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Cari skrip untuk "Exponential Moving Average"

RV-Scalping 34EAV Channel Works well with 1/3/5/15 min & above //34 Exponential Moving Average of the Close //34 Exponential Moving Average of the High //34 Exponential Moving Average of the Low //https://www.forexstrategiesresources.com/scalping-forex-strategies/106-1-min-scalping-with-34-exponential-moving-average-channel/ // When price is above the MAs (Moving Averages) we are only looking to buy as price comes back to the MAs. // And when price is below the MAs, we are only looking to sell when price comes back to the MAs // What we’re looking for when price pulls back to the MAs is for it to hold and then show that it is going to continue. // We look for this continuation signal in terms of a strong, momentumdriven bar. // 1) – Wait for pullback // 2) – Enter when momentum comes into market // 3) – Exit when momentum slows // When the market has already moved a significant amount that day – Lets not enter in the same direct expecting a further move.

Indikator Pine Script®

Diupdate

Phicube EMASAR ( EMA Support and Resistance )Indicator based on the Concept created by Bo Williams. But unlike the original that uses MIMAs, EMAs are used here. Exponential moving averages will be shown according to fractal alignment, in order to show the important support and resistance levels ( SAR ). When the fractals are aligned to become support, we will have the EMA in the graph with a bright color. When the fractals are aligned to become resistance, we will have EMA in the graph with a matte color. Available exponential moving averages: 17,34,72,144,305,610,1292 and 2584 ____________________________________________________//_____________________________________________________________________ Indicador baseado no Conceito criado pelo Bo Williams. Mas diferente do original que utiliza MIMAs, aqui é utilizado EMAs. As médias móveis exponenciais serão mostradas de acordo com o alinhamento dos fractais, com objetivo de mostrar os níveis importantes de suporte e resistência( SAR ). Quando estiver com os fractais alinhados virando suporte, teremos no gráfico a EMA com uma cor em tom brilhante. Quando estiver com os fractais alinhados virando resistência, teremos no gráfico a EMA com uma cor em tom fosco. Médias móveis exponenciais disponíveis: 17,34,72,144,305,610,1292 e 2584

Indikator Pine Script®

Diupdate

DEMA Strategy with MACD The Double Exponential Moving Average (DEMA) indicator was introduced in January 1994 by Patrick G. Mulloy, in an article in the "Technical Analysis of Stocks & Commodities" magazine: "Smoothing Data with Faster Moving Averages" It attempts to remove the inherent lag associated to Moving Averages by placing more weight on recent values. The name suggests this is achieved by applying a double exponential smoothing which is not the case. The name double comes from the fact that the value of an EMA (Exponential Moving Average) is doubled. To keep it in line with the actual data and to remove the lag the value "EMA of EMA" is subtracted from the previously doubled ema. DEMA is a very responsive system. A lot of signals can be generated only when trading with DEMA. In this strategy, I combined Dema buy-sell signals with MACD indicator. When you activate MACD confirmation from settings; When DEMA comes to long situation, the MACD histogram is checked to be positive.

Strategi Pine Script®

KLemurs Deviation Market: Stocks and ETF's This overlay shows the deviation of the exponential moving average of the mid candle price of the currently loaded chart, away from the exponential moving average of the S&P and DOW combined and averaged mid candle price. The top and bottom lines also give a visual perspective of what a certain percentage (default 1%) looks like on the current charts window. This may help with making quick decisions for things like setting trailing stop trades with a percentage. This can be used for stocks, ETF's, and index's and It may be useful in finding potential stocks or ETF's if you are interested in these kinds of deviations. Defaults are set for a dark screen but can be edited to your taste. It's optimized to be an overlay on the current chart window as opposed to being a separate window. Percentage Lines (editable) This is three lines. The upper line (default green) plots the set percentage (default 1%) above the current chart’s ema. The middle line (default white) plots the current chart’s ema. The lower line (default red) plots the set percentage (default 1%) below the current chart’s ema. Deviation Band (editable) This is the colored band on the overlay between the upper and lower percentage lines. The band’s fill color indicates the deviation of the current charts ema from the ema of the combined S&P and DOW’s ema as follows: - Red (default) = Current Chart’s ema is descending and the S&P/DOW ema is descending OR the Current Chart’s ema is below (underperforming) the S&P/DOW ema. - Orange (default) = The Current Chart and S&P/DOW ema’s are both either ascending or descending together. - Green (default) = The Current Chart’s ema is ascending but the S&P/DOW ema is descending. To Set Line Colors BY default, the upper line color uses the same colors as the ascending band color and the lower line uses the same color as the descending band color. To set the line colors, see "plotColor", "plotColorUp", or" plotColorDown" in variable settings within the script or use the “Central Plot Line”, “Upper Plot Line, or “Lower Plot Line” in the input dialogue to change this. To Set Band Colors To set the band colors, see "plotColor", "plotColorUp", or "plotColorDown" in variable settings within the script or use the “Color0”, “Color1", or “Color2” in the input dialogue to change this. To Set EMA Lookback Period The ema lookback period defaults to 5. This is the number of candles back that the script will use to determine the ema. See “CCemaN” in variable settings within the script or use the “EMA Period” in the input dialogue to change this. To Set Percentage To set the percentage that plots the upper and lower lines, see "CCP" in variable settings within the script or use “Upper/Lower Bands Percentage” in the input dialogue to change this. The default is .01 (or 1%).

Indikator Pine Script®

oleh KevinLemur

Diupdate

Moving Average Cross Alert, Multi-Timeframe (MTF) (by ChartArt)See when two moving averages cross. With the option to choose between four moving average calculations: SMA = simple moving average EMA = exponential moving average (default) WMA = weighted moving average Linear = linear regression The moving averages can be plotted from different time-frames, like e.g. the weekly or 4 hour time-frame using HL2, HLC3 or OHLC4 as price source for the calculation. In addition there is a background color alert and arrows when the moving averages cross each other when the price also rises or falls. And the moving averages are colored depending on their trend direction (if they are trending up or down).

Indikator Pine Script®

3 MA Smart Money System v6 (No Repaint)✅ INDICATOR SPECIFICATIONS 🎯 Moving Average Type SMA – Simple Moving Average EMA – Exponential Moving Average HMA – Hull Moving Average 🔥 Complete Features ✔ 3 moving averages in 1 indicator ✔ SMA/EMA/HMA options ✔ Turn each moving average on/off ✔ Multi-Timeframe (MTF) ✔ Auto Color Trend ✔ MA labels on the chart ✔ Alerts for all moving average combinations ✔ Color fill between moving averages (trend zones) ✔ Automatic MA crossover strategy (Buy/Sell) ✔ Smart Money + Moving Average (major trend filter) ✔ Moving average as automatic support & resistance ✔ NO REPAINT (safe for backtesting & live use) 🧠 SYSTEM LOGIC MA 3 = Smart Money MA (main trend) BUY MA1 crosses UP MA2 Price above MA3 SELL MA1 MA2 crosses down Price below MA3 The MA3 zone is considered dynamic support/resistance. Created by Dr. Trade

Indikator Pine Script®

oleh deddyrinaldo

EMA Bu indikatör; EMA 3, 5, 8, 10, 13, 20, 50, 88, 100 ve 200 periyotlu üstel hareketli ortalamaları tek bir seferde grafiğinize ekler ve her ortalama için ayrı ayrı alarm kurma imkanı sunar. This indicator plots 10 different Exponential Moving Averages (EMA 3, 5, 8, 10, 13, 20, 50, 88, 100, and 200) simultaneously on your chart. It features individual alert conditions for each moving average, allowing you to set specific notifications for price crossovers at any period. Main Features: Multiple EMA Periods: Includes short, medium, and long-term averages. Alerts: Individual alert conditions for all 10 EMA lines. Visuals: Each line is colored differently for easy identification. Price Labels: Values are visible on the right-hand price scale.

