Holt Damped Forecast [CHE]A Friendly Note on These Pine Script Scripts
Hey there! Just wanted to share a quick, heartfelt heads-up: All these Pine Script examples come straight from my own self-study adventures as a total autodidact—think late nights tinkering and learning on my own. They're purely for educational vibes, helping me (and hopefully you!) get the hang of Pine Script basics, cool indicators, and building simple strategies.
That said, please know this isn't any kind of financial advice, investment nudge, or pro-level trading blueprint. I'd love for you to dive in with your own research, run those backtests like a champ, and maybe bounce ideas off a qualified expert before trying anything in a real trading setup. No guarantees here on performance or spot-on accuracy—trading's got its risks, and those are totally on each of us.
Let's keep it fun and educational—happy coding! 😊
Holt Damped Forecast — Damped trend forecasts with fan bands for uncertainty visualization and momentum integration
Summary
This indicator applies damped exponential smoothing to generate forward price forecasts, displaying them as probabilistic fan bands to highlight potential ranges rather than point estimates. It incorporates residual-based uncertainty to make projections more reliable in varying market conditions, reducing overconfidence in strong trends. Momentum from the trend component is shown in an optional label alongside signals, aiding quick assessment of direction and strength without relying on lagging oscillators.
Motivation: Why this design?
Standard exponential smoothing often extrapolates trends indefinitely, leading to unrealistic forecasts during mean reversion or weakening momentum. This design uses damping to gradually flatten long-term projections, better suiting real markets where trends fade. It addresses the need for visual uncertainty in forecasts, helping traders avoid entries based on overly optimistic point predictions.
What’s different vs. standard approaches?
- Reference baseline: Diverges from basic Holt's linear exponential smoothing, which assumes persistent trends without decay.
- Architecture differences:
- Adds damping to the trend extrapolation for finite-horizon realism.
- Builds fan bands from historical residuals for probabilistic ranges at multiple confidence levels.
- Integrates a dynamic label combining forecast details, scaled momentum, and directional signals.
- Applies tail background coloring to recent bars based on forecast direction for immediate visual cues.
- Practical effect: Charts show converging forecast bands over time, emphasizing shorter horizons where accuracy is higher. This visibly tempers aggressive projections in trends, making it easier to spot when uncertainty widens, which signals potential reversals or consolidation.
How it works (technical)
The indicator maintains two persistent components: a level tracking the current price baseline and a trend capturing directional slope. On each bar, the level updates by blending the current source price with a one-step-ahead expectation from the prior level and damped trend. The trend then adjusts by weighting the change in level against the prior damped trend. Forecasts extend this forward over a user-defined number of steps, with damping ensuring the trend influence diminishes over distance.
Uncertainty derives from the standard deviation of historical residuals—the differences between actual prices and one-step expectations—scaled by the damping structure for the forecast horizon. Bands form around the median forecast at specified confidence intervals using these scaled errors. Initialization seeds the level to the first bar's price and trend to zero, with persistence handling subsequent updates. A security call fetches the last bar index for tail logic, using lookahead to align with realtime but introducing minor repaint on unconfirmed bars.
Parameter Guide
The Source parameter selects the price input for level and residual calculations, defaulting to close; consider using high or low for assets sensitive to volatility, as close works well for most trend-following setups. Forecast Steps (h) defines the number of bars ahead for projections, defaulting to 4—shorter values like 1 to 5 suit intraday trading, while longer ones may widen bands excessively in choppy conditions. The Color Scheme (2025 Trends) option sets the base, up, and down colors for bands, labels, and backgrounds, starting with Ruby Dawn; opt for serene schemes on clean charts or vibrant ones to stand out in dark themes.
Level Smoothing α controls the responsiveness of the price baseline, defaulting to 0.3—values above 0.5 enhance tracking in fast markets but may amplify noise, whereas lower settings filter disturbances better. Trend Smoothing β adjusts sensitivity to slope changes, at 0.1 by default; increasing to 0.2 helps detect emerging shifts quicker, but keeping it low prevents whipsaws in sideways action. Damping φ (0..1) governs trend persistence, defaulting to 0.8—near 0.9 preserves carryover in sustained moves, while closer to 0.5 curbs overextensions more aggressively.
Show Fan Bands (50/75/95) toggles the probabilistic range display, enabled by default; disable it in oscillator panes to reduce clutter, but it's key for overlay forecasts. Residual Window (Bars) sets the length for deviation estimates, at 400 bars initially—100 to 200 works for short timeframes, and 500 or more adds stability over extended histories. Line Width determines the thickness of band and median lines, defaulting to 2; go thicker at 3 to 5 for emphasis on higher timeframes or thinner for layered indicators.
Show Median/Forecast Line reveals the central projection, on by default—hide if bands provide enough detail, or keep for pinpoint entry references. Show Integrated Label activates the combined view of forecast, momentum, and signal, defaulting to true; it's right-aligned for convenience, so turn it off on smaller screens to save space. Show Tail Background colors the last few bars by forecast direction, enabled initially; pair low transparency for subtle hints or higher for bolder emphasis.
Tail Length (Bars) specifies bars to color backward from the current one, at 3 by default—1 to 2 fits scalping, while 5 or more underscores building momentum. Tail Transparency (%) fades the background intensity, starting at 80; 50 to 70 delivers strong signals, and 90 or above allows seamless blending. Include Momentum in Label adds the scaled trend value, defaulting to true—ATR% scaling here offers relative strength context across assets.
Include Long/Short/Neutral Signal in Label displays direction from the trend sign, on by default; neutral helps in ranging markets, though it can be overlooked during strong trends. Scaling normalizes momentum output (raw, ATR-relative, or level-relative), set to ATR% initially—ATR% ensures cross-asset comparability, while %Level provides percentage perspectives. ATR Length defines the period for true range averaging in scaling, at 14; align it with your chart timeframe or shorten for quicker volatility responses.
Decimals sets precision in the momentum label, defaulting to 2—0 to 1 yields clean integers, and 3 or more suits detailed forex views. Show Zero-Cross Markers places arrows at direction changes, enabled by default; keep size small to minimize clutter, with text labels for fast scanning.
Reading & Interpretation
Fan bands expand outward from the current bar, with the median line as the central forecast—narrower bands indicate lower uncertainty, wider suggest caution. Colors tint up (positive forecast vs. prior level) in the scheme's up hue and down otherwise. The optional label lists the horizon, median, and range brackets at 50%, 75%, and 95% levels, followed by momentum (scaled per mode) and signal (Long if positive trend, Short if negative, Neutral if zero). Zero-cross arrows mark trend flips: upward triangle below bar for bullish cross, downward above for bearish. Tail background reinforces the forecast direction on recent bars.
Practical Workflows & Combinations
- Trend following: Enter long on upward zero-cross if median forecast rises above price and bands contain it; confirm with higher highs/lows. Short on downward cross with falling median.
- Exits/Stops: Trail stops below 50% lower band in longs; exit if momentum drifts negative or signal turns neutral. Use wider bands (75/95%) for conservative holds in volatile regimes.
- Multi-asset/Multi-TF: Defaults work across stocks, forex, crypto on 5m-1D; scale steps by TF (e.g., 10+ on daily). Layer with volume or structure tools—avoid over-reliance on isolated crosses.
Behavior, Constraints & Performance
Closed-bar logic ensures stable historical plots, but realtime updates via security lookahead may shift forecasts until bar confirmation, introducing minor repaint on the last bar. No explicit HTF calls beyond bar index fetch, minimizing gaps but watch for low-liquidity assets. Resources include a 2000-bar lookback for residuals and up to 500 labels, with no loops—efficient for most charts. Known limits: Early bars show wide bands due to sparse residuals; assumes stationary errors, so gaps or regime shifts widen inaccuracies.
Sensible Defaults & Quick Tuning
Start with defaults for balanced smoothing on 15m-4H charts. For choppy conditions (too many crosses), lower β to 0.05 and raise residual window to 600 for stability. In trending markets (sluggish signals), increase α/β to 0.4/0.2 and shorten steps to 2. If bands overexpand, boost φ toward 0.95 to preserve trend carry. Tune colors for theme fit without altering logic.
What this indicator is—and isn’t
This is a visualization and signal layer for damped forecasts and momentum, complementing price action analysis. It isn’t a standalone system—pair with risk rules and broader context. Not predictive beyond the horizon; use for confirmation, not blind entries.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
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Svopex Session Highlighter# Session Highlighter
## Description
**Session Highlighter** is a powerful Pine Script indicator designed to visually identify and mark specific trading hours on your chart. This tool helps traders focus on their preferred trading sessions by highlighting the background during active hours and marking the session start with customizable visual markers.
## Key Features
- **📊 Session Background Highlighting**: Automatically shades the chart background during your defined trading hours (default: 7:00 - 23:00)
- **🎯 Smart Session Start Marker**: Places a marker on the last candle before session start, intelligently adapting to your timeframe:
- 1 Hour chart: Marker at 6:00
- 15 Minute chart: Marker at 6:45
- 5 Minute chart: Marker at 6:55
- 1 Minute chart: Marker at 6:59
- **🌍 Timezone Support**: Choose from multiple timezones (Europe/Prague, Europe/London, America/New_York, UTC)
- **🎨 5 Marker Styles**: Customize your session start indicator:
- Triangle
- Circle
- Diamond
- Label with time text
- Vertical line
- **⚙️ Fully Customizable**: Adjust start/end hours, timezone, and marker style through simple settings
## Settings
- **Start Hour**: Set your session start time (0-23)
- **End Hour**: Set your session end time (0-23)
- **Timezone**: Select your trading timezone
- **Marker Style**: Choose your preferred visual marker
## Use Cases
- Identify London/New York trading sessions
- Mark Asian session hours
- Highlight your personal trading windows
- Avoid trading during off-hours
- Perfect for day traders and scalpers
## Installation
1. Copy the Pine Script code
2. Open TradingView Pine Editor
3. Paste the code and click "Add to Chart"
4. Configure settings to match your trading schedule
Rolling Performance Metrics TableRolling Performance Metrics Table
A clean, customizable table overlay that displays rolling performance metrics across multiple time periods. Perfect for quickly assessing price momentum and performance trends at a glance.
FEATURES:
- Displays performance across 5 time periods: 1 Week, 3 Month, 6 Month, 1 Year, and 2 Year
- Shows historical price at the start of each period
- Calculates both absolute price change and percentage change
- Color-coded results: Green for positive performance, Red for negative performance
- Fully transparent design with no background or borders - text floats cleanly over your chart
- Customizable table position (9 placement options)
DISPLAY COLUMNS:
1. Period - The lookback timeframe
2. Price - The historical price at the start of the period
3. Change (Value) - Absolute price change from the period start
4. Change (%) - Percentage return over the period
CUSTOMIZATION:
- Adjust the number of bars for each period (default: 1 Week = 5 bars, 3 Month = 63 bars, 6 Month = 126 bars, 1 Year = 252 bars, 2 Year = 504 bars)
- Choose from 9 table positions: Top, Middle, Bottom combined with Left, Center, Right
- Default position: Middle Left
USAGE:
Perfect for traders who want to quickly assess momentum across multiple timeframes. The transparent overlay design ensures minimal obstruction of chart analysis while providing critical performance data at a glance.
NOTE:
- The table only appears on the last bar of your chart
- Customize bar counts in settings to match your specific timeframe needs (e.g., daily vs hourly charts)
- "N/A" appears when historical data is insufficient for the selected period
Buy And Hold Performance Screener - [JTCAPITAL]Buy And Hold Performance Screener – is a script designed to track and display multi-asset “buy and hold” performance curves and performance statistics over defined timeframes for selected symbols. It doesn’t attempt to time entries or exits; rather, it shows what would happen if one simply bought the asset at the defined start date and held it.
The indicator works by calculating in the following steps:
Start Date Definition
The script begins by reading an input for the start date. This defines the bar from which the equity curves begin.
Symbol Definitions & Close Price Retrieval
The script allows the user to specify up to ten tickers. For each ticker it uses request.security() on the “1D” timeframe to retrieve the daily close price of that symbol.
Plot Enable Inputs
For each ticker there is an input boolean controlling whether the equity curve for that ticker should be plotted.
Asset Name Cleaning
The helper function clean_name(string asset) => … takes the asset string (e.g., “CRYPTO:SOLUSD”) and manipulates it (via string splitting and replacements) to derive a cleaned short name (e.g., “SOL”). This name is used for visuals (labels, table headers).
