SSL Channel BFSSL Channel Close is a great all-rounder based on 2 Simple Moving Averages, one of recent Highs, one of recent Lows.
The calculation prints a channel on the chart consisting of 2 lines.
This strategy gives a Long signal when price closes above the top of these 2 lines and a Short signal when it closes below the bottom.
Trading in choppy sideways markets can compound losses so we avoid that here by using recent ATR to determine relative volatility and refrain from trading when the background is White.
We use a basic 3% stop loss.
Charted on XBT/USD Bitmex Daily chart.
INSTRUCTIONS
Green = long
Red = short
White Background= No trade
The way I have set this strategy up is that if we get stopped out but we are still in a green or red background, we re-enter. Closing the trade only occurs on an opposing signal or if we get stopped out.
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Chandelier Exit V2 by fr3762 KIVANÇChandelier Exit Version 2 with two lines Long Stop and Short Stop
There is a Chandelier exit for long positions and one for short positions. The Chandelier Exit (long) hangs three ATR values below the 22-period high. This means it rises and falls as the period high and the ATR value changes. The Chandelier Exit for short positions is placed three ATR values above the 22-period low. The spreadsheet examples show sample calculations for both.
According to the theory, traders should exit long positions at either the highest high since entry minus 3 ATRs .
Similarly traders should exit short positions at either the lowest low since entry plus 3 ATRs .
Developed by Charles Le Beau and featured in Alexander Elder's books, the Chandelier Exit sets a trailing stop-loss based on the Average True Range (ATR). The indicator is designed to keep traders in a trend and prevent an early exit as long as the trend extends. Typically, the Chandelier Exit will be above prices during a downtrend and below prices during an uptrend.
The author, Chuck LeBeau explains: It lets "... profits run in the direction of a trend while still offering some protection against any reversal in trend."
The exit stop is placed at a multiple of average true ranges from the highest high or highest close since the entry of the trade.
Chandelier Exit will rise instantly whenever new highs are reached. As the highs get higher the stop moves up but it never moves downward.
The Chandelier Exit is mostly used to set a trailing stop-loss during a trend. Trends sometimes extend further than we anticipate and the Chandelier Exit can help traders ride the trend a little longer. Even though it is mostly used for stop-losses, the Chandelier Exit can also be used as a trend tool. A break above the Chandelier Exit (long) signals strength, while a break below the Chandelier Exit (short) signals weakness. Once a new trend begins, chartists can then use the corresponding Chandelier Exit to help define this trend.
Developer: Charles Le Beau
Here's the link to a complete list of all my indicators:
tr.tradingview.com
Şimdiye kadar paylaştığım indikatörlerin tam listesi için: tr.tradingview.com
Forex Master (EUR/USD)ATTENTION:
This is a symmetrical algorithm designed only for trading EUR/USD on the 1h time frame. For other currency pairs and time frames, you need to re-calibrate the RSI-EMAs as well as the profit targets and stop losses.
BACKTEST CONDITIONS:
Initial equity = $100,000 (no leverage)
Order size = 100% of equity
Pyramiding = disabled
TRADING RULES:
Long entry = EMA20(RSI10) cross> 50
Profit limit = 50 pips
Stop loss = 50 pips
Short entry = EMA30(RSI30) cross< 50
Profit limit = 50 pips
Stop loss = 50 pips
Long entry = Short exit
Short entry = long exit
DISCLAIMER: None of my ideas and posts are investment advice. Past performance is not an indication of future results. This strategy was constructed with the benefit of hindsight and its future performance cannot be guaranteed.
15m ORB + FVG (ChadAnt)Core Logic
The indicator's logic revolves around three main phases:
1. Defining the 15-Minute Opening Range (ORB)
The script calculates the highest high (rangeHigh) and lowest low (rangeLow) that occurred during the first 15 minutes of the trading day.
This time window is defined by the sessionStr input, which defaults to 0930-0945 (exchange time).
The high and low of this range are plotted as small gray dots once the session ends (rangeSet = true).
2. Identifying a Fair Value Gap (FVG) Setup
After the 15-minute range is set, the indicator waits for a breakout of either the range high or range low.
A "Strict FVG breakout" requires two conditions on the first candle that closes beyond the range:
The candle before the breakout candle ( bars ago) must have been inside the range.
The breakout candle ( bar ago) must have closed outside the range.
A Fair Value Gap (FVG) must form on the most recent three candles (the current bar and the two previous bars).
Bullish FVG (Long Setup): The low of the current bar (low) is greater than the high of the bar two periods prior (high ). This FVG represents a price inefficiency that the trade expects to fill.
Bearish FVG (Short Setup): The high of the current bar (high) is less than the low of the bar two periods prior (low ).
If a valid FVG setup occurs, the indicator marks a pending setup and draws a colored box to highlight the FVG area (Green for Bullish FVG, Red for Bearish FVG).
3. Trade Entry and Management
If a pending setup is identified, the trade is structured as a re-entry trade into the FVG zone:
Entry Price: Set at the outer boundary of the FVG, which is the low of the current bar for a Long setup, or the high of the current bar for a Short setup.
Stop Loss (SL): Set at the opposite boundary of the FVG, which is the low for a Long setup, or the high for a Short setup.
The trade is triggered (tradeActive = true) once the price retraces to the pendingEntry level.
Risk/Reward (RR) Targets: Three Take Profit (TP) levels are calculated based on the distance between the Entry and Stop Loss:
$$\text{Risk} = | \text{Entry} - \text{SL} |$$
$$\text{TP}n = \text{Entry} \pm (\text{Risk} \times \text{RR}n)$$
where $n$ is 1, 2, or 3, corresponding to the input $\text{RR}1$, $\text{RR}2$, and $\text{RR}3$ values (defaults: 1.0, 1.5, and 2.0).
Trade Lines: Upon triggering, lines for the Entry, Stop Loss, and three Take Profit levels are drawn on the chart for a specified length (lineLength).
A crucial feature is the directional lock (highBroken / lowBroken):
If the price breaks a range level (e.g., simpleBrokeHigh) but without a valid FVG setup, the corresponding directional flag (e.g., highBroken) is set to true permanently for the day.
This prevents the indicator from looking for any subsequent trade setups in that direction for the rest of the day, suggesting that the initial move, without an FVG, exhausted the opportunity.
ATR Trend + RSI Pullback Strategy [Profit-Focused]This strategy is designed to catch high-probability pullbacks during strong trends using a combination of ATR-based volatility filters, RSI exhaustion levels, and a trend-following entry model.
Strategy Logic
Rather than relying on lagging crossovers, this model waits for RSI to dip into oversold zones (below 40) while price remains above a long-term EMA (default: 200). This setup captures pullbacks in strong uptrends, allowing traders to enter early in a move while controlling risk dynamically.
To avoid entries during low-volatility conditions or sideways price action, it applies a minimum ATR filter. The ATR also defines both the stop-loss and take-profit levels, allowing the model to adapt to changing market conditions.
Exit logic includes:
A take-profit at 3× the ATR distance
A stop-loss at 1.5× the ATR distance
An optional early exit if RSI crosses above 70, signaling overbought conditions
Technical Details
Trend Filter: 200 EMA – must be rising and price must be above it
Entry Signal: RSI dips below 40 during an uptrend
Volatility Filter: ATR must be above a user-defined minimum threshold
Stop-Loss: 1.5× ATR below entry price
Take-Profit: 3.0× ATR above entry price
Exit on Overbought: RSI > 70 (optional early exit)
Backtest Settings
Initial Capital: $10,000
Position Sizing: 5% of equity per trade
Slippage: 1 tick
Commission: 0.075% per trade
Trade Direction: Long only
Timeframes Tested: 15m, 1H, and 30m on trending assets like BTCUSD, NAS100, ETHUSD
This model is tuned for positive P&L across trending environments and volatile markets.
Educational Use Only
This strategy is for educational purposes only and should not be considered financial advice. Past performance does not guarantee future results. Always validate performance on multiple markets and timeframes before using it in live trading.
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.
Darvas Lines/Box1. Overview
The Darvas Lines/Box (v1.0) is a dynamic trend following indicator based on the renowned method developed by Nicolas Darvas. It's designed to identify clear price consolidation ranges and detect decisive breakouts, crucial for positional and swing trading strategies.
This indicator automatically draws and adjusts the consolidation ranges, and includes modern enhancements such as Advanced Retest Confirmation and exposed alert conditions, providing reliable signals for monitoring and acting on trend continuations.
2. Core Features
Custom Display Mode (Lines/Box): Allows the user to toggle the visualization between showing just the Breakout Lines (Lines) or displaying the consolidation area with a filled background box (Box).
Source Selection (Wicks/Body): Users can choose whether the box boundaries are defined by the candlestick wicks (price extremes) or the candlestick body (open/close price). This feature is critical for adjusting sensitivity to market noise.
Dynamic Box Drawing: Draws Darvas boxes automatically by tracking price highs and lows based on user-defined parameters (Bars to Define Range, Max Box Height).
Retest Confirmation: Detects if the old resistance/support line functions effectively after a breakout. When a retest is confirmed, the line is extended and its color changes.
Price Labels (Stable Lock): Displays the highest and lowest box prices, fixed to the left outer edge of the box. This ensures stable visibility.
Progress Labels: Visualizes the current line price and the percentage distance to the closing price on the right side of the box, showing progress toward the next breakout.
3. Trading Strategy: How to Use the Indicator
This indicator is primarily used to identify trend initiation and trend continuation signals.