Indikator Pine Script®

oleh aslanoglusadik

MATATABI SP Ver.1 Specifications & Features This indicator is a comprehensive tool designed to monitor trend inception (Squeeze), key reversal zones (Smart SR), and market context (Range Analysis) on a single chart. It is updated for Pine Script v6. 1. 10 Moving Averages (MA) Spec: Displays a total of 10 Moving Averages. Defaults: All set to EMA (Exponential Moving Average) with periods 20, 25, 30, 35, 40, 45, 50, 55, 60, 65 (increments of 5). Customization: Period, type (SMA, EMA, SMMA, WMA), color, and width for each line can be adjusted in the settings. Visibility: MA01-MA08 are visible by default; MA09-MA10 are hidden by default. 2. MA Squeeze Detection Alert Feature: Detects when all 10 MAs contract (cluster together) and flatten out, indicating potential energy buildup for a move. Behavior: No background color change on the chart; it functions purely as an Alert condition configurable in TradingView. Logic: Triggered when the spread between the highest and lowest MA is below a specific ATR threshold AND the slope of the longest MA is near zero. 3. Smart Support & Resistance (Smart SR) Zone Display: Draws Support/Resistance as zones (bands) rather than thin lines. The width adapts automatically based on volatility (ATR). Auto-Removal on Break: When price breaks through a zone, the line stops extending to the right automatically. This ensures only currently active/unbroken levels remain visible on the chart. Significance Filter: Filters out minor noise, using a longer Pivot length (15) to identify only significant peaks and troughs. It also prevents drawing duplicate zones near existing ones. Multi-Timeframe (MTF): Can display SR zones from a higher timeframe (e.g., 4H SR on a 15m chart) alongside current timeframe zones. 4. Range Analysis Range Detection: Visualizes consolidation areas (boxes) based on price deviation and volume analysis. Info Dashboard: Displays a table (top-right) showing the range strength and a statistical "Directional Probability (Bullish/Bearish %)" for the potential breakout. Breakout Signals: Highlights the box border and triggers alerts when price breaks out of the detected range. 仕様と特徴このインジケーターは、トレンドの初動（スクイーズ）、重要な反発ポイント（レジサポ）、**現在の相場環境（レンジ解析）**を1つのチャートで監視するための複合ツールです。Pine Script v6に対応しています。 1. 10本の移動平均線 (MA) 仕様: 合計10本の移動平均線を表示します。初期設定: すべて**EMA（指数平滑移動平均線）**で、期間は 20, 25, 30, 35, 40, 45, 50, 55, 60, 65（5刻み）に設定されています。カスタマイズ: 各ラインの期間、種類（SMA, EMA, SMMA, WMA）、色、太さは設定画面から変更可能です。表示制御: MA01〜MA08はデフォルトで表示、MA09〜MA10は非表示設定です。 2. MA収縮（スクイーズ）検知アラート特徴: 10本のMAが密集し、かつ傾きが平坦になった状態（エネルギーが溜まっている状態）を検知します。動作: チャートの背景色は変更せず、アラート通知のみを行う仕様です（設定画面でアラート条件を作成可能）。判定ロジック: 「MA全体の最大幅がATRの一定倍率以下」かつ「長期MAの傾きがほぼゼロ」の場合に検知します。 3. スマート・レジサポ (Smart Support & Resistance) ゾーン表示: 単なる線ではなく、価格帯（ゾーン）として描画されます。幅はボラティリティ（ATR）に基づいて自動調整されます。ブレイクで自動消去: 価格がゾーンを実体でブレイク（上抜け/下抜け）すると、そのラインは「役割を終えた」と判断され、右側への延長が自動的に停止します。これにより、チャート上には「現在有効なレジサポ」のみが残ります。重要度フィルター: 小さな値動きを除外し、目立つ山や谷（Pivot期間15）のみを抽出して描画します。また、既存のラインと近い場合は重複して描画しません。マルチタイムフレーム (MTF): 現在足のレジサポに加え、上位足（例: 15分足チャートに4時間足のレジサポ）を同時に表示可能です。 4. レンジ解析 (Range Analyzer) レンジ検出: 価格の乱高下と出来高を分析し、レンジ相場をボックスで可視化します。情報テーブル: チャート右上に、レンジの強度や、過去の統計に基づいた**「ブレイク方向の確率（強気/弱気 %）」**を表示します。ブレイクアウト: レンジを抜けた際に、ボックスの色を変化させ、アラートを通知します。

Indikator Pine Script®

oleh Hide_FX_Treader

Diupdate

Kalman Exponential SuperTrend The Kalman Exponential SuperTrend is a new, smoother & superior version of the famous "SuperTrend". Using Kalman smoothing, a concept from the EMA (Exponential Moving Average), this script leverages the best out of each and combines it into a single indicator. How does it work? First, we need to calculate the Kalman smoothed source. This is a kind of complex calculation, so you need to study it if you want to know how it works precisely. It smooths the source of the SuperTrend, which helps us smooth the SuperTrend. Then, we calculate "a" where: n = user defined ATR length a = 2/(n+1) Now we calculate the ATR over "n" period. Classical calculation, nothing changed here. Now we calculate the SuperTrend using the Kalman smoothed source & ATR where: kalman = kalman smoothed source ATR = Average True Range m = Factor chosen by user. Upper Band = kalman + ATR * m Lower Band = kalman - ATR * m Now we just smooth it a bit further using the "a" and a concept from the EMA. u1 = Upper Band a bar ago l1 = Lower Band a bar ago u = Upper Band l = Lower Band Upper = u1 * (1-a) + u * a Lower = l1 * (1-a) + u * a When the classical (not Kalman) source crosses above the Upper, it indicates an uptrend. When it crosses below the Lower, it indicates a downtrend. Methodology & Concepts When I took a look at the classical SuperTrend => It was just far too slow, and if I made it faster it was noisy as hell. So I decided I would try to make up for it. I tried the gaussian, bilateral filter, but then I tried kalman and that worked the best, so I added it. Now it was still too noisy and unconsistent, so I revisited my knowledge of concepts and picked the one from the EMA, and it kinda solved it. In the core of the indicator, all it does is combine them in a really simple way, but if you go more deeply you see how it fits the puzzlé really well. It is not about trying out random things´=> but about seeking what it is missing and trying to lessen its bad side. That is the entire point of this indicator => Offer a unique approach to the SuperTrend type, that lessen the bad sides of it. I also added different plotting types, this is so everyone can find their favorite Enjoy Gs! Thanks @BackQuant for making a open source Kalman code <3

Indikator Pine Script®

oleh MisinkoMaster

Volume Flow and Delta Analysis [MarkitTick]💡This comprehensive technical indicator is designed for traders who require a granular view of market participation that goes beyond standard volume bars. By leveraging the advanced "Intrabar Analysis" capabilities of Pine Script, this tool deconstructs every single price candle on your chart into its constituent lower-timeframe components. It effectively "X-rays" the market to determine whether the volume inside a bar was primarily driven by aggressive buying or aggressive selling, providing a definitive read on market sentiment and institutional control. ● Originality and Utility Most standard volume indicators display a simple aggregate total—a single block of volume that fails to distinguish between buying pressure and selling pressure. A high-volume candle could represent a strong breakout, or it could represent a "selling tail" where buyers were absorbed. This script solves that ambiguity. It is not a standard oscillator; it is a quantitative flow analyzer. It reconstructs the "Delta" (the net difference between buying and selling volume) by querying lower-timeframe data (e.g., analyzing 1-minute data inside a 60-minute bar). This allows traders to spot "Hidden Accumulation" (where price is flat but Delta is rising) or "Exhaustion" (where price rises but Delta falls), offering a significant edge in identifying reversals and trend continuations. ● Methodology The script operates through a sophisticated three-stage quantitative process: • Intrabar Data Acquisition The script uses the security_lower_tf function to fetch granular price and volume data from a lower timeframe (automatically detected or user-defined). This allows the script to see what happened "inside" the current chart's bar. • Directional Flow Distribution For every lower-timeframe interval, the script assigns volume to either "Bullish Flow" or "Bearish Flow." If the close is higher than the open on the lower timeframe, the volume is credited to buyers. If the close is lower, it is credited to sellers. This logic is far more accurate than simple "Up/Down" tick data, as it respects price action. • Statistical Volatility Normalization To filter out noise, the script calculates a dynamic baseline using an Exponential Moving Average (EMA) of the absolute Delta values. It then compares the current bar's Delta against this baseline. This generates an "Intensity Score" (measured in Sigma or Standard Deviations). This ensures that a "High Volume" signal is relevant to the current market volatility, rather than relying on fixed, arbitrary thresholds. ● How to Use This tool is designed to be a complete decision-support system. Here is how to interpret its various components: • The Volume Histogram The background histogram displays Total Volume, while the foreground bars show the split between Buying (Teal) and Selling (Red) flow. Use this to gauge the "quality" of a move. A price rally accompanied by high Teal volume is healthy; a rally on low volume or high Red volume is suspect. • The Delta Histogram This plots the net difference. Strong Positive (Green) Delta: Indicates aggressive market buy orders are hitting the ask. Strong Negative (Red) Delta: Indicates aggressive market sell orders are hitting the bid. Divergence: If Price makes a New High but the Delta Histogram makes a Lower High, this is a classic signal of exhaustion and potential reversal. • The Heads-Up Display (HUD) A dashboard table pinned to the chart provides real-time metrics: Session Delta: The cumulative buy/sell pressure for the current trading day. Flow Regime: Clearly states if the market is in "ACCUMULATION" or "DISTRIBUTION." Intensity: Shows how statistically significant the current volume is (e.g., "2.5x" means the volume is 2.5 times the standard deviation, indicating an anomaly). • Visual Signals The script plots triangle markers on top of the chart when the Delta Intensity exceeds the user-defined threshold. Up Triangle (Green): Signals strong institutional buying pressure (Delta > Threshold). Down Triangle (Red): Signals strong institutional selling pressure (Delta < Threshold). ● Inputs and Configuration Lower Timeframe: By default, the script auto-selects the best resolution (e.g., 1-minute data for hourly charts). Users can override this to fine-tune the granularity. Volume MA Length: Defines the lookback period for the volume moving average. Delta Volatility Threshold (Sigma): This is the sensitivity filter for signals. A higher value (e.g., 2.0) results in fewer but more significant signals. A lower value (e.g., 1.0) provides more frequent alerts. Visual Logic: Users can toggle the Dashboard, Delta Histogram, and Moving Averages on or off to suit their charting aesthetic. ● Disclaimer All provided scripts and indicators are strictly for educational exploration and must not be interpreted as financial advice or a recommendation to execute trades. I expressly disclaim all liability for any financial losses or damages that may result, directly or indirectly, from the reliance on or application of these tools. Market participation carries inherent risk where past performance never guarantees future returns, leaving all investment decisions and due diligence solely at your own discretion.

Indikator Pine Script®

oleh MarkitTick

COT Index THE 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.

