฃ ฮฑยทexp(-ฮฒ(t-tแตข)) fitted via iterative optimization
This Implementation: Simple exponential decay (0.9 multiplier) with binary event triggers (volume spike = event)
Impact: Captures self-exciting property but lacks parameter optimization; fixed decay rate may not suit all instruments
4. Kyle's Lambda Simplification:
Academic Implementation: Estimated via regression of price impact on signed order flow over multiple time intervals: ฮp = ฮป ร ฮv + ฮต
This Implementation: Simplified ratio: price_change / sqrt(volume_sum) without proper signed order flow or regression
Impact: Provides directional indicator of impact but not true market depth measurement; no statistical confidence intervals
5. Entropy Calculation:
Academic Implementation: True Shannon entropy requires probability distribution: H(X) = -ฮฃ p(x)ยทlogโ(p(x)) where p(x) is probability of each price change magnitude
This Implementation: Simple ratio of unique price changes to total observations (variety measure)
Impact: Measures diversity but not true information entropy with probability weighting; less sensitive to distribution shape
6. Memory System Constraints:
Full ML Implementation: Neural networks with backpropagation, experience replay buffers (storing state-action-reward tuples), gradient descent optimization, and eligibility traces
This Implementation: Fixed-size array queues with simple averaging; no gradient-based learning, no state representation beyond raw scores
Impact: Cannot learn complex non-linear patterns; limited to linear performance tracking
7. Limited Feature Engineering:
Advanced Implementation: Dozens of engineered features, polynomial interactions (xยฒ, xยณ), dimensionality reduction (PCA, autoencoders), feature selection algorithms
This Implementation: Raw agent scores and basic market metrics (RSI, ATR, volume ratio); minimal transformation
Impact: May miss subtle cross-feature interactions; relies on agent-level intelligence rather than feature combinations
8. Single-Instrument Data:
Full Implementation: Multi-asset correlation analysis (sector ETFs, currency pairs, volatility indices like VIX), lead-lag relationships, risk-on/risk-off regimes
This Implementation: Only OHLCV data from displayed instrument
Impact: Cannot incorporate broader market context; vulnerable to correlated moves across assets
9. Fixed Performance Horizon:
Full Implementation: Adaptive horizon based on trade duration, volatility regime, or profit target achievement
This Implementation: Fixed 8-bar evaluation window
Impact: May evaluate too early in slow markets or too late in fast markets; one-size-fits-all approach
Performance Impact Summary:
These simplifications make the script:
โ
Faster: Executes in milliseconds vs. seconds (or minutes) for full academic implementations
โ
More Accessible: Runs on any TradingView plan without external data feeds, APIs, or compute servers
โ
More Transparent: All calculations visible in Pine Script (no black-box compiled models)
โ
Lower Resource Usage: <500 bars lookback, minimal memory footprint
โ ๏ธ Less Precise: Approximations may reduce statistical edge by 5-15% vs. academic implementations
โ ๏ธ Limited Scope: Cannot capture tick-level dynamics, multi-order-book interactions, or cross-asset flows
โ ๏ธ Fixed Parameters: Some thresholds hardcoded rather than dynamically optimized
When to Upgrade to Full Implementation:
Consider professional Python/C++ versions with institutional data feeds if:
Trading with >$100K capital where precision differences materially impact returns
Operating in microsecond-competitive environments (HFT, market making)
Requiring regulatory-grade audit trails and reproducibility
Backtesting with tick-level precision for strategy validation
Need true real-time adaptation with neural network-based learning
For retail swing/day trading and position management, these approximations provide sufficient signal quality while maintaining usability, transparency, and accessibility. The core logicโmulti-agent detection with adaptive selectionโremains intact.
Technical Notes
All calculations use standard Pine Script built-in functions ( ta.ema, ta.atr, ta.rsi, ta.bb, ta.sma, ta.stdev, ta.vwap )
VPIN and Kyle's Lambda use simplified formulas optimized for OHLCV data (see "Lite" section above)
Thompson Sampling uses pseudo-random noise from price/volume decimal digits for beta distribution sampling
No repainting: All calculations use confirmed bar data (no forward-looking)
Maximum lookback: 500 bars (set via max_bars_back parameter)
Performance evaluation: 8-bar forward-looking window for reward calculation (clearly disclosed)
Confidence threshold: Minimum 0.25 (25%) enforced on all signals
Memory arrays: Dynamic sizing with FIFO queue management
Limitations and Disclaimers
Not Predictive: This indicator identifies patterns in historical data. It cannot predict future price movements with certainty.
Requires Human Judgment: Signals are entry triggers, not complete trading strategies. Must be confirmed with your own analysis, risk management rules, and market context.
Learning Period Required: The adaptive system requires 50-100 bars minimum to build statistically meaningful performance data for bandit algorithms.
