CM Stochastic POP Method 1 - Jake Bernstein_V1A good friend ucsgears recently published a Stochastic Pop Indicator designed by Jake Bernstein with a modified version he found.
I spoke to Jake this morning and asked if he had any updates to his Stochastic POP Trading Method. Attached is a PDF Jake published a while back (Please read for basic rules, which also Includes a New Method). I will release the Additional Method Tomorrow.
Jake asked me to share that he has Updated this Method Recently. Now across all symbols he has found the Stochastic Values of 60 and 30 to be the most profitable. NOTE - This can be Significantly Optimized for certain Symbols/Markets.
Jake Bernstein will be a contributor on TradingView when Backtesting/Strategies are released. Jake is one of the Top Trading System Developers in the world with 45+ years experience and he is going to teach how to create Trading Systems and how to Optimize the correct way.
Below are a few Strategy Results....Soon You Will Be Able To Find Results Like This Yourself on TradingView.com
BackTesting Results Example: EUR-USD Daily Chart Since 01/01/2005
Strategy 1:
Go Long When Stochastic Crosses Above 60. Go Short When Stochastic Crosses Below 30. Exit Long/Short When Stochastic has a Reverse Cross of Entry Value.
Results:
Total Trades = 164
Profit = 50, 126 Pips
Win% = 38.4%
Profit Factor = 1.35
Avg Trade = 306 Pips Profit
***Most Consecutive Wins = 3 ... Most Consecutive Losses = 6
Strategy 2:
Rules - Proprietary Optimization Jake Will Teach. Only Added 1 Additional Exit Rule.
Results:
Total Trades = 164
Profit = 62, 876 Pips!!!
Win% = 38.4%
Profit Factor = 1.44
Avg Trade = 383 Pips Profit
***Most Consecutive Wins = 3 ... Most Consecutive Losses = 6
Strategy 3:
Rules - Proprietary Optimization Jake Will Teach. Only added 1 Additional Exit Rule.
Results:
Winning Percent Increases to 72.6%!!! , Same Amount of Trades.
***Most Consecutive Wins = 21 ...Most Consecutive Losses = 4
Indicator Includes:
-Ability to Color Candles (CheckBox In Inputs Tab)
Green = Long Trade
Blue = No Trade
Red = Short Trade
-Color Coded Stochastic Line based on being Above/Below or In Between Entry Lines.
Link To Jakes PDF with Rules
dl.dropboxusercontent.com
Поиск скриптов по запросу "backtesting"
Average Daily Session Range PRO [Capitalize Labs]Average Daily Session Range PRO
The Average Daily Session Range PRO (ADSR PRO) is a professional-grade analytical tool designed to quantify and visualize the probabilistic range behavior of intraday sessions.
It calculates directional range statistics using historical session data to show how far price typically moves up or down from the session open.
This helps traders understand session volatility profiles, range asymmetry, and probabilistic extensions relative to prior performance.
Key Features
Asymmetric Range Modeling: Separately tracks average upside and downside excursions from each session open, revealing directional bias and volatility imbalance.
Probability Engine Modes: Choose between Rolling Window (fixed-length lookback) and Exponential Decay (weighted historical memory) to control how recent or historic data influences probabilities.
Session-Aware Statistics: Calculates values independently for each defined session, allowing region-specific insights (e.g., Tokyo, London, New York).
Dynamic Range Table: Displays key metrics such as average up/down ticks, expected range extensions, and percentage probabilities.
Adaptive Display: Works across timeframes and instruments, automatically aligning with user-defined session start and end times.
Visual Clarity: Includes clean range markers and labels optimized for both backtesting and live-chart analysis.
Intended Use
ADSR PRO is a statistical reference indicator.
It does not generate buy/sell signals or predictive forecasts.
Its purpose is to help users observe historical session behavior and volatility tendencies to support their own discretionary analysis.
Credits
Developed by Capitalize Labs, specialists in quantitative and discretionary market research tools.
Risk Warning
This material is educational research only and does not constitute financial advice, investment recommendation, or a solicitation to buy or sell any instrument.
Foreign exchange and CFDs are complex, leveraged products that carry a high risk of rapid losses; leverage amplifies both gains and losses, and you should not trade with funds you cannot afford to lose.
Market conditions can change without notice, and news or illiquidity may cause gaps and slippage; stop-loss orders are not guaranteed.
The analysis presented does not take into account your objectives, financial situation, or risk tolerance.
Before acting, assess suitability in light of your circumstances and consider seeking advice from a licensed professional.
Past performance and back-tested or hypothetical scenarios are not reliable indicators of future results, and no outcome or level mentioned here is assured.
You are solely responsible for all trading decisions, including position sizing and risk management.
No external links, promotions, or contact details are provided, in line with TradingView House Rules.
COT IndexTHE HIDDEN INTELLIGENCE IN FUTURES MARKETS
What if you could see what the smartest players in the futures markets are doing before the crowd catches on? While retail traders chase momentum indicators and moving averages, obsess over Japanese candlestick patterns, and debate whether the RSI should be set to fourteen or twenty-one periods, institutional players leave footprints in the sand through their mandatory reporting to the Commodity Futures Trading Commission. These footprints, published weekly in the Commitment of Traders reports, have been hiding in plain sight for decades, available to anyone with an internet connection, yet remarkably few traders understand how to interpret them correctly. The COT Index indicator transforms this raw institutional positioning data into actionable trading signals, bringing Wall Street intelligence to your trading screen without requiring expensive Bloomberg terminals or insider connections.
The uncomfortable truth is this: Most retail traders operate in a binary world. Long or short. Buy or sell. They apply technical analysis to individual positions, constrained by limited capital that forces them to concentrate risk in single directional bets. Meanwhile, institutional traders operate in an entirely different dimension. They manage portfolios dynamically weighted across multiple markets, adjusting exposure based on evolving market conditions, correlation shifts, and risk assessments that retail traders never see. A hedge fund might be simultaneously long gold, short oil, neutral on copper, and overweight agricultural commodities, with position sizes calibrated to volatility and portfolio Greeks. When they increase gold exposure from five percent to eight percent of portfolio allocation, this rebalancing decision reflects sophisticated analysis of opportunity cost, risk parity, and cross-market dynamics that no individual chart pattern can capture.
This portfolio reweighting activity, multiplied across hundreds of institutional participants, manifests in the aggregate positioning data published weekly by the CFTC. The Commitment of Traders report does not show individual trades or strategies. It shows the collective footprint of how actual commercial hedgers and large speculators have allocated their capital across different markets. When mining companies collectively increase forward gold sales to hedge thirty percent more production than last quarter, they are not reacting to a moving average crossover. They are making strategic allocation decisions based on production forecasts, cost structures, and price expectations derived from operational realities invisible to outside observers. This is portfolio management in action, revealed through positioning data rather than price charts.
If you want to understand how institutional capital actually flows, how sophisticated traders genuinely position themselves across market cycles, the COT report provides a rare window into that hidden world. But understand what you are getting into. This is not a tool for scalpers seeking confirmation of the next five-minute move. This is not an oscillator that flashes oversold at market bottoms with convenient precision. COT analysis operates on a timescale measured in weeks and months, revealing positioning shifts that precede major market turns but offer no precision timing. The data arrives three days stale, published only once per week, capturing strategic positioning rather than tactical entries.
If you need instant gratification, if you trade intraday moves, if you demand mechanical signals with ninety percent accuracy, close this document now. COT analysis rewards patience, position sizing discipline, and tolerance for being early. It punishes impatience, overleveraging, and the expectation that any single indicator can substitute for market understanding.
The premise is deceptively simple. Every Tuesday, large traders in futures markets must report their positions to the CFTC. By Friday afternoon, this data becomes public. Academic research spanning three decades has consistently shown that not all market participants are created equal. Some traders consistently profit while others consistently lose. Some anticipate major turning points while others chase trends into exhaustion. Bessembinder and Chan (1992) demonstrated in their seminal study that commercial hedgers, those with actual exposure to the underlying commodity or financial instrument, possess superior forecasting ability compared to speculators. Their research, published in the Journal of Finance, found statistically significant predictive power in commercial positioning, particularly at extreme levels. This finding challenged the efficient market hypothesis and opened the door to a new approach to market analysis based on positioning rather than price alone.
Think about what this means. Every week, the government publishes a report showing you exactly how the most informed market participants are positioned. Not their opinions. Not their predictions. Their actual money at risk. When agricultural producers collectively hold their largest short hedge in five years, they are not making idle speculation. They are locking in prices for crops they will harvest, informed by private knowledge of weather conditions, soil quality, inventory levels, and demand expectations invisible to outside observers. When energy companies aggressively hedge forward production at current prices, they reveal information about expected supply that no analyst report can capture. This is not technical analysis based on past prices. This is not fundamental analysis based on publicly available data. This is behavioral analysis based on how the smartest money is actually positioned, how institutions allocate capital across portfolios, and how those allocation decisions shift as market conditions evolve.
WHY SOME TRADERS KNOW MORE THAN OTHERS
Building on this foundation, Sanders, Boris and Manfredo (2004) conducted extensive research examining the behaviour patterns of different trader categories. Their work, which analyzed over a decade of COT data across multiple commodity markets, revealed a fascinating dynamic that challenges much of what retail traders are taught. Commercial hedgers consistently positioned themselves against market extremes, buying when speculators were most bearish and selling when speculators reached peak bullishness. The contrarian positioning of commercials was not random noise but rather reflected their superior information about supply and demand fundamentals. Meanwhile, large speculators, primarily hedge funds and commodity trading advisors, exhibited strong trend-following behaviour that often amplified market moves beyond fundamental values. Small traders, the retail participants, consistently entered positions late in trends, frequently near turning points, making them reliable contrary indicators.
Wang (2003) extended this research by demonstrating that the predictive power of commercial positioning varies significantly across different commodity sectors. His analysis of agricultural commodities showed particularly strong forecasting ability, with commercial net positions explaining up to fifteen percent of return variance in subsequent weeks. This finding suggests that the informational advantages of hedgers are most pronounced in markets where physical supply and demand fundamentals dominate, as opposed to purely financial markets where information asymmetries are smaller. When a corn farmer hedges six months of expected harvest, that decision incorporates private observations about rainfall patterns, crop health, pest pressure, and local storage capacity that no distant analyst can match. When an oil refinery hedges crude oil purchases and gasoline sales simultaneously, the spread relationships reveal expectations about refining margins that reflect operational realities invisible in public data.
The theoretical mechanism underlying these empirical patterns relates to information asymmetry and different participant motivations. Commercial hedgers engage in futures markets not for speculative profit but to manage business risks. An agricultural producer selling forward six months of expected harvest is not making a bet on price direction but rather locking in revenue to facilitate financial planning and ensure business viability. However, this hedging activity necessarily incorporates private information about expected supply, inventory levels, weather conditions, and demand trends that the hedger observes through their commercial operations (Irwin and Sanders, 2012). When aggregated across many participants, this private information manifests in collective positioning.
Consider a gold mining company deciding how much forward production to hedge. Management must estimate ore grades, recovery rates, production costs, equipment reliability, labor availability, and dozens of other operational variables that determine whether locking in prices at current levels makes business sense. If the industry collectively hedges more aggressively than usual, it suggests either exceptional production expectations or concern about sustaining current price levels or combination of both. Either way, this positioning reveals information unavailable to speculators analyzing price charts and economic data. The hedger sees the physical reality behind the financial abstraction.
Large speculators operate under entirely different incentives and constraints. Commodity Trading Advisors managing billions in assets typically employ systematic, trend-following strategies that respond to price momentum rather than fundamental supply and demand. When crude oil rallies from sixty dollars to seventy dollars per barrel, these systems generate buy signals. As the rally continues to eighty dollars, position sizes increase. The strategy works brilliantly during sustained trends but becomes a liability at reversals. By the time oil reaches ninety dollars, trend-following funds are maximally long, having accumulated positions progressively throughout the rally. At this point, they represent not smart money anticipating further gains but rather crowded money vulnerable to reversal. Sanders, Boris and Manfredo (2004) documented this pattern across multiple energy markets, showing that extreme speculator positioning typically marked late-stage trend exhaustion rather than early-stage trend development.
Small traders, the retail participants who fall below reporting thresholds, display the weakest forecasting ability. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns, meaning their aggregate positioning served as a reliable contrary indicator. The explanation combines several factors. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, entering trends after mainstream media coverage when institutional participants are preparing to exit. Perhaps most importantly, they trade with emotion, buying into euphoria and selling into panic at precisely the wrong times.
At major turning points, the three groups often position opposite each other with commercials extremely bearish, large speculators extremely bullish, and small traders piling into longs at the last moment. These high-divergence environments frequently precede increased volatility and trend reversals. The insiders with business exposure quietly exit as the momentum traders hit maximum capacity and retail enthusiasm peaks. Within weeks, the reversal begins, and positions unwind in the opposite sequence.
FROM RAW DATA TO ACTIONABLE SIGNALS
The COT Index indicator operationalizes these academic findings into a practical trading tool accessible through TradingView. At its core, the indicator normalizes net positioning data onto a zero to one hundred scale, creating what we call the COT Index. This normalization is critical because absolute position sizes vary dramatically across different futures contracts and over time. A commercial trader holding fifty thousand contracts net long in crude oil might be extremely bullish by historical standards, or it might be quite neutral depending on the context of total market size and historical ranges. Raw position numbers mean nothing without context. The COT Index solves this problem by calculating where current positioning stands relative to its range over a specified lookback period, typically two hundred fifty-two weeks or approximately five years of weekly data.
The mathematical transformation follows the methodology originally popularized by legendary trader Larry Williams, though the underlying concept appears in statistical normalization techniques across many fields. For any given trader category, we calculate the highest and lowest net position values over the lookback period, establishing the historical range for that specific market and trader group. Current positioning is then expressed as a percentage of this range, where zero represents the most bearish positioning ever seen in the lookback window and one hundred represents the most bullish extreme. A reading of fifty indicates positioning exactly in the middle of the historical range, suggesting neither extreme optimism nor pessimism relative to recent history (Williams and Noseworthy, 2009).
This index-based approach allows for meaningful comparison across different markets and time periods, overcoming the scaling problems inherent in analyzing raw position data. A commercial index reading of eighty-five in gold carries the same interpretive meaning as an eighty-five reading in wheat or crude oil, even though the absolute position sizes differ by orders of magnitude. This standardization enables systematic analysis across entire futures portfolios rather than requiring market-specific expertise for each contract.
The lookback period selection involves a fundamental tradeoff between responsiveness and stability. Shorter lookback periods, perhaps one hundred twenty-six weeks or approximately two and a half years, make the index more sensitive to recent positioning changes. However, it also increases noise and produces more false signals. Longer lookback periods, perhaps five hundred weeks or approximately ten years, create smoother readings that filter short-term noise but become slower to recognize regime changes. The indicator settings allow users to adjust this parameter based on their trading timeframe, risk tolerance, and market characteristics.
UNDERSTANDING CFTC DATA STRUCTURES
The indicator supports both Legacy and Disaggregated COT report formats, reflecting the evolution of CFTC reporting standards over decades of market development. Legacy reports categorize market participants into three broad groups: commercial traders (hedgers with underlying business exposure), non-commercial traders (large speculators seeking profit without commercial interest), and non-reportable traders (small speculators below reporting thresholds). Each category brings distinct motivations and information advantages to the market (CFTC, 2020).
The Disaggregated reports, introduced in September 2009 for physical commodity markets, provide finer granularity by splitting participants into five categories (CFTC, 2009). Producer and merchant positions capture those actually producing, processing, or merchandising the physical commodity. Swap dealers represent financial intermediaries facilitating derivative transactions for clients. Managed money includes commodity trading advisors and hedge funds executing systematic or discretionary strategies. Other reportables encompasses diverse participants not fitting the main categories. Small traders remain as the fifth group, representing retail participation.
This enhanced categorization reveals nuances invisible in Legacy reports, particularly distinguishing between different types of institutional capital and their distinct behavioural patterns. The indicator automatically detects which report type is appropriate for each futures contract and adjusts the display accordingly.
Importantly, Disaggregated reports exist only for physical commodity futures. Agricultural commodities like corn, wheat, and soybeans have Disaggregated reports because clear producer, merchant, and swap dealer categories exist. Energy commodities like crude oil and natural gas similarly have well-defined commercial hedger categories. Metals including gold, silver, and copper also receive Disaggregated treatment (CFTC, 2009). However, financial futures such as equity index futures, Treasury bond futures, and currency futures remain available only in Legacy format. The CFTC has indicated no plans to extend Disaggregated reporting to financial futures due to different market structures and participant categories in these instruments (CFTC, 2020).
THE BEHAVIORAL FOUNDATION
Understanding which trader perspective to follow requires appreciation of their distinct trading styles, success rates, and psychological profiles. Commercial hedgers exhibit anticyclical behaviour rooted in their fundamental knowledge and business imperatives. When agricultural producers hedge forward sales during harvest season, they are not speculating on price direction but rather locking in revenue for crops they will harvest. Their business requires converting volatile commodity exposure into predictable cash flows to facilitate planning and ensure survival through difficult periods. Yet their aggregate positioning reveals valuable information because these hedging decisions incorporate private information about supply conditions, inventory levels, weather observations, and demand expectations that hedgers observe through their commercial operations (Bessembinder and Chan, 1992).
Consider a practical example from energy markets. Major oil companies continuously hedge portions of forward production based on price levels, operational costs, and financial planning needs. When crude oil trades at ninety dollars per barrel, they might aggressively hedge the next twelve months of production, locking in prices that provide comfortable profit margins above their extraction costs. This hedging appears as short positioning in COT reports. If oil rallies further to one hundred dollars, they hedge even more aggressively, viewing these prices as exceptional opportunities to secure revenue. Their short positioning grows increasingly extreme. To an outside observer watching only price charts, the rally suggests bullishness. But the commercial positioning reveals that the actual producers of oil find these prices attractive enough to lock in years of sales, suggesting skepticism about sustaining even higher levels. When the eventual reversal occurs and oil declines back to eighty dollars, the commercials who hedged at ninety and one hundred dollars profit while speculators who chased the rally suffer losses.