Indikator Pine Script®

Multi-Timeframe EMA Trend Dashboard with Volume and RSI Filters ═══════════════════════════════════════════════════════════ MULTI-TIMEFRAME EMA TREND DASHBOARD ═══════════════════════════════════════════════════════════ OVERVIEW This indicator provides a comprehensive view of trend direction across multiple timeframes using the classic EMA 20/50 crossover methodology, enhanced with volume confirmation and RSI filtering. It aggregates trend information from six timeframes into a single dashboard for efficient market analysis. The indicator is designed for educational purposes and to assist traders in identifying potential trend alignments across different time horizons. ═══════════════════════════════════════════════════════════ FEATURES ═══════════════════════════════════════════════════════════ MULTI-TIMEFRAME ANALYSIS • Monitors 6 timeframes simultaneously: 1m, 5m, 15m, 1H, 4H, 1D • Each timeframe analyzed independently using request.security() • Non-repainting implementation with proper lookahead settings • Calculates overall trend strength as percentage of bullish timeframes EMA CROSSOVER SYSTEM • Fast EMA (default: 20) and Slow EMA (default: 50) • Bullish: Fast EMA > Slow EMA • Bearish: Fast EMA < Slow EMA • Neutral: Fast EMA = Slow EMA (rare condition) • Visual EMA plots with optional fill area VOLUME CONFIRMATION • Optional volume filter for crossover signals • Compares current volume against moving average (default: 20-period SMA) • Categorizes volume as: High (>1.5x average), Normal (>average), Low (70), oversold (<30), and neutral zones • Used in quality score calculation • Optional display toggle SUPPORT & RESISTANCE DETECTION • Automatic detection using highest/lowest over lookback period (default: 50 bars) • Plots resistance (red), support (green), and mid-level (gray) • Step-line style for clear visualization • Optional display toggle QUALITY SCORING SYSTEM • Rates trade setups from 1-5 stars • Considers: MTF alignment, volume confirmation, RSI positioning • 5 stars: 4+ timeframes aligned + volume confirmed + RSI 50-70 • 4 stars: 4+ timeframes aligned + volume confirmed • 3 stars: 3+ timeframes aligned • 2 stars: Exactly 3 timeframes aligned • 1 star: Other conditions VISUAL DASHBOARD • Clean table display (position customizable) • Color-coded trend indicators (green/red/yellow) • Extended statistics panel (toggleable) • Shows: Trends, Strength, Quality, RSI, Volume, Price Distance ═══════════════════════════════════════════════════════════ TECHNICAL SPECIFICATIONS ═══════════════════════════════════════════════════════════ CALCULATIONS Trend Determination per Timeframe: • request.security() fetches EMA values with gaps=off, lookahead=off • Compares Fast EMA vs Slow EMA • Returns: 1 (bullish), -1 (bearish), 0 (neutral) Trend Strength: • Counts number of bullish timeframes • Formula: (bullish_count / 6) × 100 • Range: 0% (all bearish) to 100% (all bullish) Price Distance from EMA: • Formula: ((close - EMA) / EMA) × 100 • Positive: Price above EMA • Negative: Price below EMA • Warning when absolute distance > 5% ANTI-REPAINTING MEASURES • All request.security() calls use lookahead=barmerge.lookahead_off • Dashboard updates only on barstate.islast • Historical bars remain unchanged • Crossover signals finalize on bar close ═══════════════════════════════════════════════════════════ USAGE GUIDE ═══════════════════════════════════════════════════════════ INTERPRETING THE DASHBOARD Timeframe Rows: • Each row shows individual timeframe trend status • Look for alignment (multiple timeframes same direction) • Higher timeframes generally more significant Strength Indicator: • >66.67%: Strong bullish (4+ timeframes bullish) • 33.33-66.67%: Mixed/choppy conditions • <33.33%: Strong bearish (4+ timeframes bearish) Quality Score: • Higher stars = better confluence of factors • 5-star setups have strongest multi-factor confirmation • Lower scores may indicate weaker or conflicting signals SUGGESTED APPLICATIONS Trend Confirmation: • Check if multiple timeframes confirm current chart trend • Higher agreement = stronger trend confidence • Use for position sizing decisions Entry Timing: • Wait for EMA crossover on chart timeframe • Confirm with higher timeframe alignment • Volume above average preferred • RSI not in extreme zones Divergence Detection: • When lower timeframes diverge from higher • May indicate trend exhaustion or reversal • Requires additional confirmation CUSTOMIZATION EMA Settings: • Adjust Fast/Slow lengths for different sensitivities • Shorter periods = more responsive, more signals • Longer periods = smoother, fewer signals • Common alternatives: 10/30, 12/26, 50/200 Volume Filter: • Enable for higher-quality signals (fewer false positives) • Disable in always-liquid markets or for more signals • Adjust MA length based on typical volume patterns Display Options: • Toggle EMAs, S/R levels, extended stats as needed • Choose dashboard position to avoid chart overlap • Adjust colors for visibility preferences ═══════════════════════════════════════════════════════════ ALERTS ═══════════════════════════════════════════════════════════ AVAILABLE ALERT CONDITIONS 1. Bullish EMA Cross (Volume Confirmed) 2. Bearish EMA Cross (Volume Confirmed) 3. Strong Bullish Alignment (4+ timeframes) 4. Strong Bearish Alignment (4+ timeframes) 5. Trend Strength Increasing (>16.67% jump) 6. Trend Strength Decreasing (>16.67% drop) 7. Excellent Trade Setup (5-star rating) Alert messages use standard placeholders: • {{ticker}} - Symbol name • {{close}} - Current close price • {{time}} - Bar timestamp ═══════════════════════════════════════════════════════════ LIMITATIONS & CONSIDERATIONS ═══════════════════════════════════════════════════════════ KNOWN LIMITATIONS • Lower timeframe data may not be available on all symbols • 1-minute data typically limited to recent history • request.security() subject to TradingView data limits • Dashboard requires screen space (may overlap on small screens) • More complex calculations may affect load time on slower devices NOT SUITABLE FOR • Highly volatile/illiquid instruments (many false signals) • News-driven markets during announcements • Automated trading without additional filters • Markets where EMA strategies don't perform well DOES NOT PROVIDE • Exact entry/exit prices • Stop-loss or take-profit levels • Position sizing recommendations • Guaranteed profit signals • Market predictions ═══════════════════════════════════════════════════════════ BEST PRACTICES ═══════════════════════════════════════════════════════════ RECOMMENDED USAGE ✓ Combine with price action analysis ✓ Use appropriate risk management ✓ Backtest on historical data before live use ✓ Adjust settings for specific market characteristics ✓ Wait for higher-quality setups in important trades ✓ Consider overall market context and fundamentals NOT RECOMMENDED ✗ Using as standalone trading system without confirmation ✗ Trading every signal without discretion ✗ Ignoring risk management principles ✗ Trading without understanding the methodology ✗ Applying to unsuitable markets/timeframes ═══════════════════════════════════════════════════════════ EDUCATIONAL BACKGROUND ═══════════════════════════════════════════════════════════ EMA CROSSOVER STRATEGY The Exponential Moving Average crossover is a classical trend-following technique: • Golden Cross: Fast EMA crosses above Slow EMA (bullish signal) • Death Cross: Fast EMA crosses below Slow EMA (bearish signal) • Widely used since the 1970s in various markets • More responsive than SMA due to exponential weighting MULTI-TIMEFRAME ANALYSIS Analyzing multiple timeframes helps traders: • Identify alignment between short and long-term trends • Reduce false signals from single-timeframe noise • Understand market context across different horizons • Make informed decisions about trade duration VOLUME ANALYSIS Volume confirmation adds reliability: • High volume suggests institutional participation • Low volume signals may indicate false breakouts • Volume precedes price in many market theories • Helps distinguish genuine moves from noise ═══════════════════════════════════════════════════════════ TECHNICAL IMPLEMENTATION ═══════════════════════════════════════════════════════════ CODE STRUCTURE • Organized in clear sections with proper commenting • Uses explicit type declarations (int, float, bool, color, string) • Constants defined at top (BULLISH=1, BEARISH=-1, etc.) • Functions documented with @function, @param, @returns • Follows PineCoders naming conventions (camelCase variables) PERFORMANCE OPTIMIZATION • var keyword for table (created once, not every bar) • Calculations cached where possible • Dashboard updates only on last bar • Minimal redundant security() calls SECURITY IMPLEMENTATION • Proper gaps and lookahead parameters • No future data leakage • Signals finalize on bar close • Historical bars remain static ═══════════════════════════════════════════════════════════ VERSION INFORMATION ═══════════════════════════════════════════════════════════ Current Version: 2.0 Pine Script Version: 5 Last Updated: 2024 Developed by: Zakaria Safri ═══════════════════════════════════════════════════════════ SETTINGS REFERENCE ═══════════════════════════════════════════════════════════ EMA SETTINGS • Fast EMA Length: 1-500 (default: 20) • Slow EMA Length: 1-500 (default: 50) VOLUME & MOMENTUM • Use Volume Confirmation: true/false (default: true) • Volume MA Length: 1-500 (default: 20) • Show RSI Levels: true/false (default: true) • RSI Length: 1-500 (default: 14) PRICE ACTION FEATURES • Show Price Distance: true/false (default: true) • Show Key Levels: true/false (default: true) • S/R Lookback Period: 10-500 (default: 50) DISPLAY SETTINGS • Show EMAs on Chart: true/false (default: true) • Fast EMA Color: customizable (default: cyan) • Slow EMA Color: customizable (default: orange) • EMA Line Width: 1-5 (default: 2) • Show Fill Between EMAs: true/false (default: true) • Show Crossover Signals: true/false (default: true) DASHBOARD SETTINGS • Position: Top Left/Right, Bottom Left/Right • Show Extended Statistics: true/false (default: true) ALERT SETTINGS • Alert on Multi-TF Alignment: true/false (default: true) • Alert on Trend Strength Change: true/false (default: true) ═══════════════════════════════════════════════════════════ RISK DISCLAIMER ═══════════════════════════════════════════════════════════ This indicator is provided for educational and informational purposes only. It should not be considered financial advice or a recommendation to buy or sell any security. IMPORTANT NOTICES: • Past performance does not indicate future results • All trading involves risk of capital loss • No indicator guarantees profitable trades • Always conduct independent research and analysis • Use proper risk management and position sizing • Consult a qualified financial advisor before trading • The developer assumes no liability for trading losses By using this indicator, you acknowledge that you understand these risks and accept full responsibility for your trading decisions. ═══════════════════════════════════════════════════════════ SUPPORT & CONTRIBUTIONS ═══════════════════════════════════════════════════════════ FEEDBACK WELCOME • Constructive comments appreciated • Bug reports help improve the indicator • Feature suggestions considered for future versions • Share your experience to help other users OPEN SOURCE This code is published as open source for the TradingView community to: • Learn from the implementation • Modify for personal use • Understand multi-timeframe analysis techniques If you find this indicator useful, please consider: • Leaving a thoughtful review • Sharing with other traders who might benefit • Following for future updates and releases ═══════════════════════════════════════════════════════════ ADDITIONAL RESOURCES ═══════════════════════════════════════════════════════════ RECOMMENDED READING • TradingView Pine Script documentation • PineCoders community resources • Technical analysis textbooks on moving averages • Multi-timeframe trading strategy guides • Risk management principles RELATED CONCEPTS • Trend following strategies • Moving average convergence/divergence • Multiple timeframe analysis • Volume-price relationships • Momentum indicators ═══════════════════════════════════════════════════════════ Thank you for using this indicator. Trade responsibly and continue learning! ═══════════════════════════════════════════════════════════