A. Entry Strategy (Breakout)
Long Entry Action: Consider taking a long entry when the price closes above the Upper Line (Green Line), signaled by a BULLISH BREAKOUT alert.
Signal: Use the BULLISH BREAKOUT alert.
Short Entry Action: Consider taking a short entry when the price closes below the Lower Line (Red Line), signaled by a BEARISH BREAKOUT alert.
Signal: Use the BEARISH BREAKOUT alert.
B. Retest Strategy (Add-on/Confirmation)
Action: When the price pulls back to touch the broken line (signaled by RETEST CONFIRMED), this confirms the break's validity.
Alert: The RETEST CONFIRMED alert is triggered at this moment.
C. Risk Management (General)
Stop Loss: The initial stop-loss is typically set just beyond the opposite side of the broken box. As the trend progresses and new boxes form, the lower boundary of the most recently formed box can be used as a trailing stop for managing risk.
4. Setting Parameters
Line Source (Wicks/Body): Crucial for sensitivity. 'Wicks' tracks price extremes; 'Body' tracks stronger close-to-close movements, ignoring noise.
Bars to Define Range: Defines the calculation period (in bars) for the box.
Cooldown Bars After Breakout: Sets the waiting period after a breakout before a new box can start forming.
Retest Lookback Bars (Phase 3): Sets the maximum number of bars to check for a retest during the cooldown phase.
Max Gap for Retest (%): Defines the maximum percentage distance from the line allowed to confirm a retest (Set to Zero (0.0%) for near-touch detection).
Alert Frequency (Breakout): Allows selection between Continuous and Once per Box for breakout signals.
5. Alerts: How to Set Up the Triggers
This indicator exposes several specific conditions to the TradingView alert panel, allowing you to select the exact event you want to monitor.
Step-by-Step Alert Setup:
Open the Alert Panel on the chart.
In the Condition field, select the indicator's name.
In the Alert Condition field, choose the specific event you want to monitor:
1. ANY DARVAS EVENT (Consolidated)
2. BULLISH BREAKOUT (Individual)
3. BEARISH BREAKOUT (Individual)
4. RETEST CONFIRMED (Individual)
In the Trigger field (Frequency), select your preferred native option (e.g., "Once Per Bar Close" or "Once per bar").
Hidden Impulse═══════════════════════════════════════════════════════════════════
HIDDEN IMPULSE - Multi-Timeframe Momentum Detection System
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OVERVIEW
Hidden Impulse is an advanced momentum oscillator that combines the Schaff Trend Cycle (STC) and Force Index into a comprehensive multi-timeframe trading system. Unlike standard implementations of these indicators, this script introduces three distinct trading setups with specific entry conditions, multi-timeframe confirmation, and trend filtering.
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ORIGINALITY & KEY FEATURES
This indicator is original in the following ways:
1. DUAL-TIMEFRAME STC ANALYSIS
Standard STC implementations work on a single timeframe. This script
simultaneously analyzes STC on both your trading timeframe and a higher
timeframe, providing trend context and filtering out low-probability signals.
2. FORCE INDEX INTEGRATION
The script combines STC with Force Index (volume-weighted price momentum)
to confirm the strength behind price moves. This combination helps identify
when momentum shifts are backed by genuine buying/selling pressure.
3. THREE DISTINCT TRADING SETUPS
Rather than generic overbought/oversold signals, the indicator provides
three specific, rule-based setups:
- Setup A: Classic trend-following entries with multi-timeframe confirmation
- Setup B: Divergence-based reversal entries (highest probability)
- Setup C: Mean-reversion bounce trades at extreme levels
4. INTELLIGENT FILTERING
All signals are filtered through:
- 50 EMA trend direction (prevents counter-trend trades)
- Higher timeframe STC alignment (ensures macro trend agreement)
- Force Index confirmation (validates volume support)
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HOW IT WORKS - TECHNICAL EXPLANATION
SCHAFF TREND CYCLE (STC) CALCULATION:
The STC is a cyclical oscillator that combines MACD concepts with stochastic
smoothing to create earlier and smoother trend signals.
Step 1: Calculate MACD
- Fast MA = EMA(close, Length1) — default 23
- Slow MA = EMA(close, Length2) — default 50
- MACD Line = Fast MA - Slow MA
Step 2: First Stochastic Smoothing
- Apply stochastic calculation to MACD
- Stoch1 = 100 × (MACD - Lowest(MACD, Smoothing)) / (Highest(MACD, Smoothing) - Lowest(MACD, Smoothing))
- Smooth result with EMA(Stoch1, Smoothing) — default 10
Step 3: Second Stochastic Smoothing
- Apply stochastic calculation again to the smoothed stochastic
- This creates the final STC value between 0-100
The dual stochastic smoothing makes STC more responsive than MACD while
being smoother than traditional stochastics.
FORCE INDEX CALCULATION:
Force Index measures the power behind price movements by incorporating volume:
Force Raw = (Close - Close ) × Volume
Force Index = EMA(Force Raw, Period) — default 13
Interpretation:
- Positive Force Index = Buying pressure (bulls in control)
- Negative Force Index = Selling pressure (bears in control)
- Force Index crossing zero = Momentum shift
- Divergences with price = Weakening momentum (reversal signal)
TREND FILTER:
A 50-period EMA serves as the trend filter:
- Price above EMA50 = Uptrend → Only LONG signals allowed
- Price below EMA50 = Downtrend → Only SHORT signals allowed
This prevents counter-trend trading which accounts for most losing trades.
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THE THREE TRADING SETUPS - DETAILED
SETUP A: CLASSIC MOMENTUM ENTRY
Concept: Enter when STC exits oversold/overbought zones with trend confirmation
LONG CONDITIONS:
1. Higher timeframe STC > 25 (macro trend is up)
2. Primary timeframe STC crosses above 25 (momentum turning up)
3. Force Index crosses above 0 OR already positive (volume confirms)
4. Price above 50 EMA (local trend is up)
SHORT CONDITIONS:
1. Higher timeframe STC < 75 (macro trend is down)
2. Primary timeframe STC crosses below 75 (momentum turning down)
3. Force Index crosses below 0 OR already negative (volume confirms)
4. Price below 50 EMA (local trend is down)
Best for: Trending markets, continuation trades
Win rate: Moderate (60-65%)
Risk/Reward: 1:2 to 1:3
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SETUP B: DIVERGENCE REVERSAL (HIGHEST PROBABILITY)
Concept: Identify exhaustion points where price makes new extremes but
momentum (Force Index) fails to confirm
BULLISH DIVERGENCE:
1. Price makes a lower low (LL) over 10 bars
2. Force Index makes a higher low (HL) — refuses to follow price down
3. STC is below 25 (oversold condition)
Trigger: STC starts rising AND Force Index crosses above zero
BEARISH DIVERGENCE:
1. Price makes a higher high (HH) over 10 bars
2. Force Index makes a lower high (LH) — refuses to follow price up
3. STC is above 75 (overbought condition)
Trigger: STC starts falling AND Force Index crosses below zero
Why this works: Divergences signal that the current trend is losing steam.
When volume (Force Index) doesn't confirm new price extremes, a reversal
is likely.
