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Gap Filling Strategy Gaps are market prices structures that appear frequently in the stock market, and can be detected when the opening price is different from the previous closing price, this is why gaps are also called "opening price jumps". While gaps can occur frequently, some of them are more significant than others, and can be observed when looking at a long term chart. The following strategy is based on the exploitation of significant gaps occurring during a new session, and posses various options that can return a wide variety of results. Type Of Gaps And Occurence I'am not a professional when it comes to gaps, but as you know the stock market close for the day, however it is still possible to place orders, your broker will hold them until the market open back. Once the market reopen the broker execute the pending orders, and when many orders where pending the market register really high volume and the price might differ from the precedent close. Gaps are generally broken down into four types: Common : Gaps occurring within a certain price range, mostly occurs during ranging markets. Break Away : Gaps breaking a support and resistance, making a new higher high/lower low. Runaway : Gaps occurring within a trend, followed by a continuation of the trend. Exhaustion : Gaps occurring at the end of a trend, followed by a reversal. As said before, some gaps are more significant than others, the significance of a gap can be determined by comparing the opening price with the previous high/low price and by looking at volume. Significant up gaps will have an opening price greater than the previous high, while significant down gap will have an opening price lower than the previous low with both high volume accompanying them. After a gap, when the price go back to the point previous to the gap we say that it has been "filled", this characteristic is what will be exploited in this strategy. Strategy Rules & Logic In this strategy, the significance of a gap is determined by the position of the opening price relative to the previous high/low and make sure the bar following the gap don't fill it. When the setting invert is set to false the strategy interpret the detected gaps as being exhaustion gaps, therefore when an up gap occur a short position is opened, when a down gap occur a long position is opened. When invert is set to true gaps are considered to be runaway or break away gaps, therefore the contrary positions are opened. Positions are exited when the gap has been filled, which in the chart is show'n when the price cross the red level who act as either a take profit (invert = false) or as a stop loss (invert = true). There are various closing conditions available that the user can select from the "close when" setting. New Session : This option close all previous positions when the market is in a new session. New Gap : This option close all previous position when a new gap has been detected. Reverse Position : This option close all previous position when a contrary position to the current one is opened. This option would reduce the number of trades. Testing On Some Stocks The analysis will be tested in different tech stocks with a main TF of 15 minutes with no spread and commissions applied. Default settings will be used. We'll be making our first analysis using AMD, who has recently formed a full reverse HS pattern, where the neckline has been crossed by the price. (by the way i have a bad feeling about it, hey ! feeling filling ! Lame jokes!) Profit: $ -12.22 Trades: 272 Profitability: 65.07 % We can see negative results, with an heavily decreasing balance. Using invert would return positive results. We will now test the strategy on NVDA, the company is one of the biggest when it comes to the Gpu market. Profit: $ -215.54 Trades: 297 Profitability: 60.27 % Not better, using invert would of course create better results. Like AMD the balance is heavily decreasing. Finally we will test the strategy on Seagate technology, a company mostly known for their mechanical hard drives. Profit: $ -4.32 Trades: 261 Profitability: 65.9 % Here the balance does not appear so heavily decreasing and even managed to reach back the initial balance before going down again. Summary A strategy based on gap filling has been briefly introduced and tested with 3 tech stocks. The results show that using invert option might be better. The advantage of this strategy against ones using technical indicators is that this one does not heavily depend on user settings, which make it way more efficient, this a big advantage of patterns based strategies. Thx to LucF for helping with the "process_orders_on_close" element, since i had to use closing price i had to remove it tho, was afraid results would differ even more from a more realistic backtest. And thx for those who continuously support me, more cool stuff is coming up. Thx for reading and i hope you'll have learned something new today !
Strategi Pine Script®
oleh alexgrover
11
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.
Penunjuk Pine Script®
oleh EdgeTools
8
Bitcoin CME-Spot Z-Spread - Strategy [presentTrading]This time is a swing trading strategy! It measures the sentiment of the Bitcoin market through the spread of CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. By applying Bollinger Bands to the spread, the strategy seeks to capture mean-reversion opportunities when prices deviate significantly from their historical norms █ Introduction and How it is Different The Bitcoin CME-Spot Bollinger Bands Strategy is designed to capture mean-reversion opportunities by exploiting the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. The strategy uses Bollinger Bands to detect when the spread between these two correlated assets has deviated significantly from its historical norm, signaling potential overbought or oversold conditions. What sets this strategy apart is its focus on spread trading between futures and spot markets rather than price-based indicators. By applying Bollinger Bands to the spread rather than individual prices, the strategy identifies price inefficiencies across markets, allowing traders to take advantage of the natural reversion to the mean that often occurs in these correlated assets. BTCUSD 8hr Performance █ Strategy, How It Works: Detailed Explanation The strategy relies on Bollinger Bands to assess the volatility and relative deviation of the spread between CME Bitcoin Futures and Bitfinex BTCUSD Spot prices. Bollinger Bands consist of a moving average and two standard deviation bands, which help measure how much the spread deviates from its historical mean. 🔶 Spread Calculation: The spread is calculated by subtracting the Bitfinex spot price from the CME Bitcoin futures price: Spread = CME Price - Bitfinex Price This spread represents the difference between the futures and spot markets, which may widen or narrow based on supply and demand dynamics in each market. By analyzing the spread, the strategy can detect when prices are too far apart (potentially overbought or oversold), indicating a trading opportunity. 🔶 Bollinger Bands Calculation: The Bollinger Bands for the spread are calculated using a simple moving average (SMA) and the standard deviation of the spread over a defined period. 1. Moving Average (SMA): The simple moving average of the spread (mu_S) over a specified period P is calculated as: mu_S = (1/P) * sum(S_i from i=1 to P) Where S_i represents the spread at time i, and P is the lookback period (default is 200 bars). The moving average provides a baseline for the normal spread behavior. 2. Standard Deviation: The standard deviation (sigma_S) of the spread is calculated to measure the volatility of the spread: sigma_S = sqrt((1/P) * sum((S_i - mu_S)^2 from i=1 to P)) 3. Upper and Lower Bollinger Bands: The upper and lower Bollinger Bands are derived by adding and subtracting a multiple of the standard deviation from the moving average. The number of standard deviations is determined by a user-defined parameter k (default is 2.618). - Upper Band: Upper Band = mu_S + (k * sigma_S) - Lower Band: Lower Band = mu_S - (k * sigma_S) These bands provide a dynamic range within which the spread typically fluctuates. When the spread moves outside of these bands, it is considered overbought or oversold, potentially offering trading opportunities. Local view 🔶 Entry Conditions: - Long Entry: A long position is triggered when the spread crosses below the lower Bollinger Band, indicating that the spread has become oversold and is likely to revert upward. Spread < Lower Band - Short Entry: A short position is triggered when the spread crosses above the upper Bollinger Band, indicating that the spread has become overbought and is likely to revert downward. Spread > Upper Band 🔶 Risk Management and Profit-Taking: The strategy incorporates multi-step take profits to lock in gains as the trade moves in favor. The position is gradually reduced at predefined profit levels, reducing risk while allowing part of the trade to continue running if the price keeps moving favorably. Additionally, the strategy uses a hold period exit mechanism. If the trade does not hit any of the take-profit levels within a certain number of bars, the position is closed automatically to avoid excessive exposure to market risks. █ Trade Direction The trade direction is based on deviations of the spread from its historical norm: - Long Trade: The strategy enters a long position when the spread crosses below the lower Bollinger Band, signaling an oversold condition where the spread is expected to narrow. - Short Trade: The strategy enters a short position when the spread crosses above the upper Bollinger Band, signaling an overbought condition where the spread is expected to widen. These entries rely on the assumption of mean reversion, where extreme deviations from the average spread are likely to revert over time. █ Usage The Bitcoin CME-Spot Bollinger Bands Strategy is ideal for traders looking to capitalize on price inefficiencies between Bitcoin futures and spot markets. It’s especially useful in volatile markets where large deviations between futures and spot prices occur. - Market Conditions: This strategy is most effective in correlated markets, like CME futures and spot Bitcoin. Traders can adjust the Bollinger Bands period and standard deviation multiplier to suit different volatility regimes. - Backtesting: Before deployment, backtesting the strategy across different market conditions and timeframes is recommended to ensure robustness. Adjust the take-profit steps and hold periods to reflect the trader’s risk tolerance and market behavior. █ Default Settings The default settings provide a balanced approach to spread trading using Bollinger Bands but can be adjusted depending on market conditions or personal trading preferences. 🔶 Bollinger Bands Period (200 bars): This defines the number of bars used to calculate the moving average and standard deviation for the Bollinger Bands. A longer period smooths out short-term fluctuations and focuses on larger, more significant trends. Adjusting the period affects the responsiveness of the strategy: - Shorter periods (e.g., 100 bars): Makes the strategy more reactive to short-term market fluctuations, potentially generating more signals but increasing the risk of false positives. - Longer periods (e.g., 300 bars): Focuses on longer-term trends, reducing the frequency of trades and focusing only on significant deviations. 🔶 Standard Deviation Multiplier (2.618): The multiplier controls how wide the Bollinger Bands are around the moving average. By default, the bands are set at 2.618 standard deviations away from the average, ensuring that only significant deviations trigger trades. - Higher multipliers (e.g., 3.0): Require a more extreme deviation to trigger trades, reducing trade frequency but potentially increasing the accuracy of signals. - Lower multipliers (e.g., 2.0): Make the bands narrower, increasing the number of trade signals but potentially decreasing their reliability. 🔶 Take-Profit Levels: The strategy has four take-profit levels to gradually lock in profits: - Level 1 (3%): 25% of the position is closed at a 3% profit. - Level 2 (8%): 20% of the position is closed at an 8% profit. - Level 3 (14%): 15% of the position is closed at a 14% profit. - Level 4 (21%): 10% of the position is closed at a 21% profit. Adjusting these take-profit levels affects how quickly profits are realized: - Lower take-profit levels: Capture gains more quickly, reducing risk but potentially cutting off larger profits. - Higher take-profit levels: Let trades run longer, aiming for bigger gains but increasing the risk of price reversals before profits are locked in. 🔶 Hold Days (20 bars): The strategy automatically closes the position after 20 bars if none of the take-profit levels are hit. This feature prevents trades from being held indefinitely, especially if market conditions are stagnant. Adjusting this: - Shorter hold periods: Reduce the duration of exposure, minimizing risks from market changes but potentially closing trades too early. - Longer hold periods: Allow trades to stay open longer, increasing the chance for mean reversion but also increasing exposure to unfavorable market conditions. By understanding how these default settings affect the strategy’s performance, traders can optimize the Bitcoin CME-Spot Bollinger Bands Strategy to their preferences, adapting it to different market environments and risk tolerances.
