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Full Session ATR Range (Live) - with Position ToggleBelow is a publication-ready text for the "Full Session ATR Range (Live) - with Position Toggle" indicator, written in a professional yet accessible style suitable for a trading community (e.g., TradingView or a blog). The text highlights the indicator's features, usage, and benefits, while avoiding overly technical jargon for a broad audience. --- ### Introducing the Full Session ATR Range (Live) Indicator with Position Toggle Enhance your trading strategy with the **Full Session ATR Range (Live) Indicator**, a powerful tool designed to provide real-time insights into market volatility and session dynamics. This customizable indicator, now available with a position toggle feature, compares the current session's range to a 10-day Average True Range (ATR), helping traders gauge market activity and anticipate potential movements. #### Key Features - **Live Range Tracking**: Displays the current session's range (high minus low) alongside a 10-day ATR, updated in real-time during market hours. - **Session Mode Flexibility**: Includes an auto-toggle option to switch between Electronic Trading Hours (ETH) and Regular Trading Hours (RTH), adapting to your preferred trading session. Manually select ETH or RTH, or let the indicator auto-detect based on market hours. - **Comprehensive Metrics**: Offers a detailed breakdown including: - Range/Avg %: Percentage of the current range relative to the 10-day ATR. - Points Left: Remaining points to reach the average range. - 100% Range Up/Dn: Potential upper and lower targets based on the ATR difference. - **Position Customization**: Adjust the table's location on your chart with options like top-left, top-right, middle-center, or bottom-right for optimal visibility. - **Visual Appeal**: Features a customizable background and text color to match your chart theme. #### How It Works The indicator calculates the 10-day ATR using daily data and tracks the current session's range, resetting at the start of each day or session change. During market hours (e.g., 6 AM - 8 PM CDT, adjustable), it updates live, providing actionable insights. When the market is closed, it displays historical ATR while marking live metrics as "n/a" to avoid confusion. The ETH/RTH toggle ensures the range reflects either the full extended session or the core trading hours, tailored to your strategy. #### Why Use It? Whether you're a day trader monitoring intraday volatility or a swing trader assessing longer-term trends, this indicator helps you: - Identify overextended or underactive sessions compared to historical norms. - Plan entries and exits with targets based on the 100% Range Up/Dn levels. - Stay informed with a clean, adjustable display that fits your workflow. #### Installation & Customization 1. Add the indicator to your TradingView chart. 2. Adjust the ATR length (default: 10 days) and table position via the input settings. 3. Choose your session mode (Auto, ETH, or RTH) and customize colors to suit your style. 4. Test during market hours for live updates—note that static values may appear outside trading sessions. #### Feedback & Support This indicator is designed for flexibility and ease of use. Share your feedback or request enhancements by commenting below or contacting the developer. Happy trading!
Pine Script® indicator
by Futurestraderbrian
Futures Key LevelsKey Levels — Sessions, Previous Ranges & Opens (Chicago-aligned sessions) What it does This indicator plots commonly used reference levels across multiple timeframes to help you frame the day and find confluence: Sessions (Chicago TZ): London, New York, and Asia session high/low ranges. Previous Period Ranges: Previous Day / Week / Month / Quarter / Year High/Low and optional Mid. Opens: Current Daily / Weekly / Monthly / Quarterly / Yearly opens. Intraday (4H): Previous 4-Hour High/Low + optional Mid. Monday Range: Captures Monday’s High/Low (and optional Mid) to use as a weekly reference. Price-scale markers: Optional markers that track key levels on the price scale without adding extra lines. How it works (concepts & calculations) Higher-timeframe values are retrieved using request.security() and update when a new period begins (e.g., previous day’s H/L become fixed at the start of the new day). Session ranges are built from bar data within session windows using time(session, "America/Chicago"): London: 02:00–05:00 CT New York: 08:30–15:00 CT Asia: 20:00–00:00 CT “Mid” levels are simple midpoints between each period’s High and Low. Merge Levels: when different levels land at the same price, their labels are merged to reduce clutter (e.g., “PDH / PWH”). Why this version is useful / original bits All-white baseline for clean charts; session colors stand out by design: London = Yellow, New York = Aquatic Blue, Asia = Red. Right-anchored mode lets you park levels to the right side of the chart with a configurable anchor distance. Label merging keeps the display minimal when multiple levels coincide. Price-scale-only markers available when you prefer fewer lines on the chart. Inputs & customization Display Style: Standard or Right Anchored (+ distance controls). Levels toggles: enable/disable each period (Daily/Weekly/Monthly/Quarterly/Yearly), Monday range, 4H range, and session ranges. Text: optional shorthand labels (e.g., PDH/PDL, PWH/PWL). Colors: global white theme, with session highlights; you can override in the Inputs. Price-scale markers: on/off toggle. How to use it Use previous High/Low as liquidity pools and areas to watch for sweeps, breaks, or retests. The Monday range often frames the rest of the week; breaks or rejections around Monday H/L can be informative. The 4H previous range gives intraday context—great for mean-reversion vs. continuation reads. Session ranges help you see where the active session expanded price and where liquidity may remain. Notes & limitations Sessions are computed in America/Chicago; higher-TF levels use the symbol’s exchange timezone. This is an indicator, not a strategy; it does not place trades or claim performance. Always combine levels with your own execution rules (structure, momentum, risk). Credit: inspired by spacemanBTC; this version adds the all-white styling, Chicago-aligned sessions, right-anchoring, label merging, and price-scale markers. Also my mentor to tell me about the levels Disclaimer This tool is for educational purposes only and is not financial advice. Markets involve risk; do your own research and manage risk appropriately.
Pine Script® indicator
by thesyjaga
1
Futures Multi-Asset Open Distance Table## Multi-Asset Open Distance Table - Quick Description This Pine Script indicator displays a **real-time table** that tracks how far **three user-selected assets** are from their key opening price levels. **What it shows:** - **Three customizable assets** (default: NQ!, ES!, YM!) - **Distance from 3 key opens** for each asset: - **1800 ET Open** (Electronic trading session start) - **0930 ET Open** (Regular market hours start) - **Weekly Open** (Beginning of trading week) **Visual features:** - **Percentage changes** from each open level - **Color coding**: Green for gains above opens, red for losses below opens - **Direction arrows**: ▲ (above), ▼ (below), ■ (unchanged) - **Customizable table position** and size **Perfect for:** - **Intraday traders** monitoring key session levels - **Multi-timeframe analysis** across different market opens - **Quick reference** to see which assets are performing relative to major opening levels - **Session-based trading strategies** using 6PM and 9:30AM opens The table updates in real-time and provides an at-a-glance view of where your chosen assets stand relative to these critical price reference points throughout the trading day.
Pine Script® indicator
by Fractaldactyl
Futures Weekly Open RangeThe weekly opening range ( high to low ) is calculated from the open of the market on Sunday (1800 EST) till the opening of the Bond Market on Monday morning (0800 EST). This is the first and most crucial range for the trading week. As ICT has taught, price is moving through an algorithm and as such is fractal; because price is fractal, the opening range can be calculated and projected to help determine if price is trending or consolidating. As well; this indicator can be used to incorporate his PO3 concept to enter above the weekly opening range for shorts if bearish, or entering below the opening range for longs if bullish. This indicator takes the high and low of weekly opening range, plots those two levels, plots the opening price for the new week, and calculates the Standard Deviations of the range and plots them both above and below of the weekly opening range. These are all plotted through the week until the start of the new week. The range is calculated by subtracting the high from the low during the specified time. The mid-point is half of that range added to the low. The Standard deviation is multiples of the range (up to 10) added to the high and subtracted from the low. At this time the indicator will only plot the Standard deviation lines on the minutes time frame below 1 hour. Only the range and range lines will be plotted on the hourly chart.