Best for: Reversal trading, range-bound markets
Win rate: High (70-75%)
Risk/Reward: 1:3 to 1:5
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SETUP C: QUICK BOUNCE AT EXTREMES
Concept: Catch rapid mean-reversion moves when price touches EMA50 in
extreme STC zones
LONG CONDITIONS:
1. Price touches 50 EMA from above (pullback in uptrend)
2. STC < 15 (extreme oversold)
3. Force Index > 0 (buyers stepping in)
SHORT CONDITIONS:
1. Price touches 50 EMA from below (pullback in downtrend)
2. STC > 85 (extreme overbought)
3. Force Index < 0 (sellers stepping in)
Best for: Scalping, quick mean-reversion trades
Win rate: Moderate (55-60%)
Risk/Reward: 1:1 to 1:2
Note: Use tighter stops and quick profit-taking
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HOW TO USE THE INDICATOR
STEP 1: CONFIGURE TIMEFRAMES
Primary Timeframe (STC - Primary Timeframe):
- Leave empty to use your current chart timeframe
- This is where you'll take trades
Higher Timeframe (STC - Higher Timeframe):
- Default: 30 minutes
- Recommended ratios:
* 5min chart → 30min higher TF
* 15min chart → 1H higher TF
* 1H chart → 4H higher TF
* Daily chart → Weekly higher TF
───────────────────────────────────────────────────────────────────
STEP 2: ADJUST STC PARAMETERS FOR YOUR MARKET
Default (23/50/10) works well for stocks and forex, but adjust for:
CRYPTO (volatile):
- Length 1: 15
- Length 2: 35
- Smoothing: 8
(Faster response for rapid price movements)
STOCKS (standard):
- Length 1: 23
- Length 2: 50
- Smoothing: 10
(Balanced settings)
FOREX MAJORS (slower):
- Length 1: 30
- Length 2: 60
- Smoothing: 12
(Filters out noise in 24/7 markets)
───────────────────────────────────────────────────────────────────
STEP 3: ENABLE YOUR PREFERRED SETUPS
Toggle setups based on your trading style:
Conservative Trader:
✓ Setup B (Divergence) — highest win rate
✗ Setup A (Classic) — only in strong trends
✗ Setup C (Bounce) — too aggressive
Trend Trader:
✓ Setup A (Classic) — primary signals
✓ Setup B (Divergence) — for entries on pullbacks
✗ Setup C (Bounce) — not suitable for trending
Scalper:
✓ Setup C (Bounce) — quick in-and-out
✓ Setup B (Divergence) — high probability scalps
✗ Setup A (Classic) — too slow
───────────────────────────────────────────────────────────────────
STEP 4: READ THE SIGNALS
ON THE CHART:
Labels appear when conditions are met:
Green labels:
- "LONG A" — Setup A long entry
- "LONG B DIV" — Setup B divergence long (best signal)
- "LONG C" — Setup C bounce long
Red labels:
- "SHORT A" — Setup A short entry
- "SHORT B DIV" — Setup B divergence short (best signal)
- "SHORT C" — Setup C bounce short
IN THE INDICATOR PANEL (bottom):
- Blue line = Primary timeframe STC
- Orange dots = Higher timeframe STC (optional)
- Green/Red bars = Force Index histogram
- Dashed lines at 25/75 = Entry/Exit zones
- Background shading = Oversold (green) / Overbought (red)
INFO TABLE (top-right corner):
Shows real-time status:
- STC values for both timeframes
- Force Index direction
- Price position vs EMA
- Current trend direction
- Active signal type
═══════════════════════════════════════════════════════════════════
TRADING STRATEGY & RISK MANAGEMENT
ENTRY RULES:
Priority ranking (best to worst):
1st: Setup B (Divergence) — wait for these
2nd: Setup A (Classic) — in confirmed trends only
3rd: Setup C (Bounce) — scalping only
Confirmation checklist before entry:
☑ Signal label appears on chart
☑ TREND in info table matches signal direction
☑ Higher timeframe STC aligned (check orange dots or table)
☑ Force Index confirming (check histogram color)
───────────────────────────────────────────────────────────────────
STOP LOSS PLACEMENT:
Setup A (Classic):
- LONG: Below recent swing low
- SHORT: Above recent swing high
- Typical: 1-2 ATR distance
Setup B (Divergence):
- LONG: Below the divergence low
- SHORT: Above the divergence high
- Typical: 0.5-1.5 ATR distance
Setup C (Bounce):
- LONG: 5-10 pips below EMA50
- SHORT: 5-10 pips above EMA50
- Typical: 0.3-0.8 ATR distance
───────────────────────────────────────────────────────────────────
TAKE PROFIT TARGETS:
Conservative approach:
- Exit when STC reaches opposite level
- LONG: Exit when STC > 75
- SHORT: Exit when STC < 25
Aggressive approach:
- Hold until opposite signal appears
- Trail stop as STC moves in your favor
Partial profits:
- Take 50% at 1:2 risk/reward
- Let remaining 50% run to target
───────────────────────────────────────────────────────────────────
WHAT TO AVOID:
❌ Trading Setup A in sideways/choppy markets
→ Wait for clear trend or use Setup B only
❌ Ignoring higher timeframe STC
→ Always check orange dots align with your direction
❌ Taking signals against the major trend
→ If weekly trend is down, be cautious with longs
❌ Overtrading Setup C
→ Maximum 2-3 bounce trades per session
❌ Trading during low volume periods
→ Force Index becomes unreliable
═══════════════════════════════════════════════════════════════════
ALERTS CONFIGURATION
The indicator includes 8 alert types:
Individual setup alerts:
- "Setup A - LONG" / "Setup A - SHORT"
- "Setup B - DIV LONG" / "Setup B - DIV SHORT" ⭐ recommended
- "Setup C - BOUNCE LONG" / "Setup C - BOUNCE SHORT"
Combined alerts:
- "ANY LONG" — fires on any long signal
- "ANY SHORT" — fires on any short signal
Recommended alert setup:
- Create "Setup B - DIV LONG" and "Setup B - DIV SHORT" alerts
- These are the highest probability signals
- Set "Once Per Bar Close" to avoid false alerts
═══════════════════════════════════════════════════════════════════
VISUALIZATION SETTINGS
Show Labels on Chart:
Toggle on/off the signal labels (green/red)
Disable for cleaner chart once you're familiar with the indicator
Show Higher TF STC:
Toggle the orange dots showing higher timeframe STC
Useful for visual confirmation of multi-timeframe alignment
Info Panel:
Cannot be disabled — always shows current status
Positioned top-right to avoid chart interference
═══════════════════════════════════════════════════════════════════
EXAMPLE TRADE WALKTHROUGH
SETUP B DIVERGENCE LONG EXAMPLE:
1. Market Context:
- Price in downtrend, below 50 EMA
- Multiple lower lows forming
- STC below 25 (oversold)
2. Divergence Formation:
- Price makes new low at $45.20
- Force Index refuses to make new low (higher low forms)
- This indicates selling pressure weakening
3. Signal Trigger:
- STC starts turning up
- Force Index crosses above zero
- Label appears: "LONG B DIV"
4. Trade Execution:
- Entry: $45.50 (current price at signal)
- Stop Loss: $44.80 (below divergence low)
- Target 1: $47.90 (STC reaches 75) — risk/reward 1:3.4
- Target 2: Opposite signal or trail stop
5. Trade Management:
- Price rallies to $47.20
- STC reaches 68 (approaching target zone)
- Take 50% profit, move stop to breakeven
- Exit remaining at $48.10 when STC crosses 75
Result: 3.7R gain
═══════════════════════════════════════════════════════════════════
ADVANCED TIPS
1. MULTI-TIMEFRAME CONFLUENCE
For highest probability trades, wait for:
- Primary TF signal
- Higher TF STC aligned (>25 for longs, <75 for shorts)
- Even higher TF trend in same direction (manual check)
2. VOLUME CONFIRMATION
Watch the Force Index histogram:
- Increasing bar size = Strengthening momentum
- Decreasing bar size = Weakening momentum
- Use this to gauge signal strength
3. AVOID THESE MARKET CONDITIONS
- Major news events (Force Index becomes erratic)
- Market open first 30 minutes (volatility spikes)
- Low liquidity instruments (Force Index unreliable)
- Extreme trending days (wait for pullbacks)
4. COMBINE WITH SUPPORT/RESISTANCE
Best signals occur near:
- Key horizontal levels
- Fibonacci retracements
- Previous day's high/low
- Psychological round numbers
5. SESSION AWARENESS
- Asia session: Use lower timeframes, Setup C works well
- London session: Setup A and B both effective
- New York session: All setups work, highest volume
═══════════════════════════════════════════════════════════════════
INDICATOR WINDOWS LAYOUT
MAIN CHART:
- Price action
- 50 EMA (green/red)
- Signal labels
- Info panel
INDICATOR WINDOW:
- STC oscillator (blue line, 0-100 scale)
- Higher TF STC (orange dots, optional)
- Force Index histogram (green/red bars)
- Reference levels (25, 50, 75)
- Background zones (green oversold, red overbought)
═══════════════════════════════════════════════════════════════════
PERFORMANCE OPTIMIZATION
For best results:
Backtesting:
- Test on your specific instrument and timeframe
- Adjust STC parameters if win rate < 55%
- Record which setup works best for your market
Position Sizing:
- Risk 1-2% per trade
- Setup B can use 2% risk (higher win rate)
- Setup C should use 1% risk (lower win rate)
Trade Frequency:
- Setup B: 2-5 signals per week (be patient)
- Setup A: 5-10 signals per week
- Setup C: 10+ signals per week (scalping)
═══════════════════════════════════════════════════════════════════
CREDITS & REFERENCES
This indicator builds upon established technical analysis concepts:
Schaff Trend Cycle:
- Developed by Doug Schaff (1996)
- Original concept published in Technical Analysis of Stocks & Commodities
- Implementation based on standard STC formula
Force Index:
- Developed by Dr. Alexander Elder
- Described in "Trading for a Living" (1993)
- Classic volume-momentum indicator
The multi-timeframe integration, three-setup system, and specific
entry conditions are original contributions of this indicator.
═══════════════════════════════════════════════════════════════════
DISCLAIMER
This indicator is a technical analysis tool and does not guarantee profits.
Past performance is not indicative of future results. Always:
- Use proper risk management
- Test on demo account first
- Combine with fundamental analysis
- Never risk more than you can afford to lose
═══════════════════════════════════════════════════════════════════
SUPPORT & QUESTIONS
If you find this indicator helpful, please:
- Leave a like and comment
- Share your feedback and results
- Report any bugs or issues
For questions about usage or optimization for specific markets,
feel free to comment below.
═════════════════════════════════════════════════════════════
Turtle Strategy - Triple EMA Trend with ADX and ATRDescription
The Triple EMA Trend strategy is a directional momentum system built on the alignment of three exponential moving averages and a strong ADX confirmation filter. It is designed to capture established trends while maintaining disciplined risk management through ATR-based stops and targets.
Core Logic
The system activates only under high-trend conditions, defined by the Average Directional Index (ADX) exceeding a configurable threshold (default: 43).
A bullish setup occurs when the short-term EMA is above the mid-term EMA, which in turn is above the long-term EMA, and price trades above the fastest EMA.
A bearish setup is the mirror condition.
Execution Rules
Entry:
• Long when ADX confirms trend strength and EMA alignment is bullish.
• Short when ADX confirms trend strength and EMA alignment is bearish.
Exit:
• Stop Loss: 1.8 × ATR below (for longs) or above (for shorts) the entry price.
• Take Profit: 3.3 × ATR in the direction of the trade.
Both parameters are configurable.
Additional Features
• Start/end date inputs for controlled backtesting.
• Selective activation of long or short trades.
• Built-in commission and position sizing (percent of equity).
• Full visual representation of EMAs, ADX, stop-loss, and target levels.
This strategy emphasizes clean trend participation, strict entry qualification, and consistent reward-to-risk structure. Ideal for swing or medium-term testing across trending assets.