Large speculators or managed money traders operate under entirely different incentives and constraints. Their systematic, momentum-driven strategies mean they amplify existing trends rather than anticipate reversals. Trend-following systems, the most common approach among large speculators, by definition require confirmation of trend through price momentum before entering positions (Sanders, Boris and Manfredo, 2004). When crude oil rallies from sixty dollars to eighty dollars per barrel over several months, trend-following algorithms generate buy signals based on moving average crossovers, breakouts, and other momentum indicators. As the rally continues, position sizes increase according to the systematic rules.
However, this approach becomes a liability at turning points. By the time oil reaches ninety dollars after a sustained rally, trend-following funds are maximally long, having accumulated positions progressively throughout the move. At this point, their positioning does not predict continued strength. Rather, it often marks late-stage trend exhaustion. The psychological and mechanical explanation is straightforward. Trend followers by definition chase price momentum, entering positions after trends establish rather than anticipating them. Eventually, they become fully invested just as the trend nears completion, leaving no incremental buying power to sustain the rally. When the first signs of reversal appear, systematic stops trigger, creating a cascade of selling that accelerates the downturn.
Small traders consistently display the weakest track record across academic studies. Wang (2003) found that small trader positioning exhibited negative correlation with subsequent returns in his analysis across multiple commodity markets. This result means that whatever small traders collectively do, the opposite typically proves profitable. The explanation for small trader underperformance combines several factors documented in behavioral finance literature. Retail traders often lack the capital reserves to weather normal market volatility, leading to premature exits from positions that would eventually prove profitable. They tend to receive information through slower channels, learning about commodity trends through mainstream media coverage that arrives after institutional participants have already positioned. Perhaps most importantly, retail traders are more susceptible to emotional decision-making, buying into euphoria and selling into panic at precisely the wrong times (Tharp, 2008).
SETTINGS, THRESHOLDS, AND SIGNAL GENERATION
The practical implementation of the COT Index requires understanding several key features and settings that users can adjust to match their trading style, timeframe, and risk tolerance. The lookback period determines the time window for calculating historical ranges. The default setting of two hundred fifty-two bars represents approximately one year on daily charts or five years on weekly charts, balancing responsiveness with stability. Conservative traders seeking only the most extreme, highest-probability signals might extend the lookback to five hundred bars or more. Aggressive traders seeking earlier entry and willing to accept more false positives might reduce it to one hundred twenty-six bars or even less for shorter-term applications.
The bullish and bearish thresholds define signal generation levels. Default settings of eighty and twenty respectively reflect academic research suggesting meaningful information content at these extremes. Readings above eighty indicate positioning in the top quintile of the historical range, representing genuine extremes rather than temporary fluctuations. Conversely, readings below twenty occupy the bottom quintile, indicating unusually bearish positioning (Briese, 2008).
However, traders must recognize that appropriate thresholds vary by market, trader category, and personal risk tolerance. Some futures markets exhibit wider positioning swings than others due to seasonal patterns, volatility characteristics, or participant behavior. Conservative traders seeking high-probability setups with fewer signals might raise thresholds to eighty-five and fifteen. Aggressive traders willing to accept more false positives for earlier entry could lower them to seventy-five and twenty-five.
The key is maintaining meaningful differentiation between bullish, neutral, and bearish zones. The default settings of eighty and twenty create a clear three-zone structure. Readings from zero to twenty represent bearish territory where the selected trader group holds unusually bearish positions. Readings from twenty to eighty represent neutral territory where positioning falls within normal historical ranges. Readings from eighty to one hundred represent bullish territory where the selected trader group holds unusually bullish positions.
The trading perspective selection determines which participant group the indicator follows, fundamentally shaping interpretation and signal meaning. For counter-trend traders seeking reversal opportunities, monitoring commercial positioning makes intuitive sense based on the academic research discussed earlier. When commercials reach extreme bearish readings below twenty, indicating unprecedented short positioning relative to recent history, they are effectively betting against the crowd. Given their informational advantages demonstrated by Bessembinder and Chan (1992), this contrarian stance often precedes major bottoms.
Trend followers might instead monitor large speculator positioning, but with inverted logic compared to commercials. When managed money reaches extreme bullish readings above eighty, the trend may be exhausting rather than accelerating. This seeming paradox reflects their late-cycle participation documented by Sanders, Boris and Manfredo (2004). Sophisticated traders thus use speculator extremes as fade signals, entering positions opposite to speculator consensus.
Small trader monitoring serves primarily as a contrary indicator for all trading styles. Extreme small trader bullishness above seventy-five or eighty typically warns of retail FOMO at market tops. Extreme small trader bearishness below twenty or twenty-five often marks capitulation bottoms where the last weak hands have sold.
VISUALIZATION AND USER INTERFACE
The visual design incorporates multiple elements working together to facilitate decision-making and maintain situational awareness during active trading. The primary COT Index line plots in bold with adjustable line width, defaulting to two pixels for clear visibility against busy price charts. An optional glow effect, controlled by a simple toggle, adds additional visual prominence through multiple plot layers with progressively increasing transparency and width.
A twenty-one period exponential moving average overlays the index line, providing trend context for positioning changes. When the index crosses above its moving average, it signals accelerating bullish sentiment among the selected trader group regardless of whether absolute positioning is extreme. Conversely, when the index crosses below its moving average, it signals deteriorating sentiment and potentially the beginning of a reversal in positioning trends.
The EMA provides a dynamic reference line for assessing positioning momentum. When the index trades far above its EMA, positioning is not only extreme in absolute terms but also building with momentum. When the index trades far below its EMA, positioning is contracting or reversing, which may indicate weakening conviction even if absolute levels remain elevated.
The data table positioned at the top right of the chart displays eleven metrics for each trader category, transforming the indicator from a simple index calculation into an analytical dashboard providing multidimensional market intelligence. Beyond the COT Index itself, users can monitor positioning extremity, which measures how unusual current levels are compared to historical norms using statistical techniques. The extremity metric clarifies whether a reading represents the ninety-fifth or ninety-ninth percentile, with values above two standard deviations indicating genuinely exceptional positioning.
Market power quantifies each group's influence on total open interest. This metric expresses each trader category's net position as a percentage of total market open interest. A commercial entity holding forty percent of total open interest commands significantly more influence than one holding five percent, making their positioning signals more meaningful.
Momentum and rate of change metrics reveal whether positions are building or contracting, providing early warning of potential regime shifts. Position velocity measures the rate of change in positioning changes, effectively a second derivative providing even earlier insight into inflection points.
Sentiment divergence highlights disagreements between commercial and speculative positioning. This metric calculates the absolute difference between normalized commercial and large speculator index values. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals.
The table also displays concentration metrics when available, showing how positioning is distributed among the largest handful of traders in each category. High concentration indicates a few dominant players controlling most of the positioning, while low concentration suggests broad-based participation across many traders.
THE ALERT SYSTEM AND MONITORING
The alert system, comprising five distinct alert conditions, enables systematic monitoring of dozens of futures markets without constant screen watching. The bullish and bearish COT signal alerts trigger when the index crosses user-defined thresholds, indicating the selected trader group has reached extreme positioning worthy of attention. These alerts fire in real-time as new weekly COT data publishes, typically Friday afternoon following the Tuesday measurement date.
Extreme positioning alerts fire at ninety and ten index levels, representing the top and bottom ten percent of the historical range, warning of particularly stretched readings that historically precede reversals with high probability. When commercials reach a COT Index reading below ten, they are expressing their most bearish stance in the entire lookback period.
The data staleness alert notifies users when COT reports have not updated for more than ten days, preventing reliance on outdated information for trading decisions. Government shutdowns or federal holidays can interrupt the normal Friday publication schedule. Using stale signals while believing them current creates dangerous false confidence.
The indicator's watermark information display positioned in the bottom right corner provides essential context at a glance. This persistent display shows the symbol and timeframe, the COT report date timestamp, days since last update, and the current signal state. A trader analyzing a potential short entry in crude oil can glance at the watermark to instantly confirm positioning context without interrupting analysis flow.
LIMITATIONS AND REALISTIC EXPECTATIONS
Practical application requires understanding both the indicator's considerable strengths and inherent limitations. COT data inherently lags price action by three days, as Tuesday positions are not published until Friday afternoon. This delay means the indicator cannot catch rapid intraday reversals or respond to surprise news events. Traders using the COT Index for timing entries must accept this latency and focus on swing trading and position trading timeframes where three-day lags matter less than in day trading or scalping.
The weekly publication schedule similarly makes the indicator unsuitable for short-term trading strategies requiring immediate feedback. The COT Index works best for traders operating on weekly or longer timeframes, where positioning shifts measured in weeks and months align with trading horizon.
Extreme COT readings can persist far longer than typical technical indicators suggest, testing the patience and capital reserves of traders attempting to fade them. When crude oil enters a sustained bull market driven by genuine supply disruptions, commercial hedgers may maintain bearish positioning for many months as prices grind higher. A commercial COT Index reading of fifteen indicating extreme bearishness might persist for three months while prices continue rallying before finally reversing. Traders without sufficient capital and risk tolerance to weather such drawdowns will exit prematurely, precisely when the signal is about to work (Irwin and Sanders, 2012).
Position sizing discipline becomes paramount when implementing COT-based strategies. Rather than risking large percentages of capital on individual signals, successful COT traders typically allocate modest position sizes across multiple signals, allowing some to take time to mature while others work more quickly.
The indicator also cannot overcome fundamental regime changes that alter the structural drivers of markets. If gold enters a true secular bull market driven by monetary debasement, commercial hedgers may remain persistently bearish as mining companies sell forward years of production at what they perceive as favorable prices. Their positioning indicates valuation concerns from a production cost perspective, but cannot stop prices from rising if investment demand overwhelms physical supply-demand balance.
Similarly, structural changes in market participation can alter the meaning of positioning extremes. The growth of commodity index investing in the two thousands brought massive passive long-only capital into futures markets, fundamentally changing typical positioning ranges. Traders relying on COT signals without recognizing this regime change would have generated numerous false bearish signals during the commodity supercycle from 2003 to 2008.
The research foundation supporting COT analysis derives primarily from commodity markets where the commercial hedger information advantage is most pronounced. Studies specifically examining financial futures like equity indices and bonds show weaker but still present effects. Traders should calibrate expectations accordingly, recognizing that COT analysis likely works better for crude oil, natural gas, corn, and wheat than for the S&P 500, Treasury bonds, or currency futures.
Another important limitation involves the reporting threshold structure. Not all market participants appear in COT data, only those holding positions above specified minimums. In markets dominated by a few large players, concentration metrics become critical for proper interpretation. A single large trader accounting for thirty percent of commercial positioning might skew the entire category if their individual circumstances are idiosyncratic rather than representative.
GOLD FUTURES DURING A HYPOTHETICAL MARKET CYCLE
Consider a practical example using gold futures during a hypothetical but realistic market scenario that illustrates how the COT Index indicator guides trading decisions through a complete market cycle. Suppose gold has rallied from fifteen hundred to nineteen hundred dollars per ounce over six months, driven by inflation concerns following aggressive monetary expansion, geopolitical uncertainty, and sustained buying by Asian central banks for reserve diversification.
Large speculators, operating primarily trend-following strategies, have accumulated increasingly bullish positions throughout this rally. Their COT Index has climbed progressively from forty-five to eighty-five. The table display shows that large speculators now hold net long positions representing thirty-two percent of total open interest, their highest in four years. Momentum indicators show positive readings, indicating positions are still building though at a decelerating rate. Position velocity has turned negative, suggesting the pace of position building is slowing.
Meanwhile, commercial hedgers have responded to the rally by aggressively selling forward production and inventory. Their COT Index has moved inversely to price, declining from fifty-five to twenty. This bearish commercial positioning represents mining companies locking in forward sales at prices they view as attractive relative to production costs. The table shows commercials now hold net short positions representing twenty-nine percent of total open interest, their most bearish stance in five years. Concentration metrics indicate this positioning is broadly distributed across many commercial entities, suggesting the bearish stance reflects collective industry view rather than idiosyncratic positioning by a single firm.
Small traders, attracted by mainstream financial media coverage of gold's impressive rally, have recently piled into long positions. Their COT Index has jumped from forty-five to seventy-eight as retail investors chase the trend. Television financial networks feature frequent segments on gold with bullish guests. Internet forums and social media show surging retail interest. This retail enthusiasm historically marks late-stage trend development rather than early opportunity.
The COT Index indicator, configured to monitor commercial positioning from a contrarian perspective, displays a clear bearish signal given the extreme commercial short positioning. The table displays multiple confirming metrics: positioning extremity shows commercials at the ninety-sixth percentile of bearishness, market power indicates they control twenty-nine percent of open interest, and sentiment divergence registers sixty-five, indicating massive disagreement between commercial hedgers and large speculators. This divergence, the highest in three years, places the market in the historically high-risk category for reversals.
The interpretation requires nuance and consideration of context beyond just COT data. Commercials are not necessarily predicting an imminent crash. Rather, they are hedging business operations at what they collectively view as favorable price levels. However, the data reveals they have sold unusually large quantities of forward production, suggesting either exceptional production expectations for the year ahead or concern about sustaining current price levels or combination of both. Combined with extreme speculator positioning indicating a crowded long trade, and small trader enthusiasm confirming retail FOMO, the confluence suggests elevated reversal risk even if the precise timing remains uncertain.
A prudent trader analyzing this situation might take several actions based on COT Index signals. Existing long positions could be tightened with closer stop losses. Profit-taking on a portion of long exposure could lock in gains while maintaining some participation. Some traders might initiate modest short positions as portfolio hedges, sizing them appropriately for the inherent uncertainty in timing reversals. Others might simply move to the sidelines, avoiding new long entries until positioning normalizes.
The key lesson from case study analysis is that COT signals provide probabilistic edges rather than deterministic predictions. They work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five percent win rate with proper risk management produces substantial profits over time, yet still means forty-five percent of signals will be premature or wrong. Traders must embrace this probabilistic reality rather than seeking the impossible goal of perfect accuracy.
INTEGRATION WITH TRADING SYSTEMS
Integration with existing trading systems represents a natural and powerful use case for COT analysis, adding a positioning dimension to price-based technical approaches or fundamental analytical frameworks. Few traders rely exclusively on a single indicator or methodology. Rather, they build systems that synthesize multiple information sources, with each component addressing different aspects of market behavior.
Trend followers might use COT extremes as regime filters, modifying position sizing or avoiding new trend entries when positioning reaches levels historically associated with reversals. Consider a classic trend-following system based on moving average crossovers and momentum breakouts. Integration of COT analysis adds nuance. When large speculator positioning exceeds ninety or commercial positioning falls below ten, the regime filter recognizes elevated reversal risk. The system might reduce position sizing by fifty percent for new signals during these high-risk periods (Kaufman, 2013).
Mean reversion traders might require COT signal confluence before fading extended moves. When crude oil becomes technically overbought and large speculators show extreme long positioning above eighty-five, both signals confirm. If only technical indicators show extremes while positioning remains neutral, the potential short signal is rejected, avoiding fades of trends with underlying institutional support (Kaufman, 2013).
Discretionary traders can monitor the indicator as a continuous awareness tool, informing bias and position sizing without dictating mechanical entries and exits. A discretionary trader might notice commercial positioning shifting from neutral to progressively more bullish over several months. This trend informs growing positive bias even without triggering mechanical signals.
Multi-timeframe analysis represents another powerful integration approach. A trader might use daily charts for trade execution and timing while monitoring weekly COT positioning for strategic context. When both timeframes align, highest-probability opportunities emerge.
Portfolio construction for futures traders can incorporate COT signals as an additional selection criterion. Markets showing strong technical setups AND favorable COT positioning receive highest allocations. Markets with strong technicals but neutral or unfavorable positioning receive reduced allocations.
ADVANCED METRICS AND INTERPRETATION
The metrics table transforms simple positioning data into multidimensional market intelligence. Position extremity, calculated as the absolute deviation from the historical mean normalized by standard deviation, helps identify truly unusual readings versus routine fluctuations. A reading above two standard deviations indicates ninety-fifth percentile or higher extremity. Above three standard deviations indicates ninety-ninth percentile or higher, genuinely rare positioning that historically precedes major events with high probability.
Market power, expressed as a percentage of total open interest, reveals whose positioning matters most from a mechanical market impact perspective. Consider two scenarios in gold futures. In scenario one, commercials show a COT Index reading of fifteen while their market power metric shows they hold net shorts representing thirty-five percent of open interest. This is a high-confidence bearish signal. In scenario two, commercials also show a reading of fifteen, but market power shows only eight percent. While positioning is extreme relative to this category's normal range, their limited market share means less mechanical influence on price.
The rate of change and momentum metrics highlight whether positions are accelerating or decelerating, often providing earlier warnings than absolute levels alone. A COT Index reading of seventy-five with rapidly building momentum suggests continued movement toward extremes. Conversely, a reading of eighty-five with decelerating or negative momentum indicates the positioning trend is exhausting.
Position velocity measures the rate of change in positioning changes, effectively a second derivative. When velocity shifts from positive to negative, it indicates that while positioning may still be growing, the pace of growth is slowing. This deceleration often precedes actual reversal in positioning direction by several weeks.
Sentiment divergence calculates the absolute difference between normalized commercial and large speculator index values. When commercials show extreme bearish positioning at twenty while large speculators show extreme bullish positioning at eighty, the divergence reaches sixty, representing near-maximum disagreement. Wang (2003) found that these high-divergence environments frequently preceded increased volatility and reversals. The mechanism is intuitive. Extreme divergence indicates the informed hedgers and momentum-following speculators have positioned opposite each other with conviction. One group will prove correct and profit while the other proves incorrect and suffers losses. The resolution of this disagreement through price movement often involves volatility.
The table also displays concentration metrics when available. High concentration indicates a few dominant players controlling most of the positioning within a category, while low concentration suggests broad-based participation. Broad-based positioning more reliably reflects collective market intelligence and industry consensus. If mining companies globally all independently decide to hedge aggressively at similar price levels, it suggests genuine industry-wide view about price valuations rather than circumstances specific to one firm.
