Rasta Long/Short — StrategyThe Rasta Long/Short Strategy is a visual and educational framework designed to help traders study momentum shifts that appear when a fast EMA interacts with a slower smoothed baseline.
It is not a signal service. Instead, it is a research tool that helps you observe transitions, structure, and behavior across different market conditions and smoothing contexts.
The script plots:
A primary EMA line (fast reaction wave).
A Smoothed line (your chosen smoothing method).
Color-coded fog regions showing directional bias.
Optional DNA rung connections between the two lines for structural comparison.
Together, these allow a deeper study of how momentum pushes, volatility compression, expansions, and drift emerge around fast/slow EMA interactions.
✦ Core Idea
The Rasta Long/Short mechanism studies how price behaves when the fast EMA crosses above or below a smoothed anchor.
Rather than predicting price, it reveals where transitions occur across different structures, timeframes, and smoothing techniques.
The Long/Short logic simply highlights flips in directional structure.
It is not intended for real-time signals or automated execution; it is intended for understanding market movement.
✦ Smoothing Types (Explained)
The strategy allows experimenting with several smoothing families to observe how they transform the fast EMA:
SMA (Simple Moving Average)
Averaged, slower response. Good for stability comparisons.
EMA (Exponential)
Faster reaction, more responsive, smoother behavior during momentum.
RMA (Wilder’s)
Used in RSI calculations; steady, well-balanced response.
WMA (Weighted)
More weight to recent bars; bridges SMA and EMA dynamics.
None
Raw EMA vs EMA interaction with no secondary smoothing.
Each smoothing type provides unique structural information and can lead to different interpretations.
✦ Modes of Study
Designed for multi-timeframe research:
1H / 4H — Momentum flow mapping and structural identification.
Daily / Weekly — Higher-timeframe rotations, macro structure transitions.
1–15m — Microstructure studies, noise vs trend emergence.
Use the built-in Strategy Tester to explore entry/exit context, but treat results as research, not predictive performance.
✦ Components (Visual Study Tools)
EMA Line (Fast)
Primary reactive wave. Shows fast directional shifts.
Smoothed Line (Slow)
Trend baseline / reference structure.
Fog Region
Highlights fast-vs-smoothed directional alignment.
DNA Rungs (Optional)
Structural “bridges” showing the exact relationship between waves on each bar.
Useful for studying separation, compression, and expansions.
✦ Educational Insights
This strategy helps illuminate:
How fast and slow EMAs interact dynamically.
How structure changes precede trend emergence.
Where volatility compresses before expansion.
How noise, drift, and clean reversals differ.
How different smoothers alter the interpretation of the same price data.
The goal is clarity — not prediction.
✦ How to Use
Apply to any timeframe or instrument.
Enable or disable fog depending on preferred visibility.
Use DNA rungs for close structural comparison.
Observe long/short flips as educational reference points — not signals.
Study transitions visually, then backtest using the Strategy Tester for pattern research.
✦ Disclaimer
This script is provided for educational and research purposes only.
It does not provide trading signals, financial advice, or recommendations.
Past behavior does not indicate future performance.
Always practice risk-aware study and consult qualified financial professionals when needed.
✦ Author
Michael Culpepper (mikeyc747)
Creator of the Rasta framework and related market structure studies.
חפש סקריפטים עבור "market structure"
AG Pro Dynamic ChannelsAG Pro Dynamic Channels V2
Discover a new lens through which to view market structure with the AG Pro Dynamic Channels V2. This advanced indicator moves beyond simple trendlines, automatically identifying, classifying, and drawing eight distinct types of support and resistance channels directly on your chart.
Built on a sophisticated pivot-point detection engine, this script intelligently distinguishes between Major and Minor price structures, as well as Internal and External channels. This provides a comprehensive and multi-dimensional map of the market's flow, helping you identify trend continuations, corrections, and potential reversals.
The indicator is complete with a powerful, fully customizable alert system designed to notify you of the two most critical events: channel breakouts and price reactions.
Key Features
Fully Automatic Channels: The script automatically analyzes price action to find pivot highs and lows, using them to construct relevant channels without any manual drawing required.
8-Channel Classification: Gain deep market insight by viewing eight distinct channel types:
Major External (Up/Down)
Major Internal (Up/Down)
Minor External (Up/Down)
Minor Internal (Up/Down)
Advanced Pivot Engine: The core logic classifies pivots into categories like Higher Highs (MHH/mHH), Lower Lows (MLL/mLL), Higher Lows (MHL/mHL), and Lower Highs (MLH/mLH) to determine the precise start and end points for each channel.
Deep Customization: Take full control of your chart's appearance. You can individually toggle the visibility, color, line style (solid, dashed, dotted), and line width for all eight channel types.
Chart Clarity: A "Delete Previous" option is available for each channel type, allowing you to keep your chart clean and focused on only the most current and relevant market structures.
Comprehensive Alert System
Never miss a key price interaction. The AG Pro Dynamic Channels V2 features a robust, built-in alert module.
Dual-Alert Conditions: Get notifications for two distinct events:
Break Alert: Triggers when price confirms a close outside of a channel, signaling a potential breakout.
React Alert: Triggers when price touches or interacts with a channel line before closing back inside, signaling a test or rejection.
16 Unique Alerts: You have full control to enable or disable "Break" and "React" alerts for all 8 channel types individually, giving you 16 unique alert conditions to monitor.
Professional Alert Messages: The embedded alert sender provides detailed messages that include the asset, timeframe, and the specific event, such as "Break Major External Up Channel" or "React Minor Internal Down Channel".
Alert Configuration: Easily set your global Alert Name, Message Frequency (e.g., Once Per Bar, Once Per Bar Close), and Alert Time Zone from the script's settings.
How to Use
Trend Identification: Use the Major External Channels (drawn from MHH and MLL pivots) to identify the primary, long-term trend direction.
Pullback & Entry Zones: Use the Internal Channels (drawn from MHL and MLH pivots) to spot corrections and potential entry zones within an established trend.
Breakout Trading: Set Break Alerts on Major channels to be notified of significant, structure-shifting moves.
Short-Term & Counter-Trend: Utilize the Minor Channels to identify shorter-term price swings and potential reversal points.
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.
The ICT Ultimate Grid | MarketMaverisk GroupThe ICT Ultimate Grid | MarketMaverisk Group
This script is a fully customizable checklist based on ICT (Inner Circle Trader) concepts. It helps traders validate entry conditions across three timeframes:
LTP (Long-Term), ITP (Intermediate-Term), and STP (Short-Term).
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✅ Purpose & Utility:
Instead of generating simple buy/sell signals, this tool assists traders in making structured, confirmation-based decisions. It presents a visual checklist with 11 customizable columns—each can be individually toggled for each timeframe and displays ✅ or ❌ confirmation status.
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🧠 Confirmation Structure:
The checklist covers the following core elements from the ICT methodology:
• ERL⇔IRL and IRL⇔ERL (presented as special confirmations below the table)
• DOL – Drow On liqudity Level
• PD – permium or discuant
• SMT – Smart Money Trap / Inter-market Divergence
• CSD – Change in State of dlivery
• MSS – Market Structure Shift
• MMXM – Market maker (buy or sell) model
• FVG – Fair Value Gap
• OB – Order Block
• BRK.B – breker Block
Each item can be enabled or disabled for LTP, ITP, and STP individually.
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📊 Visual Design:
• Clean, compact table displayed in the top-right corner of the chart.
• Clear color scheme (✅ Green = Confirmed, ❌ Red = Not Confirmed, Grey = Hidden/Disabled).
• Timeframes are stacked row-wise (LTP, ITP, STP).
• Inputs allow fine-grained control over what elements are shown in each timeframe.
• Additional rows are used to confirm:
• HTF Key Level
• Direction: Reversal ↩️ or Continuation 🔂
• Bias: Bullish 🔼 or Bearish 🔽
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📈 Use Case:
This tool is ideal for traders who follow:
• ICT-based trading approaches
• Market structure + Liquidity analysis
• Day trading, scalping, or swing setups
• Confirmation-based entries after higher-timeframe alignment
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⚙️ Recommended Timeframe Settings:
• LTP = D1 or 4H
• ITP = 1H or 15min
• STP = 5min or 3min or 1min
• Session time: Best used between 02:00 and 05:00 on london killzone & 08:00 and 12:00 on New york killzone in New York timezone (UTC -5)
(you can customize this in strategy version)
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🛠 Technical Note:
This version is an indicator and does not generate signals or alerts by itself. For full automation, a strategy version is also available upon request.
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Let me know if you’d like me to also write a “strategy description” or help you prepare the public chart layout 📊 to make your publish clean and attractivE
Twitter Model ICT [TradingFinder] MMXM ERL D + FVG + M15 MSS/SMT🔵 Introduction
The Twitter Model ICT is a trading approach based on ICT (Inner Circle Trader) models, focusing on price movement between external and internal liquidity in lower timeframes. This model integrates key concepts such as Market Structure Shift (MSS), Smart Money Technique (SMT) divergence, and CISD level break to identify precise entry points in the market.
The primary goal of this model is to determine key liquidity levels, such as the previous day’s high and low (PDH/PDL) and align them with the Fair Value Gap (FVG) in the 1-hour timeframe. The overall strategy involves framing trades around the 1H FVG and using the M15 Market Structure Shift (MSS) for entry confirmation.
The Twitter Model ICT is designed to utilize external liquidity levels, such as PDH/PDL, as key entry zones. The model identifies FVG in the 1-hour timeframe, which acts as a magnet for price movement. Additionally, traders confirm entries using M15 Market Structure Shift (MSS) and SMT divergence.
Bullish Twitter Model :
In a bullish setup, the price sweeps the previous day’s low (PDL), and after confirming reversal signals, buys are executed in internal liquidity zones. Conversely, in a bearish setup, the price sweeps the previous day’s high (PDH), and after confirming weakness signals, sells are executed.
Bearish Twitter Model :
In short setups, entries are only executed above the Midnight Open, while in long setups, entries are taken below the Midnight Open. Adhering to these principles allows traders to define precise entry and exit points and analyze price movement with greater accuracy based on liquidity and market structure.
🔵 How to Use
The Twitter Model ICT is a liquidity-based trading strategy that analyzes price movements relative to the previous day’s high and low (PDH/PDL) and Fair Value Gap (FVG). This model is applicable in both bullish and bearish directions and utilizes the 1-hour (1H) and 15-minute (M15) timeframes for entry confirmation.
The price first sweeps an external liquidity level (PDH or PDL) and then provides an entry opportunity based on Market Structure Shift (MSS) and SMT divergence. Additionally, the entry should be positioned relative to the Midnight Open, meaning long entries should occur below the Midnight Open and short entries above it.
🟣 Bullish Twitter Model
In a bullish setup, the price first sweeps the previous day’s low (PDL) and reaches an external liquidity level. Then, in the 1-hour timeframe (1H), a bullish Fair Value Gap (FVG) forms, which serves as the price target.
To confirm the entry, a Market Structure Shift (MSS) in the 15-minute timeframe (M15) should be observed, signaling a trend reversal to the upside. Additionally, SMT divergence with correlated assets can indicate weakness in selling pressure.
Under these conditions, a long position is taken below the Midnight Open, with a stop-loss placed at the lowest point of the recent bearish move. The price target for this trade is the FVG in the 1-hour timeframe.
🟣 Bearish Twitter Model
In a bearish setup, the price first sweeps the previous day’s high (PDH) and reaches an external liquidity level. Then, in the 1-hour timeframe (1H), a bearish Fair Value Gap (FVG) is identified, serving as the trade target.
To confirm entry, a Market Structure Shift (MSS) in the 15-minute timeframe (M15) should form, signaling a trend shift to the downside. If an SMT divergence is present, it can provide additional confirmation for the trade.
Once these conditions are met, a short position is taken above the Midnight Open, with a stop-loss placed at the highest level of the recent bullish move. The trade's price target is the FVG in the 1-hour timeframe.
🔵 Settings
Bar Back Check : Determining the return of candles to identify the CISD level.
CISD Level Validity : CISD level validity period based on the number of candles.
Daily Position : Determines whether only the first signal of the day is considered or if signals are evaluated throughout the entire day.
Session : Specifies in which trading sessions the indicator will be active.
Second Symbol : This setting allows you to select another asset for comparison with the primary asset. By default, "XAUUSD" (Gold) is set as the second symbol, but you can change it to any currency pair, stock, or cryptocurrency. For example, you can choose currency pairs like EUR/USD or GBP/USD to identify divergences between these two assets.
Divergence Fractal Periods : This parameter defines the number of past candles to consider when identifying divergences. The default value is 2, but you can change it to suit your preferences. This setting allows you to detect divergences more accurately by selecting a greater number of candles.