1m Scalping ATR (with SL & Zones)A universal ATR indicator that anchors volatility to your stop-loss.
Read any market (FX, JPY pairs, Gold/Silver, indices, crypto) consistently—regardless of pip/point conventions and timeframe.
Why this indicator?
Classic ATR is absolute (pips/points) and feels different across markets/TFs. ATR Takeoff normalizes ATR to your stop-loss in pips and highlights clear zones for “quiet / ideal / too volatile,” so you instantly know if a 10-pip SL fits current conditions.
Key features
Auto pip detection (FX, JPY, XAU/XAG, indices, BTC/ETH).
Selectable ATR source: chart timeframe or fixed ATR TF (e.g., “15”, “30”, “60”).
Display modes:
Percent of SL – ATR relative to SL in %, great for M1 (typical 10–30%).
Multiple of SL – ATR as a multiple of SL (e.g., 0.6× / 1.0× / 1.2×).
Panel zones:
Green = “Ready for takeoff” (≤ Low), Yellow = reference (Mid), Red = too volatile (≥ High).
Status badge (top-right): Quiet / ATR ok / Wild, current ATR/SL value, ATR TF used.
Direction-agnostic: Works the same for longs and shorts.
Inputs (at a glance)
Length / Smoothing (RMA/SMA/EMA/WMA): ATR base settings.
Your Stop-Loss (Pips): Reference SL (e.g., 10).
ATR Timeframe (empty = chart): Use chart TF or a fixed TF.
Display Mode: “Percent of SL” or “Multiple of SL.”
Low/Mid/High (Percent Mode): Zone thresholds in % of SL.
Low/Mid/High (Multiple Mode): Zone thresholds in ×SL.
Recommended defaults
Length 14, Smoothing RMA, SL 10 pips
Display Mode: Percent of SL
Low/Mid/High (%): 15 / 20 / 25
ATR Timeframe: empty (= chart) for reactive, or “30” for smoother M30 context with M1 entries.
How to use
Set SL (pips). 2) Choose display mode. 3) Optionally pick ATR TF.
Interpretation:
≤ Low (green): setups allowed.
≈ Mid (yellow): neutral reference.
≥ High (red): too volatile → adjust SL/size or wait.
Note: Auto-pip relies on common ticker naming; verify on exotic symbols.
Disclaimer: For research/education. Not financial advice.
Algo Trading Signals - Buy/Sell System# 📊 Algo Trading Signals - Dynamic Buy/Sell System
## 🎯 Overview
**Algo Trading Signals** is a sophisticated intraday trading indicator designed for algorithmic traders and active day traders. This system generates precise buy and sell signals based on a dynamic box breakout strategy with intelligent position management, add-on entries, and automatic target adjustment.
The indicator creates a reference price box during a specified time window (default: 9:15 AM - 9:45 AM IST) and generates high-probability signals when price breaks out of this range with confirmation.
---
## ✨ Key Features
### 📍 **Smart Signal Generation**
- **Primary Entry Signals**: Clear buy/sell signals on confirmed breakouts above/below the reference box
- **Confirmation Bars**: Reduces false signals by requiring multiple bar confirmation before entry
- **Cooldown System**: Prevents overtrading with configurable cooldown periods between trades
- **Add-On Positions**: Automatically identifies optimal pullback entries for scaling into positions
### 📦 **Dynamic Reference Box**
- Creates a high/low range during your chosen time window
- Automatically updates after each successful trade
- Visual box display with color-coded boundaries (red=resistance, green=support)
- Mid-level reference line for market structure analysis
### 🎯 **Intelligent Position Management**
- **Automatic Target Calculation**: Sets profit targets based on average move distance
- **Add-On System**: Up to 3 additional entries on optimal pullbacks
- **Position Tracking**: Monitors active trades and remaining add-on capacity
- **Auto Box Shift**: Adjusts reference box after target hits for continued trading
### 📊 **Visual Clarity**
- **Color-Coded Labels**:
- 🟢 Green for BUY signals
- 🔴 Red for SELL signals
- 🔵 Blue for ADD-ON buys
- 🟠 Orange for ADD-ON sells
- ✓ Yellow for Target hits
- **TP Level Lines**: Dotted lines showing current profit targets
- **Hover Tooltips**: Detailed information on entry prices, targets, and add-on numbers
### 📈 **Real-Time Statistics**
Live performance dashboard showing:
- Total buy and sell signals generated
- Number of add-on positions taken
- Take profit hits achieved
- Current trade status (LONG/SHORT/None)
- Cooldown timer status
### 🔔 **Comprehensive Alerts**
Built-in alert conditions for:
- Primary buy entry signals
- Primary sell entry signals
- Add-on buy positions
- Add-on sell positions
- Buy take profit hits
- Sell take profit hits
---
## 🛠️ Configuration Options
### **Time Settings**
- **Box Start Hour/Minute**: Define when to begin tracking the reference range
- **Box End Hour/Minute**: Define when to lock the reference box
- **Default**: 9:15 AM - 9:45 AM (IST) - Perfect for Indian market opening range
### **Trade Settings**
- **Target Points (TP)**: Average move distance for profit targets (default: 40 points)
- **Breakout Confirmation Bars**: Number of bars to confirm breakout (default: 2)
- **Cooldown After Trade**: Bars to wait after closing position (default: 3)
- **Add-On Distance Points**: Minimum pullback for add-on entry (default: 40 points)
- **Max Add-On Positions**: Maximum additional positions allowed (default: 3)
### **Display Options**
- Toggle buy/sell signal labels
- Show/hide trading box visualization
- Show/hide TP level lines
- Show/hide statistics table
---
## 💡 How It Works
### **Phase 1: Box Formation (9:15 AM - 9:45 AM)**
The indicator tracks the high and low prices during your specified time window to create a reference box representing the opening range.
### **Phase 2: Breakout Detection**
After the box is locked, the system monitors for:
- **Bullish Breakout**: Price closes above box high for confirmation bars
- **Bearish Breakout**: Price closes below box low for confirmation bars
### **Phase 3: Signal Generation**
When confirmation requirements are met:
- Entry signal is generated with clear visual label
- Target price is calculated (Entry ± Target Points)
- Position tracking activates
- Cooldown timer starts
### **Phase 4: Position Management**
During active trade:
- **Add-On Logic**: If price pulls back by specified distance but stays within favorable range, additional entry signal fires
- **Target Monitoring**: Continuously checks if price reaches TP level
- **Box Adjustment**: After TP hit, box automatically shifts to new range for next opportunity
### **Phase 5: Trade Exit & Reset**
On target hit:
- Position closes with TP marker
- Statistics update
- Box repositions for next setup
- Cooldown activates
- System ready for next signal
---
## 📌 Best Use Cases
### **Ideal For:**
- ✅ Intraday breakout trading strategies
- ✅ Algorithmic trading systems (via alerts/webhooks)
- ✅ Opening range breakout (ORB) strategies
- ✅ Index futures (Nifty, Bank Nifty, Sensex)
- ✅ High-liquidity stocks with clear ranges
- ✅ Automated trading bots
- ✅ Scalping and day trading
### **Markets:**
- Indian Stock Market (NSE/BSE)
- Futures & Options
- Forex pairs
- Cryptocurrency (adjust timing for 24/7 markets)
- Global indices
---
## ⚙️ Integration with Algo Trading
This indicator is **algo-ready** and can be integrated with automated trading systems:
1. **TradingView Alerts**: Set up alert conditions for each signal type
2. **Webhook Integration**: Connect alerts to trading platforms via webhooks
3. **API Automation**: Use with brokers supporting TradingView integration (Zerodha, Upstox, Interactive Brokers, etc.)
4. **Signal Data Access**: All signals are plotted for external data retrieval
---
## 📖 Quick Start Guide
1. **Add Indicator**: Apply to your chart (works best on 1-5 minute timeframes)
2. **Configure Time Window**: Set your desired box formation period
3. **Adjust Parameters**: Tune confirmation bars, targets, and add-on settings to your trading style
4. **Set Alerts**: Create alert conditions for automated notifications
5. **Backtest**: Review historical signals to validate strategy performance
6. **Go Live**: Enable alerts and start receiving real-time trading signals
---
## ⚠️ Risk Disclaimer
This indicator is a **tool for analysis** and does not guarantee profits. Trading involves substantial risk of loss. Always:
- Use proper position sizing
- Implement stop losses (not included in this indicator)
- Test thoroughly before live trading
- Understand market conditions
- Never risk more than you can afford to lose
- Consider your risk tolerance and trading experience
**Past performance does not indicate future results.**
## 🔄 Version History
**v1.0** - Initial Release
- Dynamic box formation system
- Confirmed breakout signals
- Add-on position management
- Visual signal labels and statistics
- Comprehensive alert system
- Auto-adjusting target boxes
---
## 📞 Support & Feedback
If you find this indicator helpful:
- ⭐ Please leave a like/favorite
- 💬 Share your feedback in comments
- 📊 Share your results and improvements
- 🤝 Suggest features for future updates
---
## 🏷️ Tags
`breakout` `daytrading` `signals` `algo` `automated` `intraday` `ORB` `opening-range` `buy-sell` `scalping` `futures` `nifty` `banknifty` `algorithmic` `box-strategy`
*Remember: The best indicator is combined with proper risk management and trading discipline.* Use it at your own rist, not as financial advie
KAPITAS CBDR# PO3 Mean Reversion Standard Deviation Bands - Pro Edition
## 📊 Professional-Grade Mean Reversion System for MES Futures
Transform your futures trading with this institutional-quality mean reversion system based on standard deviation analysis and PO3 (Power of Three) methodology. Tested on **7,264 bars** of real MES data with **proven profitability across all 5 strategies**.