DATA QUALITY AND RELIABILITY
The CFTC has maintained COT reporting in various forms since the nineteen twenties, providing nearly a century of positioning data across multiple market cycles. However, data quality and reporting standards have evolved substantially over this long period. Modern electronic reporting implemented in the late nineteen nineties and early two thousands significantly improved accuracy and timeliness compared to earlier paper-based systems.
Traders should understand that COT reports capture positions as of Tuesday's close each week. Markets remain open three additional days before publication on Friday afternoon, meaning the reported data is three days stale when received. During periods of rapid market movement or major news events, this lag can be significant. The indicator addresses this limitation by including timestamp information and staleness warnings.
The three-day lag creates particular challenges during extreme volatility episodes. Flash crashes, surprise central bank interventions, geopolitical shocks, and other high-impact events can completely transform market positioning within hours. Traders must exercise judgment about whether reported positioning remains relevant given intervening events.
Reporting thresholds also mean that not all market participants appear in disaggregated COT data. Traders holding positions below specified minimums aggregate into the non-reportable or small trader category. This aggregation affects different markets differently. In highly liquid contracts like crude oil with thousands of participants, reportable traders might represent seventy to eighty percent of open interest. In thinly traded contracts with only dozens of active participants, a few large reportable positions might represent ninety-five percent of open interest.
Another data quality consideration involves trader classification into categories. The CFTC assigns traders to commercial or non-commercial categories based on reported business purpose and activities. However, this process is not perfect. Some entities engage in both commercial and speculative activities, creating ambiguity about proper classification. The transition to Disaggregated reports attempted to address some of these ambiguities by creating more granular categories.
COMPARISON WITH ALTERNATIVE APPROACHES
Several alternative approaches to COT analysis exist in the trading community beyond the normalization methodology employed by this indicator. Some analysts focus on absolute position changes week-over-week rather than index-based normalization. This approach calculates the change in net positioning from one week to the next. The emphasis falls on momentum in positioning changes rather than absolute levels relative to history. This method potentially identifies regime shifts earlier but sacrifices cross-market comparability (Briese, 2008).
Other practitioners employ more complex statistical transformations including percentile rankings, z-score standardization, and machine learning classification algorithms. Ruan and Zhang (2018) demonstrated that machine learning models applied to COT data could achieve modest improvements in forecasting accuracy compared to simple threshold-based approaches. However, these gains came at the cost of interpretability and implementation complexity.
The COT Index indicator intentionally employs a relatively straightforward normalization methodology for several important reasons. First, transparency enhances user understanding and trust. Traders can verify calculations manually and develop intuitive feel for what different readings mean. Second, academic research suggests that most of the predictive power in COT data comes from extreme positioning levels rather than subtle patterns requiring complex statistical methods to detect. Third, robust methods that work consistently across many markets and time periods tend to be simpler rather than more complex, reducing the risk of overfitting to historical data. Fourth, the complexity costs of implementation matter for retail traders without programming teams or computational infrastructure.
PSYCHOLOGICAL ASPECTS OF COT TRADING
Trading based on COT data requires psychological fortitude that differs from momentum-based approaches. Contrarian positioning signals inherently mean betting against prevailing market sentiment and recent price action. When commercials reach extreme bearish positioning, prices have typically been rising, sometimes for extended periods. The price chart looks bullish, momentum indicators confirm strength, moving averages align positively. The COT signal says bet against all of this. This psychological difficulty explains why COT analysis remains underutilized relative to trend-following methods.
Human psychology strongly predisposes us toward extrapolation and recency bias. When prices rally for months, our pattern-matching brains naturally expect continued rally. The recent price action dominates our perception, overwhelming rational analysis about positioning extremes and historical probabilities. The COT signal asking us to sell requires overriding these powerful psychological impulses.
The indicator design attempts to support the required psychological discipline through several features. Clear threshold markers and signal states reduce ambiguity about when signals trigger. When the commercial index crosses below twenty, the signal is explicit and unambiguous. The background shifts to red, the signal label displays bearish, and alerts fire. This explicitness helps traders act on signals rather than waiting for additional confirmation that may never arrive.
The metrics table provides analytical justification for contrarian positions, helping traders maintain conviction during inevitable periods of adverse price movement. When a trader enters short positions based on extreme commercial bearish positioning but prices continue rallying for several weeks, doubt naturally emerges. The table display provides reassurance. Commercial positioning remains extremely bearish. Divergence remains high. The positioning thesis remains intact even though price action has not yet confirmed.
Alert functionality ensures traders do not miss signals due to inattention while also not requiring constant monitoring that can lead to emotional decision-making. Setting alerts for COT extremes enables a healthier relationship with markets. When meaningful signals occur, alerts notify them. They can then calmly assess the situation and execute planned responses.
However, no indicator design can completely overcome the psychological difficulty of contrarian trading. Some traders simply cannot maintain short positions while prices rally. For these traders, COT analysis might be better employed as an exit signal for long positions rather than an entry signal for shorts.
Ultimately, successful COT trading requires developing comfort with probabilistic thinking rather than certainty-seeking. The signals work over many observations by identifying higher-probability configurations, not by generating perfect calls on individual trades. A fifty-five or sixty percent win rate with proper risk management produces substantial profits over years, yet still means forty to forty-five percent of signals will be premature or wrong. COT analysis provides genuine edge, but edge means probability advantage, not elimination of losing trades.
EDUCATIONAL RESOURCES AND CONTINUOUS LEARNING
The indicator provides extensive built-in educational resources through its documentation, detailed tooltips, and transparent calculations. However, mastering COT analysis requires study beyond any single tool or resource. Several excellent resources provide valuable extensions of the concepts covered in this guide.
Books and practitioner-focused monographs offer accessible entry points. Stephen Briese published The Commitments of Traders Bible in two thousand eight, offering detailed breakdowns of how different markets and trader categories behave (Briese, 2008). Briese's work stands out for its empirical focus and market-specific insights. Jack Schwager includes discussion of COT analysis within the broader context of market behavior in his book Market Sense and Nonsense (Schwager, 2012). Perry Kaufman's Trading Systems and Methods represents perhaps the most rigorous practitioner-focused text on systematic trading approaches including COT analysis (Kaufman, 2013).
Academic journal articles provide the rigorous statistical foundation underlying COT analysis. The Journal of Futures Markets regularly publishes research on positioning data and its predictive properties. Bessembinder and Chan's earlier work on systematic risk, hedging pressure, and risk premiums in futures markets provides theoretical foundation (Bessembinder, 1992). Chang's examination of speculator returns provides historical context (Chang, 1985). Irwin and Sanders provide essential skeptical perspective in their two thousand twelve article (Irwin and Sanders, 2012). Wang's two thousand three article provides one of the most empirical analyses of COT data across multiple commodity markets (Wang, 2003).
Online resources extend beyond academic and book-length treatments. The CFTC website provides free access to current and historical COT reports in multiple formats. The explanatory materials section offers detailed documentation of report construction, category definitions, and historical methodology changes. Traders serious about COT analysis should read these official CFTC documents to understand exactly what they are analyzing.
Commercial COT data services such as Barchart provide enhanced visualization and analysis tools beyond raw CFTC data. TradingView's educational materials, published scripts library, and user community provide additional resources for exploring different approaches to COT analysis.
The key to mastering COT analysis lies not in finding a single definitive source but rather in building understanding through multiple perspectives and information sources. Academic research provides rigorous empirical foundation. Practitioner-focused books offer practical implementation insights. Direct engagement with data through systematic backtesting develops intuition about how positioning dynamics manifest across different market conditions.
SYNTHESIZING KNOWLEDGE INTO PRACTICE
The COT Index indicator represents the synthesis of academic research, trading experience, and software engineering into a practical tool accessible to retail traders equipped with nothing more than a TradingView account and willingness to learn. What once required expensive data subscriptions, custom programming capabilities, statistical software, and institutional resources now appears as a straightforward indicator requiring only basic parameter selection and modest study to understand. This democratization of institutional-grade analysis tools represents a broader trend in financial markets over recent decades.
Yet technology and data access alone provide no edge without understanding and discipline. Markets remain relentlessly efficient at eliminating edges that become too widely known and mechanically exploited. The COT Index indicator succeeds only when users invest time learning the underlying concepts, understand the limitations and probability distributions involved, and integrate signals thoughtfully into trading plans rather than applying them mechanically.
The academic research demonstrates conclusively that institutional positioning contains genuine information about future price movements, particularly at extremes where commercial hedgers are maximally bearish or bullish relative to historical norms. This informational content is neither perfect nor deterministic but rather probabilistic, providing edge over many observations through identification of higher-probability configurations. Bessembinder and Chan's finding that commercial positioning explained modest but significant variance in future returns illustrates this probabilistic nature perfectly (Bessembinder and Chan, 1992). The effect is real and statistically significant, yet it explains perhaps ten to fifteen percent of return variance rather than most variance. Much of price movement remains unpredictable even with positioning intelligence.
The practical implication is that COT analysis works best as one component of a trading system rather than a standalone oracle. It provides the positioning dimension, revealing where the smart money has positioned and where the crowd has followed, but price action analysis provides the timing dimension. Fundamental analysis provides the catalyst dimension. Risk management provides the survival dimension. These components work together synergistically.
The indicator's design philosophy prioritizes transparency and education over black-box complexity, empowering traders to understand exactly what they are analyzing and why. Every calculation is documented and user-adjustable. The threshold markers, background coloring, tables, and clear signal states provide multiple reinforcing channels for conveying the same information.
This educational approach reflects a conviction that sustainable trading success comes from genuine understanding rather than mechanical system-following. Traders who understand why commercial positioning matters, how different trader categories behave, what positioning extremes signify, and where signals fit within probability distributions can adapt when market conditions change. Traders mechanically following black-box signals without comprehension abandon systems after normal losing streaks.
The research foundation supporting COT analysis comes primarily from commodity markets where commercial hedger informational advantages are most pronounced. Agricultural producers hedging crops know more about supply conditions than distant speculators. Energy companies hedging production know more about operating costs than financial traders. Metals miners hedging output know more about ore grades than index funds. Financial futures markets show weaker but still present effects.
The journey from reading this documentation to profitable trading based on COT analysis involves several stages that cannot be rushed. Initial reading and basic understanding represents the first stage. Historical study represents the second stage, reviewing past market cycles to observe how positioning extremes preceded major turning points. Paper trading or small-size real trading represents the third stage to experience the psychological challenges. Refinement based on results and personal psychology represents the fourth stage.
Markets will continue evolving. New participant categories will emerge. Regulatory structures will change. Technology will advance. Yet the fundamental dynamics driving COT analysis, that different market participants have different information, different motivations, and different forecasting abilities that manifest in their positioning, will persist as long as futures markets exist. While specific thresholds or optimal parameters may shift over time, the core logic remains sound and adaptable.
The trader equipped with this indicator, understanding of the theory and evidence behind COT analysis, realistic expectations about probability rather than certainty, discipline to maintain positions through adverse volatility, and patience to allow signals time to develop possesses genuine edge in markets. The edge is not enormous, markets cannot allow large persistent inefficiencies without arbitraging them away, but it is real, measurable, and exploitable by those willing to invest in learning and disciplined application.
REFERENCES
Bessembinder, H. (1992) Systematic risk, hedging pressure, and risk premiums in futures markets, Review of Financial Studies, 5(4), pp. 637-667.
Bessembinder, H. and Chan, K. (1992) The profitability of technical trading rules in the Asian stock markets, Pacific-Basin Finance Journal, 3(2-3), pp. 257-284.
Briese, S. (2008) The Commitments of Traders Bible: How to Profit from Insider Market Intelligence. Hoboken: John Wiley & Sons.
Chang, E.C. (1985) Returns to speculators and the theory of normal backwardation, Journal of Finance, 40(1), pp. 193-208.
Commodity Futures Trading Commission (CFTC) (2009) Explanatory Notes: Disaggregated Commitments of Traders Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Commodity Futures Trading Commission (CFTC) (2020) Commitments of Traders: About the Report. Available at: www.cftc.gov (Accessed: 15 January 2025).
Irwin, S.H. and Sanders, D.R. (2012) Testing the Masters Hypothesis in commodity futures markets, Energy Economics, 34(1), pp. 256-269.
Kaufman, P.J. (2013) Trading Systems and Methods. 5th edn. Hoboken: John Wiley & Sons.
Ruan, Y. and Zhang, Y. (2018) Forecasting commodity futures prices using machine learning: Evidence from the Chinese commodity futures market, Applied Economics Letters, 25(12), pp. 845-849.
Sanders, D.R., Boris, K. and Manfredo, M. (2004) Hedgers, funds, and small speculators in the energy futures markets: an analysis of the CFTC's Commitments of Traders reports, Energy Economics, 26(3), pp. 425-445.
Schwager, J.D. (2012) Market Sense and Nonsense: How the Markets Really Work and How They Don't. Hoboken: John Wiley & Sons.
Tharp, V.K. (2008) Super Trader: Make Consistent Profits in Good and Bad Markets. New York: McGraw-Hill.
Wang, C. (2003) The behavior and performance of major types of futures traders, Journal of Futures Markets, 23(1), pp. 1-31.
Williams, L.R. and Noseworthy, M. (2009) The Right Stock at the Right Time: Prospering in the Coming Good Years. Hoboken: John Wiley & Sons.
FURTHER READING
For traders seeking to deepen their understanding of COT analysis and futures market positioning beyond this documentation, the following resources provide valuable extensions:
Academic Journal Articles:
Fishe, R.P.H. and Smith, A. (2012) Do speculators drive commodity prices away from supply and demand fundamentals?, Journal of Commodity Markets, 1(1), pp. 1-16.
Haigh, M.S., Hranaiova, J. and Overdahl, J.A. (2007) Hedge funds, volatility, and liquidity provision in energy futures markets, Journal of Alternative Investments, 9(4), pp. 10-38.
Kocagil, A.E. (1997) Does futures speculation stabilize spot prices? Evidence from metals markets, Applied Financial Economics, 7(1), pp. 115-125.
Sanders, D.R. and Irwin, S.H. (2011) The impact of index funds in commodity futures markets: A systems approach, Journal of Alternative Investments, 14(1), pp. 40-49.
Books and Practitioner Resources:
Murphy, J.J. (1999) Technical Analysis of the Financial Markets: A Guide to Trading Methods and Applications. New York: New York Institute of Finance.
Pring, M.J. (2002) Technical Analysis Explained: The Investor's Guide to Spotting Investment Trends and Turning Points. 4th edn. New York: McGraw-Hill.
Federal Reserve and Research Institution Publications:
Federal Reserve Banks regularly publish working papers examining commodity markets, futures positioning, and price discovery mechanisms. The Federal Reserve Bank of San Francisco and Federal Reserve Bank of Kansas City maintain active research programs in this area.
Online Resources:
The CFTC website provides free access to current and historical COT reports, explanatory materials, and regulatory documentation.
Barchart offers enhanced COT data visualization and screening tools.
TradingView's community library contains numerous published scripts and educational materials exploring different approaches to positioning analysis.
IBC Work Sessions + 4HProfessional tool for backtesting trading strategies in the Iron Balls Capital (IBC) style. The indicator visualizes key time zones and sessions, providing clear market structure for analyzing historical data and testing trading approaches.
🕓 Time Markers
- 4-hour vertical lines - mark transitions between 4-hour timeframes
- Customizable appearance - color, style (solid, dashed, dotted) and line thickness
📊 IBC Work Sessions
- Morning session: 4:00 - 8:30 UTC
- Day session: 12:00 - 18:00 UTC
- Clear labels for start and end of each session
- Background highlighting of work periods for quick identification
Arnaud Legoux Gaussian Flow | AlphaNattArnaud Legoux Gaussian Flow | AlphaNatt
A sophisticated trend-following and mean-reversion indicator that combines the power of the Arnaud Legoux Moving Average (ALMA) with advanced Gaussian distribution analysis to identify high-probability trading opportunities.
🎯 What Makes This Indicator Unique?
This indicator goes beyond traditional moving averages by incorporating Gaussian mathematics at multiple levels:
ALMA uses Gaussian distribution for superior price smoothing with minimal lag
Dynamic envelopes based on Gaussian probability zones
Multi-layer gradient visualization showing probability density
Adaptive envelope modes that respond to market conditions
📊 Core Components
1. Arnaud Legoux Moving Average (ALMA)
The ALMA is a highly responsive moving average that uses Gaussian distribution to weight price data. Unlike simple moving averages, ALMA can be fine-tuned to balance responsiveness and smoothness through three key parameters:
ALMA Period: Controls the lookback window (default: 21)
Gaussian Offset: Shifts the Gaussian curve to adjust lag vs. responsiveness (default: 0.85)
Gaussian Sigma: Controls the width of the Gaussian distribution (default: 6.0)
2. Gaussian Envelope System
The indicator features three envelope calculation modes:
Fixed Mode: Uses ATR-based fixed width for consistent envelope sizing
Adaptive Mode: Dynamically adjusts based on price acceleration and volatility
Hybrid Mode: Combines ATR and standard deviation for balanced adaptation
The envelopes represent statistical probability zones. Price moving beyond these zones suggests potential mean reversion opportunities.
3. Momentum-Adjusted Envelopes
The envelope width automatically expands during strong trends and contracts during consolidation, providing context-aware support and resistance levels.
⚡ Key Features
Multi-Layer Gradient Visualization
The indicator displays 10 gradient layers between the ALMA and envelope boundaries, creating a visual "heat map" of probability density. This helps traders quickly assess:
Distance from the mean
Potential support/resistance strength
Overbought/oversold conditions in context
Dynamic Color Coding
Cyan gradient: Price below ALMA (bullish zone)
Magenta gradient: Price above ALMA (bearish zone)
The ALMA line itself changes color based on price position
Trend Regime Detection
The indicator automatically identifies market regimes:
Strong Uptrend: Trend strength > 0.5% with price above ALMA
Strong Downtrend: Trend strength < -0.5% with price below ALMA
Weak trends and ranging conditions
📈 Trading Strategies
Mean Reversion Strategy
Look for price entering the extreme Gaussian zones (beyond 95% of envelope width) when trend strength is moderate. These represent statistical extremes where mean reversion is probable.
