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Adaptive Risk Management [sgbpulse]1. Introduction: Adaptive Risk Management is an advanced indicator designed to provide traders with a comprehensive risk management tool directly on the chart. Instead of relying on complex manual calculations, the indicator automates all critical steps of trade planning. It dynamically calculates the estimated Entry Price , the Stop Loss location, the required Position Size (Quantity) based on your capital and risk limits, and the three Take Profit targets based on your defined Reward/Risk ratios. The indicator displays all these essential data points clearly and visually on the chart, ensuring you always know the potential risk-reward profile of every trade. ARM : The A daptive R isk M anagement every trader needs to ARM themselves with. 2. The Critical Importance of Risk Management Proper risk management is the cornerstone of successful trading. Consistent profitability in the market is impossible without rigorously defining risk limits. Risk Control: This starts by setting the maximum risk amount you are willing to lose in a single trade (Risk per Trade), and limiting the total capital allocated to the position (Max Capital per Trade). Defining Boundaries (Stop Loss & Take Profit): It is mandatory to define a technical Stop Loss and a Take Profit target. A fundamental rule of risk management is that the Reward/Risk Ratio (R/R) must be a minimum of 1:1. 3. Core Features, Adaptivity, and Customization The Adaptive Risk Management indicator is engineered for use across all major trading styles, including Swing Trading, Intraday Trading, and Scalping, providing consistent risk control regardless of the chosen timeframe. Real-Time Dynamic Adaptivity: The indicator calculates all risk management parameters (Entry, Stop Loss, Quantity) dynamically with every new bar, thus adapting instantly to changing market conditions. Trend Direction Adjustment: Define the analysis direction (Long/Uptrend or Short/Downtrend). Intraday Session Data Control: Full control over whether lookback calculations will include data from Extended Trading Hours (ETH), or if the daily calculations will start actively only from the first bar of Regular Trading Hours (RTH). Status Validation: The indicator performs critical status checks and displays clear Warning Messages if risk conditions are not met. 4. Intuitive Visualization and Real-Time Data Dynamic Tracking Lines: The Entry Price and Stop Loss lines are updated with every new bar. Crucially, the length of these lines dynamically reflects the calculation's lookback range (e.g., the extent of Lookback Bars or the location of the confirmed Pivot Point), providing a visual anchor for the calculated price. Risk and Reward Zones: The indicator creates a graphical background fill between Entry and Stop Loss (marked with the risk color) and between Entry and the Reward Targets (marked with the reward color). Essential Information Labels: Labels are placed at the end of each line, providing critical data: Estimated Entry Price, Stock/Contract Quantity (Quantity), Total Entry Amount, Estimated Stop Loss, Risk per Share, Total Financial Risk (Risk Amount), Exit Amount, Estimated Take Profit 1/2/3, Reward/Risk Ratio 1/2/3, Total Reward 1/2/3, TP Exit Amount 1/2/3. 4.1. Data Window Metrics (16 Full Series) The indicator displays 16 full data series in the TradingView Data Window, allowing precise tracking of every calculation parameter: Entry Data: Estimated Entry, Quantity, Entry Amount. Risk Data (Stop Loss): Estimated Stop Loss, Risk per Share, Risk Amount, Exit Amount. Reward Data (Take Profit): Estimated Take Profit 1/2/3, Reward/Risk Ratio 1/2/3, Total Reward 1/2/3, TP Exit Amount 1/2/3. 4.2. Instant Tracking in the Status Line The indicator displays 6 critical parameters continuously in the indicator's Status Line: Estimated Entry, Quantity, Estimated Stop Loss, Estimated Take Profit 1/2/3. 5. Detailed Indicator Inputs 5.1 General Focused Trend: Defines the analysis direction (Uptrend / Downtrend). Max Capital per Trade: The maximum amount allocated to purchasing stocks/contracts (in account currency). Risk per Trade: The maximum amount the user is willing to risk in this single trade (in account currency). ATR Length: The lookback period for the Average True Range (ATR) calculation. 