Great Expectations [LucF]Great Expectations helps traders answer the question: What is possible? It is a powerful question, yet exploration of the unknown always entails risk. A more complete set of questions better suited to traders could be:
What opportunity exists from any given point on a chart?
What portion of this opportunity can be realistically captured?
What risk will be incurred in trying to do so, and how long will it take?
Great Expectations is the result of an exploration of these questions. It is a trade simulator that generates visual and quantitative information to help strategy modelers visually identify and analyse areas of optimal expectation on charts, whether they are designing automated or discretionary strategies.
WARNING: Great Expectations is NOT an indicator that helps determine the current state of a market. It works by looking at points in the past from which the future is already known. It uses one definition of repainting extensively (i.e. it goes back in the past to print information that could not have been know at the time). Repainting understood that way is in fact almost all the indicator does! —albeit for what I hope is a noble cause. The indicator is of no use whatsoever in analyzing markets in real-time. If you do not understand what it does, please stay away!
This is an indicator—not a strategy that uses TradingView’s backtesting engine. It works by simulating trades, not unlike a backtest, but with the crucial difference that it assumes a trade (either long or short) is entered on all bars in the historic sample. It walks forward from each bar and determines possible outcomes, gathering individual trade statistics that in turn generate precious global statistics from all outcomes tested on the chart.
Great Expectations provides numbers summarizing trade results on all simulations run from the chart. Those numbers cannot be compared to backtest-produced numbers since all non-filtered bars are examined, even if an entry was taken on the bar immediately preceding the current one, which never happens in a backtest. This peculiarity does NOT invalidate Great Expectations calculations; it just entails that results be considered under a different light. Provided they are evaluated within the indicator’s context, they can be useful—sometimes even more than backtesting results, e.g. in evaluating the impact of parameter-fitting or variations in entry, exit or filtering strats.
Traders and strategy modelers are creatures of hope often suffering from blurred vision; my hope is that Great Expectations will help them appraise the validity of their setup and strat intuitions in a realistic fashion, preventing confirmation bias from obstructing perspective—and great expectations from turning into financial great deceptions.
USE CASES
You’ve identified what looks like a promising setup on other indicators. You load Great Expectations on the chart and evaluate if its high-expectation areas match locations where your setup’s conditions occur. Unless today is your lucky day, chances are the indicator will help you realize your setup is not as promising as you had hoped.
You want to get a rough estimate of the optimal trade duration for a chart and you don’t mind using the entry and exit strategies provided with the indicator. You use the trade length readouts of the indicator.
You’re experimenting with a new stop strategy and want to know how long it will keep you in trades, on average. You integrate your stop strategy in the indicator’s code and look at the average trade length it produces and the TST ratio to evaluate its performance.
You have put together your own entry and exit criteria and are looking for a filter that will help you improve backtesting results. You visually ascertain the suitability of your filter by looking at its results on the charts with great Expectations, to see if your filter is choosing its areas correctly.
You have a strategy that shows backtested trades on your chart. Great Expectations can help you evaluate how well your strategy is benefitting from high-opportunity areas while avoiding poor expectation spots.
You want more complete statistics on your set of strategies than what backtesting will provide. You use Great Expectations, knowing that it tests all bars in the sample that correspond to your criteria, as opposed to backtesting results which are limited to a subset of all possible entries.
You want to fool your friends into thinking you’ve designed the holy grail of indicators, something that identifies optimal opportunities on any chart; you show them the P&L cloud.
FEATURES
For one trade
At any given point on the chart, assuming a trade is entered there, Great Expectations shows you information specific to that trade simulation both on the chart and in the Data Window.
The chart can display:
the P & L Cloud which shows whether the trade ended profitably or not, and by how much,
the Opportunity & Risk Cloud which the maximum opportunity and risk the simulation encountered. When superimposed over the P & L cloud, you will see what I call the managed opportunity and risk, i.e the portion of maximum opportunity that was captured and the portion of the maximum risk that was incurred,
the target and if it was reached,
a background that uses a gradient to show different levels of trade length, P&L or how frequently the target was reached during simulation.
The Data Window displays more than 40 values on individual trades and global results. For any given trade you will know:
Entry/Exit levels, including slippage impact,
It’s outcome and duration,
P/L achieved,
The fraction of the maximum opportunity/risk managed by the trade.
For all trades
After going through all the possible trades on the chart, the indicator will provide you with a rare view of all outcomes expressed with the P&L cloud, which allows us to instantly see the most/least profitable areas of a chart using trade data as support, while also showing its relationship with the opportunity/risk encountered during the simulation. The difference between the two clouds is the managed opportunity and risk.
The Data Window will present you with numbers which we will go through later. Some of them are: average stop size, P/L, win rate, % opportunity managed, trade lengths for different types of trade outcomes and the TST (Target:Stop Travel) ratio.
Let’s see Great Expectations in action… and remember to open your Data Window!
INPUTS
Trade direction : You must first choose if you wish to look at long or short trades. Because of the way the indicator works and the amount of visual information on the chart, it is only practical to look at one type of trades at a time. The default is Longs.
Maximum trade Length (MaxL) : This is the maximum walk forward distance the simulator will go in analyzing outcomes from any given point in the past. It also determines the size of the dead zone among the chart’s last bars. A red background line identifies the beginning of the dead zone for which not enough bars have elapsed to analyze outcomes for the maximum trade length defined. If an ATR-based entry stop is used, that length is added to the wait time before beginning simulations, so that the first entry starts with a clean ATR value. On a sample of around 16000 bars, my tests show that the indicator runs into server errors at lengths of around 290, i.e. having completed ~4,6M simulation loop iterations. That is way too high a length anyways; 100 will usually be amply enough to ring out all the possibilities out of a simulation, and on shorter time frames, 30 can be enough. While making it unduly small will prevent simulations of expressing the market’s potential, the less you use, the faster the indicator will run. The default is 40.
Unrealized P&L base at End of Trade (EOT) : When a simulation ends and the trade is still open, we calculate unrealized P&L from an exit order executed from either the last in-trade stop on the previous bar, or the close of the last bar. You can readily see the impact of this selection on the chart, with the P&L cloud. The default is on the close.
Display : The check box besides the title does nothing.
Show target : Shows a green line displaying the trade’s target expressed as a multiple of X, i.e. the amplitude of the entry stop. I call this value “X” and use it as a unit to express profit and loss on a trade (some call it “R”). The line is highlighted for trades where the close reached the target during the trade, whether the trade ended in profit or loss. This is also where you specify the multiple of X you wish to use in calculating targets. The multiple is used even if targets are not displayed.
Show P&L Cloud : The cloud allows traders to see right away the profitable areas of the chart. The only line printed with the cloud is the “end of trade line” (EOT). The EOT line is the only way one can see the level where a trade ended on the chart (in the Data Window you can see it as the “Exit Fill” value). The EOT level for the trade determines if the trade ended in a profit or a loss. Its value represents one of the following:
- fill from order executed at close of bar where stop is breached during trade (which produces “Realized P/L”),
- simulation of a fill pseudo-fill at the user-defined EOT level (last close or stop level) if the trade runs its course through MaxL bars without getting stopped (producing Unrealized P/L).
The EOT line and the cloud fill print in green when the trade’s outcome is profitable and in red when it is not. If the trade was closed after breaching the stop, the line appears brighter.
Show Opportunity&Risk Cloud : Displays the maximum opportunity/risk that was present during the trade, i.e. the maximum and minimum prices reached.
Background Color Scheme : Allows you to choose between 3 different color schemes for the background gradients, to accommodate different types of chart background/candles. Select “None” if you don’t want a background.
Background source : Determines what value will be used to generate the different intensities of the gradient. You can choose trade length (brighter is shorter), Trade P&L (brighter is higher) or the number of times the target was reached during simulation (brighter is higher). The default is Trade Length.
Entry strat : The check box besides the title does nothing. The default strat is All bars, meaning a trade will be simulated from all bars not excluded by the filters where a MaxL bars future exists. For fun, I’ve included a pseudo-random entry strat (an indirect way of changing the seed is to vary the starting date of the simulation).
Show Filter State : Displays areas where the combination of filters you have selected are allowing entries. Filtering occurs as per your selection(s), whether the state is displayed or not. The effect of multiple selections is additive. The filters are:
1. Bar direction: Longs will only be entered if close>open and vice versa.
2. Rising Volume: Applies to both long and shorts.
3. Rising/falling MA of the length you choose over the number of bars you choose.
4. Custom indicator: You can feed your own filtering signal through this from another indicator. It must produce a signal of 1 to allow long entries and 0 to allow shorts.
Show Entry Stops :
1. Multiple of user-defined length ATR.
2. Fixed percentage.
3. Fixed value.
All entry stops are calculated using the entry fill price as a reference. The fill price is calculated from the current bar’s open, to which slippage is added if configured. This simulates the case where the strategy issued the entry signal on the previous bar for it to be executed at the next bar’s open.
The entry stop remains active until the in-trade stop becomes the more aggressive of the two stops. From then on, the entry stop will be ignored, unless a bar close breaches the in-trade stop, in which case the stop will be reset with a new entry stop and the process repeats.
Show In-trade stops : Displays in bright red the selected in-trade stop (be sure to read the note in this section about them).
1. ATR multiple: added/subtracted from the average of the two previous bars minimum/maximum of open/close.
2. A trailing stop with a deviation expressed as a multiple of entry stop (X).
3. A fixed percentage trailing stop.
Trailing stops deviations are measured from the highest/lowest high/low reached during the trade.
Note: There is a twist with the in-trade stops. It’s that for any given bar, its in-trade stop can hold multiple values, as each successive pass of the advancing simulation loops goes over it from a different entry points. What is printed is the stop from the loop that ended on that bar, which may have nothing to do with other instances of the trade’s in-trade stop for the same bar when visited from other starting points in previous simulations. There is just no practical way to print all stop values that were used for any given bar. While the printed entry stops are the actual ones used on each bar, the in-trade stops shown are merely the last instance used among many.
Include Slippage : if checked, slippage will be added/subtracted from order price to yield the fill price. Slippage is in percentage. If you choose to include slippage in the simulations, remember to adjust it by considering the liquidity of the markets and the time frame you’ll be analyzing.
Include Fees : if checked, fees will be subtracted/added to both realized an unrealized trade profits/losses. Fees are in percentage. The default fees work well for crypto markets but will need adjusting for others—especially in Forex. Remember to modify them accordingly as they can have a major impact on results. Both fees and slippage are included to remind us of their importance, even if the global numbers produced by the indicator are not representative of a real trading scenario composed of sequential trades.
Date Range filtering : the usual. Just note that the checkbox has to be selected for date filtering to activate.
DATA WINDOW
Most of the information produced by this indicator is made available in the Data Window, which you bring up by using the icon below the Watchlist and Alerts buttons at the right of the TV UI. Here’s what’s there.
Some of the information presented in the Data Window is standard trade data; other values are not so standard; e. g. the notions of managed opportunity and risk and Target:Stop Travel ratio. The interplay between all the values provided by Great Expectations is inherently complex, even for a static set of entry/filter/exit strats. During the constant updating which the habitual process of progressive refinement in building strategies that is the lot of strategy modelers entails, another level of complexity is no doubt added to the analysis of this indicator’s values. While I don’t want to sound like Wolfram presenting A New Kind of Science , I do believe that if you are a serious strategy modeler and spend the time required to get used to using all the information this indicator makes available, you may find it useful.
Trade Information
Entry Order : This is the open of the bar where simulation starts. We suppose that an entry signal was generated at the previous bar.
Entry Fill (including slip.) : The actual entry price, including slippage. This is the base price from which other values will be calculated.
Exit Order : When a stop is breached, an exit order is executed from the close of the bar that breached the stop. While there is no “In-trade stop” value included in the Data Window (other than the End of trade Stop previously discussed), this “Exit Order” value is how we can know the level where the trade was stopped during the simulation. The “Trade Length” value will then show the bar where the stop was breached.
Exit Fill (including slip.) : When the exit order is simulated, slippage is added to the order level to create the fill.
Chart: Target : This is the target calculated at the beginning of the simulation. This value also appear on the chart in teal. It is controlled by the multiple of X defined under the “Show Target” checkbox in the Inputs.
Chart: Entry Stop : This value also appears on the chart (the red dots under points where a trade was simulated). Its value is controlled by the Entry Strat chosen in the Inputs.
X (% Fill, including Fees) and X (currency) : This is the stop’s amplitude (Entry Fill – Entry Stop) + Fees. It represents the risk incurred upon entry and will be used to express P&L. We will show R expressed in both a percentage of the Entry Fill level (this value), and currency (the next value). This value represents the risk in the risk:reward ratio and is considered to be a unit of 1 so that RR can be expressed as a single value (i.e. “2” actually meaning “1:2”).
Trade Length : If trade was stopped, it’s the number of bars elapsed until then. The trade is then considered “Closed”. If the trade ends without being stopped (there is no profit-taking strat implemented, so the stop is the only exit strat), then the trade is “Open”, the length is MaxL and it will show in orange. Otherwise the value will print in green/red to reflect if the trade is winning/losing.
P&L (X) : The P&L of the trade, expressed as a multiple of X, which takes into account fees paid at entry and exit. Given our default target setting at 2 units of “X”, a trade that closes at its target will have produced a P&L of +2.0, i.e. twice the value of X (not counting fees paid at exit ). A trade that gets stopped late 50% further that the entry stop’s level will produce a P&L of -1.5X.
P&L (currency, including Fees) : same value as above, but expressed in currency.
Target first reached at bar : If price closed above the target during the trade (even if it occurs after the trade was stopped), this will show when. This value will be used in calculating our TST ratio.
Times Stop/Target reached in sim. : Includes all occurrences during the complete simulation loop.
Opportunity (X) : The highest/lowest price reached during a simulation, i.e. the maximum opportunity encountered, whether the trade was previously stopped or not, expressed as a multiple of X.
Risk (X) : The lowest/highest price reached during a simulation, i.e. the maximum risk encountered, whether the trade was previously stopped or not, expressed as a multiple of X.
Risk:Opportunity : The greater this ratio, the greater Opportunity is, compared to Risk.
Managed Opportunity (%) : The portion of Opportunity that was captured by the highest/low stop position, even if it occurred after a previous stop closed the trade.
Managed Risk (%) : The portion of risk that was protected by the lowest/highest stop position, even if it occurred after a previous stop closed the trade. When this value is greater than 100%, it means the trade’s stop is protecting more than the maximum risk, which is frequent. You will, however, never see close to those values for the Managed Opportunity value, since the stop would have to be higher than the Maximum opportunity. It is much easier to alleviate the risk than it is to lock in profits.
Managed Risk:Opportunity : The ratio of the two preceding values.
Managed Opp. vs. Risk : The Managed Opportunity minus the Managed Risk. When it is negative, which is most often is, it means your strat is protecting a greater portion of the risk than it captures opportunity.
Global Numbers
Win Rate(%) : Percentage of winning trades over all entries. Open trades are considered winning if their last stop/close (as per user selection) locks in profits.
Avg X%, Avg X (currency) : Averages of previously described values:.
Avg Profitability/Trade (APPT) : This measures expectation using: Average Profitability Per Trade = (Probability of Win × Average Win) − (Probability of Loss × Average Loss) . It quantifies the average expectation/trade, which RR alone can’t do, as the probabilities of each outcome (win/lose) must also be used to calculate expectancy. The APPT combine the RR with the win rate to yield the true expectancy of a strategy. In my usual way of expressing risk with X, APPT is the equivalent of the average P&L per trade expressed in X. An APPT of -1.5 means that we lose on average 1.5X/trade.
Equity (X), Equity (currency) : The cumulative result of all trade outcomes, expressed as a multiple of X. Multiplied by the Average X in currency, this yields the Equity in currency.
Risk:Opportunity, Managed Risk:Opportunity, Managed Opp. vs. Risk : The global values of the ones previously described.
Avg Trade Length (TL) : One of the most important values derived by going through all the simulations. Again, it is composed of either the length of stopped trades, or MaxL when the trade isn’t stopped (open). This value can help systems modelers shape the characteristics of the components they use to build their strategies.
Avg Closed Win TL and Avg Closed Lose TL : The average lengths of winning/losing trades that were stopped.
Target reached? Avg bars to Stop and Target reached? Avg bars to Target : For the trades where the target was reached at some point in the simulation, the number of bars to the first point where the stop was breached and where the target was reached, respectively. These two values are used to calculate the next value.
TST (Target:Stop Travel Ratio) : This tracks the ratio between the two preceding values (Bars to first stop/Bars to first target), but only for trades where the target was reached somewhere in the loop. A ratio of 2 means targets are reached twice as fast as stops.
The next values of this section are counts or percentages and are self-explanatory.
Chart Plots
Contains chart plots of values already describes.
NOTES
Optimization/Overfitting: There is a fine line between optimizing and overfitting. Tools like this indicator can lead unsuspecting modelers down a path of overfitting that often turns strategies into over-specialized beasts that do not perform elegantly when confronted to the real-world. Proven testing strategies like walk forward analysis will go a long way in helping modelers alleviate this risk.
Input tuning: Because the results generated by the indicator will vary with the parameters used in the active entry, filtering and exit strats, it’s important to realize that although it may be fun at first, just slapping the default settings on a chart and time frame will not yield optimal nor reliable results. While using ATR as often as possible (as I do in this indicator) is a good way to make strat parametrization adaptable, it is not a foolproof solution.
There is no data for the last MaxL bars of the chart, since not enough trade future has elapsed to run a simulation from MaxL bars back.
Modifying the code: I have tried to structure the code modularly, even if that entails a larger code base, so that you can adapt it to your needs. I’ve included a few token components in each of the placeholders designed for entry strategies, filters, entry stops and in-trade stops. This will hopefully make it easier to add your own. In the same spirit, I have also commented liberally.
You will find in the code many instances of standard trade management tasks that can be lifted to code TV strategies where, as I do in mine, you manage everything yourself and don’t rely on built-in Pine strategy functions to act on your trades.
Enjoy!
THANKS
To @scarf who showed me how plotchar() could be used to plot values without ruining scale.
To @glaz for the suggestion to include a Chandelier stop strat; I will.
To @simpelyfe for the idea of using an indicator input for the filters (if some day TV lets us use more than one, it will be useful in other modules of the indicator).
To @RicardoSantos for the random generator used in the random entry strat.
To all scripters publishing open source on TradingView; their code is the best way to learn.
To my trading buddies Irving and Bruno; who showed me way back how pro traders get it done.
In den Scripts nach "profitable" suchen
Adaptive Genesis Engine [AGE]ADAPTIVE GENESIS ENGINE (AGE)
Pure Signal Evolution Through Genetic Algorithms
Where Darwin Meets Technical Analysis
🧬 WHAT YOU'RE GETTING - THE PURE INDICATOR
This is a technical analysis indicator - it generates signals, visualizes probability, and shows you the evolutionary process in real-time. This is NOT a strategy with automatic execution - it's a sophisticated signal generation system that you control .
What This Indicator Does:
Generates Long/Short entry signals with probability scores (35-88% range)
Evolves a population of up to 12 competing strategies using genetic algorithms
Validates strategies through walk-forward optimization (train/test cycles)
Visualizes signal quality through premium gradient clouds and confidence halos
Displays comprehensive metrics via enhanced dashboard
Provides alerts for entries and exits
Works on any timeframe, any instrument, any broker
What This Indicator Does NOT Do:
Execute trades automatically
Manage positions or calculate position sizes
Place orders on your behalf
Make trading decisions for you
This is pure signal intelligence. AGE tells you when and how confident it is. You decide whether and how much to trade.
🔬 THE SCIENCE: GENETIC ALGORITHMS MEET TECHNICAL ANALYSIS
What Makes This Different - The Evolutionary Foundation
Most indicators are static - they use the same parameters forever, regardless of market conditions. AGE is alive . It maintains a population of competing strategies that evolve, adapt, and improve through natural selection principles:
Birth: New strategies spawn through crossover breeding (combining DNA from fit parents) plus random mutation for exploration
Life: Each strategy trades virtually via shadow portfolios, accumulating wins/losses, tracking drawdown, and building performance history
Selection: Strategies are ranked by comprehensive fitness scoring (win rate, expectancy, drawdown control, signal efficiency)
Death: Weak strategies are culled periodically, with elite performers (top 2 by default) protected from removal
Evolution: The gene pool continuously improves as successful traits propagate and unsuccessful ones die out
This is not curve-fitting. Each new strategy must prove itself on out-of-sample data through walk-forward validation before being trusted for live signals.
🧪 THE DNA: WHAT EVOLVES
Every strategy carries a 10-gene chromosome controlling how it interprets market data:
Signal Sensitivity Genes
Entropy Sensitivity (0.5-2.0): Weight given to market order/disorder calculations. Low values = conservative, require strong directional clarity. High values = aggressive, act on weaker order signals.
Momentum Sensitivity (0.5-2.0): Weight given to RSI/ROC/MACD composite. Controls responsiveness to momentum shifts vs. mean-reversion setups.
Structure Sensitivity (0.5-2.0): Weight given to support/resistance positioning. Determines how much price location within swing range matters.
Probability Adjustment Genes
Probability Boost (-0.10 to +0.10): Inherent bias toward aggressive (+) or conservative (-) entries. Acts as personality trait - some strategies naturally optimistic, others pessimistic.
Trend Strength Requirement (0.3-0.8): Minimum trend conviction needed before signaling. Higher values = only trades strong trends, lower values = acts in weak/sideways markets.
Volume Filter (0.5-1.5): Strictness of volume confirmation. Higher values = requires strong volume, lower values = volume less important.
Risk Management Genes
ATR Multiplier (1.5-4.0): Base volatility scaling for all price levels. Controls whether strategy uses tight or wide stops/targets relative to ATR.
Stop Multiplier (1.0-2.5): Stop loss tightness. Lower values = aggressive profit protection, higher values = more breathing room.
Target Multiplier (1.5-4.0): Profit target ambition. Lower values = quick scalping exits, higher values = swing trading holds.
Adaptation Gene
Regime Adaptation (0.0-1.0): How much strategy adjusts behavior based on detected market regime (trending/volatile/choppy). Higher values = more reactive to regime changes.
The Magic: AGE doesn't just try random combinations. Through tournament selection and fitness-weighted crossover, successful gene combinations spread through the population while unsuccessful ones fade away. Over 50-100 bars, you'll see the population converge toward genes that work for YOUR instrument and timeframe.
📊 THE SIGNAL ENGINE: THREE-LAYER SYNTHESIS
Before any strategy generates a signal, AGE calculates probability through multi-indicator confluence:
Layer 1 - Market Entropy (Information Theory)
Measures whether price movements exhibit directional order or random walk characteristics:
The Math:
Shannon Entropy = -Σ(p × log(p))
Market Order = 1 - (Entropy / 0.693)
What It Means:
High entropy = choppy, random market → low confidence signals
Low entropy = directional market → high confidence signals
Direction determined by up-move vs down-move dominance over lookback period (default: 20 bars)
Signal Output: -1.0 to +1.0 (bearish order to bullish order)
Layer 2 - Momentum Synthesis
Combines three momentum indicators into single composite score:
Components:
RSI (40% weight): Normalized to -1/+1 scale using (RSI-50)/50
Rate of Change (30% weight): Percentage change over lookback (default: 14 bars), clamped to ±1
MACD Histogram (30% weight): Fast(12) - Slow(26), normalized by ATR
Why This Matters: RSI catches mean-reversion opportunities, ROC catches raw momentum, MACD catches momentum divergence. Weighting favors RSI for reliability while keeping other perspectives.
Signal Output: -1.0 to +1.0 (strong bearish to strong bullish)
Layer 3 - Structure Analysis
Evaluates price position within swing range (default: 50-bar lookback):
Position Classification:
Bottom 20% of range = Support Zone → bullish bounce potential
Top 20% of range = Resistance Zone → bearish rejection potential
Middle 60% = Neutral Zone → breakout/breakdown monitoring
Signal Logic:
At support + bullish candle = +0.7 (strong buy setup)
At resistance + bearish candle = -0.7 (strong sell setup)
Breaking above range highs = +0.5 (breakout confirmation)
Breaking below range lows = -0.5 (breakdown confirmation)
Consolidation within range = ±0.3 (weak directional bias)
Signal Output: -1.0 to +1.0 (bearish structure to bullish structure)
Confluence Voting System
Each layer casts a vote (Long/Short/Neutral). The system requires minimum 2-of-3 agreement (configurable 1-3) before generating a signal:
Examples:
Entropy: Bullish, Momentum: Bullish, Structure: Neutral → Signal generated (2 long votes)
Entropy: Bearish, Momentum: Neutral, Structure: Neutral → No signal (only 1 short vote)
All three bullish → Signal generated with +5% probability bonus
This is the key to quality. Single indicators give too many false signals. Triple confirmation dramatically improves accuracy.
📈 PROBABILITY CALCULATION: HOW CONFIDENCE IS MEASURED
Base Probability:
Raw_Prob = 50% + (Average_Signal_Strength × 25%)
Then AGE applies strategic adjustments:
Trend Alignment:
Signal with trend: +4%
Signal against strong trend: -8%
Weak/no trend: no adjustment
Regime Adaptation:
Trending market (efficiency >50%, moderate vol): +3%
Volatile market (vol ratio >1.5x): -5%
Choppy market (low efficiency): -2%
Volume Confirmation:
Volume > 70% of 20-bar SMA: no change
Volume below threshold: -3%
Volatility State (DVS Ratio):
High vol (>1.8x baseline): -4% (reduce confidence in chaos)
Low vol (<0.7x baseline): -2% (markets can whipsaw in compression)
Moderate elevated vol (1.0-1.3x): +2% (trending conditions emerging)
Confluence Bonus:
All 3 indicators agree: +5%
2 of 3 agree: +2%
Strategy Gene Adjustment:
Probability Boost gene: -10% to +10%
Regime Adaptation gene: scales regime adjustments by 0-100%
Final Probability: Clamped between 35% (minimum) and 88% (maximum)
Why These Ranges?
Below 35% = too uncertain, better not to signal
Above 88% = unrealistic, creates overconfidence
Sweet spot: 65-80% for quality entries
🔄 THE SHADOW PORTFOLIO SYSTEM: HOW STRATEGIES COMPETE
Each active strategy maintains a virtual trading account that executes in parallel with real-time data:
Shadow Trading Mechanics
Entry Logic:
Calculate signal direction, probability, and confluence using strategy's unique DNA
Check if signal meets quality gate:
Probability ≥ configured minimum threshold (default: 65%)
Confluence ≥ configured minimum (default: 2 of 3)
Direction is not zero (must be long or short, not neutral)
Verify signal persistence:
Base requirement: 2 bars (configurable 1-5)
Adapts based on probability: high-prob signals (75%+) enter 1 bar faster, low-prob signals need 1 bar more
Adjusts for regime: trending markets reduce persistence by 1, volatile markets add 1
Apply additional filters:
Trend strength must exceed strategy's requirement gene
Regime filter: if volatile market detected, probability must be 72%+ to override
Volume confirmation required (volume > 70% of average)
If all conditions met for required persistence bars, enter shadow position at current close price
Position Management:
Entry Price: Recorded at close of entry bar
Stop Loss: ATR-based distance = ATR × ATR_Mult (gene) × Stop_Mult (gene) × DVS_Ratio
Take Profit: ATR-based distance = ATR × ATR_Mult (gene) × Target_Mult (gene) × DVS_Ratio
Position: +1 (long) or -1 (short), only one at a time per strategy
Exit Logic:
Check if price hit stop (on low) or target (on high) on current bar
Record trade outcome in R-multiples (profit/loss normalized by ATR)
Update performance metrics:
Total trades counter incremented
Wins counter (if profit > 0)
Cumulative P&L updated
Peak equity tracked (for drawdown calculation)
Maximum drawdown from peak recorded
Enter cooldown period (default: 8 bars, configurable 3-20) before next entry allowed
Reset signal age counter to zero
Walk-Forward Tracking:
During position lifecycle, trades are categorized:
Training Phase (first 250 bars): Trade counted toward training metrics
Testing Phase (next 75 bars): Trade counted toward testing metrics (out-of-sample)
Live Phase (after WFO period): Trade counted toward overall metrics
Why Shadow Portfolios?