Overfitting Risk: Restoring memory parameters from one market regime to a drastically different regime (e.g., low volatility to high volatility) may cause poor initial performance until system re-adapts.
Approximation Limitations: Simplified calculations (see "Lite" section) may underperform academic implementations by 5-15% in highly efficient markets.
No Guarantee of Profit: Past performance, whether backtested or live-traded, does not guarantee future performance. All trading involves risk of loss.
Forward-Looking Bias: Performance evaluation uses 8-bar forward windowโthis creates slight look-ahead for learning (though not for signals). Real-time performance may differ from indicator's internal statistics.
Single-Instrument Limitation: Does not account for correlations with related assets or broader market regime changes.
Recommended Settings
Timeframe: 15-minute to 4-hour charts (sufficient volatility for ATR-based stops; adequate bar volume for learning)
Assets: Liquid instruments with >100k daily volume (forex majors, large-cap stocks, BTC/ETH, major indices)
Not Recommended: Illiquid small-caps, penny stocks, low-volume altcoins (microstructure metrics unreliable)
Complementary Tools: Volume profile, order book depth, market breadth indicators, fundamental catalysts
Position Sizing: Risk no more than 1-2% of capital per signal using ATR-based stop-loss
Signal Filtering: Consider external confluence (support/resistance, trendlines, round numbers, session opens)
Start With: Balanced mode, Thompson Sampling, Blend mode, default agent sensitivities (1.0)
After 30+ Signals: Review agent win rates, consider increasing sensitivity of top performers or locking to dominant agent
Alert Configuration
The script includes built-in alert conditions:
Long Signal: Fires when validated long entry confirmed
Short Signal: Fires when validated short entry confirmed
Alerts fire once per bar (after confirmation requirements met)
Set alert to "Once Per Bar Close" for reliability
Taking you to school. โ Dskyz, Trade with insight. Trade with anticipation.
Algorithm Builder - Single Trend+ (Plug&Play)Hello traders
I. SCRIPTS ACCESS AND TRIALS
1. For the trial request access, they have to be done through my website .
2. My website URL is in this script signature at the very bottom (you'll have to scroll down a bit and going past the long description) and in my profile status available here : Daveatt
Due to the new scripts publishing house rules, I won't mention the URL here directly. As I value my partnership with TradingView very much, I prefer showing you the way for finding them :)
3. You may also contact me directly for more information
II. Algorithm Builder - Single Trend+ Plug&Play
2.1 Concept
That script is an upgrade of the Single Trend:
The Algorithm Builder - Single Trend+ (Plug & Play) is made to detect the convergence of many unrelated indicators, and give a BUY or SELL signal whenever all the selected sub-indicators are converging in the same direction.
The Single Trend+ (Plug & Play) gives one single entry per identified trend - unlike the Multiple Trends editions (also available on my scripts page) which may give more than 1 entry per trend.
The traders select the sub-indicators they want, and see in real-time the BUY and SELL triangles being updated.
III. Plug & Play
Hope you're ready to be impressed. Because, what I'm about to introduce, is my best-seller feature - and available across many of my indicators.
In TradingView, there is a feature called "Indicator on Indicator" meaning you can use an external indicator as a data source for another indicator.
I'm using that feature to connect any external indicator to our Algorithm Builder Single Trend+ Plug & Play (hence the plug and play name).
Let's assume you have your RSI divergence indicator - which is not part of the Algorithm Builder - but noticed that the convergence of an RSI divergence and a MACD gives strong signals.
I mentioned an RSI divergence, but you may connect any oscillator (MACD, On balance volume, stochastic RSI, True Strenght index, and many more..) or non-oscillator (divergence, trendline break, higher highs/lower lows, candlesticks pattern, price action, harmonic patterns, ...) indicators.
Any indicator that displays visual signals are eligible for this feature .... in other words.... all possible indicators. You're welcome :)
THE SKY IS (or more likely your imagination) is the limit :)
Fear no more. The Plug&Play technology allows you to connect it and use it in the convergence/confluence calculations.
Hence, whenever the MACD and RSI divergence will be in the same direction for the first time, you'll get a signal. For the first time only because this is a Single trend edition - you may enter multiple times using our Algorithm Builders - Multiple Trends editions.
To connect your external indicator to ours, we're using a native TradingView feature, which is not available for all users.
It depends on your TradingView subscription plan ( More info here )
If you intend to use our Algorithm Plug&Play indicator, and/or our Backtest Plug&Play suites, then you must upgrade your TradingView account to enjoy those features.
We value our relationship with our customers seriously, and that's why we're warning you that a compatible TradingView account type is required - at least PRO+ or PREMIUM to add more than 1 Plug&Play indicator per account.
We go in-depth on our website why the Plug&Play is an untapped opportunity for many traders out there - URL available on my profile status and signature
IV. Why the Algorithm Builder Single Trend+ (Plug&Play) may help you
I worked with many traders during my career, and their feedback about trading is often pretty similar.