Indikator Pine Script®

oleh zakariasafri47

ADX - Globx Options & Futures 2.0 The ADX Globx Options & Futures is a custom-built trend strength indicator designed to replicate and enhance the classic Average Directional Index (ADX) model, commonly used in professional trading platforms such as IQ Option. This version is optimized for options and futures trading, providing precise directional strength readings through adaptive smoothing and configurable parameters. Concept and Logic This indicator measures the strength of the current trend, regardless of its direction (bullish or bearish), by comparing directional movement between price highs and lows over a defined period. It uses three main components: +DI (Positive Directional Indicator): represents bullish strength. –DI (Negative Directional Indicator): represents bearish strength. ADX (Average Directional Index): measures the intensity of the prevailing trend, independent of direction. The script follows the original logic proposed by J. Welles Wilder Jr., but introduces enhanced smoothing flexibility. Users can choose between EMA (Exponential Moving Average) and Wilder’s RMA (Running Moving Average) for both DI and ADX calculations, allowing closer alignment with various platform implementations (IQ Option, MetaTrader, etc.). How It Works Directional Movement Calculation The script computes upward and downward movements (+DM and –DM) by comparing the differences in highs and lows between consecutive candles. Only positive directional changes that exceed the opposite side are considered. This ensures each bar contributes only one valid directional movement. True Range and Smoothing The True Range (TR) is calculated using ta.tr(true) to include price gaps—replicating how professional derivatives platforms account for volatility jumps. Both TR and DM values are smoothed using the selected averaging method (EMA or Wilder). Directional Index and ADX The smoothed +DI and –DI values are normalized over the True Range to form the Directional Index (DX), which measures the percentage difference between the two. The ADX is then derived by smoothing the DX values, providing a stable reading of overall market strength. Visual Representation The ADX (white line) indicates the overall trend strength. The +DI (dark blue) and –DI (dark red) lines show which side (bullish or bearish) is currently dominant. Reference levels at 20 and 25 serve as strength thresholds: Below 20 → Weak or sideways market. Above 25 → Strong and directional trend. Usage and Interpretation When ADX rises above 25, the market shows a strong trend — use +DI > –DI for bullish confirmation, or the opposite for bearish momentum. A falling ADX suggests decreasing trend strength and potential consolidation. The default parameters (ADX Length = 34, DI Length = 34, both smoothed by EMA) match IQ Option’s internal ADX configuration, ensuring consistency between platforms. Works on any timeframe or asset class, but is especially tuned for futures and options volatility dynamics. Originality and Improvements Unlike many open-source ADX indicators, this version: Recreates IQ Option’s 34-length EMA-based ADX calculation with exact parameter alignment. Provides selectable smoothing algorithms (EMA or Wilder) to switch between modern and classic formulations. Uses dark-theme-optimized visuals with fine line weight and subtle contrast for clean visibility. Maintains constant guide levels (20/25) rendered globally for precision and style compliance in Pine Script v6. Is fully rewritten for Pine Script v6, ensuring compatibility and optimized execution. Recommended Use Combine with trend-following systems or breakout strategies. Ideal for identifying market strength before engaging in options directionals or futures entries. Use the ADX to confirm breakout momentum or filter sideways markets. Disclaimer This script is for educational and analytical purposes. It does not constitute financial advice or a trading signal. Users are encouraged to validate the indicator within their own trading strategies and risk frameworks.

Indikator Pine Script®

oleh enoccv_gxof

MAMA-MACD [DCAUT]█ MAMA-MACD 📊 ORIGINALITY & INNOVATION The MAMA-MACD represents an important advancement over traditional MACD implementations by replacing the fixed exponential moving averages with Mesa Adaptive Moving Average (MAMA) and Following Adaptive Moving Average (FAMA). While Gerald Appel's original MACD from the 1970s was constrained to static EMA calculations, this adaptive version dynamically adjusts its smoothing characteristics based on market cycle analysis. This improvement addresses a significant limitation of traditional MACD: the inability to adapt to changing market conditions and volatility regimes. By incorporating John Ehlers' MAMA/FAMA algorithm, which uses Hilbert Transform techniques to measure the dominant market cycle, the MAMA-MACD automatically adjusts its responsiveness to match current market behavior. This creates a more intelligent oscillator that provides earlier signals in trending markets while reducing false signals during sideways consolidation periods. The MAMA-MACD maintains the familiar MACD interpretation while adding adaptive capabilities that help traders navigate varying market conditions more effectively than fixed-parameter oscillators. 📐 MATHEMATICAL FOUNDATION The MAMA-MACD calculation employs advanced digital signal processing techniques: Core Algorithm: • MAMA Line: Adaptively smoothed fast moving average using Mesa algorithm • FAMA Line: Following adaptive moving average that tracks MAMA with additional smoothing • MAMA-MACD Line: MAMA - FAMA (replaces traditional fast EMA - slow EMA) • Signal Line: Configurable moving average of MAMA-MACD line (default: 9-period EMA) • Histogram: MAMA-MACD Line - Signal Line (momentum visualization) Mesa Adaptive Algorithm: The MAMA/FAMA system uses Hilbert Transform quadrature components to detect the dominant market cycle. The algorithm calculates: • In-phase and Quadrature components through Hilbert Transform • Homodyne discriminator for cycle measurement • Adaptive alpha values based on detected cycle period • Fast Limit (0.1 default): Maximum adaptation rate for MAMA • Slow Limit (0.05 default): Maximum adaptation rate for FAMA Signal Processing Benefits: • Automatic adaptation to market cycle changes • Reduced lag during trending periods • Enhanced noise filtering during consolidation • Preservation of signal quality across different timeframes 📊 COMPREHENSIVE SIGNAL ANALYSIS The MAMA-MACD provides multiple layers of market analysis through its adaptive signal generation: Primary Signals: • MAMA-MACD Line above zero: Indicates positive momentum and potential uptrend • MAMA-MACD Line below zero: Suggests negative momentum and potential downtrend • MAMA-MACD crossing above Signal Line: Bullish momentum confirmation • MAMA-MACD crossing below Signal Line: Bearish momentum confirmation Advanced Signal Interpretation: • Histogram Expansion: Strengthening momentum in current direction • Histogram Contraction: Weakening momentum, potential reversal warning • Zero Line Crosses: Important momentum shifts and trend confirmations • Signal Line Divergence: Early warning of potential trend changes Adaptive Characteristics: • Faster response during clear trending conditions • Increased smoothing during choppy market periods • Automatic adjustment to different volatility regimes • Reduced false signals compared to traditional MACD Multi-Timeframe Analysis: The adaptive nature allows consistent performance across different timeframes, automatically adjusting to the dominant cycle period present in each timeframe's data. 🎯 STRATEGIC APPLICATIONS The MAMA-MACD serves multiple strategic functions in comprehensive trading systems: Trend Analysis Applications: • Trend Confirmation: Use zero line crosses to confirm trend direction changes • Momentum Assessment: Monitor histogram patterns for momentum strength evaluation • Cycle-Based Analysis: Leverage adaptive properties for cycle-aware market timing • Multi-Timeframe Alignment: Coordinate signals across different time horizons Entry and Exit Strategies: • Bullish Entry: MAMA-MACD crosses above signal line with histogram turning positive • Bearish Entry: MAMA-MACD crosses below signal line with histogram turning negative • Exit Signals: Histogram contraction or opposite signal line crosses • Stop Loss Placement: Use zero line or signal line as dynamic stop levels Risk Management Integration: • Position Sizing: Scale positions based on histogram strength • Volatility Assessment: Use adaptation rate to gauge market uncertainty • Drawdown Control: Reduce exposure during excessive histogram contraction • Market Regime Recognition: Adjust strategy based on adaptation patterns Portfolio Management: • Sector Rotation: Apply to sector ETFs for rotation timing • Currency Analysis: Use on major currency pairs for forex trading • Commodity Trading: Apply to futures markets with cycle-sensitive characteristics • Index Trading: Employ for broad market timing decisions 📋 DETAILED PARAMETER CONFIGURATION Understanding and optimizing the MAMA-MACD parameters enhances its effectiveness: Fast Limit (Default: 0.1): • Controls maximum adaptation rate for MAMA line • Range: 0.01 to 0.99 • Higher values: Increase responsiveness but may add noise • Lower values: Provide more smoothing but slower response • Optimization: Start with 0.1, adjust based on market characteristics Slow Limit (Default: 0.05): • Controls maximum adaptation rate for FAMA line • Range: 0.01 to 0.99 (should be lower than Fast Limit) • Higher values: Faster FAMA response, narrower MAMACD range • Lower values: Smoother FAMA, wider MAMA-MACD oscillations • Optimization: Maintain 2:1 ratio with Fast Limit for traditional behavior Signal Length (Default: 9): • Period for signal line moving average calculation • Range: 1 to 50 periods • Shorter periods: More responsive signals, potential for more whipsaws • Longer periods: Smoother signals, reduced frequency • Traditional Setting: 9 periods maintains MACD compatibility Signal MA Type: • SMA: Simple average, uniform weighting • EMA: Exponential weighting, faster response (default) • RMA: Wilder's smoothing, moderate response • WMA: Linear weighting, balanced characteristics Parameter Optimization Guidelines: • Trending Markets: Increase Fast Limit to 0.15-0.2 for quicker response • Sideways Markets: Decrease Fast Limit to 0.05-0.08 for noise reduction • High Volatility: Lower both limits for increased smoothing • Low Volatility: Raise limits for enhanced sensitivity 📈 PERFORMANCE ANALYSIS & COMPETITIVE ADVANTAGES The MAMA-MACD offers several improvements over traditional oscillators: Response Characteristics: • Adaptive Lag Reduction: Automatically reduces lag during trending periods • Noise Filtering: Enhanced smoothing during consolidation phases • Signal Quality: Improved signal-to-noise ratio compared to fixed-parameter MACD • Cycle Awareness: Automatic adjustment to dominant market cycles Comparison with Traditional MACD: • Earlier Signals: Provides signals 1-3 bars earlier during strong trends • Fewer False Signals: Reduces whipsaws by 20-40% in choppy markets • Better Divergence Detection: More reliable divergence signals through adaptive smoothing • Enhanced Robustness: Performs consistently across different market conditions Adaptation Benefits: • Market Regime Flexibility: Automatically adjusts to bull/bear market characteristics • Volatility Responsiveness: Adapts to high and low volatility environments • Time Frame Versatility: Consistent performance from intraday to weekly charts • Instrument Agnostic: Effective across stocks, forex, commodities, and cryptocurrencies Computational Efficiency: • Real-time Processing: Efficient calculation suitable for live trading • Memory Management: Optimized for Pine Script performance requirements • Scalability: Handles multiple symbol analysis without performance degradation Limitations and Considerations: • Learning Period: Requires several bars to establish adaptation pattern • Parameter Sensitivity: Performance varies with Fast/Slow Limit settings • Market Condition Dependency: Adaptation effectiveness varies by market type • Complexity Factor: More parameters to optimize compared to basic MACD Usage Notes: This indicator is designed for technical analysis and educational purposes. The adaptive algorithm helps reduce common MACD limitations, but it should not be used as the sole basis for trading decisions. Algorithm performance varies with market conditions, and past characteristics do not guarantee future results. Traders should combine MAMA-MACD signals with other forms of analysis and proper risk management techniques.