Strategi Pine Script®
oleh PresentTrading
2
Strategy Chameleon [theUltimator5]Have you ever looked at an indicator and wondered to yourself "Is this indicator actually profitable?" Well now you can test it out for yourself with the Strategy Chameleon! Strategy Chameleon is a versatile, signal-agnostic trading strategy designed to adapt to any external indicator or trading system. Like a chameleon changes colors to match its environment, this strategy adapts to match any buy/sell signals you provide, making it the ultimate backtesting and automation tool for traders who want to test multiple strategies without rewriting code. 🎯 Key Features 1) Connects ANY external indicator's buy/sell signals Works with RSI, MACD, moving averages, custom indicators, or any Pine Script output Simply connect your indicator's signal output to the strategy inputs 2) Multiple Stop Loss Types: Percentage-based stops ATR (Average True Range) dynamic stops Fixed point stops 3) Advanced Trailing Stop System: Percentage trailing ATR-based trailing Fixed point trailing 4) Flexible Take Profit Options: Risk:Reward ratio targeting Percentage-based profits ATR-based profits Fixed point profits 5) Trading Direction Control Long Only - Bull market strategies Short Only - Bear market strategies Both - Full market strategies 6) Time-Based Filtering Optional trading session restrictions Customize active trading hours Perfect for day trading strategies 📈 How It Works Signal Detection: The strategy monitors your connected buy/sell signals Entry Logic: Executes trades when signals trigger during valid time periods Risk Management: Automatically applies your chosen stop loss and take profit levels Trailing System: Dynamically adjusts stops to lock in profits Performance Tracking: Real-time statistics table showing win rate and performance ⚙️ Setup Instructions 0) Add indicator you want to test, then add the Strategy to your chart Connect Your Signals: imgur.com Go to strategy settings → Signal Sources 1) Set "Buy Signal Source" to your indicator's buy output 2) Set "Sell Signal Source" to your indicator's sell output 3) Choose table position - This simply changes the table location on the screen 4) Set trading direction preference - Buy only? Sell only? Both directions? imgur.com 5) Set your preferred stop loss type and level You can set the stop loss to be either percentage based or ATR and fully configurable. 6) Enable trailing stops if desired imgur.com 7) Configure take profit settings 8) Toggle time filter to only consider specific time windows or trading sessions. 🚀 Use Cases Test various indicators to determine feasibility and/or profitability. Compare different signal sources quickly Validate trading ideas with consistent risk management Portfolio Management Apply uniform risk management across different strategies Standardize stop loss and take profit rules Monitor performance consistently Automation Ready Built-in alert conditions for automated trading Compatible with trading bots and webhooks Easy integration with external systems ⚠️ Important Notes This strategy requires external signals to function Default settings use 10% of equity per trade Pyramiding is disabled (one position at a time) Strategy calculates on bar close, not every tick 🔗 Integration Examples Works perfectly with: RSI strategies (connect RSI > 70 for sells, RSI < 30 for buys) Moving average crossovers MACD signal line crosses Bollinger Band strategies Custom oscillators and indicators Multi-timeframe strategies 📋 Default Settings Position Size: 10% of equity Stop Loss: 2% percentage-based Trailing Stop: 1.5% percentage-based (enabled) Take Profit: Disabled (optional) Trade Direction: Both long and short Time Filter: Disabled
Strategi Pine Script®
oleh TheUltimator5
3
Big Candle Identifier with RSI Divergence and Advanced Stops1. Strategy Objective The main goal of this strategy is to: Identify significant price momentum (big candles). Enter trades at opportune moments based on market signals (candlestick patterns and RSI divergence). Limit initial risk through a fixed stop loss. Maximize profits by using a trailing stop that activates only after the trade moves a specified distance in the profitable direction. 2. Components of the Strategy A. Big Candle Identification The strategy identifies big candles as indicators of strong momentum. A big candle is defined as: The body (absolute difference between close and open) of the current candle (body0) is larger than the bodies of the last five candles. The candle is: Bullish Big Candle: If close > open. Bearish Big Candle: If open > close. Purpose: Big candles signal potential continuation or reversal of trends, serving as the primary entry trigger. B. RSI Divergence Relative Strength Index (RSI): A momentum oscillator used to detect overbought/oversold conditions and divergence. Fast RSI: A 5-period RSI, which is more sensitive to short-term price movements. Slow RSI: A 14-period RSI, which smoothens fluctuations over a longer timeframe. Divergence: The difference between the fast and slow RSIs. Positive divergence (divergence > 0): Bullish momentum. Negative divergence (divergence < 0): Bearish momentum. Visualization: The divergence is plotted on the chart, helping traders confirm momentum shifts. C. Stop Loss Initial Stop Loss: When entering a trade, an immediate stop loss of 200 points is applied. This stop loss ensures the maximum risk is capped at a predefined level. Implementation: Long Trades: Stop loss is set below the entry price at low - 200 points. Short Trades: Stop loss is set above the entry price at high + 200 points. Purpose: Prevents significant losses if the price moves against the trade immediately after entry. D. Trailing Stop The trailing stop is a dynamic risk management tool that adjusts with price movements to lock in profits. Here’s how it works: Activation Condition: The trailing stop only starts trailing when the trade moves 200 ticks (profit) in the right direction: Long Position: close - entry_price >= 200 ticks. Short Position: entry_price - close >= 200 ticks. Trailing Logic: Once activated, the trailing stop: For Long Positions: Trails behind the price by 150 ticks (trail_stop = close - 150 ticks). For Short Positions: Trails above the price by 150 ticks (trail_stop = close + 150 ticks). Exit Condition: The trade exits automatically if the price touches the trailing stop level. Purpose: Ensures profits are locked in as the trade progresses while still allowing room for price fluctuations. E. Trade Entry Logic Long Entry: Triggered when a bullish big candle is identified. Stop loss is set at low - 200 points. Short Entry: Triggered when a bearish big candle is identified. Stop loss is set at high + 200 points. F. Trade Exit Logic Trailing Stop: Automatically exits the trade if the price touches the trailing stop level. Fixed Stop Loss: Exits the trade if the price hits the predefined stop loss level. G. 21 EMA The strategy includes a 21-period Exponential Moving Average (EMA), which acts as a trend filter. EMA helps visualize the overall market direction: Price above EMA: Indicates an uptrend. Price below EMA: Indicates a downtrend. H. Visualization Big Candle Identification: The open and close prices of big candles are plotted for easy reference. Trailing Stop: Plotted on the chart to visualize its progression during the trade. Green Line: Indicates the trailing stop for long positions. Red Line: Indicates the trailing stop for short positions. RSI Divergence: Positive divergence is shown in green. Negative divergence is shown in red. 3. Key Parameters trail_start_ticks: The number of ticks required before the trailing stop activates (default: 200 ticks). trail_distance_ticks: The distance between the trailing stop and price once the trailing stop starts (default: 150 ticks). initial_stop_loss_points: The fixed stop loss in points applied at entry (default: 200 points). tick_size: Automatically calculates the minimum tick size for the trading instrument. 4. Workflow of the Strategy Step 1: Entry Signal The strategy identifies a big candle (bullish or bearish). If conditions are met, a trade is entered with a fixed stop loss. Step 2: Initial Risk Management The trade starts with an initial stop loss of 200 points. Step 3: Trailing Stop Activation If the trade moves 200 ticks in the profitable direction: The trailing stop is activated and follows the price at a distance of 150 ticks. Step 4: Exit the Trade The trade is exited if: The price hits the trailing stop. The price hits the initial stop loss. 5. Advantages of the Strategy Risk Management: The fixed stop loss ensures that losses are capped. The trailing stop locks in profits after the trade becomes profitable. Momentum-Based Entries: The strategy uses big candles as entry triggers, which often indicate strong price momentum. Divergence Confirmation: RSI divergence helps validate momentum and avoid false signals. Dynamic Profit Protection: The trailing stop adjusts dynamically, allowing the trade to capture larger moves while protecting gains. 6. Ideal Market Conditions This strategy performs best in: Trending Markets: Big candles and momentum signals are more effective in capturing directional moves. High Volatility: Larger price swings improve the probability of reaching the trailing stop activation level (200 ticks).
Strategi Pine Script®
oleh NomaAlgo
2
Stock Rating [TrendX_]# OVERVIEW This Stock Rating indicator provides a thorough evaluation of a company (NON-FINANCIAL ONLY) ranging from 0 to 5. The rating is the average of six core financial metrics: efficiency, profitability, liquidity, solvency, valuation, and technical ratings. Each metric encompasses several financial measurements to ensure a robust and holistic evaluation of the stock. ## EFFICIENCY METRICS 1. Asset-to-Liability Ratio : Measures a company's ability to cover its liabilities with its assets. 2. Equity-to-Liability Ratio : Indicates the proportion of equity used to finance the company relative to liabilities. 3. Net Margin : Shows the percentage of revenue that translates into profit. 4. Operating Expense : Reflects the costs required for normal business operations. 5. Operating Expense Ratio : Compares operating expenses to total revenue. 6. Operating Profit Ratio : Measures operating profit as a percentage of revenue. 7. PE to Industry Relative PE/PB : Compares the company's PE ratio to the industry average. ## PROFITABILITY METRICS 1. ROA : Indicates how efficiently a company uses its assets to generate profit. 2. ROE : Measures profitability relative to shareholders' equity. 3. EBITDA : Reflects a company's operational profitability. 4. Free Cash Flow Margin : Shows the percentage of revenue that remains as free cash flow. 5. Revenue Growth : Measures the percentage increase in revenue over a period. 6. Gross Margin : Reflects the percentage of revenue exceeding the cost of goods sold. 7. Net Margin : Percentage of revenue that is net profit. 8. Operating Margin : Measures the percentage of revenue that is operating profit. ## LIQUIDITY METRICS 1. Current Ratio : Indicates the ability to cover short-term obligations with short-term assets. 2. Interest Coverage Ratio : Measures the ability to pay interest on outstanding debt. 3. Debt-to-EBITDA : Compares total debt to EBITDA. 4. Debt-to-Equity Ratio : Indicates the relative proportion of debt and equity financing. ## SOLVENCY METRICS 1. Altman Z-score : Predicts bankruptcy risk 2. Beneish M-score : Detects earnings manipulation. 3. Fulmer H-factor : Predicts business failure risk. ## VALUATION METRICS 1. Industry Relative PE/PB Comparison : Compares the company's PE and PB ratios to industry averages. 2. Momentum of PE, PB, and EV/EBITDA Multiples : Tracks the trends of PE, PB, and EV/EBITDA ratios over time. ## TECHNICAL METRICS 1. Relative Strength Index (RSI) : Measures the speed and change of price movements. 2. Supertrend : Trend-following indicator that identifies market trends. 3. Moving Average Golden-Cross : Occurs when a short-term MA crosses above mid-term and long-term MA which are determined by half-PI increment in smoothing period. 4. On-Balance Volume Golden-Cross : Measures cumulative buying and selling pressure.
Penunjuk Pine Script®
oleh TrendXvn
Customizable OCC Non Repainting Scalper Bot v7.0bThis strategy is intended to be used on an automated trading platform and should be run on a one minute chart for fastest confirmations and signal relay to crypto automation platform. The strategy has been modded to only go long at this time to focus on profitability for one direction. The open long and close long text fields allow you to use your own webhook message for this purpose. I have spent quite a bit of time and I figured I would put it out to the community to share the work and also get some feedback. Ok, so let me say that I have done absolutely everything I can to make the strategy not repaint while still maintaining it's profitability. It has been a challenge so I am publishing this to the community to help test this. What I have observed: the strategy will not repaint in real time. That is, if you have the chart open and keep it open, the signals are the same as the ones that are sent out by the strategy. In certain cases, when I reload the chart- the signals might be off from what was sent. In some ways, that is repainting, but it is repainting based on losing the real time data and recalculating from a different set of bars- since I am running it on a one minute chart then the start becomes different when you refresh. To address repainting while keeping the strategy calculating as quickly as possibly I have altered the logic in the following ways: I have made an assumption which might not work for everyone- at the first tick of the next bar, you can almost safefly assume in crypto that if you are looking at the previous bar for information, the open of the current bar was the close of the previous bar. This for the most part holds true in crypto with good liquidity. If you are trading a pair that jumps around due to low volume- this might not be the strategy to use. I might publish a different version with a different logic. I have altered the security repaint to use isbarconfirmed, so at the very end of the bar (as soon as the bar is confirmed), we recalculate to the higher time frames. So as soon as the data is available, it is at that point that we can then safely calculate higher time frames. This is unique and experimental, but seems to do well at creating good signals for entry. I have employed my own intervals by utilizing the resolution as an integer (used by the previous authors)- but in this case, I use the interval to take a snapshot of the higher time frame. With open close cross, the different moving averages can cause the repainting as they change to show the exact point of the cross. The interval feature I created minimizes this by utilizing the previous bar info until the interval is closed and then we recalculate the variants. You can use the interval offset feature to denote which minute is the one that starts and ends the interval. So for instance, Trading View uses minue 1 and minute 31 for 30 minute intervals. If you offset your 30 minute interval would start on minute 16 and do its calculations based on the last 30 minutes, As with most of my scripts, I have started using filters and a "show data" feature that will give you the ability to see the values of indicators that you cannot plot in the overlay. This allows you to figure out how to filter losing trades or market conditions. I have also added a trailing stop and created a fixed stop loss as seems to perform better than the original occ strategy. The original one seemed to repaint enough that it would close too quickly and not give the posiition enough time to become profitable. In certain cases where there was a large move, it would perform well, but for the most part the trades would not close profitably even though the backtest said that it did - probably due to the delay in execution and pinescript not having a confirmation on what the actual position price was. This is still in beta mode, so please forward test first and use at your own risk. If you spot repaint issues, please send me a message and try to explain the situation.