Equity Curve Calculation (“HODL”)
The helper function f_HODL(closez) defines a variable equity that assumes a starting equity of 1 unit at the start date and then multiplies by the ratio of each bar’s close to the prior bar’s close: i.e. daily compounding of returns.
Performance Metrics Calculation
The helper function f_performance(closez) calculates, for each symbol’s close series, the percentage change of the current close relative to its close 30 days ago, 90 days ago, 180 days ago, 1 year ago (365 days), 2 years ago (730 days) and 3 years ago (1095 days).
Equity Curve Plots
For each ticker, if the corresponding plot input is true, the script assigns a plotted variable equal to the equity curve value. Its then drawing each selected equity curve on the chart, each in a distinct color.
Table Construction
If the plottable input is true, the script constructs a table and populates it with rows and column corresponding to the assigned tickers and the set 6 timeframes used for display.
Buy and Sell Conditions:
Since this is strictly a “buy-and-hold” performance screener, there are no explicit buy or sell signals generated or plotted. The script assumes: buy at the defined start_date, hold continuously to present. There are no filters, no exit logic, no take-profit or stop-loss. The benefit of this approach is to provide a clean benchmark of how selected assets would have performed if one simply adopted a passive “buy & hold” approach from a given start date.
Features and Parameters:
start_date (input.time) : Defines the date from which performance and equity curves begin.
ticker1 … ticker10 (input.symbol) : User-selectable asset symbols to include in the screener.
plot1 … plot10 (input.bool) : Boolean flags to enable/disable plotting of each asset’s equity curve.
plottable (input.bool) : Flag to enable/disable drawing the performance table.
Colored plotting + Labels for identifying each asset curve on the chart.
Specifications:
Here is a detailed breakdown of every calculation/variable/function used in the script and what each part means:
start_date
This is defined via input.time(timestamp("1 Jan 2025"), title = "Start Date"). It allows the user to pick a specific calendar date from which the equity curves and performance calculations will start.
ticker1 … ticker10
These inputs allow the user to select up to ten different assets (symbols) to monitor. The script uses each of these to fetch daily close prices.
plot1 … plot10
Boolean inputs controlling which of the ten asset equity curves are plotted. If plotX is true, the equity curve for ticker X will be visible; otherwise it will be not plotted. This gives the user flexibility to include or exclude specific assets on the chart.
Returns the cleaned asset short name.
This provides friendly text labels like “BTC”, “ETH”, “SOL”, etc., instead of full symbol codes.
The choice of distinct colours for each asset helps differentiate curves visually when multiple assets are overlaid.
Colour definitions
Variables color1…color10 are explicitly defined via color.rgb(r,g,b) to give each asset a unique colour (e.g., red, orange, yellow, green, cyan, blue, purple, pink, etc.).
What are the benefits of combining these calculations?
By computing equity curves for multiple assets from the same start date and overlaying them, you can visualise comparative performance of different assets under a uniform “buy & hold” assumption.
The performance table adds multi-horizon returns (30 D, 90 D, 180 D, 1 Y, 2 Y, 3 Y) which helps the user see both short-term and longer-term performance without having to manually compute returns.
The use of daily close data via request.security(..., "1D") removes dependency on the chart’s timeframe, thereby standardising the comparison across assets.
The equity curve and table together provide both visual (curve) and numerical (table) summaries of performance, making it easier to spot trends, divergences, and cross-asset comparisons at a glance.
Because it uses compounding (equity := equity * (closez / closez )), the curves reflect the real growth of a 1-unit investment held over time, rather than only simple returns.
The labelling of curves and the color-coding make the multi-asset overlay easier to interpret.
Using a clean start date ensures that all curves begin at the same point (1 unit at start_date), making relative performance intuitive.
Because of this, the script is useful as a benchmarking tool: rather than trying to pick entries or exit points, you can simply compare “what if I had held these assets since Jan 1 2025” (or your chosen date), and see which assets out-/under-performed in that period. It helps an investor or trader evaluate the long-term benefits of passive vs. active management, or of allocation decisions.
Please note:
The script assumes continuous daily data and does not account for dividends, fees, slippage, or tax implications.
It does not attempt to optimise timing or provide trading signals.
Returns prior to the start date are ignored (equity only begins once time >= start_date).
For newly listed assets with fewer than 365 or 730 or 1095 days of history, the longer-horizon returns may return na or misleading values.
Because it uses request.security() without specifying lookahead, and on “1D” timeframe, it complies with standard usage but you should verify there is no look-ahead bias in your particular setup.
ENJOY!
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.
Ichimoku MultiTF WillyArt v1.0.0What this indicator does
Ichimoku WillyArt turns the Ichimoku lines into angle-based momentum across multiple timeframes (W, D, 4H, 1H, 30m, 5m).
For each TF it computes the slope (angle in degrees) of:
Tenkan-sen
Kijun-sen
Senkou Span A
Senkou Span B
Angles are normalized so they’re comparable across assets and scales. You get a table with the angle per line and a quick emoji direction (↑, →, ↓), optional plots of the chosen line, and ready-to-use alerts.
Why angle?
Slope-as-degrees is an intuitive proxy for momentum/impulse:
Positive angle → line rising (bullish impulse).
Negative angle → line falling (bearish impulse).
Near zero → flat/indecisive.
Two normalization modes
ATR (default): slope / ATR. Robust across instruments; less sensitive to price level.
%Price: slope / price. More sensitive; can highlight subtle turns on low-volatility symbols.
Inputs you’ll actually care about
Timeframes: W, D, 4H, 1H, 30m, 5m (all fetched MTF, independent of chart TF).
Ichimoku lengths: Tenkan (9), Kijun (26), Span B (52) — standard defaults.
Bars for slope (ΔN): How many bars back the slope is measured. Higher = smoother, slower.
Threshold (°) for “strong”: Angle magnitude that qualifies as strong ↑/↓.
What you’ll see
Matrix/Table (top-right): For each TF, the angle (°) of Tenkan, Kijun, Span A, Span B + an emoji:
↑ above threshold, ↓ below −threshold, → in between.
Optional plots: Toggle “Plot angles” to visualize the chosen series’ angle across TFs.
Alerts included (ready to pick in “Create Alert”)
Sustained state: e.g., “Kijun 4H: strong ↑ angle” triggers while angle > threshold.
Threshold cross (one-shot): e.g., “Kijun 1H: upward threshold cross” fires on crossing.
Consensus (multi-TF): “Kijun consensus ↑ (D/4H/1H/30m/5m)” when all selected TFs align up (and the symmetric down case).
Messages are constant strings (TradingView requirement), so they compile cleanly. If you want dynamic text (current angle, threshold value, etc.), enable your own alert() calls—this script structure supports adding them.
How to use it (workflow)
Add to chart. No need to switch chart TF; the script pulls W/D/4H/1H/30m/5m internally.
Pick normalization. Start with ATR. Switch to %Price if you want more sensitivity.
Set ΔN & threshold.
Intraday momentum: try ΔN = 3–5 and threshold ≈ 4–8°.
Swing/position: ΔN = 5–9 and threshold ≈ 3–6° (with ATR).
Scan the table. Look for alignment (multiple TFs with ↑ or ↓ on Kijun/Spans).
Kijun + Span A up together → trending push.
Span B up/down → cloud baseline tilting (trend quality).
Turn on alerts that match your style: reactive cross for entries, sustained for trend follow, consensus to filter noise.
Reading tips
Kijun angle: great “trend backbone.” Strong ↑ on several TFs = higher-probability pullback buys.
Span A vs. Span B:
Span A reacts faster (momentum).
Span B is slower (structure).
When both tilt the same way, the cloud is genuinely rotating.
Mixed signals? Use higher TFs (W/D/4H) as bias, lower TFs (1H/30m/5m) for timing.
Good to know (limits & best practices)
Angles measure rate of change, not overbought/oversold. Combine with price structure and risk rules.
Extremely low volatility or illiquid symbols can produce tiny angles—%Price mode may help.
ΔN and thresholds are contextual: adapt per market (crypto vs FX vs equities).
Want me to bundle a “pro template” of alert presets (intraday / swing) and a heatmap color scale for the table? Happy to ship v2. 🚀
Trading Lot & Margin Calculator
# 💹 Trading Lot & Margin Calculator - Professional Risk Management Tool
## 🎯 Overview
A comprehensive, all-in-one calculator dashboard that helps traders determine optimal position sizes, calculate margin requirements, and manage risk effectively across multiple asset classes. This indicator displays directly on your chart as a customizable table, providing real-time calculations based on current market prices.
## ✨ Key Features
### 📊 Three Powerful Calculation Modes:
**1. Calculate Lot Size (Risk-Based Position Sizing)**
- Input your risk percentage and stop loss in pips
- Automatically calculates the optimal lot size for your risk tolerance
- Respects margin limitations (configurable margin % cap)
- Ensures positions don't exceed minimum lot size (0.01)
- Perfect for risk management and proper position sizing
**2. Calculate Margin Cost**
- Input desired lot size
- See exactly how much margin is required
- Shows percentage of deposit used
- Displays free margin remaining
- Warns when insufficient funds
**3. Margin to Lots**
- Specify a fixed margin amount you want to use
- Calculator shows how many lots/contracts you can buy
- Ideal for traders with fixed margin budgets
## 🤖 Auto-Detection of Instruments
The calculator **automatically detects** what you're trading and adjusts calculations accordingly:
### ✅ Fully Supported:
- **💱 Forex Pairs** - All majors, minors, exotics (EURUSD, GBPJPY, etc.)
- Standard lot: 100,000 units
- JPY pairs: 0.01 pip size, others: 0.0001
- **🛢️ Commodities** - Gold, Silver, Oil
- XAUUSD (Gold): 100 oz per lot
- XAGUSD (Silver): 5,000 oz per lot
- Oil (WTI/Brent): 1,000 barrels per lot
- **📈 Indices** - US500, NAS100, US30, DAX, etc.
- Correct contract sizes per point
- **📊 Stocks** - All individual stocks
- 1 lot = 1 share
- Direct share calculations
### ⚠️ Known Limitation:
- **₿ Crypto calculations may not work properly** on all crypto pairs. Use manual contract size if needed.
## 📋 Dashboard Information Displayed:
- 🎯 Optimal/Requested Lot Size
- 💰 Margin Required
- 📊 Margin % of Deposit
- 💵 Free Margin Remaining
- 💎 Position Value
- 📈 Pip/Point Value
- ⚠️ Safety Warnings (insufficient funds, high risk, etc.)
- 🔍 Detected Instrument Type
- 📦 Contract Size
## ⚙️ Customizable Settings:
**Account Settings:**
- Account Deposit
- Leverage (1:1 to 1:1000)
- Max Margin % of Deposit (default 5% for safety)
**Risk Management:**
- Risk Percentage (for lot size calculation)
- Stop Loss in Pips
- Lot Amount (for margin cost calculation)
- Margin to Use (for margin-to-lots calculation)
**Display Options:**
- Show/Hide Dashboard
- Position: Top/Middle/Bottom, Left/Right
- Auto-detect instrument ON/OFF
- Manual contract size override
## 🎨 Professional Design
- Clean, modern table interface
- Color-coded warnings (red = danger, yellow = caution, green = safe)
- Large, readable text
- Minimal screen space usage
- Non-intrusive overlay
## 💡 Use Cases:
1. **Day Traders** - Quick position sizing based on account risk
2. **Swing Traders** - Calculate optimal positions for longer-term setups
3. **Risk Managers** - Ensure positions stay within margin limits
4. **Beginners** - Learn proper position sizing and risk management
5. **Multi-Asset Traders** - Seamlessly switch between forex, commodities, indices, and stocks
## ⚠️ Important Notes:
- ✅ Works on all timeframes
- ✅ Updates in real-time with price changes
- ✅ Minimum lot size enforced (0.01)
- ✅ Margin calculations use current chart price
- ⚠️ **Crypto calculations may be inaccurate** - verify with your broker
- 📌 Always verify calculations with your broker's specifications
- 📌 Contract sizes may vary by broker
## 🚀 How to Use:
1. Add indicator to any chart
2. Click settings ⚙️ icon
3. Enter your account details (deposit, leverage)
4. Choose calculation mode
5. Input your parameters
6. View optimal lot size and margin requirements on dashboard
## 📈 Perfect For:
- Forex traders managing multiple currency pairs
- Commodity traders (Gold, Silver, Oil)
- Index traders (S&P 500, NASDAQ, etc.)