The indicator allows displaying sessions based on various time zones. The user can select one of the following options :
UTC (Coordinated Universal Time)
Local Time of the Session
User’s Local Time
Show Open Price : Displays the New York market opening price.
Show PDH / PDL : Displays the previous day’s high and low to identify potential entry points.
Show SMT Divergence : Displays lines and labels for bullish ("+SMT") and bearish ("-SMT") divergences.
🔵 Conclusion
The Twitter Model ICT is an effective approach for analyzing and executing trades in financial markets, utilizing a combination of liquidity principles, market structure, and SMT confirmations to identify optimal entry and exit points.
By analyzing the previous day’s high and low (PDH/PDL), Fair Value Gaps (FVG), and Market Structure Shift (MSS) in the 1H and M15 timeframes, traders can pinpoint liquidity-driven trade opportunities. Additionally, considering the Midnight Open level helps traders avoid random entries and ensures better trade placement.
By applying this model, traders can interpret market movements based on liquidity flow and structural changes, allowing them to fine-tune their trading decisions with higher precision. Ultimately, the Twitter Model ICT provides a structured and logical approach for traders who seek to trade based on liquidity behavior and trend shifts in the market.
Enhanced London Session SMC SetupEnhanced London Session SMC Setup Indicator
This Pine Script-based indicator is designed for traders focusing on the London trading session, leveraging smart money concepts (SMC) to identify potential trading opportunities in the GBP/USD currency pair. The script uses multiple techniques such as Order Block Detection, Imbalance (Fair Value Gap) Analysis, Change of Character (CHoCH) detection, and Fibonacci retracement levels to aid in market structure analysis, providing a well-rounded approach to trade setups.
Features:
London Session Highlight:
The indicator visually marks the London trading session (from 08:00 AM to 04:00 PM UTC) on the chart using a blue background, signaling when the high-volume, high-impulse moves tend to occur, helping traders focus their analysis on this key session.
Order Block Detection:
Identifies significant impulse moves that may form order blocks (supply and demand zones). Order blocks are areas where institutions have executed large orders, often leading to price reversals or continuation. The indicator plots the high and low of these order blocks, providing key levels to monitor for potential entries.
Imbalance (Fair Value Gap) Detection:
Detects and highlights price imbalances or fair value gaps (FVG) where the market has moved too quickly, creating a gap in price action. These areas are often revisited by price, offering potential trade opportunities. The upper and lower bounds of the imbalance are visually marked for easy reference.
Change of Character (CHoCH) Detection:
This feature identifies potential trend reversals by detecting significant changes in market character. When the price action shifts from bullish to bearish or vice versa, a CHoCH signal is triggered, and the corresponding level is marked on the chart. This can help traders catch trend reversals at key levels.
Fibonacci Retracement Levels:
The script calculates and plots the key Fibonacci retracement levels (0.618 and 0.786 by default) based on the highest and lowest points over a user-defined swing lookback period. These levels are commonly used by traders to identify potential pullback zones where price may reverse or find support/resistance.
Directional Bias Based on Market Structure:
The indicator provides a market structure analysis by comparing the current highs and lows to the previous periods' highs and lows. This helps in identifying whether the market is in a bullish or bearish state, providing a clear directional bias for trade setups.
Alerts:
The indicator comes with built-in alert conditions to notify the trader when an order block, imbalance, CHoCH, or other significant price action event is detected, ensuring timely action can be taken.
Ideal Usage:
Timeframe: Suitable for intraday trading, particularly focusing on the London session (08:00 AM to 04:00 PM UTC).
Currency Pair: Specifically designed for GBP/USD but can be adapted to other pairs with similar market behavior.
Trading Strategy: Best used in conjunction with a price action strategy, focusing on the key levels identified (order blocks, FVG, CHoCH) and using Fibonacci retracement levels for precision entries.
Target Audience: Ideal for traders who follow smart money concepts (SMC) and are looking for a structured approach to identify high-probability setups during the London session.
ICT Donchian Smart Money Structure (Expo)█ Concept Overview
The Inner Circle Trader (ICT) methodology is focused on understanding the actions and implications of the so-called "smart money" - large institutions and professional traders who often influence market movements. Key to this is the concept of market structure and how it can provide insights into potential price moves.
Over time, however, there has been a notable shift in how some traders interpret and apply this methodology. Initially, it was designed with a focus on the fractal nature of markets. Fractals are recurring patterns in price action that are self-similar across different time scales, providing a nuanced and dynamic understanding of market structure.
However, as the ICT methodology has grown in popularity, there has been a drift away from this fractal-based perspective. Instead, many traders have started to focus more on pivot points as their primary tool for understanding market structure.
Pivot points provide static levels of potential support and resistance. While they can be useful in some contexts, relying heavily on them could provide a skewed perspective of market structure. They offer a static, backward-looking view that may not accurately reflect real-time changes in market sentiment or the dynamic nature of markets.
This shift from a fractal-based perspective to a pivot point perspective has significant implications. It can lead traders to misinterpret market structure and potentially make incorrect trading decisions.
To highlight this issue, you've developed a Donchian Structure indicator that mirrors the use of pivot points. The Donchian Channels are formed by the highest high and the lowest low over a certain period, providing another representation of potential market extremes. The fact that the Donchian Structure indicator produces the same results as pivot points underscores the inherent limitations of relying too heavily on these tools.
While the Donchian Structure indicator or pivot points can be useful tools, they should not replace the original, fractal-based perspective of the ICT methodology. These tools can provide a broad overview of market structure but may not capture the intricate dynamics and real-time changes that a fractal-based approach can offer.
It's essential for traders to understand these differences and to apply these tools correctly within the broader context of the ICT methodology and the Smart Money Concept Structure. A well-rounded approach that incorporates fractals, along with other tools and forms of analysis, is likely to provide a more accurate and comprehensive understanding of market structure.
█ Smart Money Concept - Misunderstandings
The Smart Money Concept is a popular concept among traders, and it's based on the idea that the "smart money" - typically large institutional investors, market makers, and professional traders - have superior knowledge or information, and their actions can provide valuable insight for other traders.
One of the biggest misunderstandings with this concept is the belief that tracking smart money activity can guarantee profitable trading.
█ Here are a few common misconceptions:
Following Smart Money Equals Guaranteed Success: Many traders believe that if they can follow the smart money, they will be successful. However, tracking the activity of large institutional investors and other professionals isn't easy, as they use complex strategies, have access to information not available to the public, and often intentionally hide their moves to prevent others from detecting their strategies.
Instantaneous Reaction and Results: Another misconception is that market movements will reflect smart money actions immediately. However, large institutions often slowly accumulate or distribute positions over time to avoid moving the market drastically. As a result, their actions might not produce an immediate noticeable effect on the market.
Smart Money Always Wins: It's not accurate to assume that smart money always makes the right decisions. Even the most experienced institutional investors and professional traders make mistakes, misjudge market conditions, or are affected by unpredictable events.
Smart Money Activity is Transparent: Understanding what constitutes smart money activity can be quite challenging. There are many indicators and metrics that traders use to try and track smart money, such as the COT (Commitments of Traders) reports, Level II market data, block trades, etc. However, these can be difficult to interpret correctly and are often misleading.
Assuming Uniformity Among Smart Money: 'Smart Money' is not a monolithic entity. Different institutional investors and professional traders have different strategies, risk tolerances, and investment horizons. What might be a good trade for a long-term institutional investor might not be a good trade for a short-term professional trader, and vice versa.
█ Market Structure
The Smart Money Concept Structure deals with the interpretation of price action that forms the market structure, focusing on understanding key shifts or changes in the market that may indicate where 'smart money' (large institutional investors and professional traders) might be moving in the market.
█ Three common concepts in this regard are Change of Character (CHoCH), and Shift in Market Structure (SMS), Break of Structure (BMS/BoS).
Change of Character (CHoCH): This refers to a noticeable change in the behavior of price movement, which could suggest that a shift in the market might be about to occur. This might be signaled by a sudden increase in volatility, a break of a trendline, or a change in volume, among other things.
Shift in Market Structure (SMS): This is when the overall structure of the market changes, suggesting a potential new trend. It usually involves a sequence of lower highs and lower lows for a downtrend, or higher highs and higher lows for an uptrend.
Break of Structure (BMS/BoS): This is when a previously defined trend or pattern in the price structure is broken, which may suggest a trend continuation.
A key component of this approach is the use of fractals, which are repeating patterns in price action that can give insights into potential market reversals. They appear at all scales of a price chart, reflecting the self-similar nature of markets.
█ Market Structure - Misunderstandings
One of the biggest misunderstandings about the ICT approach is the over-reliance or incorrect application of pivot points. Pivot points are a popular tool among traders due to their simplicity and easy-to-understand nature. However, when it comes to the Smart Money Concept and trying to follow the steps of professional traders or large institutions, relying heavily on pivot points can create misconceptions and lead to confusion. Here's why:
Delayed and Static Information: Pivot points are inherently backward-looking because they're calculated based on the previous period's data. As such, they may not reflect real-time market dynamics or sudden changes in market sentiment. Furthermore, they present a static view of market structure, delineating pre-defined levels of support and resistance. This static nature can be misleading because markets are fundamentally dynamic and constantly changing due to countless variables.
Inadequate Representation of Market Complexity: Markets are influenced by a myriad of factors, including economic indicators, geopolitical events, institutional actions, and market sentiment, among others. Relying on pivot points alone for reading market structure oversimplifies this complexity and can lead to a myopic understanding of market dynamics.
False Signals and Misinterpretations: Pivot points can often give false signals, especially in volatile markets. Prices might react to these levels temporarily but then continue in the original direction, leading to potential misinterpretation of market structure and sentiment. Also, a trader might wrongly perceive a break of a pivot point as a significant market event, when in fact, it could be due to random price fluctuations or temporary volatility.
Over-simplification: Viewing market structure only through the lens of pivot points simplifies the market to static levels of support and resistance, which can lead to misinterpretation of market dynamics. For instance, a trader might view a break of a pivot point as a definite sign of a trend, when it could just be a temporary price spike.
Ignoring the Fractal Nature of Markets: In the context of the Smart Money Concept Structure, understanding the fractal nature of markets is crucial. Fractals are self-similar patterns that repeat at all scales and provide a more dynamic and nuanced understanding of market structure. They can help traders identify shifts in market sentiment or direction in real-time, providing more relevant and timely information compared to pivot points.
The key takeaway here is not that pivot points should be entirely avoided or that they're useless. They can provide valuable insights and serve as a useful tool in a trader's toolbox when used correctly. However, they should not be the sole or primary method for understanding the market structure, especially in the context of the Smart Money Concept Structure.
█ Fractals
Instead, traders should aim for a comprehensive understanding of markets that incorporates a range of tools and concepts, including but not limited to fractals, order flow, volume analysis, fundamental analysis, and, yes, even pivot points. Fractals offer a more dynamic and nuanced view of the market. They reflect the recursive nature of markets and can provide valuable insights into potential market reversals. Because they appear at all scales of a price chart, they can provide a more holistic and real-time understanding of market structure.
In contrast, the Smart Money Concept Structure, focusing on fractals and comprehensive market analysis, aims to capture a more holistic and real-time view of the market. Fractals, being self-similar patterns that repeat at different scales, offer a dynamic understanding of market structure. As a result, they can help to identify shifts in market sentiment or direction as they happen, providing a more detailed and timely perspective.
Furthermore, a comprehensive market analysis would consider a broader set of factors, including order flow, volume analysis, and fundamental analysis, which could provide additional insights into 'smart money' actions.
█ Donchian Structure
Donchian Channels are a type of indicator used in technical analysis to identify potential price breakouts and trends, and they may also serve as a tool for understanding market structure. The channels are formed by taking the highest high and the lowest low over a certain number of periods, creating an envelope of price action.
Donchian Channels (or pivot points) can be useful tools for providing a general view of market structure, and they may not capture the intricate dynamics associated with the Smart Money Concept Structure. A more nuanced approach, centered on real-time fractals and a comprehensive analysis of various market factors, offers a more accurate understanding of 'smart money' actions and market structure.
█ Here is why Donchian Structure may be misleading:
Lack of Nuance: Donchian Channels, like pivot points, provide a simplified view of market structure. They don't take into account the nuanced behaviors of price action or the complex dynamics between buyers and sellers that can be critical in the Smart Money Concept Structure.
Limited Insights into 'Smart Money' Actions: While Donchian Channels can highlight potential breakout points and trends, they don't necessarily provide insights into the actions of 'smart money'. These large institutional traders often use sophisticated strategies that can't be easily inferred from price action alone.