They all tried a lot of complicated indicators, losing their capital, and finally getting back to the basics (even to the basic indicators if I might say)
The art is finding a good combination of indicators and setting strict money/risk management rules.
Easy in concept, but more than 90+% of traders lose money on the markets... which teach us that trading is not only about drawing trendlines, or using cool indicators but finding ways to ease our psychology while trading.
4.1 The Algorithm Builder trading framework
The sub-indicators (full list on our website) weren't chosen randomly. They're based on a trading method we've developed over the last 6 years - while working with traders and other trading quants.
The Algo Builders are made to detect a convergence - and as such, will give a signal once a trend has been identified.
They're not made to detect reversal but have been designed to give a signal when all sub-indicators are either ALL bullish (green) or ALL bearish (red).
We provide a framework based on indicators we selected because they:
1. make sense to be used altogether
2. work on asset classes like INDEX, CRYPTO, STOCK OPTIONS, FOREX, COMMODITIES
3. it may expand your knowledge about what detecting a convergence with pre-selected indicator really means
4.2 Supports and Resistances
The indicator displays the main algorithmic supports and resistances according to our trading method.
I think they're relevant for all asset classes, but you're absolutely free to use any different supports/resistances logic if you want to.
I'm not against it because I know that pivots, Fibonacci levels, etc. may work very well also.
4.3 Choose your favorite risk management algorithm
1/ Pre-defined Algo S/R method using:
- a supertrend of the stop-loss
- the nearest algorithmic resistances for the take profit levels.
2/ Define your own Stop-loss and Take-profits level in real-time
Stop-Loss Management
For what's following, let's assume that 2 is the stop-loss value you inserted in the indicator, and the Algorithm Builder gives a BUY signal.
This is NOT a recommendation at all, only an example to explain how this feature works.
- %Trailing: The Stop-Loss starts 2% away from the entry price - and will move up (because we're on a BUY trade as per our example) every time your trade will gain 2% profit
- Percentage: The Stop-Loss stays static 2% away from the entry price. There is no trailing here
- TP Trailing: This is a very awesome feature. The stop-loss is set 2% away when the trades start.
When the TP1 is hit, the stop-loss will be moved to the Entry price (also called breakeven).
When the TP2 is hit, the SL is moved to the previous TP1 position
- Fixed: Set the Stop-Loss at a fixed position (value should be in currency/units)
Take Profits Management
You can manage up to 2 take profit levels defined as a percentage or price value.
The expected input is in percentage value (for instance, setting the % target of TP1 to 2% will set the TP1 level 2% away from the entry price
4.4 Built-in Trade Manager
This is very likely the most loved utility script that we shared on TradingView.
It's included in your Algorithm Builder - Single Trend+, and will certainly help you immensely to analyze your charts and your trades.
We made sure that all the graphical elements on the chart will be updated in real-time whenever our user change anything on the indicator configuration.
You'll also be able to change the Trade Manager labels positions as you wish :)
4.5 Built-in Risk-to-Reward Panel
The good stuff doesn't stop here.
You'll notice that this sometimes green (when in a LONG), sometimes red (when in a SHORT) panel at the right of your chart.
It displays for the selected trading algorithmic (see 2.3.2 above), a ton of useful real-time analytics.
- Entry Price: the price when the Algorithm Builder will give a signal.
- The Trade PnL in percentage.
- Entry Stop Loss: Distance (in currency/units) between the selected stop-loss algorithm (percent, trailing, TP trailing, etc.) and the entry price.
- Entry TP1: Distance (in currency/units) between the entry price and the first take profit
- Entry TP2: Distance (in currency/units) between the entry price and the second take profit
- Risk/Reward TP1: Using the Stop-loss distance at entry, and Take Profit 1 at entry to compute the risk-to-reward ratio.
- Risk/Reward TP2: Using the Stop-loss distance at entry, and Take Profit 2 at entry to compute the risk-to-reward ratio.
For more details, please check the guides section of my website. Links are in my signature and profile status.
4.6Hard Exits
Our trading method is known for the hard exits, also called invalidation.
The Single Trend+ includes a hard exit based on a MACD - settings are flexible and you may update them.
Having a stop-loss protecting your trade is a best practice - Protecting your stop-loss also from getting hit is incredible.
We prefer invalidate a few positions, even if sometimes we don't want to. Rather than the market hard exiting on us, and leaving with our hard-working money.
4.7 Alerts
Alerts are enabled for:
- BUY/SELL triangles signals
- Trade Manager (SL, TP1, TP2)
- Hard Exits
V. Pain points that we're trying to solve with our Algorithm Builders
Issue #1 There are many informations / indicators / strategies / backtests / noise. Finding the right ones is not a simple task.
Solution #1 A reliable system that removes the external noise is much needed in trading to stay "in the game".