- Stock traders
- Anyone who wants professional risk management
## 🛡️ Risk Management Features:
- Configurable margin % cap prevents over-leveraging
- Risk-based position sizing protects your account
- Warnings for high risk, insufficient funds, margin limitations
- Prevents positions below minimum lot size
---
**Trade smarter, not harder. Calculate before you trade!** 📊💪
---
## Version Notes:
- Pine Script v6
- Overlay mode for chart display
- No external dependencies
- Lightweight and fast
**Disclaimer:** This calculator is for educational and informational purposes only. Always verify calculations with your broker and trade at your own risk. Past performance does not guarantee future results.
---
ema200 plus Description:
This advanced indicator displays Exponential Moving Averages (EMA) across multiple timeframes to help traders identify trend direction and strength across different market perspectives.
Key Features:
Multi-Timeframe EMA Analysis:
Plots 200-period EMA on four different timeframes: 30-minute, 1-hour, 4-hour, and Daily
Each timeframe is displayed with distinct colors for easy visual identification
Visual Elements:
Chart Lines: Four colored EMA lines plotted directly on the price chart
Price Labels: Clear labels showing each EMA's current value at the latest bar
Color-coded Table: Comprehensive data table showing price position relative to each EMA
Trend Identification:
Bullish Signal: When price closes above an EMA (green background in table)
Bearish Signal: When price closes below an EMA (dark background in table)
Helps identify confluence when multiple timeframes align in direction
Customizable Settings:
Adjustable EMA length (default: 200 periods)
Customizable line width and offset
Flexible table positioning (top/middle/bottom, left/center/right)
Configurable table cell size and text appearance
Swing traders analyzing multiple timeframes
Position traders looking for trend confirmation
Technical analysts seeking confluence across time horizons
This indicator provides a comprehensive view of market trends across different time perspectives, helping traders make more informed decisions based on multi-timeframe analysis.
This indicator does not provide trading advice. It is for educational and informational purposes only.
**指标名称：多时间框架200 EMA**
**描述：**
这款高级指标在多个时间框架上显示指数移动平均线（EMA），帮助交易者识别不同市场视角下的趋势方向和强度。
**主要特点：**
1. **多时间框架EMA分析：**
- 在四个不同时间框架上绘制200周期EMA：30分钟、1小时、4小时和日线
- 每个时间框架使用独特颜色显示，便于视觉识别
2. **视觉元素：**
- **图表线：** 在价格图表上直接绘制四条彩色EMA线
- **价格标签：** 清晰显示最新K线处各EMA的当前值
- **颜色编码表格：** 综合数据表格显示价格相对于各EMA的位置
3. **趋势识别：**
- **看涨信号：** 当价格收于EMA上方时（表格中显示绿色背景）
- **看跌信号：** 当价格收于EMA下方时（表格中显示深色背景）
- 帮助识别多个时间框架方向一致时的共振信号
4. **可自定义设置：**
- 可调整EMA长度（默认：200周期）
- 可自定义线宽和偏移量
- 灵活的表格定位（上/中/下，左/中/右）
- 可配置表格单元格大小和文本外观
**适合人群：**
- 分析多时间框架的摆动交易者
- 寻求趋势确认的头寸交易者
- 寻找不同时间维度共振信号的技术分析师
Trappin Previous Timeframe LevelsTrappin Previous Timeframe Levels (Trappin PTL)
Overview
Trappin PTL is a comprehensive multi-timeframe support and resistance indicator that displays key price levels from multiple timeframes on a single chart. This indicator helps traders identify critical price zones where reversals or breakouts are likely to occur, making it ideal for both intraday and swing trading strategies.
💡 Origin Story
I got tired of manually drawing these lines that I learned from watching Wallstreet Trapper on Trappin Tuesdays YouTube live streams. After repeatedly marking the same previous timeframe levels on every chart, I decided to automate the process. Hope it helps you as much as it helps me!
Key Features
📊 Multiple Timeframe Levels
The indicator tracks and displays high/low levels from:
Previous Hour (PHH/PHL) - Purple lines
Previous Day (PDH/PDL) - Green lines
Previous Week (PWH/PWL) - Yellow lines
Previous Month (PMH/PML) - Blue lines
All-Time High (ATH) - Red line
52-Week High - Orange line
🎨 Fully Customizable
Colors - Change the color of each timeframe independently
Line Styles - Choose between Solid, Dashed, or Dotted lines
Line Widths - Adjust thickness from 1-4 pixels
All settings organized in intuitive groups for easy access
📍 Smart Line Extension
Lines extend back to show when the level was established
Lines project forward to show current relevance
Historical context helps identify key support/resistance zones
🏷️ Clear Price Labels
Each level displays its exact price value (no currency symbols)
Labels positioned horizontally to avoid overlap
Adaptive text color for visibility on any chart theme (dark or light mode)
Why "Trappin"?
The name is a tribute to Wallstreet Trapper and his Trappin Tuesdays YouTube live streams, where I learned the importance of marking previous timeframe levels. The name also reflects the indicator's purpose: identifying price levels where traders often get "trapped" - whether it's bulls getting trapped below resistance or bears getting trapped above support. These levels represent zones where significant order flow and liquidity exist, making them prime areas for reversals or breakouts.
Credits
Created by resoh
Inspired by Wallstreet Trapper and Trappin Tuesdays YouTube live streams
This indicator is provided for educational and informational purposes. Always practice proper risk management and conduct your own analysis before making trading decisions.
Version History
v1.0 - Initial Release
Multi-timeframe high/low levels
All-time high tracking
52-week high tracking
Fully customizable colors, styles, and widths
Adaptive labels with price display
Smart line extension showing historical context
Price–Volume Anomaly DetectorDescription
This indicator identifies unusual relationships between price strength and trading volume. By analyzing expected intraday volume behavior and comparing it with current activity, it highlights potential exhaustion, absorption, or expansion events that may signal changing market dynamics.
How It Works
The script profiles average volume by time of day and compares current volume against this adaptive baseline. Combined with normalized price movement (ATR-based), it detects conditions where price and volume diverge:
Exhaustion: Strong price move on low volume (potential fade)
Absorption: Weak price move on high volume (potential reversal)
Expansion: Strong price move on high volume (momentum continuation)
Key Features
Adaptive time-based volume normalization
Configurable sensitivity thresholds
Optional visibility for each anomaly type
Adjustable label transparency and offset
Light Mode support: label text automatically adjusts for dark or light chart backgrounds
Lightweight overlay design
Inputs Overview
Volume Profile Resolution: Defines time bucket size for expected volume
[* ]Lookback Days: Controls how quickly the profile adapts
Price / Volume Thresholds: Tune anomaly sensitivity
Show Expansion / Exhaustion / Absorption: Toggle specific labels
Label Transparency & Offset: Adjust chart visibility
How to Use:
Apply the indicator to any chart or timeframe.
Observe where labels appear:
🔴 Exhaustion: strong price, weak volume
🔵 Absorption: weak price, strong volume
🟢 Expansion: strong price, strong volume
Use these as context clues, not trade signals — combine with broader volume or trend analysis.
How It Helps
Reveals hidden price–volume imbalances
Highlights areas where momentum may be fading or strengthening
Enhances understanding of market behavior beyond raw price action
⚠️Disclaimer:
This script is provided for educational and informational purposes only. It is not financial advice and should not be considered a recommendation to buy, sell, or hold any financial instrument. Trading involves significant risk of loss and is not suitable for every investor. Users should perform their own due diligence and consult with a licensed financial advisor before making any trading decisions. The author does not guarantee any profits or results from using this script, and assumes no liability for any losses incurred. Use this script at your own risk.
Dual Table Dashboard - Correct V3add RSI Data## 📈 Trading Applications
### 1. Trend Following Strategy
```
1. Check TABLE 1 for trend direction (AnEMA29 + PDMDR)
2. If both green → Look for longs
3. If both red → Look for shorts
4. Use TABLE 2 for entry levels
```
### 2. Support/Resistance Strategy
```
@70 levels = Resistance (sell/take profit zones)
@50 levels = Pivot (breakout levels)
@30 levels = Support (buy/accumulation zones)
```
### 3. Multi-Timeframe Alignment
```
W_RSI → Weekly bias (long-term)
D_RSI → Daily bias (medium-term)
Sto50 → Current position (swing)
Sto12 → Immediate position (day trade)
RSI(7) & RSI(3) → Entry timing (scalp)
```
### 4. Color Scanning Method
**Quick visual analysis:**
- Count greens vs reds in each row
- More greens = Bullish position
- More reds = Bearish position
- Mixed colors = Transitioning/choppy
---
## ✅ Verification & Accuracy
### Tested Against AmiBroker:
- ✅ RSI band values match within ±0.01%
- ✅ Stochastic channels match exactly
- ✅ Color logic matches exactly
- ✅ All formulas verified line-by-line
### Known Minor Differences:
Small variations (<1%) may occur due to:
1. **Platform calculation precision** - Different floating-point engines
2. **Historical data feeds** - Slight variations in past prices
3. **Weekly bar boundaries** - TradingView vs AmiBroker week definitions
4. **Initialization period** - First N bars need to "warm up"
**These minor differences don't affect trading signals!**
---
## ⚙️ Settings & Customization
### Input Parameters:
```pine
emaLen = 29 // EMA Length for angle calculation
rangePeriods = 30 // Angle normalization lookback
rangeConst = 25 // Angle normalization constant
dmiLen = 14 // DMI/ADX Length for PDMDR
```
### Available Positions:
Can be changed in the code:
- `position.top_left`
- `position.top_center`
- `position.top_right`
- `position.middle_left` (Table 2 default)
- `position.middle_center`
- `position.middle_right`
- `position.bottom_left` (Table 1 default)
- `position.bottom_center`
- `position.bottom_right`
### Text Sizes:
- `size.tiny`
- `size.small` (current default)
- `size.normal`
- `size.large`
- `size.huge`
---
## 🎯 Best Practices
### DO:
✅ Use multiple confirmations before entering trades
✅ Combine with price action and chart patterns
✅ Pay attention to color changes across timeframes
✅ Use @50 levels as key pivot points
✅ Watch for alignment between W_RSI and D_RSI
### DON'T:
❌ Trade based on color alone without confirmation
❌ Ignore the overall trend (Table 1)
❌ Enter trades against strong trend signals
❌ Overtrade when colors are mixed/choppy
❌ Ignore risk management rules
---
## 📊 Example Reading
### Bullish Setup:
```
TABLE 1:
AnEMA29: Green (15°) across all 3 bars
PDMDR: Green (1.65) and rising
TABLE 2:
W_RSI@50: Green (price above)
D_RSI@50: Green (price above)
Sto50@50: Green (price above midpoint)
Sto12@50: Green (price above midpoint)
Interpretation: Strong bullish trend confirmed across multiple timeframes
Action: Look for long entries on pullbacks to @50 or @30 levels
```
### Bearish Setup:
```
TABLE 1:
AnEMA29: Red (-12°) across all 3 bars
PDMDR: Red (0.45) and falling
TABLE 2:
W_RSI@50: Red (price below)
D_RSI@50: Red (price below)
Sto50@50: Red (price below midpoint)
Interpretation: Strong bearish trend confirmed
Action: Look for short entries on rallies to @50 or @70 levels
```
### Reversal Signal:
```
TABLE 1:
-2D: Red, -1D: Yellow, 0D: Green (momentum shifting)
TABLE 2:
Price just crossed above multiple @50 levels
Colors changing from red to green
Interpretation: Potential trend reversal in progress
Action: Wait for confirmation, consider early long entry with tight stop
```
---
## 🔍 Troubleshooting
### "Values don't match AmiBroker exactly"
- Check you're on the same timeframe
- Verify the symbol is identical
- Compare historical data (last 20 closes)
- Small differences (<1%) are normal
### "Tables are overlapping"
- Adjust positions in code
- Use different combinations (top/middle/bottom with left/center/right)
### "Colors seem wrong"
- Verify current close price
- Check if you're comparing same bar
- Ensure both platforms use same session times
### "Script takes too long"
- Use on Daily or higher timeframes
- The RSI band calculation is computationally intensive
- Don't run on tick-by-tick data
---
## 📝 Version History
**v3.0 (Final)** - Current version
- RSI band calculation verified correct
- Tables positioned bottom-left and middle-left
- All values match AmiBroker
- Production ready ✅
**v2.0**
- Fixed RSI band algorithm order (calculate before updating P/N)
- Improved variable scope handling
**v1.0**
- Initial implementation
- Had incorrect RSI band calculation
---
## 📄 Files in Package
Stock Fundamental Overlay [DarwinDarma]Stock Fundamental Overlay
Stock Fundamental Overlay is a comprehensive valuation indicator that displays multiple fundamental analysis metrics directly on your price chart.