█ Indicator Overview
We have built this Donchian Structure indicator to show that it returns the same results as using pivot points. The Donchian Structure indicator can be a useful tool for market analysis. However, it should not be seen as a direct replacement or equivalent to the original Smart Money concept, nor should any indicator based on pivot points. The indicator highlights the importance of understanding what kind of trading tools we use and how they can affect our decisions.
The Donchian Structure Indicator displays CHoCH, SMS, BoS/BMS, as well as premium and discount areas. This indicator plots everything in real-time and allows for easy backtesting on any market and timeframe. A unique candle coloring has been added to make it more engaging and visually appealing when identifying new trading setups and strategies. This candle coloring is "leading," meaning it can signal a structural change before it actually happens, giving traders ample time to plan their next trade accordingly.
█ How to use
The indicator is great for traders who want to simplify their view on the market structure and easily backtest Smart Money Concept Strategies. The added candle coloring function serves as a heads-up for structure change or can be used as trend confirmation. This new candle coloring feature can generate many new Smart Money Concepts strategies.
█ Features
Market Structure
The market structure is based on the Donchian channel, to which we have added what we call 'Structure Response'. This addition makes the indicator more useful, especially in trending markets. The core concept involves traders buying at a discount and selling or shorting at a premium, depending on the order flow. Structure response enables traders to determine the order flow more clearly. Consequently, more trading opportunities will appear in trending markets.
Structure Candles
Structure Candles highlight the current order flow and are significantly more responsive to structural changes. They can provide traders with a heads-up before a break in structure occurs
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Liquidity Sweep + BOS Retest System — Prop Firm Edition🟦 Liquidity Sweep + BOS Retest System — Prop Firm Edition
A High-Probability Smart Money Strategy Built for NQ, ES, and Funding Accounts
🚀 Overview
The Liquidity Sweep + BOS Retest System (Prop Firm Edition) is a precision-engineered SMC strategy built specifically for prop firm traders. It mirrors institutional liquidity behavior and combines it with strict account-safe entry rules to help traders pass and maintain funding accounts with consistency.
Unlike typical indicators, this system waits for three confirmations — liquidity sweep, displacement, and a clean retest — before executing any trade. Every component is optimized for low drawdown, high R:R, and prop-firm-approved risk management.
Whether you’re trading Apex, TakeProfitTrader, FFF, or OneUp Trader, this system gives you a powerful mechanical framework that keeps you within rules while identifying the market’s highest-probability reversal zones.
🔥 Key Features
1. Liquidity Sweep Detection (Stop Hunt Logic)
Automatically identifies when price clears a previous swing high/low with a sweep confirmation candle.
✔ Filters noise
✔ Eliminates early entries
✔ Locks onto true liquidity grabs
2. Automatic Break of Structure (BOS) Confirmation
Price must show true displacement by breaking structure opposite the sweep direction.
✔ Confirms momentum shift
✔ Removes fake reversals
✔ Ensures institutional intent
3. Precision Retest Entry Model
The strategy enters only when price retests the BOS level at premium/discount pricing.
✔ Zero chasing
✔ Extremely tight stop loss placement
✔ Prop-firm-friendly controlled risk
4. Built-In Risk & Trade Management
SL set at swept liquidity
TP set by user-defined R:R multiplier
Optional session filter (NY Open by default)
One trade at a time (no pyramiding)
Automatically resets logic after each trade
This prevents overtrading — the #1 cause of evaluation and account breaches.
5. Designed for Prop Firm Futures Trading
This script is optimized for:
Trailing/static drawdown accounts
Micro contract precision
Funding evaluations
Low-risk, high-probability setups
Structured, rule-based execution
It reduces randomness and emotional trading by automating the highest-quality SMC sequence.
🎯 The Trading Model Behind the System
Step 1 — Liquidity Sweep
Price must take out a recent high/low and close back inside structure.
This confirms stop-hunting behavior and marks the beginning of a potential reversal.
Step 2 — BOS (Break of Structure)
Price must break the opposite side swing with a displacement candle. This validates a directional shift.
Step 3 — Retest Entry
The system waits for price to retrace into the BOS level and signal continuation.
This creates optimal R:R entry with minimal drawdown.
📈 Best Markets
NQ (NASDAQ Futures) – Highly recommended
ES, YM, RTY
Gold (XAUUSD)
FX majors
Crypto (with high volatility)
Works best on 1m, 2m, 5m, or 15m depending on your trading style.
🧠 Why Traders Love This System
✔ No signals until all confirmations align
✔ Reduces overtrading and emotional decisions
✔ Follows market structure instead of random indicators
✔ Perfect for maintaining long-term funded accounts
✔ Built around institutional-grade concepts
✔ Makes your trading consistent, calm, and rules-based
⚙️ Recommended Settings
Session: 06:30–08:00 MST (NY Open)
R:R: 1.5R – 3R
Contracts: Start with 1–2 micros
Markets: NQ for best structure & volume
📦 What’s Included
Complete strategy logic
All plots, labels, sweep markers & BOS alerts
BOS retest entry automation
Session filtering
Stop loss & take profit system
Full SMC logic pipeline
🏁 Summary
The Liquidity Sweep + BOS Retest System is a complete, prop-firm-ready, structure-based strategy that automates one of the cleanest and most reliable SMC entry models. It is designed to keep you safe, consistent, and rule-compliant while capturing premium institutional setups.
If you want to trade with confidence, discipline, and prop-firm precision — this system is for you.
Good Luck -BG
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
Ichimoku PourSamadi Signal [TradingFinder] KijunSen Magic Number🔵 Introduction
The Ichimoku Kinko Hyo system is one of the most comprehensive market analysis tools ever created. Developed by Goichi Hosoda, a Japanese journalist in the 1930s, its purpose was to allow traders to recognize the balance between price, time, and momentum at a single glance. (In Japanese, Ichimoku literally means “one look.”)
At the core of the system lie five key components: Tenkan-sen (Conversion Line), Kijun-sen (Baseline), Chikou Span (Lagging Line), and the two leading spans, Senkou Span A and Senkou Span B, which together form the well-known Kumo or cloud representing both temporal structure and equilibrium zones in the market.
Although Ichimoku is commonly used to identify trends and support/resistance levels, a deeper layer of time philosophy exists within it. Ichimoku was not designed solely for price analysis but equally for time analysis.
In the classical model, the numerical cycles 9, 26, 52 reflect the natural rhythm of the market originally based on the Tokyo Stock Exchange’s trading schedule in the 1930s.
These values repeat across the system’s calculations, forming the foundation of Ichimoku’s time symmetry where price and time ultimately seek equilibrium.
In recent years, modern analysts have explored new approaches to extract time-based turning points from Ichimoku’s structure. One such approach is the analysis of flat segments on the Kijun-sen and Senkou B lines.
Whenever one of these lines remains flat for a period, it signals temporary balance between buyers and sellers; when the flat breaks, the market exits equilibrium and a new cycle begins.
This indicator is built precisely upon that philosophy. Following the timing methodology introduced by M.A. Poursamadi, the focus shifts away from price signals and line crossovers toward identifying flat periods on Kijun-sen (period 52) as time anchors.
From the first candle that changes the line’s slope, the tool begins a temporal count using a fixed sequence of key numbers: 5, 9, 13, 17, 26, 35, 43, 52, 63, 72, 81, 90.
Derived from both classical Ichimoku cycles and empirical testing, these numbers mark potential timing nodes where a market wave may end, a correction may begin, or a new leg may form.
Thus, this method serves not merely as another Ichimoku tool but as a temporal metronome for market structure a way to visualize moments when the market is ready to change rhythm, often before candles reveal it.
🔵 How to Use
The Kijun Timing BoX is built entirely on Ichimoku’s concept of time analysis.
Its core idea is that within every flat segment of the Kijun-sen, the market enters a temporary balance between opposing forces.
When that flat breaks, a new time cycle begins. From that first breakout candle, the indicator starts counting forward through the predefined time sequence(5, 9, 13, 17, 26, 35, 43, 52, 63, 72, 81, 90).
This counting framework creates a temporal map of market behavior, where each number represents an area where meaningful price fluctuations often occur.
A “meaningful fluctuation” does not necessarily imply reversal or continuation; rather, it marks a moment when the market’s internal energy balance shifts, typically visible as noticeable reactions on lower timeframes.
🟣 Identifying the Anchor Point
The first step is recognizing a valid flat zone on the Kijun-sen.
When this line remains flat for several candles and then changes slope, the indicator marks that bar as the Anchor, initiating the time count.
From that point onward, vertical gray lines appear at each interval in the key-number sequence, visualizing the time nodes ahead.
🟣 Reading the Timing Lines
Each numbered line represents a timing node a temporal point where a change in price rhythm is statistically more likely to occur.
At these nodes, the market may :
Enter a consolidation or minor correction phase.
Develop range-bound movement.
Or simply alter the speed and intensity of its move.
These behaviors do not imply a specific direction; they only highlight zones where time-based activity tends to cluster, giving traders a clearer view of cyclical rhythm.
🟣 Applying Time Analysis
The indicator’s primary use is to observe temporal order, not to predict price direction.
By tracking the distance between Anchors and the reactions that appear near major timing lines, traders can empirically identify each market’s characteristic rhythm—its own time DNA.
For example, one asset may consistently show significant fluctuations around the 13- and 26-bar marks,while another might react closer to 9 or 52. Recognizing such patterns helps traders understand how long typical cycles last before new phases of volatility emerge.
🟣 Combining with Other Tools
The indicator does not generate buy/sell signals on its own.
Its best use is in combination with price- or structure-based methods, to see whether meaningful price reactions occur around the same timing nodes.
In practice, it helps distinguish structured time-based fluctuations from random, noise-driven moves an insight often overlooked in conventional market analysis.
🔵 Settings
🟣 Logical Settings
KijunSen Period : Defines the baseline period used for timing analysis. Default = 52. It is the main line for detecting flats and generating time anchors.
Flat Event Filter : Controls how flat segments are validated before triggering a new timing event.
All : Every flat triggers a new Timing Box.
Automatic : Only flats longer than the historical average are used (recommended).
Custom : User manually defines the minimum flat length via Custom Count.
Update Timing Analysis BoX Per Event : If enabled, a new Timing Box is drawn each time a new flat event occurs. If disabled, the box completes its 90-bar window before refreshing.
🟣 Ichimoku Settings
TenkanSen Period : Defines the period for the Conversion Line (Tenkan-sen). Default = 9.
KijunSen Period : Sets the standard Ichimoku baseline (not the timing line). Default = 26.
Span B Period : Defines the period for Senkou Span B, the slower cloud boundary. Default = 52.
Shift Lines : Offsets cloud projection into the future. Default = 26.
🟣 Display Settings
Users can show or hide all Ichimoku lines Tenkan-sen, Kijun-sen, Chikou Span, Span A, and Span B as well as the Ichimoku Cloud.
They can also customize the color of each element to match personal chart preferences and improve visibility.
🔵 Conclusion
This analytical approach transforms Ichimoku’s time philosophy into a visual and measurable framework. A flat Kijun-sen represents a moment of market equilibrium; when its slope shifts, a new temporal cycle begins.
The purpose is not to forecast price direction but to highlight periods when meaningful fluctuations are more likely to develop.
Through this perspective, traders can observe the hidden rhythm of market time and expand their analysis beyond price into a broader time-cycle dimension.
Ultimately, the method revives Ichimoku’s original principle: the market can only be truly understood through the simultaneous harmony of price, time, and balance.
ICT Venom Trading Model [TradingFinder] SMC NY Session 2025SetupIntroduction
The ICT Venom Model is one of the most advanced strategies in the ICT framework, designed for intraday trading on major US indices such as US100, US30, and US500. This model is rooted in liquidity theory, time and price dynamics, and institutional order flow.
The Venom Model focuses on detecting Liquidity Sweeps, identifying Fair Value Gaps (FVG), and analyzing Market Structure Shifts (MSS). By combining these ICT core concepts, traders can filter false breakouts, capture sharp reversals, and align their entries with the real institutional liquidity flow during the New York Session.
Key Highlights of ICT Venom Model :
Intraday focus : Optimized for US indices (US100, US30, US500).
Time element : Critical window is 08:00–09:30 AM (Venom Box).
Liquidity sweep logic : Price grabs liquidity at 09:30 AM open.
Confirmation tools : MSS, CISD, FVG, and Order Blocks.
Dual setups : Works in both Bullish Venom and Bearish Venom conditions.
At its core, the ICT Venom Strategy is a framework that explains how institutional players manipulate liquidity pools by engineering false breakouts around the initial range of the market. Between 08:00 and 09:30 AM New York time, a range called the “Venom Box” is formed.