5.2 Intraday Session Data Control Regular Hours Limitation : If enabled, all daily lookback calculations (for Entry/Stop Loss anchor points) will begin strictly from the first Regular Trading Hours (RTH) bar. This limits the lookback range to the current RTH session, excluding preceding Extended Trading Hours (ETH) data. Only relevant for Intraday charts. Default: False (Off) 5.3 Entry Inputs Entry Method: Selects the entry price calculation method: Current Price: Uses the closing price of the current bar as the estimated entry point (Market Entry). ATR Real Bodies Margin : - Uptrend: Calculates the Maximum Real Body over the lookback period + the calculated safety margin. - Downtrend: Calculates the Minimum Real Body over the lookback period - the calculated safety margin. ATR Bars Margin : - Uptrend: Calculates the Maximum High price over the lookback period + the calculated safety margin. - Downtrend: Calculates the Minimum Low price over the lookback period - the calculated safety margin. Lookback Bars: The number of bars used to calculate the extremes in the ATR-based entry methods (Relevant only for ATR Real Bodies Margin and ATR Bars Margin methods). ATR Multiplier (Entry): The multiplier applied to the ATR value. The result of the multiplication is the calculated safety margin used to determine the estimated Entry Price. 5.4 Risk Inputs (Stop Loss) Risk Method: Selects the Stop Loss price calculation method. ATR Current Price Margin : - Uptrend: Entry Price - the calculated safety margin. - Downtrend: Entry Price + the calculated safety margin. ATR Current Bar Margin : - Uptrend: Current Bar's Low price - the calculated safety margin. - Downtrend: Current Bar's High price + the calculated safety margin. ATR Bars Margin : - Uptrend: Lowest Low over lookback period - the calculated safety margin. - Downtrend: Highest High over lookback period + the calculated safety margin. ATR Pivot Margin : - Uptrend: The first confirmed Pivot Low point - the calculated safety margin. - Downtrend: The first confirmed Pivot High point + the calculated safety margin. Lookback Bars: The lookback period for finding the extreme price used in the 'ATR Bars Margin' calculation. ATR Multiplier (Risk): The multiplier applied to the ATR value. The result of the multiplication is the calculated safety margin used to place the estimated Stop Loss. Note: If set to 0, the Stop Loss will be placed exactly at the technical anchor point, provided the Minimum Margin Value is also 0. Minimum Margin Value: The minimum price value (e.g., $0.01) the Stop Loss margin buffer must be. Pivot (Left / Right): The number of bars required on either side of the pivot bar for confirmation (relevant only for the ATR Pivot Margin method). 5.5 Reward Inputs (Take Profit) Show Take Profit 1/2/3: ON/OFF switch to control the visibility of each Take Profit target. Reward/Risk Ratio 1/ 2/ 3: Defines the R/R ratio for the profit target. Must be ≥1.0. 6. Indicator Status/Warning Messages In situations where the Stop Loss location cannot be calculated logically and validly, often caused by a mismatch between the configured Focused Trend (Uptrend/Downtrend) and the actual price action, the indicator will display a warning message, explaining the reason and suggesting corrective action. Status Message 1: Pivot reference unavailable Condition: The Stop Loss is set to the "ATR Pivot Margin" method, but the anchor point (Pivot) is missing or inaccessible. Message Displayed: "Pivot reference unavailable. Wait for valid price action, or adjust the Regular Hours Limitation setting or Pivot Left/Right inputs." Status Message 2: Calculated Stop Loss is unsafe Condition: The calculated Stop Loss is placed illogically or unsafely relative to the trend direction and the Entry price. Message Displayed: "Calculated Stop Loss is unsafe for current trend. Wait for valid price action or adjust SL Lookback/Multiplier." 7. Summary The Adaptive Risk Management (ARM) indicator provides a seamless and systematic approach to trade execution and risk control. By dynamically automating all critical trade parameters—from Entry Price and Stop Loss placement to Position Sizing and Take Profit targets—ARM removes emotional bias and ensures every trade adheres strictly to your predefined risk profile. Key Benefits: Systematic Risk Control: Strict enforcement of maximum capital allocation and risk per trade limits. Adaptivity: Dynamic calculation of prices and quantities based on real-time market data (ATR and Lookback). Clarity and Trust: Clear on-chart visualization, precise data metrics (16 series), and unambiguous Status/Warning Messages ensure transparency and reliability. ARM allows traders to focus on strategy and analysis, confident that their execution complies with the core principles of professional risk management. Important Note: Trading Risk This indicator is intended for educational and informational purposes only and does not constitute investment advice or a recommendation for trading in any form whatsoever. Trading in financial markets involves significant risk of capital loss. It is important to remember that past performance is not indicative of future results. All trading decisions are your sole responsibility. Never trade with money you cannot afford to lose.