Key Features:
• Graham Number - Benjamin Graham's intrinsic value formula
• Book Value Per Share (BVPS) - Net asset value baseline
• DCF Valuation - Discounted Cash Flow analysis (non-financial stocks)
• DDM Valuation - Dividend Discount Model (dividend-paying stocks)
• Visual Value Zones - Color-coded undervalued/overvalued regions
• Real-time Fundamental Table - Live metrics and valuations
• Price vs Graham Comparison - Quick valuation assessment
• Built-in Alerts - Notification when price crosses key levels
Valuation Models:
• Graham Number: √(22.5 × EPS × BVPS)
• DCF: Customizable discount rate, growth rate, and forecast period
• DDM: Gordon Growth Model for dividend analysis
Visual Elements:
• Plot lines for BVPS, Graham Number, and DCF values
• Shaded value zone between BVPS and Graham Number
• Background coloring: Deep value (below BVPS), Undervalued (below Graham), Overvalued (>1.5x Graham)
• Dynamic table showing all metrics with theme-aware text colors
Special Handling:
• Financial sector detection - DCF disabled for banks/financials where FCF metrics are distorted
• Automatic light/dark theme adaptation
• TTM (Trailing Twelve Months) data for current metrics
How to Use - Value Investing Approach:
1. Identifying Undervalued Stocks:
• Look for price trading BELOW the Graham Number (green zone) - potential value opportunity
• Deep value: Price below BVPS indicates trading below net asset value
• Check "Price vs Graham" % in table - negative values suggest undervaluation
• Compare multiple models: When price is below Graham, DCF, and BVPS simultaneously, stronger buy signal
2. Margin of Safety:
• Benjamin Graham recommended buying at 2/3 of intrinsic value (33% margin of safety)
• Monitor the gap between current price and valuation lines
• Larger gaps = greater margin of safety = lower downside risk
• Use the shaded "Value Zone" as your target buying range
3. Setting Alerts:
• "Price Below Graham Number" - Notifies when stock enters value territory
• "Price Below Book Value" - Extreme value alert for deep value hunters
• "Price Below DCF Value" - Cash flow-based value signal
• Set alerts on watchlist stocks to catch value opportunities
4. Customizing for Your Strategy:
• Conservative investors: Use lower growth rates (3-4%) and higher discount rates (12-15%)
• Growth-value investors: Adjust growth rate (6-8%) for quality compounders
• Dividend investors: Focus on DDM value and Div/Share metrics
• Adjust forecast years based on business predictability (stable = 10 years, cyclical = 5 years)
5. Red Flags to Avoid:
• Negative EPS or FCF (red values in table) - proceed with caution
• Financial sector stocks - Use DDM and Graham, ignore DCF
• Price far above Graham (>1.5x) with red background = overvalued territory
• No fundamental data = "N/A" in table - stock may lack reporting or be too small
• Stock persistently below BVPS for extended periods - potential value trap or business in distress
• Price significantly above ALL models (BVPS, Graham, DCF) - sentiment-driven, lacks intrinsic value foundation (fragile)
⚠️ Important Value Investing Warnings:
• Value Trap Alert: A stock staying below BVPS for months/years may signal fundamental deterioration, asset impairments, or dying industry - not just "cheap." Investigate WHY it's cheap before buying
• Sentiment Bubble Risk: When price trades far above BVPS, Graham Number, AND DCF simultaneously, the stock has no intrinsic value basis. Examples: commodity stocks during boom cycles (gold miners in gold rallies), meme stocks, hype-driven sectors. These are highly fragile and vulnerable to mean reversion
• Cyclical Trap: Commodity/cyclical stocks can appear "cheap" at peak earnings (low P/E, high FCF) but are actually expensive. Normalize earnings across the cycle before valuing
• Quality Matters: Some excellent businesses (asset-light, high ROIC) naturally trade above book value. Don't avoid quality - adjust expectations for business model
6. Monitoring Positions:
• Watch for price approaching or exceeding Graham Number - consider taking profits
• Track EPS and FCF trends quarter-to-quarter in the table
• If fundamentals deteriorate (falling BVPS, negative FCF), reassess thesis
• Use background colors for quick visual check: green = hold/buy, red = overvalued
Perfect for:
Value investors seeking multi-model fundamental analysis, long-term investors comparing intrinsic value to market price, dividend investors evaluating yield stocks, and fundamental traders looking for entry/exit signals.
Note: Only works with stocks that have financial data available. Not applicable to crypto, forex, or futures. This indicator provides analysis tools; always conduct thorough research and due diligence before investing.
PM Range Breaker [CHE] PM Range Breaker — Premarket bias with first-five range breaks, optional SWDEMA regime latch, and simple two-times-range targets
Summary
This indicator sets a once-per-day directional bias during New York premarket and then tracks a strict first-five-minutes range from the session open. After the first five complete, it marks clean breakouts and can project targets at two times the measured range. A second mode latches an EMA-based regime to inform the bias and optional background tinting. A compact panel reports live state, first-five levels, and rolling hit rates of both bias modes using a user-defined midday close for statistics.
Motivation: Why this design?
Intraday traders often get whipsawed by early noise or by fast flips in trend filters. This script commits to a bias at a single premarket minute and then waits for the market to present an objective structure: the first-five range. Breaks after that window are clearer and easier to manage. The alternative SWDEMA regime gives a slower, latched context for users who prefer a trend scaffold rather than a midpoint reference.
What’s different vs. standard approaches?
Baseline: Typical open-range-breakout lines or a single moving-average filter without daily commitment.
Architecture differences:
Bias decision at a fixed New York time using either a midpoint lookback (“Classic”) or a two-EMA regime latch (“SWDEMA”).
Strict five-minute window from session open; breakout shapes print only after that window.
Single-shot breakout direction per session (debounce) and optional two-times-range targets.
On-chart panel with hit rates using a configurable midday close for statistics.
Practical effect: Cleaner visuals, fewer repeated signals, and a traceable daily decision that can be evaluated over time.
How it works (technical)
Time handling uses New York session times for premarket decision, open, first-five end, and a midday statistics checkpoint.
Classic bias: A midpoint is computed from the highest and lowest over a user period; at the premarket minute, the bias is set long when the close is above the midpoint, short otherwise.
SWDEMA bias: Two EMAs define a regime score that requires price and trend agreement; when both agree on a confirmed bar, the regime latches. At the premarket minute, the daily bias is set from the current regime.
The first-five range captures high and low from open until the end minute, then freezes. Breakouts are detected after that window using close-based cross logic.
The script draws range lines and optional targets at two times the frozen range. A session break direction latch prevents duplicate break markers.
Statistics compare daily open and a configurable midday close to record if the chosen bias aligned with the move.
Optional elements include EMA lines, midpoint line, latched-regime background, and regime switch markers.
Data aggregation for day logic and the first-five window is sampled on one-minute data with explicit lookahead off. On charts above one minute, values update intra-bar until the underlying minute closes.
Parameter Guide
Premarket Start (NY) — Minute when the bias is decided — Default: 08:30 — Move earlier for more stability; later for recency.
Market Open (NY) — Session start used for the first-five window — Default: 09:30 — Align to instrument’s RTH if different.
First-5 End (NY) — End of the first-five window — Default: 09:35 — Extend slightly to capture wider opening ranges.
Day End (NY) for Stats — Midday checkpoint for hit rate — Default: 12:00 — Use a later time for a longer evaluation window.
Show First-5 Lines — Draw the frozen range lines — Default: On — Turn off if your chart is crowded.
Show Bias Background (Session) — Tint by daily bias during session — Default: On — Useful for directional context.
Show Break Shapes — Print breakout triangles — Default: On — Disable if you only want lines and alerts.
Show 2R Targets (Optional) — Plot targets at two times the range — Default: On — Switch off if you manage exits differently.
Line Length Right — Extension length of drawn lines — Default: 20 (bars) — Increase for slower timeframes.
High/Low Line Colors — Visual colors for range levels — Defaults: Green/Red — Adjust to your theme.
Long/Short Bias Colors — Background tints — Defaults: Green/Red with high transparency — Lower transparency for stronger emphasis.
Show Corner Panel — Enable the info panel — Default: On — Centralizes status and numbers.
Show Hit Rates in Panel — Include success rates — Default: On — Turn off to reduce panel rows.
Panel Position — Anchor on chart — Default: Top right — Move to avoid overlap.
Panel Size — Text size in panel — Default: Small — Increase on high-resolution displays.
Dark Panel — Dark theme for the panel — Default: On — Match your chart background.
Show EMA Lines — Plot blue and red EMAs — Default: Off — Enable for SWDEMA context.
Show Midpoint Line — Plot the midpoint — Default: Off — Useful for Classic mode visualization.
Midpoint Lookback Period — Bars for high-low midpoint — Default: 300 — Larger values stabilize; smaller values respond faster.
Midpoint Line Color — Color for midpoint — Default: Gray — A neutral line works best.
SWDEMA Lengths (Blue/Red) — Periods for the two EMAs — Defaults: 144 and 312 — Longer values reduce flips.
Sources (Blue/Red) — Price sources — Defaults: Close and HLC3 — Adjust if you prefer consistency.
Offsets (Blue/Red) — Pixel offsets for EMA plots — Defaults: zero — Use only for visual shift.
Show Latched Regime Background — Background by SWDEMA regime — Default: Off — Separate from session bias.
Latched Background Transparency — Opacity of regime background — Default: eighty-eight — Lower value for stronger tint.
Show Latch Switch Markers — Plot regime change markers — Default: Off — For auditing regime changes.
Bias Mode — Classic midpoint or SWDEMA latch — Default: Classic — Choose per your style.
Background Mode — Session bias or SWDEMA regime — Default: Session — Decide which background narrative you want.
Reading & Interpretation
Panel: Shows the active bias, first-five high and low, and a state that reads Building during the window, Ready once frozen, and Break arrows when a breakout occurs. Hit rates show the percentage of days where each bias mode aligned with the midday move.
Colors and shapes: Green background implies long bias; red implies short bias. Triangle markers denote the first valid breakout after the first-five window. Optional regime markers flag regime changes.
Lines: First-five high and low form the core structure. Optional targets mark a level at two times the frozen range from the breakout side.
Practical Workflows & Combinations
Trend following: Choose a bias mode. Wait for the first clean breakout after the first-five window in the direction of the bias. Confirm with structure such as higher highs and higher lows or lower highs and lower lows.
Exits and risk: Conservative users can trail behind the opposite side of the first-five range. Aggressive users can scale near the two-times-range target.
Multi-asset and multi-TF: Works well on intraday timeframes from one minute upward. For non-US sessions, adjust the time inputs to the instrument’s regular trading hours.
Behavior, Constraints & Performance
Repaint and confirmation: Bias and regime decisions use confirmed bars. Breakout signals evaluate on bar close at the chart timeframe. On higher timeframes, minute-based sources update within the live bar until the minute closes.
security and HTF: The script samples one-minute data. Lookahead is off. Values stabilize once the source minute closes.
Resources: `max_bars_back` is five thousand. Drawing objects and the panel update efficiently, with position extensions handled on the last bar.
Known limits: Midday statistics use the configured time, not the official daily close. Session logic assumes New York session timing. Targets are simple multiples of the first-five range and do not adapt to volatility beyond that structure.
Sensible Defaults & Quick Tuning
Start with Classic bias, midpoint lookback at three hundred, and all visuals on.
Too many flips in context → switch to SWDEMA mode or increase EMA lengths.
Breakouts feel noisy → extend the first-five end by a minute or two, or wait for a retest by your own rules.
Too sluggish → reduce midpoint lookback or shorten EMA lengths.
Chart cluttered → hide EMA or midpoint lines and keep only range levels and breakout shapes.
What this indicator is—and isn’t
This is a visualization and signal layer for session bias and first-five structure. It does not manage orders, position sizing, or risk. It is not predictive. Use it alongside market structure, execution rules, and independent risk controls.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
Many thanks to LonesomeTheBlue
for the original work. I adapted the midpoint calculation for this script. www.tradingview.com
[LTS] Marubozu Candle StrategyOVERVIEW
The Marubozu Candle Strategy identifies and trades wickless candles (Marubozu patterns) with dynamic take-profit and stop-loss levels based on market volatility. This indicator combines traditional Japanese candlestick pattern recognition with modern volatility-adjusted risk management and includes a comprehensive performance tracking dashboard.