Strategi Pine Script®
oleh MauricioZuniga
23
Period Dollar Cost Average BacktesterHere is a simple script to calculate the profits and other dollar cost average strategy statistics. This strategy was created to avoid asset price volatility, so the pump and dump scheme does not affect the portfolio. By dividing the investment amount into periods, the investor doesn’t need to analyze the market, fundamental analysis, or anything. The goal is to increase the asset holdings and avoid fast and robust price movements. This indicator has some configurations. Amount to buy: the amount to buy at each time Broker fee %: the fee percentage that the broker has for spot trade Frequency: the frequency of the investments. Example: 1 Day means that every day, it will buy an amount of the asset Starting Date: when the indicator will start the investment simulation Ending Date: when the indicator will end the investment simulation InfoCell With/Height: it relates to the panel for view purposes. Change the values to fit better on your screen. This indicator has three lines: Total Invested (green): total amount invested at the end of the period Total Net Profit (pink): total profit by converting the amount of the asset bought at the latest closing price Holding Profits (yellow): the amount that would be in the portfolio if the investor had invested all the capital in a signal trade at the beginning of the period. The statistics panel has some information to help you understand buying the asset in one or more trades. So, besides those three lines that were mentioned above, here are the other statistics: Entry Price: The price of the asset when the first investment was made Gross Profit: Total amount of profit, not excluding the losses Gross Losses: Total amount of losses, not excluding the profits Profit Factor: The Gross Profit divided by the Gross Loss. A value above 1 means it’s profitable. Profit/Trades: Net profit per trade. This includes the broker fees. Recovery Factor: The Net profit divided by the relative drawdown. The higher the recovery factor, the faster the recovery of a loss Total Asset Bought: The amount of the asset that was bought at the end of the investment plan Absolute Drawdown: The total amount of losses that made the account balance go below its initial value Relative Drawdown: The max drawdown that occurred, no matter the account balance amount Total Trades: number of times the investment was made in the selected period Total Fee: total Fee that was spent on the total investment Total Winning Trades: the total amount of winning trades. A trade is considered a winner if the net profit is up compared with the latest investment. Total Losing Trades: the total amount of losing trades. A trade is considered a loser if the net profit is down compared to the latest investment. Max consecutive wins: the max amount of consecutive winning trades Max consecutive losses: the max amount of consecutive losing trades The chart above uses the default configuration of the indicator. Placed on the BTCUSD market, taking the time range of January 1st, 2018 to January 1st, 2022, 4 years. Buying a BTC amount with 10 USDT every day in that period would generate a more than 500% profit. Compared to the profit amount by just holding the count, which was close to 350% profit, the dollar cost average by period would be much more profitable.
Penunjuk Pine Script®
oleh ThiagoSchmitz
2
T7 JNSARJNSAR stands for Just Nifty -0.14% Stop & Reverse. This is a Trend Following Daily Bar Trading System for NIFTY -0.14% . Original idea belongs to ILLANGO @ I coded the pine version of this system based on a request from @stocksonfire. Use it at your own risk after validation at your end. Neither me or my company is responsible for any losses you may incur using this system. Hope you like this system and enjoy trading it !!! Updated V3 code for the T7 JNSAR system earlier published here V2 and here V1 Following updates made to the code 1. Added a 22 Period Simple moving average filter over and above the standard JNSAR value for generating trading signals. This simple filter reduces the whipsaw trades drastically along with similar improvements in the max draw down and overall profitability of the system. The SMA filter is turned ON by default but can be turned OFF by user through the settings window. 2. Backtest option is now turned ON by default. Also am republishing the trading rules here again with some modification 1. Go Long when the daily close is above the JNSAR line. Go Short when the daily close is below the JNSAR line. JNSAR line is the varying green line overlayed over the price chart. Once a signal comes at market close enter in the direction of the signal @ market price @ next day market open. 2. Trade only Nifty -0.14% Index. This system was developed and backtested only for NIFTY -0.14% Index. So trade in its Futures or Options, as you may deem fit. My recommendation is to choose futures for simplicity. If you want to reduce the trading cost and go with options, trade with deep in the money options, preferably 2 strikes far from the spot price. 3. Trade all signals. Markets trend only 30-35% of the time and hence the system is only accurate to that extend. But system tends to make enough money, in this small trending window, to keep the overall profitability in good health. But one never knows when a big trend may come and when it comes its absolutely imperative that you take it. To ensure that, trade all signals and don't be choosy about what signals you are going to trade. Also I wouldn't recommend using your own analysis to trade this system. Too many drivers will crash the car. 4. Like all trend following systems, this system will have many whipsaws during flat markets along with large trade and account drawdowns. Also some months and even years may not be profitable. But to trade this system profitably, it is necessary to take these in one's stride and keep trading. As the backtester results from 1990 to 2017 proves, this system is profitable overall thus far. Take confidence from that objective fact. 5. Trade with only that amount of money you can afford to loose. Initial capital that you need to have to trade one lot of NIFTY -0.14% should be atleast - (Margin Money required to take and hold 1 lot position + maximum drawdown amount per lot)*1.2. Be prepared to add more if need be, but the above formula will give a rough idea of what you need to have to start trading and be in the game always. 6. Place an After Market Order @ Market Price with your broker after market close so that you get to execute the trade next trading day @ Market open to capture near similar price as the daily open price seen on the chart. This execution mode will give you the best chance to minimize the slippage and mimic the backtester results as closely as practically possible. 7. Follow all the 6 rules above religiously, as if your life depends on it. If you cant, then don't trade this system; You will certainly loose money. Happy Trading !!! As always am looking out for your valuable feedback.
Strategi Pine Script®
oleh syam-mohan-vs
12
Dskyz (DAFE) Quantum Sentiment Flux - Beginners Dskyz (DAFE) Quantum Sentiment Flux - Beginners: Welcome to the Dskyz (DAFE) Quantum Sentiment Flux - Beginners , a strategy and concept that’s your ultimate wingman for trading futures like MNQ, NQ, MES, and ES. This gem combines lightning-fast momentum signals, market sentiment smarts, and bulletproof risk management into a system so intuitive, even newbies can trade like pros. With clean DAFE visuals, preset modes for every vibe, and a revamped dashboard that’s basically a market GPS, this strategy makes futures trading feel like a high-octane sci-fi mission. Built on the Dskyz (DAFE) legacy of Aurora Divergence, the Quantum Sentiment Flux is designed to empower beginners while giving seasoned traders a lean, sentiment-driven edge. It uses fast/slow EMA crossovers for entries, filters trades with VIX, SPX trends, and sector breadth, and keeps your account safe with adaptive stops and cooldowns. Tuned for more action with faster signals and a slick bottom-left dashboard, this updated version is ready to light up your charts and outsmart institutional traps. Let’s dive into why this strat’s a must-have and break down its brilliance. Why Traders Need This Strategy Futures markets are a wild ride—fast moves, volatility spikes (like the April 28, 2025 NQ 1k-point drop), and institutional games that can wreck unprepared traders. Beginners often get lost in complex systems or burned by impulsive trades. The Quantum Sentiment Flux is the antidote, offering: Dead-Simple Setup: Preset modes (Aggressive, Balanced, Conservative) auto-tune signals, risk, and sizing, so you can trade without a quant degree. Sentiment Superpower: VIX filter, SPX trend, and sector breadth visuals keep you aligned with market health, dodging chop and riding trends. Ironclad Safety: Tighter ATR-based stops, 2:1 take-profits, and preset cooldowns protect your capital, even in chaotic sessions. Next-Level Visuals: Green/red entry triangles, vibrant EMAs, a sector breadth background, and a beefed-up dashboard make signals and context pop. DAFE Swagger: The clean aesthetics, sleek dashboard—ties it to Dskyz’s elite brand, making your charts a work of art. Traders need this because it’s a plug-and-play system that blends beginner-friendly simplicity with pro-level market awareness. Whether you’re just starting or scalping 5min MNQ, this strat’s your key to trading with confidence and style. Strategy Components 1. Core Signal Logic (High-Speed Momentum) The strategy’s engine is a momentum-based system using fast and slow Exponential Moving Averages (EMAs), now tuned for faster, more frequent trades. How It Works: Fast/Slow EMAs: Fast EMA (Aggressive: 5, Balanced: 7, Conservative: 9 bars) and slow EMA (12/14/18 bars) track short-term vs. longer-term momentum. Crossover Signals: Buy: Fast EMA crosses above slow EMA, and trend_dir = 1 (fast EMA > slow EMA + ATR * strength threshold). Sell: Fast EMA crosses below slow EMA, and trend_dir = -1 (fast EMA < slow EMA - ATR * strength threshold). Strength Filter: ma_strength = fast EMA - slow EMA must exceed an ATR-scaled threshold (Aggressive: 0.15, Balanced: 0.18, Conservative: 0.25) for robust signals. Trend Direction: trend_dir confirms momentum, filtering out weak crossovers in choppy markets. Evolution: Faster EMAs (down from 7–10/21–50) catch short-term trends, perfect for active futures markets. Lower strength thresholds (0.15–0.25 vs. 0.3–0.5) make signals more sensitive, boosting trade frequency without sacrificing quality. Preset tuning ensures beginners get optimized settings, while pros can tweak via mode selection. 2. Market Sentiment Filters The strategy leans hard into market sentiment with a VIX filter, SPX trend analysis, and sector breadth visuals, keeping trades aligned with the big picture. VIX Filter: Logic: Blocks long entries if VIX > threshold (default: 20, can_long = vix_close < vix_limit). Shorts are always allowed (can_short = true). Impact: Prevents longs during high-fear markets (e.g., VIX spikes in crashes), while allowing shorts to capitalize on downturns. SPX Trend Filter: Logic: Compares S&P 500 (SPX) close to its SMA (Aggressive: 5, Balanced: 8, Conservative: 12 bars). spx_trend = 1 (UP) if close > SMA, -1 (DOWN) if < SMA, 0 (FLAT) if neutral. Impact: Provides dashboard context, encouraging trades that align with market direction (e.g., longs in UP trend). Sector Breadth (Visual): Logic: Tracks 10 sector ETFs (XLK, XLF, XLE, etc.) vs. their SMAs (same lengths as SPX). Each sector scores +1 (bullish), -1 (bearish), or 0 (neutral), summed as breadth (-10 to +10). Display: Green background if breadth > 4, red if breadth < -4, else neutral. Dashboard shows sector trends (↑/↓/-). Impact: Faster SMA lengths make breadth more responsive, reflecting sector rotations (e.g., tech surging, energy lagging). Why It’s Brilliant: - VIX filter adds pro-level volatility awareness, saving beginners from panic-driven losses. - SPX and sector breadth give a 360° view of market health, boosting signal confidence (e.g., green BG + buy signal = high-probability trade). - Shorter SMAs make sentiment visuals react faster, perfect for 5min charts. 3. Risk Management The risk controls are a fortress, now tighter and more dynamic to support frequent trading while keeping accounts safe. Preset-Based Risk: Aggressive: Fast EMAs (5/12), tight stops (1.1x ATR), 1-bar cooldown. High trade frequency, higher risk. Balanced: EMAs (7/14), 1.2x ATR stops, 1-bar cooldown. Versatile for most traders. Conservative: EMAs (9/18), 1.3x ATR stops, 2-bar cooldown. Safer, fewer trades. Impact: Auto-scales risk to match style, making it foolproof for beginners. Adaptive Stops and Take-Profits: Logic: Stops = entry ± ATR * atr_mult (1.1–1.3x, down from 1.2–2.0x). Take-profits = entry ± ATR * take_mult (2x stop distance, 2:1 reward/risk). Longs: stop below entry, TP above; shorts: vice versa. Impact: Tighter stops increase trade turnover while maintaining solid risk/reward, adapting to volatility. Trade Cooldown: Logic: Preset-driven (Aggressive/Balanced: 1 bar, Conservative: 2 bars vs. old user-input 2). Ensures bar_index - last_trade_bar >= cooldown. Impact: Faster cooldowns (especially Aggressive/Balanced) allow more trades, balanced by VIX and strength filters. Contract Sizing: Logic: User sets contracts (default: 1, max: 10), no preset cap (unlike old 7/5/3 suggestion). Impact: Flexible but risks over-leverage; beginners should stick to low contracts. Built To Be Reliable and Consistent: - Tighter stops and faster cooldowns make it a high-octane system without blowing up accounts. - Preset-driven risk removes guesswork, letting newbies trade confidently. - 2:1 TPs ensure profitable trades outweigh losses, even in volatile sessions like April 27, 2025 ES slippage. 