This range acts as a trap for retail traders, and once the 09:30 AM market open occurs, price usually sweeps either the high or the low of this box to collect stop-loss liquidity. After this liquidity grab, the market often reverses sharply, giving birth to a classic Bullish Venom Setup or Bearish Venom Setup
The Venom Model (ICT Venom Trading Strategy) is not just a pattern recognition tool but a precise institutional trading model based on time, liquidity, and market structure. By understanding the Initial Balance Range, watching for Liquidity Sweeps, and entering trades from FVG zones or Order Blocks, traders can anticipate market reversals with high accuracy. This strategy is widely respected among ICT followers because it offers both risk management discipline and clear entry/exit conditions. In short, the Venom Model transforms liquidity manipulation into actionable trading opportunities.
Bullish Setup :
Bearish Setup :
🔵 How to Use
The ICT Venom Model is applied by observing price behavior during the early hours of the New York session. The first step is to define the Initial Range, also called the Venom Box, which is formed between 08:00 and 09:30 AM EST. This range marks the high and low points where institutional traders often create traps for retail participants. Once the official market opens at 09:30 AM, price usually sweeps either the top or bottom of this box to collect liquidity.
After this liquidity grab, the market tends to reverse in alignment with the true directional bias. To confirm the setup, traders look for signals such as a Market Structure Shift (MSS), Change in State of Delivery (CISD), or the appearance of a Fair Value Gap (FVG). These elements validate the reversal and provide precise levels for trade execution.
🟣 Bullish Setup
In a Bullish Venom Setup, the market first sweeps the low of the Venom Box after 09:30 AM, triggering sell-side liquidity collection. This downward move is often sharp and deceptive, designed to stop out retail long positions and attract new sellers. Once liquidity is taken, the market typically shifts direction, forming an MSS or CISD that signals a reversal to the upside.
Traders then wait for price to retrace into a Fair Value Gap or a demand-side Order Block created during the reversal leg. This retracement offers the ideal entry point for long positions. Stop-loss placement should be just below the liquidity sweep low, while profit targets are set at the Venom Box high and, if momentum continues, at higher session or daily highs.
🟣 Bearish Setup
In a Bearish Venom Setup, the process is similar but reversed. After the Initial Range is defined, if price breaks above the Venom Box high following the 09:30 AM open, it signals a false breakout designed to collect buy-side liquidity. This move usually traps eager buyers and clears out stop-losses above the high.
After the liquidity sweep, confirmation comes through an MSS or CISD pointing to a reversal downward. At this stage, traders anticipate a retracement into a Fair Value Gap or a supply-side Order Block formed during the reversal. Short entries are taken within this zone, with stop-loss positioned just above the liquidity sweep high. The logical profit targets include the Venom Box low and, in stronger bearish momentum, deeper session or daily lows.
🔵 Settings
Refine Order Block : Enables finer adjustments to Order Block levels for more accurate price responses.
Mitigation Level OB : Allows users to set specific reaction points within an Order Block, including: Proximal: Closest level to the current price. 50% OB: Midpoint of the Order Block. Distal: Farthest level from the current price.
FVG Filter : The Judas Swing indicator includes a filter for Fair Value Gap (FVG), allowing different filtering based on FVG width: FVG Filter Type: Can be set to "Very Aggressive," "Aggressive," "Defensive," or "Very Defensive." Higher defensiveness narrows the FVG width, focusing on narrower gaps.
Mitigation Level FVG : Like the Order Block, you can set price reaction levels for FVG with options such as Proximal, 50% OB, and Distal.
CISD : The Bar Back Check option enables traders to specify the number of past candles checked for identifying the CISD Level, enhancing CISD Level accuracy on the chart.
🔵 Conclusion
The ICT Venom Model is more than just a reversal setup; it is a complete intraday trading framework that blends liquidity theory, time precision, and market structure analysis. By focusing on the Initial Range between 08:00 and 09:30 AM New York time and observing how price reacts at the 09:30 AM open, traders can identify liquidity sweeps that reveal institutional intentions.
Whether in a Bullish Venom Setup or a Bearish Venom Setup, the model allows for precise entries through Fair Value Gaps (FVGs) and Order Blocks, while maintaining clear risk management with well-defined stop-loss and target levels.
Ultimately, the ICT Venom Model provides traders with a structured way to filter false moves and align their trades with institutional order flow. Its strength lies in transforming liquidity manipulation into actionable opportunities, giving intraday traders an edge in timing, accuracy, and consistency. For those who master its logic, the Venom Model becomes not only a strategy for entry and exit, but also a deeper framework for understanding how liquidity truly drives price in the New York session.
TPO Levels [VAH/POC/VAL] with Poor H/L, Single Prints & NPOCs### 🎯 Advanced Market Profile & Key Level Analysis
This script is a unique and comprehensive technical analysis tool designed to help traders understand market structure, value, and key liquidity levels using the principles of **Auction Market Theory** and **Market Profile**.
This script is unique (and shouldn't be censored) because :
It allows large history of levels to be displayed
Accurate as possible tick size
Doesn't draw a profile but only the actual levels
Supports multi-timeframe levels even on the daily mode giving macro context
There is no indicator out there that does it
While these concepts are universal, this indicator was built primarily for the dynamic, 24/7 nature of the **cryptocurrency market**. It helps you move beyond simple price action to understand *why* the market is moving, which is especially crucial in the volatile crypto space.
### ## 📊 The Concepts Behind the Calculations
To use this script effectively, it's important to understand the core concepts it is built upon. The entire script is self-contained and does not require other indicators.
* **What is Market Profile?**
Market Profile is a unique charting technique that organizes price and time data to reveal market structure. It's built from **Time Price Opportunities (TPOs)**, which are 30-minute periods of market activity. By stacking these TPOs, the script builds a distribution, showing which price levels were most accepted (heavily traded) and which were rejected (lightly traded) during a session.
* **What is the Value Area (VA)?**
The Value Area is the heart of the profile. It represents the price range where **70%** of the session's trading volume occurred. This is considered the "fair value" zone where both buyers and sellers were in general agreement.
* **Point of Control (POC):** The single price level with the most TPOs. This was the most accepted or "fairest" price of the session and acts as a gravitational line for price.
* **Value Area High (VAH):** The upper boundary of the 70% value zone.
* **Value Area Low (VAL):** The lower boundary of the 70% value zone.
VAH and VAL are dynamic support and resistance levels. Trading outside the previous session's value area can signal the start of a new trend.
***
### ## 📈 Key Features Explained
This script automatically calculates and displays the following critical market-generated information:
* **Multi-Timeframe Market Profile**
Automatically draws Daily, Weekly, and Monthly profiles, allowing you to analyze market structure across different time horizons. The script preserves up to 20 historical sessions to provide deep market context.
* **Naked Point of Control (nPOC)**
A "Naked" POC is a Point of Control from a previous session that has **not** been revisited by price. These levels often act as powerful magnets for price, representing areas of unfinished business that the market may seek to retest. The script tracks and displays Daily, Weekly, and Monthly nPOCs until they are touched.
* **Single Prints (Imbalance Zones)**
A Single Print is a price level where only one TPO traded during the session's development. This signifies a rapid, aggressive price move and an imbalanced market. These areas, like gaps in a traditional chart, are frequently revisited as the market seeks to "fill in" these thin parts of the profile.
* **Poor Structure (Unfinished Auctions)**
A **Poor High** or **Poor Low** occurs when the top or bottom of a profile is flat, with two or more TPOs at the extreme price. This suggests that the auction in that direction was weak and inconclusive. These weak structures often signal a high probability that price will eventually break that high or low.
***
### ## 💡 How to Use This Indicator
This tool is not a signal generator but an analytical framework to improve your trading decisions.
1. **Determine Market Context:** Start by asking: Is the current price trading *inside* or *outside* the previous session's Value Area?
* **Inside VA:** The market is in a state of balance or range-bound. Look for trades between the VAH and VAL.
* **Outside VA:** The market is in a state of imbalance and may be starting a trend. Look for continuation or acceptance of prices outside the prior value.
2. **Identify Key Levels:**
* Use historical **nPOCs** as potential profit targets or areas to watch for a price reaction.
* Treat historical **VAH** and **VAL** levels as significant support and resistance zones.
* Note where **Single Prints** are. These are often price magnets that may get "filled" in the future.
3. **Spot Weakness:**
* A **Poor High** suggests weak resistance that may be easily broken.
* A **Poor Low** suggests weak support, signaling a potential for a continued move lower if broken.
***
### ## ⚙️ Customization & Crypto Presets
The indicator is highly customizable, allowing you to change colors, transparency, the number of historical sessions, and more.
To help traders get started quickly, the indicator includes **built-in layout presets** specifically calibrated for major cryptocurrencies: ** BINANCE:BTCUSDT.P , BINANCE:ETHUSDT.P , and BINANCE:SOLUSDT.P **. These presets automatically adjust key visual parameters to better suit the unique price characteristics and volatility of each asset, providing an optimized view right out of the box.
***
### ## ⚠️ Disclaimer
This indicator is a tool for market analysis and should not be interpreted as direct buy or sell signals. It provides information based on historical price action, which does not guarantee future results. Trading involves significant risk, and you should always use proper risk management. This script is designed for use on standard chart types (e.g., Candlesticks, Bar) and may produce misleading information on non-standard charts.
Apex Edge - MTF Confluence PanelApex Edge – MTF Confluence Panel
Description:
The Apex Edge – MTF Confluence Panel is a powerful multi-timeframe analysis tool built to streamline trade decision-making by aggregating key confluences across three user-defined timeframes. The panel visually presents the state of five core market signals—Trend, Momentum, Sweep, Structure, and Trap—alongside a unified Score column that summarizes directional bias with clarity.
Traders can customize the number of bullish/bearish conditions required to trigger a score signal, allowing the tool to be tailored for both conservative and aggressive trading styles. This script is designed for those who value a clean, structured, and objective approach to identifying market alignment—whether scalping or swing trading.
How it Works:
Across each of the three selected timeframes, the panel evaluates:
Trend: Based on a user-configurable Hull Moving Average (HMA), the script compares price relative to trend to determine bullish, bearish, or neutral bias.
Momentum: Uses OBV (On-Balance Volume) with volume spike detection to identify bursts of strong buying or selling pressure.
Sweep: Detects potential liquidity grabs by identifying price rejections beyond prior swing highs/lows. A break below a previous low with reversal signals bullish intent (and vice versa for bearish).
Structure: Uses dynamic pivot-based logic to identify market structure breaks (BOS) beyond recent confirmed swing levels.
Trap: Flags potential false moves by measuring RSI overbought/oversold signal clusters combined with minimal price movement—highlighting exhaustion or deceptive breaks.
Score: A weighted consensus of the above components. The number of required confluences to trigger a score (default: 3) can be set by the user via input, offering flexibility in signal sensitivity.
Why It’s Useful for Traders:
Quick Decision-Making: The color-coded panel provides instant visual feedback on whether confluences align across timeframes—ideal for fast-paced environments like scalping or high-volatility news sessions.
Multi-Timeframe Confidence: Helps eliminate guesswork by confirming whether higher and lower timeframe conditions support your trade idea.
Customizability: Adjustable confluence threshold means traders can fine-tune how sensitive the system is—more signals for faster entries, stricter confluence for higher conviction trades.
Built-In Alerts: Automated alerts for score alignment, trap detection, and liquidity sweeps allow traders to stay informed even when away from the screen.
Strategic Edge: Supports directional bias confirmation and trade filtering with logic designed to mimic professional decision-making workflows.
Features:
Clean, real-time confluence table across three user-selected timeframes
Configurable score sensitivity via “Minimum Confluences for Score” input
Cell-based colour coding for at-a-glance trade direction
Built-in alerts for score alignment, traps, and sweep triggers
Note - This Indicator works great in sync with Apex Edge - Session Sweep Pro
Useful levels for TP = previous session high/low boxes or fib levels.
⚠️ Disclaimer:
This script is for informational and educational purposes only and should not be considered financial advice. Always perform your own due diligence and practice proper risk management when trading.
Institutional Support/Resistance Locator🏛️ Institutional Support/Resistance Locator
Overview
The Institutional Support/Resistance Locator identifies high-probability demand and supply zones based on strong price rejection, large candle bodies, and elevated volume . These zones are commonly targeted or defended by institutional participants, helping traders anticipate potential reversal or continuation areas.
⸻
How It Works
The indicator uses a confluence of conditions to detect zones:
• Large Body Candles: Body size must exceed the moving average body size multiplied by a user-defined factor.