A Marubozu candle is a powerful continuation pattern characterized by the complete absence of wicks on one side, indicating strong directional momentum. This strategy specifically detects:
- Bullish Marubozu: Close > Open AND Low = Open (no lower wick)
- Bearish Marubozu: Close < Open AND High = Open (no upper wick)
When price returns to test these levels, the indicator generates trading signals with predefined risk-reward parameters.
CORE METHODOLOGY
Detection Logic:
The script scans each bar for Marubozu formations using precise price comparisons. When a wickless candle appears, a horizontal line extends from the opening price, marking it as a potential support (bullish) or resistance (bearish) level. These levels remain active until price touches them or until the maximum line limit is reached.
EMA Filter (Optional):
An exponential moving average filter enhances signal quality by requiring proper trend alignment. For bullish signals, price must be above the EMA when touching the level. For bearish signals, price must be below the EMA. This filter reduces counter-trend trades and improves win rates in trending markets. Users can disable this filter for range-bound conditions.
Dynamic Risk Management:
The strategy employs ATR-based (Average True Range) position sizing rather than fixed point values. This approach adapts to market volatility automatically:
- In low volatility: Tighter stops and targets
- In high volatility: Wider stops and targets proportional to market movement
Default settings use a 2:1 reward-to-risk ratio (1x ATR for take-profit, 0.5x ATR for stop-loss), but users can adjust these multipliers to match their trading style.
HOW IT WORKS
Step 1 - Pattern Detection:
On each bar, the indicator evaluates whether the candle qualifies as a Marubozu by comparing the high, low, open, and close prices. When detected, the opening price becomes the key level.
Step 2 - Level Management:
Horizontal lines extend from each Marubozu's opening price. The indicator maintains two separate arrays: one for unbroken levels (actively extending) and one for broken levels (historical reference). Users can configure how many of each type to display, preventing chart clutter while maintaining relevant context.
Step 3 - Signal Generation:
When price returns to touch a Marubozu level, the indicator evaluates the EMA filter condition. If the filter passes (or is disabled), the script draws TP/SL boxes showing the expected profit and loss zones based on current ATR values.
Step 4 - Trade Tracking:
Each valid signal enters the tracking system, which monitors subsequent price action to determine outcomes. The script identifies whether the take-profit or stop-loss was hit first (discarding trades where both trigger on the same candle to avoid ambiguous results).
PERFORMANCE DASHBOARD
The integrated dashboard provides real-time strategy analytics to automatically convert results to dollar values for any instrument:
Tracked Metrics:
- Total Trades: Complete count of closed positions
- Wins/Losses: Individual counts with color coding
- Win Rate: Success percentage with dynamic color (green >= 50%, red < 50%)
- Total P&L: Cumulative profit/loss in dollars
- Avg Win: Mean dollar amount per winning trade
- Avg Loss: Mean dollar amount per losing trade
NOTE: The dollar values shown in the dashboard are for trading only a single share/contract/etc. You will need to manually multiply those numbers by the amount of shares/contracts you are trading to get a true value.
The dollar conversion works automatically across all markets:
- Futures contracts (ES, NQ, CL, etc.) use their contract specifications
- Forex pairs use standard lot calculations
- Stocks and crypto use their respective point values
This eliminates manual calculation and provides immediate performance feedback in meaningful currency terms.
CUSTOMIZATION OPTIONS
ATR Settings:
- ATR Period: Lookback length for volatility calculation (default: 14)
- TP Multiplier: Take-profit distance as multiple of ATR (default: 3.0)
- SL Multiplier: Stop-loss distance as multiple of ATR (default: 1.5)
EMA Settings:
- EMA Length: Period for trend filter calculation (default: 9)
- Use EMA Filter: Toggle trend confirmation requirement (default: enabled)
Visual Settings:
- Bullish Color: Color for long signals and wins (default: green)
- Bearish Color: Color for short signals and losses (default: red)
- EMA Color: Color for trend filter line (default: orange)
- Line Width: Thickness of Marubozu level lines (1-5, default: 2)
- EMA Width: Thickness of EMA line (1-5, default: 2)
Line Management:
- Max Unbroken Lines: Limit for active extending lines (default: 10)
- Max Broken Lines: Limit for historical touched lines (default: 5)
Dashboard Settings:
- Show Dashboard: Toggle performance display on/off
- Dashboard Position: Corner placement (4 options)
- Dashboard Size: Text size selection (Tiny/Small/Normal/Large)
HOW TO USE
1. Add the indicator to your chart
2. Adjust ATR multipliers based on your risk tolerance (higher values = more conservative)
3. Configure the EMA filter based on market conditions (enable for trending, disable for ranging)
4. Set line limits to match your visual preference and chart timeframe
5. Monitor the dashboard to track strategy performance in real-time
6. Use the TP/SL boxes as reference levels for manual trades or automation
Best Practices:
- Enable EMA filter in strongly trending markets
- Disable EMA filter if you want more trade signals but at lower quality
- Increase ATR multipliers in highly volatile markets
- Decrease ATR multipliers for tighter, more frequent trades
- Review avg win/loss ratio to ensure positive expectancy
UNIQUE FEATURES
Unlike basic Marubozu detectors, this strategy provides:
1. Automatic level tracking with memory management
2. Volatility-adjusted risk parameters instead of fixed values
3. Optional trend confirmation via EMA filter
4. Real-time performance analytics with automatic dollar conversion
5. Separate tracking of wins/losses with individual averages
6. Configurable visual display to prevent chart clutter
7. Complete transparency with all logic visible in open-source code
Alerts Killzones + PD/WL/ML Levels (No Labels)This indicator automatically highlights the London and New York killzones and triggers alerts at key price levels — without adding any labels or text clutter to the chart.
Features:
Highlights London (10:00–13:00) and New York (15:00–17:00) sessions (GMT+3, Romania).
Draws and updates key levels automatically:
PDH / PDL – Previous Day High & Low
WH / WL – Previous Week High & Low
MH / ML – Previous Month High & Low
Alerts when price touches any of these levels.
Alerts at session opens and closes for both London and New York.
Clean interface – no labels or extra markers on chart.
Ideal for:
Traders who follow ICT concepts, session-based setups, or liquidity sweeps and want precise alerts without chart noise.
RSI Divergence Strategy v6 What this does
Detects regular and hidden divergences between price and RSI using confirmed RSI pivots. Adds RSI@pivot entry gates, a normalized strength + volume filter, optional volume gate, delayed entries, and transparent risk management with rigid SL and activatable trailing. Visuals are throttled for clarity and include a gap-free horizontal RSI gradient.
How it works (simple)
🧮 RSI is calculated on your selected source/period.
📌 RSI pivots are confirmed with left/right lookbacks (lbL/lbR). A pivot becomes final only after lbR bars; before that, it can move (expected).
🔎 The latest confirmed pivot is compared against the previous confirmed pivot within your bar window:
• Regular Bullish = price lower low + RSI higher low
• Hidden Bullish = price higher low + RSI lower low
• Regular Bearish = price higher high + RSI lower high
• Hidden Bearish = price lower high + RSI higher high
💪 Each divergence gets a strength score that multiplies price % change, RSI change, and a volume ratio (Volume SMA / Baseline Volume SMA).
• Set Min divergence strength to filter tiny/noisy signals.
• Turn on the volume gate to require volume ratio ≥ your threshold (e.g., 1.0).
🎯 RSI@pivot gating:
• Longs only if RSI at the bullish pivot ≤ 30 (default).
• Shorts only if RSI at the bearish pivot ≥ 70 (default).
⏱ Entry timing:
• Immediate: on divergence confirm (delay = 0).
• Delayed: after N bars if RSI is still valid.
• RSI-only mode: ignore divergences; use RSI thresholds only.
🛡 Risk:
• Rigid SL is placed from average entry.
• Trailing activates only after unrealized gain ≥ threshold; it re-anchors on new highs (long) or new lows (short).
What’s NEW here (vs. the reference) — and why you may care
• Improved pivots + bar window → fewer early/misaligned signals; cleaner drawings.
• RSI@pivot gates → entries aligned with true oversold/overbought at the exact decision bar.
• Normalized strength + volume gate → ignore weak or low-volume divergences.
• Delayed entries → require the signal to persist N bars if you want more confirmation.
• Rigid SL + activatable trailing → trailing engages only after a cushion, so it’s less noisy.
• Clutter control + gradient → readable chart with a smooth RSI band look.
Suggested starting values (clear ranges)
• RSI@pivot thresholds: LONG ≤ 30 (oversold), SHORT ≥ 70 (overbought).
• Min divergence strength:
0.0 = off
3–6 = moderate filter
7–12 = strict filter for noisy LTFs
• Volume gate (ratio):
1.0 = at least baseline volume
1.2–1.5 = strong-volume only (fewer but cleaner signals)
• Pivot lookbacks:
lbL 1–2, lbR 3–4 (raise lbR to confirm later and reduce noise)
• Bar window (between pivots):
Min 5–10, Max 30–60 (increase Min if you see micro-pivots; increase Max for wider structures)
• Risk:
Rigid SL 2–5% on liquid majors; 5–10% on higher-volatility symbols
Trailing activation 1–3%, trailing 0.5–1.5% are common intraday starts
Plain-text examples
• BTCUSDT 1h → RSI 9, lbL 1, lbR 3, Min strength 5.0, Volume gate 1.0, SL 4.5%, Trail on 2.0%, Trail 1.0%.
• SPY 15m → RSI 8, lbL 1, lbR 3, Min strength 7.0, Volume gate 1.2, SL 3.0%, Trail on 1.5%, Trail 0.8%.
• EURUSD 4h → RSI 14, lbL 2, lbR 4, Min strength 4.0, Volume gate 1.0, SL 2.5%, Trail on 1.0%, Trail 0.5%.
Notes & limitations
• Pivot confirmation means the newest candidate pivot can move until lbR confirms it (expected).
• Results vary by timeframe/symbol/settings; always forward-test.
• Educational tool — no performance or profit claims.
Credits
• RSI by J. Welles Wilder Jr. (1978).
• Reference divergence script by eemani123:
• This version by tagstrading 2025 adds: improved pivot engine, RSI@pivot gating, normalized strength + optional volume gate, delayed entries, rigid SL and activatable trailing, and a gap-free RSI gradient.
Advanced HMM - 3 States CompleteHidden Markov Model
Aconsistent challenge for quantitative traders is the frequent behaviour modification of financial
markets, often abruptly, due to changing periods of government policy, regulatory environment
and other macroeconomic effects. Such periods are known as market regimes. Detecting such
changes is a common, albeit difficult, process undertaken by quantitative market participants.
These various regimes lead to adjustments of asset returns via shifts in their means, variances,
autocorrelation and covariances. This impacts the effectiveness of time series methods that rely
on stationarity. In particular it can lead to dynamically-varying correlation, excess kurtosis ("fat
tails"), heteroskedasticity (volatility clustering) and skewed returns.
There is a clear need to effectively detect these regimes. This aids optimal deployment of
quantitative trading strategies and tuning the parameters within them. The modeling task then
becomes an attempt to identify when a new regime has occurred adjusting strategy deployment,
risk management and position sizing criteria accordingly.
A principal method for carrying out regime detection is to use a statistical time series tech
nique known as a Hidden Markov Model . These models are well-suited to the task since they
involve inference on "hidden" generative processes via "noisy" indirect observations correlated
to these processes. In this instance the hidden, or latent, process is the underlying regime state,
while the asset returns are the indirect noisy observations that are influenced by these states.
MAIN FEATURES OF THE INDICATOR
The "Advanced HMM - 3 States Complete" indicator is an advanced technical analysis tool that uses Hidden Markov Model (HMM) to identify three main market regimes: BULL, BEAR, and SIDEWAYS.
🎯 KEY FEATURES:
1. HMM-based Trend Detection
3 market states: Bull (0), Bear (1), Sideways (2)
Dynamic probabilities: Calculates probability for each state based on price data
Transition matrix: Models state transitions between regimes
2. Analytical Features
Price volatility: Log returns and standard deviation
Momentum: Rate of Change (ROC)
Volume: Volume ratio vs moving average
Data normalization: Standardizes features to common scale
3. Visual Trading Signals
text
📍 BUY Signals:
- Green upward triangle below bars
- "LONG" label in green
📍 SELL Signals:
- Red downward triangle above bars
- "SHORT" label in red
📍 EXIT Signals:
- Orange X marks when transitioning to sideways
4. Information Display
Probability table (top-right): Shows percentage for each state
State label: Current regime with probability percentages
Chart background color: Reflects dominant market state
5. Automated Alerts
Alerts when new Bull/Bear market detected
Alerts when market transitions to sideways
Configurable TradingView notifications
6. Customizable Parameters
pinescript
length: 100 // Lookback period
smoothing_period: 20 // Probability smoothing
volatility_threshold: 0.5 // Volatility threshold
💡 PRACTICAL APPLICATIONS:
Identify primary trends with quantified probabilities
Entry/exit signals based on state transitions
Risk management during sideways markets
Trend confirmation when combined with other indicators
This indicator is particularly useful for market regime analysis and identifying trend transition points using advanced statistical probability methods.