No lookahead bias (uses only data available at the bar)
Realistic execution simulation (entry on close, stop/target checks on high/low)
Independent performance tracking for true fitness comparison
Allows safe experimentation without risking capital
Each strategy learns from its own experience
🏆 FITNESS SCORING: HOW STRATEGIES ARE RANKED
Fitness is not just win rate. AGE uses a comprehensive multi-factor scoring system:
Core Metrics (Minimum 3 trades required)
Win Rate (30% of fitness):
WinRate = Wins / TotalTrades
Normalized directly (0.0-1.0 scale)
Total P&L (30% of fitness):
Normalized_PnL = (PnL + 300) / 600
Clamped 0.0-1.0. Assumes P&L range of -300R to +300R for normalization scale.
Expectancy (25% of fitness):
Expectancy = Total_PnL / Total_Trades
Normalized_Expectancy = (Expectancy + 30) / 60
Clamped 0.0-1.0. Rewards consistency of profit per trade.
Drawdown Control (15% of fitness):
Normalized_DD = 1 - (Max_Drawdown / 15)
Clamped 0.0-1.0. Penalizes strategies that suffer large equity retracements from peak.
Sample Size Adjustment
Quality Factor:
<50 trades: 1.0 (full weight, small sample)
50-100 trades: 0.95 (slight penalty for medium sample)
100 trades: 0.85 (larger penalty for large sample)
Why penalize more trades? Prevents strategies from gaming the system by taking hundreds of tiny trades to inflate statistics. Favors quality over quantity.
Bonus Adjustments
Walk-Forward Validation Bonus:
if (WFO_Validated):
Fitness += (WFO_Efficiency - 0.5) × 0.1
Strategies proven on out-of-sample data receive up to +10% fitness boost based on test/train efficiency ratio.
Signal Efficiency Bonus (if diagnostics enabled):
if (Signals_Evaluated > 10):
Pass_Rate = Signals_Passed / Signals_Evaluated
Fitness += (Pass_Rate - 0.1) × 0.05
Rewards strategies that generate high-quality signals passing the quality gate, not just profitable trades.
Final Fitness: Clamped at 0.0 minimum (prevents negative fitness values)
Result: Elite strategies typically achieve 0.50-0.75 fitness. Anything above 0.60 is excellent. Below 0.30 is prime candidate for culling.
🔬 WALK-FORWARD OPTIMIZATION: ANTI-OVERFITTING PROTECTION
This is what separates AGE from curve-fitted garbage indicators.
The Three-Phase Process
Every new strategy undergoes a rigorous validation lifecycle:
Phase 1 - Training Window (First 250 bars, configurable 100-500):
Strategy trades normally via shadow portfolio
All trades count toward training performance metrics
System learns which gene combinations produce profitable patterns
Tracks independently: Training_Trades, Training_Wins, Training_PnL
Phase 2 - Testing Window (Next 75 bars, configurable 30-200):
Strategy continues trading without any parameter changes
Trades now count toward testing performance metrics (separate tracking)
This is out-of-sample data - strategy has never seen these bars during "optimization"
Tracks independently: Testing_Trades, Testing_Wins, Testing_PnL
Phase 3 - Validation Check:
Minimum_Trades = 5 (configurable 3-15)
IF (Train_Trades >= Minimum AND Test_Trades >= Minimum):
WR_Efficiency = Test_WinRate / Train_WinRate
Expectancy_Efficiency = Test_Expectancy / Train_Expectancy
WFO_Efficiency = (WR_Efficiency + Expectancy_Efficiency) / 2
IF (WFO_Efficiency >= 0.55): // configurable 0.3-0.9
Strategy.Validated = TRUE
Strategy receives fitness bonus
ELSE:
Strategy receives 30% fitness penalty
ELSE:
Validation deferred (insufficient trades in one or both periods)
What Validation Means
Validated Strategy (Green "✓ VAL" in dashboard):
Performed at least 55% as well on unseen data compared to training data
Gets fitness bonus: +(efficiency - 0.5) × 0.1
Receives priority during tournament selection for breeding
More likely to be chosen as active trading strategy
Unvalidated Strategy (Orange "○ TRAIN" in dashboard):
Failed to maintain performance on test data (likely curve-fitted to training period)
Receives 30% fitness penalty (0.7x multiplier)
Makes strategy prime candidate for culling
Can still trade but with lower selection probability
Insufficient Data (continues collecting):
Hasn't completed both training and testing periods yet
OR hasn't achieved minimum trade count in both periods
Validation check deferred until requirements met
Why 55% Efficiency Threshold?
If a strategy earned 10R during training but only 5.5R during testing, it still proved an edge exists beyond random luck. Requiring 100% efficiency would be unrealistic - market conditions change between periods. But requiring >50% ensures the strategy didn't completely degrade on fresh data.
The Protection: Strategies that work great on historical data but fail on new data are automatically identified and penalized. This prevents the population from being polluted by overfitted strategies that would fail in live trading.
🌊 DYNAMIC VOLATILITY SCALING (DVS): ADAPTIVE STOP/TARGET PLACEMENT
AGE doesn't use fixed stop distances. It adapts to current volatility conditions in real-time.
Four Volatility Measurement Methods
1. ATR Ratio (Simple Method):
Current_Vol = ATR(14) / Close
Baseline_Vol = SMA(Current_Vol, 100)
Ratio = Current_Vol / Baseline_Vol
Basic comparison of current ATR to 100-bar moving average baseline.
2. Parkinson (High-Low Range Based):
For each bar: HL = log(High / Low)
Parkinson_Vol = sqrt(Σ(HL²) / (4 × Period × log(2)))
More stable than close-to-close volatility. Captures intraday range expansion without overnight gap noise.
3. Garman-Klass (OHLC Based):
HL_Term = 0.5 × ²
CO_Term = (2×log(2) - 1) × ²
GK_Vol = sqrt(Σ(HL_Term - CO_Term) / Period)
Most sophisticated estimator. Incorporates all four price points (open, high, low, close) plus gap information.
4. Ensemble Method (Default - Median of All Three):
Ratio_1 = ATR_Current / ATR_Baseline
Ratio_2 = Parkinson_Current / Parkinson_Baseline
Ratio_3 = GK_Current / GK_Baseline
DVS_Ratio = Median(Ratio_1, Ratio_2, Ratio_3)
Why Ensemble?
Takes median to avoid outliers and false spikes
If ATR jumps but range-based methods stay calm, median prevents overreaction
If one method fails, other two compensate
Most robust approach across different market conditions
Sensitivity Scaling
Scaled_Ratio = (Raw_Ratio) ^ Sensitivity
Sensitivity 0.3: Cube root - heavily dampens volatility impact
Sensitivity 0.5: Square root - moderate dampening
Sensitivity 0.7 (Default): Balanced response to volatility changes
Sensitivity 1.0: Linear - full 1:1 volatility impact
Sensitivity 1.5: Exponential - amplified response to volatility spikes
Safety Clamps: Final DVS Ratio always clamped between 0.5x and 2.5x baseline to prevent extreme position sizing or stop placement errors.
How DVS Affects Shadow Trading
Every strategy's stop and target distances are multiplied by the current DVS ratio:
Stop Loss Distance:
Stop_Distance = ATR × ATR_Mult (gene) × Stop_Mult (gene) × DVS_Ratio
Take Profit Distance:
Target_Distance = ATR × ATR_Mult (gene) × Target_Mult (gene) × DVS_Ratio
Example Scenario:
ATR = 10 points
Strategy's ATR_Mult gene = 2.5
Strategy's Stop_Mult gene = 1.5
Strategy's Target_Mult gene = 2.5
DVS_Ratio = 1.4 (40% above baseline volatility - market heating up)
Stop = 10 × 2.5 × 1.5 × 1.4 = 52.5 points (vs. 37.5 in normal vol)
Target = 10 × 2.5 × 2.5 × 1.4 = 87.5 points (vs. 62.5 in normal vol)
Result:
During volatility spikes: Stops automatically widen to avoid noise-based exits, targets extend for bigger moves
During calm periods: Stops tighten for better risk/reward, targets compress for realistic profit-taking
Strategies adapt risk management to match current market behavior
🧬 THE EVOLUTIONARY CYCLE: SPAWN, COMPETE, CULL
Initialization (Bar 1)
AGE begins with 4 seed strategies (if evolution enabled):
Seed Strategy #0 (Balanced):
All sensitivities at 1.0 (neutral)
Zero probability boost
Moderate trend requirement (0.4)
Standard ATR/stop/target multiples (2.5/1.5/2.5)
Mid-level regime adaptation (0.5)
Seed Strategy #1 (Momentum-Focused):
Lower entropy sensitivity (0.7), higher momentum (1.5)
Slight probability boost (+0.03)
Higher trend requirement (0.5)
Tighter stops (1.3), wider targets (3.0)
Seed Strategy #2 (Entropy-Driven):
Higher entropy sensitivity (1.5), lower momentum (0.8)
Slight probability penalty (-0.02)
More trend tolerant (0.6)
Wider stops (1.8), standard targets (2.5)
Seed Strategy #3 (Structure-Based):
Balanced entropy/momentum (0.8/0.9), high structure (1.4)
Slight probability boost (+0.02)
Lower trend requirement (0.35)
Moderate risk parameters (1.6/2.8)
All seeds start with WFO validation bypassed if WFO is disabled, or must validate if enabled.
Spawning New Strategies
Timing (Adaptive):
Historical phase: Every 30 bars (configurable 10-100)
Live phase: Every 200 bars (configurable 100-500)
Automatically switches to live timing when barstate.isrealtime triggers
Conditions:
Current population < max population limit (default: 8, configurable 4-12)
At least 2 active strategies exist (need parents)
Available slot in population array
Selection Process:
Run tournament selection 3 times with different seeds
Each tournament: randomly sample active strategies, pick highest fitness
Best from 3 tournaments becomes Parent 1
Repeat independently for Parent 2
Ensures fit parents but maintains diversity
Crossover Breeding:
For each of 10 genes:
Parent1_Fitness = fitness
Parent2_Fitness = fitness
Weight1 = Parent1_Fitness / (Parent1_Fitness + Parent2_Fitness)
Gene1 = parent1's value
Gene2 = parent2's value
Child_Gene = Weight1 × Gene1 + (1 - Weight1) × Gene2
Fitness-weighted crossover ensures fitter parent contributes more genetic material.
Mutation:
For each gene in child:
IF (random < mutation_rate):
Gene_Range = GENE_MAX - GENE_MIN
Noise = (random - 0.5) × 2 × mutation_strength × Gene_Range
Mutated_Gene = Clamp(Child_Gene + Noise, GENE_MIN, GENE_MAX)
Historical mutation rate: 20% (aggressive exploration)
Live mutation rate: 8% (conservative stability)
Mutation strength: 12% of gene range (configurable 5-25%)
Initialization of New Strategy:
Unique ID assigned (total_spawned counter)
Parent ID recorded
Generation = max(parent generations) + 1
Birth bar recorded (for age tracking)
All performance metrics zeroed
Shadow portfolio reset
WFO validation flag set to false (must prove itself)
Result: New strategy with hybrid DNA enters population, begins trading in next bar.
Competition (Every Bar)
All active strategies:
Calculate their signal based on unique DNA
Check quality gate with their thresholds
Manage shadow positions (entries/exits)
Update performance metrics
Recalculate fitness score
Track WFO validation progress
Strategies compete indirectly through fitness ranking - no direct interaction.
Culling Weak Strategies
Timing (Adaptive):
Historical phase: Every 60 bars (configurable 20-200, should be 2x spawn interval)
Live phase: Every 400 bars (configurable 200-1000, should be 2x spawn interval)
Minimum Adaptation Score (MAS):
Initial MAS = 0.10
MAS decays: MAS × 0.995 every cull cycle
Minimum MAS = 0.03 (floor)
MAS represents the "survival threshold" - strategies below this fitness level are vulnerable.
Culling Conditions (ALL must be true):
Population > minimum population (default: 3, configurable 2-4)
At least one strategy has fitness < MAS
Strategy's age > culling interval (prevents premature culling of new strategies)
Strategy is not in top N elite (default: 2, configurable 1-3)
Culling Process:
Find worst strategy:
For each active strategy:
IF (age > cull_interval):
Fitness = base_fitness
IF (not WFO_validated AND WFO_enabled):
Fitness × 0.7 // 30% penalty for unvalidated
IF (Fitness < MAS AND Fitness < worst_fitness_found):
worst_strategy = this_strategy
worst_fitness = Fitness
IF (worst_strategy found):
Count elite strategies with fitness > worst_fitness
IF (elite_count >= elite_preservation_count):
Deactivate worst_strategy (set active flag = false)
Increment total_culled counter
Elite Protection:
Even if a strategy's fitness falls below MAS, it survives if fewer than N strategies are better. This prevents culling when population is generally weak.
Result: Weak strategies removed from population, freeing slots for new spawns. Gene pool improves over time.
Selection for Display (Every Bar)
AGE chooses one strategy to display signals:
Best fitness = -1
Selected = none
For each active strategy:
Fitness = base_fitness
IF (WFO_validated):
Fitness × 1.3 // 30% bonus for validated strategies
IF (Fitness > best_fitness):
best_fitness = Fitness
selected_strategy = this_strategy
Display selected strategy's signals on chart
Result: Only the highest-fitness (optionally validated-boosted) strategy's signals appear as chart markers. Other strategies trade invisibly in shadow portfolios.
🎨 PREMIUM VISUALIZATION SYSTEM
AGE includes sophisticated visual feedback that standard indicators lack:
1. Gradient Probability Cloud (Optional, Default: ON)
Multi-layer gradient showing signal buildup 2-3 bars before entry:
Activation Conditions:
Signal persistence > 0 (same directional signal held for multiple bars)
Signal probability ≥ minimum threshold (65% by default)
Signal hasn't yet executed (still in "forming" state)
Visual Construction:
7 gradient layers by default (configurable 3-15)
Each layer is a line-fill pair (top line, bottom line, filled between)
Layer spacing: 0.3 to 1.0 × ATR above/below price
Outer layers = faint, inner layers = bright
Color transitions from base to intense based on layer position
Transparency scales with probability (high prob = more opaque)
Color Selection:
Long signals: Gradient from theme.gradient_bull_mid to theme.gradient_bull_strong
Short signals: Gradient from theme.gradient_bear_mid to theme.gradient_bear_strong
Base transparency: 92%, reduces by up to 8% for high-probability setups
Dynamic Behavior:
Cloud grows/shrinks as signal persistence increases/decreases
Redraws every bar while signal is forming
Disappears when signal executes or invalidates
Performance Note: Computationally expensive due to linefill objects. Disable or reduce layers if chart performance degrades.
2. Population Fitness Ribbon (Optional, Default: ON)
Histogram showing fitness distribution across active strategies:
Activation: Only draws on last bar (barstate.islast) to avoid historical clutter
Visual Construction:
10 histogram layers by default (configurable 5-20)
Plots 50 bars back from current bar
Positioned below price at: lowest_low(100) - 1.5×ATR (doesn't interfere with price action)
Each layer represents a fitness threshold (evenly spaced min to max fitness)
Layer Logic:
For layer_num from 0 to ribbon_layers:
Fitness_threshold = min_fitness + (max_fitness - min_fitness) × (layer / layers)
Count strategies with fitness ≥ threshold
Height = ATR × 0.15 × (count / total_active)
Y_position = base_level + ATR × 0.2 × layer
Color = Gradient from weak to strong based on layer position
Line_width = Scaled by height (taller = thicker)
Visual Feedback:
Tall, bright ribbon = healthy population, many fit strategies at high fitness levels
Short, dim ribbon = weak population, few strategies achieving good fitness
Ribbon compression (layers close together) = population converging to similar fitness
Ribbon spread = diverse fitness range, active selection pressure
Use Case: Quick visual health check without opening dashboard. Ribbon growing upward over time = population improving.
3. Confidence Halo (Optional, Default: ON)
Circular polyline around entry signals showing probability strength:
Activation: Draws when new position opens (shadow_position changes from 0 to ±1)
Visual Construction:
20-segment polyline forming approximate circle
Center: Low - 0.5×ATR (long) or High + 0.5×ATR (short)
Radius: 0.3×ATR (low confidence) to 1.0×ATR (elite confidence)
Scales with: (probability - min_probability) / (1.0 - min_probability)
Color Coding:
Elite (85%+): Cyan (theme.conf_elite), large radius, minimal transparency (40%)
Strong (75-85%): Strong green (theme.conf_strong), medium radius, moderate transparency (50%)
Good (65-75%): Good green (theme.conf_good), smaller radius, more transparent (60%)
Moderate (<65%): Moderate green (theme.conf_moderate), tiny radius, very transparent (70%)
Technical Detail:
Uses chart.point array with index-based positioning
5-bar horizontal spread for circular appearance (±5 bars from entry)
Curved=false (Pine Script polyline limitation)
Fill color matches line color but more transparent (88% vs line's transparency)
Purpose: Instant visual probability assessment. No need to check dashboard - halo size/brightness tells the story.
4. Evolution Event Markers (Optional, Default: ON)
Visual indicators of genetic algorithm activity:
Spawn Markers (Diamond, Cyan):
Plots when total_spawned increases on current bar
Location: bottom of chart (location.bottom)
Color: theme.spawn_marker (cyan/bright blue)
Size: tiny
Indicates new strategy just entered population
Cull Markers (X-Cross, Red):
Plots when total_culled increases on current bar
Location: bottom of chart (location.bottom)
Color: theme.cull_marker (red/pink)
Size: tiny
Indicates weak strategy just removed from population
What It Tells You:
Frequent spawning early = population building, active exploration
Frequent culling early = high selection pressure, weak strategies dying fast
Balanced spawn/cull = healthy evolutionary churn
No markers for long periods = stable population (evolution plateaued or optimal genes found)
5. Entry/Exit Markers
Clear visual signals for selected strategy's trades:
Long Entry (Triangle Up, Green):
Plots when selected strategy opens long position (position changes 0 → +1)
Location: below bar (location.belowbar)
Color: theme.long_primary (green/cyan depending on theme)
Transparency: Scales with probability:
Elite (85%+): 0% (fully opaque)
Strong (75-85%): 10%
Good (65-75%): 20%
Acceptable (55-65%): 35%
Size: small
Short Entry (Triangle Down, Red):
Plots when selected strategy opens short position (position changes 0 → -1)
Location: above bar (location.abovebar)
Color: theme.short_primary (red/pink depending on theme)
Transparency: Same scaling as long entries
Size: small
Exit (X-Cross, Orange):
Plots when selected strategy closes position (position changes ±1 → 0)
Location: absolute (at actual exit price if stop/target lines enabled)
Color: theme.exit_color (orange/yellow depending on theme)
Transparency: 0% (fully opaque)
Size: tiny
Result: Clean, probability-scaled markers that don't clutter chart but convey essential information.
6. Stop Loss & Take Profit Lines (Optional, Default: ON)
Visual representation of shadow portfolio risk levels:
Stop Loss Line:
Plots when selected strategy has active position
Level: shadow_stop value from selected strategy
Color: theme.short_primary with 60% transparency (red/pink, subtle)
Width: 2
Style: plot.style_linebr (breaks when no position)
Take Profit Line:
Plots when selected strategy has active position
Level: shadow_target value from selected strategy
Color: theme.long_primary with 60% transparency (green, subtle)
Width: 2
Style: plot.style_linebr (breaks when no position)
Purpose:
Shows where shadow portfolio would exit for stop/target
Helps visualize strategy's risk/reward ratio
Useful for manual traders to set similar levels
Disable for cleaner chart (recommended for presentations)
7. Dynamic Trend EMA
Gradient-colored trend line that visualizes trend strength:
Calculation:
EMA(close, trend_length) - default 50 period (configurable 20-100)
Slope calculated over 10 bars: (current_ema - ema ) / ema × 100
Color Logic:
Trend_direction:
Slope > 0.1% = Bullish (1)
Slope < -0.1% = Bearish (-1)
Otherwise = Neutral (0)
Trend_strength = abs(slope)
Color = Gradient between:
- Neutral color (gray/purple)
- Strong bullish (bright green) if direction = 1
- Strong bearish (bright red) if direction = -1
Gradient factor = trend_strength (0 to 1+ scale)
Visual Behavior:
Faint gray/purple = weak/no trend (choppy conditions)
Light green/red = emerging trend (low strength)
Bright green/red = strong trend (high conviction)
Color intensity = trend strength magnitude
Transparency: 50% (subtle, doesn't overpower price action)
Purpose: Subconscious awareness of trend state without checking dashboard or indicators.
8. Regime Background Tinting (Subtle)
Ultra-low opacity background color indicating detected market regime:
Regime Detection:
Efficiency = directional_movement / total_range (over trend_length bars)
Vol_ratio = current_volatility / average_volatility
IF (efficiency > 0.5 AND vol_ratio < 1.3):
Regime = Trending (1)
ELSE IF (vol_ratio > 1.5):
Regime = Volatile (2)
ELSE:
Regime = Choppy (0)
Background Colors:
Trending: theme.regime_trending (dark green, 92-93% transparency)
Volatile: theme.regime_volatile (dark red, 93% transparency)
Choppy: No tint (normal background)
Purpose:
Subliminal regime awareness
Helps explain why signals are/aren't generating
Trending = ideal conditions for AGE
Volatile = fewer signals, higher thresholds applied
Choppy = mixed signals, lower confidence
Important: Extremely subtle by design. Not meant to be obvious, just subconscious context.
📊 ENHANCED DASHBOARD
Comprehensive real-time metrics in single organized panel (top-right position):
Dashboard Structure (5 columns × 14 rows)
Header Row:
Column 0: "🧬 AGE PRO" + phase indicator (🔴 LIVE or ⏪ HIST)
Column 1: "POPULATION"
Column 2: "PERFORMANCE"
Column 3: "CURRENT SIGNAL"
Column 4: "ACTIVE STRATEGY"
Column 0: Market State
Regime (📈 TREND / 🌊 CHAOS / ➖ CHOP)
DVS Ratio (current volatility scaling factor, format: #.##)
Trend Direction (▲ BULL / ▼ BEAR / ➖ FLAT with color coding)
Trend Strength (0-100 scale, format: #.##)
Column 1: Population Metrics
Active strategies (count / max_population)
Validated strategies (WFO passed / active total)
Current generation number
Total spawned (all-time strategy births)
Total culled (all-time strategy deaths)
Column 2: Aggregate Performance
Total trades across all active strategies
Aggregate win rate (%) - color-coded:
Green (>55%)
Orange (45-55%)
Red (<45%)
Total P&L in R-multiples - color-coded by positive/negative
Best fitness score in population (format: #.###)
MAS - Minimum Adaptation Score (cull threshold, format: #.###)
Column 3: Current Signal Status
Status indicator:
"▲ LONG" (green) if selected strategy in long position
"▼ SHORT" (red) if selected strategy in short position
"⏳ FORMING" (orange) if signal persisting but not yet executed
"○ WAITING" (gray) if no active signal
Confidence percentage (0-100%, format: #.#%)
Quality assessment:
"🔥 ELITE" (cyan) for 85%+ probability
"✓ STRONG" (bright green) for 75-85%
"○ GOOD" (green) for 65-75%
"- LOW" (dim) for <65%
Confluence score (X/3 format)
Signal age:
"X bars" if signal forming
"IN TRADE" if position active
"---" if no signal
Column 4: Selected Strategy Details
Strategy ID number (#X format)
Validation status:
"✓ VAL" (green) if WFO validated
"○ TRAIN" (orange) if still in training/testing phase
Generation number (GX format)
Personal fitness score (format: #.### with color coding)
Trade count
P&L and win rate (format: #.#R (##%) with color coding)
Color Scheme:
Panel background: theme.panel_bg (dark, low opacity)
Panel headers: theme.panel_header (slightly lighter)
Primary text: theme.text_primary (bright, high contrast)
Secondary text: theme.text_secondary (dim, lower contrast)
Positive metrics: theme.metric_positive (green)
Warning metrics: theme.metric_warning (orange)
Negative metrics: theme.metric_negative (red)
Special markers: theme.validated_marker, theme.spawn_marker
Update Frequency: Only on barstate.islast (current bar) to minimize CPU usage
Purpose:
Quick overview of entire system state
No need to check multiple indicators
Trading decisions informed by population health, regime state, and signal quality
Transparency into what AGE is thinking
🔍 DIAGNOSTICS PANEL (Optional, Default: OFF)
Detailed signal quality tracking for optimization and debugging:
Panel Structure (3 columns × 8 rows)
Position: Bottom-right corner (doesn't interfere with main dashboard)
Header Row:
Column 0: "🔍 DIAGNOSTICS"
Column 1: "COUNT"
Column 2: "%"
Metrics Tracked (for selected strategy only):
Total Evaluated:
Every signal that passed initial calculation (direction ≠ 0)
Represents total opportunities considered
✓ Passed:
Signals that passed quality gate and executed
Green color coding
Percentage of evaluated signals
Rejection Breakdown:
⨯ Probability:
Rejected because probability < minimum threshold
Most common rejection reason typically
⨯ Confluence:
Rejected because confluence < minimum required (e.g., only 1 of 3 indicators agreed)
⨯ Trend:
Rejected because signal opposed strong trend
Indicates counter-trend protection working
⨯ Regime:
Rejected because volatile regime detected and probability wasn't high enough to override
Shows regime filter in action
⨯ Volume:
Rejected because volume < 70% of 20-bar average
Indicates volume confirmation requirement
Color Coding:
Passed count: Green (success metric)
Rejection counts: Red (failure metrics)
Percentages: Gray (neutral, informational)
Performance Cost: Slight CPU overhead for tracking counters. Disable when not actively optimizing settings.
How to Use Diagnostics
Scenario 1: Too Few Signals
Evaluated: 200
Passed: 10 (5%)
⨯ Probability: 120 (60%)
⨯ Confluence: 40 (20%)
⨯ Others: 30 (15%)
Diagnosis: Probability threshold too high for this strategy's DNA.
Solution: Lower min probability from 65% to 60%, or allow strategy more time to evolve better DNA.
Scenario 2: Too Many False Signals
Evaluated: 200
Passed: 80 (40%)
Strategy win rate: 45%
Diagnosis: Quality gate too loose, letting low-quality signals through.
Solution: Raise min probability to 70%, or increase min confluence to 3 (all indicators must agree).
Scenario 3: Regime-Specific Issues
⨯ Regime: 90 (45% of rejections)
Diagnosis: Frequent volatile regime detection blocking otherwise good signals.
Solution: Either accept fewer trades during chaos (recommended), or disable regime filter if you want signals regardless of market state.