Pine Script® indicator
by Fractaldactyl
1
Really Key Levels█  OVERVIEW This indicator shows the most useful and universally used key trading levels (and only those) in a visually appealing way. Its originality lies in the fact that it was developed due to being unable to find an indicator that wasn't cluttered with other features or far less relevant levels, or one that would indicate the bar causing the level (i.e., not just using a horizontal line over the whole chart), or one that was well-programmed and didn’t frequently refresh for many seconds for no obvious reason, taking far too long to do so for such a seemingly simple indicator. █  FEATURES Shows the most frequently used key levels in a visually appealing way Indicates the bar that causes the level, with the line starting at that bar Works correctly and consistently on both RTH and ETH charts Lines can be optionally extended both left and right, if the user prefers Works with US/European stocks and US futures (at least) Configurable futures regular session (default time is for CME futures, e.g., ES/NQ, etc.) Users can configure line colour, style, and thickness Adjustable label locations to prevent overlap with other indicator labels Nice defaults that look good, and a well-contrasting label text colour Well-documented, high-quality, open-source code for those who are interested █  CONCEPTS The indicator shows the following levels by a line starting at the bar that causes them: Current Day RTH High/Low (visible and updated only during RTH; visible with no further updates in the post-market) Current Day RTH Open (only after the RTH open) Pre-Market High/Low (as it develops in the pre-market and fixed after RTH open) Previous Day RTH Close Previous Day RTH High/Low Previous Day Pre-Market High-Low Two Days Ago RTH Close Other levels may be added in future versions, if requested and if they are Really Key Levels. Regarding futures: despite being a 23-hour market (for CME futures, 5 p.m. the previous day to 4 p.m. the current day), most trading activity takes place together with the RTH on stock exchanges in New York, 08:30 to 3 p.m. Central (Chicago) time. Therefore, a user-configurable regular market is defined at those times, with times before this (from 5 p.m. the previous day) being considered pre-market, and times after this (until 4 p.m.) being considered post-market. Care was taken so that the code uses no hard-coded time zones, exchanges, or session times. For this reason, it can in principle work globally. However, it very much depends on the information provided by the exchange, which is reflected in built-in Pine Script variables (see Limitations below). █  LIMITATIONS Pre-market levels are not shown when viewing an RTH chart. The indicator was developed and tested on US/European stocks and US futures. It may or may not work for stocks and futures in other countries (depending on their pre- and post-market definitions and what information the exchange provides to TradingView via the relevant built-in Pine Script variable). It does not work on other security types, especially those with a 24-hour market that don't have a uniquely defined daily close, implicit H/L time window, or a pre-market.
Pine Script® indicator
by BruzX
1
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.
Pine Script® indicator
by EdgeTools
6
Cash And Carry Arbitrage BTC Compare Month 6 by SeoNo1Detailed Explanation of the BTC Cash and Carry Arbitrage Script Script Title: BTC Cash And Carry Arbitrage Month 6 by SeoNo1 Short Title: BTC C&C ABT Month 6 Version: Pine Script v5 Overlay: True (The indicators are plotted directly on the price chart) Purpose of the Script This script is designed to help traders analyze and track arbitrage opportunities between the spot market and futures market for Bitcoin (BTC). Specifically, it calculates the spread and Annual Percentage Yield (APY) from a cash-and-carry arbitrage strategy until a specific expiry date (in this case, June 27, 2025). The strategy helps identify profitable opportunities when the futures price of BTC is higher than the spot price. Traders can then buy BTC in the spot market and short BTC futures contracts to lock in a risk-free profit. 1. Input Settings Spot Symbol: The real-time BTC spot price from Binance (BTCUSDT). Futures Symbol: The BTC futures contract that expires in June 2025 (BTCUSDM2025). Expiry Date: The expiration date of the futures contract, set to June 27, 2025. These inputs allow users to adjust the symbols or expiry date according to their trading needs. 2. Price Data Retrieval Spot Price: Fetches the latest closing price of BTC from the spot market. Futures Price: Fetches the latest closing price of BTC futures. Spread: The difference between the futures price and the spot price (futures_price - spot_price). The spread indicates how much higher (or lower) the futures price is compared to the spot market. 3. Time to Maturity (TTM) and Annual Percentage Yield (APY) Calculation Current Date: Gets the current timestamp. Time to Maturity (TTM): The number of days left until the futures contract expires. APY Calculation: Formula: APY = ( Spread / Spot Price ) x ( 365 / TTM Days ) x 100 This represents the annualized return from holding a cash-and-carry arbitrage position if the trader buys BTC at the spot price and sells BTC futures. 4. Display Information Table on the Chart A table is created on the chart's top-right corner showing the following data: Metric: Labels such as Spread and APY Value: Displays the calculated spread and APY The table automatically updates at the latest bar to display the most recent data. 5. Alert Condition This sets an alert condition that triggers every time the script runs. In practice, users can modify this alert to trigger based on specific conditions (e.g., APY exceeds a threshold). 6. Plotting the APY and Spread APY Plot: Displays the annualized yield as a blue line on the chart. Spread Plot: Visualizes the futures-spot spread as a red line. This helps traders quickly identify arbitrage opportunities when the spread or APY reaches desirable levels. How to Use the Script Monitor Arbitrage Opportunities: A positive spread indicates a potential cash-and-carry arbitrage opportunity. The larger the APY, the more profitable the arbitrage opportunity could be. Timing Trades: Execute a buy on the BTC spot market and simultaneously sell BTC futures when the APY is attractive. Close both positions upon futures contract expiry to realize profits. Risk Management: Ensure you have sufficient margin to hold both positions until expiry. Monitor funding rates and volatility, which could affect returns. Conclusion This script is an essential tool for traders looking to exploit price discrepancies between the BTC spot market and futures market through a cash-and-carry arbitrage strategy. It provides real-time data on spreads, annualized returns (APY), and visual alerts, helping traders make informed decisions and maximize their profit potential.
Pine Script® indicator
by seono1
Lot Size + Margin InfoThis indicator is designed to give Futures & Options traders instant access to lot size and estimated margin requirements for the instrument they are viewing — directly on their TradingView chart. It combines real-time symbol detection with a built-in, regularly updated margin lookup table (sourced from Kotak Securities’ published margin requirements), while also handling fallback logic for unknown or unsupported symbols. --- ### What It Does * Automatically Detects the Instrument Type Identifies whether the current chart’s symbol is a futures contract, option, or a cash/spot instrument. * Shows Accurate Lot Size For supported F\&O symbols, it fetches the correct lot size directly from exchange data. For options, it retrieves the lot size from the option’s point value. For cash/spot symbols with linked futures, it uses the futures lot size. * Calculates Estimated Margin * For futures: `Lot Size × Current Price × Margin%` (Margin% sourced from the internal lookup table). * For options: `Lot Size × Current Price` (simple multiplication, as options margin ≈ premium cost). * For unsupported or non-FnO symbols: Displays "No FnO". * Fallback Margin Logic If a symbol is missing from the margin lookup table, the script applies a user-defined default margin percentage and highlights the data in orange to indicate it’s using fallback values. * Debug Mode for Transparency A toggle to display the exact symbol string used for fetching lot size and margin, so traders can verify the data source. --- ### How It Works 1. Symbol Normalization The script standardizes symbol names to match the margin table format (e.g., converting `"NIFTY1!"` to `"NIFTY"`). 2. Type-Based Handling * Futures – Uses point value for lot size, applies specific margin % from the table. * Options – Uses option point value for lot size, margin is simply premium × lot size. * Cash Symbols with Linked Futures – Attempts to find and use the associated futures contract for lot/margin data. * Unsupported Symbols – Displays `"No FnO"`. 3. Margin Table Integration The margin % table is manually updated from a reliable broker’s margin sheet (Kotak Securities) — ensuring alignment with real trading conditions. 4. Customizable Display * Position (Top Right / Bottom Left / Bottom Right) * Table background color, text color, font size, border width * Editable label text for lot size and margin display * Toggleable lot size and margin sections --- ### How to Use 1. Add the Indicator to Your Chart – Works on any NSE Futures, Options, or Cash symbol with linked F\&O. 2. Configure Display Settings – Choose whether to show lot size, margin, or both, and place the info table where you prefer. 3. Adjust Fallback Margin % – If you trade less common contracts, set your default margin % to reflect your broker’s requirement. 4. Enable Debug Mode (Optional) – To see the exact symbol source the script is using. --- ### Best For * Intraday & Positional F\&O Traders who need instant clarity on lot size and margin before entering trades. * Options Sellers & Buyers who want quick cost estimates. * Traders Switching Symbols Quickly — saves time by removing the need to check the broker’s margin sheet manually. --- 💡 Pro Tip: Since margin requirements can change, keep the script updated whenever your broker revises margin data. This version’s margin table is updated as of 13-08-2025.