🔧 TECHNICAL IMPLEMENTATION:
Composite observation: Weighted combination of returns (40%), momentum (30%), and volatility (30%)
Gaussian emission probabilities: Different distributions for each state
Manual HMM updates: Avoids matrix computation limitations in Pine Script
Real-time smoothing: EMA applied to state probabilities
The indicator provides institutional-grade regime detection in a visually intuitive package suitable for both discretionary and systematic traders.
Pivot Regime Anchored VWAP [CHE] Pivot Regime Anchored VWAP — Detects body-based pivot regimes to classify swing highs and lows, anchoring volume-weighted average price lines directly at higher highs and lower lows for adaptive reference levels.
Summary
This indicator identifies shifts between top and bottom regimes through breakouts in candle body highs and lows, labeling swing points as higher highs, lower highs, lower lows, or higher lows. It then draws anchored volume-weighted average price lines starting from the most recent higher high and lower low, providing dynamic support and resistance that evolve with volume flow. These anchored lines differ from standard volume-weighted averages by resetting only at confirmed swing extremes, reducing noise in ranging markets while highlighting momentum shifts in trends.
Motivation: Why this design?
Traders often struggle with static reference lines that fail to adapt to changing market structures, leading to false breaks in volatile conditions or missed continuations in trends. By anchoring volume-weighted average price calculations to body pivot regimes—specifically at higher highs for resistance and lower lows for support—this design creates reference levels tied directly to price structure extremes. This approach addresses the problem of generic moving averages lagging behind swing confirmations, offering a more context-aware tool for intraday or swing trading.
What’s different vs. standard approaches?
- Baseline reference: Traditional volume-weighted average price indicators compute a running total from session start or fixed periods, often ignoring price structure.
- Architecture differences:
- Regime detection via body breakout logic switches between high and low focus dynamically.
- Anchoring limited to confirmed higher highs and lower lows, with historical recalculation for accurate line drawing.
- Polyline rendering rebuilds only on the last bar to manage performance.
- Practical effect: Charts show fewer, more meaningful lines that start at swing points, making it easier to spot confluences with structure breaks rather than cluttered overlays from continuous calculations.
How it works (technical)
The indicator first calculates the maximum and minimum of each candle's open and close to define body highs and lows. It then scans a lookback window for the highest body high and lowest body low. A top regime triggers when the body high from the lookback period exceeds the window's highest, and a bottom regime when the body low falls below the window's lowest. These regime shifts confirm pivots only when crossing from one state to the other.
For top pivots, it compares the new body high against the previous swing high: if greater, it marks a higher high and anchors a new line; otherwise, a lower high. The same logic applies inversely for bottom pivots. Anchored lines use cumulative price-volume products and volumes from the anchor bar onward, subtracting prior cumulatives to isolate the segment. On pivot confirmation, it loops backward from the current bar to the anchor, computing and storing points for the line. New points append as bars advance, ensuring the line reflects ongoing volume weighting.
Initialization uses persistent variables to track the last swing values and anchor bars, starting with neutral states. Data flows from regime detection to pivot classification, then to anchoring and point accumulation, with lines rendered globally on the final bar.
Parameter Guide
Pivot Length — Controls the lookback window for detecting body breakouts, influencing pivot frequency and sensitivity to recent action. Shorter values catch more pivots in choppy conditions; longer smooths for major swings. Default: 30 (bars). Trade-offs/Tips: Min 1; for intraday, try 10–20 to reduce lag but watch for noise; on daily, 50+ for stability.
Show Pivot Labels — Toggles display of text markers at swing points, aiding quick identification of higher highs, lower highs, lower lows, or higher lows. Default: true. Trade-offs/Tips: Disable in multi-indicator setups to declutter; useful for backtesting structure.
HH Color — Sets the line and label color for higher high anchored lines, distinguishing resistance levels. Default: Red (solid). Trade-offs/Tips: Choose contrasting hues for dark/light themes; pair with opacity for fills if added later.
LL Color — Sets the line and label color for lower low anchored lines, distinguishing support levels. Default: Lime (solid). Trade-offs/Tips: As above; green shades work well for bullish contexts without overpowering candles.
Reading & Interpretation
Higher high labels and red lines indicate potential resistance zones where volume weighting begins at a new swing top, suggesting sellers may defend prior highs. Lower low labels and lime lines mark support from a fresh swing bottom, with the line's slope reflecting buyer commitment via volume. Lower highs or higher lows appear as labels without new anchors, signaling possible range-bound action. Line proximity to price shows overextension; crosses may hint at regime shifts, but confirm with volume spikes.
Practical Workflows & Combinations
- Trend following: Enter longs above a rising lower low anchored line after higher low confirmation; filter with rising higher highs for uptrends. Use line breaks as trailing stops.
- Exits/Stops: In downtrends, exit shorts below a higher high line; set aggressive stops above it for scalps, conservative below for swings. Pair with momentum oscillators for divergence.
- Multi-asset/Multi-TF: Defaults suit forex/stocks on 1H–4H; on crypto 15M, shorten length to 15. Scale colors for dark themes; combine with higher timeframe anchors for confluence.
Behavior, Constraints & Performance
Closed-bar logic ensures pivots confirm after the lookback period, with no repainting on historical bars—live bars may adjust until regime shift. No higher timeframe calls, so minimal repaint risk beyond standard delays. Resources include a 2000-bar history limit, label/polyline caps at 200/50, and loops for historical point filling (up to current bar count from anchor, typically under 500 iterations). Known limits: In extreme gaps or low-volume periods, anchors may skew; lines absent until first pivots.
Sensible Defaults & Quick Tuning
Start with the 30-bar length for balanced pivot detection across most assets. For too-frequent pivots in ranges, increase to 50 for fewer signals. If lines lag in trends, reduce to 20 and enable labels for visual cues. In low-volatility assets, widen color contrasts; test on 100-bar history to verify stability.
What this indicator is—and isn’t
This is a structure-aware visualization layer for anchoring volume-weighted references at swing extremes, enhancing manual analysis of regimes and levels. It is not a standalone signal generator or predictive model—always integrate with broader context like order flow or news. Use alongside risk management and position sizing, not as isolated buy/sell triggers.
Many thanks to LuxAlgo for the original script "McDonald's Pattern ". The implementation for body pivots instead of wicks uses a = max(open, close), b = min(open, close) and then highest(a, length) / lowest(b, length). This filters noise from the wicks and detects breakouts over/under bodies. Unusual and targeted, super innovative.
Disclaimer
The content provided, including all code and materials, is strictly for educational and informational purposes only. It is not intended as, and should not be interpreted as, financial advice, a recommendation to buy or sell any financial instrument, or an offer of any financial product or service. All strategies, tools, and examples discussed are provided for illustrative purposes to demonstrate coding techniques and the functionality of Pine Script within a trading context.
Any results from strategies or tools provided are hypothetical, and past performance is not indicative of future results. Trading and investing involve high risk, including the potential loss of principal, and may not be suitable for all individuals. Before making any trading decisions, please consult with a qualified financial professional to understand the risks involved.
By using this script, you acknowledge and agree that any trading decisions are made solely at your discretion and risk.
Do not use this indicator on Heikin-Ashi, Renko, Kagi, Point-and-Figure, or Range charts, as these chart types can produce unrealistic results for signal markers and alerts.
Best regards and happy trading
Chervolino
USD Session 8FX - LDN & NY (TF-invariant, Live + Table)What it is
A USD strength/weakness meter for the London (08:00–08:45) or New York (15:30–16:00/16:15) session. It blends the movement of 8 markets—EURUSD, GBPUSD, AUDUSD, NZDUSD, USDCHF, USDCAD, USDJPY, XAUUSD—into one Score that is timeframe-invariant (it uses a 1-minute “boundary TF” under the hood so changing chart TF doesn’t change the math).
Core logic (simple)
During the chosen session window, it records each symbol’s start and live end prices, computes returns, optionally normalizes by ATR (volatility), applies your weights, and averages anti-USD (EUR/GBP/AUD/NZD/XAU) vs USD-base (CHF/CAD/JPY) groups.
The final Score is the normalized sum of weighted contributions:
Score > 0 → “USD Strong”
Score < 0 → “USD Weak”
At the session close it freezes (“Locked”) the results so you can review them later.
What you see
Main plot: the USD Score line (with a 0 baseline).
Optional lines: Anti-USD average vs USD-base average (post-normalization, pre-weights).
Session background shading (London silver, New York aqua).
Live table with:
Each symbol’s % change, its weight, and its contribution to the Score.
TOP badges for the two biggest drivers (by absolute contribution).
A Side column (only for the two TOPs) showing BUY/SELL aligned with the USD verdict (e.g., if USD Strong → SELL anti-USD pairs like EURUSD, BUY USD-base like USDCHF).
Verdict row with USD Strong/Weak, the Score value, the window text, and whether you’re LIVE / CLOSED / FROZEN.
Trade Gate panel:
Shows Verdict (USD Strong/Weak), Bias OK/weak (|Score| vs your threshold), Top-1/Top-2 VWAP checks, an overall GATE: OK/NO, and an Entry hint string (e.g., “SELL EURUSD, BUY USDCHF”) when conditions align.
VWAP “Trade Gate”
It confirms alignment between the USD bias and price vs VWAP for the top movers:
If USD Strong: anti-USD symbols should be below VWAP (short bias), USD-base symbols above VWAP (long bias).
If USD Weak: the opposite.
Gate = OK only if |Score| ≥ minAbsScore and at least one of the two TOP symbols is on the correct side of VWAP.
Tip: set vwapTF to an intraday value (“1”, “5”, “15”) for reliable VWAP on higher-TF charts.
Alerts
At session close: “USD Strong/Weak – session close”.
Live threshold: alerts when |Score| crosses your intraday threshold up/down.
Entry hint (Gate OK): triggers when the Gate flips from NO → OK inside the window.
If you create an alert of type “Any alert() function call”, you also get a dynamic message like:
ENTRY HINT • Hint: SELL EURUSD, BUY USDCHF
Key inputs you can tweak
Session: London vs New York; NY end time 16:00 or 16:15.
Timezone: default Europe/Tirane.
Boundary TF: default “1” (keeps the indicator TF-invariant).
minAbsScore: sensitivity threshold for “Bias OK”.
ATR normalization (len): stabilizes comparisons across different volatility regimes.
VWAP settings: toggle panel and set vwapTF.
How to use (playbook)
Choose the session (e.g., New York 15:30–16:15), keep Boundary TF = 1.
If you’re on a higher-TF chart, set vwapTF = "1" or "5".
Watch Score and Verdict; when |Score| ≥ minAbsScore, bias is meaningful.
Check Top-1/Top-2 and the Trade Gate:
If Gate = OK, use the Entry hint (e.g., “SELL EURUSD, BUY USDCHF”) as the aligned idea.
Use your own execution rules (e.g., structure, risk, stops) on the suggested symbols.
After close, review the Frozen table to validate behavior and refine thresholds/weights.
Notes & edge cases
If some markets are illiquid/holiday, a few returns may be na; the script handles that gracefully.
If ta.vwap is na on high TFs, the Gate will simply not confirm—set vwapTF intraday.
You can customize weights (e.g., reduce XAUUSD to -0.3 or similar) to suit your basket philosophy.
If you want, I can add toggles to show Side for all 8 symbols, or print a one-line summary (e.g., “USD Strong • Score 0.23 • Gate OK • SELL EURUSD, BUY USDCHF”) in the top-left of the pane.
Historical Matrix Analyzer [PhenLabs]📊Historical Matrix Analyzer
Version: PineScriptv6
📌Description
The Historical Matrix Analyzer is an advanced probabilistic trading tool that transforms technical analysis into a data-driven decision support system. By creating a comprehensive 56-cell matrix that tracks every combination of RSI states and multi-indicator conditions, this indicator reveals which market patterns have historically led to profitable outcomes and which have not.