• High Volume: Volume must exceed the moving average volume by a configurable multiplier.
• Wick Rejection: Candles must show strong upper or lower wicks indicating aggressive rejection.
• If all criteria are met:
• Bullish candles form a Demand Zone.
• Bearish candles form a Supply Zone.
Each zone is plotted for a customizable number of future bars, representing areas where institutions may re-engage with the market.
⸻
Key Features
• ✅ Highlights institutional demand and supply areas dynamically
• ✅ Customizable sensitivity: body, volume, wick, padding, and zone extension
• ✅ Zones plotted as translucent regions with auto-expiry
• ✅ Works across all timeframes and markets
⸻
How to Use
• Trend Traders: Use demand zones for potential bounce entries in uptrends, and supply zones for pullback short entries in downtrends.
• Range Traders: Use zones as potential reversal points inside sideways market structures.
• Scalpers & Intraday Traders: Combine with volume or price action near zones for refined entries.
Always validate zone reactions with supporting indicators or price behavior.
⸻
Why This Combination?
The combination of wick rejection, volume confirmation, and large candle structure is designed to reflect footprints of smart money. Rather than relying on fixed pivots or subjective zones, this logic adapts to the current market context with statistically grounded conditions.
⸻
Why It’s Worth Using
This tool offers traders a structured way to interpret institutional activity on charts without relying on guesswork. By plotting potential high-impact areas, it helps improve reaction time.
⸻
Note :
• This script is open-source and non-commercial.
• No performance guarantees or unrealistic claims are made.
• It is intended for educational and analytical purposes only.
FVG, Swing, Target, D/W/M High Low Detector Basic by Trader Riaz"FVG, Swing, Target, D/W/M High Low Detector Basic by Trader Riaz " is a powerful TradingView indicator designed to enhance your trading strategy by identifying key market structures and levels. This all-in-one tool detects Fair Value Gaps (FVGs), Swing Highs/Lows, and previous Day, Previous Week, and Previous Month Highs/Lows, helping traders make informed decisions with ease.
Key Features:
Bullish & Bearish FVG Detection: Highlights Fair Value Gaps with customizable colors, labels, and extension options.
Swing Highs & Lows: Automatically detects and marks Swing Highs and Lows with adjustable display settings and extensions.
Next Target Levels: Identifies potential price targets based on market direction (rising or falling).
Daily, Weekly, and Monthly High/Low Levels: Displays previous day, week, and month highs/lows with customizable colors.
Customizable Settings: Fully adjustable inputs for colors, number of levels to display, and extension periods.
Clean Visuals: Intuitive and non-intrusive design with dashed lines, labels, and tooltips for better chart readability.
This indicator is ideal for traders looking to identify key price levels, improve market structure analysis, and enhance their trading strategies.
Happy Trading,
Trader Riaz
ICT FVG & Swing Detector Basic by Trader RiazICT FVG & Swing Detector Basic by Trader Riaz
Unlock Precision Trading with the Ultimate Fair Value Gap (FVG) and Swing Detection Tool!
Developed by Trader Riaz , the ICT FVG and Swing Detector Basic is a powerful Pine Script indicator designed to help traders identify key market structures with ease. Whether you're a day trader, swing trader, or scalper, this indicator provides actionable insights by detecting Bullish and Bearish Fair Value Gaps (FVGs) and Swing Highs/Lows on any timeframe. Perfect for trading forex, stocks, crypto, and more on TradingView!
Key Features:
1: Bullish and Bearish FVG Detection
- Automatically identifies Bullish FVGs (highlighted in green) and Bearish FVGs (highlighted in red) to spot potential reversal or continuation zones.
- Displays FVGs as shaded boxes with a dashed midline at 70% opacity, making it easy to see the midpoint of the gap for precise entries and exits.
- Labels are placed inside the FVG boxes at the extreme right for clear visibility.
2: Customizable FVG Display
- Control the number of Bullish and Bearish FVGs displayed on the chart with user-defined inputs (fvg_bull_count and fvg_bear_count).
- Toggle the visibility of Bullish and Bearish FVGs with simple checkboxes (show_bull_fvg and show_bear_fvg) to declutter your chart.
3: Swing High and Swing Low Detection
- Detects Swing Highs (blue lines) and Swing Lows (red lines) to identify key market turning points.
- Labels are positioned at the extreme right edge of the lines for better readability and alignment.
- Customize the number of Swing Highs and Lows displayed (swing_high_count and swing_low_count) to focus on the most recent market structures.
4: Fully Customizable Display
- Toggle visibility for Swing Highs and Lows (show_swing_high and show_swing_low) to suit your trading style.
- Adjust the colors of Swing High and Low lines (swing_high_color and swing_low_color) to match your chart preferences.
5: Clean and Efficient Design
- Built with Pine Script v6 for optimal performance on TradingView.
- Automatically removes older FVGs and Swing points when the user-defined count is exceeded, keeping your chart clean and focused.
- Labels are strategically placed to avoid clutter while providing clear information.
Why Use This Indicator?
Precision Trading: Identify high-probability setups with FVGs and Swing points, commonly used in Smart Money Concepts (SMC) and Institutional Trading strategies.
User-Friendly: Easy-to-use inputs allow traders of all levels to customize the indicator to their needs.
Versatile: Works on any market (Forex, Stocks, Crypto, Commodities) and timeframe (1M, 5M, 1H, 4H, Daily, etc.).
Developed by Trader Riaz: Backed by the expertise of Trader Riaz, a seasoned trader dedicated to creating tools that empower the TradingView community.
How to Use:
- Add the Custom FVG and Swing Detector to your chart on TradingView.
- Adjust the input settings to control the number of FVGs and Swing points displayed.
- Toggle visibility for Bullish/Bearish FVGs and Swing Highs/Lows as needed.
- Use the identified FVGs and Swing points to plan your trades, set stop-losses, and target key levels.
Ideal For:
- Traders using Smart Money Concepts (SMC), Price Action, or Market Structure strategies.
- Those looking to identify liquidity grabs, imbalances, and trend reversals.
- Beginners and advanced traders seeking a reliable tool to enhance their technical analysis.
Happy trading!
paranimonipobre
Chart Description: Buy Low, Sell High with Market Structure
This chart utilizes a dynamic trading strategy based on Bollinger Bands, RSI, and market structure analysis to identify high-probability buy and sell signals while aligning with prevailing trends.
Key Elements:
Bollinger Bands:
The upper (red) and lower (green) bands define volatility boundaries based on standard deviations.
The middle line (blue) represents the 20-period simple moving average.
Market Structure:
Swing highs (red triangles labeled "SH") and swing lows (green triangles labeled "SL") are identified to analyze the trend.
Background colors indicate trend direction:
Green Background: Uptrend (Higher Lows).
Red Background: Downtrend (Lower Highs).
RSI Indicator:
Shown in a separate pane, with overbought (red) at 70 and oversold (green) at 30.
Helps confirm signal validity by identifying momentum extremes.
Buy and Sell Signals:
Buy Signals (Green):
Triggered when the price crosses above the lower Bollinger Band, RSI is oversold (<30), and the market is in an uptrend.
Displayed as green "BUY" labels below bars.
Sell Signals (Red):
Triggered when the price crosses below the upper Bollinger Band, RSI is overbought (>70), and the market is in a downtrend.
Displayed as red "SELL" labels above bars.
How to Use:
Trend Identification:
Follow market structure analysis to determine the current trend direction.
Trade only in the direction of the trend (e.g., buy in an uptrend, sell in a downtrend).
Signal Confirmation:
Look for signals aligning with Bollinger Bands, RSI levels, and market structure.
Ignore signals that conflict with the trend to avoid false entries.
Market Conditions:
Best suited for trending markets with clear higher lows or lower highs.
Signals in choppy or sideways markets may require additional confirmation.
Trading IQ - ICT LibraryLibrary "ICTlibrary"
Used to calculate various ICT related price levels and strategies. An ongoing project.
Hello Coders!
This library is meant for sourcing ICT related concepts. While some functions might generate more output than you require, you can specify "Lite Mode" as "true" in applicable functions to slim down necessary inputs.
isLastBar(userTF)
Identifies the last bar on the chart before a timeframe change
Parameters:
userTF (simple int) : the timeframe you wish to calculate the last bar for, must be converted to integer using 'timeframe.in_seconds()'
Returns: bool true if bar on chart is last bar of higher TF, dalse if bar on chart is not last bar of higher TF
necessaryData(atrTF)
returns necessaryData UDT for historical data access
Parameters:
atrTF (float) : user-selected timeframe ATR value.
Returns: logZ. log return Z score, used for calculating order blocks.
method gradBoxes(gradientBoxes, idColor, timeStart, bottom, top, rightCoordinate)
creates neon like effect for box drawings
Namespace types: array
Parameters:
gradientBoxes (array) : an array.new() to store the gradient boxes
idColor (color)
timeStart (int) : left point of box
bottom (float) : bottom of box price point
top (float) : top of box price point
rightCoordinate (int) : right point of box
Returns: void
checkIfTraded(tradeName)
checks if recent trade is of specific name
Parameters:
tradeName (string)
Returns: bool true if recent trade id matches target name, false otherwise
checkIfClosed(tradeName)
checks if recent closed trade is of specific name
Parameters:
tradeName (string)
Returns: bool true if recent closed trade id matches target name, false otherwise
IQZZ(atrMult, finalTF)
custom ZZ to quickly determine market direction.
Parameters:
atrMult (float) : an atr multiplier used to determine the required price move for a ZZ direction change
finalTF (string) : the timeframe used for the atr calcuation
Returns: dir market direction. Up => 1, down => -1
method drawBos(id, startPoint, getKeyPointTime, getKeyPointPrice, col, showBOS, isUp)
calculates and draws Break Of Structure
Namespace types: array
Parameters:
id (array)
startPoint (chart.point)
getKeyPointTime (int) : the actual time of startPoint, simplystartPoint.time
getKeyPointPrice (float) : the actual time of startPoint, simplystartPoint.price
col (color) : color of the BoS line / label
showBOS (bool) : whether to show label/line. This function still calculates internally for other ICT related concepts even if not drawn.
isUp (bool) : whether BoS happened during price increase or price decrease.
Returns: void
method drawMSS(id, startPoint, getKeyPointTime, getKeyPointPrice, col, showMSS, isUp, upRejections, dnRejections, highArr, lowArr, timeArr, closeArr, openArr, atrTFarr, upRejectionsPrices, dnRejectionsPrices)
calculates and draws Market Structure Shift. This data is also used to calculate Rejection Blocks.
Namespace types: array
Parameters:
id (array)
startPoint (chart.point)
getKeyPointTime (int) : the actual time of startPoint, simplystartPoint.time
getKeyPointPrice (float) : the actual time of startPoint, simplystartPoint.price
col (color) : color of the MSS line / label
showMSS (bool) : whether to show label/line. This function still calculates internally for other ICT related concepts even if not drawn.
isUp (bool) : whether MSS happened during price increase or price decrease.
upRejections (array)
dnRejections (array)
highArr (array) : array containing historical highs, should be taken from the UDT "necessaryData" defined above
lowArr (array) : array containing historical lows, should be taken from the UDT "necessaryData" defined above
timeArr (array) : array containing historical times, should be taken from the UDT "necessaryData" defined above
closeArr (array) : array containing historical closes, should be taken from the UDT "necessaryData" defined above
openArr (array) : array containing historical opens, should be taken from the UDT "necessaryData" defined above
atrTFarr (array) : array containing historical atr values (of user-selected TF), should be taken from the UDT "necessaryData" defined above
upRejectionsPrices (array) : array containing up rejections prices. Is sorted and used to determine selective looping for invalidations.
dnRejectionsPrices (array) : array containing down rejections prices. Is sorted and used to determine selective looping for invalidations.