Pine Script® indicator
by Hitesh2603
BTC Future Gamma-Weighted Momentum Model (BGMM)The BTC Future Gamma-Weighted Momentum Model (BGMM) is a quantitative trading strategy that utilizes the Gamma-weighted average price (GWAP) in conjunction with a momentum-based approach to predict price movements in the Bitcoin futures market. The model combines the concept of weighted price movements with trend identification, where the Gamma factor amplifies the weight assigned to recent prices. It leverages the idea that historical price trends and weighting mechanisms can be utilized to forecast future price behavior. Theoretical Background: 1. Momentum in Financial Markets: Momentum is a well-established concept in financial market theory, referring to the tendency of assets to continue moving in the same direction after initiating a trend. Any observed market return over a given time period is likely to continue in the same direction, a phenomenon known as the “momentum effect.” Deviations from a mean or trend provide potential trading opportunities, particularly in highly volatile assets like Bitcoin. Numerous empirical studies have demonstrated that momentum strategies, based on price movements, especially those correlating long-term and short-term trends, can yield significant returns (Jegadeesh & Titman, 1993). Given Bitcoin’s volatile nature, it is an ideal candidate for momentum-based strategies. 2. Gamma-Weighted Price Strategies: Gamma weighting is an advanced method of applying weights to price data, where past price movements are weighted by a Gamma factor. This weighting allows for the reinforcement or reduction of the influence of historical prices based on an exponential function. The Gamma factor (ranging from 0.5 to 1.5) controls how much emphasis is placed on recent data: a value closer to 1 applies an even weighting across periods, while a value closer to 0 diminishes the influence of past prices. Gamma-based models are used in financial analysis and modeling to enhance a model’s adaptability to changing market dynamics. This weighting mechanism is particularly advantageous in volatile markets such as Bitcoin futures, as it facilitates quick adaptation to changing market conditions (Black-Scholes, 1973). Strategy Mechanism: The BTC Future Gamma-Weighted Momentum Model (BGMM) utilizes an adaptive weighting strategy, where the Bitcoin futures prices are weighted according to the Gamma factor to calculate the Gamma-Weighted Average Price (GWAP). The GWAP is derived as a weighted average of prices over a specific number of periods, with more weight assigned to recent periods. The calculated GWAP serves as a reference value, and trading decisions are based on whether the current market price is above or below this level. 1. Long Position Conditions: A long position is initiated when the Bitcoin price is above the GWAP and a positive price movement is observed over the last three periods. This indicates that an upward trend is in place, and the market is likely to continue in the direction of the momentum. 2. Short Position Conditions: A short position is initiated when the Bitcoin price is below the GWAP and a negative price movement is observed over the last three periods. This suggests that a downtrend is occurring, and a continuation of the negative price movement is expected. Backtesting and Application to Bitcoin Futures: The model has been tested exclusively on the Bitcoin futures market due to Bitcoin’s high volatility and strong trend behavior. These characteristics make the market particularly suitable for momentum strategies, as strong upward or downward movements are often followed by persistent trends that can be captured by a momentum-based approach. Backtests of the BGMM on the Bitcoin futures market indicate that the model achieves above-average returns during periods of strong momentum, especially when the Gamma factor is optimized to suit the specific dynamics of the Bitcoin market. The high volatility of Bitcoin, combined with adaptive weighting, allows the model to respond quickly to price changes and maximize trading opportunities. Scientific Citations and Sources: • Jegadeesh, N., & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. The Journal of Finance, 48(1), 65–91. • Black, F., & Scholes, M. (1973). The Pricing of Options and Corporate Liabilities. Journal of Political Economy, 81(3), 637–654. • Fama, E. F., & French, K. R. (1992). The Cross-Section of Expected Stock Returns. The Journal of Finance, 47(2), 427–465.
Pine Script® strategy
by EdgeTools
Intrabar Volume Delta — RealTime + History (Stocks/Crypto/Forex)Intrabar Volume Delta Grid — RealTime + History (Stocks/Crypto/Forex) # Short Description Shows intrabar Up/Down volume, Delta (absolute/relative) and UpShare% in a compact grid for both real-time and historical bars. Includes an MTF (M1…D1) dashboard, contextual coloring, density controls, and alerts on Δ and UpShare%. Smart historical splitting (“History Mode”) for Crypto/Futures/FX. --- # What it does (Quick) * **UpVol / DownVol / Δ / UpShare%** — visualizes order-flow inside each candle. * **Real-time** — accumulates intrabar volume live by tick-direction. * **History Mode** — splits Up/Down on closed bars via simple or range-aware logic. * **MTF Dashboard** — one table view across M1, M5, M15, M30, H1, H4, D1 (Vol, Up/Down, Δ%, Share, Trend). * **Contextual opacity** — stronger signals appear bolder. * **Label density** — draw every N-th bar and limit to last X bars for performance. * **Alerts** — thresholds for |Δ|, Δ%, and UpShare%. --- # How it works (Real-Time vs History) * **Real-time (open bar):** volume increments into **UpVolRT** or **DownVolRT** depending on last price move (↑ goes to Up, ↓ to Down). This approximates live order-flow even when full tick history isn’t available. * **History (closed bars):** * **None** — no split (Up/Down = 0/0). Safest for equities/indices with unreliable tick history. * **Approx (Close vs Open)** — all volume goes to candle direction (green → Up 100%, red → Down 100%). Fast but yields many 0/100% bars. * **Price Action Based** — splits by Close position within High-Low range; strength = |Close−mid|/(High−Low). Above mid → more Up; below mid → more Down. Falls back to direction if High==Low. * **Auto** — **Stocks/Index → None**, **Crypto/Futures/FX → Approx**. If you see too many 0/100 bars, switch to **Price Action Based**. --- # Rows & Meaning * **Volume** — total bar volume (no split). * **UpVol / DownVol** — directional intrabar volume. * **Delta (Δ)** — UpVol − DownVol. * **Absolute**: raw units * **Relative (Δ%)**: Δ / (Up+Down) × 100 * **Both**: shows both formats * **UpShare%** — UpVol / (Up+Down) × 100. >50% bullish, <50% bearish. * Helpful icons: ▲ (>65%), ▼ (<35%). --- # MTF Dashboard (🔧 Enable Dashboard) A single table with **Vol, Up, Down, Δ%, Share, Trend (🔼/🔽/⏭️)** for selected timeframes (M1…D1). Great for a fast “panorama” read of flow alignment across horizons. --- # Inputs (Grouped) ## Display * Toggle rows: **Volume / Up / Down / Delta / UpShare** * **Delta Display**: Absolute / Relative / Both ## Realtime & History * **History Mode**: Auto / None / Approx / Price Action Based * **Compact Numbers**: 1.2k, 1.25M, 3.4B… ## Theme & UI * **Theme Mode**: Auto / Light / Dark * **Row Spacing**: vertical spacing between rows * **Top Row Y**: moves the whole grid vertically * **Draw Guide Lines**: faint dotted guides * **Text Size**: Tiny / Small / Normal / Large ## 🔧 Dashboard Settings * **Enable Dashboard** * **📏 Table Text Size**: Tiny…Huge * **🦓 Zebra Rows** * **🔲 Table Border** ## ⏰ Timeframes (for Dashboard) * **M1…D1** toggles ## Contextual Coloring * **Enable Contextual Coloring**: opacity by signal strength * **Δ% cap / Share offset cap**: saturation caps * **Min/Max transparency**: solid vs faint extremes ## Label Density & Size * **Show every N-th bar**: draw labels only every Nth bar * **Limit to last X bars**: keep labels only in the most recent X bars ## Colors * Up / Down / Text / Guide ## Alerts * **Delta Threshold (abs)** — |Δ| in volume units * **UpShare > / <** — bullish/bearish thresholds * **Enable Δ% Alert**, **Δ% > +**, **Δ% < −** — relative delta levels --- # How to use (Quick Start) 1. Add the indicator to your chart (overlay=false → separate pane). 2. **History Mode**: * Crypto/Futures/FX → keep **Auto** or switch to **Price Action Based** for richer history. * Stocks/Index → prefer **None** or **Price Action Based** for safer splits. 3. **Label Density**: start with **Limit to last X bars = 30–150** and **Show every N-th bar = 2–4**. 4. **Contextual Coloring**: keep on to emphasize strong Δ% / Share moves. 