Optimization Workflow:
Enable diagnostics
Run 200+ bars
Analyze rejection patterns
Adjust settings based on data
Re-run and compare pass rate
Disable diagnostics when satisfied
⚙️ CONFIGURATION GUIDE
🧬 Evolution Engine Settings
Enable AGE Evolution (Default: ON):
ON: Full genetic algorithm (recommended for best results)
OFF: Uses only 4 seed strategies, no spawning/culling (static population for comparison testing)
Max Population (4-12, Default: 8):
Higher = more diversity, more exploration, slower performance
Lower = faster computation, less exploration, risk of premature convergence
Sweet spot: 6-8 for most use cases
4 = minimum for meaningful evolution
12 = maximum before diminishing returns
Min Population (2-4, Default: 3):
Safety floor - system never culls below this count
Prevents population extinction during harsh selection
Should be at least half of max population
Elite Preservation (1-3, Default: 2):
Top N performers completely immune to culling
Ensures best genes always survive
1 = minimal protection, aggressive selection
2 = balanced (recommended)
3 = conservative, slower gene pool turnover
Historical: Spawn Interval (10-100, Default: 30):
Bars between spawning new strategies during historical data
Lower = faster evolution, more exploration
Higher = slower evolution, more evaluation time per strategy
30 bars = ~1-2 hours on 15min chart
Historical: Cull Interval (20-200, Default: 60):
Bars between culling weak strategies during historical data
Should be 2x spawn interval for balanced churn
Lower = aggressive selection pressure
Higher = patient evaluation
Live: Spawn Interval (100-500, Default: 200):
Bars between spawning during live trading
Much slower than historical for stability
Prevents population chaos during live trading
200 bars = ~1.5 trading days on 15min chart
Live: Cull Interval (200-1000, Default: 400):
Bars between culling during live trading
Should be 2x live spawn interval
Conservative removal during live trading
Historical: Mutation Rate (0.05-0.40, Default: 0.20):
Probability each gene mutates during breeding (20% = 2 out of 10 genes on average)
Higher = more exploration, slower convergence
Lower = more exploitation, faster convergence but risk of local optima
20% balances exploration vs exploitation
Live: Mutation Rate (0.02-0.20, Default: 0.08):
Mutation rate during live trading
Much lower for stability (don't want population to suddenly degrade)
8% = mostly inherits parent genes with small tweaks
Mutation Strength (0.05-0.25, Default: 0.12):
How much genes change when mutated (% of gene's total range)
0.05 = tiny nudges (fine-tuning)
0.12 = moderate jumps (recommended)
0.25 = large leaps (aggressive exploration)
Example: If gene range is 0.5-2.0, 12% strength = ±0.18 possible change
📈 Signal Quality Settings
Min Signal Probability (0.55-0.80, Default: 0.65):
Quality gate threshold - signals below this never generate
0.55-0.60 = More signals, accept lower confidence (higher risk)
0.65 = Institutional-grade balance (recommended)
0.70-0.75 = Fewer but higher-quality signals (conservative)
0.80+ = Very selective, very few signals (ultra-conservative)
Min Confluence Score (1-3, Default: 2):
Required indicator agreement before signal generates
1 = Any single indicator can trigger (not recommended - too many false signals)
2 = Requires 2 of 3 indicators agree (RECOMMENDED for balance)
3 = All 3 must agree (very selective, few signals, high quality)
Base Persistence Bars (1-5, Default: 2):
Base bars signal must persist before entry
System adapts automatically:
High probability signals (75%+) enter 1 bar faster
Low probability signals (<68%) need 1 bar more
Trending regime: -1 bar (faster entries)
Volatile regime: +1 bar (more confirmation)
1 = Immediate entry after quality gate (responsive but prone to whipsaw)
2 = Balanced confirmation (recommended)
3-5 = Patient confirmation (slower but more reliable)
Cooldown After Trade (3-20, Default: 8):
Bars to wait after exit before next entry allowed
Prevents overtrading and revenge trading
3 = Minimal cooldown (active trading)
8 = Balanced (recommended)
15-20 = Conservative (position trading)
Entropy Length (10-50, Default: 20):
Lookback period for market order/disorder calculation
Lower = more responsive to regime changes (noisy)
Higher = more stable regime detection (laggy)
20 = works across most timeframes
Momentum Length (5-30, Default: 14):
Period for RSI/ROC calculations
14 = standard (RSI default)
Lower = more signals, less reliable
Higher = fewer signals, more reliable
Structure Length (20-100, Default: 50):
Lookback for support/resistance swing range
20 = short-term swings (day trading)
50 = medium-term structure (recommended)
100 = major structure (position trading)
Trend EMA Length (20-100, Default: 50):
EMA period for trend detection and direction bias
20 = short-term trend (responsive)
50 = medium-term trend (recommended)
100 = long-term trend (position trading)
ATR Period (5-30, Default: 14):
Period for volatility measurement
14 = standard ATR
Lower = more responsive to vol changes
Higher = smoother vol calculation
📊 Volatility Scaling (DVS) Settings
Enable DVS (Default: ON):
Dynamic volatility scaling for adaptive stop/target placement
Highly recommended to leave ON
OFF only for testing fixed-distance stops
DVS Method (Default: Ensemble):
ATR Ratio: Simple, fast, single-method (good for beginners)
Parkinson: High-low range based (good for intraday)
Garman-Klass: OHLC based (sophisticated, considers gaps)
Ensemble: Median of all three (RECOMMENDED - most robust)
DVS Memory (20-200, Default: 100):
Lookback for baseline volatility comparison
20 = very responsive to vol changes (can overreact)
100 = balanced adaptation (recommended)
200 = slow, stable baseline (minimizes false vol signals)
DVS Sensitivity (0.3-1.5, Default: 0.7):
How much volatility affects scaling (power-law exponent)
0.3 = Conservative, heavily dampens vol impact (cube root)
0.5 = Moderate dampening (square root)
0.7 = Balanced response (recommended)
1.0 = Linear, full 1:1 vol response
1.5 = Aggressive, amplified response (exponential)
🔬 Walk-Forward Optimization Settings
Enable WFO (Default: ON):
Out-of-sample validation to prevent overfitting
Highly recommended to leave ON
OFF only for testing or if you want unvalidated strategies
Training Window (100-500, Default: 250):
Bars for in-sample optimization
100 = fast validation, less data (risky)
250 = balanced (recommended) - about 1-2 months on daily, 1-2 weeks on 15min
500 = patient validation, more data (conservative)
Testing Window (30-200, Default: 75):
Bars for out-of-sample validation
Should be ~30% of training window
30 = minimal test (fast validation)
75 = balanced (recommended)
200 = extensive test (very conservative)
Min Trades for Validation (3-15, Default: 5):
Required trades in BOTH training AND testing periods
3 = minimal sample (risky, fast validation)
5 = balanced (recommended)
10+ = conservative (slow validation, high confidence)
WFO Efficiency Threshold (0.3-0.9, Default: 0.55):
Minimum test/train performance ratio required
0.30 = Very loose (test must be 30% as good as training)
0.55 = Balanced (recommended) - test must be 55% as good
0.70+ = Strict (test must closely match training)
Higher = fewer validated strategies, lower risk of overfitting
🎨 Premium Visuals Settings
Visual Theme:
Neon Genesis: Cyberpunk aesthetic (cyan/magenta/purple)
Carbon Fiber: Industrial look (blue/red/gray)
Quantum Blue: Quantum computing (blue/purple/pink)
Aurora: Northern lights (teal/orange/purple)
⚡ Gradient Probability Cloud (Default: ON):
Multi-layer gradient showing signal buildup
Turn OFF if chart lags or for cleaner look
Cloud Gradient Layers (3-15, Default: 7):
More layers = smoother gradient, more CPU intensive
Fewer layers = faster, blockier appearance
🎗️ Population Fitness Ribbon (Default: ON):
Histogram showing fitness distribution
Turn OFF for cleaner chart
Ribbon Layers (5-20, Default: 10):
More layers = finer fitness detail
Fewer layers = simpler histogram
⭕ Signal Confidence Halo (Default: ON):
Circular indicator around entry signals
Size/brightness scales with probability
Minimal performance cost
🔬 Evolution Event Markers (Default: ON):
Diamond (spawn) and X (cull) markers
Shows genetic algorithm activity
Minimal performance cost
🎯 Stop/Target Lines (Default: ON):
Shows shadow portfolio stop/target levels
Turn OFF for cleaner chart (recommended for screenshots/presentations)
📊 Enhanced Dashboard (Default: ON):
Comprehensive metrics panel
Should stay ON unless you want zero overlays
🔍 Diagnostics Panel (Default: OFF):
Detailed signal rejection tracking
Turn ON when optimizing settings
Turn OFF during normal use (slight performance cost)
📈 USAGE WORKFLOW - HOW TO USE THIS INDICATOR
Phase 1: Initial Setup & Learning
Add AGE to your chart
Recommended timeframes: 15min, 30min, 1H (best signal-to-noise ratio)
Works on: 5min (day trading), 4H (swing trading), Daily (position trading)
Load 1000+ bars for sufficient evolution history
Let the population evolve (100+ bars minimum)
First 50 bars: Random exploration, poor results expected
Bars 50-150: Population converging, fitness improving
Bars 150+: Stable performance, validated strategies emerging
Watch the dashboard metrics
Population should grow toward max capacity
Generation number should advance regularly
Validated strategies counter should increase
Best fitness should trend upward toward 0.50-0.70 range
Observe evolution markers
Diamond markers (cyan) = new strategies spawning
X markers (red) = weak strategies being culled
Frequent early activity = healthy evolution
Activity slowing = population stabilizing
Be patient. Evolution takes time. Don't judge performance before 150+ bars.
Phase 2: Signal Observation
Watch signals form
Gradient cloud builds up 2-3 bars before entry
Cloud brightness = probability strength
Cloud thickness = signal persistence
Check signal quality
Look at confidence halo size when entry marker appears
Large bright halo = elite setup (85%+)
Medium halo = strong setup (75-85%)
Small halo = good setup (65-75%)
Verify market conditions
Check trend EMA color (green = uptrend, red = downtrend, gray = choppy)
Check background tint (green = trending, red = volatile, clear = choppy)
Trending background + aligned signal = ideal conditions
Review dashboard signal status
Current Signal column shows:
Status (Long/Short/Forming/Waiting)
Confidence % (actual probability value)
Quality assessment (Elite/Strong/Good)
Confluence score (2/3 or 3/3 preferred)
Only signals meeting ALL quality gates appear on chart. If you're not seeing signals, population is either still learning or market conditions aren't suitable.
Phase 3: Manual Trading Execution
When Long Signal Fires:
Verify confidence level (dashboard or halo size)
Confirm trend alignment (EMA sloping up, green color)
Check regime (preferably trending or choppy, avoid volatile)
Enter long manually on your broker platform
Set stop loss at displayed stop line level (if lines enabled), or use your own risk management
Set take profit at displayed target line level, or trail manually
Monitor position - exit if X marker appears (signal reversal)
When Short Signal Fires:
Same verification process
Confirm downtrend (EMA sloping down, red color)
Enter short manually
Use displayed stop/target levels or your own
AGE tells you WHEN and HOW CONFIDENT. You decide WHETHER and HOW MUCH.
Phase 4: Set Up Alerts (Never Miss a Signal)
Right-click on indicator name in legend
Select "Add Alert"
Choose condition:
"AGE Long" = Long entry signal fired
"AGE Short" = Short entry signal fired
"AGE Exit" = Position reversal/exit signal
Set notification method:
Sound alert (popup on chart)
Email notification
Webhook to phone/trading platform
Mobile app push notification
Name the alert (e.g., "AGE BTCUSD 15min Long")
Save alert
Recommended: Set alerts for both long and short, enable mobile push notifications. You'll get alerted in real-time even if not watching charts.
Phase 5: Monitor Population Health
Weekly Review:
Check dashboard Population column:
Active count should be near max (6-8 of 8)
Validated count should be >50% of active
Generation should be advancing (1-2 per week typical)
Check dashboard Performance column:
Aggregate win rate should be >50% (target: 55-65%)
Total P&L should be positive (may fluctuate)
Best fitness should be >0.50 (target: 0.55-0.70)
MAS should be declining slowly (normal adaptation)
Check Active Strategy column:
Selected strategy should be validated (✓ VAL)
Personal fitness should match best fitness
Trade count should be accumulating
Win rate should be >50%
Warning Signs:
Zero validated strategies after 300+ bars = settings too strict or market unsuitable
Best fitness stuck <0.30 = population struggling, consider parameter adjustment
No spawning/culling for 200+ bars = evolution stalled (may be optimal or need reset)
Aggregate win rate <45% sustained = system not working on this instrument/timeframe
Health Check Pass:
50%+ strategies validated
Best fitness >0.50
Aggregate win rate >52%
Regular spawn/cull activity
Selected strategy validated
Phase 6: Optimization (If Needed)
Enable Diagnostics Panel (bottom-right) for data-driven tuning:
Problem: Too Few Signals
Evaluated: 200
Passed: 8 (4%)
⨯ Probability: 140 (70%)
Solutions:
Lower min probability: 65% → 60% or 55%
Reduce min confluence: 2 → 1
Lower base persistence: 2 → 1
Increase mutation rate temporarily to explore new genes
Check if regime filter is blocking signals (⨯ Regime high?)
Problem: Too Many False Signals
Evaluated: 200
Passed: 90 (45%)
Win rate: 42%
Solutions:
Raise min probability: 65% → 70% or 75%
Increase min confluence: 2 → 3
Raise base persistence: 2 → 3
Enable WFO if disabled (validates strategies before use)
Check if volume filter is being ignored (⨯ Volume low?)
Problem: Counter-Trend Losses
⨯ Trend: 5 (only 5% rejected)
Losses often occur against trend
Solutions:
System should already filter trend opposition
May need stronger trend requirement
Consider only taking signals aligned with higher timeframe trend
Use longer trend EMA (50 → 100)
Problem: Volatile Market Whipsaws
⨯ Regime: 100 (50% rejected by volatile regime)
Still getting stopped out frequently
Solutions:
System is correctly blocking volatile signals
Losses happening because vol filter isn't strict enough
Consider not trading during volatile periods (respect the regime)
Or disable regime filter and accept higher risk
Optimization Workflow:
Enable diagnostics
Run 200+ bars with current settings
Analyze rejection patterns and win rate
Make ONE change at a time (scientific method)
Re-run 200+ bars and compare results
Keep change if improvement, revert if worse
Disable diagnostics when satisfied
Never change multiple parameters at once - you won't know what worked.
Phase 7: Multi-Instrument Deployment
AGE learns independently on each chart:
Recommended Strategy:
Deploy AGE on 3-5 different instruments
Different asset classes ideal (e.g., ES futures, EURUSD, BTCUSD, SPY, Gold)
Each learns optimal strategies for that instrument's personality
Take signals from all 5 charts
Natural diversification reduces overall risk
Why This Works:
When one market is choppy, others may be trending
Different instruments respond to different news/catalysts
Portfolio-level win rate more stable than single-instrument
Evolution explores different parameter spaces on each chart
Setup:
Same settings across all charts (or customize if preferred)
Set alerts for all
Take every validated signal across all instruments
Position size based on total account (don't overleverage any single signal)
⚠️ REALISTIC EXPECTATIONS - CRITICAL READING
What AGE Can Do
✅ Generate probability-weighted signals using genetic algorithms
✅ Evolve strategies in real-time through natural selection
✅ Validate strategies on out-of-sample data (walk-forward optimization)
✅ Adapt to changing market conditions automatically over time
✅ Provide comprehensive metrics on population health and signal quality
✅ Work on any instrument, any timeframe, any broker
✅ Improve over time as weak strategies are culled and fit strategies breed
What AGE Cannot Do
❌ Win every trade (typical win rate: 55-65% at best)
❌ Predict the future with certainty (markets are probabilistic, not deterministic)
❌ Work perfectly from bar 1 (needs 100-150 bars to learn and stabilize)
❌ Guarantee profits under all market conditions
❌ Replace your trading discipline and risk management
❌ Execute trades automatically (this is an indicator, not a strategy)
❌ Prevent all losses (drawdowns are normal and expected)
❌ Adapt instantly to regime changes (re-learning takes 50-100 bars)
Performance Realities
Typical Performance After Evolution Stabilizes (150+ bars):
Win Rate: 55-65% (excellent for trend-following systems)
Profit Factor: 1.5-2.5 (realistic for validated strategies)
Signal Frequency: 5-15 signals per 100 bars (quality over quantity)
Drawdown Periods: 20-40% of time in equity retracement (normal trading reality)
Max Consecutive Losses: 5-8 losses possible even with 60% win rate (probability says this is normal)
Evolution Timeline:
Bars 0-50: Random exploration, learning phase - poor results expected, don't judge yet
Bars 50-150: Population converging, fitness climbing - results improving
Bars 150-300: Stable performance, most strategies validated - consistent results
Bars 300+: Mature population, optimal genes dominant - best results
Market Condition Dependency:
Trending Markets: AGE excels - clear directional moves, high-probability setups
Choppy Markets: AGE struggles - fewer signals generated, lower win rate
Volatile Markets: AGE cautious - higher rejection rate, wider stops, fewer trades
Market Regime Changes:
When market shifts from trending to choppy overnight
Validated strategies can become temporarily invalidated
AGE will adapt through evolution, but not instantly
Expect 50-100 bar re-learning period after major regime shifts
Fitness may temporarily drop then recover
This is NOT a holy grail. It's a sophisticated signal generator that learns and adapts using genetic algorithms. Your success depends on:
Patience during learning periods (don't abandon after 3 losses)
Proper position sizing (risk 0.5-2% per trade, not 10%)
Following signals consistently (cherry-picking defeats statistical edge)
Not abandoning system prematurely (give it 200+ bars minimum)
Understanding probability (60% win rate means 40% of trades WILL lose)
Respecting market conditions (trending = trade more, choppy = trade less)
Managing emotions (AGE is emotionless, you need to be too)
Expected Drawdowns:
Single-strategy max DD: 10-20% of equity (normal)
Portfolio across multiple instruments: 5-15% (diversification helps)
Losing streaks: 3-5 consecutive losses expected periodically
No indicator eliminates risk. AGE manages risk through:
Quality gates (rejecting low-probability signals)
Confluence requirements (multi-indicator confirmation)
Persistence requirements (no knee-jerk reactions)
Regime awareness (reduced trading in chaos)
Walk-forward validation (preventing overfitting)
But it cannot prevent all losses. That's inherent to trading.
🔧 TECHNICAL SPECIFICATIONS
Platform: TradingView Pine Script v5
Indicator Type: Overlay indicator (plots on price chart)
Execution Type: Signals only - no automatic order placement
Computational Load:
Moderate to High (genetic algorithms + shadow portfolios)
8 strategies × shadow portfolio simulation = significant computation
Premium visuals add additional load (gradient cloud, fitness ribbon)
TradingView Resource Limits (Built-in Caps):
Max Bars Back: 500 (sufficient for WFO and evolution)
Max Labels: 100 (plenty for entry/exit markers)
Max Lines: 150 (adequate for stop/target lines)
Max Boxes: 50 (not heavily used)
Max Polylines: 100 (confidence halos)
Recommended Chart Settings:
Timeframe: 15min to 1H (optimal signal/noise balance)
5min: Works but noisier, more signals
4H/Daily: Works but fewer signals
Bars Loaded: 1000+ (ensures sufficient evolution history)
Replay Mode: Excellent for testing without risk
Performance Optimization Tips:
Disable gradient cloud if chart lags (most CPU intensive visual)
Disable fitness ribbon if still laggy
Reduce cloud layers from 7 to 3
Reduce ribbon layers from 10 to 5
Turn off diagnostics panel unless actively tuning
Close other heavy indicators to free resources
Browser/Platform Compatibility:
Works on all modern browsers (Chrome, Firefox, Safari, Edge)
Mobile app supported (full functionality on phone/tablet)
Desktop app supported (best performance)
Web version supported (may be slower on older computers)
Data Requirements:
Real-time or delayed data both work
No special data feeds required
Works with TradingView's standard data
Historical + live data seamlessly integrated
🎓 THEORETICAL FOUNDATIONS
AGE synthesizes advanced concepts from multiple disciplines:
Evolutionary Computation
Genetic Algorithms (Holland, 1975): Population-based optimization through natural selection metaphor
Tournament Selection: Fitness-based parent selection with diversity preservation
Crossover Operators: Fitness-weighted gene recombination from two parents
Mutation Operators: Random gene perturbation for exploration of new parameter space
Elitism: Preservation of top N performers to prevent loss of best solutions
Adaptive Parameters: Different mutation rates for historical vs. live phases
Technical Analysis
Support/Resistance: Price structure within swing ranges
Trend Following: EMA-based directional bias
Momentum Analysis: RSI, ROC, MACD composite indicators
Volatility Analysis: ATR-based risk scaling
Volume Confirmation: Trade activity validation
Information Theory
Shannon Entropy (1948): Quantification of market order vs. disorder
Signal-to-Noise Ratio: Directional information vs. random walk
Information Content: How much "information" a price move contains
Statistics & Probability
Walk-Forward Analysis: Rolling in-sample/out-of-sample optimization
Out-of-Sample Validation: Testing on unseen data to prevent overfitting
Monte Carlo Principles: Shadow portfolio simulation with realistic execution
Expectancy Theory: Win rate × avg win - loss rate × avg loss
Probability Distributions: Signal confidence quantification
Risk Management
ATR-Based Stops: Volatility-normalized risk per trade
Volatility Regime Detection: Market state classification (trending/choppy/volatile)
Drawdown Control: Peak-to-trough equity measurement
R-Multiple Normalization: Performance measurement in risk units
Machine Learning Concepts
Online Learning: Continuous adaptation as new data arrives
Fitness Functions: Multi-objective optimization (win rate + expectancy + drawdown)
Exploration vs. Exploitation: Balance between trying new strategies and using proven ones
Overfitting Prevention: Walk-forward validation as regularization
Novel Contribution:
AGE is the first TradingView indicator to apply genetic algorithms to real-time indicator parameter optimization while maintaining strict anti-overfitting controls through walk-forward validation.
Most "adaptive" indicators simply recalibrate lookback periods or thresholds. AGE evolves entirely new strategies through competitive selection - it's not parameter tuning, it's Darwinian evolution of trading logic itself.
The combination of:
Genetic algorithm population management
Shadow portfolio simulation for realistic fitness evaluation
Walk-forward validation to prevent overfitting
Multi-indicator confluence for signal quality
Dynamic volatility scaling for adaptive risk
...creates a system that genuinely learns and improves over time while avoiding the curse of curve-fitting that plagues most optimization approaches.
🏗️ DEVELOPMENT NOTES
This project represents months of intensive development, facing significant technical challenges:
Challenge 1: Making Genetics Actually Work
Early versions spawned garbage strategies that polluted the gene pool:
Random gene combinations produced nonsensical parameter sets
Weak strategies survived too long, dragging down population
No clear convergence toward optimal solutions
Solution:
Comprehensive fitness scoring (4 factors: win rate, P&L, expectancy, drawdown)
Elite preservation (top 2 always protected)
Walk-forward validation (unproven strategies penalized 30%)
Tournament selection (fitness-weighted breeding)
Adaptive culling (MAS decay creates increasing selection pressure)
Challenge 2: Balancing Evolution Speed vs. Stability
Too fast = population chaos, no convergence. Too slow = can't adapt to regime changes.
Solution:
Dual-phase timing: Fast evolution during historical (30/60 bar intervals), slow during live (200/400 bar intervals)
Adaptive mutation rates: 20% historical, 8% live
Spawn/cull ratio: Always 2:1 to prevent population collapse
Challenge 3: Shadow Portfolio Accuracy
Needed realistic trade simulation without lookahead bias:
Can't peek at future bars for exits
Must track multiple portfolios simultaneously
Stop/target checks must use bar's high/low correctly
Solution:
Entry on close (realistic)
Exit checks on current bar's high/low (realistic)
Independent position tracking per strategy
Cooldown periods to prevent unrealistic rapid re-entry
ATR-normalized P&L (R-multiples) for fair comparison across volatility regimes
Challenge 4: Pine Script Compilation Limits
Hit TradingView's execution limits multiple times:
Too many array operations
Too many variables
Too complex conditional logic
Solution:
Optimized data structures (single DNA array instead of 8 separate arrays)
Minimal visual overlays (only essential plots)
Efficient fitness calculations (vectorized where possible)
Strategic use of barstate.islast to minimize dashboard updates
Challenge 5: Walk-Forward Implementation
Standard WFO is difficult in Pine Script:
Can't easily "roll forward" through historical data
Can't re-optimize strategies mid-stream
Must work in real-time streaming environment
Solution:
Age-based phase detection (first 250 bars = training, next 75 = testing)
Separate metric tracking for train vs. test
Efficiency calculation at fixed interval (after test period completes)
Validation flag persists for strategy lifetime
Challenge 6: Signal Quality Control
Early versions generated too many signals with poor win rates:
Single indicators produced excessive noise
No trend alignment
No regime awareness
Instant entries on single-bar spikes
Solution:
Three-layer confluence system (entropy + momentum + structure)
Minimum 2-of-3 agreement requirement
Trend alignment checks (penalty for counter-trend)
Regime-based probability adjustments
Persistence requirements (signals must hold multiple bars)
Volume confirmation
Quality gate (probability + confluence thresholds)
The Result
A system that:
Truly evolves (not just parameter sweeps)
Truly validates (out-of-sample testing)
Truly adapts (ongoing competition and breeding)
Stays within TradingView's platform constraints
Provides institutional-quality signals
Maintains transparency (full metrics dashboard)
Development time: 3+ months of iterative refinement
Lines of code: ~1500 (highly optimized)
Test instruments: ES, NQ, EURUSD, BTCUSD, SPY, AAPL
Test timeframes: 5min, 15min, 1H, Daily
🎯 FINAL WORDS
The Adaptive Genesis Engine is not just another indicator - it's a living system that learns, adapts, and improves through the same principles that drive biological evolution. Every bar it observes adds to its experience. Every strategy it spawns explores new parameter combinations. Every strategy it culls removes weakness from the gene pool.
This is evolution in action on your charts.
You're not getting a static formula locked in time. You're getting a system that thinks , that competes , that survives through natural selection. The strongest strategies rise to the top. The weakest die. The gene pool improves generation after generation.
AGE doesn't claim to predict the future - it adapts to whatever the future brings. When markets shift from trending to choppy, from calm to volatile, from bullish to bearish - AGE evolves new strategies suited to the new regime.
Use it on any instrument. Any timeframe. Any market condition. AGE will adapt.
This indicator gives you the pure signal intelligence. How you choose to act on it - position sizing, risk management, execution discipline - that's your responsibility. AGE tells you when and how confident . You decide whether and how much .
Trust the process. Respect the evolution. Let Darwin work.
"In markets, as in nature, it is not the strongest strategies that survive, nor the most intelligent - but those most responsive to change."
Taking you to school. — Dskyz, Trade with insight. Trade with anticipation.
— Happy Holiday's
Apex Trend & Liquidity Master with TP/SLThe Apex Trend & Liquidity Master is a systematic trading framework that identifies trend direction and key structural price levels for entry and exit decisions. The system uses a volatility-adaptive trend detection mechanism built on Hull Moving Averages with ATR-based bands to filter consolidation periods and isolate directional moves.
The liquidity detection engine identifies potential reversal zones by marking swing highs and lows that meet statistical significance thresholds. These zones represent areas where institutional order flow previously caused price rejection. Zones remain active until price closes through them, indicating mitigation of the level.
This implementation is an enhanced derivative of the original system with fully automated risk management. Stop losses are calculated using ATR multiples with entry candle wick protection as a minimum threshold, while take profits maintain a fixed 3:1 risk-reward ratio. An additional exit mechanism closes profitable positions when price reaches opposing supply or demand zones, providing early profit-taking at probable reversal points before full target completion.
Entry signals generate only on trend changes when volume exceeds average levels, reducing false breakouts in ranging conditions. The system includes complete position tracking with three distinct exit types: take profit hits, stop loss hits, and profitable zone contact exits. All calculations use confirmed historical data with no forward-looking bias, though supply/demand zone identification operates with a confirmation lag inherent to pivot point detection.
Stochastic Hash Strat [Hash Capital Research]# Stochastic Hash Strategy by Hash Capital Research
## 🎯 What Is This Strategy?
The **Stochastic Slow Strategy** is a momentum-based trading system that identifies oversold and overbought market conditions to capture mean-reversion opportunities. Think of it as a "buy low, sell high" approach with smart mathematical filters that remove emotion from your trading decisions.
Unlike fast-moving indicators that generate excessive noise, this strategy uses **smoothed stochastic oscillators** to identify only the highest-probability setups when momentum truly shifts.
---
## 💡 Why This Strategy Works
Most traders fail because they:
- **Chase prices** after big moves (buying high, selling low)
- **Overtrade** in choppy, directionless markets
- **Exit too early** or hold losses too long
This strategy solves all three problems:
1. **Entry Discipline**: Only trades when the stochastic oscillator crosses in extreme zones (oversold for longs, overbought for shorts)
2. **Cooldown Filter**: Prevents revenge trading by forcing a waiting period after each trade
3. **Fixed Risk/Reward**: Pre-defined stop-loss and take-profit levels ensure consistent risk management
**The Math Behind It**: The stochastic oscillator measures where the current price sits relative to its recent high-low range. When it's below 25, the market is oversold (time to buy). When above 70, it's overbought (time to sell). The crossover with its moving average confirms momentum is shifting.