Returns: void
method getTime(id, compare, timeArr)
gets time of inputted price (compare) in an array of data
this is useful when the user-selected timeframe for ICT concepts is greater than the chart's timeframe
Namespace types: array
Parameters:
id (array) : the array of data to search through, to find which index has the same value as "compare"
compare (float) : the target data point to find in the array
timeArr (array) : array of historical times
Returns: the time that the data point in the array was recorded
method OB(id, highArr, signArr, lowArr, timeArr, sign)
store bullish orderblock data
Namespace types: array
Parameters:
id (array)
highArr (array) : array of historical highs
signArr (array) : array of historical price direction "math.sign(close - open)"
lowArr (array) : array of historical lows
timeArr (array) : array of historical times
sign (int) : orderblock direction, -1 => bullish, 1 => bearish
Returns: void
OTEstrat(OTEstart, future, closeArr, highArr, lowArr, timeArr, longOTEPT, longOTESL, longOTElevel, shortOTEPT, shortOTESL, shortOTElevel, structureDirection, oteLongs, atrTF, oteShorts)
executes the OTE strategy
Parameters:
OTEstart (chart.point)
future (int) : future time point for drawings
closeArr (array) : array of historical closes
highArr (array) : array of historical highs
lowArr (array) : array of historical lows
timeArr (array) : array of historical times
longOTEPT (string) : user-selected long OTE profit target, please create an input.string() for this using the example below
longOTESL (int) : user-selected long OTE stop loss, please create an input.string() for this using the example below
longOTElevel (float) : long entry price of selected retracement ratio for OTE
shortOTEPT (string) : user-selected short OTE profit target, please create an input.string() for this using the example below
shortOTESL (int) : user-selected short OTE stop loss, please create an input.string() for this using the example below
shortOTElevel (float) : short entry price of selected retracement ratio for OTE
structureDirection (string) : current market structure direction, this should be "Up" or "Down". This is used to cancel pending orders if market structure changes
oteLongs (bool) : input.bool() for whether OTE longs can be executed
atrTF (float) : atr of the user-seleceted TF
oteShorts (bool) : input.bool() for whether OTE shorts can be executed
@exampleInputs
oteLongs = input.bool(defval = false, title = "OTE Longs", group = "Optimal Trade Entry")
longOTElevel = input.float(defval = 0.79, title = "Long Entry Retracement Level", options = , group = "Optimal Trade Entry")
longOTEPT = input.string(defval = "-0.5", title = "Long TP", options = , group = "Optimal Trade Entry")
longOTESL = input.int(defval = 0, title = "How Many Ticks Below Swing Low For Stop Loss", group = "Optimal Trade Entry")
oteShorts = input.bool(defval = false, title = "OTE Shorts", group = "Optimal Trade Entry")
shortOTElevel = input.float(defval = 0.79, title = "Short Entry Retracement Level", options = , group = "Optimal Trade Entry")
shortOTEPT = input.string(defval = "-0.5", title = "Short TP", options = , group = "Optimal Trade Entry")
shortOTESL = input.int(defval = 0, title = "How Many Ticks Above Swing Low For Stop Loss", group = "Optimal Trade Entry")
Returns: void (0)
displacement(logZ, atrTFreg, highArr, timeArr, lowArr, upDispShow, dnDispShow, masterCoords, labelLevels, dispUpcol, rightCoordinate, dispDncol, noBorders)
calculates and draws dispacements
Parameters:
logZ (float) : log return of current price, used to determine a "significant price move" for a displacement
atrTFreg (float) : atr of user-seleceted timeframe
highArr (array) : array of historical highs
timeArr (array) : array of historical times
lowArr (array) : array of historical lows
upDispShow (int) : amount of historical upside displacements to show
dnDispShow (int) : amount of historical downside displacements to show
masterCoords (map) : a map to push the most recent displacement prices into, useful for having key levels in one data structure
labelLevels (string) : used to determine label placement for the displacement, can be inside box, outside box, or none, example below
dispUpcol (color) : upside displacement color
rightCoordinate (int) : future time for displacement drawing, best is "last_bar_time"
dispDncol (color) : downside displacement color
noBorders (bool) : input.bool() to remove box borders, example below
@exampleInputs
labelLevels = input.string(defval = "Inside" , title = "Box Label Placement", options = )
noBorders = input.bool(defval = false, title = "No Borders On Levels")
Returns: void
method getStrongLow(id, startIndex, timeArr, lowArr, strongLowPoints)
unshift strong low data to array id
Namespace types: array
Parameters:
id (array)
startIndex (int) : the starting index for the timeArr array of the UDT "necessaryData".
this point should start from at least 1 pivot prior to find the low before an upside BoS
timeArr (array) : array of historical times
lowArr (array) : array of historical lows
strongLowPoints (array) : array of strong low prices. Used to retrieve highest strong low price and see if need for
removal of invalidated strong lows
Returns: void
method getStrongHigh(id, startIndex, timeArr, highArr, strongHighPoints)
unshift strong high data to array id
Namespace types: array
Parameters:
id (array)
startIndex (int) : the starting index for the timeArr array of the UDT "necessaryData".
this point should start from at least 1 pivot prior to find the high before a downside BoS
timeArr (array) : array of historical times
highArr (array) : array of historical highs
strongHighPoints (array)
Returns: void
equalLevels(highArr, lowArr, timeArr, rightCoordinate, equalHighsCol, equalLowsCol, liteMode)
used to calculate recent equal highs or equal lows
Parameters:
highArr (array) : array of historical highs
lowArr (array) : array of historical lows
timeArr (array) : array of historical times
rightCoordinate (int) : a future time (right for boxes, x2 for lines)
equalHighsCol (color) : user-selected color for equal highs drawings
equalLowsCol (color) : user-selected color for equal lows drawings
liteMode (bool) : optional for a lite mode version of an ICT strategy. For more control over drawings leave as "True", "False" will apply neon effects
Returns: void
quickTime(timeString)
used to quickly determine if a user-inputted time range is currently active in NYT time
Parameters:
timeString (string) : a time range
Returns: true if session is active, false if session is inactive
macros(showMacros, noBorders)
used to calculate and draw session macros
Parameters:
showMacros (bool) : an input.bool() or simple bool to determine whether to activate the function
noBorders (bool) : an input.bool() to determine whether the box anchored to the session should have borders
Returns: void
po3(tf, left, right, show)
use to calculate HTF po3 candle
@tip only call this function on "barstate.islast"
Parameters:
tf (simple string)
left (int) : the left point of the candle, calculated as bar_index + left,
right (int) : :the right point of the candle, calculated as bar_index + right,
show (bool) : input.bool() whether to show the po3 candle or not
Returns: void
silverBullet(silverBulletStratLong, silverBulletStratShort, future, userTF, H, L, H2, L2, noBorders, silverBulletLongTP, historicalPoints, historicalData, silverBulletLongSL, silverBulletShortTP, silverBulletShortSL)
used to execute the Silver Bullet Strategy
Parameters:
silverBulletStratLong (simple bool)
silverBulletStratShort (simple bool)
future (int) : a future time, used for drawings, example "last_bar_time"
userTF (simple int)
H (float) : the high price of the user-selected TF
L (float) : the low price of the user-selected TF
H2 (float) : the high price of the user-selected TF
L2 (float) : the low price of the user-selected TF
noBorders (bool) : an input.bool() used to remove the borders from box drawings
silverBulletLongTP (series silverBulletLevels)
historicalPoints (array)
historicalData (necessaryData)
silverBulletLongSL (series silverBulletLevels)
silverBulletShortTP (series silverBulletLevels)
silverBulletShortSL (series silverBulletLevels)
Returns: void
method invalidFVGcheck(FVGarr, upFVGpricesSorted, dnFVGpricesSorted)
check if existing FVGs are still valid
Namespace types: array
Parameters:
FVGarr (array)
upFVGpricesSorted (array) : an array of bullish FVG prices, used to selective search through FVG array to remove invalidated levels
dnFVGpricesSorted (array) : an array of bearish FVG prices, used to selective search through FVG array to remove invalidated levels
Returns: void (0)
method drawFVG(counter, FVGshow, FVGname, FVGcol, data, masterCoords, labelLevels, borderTransp, liteMode, rightCoordinate)
draws FVGs on last bar
Namespace types: map
Parameters:
counter (map) : a counter, as map, keeping count of the number of FVGs drawn, makes sure that there aren't more FVGs drawn
than int FVGshow
FVGshow (int) : the number of FVGs to show. There should be a bullish FVG show and bearish FVG show. This function "drawFVG" is used separately
for bearish FVG and bullish FVG.
FVGname (string) : the name of the FVG, "FVG Up" or "FVG Down"
FVGcol (color) : desired FVG color
data (FVG)
masterCoords (map) : a map containing the names and price points of key levels. Used to define price ranges.
labelLevels (string) : an input.string with options "Inside", "Outside", "Remove". Determines whether FVG labels should be inside box, outside,
or na.
borderTransp (int)
liteMode (bool)
rightCoordinate (int) : the right coordinate of any drawings. Must be a time point.
Returns: void
invalidBlockCheck(bullishOBbox, bearishOBbox, userTF)
check if existing order blocks are still valid
Parameters:
bullishOBbox (array) : an array declared using the UDT orderBlock that contains bullish order block related data
bearishOBbox (array) : an array declared using the UDT orderBlock that contains bearish order block related data
userTF (simple int)
Returns: void (0)
method lastBarRejections(id, rejectionColor, idShow, rejectionString, labelLevels, borderTransp, liteMode, rightCoordinate, masterCoords)
draws rejectionBlocks on last bar
Namespace types: array
Parameters:
id (array) : the array, an array of rejection block data declared using the UDT rejection block
rejectionColor (color) : the desired color of the rejection box
idShow (int)
rejectionString (string) : the desired name of the rejection blocks
labelLevels (string) : an input.string() to determine if labels for the block should be inside the box, outside, or none.
borderTransp (int)
liteMode (bool) : an input.bool(). True = neon effect, false = no neon.
rightCoordinate (int) : atime for the right coordinate of the box
masterCoords (map) : a map that stores the price of key levels and assigns them a name, used to determine price ranges
Returns: void
method OBdraw(id, OBshow, BBshow, OBcol, BBcol, bullishString, bearishString, isBullish, labelLevels, borderTransp, liteMode, rightCoordinate, masterCoords)
draws orderblocks and breaker blocks for data stored in UDT array()
Namespace types: array
Parameters:
id (array) : the array, an array of order block data declared using the UDT orderblock
OBshow (int) : the number of order blocks to show
BBshow (int) : the number of breaker blocks to show
OBcol (color) : color of order blocks
BBcol (color) : color of breaker blocks
bullishString (string) : the title of bullish blocks, which is a regular bullish orderblock or a bearish orderblock that's converted to breakerblock
bearishString (string) : the title of bearish blocks, which is a regular bearish orderblock or a bullish orderblock that's converted to breakerblock
isBullish (bool) : whether the array contains bullish orderblocks or bearish orderblocks. If bullish orderblocks,
the array will naturally contain bearish BB, and if bearish OB, the array will naturally contain bullish BB
labelLevels (string) : an input.string() to determine if labels for the block should be inside the box, outside, or none.
borderTransp (int)
liteMode (bool) : an input.bool(). True = neon effect, false = no neon.
rightCoordinate (int) : atime for the right coordinate of the box
masterCoords (map) : a map that stores the price of key levels and assigns them a name, used to determine price ranges
Returns: void
FVG
UDT for FVG calcualtions
Fields:
H (series float) : high price of user-selected timeframe
L (series float) : low price of user-selected timeframe
direction (series string) : FVG direction => "Up" or "Down"
T (series int) : => time of bar on user-selected timeframe where FVG was created
fvgLabel (series label) : optional label for FVG
fvgLineTop (series line) : optional line for top of FVG
fvgLineBot (series line) : optional line for bottom of FVG
fvgBox (series box) : optional box for FVG
labelLine
quickly pair a line and label together as UDT
Fields:
lin (series line) : Line you wish to pair with label
lab (series label) : Label you wish to pair with line
orderBlock
UDT for order block calculations
Fields:
orderBlockData (array) : array containing order block x and y points
orderBlockBox (series box) : optional order block box
vioCount (series int) : = 0 violation count of the order block. 0 = Order Block, 1 = Breaker Block
traded (series bool)
status (series string) : = "OB" status == "OB" => Level is order block. status == "BB" => Level is breaker block.
orderBlockLab (series label) : options label for the order block / breaker block.
strongPoints
UDT for strong highs and strong lows
Fields:
price (series float) : price of the strong high or strong low
timeAtprice (series int) : time of the strong high or strong low
strongPointLabel (series label) : optional label for strong point
strongPointLine (series line) : optional line for strong point
overlayLine (series line) : optional lines for strong point to enhance visibility
overlayLine2 (series line) : optional lines for strong point to enhance visibility
displacement
UDT for dispacements
Fields:
highPrice (series float) : high price of displacement
lowPrice (series float) : low price of displacement
timeAtPrice (series int) : time of bar where displacement occurred
displacementBox (series box) : optional box to draw displacement
displacementLab (series label) : optional label for displacement
po3data
UDT for po3 calculations
Fields:
dHigh (series float) : higher timeframe high price
dLow (series float) : higher timeframe low price
dOpen (series float) : higher timeframe open price
dClose (series float) : higher timeframe close price
po3box (series box) : box to draw po3 candle body
po3line (array) : line array to draw po3 wicks
po3Labels (array) : label array to label price points of po3 candle
macros
UDT for session macros
Fields:
sessions (array) : Array of sessions, you can populate this array using the "quickTime" function located above "export macros".
prices (matrix) : Matrix of session data -> open, high, low, close, time
sessionTimes (array) : Array of session names. Pairs with array sessions.
sessionLines (matrix) : Optional array for sesion drawings.