Pine Script® göstergesi
sgbpulse tarafından
FAIR VALUE CEDEARSFair Value CEDEARS y ETFs Important: load together with the CEDEARdata library. Returns the “Fair Value” of CEDEAR and CEDEAR-based ETF prices traded on ByMA, using as a reference the price of the underlying ordinary share or ETF traded on the NYSE or NASDAQ. It multiplies the NYSE/NASDAQ price by the CEDEAR or ETF conversion ratio and converts the currency to ARS or Dólar MEP using the exchange rate implied by the AL30/AL30C ratio for tickers quoted in ARS (e.g., AAPL) and AL30D/AL30C for tickers quoted in Dólar MEP (e.g., AAPLD). If the CEDEAR or ETF quote is higher than Fair Value, it highlights the difference in red; if it is lower, it highlights it in green. If any of the markets is closed or in an auction period, it notifies the user and changes the background color. By default, the CEDEAR or ETF quote used is the last price, but the user may choose to use the BID or OFFER instead. This allows CEDEAR and ETF buyers to compare Fair Value against the OFFER, while sellers may prefer to measure Fair Value against the BID of the local instrument. BCBA:AAPL BCBA:AAPLD NASDAQ:AAPL BCBA:SPY BCBA:TSLA BCBA:TSLAD CEDEARS ETFs ByMA
Pine Script® göstergesi
EcoValores tarafından
Absorption RatioThe Hidden Connections Between Markets Financial markets are not isolated islands. When panic spreads, seemingly unrelated assets suddenly begin moving in lockstep. Stocks, bonds, commodities, and currencies that normally provide diversification benefits start falling together. This phenomenon, where correlations spike during crises, has devastated portfolios throughout history. The Absorption Ratio provides a quantitative measure of this hidden fragility. The concept emerged from research at State Street Associates, where Mark Kritzman, Yuanzhen Li, Sebastien Page, and Roberto Rigobon developed a novel application of principal component analysis to measure systemic risk. Their 2011 paper in the Journal of Portfolio Management demonstrated that when markets become tightly coupled, the variance explained by the first few principal components increases dramatically. This concentration of variance signals elevated systemic risk. What the Absorption Ratio Measures Principal component analysis, or PCA, is a statistical technique that identifies the underlying factors driving a set of variables. When applied to asset returns, the first principal component typically captures broad market movements. The second might capture sector rotations or risk-on/risk-off dynamics. Additional components capture increasingly idiosyncratic patterns. The Absorption Ratio measures the fraction of total variance absorbed or explained by a fixed number of principal components. In the original research, Kritzman and colleagues used the first fifth of the eigenvectors. When this fraction is high, it means a small number of factors are driving most of the market movements. Assets are moving together, and diversification provides less protection than usual. Consider an analogy: imagine a room full of people having independent conversations. Each person speaks at different times about different topics. The total "variance" of sound in the room comes from many independent sources. Now imagine a fire alarm goes off. Suddenly everyone is talking about the same thing, moving in the same direction. The variance is now dominated by a single factor. The Absorption Ratio captures this transition from diverse, independent behavior to unified, correlated movement. The Implementation Approach TradingView does not support matrix algebra required for true principal component analysis. This implementation uses a closely related proxy: the average absolute correlation across a universe of major asset classes. This approach captures the same underlying phenomenon because when assets are highly correlated, the first principal component explains more variance by mathematical necessity. The asset universe includes eight ETFs representing major investable categories: SPY and QQQ for large cap US equities, IWM for small caps, EFA for developed international markets, EEM for emerging markets, TLT for long-term treasuries, GLD for gold, and USO for oil. This selection provides exposure to equities across geographies and market caps, plus traditional diversifying assets. From eight