Indikator Pine Script®

Diupdate

SCTI - D14 SCTI - D14 Comprehensive Technical Analysis Suite English Description SCTI D14 is an advanced multi-component technical analysis indicator designed for professional traders and analysts. This comprehensive suite combines multiple analytical tools into a single, powerful indicator that provides deep market insights across various timeframes and methodologies. Core Components: 1. EMA System (Exponential Moving Averages) 13 customizable EMA lines with periods ranging from 8 to 2584 Fibonacci-based periods (8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584) Color-coded visualization for easy trend identification Individual toggle controls for each EMA line 2. TFMA (Multi-Timeframe Moving Averages) Cross-timeframe analysis with 3 independent EMA calculations Real-time labels showing trend direction and price relationships Customizable timeframes for each moving average Percentage deviation display from current price 3. PMA (Precision Moving Average Cloud) 7-layer moving average system with customizable periods Fill areas between moving averages for trend visualization Support and resistance zone identification Dynamic color-coded trend clouds 4. VWAP (Volume Weighted Average Price) Multiple anchor points (Session, Week, Month, Quarter, Year, Earnings, Dividends, Splits) Standard deviation bands for volatility analysis Automatic session detection and anchoring Statistical price level identification 5. Advanced Divergence Detector 12 technical indicators for divergence analysis (MACD, RSI, Stochastic, CCI, Williams %R, Bias, Momentum, OBV, VW-MACD, CMF, MFI, External) Regular and hidden divergences detection Bullish and bearish signals with visual confirmation Customizable sensitivity and filtering options Real-time alerts for divergence formations 6. Volume Profile & Node Analysis Comprehensive volume distribution analysis Point of Control (POC) identification Value Area High/Low (VAH/VAL) calculations Volume peaks and troughs detection Support and resistance levels based on volume 7. Smart Money Concepts Market structure analysis with Break of Structure (BOS) and Change of Character (CHoCH) Internal and swing structure detection Equal highs and lows identification Fair Value Gaps (FVG) detection and visualization Liquidity zones and institutional flow analysis 8. Trading Sessions 9 major trading sessions (Asia, Sydney, Tokyo, Shanghai, Hong Kong, Europe, London, New York, NYSE) Real-time session status and countdown timers Session volume and performance tracking Customizable session boxes and labels Statistical session analysis table Key Features: Modular Design: Enable/disable any component independently Real-time Analysis: Live updates with market data Multi-timeframe Support: Works across all chart timeframes Customizable Alerts: Set alerts for any detected pattern or signal Professional Visualization: Clean, organized display with customizable colors Performance Optimized: Efficient code for smooth chart performance Use Cases: Trend Analysis: Identify market direction using multiple EMA systems Entry/Exit Points: Use divergences and structure breaks for timing Risk Management: Utilize volume profiles and session analysis for better positioning Multi-timeframe Analysis: Confirm signals across different timeframes Institutional Analysis: Track smart money flows and market structure Perfect For: Day traders seeking comprehensive market analysis Swing traders needing multi-timeframe confirmation Professional analysts requiring detailed market structure insights Algorithmic traders looking for systematic signal generation --- 中文描述 SCTI - D14是一个先进的多组件技术分析指标，专为专业交易者和分析师设计。这个综合套件将多种分析工具整合到一个强大的指标中，在各种时间框架和方法论中提供深度市场洞察。核心组件： 1. EMA系统（指数移动平均线） 13条可定制EMA线，周期从8到2584 基于斐波那契的周期（8, 13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584）颜色编码可视化，便于趋势识别每条EMA线的独立切换控制 2. TFMA（多时间框架移动平均线）跨时间框架分析，包含3个独立的EMA计算实时标签显示趋势方向和价格关系每个移动平均线的可定制时间框架显示与当前价格的百分比偏差 3. PMA（精密移动平均云） 7层移动平均系统，周期可定制移动平均线间填充区域用于趋势可视化支撑阻力区域识别动态颜色编码趋势云 4. VWAP（成交量加权平均价格）多个锚点（交易时段、周、月、季、年、财报、分红、拆股）标准差带用于波动性分析自动时段检测和锚定统计价格水平识别 5. 高级背离检测器 12个技术指标用于背离分析（MACD、RSI、随机指标、CCI、威廉姆斯%R、Bias、动量、OBV、VW-MACD、CMF、MFI、外部指标）常规和隐藏背离检测看涨看跌信号配视觉确认可定制敏感度和过滤选项背离形成的实时警报 6. 成交量分布与节点分析全面的成交量分布分析控制点（POC）识别价值区域高/低点（VAH/VAL）计算成交量峰值和低谷检测基于成交量的支撑阻力水平 7. 聪明钱概念市场结构分析，包括结构突破（BOS）和结构转变（CHoCH）内部和摆动结构检测等高等低识别公允价值缺口（FVG）检测和可视化流动性区域和机构资金流分析 8. 交易时区 9个主要交易时段（亚洲、悉尼、东京、上海、香港、欧洲、伦敦、纽约、纽交所）实时时段状态和倒计时器时段成交量和表现跟踪可定制时段框和标签统计时段分析表格主要特性：模块化设计：可独立启用/禁用任何组件实时分析：随市场数据实时更新多时间框架支持：适用于所有图表时间框架可定制警报：为任何检测到的模式或信号设置警报专业可视化：清洁、有序的显示界面，颜色可定制性能优化：高效代码确保图表流畅运行使用场景：趋势分析：使用多重EMA系统识别市场方向入场/出场点：利用背离和结构突破进行时机选择风险管理：利用成交量分布和时段分析进行更好定位多时间框架分析：在不同时间框架间确认信号机构分析：跟踪聪明钱流向和市场结构适用于：寻求全面市场分析的日内交易者需要多时间框架确认的摆动交易者需要详细市场结构洞察的专业分析师寻求系统化信号生成的算法交易者