5. **Dashboard**: enable and pick only the TFs you actually use. 6. **Alerts**: set thresholds (ideas below). --- # Alerts (in TradingView) Add alert → pick this indicator → choose any of: * **Delta exceeds threshold** (|Δ| > X) * **UpShare above threshold** (UpShare% > X) * **UpShare below threshold** (UpShare% < X) * **Relative Delta above +X%** * **Relative Delta below −X%** **Starter thresholds (tune per symbol & TF):** * **Crypto M1/M5**: Δ% > +25…35 (bullish), Δ% < −25…−35 (bearish) * **FX (tick volume)**: UpShare > 60–65% or < 40–35% * **Stocks (liquid)**: set **Absolute Δ** by typical volume scale (e.g., 50k / 100k / 500k) --- # Notes by Market Type * **Crypto/Futures**: 24/7 and high liquidity — **Price Action Based** often gives nicer history splits than Approx. * **Forex (FX)**: TradingView volume is typically **tick volume** (not true exchange volume). Treat Δ/Share as tick-based flow, still very useful intraday. * **Stocks/Index**: historical tick detail can be limited. **None** or **Price Action Based** is a safer default. If you see too many 0/100% shares, switch away from Approx. --- # “All Timeframes” accuracy * Works on **any TF** (M1 → D1/W1). * **Real-time accuracy** is strong for the open bar (live accumulation). * **Historical accuracy** depends on your **History Mode** (None = safest, Approx = fastest/simplest, Price Action Based = more nuanced). * The MTF dashboard uses `request.security` and therefore follows the same logic per TF. --- # Trade Ideas (Use-Cases) * **Scalping (M1–M5)**: a spike in Δ% + UpShare>65% + rising total Vol → momentum entries. * **Intraday (M5–M30–H1)**: when multiple TFs show aligned Δ%/Share (e.g., M5 & M15 bullish), join the trend. * **Swing (H4–D1)**: persistent Δ% > 0 and UpShare > 55–60% → structural accumulation bias. --- # Advantages * **True-feeling live flow** on the open bar. * **Adaptable history** (three modes) to match data quality. * **Clean visual layout** with guides, compact numbers, contextual opacity. * **MTF snapshot** for quick bias read. * **Performance controls** (last X bars, every N-th bar). --- # Limitations & Care * **FX uses tick volume** — interpret Δ/Share accordingly. * **History Mode is an approximation** — confirm with trend/structure/liquidity context. * **Illiquid symbols** can produce noisy or contradictory signals. * **Too many labels** can slow charts → raise N, lower X, or disable guides. --- # Best Practices (Checklist) * Crypto/Futures: prefer **Price Action Based** for history. * Stocks: **None** or **Price Action Based**; be cautious with **Approx**. * FX: pair Δ% & UpShare% with session context (London/NY) and volatility. * If labels overlap: tweak **Row Spacing** and **Text Size**. * In the dashboard, keep only the TFs you actually act on. * Alerts: start around **Δ% 25–35** for “punchy” moves, then refine per asset. --- # FAQ **1) Why do some closed bars show 0%/100% UpShare?** You’re on **Approx** history mode. Switch to **Price Action Based** for smoother splits. **2) Δ% looks strong but price doesn’t move — why?** Δ% is an **order-flow** measure. Price also depends on liquidity pockets, sessions, news, higher-timeframe structure. Use confirmations. **3) Performance slowdown — what to do?** Lower **Limit to last X bars** (e.g., 30–100), increase **Show every N-th bar** (2–6), or disable **Draw Guide Lines**. **4) Dashboard values don’t “match” the grid exactly?** Dashboard is multi-TF via `request.security` and follows the history logic per TF. Differences are normal. --- # Short “Store” Marketing Blurb Intrabar Volume Delta Grid reveals the order-flow inside every candle (Up/Down, Δ, UpShare%) — live and on history. With smart history splitting, an MTF dashboard, contextual emphasis, and flexible alerts, it helps you spot momentum and bias across Crypto, Forex (tick volume), and Stocks. Tidy labels and compact numbers keep the panel readable and fast.
Pine Script® indicator
by a_jabbaroff
Clean Multi-Indicator Alignment System Overview A sophisticated multi-indicator alignment system designed for 24/7 trading across all markets, with pure signal-based exits and no time restrictions. Perfect for futures, forex, and crypto markets that operate around the clock. Key Features 🎯 Multi-Indicator Confluence System EMA Cross Strategy: Fast EMA (5) and Slow EMA (10) for precise trend direction VWAP Integration: Institution-level price positioning analysis RSI Momentum: 7-period RSI for momentum confirmation and reversal detection MACD Signals: Optimized 8/17/5 configuration for scalping responsiveness Volume Confirmation: Customizable volume multiplier (default 1.6x) for signal validation 🚀 Advanced Entry Logic Initial Full Alignment: Requires all 5 indicators + volume confirmation Smart Continuation Entries: EMA9 pullback entries when trend momentum remains intact Flexible Time Controls: Optional session filtering or 24/7 operation 🎪 Pure Signal-Based Exits No Forced Closes: Positions exit only on technical signal reversals Dual Exit Conditions: EMA9 breakdown + RSI flip OR MACD cross + EMA20 breakdown Trend Following: Allows profitable trends to run their full course Perfect for Swing Scalping: Ideal for multi-session position holding 📊 Visual Interface Real-Time Status Dashboard: Live alignment monitoring for all indicators Color-Coded Candles: Instant visual confirmation of entry/exit signals Clean Chart Display: Toggle-able EMAs and VWAP with professional styling Signal Differentiation: Clear labels for entries, X-crosses for exits 🔔 Alert System Entry Notifications: Separate alerts for buy/sell signals Exit Warnings: Technical breakdown alerts for position management Mobile Ready: Push notifications to TradingView mobile app Market Applications Perfect For: Gold Futures (GC): 24-hour precious metals trading NASDAQ Futures (NQ): High-volatility index scalping Forex Markets: Currency pairs with continuous operation Crypto Trading: 24/7 cryptocurrency momentum plays Energy Futures: Oil, gas, and commodity swing trades Optimal Timeframes: 1-5 Minutes: Ultra-fast scalping during high volatility 5-15 Minutes: Balanced approach for most markets 15-30 Minutes: Swing scalping for trend following 🧠 Smart Position Management Tracks implied position direction Prevents conflicting signals Allows trend continuation entries State-aware exit logic ⚡ Scalping Optimized Fast-reacting indicators with shorter periods Volume-based confirmation reduces false signals Clean entry/exit visualization Minimal lag for time-sensitive trades Configuration Options All parameters fully customizable: EMA Lengths: Adjustable from 1-30 periods RSI Period: 1-14 range for different market conditions MACD Settings: Fast (1-15), Slow (1-30), Signal (1-10) Volume Confirmation: 0.5-5.0x multiplier range Visual Preferences: Colors, displays, and table options Risk Management Features Clear visual exit signals prevent emotion-based decisions Volume confirmation reduces false breakouts Multi-indicator confluence improves signal quality Optional time filtering for session-specific strategies Best Use Cases Futures Scalping: NQ, ES, GC during active sessions Forex Swing Trading: Major pairs during overlap periods Crypto Momentum: Bitcoin, Ethereum trend following 24/7 Automated Systems: Algorithmic trading implementation Multi-Market Scanning: Portfolio-wide signal monitoring
Pine Script® indicator
by Fizeks
1
Market Regime IndexThe Market Regime Index is a top-down macro regime nowcasting tool that offers a consolidated view of the market’s risk appetite. It tracks 32 of the world’s most influential markets across asset classes to determine investor sentiment by applying trend-following signals to each independent asset. It features adjustable parameters and a built-in alert system that notifies investors when conditions transition between Risk-On and Risk-Off regimes. The selected markets are grouped into equities (7), fixed income (9), currencies (7), commodities (5), and derivatives (4): Equities = S&P 500 E-mini Index Futures, Nasdaq-100 E-mini Index Futures, Russell 2000 E-mini Index Futures, STOXX Europe 600 Index Futures, Nikkei 225 Index Futures, MSCI Emerging Markets Index Futures, and S&P 500 High Beta (SPHB)/Low Beta (SPLV) Ratio. Fixed Income = US 10Y Treasury Yield, US 2Y Treasury Yield, US 10Y-02Y Yield Spread, German 10Y Bund Yield, UK 10Y Gilt Yield, US 10Y Breakeven Inflation Rate, US 10Y TIPS Yield, US High Yield Option-Adjusted Spread, and US Corporate Option-Adjusted Spread. Currencies = US Dollar Index (DXY), Australian Dollar/US Dollar, Euro/US Dollar, Chinese Yuan/US Dollar, Pound Sterling/US Dollar, Japanese Yen/US Dollar, and Bitcoin/US Dollar. Commodities = ICE Brent Crude Oil Futures, COMEX Gold Futures, COMEX Silver Futures, COMEX Copper Futures, and S&P Goldman Sachs Commodity Index (GSCI) Futures. Derivatives = CBOE S&P 500 Volatility Index (VIX), ICE US Bond Market Volatility Index (MOVE), CBOE 3M Implied Correlation Index, and CBOE VIX Volatility Index (VVIX)/VIX. All assets are directionally aligned with their historical correlation to the S&P 500. Each asset contributes equally based on its individual bullish or bearish signal. The overall market regime is calculated as the difference between the number of Risk-On and Risk-Off signals divided by the total number of assets, displayed as the percentage of markets confirming each regime. Green indicates Risk-On and occurs when the number of Risk-On signals exceeds Risk-Off signals, while red indicates Risk-Off and occurs when the number of Risk-Off signals exceeds Risk-On signals. Bullish Signal = (Fast MA – Slow MA) > (ATR × ATR Margin) Bearish Signal = (Fast MA – Slow MA) < –(ATR × ATR Margin) Market Regime = (Risk-On signals – Risk-Off signals) ÷ Total assets This indicator is designed with flexibility in mind, allowing users to include or exclude individual assets that contribute to the market regime and adjust the input parameters used for trend signal detection. These parameters apply to each independent asset, and the overall regime signal is smoothed by the signal length to reduce noise and enhance reliability. Investors can position according to the prevailing market regime by selecting factors that have historically outperformed under each regime environment to minimise downside risk and maximise upside potential: Risk-On Equity Factors = High Beta > Cyclicals > Low Volatility > Defensives. Risk-Off Equity Factors = Defensives > Low Volatility > Cyclicals > High Beta. Risk-On Fixed Income Factors = High Yield > Investment Grade > Treasuries. Risk-Off Fixed Income Factors = Treasuries > Investment Grade > High Yield. Risk-On Commodity Factors = Industrial Metals > Energy > Agriculture > Gold. Risk-Off Commodity Factors = Gold > Agriculture > Energy > Industrial Metals. Risk-On Currency Factors = Cryptocurrencies > Foreign Currencies > US Dollar. Risk-Off Currency Factors = US Dollar > Foreign Currencies > Cryptocurrencies. In summary, the Market Regime Index is a comprehensive macro risk-management tool that identifies the current market regime and helps investors align portfolio risk with the market’s underlying risk appetite. Its intuitive, color-coded design makes it an indispensable resource for investors seeking to navigate shifting market conditions and enhance risk-adjusted performance by selecting factors that have historically outperformed. While it has proven historically valuable, asset-specific characteristics and correlations evolve over time as market dynamics change.
Pine Script® indicator
by QuantitativeAlpha
Synthetic Implied APROverview The Synthetic Implied APR is an artificial implied APR, designed to imitate the implied APR seen when trading cryptocurrency funding rates. It combines real-time funding rates with premium data to calculate an artificial market expectation of the annualized funding rate. The (actual) implied APR is the market's expectation of the annualized funding rate. This is dependent on bid/ask impacts of the implied APR, something which is currently unavailable to fetch with TradingView. In essence, an implied APR of X% means traders believe that asset's funding fees to average X% when annualized. What's important to understand, is that the actual value of the synthetic implied APR is not relevant. We only simply use its relative changes when we trade (i.e if it crosses above/below its MA for a given weight). Even for the same asset, the implied APRs will change depending on days to maturity. How it calculates The synthetic implied APR is calculated with these steps: Collects premium data from perpetual futures markets using optimized lower timeframe requests (check my 'Predicted Funding Rates' indicator) Calculates the funding rate by adding the premium to an interest rate component (clamped within exchange limits) Derives the underlying APR from the 8-hour funding rate (funding rate × 3 × 365) Apply a weighed formula that imitates both the direction (underlying APR) with the volatility of prices (from the premium index and funding) premium_component = (prem_avg / 50 ) * 365 weighedprem = (weight * fr) + ((1 - weight) * apr) + (premium_component * 0.3) impliedAPR = math.avg(weighedprem, ta.sma(apr, maLength)) How to use it: Generally Preface: Funding rates are an indication of market sentiment If funding is positive, generally the market is bullish as longs are willing to pay shorts funding If funding is negative, generally the market is bearish as shorts are willing to pay longs funding So, this script can be used like a typical oscillator: Bullish: If implied APR > MA OR if implied APR MA is green Bearish: If implied APR < MA OR if implied APR MA is red The components: Synthetic Implied APR: The main metric. At current setting of 0.7, it imitates volatility Weight: The higher the value, the smoother the synthetic implied APR is (and MA too). This value is very important to the imitation. At 0.7, it imitates the actual volatility of the implied APR. At weight = 1, it becomes very smooth. Perfect for trading Synthetic Implied APR Moving Average: A moving average of the Synthetic implied APR. Can choose from multiple selections, (SMA, EMA, WMA, HMA, VWMA, RMA) How to use it: Trading Funding When trading funding there're multiple ways to use it with different settings Trade funding rates with trend changes Settings: Weight = 1 Method 1: When the implied APR MA turns green, long funding rates (or short if red) Method 2: When the implied APR crosses above the MA, long funding rates (or short when crosses below) Trade funding rates with MA pullbacks Settings: Weight = 0.7, timeframe 15m In an uptrend: When implied APR crosses below then above the script, long funding opportunity In an downtrend: When implied APR crosses above then below the script, shortfunding opportunity You can determine the trend with the method before, using a weight of 1 To trade funding rates, it's best to have these 3 scripts at these settings: Predicted Funding Rates: This allows you to see the predicted funding rates and see if they've maxxed out for added confluence too (+/-0.01% usually for Binance BTC futures) Synthetic implied APR: At weight 1, the MA provides a good trend (whether close above/below or colour change) Synthetic implied APR: At weight 0.7, it provides a good imitation of volatility How to use it: Trading Futures When trading futures: You can determine roughly what the trend is, if the assumption is made that funding rates can help identify trends if used as a sentiment indicator. It should be supplemented with traditional trend trading methods To prevent whipsaws, weight should remain high Long trend: When the implied APR MA turns green OR when it crosses above its MA Short trend: When the implied APR MA turns red OR when it below above its MA Why it's original This indicator introduces a unique synthetic weighting system that combines funding rates, underlying APR, and premium components in a way not found in existing TradingView scripts. Trading funding rates is a niche area, there aren't that many scripts currently available. And to my knowledge, there's no synthetic implied APR scripts available on TradingView either. So I believe this script to be original in that sense. Notes Because it depends on my triangular weighting algos, optimal accuracy is found on timeframes that are 4H or less. On higher timeframes, the accuracy drops off. Best timeframes for intraday trading using this are 15m or 1 hour The higher the timeframe, the lower the MA one should use. At 1 hour, 200 or higher is best. At say, 4h, length of 50 is best Only works for coins that have a Binance premium index Inputs Funding Period - Select between "1 Hour" or "8 Hour" funding cycles. 8 hours is standard for Binance Table - Toggle the information dashboard on/off to show or hide real-time metrics including funding rate, premium, and APR value Weight - Controls the balance between funding rate (higher values = smoother) and APR (lower values = more responsive) in the calculation, ranging from 0.0 to 1.0. Default is 0.7, this imitates the volatility Auto Timeframe Implied Length - Automatically calculates optimal smoothing length based on your chart timeframe for consistent behavior across different time periods Manual Implied Length - Sets a fixed smoothing length (in bars) when auto mode is disabled, with lower values being more responsive and higher values being smoother Show Implied APR MA - Displays an additional moving average line of the Synthetic Implied APR to help identify trend direction and crossover signals MA Type for Implied APR - Selects the calculation method (SMA, EMA, WMA, HMA, VWMA, or RMA) for the moving average, each offering different responsiveness and lag characteristics MA Length for Implied APR - Sets the lookback period (1-500 bars) for the moving average, with shorter lengths providing more signals and longer lengths filtering noise Show Underlying APR - Displays the raw APR calculation (without synthetic weighting) as a reference line to compare against the main indicator Bullish Color - Sets the color for positive values in the table and rising MA line Bearish Color - Sets the color for negative values in the table and falling MA line Table Background - Customizes the background color and transparency of the information dashboard Table Text Color - Sets the color for label text in the left column of the information table Table Text Size - Controls the font size of table text with options from Tiny to Huge
Pine Script® indicator
by stormeta