assets, there are twenty-eight unique pairwise correlations. The indicator calculates each using a rolling window, takes the absolute value to measure coupling strength regardless of direction, and averages across all pairs. This average correlation is then transformed to match the typical range of published Absorption Ratio values. The transformation maps zero average correlation to an AR of 0.50 and perfect correlation to an AR of 1.00. This scaling aligns with empirical observations that the AR typically fluctuates between 0.60 and 0.95 in practice. Interpreting the Regimes The indicator classifies systemic risk into four regimes based on AR levels. The Extreme regime occurs when the AR exceeds 0.90. At this level, nearly all asset classes are moving together. Diversification has largely failed. Historically, this regime has coincided with major market dislocations: the 2008 financial crisis, the 2020 COVID crash, and significant correction periods. Portfolios constructed under normal correlation assumptions will experience larger drawdowns than expected. The High regime, between 0.80 and 0.90, indicates elevated systemic risk. Correlations across asset classes are above normal. This often occurs during the build-up to stress events or during volatile periods where fear is spreading but has not reached panic levels. Risk management should be more conservative. The Normal regime covers AR values between 0.60 and 0.80. This represents typical market conditions where some correlation exists between assets but diversification still provides meaningful benefits. Standard portfolio construction assumptions are reasonable. The Low regime, below 0.60, indicates that assets are behaving relatively independently. Diversification is working well. Idiosyncratic factors dominate returns rather than systematic risk. This environment is favorable for active management and security selection strategies. The Relationship to Portfolio Construction The implications for portfolio management are significant. Modern portfolio theory assumes correlations are stable and uses historical estimates to construct efficient portfolios. The Absorption Ratio reveals that this assumption is violated precisely when it matters most. When AR is elevated, the effective number of independent bets in a diversified portfolio shrinks. A portfolio holding stocks, bonds, commodities, and real estate might behave as if it holds only one or two positions during high AR periods. Position sizing based on normal correlation estimates will underestimate portfolio risk. Conversely, when AR is low, true diversification opportunities expand. The same nominal portfolio provides more independent return streams. Risk can be deployed more aggressively while maintaining the same effective exposure. Component Analysis The indicator separately tracks equity correlations and cross-asset correlations. These components tell different stories about market structure. Equity correlations measure coupling within the stock market. High equity correlation indicates broad risk-on or risk-off behavior where all stocks move together. This is common during both rallies and selloffs driven by macroeconomic factors. Stock pickers face headwinds when equity correlations are elevated because individual company fundamentals matter less than market beta. Cross-asset correlations measure coupling between different asset classes. When stocks, bonds, and commodities start moving together, traditional hedges fail. The classic 60/40 stock/bond portfolio, for example, assumes negative or low correlation between equities and treasuries. When cross-asset correlation spikes, this assumption breaks down. During the 2022 market environment, for instance, both stocks and bonds fell significantly as inflation and rate hikes affected all assets simultaneously. High cross-asset correlation warned that the usual defensive allocations would not provide their expected protection. Mean Reversion Characteristics Like most risk metrics, the Absorption Ratio tends to mean-revert over time. Extremely high AR readings eventually normalize as panic subsides and assets return to more independent behavior. Extremely low readings tend to rise as some level of systematic risk always reasserts itself. The indicator tracks AR in statistical terms by calculating its Z-score