Signals:
Long: Price in lower Gaussian zone with trend strength > -0.5%
Short: Price in upper Gaussian zone with trend strength < 0.5%
Trend Continuation Strategy
Enter when price crosses the ALMA during confirmed strong trend conditions, riding momentum while using the envelope as a trailing stop reference.
Signals:
Long: Price crosses above ALMA during strong uptrend
Short: Price crosses below ALMA during strong downtrend
🎨 Visualization Guide
The gradient layers create a "probability cloud" around the ALMA:
Darker shades (near ALMA): High probability zone - price tends to stay here
Lighter shades (near envelope edges): Lower probability - potential reversal zones
Price at envelope extremes: Statistical outliers - strongest mean reversion setups
⚙️ Customization Options
ALMA Parameters
Adjust period for different timeframes (lower for day trading, higher for swing trading)
Modify offset to tune responsiveness vs. smoothness
Change sigma to control distribution width
Envelope Configuration
Choose envelope mode based on market characteristics
Adjust multiplier to match instrument volatility
Modify gradient depth for visual preference (5-15 layers)
Signal Enhancement
Momentum Length: Lookback for trend strength calculation
Signal Smoothing: Additional EMA smoothing to reduce noise
🔔 Built-in Alerts
The indicator includes six pre-configured alert conditions:
ALMA Trend Long - Price crosses above ALMA in strong uptrend
ALMA Trend Short - Price crosses below ALMA in strong downtrend
Mean Reversion Long - Price enters lower Gaussian zone
Mean Reversion Short - Price enters upper Gaussian zone
Strong Uptrend Detected - Momentum confirms strong bullish regime
Strong Downtrend Detected - Momentum confirms strong bearish regime
💡 Best Practices
Use on clean, liquid markets with consistent volatility
Combine with volume analysis for confirmation
Adjust envelope multiplier based on backtesting for your specific instrument
Higher timeframes (4H+) generally provide more reliable signals
Use adaptive mode for trending markets, hybrid for mixed conditions
⚠️ Important Notes
This indicator works best in markets with normal price distribution
Extreme news events can invalidate Gaussian assumptions temporarily
Always use proper risk management - no indicator is perfect
Backtest parameters on your specific instrument and timeframe
🔬 Technical Background
The Arnaud Legoux Moving Average was developed to solve the classic dilemma of moving averages: the trade-off between lag and noise. By applying Gaussian distribution weighting, ALMA achieves superior smoothing while maintaining responsiveness to price changes.
The envelope system extends this concept by creating probability zones based on volatility and momentum, effectively mapping where price is "likely" vs "unlikely" to be found based on statistical principles.
Created by AlphaNatt - For educational purposes. Always practice proper risk management. Not financial advice. Always DYOR.
Scalp BTC/ETH — Reversal & Continuation (v1, Pine v6)Scalp BTC/ETH — Reversal & Continuation (1m à 10m)
Cet indicateur détecte des opportunités de micro-scalping sur futures (BTC/ETH) basées sur deux mécaniques courtes validées par structure de prix :
A) Reversal de pression (contre-mouvement contrôlé)
Détection d’une sur-extension brutale suivie d’une absorption sur la bougie suivante.
Objectif : capturer la première respiration après un excès de prix (rejet court).
B) Continuation courte (momentum + reprise)
Détection de 3 bougies directionnelles consécutives suivies d’un pullback léger, puis signal sur la reprise du mouvement initial.
Gestion intégrée (scénario standard TP dynamique)
TP1 → 50% de la position à un gain fixe (% adaptable au timeframe)
Stop déplacé au Break-Even sur le restant
Sortie finale sur bougie inverse significative
(correction ≥ X% du corps précédent) ou timeout (max bars en trade)
Scalp BTC/ETH — Reversal & Continuation (1m to 10m)
This indicator detects short-term futures scalping setups on BTC & ETH using two mechanical price-action models designed for fast execution:
A) Reversal Compression (counter-move entry)
Identifies a sharp impulse (overextension) followed by absorption / failure to extend on the next candle.
Objective: capture the first corrective pullback after exhaustion.
B) Controlled Continuation (momentum follow-through)
Identifies 3 consecutive trend candles, then a shallow pullback, and triggers an entry on the resumption of the main leg.
Built-in trade logic (dynamic TP structure)
TP1 → scale out 50% of the position at a fixed percentage (auto-scaled per timeframe)
Stop moved to Break-Even after TP1
Final exit on either:
• a meaningful opposite candle (≥ X% correction of prior body), or
• a timeout (max bars in trade)
Technical characteristics
Designed for 1m / 3m / 5m / 7m / 10m
No repainting (bar-close confirmed logic)
Works for both LONG & SHORT
Built-in alert events:
ENTRY_LONG / ENTRY_SHORT / TP1 / EXIT_STOP / EXIT_INVERSE / EXIT_TIMEOUT
Suitable for manual execution, semi-automation (alerts) or full bot integration (webhook JSON)
Purpose
Provide a repeatable, rule-based, non-subjective framework to harvest micro-moves with controlled risk, without relying on lagging indicators or long-term prediction.
(A Strategy / backtesting version is planned as a next iteration.)
ICT Liquidity Sweep Asia/London 1 Trade per High & Low🧠 ICT Liquidity Sweep Asia/London — 1 Trade per High & Low
This strategy is inspired by the ICT (Inner Circle Trader) concepts of liquidity sweeps and market structure, focusing on the Asia and London sessions.
It automatically identifies liquidity grabs (sweeps) above or below key session highs/lows and enters trades with a fixed risk/reward ratio (RR).
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⚙️ Core Logic
-Asia Session: 8:00 PM – 11:59 PM (New York time)
-London Session: 2:00 AM – 5:00 AM (New York time)
-The script marks the Asia High/Low and London High/Low ranges for each day.
-When the market sweeps above a session high → potential Short setup
-When the market sweeps below a session low → potential Long setup
-A trade is triggered when the confirmation candle closes in the opposite direction of the sweep (bearish after a high sweep, bullish after a low sweep).
-Only one trade per sweep type (1 per High, 1 per Low) is allowed per session.
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📈 Risk Management
-Configurable Risk/Reward Target (default = 2:1)
-Configurable Position Size (number of contracts)
-Each trade uses a fixed Stop Loss (beyond the wick of the sweep) and a Take Profit calculated from the RR setting.
-All trades are automatically logged in the Strategy Tester with performance metrics.
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💡 Features
✅ Visual session highlighting (Asia = Aqua, London = Orange)
✅ Automatic liquidity line plotting (session highs/lows)
✅ Entry & exit labels (optional visual display)
✅ Customizable RR and contract size
✅ Works on any instrument (ideal for indices, futures, or forex)
✅ Compatible with all timeframes (optimized for 1M–15M)
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⚠️ Notes
-Best used on New York time-based charts.
-Designed for educational and backtesting purposes — not financial advice.
-Use as a foundation for further optimization (e.g., SMT confirmation, FVG filter, or time-based restrictions).
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🧩 Recommended Use
Pair this with:
-ICT’s concepts like CISD (Change in State of Delivery) and FVGs (Fair Value Gaps)
-Higher timeframe liquidity maps
-Session bias or daily narrative filters
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Author: jygirouard
Strategy Version: 1.3
Type: ICT Liquidity Sweep Automation
Timezone: America/New_York
Pivot Regime Anchored VWAP [CHE] Pivot Regime Anchored VWAP — Detects body-based pivot regimes to classify swing highs and lows, anchoring volume-weighted average price lines directly at higher highs and lower lows for adaptive reference levels.
Summary
This indicator identifies shifts between top and bottom regimes through breakouts in candle body highs and lows, labeling swing points as higher highs, lower highs, lower lows, or higher lows. It then draws anchored volume-weighted average price lines starting from the most recent higher high and lower low, providing dynamic support and resistance that evolve with volume flow. These anchored lines differ from standard volume-weighted averages by resetting only at confirmed swing extremes, reducing noise in ranging markets while highlighting momentum shifts in trends.
Motivation: Why this design?
Traders often struggle with static reference lines that fail to adapt to changing market structures, leading to false breaks in volatile conditions or missed continuations in trends. By anchoring volume-weighted average price calculations to body pivot regimes—specifically at higher highs for resistance and lower lows for support—this design creates reference levels tied directly to price structure extremes. This approach addresses the problem of generic moving averages lagging behind swing confirmations, offering a more context-aware tool for intraday or swing trading.
What’s different vs. standard approaches?
- Baseline reference: Traditional volume-weighted average price indicators compute a running total from session start or fixed periods, often ignoring price structure.
- Architecture differences:
- Regime detection via body breakout logic switches between high and low focus dynamically.
- Anchoring limited to confirmed higher highs and lower lows, with historical recalculation for accurate line drawing.
- Polyline rendering rebuilds only on the last bar to manage performance.
- Practical effect: Charts show fewer, more meaningful lines that start at swing points, making it easier to spot confluences with structure breaks rather than cluttered overlays from continuous calculations.
How it works (technical)
The indicator first calculates the maximum and minimum of each candle's open and close to define body highs and lows. It then scans a lookback window for the highest body high and lowest body low. A top regime triggers when the body high from the lookback period exceeds the window's highest, and a bottom regime when the body low falls below the window's lowest. These regime shifts confirm pivots only when crossing from one state to the other.
For top pivots, it compares the new body high against the previous swing high: if greater, it marks a higher high and anchors a new line; otherwise, a lower high. The same logic applies inversely for bottom pivots. Anchored lines use cumulative price-volume products and volumes from the anchor bar onward, subtracting prior cumulatives to isolate the segment. On pivot confirmation, it loops backward from the current bar to the anchor, computing and storing points for the line. New points append as bars advance, ensuring the line reflects ongoing volume weighting.
Initialization uses persistent variables to track the last swing values and anchor bars, starting with neutral states. Data flows from regime detection to pivot classification, then to anchoring and point accumulation, with lines rendered globally on the final bar.
Parameter Guide
Pivot Length — Controls the lookback window for detecting body breakouts, influencing pivot frequency and sensitivity to recent action. Shorter values catch more pivots in choppy conditions; longer smooths for major swings. Default: 30 (bars). Trade-offs/Tips: Min 1; for intraday, try 10–20 to reduce lag but watch for noise; on daily, 50+ for stability.
Show Pivot Labels — Toggles display of text markers at swing points, aiding quick identification of higher highs, lower highs, lower lows, or higher lows. Default: true. Trade-offs/Tips: Disable in multi-indicator setups to declutter; useful for backtesting structure.
HH Color — Sets the line and label color for higher high anchored lines, distinguishing resistance levels. Default: Red (solid). Trade-offs/Tips: Choose contrasting hues for dark/light themes; pair with opacity for fills if added later.
LL Color — Sets the line and label color for lower low anchored lines, distinguishing support levels. Default: Lime (solid). Trade-offs/Tips: As above; green shades work well for bullish contexts without overpowering candles.
Reading & Interpretation
Higher high labels and red lines indicate potential resistance zones where volume weighting begins at a new swing top, suggesting sellers may defend prior highs. Lower low labels and lime lines mark support from a fresh swing bottom, with the line's slope reflecting buyer commitment via volume. Lower highs or higher lows appear as labels without new anchors, signaling possible range-bound action. Line proximity to price shows overextension; crosses may hint at regime shifts, but confirm with volume spikes.
Practical Workflows & Combinations
- Trend following: Enter longs above a rising lower low anchored line after higher low confirmation; filter with rising higher highs for uptrends. Use line breaks as trailing stops.
- Exits/Stops: In downtrends, exit shorts below a higher high line; set aggressive stops above it for scalps, conservative below for swings. Pair with momentum oscillators for divergence.
- Multi-asset/Multi-TF: Defaults suit forex/stocks on 1H–4H; on crypto 15M, shorten length to 15. Scale colors for dark themes; combine with higher timeframe anchors for confluence.
Behavior, Constraints & Performance
Closed-bar logic ensures pivots confirm after the lookback period, with no repainting on historical bars—live bars may adjust until regime shift. No higher timeframe calls, so minimal repaint risk beyond standard delays. Resources include a 2000-bar history limit, label/polyline caps at 200/50, and loops for historical point filling (up to current bar count from anchor, typically under 500 iterations). Known limits: In extreme gaps or low-volume periods, anchors may skew; lines absent until first pivots.
Sensible Defaults & Quick Tuning
Start with the 30-bar length for balanced pivot detection across most assets. For too-frequent pivots in ranges, increase to 50 for fewer signals. If lines lag in trends, reduce to 20 and enable labels for visual cues. In low-volatility assets, widen color contrasts; test on 100-bar history to verify stability.
What this indicator is—and isn’t
This is a structure-aware visualization layer for anchoring volume-weighted references at swing extremes, enhancing manual analysis of regimes and levels. It is not a standalone signal generator or predictive model—always integrate with broader context like order flow or news. Use alongside risk management and position sizing, not as isolated buy/sell triggers.
Many thanks to LuxAlgo for the original script "McDonald's Pattern ". The implementation for body pivots instead of wicks uses a = max(open, close), b = min(open, close) and then highest(a, length) / lowest(b, length). This filters noise from the wicks and detects breakouts over/under bodies. Unusual and targeted, super innovative.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Historical Matrix Analyzer [PhenLabs]📊Historical Matrix Analyzer
Version: PineScriptv6
📌Description
The Historical Matrix Analyzer is an advanced probabilistic trading tool that transforms technical analysis into a data-driven decision support system. By creating a comprehensive 56-cell matrix that tracks every combination of RSI states and multi-indicator conditions, this indicator reveals which market patterns have historically led to profitable outcomes and which have not.
At its core, the indicator continuously monitors seven distinct RSI states (ranging from Extreme Oversold to Extreme Overbought) and eight unique indicator combinations (MACD direction, volume levels, and price momentum). For each of these 56 possible market states, the system calculates average forward returns, win rates, and occurrence counts based on your configurable lookback period. The result is a color-coded probability matrix that shows you exactly where you stand in the historical performance landscape.
The standout feature is the Current State Panel, which provides instant clarity on your active market conditions. This panel displays signal strength classifications (from Strong Bullish to Strong Bearish), the average return percentage for similar past occurrences, an estimated win rate using Bayesian smoothing to prevent small-sample distortions, and a confidence level indicator that warns you when insufficient data exists for reliable conclusions.
🚀Points of Innovation
Multi-dimensional state classification combining 7 RSI levels with 8 indicator combinations for 56 unique trackable market conditions
Bayesian win rate estimation with adjustable smoothing strength to provide stable probability estimates even with limited historical samples
Real-time active cell highlighting with “NOW” marker that visually connects current market conditions to their historical performance data
Configurable color intensity sensitivity allowing traders to adjust heat-map responsiveness from conservative to aggressive visual feedback
Dual-panel display system separating the comprehensive statistics matrix from an easy-to-read current state summary panel
Intelligent confidence scoring that automatically warns traders when occurrence counts fall below reliable thresholds
🔧Core Components
RSI State Classification: Segments RSI readings into 7 distinct zones (Extreme Oversold <20, Oversold 20-30, Weak 30-40, Neutral 40-60, Strong 60-70, Overbought 70-80, Extreme Overbought >80) to capture momentum extremes and transitions
Multi-Indicator Condition Tracking: Simultaneously monitors MACD crossover status (bullish/bearish), volume relative to moving average (high/low), and price direction (rising/falling) creating 8 binary-encoded combinations
Historical Data Storage Arrays: Maintains rolling lookback windows storing RSI states, indicator states, prices, and bar indices for precise forward-return calculations
Forward Performance Calculator: Measures price changes over configurable forward bar periods (1-20 bars) from each historical state, accumulating total returns and win counts per matrix cell
Bayesian Smoothing Engine: Applies statistical prior assumptions (default 50% win rate) weighted by user-defined strength parameter to stabilize estimated win rates when sample sizes are small
Dynamic Color Mapping System: Converts average returns into color-coded heat map with intensity adjusted by sensitivity parameter and transparency modified by confidence levels
🔥Key Features
56-Cell Probability Matrix: Comprehensive grid displaying every possible combination of RSI state and indicator condition, with each cell showing average return percentage, estimated win rate, and occurrence count for complete statistical visibility
Current State Info Panel: Dedicated display showing your exact position in the matrix with signal strength emoji indicators, numerical statistics, and color-coded confidence warnings for immediate situational awareness
Customizable Lookback Period: Adjustable historical window from 50 to 500 bars allowing traders to focus on recent market behavior or capture longer-term pattern stability across different market cycles
Configurable Forward Performance Window: Select target holding periods from 1 to 20 bars ahead to align probability calculations with your trading timeframe, whether day trading or swing trading
Visual Heat Mapping: Color-coded cells transition from red (bearish historical performance) through gray (neutral) to green (bullish performance) with intensity reflecting statistical significance and occurrence frequency
Intelligent Data Filtering: Minimum occurrence threshold (1-10) removes unreliable patterns with insufficient historical samples, displaying gray warning colors for low-confidence cells
Flexible Layout Options: Independent positioning of statistics matrix and info panel to any screen corner, accommodating different chart layouts and personal preferences
Tooltip Details: Hover over any matrix cell to see full RSI label, complete indicator status description, precise average return, estimated win rate, and total occurrence count
🎨Visualization
Statistics Matrix Table: A 9-column by 8-row grid with RSI states labeling vertical axis and indicator combinations on horizontal axis, using compact abbreviations (XOverS, OverB, MACD↑, Vol↓, P↑) for space efficiency
Active Cell Indicator: The current market state cell displays “⦿ NOW ⦿” in yellow text with enhanced color saturation to immediately draw attention to relevant historical performance
Signal Strength Visualization: Info panel uses emoji indicators (🔥 Strong Bullish, ✅ Bullish, ↗️ Weak Bullish, ➖ Neutral, ↘️ Weak Bearish, ⛔ Bearish, ❄️ Strong Bearish, ⚠️ Insufficient Data) for rapid interpretation
Histogram Plot: Below the price chart, a green/red histogram displays the current cell’s average return percentage, providing a time-series view of how historical performance changes as market conditions evolve
Color Intensity Scaling: Cell background transparency and saturation dynamically adjust based on both the magnitude of average returns and the occurrence count, ensuring visual emphasis on reliable patterns
Confidence Level Display: Info panel bottom row shows “High Confidence” (green), “Medium Confidence” (orange), or “Low Confidence” (red) based on occurrence counts relative to minimum threshold multipliers
📖Usage Guidelines
RSI Period
Default: 14
Range: 1 to unlimited
Description: Controls the lookback period for RSI momentum calculation. Standard 14-period provides widely-recognized overbought/oversold levels. Decrease for faster, more sensitive RSI reactions suitable for scalping. Increase (21, 28) for smoother, longer-term momentum assessment in swing trading. Changes affect how quickly the indicator moves between the 7 RSI state classifications.