VWAP Momentum Oscillator How It Works Core Calculation Method The oscillator combines four key market measurements into a single, normalized reading: 1. Price-VWAP Deviation: `(Close - VWAP) / VWAP × 100` 2. VWAP-MA Momentum: `(VWAP - MovingAverage) / MovingAverage × 100` 3. Anchored VWAP Strength: Average of high/low anchor deviations from rolling VWAP 4. Range Position: `(Close - PeriodLow) / (PeriodHigh - PeriodLow) × 100 - 50` Dynamic Signal Line The signal line uses an EMA that automatically adjusts its length based on your chart timeframe: - Futures: Always covers 23 hours of trading (1,380 minutes) - Stocks: Always covers 6.5 hours of trading (390 minutes) - Examples: 276 periods on 5-min futures chart, 1,380 periods on 1-min futures chart Trading Signals 🟢 Buy Signals - Condition: Main oscillator crosses above signal line while below zero - Logic: Momentum turning bullish from oversold conditions - Visual: Green "BUY" label below price action 🔴 Sell Signals - Condition: Main oscillator crosses below signal line while above zero - Logic: Momentum turning bearish from overbought conditions - Visual: Red "SELL" label above price action ⚠️ Extreme Warnings - Extreme Overbought: Red triangle when oscillator crosses above +4.0 - Extreme Oversold: Green triangle when oscillator crosses below -4.0 - Purpose: Risk management alerts, not entry/exit signals Oscillator Zones Interpretation Guide - Above +2.0: Strong bullish momentum zone (green background) - 0 to +2.0: Mild bullish territory - 0 to -2.0: Mild bearish territory - Below -2.0: Strong bearish momentum zone (red background) - Above +4.0: Extreme overbought (caution advised) - Below -4.0: Extreme oversold (potential reversal zone) Customization Options Moving Average Settings - EMA/SMA Toggle: Choose between exponential or simple moving average - Color Customization: Adjust MA line color and width Visual Controls - Bullish/Bearish Colors: Customize momentum zone colors - Signal Line: Toggle visibility and adjust color - Line Widths: Control thickness of all plot lines Anchor Modes - NY Session Only: Anchors reset at NY market open (9:30 AM ET) - 24H NY Day: Anchors reset at NY calendar day change (midnight ET) Best Practices Timeframe Selection - Scalping: 1-5 minute charts for quick momentum changes - Day Trading: 5-15 minute charts for clearer trend signals - Swing Trading: 1-4 hour charts for major momentum shifts Signal Confirmation - Wait for crossovers: Don't trade on oscillator position alone - Respect extreme levels: Exercise caution above +4 or below -4 - Use with price action: Combine with support/resistance levels Risk Management - Extreme zones: Reduce position size when oscillator is extended - Failed signals: Exit quickly if momentum doesn't follow through - Market context: Consider overall trend direction and market volatility Technical Specifications Calculation Components - Base Length: 1,380 periods (futures) / 390 periods (stocks) - Signal Line: Dynamic EMA covering one full trading day - Smoothing: 3-period SMA on raw oscillator (adjustable) - Update Frequency: Real-time on every price tick Performance Notes - Resource Efficient: Optimized calculations minimize CPU usage - Memory Friendly: Uses incremental VWAP calculations - Fast Loading: Minimal historical data requirements Version History & Development This oscillator evolved from advanced VWAP overlay strategies, transforming complex multi-line analysis into a single, actionable momentum gauge. The indicator maintains the sophistication of institutional VWAP analysis while providing the clarity needed for retail trading decisions. Core Philosophy Traditional VWAP indicators show where price is relative to volume-weighted averages, but they don't quantify momentum or provide clear entry/exit signals. This oscillator solves that problem by normalizing all VWAP relationships into a single, bounded indicator that works consistently across all timeframes and asset classes. --- Open Source License: This indicator is provided free for the TradingView community. Feel free to modify and enhance according to your trading needs.
Pine Script® indicator
by PrestigeWorldwideTradingCo
Request█   OVERVIEW This library is a tool for Pine Script™ programmers that consolidates access to a wide range of lesser-known data feeds available on TradingView, including metrics from the FRED database, FINRA short sale volume, open interest, and COT data. The functions in this library simplify requests for these data feeds, making them easier to retrieve and use in custom scripts. █   CONCEPTS Federal Reserve Economic Data (FRED) FRED (Federal Reserve Economic Data) is a comprehensive online database curated by the Federal Reserve Bank of St. Louis. It provides free access to extensive economic and financial data from U.S. and international sources. FRED includes numerous economic indicators such as GDP, inflation, employment, and interest rates. Additionally, it provides financial market data, regional statistics, and international metrics such as exchange rates and trade balances. Sourced from reputable organizations, including U.S. government agencies, international institutions, and other public and private entities, FRED enables users to analyze over 825,000 time series, download their data in various formats, and integrate their information into analytical tools and programming workflows. On TradingView, FRED data is available from ticker identifiers with the "FRED:" prefix. Users can search for FRED symbols in the "Symbol Search" window, and Pine scripts can retrieve data for these symbols via `request.*()` function calls. FINRA Short Sale Volume FINRA (the Financial Industry Regulatory Authority) is a non-governmental organization that supervises and regulates U.S. broker-dealers and securities professionals. Its primary aim is to protect investors and ensure integrity and transparency in financial markets. FINRA's Short Sale Volume data provides detailed information about daily short-selling activity across U.S. equity markets. This data tracks the volume of short sales reported to FINRA's trade reporting facilities (TRFs), including shares sold on FINRA-regulated Alternative Trading Systems (ATSs) and over-the-counter (OTC) markets, offering transparent access to short-selling information not typically available from exchanges. This data helps market participants, researchers, and regulators monitor trends in short-selling and gain insights into bearish sentiment, hedging strategies, and potential market manipulation. Investors often use this data alongside other metrics to assess stock performance, liquidity, and overall trading activity. It is important to note that FINRA's Short Sale Volume data does not consolidate short sale information from public exchanges and excludes trading activity that is not publicly disseminated. TradingView provides ticker identifiers for requesting Short Sale Volume data with the format "FINRA:_SHORT_VOLUME", where "" is a supported U.S. equities symbol (e.g., "AAPL"). Open Interest (OI) Open interest is a cornerstone indicator of market activity and sentiment in derivatives markets such as options or futures. In contrast to volume, which measures the number of contracts opened or closed within a period, OI measures the number of outstanding contracts that are not yet settled. This distinction makes OI a more robust indicator of how money flows through derivatives, offering meaningful insights into liquidity, market interest, and trends. Many traders and investors analyze OI alongside volume and price action to gain an enhanced perspective on market dynamics and reinforce trading decisions. TradingView offers many ticker identifiers for requesting OI data with the format "_OI", where "" represents a derivative instrument's ticker ID (e.g., "COMEX:GC1!"). Commitment of Traders (COT) Commitment of Traders data provides an informative weekly breakdown of the aggregate positions held by various market participants, including commercial hedgers, non-commercial speculators, and small traders, in the U.S. derivative markets. Tallied and managed by the Commodity Futures Trading Commission (CFTC) , these reports provide traders and analysts with detailed insight into an asset's open interest and help them assess the actions of various market players. COT data is valuable for gaining a deeper understanding of market dynamics, sentiment, trends, and liquidity, which helps traders develop informed trading strategies. TradingView has numerous ticker identifiers that provide access to time series containing data for various COT metrics. To learn about COT ticker IDs and how they work, see our LibraryCOT publication. █   USING THE LIBRARY Common function characteristics  • This library's functions construct ticker IDs with valid formats based on their specified parameters, then use them as the `symbol` argument in request.security() to retrieve data from the specified context.  • Most of these functions automatically select the timeframe of a data request because the data feeds are not available for all timeframes.  • All the functions have two overloads. The first overload of each function uses values with the "simple" qualifier to define the requested context, meaning the context does not change after the first script execution. The second accepts "series" values, meaning it can request data from different contexts across executions.  • The `gaps` parameter in most of these functions specifies whether the returned data is `na` when a new value is unavailable for request. By default, its value is `false`, meaning the call returns the last retrieved data when no new data is available.  • The `repaint` parameter in applicable functions determines whether the request can fetch the latest unconfirmed values from a higher timeframe on realtime bars, which might repaint after the script restarts. If `false`, the function only returns confirmed higher-timeframe values to avoid repainting. The default value is `true`. `fred()` The `fred()` function retrieves the most recent value of a specified series from the Federal Reserve Economic Data (FRED) database. With this function, programmers can easily fetch macroeconomic indicators, such as GDP and unemployment rates, and use them directly in their scripts. How it works The function's `fredCode` parameter accepts a "string" representing the unique identifier of a specific FRED series. Examples include "GDP" for the "Gross Domestic Product" series and "UNRATE" for the "Unemployment Rate" series. Over 825,000 codes are available. To access codes for available series, search the FRED website . The function adds the "FRED:" prefix to the specified `fredCode` to construct a valid FRED ticker ID (e.g., "FRED:GDP"), which it uses in request.security() to retrieve the series data. Example Usage This line of code requests the latest value from the Gross Domestic Product series and assigns the returned value to a `gdpValue` variable: float gdpValue = fred("GDP") `finraShortSaleVolume()` The `finraShortSaleVolume()` function retrieves EOD data from a FINRA Short Sale Volume series. Programmers can call this function to retrieve short-selling information for equities listed on supported exchanges, namely NASDAQ, NYSE, and NYSE ARCA. How it works The `symbol` parameter determines which symbol's short sale volume information is retrieved by the function. If the value is na , the function requests short sale volume data for the chart's symbol. The argument can be the name of the symbol from a supported exchange (e.g., "AAPL") or a ticker ID with an exchange prefix ("NASDAQ:AAPL"). If the `symbol` contains an exchange prefix, it must be one of the following: "NASDAQ", "NYSE", "AMEX", or "BATS". The function constructs a ticker ID in the format "FINRA:ticker_SHORT_VOLUME", where "ticker" is the symbol name without the exchange prefix (e.g., "AAPL"). It then uses the ticker ID in request.security() to retrieve the available data. Example Usage This line of code retrieves short sale volume for the chart's symbol and assigns the result to a `shortVolume` variable: float shortVolume = finraShortSaleVolume(syminfo.tickerid) This example requests short sale volume for the "NASDAQ:AAPL" symbol, irrespective of the current chart: float shortVolume = finraShortSaleVolume("NASDAQ:AAPL") `openInterestFutures()` and `openInterestCrypto()` The `openInterestFutures()` function retrieves EOD open interest (OI) data for futures contracts. The `openInterestCrypto()` function provides more granular OI data for cryptocurrency contracts. How they work The `openInterestFutures()` function retrieves EOD closing OI information. Its design is focused primarily on retrieving OI data for futures, as only EOD OI data is available for these instruments. If the chart uses an intraday timeframe, the function requests data from the "1D" timeframe. Otherwise, it uses the chart's timeframe. The `openInterestCrypto()` function retrieves opening, high, low, and closing OI data for a cryptocurrency contract on a specified timeframe. Unlike `openInterest()`, this function can also retrieve granular data from intraday timeframes. Both functions contain a `symbol` parameter that determines the symbol for which the calls request OI data. The functions construct a valid OI ticker ID from the chosen symbol by appending "_OI" to the end (e.g., "CME:ES1!_OI"). The `openInterestFutures()` function requests and returns a two-element tuple containing the futures instrument's EOD closing OI and a "bool" condition indicating whether OI is rising. The `openInterestCrypto()` function requests and returns a five-element tuple containing the cryptocurrency contract's opening, high, low, and closing OI, and a "bool" condition indicating whether OI is rising. Example usage This code line calls `openInterest()` to retrieve EOD OI and the OI rising condition for a futures symbol on the chart, assigning the values to two variables in a tuple: = openInterestFutures(syminfo.tickerid) This line retrieves the EOD OI data for "CME:ES1!", irrespective of the current chart's symbol: = openInterestFutures("CME:ES1!") This example uses `openInterestCrypto()` to retrieve OHLC OI data and the OI rising condition for a cryptocurrency contract on the chart, sampled at the chart's timeframe. It assigns the returned values to five variables in a tuple: = openInterestCrypto(syminfo.tickerid, timeframe.period) This call retrieves OI OHLC and rising information for "BINANCE:BTCUSDT.P" on the "1D" timeframe: = openInterestCrypto("BINANCE:BTCUSDT.P", "1D") `commitmentOfTraders()` The `commitmentOfTraders()` function retrieves data from the Commitment of Traders (COT) reports published by the Commodity Futures Trading Commission (CFTC). This function significantly simplifies the COT request process, making it easier for programmers to access and utilize the available data. How It Works This function's parameters determine different parts of a valid ticker ID for retrieving COT data, offering a streamlined alternative to constructing complex COT ticker IDs manually. The `metricName`, `metricDirection`, and `includeOptions` parameters are required. They specify the name of the reported metric, the direction, and whether it includes information from options contracts. The function also includes several optional parameters. The `CFTCCode` parameter allows programmers to request data for a specific report code. If unspecified, the function requests data based on the chart symbol's root prefix, base currency, or quoted currency, depending on the `mode` argument. The call can specify the report type ("Legacy", "Disaggregated", or "Financial") and metric type ("All", "Old", or "Other") with the `typeCOT` and `metricType` parameters. Explore the CFTC website to find valid report codes for specific assets. To find detailed information about the metrics included in the reports and their meanings, see the CFTC's Explanatory Notes . View the function's documentation below for detailed explanations of its parameters. For in-depth information about COT ticker IDs and more advanced functionality, refer to our previously published COT library . Available metrics Different COT report types provide different metrics . The tables below list all available metrics for each type and their applicable directions: +------------------------------+------------------------+ | Legacy (​COT) Metric Names | Directions | +------------------------------+------------------------+ | Open Interest | No direction | | Noncommercial Positions | Long, Short, Spreading | | Commercial Positions | Long, Short | | Total Reportable Positions | Long, Short | | Nonreportable Positions | Long, Short | | Traders Total | No direction | | Traders Noncommercial | Long, Short, Spreading | | Traders Commercial | Long, Short | | Traders Total Reportable | Long, Short | | Concentration Gross ​LT 4 TDR | Long, Short | | Concentration Gross ​LT 8 TDR | Long, Short | | Concentration Net ​LT 4 TDR | Long, Short | | Concentration Net ​LT 8 TDR | Long, Short | +------------------------------+------------------------+ +-----------------------------------+------------------------+ | Disaggregated (COT2) Metric Names | Directions | +-----------------------------------+------------------------+ | Open Interest | No Direction | | Producer Merchant Positions | Long, Short | | Swap Positions | Long, Short, Spreading | | Managed Money Positions | Long, Short, Spreading | | Other Reportable Positions | Long, Short, Spreading | | Total Reportable Positions | Long, Short | | Nonreportable Positions | Long, Short | | Traders Total | No Direction | | Traders Producer Merchant | Long, Short | | Traders Swap | Long, Short, Spreading | | Traders Managed Money | Long, Short, Spreading | | Traders Other Reportable | Long, Short, Spreading | | Traders Total Reportable | Long, Short | | Concentration Gross LE 4 TDR | Long, Short | | Concentration Gross LE 8 TDR | Long, Short | | Concentration Net LE 4 TDR | Long, Short | | Concentration Net LE 8 TDR | Long, Short | +-----------------------------------+------------------------+ +-------------------------------+------------------------+ | Financial (COT3) Metric Names | Directions | +-------------------------------+------------------------+ | Open Interest | No Direction | | Dealer Positions | Long, Short, Spreading | | Asset Manager Positions | Long, Short, Spreading | | Leveraged Funds Positions | Long, Short, Spreading | | Other Reportable Positions | Long, Short, Spreading | | Total Reportable Positions | Long, Short | | Nonreportable Positions | Long, Short | | Traders Total | No Direction | | Traders Dealer | Long, Short, Spreading | | Traders Asset Manager | Long, Short, Spreading | | Traders Leveraged Funds | Long, Short, Spreading | | Traders Other Reportable | Long, Short, Spreading | | Traders Total Reportable | Long, Short | | Concentration Gross LE 4 TDR | Long, Short | | Concentration Gross LE 8 TDR | Long, Short | | Concentration Net LE 4 TDR | Long, Short | | Concentration Net LE 8 TDR | Long, Short | +-------------------------------+------------------------+ Example usage This code line retrieves "Noncommercial Positions (Long)" data, without options information, from the "Legacy" report for the chart symbol's root, base currency, or quote currency: float nonCommercialLong = commitmentOfTraders("Noncommercial Positions", "Long", false) This example retrieves "Managed Money Positions (Short)" data, with options included, from the "Disaggregated" report: float disaggregatedData = commitmentOfTraders("Managed Money Positions", "Short", true, "", "Disaggregated") █   NOTES  • This library uses dynamic requests , allowing dynamic ("series") arguments for the parameters defining the context (ticker ID, timeframe, etc.) of a `request.