Issue #2 Trading could be quite stressful - The majority doesn't lose in trading because technical analysis is hard, but because managing our psychology is one of the hardest things a human can do.
Solution #2 Some ways to reduce the "trading stress" could be: getting better quality signals and trading like a "machine". Forgetting about Twitter and trusting the system you designed.
Issue #3 Trading without strict rules and only based on what we feel, or what we think the market should do is the fastest way to kiss our money goodbye.
Only 1 indicator generally is not enough. Traders generally use a combination of several indicators but they're monitoring them individually.
It's normal then to feel exhausted at the end of the day ^^ (to say the least)... and exhaustion leads to mistakes which leads to..... (I'm sure you got it) ... capital loss.
Solution #3 As a trader, I needed a trading framework and a method. I offer our trading method but they're plenty others out there. We cannot claim obviously it's the best ever ....but let's say we're using those exact same
scripts ourselves for our trading. And this what we've been recommending our clients to trade with for the past years. Also, having a tool detecting the convergence of several indicators and giving 1 unique signal
for BUY/SELL position will save you a lot of time/energy, and perhaps might help you out getting better trading performance.
VI. Resolving a complex puzzle and having fun in the process
Trading has to stay a passion and not (only be) a source of intense stress.
The most successful traders I know are "trading geeks" - literally always looking for optimizing, searching for the best possible entries, setups, indicators, tools, etc.
For them, it's not even about the money anymore, but only about beating their previous performance.
Why are they doing this? Because it's fun
Might appears as a bold statement, but I guarantee that looking for setups is fun.
One of our users even told us, that it's like playing with "Legos" and we couldn't possibly agree more.
VII. Designing a system that "makes sense"
Another bold statement now. Brace yourselves ladies and gentlemen
The Algorithm Builders allow to design trading systems quickly. What could takes days/weeks/months to find out... might be now within your reach in less than a few hours.
With a bit of practice, less than an hour might be enough per asset/timeframe to find a system that makes sense to you and adapted to your trading capital and psychology.
Assuming our users read our guides and are fully committed to learning a new way of trading - then we do guarantee you'll be able to design kick-ass trading systems that make sense.
"Making sense" doesn't mean at all it's guaranteed to win, it means you're the one defining the convergence of indicators, using your Algorithm Builder, and observe that most of the time - whenever there is a BUY signal, the candlesticks are going upwards - whenever there is a SELL signal, it's going downwards.
This is a necessary step to make real progress from a trading analyst perspective - and hopefully could lead to profits.
VII. Algorithm Builder versus the main trader enemy(=psychology)
This indicator has the goal to help solving one of the MAIN issues encountered by traders.
Most of traders realize, they can't perform with only 1 indicator (or 1 price pattern or 1 price action) and need a combination of multiple indicators before getting in a trade.
Far from being a magic pill, if it could at least reduce the stress you have while trading, then we'll consider we made a great job - it's a technical "useless noise remover", and needs to be followed strictly.
Such trust in a trading system can only be built by testing your Algorithm Builder configuration on either:
1. a demo account
2. or a live account with small bids. And then, increasing progressively the bids if your capital increases progressively.
Though, you should still use your common sense. (for instance: if we get a BUY signal right on a big timeframe resistance we're hitting for the first time).
I'm aware this is a new way of trading but for many, and while we cannot foresee the future, neither predict performance, we believe it might save you a lot of time to find good signals.
My maximum level of happiness will be reached the day when our users will contact me and showing me setups being mine.
I'm sure that even I can learn from my users and, we can all learn from each other Algorithm Builder configuration
VIII. What is a wrong or bad configuration?
Simply put. If you see that most of your signals react such as described below:
1. a buy triangle predicts, most of the time an upwards move
2. a sell triangle predicts, most of the time a downwards move
3. you estimated yourself the stop-loss needed to give enough room for your trades.
4. take profits based on algorithmic support and resistances or your own take profit method.
So what's a good Algorithm Builder configuration? A configuration you're happy with and makes sense.
A better Algorithm Builder setup is one used in demo or a live account w/ small bids for a few weeks, and you're consistent in your trading performance.
If you have any doubt or question, please hit me up directly or ask in the comments section of this script.
I'll never claim I have the best trading methodology or the best indicators. You only will be the judge, and I'll appreciate all the questions and feedback you're sending my way.
They help me a ton to develop indicators based on all the requests I received.
Kind regards,
Dave
Algo Bands [ProjeAdam]OVERVIEW:
The Algo Bands indicator is a technical analysis tool that calculates the highest, lowest, and average price levels over a user-defined number of bars. It generates buy and sell signals based on price interactions with these levels, visualizing them as bands on the chart. Additionally, the indicator provides multi-timeframe analysis and integrates alerts for timely trading decisions.
ALGORITHM:
1. Initialization and Function Definition
The Algo Bands indicator starts by defining functions to calculate critical price levels:
- High Band : A smoothed average of recent high price levels.