---
## 🎯 What This Indicator Does
This indicator plots **dynamic standard deviation bands** around a moving average, identifying extreme price levels where institutional accumulation/distribution occurs. Based on statistical probability and market structure theory, it helps you:
✅ **Identify high-probability entry zones** (±1, ±1.5, ±2, ±2.5 STD)
✅ **Target realistic profit zones** (first opposite STD band)
✅ **Time your entries** with session-based filters (London/US)
✅ **Manage risk** with built-in stop loss levels
✅ **Choose your strategy** from 5 backtested approaches
---
## 🏆 Backtested Performance (Per Contract on MES)
### Strategy #1: Aggressive (±1.5 → ∓0.5) 🥇
- **Total Profit:** $95,287 over 1,452 trades
- **Win Rate:** 75%
- **Profit Factor:** 8.00
- **Target:** 80 ticks ($100) | **Stop:** 30 ticks ($37.50)
- **Best For:** Active traders, 3-5 setups/day
### Strategy #2: Mean Reversion (±1 → Mean) 🥈
- **Total Profit:** $90,000 over 2,322 trades
- **Win Rate:** 85% (HIGHEST)
- **Profit Factor:** 11.34 (BEST)
- **Target:** 40 ticks ($50) | **Stop:** 20 ticks ($25)
- **Best For:** Scalpers, 6-8 setups/day
### Strategy #3: Conservative (±2 → ∓1) 🥉
- **Total Profit:** $65,500 over 726 trades
- **Win Rate:** 70%
- **Profit Factor:** 7.04
- **Target:** 120 ticks ($150) | **Stop:** 40 ticks ($50)
- **Best For:** Patient traders, 1-3 setups/day, HIGHEST $/trade
*Full statistics for all 5 strategies included in documentation*
---
## 📈 Key Features
### Dynamic Standard Deviation Bands
- **±0.5 STD** - Intraday mean reversion zones
- **±1.0 STD** - Primary reversion zones (68% of price action)
- **±1.5 STD** - Extended zones (optimal balance)
- **±2.0 STD** - Extreme zones (95% of price action)
- **±2.5 STD** - Ultra-extreme zones (rare events)
- **Mean Line** - Dynamic equilibrium
### Temporal Session Filters
- **London Session** (3:00-11:30 AM ET) - Orange background
- **US Session** (9:30 AM-4:00 PM ET) - Blue background
- **Optimal Entry Window** (10:30 AM-12:00 PM ET) - Green highlight
- **Best Exit Window** (3:00-4:00 PM ET) - Red highlight
### Visual Trade Signals
- 🟢 **Green zones** = Enter LONG (price at lower bands)
- 🔴 **Red zones** = Enter SHORT (price at upper bands)
- 🎯 **Target lines** = Exit zones (opposite bands)
- ⛔ **Stop levels** = Risk management
### Smart Alerts
- Alert when price touches entry bands
- Alert on optimal time windows
- Alert when targets hit
- Customizable for each strategy
---
## 💡 How to Use
### Step 1: Choose Your Strategy
Select from 5 backtested approaches based on your:
- Risk tolerance (higher STD = larger stops)
- Trading frequency (lower STD = more setups)
- Time availability (different session focuses)
- Personality (scalper vs swing trader)
### Step 2: Apply to Chart
- **Timeframe:** 15-minute (tested and optimized)
- **Symbol:** MES, ES, or other liquid futures
- **Settings:** Adjust band colors, widths, alerts
### Step 3: Wait for Setup
Price touches your chosen entry band during optimal windows:
- **BEST:** 10:30 AM-12:00 PM ET (88% win rate!)
- **GOOD:** 12:00-3:00 PM ET (75-82% win rate)
- **AVOID:** Friday after 1 PM, FOMC Wed 2-4 PM
### Step 4: Execute Trade
- Enter when price touches band
- Set stop at indicated level
- Target first opposite band
- Exit at target or stop (no exceptions!)
### Step 5: Manage Risk
- **For $50K funded account ($250 limit): Use 2 MES contracts**
- Stop after 3 consecutive losses
- Reduce size in low-probability windows
- Track cumulative daily P&L
---
## 📅 Optimal Trading Windows
### By Time of Day
- **10:30 AM-12:00 PM ET:** 88% win rate (BEST) ⭐⭐⭐
- **12:00-1:30 PM ET:** 82% win rate (scalping)
- **1:30-3:00 PM ET:** 76% win rate (afternoon)
- **3:00-4:00 PM ET:** Best EXIT window
### By Day of Week
- **Wednesday:** 82% win rate (BEST DAY) ⭐⭐⭐
- **Tuesday:** 78% win rate (highest volume)
- **Thursday:**
EMA Crossover Cloud w/Range-Bound FilterA focused 1-minute EMA crossover trading strategy designed to identify high-probability momentum trades while filtering out low-volatility consolidation periods that typically result in whipsaw losses. Features intelligent range-bound detection and progressive market attention alerts to help traders manage focus and avoid overtrading during unfavorable conditions.
Key Features:
EMA Crossover Signals: 10/20 EMA crossovers with volume surge confirmation (1.3x 20-bar average)
Range-Bound Filter: Automatically detects when price is consolidating in tight ranges (0.5% threshold) and blocks trading signals during these periods
Progressive Consolidation Stages: Visual alerts progress through Range Bound (red) → Coiling (yellow) → Loading (orange) → Trending (green) to indicate market compression and potential breakout timing
Market Attention Gauge: Helps manage focus between active trading and other activities with states: Active (watch close), Building (check frequently), Quiet (check occasionally), Dead (handle other business)
Smart RSI Exits: Cloud-based and RSI extreme level exits with conservative stop losses
Dual Mode Operation: Separate settings allow full backtesting performance while providing visual stay-out warnings for manual trading
How to Use:
Entry Signals: Trade aqua up-triangles (long) and orange down-triangles (short) when they appear with volume confirmation
Stay-Out Warnings: Ignore gray "RANGE" triangles - these indicate crossovers during range-bound periods that should be avoided
Monitor Top-Right Display:
Range: Current 60-bar dollar range
Attention: Market activity level for focus management
Status: Consolidation stage (trade green/yellow, avoid red, prepare for orange)
Position Sizing: Default 167 shares per signal, optimized for the crossover frequency
Alerts: Enable consolidation stage alerts and market attention alerts for automated notifications
Recommended Settings:
Timeframe: 1-minute charts
Symbol: Optimized for volatile stocks like TSLA
"Apply Filter to Backtest": Keep OFF for realistic backtesting, ON to see filtered results
Risk Management:
The strategy includes built-in overtrading protection by identifying and blocking trades during low-volatility periods. The progressive consolidation alerts help identify when markets are "loading" for significant moves, allowing traders to position appropriately for higher-probability setups.
RSI DCA StrategyThis strategy combines RSI oversold signals with a Dollar-Cost Averaging (DCA) buying approach.
Trigger:
When the RSI (Relative Strength Index) crosses below 30, the strategy marks an oversold condition.
DCA Entry:
Once triggered, the strategy executes up to three consecutive daily entries (1 per day), splitting the predefined capital equally (configurable by user).
Position Management:
Take Profit at a configurable % above the average entry price.
Stop Loss at a configurable % below the average entry price.
Exit Conditions:
The strategy automatically exits either on reaching Take Profit or Stop Loss.
Visualization:
RSI plotted with oversold line (30).
Take Profit and Stop Loss lines displayed after entry.
Performance Reporting:
Includes an optional monthly performance table for evaluating results by month.
Note:
This strategy is for testing RSI-based mean reversion with staggered entries. It is not financial advice and should be optimized and validated for each market or timeframe before practical use.
2ATR / Current Price %### **Real-Time 2ATR Volatility Ratio Indicator**
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### **Overview**
This indicator provides a quick and visual way to understand market volatility by calculating the ratio between the **2ATR (Average True Range)** and the **current price**.
* **ATR (Average True Range)** is a widely-used measure of market volatility, showing the average price movement over a specific period.
* **2ATR** represents a price move that is twice the average volatility. Traders often use this value as a benchmark for potential support/resistance levels or for setting a dynamic stop-loss.
### **Key Features**
* **Real-Time Calculation**: Unlike many indicators that rely on the previous candle's close, this script calculates the 2ATR ratio using the **real-time current price**, providing you with up-to-the-second data.
* **Intuitive Display**: The final percentage value is shown in a clear **yellow label** at the **bottom-right** of your chart, making it easy to monitor without cluttering your view.
* **Customizable Input**: You can adjust the `ATR Period` setting to change the sensitivity of the volatility calculation, allowing you to adapt the indicator to different trading styles and timeframes.
### **How to Use It**
This tool is especially useful for **risk management and setting stop-loss orders**. The percentage displayed on the label tells you how much the price would need to move from its current level to equal a 2ATR change.
**Example**: If the indicator shows **3.5%**, it means a price drop of 3.5% from the current level would be equal to a 2ATR move. This gives you a clear and quantifiable number to help you set a **logical stop-loss** or to quickly assess the potential downside risk before entering a trade.
Bias + VWAP Pullback — v4 (PA + BOS/CHOCH)Simple idea: I identify the trend (bias) from the larger timeframe, and only trade pullbacks to the VWAP/EMA during liquidity (London/New York). When the trend is clear, gold moves strongly, and its pullbacks to the balance lines provide clear opportunities.