4. Trade Entry and Exit Logic The entry/exit rules are simple yet razor-sharp, now with VIX filtering and faster signals: Entry Conditions: Long Entry: buy_signal (fast EMA crosses above slow EMA, trend_dir = 1), no position (strategy.position_size = 0), cooldown passed (can_trade), and VIX < 20 (can_long). Enters with user-defined contracts. Short Entry: sell_signal (fast EMA crosses below slow EMA, trend_dir = -1), no position, cooldown passed, can_short (always true). Logic: Tracks last_entry_bar for visuals, last_trade_bar for cooldowns. Exit Conditions: Stop-Loss/Take-Profit: ATR-based stops (1.1–1.3x) and TPs (2x stop distance). Longs exit if price hits stop (below) or TP (above); shorts vice versa. No Other Exits: Keeps it straightforward, relying on stops/TPs. 5. DAFE Visuals The visuals are pure DAFE magic, blending clean function with informative metrics utilized by professionals, now enhanced by faster signals and a responsive breadth background: EMA Plots: Display: Fast EMA (blue, 2px), slow EMA (orange, 2px), using faster lengths (5–9/12–18). Purpose: Highlights momentum shifts, with crossovers signaling entries. Sector Breadth Background: Display: Green (90% transparent) if breadth > 4, red (90%) if breadth < -4, else neutral. Purpose: Faster breadth_sma_len (5–12 vs. 10–50) reflects sector shifts in real-time, reinforcing signal strength. - Visuals are intuitive, turning complex signals into clear buy/sell cues. - Faster breadth background reacts to market rotations (e.g., tech vs. energy), giving a pro-level edge. 6. Sector Breadth Dashboard The new bottom-left dashboard is a game-changer, a 3x16 table (black/gray theme) that’s your market command center: Metrics: VIX: Current VIX (red if > 20, gray if not). SPX: Trend as “UP” (green), “DOWN” (red), or “FLAT” (gray). Trade Longs: “OK” (green) if VIX < 20, “BLOCK” (red) if not. Sector Breadth: 10 sectors (Tech, Financial, etc.) with trend arrows (↑ green, ↓ red, - gray). Placeholder Row: Empty for future metrics (e.g., ATR, breadth score). Purpose: Consolidates regime, volatility, market trend, and sector data, making decisions a breeze. - VIX and SPX metrics add context, helping beginners avoid bad trades (e.g., no longs if “BLOCK”). Sector arrows show market health at a glance, like a cheat code for sentiment. Key Features Beginner-Ready: Preset modes and clear visuals make futures trading a breeze. Sentiment-Driven: VIX filter, SPX trend, and sector breadth keep you in sync with the market. High-Frequency: Faster EMAs, tighter stops, and short cooldowns boost trade volume. Safe and Smart: Adaptive stops/TPs and cooldowns protect capital while maximizing wins. Visual Mastery: DAFE’s clean flair, EMAs, dashboard—makes trading fun and clear. Backtestable: Lean code and fixed qty ensure accurate historical testing. How to Use Add to Chart: Load on a 5min MNQ/ES chart in TradingView. Pick Preset: Aggressive (scalping), Balanced (versatile), or Conservative (safe). Balanced is default. Set Contracts: Default 1, max 10. Stick low for safety. Check Dashboard: Bottom-left shows preset, VIX, SPX, and sectors. “OK” + green breadth = strong buy. Backtest: Run in strategy tester to compare modes. Live Trade: Connect to Tradovate or similar. Watch for slippage (e.g., April 27, 2025 ES issues). Replay Test: Try April 28, 2025 NQ drop to see VIX filter and stops in action. Why It’s Brilliant The Dskyz (DAFE) Quantum Sentiment Flux - Beginners is a masterpiece of simplicity and power. It takes pro-level tools—momentum, VIX, sector breadth—and wraps them in a system anyone can run. Faster signals and tighter stops make it a trading machine, while the VIX filter and dashboard keep you ahead of market chaos. The DAFE visuals and bottom-left command center turn your chart into a futuristic cockpit, guiding you through every trade. For beginners, it’s a safe entry to futures; for pros, it’s a scalping beast with sentiment smarts. This strat doesn’t just trade—it transforms how you see the market. Final Notes This is more than a strategy—it’s your launchpad to mastering futures with Dskyz (DAFE) flair. The Quantum Sentiment Flux blends accessibility, speed, and market savvy to help you outsmart the game. Load it, watch those triangles glow, and let’s make the markets your canvas! Official Statement from Pine Script Team (see TradingView help docs and forums): "This warning may appear when you call functions such as ta.sma inside a request.security in a loop. There is no runtime impact. If you need to loop through a dynamic list of tickers, this cannot be avoided in the present version... Values will still be correct. Ignore this warning in such contexts." (This publishing will most likely be taken down do to some miscellaneous rule about properly displaying charting symbols, or whatever. Once I've identified what part of the publishing they want to pick on, I'll adjust and repost.) Use it with discipline. Use it with clarity. Trade smarter. **I will continue to release incredible strategies and indicators until I turn this into a brand or until someone offers me a contract. Created by Dskyz, powered by DAFE Trading Systems. Trade fast, trade bold.
Strategi Pine Script®
oleh DskyzInvestments
1
[3Commas] HA & MAHA & MA 🔷What it does: This tool is designed to test a trend-following strategy using Heikin Ashi candles and moving averages. It enters trades after pullbacks, aiming to let profits run once the risk-to-reward ratio reaches 1:1 while securing the position. 🔷Who is it for: It is ideal for traders looking to compare final results using fixed versus dynamic take profits by adjusting parameters and trade direction—a concept applicable to most trading strategies. 🔷How does it work: We use moving averages to define the market trend, then wait for opposite Heikin Ashi candles to form against it. Once these candles reverse in favor of the trend, we enter the trade, using the last swing created by the pullback as the stop loss. By applying the breakeven ratio, we protect the trade and let it run, using the slower moving average as a trailing stop. A buy signal is generated when: The previous candle is bearish (ha_bear ), indicating a pullback. The fast moving average (ma1) is above the slow moving average (ma2), confirming an uptrend. The current candle is bullish (ha_bull), showing trend continuation. The Heikin Ashi close is above the fast moving average (ma1), reinforcing the bullish bias. The real price close is above the open (close > open), ensuring bullish momentum in actual price data. The signal is confirmed on the closed candle (barstate.isconfirmed) to avoid premature signals. dir is undefined (na(dir)), preventing repeated signals in the same direction. A sell signal is generated when: The previous candle is bullish (ha_bull ), indicating a temporary upward move before a potential reversal. The fast moving average (ma1) is below the slow moving average (ma2), confirming a downtrend. The current candle is bearish (ha_bear), showing trend continuation to the downside. The Heikin Ashi close is below the fast moving average (ma1), reinforcing bearish pressure. The real price close is below the open (close < open), confirming bearish momentum in actual price data. The signal is confirmed after the candle closes (barstate.isconfirmed), avoiding premature entries. dir is undefined (na(dir)), preventing consecutive signals in the same direction. In simple terms, this setup looks for trend continuation after a pullback, confirming entries with both Heikin Ashi and real price action, supported by moving average alignment to avoid false signals. If the price reaches a 1:1 risk-to-reward ratio, the stop will be moved to the entry point. However, if the slow moving average surpasses this level, it will become the new exit point, acting as a trailing stop 🔷Why It’s Unique Easily visualizes the benefits of using risk-to-reward ratios when trading instead of fixed percentages. Provides a simple and straightforward approach to trading, embracing the "keep it simple" concept. Offers clear visualization of DCA Bot entry and exit points based on user preferences. Includes an option to review the message format before sending signals to bots, with compatibility for multi-pair and futures contract pairs. 🔷 Considerations Before Using the Indicator ⚠️Very important: The indicator must be used on charts with real price data, such as Japanese candlesticks, line charts, etc. Do not use it on Heikin Ashi charts, as this may lead to unrealistic results. 🔸Since this is a trend-following strategy, use it on timeframes above 4 hours, where market noise is reduced and trends are clearer. Also, carefully review the statistics before using it, focusing on pairs that tend to have long periods of well-defined trends. 🔸Disadvantages: False Signals in Ranges: Consolidating markets can generate unreliable signals. Lagging Indicator: Being based on moving averages, it may react late to sudden price movements. 🔸Advantages: Trend Focused: Simplifies the identification of trending markets. Noise Reduction: Uses Heikin Ashi candles to identify trend continuation after pullbacks. Broad Applicability: Suitable for forex, crypto, stocks, and commodities. 🔸The strategy provides a systematic way to analyze markets but does not guarantee successful outcomes. Use it as an additional tool rather than relying solely on an automated system. Trading results depend on various factors, including market conditions, trader discipline, and risk management. Past performance does not ensure future success, so always approach the market cautiously. 🔸Risk Management: Define stop-loss levels, position sizes, and profit targets before entering any trade. Be prepared for potential losses and ensure your approach aligns with your overall trading plan. 🔷 STRATEGY PROPERTIES Symbol: BINANCE:BTCUSDT (Spot). Timeframe: 4h. Test Period: All historical data available. Initial Capital: 10000 USDT. Order Size per Trade: 1% of Capital, you can use a higher value e.g. 5%, be cautious that the Max Drawdown does not exceed 10%, as it would indicate a very risky trading approach. Commission: Binance commission 0.1%, adjust according to the exchange being used, lower numbers will generate unrealistic results. By using low values e.g. 5%, it allows us to adapt over time and check the functioning of the strategy. Slippage: 5 ticks, for pairs with low liquidity or very large orders, this number should be increased as the order may not be filled at the desired level. Margin for Long and Short Positions: 100%. Indicator Settings: Default Configuration. MA1 Length: 9. MA2 Length: 18. MA Calculations: EMA. Take Profit Ratio: Disable. Ratio 1:4. Breakeven Ratio: Enable, Ratio 1:1. Strategy: Long & Short. 🔷 STRATEGY RESULTS ⚠️Remember, past results do not guarantee future performance. Net Profit: +324.88 USDT (+3.25%). Max Drawdown: -81.18 USDT (-0.78%). Total Closed Trades: 672. Percent Profitable: 35.57%. Profit Factor: 1.347. Average Trade: +0.48 USDT (+0.48%). Average # Bars in Trades: 13. 🔷 HOW TO USE 🔸 Adjust Settings: The default values—MA1 (9) and MA2 (18) with EMA calculation—generally work well. However, you can increase these values, such as 20 and 40, to better identify stronger trends. 🔸 Choose a Symbol that Typically Trends: Select an asset that tends to form clear trends. Keep in mind that the Strategy Tester results may show poor performance for certain assets, making them less suitable for sending signals to bots. 🔸 Experiment with Ratios: Test different take profit and breakeven ratios to compare various scenarios—especially to observe how the strategy performs when only the trade is protected. 🔸This is an example of how protecting the trade works: once the price moves in favor of the position with a 1:1 risk-to-reward ratio, the stop loss is moved to the entry price. If the Slow MA surpasses this level, it will act as a trailing stop, aiming to follow the trend and maximize potential gains. 🔸In contrast, in this example, for the same trade, if we set a take profit at a 1:3 risk-to-reward ratio—which is generally considered a good risk-reward relationship—we can see how a significant portion of the upward move is left on the table. 🔸Results Review: It is important to check the Max Drawdown. This value should ideally not exceed 10% of your capital. Consider adjusting the trade size to ensure this threshold is not surpassed. Remember to include the correct values for commission and slippage according to the symbol and exchange where you are conducting the tests. Otherwise, the results will not be realistic. If you are satisfied with the results, you may consider automating your trades. However, it is strongly recommended to use a small amount of capital or a demo account to test proper execution before committing real funds. 🔸Create alerts to trigger the DCA Bot: Verify Messages: Ensure the message matches the one specified by the DCA Bot. Multi-Pair Configuration: For multi-pair setups, enable the option to add the symbol in the correct format. Signal Settings: Enable whether you want to receive long or short signals (Entry | TP | SL), copy and paste the the messages for the DCA Bots configured. Alert Setup: When creating an alert, set the condition to the indicator and choose "alert() function call only. Enter any desired Alert Name. Open the Notifications tab, enable Webhook URL, and paste the Webhook URL. For more details, refer to the section: "How to use TradingView Custom Signals". Finalize Alerts: Click Create, you're done! Alerts will now be sent automatically in the correct format. 🔷 INDICATOR SETTINGS MA 1: Fast MA Length MA 2: Slow MA Length MA Calc: MA's Calculations (SMA,EMA, RMA,WMA) TP Ratio: This is the take profit ratio relative to the stop loss, where the trade will be closed in profit. BE Ratio: This is the breakeven ratio relative to the stop loss, where the stop loss will be updated to breakeven or if the MA2 is greater than this level. Strategy: Order Type direction in which trades are executed. Use Custom Test Period: When enabled signals only works in the selected time window. If disabled it will use all historical data available on the chart. Test Start and End: Once the Custom Test Period is enabled, here you select the start and end date that you want to analyze. Check Messages: Enable the table to review the messages to be sent to the bot. Entry | TP | SL: Enable this options to send Buy Entry, Take Profit (TP), and Stop Loss (SL) signals. Deal Entry and Deal Exit : Copy and paste the message for the deal start signal and close order at Market Price of the DCA Bot. This is the message that will be sent with the alert to the Bot, you must verify that it is the same as the bot so that it can process properly so that it executes and starts the trade. DCA Bot Multi-Pair: You must activate it if you want to use the signals in a DCA Bot Multi-pair in the text box you must enter (using the correct format) the symbol in which you are creating the alert, you can check the format of each symbol when you create the bot. 👨🏻‍💻💭 We hope this tool helps enhance your trading. Your feedback is invaluable, so feel free to share any suggestions for improvements or new features you'd like to see implemented. __ The information and publications within the 3Commas TradingView account are not meant to be and do not constitute financial, investment, trading, or other types of advice or recommendations supplied or endorsed by 3Commas and any of the parties acting on behalf of 3Commas, including its employees, contractors, ambassadors, etc.