At its core, the indicator continuously monitors seven distinct RSI states (ranging from Extreme Oversold to Extreme Overbought) and eight unique indicator combinations (MACD direction, volume levels, and price momentum). For each of these 56 possible market states, the system calculates average forward returns, win rates, and occurrence counts based on your configurable lookback period. The result is a color-coded probability matrix that shows you exactly where you stand in the historical performance landscape.
The standout feature is the Current State Panel, which provides instant clarity on your active market conditions. This panel displays signal strength classifications (from Strong Bullish to Strong Bearish), the average return percentage for similar past occurrences, an estimated win rate using Bayesian smoothing to prevent small-sample distortions, and a confidence level indicator that warns you when insufficient data exists for reliable conclusions.
🚀Points of Innovation
Multi-dimensional state classification combining 7 RSI levels with 8 indicator combinations for 56 unique trackable market conditions
Bayesian win rate estimation with adjustable smoothing strength to provide stable probability estimates even with limited historical samples
Real-time active cell highlighting with “NOW” marker that visually connects current market conditions to their historical performance data
Configurable color intensity sensitivity allowing traders to adjust heat-map responsiveness from conservative to aggressive visual feedback
Dual-panel display system separating the comprehensive statistics matrix from an easy-to-read current state summary panel
Intelligent confidence scoring that automatically warns traders when occurrence counts fall below reliable thresholds
🔧Core Components
RSI State Classification: Segments RSI readings into 7 distinct zones (Extreme Oversold <20, Oversold 20-30, Weak 30-40, Neutral 40-60, Strong 60-70, Overbought 70-80, Extreme Overbought >80) to capture momentum extremes and transitions
Multi-Indicator Condition Tracking: Simultaneously monitors MACD crossover status (bullish/bearish), volume relative to moving average (high/low), and price direction (rising/falling) creating 8 binary-encoded combinations
Historical Data Storage Arrays: Maintains rolling lookback windows storing RSI states, indicator states, prices, and bar indices for precise forward-return calculations
Forward Performance Calculator: Measures price changes over configurable forward bar periods (1-20 bars) from each historical state, accumulating total returns and win counts per matrix cell
Bayesian Smoothing Engine: Applies statistical prior assumptions (default 50% win rate) weighted by user-defined strength parameter to stabilize estimated win rates when sample sizes are small
Dynamic Color Mapping System: Converts average returns into color-coded heat map with intensity adjusted by sensitivity parameter and transparency modified by confidence levels
🔥Key Features
56-Cell Probability Matrix: Comprehensive grid displaying every possible combination of RSI state and indicator condition, with each cell showing average return percentage, estimated win rate, and occurrence count for complete statistical visibility
Current State Info Panel: Dedicated display showing your exact position in the matrix with signal strength emoji indicators, numerical statistics, and color-coded confidence warnings for immediate situational awareness
Customizable Lookback Period: Adjustable historical window from 50 to 500 bars allowing traders to focus on recent market behavior or capture longer-term pattern stability across different market cycles
Configurable Forward Performance Window: Select target holding periods from 1 to 20 bars ahead to align probability calculations with your trading timeframe, whether day trading or swing trading
Visual Heat Mapping: Color-coded cells transition from red (bearish historical performance) through gray (neutral) to green (bullish performance) with intensity reflecting statistical significance and occurrence frequency
Intelligent Data Filtering: Minimum occurrence threshold (1-10) removes unreliable patterns with insufficient historical samples, displaying gray warning colors for low-confidence cells
Flexible Layout Options: Independent positioning of statistics matrix and info panel to any screen corner, accommodating different chart layouts and personal preferences
Tooltip Details: Hover over any matrix cell to see full RSI label, complete indicator status description, precise average return, estimated win rate, and total occurrence count
🎨Visualization
Statistics Matrix Table: A 9-column by 8-row grid with RSI states labeling vertical axis and indicator combinations on horizontal axis, using compact abbreviations (XOverS, OverB, MACD↑, Vol↓, P↑) for space efficiency
Active Cell Indicator: The current market state cell displays “⦿ NOW ⦿” in yellow text with enhanced color saturation to immediately draw attention to relevant historical performance
Signal Strength Visualization: Info panel uses emoji indicators (🔥 Strong Bullish, ✅ Bullish, ↗️ Weak Bullish, ➖ Neutral, ↘️ Weak Bearish, ⛔ Bearish, ❄️ Strong Bearish, ⚠️ Insufficient Data) for rapid interpretation
Histogram Plot: Below the price chart, a green/red histogram displays the current cell’s average return percentage, providing a time-series view of how historical performance changes as market conditions evolve
Color Intensity Scaling: Cell background transparency and saturation dynamically adjust based on both the magnitude of average returns and the occurrence count, ensuring visual emphasis on reliable patterns
Confidence Level Display: Info panel bottom row shows “High Confidence” (green), “Medium Confidence” (orange), or “Low Confidence” (red) based on occurrence counts relative to minimum threshold multipliers
📖Usage Guidelines
RSI Period
Default: 14
Range: 1 to unlimited
Description: Controls the lookback period for RSI momentum calculation. Standard 14-period provides widely-recognized overbought/oversold levels. Decrease for faster, more sensitive RSI reactions suitable for scalping. Increase (21, 28) for smoother, longer-term momentum assessment in swing trading. Changes affect how quickly the indicator moves between the 7 RSI state classifications.
MACD Fast Length
Default: 12
Range: 1 to unlimited
Description: Sets the faster exponential moving average for MACD calculation. Standard 12-period setting works well for daily charts and captures short-term momentum shifts. Decreasing creates more responsive MACD crossovers but increases false signals. Increasing smooths out noise but delays signal generation, affecting the bullish/bearish indicator state classification.
MACD Slow Length
Default: 26
Range: 1 to unlimited
Description: Defines the slower exponential moving average for MACD calculation. Traditional 26-period setting balances trend identification with responsiveness. Must be greater than Fast Length. Wider spread between fast and slow increases MACD sensitivity to trend changes, impacting the frequency of indicator state transitions in the matrix.
MACD Signal Length
Default: 9
Range: 1 to unlimited
Description: Smoothing period for the MACD signal line that triggers bullish/bearish state changes. Standard 9-period provides reliable crossover signals. Shorter values create more frequent state changes and earlier signals but with more whipsaws. Longer values produce more confirmed, stable signals but with increased lag in detecting momentum shifts.
Volume MA Period
Default: 20
Range: 1 to unlimited
Description: Lookback period for volume moving average used to classify volume as “high” or “low” in indicator state combinations. 20-period default captures typical monthly trading patterns. Shorter periods (10-15) make volume classification more reactive to recent spikes. Longer periods (30-50) require more sustained volume changes to trigger state classification shifts.
Statistics Lookback Period
Default: 200
Range: 50 to 500
Description: Number of historical bars used to calculate matrix statistics. 200 bars provides substantial data for reliable patterns while remaining responsive to regime changes. Lower values (50-100) emphasize recent market behavior and adapt quickly but may produce volatile statistics. Higher values (300-500) capture long-term patterns with stable statistics but slower adaptation to changing market dynamics.
Forward Performance Bars
Default: 5
Range: 1 to 20
Description: Number of bars ahead used to calculate forward returns from each historical state occurrence. 5-bar default suits intraday to short-term swing trading (5 hours on hourly charts, 1 week on daily charts). Lower values (1-3) target short-term momentum trades. Higher values (10-20) align with position trading and longer-term pattern exploitation.
Color Intensity Sensitivity
Default: 2.0
Range: 0.5 to 5.0, step 0.5
Description: Amplifies or dampens the color intensity response to average return magnitudes in the matrix heat map. 2.0 default provides balanced visual emphasis. Lower values (0.5-1.0) create subtle coloring requiring larger returns for full saturation, useful for volatile instruments. Higher values (3.0-5.0) produce vivid colors from smaller returns, highlighting subtle edges in range-bound markets.
Minimum Occurrences for Coloring
Default: 3
Range: 1 to 10
Description: Required minimum sample size before applying color-coded performance to matrix cells. Cells with fewer occurrences display gray “insufficient data” warning. 3-occurrence default filters out rare patterns. Lower threshold (1-2) shows more data but includes unreliable single-event statistics. Higher thresholds (5-10) ensure only well-established patterns receive visual emphasis.
Table Position
Default: top_right
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the 56-cell statistics matrix table. Position to avoid overlapping critical price action or other indicators on your chart. Consider chart orientation and candlestick density when selecting optimal placement.
Show Current State Panel
Default: true
Options: true, false
Description: Toggle visibility of the dedicated current state information panel. When enabled, displays signal strength, RSI value, indicator status, average return, estimated win rate, and confidence level for active market conditions. Disable to declutter charts when only the matrix table is needed.
Info Panel Position
Default: bottom_left
Options: top_left, top_right, bottom_left, bottom_right
Description: Screen location for the current state information panel (when enabled). Position independently from statistics matrix to optimize chart real estate. Typically placed opposite the matrix table for balanced visual layout.
Win Rate Smoothing Strength
Default: 5
Range: 1 to 20
Description: Controls Bayesian prior weighting for estimated win rate calculations. Acts as virtual sample size assuming 50% win rate baseline. Default 5 provides moderate smoothing preventing extreme win rate estimates from small samples. Lower values (1-3) reduce smoothing effect, allowing win rates to reflect raw data more directly. Higher values (10-20) increase conservatism, pulling win rate estimates toward 50% until substantial evidence accumulates.
✅Best Use Cases
Pattern-based discretionary trading where you want historical confirmation before entering setups that “look good” based on current technical alignment
Swing trading with holding periods matching your forward performance bar setting, using high-confidence bullish cells as entry filters
Risk assessment and position sizing, allocating larger size to trades originating from cells with strong positive average returns and high estimated win rates
Market regime identification by observing which RSI states and indicator combinations are currently producing the most reliable historical patterns
Backtesting validation by comparing your manual strategy signals against the historical performance of the corresponding matrix cells
Educational tool for developing intuition about which technical condition combinations have actually worked versus those that feel right but lack historical evidence
⚠️Limitations
Historical patterns do not guarantee future performance, especially during unprecedented market events or regime changes not represented in the lookback period
Small sample sizes (low occurrence counts) produce unreliable statistics despite Bayesian smoothing, requiring caution when acting on low-confidence cells
Matrix statistics lag behind rapidly changing market conditions, as the lookback period must accumulate new state occurrences before updating performance data
Forward return calculations use fixed bar periods that may not align with actual trade exit timing, support/resistance levels, or volatility-adjusted profit targets
💡What Makes This Unique
Multi-Dimensional State Space: Unlike single-indicator tools, simultaneously tracks 56 distinct market condition combinations providing granular pattern resolution unavailable in traditional technical analysis
Bayesian Statistical Rigor: Implements proper probabilistic smoothing to prevent overconfidence from limited data, a critical feature missing from most pattern recognition tools
Real-Time Contextual Feedback: The “NOW” marker and dedicated info panel instantly connect current market conditions to their historical performance profile, eliminating guesswork
Transparent Occurrence Counts: Displays sample sizes directly in each cell, allowing traders to judge statistical reliability themselves rather than hiding data quality issues
Fully Customizable Analysis Window: Complete control over lookback depth and forward return horizons lets traders align the tool precisely with their trading timeframe and strategy requirements
🔬How It Works
1. State Classification and Encoding
Each bar’s RSI value is evaluated and assigned to one of 7 discrete states based on threshold levels (0: <20, 1: 20-30, 2: 30-40, 3: 40-60, 4: 60-70, 5: 70-80, 6: >80)
Simultaneously, three binary conditions are evaluated: MACD line position relative to signal line, current volume relative to its moving average, and current close relative to previous close
These three binary conditions are combined into a single indicator state integer (0-7) using binary encoding, creating 8 possible indicator combinations
The RSI state and indicator state are stored together, defining one of 56 possible market condition cells in the matrix
2. Historical Data Accumulation
As each bar completes, the current state classification, closing price, and bar index are stored in rolling arrays maintained at the size specified by the lookback period
When the arrays reach capacity, the oldest data point is removed and the newest added, creating a sliding historical window
This continuous process builds a comprehensive database of past market conditions and their subsequent price movements
3. Forward Return Calculation and Statistics Update
On each bar, the indicator looks back through the stored historical data to find bars where sufficient forward bars exist to measure outcomes
For each historical occurrence, the price change from that bar to the bar N periods ahead (where N is the forward performance bars setting) is calculated as a percentage return
This percentage return is added to the cumulative return total for the specific matrix cell corresponding to that historical bar’s state classification
Occurrence counts are incremented, and wins are tallied for positive returns, building comprehensive statistics for each of the 56 cells
The Bayesian smoothing formula combines these raw statistics with prior assumptions (neutral 50% win rate) weighted by the smoothing strength parameter to produce estimated win rates that remain stable even with small samples
💡Note:
The Historical Matrix Analyzer is designed as a decision support tool, not a standalone trading system. Best results come from using it to validate discretionary trade ideas or filter systematic strategy signals. Always combine matrix insights with proper risk management, position sizing rules, and awareness of broader market context. The estimated win rate feature uses Bayesian statistics specifically to prevent false confidence from limited data, but no amount of smoothing can create reliable predictions from fundamentally insufficient sample sizes. Focus on high-confidence cells (green-colored confidence indicators) with occurrence counts well above your minimum threshold for the most actionable insights.