relative to the trailing distribution. When AR reaches extreme Z-scores, the probability of normalization increases. This creates potential opportunities for strategies that bet on mean reversion in systemic risk. A buy signal triggers when AR recovers from extremely elevated levels, suggesting the worst of the correlation spike may be over. A sell signal triggers when AR rises from unusually low levels, warning that complacency about diversification benefits may be excessive. Momentum and Trend The rate of change in AR carries information beyond the absolute level. Rapidly rising AR suggests correlations are increasing and systemic risk is building. Even if AR has not yet reached the high regime, acceleration in coupling should prompt increased vigilance. Falling AR momentum indicates normalizing conditions. Correlations are decreasing and assets are returning to more independent behavior. This often occurs in the recovery phase following stress events. Practical Application For asset allocators, the AR provides guidance on how much diversification benefit to expect from a given allocation. During high AR periods, reducing overall portfolio risk makes sense because the usual diversifiers provide less protection. During low AR periods, standard or even aggressive allocations are more appropriate. For risk managers, the AR serves as an early warning indicator. Rising AR often precedes large market moves and volatility spikes. Tightening risk limits before correlations reach extreme levels can protect capital. For systematic traders, the AR provides a regime filter. Mean reversion strategies may work better during high AR periods when panics create overshooting. Momentum strategies may work better during low AR periods when trends can develop independently across assets. Limitations and Considerations The proxy methodology introduces some approximation error relative to true PCA-based AR calculations. The asset universe, while representative, does not include all possible diversifiers. Correlation estimates are inherently backward-looking and can change rapidly. The transformation from average correlation to AR scale is calibrated to match typical published ranges but is not mathematically equivalent to the eigenvalue ratio. Users should interpret levels directionally rather than as precise measurements. Correlation regimes can persist longer than expected. Mean reversion signals indicate elevated probability of normalization but do not guarantee timing. High AR can remain elevated throughout extended crisis periods. References Kritzman, M., Li, Y., Page, S., and Rigobon, R. (2011). Principal Components as a Measure of Systemic Risk. Journal of Portfolio Management, 37(4), 112-126. Kritzman, M., and Li, Y. (2010). Skulls, Financial Turbulence, and Risk Management. Financial Analysts Journal, 66(5), 30-41. Billio, M., Getmansky, M., Lo, A., and Pelizzon, L. (2012). Econometric Measures of Connectedness and Systemic Risk in the Finance and Insurance Sectors. Journal of Financial Economics, 104(3), 535-559.
Pine Script® göstergesi
EdgeTools tarafından
RiskCraft - Advanced Risk Management SystemRiskCraft – Risk Intelligence Dashboard Trade like you actually respect risk "I know the setup looks good… but how much am I actually risking right now?" RiskCraft is an open-source Pine Script v6 indicator that keeps risk transparent directly on the chart. It is not a signal generator; it is a risk desk that calculates size, frames volatility, and reminds you when your behaviour drifts away from the plan. Core utilities Calculates professional-style position sizing in real time. Reads volatility and market regime before position size is confirmed. Adjusts risk based on the trader’s emotional state and confidence inputs. Maps session risk across Asian, London, and New York hours. Draws exactly one stop line and one target line in the preferred direction. Provides rotating education tips plus contextual warnings when risk escalates. It is intentionally conservative and keeps you in the game long enough for any separate entry logic to matter. --- Chart layout checklist Use a clean chart on a liquid symbol (e.g., AMEX:SPY or major FX pairs). Main RiskCraft dashboard placed on the right edge. Session Risk box on the left with UTC time visible. Floating risk badge above price. Stop/target