- Low Band : A smoothed average of recent low price levels.
- Average Band : The midpoint between the High Band and Low Band.
The smoothing process utilizes a Smoothed Moving Average (SMMA) to reduce noise and ensure accurate signal generation.
2. Inputs and Band Calculation
The indicator accepts customizable inputs for flexibility in trading strategies:
- Backward Length : The number of bars to consider for calculating high and low values.
- Number of Lines : Specifies how many recent high or low values are averaged.
- Smoothing Period : The length of the SMMA to smooth price data.
Using these inputs:
- The High Band is calculated as the smoothed average of the highest price values.
- The Low Band is calculated as the smoothed average of the lowest price values.
- The Average Band is the midpoint of the High and Low Bands.
3. Plotting the Bands
The Algo Bands indicator plots three main lines on the price chart:
- High Band : Plotted as a red step line, representing resistance levels.
- Low Band : Plotted as a green step line, indicating support levels.
- Average Band : Plotted as an orange line, showing the midpoint or equilibrium price.
4. Buy and Sell Conditions
Sell Condition:
The indicator triggers a sell signal when either of the following conditions is met:
A. Crossunder Condition :
- The closing price crosses below the High Band.
- The candle closes below its open price, confirming bearish sentiment.
- The closing price remains below both the High Band and the previous bar's open price.
B. Rejection Condition :
- The high price exceeds the High Band during the bar.
- However, the closing price fails to hold above the High Band and closes lower than both the High Band and the open price.
Buy Condition:
The indicator triggers a buy signal when either of the following conditions is met:
A. Crossover Condition :
- The closing price crosses above the Low Band.
- The candle closes above its open price, indicating bullish momentum.
- The closing price remains above both the Low Band and the previous bar's open price.
B. Rejection Condition :
- The low price dips below the Low Band during the bar.
- However, the closing price recovers and closes higher than both the Low Band and the open price.
5. Signal Visualization
The indicator visually represents buy and sell signals as follows:
- Sell Signals : Displayed as a red downward label (๐ด) above the bar.
- Buy Signals : Displayed as a green upward label (๐ข) below the bar.
The background colors between the bands also reflect market direction:
- Red for bearish trends.
- Green for bullish trends.
6. Alerts
The Algo Bands indicator includes customizable alerts to notify traders of trading signals:
- Alerts are triggered when Buy or Sell conditions are met.
- Integration with Telegram allows real-time notifications for immediate action.
7. Multi-Timeframe Analysis
The indicator supports analysis across multiple timeframes, including:
- 1 Hour
- 4 Hours
- Daily
It calculates the High and Low Bands for these timeframes to provide a comprehensive view of the market trend.
HOW DOES THE INDICATOR WORK?
1. Price Band Calculation :
- The highest and lowest price values are dynamically identified for a user-defined range.
- These values are smoothed using SMMA to produce the High Band and Low Band.
2. Signal Generation :
- Sell signals occur when the price crosses below or rejects the High Band.
- Buy signals occur when the price crosses above or rejects the Low Band.
3. Visualization :
- The bands are plotted on the chart to display resistance, support, and price equilibrium.
- Buy and Sell signals are marked with labels and color-coded backgrounds.
4. Alerts :
- Custom alerts notify traders in real time when signals are triggered.
BENEFITS OF THE ALGO BANDS INDICATOR:
- Trend Identification : Identifies support, resistance, and price equilibrium levels.
- Clear Buy/Sell Signals : Helps traders make timely entry and exit decisions.
- Noise Reduction : SMMA smoothing minimizes false signals.
- Multi-Timeframe Analysis : Provides insights across 1-hour, 4-hour, and daily timeframes.
- Customizable Parameters : Users can adjust settings for their trading style.
- Real-Time Alerts : Immediate notifications ensure timely actions.
- Visual Clarity : Labels and background colors enhance signal visibility.
- Ease of Use : Suitable for traders of all levels, from beginners to experts.
If you have any ideas what to add to my work to add more sources or make calculations cooler, suggest in DM .
Algorithmic Kalman Filter [CRYPTIK1]Price action is chaos. Markets are driven by high-frequency algorithms, emotional reactions, and raw speculation, creating a constant stream of noise that obscures the true underlying trend. A simple moving average is too slow, too primitive to navigate this environment effectively. It lags, it gets chopped up, and it fails when you need it most.
This script implements an Algorithmic Kalman Filter (AKF), a sophisticated signal processing algorithm adapted from aerospace and robotic guidance systems. Its purpose is singular: to strip away market noise and provide a hyper-adaptive, self-correcting estimate of an asset's true trajectory.
The Concept: An Adaptive Intelligence
Unlike a moving average that mindlessly averages past data, the Kalman Filter operates on a two-step principle: Predict and Update.
Predict: On each new bar, the filter makes a prediction of the true price based on its previous state.