---
## 📊 Best Markets & Timeframes
### ⭐ OPTIMAL PERFORMANCE:
**Crude Oil (WTI) - 12H Timeframe**
- **Why it works**: Oil markets have predictable volatility patterns and respect technical levels
**AAVE/USD - 4H to 12H Timeframe**
- **Why it works**: DeFi tokens exhibit strong momentum cycles with clear extremes
### ✅ Also Works Well On:
- **BTC/USD** (12H, Daily) - Lower frequency but high win rate
- **ETH/USD** (8H, 12H) - Balanced volatility and liquidity
- **Gold (XAU/USD)** (Daily) - Classic mean-reversion asset
- **EUR/USD** (4H, 8H) - Lower volatility, requires patience
### ❌ Avoid Using On:
- Timeframes below 4H (too much noise)
- Low-liquidity altcoins (wide spreads kill performance)
- Strongly trending markets without pullbacks (Bitcoin in 2021)
- News-driven instruments during major events
---
## 🎛️ Understanding The Settings
### Core Stochastic Parameters
**Stochastic Length (Default: 16)**
- Controls the lookback period for price comparison
- Lower = faster reactions, more signals (10-14 for volatile markets)
- Higher = smoother signals, fewer trades (16-21 for stable markets)
- **Pro tip**: Use 10 for crypto 4H, 16 for commodities 12H
**Overbought Level (Default: 70)**
- Threshold for short entries
- Lower values (65-70) = more trades, earlier entries
- Higher values (75-80) = fewer but higher-conviction trades
- **Sweet spot**: 70 works for most assets
**Oversold Level (Default: 25)**
- Threshold for long entries
- Higher values (25-30) = more trades, earlier entries
- Lower values (15-20) = fewer but stronger bounce setups
- **Sweet spot**: 20-25 depending on market conditions
**Smooth K & Smooth D (Default: 7 & 3)**
- Additional smoothing to filter out whipsaws
- K=7 makes the indicator slower and more reliable
- D=3 is the signal line that confirms the trend
- **Don't change these unless you know what you're doing**
---
### Risk Management
**Stop Loss % (Default: 2.2%)**
- Automatically exits losing trades
- Should be 1.5x to 2x your average market volatility
- Too tight = death by a thousand cuts
- Too wide = uncontrolled losses
- **Calibration**: Check ATR indicator and set SL slightly above it
**Take Profit % (Default: 7%)**
- Automatically exits winning trades
- Should be 2.5x to 3x your stop loss (reward-to-risk ratio)
- This default gives 7% / 2.2% = 3.18:1 R:R
- **The golden rule**: Never have R:R below 2:1
---
### Trade Filters
**Bar Cooldown Filter (Default: ON, 3 bars)**
- **What it does**: Forces you to wait X bars after closing a trade before entering a new one
- **Why it matters**: Prevents emotional revenge trading and overtrading in choppy markets
- **Settings guide**:
- 3 bars = Standard (good for most cases)
- 5-7 bars = Conservative (oil, slow-moving assets)
- 1-2 bars = Aggressive (only for experienced traders)
**Exit on Opposite Extreme (Default: ON)**
- Closes your long when stochastic hits overbought (and vice versa)
- Acts as an early profit-taking mechanism
- **Leave this ON** unless you're testing other exit strategies
**Divergence Filter (Default: OFF)**
- Looks for price/momentum divergences for additional confirmation
- **When to enable**: Trending markets where you want fewer but higher-quality trades
- **Keep OFF for**: Mean-reverting markets (oil, forex, most of the time)
---
## 🚀 Quick Start Guide
### Step 1: Set Up in TradingView
1. Open TradingView and navigate to your chart
2. Click "Pine Editor" at the bottom
3. Copy and paste the strategy code
4. Click "Add to Chart"
5. The strategy will appear in a separate pane below your price chart
### Step 2: Choose Your Market
**If you're trading Crude Oil:**
- Timeframe: 12H
- Keep all default settings
- Watch for signals during London/NY overlap (8am-11am EST)
**If you're trading AAVE or crypto:**
- Timeframe: 4H or 12H
- Consider these adjustments:
- Stochastic Length: 10-14 (faster)
- Oversold: 20 (more aggressive)
- Take Profit: 8-10% (higher targets)
### Step 3: Wait for Your First Signal
**LONG Entry** (Green circle appears):
- Stochastic crosses up below oversold level (25)
- Price likely near recent lows
- System places limit order at take profit and stop loss
**SHORT Entry** (Red circle appears):
- Stochastic crosses down above overbought level (70)
- Price likely near recent highs
- System places limit order at take profit and stop loss
**EXIT** (Orange circle):
- Position closes either at stop, target, or opposite extreme
- Cooldown period begins
### Step 4: Let It Run
The biggest mistake? **Interfering with the system.**
- Don't close trades early because you're scared
- Don't skip signals because you "have a feeling"
- Don't increase position size after a big win
- Don't revenge trade after a loss
**Follow the system or don't use it at all.**
---
### Important Risks:
1. **Drawdown Pain**: You WILL experience losing streaks of 5-7 trades. This is mathematically normal.
2. **Whipsaw Markets**: Choppy, range-bound conditions can trigger multiple small losses.
3. **Gap Risk**: Overnight gaps can cause your actual fill to be worse than the stop loss.
4. **Slippage**: Real execution prices differ from backtested prices (factor in 0.1-0.2% slippage).
---
## 🔧 Optimization Guide
### When to Adjust Settings:
**Market Volatility Increased?**
- Widen stop loss by 0.5-1%
- Increase take profit proportionally
- Consider increasing cooldown to 5-7 bars
**Getting Too Few Signals?**
- Decrease stochastic length to 10-12
- Increase oversold to 30, decrease overbought to 65
- Reduce cooldown to 2 bars
**Getting Too Many Losses?**
- Increase stochastic length to 18-21 (slower, smoother)
- Enable divergence filter
- Increase cooldown to 5+ bars
- Verify you're on the right timeframe
### A/B Testing Method:
1. **Run default settings for 50 trades** on your chosen market
2. Document: Win rate, profit factor, max drawdown, emotional tolerance
3. **Change ONE variable** (e.g., oversold from 25 to 20)
4. Run another 50 trades
5. Compare results
6. Keep the better version
**Never change multiple settings at once** or you won't know what worked.
---
## 📚 Educational Resources
### Key Concepts to Learn:
**Stochastic Oscillator**
- Developed by George Lane in the 1950s
- Measures momentum by comparing closing price to price range
- Formula: %K = (Close - Low) / (High - Low) × 100
- Similar to RSI but more sensitive to price movements
**Mean Reversion vs. Trend Following**
- This is a **mean reversion** strategy (price returns to average)
- Works best in ranging markets with defined support/resistance
- Fails in strong trending markets (2017 Bitcoin, 2020 Tech stocks)
- Complement with trend filters for better results
**Risk:Reward Ratio**
- The cornerstone of profitable trading
- Winning 40% of trades with 3:1 R:R = profitable
- Winning 60% of trades with 1:1 R:R = breakeven (after fees)
- **This strategy aims for 45% win rate with 2.5-3:1 R:R**
### Recommended Reading:
- *"Trading Systems and Methods"* by Perry Kaufman (Chapter on Oscillators)
- *"Mean Reversion Trading Systems"* by Howard Bandy
- *"The New Trading for a Living"* by Dr. Alexander Elder
---
## 🛠️ Troubleshooting
### "I'm not seeing any signals!"
**Check:**
- Is your timeframe 4H or higher?
- Is the stochastic actually reaching extreme levels (check if your asset is stuck in middle range)?
- Is cooldown still active from a previous trade?
- Are you on a low-liquidity pair?
**Solution**: Switch to a more volatile asset or lower the overbought/oversold thresholds.
---
### "The strategy keeps losing money!"
**Check:**
- What's your win rate? (Below 35% is concerning)
- What's your profit factor? (Below 0.8 means serious issues)
- Are you trading during major news events?
- Is the market in a strong trend?
**Solution**:
1. Verify you're using recommended markets/timeframes
2. Increase cooldown period to avoid choppy markets
3. Reduce position size to 5% while you diagnose
4. Consider switching to daily timeframe for less noise
---
### "My stop losses keep getting hit!"
**Check:**
- Is your stop loss tighter than the average ATR?
- Are you trading during high-volatility sessions?
- Is slippage eating into your buffer?
**Solution**:
1. Calculate the 14-period ATR
2. Set stop loss to 1.5x the ATR value
3. Avoid trading right after market open or major news
4. Factor in 0.2% slippage for crypto, 0.1% for oil
---
## 💪 Pro Tips from the Trenches
### Psychological Discipline
**The Three Deadly Sins:**
1. **Skipping signals** - "This one doesn't feel right"
2. **Early exits** - "I'll just take profit here to be safe"
3. **Revenge trading** - "I need to make back that loss NOW"
**The Solution:** Treat your strategy like a business system. Would McDonald's skip making fries because the cashier "doesn't feel like it today"? No. Systems work because of consistency.
---
### Position Management
**Scaling In/Out** (Advanced)
- Enter 50% position at signal
- Add 50% if stochastic reaches 10 (oversold) or 90 (overbought)
- Exit 50% at 1.5x take profit, let the rest run
**This is NOT for beginners.** Master the basic system first.
---
### Market Awareness
**Oil Traders:**
- OPEC meetings = volatility spikes (avoid or widen stops)
- US inventory reports (Wed 10:30am EST) = avoid trading 2 hours before/after
- Summer driving season = different patterns than winter
**Crypto Traders:**
- Monday-Tuesday = typically lower volatility (fewer signals)
- Thursday-Sunday = higher volatility (more signals)
- Avoid trading during exchange maintenance windows
---
## ⚖️ Legal Disclaimer
This trading strategy is provided for **educational purposes only**.
- Past performance does not guarantee future results
- Trading involves substantial risk of loss
- Only trade with capital you can afford to lose
- No one associated with this strategy is a licensed financial advisor
- You are solely responsible for your trading decisions
**By using this strategy, you acknowledge that you understand and accept these risks.**
---
## 🙏 Acknowledgments
Strategy development inspired by:
- George Lane's original Stochastic Oscillator work
- Modern quantitative trading research
- Community feedback from hundreds of backtests
Built with ❤️ for retail traders who want systematic, disciplined approaches to the markets.
---
**Good luck, stay disciplined, and trade the system, not your emotions.**
J.P. Morgan Efficiente 5 IndexJ.P. MORGAN EFFICIENTE 5 INDEX REPLICATION
Walk into any retail trading forum and you'll find the same scene playing out thousands of times a day: traders huddled over their screens, drawing trendlines on candlestick charts, hunting for the perfect entry signal, convinced that the next RSI crossover will unlock the path to financial freedom. Meanwhile, in the towers of lower Manhattan and the City of London, portfolio managers are doing something entirely different. They're not drawing lines. They're not hunting patterns. They're building fortresses of diversification, wielding mathematical frameworks that have survived decades of market chaos, and most importantly, they're thinking in portfolios while retail thinks in positions.
This divide is not just philosophical. It's structural, mathematical, and ultimately, profitable. The uncomfortable truth that retail traders must confront is this: while you're obsessing over whether the 50-day moving average will cross the 200-day, institutional investors are solving quadratic optimization problems across thirteen asset classes, rebalancing monthly according to Markowitz's Nobel Prize-winning framework, and targeting precise volatility levels that allow them to sleep at night regardless of what the VIX does tomorrow. The game you're playing and the game they're playing share the same field, but the rules are entirely different.
The question, then, is not whether retail traders can access institutional strategies. The question is whether they're willing to fundamentally change how they think about markets. Are you ready to stop painting lines and start building portfolios?
THE INSTITUTIONAL FRAMEWORK: HOW THE PROFESSIONALS ACTUALLY THINK
When Harry Markowitz published "Portfolio Selection" in The Journal of Finance in 1952, he fundamentally altered how sophisticated investors approach markets. His insight was deceptively simple: returns alone mean nothing. Risk-adjusted returns mean everything. For this revelation, he would eventually receive the Nobel Prize in Economics in 1990, and his framework would become the foundation upon which trillions of dollars are managed today (Markowitz, 1952).
Modern Portfolio Theory, as it came to be known, introduced a revolutionary concept: through diversification across imperfectly correlated assets, an investor could reduce portfolio risk without sacrificing expected returns. This wasn't about finding the single best asset. It was about constructing the optimal combination of assets. The mathematics are elegant in their logic: if two assets don't move in perfect lockstep, combining them creates a portfolio whose volatility is lower than the weighted average of the individual volatilities. This "free lunch" of diversification became the bedrock of institutional investment management (Elton et al., 2014).
But here's where retail traders miss the point entirely: this isn't about having ten different stocks instead of one. It's about systematic, mathematically rigorous allocation across asset classes with fundamentally different risk drivers. When equity markets crash, high-quality government bonds often rally. When inflation surges, commodities may provide protection even as stocks and bonds both suffer. When emerging markets are in vogue, developed markets may lag. The professional investor doesn't predict which scenario will unfold. Instead, they position for all of them simultaneously, with weights determined not by gut feeling but by quantitative optimization.
This is what J.P. Morgan Asset Management embedded into their Efficiente Index series. These are not actively managed funds where a portfolio manager makes discretionary calls. They are rules-based, systematic strategies that execute the Markowitz framework in real-time, rebalancing monthly to maintain optimal risk-adjusted positioning across global equities, fixed income, commodities, and defensive assets (J.P. Morgan Asset Management, 2016).
THE EFFICIENTE 5 STRATEGY: DECONSTRUCTING INSTITUTIONAL METHODOLOGY
The Efficiente 5 Index, specifically, targets a 5% annualized volatility. Let that sink in for a moment. While retail traders routinely accept 20%, 30%, or even 50% annual volatility in pursuit of returns, institutional allocators have determined that 5% volatility provides an optimal balance between growth potential and capital preservation. This isn't timidity. It's mathematics. At higher volatility levels, the compounding drag from large drawdowns becomes mathematically punishing. A 50% loss requires a 100% gain just to break even. The institutional solution: constrain volatility at the portfolio level, allowing the power of compounding to work unimpeded (Damodaran, 2008).
The strategy operates across thirteen exchange-traded funds spanning five distinct asset classes: developed equity markets (SPY, IWM, EFA), fixed income across the risk spectrum (TLT, LQD, HYG), emerging markets (EEM, EMB), alternatives (IYR, GSG, GLD), and defensive positioning (TIP, BIL). These aren't arbitrary choices. Each ETF represents a distinct factor exposure, and together they provide access to the primary drivers of global asset returns (Fama and French, 1993).
The methodology, as detailed in replication research by Jungle Rock (2025), follows a precise monthly cadence. At the end of each month, the strategy recalculates expected returns and volatilities for all thirteen assets using a 126-day rolling window. This six-month lookback balances responsiveness to changing market conditions against the noise of short-term fluctuations. The optimization engine then solves for the portfolio weights that maximize expected return subject to the 5% volatility target, with additional constraints to prevent excessive concentration.
These constraints are critical and reveal institutional wisdom that retail traders typically ignore. No single ETF can exceed 20% of the portfolio, except for TIP and BIL which can reach 50% given their defensive nature. At the asset class level, developed equities are capped at 50%, bonds at 50%, emerging markets at 25%, and alternatives at 25%. These aren't arbitrary limits. They're guardrails preventing the optimization from becoming too aggressive during periods when recent performance might suggest concentrating heavily in a single area that's been hot (Jorion, 1992).
After optimization, there's one final step that appears almost trivial but carries profound implications: weights are rounded to the nearest 5%. In a world of fractional shares and algorithmic execution, why round to 5%? The answer reveals institutional practicality over mathematical purity. A portfolio weight of 13.7% and 15.0% are functionally similar in their risk contribution, but the latter is vastly easier to communicate, to monitor, and to execute at scale. When you're managing billions, parsimony matters.
WHY THIS MATTERS FOR RETAIL: THE GAP BETWEEN APPROACH AND EXECUTION
Here's the uncomfortable reality: most retail traders are playing a different game entirely, and they don't even realize it. When a retail trader says "I'm bullish on tech," they buy QQQ and that's their entire technology exposure. When they say "I need some diversification," they buy ten different stocks, often in correlated sectors. This isn't diversification in the Markowitzian sense. It's concentration with extra steps.
The institutional approach represented by the Efficiente 5 is fundamentally different in several ways. First, it's systematic. Emotions don't drive the allocation. The mathematics do. When equities have rallied hard and now represent 55% of the portfolio despite a 50% cap, the system sells equities and buys bonds or alternatives, regardless of how bullish the headlines feel. This forced contrarianism is what retail traders know they should do but rarely execute (Kahneman and Tversky, 1979).
Second, it's forward-looking in its inputs but backward-looking in its process. The strategy doesn't try to predict the next crisis or the next boom. It simply measures what volatility and returns have been recently, assumes the immediate future resembles the immediate past more than it resembles some forecast, and positions accordingly. This humility regarding prediction is perhaps the most institutional characteristic of all.
Third, and most critically, it treats the portfolio as a single organism. Retail traders typically view their holdings as separate positions, each requiring individual management. The institutional approach recognizes that what matters is not whether Position A made money, but whether the portfolio as a whole achieved its risk-adjusted return target. A position can lose money and still be a valuable contributor if it reduced portfolio volatility or provided diversification during stress periods.
THE MATHEMATICAL FOUNDATION: MEAN-VARIANCE OPTIMIZATION IN PRACTICE
At its core, the Efficiente 5 strategy solves a constrained optimization problem each month. In technical terms, this is a quadratic programming problem: maximize expected portfolio return subject to a volatility constraint and position limits. The objective function is straightforward: maximize the weighted sum of expected returns. The constraint is that the weighted sum of variances and covariances must not exceed the volatility target squared (Markowitz, 1959).
The challenge, and this is crucial for understanding the Pine Script implementation, is that solving this problem properly requires calculating a covariance matrix. This 13x13 matrix captures not just the volatility of each asset but the correlation between every pair of assets. Two assets might each have 15% volatility, but if they're negatively correlated, combining them reduces portfolio risk. If they're positively correlated, it doesn't. The covariance matrix encodes these relationships.
True mean-variance optimization requires matrix algebra and quadratic programming solvers. Pine Script, by design, lacks these capabilities. The language doesn't support matrix operations, and certainly doesn't include a QP solver. This creates a fundamental challenge: how do you implement an institutional strategy in a language not designed for institutional mathematics?
The solution implemented here uses a pragmatic approximation. Instead of solving the full covariance problem, the indicator calculates a Sharpe-like ratio for each asset (return divided by volatility) and uses these ratios to determine initial weights. It then applies the individual and asset-class constraints, renormalizes, and produces the final portfolio. This isn't mathematically equivalent to true mean-variance optimization, but it captures the essential spirit: weight assets according to their risk-adjusted return potential, subject to diversification constraints.
For retail implementation, this approximation is likely sufficient. The difference between a theoretically optimal portfolio and a very good approximation is typically modest, and the discipline of systematic rebalancing across asset classes matters far more than the precise weights. Perfect is the enemy of good, and a good approximation executed consistently will outperform a perfect solution that never gets implemented (Arnott et al., 2013).
RETURNS, RISKS, AND THE POWER OF COMPOUNDING
The Efficiente 5 Index has, historically, delivered on its promise of 5% volatility with respectable returns. While past performance never guarantees future results, the framework reveals why low-volatility strategies can be surprisingly powerful. Consider two portfolios: Portfolio A averages 12% returns with 20% volatility, while Portfolio B averages 8% returns with 5% volatility. Which performs better over time?
The arithmetic return favors Portfolio A, but compound returns tell a different story. Portfolio A will experience occasional 20-30% drawdowns. Portfolio B rarely draws down more than 10%. Over a twenty-year horizon, the geometric return (what you actually experience) for Portfolio B may match or exceed Portfolio A, simply because it never gives back massive gains. This is the power of volatility management that retail traders chronically underestimate (Bernstein, 1996).
Moreover, low volatility enables behavioral advantages. When your portfolio draws down 35%, as it might with a high-volatility approach, the psychological pressure to sell at the worst possible time becomes overwhelming. When your maximum drawdown is 12%, as might occur with the Efficiente 5 approach, staying the course is far easier. Behavioral finance research has consistently shown that investor returns lag fund returns primarily due to poor timing decisions driven by emotional responses to volatility (Dalbar, 2020).
The indicator displays not just target and actual portfolio weights, but also tracks total return, portfolio value, and realized volatility. This isn't just data. It's feedback. Retail traders can see, in real-time, whether their actual portfolio volatility matches their target, whether their risk-adjusted returns are improving, and whether their allocation discipline is holding. This transparency transforms abstract concepts into concrete metrics.
WHAT RETAIL TRADERS MUST LEARN: THE MINDSET SHIFT
The path from retail to institutional thinking requires three fundamental shifts. First, stop thinking in positions and start thinking in portfolios. Your question should never be "Should I buy this stock?" but rather "How does this position change my portfolio's expected return and volatility?" If you can't answer that question quantitatively, you're not ready to make the trade.
Second, embrace systematic rebalancing even when it feels wrong. Perhaps especially when it feels wrong. The Efficiente 5 strategy rebalances monthly regardless of market conditions. If equities have surged and now exceed their target weight, the strategy sells equities and buys bonds or alternatives. Every retail trader knows this is what you "should" do, but almost none actually do it. The institutional edge isn't in having better information. It's in having better discipline (Swensen, 2009).
Third, accept that volatility is not your friend. The retail mythology that "higher risk equals higher returns" is true on average across assets, but it's not true for implementation. A 15% return with 30% volatility will compound more slowly than a 12% return with 10% volatility due to the mathematics of return distributions. Institutions figured this out decades ago. Retail is still learning.
The Efficiente 5 replication indicator provides a bridge. It won't solve the problem of prediction no indicator can. But it solves the problem of allocation, which is arguably more important. By implementing institutional methodology in an accessible format, it allows retail traders to see what professional portfolio construction actually looks like, not in theory but in executable code. The the colorful lines that retail traders love to draw, don't disappear. They simply become less central to the process. The portfolio becomes central instead.
IMPLEMENTATION CONSIDERATIONS AND PRACTICAL REALITY
Running this indicator on TradingView provides a dynamic view of how institutional allocation would evolve over time. The labels on each asset class line show current weights, updated continuously as prices change and rebalancing occurs. The dashboard displays the full allocation across all thirteen ETFs, showing both target weights (what the optimization suggests) and actual weights (what the portfolio currently holds after price movements).
Several key insights emerge from watching this process unfold. First, the strategy is not static. Weights change monthly as the optimization recalibrates to recent volatility and returns. What worked last month may not be optimal this month. Second, the strategy is not market-timing. It doesn't try to predict whether stocks will rise or fall. It simply measures recent behavior and positions accordingly. If volatility has risen, the strategy shifts toward defensive assets. If correlations have changed, the diversification benefits adjust.
Third, and perhaps most importantly for retail traders, the strategy demonstrates that sophistication and complexity are not synonyms. The Efficiente 5 methodology is sophisticated in its framework but simple in its execution. There are no exotic derivatives, no complex market-timing rules, no predictions of future scenarios. Just systematic optimization, monthly rebalancing, and discipline. This simplicity is a feature, not a bug.
The indicator also highlights limitations that retail traders must understand. The Pine Script implementation uses an approximation of true mean-variance optimization, as discussed earlier. Transaction costs are not modeled. Slippage is ignored. Tax implications are not considered. These simplifications mean the indicator is educational and analytical, not a fully operational trading system. For actual implementation, traders would need to account for these real-world factors.
Moreover, the strategy requires access to all thirteen ETFs and sufficient capital to hold meaningful positions in each. With 5% as the rounding increment, practical implementation probably requires at least $10,000 to avoid having positions that are too small to matter. The strategy is also explicitly designed for a 5% volatility target, which may be too conservative for younger investors with long time horizons or too aggressive for retirees living off their portfolio. The framework is adaptable, but adaptation requires understanding the trade-offs.
CAN RETAIL TRULY COMPETE WITH INSTITUTIONS?
The honest answer is nuanced. Retail traders will never have the same resources as institutions. They won't have Bloomberg terminals, proprietary research, or armies of analysts. But in portfolio construction, the resource gap matters less than the mindset gap. The mathematics of Markowitz are available to everyone. ETFs provide liquid, low-cost access to institutional-quality building blocks. Computing power is essentially free. The barriers are not technological or financial. They're conceptual.
If a retail trader understands why portfolios matter more than positions, why systematic discipline beats discretionary emotion, and why volatility management enables compounding, they can build portfolios that rival institutional allocation in their elegance and effectiveness. Not in their scale, not in their execution costs, but in their conceptual soundness. The Efficiente 5 framework proves this is possible.
What retail traders must recognize is that competing with institutions doesn't mean day-trading better than their algorithms. It means portfolio-building better than their average client. And that's achievable because most institutional clients, despite having access to the best managers, still make emotional decisions, chase performance, and abandon strategies at the worst possible times. The retail edge isn't in outsmarting professionals. It's in out-disciplining amateurs who happen to have more money.
The J.P. Morgan Efficiente 5 Index Replication indicator serves as both a tool and a teacher. As a tool, it provides a systematic framework for multi-asset allocation based on proven institutional methodology. As a teacher, it demonstrates daily what portfolio thinking actually looks like in practice. The colorful lines remain on the chart, but they're no longer the focus. The portfolio is the focus. The risk-adjusted return is the focus. The systematic discipline is the focus.
Stop painting lines. Start building portfolios. The institutions have been doing it for seventy years. It's time retail caught up.
REFERENCES
Arnott, R. D., Hsu, J., & Moore, P. (2013). Fundamental Indexation. Financial Analysts Journal, 61(2), 83-99.
Bernstein, W. J. (1996). The Intelligent Asset Allocator. New York: McGraw-Hill.
Dalbar, Inc. (2020). Quantitative Analysis of Investor Behavior. Boston: Dalbar.
Damodaran, A. (2008). Strategic Risk Taking: A Framework for Risk Management. Upper Saddle River: Pearson Education.
Elton, E. J., Gruber, M. J., Brown, S. J., & Goetzmann, W. N. (2014). Modern Portfolio Theory and Investment Analysis (9th ed.). Hoboken: John Wiley & Sons.
Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56.
Jorion, P. (1992). Portfolio optimization in practice. Financial Analysts Journal, 48(1), 68-74.
J.P. Morgan Asset Management. (2016). Guide to the Markets. New York: J.P. Morgan.
Jungle Rock. (2025). Institutional Asset Allocation meets the Efficient Frontier: Replicating the JPMorgan Efficiente 5 Strategy. Working Paper.
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263-291.
Markowitz, H. (1952). Portfolio Selection. The Journal of Finance, 7(1), 77-91.
Markowitz, H. (1959). Portfolio Selection: Efficient Diversification of Investments. New York: John Wiley & Sons.
Swensen, D. F. (2009). Pioneering Portfolio Management: An Unconventional Approach to Institutional Investment. New York: Free Press.
Intrinsic Value AnalyzerThe Intrinsic Value Analyzer is an all-in-one valuation tool that automatically calculates the fair value of a stock using industry-standard valuation techniques. It estimates intrinsic value through Discounted Cash Flow (DCF), Enterprise Value to Revenue (EV/REV), Enterprise Value to EBITDA (EV/EBITDA), and Price to Earnings (P/EPS). The model features adjustable parameters and a built-in alert system that notifies investors in real time when valuation multiples reach predefined thresholds. It also includes a comprehensive, color-coded table that compares the company’s historical average growth rates, valuation multiples, and financial ratios with the most recent values, helping investors quickly assess how current values align with historical averages.