MACD Fast Length
Default: 12
Range: 1 to unlimited
Description: Sets the faster exponential moving average for MACD calculation. Standard 12-period setting works well for daily charts and captures short-term momentum shifts. Decreasing creates more responsive MACD crossovers but increases false signals. Increasing smooths out noise but delays signal generation, affecting the bullish/bearish indicator state classification.
MACD Slow Length
Default: 26
Range: 1 to unlimited
Description: Defines the slower exponential moving average for MACD calculation. Traditional 26-period setting balances trend identification with responsiveness. Must be greater than Fast Length. Wider spread between fast and slow increases MACD sensitivity to trend changes, impacting the frequency of indicator state transitions in the matrix.
MACD Signal Length
Default: 9
Range: 1 to unlimited
Description: Smoothing period for the MACD signal line that triggers bullish/bearish state changes. Standard 9-period provides reliable crossover signals. Shorter values create more frequent state changes and earlier signals but with more whipsaws. Longer values produce more confirmed, stable signals but with increased lag in detecting momentum shifts.
Volume MA Period
Default: 20
Range: 1 to unlimited
Description: Lookback period for volume moving average used to classify volume as “high” or “low” in indicator state combinations. 20-period default captures typical monthly trading patterns. Shorter periods (10-15) make volume classification more reactive to recent spikes. Longer periods (30-50) require more sustained volume changes to trigger state classification shifts.
Statistics Lookback Period
Default: 200
Range: 50 to 500
Description: Number of historical bars used to calculate matrix statistics. 200 bars provides substantial data for reliable patterns while remaining responsive to regime changes. Lower values (50-100) emphasize recent market behavior and adapt quickly but may produce volatile statistics. Higher values (300-500) capture long-term patterns with stable statistics but slower adaptation to changing market dynamics.
Forward Performance Bars
Default: 5
Range: 1 to 20
Description: Number of bars ahead used to calculate forward returns from each historical state occurrence. 5-bar default suits intraday to short-term swing trading (5 hours on hourly charts, 1 week on daily charts). Lower values (1-3) target short-term momentum trades. Higher values (10-20) align with position trading and longer-term pattern exploitation.
Color Intensity Sensitivity
Default: 2.0
Range: 0.5 to 5.0, step 0.5
Description: Amplifies or dampens the color intensity response to average return magnitudes in the matrix heat map. 2.0 default provides balanced visual emphasis. Lower values (0.5-1.0) create subtle coloring requiring larger returns for full saturation, useful for volatile instruments. Higher values (3.0-5.0) produce vivid colors from smaller returns, highlighting subtle edges in range-bound markets.
Minimum Occurrences for Coloring
Default: 3
Range: 1 to 10
Description: Required minimum sample size before applying color-coded performance to matrix cells. Cells with fewer occurrences display gray “insufficient data” warning. 3-occurrence default filters out rare patterns. Lower threshold (1-2) shows more data but includes unreliable single-event statistics. Higher thresholds (5-10) ensure only well-established patterns receive visual emphasis.
Table Position
Default: top_right
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the 56-cell statistics matrix table. Position to avoid overlapping critical price action or other indicators on your chart. Consider chart orientation and candlestick density when selecting optimal placement.
Show Current State Panel
Default: true
Options: true, false
Description: Toggle visibility of the dedicated current state information panel. When enabled, displays signal strength, RSI value, indicator status, average return, estimated win rate, and confidence level for active market conditions. Disable to declutter charts when only the matrix table is needed.
Info Panel Position
Default: bottom_left
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the current state information panel (when enabled). Position independently from statistics matrix to optimize chart real estate. Typically placed opposite the matrix table for balanced visual layout.
Win Rate Smoothing Strength
Default: 5
Range: 1 to 20
Description: Controls Bayesian prior weighting for estimated win rate calculations. Acts as virtual sample size assuming 50% win rate baseline. Default 5 provides moderate smoothing preventing extreme win rate estimates from small samples. Lower values (1-3) reduce smoothing effect, allowing win rates to reflect raw data more directly. Higher values (10-20) increase conservatism, pulling win rate estimates toward 50% until substantial evidence accumulates.
✅Best Use Cases
Pattern-based discretionary trading where you want historical confirmation before entering setups that “look good” based on current technical alignment
Swing trading with holding periods matching your forward performance bar setting, using high-confidence bullish cells as entry filters
Risk assessment and position sizing, allocating larger size to trades originating from cells with strong positive average returns and high estimated win rates
Market regime identification by observing which RSI states and indicator combinations are currently producing the most reliable historical patterns
Backtesting validation by comparing your manual strategy signals against the historical performance of the corresponding matrix cells
Educational tool for developing intuition about which technical condition combinations have actually worked versus those that feel right but lack historical evidence
⚠️Limitations
Historical patterns do not guarantee future performance, especially during unprecedented market events or regime changes not represented in the lookback period
Small sample sizes (low occurrence counts) produce unreliable statistics despite Bayesian smoothing, requiring caution when acting on low-confidence cells
Matrix statistics lag behind rapidly changing market conditions, as the lookback period must accumulate new state occurrences before updating performance data
Forward return calculations use fixed bar periods that may not align with actual trade exit timing, support/resistance levels, or volatility-adjusted profit targets
💡What Makes This Unique
Multi-Dimensional State Space: Unlike single-indicator tools, simultaneously tracks 56 distinct market condition combinations providing granular pattern resolution unavailable in traditional technical analysis
Bayesian Statistical Rigor: Implements proper probabilistic smoothing to prevent overconfidence from limited data, a critical feature missing from most pattern recognition tools
Real-Time Contextual Feedback: The “NOW” marker and dedicated info panel instantly connect current market conditions to their historical performance profile, eliminating guesswork
Transparent Occurrence Counts: Displays sample sizes directly in each cell, allowing traders to judge statistical reliability themselves rather than hiding data quality issues
Fully Customizable Analysis Window: Complete control over lookback depth and forward return horizons lets traders align the tool precisely with their trading timeframe and strategy requirements
🔬How It Works
1. State Classification and Encoding
Each bar’s RSI value is evaluated and assigned to one of 7 discrete states based on threshold levels (0: <20, 1: 20-30, 2: 30-40, 3: 40-60, 4: 60-70, 5: 70-80, 6: >80)
Simultaneously, three binary conditions are evaluated: MACD line position relative to signal line, current volume relative to its moving average, and current close relative to previous close
These three binary conditions are combined into a single indicator state integer (0-7) using binary encoding, creating 8 possible indicator combinations
The RSI state and indicator state are stored together, defining one of 56 possible market condition cells in the matrix
2. Historical Data Accumulation
As each bar completes, the current state classification, closing price, and bar index are stored in rolling arrays maintained at the size specified by the lookback period
When the arrays reach capacity, the oldest data point is removed and the newest added, creating a sliding historical window
This continuous process builds a comprehensive database of past market conditions and their subsequent price movements
3. Forward Return Calculation and Statistics Update
On each bar, the indicator looks back through the stored historical data to find bars where sufficient forward bars exist to measure outcomes
For each historical occurrence, the price change from that bar to the bar N periods ahead (where N is the forward performance bars setting) is calculated as a percentage return
This percentage return is added to the cumulative return total for the specific matrix cell corresponding to that historical bar’s state classification
Occurrence counts are incremented, and wins are tallied for positive returns, building comprehensive statistics for each of the 56 cells
The Bayesian smoothing formula combines these raw statistics with prior assumptions (neutral 50% win rate) weighted by the smoothing strength parameter to produce estimated win rates that remain stable even with small samples
💡Note:
The Historical Matrix Analyzer is designed as a decision support tool, not a standalone trading system. Best results come from using it to validate discretionary trade ideas or filter systematic strategy signals. Always combine matrix insights with proper risk management, position sizing rules, and awareness of broader market context. The estimated win rate feature uses Bayesian statistics specifically to prevent false confidence from limited data, but no amount of smoothing can create reliable predictions from fundamentally insufficient sample sizes. Focus on high-confidence cells (green-colored confidence indicators) with occurrence counts well above your minimum threshold for the most actionable insights.
Scalper Pro Pattern Recognition & Price ActionOVERVIEW
Scalper Pro is a comprehensive multi-timeframe trading indicator that combines Smart Money Concepts (SMC) with traditional technical analysis to provide scalpers and day traders with high-probability entry and exit signals. This indicator integrates multiple analytical frameworks into a unified visual system designed specifically for short-term trading strategies.
ORIGINALITY & PURPOSE
What Makes This Script Original
This script is not a simple mashup of existing indicators. Instead, it represents a carefully orchestrated integration of complementary analytical methods that work together to solve a specific problem: identifying high-probability scalping opportunities in volatile markets.
The unique value proposition:
Adaptive Trend Filtering System - Combines a customized SuperTrend algorithm with dual-period range filters (Cirrus Cloud) and Hull Moving Average trend cloud to create a three-layer trend confirmation system
Smart Money Concepts Integration - Incorporates institutional trading concepts (Order Blocks, Fair Value Gaps, Break of Structure) with retail technical indicators for a complete market structure view
Dynamic Risk Management - Automatically calculates stop-loss and take-profit levels based on ATR volatility, providing objective position sizing
ADX-Based Market Regime Detection - Identifies ranging vs. trending markets through ADX analysis with visual bar coloring to prevent whipsaws during consolidation
Why Combine These Specific Components
Each component addresses a specific weakness in scalping:
SuperTrend provides the primary directional bias but can generate false signals in ranging markets
Range Filters smooth out noise and confirm trend direction, reducing SuperTrend false positives
ADX Analysis prevents trading during low-volatility consolidation when most indicators fail
SMC Elements identify institutional activity zones where price is likely to react strongly
ATR-Based Risk Management adapts position sizing to current volatility conditions
The synergy creates a system where signals are only generated when multiple confirmation layers align, significantly reducing false signals common in single-indicator approaches.
HOW IT WORKS
Core Calculation Methodology
1. SuperTrend Signal Generation
The script uses a modified SuperTrend algorithm with the following calculation:
ATR = Average True Range (default: 10 periods)
Factor = 7 (default sensitivity multiplier)
Upper Band = Source + (Factor × ATR)
Lower Band = Source - (Factor × ATR)
Directional Logic:
When price crosses above SuperTrend → Bullish signal
When price crosses below SuperTrend → Bearish signal
SuperTrend value is plotted as dynamic support/resistance
Key Modification: The sensitivity parameter (nsensitivity * 7) allows users to adjust the aggressiveness of trend detection without changing the core ATR calculation.
2. Range Filter System (Cirrus Cloud)
The Range Filter uses a smoothed range calculation to filter out market noise:
Smooth Range Calculation:
WPER = (Period × 2) - 1
AVRNG = EMA(|Price - Price |, Period)
Smooth Range = EMA(AVRNG, WPER) × Multiplier
Two-Layer System:
Layer 1: 22-period with 6x multiplier (broader trend)
Layer 2: 15-period with 5x multiplier (tighter price action)
Visual Output: The space between these two filters is colored:
Green fill = Bullish trend (Layer 1 > Layer 2)
Red fill = Bearish trend (Layer 1 < Layer 2)
This creates a "cloud" that expands during strong trends and contracts during consolidation.
3. ADX Market Regime Detection
Calculation:
+DM = Positive Directional Movement
-DM = Negative Directional Movement
True Range = RMA of True Range (15 periods)
+DI = 100 × RMA(+DM, 15) / True Range
-DI = 100 × RMA(-DM, 15) / True Range
ADX = 100 × RMA(|+DI - -DI| / (+DI + -DI), 15)
Threshold System:
ADX < Threshold (default 15) = Ranging market → Bar color changes to purple
ADX > Threshold = Trending market → Normal bar coloring applies
Purpose: This prevents taking trend-following signals during sideways markets where most indicators produce whipsaws.
4. Smart Money Concepts (SMC) Integration
Order Blocks (OB):
Identified using swing high/low detection with customizable pivot length
Bullish OB: Last down-close candle before bullish Break of Structure (BOS)
Bearish OB: Last up-close candle before bearish BOS
Extended forward until price breaks through them
Fair Value Gaps (FVG):
Detected when a three-candle gap exists:
Bullish FVG: Low > High
Bearish FVG: High < Low
Filtered by price delta percentage to ensure significant gaps
Displayed as boxes that delete when price fills the gap
Break of Structure (BOS) vs. Change of Character (CHoCH):
BOS = Price breaks the previous structural high/low in the current trend direction
CHoCH = Price breaks structure in the opposite direction (potential trend reversal)
Both internal (minor) and swing (major) structures are tracked
Equal Highs/Lows (EQH/EQL):
Detected when consecutive swing highs/lows are within ATR threshold
Often indicates liquidity pools that price may sweep before reversing
5. ATR-Based Risk Management
Calculation:
ATR Band = ATR(14) × Risk Multiplier (default 3%)
Stop Loss = Entry - ATR Band (for longs) or Entry + ATR Band (for shorts)
Take Profit Levels:
TP1 = Entry + (Entry - Stop Loss) × 1
TP2 = Entry + (Entry - Stop Loss) × 2
TP3 = Entry + (Entry - Stop Loss) × 3
Dynamic Labels: Stop loss and take profit levels are automatically calculated and displayed as labels on the chart when new signals trigger.
6. Hull Moving Average Trend Cloud
HMA = WMA(2 × WMA(Close, Period/2) - WMA(Close, Period), sqrt(Period))
Period = 600 bars (long-term trend)
The HMA provides a smoothed long-term trend reference that's more responsive than traditional moving averages while filtering out short-term noise.
HOW TO USE THE INDICATOR
Entry Signals
Primary Buy Signal:
SuperTrend changes to green (price crosses above)
ADX shows market is NOT ranging (bars are NOT purple)
Price is within or near a bullish Order Block OR bullish FVG
Cirrus Cloud shows green fill (Layer 1 > Layer 2)
Primary Sell Signal:
SuperTrend changes to red (price crosses below)
ADX shows market is NOT ranging
Price is within or near a bearish Order Block OR bearish FVG
Cirrus Cloud shows red fill (Layer 1 < Layer 2)
Confirmation Layers
Higher Probability Trades Include:
Bullish/Bearish BOS in the same direction as signal
Equal highs/lows being swept before entry
Price respecting premium/discount zones (above/below equilibrium)
Multiple timeframe alignment (use MTF settings)
Exit Strategy
The indicator provides three take-profit levels:
TP1: Conservative target (1:1 risk-reward)
TP2: Moderate target (2:1 risk-reward)
TP3: Aggressive target (3:1 risk-reward)
Suggested Exit Approach:
Close 1/3 position at TP1
Move stop to breakeven
Close 1/3 position at TP2
Trail remaining position or exit at TP3
Risk Management
Stop Loss:
Use the ATR-based stop loss level displayed on chart
Alternatively, use percentage-based stop (adjustable in settings)
Never risk more than 1-2% of account per trade
Position Sizing:
Position Size = (Account Risk $) / (Entry Price - Stop Loss Price)
CUSTOMIZABLE SETTINGS
Core Parameters
Buy/Sell Signals:
Toggle signals on/off
Adjust SuperTrend sensitivity (0.5 - 2.0)
Risk Management:
Show/hide TP/SL levels
ATR period (default: 14)
Risk percentage (default: 3%)
Number of decimal places for price labels
Trend Features:
Cirrus Cloud display toggle
Range filter periods (x1, x2, x3, x4)
Hull MA length for trend cloud
Smart Money Concepts:
Order Block settings (swing length, display count)
Fair Value Gap parameters (auto-threshold, extend length)
Structure detection (internal vs swing)
EQH/EQL threshold
ADX Settings:
ADX length (default: 15)
Sideways threshold (10-30, default: 15)
Bar color toggle
Display Options:
Previous day/week/month high/low levels
Premium/Discount/Equilibrium zones
Trend candle coloring (colored or monochrome)
BEST PRACTICES & TRADING TIPS
Optimal Use Cases
Scalping on lower timeframes (1m, 5m, 15m)
Rapid entry/exit with clear TP levels
ADX filter prevents choppy market entries
Day trading on medium timeframes (30m, 1H)
Stronger trend confirmation
Better risk-reward ratios
Swing trading entries on higher timeframes (4H, Daily)
Higher-probability structural setups
Larger ATR-based stops accommodate volatility
Market Conditions
Best Performance:
Trending markets with clear directional bias
Post-news volatility with defined structure
Markets respecting support/resistance levels
Avoid Trading When:
ADX indicator shows purple bars (ranging market)
Multiple conflicting signals across timeframes
Major news events without clear price structure
Low volume periods (market open/close)
Common Mistakes to Avoid
Ignoring the ADX filter - Taking signals during ranging markets leads to whipsaws
Not waiting for confirmation - Enter only when multiple layers align
Overtrading - Fewer high-quality setups outperform many mediocre ones
Ignoring risk management - Always use the calculated stop losses
Fighting the trend - Trade WITH the SuperTrend and Cirrus Cloud direction
TECHNICAL SPECIFICATIONS
Indicator Type: Overlay (plots on price chart)
Calculation Resources:
Max labels: 500
Max lines: 500
Max boxes: 500
Max bars back: 500
Pine Script Version: 5
Compatible Timeframes: All timeframes (optimized for 1m to 1D)
Compatible Instruments:
Forex pairs
Crypto assets
Stock indices
Individual stocks
Commodities
THEORETICAL FOUNDATION
Trend-Following Concepts
This indicator is based on the principle that markets trend more often than they range, and that trends tend to persist. The SuperTrend component captures this momentum while the range filters prevent premature entries during pullbacks.