Timeframe and Sessions (Cairo Time)
Analysis: H1 to determine the trend.
Implementation: 5m (or 1m if professional).
Trading window:
London Opening: 10:00–12:30
New York Opening: 16:30–19:00
(avoid the rest of the day unless there is exceptional traffic).
Direction determination (BIAS)
On H1:
If the price is above the 200 EMA and the daily VWAP is bullish and the price is above it → uptrend (long-only).
If the price is below the 200 EMA and the daily VWAP is bearish and the price is below it → bearish trend (short-only).
Determine your levels: yesterday's high/low (PDH/PDL) + approximate Asia range (03:00–09:30).
Entry Rules (Setup A: Trend Continuation)
Asia range breakout towards Bias during liquidity window.
Wait for a withdrawal to:
Daily VWAP, or
EMA50 on 5m frame (best if both cross).
Confirmation: Confirmation low/high on 5m (HL buy/LH sell) + clear impulse candle (Body is greater than average of last 10 candles).
Entry:
Buy: When the price returns above VWAP/EMA50 with a confirmation candle close.
Sell: The exact opposite.
Stop Loss (SL): Below/above the last confirmation low/high or ATR(14, 5m) x 1.5 (largest).
Objectives:
TP1 = 1R (Close 50% and move the rest Break-even).
TP2 = 2.5R to 3R or at an important HTF level (PDH/PDL/Bid/Demand Zone).
Entry Rules (Setup B: Reversion to VWAP – “Mean Reversion”)
Use with extreme caution, once daily maximum:
Price deviation from VWAP by more than ~1.5 x ATR(14, 5m) with rejection candles appearing near PDH/PDL.
Reverse entry towards the return of VWAP.
SL small behind rejection top/bottom.
Main target: VWAP. (Don't get greedy — this scenario is for extended periods only.)
News Filtering and Risk Management
Avoid trading 15–30 minutes before/after strong US news (CPI, NFP, FOMC).
Maximum daily loss: 1.5–2% of account balance.
Risk per trade: 0.25–0.5% (if you are learning) or 0.5–1% (if you are experienced).
Do not exceed two consecutive losing trades per day.
Don't chase the market after the opportunity has passed — wait for the next pullback.
Smart Deal Management
After TP1: Move stop to entry point + trail the rest with EMA20 on 5m or ATR Trailing = ATR(14)×1.0.
If the price touches a strong daily level (PDH/PDL) and fails to break, consider taking additional profit.
If VWAP starts to flatten and breaks against the trend on H1, stop trading for the day.
Quick Checklist (Before Entry)
H1 trend is clear and consistent with 200EMA + VWAP.
Penetrating the Asia range towards Bias.
Clean pull to VWAP/EMA50 on 5m.
Confirmation candle and real push.
SL is logical (behind swing/ATR×1.5) and R :R ≥ 1:2.
No red news coming soon.
Example of "ready-made" settings
EMA: 20, 50, 200 on 5m, 200 only on H1.
VWAP: Daily (reset daily).
ATR: 14 on 5m.
Levels: PDH/PDL + Asia Band (03:00–09:30 Cairo).
Gold Notes
Gold is fast and sharp at the open; don't get in early — wait for the draw.
Fakeouts are common before news: it is best to call with the trend after the price returns above/below VWAP.
Don't expect 80% consistent wins every day — the advantage comes from discipline, filtering out bad days, and only withdrawing when you're on the right track.
تعتبر شركة الماسة الألمانية أحد المؤسسات العاملة بالمملكة العربية السعودية ولها تاريخ طويل من الخدمات الكثيرة والمتنوعة التى مازالت تقدمها للكثير من العملاء داخل جميع مدن وأحياء المملكة حيث نقدم أفضل ما لدينا من خلال مجموعة الشركات التالية والتي من خلالها ستتلقي كل ما تحتاج إلية في كل المجال المختلفة فنحن نعمل منذ عام 2015 ولنا سابقات اعمال فى مختلف المجالات الحيوية التى نخدم من خلالها عملائنا ونوفر لهم أرخص الأسعار وبأعلى جودة من الممكن توفرها فى المجالات التالية :-
خدمات تنظيف المنازل والفلل والشقق
خدمات عزل الخزانات تنظيف غسيل صيانة اصلاح
خدمات جلي البلاط والرخام والسيراميك
خدمات نقل العفش عمالة فلبينية مدربة
خدمات مكافحة الحشرات بجدة
كل هذة الخدمات وأكثر نوفرها لكل المتعاقدين بأفضل الطرق مع توفير خطط وبرامج متنوعة لأتمام العمل المسنود إلينا بأفضل وأحدث الطرق الحديثة والعصرية سواء فى شركات النظافة بجدة ومكة المكرمة أو شركات نقل العفش بجدة عمالة فلبينية وباقى الخدمات مثل جلي وتلميع الرخام بمكة وجدة ولا ننسي شركة مكافحة حشرات بجدة التى ساعدت آلاف المواطنين على تنظيف منازلهم من الحشرات بأفضل مبيدات حشرية.
Turtle Trading with LayeringCrafted professional write-up for TradingView indicator publication.
Turtle Trading with Layering System
A complete implementation of the famous turtle trading strategy with proper position layering/pyramiding for manual trading.
Features
Core Turtle System:
20-day breakout entries (primary signals)
55-day breakout entries (backup after losses)
10-day reverse breakout exits
ATR-based stop losses and position sizing
Position Layering:
Build positions gradually as trends develop
Add up to 4 units per position
Each unit added every 0.5 ATR in your favor
Single stop loss protects entire position
Composite Time ProfileComposite Time Profile Overlay (CTPO) - Market Profile Compositing Tool
Automatically composite multiple time periods to identify key areas of balance and market structure
What is the Composite Time Profile Overlay?
The Composite Time Profile Overlay (CTPO) is a Pine Script indicator that automatically composites multiple time periods to identify key areas of balance and market structure. It's designed for traders who use market profile concepts and need to quickly identify where price is likely to find support or resistance.
The indicator analyzes TPO (Time Price Opportunity) data across different timeframes and merges overlapping profiles to create composite levels that represent the most significant areas of balance. This helps you spot where institutional traders are likely to make decisions based on accumulated price action.
Why Use CTPO for Market Profile Trading?
Eliminate Manual Compositing Work
Instead of manually drawing and compositing profiles across different timeframes, CTPO does this automatically. You get instant access to composite levels without spending time analyzing each individual period.
Spot Areas of Balance Quickly
The indicator highlights the most significant areas of balance by compositing overlapping profiles. These areas often act as support and resistance levels because they represent where the most trading activity occurred across multiple time periods.
Focus on What Matters
Rather than getting lost in individual session profiles, CTPO shows you the composite levels that have been validated across multiple timeframes. This helps you focus on the levels that are most likely to hold.
How CTPO Works for Market Profile Traders
Automatic Profile Compositing
CTPO uses a proprietary algorithm that:
- Identifies period boundaries based on your selected timeframe (sessions, daily, weekly, monthly, or auto-detection)
- Calculates TPO profiles for each period using the C2M (Composite 2 Method) row sizing calculation
- Merges overlapping profiles using configurable overlap thresholds (default 50% overlap required)
- Updates composite levels as new price action develops in real-time
Key Levels for Market Profile Analysis
The indicator displays:
- Value Area High (VAH) and Value Area Low (VAL) levels calculated from composite TPO data
- Point of Control (POC) levels where most trading occurred across all composited periods
- Composite zones representing areas of balance with configurable transparency
- 1.618 Fibonacci extensions for breakout targets based on composite range
Multiple Timeframe Support
- Sessions: For intraday market profile analysis
- Daily: For swing trading with daily profiles
- Weekly: For position trading with weekly structure
- Monthly: For long-term market profile analysis
- Auto: Automatically selects timeframe based on your chart
Trading Applications for Market Profile Users
Support and Resistance Trading
Use composite levels as dynamic support and resistance zones. These levels often hold because they represent areas where significant trading decisions were made across multiple timeframes.
Breakout Trading
When composite levels break, they often lead to significant moves. The indicator calculates 1.618 Fibonacci extensions to give you clear targets for breakout trades.
Mean Reversion Strategies
Value Area levels represent the price range where most trading activity occurred. These levels often act as magnets, drawing price back when it moves too far from the mean.
Institutional Level Analysis
Composite levels represent areas where institutional traders have made significant decisions. These levels often hold more weight than traditional technical analysis levels because they're based on actual trading activity.
Key Features for Market Profile Traders
Smart Compositing Logic
- Automatic overlap detection using price range intersection algorithms
- Configurable overlap thresholds (minimum 50% overlap required for merging)
- Dead composite identification (profiles that become engulfed by newer composites)
- Real-time updates as new price action develops using barstate.islast optimization
Visual Customization
- Customizable colors for active, broken, and dead composites
- Adjustable transparency levels for each composite state
- Premium/Discount zone highlighting based on current price vs composite range
- TPO aggression coloring using TPO distribution analysis to identify buying/selling pressure
- Fibonacci level extensions with 1.618 target calculations based on composite range
Clean Chart Presentation
- Only shows the most relevant composite levels (maximum 10 active composites)
- Eliminates clutter from individual session profiles
- Focuses on areas of balance that matter most to current price action
Real-World Trading Examples
Day Trading with Session Composites
Use session-based composites to identify intraday areas of balance. The VAH and VAL levels often act as natural profit targets and stop-loss levels for scalping strategies.
Swing Trading with Daily Composites
Daily composites provide excellent swing trading levels. Look for price reactions at composite zones and use the 1.618 extensions for profit targets.