guide lines enabled. Education panel visible in the bottom-right corner. --- 1. On-chart components Right-side dashboard : account risk %, position size/value, stop, target, risk/reward, regime, trend strength, emotional state, behavioural score, correlation, and preferred trade direction. Session Risk box : highlights active session (Asian, London, NY), current UTC time, and risk label (High/Med/Low) per session. Floating risk badge : keeps actual account risk percent visible with colour-coded wording from Ultra Cautious to Very Aggressive. Stop/target lines : exactly one dashed stop and one dashed target aligned with the preferred bias. Education panel : rotates core principles and AI-style warnings tied to volatility, risk %, and behaviour flags. --- 2. Volatility engine – ATR with context 📈 atr = ta.atr(atrLength) atrPercent = (atr / close) * 100 atrSMA = ta.sma(atr, atrLength) volatilityRatio = atr / atrSMA isHighVol = volatilityRatio > volThreshold ATR vs ATR SMA shows how wild price is relative to recent history. Volatility ratio above the threshold flips isHighVol , which immediately trims risk. An ATR percentile rank over the last 100 bars indicates calm versus chaotic regimes. Daily ATR sampling via request.security() gives higher time-frame context for intraday sessions. When volatility spikes the script dials position size down automatically instead of cheering for maximum exposure. --- 3. Market regime radar – Danger or Drift 🌊 ema20 = ta.ema(close, 20) ema50 = ta.ema(close, 50) ema200 = ta.ema(close, 200) trendScore = (close > ema20 ? 1 : -1) + (ema20 > ema50 ? 1 : -1) + (ema50 > ema200 ? 1 : -1) = ta.dmi(14, 14) Regimes covered: Danger : high volatility with weak trend. Volatile : volatility elevated but structure still directional. Choppy : low ADX and noisy action. Trending : directional flows without extreme volatility. Mixed : anything between. Each regime maps to a 1–10 risk score and a multiplier that feeds the final position size. Danger and Choppy clamp size; Trending restores normal risk. --- 4. Behaviour engine – trader inputs matter 🧠 You provide: Emotional state : Confident, Neutral, FOMO, Revenge, Fearful. Confidence : slider from 1 to 10. Toggle for behavioural adjustment on/off. Behind the scenes: Each state triggers an emotional multiplier . Confidence produces a confidence multiplier . Combined they form behavioralFactor and a 0–100 Behavioural Score . High-risk emotions or low conviction clamp the final risk. Calm inputs allow normal size. The dashboard prints both fields to keep accountability on-screen. --- 5. Correlation guardrail – avoid stacking identical risk 📊 Optional correlation mode compares the active symbol to a reference (default AMEX:SPY ): corrClose = request.security(correlationSymbol, timeframe.period, close) priceReturn = ta.change(close) / close corrReturn = ta.change(corrClose) / corrClose correlation = calcCorrelation() Absolute correlation above the threshold applies a correlation multiplier (< 1) to reduce size. Dashboard row shows the live correlation and reference ticker. When disabled, the row simply echoes the current symbol, keeping the table readable. --- 6. Position sizing engine – heart of the script 💰 baseRiskAmount = accountSize * (baseRiskPercent / 100) adjustedRisk = baseRiskAmount * behavioralFactor * regimeAdjustment * volAdjustment * correlationAdjustment finalRiskAmount = math.min(adjustedRisk, accountSize * (maxRiskCap / 100)) stopDistance = atr * atrStopMultiplier takeProfit = atr * atrTargetMultiplier positionSize = stopDistance > 0 ? finalRiskAmount / stopDistance : 0 positionValue = positionSize * close Outputs shown on the dashboard: Position size in units and value in currency. Actual risk % back on account after adjustments. Risk/Reward derived from ATR-based stop and target. --- 7. Intelligent trade direction – bias without signals 🎯 Direction score ingredients: EMA stack alignment. Price versus EMA20. RSI momentum relative to 50. MACD line vs signal. Directional Movement (DI+/DI–). The resulting Trade Direction row prints LONG, SHORT, or NEUTRAL. No orders are generated—this is guidance so you only risk capital when the structure supports it. --- 8. Stop/target guide lines – two lines only ✂️ if showStopLines if preferLong // long stop below, target above else if preferShort // short stop