Smart Money Theory
The SMC elements are based on the concept that institutional traders (banks, hedge funds) leave footprints in the form of:
Order Blocks: Areas where large orders were placed
Fair Value Gaps: Inefficient price movements that may be revisited
Liquidity Sweeps: Stop hunts before continuation (EQH/EQL)
Volatility-Based Position Sizing
Using ATR for stop-loss placement ensures that stop distances adapt to current market conditions:
Tight stops in low volatility (avoids excessive risk)
Wider stops in high volatility (avoids premature stop-outs)
PERFORMANCE EXPECTATIONS
Realistic Expectations
Win Rate:
Expected: 45-55% (trend-following systems rarely exceed 60%)
Higher win rates on trending days
Lower win rates during consolidation (even with ADX filter)
Risk-Reward Ratio:
Target: 1.5:1 minimum (TP2)
Achievable: 2:1 to 3:1 on strong trends
Drawdowns:
Normal: 10-15% of account during choppy periods
Maximum: Should not exceed 20% with proper risk management
Optimization Tips
Backtesting Recommendations:
Test on at least 1 year of historical data
Include different market conditions (trending, ranging, volatile)
Adjust SuperTrend sensitivity per instrument
Optimize ADX threshold for your specific market
Record trades to identify personal execution errors
FREQUENTLY ASKED QUESTIONS
Q: Can I use this for automated trading?
A: The indicator provides signals, but you'll need to code a strategy script separately for automation. The signals can trigger alerts that connect to trading bots.
Q: Why do I see conflicting signals?
A: This is normal during transition periods. Wait for all confirmation layers to align before entering.
Q: How often should I expect signals?
A: Depends on timeframe and market conditions. On 5m charts during trending markets: 3-7 quality setups per session.
Q: Can I use only some features?
A: Yes, all components can be toggled on/off. However, the system works best with all confirmations active.
Q: What's the difference between internal and swing structures?
A: Internal = minor price structures (smaller pivots). Swing = major price structures (larger pivots). Both provide different levels of confirmation.
DISCLAIMER
This indicator is a tool for technical analysis and should not be the sole basis for trading decisions. Past performance does not guarantee future results. Always:
Use proper risk management
Test on demo accounts first
Never risk more than you can afford to lose
Combine with fundamental analysis when applicable
Understand that no indicator is 100% accurate
License: Mozilla Public License 2.0
Author: DrFXGOD
VERSION HISTORY & UPDATES
Initial Release - Version 1.0
Integrated SuperTrend, Range Filters, ADX, SMC concepts
ATR-based risk management
Multi-timeframe support
Customizable visual elements
SUPPORT & DOCUMENTATION
For questions, suggestions, or bug reports, please comment on the script page or contact the author through TradingView.
Additional Resources:
Smart Money Concepts: Research ICT (Inner Circle Trader) materials
ATR and Volatility: Refer to Wilder's original ATR documentation
SuperTrend Indicator: Study original SuperTrend strategy papers
SALSA MultiStrategy DashboardENGLISH VERSION (Primary)
Why I Created This Unified Dashboard
The Problem with Analysis Fragmentation:
As an active trader, I found myself constantly struggling with chart clutter - having 5-8 separate indicators open simultaneously. This created cognitive overload and made it difficult to identify confluence across different technical approaches. The constant switching between indicators and managing multiple windows was disrupting my trading workflow and decision-making process.
My Solution:
I developed the SALSA MultiStrategy Dashboard to solve this specific problem by integrating complementary technical methodologies into a single, cohesive view. This isn't just a random collection of indicators, but a carefully curated selection that work together to provide comprehensive market analysis.
What Makes This Dashboard Unique
Integrated Analysis Framework:
Squeeze Momentum System: Identifies consolidation periods and potential breakout directions with color-coded momentum signals
ADX Trend Strength Analysis: Customizable key level (default: 23) for trend strength assessment with visual scaling
RSI with Built-in Divergence: Dual-timeframe RSI analysis with automatic divergence detection
Multi-Timeframe Confirmation: Additional oscillators (MFI, Stochastic, AO, MACD, CCI) for signal validation
Key Innovations:
Unified Scaling System: All indicators share a common scale, making visual comparison intuitive
Integrated Divergence Detection: Consistent divergence logic applied across both Squeeze Momentum and RSI
Smart Color Coding: Visual cues that highlight momentum shifts and trend strength
Trading Status Module: Real-time market condition assessment based on multiple factor confluence
How It Works - Technical Foundation
Squeeze Momentum Component:
Uses Bollinger Bands® and Keltner Channels to detect market compression
Momentum calculation based on linear regression of price action
Color transitions indicate momentum shifts (Cyan/Blue for bullish, Yellow/Orange for bearish)
ADX with Custom Key Levels:
Implements Wilder's ADX with adjustable key level threshold
Visual scaling adapts to market conditions
Separate +DI/-DI plotting options for additional trend direction insight
Advanced RSI System:
Standard RSI with fast-slow momentum divergence detection
Configurable overbought/oversold levels
SMA smoothing for reduced noise
Practical Usage Guidelines
For Trend Identification:
Watch for Squeeze Momentum breaking above/below zero with corresponding ADX above key level
Look for RSI confirmation in the same direction
Use additional oscillators for secondary confirmation
For Divergence Trading:
Monitor for regular and hidden divergences on both Squeeze and RSI
Wait for price action confirmation
Use ADX trend strength to filter high-probability setups
Customization Options:
Toggle individual components on/off based on your trading style
Adjust sensitivity parameters for different timeframes
Modify key levels to match specific market conditions
SPANISH VERSION (Secondary)
Por Qué Creé Este Dashboard Unificado
El Problema con la Fragmentación del Análisis:
Como trader activo, me encontraba constantemente luchando con el desorden en los gráficos - teniendo 5-8 indicadores separados abiertos simultáneamente. Esto creaba sobrecarga cognitiva y dificultaba identificar confluencia entre diferentes enfoques técnicos. El cambio constante entre indicadores y la gestión de múltiples ventanas estaba interrumpiendo mi flujo de trabajo y proceso de decisión.
Mi Solución:
Desarrollé el SALSA MultiStrategy Dashboard para resolver este problema específico integrando metodologías técnicas complementarias en una vista única y cohesiva. Esto no es solo una colección aleatoria de indicadores, sino una selección cuidadosamente curada que trabajan juntos para proporcionar análisis de mercado integral.
Componentes Principales y Su Función
Sistema de Momento Squeeze:
Detecta períodos de consolidación y direcciones potenciales de ruptura
Señales de momento codificadas por colores para identificación visual rápida
Análisis de Fuerza de Tendencia ADX:
Nivel clave personalizable (por defecto: 23) para evaluación de fuerza de tendencia
Escalado visual adaptativo a condiciones de mercado
RSI con Detección de Divergencia Integrada:
Análisis RSI de doble marco temporal con detección automática de divergencias
Niveles de sobrecompra/sobreventa configurables
Cómo Utilizar el Dashboard
Para Trading de Tendencia:
Squeeze Momentum rompiendo arriba/abajo de cero con ADX sobre nivel clave
Confirmación RSI en la misma dirección
Osciladores adicionales para confirmación secundaria
Para Trading por Divergencia:
Monitorear divergencias regulares y ocultas en Squeeze y RSI
Esperar confirmación de acción del precio
Usar fuerza de tendencia ADX para filtrar setups de alta probabilidad
Important Compliance Notes:
Title: "SALSA MultiStrategy Dashboard" (English only, no emojis)
Language: English first, Spanish translation provided
Originality: Focus on solving the specific problem of analysis fragmentation
Chart Requirements: Clean chart showing only this indicator's output
Open Source: Complete transparency about methodology and calculations
Trading Disclaimer:
This tool is designed for educational and analytical purposes to help traders develop a systematic approach to market analysis. It is not financial advice. Always conduct your own research and backtesting before making trading decisions.
HA Reversal + Doji 🔥 Heikin Ashi Reversal + Stochastic Filter (Precision Entry System)
This indicator is designed to detect high–quality reversal entries using a Heikin Ashi candle pattern (Doji + 2 no–wick confirmation) combined with a strict Stochastic filter that uses memory of extreme touches to control trade direction.
✅ Entry Logic
🔹 Bullish BUY Signal
A BUY is triggered only when:
A valid reversal pattern is detected:
Doji candle (pivot) 3 bars back
Followed by 2 bullish candles with no lower wicks
Stochastic touched Oversold (≤ 20) at least once before the signal
Pattern + Stoch alignment = BUY
🔹 Bearish SELL Signal
A SELL is triggered only when:
Valid bearish reversal pattern:
Doji candle (pivot) 3 bars back
Followed by 2 bearish candles with no upper wicks
Stochastic touched Overbought (≥ 80) before the signal
Pattern + Stoch alignment = SELL
🧠 Stochastic “Memory” Filter
This is not a basic OB/OS filter — it uses event memory:
If Stochastic touches Oversold, the system becomes ready for BUY
If it touches Overbought, it becomes ready for SELL
Both directions can be armed at once
Once a BUY or SELL actually triggers, memory resets to neutral
Prevents “signal spam” during chop and keeps direction meaningful
🎯 Why This Works
✔ Filters out random countertrend noise
✔ Only trades after momentum exhaustion
✔ Uses strict Heikin Ashi reversal structure
✔ Works great across crypto, forex, indices, metals
✔ Designed for precision entries and swing continuation traps
⚙️ Customizable Options
Doji detection mode (body % / ticks / hybrid)
Wick tolerance
Heikin Ashi source (chart or calculated)
Stochastic source (raw or smoothed)
Option to avoid duplicate same-direction signals
Visual aids: pattern markers, blocked signals, doji debugging
📌 Best Use Cases
Reversal scalping on 5m/15m
Swing entries on 1H/4H
Trend exhaustion confirmation
Smart Money Concepts entry refinement
Entry timing after liquidity sweeps
🚨 Important
This is not a repainting system. Signals are generated at bar close only. Always combine with proper risk management and market context.
Let me know if you want:
✅ A shorter description
✅ An SEO optimized TradingView title
✅ A strategy version with backtesting
✅ Alerts version for automation
Equinox Wolf - ICT MacrosEquinox Wolf – ICT Macros plots the key ICT session macro windows on your chart so you can focus on how price behaves inside each time range. The script anchors every session to America/New_York time, updates live or in backtesting, and only keeps the current trading day on screen, avoiding clutter from prior sessions. Each window can be toggled individually, the box fill, borders, and high/low/equilibrium levels share global color and style controls, and the levels extend forward until the next macro begins. Use it to highlight the ICT LND, NYAM, lunch, afternoon, and final-hour ranges and monitor how price reacts around their highs, lows, and midpoints.
Tristan's Devil Mark (Short)"Devil's Mark" in trading refers to a specific candlestick pattern where a candle opens and moves significantly in one direction without creating a wick on that side. This creates an "inefficiency" in the market, and traders use this as a signal that price will likely return to that level to "rebalance" the imbalance and print the missing wick.
This strategy marks every green candle with no bottom wick using a purple downward wedge above the candle. This is highlighting a candle where buyers dominated from the open, but creating inefficiency below.
The purple wedge marks candles that opened at their lowest point and closed higher.
These candles indicate buyer dominance from the start of the period. In downtrends, a green candle with no bottom wick may indicate a potential short-term reversal.
Wait for the candle to close, and short it. Wait for the price to go below the bottom of the body of the marked candle.
Combine with Trend Analysis
Look for these candles in uptrends to confirm continuation momentum.
In downtrends, a green candle with no bottom wick may indicate a potential short-term reversal.
Support/Resistance Filters
Use horizontal support/resistance levels or moving averages to filter trades.
A green no-wick candle bouncing off support is a stronger bullish signal.
Timeframe Consideration
Works on any timeframe; adjust your strategy accordingly.
For intraday scalping, use 1–15 minute charts; for swing trades, use daily or 4-hour charts.
Backtesting and Pattern Recognition
Since the indicator works on historical bars, review past setups to identify patterns where this candle type reliably predicts price movement.
X 4H ORThis indicator plots the 30-second opening range (high/low) for six New-York–time anchors—2am, 6am, 10am, 2pm, 6pm, and 10pm—and extends each box to a fixed end time (e.g., 2am→9am, 6am→1pm, etc.). It samples true 30-second data regardless of the chart timeframe, so the captured highs/lows are precise.
What it does
Builds the first 30s OR for each selected anchor and draws a time-anchored box for that session.
Archives every day’s boxes (up to a cap) so you can study how price interacts with past ranges.
Adds per-anchor show toggles to display the latest box for that anchor.
Adds a global History toggle to show/hide all archived boxes without deleting them (clean view vs. context view).
Uses borderless, color-coded fills per anchor to avoid edge distortion while keeping levels easy to read.
Why it’s useful
Quickly spot session inflection zones where liquidity, breakouts, or reversals cluster.
Compare how current price trades relative to recent session ranges for bias and risk framing.
Perform lightweight post-session review/backtesting on OR breaks, retests, and range rotations.
Keep charts decluttered on demand (latest only), or flip on history for deeper context.
USDJPY Fair Value Gap + Session Strategy🎯 Overview
This strategy combines Fair Value Gaps (FVGs) with session-based order flow analysis, specifically optimized for USDJPY. It identifies price inefficiencies left behind by institutional order flow during high-volatility trading sessions, offering a modern alternative to traditional lagging indicators.
🔬 What Are Fair Value Gaps?
Fair Value Gaps represent areas where aggressive institutional buying or selling created "gaps" in the market structure:
Bullish FVG: Price moves up so aggressively that it leaves unfilled buy orders behind
Bearish FVG: Price moves down so quickly that it leaves unfilled sell orders behind
Research shows approximately 80% of FVGs get "filled" (price returns to the gap) within 20-60 bars, making them highly predictable trading zones.
(see the generated image above)
(see the generated image above)
FVG Detection Logic:
text
// Bullish FVG: Gap between high and current low
bullishFVG = low > high and high > high
// Bearish FVG: Gap between low and current high
bearishFVG = high < low and low < low
🌏 Session-Based Trading
Why Sessions Matter for USDJPY
(see the generated image above)
Tokyo Session (00:00-09:00 UTC)
Highest volatility during first hour (00:00-01:00 UTC)
Average movement: 51-60 pips
Best for breakout strategies
London/NY Overlap (13:00-16:00 UTC)
Maximum liquidity and institutional participation
Tightest spreads and most reliable FVG formations
Optimal for continuation trades
Monday Premium Effect
USDJPY moves 120+ pips on Mondays due to weekend positioning
Enhanced FVG formation during session opens
📊 Strategy Components
(see the generated image above)
1. Fair Value Gap Detection
Identifies bullish and bearish FVGs automatically
Age limit: FVGs expire after 20 bars to avoid stale setups
Size filter: Minimum gap size to filter out noise
2. Session Filtering
Tokyo Open focus: Trades during first hour of Asian session
London/NY Overlap: Captures high-liquidity institutional flows
Weekend gap strategy: Enhanced signals on Monday opens
3. Volume Confirmation
Requires 1.5x average volume spike
Confirms institutional participation
Reduces false signals
4. Trend Alignment
50 EMA filter ensures trades align with higher timeframe trend
Long trades above EMA, short trades below
Prevents costly counter-trend trades
5. Risk Management
2:1 Risk/Reward minimum ensures profitability with 40%+ win rate
Percentage-based stops adapt to USDJPY volatility (0.3% default)
Configurable position sizing
🎯 Entry Conditions
(see the generated image above)
Long Entry (BUY)
✅ Bullish FVG detected in previous bars
✅ Price returns to FVG zone during active trading session
✅ Volume spike above 1.5x average
✅ Price above 50 EMA (trend confirmation)
✅ Bullish candle closes within FVG zone
✅ Trading during Tokyo open OR London/NY overlap
Short Entry (SELL)
✅ Bearish FVG detected in previous bars
✅ Price returns to FVG zone during active trading session
✅ Volume spike above 1.5x average
✅ Price below 50 EMA (trend confirmation)
✅ Bearish candle closes within FVG zone
✅ Trading during Tokyo open OR London/NY overlap
📈 Expected Performance
Backtesting Results (Based on Similar Strategies):
Win Rate: 44-59% (profitable due to high R:R ratio)
Average Winner: 60-90 pips during London/NY sessions
Average Loser: 30-40 pips (tight stops at FVG boundaries)
Risk/Reward: 2:1 minimum, often 3:1 during strong trends
Best Performance: Monday Tokyo opens and Wednesday London/NY overlaps
Why This Works for USDJPY:
90% correlation with US-Japan bond yield spreads
High volatility provides sufficient pip movement
Heavy institutional/central bank participation creates clear FVGs
Consistent volatility patterns across trading sessions
⚙️ Configurable Parameters
Session Settings:
Trade Tokyo Session (Enable/Disable)
Trade London/NY Overlap (Enable/Disable)
FVG Settings:
FVG Minimum Size (Filter small gaps)
Maximum FVG Age (20 bars default)
Show FVG Markers (Visual display)
Volume Settings:
Use Volume Filter (Enable/Disable)
Volume Multiplier (1.5x default)
Volume Average Period (20 bars)
Trend Settings:
Use Trend Filter (Enable/Disable)
Trend EMA Period (50 default)
Risk Management:
Risk/Reward Ratio (2.0 default)
Stop Loss Percentage (0.3% default)
🎨 Visual Indicators
🟡 Yellow Line: 50 EMA trend filter
🟢 Green Triangles: Long entry signals
🔴 Red Triangles: Short entry signals
🟢 Green Dots: Bullish FVG zones
🔴 Red Dots: Bearish FVG zones
🟦 Blue Background: Tokyo open session
🟧 Orange Background: London/NY overlap
📊 Recommended Settings
Optimal Timeframes:
Primary: 5-minute charts (scalping)
Secondary: 15-minute charts (swing trading)
Parameter Optimization:
Conservative: Stop Loss 0.2%, R:R 2:1, Volume 2.0x
Balanced: Stop Loss 0.3%, R:R 2:1, Volume 1.5x (default)
Aggressive: Stop Loss 0.4%, R:R 1.5:1, Volume 1.2x
Risk Management:
Maximum 1-2% of account per trade
Daily loss limit: Stop after 3-5 consecutive losses
Use fixed percentage position sizing
⚠️ Important Considerations
Avoid Trading During:
Major news events (BOJ interventions, NFP, FOMC)
Holiday periods with reduced liquidity
Low volatility Asian afternoon sessions
When US-Japan yield differential narrows sharply
Best Practices:
Limit to 2-3 trades per session maximum
Always respect the 50 EMA trend filter
Never risk more than planned per trade
Paper trade for 2-4 weeks before live implementation
Track performance by session and day of week
🚀 How to Use
Add the script to your USDJPY chart
Set timeframe to 5-minute or 15-minute
Adjust parameters based on your risk tolerance
Enable strategy alerts for automated notifications
Wait for visual signals (triangles) to appear
Enter trades according to your risk management rules
📚 Strategy Foundation
This strategy is based on:
Smart Money Concepts (SMC): Institutional order flow tracking
Market Microstructure: Understanding how FVGs form in electronic trading
Quantified Risk Management: Statistical edge through proper R:R ratios
Session Liquidity Patterns: Exploiting predictable volatility cycles
LW Outside Day Strategy[SpeculationLab]This strategy is inspired by the “Outside Day” concept introduced by Larry Williams in Long-Term Secrets to Short-Term Trading, and has been extended with configurable risk management tools and realistic backtesting parameters.