Update: It then measures the error between its prediction and the actual closing price. It uses this error to intelligently correct its estimate, learning from its mistakes in real-time.
The result is a flawlessly smooth line that adapts to volatility. It remains stable during chop and reacts swiftly to new trends, giving you a crystal-clear view of the market's real intention.
How to Wield the Filter: The Core Settings
The power of the AKF lies in its two tuning parameters, which allow you to calibrate the filter's "brain" to any asset or timeframe.
Process Noise (Q) - Responsiveness: This controls how much you expect the true trend to change.
A higher Q value makes the filter more sensitive and responsive to recent price action. Use this for highly volatile assets or lower timeframes.
A lower Q value makes the filter smoother and more stable, trusting that the underlying trend is slow-moving. Use this for higher timeframes or ranging markets.
Measurement Noise (R) - Smoothness: This controls how much you trust the incoming price data.
A higher R value tells the filter that the price is extremely noisy and to be more skeptical. This results in a much smoother, slower-moving line.
A lower R value tells the filter to trust the price data more, resulting in a line that tracks price more closely.
The interaction between Q and R is what gives the filter its power. The default settings provide a solid baseline, but a true operator will fine-tune these to perfectly match the rhythm of their chosen market.
Tactical Application
The AKF is not just a line; it's a complete framework for viewing the market.
Trend Identification: The primary signal. The filter's color code provides an unambiguous definition of the trend. Teal for an uptrend, Pink for a downtrend. No more guesswork.
Dynamic Support & Resistance: The filter itself acts as a dynamic level. Watch for price to pull back and find support on a rising (Teal) filter in an uptrend, or to be rejected by a falling (Pink) filter in a downtrend.
A Higher-Order Filter: Use the AKF's trend state to filter signals from your primary strategy. For example, only take long signals when the AKF is Teal. This single rule can dramatically reduce noise and eliminate low-probability trades.
This is a professional-grade tool for traders who are serious about gaining a statistical edge. Ditch the lagging averages. Extract the signal from the noise.
Algo Trading Signals - Buy/Sell System# ๐ Algo Trading Signals - Dynamic Buy/Sell System
## ๐ฏ Overview
**Algo Trading Signals** is a sophisticated intraday trading indicator designed for algorithmic traders and active day traders. This system generates precise buy and sell signals based on a dynamic box breakout strategy with intelligent position management, add-on entries, and automatic target adjustment.
The indicator creates a reference price box during a specified time window (default: 9:15 AM - 9:45 AM IST) and generates high-probability signals when price breaks out of this range with confirmation.
---
## โจ Key Features
### ๐ **Smart Signal Generation**
- **Primary Entry Signals**: Clear buy/sell signals on confirmed breakouts above/below the reference box
- **Confirmation Bars**: Reduces false signals by requiring multiple bar confirmation before entry
- **Cooldown System**: Prevents overtrading with configurable cooldown periods between trades
- **Add-On Positions**: Automatically identifies optimal pullback entries for scaling into positions
### ๐ฆ **Dynamic Reference Box**
- Creates a high/low range during your chosen time window
- Automatically updates after each successful trade
- Visual box display with color-coded boundaries (red=resistance, green=support)
- Mid-level reference line for market structure analysis
### ๐ฏ **Intelligent Position Management**
- **Automatic Target Calculation**: Sets profit targets based on average move distance
- **Add-On System**: Up to 3 additional entries on optimal pullbacks
- **Position Tracking**: Monitors active trades and remaining add-on capacity
- **Auto Box Shift**: Adjusts reference box after target hits for continued trading
### ๐ **Visual Clarity**
- **Color-Coded Labels**:
- ๐ข Green for BUY signals
- ๐ด Red for SELL signals
- ๐ต Blue for ADD-ON buys
- ๐ Orange for ADD-ON sells
- โ Yellow for Target hits
- **TP Level Lines**: Dotted lines showing current profit targets
- **Hover Tooltips**: Detailed information on entry prices, targets, and add-on numbers
### ๐ **Real-Time Statistics**
Live performance dashboard showing:
- Total buy and sell signals generated
- Number of add-on positions taken
- Take profit hits achieved
- Current trade status (LONG/SHORT/None)
- Cooldown timer status
### ๐ **Comprehensive Alerts**
Built-in alert conditions for:
- Primary buy entry signals
- Primary sell entry signals
- Add-on buy positions
- Add-on sell positions
- Buy take profit hits
- Sell take profit hits
---
## ๐ ๏ธ Configuration Options
### **Time Settings**
- **Box Start Hour/Minute**: Define when to begin tracking the reference range
- **Box End Hour/Minute**: Define when to lock the reference box
- **Default**: 9:15 AM - 9:45 AM (IST) - Perfect for Indian market opening range
### **Trade Settings**
- **Target Points (TP)**: Average move distance for profit targets (default: 40 points)
- **Breakout Confirmation Bars**: Number of bars to confirm breakout (default: 2)
- **Cooldown After Trade**: Bars to wait after closing position (default: 3)
- **Add-On Distance Points**: Minimum pullback for add-on entry (default: 40 points)
- **Max Add-On Positions**: Maximum additional positions allowed (default: 3)
### **Display Options**
- Toggle buy/sell signal labels
- Show/hide trading box visualization
- Show/hide TP level lines
- Show/hide statistics table
---
## ๐ก How It Works
### **Phase 1: Box Formation (9:15 AM - 9:45 AM)**
The indicator tracks the high and low prices during your specified time window to create a reference box representing the opening range.