OTEtimes
UDT for data storage and drawings associated with OTE strategy
Fields:
upTimes (array) : time of highest point before trade is taken
dnTimes (array) : time of lowest point before trade is taken
tpLineLong (series line) : line to mark tp level long
tpLabelLong (series label) : label to mark tp level long
slLineLong (series line) : line to mark sl level long
slLabelLong (series label) : label to mark sl level long
tpLineShort (series line) : line to mark tp level short
tpLabelShort (series label) : label to mark tp level short
slLineShort (series line) : line to mark sl level short
slLabelShort (series label) : label to mark sl level short
sweeps
UDT for data storage and drawings associated with liquidity sweeps
Fields:
upSweeps (matrix) : matrix containing liquidity sweep price points and time points for up sweeps
dnSweeps (matrix) : matrix containing liquidity sweep price points and time points for down sweeps
upSweepDrawings (array) : optional up sweep box array. Pair the size of this array with the rows or columns,
dnSweepDrawings (array) : optional up sweep box array. Pair the size of this array with the rows or columns,
raidExitDrawings
UDT for drawings associated with the Liquidity Raid Strategy
Fields:
tpLine (series line) : tp line for the liquidity raid entry
tpLabel (series label) : tp label for the liquidity raid entry
slLine (series line) : sl line for the liquidity raid entry
slLabel (series label) : sl label for the liquidity raid entry
m2022
UDT for data storage and drawings associated with the Model 2022 Strategy
Fields:
mTime (series int) : time of the FVG where entry limit order is placed
mIndex (series int) : array index of FVG where entry limit order is placed. This requires an array of FVG data, which is defined above.
mEntryDistance (series float) : the distance of the FVG to the 50% range. M2022 looks for the fvg closest to 50% mark of range.
mEntry (series float) : the entry price for the most eligible fvg
fvgHigh (series float) : the high point of the eligible fvg
fvgLow (series float) : the low point of the eligible fvg
longFVGentryBox (series box) : long FVG box, used to draw the eligible FVG
shortFVGentryBox (series box) : short FVG box, used to draw the eligible FVG
line50P (series line) : line used to mark 50% of the range
line100P (series line) : line used to mark 100% (top) of the range
line0P (series line) : line used to mark 0% (bottom) of the range
label50P (series label) : label used to mark 50% of the range
label100P (series label) : label used to mark 100% (top) of the range
label0P (series label) : label used to mark 0% (bottom) of the range
sweepData (array)
silverBullet
UDT for data storage and drawings associated with the Silver Bullet Strategy
Fields:
session (series bool)
sessionStr (series string) : name of the session for silver bullet
sessionBias (series string)
sessionHigh (series float) : = high high of session // use math.max(silverBullet.sessionHigh, high)
sessionLow (series float) : = low low of session // use math.min(silverBullet.sessionLow, low)
sessionFVG (series float) : if applicable, the FVG created during the session
sessionFVGdraw (series box) : if applicable, draw the FVG created during the session
traded (series bool)
tp (series float) : tp of trade entered at the session FVG
sl (series float) : sl of trade entered at the session FVG
sessionDraw (series box) : optional draw session with box
sessionDrawLabel (series label) : optional label session with label
silverBulletDrawings
UDT for trade exit drawings associated with the Silver Bullet Strategy
Fields:
tpLine (series line) : tp line drawing for strategy
tpLabel (series label) : tp label drawing for strategy
slLine (series line) : sl line drawing for strategy
slLabel (series label) : sl label drawing for strategy
unicornModel
UDT for data storage and drawings associated with the Unicorn Model Strategy
Fields:
hPoint (chart.point)
hPoint2 (chart.point)
hPoint3 (chart.point)
breakerBlock (series box) : used to draw the breaker block required for the Unicorn Model
FVG (series box) : used to draw the FVG required for the Unicorn model
topBlock (series float) : price of top of breaker block, can be used to detail trade entry
botBlock (series float) : price of bottom of breaker block, can be used to detail trade entry
startBlock (series int) : start time of the breaker block, used to set the "left = " param for the box
includes (array) : used to store the time of the breaker block, or FVG, or the chart point sequence that setup the Unicorn Model.
entry (series float) : // eligible entry price, for longs"math.max(topBlock, FVG.get_top())",
tpLine (series line) : optional line to mark PT
tpLabel (series label) : optional label to mark PT
slLine (series line) : optional line to mark SL
slLabel (series label) : optional label to mark SL
rejectionBlocks
UDT for data storage and drawings associated with rejection blocks
Fields:
rejectionPoint (chart.point)
bodyPrice (series float) : candle body price closest to the rejection point, for "Up" rejections => math.max(open, close),
rejectionBox (series box) : optional box drawing of the rejection block
rejectionLabel (series label) : optional label for the rejection block
equalLevelsDraw
UDT for data storage and drawings associated with equal highs / equal lows
Fields:
connector (series line) : single line placed at the first high or low, y = avgerage of distinguished equal highs/lows
connectorLab (series label) : optional label to be placed at the highs or lows
levels (array) : array containing the equal highs or lows prices
times (array) : array containing the equal highs or lows individual times
startTime (series int) : the time of the first high or low that forms a sequence of equal highs or lows
radiate (array) : options label to "radiate" the label in connector lab. Can be used for anything
necessaryData
UDT for data storage of historical price points.
Fields:
highArr (array) : array containing historical high points
lowArr (array) : array containing historical low points
timeArr (array) : array containing historical time points
logArr (array) : array containing historical log returns
signArr (array) : array containing historical price directions
closeArr (array) : array containing historical close points
binaryTimeArr (array) : array containing historical time points, uses "push" instead of "unshift" to allow for binary search
binaryCloseArr (array) : array containing historical close points, uses "push" instead of "unshift" to allow the correct
binaryOpenArr (array) : array containing historical optn points, uses "push" instead of "unshift" to allow the correct
atrTFarr (array) : array containing historical user-selected TF atr points
openArr (array) : array containing historical open points
Pure Price Action Order & Breaker Blocks [LuxAlgo]The Pure Price Action Order & Breaker Blocks indicator is a pure price action adaptation of our previously published and highly popular Order-Blocks-Breaker-Blocks script.
Similar to its earlier version, this indicator detects order blocks that can automatically turn into breaker blocks on the chart once mitigated. However, the key difference/uniqueness is that the pure price action version relies solely on price patterns, eliminating the need for length definitions. In other words, it removes the limitation of user-defined inputs, ensuring a robust and objective analysis of market dynamics.
🔶 USAGE
An order block is a significant area on a price chart where there was a notable accumulation or distribution of orders, often identified by a strong price move followed by consolidation. Traders use order blocks to identify potential support or resistance levels.
A mitigated order block refers to an order block that has been invalidated due to subsequent market movements. It may no longer hold the same significance in the current market context. However, when the price mitigates an order block, a breaker block is confirmed. It is possible that the price might trade back to this breaker block, potentially offering a new trading opportunity.
Users can optionally enable the "Historical Polarity Changes" labels within the settings menu to see where breaker blocks might have previously provided effective trade setups.
This feature is most effective when using replay mode. Please note that these labels are subject to backpainting.
🔶 DETAILS
The swing points detection feature relies exclusively on price action, eliminating the need for numerical user-defined settings.
The first step involves detecting short-term swing points, where a short-term swing high (STH) is identified as a price peak surrounded by lower highs on both sides. Similarly, a short-term swing low is recognized as a price trough surrounded by higher lows on both sides.
Intermediate-term swing and long-term swing points are detected using the same approach but with a slight modification. Instead of directly analyzing price candles, we now utilize the previously detected short-term swing points. For intermediate-term swing points, we rely on short-term swing points, while for long-term swing points, we use the intermediate-term ones.
🔶 SETTINGS
Detection: Market structure used to detect swing points for creating order blocks.
Show Last Bullish OB: Number of the most recent bullish order/breaker blocks to display on the chart.
Show Last Bearish OB: Number of the most recent bearish order/breaker blocks to display on the chart.
Use Candle Body: Allows users to use candle bodies as order block areas instead of the full candle range.
🔹 Style
Show Historical Polarity Changes: Allows users to see labels indicating where a swing high/low previously occurred within a breaker block.
🔶 RELATED SCRIPTS
Pure-Price-Action-Structures.
Order-Blocks-Breaker-Blocks.
Predictive Trend and Structure (Expo)█ Overview
The Predictive Trend and Structure indicator is designed for traders seeking to identify future trend directions and interruptions in trend continuation. This indicator is unique because it employs standard deviation to predict upcoming trend directions and potential trend continuation levels. This enables traders to stay ahead of the market.
█ How It Works
This indicator primarily functions based on the calculated standard deviation of the trend over a specified period. It evaluates the trend direction by comparing the current trend value to its previous one and scales the standard deviation, allowing for adjustments in sensitivity to price fluctuations.
█ How to Use
Trend
You can easily identify when a future trend begins by observing where the trend level is displayed. If the price breaks above and remains above the trend, it indicates a bullish trend. Conversely, if the price breaks below and stays below, it signifies a bearish trend.
Support and Resistance
With the Predictive Structure enabled, the indicator aids in identifying potential support and resistance levels.
Trend Continuation Break
Trend continuation breaks occur when prices breaks support or resistance, indicating the existing trend may persist. The indicator plots these levels in advance, allowing traders to quickly identify where trend continuation might occur.
█ Settings
Period for Std Dev: Determines the number of periods used for the standard deviation calculation, impacting the indicator's sensitivity to price changes.
Standard Deviation Scaler: Scales the computed standard deviation, affecting the deviations needed to confirm trends and the indicator's focus on significant trend changes.
Predictive Structure: Enables or disables the prediction of market structures like potential levels of structure breaks/trend continuation breaks.
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Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
Fibonacci Structure & Trend Channel (Expo)█ Overview
The Fibonacci Structure & Trend Channel (Expo) is designed to identify trend direction and potential reversal levels and offer insights into price structure based on Fibonacci ratios. The algorithm plots a Fibonacci channel, making it easier for traders to identify potential retracement points. Additionally, the Fibonacci market structure is plotted to enhance traders' understanding of the underlying order flow.
█ How to Use
Identify Trends
Use the plotted Fibonacci Trend Line to identify the direction of the market trend. A green line typically signifies a bullish trend, while a red line signifies a bearish trend.
Retracement Levels
The plotted Fibonacci levels can act as potential support or resistance levels. Look for price action signs at these levels for entry or exit points.
Channel Trading
If you enable the Fibonacci channel, the upper and lower bounds can act as overbought or oversold levels.
Market Structure
The plotted Fibonacci market structure serves as a valuable tool for dissecting the underlying order flow and gauging the strength or weakness of a trend. By analyzing these structures, traders can identify key levels where supply and demand intersect, which often act as pivotal points for trend reversals or accelerations. This visual representation simplifies complex market dynamics. Whether you're looking to catch a new trend early or seeking confirmation for a potential reversal, understanding the market structure plotted by the Fibonacci ratios can provide actionable insights for various trading strategies.
Use the Table
The information table can provide quick insights into the current trend and when it started.
█ Settings
The Fibonacci settings allow traders to specify the Fibonacci retracement levels that will be used to calculate the trend and its channel.
The Fibonacci Structure Trend Channel structure settings enable traders to fine-tune how the indicator identifies and plots the underlying price structure.
-----------------
Disclaimer
The information contained in my Scripts/Indicators/Ideas/Algos/Systems does not constitute financial advice or a solicitation to buy or sell any securities of any type. I will not accept liability for any loss or damage, including without limitation any loss of profit, which may arise directly or indirectly from the use of or reliance on such information.
All investments involve risk, and the past performance of a security, industry, sector, market, financial product, trading strategy, backtest, or individual's trading does not guarantee future results or returns. Investors are fully responsible for any investment decisions they make. Such decisions should be based solely on an evaluation of their financial circumstances, investment objectives, risk tolerance, and liquidity needs.
My Scripts/Indicators/Ideas/Algos/Systems are only for educational purposes!
HTF Candles with PVSRA Volume Coloring (PCS Series)This indicator displays higher timeframe (HTF) candles using a PVSRA-inspired color model that blends price and volume strength, allowing traders to visualize higher-timeframe activity directly on lower-timeframe charts without switching screens.
OVERVIEW
This script visualizes higher-timeframe (HTF) candles directly on lower-timeframe charts using a custom PVSRA (Price, Volume & Support/Resistance Analysis) color model.