ICT First Presented FVG with Volume Imbalance [1st P. FVG + VI]The indicator identifies and highlights the first presented Fair Value Gap (FVG) occurringthe morning (09:30–10:00) and afternoon (13:30–14:00) session's first 30 minutes. It includes an optional feature to extend FVG zones when a volume imbalance (V.I.) is detected, providing additional context for areas of potential price inefficiency. This powerful combination helps traders identify significant market structure gaps that often act as support/resistance zones and potential price targets.
What is an FVG?
A Fair Value Gap, often abbreviated as FVG, is a price range on a chart where there is an inefficiency or imbalance in trading. This typically happens when price moves rapidly in one direction, leaving a gap between the wicks or bodies of three consecutive candles. For example, in a bullish move, if the low of the third candle is higher than the high of the first candle, the space between them is the FVG.
What is a Volume Imbalance?
A volume imbalance is a smaller, more precise inefficiency within price action, often visible as a "crack" or thin area in the price delivery. It represents a spot where the volume traded was not balanced between buyers and sellers, often seen as a thin wick or a gap between candle bodies.
FVG + Volume Imbalance:
When you have a fair value gap that contains a volume imbalance, it becomes a more significant area of interest. ICT teaches that you should not ignore a volume imbalance if it’s part of an FVG. In fact, you should use the volume imbalance in conjunction with the FVG to define your trading range more accurately
📊 Volume Imbalance Integration
Toggle Option: Enable/disable volume imbalance detection based on preference
Extended Boundaries: When enabled, FVG boundaries expand to include volume imbalance zones
Accurate Gap Sizing: Total gap calculation includes volume imbalance extensions
Multi-Scenario Support: Handles volume imbalances at start, end, or both sides of FVG formations
📈 Multiple Display Modes
Current Day: Shows only today's FVGs for clean chart analysis
Current Week: Displays all weekly FVGs for broader context
Forward Extension: Extends FVG boxes and CE, Upper/Lower Quadrant lines into the future
📊 Visualization
Bullish FVGs appear in semi-transparent blue or purple zones (depending on session).
Bearish FVGs appear in red or orange zones.
Optional dotted lines mark the CE (midpoint) of each FVG for additional reference.
Quadrant Division: Additional 25%/75% lines for large FVGs (configurable minimum gap size)
🎯 Smart Filtering
First Presentation Only: Only displays the initial FVG in each session, avoiding clutter
Minimum Gap Size: Configurable tick-based thresholds for AM and PM sessions
Core FVG Validation: Ensures only valid Fair Value Gaps are displayed
⚙️ Configuration Options
Display Settings
Show Mode: Current Day or Current Week view
Forward Extension: 1-500 bars projection
Day Labels: Toggle weekday labels in weekly mode
Text Color: Customizable label colors
Volume Imbalance Settings
Include Volume Imbalance: Master toggle for enhanced boundary calculation
Automatic Detection: Identifies imbalance scenarios without additional input
Session-Specific Settings
AM Session (09:30-10:00):
Enable/disable AM FVG detection
Customizable bullish/bearish colors
CE line visibility and coloring
Minimum gap size in ticks
PM Session (13:30-14:00):
Enable/disable PM FVG detection
Customizable bullish/bearish colors
CE line visibility and coloring
Minimum gap size in ticks
Quadrant Settings
Enable/Disable: Toggle quadrant line display
Minimum Gap: Tick threshold for quadrant activation
Line Style: Dotted, dashed, or solid
Color: Customizable quadrant line color
How It Works
FVG Boundary Calculation
Traditional FVG: High to Low (bullish) or Low to High (bearish)
Enhanced FVG: Extended boundaries to include volume imbalance zones when enabled
Total Gap Size: Calculated including any volume imbalance extensions
Volume Imbalance Detection
The indicator identifies volume imbalances by detecting bars where:
Bullish Imbalance: Current bar's body is completely above previous bar's body
Bearish Imbalance: Current bar's body is completely below previous bar's body
⚠️ Disclaimer
This script is a technical visualization tool only.
It does not provide financial advice, signals, or predictions. Always perform independent analysis and manage risk appropriately before making trading decisions.
Elite_Pro_SignalsA sophisticated trading indicator that combines 8 powerful technical factors into a single confidence score to identify high-probability reversal signals.
8-Factor Confidence Scoring - Weighted analysis of multiple technical aspects
Smart Trend Alignment - Multi-timeframe EMA convergence
Advanced Pattern Recognition - Pin Bars, Engulfing, Inside Bars, Hammer/Shooting Star
Supply/Demand Zones - Automatic key level detection
Support/Resistance Confluence - Price action at significant levels
⚡ Smart Filters
Market Regime Detection - Avoid choppy/low-volatility conditions
Volume Confirmation - Ensure institutional participation
Liquidity Sweep Validation - Smart money movement detection
Candle Quality Filter - Eliminate false signals from tiny candles
🔧 How It Works
Confidence Scoring System (0-100%)
text
Wick Strength (30%) + Trend Alignment (25%) + Pattern Recognition (15%) +
Supply/Demand Zones (12%) + Support/Resistance (10%) + RSI Momentum (5%) +
Volume & Liquidity (5%)
Signal Generation
🟢 BUY Signals - Bullish rejection + Uptrend + High confidence
🔴 SELL Signals - Bearish rejection + Downtrend + High confidence
🎨 Visual Features
Clear Buy/Sell Arrows - Easy-to-spot signals
Confidence Background - Color-coded confidence levels
Info Table - Real-time metrics and analysis
Multi-Timeframe EMAs - Trend direction visualization
Professional Alerts - Real-time notifications
⚙️ Customization
Confidence Weights
Adjust the importance of each factor to match your trading style
Strategy Parameters
EMA periods (Fast: 20, Slow: 50)
RSI levels (Oversold: 25, Overbought: 80)
Minimum confidence threshold (70% recommended)
Advanced Filters
Volume multiplier settings
Liquidity sweep sensitivity
Market regime filters
Zone detection parameters
📈 Recommended Usage
Timeframes
Primary: 5-minute to 1-hour charts
Best Results: 15-minute with 1-hour trend alignment
Markets
Forex Pairs (EURUSD, GBPUSD, XAUUSD)
Indices (US30, NAS100, DE40)
Commodities (Gold, Oil)
Trading Sessions
London & New York overlap (Highest volatility)
Avoid Asian session (Low signal quality)
🔍 Signal Interpretation
High-Confidence Signals (80%+)
Strong trend alignment
Clear rejection patterns
Volume confirmation
Multiple confluence factors
Medium-Confidence Signals (60-80%)
Good setup but missing 1-2 factors
Requires additional confirmation
Low-Confidence Signals (<60%)
Avoid trading
Wait for better setups
Multi-Timeframe SFP (Swing Failure Pattern)How to Use
1. Set Pivot Timeframe: Choose the timeframe for identifying major swing points (e.g., 'D' for Daily pivots).
2. Set SFP Timeframe: Choose the timeframe to find the SFP candle (e.g., '240' for the 4-Hour chart).
3. Set Confirmation Bars: Set how many SFP Timeframe bars must pass without invalidating the level. A value of '0' confirms immediately on the SFP bar's close. A value of '1' waits for one more bar to close.
4. Adjust Filters (Optional): Enable the 'Wick % Filter' to add a quality check for strong rejections.
5. Watch & Wait: The indicator will draw lines and labels and fire alerts for fully confirmed signals.
In-Depth Explanation
1. Overview
The Dynamic Pivot SFP Engine is a multi-timeframe tool designed to identify and validate Swing Failure Patterns (SFPs) at significant price levels.
An SFP is a common price action pattern where price briefly trades beyond a previous swing high or low (sweeping liquidity) but then fails to hold those new prices, closing back inside the previous range. This "failure" often signals a reversal.
This indicator enhances SFP detection by separating the Pivot (Liquidity) from the SFP (Rejection), allowing you to monitor them on different timeframes.
2. The Core Multi-Timeframe Logic
The indicator's power comes from two key inputs:
• Pivot Timeframe (Pivot Timeframe)
This is the "high timeframe" used to establish significant support and resistance levels. The script finds standard pivots (swing highs and lows) on this timeframe based on the Pivot Left Strength and Pivot Right Strength inputs. These pivots are the "liquidity" levels the SFP will target. The Pivot Lookback input controls how long (in Pivot Timeframe bars) a pivot remains active and monitored.
• SFP Timeframe (SFP Timeframe)
This is the "execution timeframe" where the script looks for the actual SFP. On every new bar of this timeframe, the script checks if price has swept and rejected any of the active pivots.
Example Setup:
You might set Pivot Timeframe to 'D' (Daily) to find major daily swing points. You then set SFP Timeframe to '240' (4-Hour) to find a 4-hour candle that sweeps a daily pivot and closes back below/above it.
3. The SFP Confirmation Process
An SFP is not confirmed instantly. It must pass a rigorous, multi-step validation process.
Step 1: The SFP Candle (The Sweep)
A potential SFP is identified when an SFP Timeframe bar does the following:
• Bearish SFP: The bar's high trades above an active pivot high, but the bar closes below that same pivot high.
• Bullish SFP: The bar's low trades below an active pivot low, but the bar closes above that same pivot low.
Step 2: The Wick Filter (Optional Quality Check)
If Enable Wick % Filter is checked, the SFP candle from Step 1 is also measured.
• For a bearish SFP, the upper wick (from the high to the open/close) must be at least Min. Wick % of the entire candle's range (high-to-low).
• For a bullish SFP, the lower wick (from the low to the open/close) must meet the same percentage requirement.
If the SFP candle fails this test, it is discarded, even if it met the sweep/close criteria.
Step 3: The Validation Window (The Confirmation)
This is the most critical feature, controlled by Confirmation Bars.
• If Confirmation Bars = 0: The SFP is confirmed immediately on the SFP candle's close (assuming it passed the optional wick check). The label, line, and alert are triggered at this moment.
• If Confirmation Bars > 0: The SFP enters a "pending" state. The script will wait for $N$ more SFP Timeframe bars to close.
o Invalidation: If, during this waiting period, any bar closes back across the pivot (e.g., a close above the pivot for a bearish SFP), the SFP is considered failed and invalidated. All pending plots are deleted.
o Confirmation: If the $N$ confirmation bars all complete without invalidating the level, the SFP is finally confirmed. The label, line, and alert are only triggered after this entire process is complete. This adds a significant layer of robustness, ensuring the rejection holds for a period of time.
4. Visuals & Alerts
• Lines: A horizontal line is drawn from the original pivot to the SFP bar, showing which level was targeted. Note: These lines will only be drawn on chart timeframes equal to or lower than the 'SFP Timeframe'.
• Labels: A label is placed at the SFP's extreme (the high/low of the SFP bar). The label text conveniently includes the Ticker, Pivot TF, SFP TF, and Confirmation bar settings (e.g., "Bearish SFP BTCUSD / Pivot: 1D / SFP: 4H | Conf: 1").
• MTF Boxes (Show SFP Box, Show Conf. Boxes): These boxes highlight the SFP and confirmation bars. Crucially, they are only visible when your chart timeframe is lower than the SFP Timeframe. For example, if your SFP Timeframe is '240' (4H), you will only see these boxes on the 1H, 15M, 5M, etc., charts. This allows you to see the higher-timeframe SFP unfolding on your lower-timeframe chart.
• Alerts (Enable Alerts): An alert is fired only when an SFP is fully confirmed (i.e., after the Confirmation Bars have passed successfully). For efficient, real-time monitoring, it is highly recommended to run this indicator server-side by creating an alert on TradingView set to trigger on "Any alert() function call".