Concept
The “Outside Day” is a classic price action pattern that reflects strong market rejection or continuation pressure.
An Outside Bar occurs when the current bar’s high exceeds the previous high and the low falls below the previous low.
A body-size filter ensures only significant candles are included.
Entry Logic
Buy setup: Price closes below the previous low (bullish rejection).
Sell setup: Price closes above the previous high (bearish rejection).
Only confirmed bars are used (no intrabar signals).
Stop-Loss Modes
Prev Low/High: Uses the previous swing point ± ATR-based buffer.
ATR: Dynamic stop based on Average True Range × multiplier.
Fixed Pips: User-defined fixed distance (for forex testing).
Take-Profit Modes
Prev High/Low (PHL): Exits near the opposite swing.
Risk-Reward (RR): Targets a user-defined multiple of the stop distance (default = 2 : 1).
Following Price Open (FPO): Exits on the next bar’s open if price opens in profit (used to test overnight price continuation).
Risk Management & Backtest Settings
Default risk per trade is set at 10% of account equity (user-adjustable).
Commission = 0.1% and slippage = 2 ticks are applied to simulate realistic conditions.
For reliable statistics, test on data that yields over 100 trades.
Suitable for daily and 4-hour timeframes across stocks, forex, and crypto markets.
Visual Elements
Green and red triangles show entry signals.
Stop-loss (red) and take-profit (green) reference lines are drawn for clarity.
Optional alerts notify when a valid setup forms.
Disclaimer
This script is for educational and research purposes only.
It does not constitute financial advice or guarantee profits.
Always backtest thoroughly and manage your own risk.
Enhancements over Classic Outside Bar Models
Adjustable stop and target logic with ATR and buffer multipliers.
“Following Price Open” exit logic for realistic day-end management.
Optimized to avoid repainting and bar-confirmation issues.
Built with realistic trading costs and position sizing.
策略逻辑
外包线识别
当日最高价高于前一日最高价，且当日最低价低于前一日最低价，即形成外包线。
同时过滤掉较小实体的 K 线，仅保留实体显著大于前一根的形态。
方向过滤
收盘价低于前一日最低价 → 视为买入信号。
收盘价高于前一日最高价 → 视为卖出信号。
止损设置（可选参数）
前低/高止损：以形态前低/前高为止损，带有缓冲倍数。
ATR 止损：根据平均波动率（ATR）动态调整。
固定点数止损：按照用户设定的点数作为止损范围。
止盈设置（可选参数）
前高/低止盈（PHL）：以前高/前低为目标。
固定盈亏比（RR）：根据用户设定的风险回报比自动计算。
隔夜开盘（FPO）：若次日开盘价高于进场价（多单）或低于进场价（空单），则平仓。
信号标记
在图表中标注买入/卖出信号（三角形标记）。
绘制止损与目标位参考线。
使用说明
适用周期：建议用于 日线图（Daily）。
适用市场：股票、外汇、加密货币等各类市场均可。
提示：此策略为历史研究与学习用途，不构成投资建议。实际交易请结合自身风险管理。
Stochastic Enhanced [DCAUT]█ Stochastic Enhanced
📊 ORIGINALITY & INNOVATION
The Stochastic Enhanced indicator builds upon George Lane's classic momentum oscillator (developed in the late 1950s) by providing comprehensive smoothing algorithm flexibility. While traditional implementations limit users to Simple Moving Average (SMA) smoothing, this enhanced version offers 21 advanced smoothing algorithms, allowing traders to optimize the indicator's characteristics for different market conditions and trading styles.
Key Improvements:
Extended from single SMA smoothing to 21 professional-grade algorithms including adaptive filters (KAMA, FRAMA), zero-lag methods (ZLEMA, T3), and advanced digital filters (Kalman, Laguerre)
Maintains backward compatibility with traditional Stochastic calculations through SMA default setting
Unified smoothing algorithm applies to both %K and %D lines for consistent signal processing characteristics
Enhanced visual feedback with clear color distinction and background fill highlighting for intuitive signal recognition
Comprehensive alert system covering crossovers and zone entries for systematic trade management
Differentiation from Traditional Stochastic:
Traditional Stochastic indicators use fixed SMA smoothing, which introduces consistent lag regardless of market volatility. This enhanced version addresses the limitation by offering adaptive algorithms that adjust to market conditions (KAMA, FRAMA), reduce lag without sacrificing smoothness (ZLEMA, T3, HMA), or provide superior noise filtering (Kalman Filter, Laguerre filters). The flexibility helps traders balance responsiveness and stability according to their specific needs.
📐 MATHEMATICAL FOUNDATION
Core Stochastic Calculation:
The Stochastic Oscillator measures the position of the current close relative to the high-low range over a specified period:
Step 1: Raw %K Calculation
%K_raw = 100 × (Close - Lowest Low) / (Highest High - Lowest Low)
Where:
Close = Current closing price
Lowest Low = Lowest low over the %K Length period
Highest High = Highest high over the %K Length period
Result ranges from 0 (close at period low) to 100 (close at period high)
Step 2: Smoothed %K Calculation
%K = MA(%K_raw, K Smoothing Period, MA Type)
Where:
MA = Selected moving average algorithm (SMA, EMA, etc.)
K Smoothing = 1 for Fast Stochastic, 3+ for Slow Stochastic
Traditional Fast Stochastic uses %K_raw directly without smoothing
Step 3: Signal Line %D Calculation
%D = MA(%K, D Smoothing Period, MA Type)
Where:
%D acts as a signal line and moving average of %K
D Smoothing typically set to 3 periods in traditional implementations
Both %K and %D use the same MA algorithm for consistent behavior
Available Smoothing Algorithms (21 Options):
Standard Moving Averages:
SMA (Simple): Equal-weighted average, traditional default, consistent lag characteristics
EMA (Exponential): Recent price emphasis, faster response to changes, exponential decay weighting
RMA (Rolling/Wilder's): Smoothed average used in RSI, less reactive than EMA
WMA (Weighted): Linear weighting favoring recent data, moderate responsiveness
VWMA (Volume-Weighted): Incorporates volume data, reflects market participation intensity
Advanced Moving Averages:
HMA (Hull): Reduced lag with smoothness, uses weighted moving averages and square root period
ALMA (Arnaud Legoux): Gaussian distribution weighting, minimal lag with good noise reduction
LSMA (Least Squares): Linear regression based, fits trend line to data points
DEMA (Double Exponential): Reduced lag compared to EMA, uses double smoothing technique
TEMA (Triple Exponential): Further lag reduction, triple smoothing with lag compensation
ZLEMA (Zero-Lag Exponential): Lag elimination attempt using error correction, very responsive
TMA (Triangular): Double-smoothed SMA, very smooth but slower response
Adaptive & Intelligent Filters:
T3 (Tilson T3): Six-pass exponential smoothing with volume factor adjustment, excellent smoothness
FRAMA (Fractal Adaptive): Adapts to market fractal dimension, faster in trends, slower in ranges
KAMA (Kaufman Adaptive): Efficiency ratio based adaptation, responds to volatility changes
McGinley Dynamic: Self-adjusting mechanism following price more accurately, reduced whipsaws
Kalman Filter: Optimal estimation algorithm from aerospace engineering, dynamic noise filtering
Advanced Digital Filters:
Ultimate Smoother: Advanced digital filter design, superior noise rejection with minimal lag
Laguerre Filter: Time-domain filter with N-order implementation, adjustable lag characteristics
Laguerre Binomial Filter: 6-pole Laguerre filter, extremely smooth output for long-term analysis
Super Smoother: Butterworth filter implementation, removes high-frequency noise effectively
📊 COMPREHENSIVE SIGNAL ANALYSIS
Absolute Level Interpretation (%K Line):
%K Above 80: Overbought condition, price near period high, potential reversal or pullback zone, caution for new long entries
%K in 70-80 Range: Strong upward momentum, bullish trend confirmation, uptrend likely continuing
%K in 50-70 Range: Moderate bullish momentum, neutral to positive outlook, consolidation or mild uptrend
%K in 30-50 Range: Moderate bearish momentum, neutral to negative outlook, consolidation or mild downtrend
%K in 20-30 Range: Strong downward momentum, bearish trend confirmation, downtrend likely continuing
%K Below 20: Oversold condition, price near period low, potential bounce or reversal zone, caution for new short entries
Crossover Signal Analysis:
%K Crosses Above %D (Bullish Cross): Momentum shifting bullish, faster line overtakes slower signal, consider long entry especially in oversold zone, strongest when occurring below 20 level
%K Crosses Below %D (Bearish Cross): Momentum shifting bearish, faster line falls below slower signal, consider short entry especially in overbought zone, strongest when occurring above 80 level
Crossover in Midrange (40-60): Less reliable signals, often in choppy sideways markets, require additional confirmation from trend or volume analysis
Multiple Failed Crosses: Indicates ranging market or choppy conditions, reduce position sizes or avoid trading until clear directional move
Advanced Divergence Patterns (%K Line vs Price):
Bullish Divergence: Price makes lower low while %K makes higher low, indicates weakening bearish momentum, potential trend reversal upward, more reliable when %K in oversold zone
Bearish Divergence: Price makes higher high while %K makes lower high, indicates weakening bullish momentum, potential trend reversal downward, more reliable when %K in overbought zone
Hidden Bullish Divergence: Price makes higher low while %K makes lower low, indicates trend continuation in uptrend, bullish trend strength confirmation
Hidden Bearish Divergence: Price makes lower high while %K makes higher high, indicates trend continuation in downtrend, bearish trend strength confirmation
Momentum Strength Analysis (%K Line Slope):
Steep %K Slope: Rapid momentum change, strong directional conviction, potential for extended moves but also increased reversal risk
Gradual %K Slope: Steady momentum development, sustainable trends more likely, lower probability of sharp reversals
Flat or Horizontal %K: Momentum stalling, potential reversal or consolidation ahead, wait for directional break before committing
%K Oscillation Within Range: Indicates ranging market, sideways price action, better suited for range-trading strategies than trend following
🎯 STRATEGIC APPLICATIONS
Mean Reversion Strategy (Range-Bound Markets):
Identify ranging market conditions using price action or Bollinger Bands
Wait for Stochastic to reach extreme zones (above 80 for overbought, below 20 for oversold)
Enter counter-trend position when %K crosses %D in extreme zone (sell on bearish cross above 80, buy on bullish cross below 20)
Set profit targets near opposite extreme or midline (50 level)
Use tight stop-loss above recent swing high/low to protect against breakout scenarios
Exit when Stochastic reaches opposite extreme or %K crosses %D in opposite direction
Trend Following with Momentum Confirmation:
Identify primary trend direction using higher timeframe analysis or moving averages
Wait for Stochastic pullback to oversold zone (<20) in uptrend or overbought zone (>80) in downtrend
Enter in trend direction when %K crosses %D confirming momentum shift (bullish cross in uptrend, bearish cross in downtrend)
Use wider stops to accommodate normal trend volatility
Add to position on subsequent pullbacks showing similar Stochastic pattern
Exit when Stochastic shows opposite extreme with failed cross or bearish/bullish divergence
Divergence-Based Reversal Strategy:
Scan for divergence between price and Stochastic at swing highs/lows
Confirm divergence with at least two price pivots showing divergent Stochastic readings
Wait for %K to cross %D in direction of anticipated reversal as entry trigger
Enter position in divergence direction with stop beyond recent swing extreme
Target profit at key support/resistance levels or Fibonacci retracements
Scale out as Stochastic reaches opposite extreme zone
Multi-Timeframe Momentum Alignment:
Analyze Stochastic on higher timeframe (4H or Daily) for primary trend bias
Switch to lower timeframe (1H or 15M) for precise entry timing
Only take trades where lower timeframe Stochastic signal aligns with higher timeframe momentum direction
Higher timeframe Stochastic in bullish zone (>50) = only take long entries on lower timeframe
Higher timeframe Stochastic in bearish zone (<50) = only take short entries on lower timeframe
Exit when lower timeframe shows counter-signal or higher timeframe momentum reverses
Zone Transition Strategy:
Monitor Stochastic for transitions between zones (oversold to neutral, neutral to overbought, etc.)
Enter long when Stochastic crosses above 20 (exiting oversold), signaling momentum shift from bearish to neutral/bullish
Enter short when Stochastic crosses below 80 (exiting overbought), signaling momentum shift from bullish to neutral/bearish
Use zone midpoint (50) as dynamic support/resistance for position management
Trail stops as Stochastic advances through favorable zones
Exit when Stochastic fails to maintain momentum and reverses back into prior zone
📋 DETAILED PARAMETER CONFIGURATION
%K Length (Default: 14):
Lower Values (5-9): Highly sensitive to price changes, generates more frequent signals, increased false signals in choppy markets, suitable for very short-term trading and scalping
Standard Values (10-14): Balanced sensitivity and reliability, traditional default (14) widely used,适合 swing trading and intraday strategies
Higher Values (15-21): Reduced sensitivity, smoother oscillations, fewer but potentially more reliable signals, better for position trading and lower timeframe noise reduction
Very High Values (21+): Slow response, long-term momentum measurement, fewer trading signals, suitable for weekly or monthly analysis
%K Smoothing (Default: 3):
Value 1: Fast Stochastic, uses raw %K calculation without additional smoothing, most responsive to price changes, generates earliest signals with higher noise
Value 3: Slow Stochastic (default), traditional smoothing level, reduces false signals while maintaining good responsiveness, widely accepted standard
Values 5-7: Very slow response, extremely smooth oscillations, significantly reduced whipsaws but delayed entry/exit timing
Recommendation: Default value 3 suits most trading scenarios, active short-term traders may use 1, conservative long-term positions use 5+
%D Smoothing (Default: 3):
Lower Values (1-2): Signal line closely follows %K, frequent crossover signals, useful for active trading but requires strict filtering
Standard Value (3): Traditional setting providing balanced signal line behavior, optimal for most trading applications
Higher Values (4-7): Smoother signal line, fewer crossover signals, reduced whipsaws but slower confirmation, better for trend trading
Very High Values (8+): Signal line becomes slow-moving reference, crossovers rare and highly significant, suitable for long-term position changes only
Smoothing Type Algorithm Selection:
For Trending Markets:
ZLEMA, DEMA, TEMA: Reduced lag for faster trend entry, quick response to momentum shifts, suitable for strong directional moves
HMA, ALMA: Good balance of smoothness and responsiveness, effective for clean trend following without excessive noise
EMA: Classic choice for trending markets, faster than SMA while maintaining reasonable stability
For Ranging/Choppy Markets:
Kalman Filter, Super Smoother: Superior noise filtering, reduces false signals in sideways action, helps identify genuine reversal points
Laguerre Filters: Smooth oscillations with adjustable lag, excellent for mean reversion strategies in ranges
T3, TMA: Very smooth output, filters out market noise effectively, clearer extreme zone identification
For Adaptive Market Conditions:
KAMA: Automatically adjusts to market efficiency, fast in trends and slow in congestion, reduces whipsaws during transitions
FRAMA: Adapts to fractal market structure, responsive during directional moves, conservative during uncertainty
McGinley Dynamic: Self-adjusting smoothing, follows price naturally, minimizes lag in trending markets while filtering noise in ranges
For Conservative Long-Term Analysis:
SMA: Traditional choice, predictable behavior, widely understood characteristics
RMA (Wilder's): Smooth oscillations, reduced sensitivity to outliers, consistent behavior across market conditions
Laguerre Binomial Filter: Extremely smooth output, ideal for weekly/monthly timeframe analysis, eliminates short-term noise completely
Source Selection:
Close (Default): Standard choice using closing prices, most common and widely tested
HLC3 or OHLC4: Incorporates more price information, reduces impact of sudden spikes or gaps, smoother oscillator behavior
HL2: Midpoint of high-low range, emphasizes intrabar volatility, useful for markets with wide intraday ranges
Custom Source: Can use other indicators as input (e.g., Heikin Ashi close, smoothed price), creates derivative momentum indicators
📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES
Responsiveness Characteristics:
Traditional SMA-Based Stochastic:
Fixed lag regardless of market conditions, consistent delay of approximately (K Smoothing + D Smoothing) / 2 periods
Equal treatment of trending and ranging markets, no adaptation to volatility changes
Predictable behavior but suboptimal in varying market regimes
Enhanced Version with Adaptive Algorithms:
KAMA and FRAMA reduce lag by up to 40-60% in strong trends compared to SMA while maintaining similar smoothness in ranges
ZLEMA and T3 provide near-zero lag characteristics for early entry signals with acceptable noise levels
Kalman Filter and Super Smoother offer superior noise rejection, reducing false signals in choppy conditions by estimations of 30-50% compared to SMA
Performance improvements vary by algorithm selection and market conditions
Signal Quality Improvements:
Adaptive algorithms help reduce whipsaw trades in ranging markets by adjusting sensitivity dynamically
Advanced filters (Kalman, Laguerre, Super Smoother) provide clearer extreme zone readings for mean reversion strategies
Zero-lag methods (ZLEMA, DEMA, TEMA) generate earlier crossover signals in trending markets for improved entry timing
Smoother algorithms (T3, Laguerre Binomial) reduce false extreme zone touches for more reliable overbought/oversold signals
Comparison with Standard Implementations:
Versus Basic Stochastic: Enhanced version offers 21 smoothing options versus single SMA, allowing optimization for specific market characteristics and trading styles
Versus RSI: Stochastic provides range-bound measurement (0-100) with clear extreme zones, RSI measures momentum speed, Stochastic offers clearer visual overbought/oversold identification
Versus MACD: Stochastic bounded oscillator suitable for mean reversion, MACD unbounded indicator better for trend strength, Stochastic excels in range-bound and oscillating markets
Versus CCI: Stochastic has fixed bounds (0-100) for consistent interpretation, CCI unbounded with variable extremes, Stochastic provides more standardized extreme readings across different instruments
Flexibility Advantages:
Single indicator adaptable to multiple strategies through algorithm selection rather than requiring different indicator variants
Ability to optimize smoothing characteristics for specific instruments (e.g., smoother for crypto volatility, faster for forex trends)
Multi-timeframe analysis with consistent algorithm across timeframes for coherent momentum picture
Backtesting capability with algorithm as optimization parameter for strategy development
Limitations and Considerations:
Increased complexity from multiple algorithm choices may lead to over-optimization if parameters are curve-fitted to historical data
Adaptive algorithms (KAMA, FRAMA) have adjustment periods during market regime changes where signals may be less reliable
Zero-lag algorithms sacrifice some smoothness for responsiveness, potentially increasing noise sensitivity in very choppy conditions
Performance characteristics vary significantly across algorithms, requiring understanding and testing before live implementation
Like all oscillators, Stochastic can remain in extreme zones for extended periods during strong trends, generating premature reversal signals
USAGE NOTES
This indicator is designed for technical analysis and educational purposes to provide traders with enhanced flexibility in momentum analysis. The Stochastic Oscillator has limitations and should not be used as the sole basis for trading decisions.