Strategi Pine Script®
oleh 3Commas
1
Smart MA Crossover BacktesterSmart MA Crossover Backtester - Strategy Overview Strategy Name: Smart MA Crossover Backtester Published on: TradingView Applicable Markets: Works well on crypto (tested profitably on ETH) Strategy Concept The Smart MA Crossover Backtester is an improved Moving Average (MA) crossover strategy that incorporates a trend filter and an ATR-based stop loss & take profit mechanism for better risk management. It aims to capture trends efficiently while reducing false signals by only trading in the direction of the long-term trend. Core Components & Logic Moving Averages (MA) for Entry Signals Fast Moving Average (9-period SMA) Slow Moving Average (21-period SMA) A trade signal is generated when the fast MA crosses the slow MA. Trend Filter (200-period SMA) Only enters long positions if price is above the 200-period SMA (bullish trend). Only enters short positions if price is below the 200-period SMA (bearish trend). This helps in avoiding counter-trend trades, reducing whipsaws. ATR-Based Stop Loss & Take Profit Uses the Average True Range (ATR) with a multiplier of 2 to calculate stop loss. Risk-Reward Ratio = 1:2 (Take profit is set at 2x ATR). This ensures dynamic stop loss and take profit levels based on market volatility. Trading Rules ✅ Long Entry (Buy Signal): Fast MA (9) crosses above Slow MA (21) Price is above the 200 MA (bullish trend filter active) Stop Loss: Below entry price by 2× ATR Take Profit: Above entry price by 4× ATR ✅ Short Entry (Sell Signal): Fast MA (9) crosses below Slow MA (21) Price is below the 200 MA (bearish trend filter active) Stop Loss: Above entry price by 2× ATR Take Profit: Below entry price by 4× ATR Why This Strategy Works Well for Crypto (ETH)? 🔹 Crypto markets are highly volatile – ATR-based stop loss adapts dynamically to market conditions. 🔹 Long-term trend filter (200 MA) ensures trading in the dominant direction, reducing false signals. 🔹 Risk-reward ratio of 1:2 allows for profitable trades even with a lower win rate. This strategy has been tested on Ethereum (ETH) and has shown profitable performance, making it a strong choice for crypto traders looking for trend-following setups with solid risk management. 🚀
Strategi Pine Script®
oleh pratik-r
1
HTC peppermint_07 CCI w signal + s&r RSI This CCI version enhances the traditional Commodity Channel Index (CCI) by integrating a dynamically calculated Relative Strength Index (RSI) that acts as support and resistance as shown in the screenshot, it can add as a confirmation to the divergence found in the CCI. Key Features: Enhanced CCI: The primary plot (black line but customizable) represents the standard CCI, providing insight into price momentum and potential overbought/oversold conditions. Dynamic RSI Support/Resistance: The upper and lower bands (medium cyan line) are derived from a smoothed RSI, dynamically adjusting to the current market volatility. These bands serve as potential support and resistance levels for the CCI as additional confirmation for the divergence. Overbought/Oversold Zones: The traditional overbought (+100) and oversold (-100) levels for CCI are marked with horizontal dotted lines. Benefits: Improved Entry/Exit Signals: Combining CCI with dynamic RSI support/resistance may offer more precise trading signals compared to using CCI alone. Dynamic Adaptation: The RSI-based bands adapt to changing market conditions, potentially providing more relevant support and resistance levels. Divergence Confirmation: dynamic s&r RSI adds confluence to potential trend reversals identified by the CCI. Potential Usage: Traders might use this indicator to: Identify potential overbought/oversold conditions using the CCI and its relationship to the dynamic RSI bands. Look for breakouts beyond the dynamic support/resistance levels as potential entry points. Confirm potential trend reversals using RSI divergence (cyan and red label above divergence) signals. Further Development Considerations: Customizable Parameters: Allowing users to adjust the CCI length, RSI periods, and smoothing factors would enhance flexibility. Alert Conditions: Adding alerts for breakouts, overbought/oversold conditions, and divergence signals would improve usability. Backtesting: Thoroughly backtesting the indicator's performance across different assets and timeframes is essential before using it for live trading. DISCLAIMER: !! indicator is a custom technical analysis tool designed for educational and informational purposes only. It should not be construed as financial advice or a recommendation to buy or sell any security. Trading involves substantial risk of loss and may not be suitable for all investors.   Key Points to Consider: No Guarantee of Profitability: The indicator's past performance is not indicative of future results. No trading strategy can guarantee profits or eliminate the risk of losses. You could lose some or all of your investment. Use at Your Own Risk: Use of this indicator is solely at your own discretion and risk. You are responsible for your trading decisions. The developers and distributors of this indicator are not liable for any losses incurred as a result of using it. Not Financial Advice: This indicator does not provide financial advice. Consult with a qualified financial advisor before making any investment decisions. Backtesting Limitations: Backtested results, if presented, should be viewed with caution. Past performance may not reflect future results due to various factors, including changing market conditions and the limitations of backtesting methodologies. Indicator Limitations: Technical indicators, including this one, are not perfect. They can generate false signals, and their effectiveness can vary depending on market conditions and the specific parameters used. Parameter Optimization: Optimizing indicator parameters for past performance can lead to overfitting, which may not translate to future profitability. No Warranty: The indicator is provided "as is" without any warranty of any kind, either express or implied, including but not limited to warranties of merchantability, fitness for a particular purpose, or non-infringement.   Changes and Updates: The developers may make changes or updates to the indicator without notice. By using the "HTC peppermint_07 CCI w signal + s&r RSI" indicator, you acknowledge and agree to the terms of this disclaimer. If you do not agree with these terms, do not use the indicator.
Penunjuk Pine Script®
oleh peppermint07
Martingale with MACD+KDJ opening conditionsStrategy Overview: This strategy is based on a Martingale trading approach, incorporating MACD and KDJ indicators. It features pyramiding, trailing stops, and dynamic profit-taking mechanisms, suitable for both long and short trades. The strategy increases position size progressively using a Multiplier, a key feature of Martingale systems. Key Concepts: Martingale Strategy: A trading system where positions are doubled or increased after a loss to recover previous losses with a single successful trade. In this script, the position size is incremented using a Multiplier for each addition. Pyramiding: Allows adding to existing trades when market conditions are favorable, enhancing profitability during trends. Settings: Basic Inputs: Initial Order: Defines the starting size of the position. Default: 150.0 MACD Settings: Customize the fast, slow, and signal smoothing lengths. Default: Fast Length: 9, Slow Length: 26, Signal Smoothing: 9 KDJ Settings: Customize the length and smoothing parameters for KDJ. Default: Length: 14, Smooth K: 3, Smooth D: 3 Max Additions: Sets the number of additional positions (pyramiding). Default: 5 (Min: 1, Max: 10) Position Sizing: Percent to add to positions on favorable conditions. Default: 1.0% Martingale Multiplier: Add Multiplier: This value controls the scaling of additional positions according to the Martingale principle. After each loss, a new position is added, and its size is increased by the Multiplier factor. For example, with a multiplier of 2, each new addition will be twice as large as the previous one, accelerating recovery if the price moves favorably. Default: 1.0 (no multiplication) Can be adjusted up to 10x to aggressively increase position size after losses. Trade Execution: Long Trades: Entry Condition: A long position is opened when the MACD line crosses over the signal line, and the KDJ’s %K crosses above %D. Additions (Martingale): After the initial long position, new positions are added if the price drops by the defined percentage, and each new addition is increased using the Multiplier. This continues up to the set Max Additions. Short Trades: Entry Condition: A short position is opened when the MACD line crosses under the signal line, and the KDJ’s %K crosses below %D. Additions (Martingale): After the initial short position, new positions are added if the price rises by the defined percentage, and each new addition is increased using the Multiplier. Exit Conditions: Take Profit: Exits are triggered when the price reaches the take-profit threshold. Stop Loss: If the price moves unfavorably, the position will be closed at the set stop-loss level. Trailing Stop: Adjusts dynamically as the price moves in favor of the trade to lock in profits. On-Chart Visuals: Long Signals: Blue triangles below the bars indicate long entries, and green triangles mark additional long positions. Short Signals: Red triangles above the bars indicate short entries, and orange triangles mark additional short positions. Information Table: The strategy displays a table with key metrics: Open Price: The entry price of the trade. Average Price: The average price of the current position. Additions: The number of additional positions taken. Next Add Price: The price level for the next position. Take Profit: The price at which profits will be taken. Stop Loss: The stop-loss level to minimize risk. Usage Instructions: Adjust the parameters to your trading style using the input settings. The Multiplier amplifies your position size after each addition, so use it cautiously, especially in volatile markets. Monitor the signals and table on the chart for entry/exit decisions and trade management.