The model calculates the historical Compounded Annual Growth Rates (CAGR) and average valuation multiples over the selected Lookback Period. It then projects Revenue, Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), Earnings per Share (EPS), and Free Cash Flow (FCF) for the selected Forecast Period and discounts their future values back to the present using the Weighted Average Cost of Capital (WACC) or the Cost of Equity. By default, the model automatically applies the historical averages displayed in the table as the growth forecasts and target multiples. These assumptions can be modified in the menu by entering custom REV-G, EBITDA-G, EPS-G, and FCF-G growth forecasts, as well as EV/REV, EV/EBITDA, and P/EPS target multiples. When new input values are entered, the model recalculates the fair value in real time, allowing users to see how changes in these assumptions affect the company’s fair value.
DCF = (Sum of (FCF × (1 + FCF-G) ^ t ÷ (1 + WACC) ^ t) for each year t until Forecast Period + ((FCF × (1 + FCF-G) ^ Forecast Period × (1 + LT Growth)) ÷ ((WACC - LT Growth) × (1 + WACC) ^ Forecast Period)) + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
EV/REV = ((Revenue × (1 + REV-G) ^ Forecast Period × EV/REV Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
EV/EBITDA = ((EBITDA × (1 + EBITDA-G) ^ Forecast Period × EV/EBITDA Target) ÷ (1 + WACC) ^ Forecast Period + Cash - Debt - Preferred Equity - Minority Interest) ÷ Shares Outstanding
P/EPS = (EPS × (1 + EPS-G) ^ Forecast Period × P/EPS Target) ÷ (1 + Cost of Equity) ^ Forecast Period
The discounted one-year average analyst price target (1Y PT) is also displayed alongside the valuation labels to provide an overview of consensus estimates. For the DCF model, the terminal long-term FCF growth rate (LT Growth) is based on the selected country to reflect expected long-term nominal GDP growth and can be modified in the menu. For metrics involving FCF, users can choose between reported FCF, calculated as Cash From Operations (CFO) - Capital Expenditures (CAPEX), or standardized FCF, calculated as Earnings Before Interest and Taxes (EBIT) × (1 - Average Tax Rate) + Depreciation and Amortization - Change in Net Working Capital - CAPEX. Historical average values displayed in the left column of the table are based on Fiscal Year (FY) data, while the latest values in the right column use the most recent Trailing Twelve Month (TTM) or Fiscal Quarter (FQ) data. The indicator displays color-coded price labels for each fair value estimate, showing the percentage upside or downside from the current price. Green indicates undervaluation, while red indicates overvaluation. The table follows a separate color logic:
REV-G, EBITDA-G, EPS-G, FCF-G = Green indicates positive annual growth when the CAGR is positive. Red indicates negative annual growth when the CAGR is negative.
EV/REV = Green indicates undervaluation when EV/REV ÷ REV-G is below 1. Red indicates overvaluation when EV/REV ÷ REV-G is above 2. Gray indicates fair value.
EV/EBITDA = Green indicates undervaluation when EV/EBITDA ÷ EBITDA-G is below 1. Red indicates overvaluation when EV/EBITDA ÷ EBITDA-G is above 2. Gray indicates fair value.
P/EPS = Green indicates undervaluation when P/EPS ÷ EPS-G is below 1. Red indicates overvaluation when P/EPS ÷ EPS-G is above 2. Gray indicates fair value.
EBITDA% = Green indicates profitable operations when the EBITDA margin is positive. Red indicates unprofitable operations when the EBITDA margin is negative.
FCF% = Green indicates strong cash conversion when FCF/EBITDA > 50%. Red indicates unsustainable FCF when FCF/EBITDA is negative. Gray indicates normal cash conversion.
ROIC = Green indicates value creation when ROIC > WACC. Red indicates value destruction when ROIC is negative. Gray indicates positive but insufficient returns.
ND/EBITDA = Green indicates low leverage when ND/EBITDA is below 1. Red indicates high leverage when ND/EBITDA is above 3. Gray indicates moderate leverage.
YIELD = Green indicates positive shareholder return when Shareholder Yield > 1%. Red indicates negative shareholder return when Shareholder Yield < -1%.
The Return on Invested Capital (ROIC) is calculated as EBIT × (1 - Average Tax Rate) ÷ (Average Debt + Average Equity - Average Cash). Shareholder Yield (YIELD) is calculated as the CAGR of Dividend Yield - Change in Shares Outstanding. The Weighted Average Cost of Capital (WACC) is displayed at the top left of the table and is derived from the current Market Cap (MC), Debt, Cost of Equity, and Cost of Debt. The Cost of Equity is calculated using the Equity Beta, Index Return, and Risk-Free Rate, which are based on the selected country. The Equity Beta (β) is calculated as the 5-year Blume-adjusted beta between the weekly logarithmic returns of the underlying stock and the selected country’s stock market index. For accurate calculations, it is recommended to use the stock ticker listed on the primary exchange corresponding to the company’s main index.
Cost of Debt = (Interest Expense on Debt ÷ Average Debt) × (1 - Average Tax Rate)
Cost of Equity = Risk-Free Rate + Equity Beta (β) × (Index Return - Risk-Free Rate)
WACC = (MC ÷ (MC + Debt)) × Cost of Equity + (Debt ÷ (MC + Debt)) × Cost of Debt
This indicator works best for operationally stable and profitable companies that are primarily valued based on fundamentals rather than speculative growth, such as those in the industrial, consumer, technology, and healthcare sectors. It is less suitable for early-stage, unprofitable, or highly cyclical companies, including energy, real estate, and financial institutions, as these often have irregular cash flows or distorted balance sheets. It is also worth noting that TradingView’s financial data provider, FactSet, standardizes financial data from official company filings to align with a consistent accounting framework. While this improves comparability across companies, industries, and countries, it may also result in differences from officially reported figures.
In summary, the Intrinsic Value Analyzer is a comprehensive valuation tool designed to help long-term investors estimate a company’s fair value while comparing historical averages with the latest values. Fair value estimates are driven by growth forecasts, target multiples, and discount rates, and should always be interpreted within the context of the underlying assumptions. By default, the model applies historical averages and current discount rates, which may not accurately reflect future conditions. Investors are therefore encouraged to adjust inputs in the menu to better understand how changes in these key assumptions influence the company’s fair value.
Luxy Momentum, Trend, Bias and Breakout Indicators V7
TABLE OF CONTENTS
This is Version 7 (V7) - the latest and most optimized release. If you are using any older versions (V6, V5, V4, V3, etc.), it is highly recommended to replace them with V7.
Why This Indicator is Different
Who Should Use This
Core Components Overview
The UT Bot Trading System
Understanding the Market Bias Table
Candlestick Pattern Recognition
Visual Tools and Features
How to Use the Indicator
Performance and Optimization
FAQ
---
### CREDITS & ATTRIBUTION
This indicator implements proven trading concepts using entirely original code developed specifically for this project.
### CONCEPTUAL FOUNDATIONS
• UT Bot ATR Trailing System
- Original concept by @QuantNomad: (search "UT-Bot-Strategy"
- Our version is a complete reimplementation with significant enhancements:
- Volume-weighted momentum adjustment
- Composite stop loss from multiple S/R layers
- Multi-filter confirmation system (swing, %, 2-bar, ZLSMA)
- Full integration with multi-timeframe bias table
- Visual audit trail with freeze-on-touch
- NOTE: No code was copied - this is a complete reimplementation with enhancements.
• Standard Technical Indicators (Public Domain Formulas):
- Supertrend: ATR-based trend calculation with custom gradient fills
- MACD: Gerald Appel's formula with separation filters
- RSI: J. Welles Wilder's formula with pullback zone logic
- ADX/DMI: Custom trend strength formula inspired by Wilder's directional movement concept, reimplemented with volume weighting and efficiency metrics
- ZLSMA: Zero-lag formula enhanced with Hull MA and momentum prediction
### Custom Implementations
- Trend Strength: Inspired by Wilder's ADX concept but using volume-weighted pressure calculation and efficiency metrics (not traditional +DI/-DI smoothing)
- All code implementations are original
### ORIGINAL FEATURES (70%+ of codebase)
- Multi-Timeframe Bias Table with live updates
- Risk Management System (R-multiple TPs, freeze-on-touch)
- Opening Range Breakout tracker with session management
- Composite Stop Loss calculator using 6+ S/R layers
- Performance optimization system (caching, conditional calcs)
- VIX Fear Index integration
- Previous Day High/Low auto-detection
- Candlestick pattern recognition with interactive tooltips
- Smart label and visual management
- All UI/UX design and table architecture
### DEVELOPMENT PROCESS
**AI Assistance:** This indicator was developed over 2+ months with AI assistance (ChatGPT/Claude) used for:
- Writing Pine Script code based on design specifications
- Optimizing performance and fixing bugs
- Ensuring Pine Script v6 compliance
- Generating documentation
**Author's Role:** All trading concepts, system design, feature selection, integration logic, and strategic decisions are original work by the author. The AI was a coding tool, not the system designer.
**Transparency:** We believe in full disclosure - this project demonstrates how AI can be used as a powerful development tool while maintaining creative and strategic ownership.
---
1. WHY THIS INDICATOR IS DIFFERENT
Most traders use multiple separate indicators on their charts, leading to cluttered screens, conflicting signals, and analysis paralysis. The Suite solves this by integrating proven technical tools into a single, cohesive system.
Key Advantages:
All-in-One Design: Instead of loading 5-10 separate indicators, you get everything in one optimized script. This reduces chart clutter and improves TradingView performance.
Multi-Timeframe Bias Table: Unlike standard indicators that only show the current timeframe, the Bias Table aggregates trend signals across multiple timeframes simultaneously. See at a glance whether 1m, 5m, 15m, 1h are aligned bullish or bearish - no more switching between charts.
Smart Confirmations: The indicator doesn't just give signals - it shows you WHY. Every entry has multiple layers of confirmation (MA cross, MACD momentum, ADX strength, RSI pullback, volume, etc.) that you can toggle on/off.
Dynamic Stop Loss System: Instead of static ATR stops, the SL is calculated from multiple support/resistance layers: UT trailing line, Supertrend, VWAP, swing structure, and MA levels. This creates more intelligent, price-action-aware stops.
R-Multiple Take Profits: Built-in TP system calculates targets based on your initial risk (1R, 1.5R, 2R, 3R). Lines freeze when touched with visual checkmarks, giving you a clean audit trail of partial exits.
Educational Tooltips Everywhere: Every single input has detailed tooltips explaining what it does, typical values, and how it impacts trading. You're not guessing - you're learning as you configure.
Performance Optimized: Smart caching, conditional calculations, and modular design mean the indicator runs fast despite having 15+ features. Turn off what you don't use for even better performance.
No Repainting: All signals respect bar close. Alerts fire correctly. What you see in history is what you would have gotten in real-time.
What Makes It Unique:
Integrated UT Bot + Bias Table: No other indicator combines UT Bot's ATR trailing system with a live multi-timeframe dashboard. You get precision entries with macro trend context.
Candlestick Pattern Recognition with Interactive Tooltips: Patterns aren't just marked - hover over any emoji for a full explanation of what the pattern means and how to trade it.
Opening Range Breakout Tracker: Built-in ORB system for intraday traders with customizable session times and real-time status updates in the Bias Table.
Previous Day High/Low Auto-Detection: Automatically plots PDH/PDL on intraday charts with theme-aware colors. Updates daily without manual input.
Dynamic Row Labels in Bias Table: The table shows your actual settings (e.g., "EMA 10 > SMA 20") not generic labels. You know exactly what's being evaluated.
Modular Filter System: Instead of forcing a fixed methodology, the indicator lets you build your own strategy. Start with just UT Bot, add filters one at a time, test what works for your style.
---
2. WHO WHOULD USE THIS
Designed For:
Intermediate to Advanced Traders: You understand basic technical analysis (MAs, RSI, MACD) and want to combine multiple confirmations efficiently. This isn't a "one-click profit" system - it's a professional toolkit.
Multi-Timeframe Traders: If you trade one asset but check multiple timeframes for confirmation (e.g., enter on 5m after checking 15m and 1h alignment), the Bias Table will save you hours every week.
Trend Followers: The indicator excels at identifying and following trends using UT Bot, Supertrend, and MA systems. If you trade breakouts and pullbacks in trending markets, this is built for you.
Intraday and Swing Traders: Works equally well on 5m-1h charts (day trading) and 4h-D charts (swing trading). Scalpers can use it too with appropriate settings adjustments.
Discretionary Traders: This isn't a black-box system. You see all the components, understand the logic, and make final decisions. Perfect for traders who want tools, not automation.
Works Across All Markets:
Stocks (US, international)
Cryptocurrency (24/7 markets supported)
Forex pairs
Indices (SPY, QQQ, etc.)
Commodities
NOT Ideal For :
Complete Beginners: If you don't know what a moving average or RSI is, start with basics first. This indicator assumes foundational knowledge.
Algo Traders Seeking Black Box: This is discretionary. Signals require context and confirmation. Not suitable for blind automated execution.
Mean-Reversion Only Traders: The indicator is trend-following at its core. While VWAP bands support mean-reversion, the primary methodology is trend continuation.
---
3. CORE COMPONENTS OVERVIEW
The indicator combines these proven systems:
Trend Analysis:
Moving Averages: Four customizable MAs (Fast, Medium, Medium-Long, Long) with six types to choose from (EMA, SMA, WMA, VWMA, RMA, HMA). Mix and match for your style.
Supertrend: ATR-based trend indicator with unique gradient fill showing trend strength. One-sided ribbon visualization makes it easier to see momentum building or fading.
ZLSMA : Zero-lag linear-regression smoothed moving average. Reduces lag compared to traditional MAs while maintaining smooth curves.
Momentum & Filters:
MACD: Standard MACD with separation filter to avoid weak crossovers.
RSI: Pullback zone detection - only enter longs when RSI is in your defined "buy zone" and shorts in "sell zone".
ADX/DMI: Trend strength measurement with directional filter. Ensures you only trade when there's actual momentum.
Volume Filter: Relative volume confirmation - require above-average volume for entries.
Donchian Breakout: Optional channel breakout requirement.
Signal Systems:
UT Bot: The primary signal generator. ATR trailing stop that adapts to volatility and gives clear entry/exit points.
Base Signals: MA cross system with all the above filters applied. More conservative than UT Bot alone.
Market Bias Table: Multi-timeframe dashboard showing trend alignment across 7 timeframes plus macro bias (3-day, weekly, monthly, quarterly, VIX).
Candlestick Patterns: Six major reversal patterns auto-detected with interactive tooltips.
ORB Tracker: Opening range high/low with breakout status (intraday only).
PDH/PDL: Previous day levels plotted automatically on intraday charts.
VWAP + Bands : Session-anchored VWAP with up to three standard deviation band pairs.
---
4. THE UT BOT TRADING SYSTEM
The UT Bot is the heart of the indicator's signal generation. It's an advanced ATR trailing stop that adapts to market volatility.
Why UT Bot is Superior to Fixed Stops:
Traditional ATR stops use a fixed multiplier (e.g., "stop = entry - 2×ATR"). UT Bot is smarter:
It TRAILS the stop as price moves in your favor
It WIDENS during high volatility to avoid premature stops
It TIGHTENS during consolidation to lock in profits
It FLIPS when price breaks the trailing line, signaling reversals
Visual Elements You'll See:
Orange Trailing Line: The actual UT stop level that adapts bar-by-bar
Buy/Sell Labels: Aqua triangle (long) or orange triangle (short) when the line flips
ENTRY Line: Horizontal line at your entry price (optional, can be turned off)
Suggested Stop Loss: A composite SL calculated from multiple support/resistance layers:
- UT trailing line
- Supertrend level
- VWAP
- Swing structure (recent lows/highs)
- Long-term MA (200)
- ATR-based floor
Take Profit Lines: TP1, TP1.5, TP2, TP3 based on R-multiples. When price touches a TP, it's marked with a checkmark and the line freezes for audit trail purposes.
Status Messages: "SL Touched ❌" or "SL Frozen" when the trade leg completes.
How UT Bot Differs from Other ATR Systems:
Multiple Filters Available: You can require 2-bar confirmation, minimum % price change, swing structure alignment, or ZLSMA directional filter. Most UT implementations have none of these.
Smart SL Calculation: Instead of just using the UT line as your stop, the indicator suggests a better SL based on actual support/resistance. This prevents getting stopped out by wicks while keeping risk controlled.
Visual Audit Trail: All SL/TP lines freeze when touched with clear markers. You can review your trades weeks later and see exactly where entries, stops, and targets were.
Performance Options: "Draw UT visuals only on bar close" lets you reduce rendering load without affecting logic or alerts - critical for slower machines or 1m charts.
Trading Logic:
UT Bot flips direction (Buy or Sell signal appears)
Check Bias Table for multi-timeframe confirmation
Optional: Wait for Base signal or candlestick pattern
Enter at signal bar close or next bar open
Place stop at "Suggested Stop Loss" line
Scale out at TP levels (TP1, TP2, TP3)
Exit remaining position on opposite UT signal or stop hit
---
5. UNDERSTANDING THE MARKET BIAS TABLE
This is the indicator's unique multi-timeframe intelligence layer. Instead of looking at one chart at a time, the table aggregates signals across seven timeframes plus macro trend bias.
Why Multi-Timeframe Analysis Matters:
Professional traders check higher and lower timeframes for context:
Is the 1h uptrend aligning with my 5m entry?
Are all short-term timeframes bullish or just one?
Is the daily trend supportive or fighting me?
Doing this manually means opening multiple charts, checking each indicator, and making mental notes. The Bias Table does it automatically in one glance.
Table Structure:
Header Row:
On intraday charts: 1m, 5m, 15m, 30m, 1h, 2h, 4h (toggle which ones you want)
On daily+ charts: D, W, M (automatic)
Green dot next to title = live updating
Headline Rows - Macro Bias:
These show broad market direction over longer periods:
3 Day Bias: Trend over last 3 trading sessions (uses 1h data)
Weekly Bias: Trend over last 5 trading sessions (uses 4h data)
Monthly Bias: Trend over last 30 daily bars
Quarterly Bias: Trend over last 13 weekly bars
VIX Fear Index: Market regime based on VIX level - bullish when low, bearish when high
Opening Range Breakout: Status of price vs. session open range (intraday only)
These rows show text: "BULLISH", "BEARISH", or "NEUTRAL"
Indicator Rows - Technical Signals:
These evaluate your configured indicators across all active timeframes:
Fast MA > Medium MA (shows your actual MA settings, e.g., "EMA 10 > SMA 20")
Price > Long MA (e.g., "Price > SMA 200")
Price > VWAP
MACD > Signal
Supertrend (up/down/neutral)
ZLSMA Rising
RSI In Zone
ADX ≥ Minimum
These rows show emojis: GREEB (bullish), RED (bearish), GRAY/YELLOW (neutral/NA)
AVG Column:
Shows percentage of active timeframes that are bullish for that row. This is the KEY metric:
AVG > 70% = strong multi-timeframe bullish alignment
AVG 40-60% = mixed/choppy, no clear trend
AVG < 30% = strong multi-timeframe bearish alignment
How to Use the Table:
For a long trade:
Check AVG column - want to see > 60% ideally
Check headline bias rows - want to see BULLISH, not BEARISH
Check VIX row - bullish market regime preferred
Check ORB row (intraday) - want ABOVE for longs
Scan indicator rows - more green = better confirmation
For a short trade:
Check AVG column - want to see < 40% ideally
Check headline bias rows - want to see BEARISH, not BULLISH
Check VIX row - bearish market regime preferred
Check ORB row (intraday) - want BELOW for shorts
Scan indicator rows - more red = better confirmation
When AVG is 40-60%:
Market is choppy, mixed signals. Either stay out or reduce position size significantly. These are low-probability environments.
Unique Features:
Dynamic Labels: Row names show your actual settings (e.g., "EMA 10 > SMA 20" not generic "Fast > Slow"). You know exactly what's being evaluated.
Customizable Rows: Turn off rows you don't care about. Only show what matters to your strategy.
Customizable Timeframes: On intraday charts, disable 1m or 4h if you don't trade them. Reduces calculation load by 20-40%.
Automatic HTF Handling: On Daily/Weekly/Monthly charts, the table automatically switches to D/W/M columns. No configuration needed.
Performance Smart: "Hide BIAS table on 1D or above" option completely skips all table calculations on higher timeframes if you only trade intraday.
---
6. CANDLESTICK PATTERN RECOGNITION
The indicator automatically detects six major reversal patterns and marks them with emojis at the relevant bars.
Why These Six Patterns:
These are the most statistically significant reversal patterns according to trading literature:
High win rate when appearing at support/resistance
Clear visual structure (not subjective)
Work across all timeframes and assets
Studied extensively by institutions
The Patterns:
Bullish Patterns (appear at bottoms):
Bullish Engulfing: Green candle completely engulfs prior red candle's body. Strong reversal signal.
Hammer: Small body with long lower wick (at least 2× body size). Shows rejection of lower prices by buyers.
Morning Star: Three-candle pattern (large red → small indecision → large green). Very strong bottom reversal.
Bearish Patterns (appear at tops):
Bearish Engulfing: Red candle completely engulfs prior green candle's body. Strong reversal signal.
Shooting Star: Small body with long upper wick (at least 2× body size). Shows rejection of higher prices by sellers.
Evening Star: Three-candle pattern (large green → small indecision → large red). Very strong top reversal.
Interactive Tooltips:
Unlike most pattern indicators that just draw shapes, this one is educational:
Hover your mouse over any pattern emoji
A tooltip appears explaining: what the pattern is, what it means, when it's most reliable, and how to trade it
No need to memorize - learn as you trade
Noise Filter:
"Min candle body % to filter noise" setting prevents false signals:
Patterns require minimum body size relative to price
Filters out tiny candles that don't represent real buying/selling pressure
Adjust based on asset volatility (higher % for crypto, lower for low-volatility stocks)
How to Trade Patterns:
Patterns are NOT standalone entry signals. Use them as:
Confirmation: UT Bot gives signal + pattern appears = stronger entry
Reversal Warning: In a trade, opposite pattern appears = consider tightening stop or taking profit
Support/Resistance Validation: Pattern at key level (PDH, VWAP, MA 200) = level is being respected
Best combined with:
UT Bot or Base signal in same direction
Bias Table alignment (AVG > 60% or < 40%)
Appearance at obvious support/resistance
---
7. VISUAL TOOLS AND FEATURES
VWAP (Volume Weighted Average Price):
Session-anchored VWAP with standard deviation bands. Shows institutional "fair value" for the trading session.
Anchor Options: Session, Day, Week, Month, Quarter, Year. Choose based on your trading timeframe.
Bands: Up to three pairs (X1, X2, X3) showing statistical deviation. Price at outer bands often reverses.
Auto-Hide on HTF: VWAP hides on Daily/Weekly/Monthly charts automatically unless you enable anchored mode.
Use VWAP as:
Directional bias (above = bullish, below = bearish)
Mean reversion levels (outer bands)
Support/resistance (the VWAP line itself)
Previous Day High/Low:
Automatically plots yesterday's high and low on intraday charts:
Updates at start of each new trading day
Theme-aware colors (dark text for light charts, light text for dark charts)
Hidden automatically on Daily/Weekly/Monthly charts
These levels are critical for intraday traders - institutions watch them closely as support/resistance.
Opening Range Breakout (ORB):
Tracks the high/low of the first 5, 15, 30, or 60 minutes of the trading session:
Customizable session times (preset for NYSE, LSE, TSE, or custom)
Shows current breakout status in Bias Table row (ABOVE, BELOW, INSIDE, BUILDING)
Intraday only - auto-disabled on Daily+ charts
ORB is a classic day trading strategy - breakout above opening range often leads to continuation.
Extra Labels:
Change from Open %: Shows how far price has moved from session open (intraday) or daily open (HTF). Green if positive, red if negative.
ADX Badge: Small label at bottom of last bar showing current ADX value. Green when above your minimum threshold, red when below.
RSI Badge: Small label at top of last bar showing current RSI value with zone status (buy zone, sell zone, or neutral).
These labels provide quick at-a-glance confirmation without needing separate indicator windows.
---
8. HOW TO USE THE INDICATOR
Step 1: Add to Chart
Load the indicator on your chosen asset and timeframe
First time: Everything is enabled by default - the chart will look busy
Don't panic - you'll turn off what you don't need
Step 2: Start Simple
Turn OFF everything except:
UT Bot labels (keep these ON)
Bias Table (keep this ON)
Moving Averages (Fast and Medium only)
Suggested Stop Loss and Take Profits
Hide everything else initially. Get comfortable with the basic UT Bot + Bias Table workflow first.
Step 3: Learn the Core Workflow
UT Bot gives a Buy or Sell signal
Check Bias Table AVG column - do you have multi-timeframe alignment?
If yes, enter the trade
Place stop at Suggested Stop Loss line
Scale out at TP levels
Exit on opposite UT signal
Trade this simple system for a week. Get a feel for signal frequency and win rate with your settings.
Step 4: Add Filters Gradually
If you're getting too many losing signals (whipsaws in choppy markets), add filters one at a time:
Try: "Require 2-Bar Trend Confirmation" - wait for 2 bars to confirm direction
Try: ADX filter with minimum threshold - only trade when trend strength is sufficient
Try: RSI pullback filter - only enter on pullbacks, not chasing
Try: Volume filter - require above-average volume
Add one filter, test for a week, evaluate. Repeat.
Step 5: Enable Advanced Features (Optional)
Once you're profitable with the core system, add:
Supertrend for additional trend confirmation
Candlestick patterns for reversal warnings
VWAP for institutional anchor reference
ORB for intraday breakout context
ZLSMA for low-lag trend following
Step 6: Optimize Settings
Every setting has a detailed tooltip explaining what it does and typical values. Hover over any input to read:
What the parameter controls
How it impacts trading
Suggested ranges for scalping, day trading, and swing trading
Start with defaults, then adjust based on your results and style.
Step 7: Set Up Alerts
Right-click chart → Add Alert → Condition: "Luxy Momentum v6" → Choose:
"UT Bot — Buy" for long entries
"UT Bot — Sell" for short entries
"Base Long/Short" for filtered MA cross signals
Optionally enable "Send real-time alert() on UT flip" in settings for immediate notifications.
Common Workflow Variations:
Conservative Trader:
UT signal + Base signal + Candlestick pattern + Bias AVG > 70%
Enter only at major support/resistance
Wider UT sensitivity, multiple filters
Aggressive Trader:
UT signal + Bias AVG > 60%
Enter immediately, no waiting
Tighter UT sensitivity, minimal filters
Swing Trader:
Focus on Daily/Weekly Bias alignment
Ignore intraday noise
Use ORB and PDH/PDL less (or not at all)
Wider stops, patient approach
---
9. PERFORMANCE AND OPTIMIZATION
The indicator is optimized for speed, but with 15+ features running simultaneously, chart load time can add up. Here's how to keep it fast:
Biggest Performance Gains:
Disable Unused Timeframes: In "Time Frames" settings, turn OFF any timeframe you don't actively trade. Each disabled TF saves 10-15% calculation time. If you only day trade 5m, 15m, 1h, disable 1m, 2h, 4h.
Hide Bias Table on Daily+: If you only trade intraday, enable "Hide BIAS table on 1D or above". This skips ALL table calculations on higher timeframes.
Draw UT Visuals Only on Bar Close: Reduces intrabar rendering of SL/TP/Entry lines. Has ZERO impact on logic or alerts - purely visual optimization.
Additional Optimizations:
Turn off VWAP bands if you don't use them
Disable candlestick patterns if you don't trade them
Turn off Supertrend fill if you find it distracting (keep the line)
Reduce "Limit to 10 bars" for SL/TP lines to minimize line objects
Performance Features Built-In:
Smart Caching: Higher timeframe data (3-day bias, weekly bias, etc.) updates once per day, not every bar
Conditional Calculations: Volume filter only calculates when enabled. Swing filter only runs when enabled. Nothing computes if turned off.
Modular Design: Every component is independent. Turn off what you don't need without breaking other features.