### **Phase 2: Breakout Detection**
After the box is locked, the system monitors for:
- **Bullish Breakout**: Price closes above box high for confirmation bars
- **Bearish Breakout**: Price closes below box low for confirmation bars
### **Phase 3: Signal Generation**
When confirmation requirements are met:
- Entry signal is generated with clear visual label
- Target price is calculated (Entry ยฑ Target Points)
- Position tracking activates
- Cooldown timer starts
### **Phase 4: Position Management**
During active trade:
- **Add-On Logic**: If price pulls back by specified distance but stays within favorable range, additional entry signal fires
- **Target Monitoring**: Continuously checks if price reaches TP level
- **Box Adjustment**: After TP hit, box automatically shifts to new range for next opportunity
### **Phase 5: Trade Exit & Reset**
On target hit:
- Position closes with TP marker
- Statistics update
- Box repositions for next setup
- Cooldown activates
- System ready for next signal
---
## ๐ Best Use Cases
### **Ideal For:**
- โ
Intraday breakout trading strategies
- โ
Algorithmic trading systems (via alerts/webhooks)
- โ
Opening range breakout (ORB) strategies
- โ
Index futures (Nifty, Bank Nifty, Sensex)
- โ
High-liquidity stocks with clear ranges
- โ
Automated trading bots
- โ
Scalping and day trading
### **Markets:**
- Indian Stock Market (NSE/BSE)
- Futures & Options
- Forex pairs
- Cryptocurrency (adjust timing for 24/7 markets)
- Global indices
---
## โ๏ธ Integration with Algo Trading
This indicator is **algo-ready** and can be integrated with automated trading systems:
1. **TradingView Alerts**: Set up alert conditions for each signal type
2. **Webhook Integration**: Connect alerts to trading platforms via webhooks
3. **API Automation**: Use with brokers supporting TradingView integration (Zerodha, Upstox, Interactive Brokers, etc.)
4. **Signal Data Access**: All signals are plotted for external data retrieval
---
## ๐ Quick Start Guide
1. **Add Indicator**: Apply to your chart (works best on 1-5 minute timeframes)
2. **Configure Time Window**: Set your desired box formation period
3. **Adjust Parameters**: Tune confirmation bars, targets, and add-on settings to your trading style
4. **Set Alerts**: Create alert conditions for automated notifications
5. **Backtest**: Review historical signals to validate strategy performance
6. **Go Live**: Enable alerts and start receiving real-time trading signals
---
## โ ๏ธ Risk Disclaimer
This indicator is a **tool for analysis** and does not guarantee profits. Trading involves substantial risk of loss. Always:
- Use proper position sizing
- Implement stop losses (not included in this indicator)
- Test thoroughly before live trading
- Understand market conditions
- Never risk more than you can afford to lose
- Consider your risk tolerance and trading experience
**Past performance does not indicate future results.**
## ๐ Version History
**v1.0** - Initial Release
- Dynamic box formation system
- Confirmed breakout signals
- Add-on position management
- Visual signal labels and statistics
- Comprehensive alert system
- Auto-adjusting target boxes
---
## ๐ Support & Feedback
If you find this indicator helpful:
- โญ Please leave a like/favorite
- ๐ฌ Share your feedback in comments
- ๐ Share your results and improvements
- ๐ค Suggest features for future updates
---
## ๐ท๏ธ Tags
`breakout` `daytrading` `signals` `algo` `automated` `intraday` `ORB` `opening-range` `buy-sell` `scalping` `futures` `nifty` `banknifty` `algorithmic` `box-strategy`
*Remember: The best indicator is combined with proper risk management and trading discipline.* Use it at your own rist, not as financial advie
Algorithm Builder UNIVERSAL (m30)Hello traders ๐
I. ๐ SCRIPTS ACCESS AND TRIALS ๐
1. Every 3 weeks trial request access has to be done through my website .
2. My website URL is in this script signature at the very bottom (you'll have to scroll down a bit and going past the long description) and in my profile status available here : Daveatt
3. Many video tutorials explaining clearly how all our indicators work are available on your website > guides section.
4. You may also contact me directly for more information regarding the trading method included in the indicator or how to access it
2.1 Forewords
This indicator/trading framework is available only to our PREMIUM users.