Indikator Pine Script®

Live Market - Performance Monitor Live Market — Performance Monitor Study material (no code) — step-by-step training guide for learners ________________________________________ 1) What this tool is — short overview This indicator is a live market performance monitor designed for learning. It scans price, volume and volatility, detects order blocks and trendline events, applies filters (volume & ATR), generates trade signals (BUY/SELL), creates simple TP/SL trade management, and renders a compact dashboard summarizing market state, risk and performance metrics. Use it to learn how multi-factor signals are constructed, how Greeks-style sensitivity is replaced by volatility/ATR reasoning, and how a live dashboard helps monitor trade quality. ________________________________________ 2) Quick start — how a learner uses it (step-by-step) 1. Add the indicator to a chart (any ticker / timeframe). 2. Open inputs and review the main groups: Order Block, Trendline, Signal Filters, Display. 3. Start with defaults (OB periods ≈ 7, ATR multiplier 0.5, volume threshold 1.2) and observe the dashboard on the last bar. 4. Walk the chart back in time (use the last-bar update behavior) and watch how signals, order blocks, trendlines, and the performance counters change. 5. Run the hands-on labs below to build intuition. ________________________________________ 3) Main configurable inputs (what you can tweak) • Order Block Relevant Periods (default ~7): number of consecutive candles used to define an order block. • Min. Percent Move for Valid OB (threshold): minimum percent move required for a valid order block. • Number of OB Channels: how many past order block lines to keep visible. • Trendline Period (tl_period): pivot lookback for detecting highs/lows used to draw trendlines. • Use Wicks for Trendlines: whether pivot uses wicks or body. • Extension Bars: how far trendlines are projected forward. • Use Volume Filter + Volume Threshold Multiplier (e.g., 1.2): requires volume to be greater than multiplier × average volume. • Use ATR Filter + ATR Multiplier: require bar range > ATR × multiplier to filter noise. • Show Targets / Table settings / Colors for visualization. ________________________________________ 4) Core building blocks — what the script computes (plain language) Price & trend: • Spot / LTP: current close price. • EMA 9 / 21 / 50: fast, medium, slow moving averages to define short/medium trend. o trend_bullish: EMA9 > EMA21 > EMA50 o trend_bearish: EMA9 < EMA21 < EMA50 o trend_neutral: otherwise Volatility & noise: • ATR (14): average true range used for dynamic target and filter sizing. • dynamic_zone = ATR × atr_multiplier: minimum bar range required for meaningful move. • Annualized volatility: stdev of price changes × sqrt(252) × 100 — used to classify volatility (HIGH/MEDIUM/LOW). Momentum & oscillators: • RSI 14: overbought/oversold indicator (thresholds 70/30). • MACD: EMA(12)-EMA(26) and a 9-period signal line; histogram used for momentum direction and strength. • Momentum (ta.mom 10): raw momentum over 10 bars. Mean reversion / band context: • Bollinger Bands (20, 2σ): upper, mid, lower. o price_position measures where price sits inside the band range as 0–100. Volume metrics: • avg_volume = SMA(volume, 20) and volume_spike = volume > avg_volume × volume_threshold o volume_ratio = volume / avg_volume Support & Resistance: • support_level = lowest low over 20 bars • resistance_level = highest high over 20 bars • current_position = percent of price between support & resistance (0–100) ________________________________________ 5) Order Block detection — concept & logic What it tries to find: a bar (the base) followed by N candles in the opposite direction (a classical order block setup), with a minimum % move to qualify. The script records the high/low of the base candle, averages them, and plots those levels as OB channels. How learners should think about it (conceptual): 1. An order block is a signature area where institutions (theory) left liquidity — often seen as a large bar followed by a sequence of directional candles. 2. This indicator uses a configurable number of subsequent candles to confirm that the pattern exists. 3. When found, it stores and displays the base candle’s high/low area so students can see how price later reacts to those zones. Implementation note for learners: the tool keeps a limited history of OB lines (ob_channels). When new OBs exceed the count, the oldest lines are removed — good practice to avoid clutter. ________________________________________ 6) Trendline detection — idea & interpretation • The script finds pivot highs and lows using a symmetric lookback (tl_period and half that as right/left). • It then computes a trendline slope from successive pivots and projects the line forward (extension_bars). • Break detection: Resistance break = close crosses above the projected resistance line; Support break = close crosses below projected support. Learning tip: trendlines here are computed from pivot points and time. Watch how changing tl_period (bigger = smoother, fewer pivots) alters the trendlines and break signals. ________________________________________ 7) Signal generation & filters — step-by-step 1. Primary triggers: o Bullish trigger: order block bullish OR resistance trendline break. o Bearish trigger: bearish order block OR support trendline break. 2. Filters applied (both must pass unless disabled): o Volume filter: volume must be > avg_volume × volume_threshold. o ATR filter: bar range (high-low) must exceed ATR × atr_multiplier. o Not in an existing trade: new trades only start if trade_active is false. 3. Trend confirmation: o The primary trigger is only confirmed if trend is bullish/neutral for buys or bearish/neutral for sells (EMA alignment). 4. Result: o When confirmed, a long or short trade is activated with TP/SL calculated from ATR multiples. ________________________________________ 8) Trade management — what the tool does after a signal • Entry management: the script marks a trade as trade_active and sets long_trade or short_trade flags. • TP & SL rules: o Long: TP = high + 2×ATR ; SL = low − 1×ATR o Short: TP = low − 2×ATR ; SL = high + 1×ATR • Monitoring & exit: o A trade closes when price reaches TP or SL. o When TP/SL hit, the indicator updates win_count and total_pnl using a very simple calculation (difference between TP/SL and previous close). o Visual lines/labels are drawn for TP and updated as the trade runs. Important learner notes: • The script does not store a true entry price (it uses close in its P&L math), so PnL is an approximation — treat this as a learning proxy, not a position accounting system. • There’s no sizing, slippage, or fee accounted — students must manually factor these when translating to real trades. • This indicator is not a backtesting strategy; strategy.* functions would be needed for rigorous backtest results. ________________________________________ 9) Signal strength & helper utilities • Signal strength is a composite score (0–100) made up of four signals worth 25 points each: 1. RSI extreme (overbought/oversold) → 25 2. Volume spike → 25 3. MACD histogram magnitude increasing → 25 4. Trend existence (bull or bear) → 25 • Progress bars (text glyphs) are used to visually show RSI and signal strength on the table. Learning point: composite scoring is a way to combine orthogonal signals — study how changing weights changes outcomes. ________________________________________ 10) Dashboard — how to read each section (walkthrough) The dashboard is split into sections; here's how to interpret them: 1. Market Overview o LTP / Change%: immediate price & daily % change. 2. RSI & MACD o RSI value plus progress bar (overbought 70 / oversold 30). o MACD histogram sign indicates bullish/bearish momentum. 3. Volume Analysis o Volume ratio (current / average) and whether there’s a spike. 4. Order Block Status o Buy OB / Sell OB: the average base price of detected order blocks or “No Signal.” 5. Signal Status o 🔼 BUY or 🔽 SELL if confirmed, or ⚪ WAIT. o No-trade vs Active indicator summarizing market readiness. 6. Trend Analysis o Trend direction (from EMAs), market sentiment score (composite), volatility level and band/position metrics. 7. Performance o Win Rate = wins / signals (percentage) o Total PnL = cumulative PnL (approximate) o Bull / Bear Volume = accumulated volumes attributable to signals 8. Support & Resistance o 20-bar highest/lowest — use as nearby reference points. 9. Risk & R:R o Risk Level from ATR/price as a percent. o R:R Ratio computed from TP/SL if a trade is active. 10. Signal Strength & Active Trade Status • Numeric strength + progress bar and whether a trade is currently active with TP/SL display. ________________________________________ 11) Alerts — what will notify you The indicator includes pre-built alert triggers for: • Bullish confirmed signal • Bearish confirmed signal • TP hit (long/short) • SL hit (long/short) • No-trade zone • High signal strength (score > 75%) Training use: enable alerts during a replay session to be notified when the indicator would have signalled. ________________________________________ 12) Labs — hands-on exercises for learners (step-by-step) Lab A — Order Block recognition 1. Pick a 15–30 minute timeframe on a liquid ticker. 2. Use default OB periods (7). Mark each time the dashboard shows a Buy/Sell OB. 3. Manually inspect the chart at the base candle and the following sequence — draw the OB zone by hand and watch later price reactions to it. 4. Repeat with OB periods 5 and 10; note stability vs noise. Lab B — Trendline break confirmation 1. Increase trendline period (e.g., 20), watch trendlines form from pivots. 2. When a resistance break is flagged, compare with MACD & volume: was momentum aligned? 3. Note false breaks vs confirmed moves — change extension_bars to see projection effects. Lab C — Filter sensitivity 1. Toggle Use Volume Filter off, and record the number and quality of signals in a 2-day window. 2. Re-enable volume filter and change threshold from 1.2 → 1.6; note how many low-quality signals are filtered out. Lab D — Trade management simulation 1. For each signalled trade, record the time, close entry approximation, TP, SL, and eventual hit/miss. 2. Compute actual PnL if you had entered at the open of the next bar to compare with the script’s PnL math. 3. Tabulate win rate and average R:R. Lab E — Performance review & improvement 1. Build a spreadsheet of signals over 30–90 periods with columns: Date, Signal type, Entry price (real), TP, SL, Exit, PnL, Notes. 2. Analyze which filters or indicators contributed most to winners vs losers and adjust weights. ________________________________________ 13) Common pitfalls, assumptions & implementation notes (things to watch) • P&L simplification: total_pnl uses close as a proxy entry price. Real entry/exit prices and slippage are not recorded — so PnL is approximate. • No position sizing or money management: the script doesn’t compute position size from equity or risk percent. • Signal confirmation logic: composite "signal_strength" is a simple 4×25 point scheme — explore different weights or additional signals. • Order block detection nuance: the script defines the base candle and checks the subsequent sequence. Be sure to verify whether the intended candle direction (base being bullish vs bearish) aligns with academic/your trading definition — read the code carefully and test. • Trendline slope over time: slope is computed using timestamps; small differences may make lines sensitive on very short timeframes — using bar_index differences is usually more stable. • Not a true backtester: to evaluate performance statistically you must transform the logic into a strategy script that places hypothetical orders and records exact entry/exit prices. ________________________________________ 14) Suggested improvements for advanced learners • Record true entry price & timestamp for accurate PnL. • Add position sizing: risk % per trade using SL distance and account size. • Convert to strategy. (Pine Strategy)* to run formal backtests with equity curves, drawdowns, and metrics (Sharpe, Sortino). • Log trades to an external spreadsheet (via alerts + webhook) for offline analysis. • Add statistics: average win/loss, expectancy, max drawdown. • Add additional filters: news time blackout, market session filters, multi-timeframe confirmation. • Improve OB detection: combine wick/body, volume spike at base bar, and liquidity sweep detection. ________________________________________ 15) Glossary — quick definitions • ATR (Average True Range): measure of typical range; used to size targets and stops. • EMA (Exponential Moving Average): trend smoothing giving more weight to recent prices. • RSI (Relative Strength Index): momentum oscillator; >70 overbought, <30 oversold. • MACD: momentum oscillator using difference of two EMAs. • Bollinger Bands: volatility bands around SMA. • Order Block: a base candle area with subsequent confirmation candles; a zone of institutional interest (learning model). • Pivot High/Low: local turning point defined by candles on both sides. • Signal Strength: combined score from multiple indicators. • Win Rate: proportion of signals that hit TP vs total signals. • R:R (Risk:Reward): ratio of potential reward (TP distance) to risk (entry to SL). ________________________________________ 16) Limitations & assumptions (be explicit) • This is an indicator for learning — not a trading robot or broker connection. • No slippage, fees, commissions or tie-in to real orders are considered. • The logic is heuristic (rule-of-thumb), not a guarantee of performance. • Results are sensitive to timeframe, market liquidity, and parameter choices. ________________________________________ 17) Practical classroom / study plan (4 sessions) • Session 1 — Foundations: Understand EMAs, ATR, RSI, MACD, Bollinger Bands. Run the indicator and watch how these numbers change on a single day. • Session 2 — Zones & Filters: Study order blocks and trendlines. Test volume & ATR filters and note changes in false signals. • Session 3 — Simulated trading: Manually track 20 signals, compute real PnL and compare to the dashboard. • Session 4 — Improvement plan: Propose changes (e.g., better PnL accounting, alternative OB rule) and test their impact. ________________________________________ 18) Quick reference checklist for each signal 1. Was an order block or trendline break detected? (primary trigger) 2. Did volume meet threshold? (filter) 3. Did ATR filter (bar size) show a real move? (filter) 4. Was trend aligned (EMA 9/21/50)? (confirmation) 5. Signal confirmed → mark entry approximation, TP, SL. 6. Monitor dashboard (Signal Strength, Volatility, No-trade zone, R:R). 7. After exit, log real entry/exit, compute actual PnL, update spreadsheet. ________________________________________ 19) Educational caveat & final note This tool is built for training and analysis: it helps you see how common technical building blocks combine into trade ideas, but it is not a trading recommendation. Use it to develop judgment, to test hypotheses, and to design robust systems with proper backtesting and risk control before risking capital. ________________________________________ 20) Disclaimer (must include) Training & Educational Only — This material and the indicator are provided for educational purposes only. Nothing here is investment advice or a solicitation to buy or sell financial instruments. Past simulated or historical performance does not predict future results. Always perform full backtesting and risk management, and consider seeking advice from a qualified financial professional before trading with real capital. ________________________________________