Typical Load Times:
5m chart, all features ON, 7 timeframes: ~2-3 seconds
5m chart, core features only, 3 timeframes: ~1 second
1m chart, all features: ~4-5 seconds (many bars to calculate)
If loading takes longer, you likely have too many indicators on the chart total (not just this one).
---
10. FAQ
Q: How is this different from standard UT Bot indicators?
A: Standard UT Bot (originally by @QuantNomad) is just the ATR trailing line and flip signals. This implementation adds:
- Volume weighting and momentum adjustment to the trailing calculation
- Multiple confirmation filters (swing, %, 2-bar, ZLSMA)
- Smart composite stop loss system from multiple S/R layers
- R-multiple take profit system with freeze-on-touch
- Integration with multi-timeframe Bias Table
- Visual audit trail with checkmarks
Q: Can I use this for automated trading?
A: The indicator is designed for discretionary trading. While it has clear signals and alerts, it's not a mechanical system. Context and judgment are required.
Q: Does it repaint?
A: No. All signals respect bar close. UT Bot logic runs intrabar but signals only trigger on confirmed bars. Alerts fire correctly with no lookahead.
Q: Do I need to use all the features?
A: Absolutely not. The indicator is modular. Many profitable traders use just UT Bot + Bias Table + Moving Averages. Start simple, add complexity only if needed.
Q: How do I know which settings to use?
A: Every single input has a detailed tooltip. Hover over any setting to see:
What it does
How it affects trading
Typical values for scalping, day trading, swing trading
Start with defaults, adjust gradually based on results.
Q: Can I use this on crypto 24/7 markets?
A: Yes. ORB will not work (no defined session), but everything else functions normally. Use "Day" anchor for VWAP instead of "Session".
Q: The Bias Table is blank or not showing.
A: Check:
"Show Table" is ON
Table position isn't overlapping another indicator's table (change position)
At least one row is enabled
"Hide BIAS table on 1D or above" is OFF (if on Daily+ chart)
Q: Why are candlestick patterns not appearing?
A: Patterns are relatively rare by design - they only appear at genuine reversal points. Check:
Pattern toggles are ON
"Min candle body %" isn't too high (try 0.05-0.10)
You're looking at a chart with actual reversals (not strong trending market)
Q: UT Bot is too sensitive/not sensitive enough.
A: Adjust "Sensitivity (Key×ATR)". Lower number = tighter stop, more signals. Higher number = wider stop, fewer signals. Read the tooltip for guidance.
Q: Can I get alerts for the Bias Table?
A: The Bias Table is a dashboard for visual analysis, not a signal generator. Set alerts on UT Bot or Base signals, then manually check Bias Table for confirmation.
Q: Does this work on stocks with low volume?
A: Yes, but turn OFF the volume filter. Low volume stocks will never meet relative volume requirements.
Q: How often should I check the Bias Table?
A: Before every entry. It takes 2 seconds to glance at the AVG column and headline rows. This one check can save you from fighting the trend.
Q: What if UT signal and Base signal disagree?
A: UT Bot is more aggressive (ATR trailing). Base signals are more conservative (MA cross + filters). If they disagree, either:
Wait for both to align (safest)
Take the UT signal but with smaller size (aggressive)
Skip the trade (conservative)
There's no "right" answer - depends on your risk tolerance.
---
FINAL NOTES
The indicator gives you an edge. How you use that edge determines results.
For questions, feedback, or support, comment on the indicator page or message the author.
Happy Trading!
Larry Williams Bonus Track PatternThis strategy trades the day immediately following an Inside Day, under specific directional and timing conditions. It is designed for daily-based setups but executed on intraday charts to ensure orders are placed exactly at the open of the following day, rather than at the daily bar close.
Entry Conditions
Only trades on Monday, Thursday, or Friday.
The previous day must be an Inside Day (its high is lower than the prior high and its low is higher than the prior low).
The bar before the Inside Day must be bullish (close > open).
On the following day (t):
The daily open must be below both the Inside Day’s high and the highest high of the two days before that.
A buy stop is placed at the highest high of the three previous days (Inside Day and the two days before it).
If the new day’s open is already above that level (gap up), the strategy enters long immediately at the open.
Exit Rules
Stop Loss: Fixed, defined in points or percentage (user input).
FPO (First Profitable Open): the position is closed at the first daily open after the entry day where the open price is above the average entry price (the first profitable open).
Notes
The script must be applied on an intraday timeframe (e.g., 15-minute or 1-hour) so that the strategy can:
Detect the Inside Day pattern using daily data (request.security).
Execute orders in real time at the next day’s open.
Running it directly on the daily timeframe will delay executions by one bar due to Pine Script’s evaluation model.
cd_VWAP_mtg_CxCd_VWAP_mtg_Cx
Overview
The most important condition for being successful and profitable in the market is to consistently follow the same rules without compromise, while the price constantly moves in countless different ways.
Regardless of the concept or trading school, those who have rules win.
In this indicator, we will define and use three main sections to set and apply our rules.
The indicator uses the VWAP (Volume Weighted Average Price) — price weighted by volume.
Two VWAPs can be displayed either by manually entering date and time, or by selecting from the menu.
From the menu, you can select the following reference levels:
• HTF Open: Opening candle of the higher timeframe
• ATH / ATL: All-Time High / All-Time Low candles
• PMH / PML, PWH / PWL, PDH / PDL, PH4H / PH4L: Previous Month, Week, Day, or H4 Highs/Lows
• MH / ML, WH / WL, DH / DL, H4H / H4L: Current Month, Week, Day, or H4 Highs/Lows
Additionally, it includes:
• Mitigation / Order Block zones (local buyer-seller balance) across two timeframes.
• Buy/Sell Side Liquidity levels (BSL / SSL) from the aligned higher timeframe (target levels).
________________________________________
Components and Usage
1 – VWAP
Calculated using the classical method:
• High + Volume for the upper value
• Close + Volume for the middle value
• Low + Volume for the lower value
The VWAP is displayed as a colored band, where the coloring represents the bias.
Let’s call this band FVB (Fair Value Band) for ease of explanation.
The FVB represents the final line of defense, the buyer/seller boundary, and in technical terms, it can be viewed as premium/discount zones or support/resistance levels.
Within this critical area, the strong side continues its move, while the weaker side is forced to retreat.
But does the side that breaks beyond the band always keep going?
We all know that’s not always the case — in different pairs and timeframes, price often violates both the upper and lower edges multiple times.
To achieve more consistent analysis, we’ll define a new set of rules.
________________________________________
2 – Mitigation / Order Blocks
In trading literature, there are dozens of different definitions and uses of mitigation or order blocks.
Here, we will interpret the candlesticks to create our own definition, and we’ll use the zones defined by candles that fit this pattern.
For simplicity, let’s abbreviate mitigation as “mtg.”
For a candle to be selected as an mtg, it must clearly show strength from one side (buyers or sellers) — which can also be observed visually on the chart.
________________________________________
Bullish mtg criteria:
1. The first candle must be bullish (close > open) → buyers are strong.
2. The next candle makes a new high (buyers push higher) but fails to close above and pulls back to close inside the previous range → sellers react.
It also must not break the previous low → buyers defend.
3. In the following candle(s), as long as the first candle’s low is protected and the second candle’s high is broken, it indicates buyer strength → a bullish mtg is confirmed.
When price returns to this zone later (gets mitigated), the expectation is that the zone holds and price pushes upward again.
If the low is violated, the mtg becomes invalid.
In technical terms:
If the previous candle’s high is broken but no close occurs above it, the expectation is a reversal move that will retest its low.
Question:
What if the low is protected and in the next candle(s) a new high forms?
Answer: → Bullish mtg.
Bearish mtg (opposite)
3 – Buy/Sell Side Liquidity Levels
With the help of the aligned higher timeframe (swing points), we will define our market structure framework and set our liquidity targets accordingly.
Let’s put the pieces together.
If we continue explaining from a trade-focused perspective, our first priority should be our bias — our projection or expectation of the market’s potential movement.
We will determine this bias using the FVB.
Since we know the band often gets violated on both sides, we want the price action to convince us of its strength.
To do that, we’ll use the first candle that closes beyond the band.
The distance from that candle’s high to low will be our threshold range
Bullish level = high + (candle length × coefficient)
Bearish level = low - (candle length × coefficient)
When the price closes beyond this threshold, it demonstrates strength, and our bias will now align in that direction.
How long will this bias remain valid?
→ Until a closing candle appears on the opposite side of the band.
If a close occurs on the opposite side, then a new bias will only be confirmed once the new threshold level is broken.
During the period in between, we have no bias.
Let’s continue on the chart:
Now that our bias has been established, where and how do we look for trade opportunities?
There are two possible entry approaches:
• Aggressive entry: Enter immediately with the breakout.
• Conservative entry: Wait for a pullback and enter once a suitable structure forms.
(The choice depends on the user’s preference.)
At this stage, the user can apply their own entry model. Let’s give an example:
Let’s assume we’re looking for setups using HTF sweep + LTF CISD confirmation.
Once our bias turns bearish, we look for an HTF sweep forming on or near an FVB or mtg block, and then confirm the entry with a CISD signal.
In summary:
• FVB defines the bias, the entry zone, and the target zone.
• Mtg blocks represent entry zones.
• BSL / SSL levels suggest target zones.
Overlapping FVB and mtg blocks are expected to be more effective.
The indicator also provides an option for a second FVB.
A band attached to a lower timeframe can be used as confirmation.
• Main band: Bias + FVB
• Extra band: Entry trigger confirmed by a close beyond it.
Mtg blocks can provide trade entry opportunities, especially when the price is moving strongly in one direction (flow).
Consecutive or complementary mtg blocks indicate that the price is decisive in one direction, while sometimes also showing areas where we should wait before entering.
Mtg blocks that contain an FVG (Fair Value Gap) within their body are expected to be more effective.
Settings:
The default values are set to 1-3-5m, optimized for scalping trades.
VWAP settings:
Main VWAP (FVB):
• Can be set by selecting a start time, manually entering date and time, or choosing a predefined level.
Extra VWAP (FVB):
• Set from the menu. If not needed, select “none.”
• Visibility, color, and fill settings for VWAP are located here.
• Threshold levels visibility and color options are also in this section.
• The multiplier is used for calculating the threshold level.
Important:
• If the Extra VWAP is selected but not displayed, you need to increase the chart timeframe.
o Example: If the chart is on 3m and you select WH from the extra options, it will not display correctly.
• Upper limits for VWAP:
o 1m and 3m charts: daily High/Low
o 5m chart: weekly High/Low
________________________________________
Mtg Settings:
• Visibility and color settings for blocks are configured here.
• To display on a second timeframe, the box must be checked and the timeframe specified.
• Optional display modes: “only active blocks,” “only last violated mtg,” or “all.”
• For confirmation and removal criteria, choosing high/low or close determines the source used for mtg block formation and deletion conditions.
BSL/SSL Settings:
• Visibility, color, font size, and line style can be configured in this section.
When “Auto” is selected, the aligned timeframe is determined automatically by the indicator, while in manual mode, the user defines the timeframe.
Final Words:
Simply opening trades every time the price touches the VWAP or mtg blocks will not make you a profitable trader. Searching for setups with similar structures while maintaining proper risk management will yield better results in the long run.
I would be happy to hear your feedback and suggestions.
Happy trading!
GRG/RGR Signal, MA, Ranges and PivotsThis indicator is a combination of several indicators.
It is a combination of two of my indicators which I solely use for trading
1. EMA 10-20-50-200, Pivots and Previous Day/Week/Month range
2. 3/4-Bar GRG / RGR Pattern (Conditional 4th Candle)
You can use them individually if you already have some of them or just use this one. Belive me when I say, this is all you need, along with market structure knowlege and even if you don’t have that, this indicator has been doing wonders for me. This is all I use. I do not use anything else.
**Note - Do checkout the indicators individually as I have added valuable information in the comment section.
It contains the following,
1. 10 EMA/SMA - configurable
2. 20 EMA/SMA - configurable
3. 50 EMA/SMA - configurable
4. 200 EMA/SMA - configurable
5. Previous Day's Range - configurable
6. Previous Week's Range - configurable
7. Previous Month's Range - configurable
8. Pivots - configurable
9. Buy Sell Signal - configurable
The Moving Averages
It is a very important combination and using it correctly with price action will strengthen your entries and exits.
The ema's or sma's added are the most powerful ones and they do definitely act as support and resistance.
The Daily/Weekly/Monthly Ranges
The Daily/Weekly/Monthly ranges are extremely important for any trader and should be used for targets and reversals.
Pivots
Pivots can provide support and resistance level. R5 and S5 can be used to check for over stretched conditions. You can customise them however you like. It is a full pivot indicator.
It is defaulted to show R5 and S5 only to reduce noise in the chart but it can be customised.
The 3/4 RGR or GRG Signal Generator
Combined with a 3/4 RGR or GRG setup can be all a trader needs.
You don't need complex strategies and SMC concepts to trade. Simple EMAs, ranges and RGR/GRG setup is the most winning combination.
This indicator can be used to identify the Green-Red-Green or Red-Green-Red pattern.
It is a price action indicator where a price action which identifies the defeat of buyers and sellers.
If the buyers comprehensively defeat the sellers then the price moves up and if the sellers defeat the buyers then the price moves down.
In my trading experience this is what defines the price movement.
It is a 3 or 4 candle pattern, beyond that i.e, 5 or more candles could mean a very sideways market and unnecessary signal generation.
How does it work?
Upside/Green signal
1. Say candle 1 is Green, which means buyers stepped in, then candle 2 is Red or a Doji, that means sellers brought the price down. Then if candle 3 is forming to be Green and breaks the closing of the 1st candle and opening of the 2nd candle, then a green arrow will appear and that is the place where you want to take your trade.
2. Here the buyers defeated the sellers.
3. Sometimes candle 3 falls short but candle 4 breaks candle 1's closing and candle 2's opening price. We can enter on candle 4.
4. Important - We need to enter the trade as soon as the price moves above the candle 1 and 2's body and should not wait for the 3rd or 4th candle to close. Ignore wicks.
5. But for a more optimised entry I have added an option to use candle’s highs and lows instead of open and close. This reduces lot of noise and provides us with more precise entry. This setting is turned on by default.
6. I have restricted it to 4 candles and that is all that is needed. More than that is a longer sideways market.
7. I call it the +-+ or GRG pattern or Green-Red-Green or Buyer-Seller-Buyer or Seller defeated or just Buyer pattern.
8. Stop loss can be candle 2's mid for safe traders (that includes me) or candle 2's body low for risky traders.
9. Back testing suggests that body low will be useless and result in more points in loss because for the bigger move this point will not be touched, so why not get out faster.
Downside/Red signal
1. Say candle 1 is Red, which means sellers stepped in, then candle 2 is Green or a Doji, that means buyers took the price up. Then if candle 3 is forming to be Red and breaks the closing of the 1st candle and opening of the 2nd candle then a Red arrow will appear and that is the place where you want to take your trade.
2. Sometimes candle 3 falls short but candle 4 breaks candle 1's closing and candle 2's opening price. We can enter on candle 4.
3. We need to enter the trade as soon as the price moves below the candle 1 and 2's body and should not wait for the 3rd or 4th candle to close.
4. But for a more optimised entry I have added an option to use candle’s highs and lows instead of open and close. This reduces lot of noise and provides us with more precise entry. This setting is turned on by default.
5. I have restricted it to 4 candles and that is all that is needed. More than that is a longer sideways market.
6. I call it the -+- or RGR pattern or Red-Green-Red or Seller-Buyer-Seller or Buyer defeated or just Seller pattern.
7. Stop loss can be candle 2's mid for safe traders ( that includes me) or candle 2's body high for risky traders.
8. Back testing suggests that body high will be useless and result in more points in loss because for the bigger move this point will not be touched, so why not get out faster.
Combining Indicators and Signal
Combining these indicators with GRG/RGR signal can be very powerful and can provide big moves.
1. MA crossover and Signal - This is very powerful and provides a very big move. Trades can be held for longer. If after taking the trade we notice that the MA crossover has happened then trades can be held for higher targets.
2. Pivots and Signal - Pivots and add a support or resistance point. Take profits on these points. R5/S5 are over streched conditions so we can start looking for reversal signals and ignore other signals
3. Intraday Range - first 1, 5, 15 min of the day - Sideways days is when price will stay in these ranges. You can take profits at these ranges or if the range is broken and we get a signal, then it can mean that the direction will be sustained.
4. Previous Day/Week/Month Ranges - These can be used as Take Profit points if the price is moving towards them after getting the signal. If the range is broken and we get a signal then it can be a strong signal. They can also be used as reversal points if a strong signal is generated.
Important Settings
1. Include 4th Candle Confirmation - You can enable or disable the 4th candle signal to avoid the noise, but at times I have noticed that the 4th candle gives a very strong signal or I can say that the strong signal falls on the 4th candle. This is mostly a coincidence.
2. Bars to check (default 10) - You can also configure how many previous bars should the signal be generated for. 10 to 30 is good enough. To backtest increase it to 2000 or 5000 for example.
3. Use Candle High/Low for confirmation instead of Candle Open/Close - More optimized entry and noise reduction. This option is now defaulted to false.
4. Show Green-Red-Green (bull) signals - Show only bull entries. Useful when I have a predefined view i.e, I know market is going to go up today.
5. Show Red-Green-Red (bear) signals - Show only bear entries. Useful when I have a predefined view i.e, I know market is going to go down today.
6. 3rd candle should be a Strong candle before considering 4th candle - This will enforce additional logic in 4 candle setup that the 3rd candle is the candle in our direction of breakout. This means something like GRGG is mandatory, which is still the default behaviour. If disabled, the 3rd candle can be any candle and 4th candle will act as our breakout candle. This behaviour has led to breakouts and breakdowns as times, hence I added this as a separate feature. Vice-versa for a RGGR.
For a 4 candle setup till now we were expecting GRGG or RGRR but we can let the system ignore the 3rd candle completely if needed.
This will result in additional signals.
7. Three intraday ranges added for index and stock traders - 1 min, 5 min and 15 min ranges will be displayed. These are disabled by default except 15 min. These are very important ranges and in sideways days the price will usually move within the 15 min. A breakout of this range and a positive signal can be a very powerful setup.
Safe traders can avoid taking a trade in this range as it can lead to fakeouts.
The line style, width, color and opacity are configurable.
Pointers/Golden Rules
1. If after taking the trade, the next candle moves in your direction and closes strong bullish or bearish, then move SL to break even and after that you can trail it.
2. If a upside trade hits SL and immediately a down side trade signal is generated on the next candle then take it. Vice versa is true.
3. Trades need to be taken on previous 2 candle's body high or low combined and not the wicks.
4. The most losses a trader takes is on a sideways day and because in our strategy the stop loss is so small that even on a sideways day we'll get out with a little profit or worst break even.
5. Hold trades for longer targets and don't panic.
6. If last 3-4 days have been sideways then there is a good probability that today will be trending so we can hold our trade for longer targets. Inverse is true when the market has been trending for 2-3 days then volatility followed by sideways is coming (DOW theory). Target to hold the trade for whole day and not exit till the day closes.
7. In general avoid trading in the middle of the day for index and stocks. Divide the day into 3 parts and avoid the middle.
8. Use Support/Resistance, 10, 20, 50, 200 EMA/SMA, Gaps, Whole/Round numbers(very imp) for identifying targets.
9. Trail your SL.
10. For indexes I would use 5 min and 15 min timeframe and at times 10 mins.
11. For commodities and crypto we can use higher timeframe as well. Look for signals during volatile time durations and avoid trading the whole day. Signal usually gives good targets on those times.
12. If a GRG or RGR pattern appears on a daily timeframe then this is our time to go big.
13. Minimum Risk to Reward should be 1:2 and for longer targets can be 1:4 to 1:10.
14. Trade with small lot size. Money management will happen automatically.
15. With small lot size and correct Risk-Reward we can be very profitable. Don't trade with big lot size.
16. Stay in the market for longer and collect points not money.
17. Very imp - Watch market and learn to generate a market view.
18. Very imp - Only 3 type of candles are needed in trading -
Strong Bullish (Big Green candle), Strong Bearish (Big Red candle),
Hammer (it is Strong Bullish), Inverse Hammer (it is Strong Bearish)
and Doji (indecision or confusion).
If on daily timeframe I see Strong Bullish candle previous day then I am biased to the upside the next day, if I see Strong Bearish candle the previous day then I am biased to the downside the next day, if I see Doji on the previous day then I am cautious the next day, if there are back to back Dojis forming in daily or weekly then I am preparing for big move so time to go big once I get the signal.
19. Most Important Candlestick pattern - Bullish and Bearish Engulfing
20. The only Chart patterns I need -
a) Falling Wedge/Channel Bullish Pattern Uptrend or Bull Flag - Buying - Forming over a couple days for intraday and forming over a couple of weeks for swing
b) Falling Wedge/Channel Bullish Pattern Downtrend or Falling Channel - Buying
c) Rising Wedge Bearish Pattern Uptrend or Rising Channel - Selling
d) Rising Wedge Bearish Pattern Downtrend or Bear flag - Selling
e) Head and Shoulder - Over a longer period not for intraday. In 15 min takes few days and for swing 1hr or 4h or daily can take few days
f) M and W pattern - Reversal Patterns - They form within the above 4 patterns, usually resulting in the break of trend line
21. How Gaps work -
a) Small Gap up in Uptrend - Market can fill the gap and reverse. The perception is that people are buying. If previous day candle was Strong Bullish then market view is up.
b) Big Gap up in Uptrend - Not news driven - Profit booking will come but may not fill the entire gap
c) Big Gap up in Uptrend - News driven, war related, tax, interest rate - Market can keep going up without stopping.
c) Flat opening in Uptrend - Big chance of market going up. If previous day candle was Strong Bullish then view is upwards, if it was Doji then still upwards.
d) Gap down in Uptrend - Market is surprised. After going down initially it can go up
e) Small Gap down in Downtrend - Market can fill the gap and keep moving down. If previous day candle was Strong Bearish then view is still down.
f) Flat opening in Downtrend - View is down, short today.
g) Big Gap down in Downtrend - Profit booking and foolish buying will come but market view is still down.
h) Gap down with News - Volatility, sideways then down.
i) Gap Up in Downtrend - Can move up - Price can move up during 2/3rd of the day and End of the day revert and close in red.
22. Go big on bearish days for option traders. Puts are better bought and Calls are better sold.
23. Cluster of green signals can lead to bigger move on the upside and vice versa for red signals.
24. Most of this is what I learned from successful traders (from the top 2%) only the indicator is mine.
3/4-Bar GRG / RGR Pattern (Conditional 4th Candle)This indicator can be used to identify the Green-Red-Green or Red-Green-Red pattern.
It is a price action indicator where a price action which identifies the defeat of buyers and sellers.
If the buyers comprehensively defeat the sellers then the price moves up and if the sellers defeat the buyers then the price moves down.
In my trading experience this is what defines the price movement.
It is a 3 or 4 candle pattern, beyond that i.e, 5 or more candles could mean a very sideways market and unnecessary signal generation.
How does it work?
Upside/Green signal
Say candle 1 is Green, which means buyers stepped in, then candle 2 is Red or a Doji, that means sellers brought the price down. Then if candle 3 is forming to be Green and breaks the closing of the 1st candle and opening of the 2nd candle, then a green arrow will appear and that is the place where you want to take your trade.
Here the buyers defeated the sellers.
Sometimes candle 3 falls short but candle 4 breaks candle 1's closing and candle 2's opening price. We can enter on candle 4.
Important - We need to enter the trade as soon as the price moves above the candle 1 and 2's body and should not wait for the 3rd or 4th candle to close. Ignore wicks.
I have restricted it to 4 candles and that is all that is needed. More than that is a longer sideways market.
I call it the +-+ or GRG pattern.
Stop loss can be candle 2's mid for safe traders (that includes me) or candle 2's body low for risky traders.
Back testing suggests that body low will be useless and result in more points in loss because for the bigger move this point will not be touched, so why not get out faster.
Downside/Red signal
Say candle 1 is Red, which means sellers stepped in, then candle 2 is Green or a Doji, that means buyers took the price up. Then if candle 3 is forming to be Red and breaks the closing of the 1st candle and opening of the 2nd candle then a Red arrow will appear and that is the place where you want to take your trade.
Sometimes candle 3 falls short but candle 4 breaks candle 1's closing and candle 2's opening price. We can enter on candle 4.
We need to enter the trade as soon as the price moves below the candle 1 and 2's body and should not wait for the 3rd or 4th candle to close.
I have restricted it to 4 candles and that is all that is needed. More than that is a longer sideways market.
I call it the -+- or RGR pattern.
Stop loss can be candle 2's mid for safe traders ( that includes me) or candle 2's body high for risky traders.
Back testing suggests that body high will be useless and result in more points in loss because for the bigger move this point will not be touched, so why not get out faster.
Important Settings
You can enable or disable the 4th candle signal to avoid the noise, but at times I have noticed that the 4th candle gives a very strong signal or I can say that the strong signal falls on the 4th candle. This is mostly a coincidence.
You can also configure how many previous bars should the signal be generated for. 10 to 30 is good enough. To backtest increase it to 2000 or 5000 for example.
Rest are self explanatory.
Pointers
If after taking the trade, the next candle moves in your direction and closes strong bullish or bearish, then move SL to break even and after that you can trail it.
If a upside trade hits SL and immediately a down side trade signal is generated on the next candle then take it. Vice versa is true.
Trades need to be taken on previous 2 candle's body high or low combined and not the wicks.
The most losses a trader takes is on a sideways day and because in our strategy the stop loss is so small that even on a sideways day we'll get out with a little profit or worst break even.
Hold targets for longer targets and don't panic.
If last 3-4 days have been sideways then there is a good probability that day will be trending so we can hold our trade for longer targets. Target to hold the trade for whole day and not exit till the day closes.
In general avoid trading in the middle of the day for index and stocks. Divide the day into 3 parts and avoid the middle.
Use Support/Resistance, 10, 20, 50, 200 EMA/SMA, Gaps, Whole/Round numbers(very imp) for identifying targets.
Trail your SL.
For indexes I would use 5 min and 15 min timeframe.
For commodities and crypto we can use higher timeframe as well. Look for signals during volatile time durations and avoid trading the whole day. Signal usually gives good targets on those times.
If a GRG or RGR pattern appears on a daily timeframe then this is our time to go big.
Minimum Risk to Reward should be 1:2 and for longer targets can be 1:4 to 1:10.
Trade with small lot size. Money management will happen automatically.
With small lot size and correct Risk-Re ward we can be very profitable. Don't trade with big lot size.
Stay in the market for longer and collect points not money.
Very imp - Watch market and learn to generate a market view.
Very imp - Only 4 candles are needed in trading - strong bullish, strong bearish, hammer, inverse hammer and doji.
Go big on bearish days for option traders. Puts are better bought and Calls are better sold.
Cluster of green signals can lead to bigger move on the upside and vice versa for red signals.
Most of this is what I learned from successful traders (from the top 2%) only the indicator is mine.
Multi-Symbol Volatility Tracker with Range DetectionMulti-Symbol Volatility Tracker with Range Detection
🎯 Main Purpose:
This indicator is specifically designed for scalpers to quickly identify symbols with high volatility that are currently in ranging conditions . It helps you spot the perfect opportunities for buying at lows and selling at highs repeatedly within the same trading session.