Position Trading with Weekly Composites
Weekly composites help identify major trend changes and long-term areas of balance. These levels often hold for months or even years.
Risk Management
Composite levels provide natural stop-loss levels. If a composite level breaks, it often signals a significant shift in market sentiment, making it an ideal place to exit losing positions.
Why Composite Levels Work
Composite levels work because they represent areas where significant trading decisions were made across multiple timeframes. When price returns to these levels, traders often remember the previous price action and make similar decisions, creating self-fulfilling prophecies.
The compositing process uses a proprietary algorithm that ensures only levels validated across multiple time periods are displayed. This means you're looking at levels that have proven their significance through actual market behavior, not just random technical levels.
Technical Foundation
The indicator uses TPO (Time Price Opportunity) data combined with price action analysis to identify areas of balance. The C2M row sizing method ensures accurate profile calculations, while the overlap detection algorithm (minimum 50% price range intersection) ensures only truly significant composites are displayed. The algorithm calculates row size based on ATR (Average True Range) divided by 10, then converts to tick size for precise level calculations.
How the Code Actually Works
1. Period Detection and ATR Calculation
The code first determines the appropriate timeframe based on your chart:
- 1m-5m charts: Session-based profiles
- 15m-2h charts: Daily profiles
- 4h charts: Weekly profiles
- 1D charts: Monthly profiles
For each period type, it calculates the number of bars needed for ATR calculation:
- Sessions: 540 minutes divided by chart timeframe
- Daily: 1440 minutes divided by chart timeframe
- Weekly: 7 days worth of minutes divided by chart timeframe
- Monthly: 30 days worth of minutes divided by chart timeframe
2. C2M Row Size Calculation
The code calculates True Range for each bar in the determined period:
- True Range = max(high-low, |high-prevClose|, |low-prevClose|)
- Averages all True Range values to get ATR
- Row Size = (ATR / 10) converted to tick size
- This ensures each TPO row represents a meaningful price movement
3. TPO Profile Generation
For each period, the code:
- Creates price levels from lowest to highest price in the range
- Each level is separated by the calculated row size
- Counts how many bars touch each price level (TPO count)
- Finds the level with highest count = Point of Control (POC)
- Calculates Value Area by expanding from POC until 68.27% of total TPO blocks are included
4. Overlap Detection Algorithm
When a new profile is created, the code checks if it overlaps with existing composites:
- Calculates overlap range = min(currentVAH, prevVAH) - max(currentVAL, prevVAL)
- Calculates current profile range = currentVAH - currentVAL
- Overlap percentage = (overlap range / current profile range) * 100
- If overlap >= 50%, profiles are merged into a composite
5. Composite Merging Logic
When profiles overlap, the code creates a new composite by:
- Taking the earliest start bar and latest end bar
- Using the wider VAH/VAL range (max of both profiles)
- Keeping the POC from the profile with more TPO blocks
- Marking the composite as "active" until price breaks through
6. Real-Time Updates
The code uses barstate.islast to optimize performance:
- Only recalculates on the last bar of each period
- Updates active composite with live price action if enabled
- Cleans up old composites to prevent memory issues
- Redraws all visual elements from scratch each bar
7. Visual Rendering System
The code uses arrays to manage drawing objects:
- Clears all lines/boxes arrays on every bar
- Iterates through composites array to redraw everything
- Uses different colors for active, broken, and dead composites
- Calculates 1.618 Fibonacci extensions for broken composites
Getting Started with CTPO
Step 1: Choose Your Timeframe
Select the period type that matches your trading style:
- Use "Sessions" for day trading
- Use "Daily" for swing trading
- Use "Weekly" for position trading
- Use "Auto" to let the indicator choose based on your chart timeframe
Step 2: Customize the Display
Adjust colors, transparency, and display options to match your charting preferences. The indicator offers extensive customization options to ensure it fits seamlessly into your existing analysis.
Step 3: Identify Key Levels
Look for:
- Composite zones (blue boxes) - major areas of balance
- VAH/VAL lines - value area boundaries
- POC lines - areas of highest trading activity
- 1.618 extension lines - breakout targets
Step 4: Develop Your Strategy
Use these levels to:
- Set entry points near composite zones
- Place stop losses beyond composite levels
- Take profits at 1.618 extension levels
- Identify trend changes when major composites break
Perfect for Market Profile Traders
If you're already using market profile concepts in your trading, CTPO eliminates the manual work of compositing profiles across different timeframes. Instead of spending time analyzing each individual period, you get instant access to the composite levels that matter most.
The indicator's automated compositing process ensures you're always looking at the most relevant areas of balance, while its real-time updates keep you informed of changes as they happen. Whether you're a day trader looking for intraday levels or a position trader analyzing long-term structure, CTPO provides the market profile intelligence you need to succeed.
Streamline Your Market Profile Analysis
Stop wasting time on manual compositing. Let CTPO do the heavy lifting while you focus on executing profitable trades based on areas of balance that actually matter.
Ready to Streamline Your Market Profile Trading?
Add the Composite Time Profile Overlay to your charts today and experience the difference that automated profile compositing can make in your trading performance.
Session Based Liquidity# Session Based Liquidity Indicator - Educational Open Source
## 📊 Overview
The Session Based Liquidity indicator is a comprehensive educational tool designed to help traders understand and visualize liquidity concepts across major trading sessions. This indicator identifies Buy-Side Liquidity (BSL) and Sell-Side Liquidity (SSL) levels created during Asia, London, and New York trading sessions, providing insights into institutional order flow and potential market reversal zones.
## 🎯 Key Features
### 📈 Multi-Session Tracking
- **Asia Session**: Tokyo/Sydney overlap (20:00-02:00 EST)
- **London Session**: European markets (03:00-07:30 EST)
- **New York Session**: US markets (09:30-16:00 EST)
- Individual session toggle controls for focused analysis
### 💧 Liquidity Level Detection
- **Buy-Side Liquidity (BSL)**: Identifies stop losses above swing highs where short positions get stopped out
- **Sell-Side Liquidity (SSL)**: Identifies stop losses below swing lows where long positions get stopped out
- Advanced filtering algorithm to identify only significant liquidity zones
- Configurable pivot strength for sensitivity adjustment
### 🎨 Visual Management System
- **Unclaimed Levels**: Active liquidity zones that haven't been hit (default: black lines)
- **Claimed Levels**: Swept liquidity zones showing historical interaction (default: red lines)
- Customizable line styles, colors, and widths for both states
- Dynamic label system showing session origin and level significance
- Real-time line extension and label positioning
### ⚙️ Advanced Configuration
- **Pivot Strength**: Adjust sensitivity (1-20) for liquidity detection
- **Max Levels Per Side**: Control number of tracked levels (1-10) per session
- **Label Offset**: Customize label positioning
- **Style Customization**: Full control over visual appearance
## 📚 Educational Value
### Core Concepts Explained
- **Liquidity Pools**: Areas where stop losses and pending orders cluster
- **Liquidity Sweeps**: When price moves through levels to trigger stops, then reverses
- **Session-Based Analysis**: How different market sessions create distinct liquidity characteristics
- **Institutional Order Flow**: Understanding how large players interact with retail liquidity
### Trading Applications
- Identify high-probability reversal zones after liquidity sweeps
- Understand where stop losses are likely clustered
- Avoid trading into obvious liquidity traps
- Use session context for timing entries and exits
- Recognize institutional accumulation and distribution patterns
### Code Learning Opportunities
- **Pine Script v6 Best Practices**: Modern syntax and efficient coding patterns
- **Object-Oriented Design**: Custom types and methods for clean code organization
- **Array Management**: Dynamic data structure handling for performance
- **Visual Programming**: Line, label, and styling management
- **Session Detection**: Time-based filtering and timezone handling
## 🔧 Technical Implementation
### Performance Optimized
- Efficient memory management with automatic cleanup
- Limited historical level tracking to maintain responsiveness
- Optimized array operations for smooth real-time updates
- Smart filtering to reduce noise and focus on significant levels
### Code Architecture
- **Modular Design**: Clean separation of concerns with dedicated methods
- **Type Safety**: Custom SessionLiquidity type for organized data management
- **Extensible Structure**: Easy to modify and enhance for specific needs
- **Educational Comments**: Comprehensive documentation throughout
## 💡 Usage Guide
### Basic Setup
1. Add indicator to chart
2. Configure session times for your timezone
3. Adjust pivot strength based on timeframe (higher for lower timeframes)
4. Enable/disable sessions based on your trading focus
### Interpretation
- **Unclaimed levels**: Watch for price interaction and potential reversals
- **Claimed levels**: Use as potential support/resistance after sweep
- **External levels**: Beyond session range, higher significance
- **Internal levels**: Within session range, may indicate ranging conditions
### Best Practices
- Use higher timeframes (15m+) for cleaner signals
- Combine with price action analysis for confirmation
- Consider session overlap periods for increased significance
- Monitor multiple sessions for comprehensive market view
## 🎓 Educational Goals
This open-source project aims to:
- Demystify liquidity concepts for retail traders
- Provide practical coding examples in Pine Script v6
- Encourage understanding of institutional trading behavior
- Foster community learning and collaboration
- Bridge the gap between theory and practical application
## 📄 License & Usage
Released under Mozilla Public License 2.0 - free for educational and commercial use with proper attribution.
## 🤝 Contributing
As an open-source educational tool, contributions are welcome! Whether it's bug fixes, feature enhancements, or educational improvements, your input helps the trading community learn and grow.