We decided to call it "UNIVERSAL" because the tool gives very decent signals for STOCKS/PENNY STOCKS/FOREX/CRYPTO (USD & BTC pairing)/INDICES/COMMODITIES trading
(the asset classes that I'm not listing are not tested yet; though we should cover a wide range of tradable assets here)
This is a by-product of defining an algorithmic trading method... we were surprised ourselves those past few weeks while trading with it.
The entries displayed are most of the time amazing, and the invalidations allow to reduce the losses considerably (more wins and less losses => delighted trader)
A few examples below showing why it's "universal"
FOREX
INDICES
COMMODITIES
CRYPTO (BTC pairing)
The tool works in m30 timeframe but won't work with any other timeframe . Even if applied on a 30-minutes chart; we included higher timeframe indicators to enter more securely.
It includes :
- our proprietary method with fixed entries
- a hard exit system (built-in stop-loss)
- ๐ Compatible with dynamic alerts ๐
Dynamic alerts are bringing automated trading to a whole new level. The third-party solutions capturing TradingView alerts are able to use them.
- (optional) we let the users decide to use or not our built-in Trade Manager.
Regardless of the Trade Manager is used or not, one must mandatory exit, once a vertical hard exit bar appears.
- the Algorithmic Supports and Resistances used as safeguards and take profit zones.
Final words
We made it as simple as we could (to be honest it's a very simple system for the end-user) - even with several hundreds of calculations in the indicator.
Please hit me up for any question/feedback/comment
Become the BEST trader that you can be
Dave
[Algo/Fract] QuantBuilt for traders ready to Level Up.
Combine algorithmic strength tracking with fractal structure to deliver quant-style clarity on a live chart.
You trade with intuition. Quant trades with Data.
Together, you read the Unseen.
Features included are:
TT Candles
Quant Strength Index
Structural Retest Areas
Fractal Trend Colors
Gain Access at: www.algofract.com
or by visiting our Whop Marketplace: whop.com
Algorithmic Value Oscillator [CRYPTIK1]Algorithmic Value Oscillator
Introduction: What is the AVO? Welcome to the Algorithmic Value Oscillator (AVO), a powerful, modern momentum indicator that reframes the classic "overbought" and "oversold" concept. Instead of relying on a fixed lookback period like a standard RSI, the AVO measures the current price relative to a significant, higher-timeframe Value Zone .
This gives you a more contextual and structural understanding of price. The core question it answers is not just "Is the price moving up or down quickly?" but rather, " Where is the current price in relation to its recently established area of value? "
This allows traders to identify true "premium" (overbought) and "discount" (oversold) levels with greater accuracy, all presented with a clean, futuristic aesthetic designed for the modern trader.
The Core Concept: Price vs. Value The market is constantly trying to find equilibrium. The AVO is built on the principle that the high and low of a significant prior period (like the previous day or week) create a powerful area of perceived value.
The Value Zone: The range between the high and low of the selected higher timeframe.
Premium Territory (Distribution Zone): When the oscillator moves into the glowing pink/purple zone above +100, it is trading at a premium.
Discount Territory (Accumulation Zone): When the oscillator moves into the glowing teal/blue zone below -100, it is trading at a discount.
Key Features
1. Glowing Gradient Oscillator: The main oscillator line is a dynamic visual guide to momentum.
The line changes color smoothly from light blue to neon teal as bullish momentum increases.
It shifts from hot pink to bright purple as bearish momentum increases.
Multiple transparent layers create a professional "glow" effect, making the trend easy to see at a glance.
2. Dynamic Volatility Histogram: This histogram at the bottom of the indicator is a custom volatility meter. It has been engineered to be adaptive, ensuring that the visual differences between high and low volatility are always clear and dramatic, no matter your zoom level. It uses a multi-color gradient to visualize the intensity of market volatility.
3. Volatility Regime Dashboard: This simple on-screen table analyzes the histogram and provides a clear, one-word summary of the current market state: Compressing, Stable, or Expanding.
How to Use the AVO: Trading Strategies
1. Reversion Trading This is the most direct way to use the indicator.
Look for Buys: When the AVO line drops into the teal "Accumulation Zone" (below -100), the price is trading at a discount. Watch for the oscillator to form a bottom and start turning up as a signal that buying pressure is returning.
Look for Sells: When the AVO line moves into the pink "Distribution Zone" (above +100), the price is trading at a premium. Watch for the oscillator to form a peak and start turning down as a signal that selling pressure is increasing.
2. Best Practices & Settings
Timeframe Synergy: The AVO is most effective when your chart timeframe is lower than your selected "Value Zone Source." For example, if you trade on the 1-hour chart, set your Value Zone to "Previous Day."
Confirmation is Key: This indicator provides powerful context, but it should not be used in isolation. Always combine its readings with your primary analysis, such as market structure and support/resistance levels.