above, target below Lines refresh each bar to keep clutter low. When the direction score is neutral, no lines appear. Use them as visual anchors, not auto-orders. --- 9. Session Risk map – global volatility clock 🌍 Tracks Asian, London, and New York windows via UTC. Computes average ATR per session versus global ATR SMA. Labels each session High/Med/Low and colours the cells accordingly. Top row shows the active session plus current UTC time so you always know the regime you are trading. One glance tells you whether you are trading quiet drift or the part of the day that hunts stops. --- 10. Floating risk badge – honesty above price 🪪 Text ranges from Ultra Cautious through Very Aggressive. Colour matches the risk palette inputs (High/Med/Low). Updates on the last bar only, keeping historical clutter off the chart. Account risk becomes impossible to ignore while you stare at price. --- 11. Education engine & warnings 📚 Rotates evergreen principles (risk 1–2%, journal trades, respect plan). Triggers contextual warnings when volatility and risk % conflict. Flags when emotional state = FOMO or Revenge. Highlights sub-standard risk/reward setups. When multiple danger flags stack, an AI-style warning overrides the tip text so you can course-correct before capital is exposed. --- 12. Alerts – hard guard rails 🚨 Excessive Risk Alert : actual risk % crosses custom threshold. High Volatility Alert : ATR behaviour signals danger regime. Emotional State Warning : FOMO or Revenge selected. Poor Risk/Reward Alert : risk/reward drops below your standard. All alerts reinforce discipline; none suggest entries or exits. --- 13. Multi-market behaviour 🕒 Intraday (1m–1h): session box and badge react quickly; ideal for scalpers needing constant risk context. Higher time frames (1D–1W): dashboard shifts slowly, supporting swing planning. Asset classes confirmed in validation: crypto majors, large-cap equities, indices, major FX pairs, and liquid commodities. Risk logic is price-based, so it adapts across markets without bespoke tuning. 15. Key inputs & recommended defaults Account Size : 10,000 (modify to match actual account; min 100). Base Risk % : 1.0 with a Maximum Risk Cap of 2.5%. ATR Period : 14, Stop Multiplier 2.0, Target Multiplier 3.0. High Vol Threshold : 1.5 for ATR ratio. Behavioural Adjustment : enabled by default; disable for fixed risk. Correlation Check : optional; default symbol AMEX:SPY , threshold 0.7. Display toggles : main dashboard, risk badge, session map, education panel, and stop lines can be individually disabled to reduce clutter. 16. Usage notes & limits Indicator mode only; no automated entries or exits. Trade history panel intentionally disabled (requires strategy context). Correlation analysis depends on additional data requests and may lag slightly on illiquid symbols. Session timing uses UTC; adjust expectations if you trade localized instruments. HTF ATR sampling uses daily data, so bar replay on lower charts may show brief data gaps while HTF loads. What does everyone think RISK really means?
Pine Script® göstergesi
officialjackofalltrades tarafından
Daily Dollar Cost Averaging (DCA) Simulator & Yearly PerformanceThis indicator simulates a "Daily Dollar Cost Averaging" strategy directly on your chart. Unlike standard backtesters that trade based on signals, this script calculates the performance of a portfolio where a fixed dollar amount is invested every single day, regardless of price action. Key Features: Daily Accumulation: Simulates buying a specific dollar amount (e.g., $10) at the market close every day. Yearly Breakdown Table: A detailed dashboard displayed on the chart that breaks down performance by year. It tracks total invested, average entry price, total holdings, current value, and PnL percentage for each individual year. Global Stats: The bottom row of the table summarizes the total performance of the entire strategy since the start date. Breakeven Line: Plots a yellow line on the chart representing your "Global Average Price." When the current price is above this line, the total strategy is in profit. How to Use: Add to chart (Works best on the Daily (D) timeframe). Open settings to adjust your Daily Investment Amount and Start Year. The table will automatically update to show how a daily investment strategy would have performed over time.