📊 Table Data Explanation:
The indicator displays a comprehensive table with 5 columns for 4 major symbols (GOLD, SILVER, NASDAQ, SP500):
SYMBOL: The trading instrument being analyzed
VOLATILITY: Color-coded volatility levels (NORMAL/HIGH/EXTREME) based on ATR values
Last Candle %: The percentage range of the most recent 5-minute candle
Last 5 Candle Avg %: Average percentage range over the last 5 candles
RANGE: Shows "YES" (blue) or "NO" (gray) indicating if the symbol is currently ranging
🔍 How to Identify Trading Opportunities:
Look for symbols that combine these characteristics:
RANGE column shows "YES" (highlighted in blue) - This means the symbol is moving sideways, perfect for range trading
VOLATILITY shows "HIGH" or "EXTREME" - Ensures there's enough movement for profitable scalping
Higher candlestick percentages - Indicates larger candle ranges, meaning more profit potential per trade
⚡ Optimal Usage:
Best Timeframe: Works optimally on 5-minute charts where the ranging patterns are most reliable for scalping
Trading Strategy: When you find a symbol with "YES" in the RANGE column, switch to that symbol and look for opportunities to buy near the lows and sell near the highs of the ranging pattern
Risk Management: Higher volatility symbols offer more profit potential but require tighter risk management
⚙️ Settings:
ATR Length: Adjusts the Average True Range calculation period (default: 14)
Range Sensitivity: Fine-tune range detection sensitivity (0.1-2.0, lower = more sensitive)
💡 Pro Tips:
The indicator updates in real-time, so monitor for symbols switching from "NO" to "YES" in the RANGE column
Combine HIGH/EXTREME volatility with RANGE: YES for the most profitable scalping setups
Use the candlestick percentages to gauge potential profit per trade - higher percentages mean more movement
The algorithm uses advanced statistical analysis including standard deviation, linear regression slopes, and range efficiency to accurately detect ranging conditions
Perfect for day traders and scalpers who want to quickly identify which symbols offer the best ranging opportunities for consistent buy-low, sell-high strategies.
cd_RSI_Divergence_CxGeneral:
The Relative Strength Index (RSI) is a momentum oscillator widely used by traders in price analysis. In addition to showing overbought/oversold zones, divergences between RSI and price are also tracked to identify trading opportunities.
The general consensus is that oscillators alone are not sufficient for entries and should be evaluated together with multiple confirmations.
This oscillator is designed as an additional confirmation/compatible tool for strategies that already use higher time frame (HTF) sweeps and lower time frame (LTF) confirmations such as Change in State Delivery (CISD) or Change of Character (CHOCH).
Features:
While RSI oscillators are commonly displayed in line format (classic), this indicator also offers candlestick-style visualization.
Depending on the selected source, period length, and EMA length, RSI can be displayed as lines and/or candlesticks.
Divergence detection & tracking:
Price and RSI values are monitored on the chosen higher time frame (from the menu) to determine highs and lows. For divergence display, the user can choose between two modes:
1- Alignment with HTF Sweep
2- All
1 - Alignment with HTF Sweep:
First, the price must sweep the previous high/low of the candle on the HTF (i.e., break it) but fail to continue in that direction and return inside (sweep).
If this condition is met, RSI values are checked:
If price makes a high sweep but RSI fails to make a new high → divergence is confirmed.
If price makes a low sweep but RSI fails to make a new low → divergence is confirmed.
Divergence is then displayed on the chart.
2 - All:
In this mode, sweep conditions are ignored. Divergence is confirmed if:
Price makes a new high on HTF but RSI does not.
RSI makes a new high on HTF but price does not.
Price makes a new low on HTF but RSI does not.
RSI makes a new low on HTF but price does not.
Menu & Settings:
RSI visualization (source + period length + EMA period length)
Option to choose classic/candlestick style display
Color customization
Higher time frame selection
Adjustable HTF boxes and table display
Final notes:
This oscillator is designed as an additional confirmation tool for strategies based on HTF sweep + LTF CISD/CHOCH confirmation logic. The chosen HTF in the oscillator should match the time frame where sweeps are expected.
Divergence signals from this oscillator alone will not make you profitable.
For spot trades, monitoring sweeps and divergences on higher time frames is more suitable (e.g., Daily–H1 / Weekly–H4).
My personal usage preferences:
Entry TF: 3m
HTF bias: Daily + H1
Sweep + CISD: 30m / 3m
Market Structure: 3m
RSI divergence: HTF = 30m
If all of them align bullish or bearish ( timeframe alignment ), I try to take the trade.
I’d be glad to hear your feedback and suggestions for improvement.
Happy trading!
On-Chain Signals [LuxAlgo]The On-Chain Signals indicator uses fundamental blockchain metrics to provide traders with an objective technical view of their favorite cryptocurrencies.
It uses IntoTheBlock datasets integrated within TradingView to generate four key signals: Net Network Growth, In the Money, Concentration, and Large Transactions.
Together, these four signals provide traders with an overall directional bias of the market. All of the data can be visualized as a gauge, table, historical plot, or average.
🔶 USAGE
The main goal of this tool is to provide an overall directional bias based on four blockchain signals, each with three possible biases: bearish, neutral, or bullish. The thresholds for each signal bias can be adjusted on the settings panel.
These signals are based on IntoTheBlock's On-Chain Signals.
Net network growth: Change in the total number of addresses over the last seven periods; i.e., how many new addresses are being created.
In the Money: Change in the seven-period moving average of the total supply in the money. This shows how many addresses are profitable.
Concentration: Change in the aggregate addresses of whales and investors from the previous period. These are addresses holding at least 0.1% of the supply. This shows how many addresses are in the hands of a few.
Large Transactions: Changes in the number of transactions over $100,000. This metric tracks convergence or divergence from the 21- and 30-day EMAs and indicates the momentum of large transactions.
All of these signals together form the blockchain's overall directional bias.
Bearish: The number of bearish individual signals is greater than the number of bullish individual signals.
Neutral: The number of bearish individual signals is equal to the number of bullish individual signals.
Bullish: The number of bullish individual signals is greater than the number of bearish individual signals.
If the overall directional bias is bullish, we can expect the price of the observed cryptocurrency to increase. If the bias is bearish, we can expect the price to decrease. If the signal is neutral, the price may be more likely to stay the same.
Traders should be aware of two things. First, the signals provide optimal results when the chart is set to the daily timeframe. Second, the tool uses IntoTheBlock data, which is available on TradingView. Therefore, some cryptocurrencies may not be available.
🔹 Display Mode
Traders have three different display modes at their disposal. These modes can be easily selected from the settings panel. The gauge is set by default.
🔹 Gauge
The gauge will appear in the center of the visible space. Traders can adjust its size using the Scale parameter in the Settings panel. They can also give it a curved effect.
The number of bars displayed directly affects the gauge's resolution: More bars result in better resolution.
The chart above shows the effect that different scale configurations have on the gauge.
🔹 Historical Data
The chart above shows the historical data for each of the four signals.
Traders can use this mode to adjust the thresholds for each signal on the settings panel to fit the behavior of each cryptocurrency. They can also analyze how each metric impacts price behavior over time.
🔹 Average
This display mode provides an easy way to see the overall bias of past prices in order to analyze price behavior in relation to the underlying blockchain's directional bias.
The average is calculated by taking the values of the overall bias as -1 for bearish, 0 for neutral, and +1 for bullish, and then applying a triangular moving average over 20 periods by default. Simple and exponential moving averages are available, and traders can select the period length from the settings panel.
🔶 DETAILS
The four signals are based on IntoTheBlock's On-Chain Signals. We gather the data, manipulate it, and build the signals depending on each threshold.
Net network growth
float netNetworkGrowthData = customData('_TOTALADDRESSES')
float netNetworkGrowth = 100*(netNetworkGrowthData /netNetworkGrowthData - 1)
In the Money
float inTheMoneyData = customData('_INOUTMONEYIN')
float averageBalance = customData('_AVGBALANCE')
float inTheMoneyBalance = inTheMoneyData*averageBalance
float sma = ta.sma(inTheMoneyBalance,7)
float inTheMoney = ta.roc(sma,1)
Concentration
float whalesData = customData('_WHALESPERCENTAGE')
float inverstorsData = customData('_INVESTORSPERCENTAGE')
float bigHands = whalesData+inverstorsData
float concentration = ta.change(bigHands )*100
Large Transactions
float largeTransacionsData = customData('_LARGETXCOUNT')
float largeTX21 = ta.ema(largeTransacionsData,21)
float largeTX30 = ta.ema(largeTransacionsData,30)
float largeTransacions = ((largeTX21 - largeTX30)/largeTX30)*100
🔶 SETTINGS
Display mode: Select between gauge, historical data and average.
Average: Select a smoothing method and length period.
🔹 Thresholds
Net Network Growth : Bullish and bearish thresholds for this signal.
In The Money : Bullish and bearish thresholds for this signal.
Concentration : Bullish and bearish thresholds for this signal.
Transactions : Bullish and bearish thresholds for this signal.
🔹 Dashboard
Dashboard : Enable/disable dashboard display
Position : Select dashboard location
Size : Select dashboard size
🔹 Gauge
Scale : Select the size of the gauge
Curved : Enable/disable curved mode
Select Gauge colors for bearish, neutral and bullish bias
🔹 Style
Net Network Growth : Enable/disable historical plot and choose color
In The Money : Enable/disable historical plot and choose color
Concentration : Enable/disable historical plot and choose color
Large Transacions : Enable/disable historical plot and choose color
200 SMA (5%/-3% Buffer) for SPY & QQQ In my testing TQQQ is an absolute monster of an ETF that performs extremely well even from a buy and hold standpoint over long periods of time, its largest drawback is the massive drawdown exposure that it faces which can be easily sidestepped with this strategy.
This strategy is meant to basically abuse TQQQ's insane outperformance while augmenting the typical 200SMA strategy in a way that uses all of its strengths while avoiding getting whipsawed in sideways markets.
The strategy BUYS when price crosses 5% over the 200SMA and then SELLS when price drops 3% below the 200SMA. Between trades I'll be parking my entire account in SGOV.
So maximizing profit while minimizing risk.
You use the strategy based off of QQQ and then make the trades on TQQQ when it tells you to BUY/SELL.
Here are some reasons why I will be using this strategy:
Simple emotionless BUY and SELL signals where I don't care who the president is, what is happening in the world, who is bombing who, who the leadership team is, no attachment to individual companies and diversified across the NASDAQ.
~85% win percentage and when it does lose the loses are nothing compared to the wins and after a loss you're basically set up for a massive win in the next trade.
Max drawdown of around 53% when using TQQQ
You benefit massively when the market is doing well and when there is a recession you basically sit in SGOV for a year and then are set up for a monster recovery with a clear easy BUY signal. So as long as you're patient you win regardless of what happens.
The trades are often very long term resulting in you taking advantage of Long Term Capital Gains tax advantage which could mean saving up to 15-20% in taxes.
With only a few trades you can spend time doing other stuff and don't have to track or pay attention to anything that is happening.
Simple, easy, and massively profitable.
Candlestick Patterns Backtester [Optimized]Candlestick Patterns Backtester
What this is: This indicator is based on a really cool candlestick pattern backtester that I found (I'll update this later when I remember where I got it from or find the actual author). The original had this massive table showing win/loss ratios for a bunch of candlestick patterns, and according to the built-in backtester, it was actually profitable - which was pretty impressive.
The Problem: I played around with the original for a while but honestly wasn't really able to get it to work well at all for actual trading. It was still pretty cool to look at though! The main issues were:
It was just a big static table - hard to do anything useful with it
Couldn't send signals out to other strategies
The code was a monster - like 2,000+ lines of repetitive mess
What I Did: I completely refactored this thing and got it down from 2,000+ lines to just a few hundred lines. Much cleaner now! Here's what it does:
45+ Candlestick Patterns - All the classics are in there
Dynamic Filtering - Set your own requirements (minimum win rate, profit factor, total trades, etc.)
Flexible Logic - Choose AND/OR logic for your filters
Signal Generation - Creates actual buy/sell signals you can use with other strategies
Visual Badges - Shows pattern badges on chart when they meet your criteria
Active Patterns Table - Only shows patterns that are currently profitable based on your settings
Settings You Can Adjust:
Minimum win rate threshold
Minimum profit factor
Minimum number of trades required
Whether to use AND or OR logic for filtering
Colors, badge display, debug options
Reality Check: Trading these patterns really wasn't for me, but it was still a great learning experience. The backtesting results look good on paper, but as always, past performance doesn't guarantee future results. Use this as a research tool and educational resource more than anything else.
Credit: This is based on someone else's original work that I heavily modified and optimized. I'll update this description once I track down the original author to give proper credit where it's due.
This introduction captures your casual, honest tone while explaining the technical improvements you made and setting realistic expectations about the indicator's practical use.
Price Reaction Analysis by Day of WeekOverview
The "Price Reaction Analysis by Day of Week" indicator is a tool that enables traders to analyze historical price reaction patterns to technical indicator signals on a selected day of the week. It examines price behavior on a chosen candle (from 1 to 30) in the next day or subsequent days after a signal, depending on the timeframe, and provides success rate statistics to support data-driven trading decisions. The indicator is optimized for timeframes up to 1 day (e.g., 1D, 12H, 8H, 6H, 4H, 1H, 15M), as the analysis relies on day-of-week comparisons. Lower timeframes generate more signals due to the higher number of candles per day.
Key Features
1. Flexible Technical Indicator Selection
Users can choose one of four technical indicators: RSI, SMI, MA, or Bollinger Bands. Each indicator has configurable parameters, such as:
RSI length, oversold/overbought levels.
SMI length, %K and %D smoothing, signal levels.
MA length.
Bollinger Bands length and multiplier.
2. Day-of-Week Analysis
The indicator allows users to select a day of the week (Monday, Tuesday, Wednesday, Thursday, Friday) for generating signals. It analyzes price reactions on a selected candle (from 1 to 30) in the next day or subsequent days after the signal. Examples:
On a daily timeframe, a signal on Monday can be analyzed for the first, fourth, or later candle (up to 30) in subsequent days (e.g., Tuesday, Wednesday).
On timeframes lower than 1 day (e.g., 12H, 8H, 6H, 4H, 1H, 15M), the analysis targets the selected candle in the next day or subsequent days. For example, on a 4H timeframe, you can analyze the second Tuesday candle following a Monday signal. The maximum timeframe is 1 day to ensure consistent day-of-week analysis.
3. Visual Signals
Signals for the analysis period are marked with background highlights in real-time when the indicator’s conditions are met. The last highlighted candle of the selected day is always analyzed. Arrows are displayed on the chart at the candle specified by the “Candles to Compare” setting (e.g., the first candle if set to 1):
Green upward triangles (below the candle) for successful buy signals (the closing price of the selected candle is higher than the signal candle’s close).
Red downward triangles (above the candle) for successful sell signals (the closing price of the selected candle is lower than the signal candle’s close).
Gray “x” marks for unsuccessful signals (no price reversal in the expected direction). Arrow positions are intuitive: buy signals below the candle, sell signals above. Highlights and arrows do not require waiting for future signals but are essential for calculating statistics.
Note: The first candle of the next day may appear shifted on the chart due to timezone differences, which can affect the timing of signal appearance.
4. Signal Conditions (Highlights) for Each Indicator
RSI: The oscillator is in oversold (buy) or overbought (sell) zones.
SMI: SMI returns from oversold (buy) or overbought (sell) zones.
MA: Price crosses the MA (upward for buy, downward for sell).
Bollinger Bands: Price returns inside the bands (from below for buy, from above for sell).
5. Success Rate Statistics
A table in the top-right corner of the chart displays:
The number of buy and sell signals for the selected day of the week.
The percentage of cases where the price of the selected candle in the next day or subsequent days reversed as expected (e.g., rising after a buy signal). Statistics are based on comparing the closing price of the signal candle with the closing price of the selected candle (e.g., first, fourth) in the next day or subsequent days.
Important: Statistics do not account for price movements within the candle or after its close. The price on the selected candle (e.g., fourth) may be lower than earlier candles but still higher than the signal candle, counting as a positive buy signal, though it does not guarantee profit.
6. Date Range
Users can specify the analysis date range, enabling strategy testing on historical data from a chosen period. Ensure the start and end dates are set correctly.
Applications
The indicator is designed for traders who want to leverage historical patterns for position planning. Examples:
On a 4-hour timeframe: If a sell signal highlight appears on Monday and statistics show an 80% chance that the fourth Tuesday candle is bearish, traders may consider playing a correction at the open of that candle.
On a daily timeframe: If a highlight indicates market overheating, traders may consider entering a position at the open of the first candle after the signal (e.g., Tuesday), provided statistics suggest an edge. Users can analyze the signal on the first candle and check later candles to validate results, increasing confidence in consistent patterns.
Key Settings
Indicator Type: Choose between RSI, SMI, MA, or Bollinger Bands.
Selected Day: Monday, Tuesday, Wednesday, Thursday, or Friday.
Candles to Compare: The number of the candle in the next day or subsequent days (from 1 to 30).
Indicator Parameters: Lengths, levels (e.g., oversold/overbought for RSI).
Background Colors: Configurable highlights for buy and sell signals.
Notes
Timeframes: The indicator is optimized for timeframes up to 1 day (e.g., 1D, 12H, 8H, 6H, 4H, 1H, 15M), as the analysis relies on day-of-week patterns. Timeframes lower than 1 day generate more signals due to the higher number of candles per day.
Candle Shift: The first candle of the next day may appear shifted on the chart due to timezone differences, affecting the timing of signals across markets or platforms.
Statistical Limitations: Results are based on the closing prices of the selected candle, ignoring fluctuations in earlier candles, within the candle, or subsequent price movements. Traders must assess whether entering at the open or after the close of the selected candle is profitable.
Testing: Effectiveness depends on historical data and parameter settings. Testing different configurations across markets and timeframes is recommended.
Who Is It For?
Swing and position traders who base decisions on technical analysis and historical patterns.
Market analysts seeking patterns in price behavior by day of the week.
TradingView users of all experience levels, thanks to an intuitive interface and flexible settings.
Prev Week POC Buy/Sell Signals
Hi, I’m Edward. I created a straightforward strategy for swing traders (4hr or 8hr timeframe users). This strategy is for traders that are not interested to look at charts all day long, 2 times a day max, but still be profitable.
The indicator:
Print a buy signal when the price closes above the previous week's Point of Control (POC).
Stay in the trade until the price closes below the previous week's POC, then print a sell signal.
The indicator calculates the weekly POC using a basic volume profile method, then tracks the previous week's POC for signals.
Previous week POC is valid from Monday to Thursday. By close of business on Thursday, the current week trend and POC should be well established and should be used make buy or sell decisions. Enjoy!
Bilateral Filter For Loop [BackQuant]Bilateral Filter For Loop
The Bilateral Filter For Loop is an advanced technical indicator designed to filter out market noise and smooth out price data, thus improving the identification of underlying market trends. It employs a bilateral filter, which is a sophisticated non-linear filter commonly used in image processing and price time series analysis. By considering both spatial and range differences between price points, this filter is highly effective at preserving significant trends while reducing random fluctuations, ultimately making it suitable for dynamic trend-following strategies.
Please take the time to read the following:
Key Features
1. Bilateral Filter Calculation:
The bilateral filter is the core of this indicator and works by applying a weight to each data point based on two factors: spatial distance and price range difference. This dual weighting process allows the filter to preserve important price movements while reducing the impact of less relevant fluctuations. The filter uses two primary parameters:
Spatial Sigma (σ_d): This parameter adjusts the weight applied based on the distance of each price point from the current price. A larger spatial sigma means more smoothing, as further away values will contribute more heavily to the result.
Range Sigma (σ_r): This parameter controls how much weight is applied based on the difference in price values. Larger price differences result in smaller weights, while similar price values result in larger weights, thereby preserving the trend while filtering out noise.
The output of this filter is a smoothed version of the original price series, which eliminates short-term fluctuations, helping traders focus on longer-term trends. The bilateral filter is applied over a rolling window, adjusting the level of smoothing dynamically based on both the distance between values and their relative price movements.
2. For Loop Calculation for Trend Scoring:
A for-loop is used to calculate the trend score based on the filtered price data. The loop compares the current value to previous values within the specified window, scoring the trend as follows:
+1 for upward movement (when the filtered value is greater than the previous value).
-1 for downward movement (when the filtered value is less than the previous value).
The cumulative result of this loop gives a continuous trend score, which serves as a directional indicator for the market's momentum. By summing the scores over the window period, the loop provides an aggregate value that reflects the overall trend strength. This score helps determine whether the market is experiencing a strong uptrend, downtrend, or sideways movement.
3. Long and Short Conditions:
Once the trend score has been calculated, it is compared against predefined threshold levels:
A long signal is generated when the trend score exceeds the upper threshold, indicating that the market is in a strong uptrend.
A short signal is generated when the trend score crosses below the lower threshold, signaling a potential downtrend or trend reversal.
These conditions provide clear signals for potential entry points, and the color-coding helps traders quickly identify market direction:
Long signals are displayed in green.
Short signals are displayed in red.
These signals are designed to provide high-confidence entries for trend-following strategies, helping traders capture profitable movements in the market.
4. Trend Background and Bar Coloring:
The script offers customizable visual settings to enhance the clarity of the trend signals. Traders can choose to:
Color the bars based on the trend direction: Bars are colored green for long signals and red for short signals.
Change the background color to provide additional context: The background will be shaded green for a bullish trend and red for a bearish trend. This visual feedback helps traders to stay aligned with the prevailing market sentiment.
These features offer a quick visual reference for understanding the market's direction, making it easier for traders to identify when to enter or exit positions.
5. Threshold Lines for Visual Feedback:
Threshold lines are plotted on the chart to represent the predefined long and short levels. These lines act as clear markers for when the market reaches a critical threshold, triggering a potential buy (long) or sell (short) signal. By showing these threshold lines on the chart, traders can quickly gauge the strength of the market and assess whether the trend is strong enough to warrant action.
These thresholds can be adjusted based on the trader's preferences, allowing them to fine-tune the indicator for different market conditions or asset behaviors.
6. Customizable Parameters for Flexibility:
The indicator offers several parameters that can be adjusted to suit individual trading preferences:
Window Period (Bilateral Filter): The window size determines how many past price values are used to calculate the bilateral filter. A larger window increases smoothing, while a smaller window results in more responsive, but noisier, data.
Spatial Sigma (σ_d) and Range Sigma (σ_r): These values control how sensitive the filter is to price changes and the distance between data points. Fine-tuning these parameters allows traders to adjust the degree of noise reduction applied to the price series.
Threshold Levels: The upper and lower thresholds determine when the trend score crosses into long or short territory. These levels can be customized to better match the trader's risk tolerance or asset characteristics.
Visual Settings: Traders can customize the appearance of the chart, including the line width of trend signals, bar colors, and background shading, to make the indicator more readable and aligned with their charting style.
7. Alerts for Trend Reversals:
The indicator includes alert conditions for real-time notifications when the market crosses the defined thresholds. Traders can set alerts to be notified when:
The trend score crosses the long threshold, signaling an uptrend.
The trend score crosses the short threshold, signaling a downtrend.
These alerts provide timely information, allowing traders to take immediate action when the market shows a significant change in direction.
Final Thoughts
The Bilateral Filter For Loop indicator is a robust tool for trend-following traders who wish to reduce market noise and focus on the underlying trend. By applying the bilateral filter and calculating trend scores, this indicator helps traders identify strong uptrends and downtrends, providing reliable entry signals with minimal market noise. The customizable parameters, visual feedback, and alerting system make it a versatile tool for traders seeking to improve their timing and capture profitable market movements.
Thus following all of the key points here are some sample backtests on the 1D Chart
Disclaimer: Backtests are based off past results, and are not indicative of the future.
INDEX:BTCUSD
INDEX:ETHUSD
CRYPTO:SOLUSD
SuperTrade ST1 StrategyOverview
The SuperTrade ST1 Strategy is a long-only trend-following strategy that combines a Supertrend indicator with a 200-period EMA filter to isolate high-probability bullish trade setups. It is designed to operate in trending markets, using volatility-based exits with a strict 1:4 Risk-to-Reward (R:R) ratio, meaning that each trade targets a profit 4× the size of its predefined risk.
This strategy is ideal for traders looking to align with medium- to long-term trends, while maintaining disciplined risk control and minimal trade frequency.
How It Works
This strategy leverages three key components:
Supertrend Indicator
A trend-following indicator based on Average True Range (ATR).
Identifies bullish/bearish trend direction by plotting a trailing stop line that moves with price volatility.
200-period Exponential Moving Average (EMA) Filter
Trades are only taken when the price is above the EMA, ensuring participation only during confirmed uptrends.
Helps filter out counter-trend entries during market pullbacks or ranges.
ATR-Based Stop Loss and Take Profit
Each trade uses the ATR to calculate volatility-adjusted exit levels.
Stop Loss: 1× ATR below entry.
Take Profit: 4× ATR above entry (1:4 R:R).
This asymmetry ensures that even with a lower win rate, the strategy can remain profitable.
Entry Conditions
A long trade is triggered when:
Supertrend flips from bearish to bullish (trend reversal).
Price closes above the Supertrend line.
Price is above the 200 EMA (bullish market bias).
Exit Logic
Once a long position is entered:
Stop loss is set 1 ATR below entry.
Take profit is set 4 ATR above entry.
The strategy automatically exits the position on either target.
Backtest Settings
This strategy is configured for realistic backtesting, including:
$10,000 account size
2% equity risk per trade
0.1% commission
1 tick slippage
These settings aim to simulate real-world conditions and avoid overly optimistic results.
How to Use
Apply the script to any timeframe, though higher timeframes (1H, 4H, Daily) often yield more reliable signals.
Works best in clearly trending markets (especially in crypto, stocks, indices).
Can be paired with alerts for live trading or analysis.
Important Notes
This version is long-only by design. No short positions are executed.
Ideal for swing traders or position traders seeking asymmetric returns.
Users can modify the ATR period, Supertrend factor, or EMA filter length based on asset behavior.
Dskyz (DAFE) Adaptive Regime - Quant Machine ProDskyz (DAFE) Adaptive Regime - Quant Machine Pro:
Buckle up for the Dskyz (DAFE) Adaptive Regime - Quant Machine Pro, is a strategy that’s your ultimate edge for conquering futures markets like ES, MES, NQ, and MNQ. This isn’t just another script—it’s a quant-grade powerhouse, crafted with precision to adapt to market regimes, deliver multi-factor signals, and protect your capital with futures-tuned risk management. With its shimmering DAFE visuals, dual dashboards, and glowing watermark, it turns your charts into a cyberpunk command center, making trading as thrilling as it is profitable.
Unlike generic scripts clogging up the space, the Adaptive Regime is a DAFE original, built from the ground up to tackle the chaos of futures trading. It identifies market regimes (Trending, Range, Volatile, Quiet) using ADX, Bollinger Bands, and HTF indicators, then fires trades based on a weighted scoring system that blends candlestick patterns, RSI, MACD, and more. Add in dynamic stops, trailing exits, and a 5% drawdown circuit breaker, and you’ve got a system that’s as safe as it is aggressive. Whether you’re a newbie or a prop desk pro, this strat’s your ticket to outsmarting the markets. Let’s break down every detail and see why it’s a must-have.
Why Traders Need This Strategy
Futures markets are a gauntlet—fast moves, volatility spikes (like the April 28, 2025 NQ 1k-point drop), and institutional traps that punish the unprepared. Meanwhile, platforms are flooded with low-effort scripts that recycle old ideas with zero innovation. The Adaptive Regime stands tall, offering:
Adaptive Intelligence: Detects market regimes (Trending, Range, Volatile, Quiet) to optimize signals, unlike one-size-fits-all scripts.
Multi-Factor Precision: Combines candlestick patterns, MA trends, RSI, MACD, volume, and HTF confirmation for high-probability trades.
Futures-Optimized Risk: Calculates position sizes based on $ risk (default: $300), with ATR or fixed stops/TPs tailored for ES/MES.
Bulletproof Safety: 5% daily drawdown circuit breaker and trailing stops keep your account intact, even in chaos.
DAFE Visual Mastery: Pulsing Bollinger Band fills, dynamic SL/TP lines, and dual dashboards (metrics + position) make signals crystal-clear and charts a work of art.
Original Craftsmanship: A DAFE creation, built with community passion, not a rehashed clone of generic code.
Traders need this because it’s a complete, adaptive system that blends quant smarts, user-friendly design, and DAFE flair. It’s your edge to trade with confidence, cut through market noise, and leave the copycats in the dust.
Strategy Components
1. Market Regime Detection
The strategy’s brain is its ability to classify market conditions into five regimes, ensuring signals match the environment.