Strategi Pine Script®
oleh aaronxu567
1
SL ManagerSTOP LOSS MANAGER Overview: The "SL Manager" indicator is designed to assist traders in managing their stop loss (SL) and take profit (TP) levels for both long and short positions. This tool helps you visualize intermediate levels, enhancing your trading decisions by providing crucial information on the chart. Usage: This indicator is particularly useful for traders who want to manage their trades more effectively by visualizing potential adjustment points for their stop loss and take profit levels. It helps in making informed decisions to maximize profits and minimize risks by providing clear levels to take partial profits and adjust stop losses. Features: Position Input: Select between "long" and "short" positions. Entry Price: Specify the entry price of your trade. Take Profit: Define the price level at which you want to take profit. Stop Loss: Set the stop loss price level to manage your risk. Intermediate Levels: For both long and short positions, the indicator calculates and plots the following intermediate levels: 50% Take Profit (TP 50%): Midway between the entry price and the take profit level, where you can take partial profits and move your SL up to the 25% mark. 75% Take Profit (TP 75%): Three-quarters of the way from the entry price to the take profit level, where you can take partial profits and move your SL to breakeven. Stop Loss Move to 25% (SL Move to 25%): A level where the stop loss can be adjusted to lock in profits. Visualization: The indicator plots the calculated levels directly on the chart, provided the data for the current day is available. Different color codes and line styles distinguish between the various levels: TP 50% and TP 75% are plotted in green. SL Move to 25% is plotted in red . Entry/Breakeven is plotted in blue.
Penunjuk Pine Script®
oleh ninefnarsa
Machine Learning: SuperTrend Strategy TP/SL [YinYangAlgorithms]The SuperTrend is a very useful Indicator to display when trends have shifted based on the Average True Range (ATR). Its underlying ideology is to calculate the ATR using a fixed length and then multiply it by a factor to calculate the SuperTrend +/-. When the close crosses the SuperTrend it changes direction. This Strategy features the Traditional SuperTrend Calculations with Machine Learning (ML) and Take Profit / Stop Loss applied to it. Using ML on the SuperTrend allows for the ability to sort data from previous SuperTrend calculations. We can filter the data so only previous SuperTrends that follow the same direction and are within the distance bounds of our k-Nearest Neighbour (KNN) will be added and then averaged. This average can either be achieved using a Mean or with an Exponential calculation which puts added weight on the initial source. Take Profits and Stop Losses are then added to the ML SuperTrend so it may capitalize on Momentum changes meanwhile remaining in the Trend during consolidation. By applying Machine Learning logic and adding a Take Profit and Stop Loss to the Traditional SuperTrend, we may enhance its underlying calculations with potential to withhold the trend better. The main purpose of this Strategy is to minimize losses and false trend changes while maximizing gains. This may be achieved by quick reversals of trends where strategic small losses are taken before a large trend occurs with hopes of potentially occurring large gain. Due to this logic, the Win/Loss ratio of this Strategy may be quite poor as it may take many small marginal losses where there is consolidation. However, it may also take large gains and capitalize on strong momentum movements. Tutorial: In this example above, we can get an idea of what the default settings may achieve when there is momentum. It focuses on attempting to hit the Trailing Take Profit which moves in accord with the SuperTrend just with a multiplier added. When momentum occurs it helps push the SuperTrend within it, which on its own may act as a smaller Trailing Take Profit of its own accord. We’ve highlighted some key points from the last example to better emphasize how it works. As you can see, the White Circle is where profit was taken from the ML SuperTrend simply from it attempting to switch to a Bullish (Buy) Trend. However, that was rejected almost immediately and we went back to our Bearish (Sell) Trend that ended up resulting in our Take Profit being hit (Yellow Circle). This Strategy aims to not only capitalize on the small profits from SuperTrend to SuperTrend but to also capitalize when the Momentum is so strong that the price moves X% away from the SuperTrend and is able to hit the Take Profit location. This Take Profit addition to this Strategy is crucial as momentum may change state shortly after such drastic price movements; and if we were to simply wait for it to come back to the SuperTrend, we may lose out on lots of potential profit. If you refer to the Yellow Circle in this example, you’ll notice what was talked about in the Summary/Overview above. During periods of consolidation when there is little momentum and price movement and we don’t have any Stop Loss activated, you may see ‘Signal Flashing’. Signal Flashing is when there are Buy and Sell signals that keep switching back and forth. During this time you may be taking small losses. This is a normal part of this Strategy. When a signal has finally been confirmed by Momentum, is when this Strategy shines and may produce the profit you desire. You may be wondering, what causes these jagged like patterns in the SuperTrend? It's due to the ML logic, and it may be a little confusing, but essentially what is happening is the Fast Moving SuperTrend and the Slow Moving SuperTrend are creating KNN Min and Max distances that are extreme due to (usually) parabolic movement. This causes fewer values to be added to and averaged within the ML and causes less smooth and more exponential drastic movements. This is completely normal, and one of the perks of using k-Nearest Neighbor for ML calculations. If you don’t know, the Min and Max Distance allowed is derived from the most recent(0 index of data array) to KNN Length. So only SuperTrend values that exhibit distances within these Min/Max will be allowed into the average. Since the KNN ML logic can cause these exponential movements in the SuperTrend, they likewise affect its Take Profit. The Take Profit may benefit from this movement like displayed in the example above which helped it claim profit before then exhibiting upwards movement. By default our Stop Loss Multiplier is kept quite low at 0.0000025. Keeping it low may help to reduce some Signal Flashing while not taking extra losses more so than not using it at all. However, if we increase it even more to say 0.005 like is shown in the example above. It can really help the trend keep momentum. Please note, although previous results don’t imply future results, at 0.0000025 Stop Loss we are currently exhibiting 69.27% profit while at 0.005 Stop Loss we are exhibiting 33.54% profit. This just goes to show that although there may be less Signal Flashing, it may not result in more profit. We will conclude our Tutorial here. Hopefully this has given you some insight as to how Machine Learning, combined with Trailing Take Profit and Stop Loss may have positive effects on the SuperTrend when turned into a Strategy. Settings: SuperTrend: ATR Length: ATR Length used to create the Original Supertrend. Factor: Multiplier used to create the Original Supertrend. Stop Loss Multiplier: 0 = Don't use Stop Loss. Stop loss can be useful for helping to prevent false signals but also may result in more loss when hit and less profit when switching trends. Take Profit Multiplier: Take Profits can be useful within the Supertrend Strategy to stop the price reverting all the way to the Stop Loss once it's been profitable. Machine Learning: Only Factor Same Trend Direction: Very useful for ensuring that data used in KNN is not manipulated by different SuperTrend Directional data. Please note, it doesn't affect KNN Exponential. Rationalized Source Type: Should we Rationalize only a specific source, All or None? Machine Learning Type: Are we using a Simple ML Average, KNN Mean Average, KNN Exponential Average or None? Machine Learning Smoothing Type: How should we smooth our Fast and Slow ML Datas to be used in our KNN Distance calculation? SMA, EMA or VWMA? KNN Distance Type: We need to check if distance is within the KNN Min/Max distance, which distance checks are we using. Machine Learning Length: How far back is our Machine Learning going to keep data for. k-Nearest Neighbour (KNN) Length: How many k-Nearest Neighbours will we account for? Fast ML Data Length: What is our Fast ML Length?? This is used with our Slow Length to create our KNN Distance. Slow ML Data Length: What is our Slow ML Length?? This is used with our Fast Length to create our KNN Distance. If you have any questions, comments, ideas or concerns please don't hesitate to contact us. HAPPY TRADING!
Strategi Pine Script®
oleh YinYangAlgorithms
11
MA RSI @KINGThis Pine Script is designed to create a trading indicator with moving averages (MA) and relative strength index (RSI), along with arrow signals and background color changes based on those signals. Here's a description of its functions: 1. Moving Averages and RSI Calculation: - Two moving averages (`fastMA` and `slowMA`) are calculated based on user-input lengths. - The Relative Strength Index (`rsi`) is calculated based on a user-defined length. 2. Crossover Conditions: - `crossoverUp` is true when the fastMA crosses above the slowMA and RSI is above an overbought level. - `crossoverDown` is true when the fastMA crosses below the slowMA and RSI is below an oversold level. 3. Arrow Signals: - Triangle-shaped arrows (`arrowUp` and `arrowDown`) are plotted below and above bars, indicating buy (green) and sell (red) signals, respectively. 4. Background Color Changes: - The background color (`bgColor`) changes based on buy and sell signals. - If there's a buy signal (`crossoverUp`), the background color is set to a light blue with 40% transparency. - If there's a sell signal (`crossoverDown`), the background color is set to a light red with 40% transparency. - On the next opposite signal, the background color is scaled up (transparency set to 80%) to indicate a stronger signal. In summary, this script provides visual cues through arrows and background color changes to assist traders in identifying potential buy and sell signals based on moving average crossovers and RSI conditions. The background color variations aim to highlight the strength of the signal, with scaling based on consecutive signals in the same direction. ******************************************************************************** 1. Buy Signal: - Condition: The arrow points up (green) with a background color indicating a buy signal. - Confirmation: Ensure that there is a strong upward crossover (fastMA above slowMA) and RSI is above the overbought level. 2. Sell Signal: - Condition: The arrow points down (red) with a background color indicating a sell signal. - Confirmation: Ensure that there is a strong downward crossover (fastMA below slowMA) and RSI is below the oversold level. 3. Exit Signal: - Condition: No arrow is present, and the background color is reset. - Confirmation: Confirm that there is no active buy or sell signal. Example Trading Rules: Opening a Long Position (Buy): - Enter a long (buy) position when: - The green arrow appears with a light blue background. - Confirm that the fastMA is above the slowMA. - Confirm that RSI is above the overbought level. Opening a Short Position (Sell): - Enter a short (sell) position when: - The red arrow appears with a light red background. - Confirm that the fastMA is below the slowMA. - Confirm that RSI is below the oversold level. Exiting a Position: - Close the position when: - There is no arrow present (neither green nor red). - The background color is reset, indicating no active signal. Risk Management: Position Sizing: Determine the size of your positions based on your risk tolerance and the size of your trading account. Stop-Loss and Take-Profit: Set stop-loss orders to limit potential losses and take-profit orders to secure profits. Risk-Reward Ratio: Consider maintaining a favorable risk-reward ratio in your trades. Notes: Backtesting: Before applying this strategy in a live market, it's crucial to backtest it using historical data to assess its performance. Market Conditions: Adapt the strategy to different market conditions, and be aware that no strategy is guaranteed to be profitable. Continuous Monitoring: Regularly monitor the performance of the strategy and make adjustments as needed. Educational Purpose: This strategy is for educational purposes only. Always consult with financial professionals and use your judgment when making trading decisions. Remember that trading involves risk, and past performance is not indicative of future results. It's recommended to paper trade or use a demo account to test the strategy before risking real capital. Best wishes on your trading journey! May your strategies be profitable, your risks well-managed, and your decisions guided by wisdom and success. Happy trading!