*()` function call. With this feature, a single `request.*()` call instance can flexibly retrieve data from different feeds across historical executions. Additionally, scripts can use such calls in the local scopes of loops, conditional structures, and even exported library functions, as demonstrated in this script. All scripts coded in Pine Script™ v6 have dynamic requests enabled by default. To learn more about the behaviors and limitations of this feature, see the Dynamic requests section of the Pine Script™ User Manual.  • The library's example code offers a simple demonstration of the exported functions. The script retrieves available data using the function specified by the "Series type" input. The code requests a FRED series or COT (Legacy), FINRA Short Sale Volume, or Open Interest series for the chart's symbol with specific parameters, then plots the retrieved data as a step-line with diamond markers. Look first. Then leap. █   EXPORTED FUNCTIONS This library exports the following functions: fred(fredCode, gaps)   Requests a value from a specified Federal Reserve Economic Data (FRED) series. FRED is a comprehensive source that hosts numerous U.S. economic datasets. To explore available FRED datasets and codes, search for specific categories or keywords at fred.stlouisfed.org Calls to this function count toward a script's `request.*()` call limit.   Parameters:      fredCode (series string) : The unique identifier of the FRED series. The function uses the value to create a valid ticker ID for retrieving FRED data in the format `"FRED:fredCode"`. For example, `"GDP"` refers to the "Gross Domestic Product" series ("FRED:GDP"), and `"GFDEBTN"` refers to the "Federal Debt: Total Public Debt" series ("FRED:GFDEBTN").      gaps (simple bool) : Optional. If `true`, the function returns a non-na value only when a new value is available from the requested context. If `false`, the function returns the latest retrieved value when new data is unavailable. The default is `false`.   Returns: (float) The value from the requested FRED series. finraShortSaleVolume(symbol, gaps, repaint)   Requests FINRA daily short sale volume data for a specified symbol from one of the following exchanges: NASDAQ, NYSE, NYSE ARCA. If the chart uses an intraday timeframe, the function requests data from the "1D" timeframe. Otherwise, it uses the chart's timeframe. Calls to this function count toward a script's `request.*()` call limit.   Parameters:      symbol (series string) : The symbol for which to request short sale volume data. If the specified value contains an exchange prefix, it must be one of the following: "NASDAQ", "NYSE", "AMEX", "BATS".      gaps (simple bool) : Optional. If `true`, the function returns a non-na value only when a new value is available from the requested context. If `false`, the function returns the latest retrieved value when new data is unavailable. The default is `false`.      repaint (simple bool) : Optional. If `true` and the chart's timeframe is intraday, the value requested on realtime bars may change its time offset after the script restarts its executions. If `false`, the function returns the last confirmed period's values to avoid repainting. The default is `true`.   Returns: (float) The short sale volume for the specified symbol or the chart's symbol. openInterestFutures(symbol, gaps, repaint)   Requests EOD open interest (OI) and OI rising information for a valid futures symbol. If the chart uses an intraday timeframe, the function requests data from the "1D" timeframe. Otherwise, it uses the chart's timeframe. Calls to this function count toward a script's `request.*()` call limit.   Parameters:      symbol (series string) : The symbol for which to request open interest data.      gaps (simple bool) : Optional. If `true`, the function returns non-na values only when new values are available from the requested context. If `false`, the function returns the latest retrieved values when new data is unavailable. The default is `false`.      repaint (simple bool) : Optional. If `true` and the chart's timeframe is intraday, the value requested on realtime bars may change its time offset after the script restarts its executions. If `false`, the function returns the last confirmed period's values to avoid repainting. The default is `true`.   Returns: ( ) A tuple containing the following values:     - The closing OI value for the symbol.     - `true` if the closing OI is above the previous period's value, `false` otherwise. openInterestCrypto(symbol, timeframe, gaps, repaint)   Requests opening, high, low, and closing open interest (OI) data and OI rising information for a valid cryptocurrency contract on a specified timeframe. Calls to this function count toward a script's `request.*()` call limit.   Parameters:      symbol (series string) : The symbol for which to request open interest data.      timeframe (series string) : The timeframe of the data request. If the timeframe is lower than the chart's timeframe, it causes a runtime error.      gaps (simple bool) : Optional. If `true`, the function returns non-na values only when new values are available from the requested context. If `false`, the function returns the latest retrieved values when new data is unavailable. The default is `false`.      repaint (simple bool) : Optional. If `true` and the `timeframe` represents a higher timeframe, the function returns unconfirmed values from the timeframe on realtime bars, which repaint when the script restarts its executions. If `false`, it returns only confirmed higher-timeframe values to avoid repainting. The default is `true`.   Returns: ( ) A tuple containing the following values:     - The opening, high, low, and closing OI values for the symbol, respectively.     - `true` if the closing OI is above the previous period's value, `false` otherwise. commitmentOfTraders(metricName, metricDirection, includeOptions, CFTCCode, typeCOT, mode, metricType)   Requests Commitment of Traders (COT) data with specified parameters. This function provides a simplified way to access CFTC COT data available on TradingView. Calls to this function count toward a script's `request.*()` call limit. For more advanced tools and detailed information about COT data, see TradingView's LibraryCOT library.   Parameters:      metricName (series string) : One of the valid metric names listed in the library's documentation and source code.      metricDirection (series string) : Metric direction. Possible values are: "Long", "Short", "Spreading", and "No direction". Consult the library's documentation or code to see which direction values apply to the specified metric.      includeOptions (series bool) : If `true`, the COT symbol includes options information. Otherwise, it does not.      CFTCCode (series string) : Optional. The CFTC code for the asset. For example, wheat futures (root "ZW") have the code "001602". If one is not specified, the function will attempt to get a valid code for the chart symbol's root, base currency, or main currency.      typeCOT (series string) : Optional. The type of report to request. Possible values are: "Legacy", "Disaggregated", "Financial". The default is "Legacy".      mode (series string) : Optional. Specifies the information the function extracts from a symbol. Possible modes are:   - "Root": The function extracts the futures symbol's root prefix information (e.g., "ES" for "ESH2020").   - "Base currency": The function extracts the first currency from a currency pair (e.g., "EUR" for "EURUSD").   - "Currency": The function extracts the currency of the symbol's quoted values (e.g., "JPY" for "TSE:9984" or "USDJPY").   - "Auto": The function tries the first three modes (Root -> Base currency -> Currency) until it finds a match.   The default is "Auto". If the specified mode is not available for the symbol, it causes a runtime error.      metricType (series string) : Optional. The metric type. Possible values are: "All", "Old", "Other". The default is "All".   Returns: (float) The specified Commitment of Traders data series. If no data is available, it causes a runtime error.
Pine Script® library
by TradingView
9