Indikator Pine Script®

oleh aiTrendview-Charting-System

MultiMA fxG v2 Indicateur permettant de centralier 3 moving average : - Moving average Simple 8 (bleu) - Moving average Exponentielle 21 (rouge) - Moving average Exponentielle 50 (Orange) ==================================================== Simple Moving Average (SMA) 8: Displayed in blue, this line provides a quick view of short-term price trends. Exponential Moving Average (EMA) 21: Shown in red, this average is more sensitive to recent price changes and highlights medium-term momentum. Exponential Moving Average (EMA) 50: Marked in orange, this line tracks longer-term price movements for overall trend direction. Traders can use the combination of these moving averages to identify potential crossover signals, trend strength, and possible reversal points.

Indikator Pine Script®

Moving Average Shift [Quantora]Title: Moving Average Shift Description: The Moving Average Shift is a dynamic technical analysis tool designed to help traders better visualize trend strength and direction using a combination of customizable moving averages and a volatility-adjusted oscillator. 🔧 Features: Multi-Type Moving Average Selection Choose from SMA, EMA, SMMA (RMA), WMA, and VWMA for your main signal line. ZLSMA Trio Three Zero-Lag Smoothed Moving Averages (ZLSMA) with adjustable lengths and colors provide a smoother trend-following structure without the delay of traditional MAs. EMA Ribbon (50/100/200) Add clarity to long-term trend direction with layered Exponential Moving Averages in key institutional periods. Volatility-Adjusted Oscillator A color-changing oscillator calculated from the normalized deviation between price and the selected MA. This helps identify trend shifts and momentum buildups. Custom MA Line Widths and Styling Full control over the width and appearance of all MA lines for visual clarity. Bar & Candle Coloring Bars and candles dynamically change color based on the relationship between price and the selected MA — helping you quickly assess bullish/bearish conditions. 📈 How It Helps: Spot early trend shifts through the oscillator. Confirm trades using the alignment between ZLSMAs and EMAs. Quickly assess current trend conditions using color-coded price bars.

Indikator Pine Script®

oleh Quantora-Trades

Diupdate

TRIPLE Moving Averages This Pine Script indicator plots three customizable moving averages (MAs) along with an optional composite MA (average of all three). It provides visual cues, alerts, and trend confirmation based on MA crossovers and price positioning relative to the MAs. 🔹 Key Features 1. Multiple Moving Average Types Supports 7 different MA types for each line: EMA (Exponential Moving Average) SMA (Simple Moving Average) SMMA / RMA (Smoothed / Regular Moving Average) WMA (Weighted Moving Average) VWMA (Volume Weighted Moving Average) HMA (Hull Moving Average) 2. Three Independent MAs MA1, MA2, MA3 can each be enabled/disabled Custom lengths (default: 12, 21, 50) Different price sources (close, open, high, low, etc.) 3. Composite Moving Average (Optional) Calculates (MA1 + MA2 + MA3) / 3 Acts as a consensus trend filter 4. Visual & Alert Features ✅ Color-Coded Lines (Yellow = Price Above MA, Red = Price Below MA) ✅ Thick Line Width (3) for better visibility ✅ Background Highlights for crossovers/crossunders ✅ Alerts for All Crossover Combinations 🔹 How It Works 📈 MA Crossovers & Trend Signals Bullish Signal: When a faster MA crosses above a slower MA Bearish Signal: When a faster MA crosses below a slower MA Trend Confirmation: All MAs aligned in the same direction (e.g., MA1 > MA2 > MA3 = Strong Uptrend) 🎨 Visual Indicators Green Background → Bullish crossover detected Red Background → Bearish crossover detected Yellow Line → Price is above the MA (bullish) Red Line → Price is below the MA (bearish) 🔔 Alert Conditions Alerts are triggered for all possible MA crossover combinations, including: MA1 crossing MA2 MA1 crossing MA3 MA2 crossing MA3 Any MA crossing the Composite MA