Important Considerations:
Algorithm performance varies with market conditions - no single smoothing method is optimal for all scenarios
Extreme zone signals (overbought/oversold) indicate potential reversal areas but not guaranteed turning points, especially in strong trends
Crossover signals may generate false entries during sideways choppy markets regardless of smoothing algorithm
Divergence patterns require confirmation from price action or additional indicators before trading
Past indicator characteristics and backtested results do not guarantee future performance
Always combine Stochastic analysis with proper risk management, position sizing, and multi-indicator confirmation
Test selected algorithm on historical data of specific instrument and timeframe before live trading
Market regime changes may require algorithm adjustment for optimal performance
The enhanced smoothing options are intended to provide tools for optimizing the indicator's behavior to match individual trading styles and market characteristics, not to create a perfect predictive tool. Responsible usage includes understanding the mathematical properties of selected algorithms and their appropriate application contexts.
Session-Conditioned Regime ATRWhy this exists
Classic ATR is great—until the open. The first few bars often inherit overnight gaps and 24-hour noise that have nothing to do with the intraday regime you actually trade. That inflates early ATR, scrambles thresholds, and invites hyper-recency bias (“today is crazy!”) when it’s just the open being the open.
This tool was built to:
Separate session reality from 24h noise. Measure volatility only inside your defined session (e.g., NYSE 09:30–16:00 ET).
Judge candles against the current regime, not the last 2–3 bars. A rolling statistic from the last N completed sessions defines what “typical” means right now.
Label “large” and “small” objectively. Bars are colored only when True Range meaningfully departs from the session regime—no gut feel, no open-bar distortion (gap inclusion optional).
Overview
Purpose: objectively identify unusually big or small candles within the active trading session, compared to the recent session regime.
Use cases: volatility filters, entry/exit confirmation, session bias detection, adaptive sizing.
This indicator replaces generic ATR with a session-conditioned, regime-aware measure. It colors candles only when their True Range (TR) is abnormally large/small versus the last N completed sessions of the same session window.
How it works
Session gating: Only bars inside the selected session are evaluated (presets for NYSE, CME RTH, FX NY; custom supported).
Per-bar TR: TR = max(high, prevRef) − min(low, prevRef).
prevRef is the prior close for in-session bars.
First bar of the session can include the overnight gap (optional; default off).
Regime statistic: For any bar in session k, aggregate all in-session TRs from the previous N completed sessions (k−N … k−1), then compute Median (default) or Mean.
Today’s anchor: Running statistic from today’s session start → current bar (for context and the on-chart ratio).
Color logic:
Big if TR ≥ bigMult × RegimeStat
Small if TR ≤ smallMult × RegimeStat
Colored states: big bull, big bear, small bull, small bear.
Non-triggering bars retain the chart’s native colors.
Panel (top-right by default)
Regime ATR (Nd): session-conditioned statistic over the past N completed sessions.
Today ATR (anchored): running statistic for the current session.
Ratio (Today/Regime): intraday volatility vs regime.
Sample size n: number of bars used in the regime calculation.
Inputs
Session Preset: NYSE (09:30–16:00 ET), CME RTH (08:30–15:00 CT), FX NY (08:00–17:00 ET), Custom (session + IANA timezone).
Regime Window: number of completed sessions (default 5).
Statistic: Median (robust) or Mean.
Include Open Gap: include overnight gap in the first in-session bar’s TR (default off).
Big/Small thresholds: multipliers relative to RegimeStat (defaults: Big=1.5×, Small=0.67×).
Colors: four independent colors for big/small × bull/bear.
Panel position & text size.
Hidden outputs: expose RegimeStat, TodayStat, Ratio, and Z-score to other scripts.
Alerts
RegimeATR: BIG bar — triggers when a bar meets the “Big” condition.
RegimeATR: SMALL bar — triggers when a bar meets the “Small” condition.
Hidden outputs (for strategies/screeners)
RegimeATR_stat, TodayATR_stat, Today_vs_Regime_Ratio, BarTR_Zscore.
Notes & limitations
No look-ahead: calculations only use information available up to that bar. Historical colors reflect what would have been known then.
Warm-up: colors begin once there are at least N completed sessions; before that, regime is undefined by design.
Changing inputs (session window, multipliers, median/mean, gap toggle) recomputes the full series using the same rolling regime logic per bar.
Designed for standard candles. Styling respects existing chart colors when no condition triggers.
Practical tips
For a broader or tighter notion of “unusual,” adjust Big/Small multipliers.
Prefer Median in markets prone to outliers; use Mean if you want Z-score alignment with the panel’s regime mean/std.
Use the Ratio readout to spot compression/expansion days quickly (e.g., <0.7× = compressed session, >1.3× = expanded).
Roadmap
More session presets:
24h continuous (crypto, index CFDs).
23h/Globex futures (CME ETH with a 60-minute maintenance break).
Regional equities (LSE, Xetra, TSE), Asia/Europe/NY overlaps for FX.
Half-day/holiday templates and dynamic calendars.
Multi-regime comparison: track multiple overlapping regimes (e.g., RTH vs ETH for futures) and show separate stats/ratios.
Robust stats options: trimmed mean, MAD/Huber alternatives; optional percentile thresholds instead of fixed multipliers.
Subpanel visuals: rolling TodayATR and Ratio plots; optional Z-score ribbon.
Screener/strategy hooks: export boolean series for BIG/SMALL, plus a lightweight strategy template for backtesting entries/exits conditioned on regime volatility.
Performance/QOL: per-symbol presets, smarter warm-up, and finer control over sample caps for ultra-low TF charts.
Changelog
v0.9b (Beta)
Session presets (NYSE/CME RTH/FX NY/Custom) with timezone handling.
Panel enhancements: ratio + sample size n.
Four-state bar coloring (big/small × bull/bear).
Alerts for BIG/SMALL bars.
Hidden Z-score stream for downstream use.
Gap-in-TR toggle for the first in-session bar.
Disclaimer
For educational purposes only. Not investment advice. Validate thresholds and session settings across symbols/timeframes before live use.
Bitcoin Halving Strategy
A systematic, data-driven trading strategy based on Bitcoin's 4-year halving cycles. This strategy capitalizes on historical price patterns that emerge around halving events, providing clear entry and exit signals for both accumulation and profit-taking phases.
🎯 Strategy Overview
This automated trading system identifies optimal buy and sell zones based on the predictable Bitcoin halving cycle that occurs approximately every 4 years. By analyzing historical data from all previous halvings (2012, 2016, 2020, 2024), the strategy pinpoints high-probability trading opportunities.
📊 Key Features
Automated Signal Generation: Buy signals at halving events and DCA zones, sell signals at profit-taking peaks
Multi-Phase Analysis: Tracks Accumulation, Profit Taking, Bear Market, and DCA phases
Visual Dashboard: Real-time performance metrics, phase countdown, and position tracking
Backtesting Enabled: Comprehensive historical performance analysis with configurable parameters
Risk Management: Built-in position sizing, slippage control, and optional short trading
⚙️ Strategy Logic
Buy Signals:
At halving event (Week 0)
DCA zone entry (Week 135 post-halving)
Sell Signals:
Profit-taking zone (Week 80 post-halving)
Optional short position entry for advanced traders
📈 Performance Highlights
Captures major bull run profits while avoiding prolonged bear markets
Clear visual indicators for all phases and transitions
Customizable timing parameters for personalized risk tolerance
Professional dashboard with live P&L, win rate, and drawdown metrics
🛠️ Customization Options
Adjustable phase timing (profit start/end, DCA timing)
Position sizing control
Enable/disable short trading
Visual customization (colors, labels, zones)
Table positioning and transparency
⚠️ Risk Disclosure
Past performance does not guarantee future results. This strategy is based on historical halving cycle patterns and should be used as part of a comprehensive trading plan. Always conduct your own research and consider your risk tolerance before trading.
💡 Ideal For
Long-term Bitcoin investors seeking systematic entry/exit points
Swing traders capitalizing on multi-month trends
Portfolio managers implementing cycle-based allocation strategies
Twisted Forex's Doji + Area StrategyTitle
Twisted Forex’s Doji + Area Strategy
Description
What this strategy does
This strategy looks for doji candles forming inside or near supply/demand areas . Areas are built from swing pivots and sized with ATR, then tracked for retests (“confirmations”). When a doji prints close to an area and quality checks pass, the strategy places a trade with the stop beyond the doji and a configurable R:R target.
How areas (zones) are built
• Swings are detected with a user-set pivot length.
• Each swing spawns a horizontal area centered at the pivot price with half-height = zoneHalfATR × ATR .
• Duplicates are de-duplicated by center distance (ATR-scaled).
• Areas fade when broken beyond a buffer or after an optional age (expiry).
• Retests are recorded when price touches and then bounces away from the area; repeated reactions increase the zone’s “strength”.
Signal logic (summary)
Doji detection: strict or loose body criteria with optional minimum wick fractions and ATR-scaled minimum range.
Proximity: price must be inside/near a supply or demand area (proxATR × ATR).
Side resolution: overlap is resolved by (a) which side price penetrates more, (b) fast/slow EMA trend, or (c) nearest distance. Optional “previous candle flip” can bias long after a bearish candle and short after a bullish one.
Optional 1-bar confirmation: the bar after the doji must close away from the area by confirmATR × ATR .
Quality filter (Off/Soft/Strict): four checks—(i) wick rejection past the edge, (ii) doji closes in an edge “band” of the area, (iii) fresh touch (cooldown), (iv) approach impulse over a short lookback. In Strict , thresholds auto-tighten.
Orders & exits
• Long: stop below doji low minus buffer; Short: above doji high plus buffer.
• Target = rrMultiple × risk distance .
• Pyramiding is off by default.
Position sizing
You can size from the script or from Strategy Properties:
• Script-driven (default): set Position sizing = “Risk % of equity” and choose riskPercent (e.g., 1.0%). The script applies safe floors/rounding (FX micro-lots by default) so quantity never rounds to zero.
• Properties-driven : toggle Use TV Properties → Order size ON, then pick “Percent of equity” in Properties (e.g., 1%). The header includes safe defaults so trades still place.
Key inputs to explore
• Zone building : pivotLen, zoneHalfATR, minDepartureATR, expiryBars, breakATR, leftBars, dedupeATR.
• Doji & proximity : strictDoji, dojiBodyFrac, minWickFrac, minRangeATR, proxATR, minBarsBetween.
• Overlap resolution : usePenetration, useTrend (EMA 21/55), “previous candle flip”, needNextBarConf & confirmATR.
• Quality : qualityMode (Off/Soft/Strict), minQualPass/kStrict, wickPenATR, edgeBandFrac, approachLookback, approachMinATR, freshTouchBars.
• Zone strength gating : minStrengthSoft / minStrengthStrict.
• HTF confluence (optional) : useHTFTrend (HTF EMA 34/89) and/or useHTFZoneProx (HTF swing bands).
Tips to make it cleaner / higher quality
• Turn needNextBarConf ON and use confirmATR = 0.10–0.15 .
• Increase approachMinATR (e.g., 0.35–0.45) to require a stronger pre-touch impulse.
• Raise minStrengthSoft/Strict (e.g., 4–6) so only well-reacted zones can signal.
• Use signalsOnlyConfirmed ON if you prefer trades only from zones with retests (the script falls back gracefully when none exist yet).
• Nudge proxATR to 0.5–0.6 to demand tighter proximity to the level.
• Optional: enable useHTFTrend to filter counter-trend setups.
Default settings used in this publication
• Initial capital: 100,000 (illustrative).
• Slippage: 1 tick; Commission: 0% (you can raise commission if you prefer—spread is partly modeled by slippage).
• Sizing: Risk % of equity via inputs; riskPercent = 1.0% ; FX uses micro-lot floors by default.
• Quality: Off by default (Soft/Strict available).
• HTF trend gate: Off by default.
Backtesting notes
For a meaningful sample size, test on liquid symbols/timeframes that yield 100+ trades (e.g., majors on 5–15m over 1–2 years). Backtests are modelled and broker costs/spread vary—validate on your feed and forward-test.
How to read the chart
Shaded bands are supply (above) and demand (below). Brighter bands are the nearest K per side (visual aid). BUY/SELL labels mark entries; colored dots show entry/SL/TP levels. You can hide zones or unconfirmed zones for a cleaner view.
Disclaimer
This is educational material, not financial advice. Trading involves risk. Always test and size responsibly.
BTC 5-MA Multi Cross Strategy By Hardik Prajapati Ai TradelabThis strategy is built around the five most powerful and commonly used moving averages in crypto trading — 5, 20, 50, 100, and 200-period SMAs (Simple Moving Averages) — applied on a 1-hour Bitcoin chart.
Core Idea:
The strategy aims to identify strong bullish trends by confirming when the price action crosses above all key moving averages. This alignment of multiple MAs indicates momentum shift and helps filter out false breakouts.
⸻
⚙️ How It Works:
1. Calculates 5 Moving Averages:
• 5 MA → Short-term momentum (fastest signal)
• 20 MA → Near-term trend confirmation
• 50 MA → Mid-term trend filter
• 100 MA → Long-term trend foundation
• 200 MA → Macro-trend direction (strongest support/resistance)
2. Buy Condition (Entry):
• A Buy is triggered when:
• The price crosses above the 5 MA, and
• The closing price remains above all other MAs (20, 50, 100, 200)
This signals that momentum is aligned across all time horizons — a strong uptrend confirmation.
3. Sell Condition (Exit):
• The position is closed when price crosses below the 20 MA, showing weakness in short-term momentum.
4. Visual Signals:
• 🟢 BUY triangle below candles → Entry signal
• 🔴 SELL triangle above candles → Exit signal
• Colored MAs plotted for trend clarity.
⸻
📈 Recommended Usage:
• Chart: BTC/USDT
• Timeframe: 1 Hour
• Type: Trend-following crossover strategy
• Ideal for: Identifying major breakout moves and confirming trend reversals.
⸻
⚠️ Notes:
• This script is meant for educational and backtesting purposes only.
• Always apply additional confirmation tools (like RSI, Volume, or VIX-style filters) before live trading.
• Works best during trending markets; may produce whipsaws in sideways zones.
PG DMean & Price Sync ver 9.4 - ConsolidatedPG DMean & Price Sync Strategy (SD Filter)
This strategy combines the momentum-oscillator properties of the Detrended Mean (DMean) with a Standard Deviation (SD) Price Filter for confirming trend direction, aiming to isolate high-conviction trades while actively managing risk.
🔑 Core Logic
DMean Momentum Signal: The strategy's primary engine is the DMean, which measures the percentage difference between the current closing price and a longer-term Moving Average (price_ma). It is then smoothed by a DMean Signal line (MA of the DMean).
Entry Signal: A trade is triggered when the DMean line crosses above (for Long) or below (for Short) its Signal Line, but it must clear a user-defined Dead Zone Threshold to confirm momentum commitment.
SD Filter Confirmation (Price Sync): A Standard Deviation Channel, based on a separate user-defined price source and period, is used to filter trades.
Long Filter: Allows Long entries only when the price is trading above the lower SD band, suggesting the current price action is stronger than the recent average volatility to the downside.
Short Filter: Allows Short entries only when the price is currently below the Filter Basis (SMA), confirming a bearish stance within the SD channel.
🛡️ Risk & Exit Management
Primary Exit: All trades are exited by reverse DMean Crossover/Crossunder, meaning the position is closed when the DMean momentum reverses against the open trade (e.g., DMean crosses under the Signal to exit a Long).
Hard Stop Loss (Short Trades): A mandatory percentage-based Hard Stop Loss is implemented only for short positions to protect against sudden upward price spikes, closing the trade if the loss exceeds the set percentage. (Note: This version does not include a Hard SL for Long trades).
📊 Performance Dashboard
A custom Performance Dashboard Table is displayed at the bottom right of the chart to provide real-time, at-a-glance comparison of the strategy's equity performance versus a simple Buy & Hold over the selected backtesting date range.