Penunjuk Pine Script®
oleh ClearTradingMind
2
Customizable Non-Repainting HTF MACD MFI Scalper Bot Strategy v2Customizable Non-Repainting HTF MACD MFI Scalper Bot Strategy v2 This script was originally shared by Wunderbit as a free open source script for the community to work with. This is my second published iteration of this idea. WHAT THIS SCRIPT DOES: It is intended for use on an algorithmic bot trading platform but can be used for scalping and manual trading. This strategy is based on the trend-following momentum indicator . It includes the Money Flow index as an additional point for entry. This is a new and improved version geared for lower timeframes (15-5 minutes), but can be run on larger ones as well. I am testing it live as my high frequency trader. HOW IT DOES IT: It uses a combination of MACD and MFI indicators to create entry signals. Parameters for each indicator have been surfaced for user configurability. Take profits are now trailing profits, and the stop loss is now fixed. Why? I found that the trailing stop loss with ATR in the previous version yields very good results for back tests but becomes very difficult to deploy live due to transaction fees. As you can see the average trade is a higher profit percentage than the previous version. HOW IS MY VERSION ORIGINAL: Now instead of using ATR stop loss, we have a fixed stop loss - counter intuitively to what some may believe this performs better in live trading scenarios since it gives the strategy room to move. I noticed that the ATR trailing stop was stopping out too fast and was eating away balance due to transaction fees. The take profit on the other hand is now a trailing profit with a customizable deviation. This ensures that you can have a minimum profit you want to take in order to exit. I have depracated the old ATR trailing stop as it became too confusing to have those as different options. I kept the old version for others that want to experiment with it. The source code still requires some cleanup, but its fully functional. I added in a way to show RSI values and ATR values with a checkbox so that you can use the new an improved ATR Filter (and grab the right RSI values for the RSI filter). This will help to filter out times of very low volatility where we are unlikely to find a profitable trade. Use the "Show Data" checkbox to see what the values are on the indicator pane, then use those values to gauge what you want to filter out. Both versions Delayed Signals : The script has been refactored to use a time frame drop down. The higher time frame can be run on a faster chart (recommended on one minute chart for fastest signal confirmation and relay to algotrading platform.) Repainting Issues : All indicators have been recoded to use the security function that checks to see if the current calculation is in realtime, if it is, then it uses the previous bar for calculation. If you are still experiencing repainting issues based on intended (or non intended use), please provide a report with screenshot and explanation so I can try to address. Filtering : I have added to additional filters an ABOVE EMA Filter and a BELOW RSI Filter (both can be turned on and off) Customizable Long and Close Messages : This allows someone to use the script for algorithmic trading without having to alter code. It also means you can use one indicator for all of your different alterts required for your bots. HOW TO USE IT: It is intended to be used in the 5-30 minute time frames, but you might be able to get a good configuration for higher time frames. I welcome feedback from other users on what they have found. Find a pair with high volatility (example KUCOIN:ETH3LUSDT ) - I have found it works particularly well with 3L and 3S tokens for crypto. although it the limitation is that confrigurations I have found to work typically have low R/R ratio, but very high win rate and profit factor. Ideally set one minute chart for bots, but you can use other charts for manual trading. The signal will be delayed by one bar but I have found configurations that still test well. Select a time frame in configuration for your indicator calculations. Select the strategy config for time frame (resolution). I like to use 5 and 15 minutes for scalping scenarios, but I am interested in hearing back from other community memebers. Optimize your indicator without filters : customize your settings for MACD and MFI that are profitable with your chart and selected time frame calculation. Try different Take Profits (try about 2-5%) and stop loss (try about 5-8%). See if your back test is profitable and continue to optimize. Use the Trend, RSI, ATR Filter to further refine your signals for entry. You will get less entries but you can increase your win ratio. You can use the open and close messages for a platform integration, but I choose to set mine up on the destination platform and let the platform close it. With certain platforms you cannot be sure what your entry point actually was compared to Trading View due to slippage and timing, so I let the platform decide when it is actually profitable. Limitations: this works rather well for short term, and does some good forward testing but back testing large data sets is a problem when switching from very small time frame to large time frame. For instance, finding a configuration that works on a one minute chart but then changing to a 1 hour chart means you lose some of your intra bar calclulations. There are some new features in pine script which might be able to address, this, but I have not had a chance to work on that issue.
Strategi Pine Script®
oleh MauricioZuniga
14
Short Selling EMA Cross (By Coinrule)BINANCE:AVAXUSDT This short selling script works best in periods of downtrends and general bearish market conditions, with the ultimate goal to sell as the the price decreases further and buy back before a rebound. This script can work well on coins you are planning to hodl for long-term and works especially well whilst using an automated bot that can execute your trades for you. It allows you to hedge your investment by allocating a % of your coins to trade with, whilst not risking your entire holding. This mitigates unrealised losses from hodling as it provides additional cash from the profits made. You can then choose to to hodl this cash, or use it to reinvest when the market reaches attractive buying levels. Entry The exponential moving average ( EMA ) 20 and EMA 50 have been used for the variables determining the entry to the short. EMAs can operate better than simple moving averages due to the additional weighting placed on the most recent data points, whereas simple moving averages weight all the data the same. This means that price is tracked more closely and the most recent volatile moves can be captured and exploited more efficiently using EMAs. Our backtesting data revealed that the most profitable timeframe was the 30-minute timeframe, this also enabled a good frequency of trades and high profitability. A fast (shorter term) exponential moving average , in this strategy the EMA 20, crossing under a slow (longer term) moving average, in this example the EMA 50, signals the price of an asset has started to trend to the downside, as the most recent data signals price is declining compared to earlier data. The entry acts on this principle and executes when the EMA 20 crosses under the EMA 50. Enter Short: EMA 20 crosses under EMA 50. Exit This script utilises a take profit and stop loss for the exit. The take profit is set at -8% and the stop loss is set at +16% from the entry price. This would normally be a poor trade due to the risk:reward equalling 0.5. However, when looking at the backtesting data, the high profitability of the strategy (93.33%) leads to increased confidence and showcases the high probability of success according to historical data. The take profit (-8%) and the stop loss (+16%) of the strategy are widely placed to ensure the move is captured without being stopped out due to relief rallies. The stop loss also plays a role of mitigating losses and minimising risk of being stuck in a short position once there has been a fundamental trend reversal and the market has become bullish . Exit Short: -8% price decrease from entry price. OR Exit Short: +16% price increase from entry price. Tip: Research what coins have consistent and large token unlocks / highly inflationary tokenomics, and target these during bear markets to short as they will most likely have substantial selling pressure that outweighs demand - leading to declining prices. The strategy assumes each order is using 30% of the available coins to make the results more realistic and to simulate you only ran this strategy on 30% of your holdings. A trading fee of 0.1% is also taken into account and is aligned to the base fee applied on Binance. The backtesting data was recorded from December 1st 2021, just as the market was beginning its downtrend. We therefore recommend analysing the market conditions prior to utilising this strategy as it operates best on weak coins during downtrends and bearish conditions.
Strategi Pine Script®
oleh Coinrule
Grover Llorens Activator Strategy AnalysisThe Grover Llorens Activator is a trailing stop indicator deeply inspired by the parabolic SAR indicator, and aim to provide early exit points and reversal detection. The indicator was posted not so long ago, you can find it here : Today a strategy using the indicator is proposed, and its profitability is analyzed on 3 different markets with the main time frame being 1 hour, remember that lower time frames involve lower absolute price changes, therefore we are way more affected by the spread, and we can require a larger position sizing depending on our investment target, trading higher time-frames is always a good practice and this is why 1 hour is selected. Based on the result we might make various conclusions regarding the indicator accuracy and might have ideas on future improvements of the indicator. I'am not great when it comes to strategy design, i still hope to share correct and useful information in this post, let me know your thoughts on the post format and if i should make more of these. Setup And Rules The analysis is solely based on the indicator signals, money management isn't taken into account, this allow us to have an idea on the indicator robustness and resilience, particularly on extremely volatile markets and ones exhibiting a chaotic structure, altho it is normally good practice to close any position before a market closure in order to avoid any potential major gaps. The settings used are 480 for length and 14 for mult, this create relatively mid term signals that are suited for a trend indicator such as the Grover Llorens Activator, unfortunately we can't infer the indicator optimal settings, thats how it is with any technical indicator anyway. Here are the rules of our strategy : long : closing price cross over the indicator short : closing price cross under the indicator We use constant position sizing, once a signal is triggered all the previous positions are closed. Description Of The Statistics Used Various statistics are presented in this post, here is a brief description of the main ones : Percent Profitability (higher = better): Percentage of winning trades, that is : winning trades/total number of trades × 100 Maximum Drawdown (lower = better) : The highest difference between a peak and a valley in the balance, that is : peak - valley , in percentage : (peak - valley)/peak × 100 Profit Factor (higher = better) : Gross profit divided by gross loss, values under 1 represent gross losses superior to the gross profits Remember that more volatility = more risk, since higher absolute price changes can logically cause larger losses. EURUSD The first market analyzed is the Forex market with the EURUSD major pair with a position sizing of 1000 units (1 micro lot). Since October EURUSD is not showing any particular strong trend but posses a discrete rising motion, fortunately cycles can be observed. The equity was rising until two trades appeared causing a decline in the equity. Before October a bearish market could be observed. We can see that the equity is rising, the trend still posses various retracements that affect our indicator, however we can see that the indicator totally nail the end of the trend, thats the power of converging toward the price. In short : $ 86.63 net profit 340 closed trades 37.65 % profitable (thats a lot of loosing trades) 1.19 profit factor $ 76.67 max drawdown Applying a spread would create negative results (in general the average spread is used), not a great start... BTCUSD The cryptocurrency market is relatively more volatile than others, which also mean potentially higher returns, we test the indicator using certainly the most traded cryptocurrency, BTCUSD. We will use a position sizing of 1 unit. In the case of BTCUSD the strategy balance is relatively stationary around the initial capital, with of course high dispersion. from september to december the market is bearish with various ranging periods, no apparent cycles can be observed, except maybe in the ranging period of october, this ranging period is followed by a non linear trend (relatively parabolic) that the indicator failed to capture in its integrity (this is a recurrent problem and it is starting to piss me off xD). In short : $ 2010.64 net profit (aka how i bet the crypto market) 395 closed trades 38.23 % profitable 1.036 profit factor $ 5738.01 max drawdown (aka how i lost to the crypto market) AMD AMD stand for Advanced Micro Devices and is a company focused on the development of computer technology, i love the microprocessor market and i really like AMD who start this year in a pretty great way with a net bullish trend. The performance of the indicator on AMD is decent (at last !) with the equity producing many new higher highs. The indicator performance still drop in the middle end of 2019 with a large equity drawdown of 17$ caused by the gap of august 8. Unfortunately AMD, like lot of well behaving stocks can only tells us that the indicator has good performances on heavily trending markets with no excess of noise or chaotic structures. In short : $ 17.86 net profit (Enough for a consistent lunch) 295 closed trades 36.27 % profitable 1.414 profit factor $ 10.37 max drawdown. Conclusion A strategy using the recently proposed Grover Llorens activator has been presented. We can easily conclude that the indicator can't possibly generate long term returns under chaotic and volatile markets, and could even produce unnecessary trades in trending markets without much parasitic fluctuations such as noise and retracements (think about a simple linear trend) since the indicator converge toward the price and would therefore automatically cross over/under the trend, thus guaranteeing a false signal. However we have seen its ability to provide accurate early reversal detection shine from time to time, thus over performing lagging indicators in this aspect, however the duration of price fluctuations isn't fixed at a certain period, the rate of convergence should be way faster during volatile fluctuations, of moderate speed during more cyclic fluctuations, and really slow with apparent long term trends, this could be achieved by making the indicator adaptive, but it won't really make it necessarily perform better. That said i still believe that converging trend indicators are really interesting and aim to capture the non lasting behavior of price fluctuations, they shouldn't receive so much hate (think about the poor p-sar). Thanks for reading !