Unlike standard HTF indicators, it aggregates real-time OHLC and volume data bar-by-bar and dynamically draws synthetic HTF candles that update as the higher-timeframe bar evolves.
This allows traders to interpret momentum, trend continuation, and volume pressure from broader market structures without switching charts.
INTEGRATION LOGIC
This script merges higher-timeframe candle projection with PVSRA volume analysis to provide a single, multi-timeframe momentum view.
The HTF structure reveals directional context, while PVSRA coloring exposes the underlying strength of buying and selling pressure.
By combining both, traders can see when a higher-timeframe candle is building with strong or weak volume, enabling more informed intraday decisions than either tool could offer alone.
HOW IT WORKS
Aggregates price data : Groups lower-timeframe bars to calculate higher-timeframe Open, High, Low, Close, and total Volume.
Applies PVSRA logic : Compares each HTF candle’s volume to the average of the last 10 bars:
• >200% of average = strong activity
• >150% of average = moderate activity
• ≤150% = normal activity
Assigns colors :
• Green/Blue = bullish high-volume
• Red/Fuchsia = bearish high-volume
• White/Gray = neutral or low-volume moves
Draws dynamic outlines : Outlines update live while the current HTF candle is forming.
Supports symbol override : Calculations can use another instrument for correlation analysis.
This multi-timeframe aggregation avoids repainting issues in request.security() and ensures accurate real-time HTF representation.
FEATURES
Dual HTF Display : Visualize two higher timeframes simultaneously (e.g., 4H and 1D).
Dynamic PVSRA Coloring : Volume-weighted candle colors reveal bullish or bearish dominance.
Customizable Layout : Adjust candle width, spacing, offset, and color schemes.
Candle Outlines : Highlight the forming HTF candle to monitor developing structure.
Symbol Override : Display HTF candles from another instrument for cross-analysis.
SETTINGS
HTF 1 & HTF 2 : enable/disable, set timeframes, choose label colors, show/hide outlines.
Number of Candles : choose how many HTF candles to plot (1–10).
Offset Position : distance to the right of the current price where HTF candles begin.
Spacing & Width : adjust separation and scaling of candle groups.
Show Wicks/Borders : toggle wick and border visibility.
PVSRA Colors : enable or disable volume-based coloring.
Symbol Override : use a secondary ticker for HTF data if desired.
USAGE TIPS
Set the indicator’s visual order to “Bring to front.”
Always choose HTFs higher than your active chart timeframe.
Use PVSRA colors to identify strong momentum and potential reversals.
Adjust candle spacing and width for your chart layout.
Outlines are not shown on chart timeframes below 5 minutes.
TRADING STRATEGY
Strategy Overview : Combine HTF structure and PVSRA volume signals to
• Identify zones of high institutional activity and potential reversals.
• Wait for confirmation through consolidation or a pullback to key levels.
• Trade in alignment with dominant higher-timeframe structure rather than chasing volatility.
Setup :
• Chart timeframe: lower (5m, 15m, 1H)
• HTF 1: 4H or 1D
• HTF 2: 1D or 1W
• PVSRA Colors: enabled
• Outlines: enabled
Entry Concept :
High-volume candles (green or red) often indicate market-maker activity , such zones often reflect liquidity absorption by larger players and are not necessarily ideal entry points.
Wait for the next consolidation or pullback toward a support or resistance level before acting.
Bullish scenario :
• After a high-volume or rejection candle near a low, price consolidates and forms a higher low.
• Enter long only when structure confirms strength above support.
Bearish scenario :
• After a high-volume or rejection candle near a top, price consolidates and forms a lower high.
• Enter short once resistance holds and momentum weakens.
Exit Guidelines :
• Exit when next HTF candle shifts in color or momentum fades.
• Exit if price structure breaks opposite to your trade direction.
• Always use stop-loss and take-profit levels.
Additional Tips :
• Never enter directly on strong green/red high-volume candles, these are usually areas of institutional absorption.
• Wait for market structure confirmation and volume normalization.
• Combine with RSI, moving averages, or support/resistance for timing.
• Avoid trading when HTF candles are mixed or low-volume (unclear bias).
• Outlines hidden below 5m charts.
Risk Management :
• Use stop-loss and take-profit on all positions.
• Limit risk to 1–2% per trade.
• Adjust position size for volatility.
FINAL NOTES
This script helps traders synchronize lower-timeframe execution with higher-timeframe momentum and volume dynamics.
Test it on demo before live use, and adjust settings to fit your trading style.
DISCLAIMER
This script is for educational purposes only and does not constitute financial advice.
SUPPORT & UPDATES
Future improvements may include alert conditions and additional visualization modes. Feedback is welcome in the comments section.
CREDITS & LICENSE
Created by @seoco — open source for community learning.
Licensed under Mozilla Public License 2.0 .
Curved Radius Supertrend [BOSWaves]Curved Radius Supertrend — Adaptive Parabolic Trend Framework with Dynamic Acceleration Geometry
Overview
The Curved Radius Supertrend introduces an evolution of the classic Supertrend indicator - engineered with a dynamic curvature engine that replaces rigid ATR bands with parabolic, radius-based motion. Traditional Supertrend systems rely on static band displacement, reacting linearly to volatility and often lagging behind emerging price acceleration. The Curved Radius Supertend model redefines this by integrating controlled acceleration and curvature geometry, allowing the trend bands to adapt fluidly to both velocity and duration of price movement.
The result is a smoother, more organic trend flow that visually captures the momentum curve of price action - not just its direction. Instead of sharp pivots or whipsaws, traders experience a structurally curved trajectory that mirrors real market inertia. This makes it particularly effective for identifying sustained directional phases, detecting early trend rotations, and filtering out noise that plagues standard Supertrend methodologies.
Unlike conventional band-following systems, the Curved Radius framework is time-reactive and velocity-aware, providing a nuanced signal structure that blends geometric precision with volatility sensitivity.
Theoretical Foundation
The Curved Radius Supertrend draws from the intersection of mathematical curvature dynamics and adaptive volatility processing. Standard Supertrend algorithms extend from Average True Range (ATR) envelopes - a linear measure of volatility that moves proportionally with price deviation. However, markets do not expand or contract linearly. Trend velocity typically accelerates and decelerates in nonlinear arcs, forming natural parabolas across price phases.
By embedding a radius-based acceleration function, the indicator models this natural behavior. The core variable, radiusStrength, controls how aggressively curvature accelerates over time. Instead of simply following price distance, the band now evolves according to temporal acceleration - each bar contributes incremental velocity, bending the trend line into a radius-like curve.
This structural design allows the indicator to anticipate rather than just respond to price action, capturing momentum transitions as curved accelerations rather than binary flips. In practice, this eliminates the stutter effect typical of standard Supertrends and replaces it with fluid directional motion that better reflects actual trend geometry.
How It Works
The Curved Radius Supertrend is constructed through a multi-stage process designed to balance price responsiveness with geometric stability:
1. Baseline Supertrend Core
The framework begins with a standard ATR-derived upper and lower band calculation. These define the volatility envelope that constrains potential price zones. Directional bias is determined through crossover logic - prices above the lower band confirm an uptrend, while prices below the upper band confirm a downtrend.
2. Curvature Acceleration Engine
Once a trend direction is established, a curvature engine is activated. This system uses radiusStrength as a coefficient to simulate acceleration per bar, incrementally increasing velocity over time. The result is a parabolic displacement from the anchor price (the price level at trend change), creating a curved motion path that dynamically widens or tightens as the trend matures.
Mathematically, this acceleration behaves quadratically - each new bar compounds the previous velocity, forming an exponential rate of displacement that resembles curved inertia.
3. Adaptive Smoothing Layer
After the radius curve is applied, a smoothing stage (defined by the smoothness parameter) uses a simple moving average to regulate curve noise. This ensures visual coherence without sacrificing responsiveness, producing flowing arcs rather than jagged band steps.
4. Directional Visualization and Outer Envelope
Directional state (bullish or bearish) dictates both the color gradient and band displacement. An outer envelope is plotted one ATR beyond the curved band, creating a layered trend visualization that shows the extent of volatility expansion.
5. Signal Events and Alerts
Each directional transition triggers a 'BUY' or 'SELL' signal, clearly labeling phase shifts in market structure. Alerts are built in for automation and backtesting.
Interpretation
The Curved Radius Supertrend reframes how traders visualize and confirm trends. Instead of simply plotting a trailing stop, it maps the dynamic curvature of trend development.
Uptrend Phases : The band curves upward with increasing acceleration, reflecting the market’s growing directional velocity. As curvature steepens, conviction strengthens.
Downtrend Phases : The band bends downward in a mirrored acceleration pattern, indicating sustained bearish momentum.
Trend Change Points : When the direction flips and a new anchor point forms, the curve resets - providing a clean, early visual confirmation of structural reversal.
Smoothing and Radius Interplay : A lower radius strength produces a tighter, more reactive curve ideal for scalping or short timeframes. Higher values generate broad, sweeping arcs optimized for swing or positional analysis.
Visually, this curvature system translates market inertia into shape - revealing how trends bend, accelerate, and ultimately exhaust.
Strategy Integration
The Curved Radius Supertrend is versatile enough to integrate seamlessly into multiple trading frameworks:
Trend Following : Use BUY/SELL flips to identify emerging directional bias. Strong curvature continuation confirms sustained momentum.
Momentum Entry Filtering : Combine with oscillators or volume tools to filter entries only when the curve slope accelerates (high momentum conditions).
Pullback and Re-entry Timing : The smooth curvature of the radius band allows traders to identify shallow retracements without premature exits. The band acts as a dynamic, self-adjusting support/resistance arc.
Volatility Compression and Expansion : Flattening curvature indicates volatility compression - a potential pre-breakout zone. Rapid re-steepening signals expansion and directional conviction.
Stop Placement Framework : The curved band can serve as a volatility-adjusted trailing stop. Because the curve reflects acceleration, it adapts naturally to market rhythm - widening during momentum surges and tightening during stagnation.
Technical Implementation Details
Curved Radius Engine : Parabolic acceleration algorithm that applies quadratic velocity based on bar count and radiusStrength.
Anchor Logic : Resets curvature at each trend change, establishing a new reference base for directional acceleration.
Smoothing Layer : SMA-based curve smoothing for noise reduction.
Outer Envelope : ATR-derived band offset visualizing volatility extension.
Directional Coloring : Candle and band coloration tied to current trend state.
Signal Engine : Built-in BUY/SELL markers and alert conditions for automation or script integration.
Optimal Application Parameters
Timeframe Guidance :
1-5 min (Scalping) : 0.08–0.12 radius strength, minimal smoothing for rapid responsiveness.
15 min : 0.12–0.15 radius strength for intraday trends.
1H : 0.15–0.18 radius strength for structured short-term swing setups.
4H : 0.18–0.22 radius strength for macro-trend shaping.
Daily : 0.20–0.25 radius strength for broad directional curves.
Weekly : 0.25–0.30 radius strength for smooth macro-level cycles.
The suggested radius strength ranges provide general structural guidance. Optimal values may vary across assets and volatility regimes, and should be refined through empirical testing to account for instrument-specific behavior and prevailing market conditions.
Asset Guidance :
Cryptocurrency : Higher radius and multiplier values to stabilize high-volatility environments.
Forex : Midrange settings (0.12-0.18) for clean curvature transitions.
Equities : Balanced curvature for trending sectors or momentum rotation setups.
Indices/Futures : Moderate radius values (0.15-0.22) to capture cyclical macro swings.
Performance Characteristics
High Effectiveness :
Trending environments with directional expansion.
Markets exhibiting clean momentum arcs and low structural noise.
Reduced Effectiveness :
Range-bound or low-volatility conditions with repeated false flips.
Ultra-short-term timeframes (<1m) where curvature acceleration overshoots.
Integration Guidelines
Confluence Framework : Combine with structure tools (order blocks, BOS, liquidity zones) for entry validation.
Risk Management : Trail stops along the curved band rather than fixed points to align with adaptive market geometry.
Multi-Timeframe Confirmation : Use higher timeframe curvature as a trend filter and lower timeframe curvature for execution timing.
Curve Compression Awareness : Treat flattening arcs as potential exhaustion zones - ideal for scaling out or reducing exposure.
Disclaimer
The Curved Radius Supertrend is a geometric trend model designed for professional traders and analysts. It is not a predictive system or a guaranteed profit method. Its performance depends on correct parameter calibration and sound risk management. BOSWaves recommends using it as part of a comprehensive analytical framework, incorporating volume, liquidity, and structural context to validate directional signals.