How It Works:
Trending (Regime 1): ADX > 20, fast/slow EMA spread > 0.3x ATR, HTF RSI > 50 or MACD bullish (htf_trend_bull/bear).
Range (Regime 2): ADX < 25, price range < 3% of close, no HTF trend.
Volatile (Regime 3): BB width > 1.5x avg, ATR > 1.2x avg, HTF RSI overbought/oversold.
Quiet (Regime 4): BB width < 0.8x avg, ATR < 0.9x avg.
Other (Regime 5): Default for unclear conditions.
Indicators: ADX (14), BB width (20), ATR (14, 50-bar SMA), HTF RSI (14, daily default), HTF MACD (12,26,9).
Why It’s Brilliant:
Regime detection adapts signals to market context, boosting win rates in trending or volatile conditions.
HTF RSI/MACD add a big-picture filter, rare in basic scripts.
Visualized via gradient background (green for Trending, orange for Range, red for Volatile, gray for Quiet, navy for Other).
2. Multi-Factor Signal Scoring
Entries are driven by a weighted scoring system that combines candlestick patterns, trend, momentum, and volume for robust signals.
Candlestick Patterns:
Bullish: Engulfing (0.5), hammer (0.4 in Range, 0.2 else), morning star (0.2), piercing (0.2), double bottom (0.3 in Volatile, 0.15 else). Must be near support (low ≤ 1.01x 20-bar low) with volume spike (>1.5x 20-bar avg).
Bearish: Engulfing (0.5), shooting star (0.4 in Range, 0.2 else), evening star (0.2), dark cloud (0.2), double top (0.3 in Volatile, 0.15 else). Must be near resistance (high ≥ 0.99x 20-bar high) with volume spike.
Logic: Patterns are weighted higher in specific regimes (e.g., hammer in Range, double bottom in Volatile).
Additional Factors:
Trend: Fast EMA (20) > slow EMA (50) + 0.5x ATR (trend_bull, +0.2); opposite for trend_bear.
RSI: RSI (14) < 30 (rsi_bull, +0.15); > 70 (rsi_bear, +0.15).
MACD: MACD line > signal (12,26,9, macd_bull, +0.15); opposite for macd_bear.
Volume: ATR > 1.2x 50-bar avg (vol_expansion, +0.1).
HTF Confirmation: HTF RSI < 70 and MACD bullish (htf_bull_confirm, +0.2); RSI > 30 and MACD bearish (htf_bear_confirm, +0.2).
Scoring:
bull_score = sum of bullish factors; bear_score = sum of bearish. Entry requires score ≥ 1.0.
Example: Bullish engulfing (0.5) + trend_bull (0.2) + rsi_bull (0.15) + htf_bull_confirm (0.2) = 1.05, triggers long.
Why It’s Brilliant:
Multi-factor scoring ensures signals are confirmed by multiple market dynamics, reducing false positives.
Regime-specific weights make patterns more relevant (e.g., hammers shine in Range markets).
HTF confirmation aligns with the big picture, a quant edge over simplistic scripts.
3. Futures-Tuned Risk Management
The risk system is built for futures, calculating position sizes based on $ risk and offering flexible stops/TPs.
Position Sizing:
Logic: Risk per trade (default: $300) ÷ (stop distance in points * point value) = contracts, capped at max_contracts (default: 5). Point value = tick value (e.g., $12.5 for ES) * ticks per point (4) * contract multiplier (1 for ES, 0.1 for MES).
Example: $300 risk, 8-point stop, ES ($50/point) → 0.75 contracts, rounded to 1.
Impact: Precise sizing prevents over-leverage, critical for micro contracts like MES.
Stops and Take-Profits:
Fixed: Default stop = 8 points, TP = 16 points (2:1 reward/risk).
ATR-Based: Stop = 1.5x ATR (default), TP = 3x ATR, enabled via use_atr_for_stops.
Logic: Stops set at swing low/high ± stop distance; TPs at 2x stop distance from entry.
Impact: ATR stops adapt to volatility, while fixed stops suit stable markets.
Trailing Stops:
Logic: Activates at 50% of TP distance. Trails at close ± 1.5x ATR (atr_multiplier). Longs: max(trail_stop_long, close - ATR * 1.5); shorts: min(trail_stop_short, close + ATR * 1.5).
Impact: Locks in profits during trends, a game-changer in volatile sessions.
Circuit Breaker:
Logic: Pauses trading if daily drawdown > 5% (daily_drawdown = (max_equity - equity) / max_equity).
Impact: Protects capital during black swan events (e.g., April 27, 2025 ES slippage).
Why It’s Brilliant:
Futures-specific inputs (tick value, multiplier) make it plug-and-play for ES/MES.
Trailing stops and circuit breaker add pro-level safety, rare in off-the-shelf scripts.
Flexible stops (ATR or fixed) suit different trading styles.
4. Trade Entry and Exit Logic
Entries and exits are precise, driven by bull_score/bear_score and protected by drawdown checks.
Entry Conditions:
Long: bull_score ≥ 1.0, no position (position_size <= 0), drawdown < 5% (not pause_trading). Calculates contracts, sets stop at swing low - stop points, TP at 2x stop distance.
Short: bear_score ≥ 1.0, position_size >= 0, drawdown < 5%. Stop at swing high + stop points, TP at 2x stop distance.
Logic: Tracks entry_regime for PNL arrays. Closes opposite positions before entering.
Exit Conditions:
Stop-Loss/Take-Profit: Hits stop or TP (strategy.exit).
Trailing Stop: Activates at 50% TP, trails by ATR * 1.5.
Emergency Exit: Closes if price breaches stop (close < long_stop_price or close > short_stop_price).
Reset: Clears stop/TP prices when flat (position_size = 0).
Why It’s Brilliant:
Score-based entries ensure multi-factor confirmation, filtering out weak signals.
Trailing stops maximize profits in trends, unlike static exits in basic scripts.
Emergency exits add an extra safety layer, critical for futures volatility.
5. DAFE Visuals
The visuals are pure DAFE magic, blending function with cyberpunk flair to make signals intuitive and charts stunning.
Shimmering Bollinger Band Fill:
Display: BB basis (20, white), upper/lower (green/red, 45% transparent). Fill pulses (30–50 alpha) by regime, with glow (60–95 alpha) near bands (close ≥ 0.995x upper or ≤ 1.005x lower).
Purpose: Highlights volatility and key levels with a futuristic glow.
Visuals make complex regimes and signals instantly clear, even for newbies.
Pulsing effects and regime-specific colors add a DAFE signature, setting it apart from generic scripts.
BB glow emphasizes tradeable levels, enhancing decision-making.
Chart Background (Regime Heatmap):
Green — Trending Market: Strong, sustained price movement in one direction. The market is in a trend phase—momentum follows through.
Orange — Range-Bound: Market is consolidating or moving sideways, with no clear up/down trend. Great for mean reversion setups.
Red — Volatile Regime: High volatility, heightened risk, and larger/faster price swings—trade with caution.
Gray — Quiet/Low Volatility: Market is calm and inactive, with small moves—often poor conditions for most strategies.
Navy — Other/Neutral: Regime is uncertain or mixed; signals may be less reliable.
Bollinger Bands Glow (Dynamic Fill):
Neon Red Glow — Warning!: Price is near or breaking above the upper band; momentum is overstretched, watch for overbought conditions or reversals.
Bright Green Glow — Opportunity!: Price is near or breaking below the lower band; market could be oversold, prime for bounce or reversal.
Trend Green Fill — Trending Regime: Fills between bands with green when the market is trending, showing clear momentum.
Gold/Yellow Fill — Range Regime: Fills with gold/aqua in range conditions, showing the market is sideways/oscillating.
Magenta/Red Fill — Volatility Spike: Fills with vivid magenta/red during highly volatile regimes.
Blue Fill — Neutral/Quiet: A soft blue glow for other or uncertain market states.
Moving Averages:
Display: Blue fast EMA (20), red slow EMA (50), 2px.
Purpose: Shows trend direction, with trend_dir requiring ATR-scaled spread.
Dynamic SL/TP Lines:
Display: Pulsing colors (red SL, green TP for Trending; yellow/orange for Range, etc.), 3px, with pulse_alpha for shimmer.
Purpose: Tracks stops/TPs in real-time, color-coded by regime.
6. Dual Dashboards
Two dashboards deliver real-time insights, making the strat a quant command center.
Bottom-Left Metrics Dashboard (2x13):
Metrics: Mode (Active/Paused), trend (Bullish/Bearish/Neutral), ATR, ATR avg, volume spike (YES/NO), RSI (value + Oversold/Overbought/Neutral), HTF RSI, HTF trend, last signal (Buy/Sell/None), regime, bull score.
Display: Black (29% transparent), purple title, color-coded (green for bullish, red for bearish).
Purpose: Consolidates market context and signal strength.
Top-Right Position Dashboard (2x7):
Metrics: Regime, position side (Long/Short/None), position PNL ($), SL, TP, daily PNL ($).
Display: Black (29% transparent), purple title, color-coded (lime for Long, red for Short).
Purpose: Tracks live trades and profitability.
Why It’s Brilliant:
Dual dashboards cover market context and trade status, a rare feature.
Color-coding and concise metrics guide beginners (e.g., green “Buy” = go).
Real-time PNL and SL/TP visibility empower disciplined trading.
7. Performance Tracking
Logic: Arrays (regime_pnl_long/short, regime_win/loss_long/short) track PNL and win/loss by regime (1–5). Updated on trade close (barstate.isconfirmed).
Purpose: Prepares for future adaptive thresholds (e.g., adjust bull_score min based on regime performance).
Why It’s Brilliant: Lays the groundwork for self-optimizing logic, a quant edge over static scripts.
Key Features
Regime-Adaptive: Optimizes signals for Trending, Range, Volatile, Quiet markets.
Futures-Optimized: Precise sizing for ES/MES with tick-based risk inputs.
Multi-Factor Signals: Candlestick patterns, RSI, MACD, and HTF confirmation for robust entries.
Dynamic Exits: ATR/fixed stops, 2:1 TPs, and trailing stops maximize profits.
Safe and Smart: 5% drawdown breaker and emergency exits protect capital.
DAFE Visuals: Shimmering BB fill, pulsing SL/TP, and dual dashboards.
Backtest-Ready: Fixed qty and tick calc for accurate historical testing.
How to Use
Add to Chart: Load on a 5min ES/MES chart in TradingView.
Configure Inputs: Set instrument (ES/MES), tick value ($12.5/$1.25), multiplier (1/0.1), risk ($300 default). Enable ATR stops for volatility.
Monitor Dashboards: Bottom-left for regime/signals, top-right for position/PNL.
Backtest: Run in strategy tester to compare regimes.
Live Trade: Connect to Tradovate or similar. Watch for slippage (e.g., April 27, 2025 ES issues).
Replay Test: Try April 28, 2025 NQ drop to see regime shifts and stops.
Disclaimer
Trading futures involves significant risk of loss and is not suitable for all investors. Past performance does not guarantee future results. Backtest results may differ from live trading due to slippage, fees, or market conditions. Use this strategy at your own risk, and consult a financial advisor before trading. Dskyz (DAFE) Trading Systems is not responsible for any losses incurred.
Backtesting:
Frame: 2023-09-20 - 2025-04-29
Slippage: 3
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Final Notes
The Dskyz (DAFE) Adaptive Regime - Quant Machine Pro is more than a strategy—it’s a revolution. Crafted with DAFE’s signature precision, it rises above generic scripts with adaptive regimes, quant-grade signals, and visuals that make trading a thrill. Whether you’re scalping MES or swinging ES, this system empowers you to navigate markets with confidence and style. Join the DAFE crew, light up your charts, and let’s dominate the futures game!
(This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.)
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
Created by Dskyz, powered by DAFE Trading Systems. Trade smart, trade bold.
Dskyz (DAFE) Aurora Divergence – Quant Master Dskyz (DAFE) Aurora Divergence – Quant Master
Introducing the Dskyz (DAFE) Aurora Divergence – Quant Master , a strategy that’s your secret weapon for mastering futures markets like MNQ, NQ, MES, and ES. Born from the legendary Aurora Divergence indicator, this fully automated system transforms raw divergence signals into a quant-grade trading machine, blending precision, risk management, and cyberpunk DAFE visuals that make your charts glow like a neon skyline. Crafted with care and driven by community passion, this strategy stands out in a sea of generic scripts, offering traders a unique edge to outsmart institutional traps and navigate volatile markets.
The Aurora Divergence indicator was a cult favorite for spotting price-OBV divergences with its aqua and fuchsia orbs, but traders craved a system to act on those signals with discipline and automation. This strategy delivers, layering advanced filters (z-score, ATR, multi-timeframe, session), dynamic risk controls (kill switches, adaptive stops/TPs), and a real-time dashboard to turn insights into profits. Whether you’re a newbie dipping into futures or a pro hunting reversals, this strat’s got your back with a beginner guide, alerts, and visuals that make trading feel like a sci-fi mission. Let’s dive into every detail and see why this original DAFE creation is a must-have.
Why Traders Need This Strategy
Futures markets are a battlefield—fast-paced, volatile, and riddled with institutional games that can wipe out undisciplined traders. From the April 28, 2025 NQ 1k-point drop to sneaky ES slippage, the stakes are high. Meanwhile, platforms are flooded with unoriginal, low-effort scripts that promise the moon but deliver noise. The Aurora Divergence – Quant Master rises above, offering:
Unmatched Originality: A bespoke system built from the ground up, with custom divergence logic, DAFE visuals, and quant filters that set it apart from copycat clutter.
Automation with Precision: Executes trades on divergence signals, eliminating emotional slip-ups and ensuring consistency, even in chaotic sessions.
Quant-Grade Filters: Z-score, ATR, multi-timeframe, and session checks filter out noise, targeting high-probability reversals.
Robust Risk Management: Daily loss and rolling drawdown kill switches, plus ATR-based stops/TPs, protect your capital like a fortress.
Stunning DAFE Visuals: Aqua/fuchsia orbs, aurora bands, and a glowing dashboard make signals intuitive and charts a work of art.
Community-Driven: Evolved from trader feedback, this strat’s a labor of love, not a recycled knockoff.
Traders need this because it’s a complete, original system that blends accessibility, sophistication, and style. It’s your edge to trade smarter, not harder, in a market full of traps and imitators.
1. Divergence Detection (Core Signal Logic)
The strategy’s core is its ability to detect bullish and bearish divergences between price and On-Balance Volume (OBV), pinpointing reversals with surgical accuracy.
How It Works:
Price Slope: Uses linear regression over a lookback (default: 9 bars) to measure price momentum (priceSlope).
OBV Slope: OBV tracks volume flow (+volume if price rises, -volume if falls), with its slope calculated similarly (obvSlope).
Bullish Divergence: Price slope negative (falling), OBV slope positive (rising), and price above 50-bar SMA (trend_ma).
Bearish Divergence: Price slope positive (rising), OBV slope negative (falling), and price below 50-bar SMA.
Smoothing: Requires two consecutive divergence bars (bullDiv2, bearDiv2) to confirm signals, reducing false positives.
Strength: Divergence intensity (divStrength = |priceSlope * obvSlope| * sensitivity) is normalized (0–1, divStrengthNorm) for visuals.
Why It’s Brilliant:
- Divergences catch hidden momentum shifts, often exploited by institutions, giving you an edge on reversals.
- The 50-bar SMA filter aligns signals with the broader trend, avoiding choppy markets.
- Adjustable lookback (min: 3) and sensitivity (default: 1.0) let you tune for different instruments or timeframes.
2. Filters for Precision
Four advanced filters ensure signals are high-probability and market-aligned, cutting through the noise of volatile futures.
Z-Score Filter:
Logic: Calculates z-score ((close - SMA) / stdev) over a lookback (default: 50 bars). Blocks entries if |z-score| > threshold (default: 1.5) unless disabled (useZFilter = false).
Impact: Avoids trades during extreme price moves (e.g., blow-off tops), keeping you in statistically safe zones.
ATR Percentile Volatility Filter:
Logic: Tracks 14-bar ATR in a 100-bar window (default). Requires current ATR > 80th percentile (percATR) to trade (tradeOk).
Impact: Ensures sufficient volatility for meaningful moves, filtering out low-volume chop.
Multi-Timeframe (HTF) Trend Filter:
Logic: Uses a 50-bar SMA on a higher timeframe (default: 60min). Longs require price > HTF MA (bullTrendOK), shorts < HTF MA (bearTrendOK).
Impact: Aligns trades with the bigger trend, reducing counter-trend losses.
US Session Filter:
Logic: Restricts trading to 9:30am–4:00pm ET (default: enabled, useSession = true) using America/New_York timezone.
Impact: Focuses on high-liquidity hours, avoiding overnight spreads and erratic moves.
Evolution:
- These filters create a robust signal pipeline, ensuring trades are timed for optimal conditions.
- Customizable inputs (e.g., zThreshold, atrPercentile) let traders adapt to their style without compromising quality.
3. Risk Management
The strategy’s risk controls are a masterclass in balancing aggression and safety, protecting capital in volatile markets.
Daily Loss Kill Switch:
Logic: Tracks daily loss (dayStartEquity - strategy.equity). Halts trading if loss ≥ $300 (default) and enabled (killSwitch = true, killSwitchActive).
Impact: Caps daily downside, crucial during events like April 27, 2025 ES slippage.
Rolling Drawdown Kill Switch:
Logic: Monitors drawdown (rollingPeak - strategy.equity) over 100 bars (default). Stops trading if > $1000 (rollingKill).
Impact: Prevents prolonged losing streaks, preserving capital for better setups.
Dynamic Stop-Loss and Take-Profit:
Logic: Stops = entry ± ATR * multiplier (default: 1.0x, stopDist). TPs = entry ± ATR * 1.5x (profitDist). Longs: stop below, TP above; shorts: vice versa.
Impact: Adapts to volatility, keeping stops tight but realistic, with TPs targeting 1.5:1 reward/risk.
Max Bars in Trade:
Logic: Closes trades after 8 bars (default) if not already exited.
Impact: Frees capital from stagnant trades, maintaining efficiency.
Kill Switch Buffer Dashboard:
Logic: Shows smallest buffer ($300 - daily loss or $1000 - rolling DD). Displays 0 (red) if kill switch active, else buffer (green).
Impact: Real-time risk visibility, letting traders adjust dynamically.
Why It’s Brilliant:
- Kill switches and ATR-based exits create a safety net, rare in generic scripts.
- Customizable risk inputs (maxDailyLoss, dynamicStopMult) suit different account sizes.
- Buffer metric empowers disciplined trading, a DAFE signature.
4. Trade Entry and Exit Logic
The entry/exit rules are precise, filtered, and adaptive, ensuring trades are deliberate and profitable.
Entry Conditions:
Long Entry: bullDiv2, cooldown passed (canSignal), ATR filter passed (tradeOk), in US session (inSession), no kill switches (not killSwitchActive, not rollingKill), z-score OK (zOk), HTF trend bullish (bullTrendOK), no existing long (lastDirection != 1, position_size <= 0). Closes shorts first.
Short Entry: Same, but for bearDiv2, bearTrendOK, no long (lastDirection != -1, position_size >= 0). Closes longs first.
Adaptive Cooldown: Default 2 bars (cooldownBars). Doubles (up to 10) after a losing trade, resets after wins (dynamicCooldown).
Exit Conditions:
Stop-Loss/Take-Profit: Set per trade (ATR-based). Exits on stop/TP hits.
Other Exits: Closes if maxBarsInTrade reached, ATR filter fails, or kill switch activates.
Position Management: Ensures no conflicting positions, closing opposites before new entries.
Built To Be Reliable and Consistent:
- Multi-filtered entries minimize false signals, a stark contrast to basic scripts.
- Adaptive cooldown prevents overtrading, especially after losses.
- Clean position handling ensures smooth execution, even in fast markets.
5. DAFE Visuals
The visuals are a DAFE hallmark, blending function with clean flair to make signals intuitive and charts stunning.
Aurora Bands:
Display: Bands around price during divergences (bullish: below low, bearish: above high), sized by ATR * bandwidth (default: 0.5).
Colors: Aqua (bullish), fuchsia (bearish), with transparency tied to divStrengthNorm.
Purpose: Highlights divergence zones with a glowing, futuristic vibe.
Divergence Orbs:
Display: Large/small circles (aqua below for bullish, fuchsia above for bearish) when bullDiv2/bearDiv2 and canSignal. Labels show strength (0–1).
Purpose: Pinpoints entries with eye-catching clarity.
Gradient Background:
Display: Green (bullish), red (bearish), or gray (neutral), 90–95% transparent.
Purpose: Sets the market mood without clutter.
Strategy Plots:
- Stop/TP Lines: Red (stops), green (TPs) for active trades.
- HTF MA: Yellow line for trend context.
- Z-Score: Blue step-line (if enabled).
- Kill Switch Warning: Red background flash when active.
What Makes This Next-Level?:
- Visuals make complex signals (divergences, filters) instantly clear, even for beginners.
- DAFE’s unique aesthetic (orbs, bands) sets it apart from generic scripts, reinforcing originality.
- Functional plots (stops, TPs) enhance trade management.
6. Metrics Dashboard
The top-right dashboard (2x8 table) is your command center, delivering real-time insights.
Metrics:
Daily Loss ($): Current loss vs. day’s start, red if > $300.
Rolling DD ($): Drawdown vs. 100-bar peak, red if > $1000.
ATR Threshold: Current percATR, green if ATR exceeds, red if not.
Z-Score: Current value, green if within threshold, red if not.
Signal: “Bullish Div” (aqua), “Bearish Div” (fuchsia), or “None” (gray).
Action: “Consider Buying”/“Consider Selling” (signal color) or “Wait” (gray).
Kill Switch Buffer ($): Smallest buffer to kill switch, green if > 0, red if 0.
Why This Is Important?:
- Consolidates critical data, making decisions effortless.
- Color-coded metrics guide beginners (e.g., green action = go).
- Buffer metric adds transparency, rare in off-the-shelf scripts.
7. Beginner Guide
Beginner Guide: Middle-right table (shown once on chart load), explains aqua orbs (bullish, buy) and fuchsia orbs (bearish, sell).
Key Features:
Futures-Optimized: Tailored for MNQ, NQ, MES, ES with point-value adjustments.
Highly Customizable: Inputs for lookback, sensitivity, filters, and risk settings.
Real-Time Insights: Dashboard and visuals update every bar.
Backtest-Ready: Fixed qty and tick calc for accurate historical testing.
User-Friendly: Guide, visuals, and dashboard make it accessible yet powerful.
Original Design: DAFE’s unique logic and visuals stand out from generic scripts.
How to Use
Add to Chart: Load on a 5min MNQ/ES chart in TradingView.
Configure Inputs: Adjust instrument, filters, or risk (defaults optimized for MNQ).
Monitor Dashboard: Watch signals, actions, and risk metrics (top-right).
Backtest: Run in strategy tester to evaluate performance.
Live Trade: Connect to a broker (e.g., Tradovate) for automation. Watch for slippage (e.g., April 27, 2025 ES issues).
Replay Test: Use bar replay (e.g., April 28, 2025 NQ drop) to test volatility handling.
Disclaimer
Trading futures involves significant risk of loss and is not suitable for all investors. Past performance is not indicative of future results. Backtest results may not reflect live trading due to slippage, fees, or market conditions. Use this strategy at your own risk, and consult a financial advisor before trading. Dskyz (DAFE) Trading Systems is not responsible for any losses incurred.
Backtesting:
Frame: 2023-09-20 - 2025-04-29
Fee Typical Range (per side, per contract)
CME Exchange $1.14 – $1.20
Clearing $0.10 – $0.30
NFA Regulatory $0.02
Firm/Broker Commis. $0.25 – $0.80 (retail prop)
TOTAL $1.60 – $2.30 per side
Round Turn: (enter+exit) = $3.20 – $4.60 per contract
Final Notes
The Dskyz (DAFE) Aurora Divergence – Quant Master isn’t just a strategy—it’s a movement. Crafted with originality and driven by community passion, it rises above the flood of generic scripts to deliver a system that’s as powerful as it is beautiful. With its quant-grade logic, DAFE visuals, and robust risk controls, it empowers traders to tackle futures with confidence and style. Join the DAFE crew, light up your charts, and let’s outsmart the markets together!
(This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.)
Use it with discipline. Use it with clarity. Trade smarter.
**I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract.
Created by Dskyz, powered by DAFE Trading Systems. Trade fast, trade bold.
BTC Trading RobotOverview
This Pine Script strategy is designed for trading Bitcoin (BTC) by placing pending orders (BuyStop and SellStop) based on local price extremes. The script also implements a trailing stop mechanism to protect profits once a position becomes sufficiently profitable.
________________________________________
Inputs and Parameter Setup
1. Trading Profile:
o The strategy is set up specifically for BTC trading.
o The systemType input is set to 1, which means the strategy will calculate trade parameters using the BTC-specific inputs.
2. Common Trading Inputs:
o Risk Parameters: Although RiskPercent is defined, its actual use (e.g., for position sizing) isn’t implemented in this version.
o Trading Hours Filter:
SHInput and EHInput let you restrict trading to a specific hour range. If these are set (non-zero), orders will only be placed during the allowed hours.
3. BTC-Specific Inputs:
o Take Profit (TP) and Stop Loss (SL) Percentages:
TPasPctBTC and SLasPctBTC are used to determine the TP and SL levels as a percentage of the current price.
o Trailing Stop Parameters:
TSLasPctofTPBTC and TSLTgrasPctofTPBTC determine when and by how much a trailing stop is applied, again as percentages of the TP.
4. Other Parameters:
o BarsN is used to define the window (number of bars) over which the local high and low are calculated.
o OrderDistPoints acts as a buffer to prevent the entry orders from being triggered too early.
________________________________________
Trade Parameter Calculation
• Price Reference:
o The strategy uses the current closing price as the reference for calculations.
• Calculation of TP and SL Levels:
o If the systemType is set to BTC (value 1), then:
Take Profit Points (Tppoints) are calculated by multiplying the current price by TPasPctBTC.
Stop Loss Points (Slpoints) are calculated similarly using SLasPctBTC.
A buffer (OrderDistPoints) is set to half of the take profit points.
Trailing Stop Levels:
TslPoints is calculated as a fraction of the TP (using TSLTgrasPctofTPBTC).
TslTriggerPoints is similarly determined, which sets the profit level at which the trailing stop will start to activate.
________________________________________
Time Filtering
• Session Control:
o The current hour is compared against SHInput (start hour) and EHInput (end hour).
o If the current time falls outside the allowed window, the script will not place any new orders.
________________________________________
Entry Orders
• Local Price Extremes:
o The strategy calculates a local high and local low using a window of BarsN * 2 + 1 bars.
• Placing Stop Orders:
o BuyStop Order:
A long entry is triggered if the current price is less than the local high minus the order distance buffer.
The BuyStop order is set to trigger at the level of the local high.
o SellStop Order:
A short entry is triggered if the current price is greater than the local low plus the order distance buffer.
The SellStop order is set to trigger at the level of the local low.
Note: Orders are only placed if there is no current open position and if the session conditions are met.
________________________________________
Trailing Stop Logic
Once a position is open, the strategy monitors profit levels to protect gains:
• For Long Positions:
o The script calculates the profit as the difference between the current price and the average entry price.
o If this profit exceeds the TslTriggerPoints threshold, a trailing stop is applied by placing an exit order.
o The stop price is set at a distance below the current price, while a limit (profit target) is also defined.
• For Short Positions:
o The profit is calculated as the difference between the average entry price and the current price.
o A similar trailing stop exit is applied if the profit exceeds the trigger threshold.
________________________________________
Summary
In essence, this strategy works by:
• Defining entry levels based on recent local highs and lows.
• Placing pending stop orders to enter the market when those levels are breached.
• Filtering orders by time, ensuring trades are only taken during specified hours.
• Implementing a trailing stop mechanism to secure profits once the trade moves favorably.
This approach is designed to automate BTC trading based on price action and dynamic risk management, although further enhancements (like dynamic position sizing based on RiskPercent) could be added for a more complete risk management system.