Strategi Pine Script®
oleh alexgrover
20
Red and Green Ignored Bar by Oliver VelezOn this occasion I present a script that detects Ignored Red Candles and Ignored Green Candles, basically it is a Price Action event that indicates a possible continuation of the current trend and gives the opportunity to climb it with a Very tight risk, before delving into detail I would like to leave this note: Note: the detection of this event does not guarantee that the signal will be good, the trader must have the ability to determine its quality based on aspects such as trend, maturity, support / resistance levels, expansion / contraction of the market, risk / benefit, etc, if you do not have knowledge about this you should not use this indicator since using it without a robust trading plan and experience could cause you to partially or totally lose your money, if this is your case you should train before If you try to extract money from the market, this script was created to be another tool in your trading plan in order to configure the rules at your discretion, execute them consistently and have AUTOMATIC ALERTS when the event occurs, which is where I find more value because you can have many instruments waiting for the event to be generated, in the time frame you want and without having to observe the mer When the alert is generated, the Trader should evaluate the quality of the alert and define whether or not to execute it (higher timeframes, they can give you more time to execute the operation correctly). Let's continue…. This event was created by Oliver Velez recognized trader / mentor of price action, the event has a very interesting particularity since it allows to take a position with a very limited risk in trend movements, this achieves favorable operations of good ratio and small losses when taking An adjusted risk, if the trade works, a good ratio is quickly achieved and we agree with a key point in the “Keep small losses and big profits” trading, this makes it easier to have a positive mathematical hope when your level of Success is not very high, so leave you in the field of profitability. THE EVENT: The event has a bullish configuration (Ignored Red Candle) and a bearish configuration (Ignored Green Candle), below I detail the “Hard” rules (later I explain why “Hard”): 1- Last 3 bars have to be GREEN-RED-GREEN (possible bullish configuration) or RED-GREEN-RED (possible bearish configuration), the first bar is called Control Bar, the second is called Ignored Bar and the third Signal Bar as shown in the following image: 2- Be in a trend determined by simple moving averages (Slow of 20 periods and Fast of 8 periods), as a general rule you can take the direction of MA20 but the Trader has to determine if there is a trend movement or not. 3- Control bar of good range, little tail and with a body greater than 55%. 4- Ignored bar preferably narrow range, little tail and that is located in the upper 1/3 of the control bar. 5- Signal bar cannot override the minimum of the ignored bar. 6- Activation / Confirmation of event by means of signal bar in overcoming the body of the ignored bar. Some examples of ignored bars (with “Hard” and “Flexible” rules): Features and configuration of the indicator: To access the indicator settings, press the wheel next to the indicator name VVI_VRI "Configuration options". - Operation mode (Filtering Type): • Filtering Complete: all filters activated according to the configuration below. • Without Filtering: all filters deactivated, all VRI / VVI are displayed without any selection criteria. • Trend Filter only: shows only VRI / VVI that are in accordance with what is set in “Trend Settings” - Configuration Moving Averages: • See Slow Media: slow moving average display with direction detection and color change. • See Fast Media: display of fast moving average with direction detection and color change. • Type: possibility to choose the type of media: DEMA, EMA, HullMA, SMA, SSMA, SSMA, TEMA, TMA, VWMA, WMA, ZEMA) • Period: number of previous bars. • Source: possibility to choose the type of source, open, close, high, low, hl2 hlc3, ohlc4. • Reaction: this configuration affects the color change before a change of direction, 1 being an immediate reaction and higher values, a more delayed reaction obtaining les false "changes of direction", a value of 3 filters the direction quite well. - Trend Configuration • Uptrend Condition P / VRI: possibility to select any of these conditions: o Bullish MA direction o Quick bullish MA direction o Slow and fast bullish MA direction o Price higher than slow MA o Price higher than fast MA o Price higher than slow and fast MA o Price higher than slow MA and bullish direction o Price higher than fast MA and bullish direction o Price higher than slow, fast MA and bullish direction o No condition • Condition P / VVI bear trend: possibility of selecting any of these conditions: o Slow bearish MA direction o Fast bearish MA direction o Slow and fast bearish MA direction o Price less than slow MA o Price less than fast MA o Price less than slow and fast MA o Price lower than slow MA and bearish direction o Price less than fast MA and bearish direction o Price less than slow, fast MA and bearish direction o No condition - Control bar configuration • Minimum body percentage%: possibility to select what body percentage the bar must have. • Paint control bar: when selected, paint the control bar. • See control bar label: when selected, a label with the legend BC is plotted. - Configuration bar ignored • Above X% of the control bar: possibility to select above what percentage of the control bar the ignored bar must be located. • Paint ignored bar: when selected, paint the ignored bar. - Signal bar configuration • You cannot override the minimum of the ignored bar: when selected, the condition is added that the signal bar cannot override the minimum of the ignored bar. • Paint signal bar: when selected, paint the signal bar. • See arrow: when selected it shows the direction arrow of the possible movement. • See bear and arrow: when selected it shows bear and arrow label • See bull and arrow: when selected it shows bull and arrow label The following image shows the ignored bar and painted signal: - Take profit / loss The profit / loss taking varies depending on the trader and its risk / monetary plan, the proposal is a recommendation based on the nature of the event that is to have a small risk unit (stop below the minimum of the ignored bar), look for objectives in ratios greater than 2: 1 and eliminate the risk in 1: 1 by taking the stop to BE, all parameters are configurable and are the following: • See recommended stop loss and take profit: trace the levels of Stop, BE, TP1 and TP2, as well as their prices to know them quickly based on the assumed risk • To: select which event you want to draw the SL and TP (VRI, VVI) • Extend stop loss line x bars: allows extending the stop line by x number of bars • Extend take profit line x bars: allows extending the stop line by x number of bars • Ratio to move to break even: allows you to select the minimum ratio to move stop to break even (default 1: 1) • Take profit 1 ratio: allows you to select the ratio for take profit 1 (default 2: 1) • Take profit 2 ratio: allows you to select the ratio for take profit 2 (default 4: 1) - Alerts • It is possible to configure the following alerts: -VRI DETECTED -VVI DETECTED -VRI / VVI DETECTED Final Notes: - The term hard rules refers to the fact that an event is sought with the rules detailed above to obtain a high quality event but this brings 2 situations to consider, less number of events and events that are generated in a strong impulse may be leaked, a very large control bar followed by an ignored narrow body away from moving averages, despite having a good chance of continuing, taking a stop very tight in a strong impulse you can touch it by the simple fact of the own volatility at that time. - The setting of the parameters “Minimum body percentage% (control bar)”, “Above x% of the control bar (bar ignored)” and “Cannot override the minimum of the ignored bar” can bring large Benefits in terms of number of events and that can also be of high quality, feel free to find the best configuration for your instrument to operate. - It is recommended to look for trending events, near moving averages and at an early stage of it. - The display of several nearby VRIs or VVIs in an advanced trend may indicate a depletion of it. - The alerts can be worked in 2 ways: at the closing of the candle (confirms event but the risk unit may be larger or smaller) or immediately the body of the ignored bar is exceeded, in case you are operating from the mobile and miss many events because of the short time I recommend that you operate in a superior time frame to have more time. - The indicator is configured with “flexible” rules to have more events, but without any important criteria, each trader has to look for the best configuration that suits his instrument. - It is recommended to partially close the operation based on the ratio and always keep a part of the position to apply manual trailing stop and try to maximize profits. The code is open feel free to use and modify it, a mention in credits is appreciated. If you liked this SCRIPT THUMB UP! Greetings to all, I wish you much green!
Penunjuk Pine Script®
oleh Dreadblitz
36
Complete Trend Trading System [Fhenry0331]This system was designed for the beginner trader to make money swing trading. Your losses will be small and your gains will be mostly large. You will show consistent profit. Period. The system works on any security you like to trade. I used GBPUSD as an example because of the up swing and down swing it had recently. I tried to put as much information of how the system works in the chart. Hope it helps and is not to cluttered. I will reiterate how the system works here: Everything is based off of closed price. Legend Uptrend: Buy Green bar: initial start of an uptrend or uptrend continuing. Place order above that bar. If the initial bar does not stray too far from the MVWAP , I will place orders above subsequent bars if no filled occurred. If initial start of the trend is missed, I will wait for the pullback. A pullback is a close below the MVWAP, and a close above the EMA (Low), RSI is above 50. Orders are placed above the pullback bars with plotted char "B" and also plotted green triangle up. Again orders are placed above those bars. the bars do not notate automatic buys. Don't chase anything. You will miss the initial bar on something because of news or earnings and it rocket up. Just wait, it will pullback. If it doesn't, to hell with it, on to the next. Take profits: In the indicator you will see "T." That notates to take some profits. It is a suggestion. I was always told to take profits into spikes, as well as you can never lose money if you take profits. Up to you if you want to scale out and take the suggested profits or not. Exit Completely: In an uptrend, close your entire position on bars colored yellow or red. (Again, closed bars) In uptrend bars colored orange and black, do nothing, they are just pullback bars. Look for the buy pullback signal, then follow pullback buy rules for an uptrend. Downtrend: Short Red bar: initial start of a downtrend or downtrend continuing. Place order below the bar. If the initial bar does not stray too far fro the MVWAP, place orders below subsequent bars. If initial start on the downtrend is missed, wait for the pullback. A pullback is a close above the MVWAP, and close below the EMA(Low). RSI is below 50. Orders are placed below the pullback bars with the plotted char "S" and also plotted red triangle. Again those bars are not automatic shorts, orders are placed below them. Don't chase anything. Wait for price to come into your plan. The idea FOMO is the stupidest thing ever, how can you miss out on something when it is always there. The market is always there and something will come into your zone. Chill. "T": same as in uptrend, suggestion to take some profits. Exit Completely: In a downtrend, close your entire position on bars colored orange or green. In downtrend you will see bars colored yellow and black, do nothing, they are pullback bars. Look for the pullback short signal and follow pullback short rules. If you have any questions get at me. Take a look at it on what you trade. Flip it through different securities. Best of luck in all you do. P.S. You should not take a trade right before earnings. You should also exit a trade right before earnings.
Penunjuk Pine Script®
oleh NYBKLYN
36
T7 JNSARUpdated code for the T7 JNSAR system earlier published here - Following updates made to the code 1. Buy / Sell arrows now appear when the corresponding conditions are met. 2. Support for Heikin-Ashi Candles added 3. Different Backtesting Position Sizing Algorithms added for evaluation Also am republishing the trading rules here again with some modification 1. Go Long when the daily close is above the JNSAR line. Go Short when the daily close is below the JNSAR line. JNSAR line is the varying green line overlayed over the price chart. Once a signal comes at market close enter in the direction of the signal @ market price @ next day market open. 2. Trade only Nifty Index. This system was developed and backtested only for NIFTY Index. So trade in its Futures or Options, as you may deem fit. My recommendation is to choose futures for simplicity. If you want to reduce the trading cost and go with options, trade with deep in the money options, preferably 2 strikes far from the spot price. 3. Trade all signals. Markets trend only 30-35% of the time and hence the system is only accurate to that extend. But system tends to make enough money, in this small trending window, to keep the overall profitability in good health. But one never knows when a big trend may come and when it comes its absolutely imperative that you take it. To ensure that, trade all signals and don't be choosy about what signals you are going to trade. Also I wouldn't recommend using your own analysis to trade this system. Too many drivers will crash the car. 4. Like all trend following systems, this system will have many whipsaws during flat markets along with large trade and account drawdowns. Also some months and even years may not be profitable. But to trade this system profitably, it is necessary to take these in one's stride and keep trading. As the backtester results from 1990 to 2016 proves, this system is profitable overall thus far. Take confidence from that objective fact. 5. Trade with only that amount of money you can afford to loose. Initial capital that you need to have to trade one lot of NIFTY should be atleast - (Margin Money required to take and hold 1 lot position + maximum drawdown amount per lot)*1.2. Be prepared to add more if need be, but the above formula will give a rough idea of what you need to have to start trading and be in the game always. 6. Place an After Market Order @ Market Price with your broker after market close so that you get to execute the trade next trading day @ Market open to capture near similar price as the daily open price seen on the chart. This execution mode will give you the best chance to minimise the slippage and mimic the backtester results as closely as practically possible. 7. Follow all the 6 rules above religiously, as if your life depends on it. If you cant, then don't trade this system; You will certainly loose money. Happy Trading !!! As always am looking out for your valuable feedback.
Strategi Pine Script®
oleh syam-mohan-vs
8