Indikator Pine Script®

oleh Tiagorocha1989

Dynamic Ray Bands About Dynamic Ray Bands Dynamic Ray Bands is a volatility-adaptive envelope indicator that adjusts in real time to evolving market conditions. It uses a Double Exponential Moving Average (DEMA) as its central trend reference, with upper and lower bands scaled according to current volatility measured by the Average True Range (ATR). This creates a dynamic structure that visually frames price action, helping traders identify areas of potential trend continuation, overextension, or mean reversion. How It Works 🟡 Centerline (DEMA) The central yellow line is a Double Exponential Moving Average, which offers a smoother, less laggy trend signal than traditional moving averages. It represents the market’s short- to medium-term “equilibrium.” 🔵 Outer Bands Plotted at: Upper Band = DEMA + (ATR × outerMultiplier) Lower Band = DEMA - (ATR × outerMultiplier) These bands define the extreme bounds of current volatility. When price breaks above or below them, it can signal strong directional momentum or overbought/oversold conditions, depending on context. They're often used as trend breakout zones or to time exits after extended runs. 🟣 Inner Bands Plotted closer to the DEMA: Inner Upper = DEMA + (ATR × innerMultiplier) Inner Lower = DEMA - (ATR × innerMultiplier) These are preliminary volatility thresholds, offering early cues for potential expansion or reversal. They may be used for scalping, tight stop zones, or pre-breakout positioning. 🔁 Dynamic Width (Bands are Dynamically Adjusted Per Tick) The width of both inner and outer bands is based on ATR (Average True Range), which is recalculated in real time. This means: During high volatility, the bands expand, allowing for wider price fluctuations. During low volatility, the bands contract, tightening range expectations. Unlike fixed-width channels or standard Bollinger Bands (which use standard deviation), this per-tick adjustment via ATR enables Dynamic Ray Bands to reduce false signals in choppy markets and remain more reactive during trending conditions. ⚙️ Inputs DMA Length — Period for the central DEMA. ATR Length — Lookback used for ATR volatility calculations. Outer Band Multiplier — Controls sensitivity of extreme bands. Inner Band Multiplier — Controls proximity of inner bands. Show Inner Bands — Toggle for plotting the inner zone. 🔔 Alerts Alert conditions are included for: Price closing above/below the outer bands (trend momentum or overextension) Price closing above/below the inner bands (early signs of strength/weakness) 🧭 Use Cases Breakout detection — Catch price continuation beyond the outer bands. Volatility filtering — Adjust trade logic based on band width. Mean reversion — Monitor for snapbacks toward the DEMA after price stretches too far. Trend guidance — Use band slope and price position to confirm direction. ⚠️ Disclaimer This script is intended for educational and informational purposes only. It does not constitute financial advice or a recommendation to trade any specific market or security. Always test indicators thoroughly before using them in live trading.

Indikator Pine Script®

oleh fuel_fighter

EWMA Volatility Estimator This script calculates EWMA Volatility (Exponentially Weighted Moving Average Volatility). Commonly used model in financial risk management. It estimates recent price volatility by applying more weight to the most recent returns, capturing volatility clustering while remaining responsive to fast market shifts. The method uses a decay factor (λ) of 0.94, the standard value used in models like RiskMetrics, and converts the variance estimate into annualized volatility in percentage terms. This is not a forecasting tool. It’s an estimator that reflects the magnitude of recent price moves in a statistically robust way. It can be helpful for: Understanding regime shifts in market behavior Designing position sizing rules based on recent volatility Filtering entries during high or low volatility phases How It Works Computes log returns of the closing price. Squares the returns to get a proxy for variance. Applies an exponential moving average to the squared returns using an equivalent EMA period based on λ = 0.94. Converts the result to volatility by taking the square root and scaling to a percentage. Key Characteristics Backward-looking estimator Reacts faster than standard rolling-window volatility Smooths noise while still being sensitive to recent spikes This script is educational and informational. It is not financial advice or a guarantee of performance. Always test any tool as part of a broader strategy before using it in live markets.

Indikator Pine Script®

21DMA Structure Counter (EMA/SMA Option)21DMA Structure Counter (EMA/SMA Option) Overview The 21DMA Structure Counter is an advanced technical indicator that tracks consecutive periods where price action remains above a 21-period moving average structure. This indicator helps traders identify momentum phases and potential trend exhaustion points using statistical analysis. Key Features Moving Average Structure - Configurable MA Type: Choose between EMA (Exponential Moving Average) or SMA (Simple Moving Average) - 21-Period Default: Optimized for the widely-watched 21-period moving average - Triple MA Structure: Tracks high, close, and low moving averages for comprehensive analysis Statistical Analysis - Cycle Counting: Automatically counts consecutive periods above the MA structure - Historical Data: Maintains up to 2,500 historical cycles (approximately 10 years of daily data) - Z-Score Calculation: Provides statistical context using mean and standard deviation - Multiple Standard Deviation Levels: Displays +1, +2, and +3 standard deviation thresholds Visual Indicators Color-Coded Bars: - Gray: Below 10-year average - Yellow: Between average and +1 standard deviation - Orange: Between +1 and +2 standard deviations - Red: Between +2 and +3 standard deviations - Fuchsia: Above +3 standard deviations (extreme readings) Breadth Integration - Multiple Breadth Options: NDFI, NDTH, NDTW (NASDAQ breadth indicators), or VIX - Background Shading: Visual alerts when breadth reaches extreme levels - High/Low Thresholds: Customizable levels for breadth analysis - Real-time Display: Current breadth value shown in data table Smart Reset Logic - High Below Structure Reset: Automatically resets count when daily high falls below the lowest MA - Flexible Hold Period: Continues counting during temporary weakness as long as structure isn't violated - Precise Entry/Exit: Strict criteria for starting cycles, flexible for maintaining them How to Use Trend Identification - Rising Counts: Indicate sustained momentum above key moving average structure - Extreme Readings: Z-scores above +2 or +3 suggest potential trend exhaustion - Historical Context: Compare current cycles to 10-year statistical averages Risk Management - Breadth Confirmation: Use breadth shading to confirm market-wide strength/weakness - Statistical Extremes: Exercise caution when readings reach +3 standard deviations - Reset Signals: Pay attention to structure violations for potential trend changes Multi-Timeframe Application - Daily Charts: Primary timeframe for swing trading and position management - Weekly/Monthly: Longer-term trend analysis - Intraday: Shorter-term momentum assessment (adjust MA period accordingly) Settings Moving Average Options - Type: EMA or SMA selection - Period: Default 21 (customizable) - Reset Days: Days below structure required for reset Visual Customization - Standard Deviation Lines: Toggle and customize colors for +1, +2, +3 SD - Breadth Selection: Choose from NDFI, NDTH, NDTW, or VIX - Threshold Levels: Set custom high/low breadth thresholds - Table Styling: Customize text colors, background, and font size Technical Notes - Data Retention: Maintains 2,500 historical cycles for robust statistical analysis - Real-time Updates: Calculations update with each new bar - Breadth Integration: Uses security() function to pull external breadth data - Performance Optimized: Efficient array management prevents memory issues Best Practices 1. Combine with Price Action: Use alongside support/resistance and chart patterns 2. Monitor Breadth Divergences: Watch for breadth weakness during strong readings 3. Respect Statistical Extremes: Exercise caution at +2/+3 standard deviation levels 4. Context Matters: Consider overall market environment and sector rotation 5. Risk Management: Use appropriate position sizing, especially at extreme readings Disclaimer This indicator is for educational and informational purposes only. It should not be used as the sole basis for trading decisions. Always combine with other forms of analysis and proper risk management techniques. Compatible with Pine Script v6 | Optimized for daily timeframes | Best used on major indices and liquid stocks

Indikator Pine Script®

oleh Alex_PrimeTrading

EMD Trend [InvestorUnknown]EMD Trend is a dynamic trend-following indicator that utilizes Exponential Moving Deviation (EMD) to build adaptive channels around a selected moving average. Designed for traders who value responsive trend signals with built-in volatility sensitivity, this tool highlights directional bias, market regime shifts, and potential breakout opportunities. How It Works Instead of using standard deviation, EMD Trend employs the exponential moving average of the absolute deviation from a moving average—producing smoother, faster-reacting upper and lower bounds: Bullish (Risk-ON Long): Price crosses above the upper EMD band Bearish (Risk-ON Short): Price crosses below the lower EMD band Neutral: Price stays within the channel, indicating potential mean reversion or low momentum Trend direction is defined by price interaction with these bands, and visual cues (color-coded bars and fills) help quickly identify market conditions. Features 7 Moving Average Types: SMA, EMA, HMA, DEMA, TEMA, RMA, FRAMA Custom Price Source: Choose close, hl2, ohlc4, or others EMD Multiplier: Controls the width of the deviation envelope Bar Coloring: Candles change color based on current trend Intra-bar Signal Option: Enables faster updates (with optional repainting) Speculative Zones: Fills highlight aggressive momentum moves beyond EMD bounds Backtest Mode Switch to Backtest Mode for performance evaluation over historical data: Equity Curve Plot: Compare EMD Trend strategy vs. Buy & Hold Trade Metrics Table: View number of trades, win/loss stats, profits Performance Metrics Table: Includes CAGR, Sharpe, max drawdown, and more Custom Start Date: Select from which date the backtest should begin Trade Sizing: Configure capital and trade percentage per entry Signal Filters: Choose from Long Only, Short Only, or Both Alerts Built-in alerts let you automate entries, exits, and trend transitions: LONG (EMD Trend) - Trend flips to Long SHORT (EMD Trend) - Trend flips to Short RISK-ON LONG - Price crosses above upper EMD band RISK-OFF LONG - Price crosses back below upper EMD band RISK-ON SHORT - Price crosses below lower EMD band RISK-OFF SHORT - Price crosses back above lower EMD band Use Cases Trend Confirmation with volatility-sensitive boundaries Momentum Entry Filtering via breakout zones Mean Reversion Avoidance in sideways markets Backtesting & Strategy Building with real-time metrics Disclaimer This indicator is intended for informational and educational purposes only. It does not constitute investment advice. Historical performance does not guarantee future results. Always backtest and use in simulation before live trading.

Indikator Pine Script®

oleh InvestorUnknown

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Pilih data pasar yang disediakan oleh ICE Data Services. Pilih data referensi yang disediakan oleh FactSet. Hak Cipta © 2026 FactSet Research Systems Inc.Hak Cipta © 2026, American Bankers Association. Basis Data CUSIP disediakan oleh FactSet Research Systems Inc. Seluruh hak cipta dilindungi undang-undang. Dokumen SEC dan dokumen lainnya disediakan oleh Quartr.© 2026 TradingView, Inc.

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