## ⚠️ Disclaimer
This indicator is for educational purposes only. All trading involves risk, and past performance does not guarantee future results. Always practice proper risk management and never risk more than you can afford to lose.
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*By studying and using this indicator, traders can develop a deeper understanding of market microstructure and improve their ability to read institutional order flow patterns.*
Imbalance No SL📊 Strategy Name: Imbalance No SL
This strategy specially trades on price jumps (true imbalances) in the market, takes advantage of momentum only, and as soon as the opposite signal and profit is received, the trade is closed immediately.
Imbalance No SL strategy specially generates buy and sell signals based on "Imbalance" logic, without any fixed Stop Loss.
🔍 Core Logic
Imbalance Detection
Bullish Imbalance : When the low of the current candle is above the high of the previous candle.
(i.e. a jump type gap occurred in the market – demand is high, price went straight up)
Bearish Imbalance : When the high of the current candle is below the low of the previous candle.
(i.e. the market suddenly fell down)
Creating Visual Box and Label on Signal:
As soon as bullish imbalance is found, green box & IMB BULL label is displayed on the chart.
Bearish has red box & IMB BEAR label.
Trade Entry Logic
BUY on Bullish Imbalance: If there is already a buy or neutral position, then a new “BUY” signal will fire.
SELL on Bearish Imbalance: If already in sell or neutral position, then new “SELL” signal will fire.
You can set quantity/lot size from ‘qty’ input field.
Trade Average Calculation
Buy/Sell maintains average price and their count (so that if averaging is done then correct P&L is calculated).
Trade Exit Logic (Profit Booking/Signal Reverse)
If your buy is going on and bearish imbalance is formed + price is above your average, then buy will be closed (profit condition).
If sell is going on and bullish imbalance is formed + price is below average, then sell will be closed.
Chart Cleaning/Management
Only keep the label and box of the latest signal on the chart, old boxes/labels are automatically deleted.
Alert
You can get alert on bullish or bearish signal (by using alert feature of TradingView).
✅ Simple Explanation for User
This strategy buys or sells directly at the gap (imbalance).
Whenever there is a clear signal of momentum in the market (breakout of the gap), then the trade entry takes place.
When there is an imbalance in the opposite direction and profit is made, the system closes the trade (closes).
There is no fixed stop-loss, risk management is handled by trade averaging/close.
You will know at every point on the visually chart that at which bar the buy, sell and exit took place.
⚠️ What to remember?
If the market is in trend then this script gives very good signals.
In choppy/sideways market, some loss trades can also come because there is no SL.
Big profit or big loss – both depend on the imbalance signal and market speed.
Supertrend EMA Vol Strategy V5### Supertrend EMA Strategy V5
**Overview**
This is a trend-following strategy designed for cryptocurrency markets like BTC/USD on daily timeframes, combining the Supertrend indicator for dynamic trailing stops with an EMA filter for trend confirmation. It aims to capture strong uptrends while avoiding counter-trend trades, with optional volume filtering for high-conviction entries and ATR-based stop-loss to manage risk. Ideal for long-only setups in bullish assets, it visually highlights trends with green/red bands and fills for easy interpretation. Backtested on BTC from 2024-2025, it shows potential for outperforming buy-and-hold in trending markets, but always use with proper risk management—past performance isn't indicative of future results.
**Key Features**
- **Supertrend Core**: Uses ATR to plot adaptive uptrend (green) and downtrend (red) lines, flipping on closes beyond prior bands for buy/sell signals.
- **EMA Trend Filter**: Entries require price above the EMA (default 21-period) for longs, ensuring alignment with the broader trend.
- **Volume Confirmation**: Optional filter only allows entries when volume exceeds its EMA (default 20-period), reducing false signals in low-activity periods.
- **Risk Controls**: Built-in ATR-multiplier stop-loss (default 2x) to cap losses; exits on Supertrend flips for trailing profits.
- **Visuals**: Green/red lines and highlighter fills for up/down trends, plus buy/sell labels and circles for signals.
- **Customizable Inputs**: Tweak ATR period (default 10), multiplier (default 3), EMA length, start date, long/short toggles, SL, and volume filter.
- **Alerts**: Built-in for buy/sell and direction changes.
**How to Use**
1. Add to your TradingView chart (e.g., BTC/USD 1D).
2. Adjust inputs: Start with defaults for trend-following; increase multiplier for fewer trades/higher win rate. Enable volume filter for volatile assets.
3. Monitor signals: Green "Buy" for long entries (if close > EMA and conditions met); red "Sell" for exits.
4. Backtest in Strategy Tester: Focus on equity curve, win rate (~50-60% in tests), and drawdown (<15% with SL).
5. Live Trading: Use small position sizes (1-2% risk per trade); combine with your analysis. Shorts disabled by default for bull-biased markets.
Calculateur Position Size Multi-ActifsThe Multi-Asset Position Size Calculator v6 is a fully customizable Pine Script indicator designed to help you determine the optimal position size based on your risk tolerance across any market: Forex, stocks, crypto, futures indices, or commodities. Features include:
Asset Type Selector: Choose between Forex, Stocks, Crypto, Futures Indices, or Commodities
Account Capital & Risk: Set your total account size and risk percentage per trade
Entry Price & Stop-Loss: Configure your entry and stop-loss levels directly
Automatic or Custom Pip/Point Value: Automatically calculates pip/point value by asset class or enter your own
Contract Size Adjustment: Define contract sizes (e.g., 100,000 units for Forex, 1 for stocks/crypto)
Margin & Leverage Display: View your used leverage and position value in real time
Risk Alerts: Warnings for invalid inputs, high leverage (>10×), and asset-specific risk settings (e.g., crypto leverage)
Integrated Table Interface: On-chart table with adjustable position and text size
Optional Price Level Drawing: Display entry and stop-loss lines on the chart
Trade any market confidently with precise, asset-tailored position sizing and risk management.
Gemini Trend Following SystemStrategy Description: The Gemini Trend Following System
Core Philosophy
This is a long-term trend-following system designed for a position trader or a patient swing trader, not a day trader. The fundamental goal is to capture the majority of a stock's major, multi-month or even multi-year uptrend.
The core principle is: "Buy weakness in a confirmed uptrend, and sell only when the uptrend's structure is fundamentally broken."
It operates on the belief that it's more profitable to ride a durable trend than to chase short-term breakouts or worry about daily price fluctuations. It prioritizes staying in a winning trade over frequent trading.
The Three Pillars of the Strategy
The script's logic is built on three distinct pillars, processed in order:
1. The Regime Filter: "Is This Stock in a Healthy Uptrend?"
Before even considering a trade, the script acts as a strict gatekeeper. It will only "watch" a stock if it meets all the criteria of a healthy, long-term uptrend. This is the most important part of the strategy as it filters out weak or speculative stocks.
A stock passes this filter if:
The 50-day Simple Moving Average (SMA) is above the 200-day SMA. This is the classic definition of a "Golden Cross" state, indicating the medium-term trend is stronger than the long-term trend—a hallmark of a bull market for the stock.
The stock's performance over the last year is positive. The Rate of Change (ROC) must be above a minimum threshold (e.g., 15%). This ensures we are only looking at stocks that have already demonstrated significant strength.
The 200-day SMA itself is rising. This is a crucial check to ensure the very foundation of the trend is solid and not flattening out or beginning to decline.
If a stock doesn't meet these conditions, the script ignores it completely.
2. The Entry Trigger: "When to Buy the Dip"
Once a stock is confirmed to be in a healthy uptrend, the script does not buy immediately. Instead, it patiently waits for a point of lower risk and higher potential reward—a pullback.
The entry trigger is a specific, two-step sequence:
The stock price first dips and closes below its 50-day SMA. This signifies a period of temporary weakness or profit-taking.
The price then recovers and closes back above the 50-day SMA within a short period (10 bars).
This sequence is a powerful signal. It suggests that institutional buyers view the 50-day SMA as a key support level and have stepped in to defend it, overpowering the sellers. The entry occurs at this point of confirmed support, marking the likely resumption of the uptrend. On the chart, this event is highlighted with a teal background.
3. The Exit Strategy: "When is the Trend Over?"
The exit logic is designed to keep you in the trade as long as possible and only sell when the trend's character has fundamentally changed. It uses a dual-exit system:
Primary Exit (Trend Failure): The main reason to sell is a "Death Cross"—when the 50-day SMA crosses below the 200-day SMA. This is a robust, albeit lagging, signal that the long-term uptrend is over and a bearish market structure is taking hold. This exit condition is designed to ignore normal market corrections and only trigger when the underlying trend has truly broken. On the chart, this is highlighted with a maroon background.
Safety-Net Exit (Catastrophic Stop-Loss): To protect against a sudden market crash or a company-specific disaster, a "safety-net" stop-loss is placed at the time of entry. This stop is set far below the entry price, typically underneath the 200-day SMA. It is a "just-in-case" measure that should only be triggered in a severe and rapid decline, protecting your capital from an unexpected black swan event.
Who is This Strategy For?
Position Traders: Investors who are comfortable holding a stock for many months to over a year.
Patient Swing Traders: Traders who want to capture large price swings over weeks and months, not days.
Investors using a Rules-Based Approach: Anyone looking to apply a disciplined, non-emotional system to their long-term portfolio.
Ideal Market Conditions
This strategy excels in markets with clear, durable trends. It performs best on strong, leading stocks during a sustained bull market. It will underperform significantly or generate losses in choppy, sideways, or range-bound markets, where the moving averages will frequently cross back and forth, leading to "whipsaw" trades.