Pine Script® göstergesi
oojeson tarafından
Dual Account Position Size CalculatorA quick and easy to use position sizing calculator for use on the daily TF only. inputs for two different account sizes and risk %. Calculates risk to low of day (plus a small buffer which can be changed based on ATR). Shows # of shares to buy, stop loss, portfolio %. Will show on smaller timeframes , but be aware that the stop level will no longer be low of day, so it will not calculate properly. Always use on the daily.
Pine Script® göstergesi
gfunbaby tarafından
Monthly DCA & Last 10 YearsThis Pine Script indicator simulates a Monthly Dollar Cost Averaging (DCA) strategy to help long-term investors visualize historical performance. Instead of complex timing, the script automatically executes a hypothetical fixed-dollar purchase (e.g., $100) on the first trading day of every month. It visually marks entry points with green "B" labels and plots a dynamic yellow line representing your Global Break-Even Price, allowing you to instantly see if the current price is above or below your average cost basis. To provide deep insight, it generates a detailed performance table in the bottom-right corner that breaks down metrics year-by-year—including total capital invested, shares/coins accumulated, and Profit/Loss percentage—along with a grand total summary of the entire investment period.
Pine Script® göstergesi
oojeson tarafından
90% Buying Power Position Size Helper90% Buying Power Position Size Helper — Script Description This tool calculates a recommended share size based on your available buying power and the current market price. TradingView does not provide access to live broker balances, so this script allows you to manually enter your current buying power and instantly see how many shares you can buy using a chosen percentage of it (default: 90%). How It Works • Enter your Buying Power ($) • Choose the Percent to Use (e.g., 90%). • The script divides the selected portion of your buying power by the current price of the symbol. • A small display in the chart corner shows the recommended number of shares to buy. Formula shares = floor((buying_power * percent_to_use / 100) / price) What It’s For • Day traders who size positions based on account buying power • Traders who want a quick way to calculate share size per trade • Anyone who sizes entries using a fixed percentage of their account What It Doesn’t Do Due to TradingView limitations, the script cannot: • Read your live buying power or broker balance • Auto-fill orders or submit trades • Retrieve real account data from your broker You simply update the buying power input whenever your account changes, and the script does the rest. Why It’s Useful • Keeps you consistent with position sizing • Reduces manual math during fast trading • Prevents oversizing or undersizing trades • Helps maintain discipline and risk control
Pine Script® göstergesi
TraderBecks tarafından
Fixed Dollar Risk Lines V2*This is a small update to the original concept that adds greater customization of the visual elements of the script. Since some folks have liked the original I figured I'd put this out there.* Fixed Dollar Risk Lines is a utility indicator that converts a user-defined dollar risk into price distance and plots risk lines above and below the current price for popular futures contracts. It helps you place stops or entries at a consistent dollar risk per trade, regardless of the market’s tick value or tick size. What it does: -You choose a dollar amount to risk (e.g., $100) and a futures contract (ES, NQ, GC, YM, RTY, PL, SI, CL, BTC). The script automatically: -Looks up the contract’s tick value and tick size -Converts your dollar risk into number of ticks -Converts ticks into price distance Plots: -Long Risk line below current price -Short Risk line above current price -Optional labels show exact price levels and an information table summarizes your settings. Key features -Consistent dollar risk across instruments -Supports major futures contracts with built‑in tick values and sizes -Toggle Long and Short risk lines independently -Customizable line width and colors (lines and labels) -Right‑axis price level display for quick reading -Compact info table with contract, risk, and computed prices Typical use -Long setups: use the green line as a stop level below entry to match your chosen dollar risk. -Short setups: use the red line as a stop level above entry to match your chosen dollar risk. -Quickly compare how the same dollar risk translates to distance on different contracts. Inputs -Risk Amount (USD) -Futures Contract (ES, NQ, GC, YM, RTY, PL, SI, CL, BTC) -Show Long/Short lines (toggles) -Line Width -Colors for lines and labels Notes -Designed for futures symbols that match the listed contracts’ tick specs. If your symbol has different tick value/size than the defaults, results will differ. -Intended for educational/informational use; not financial advice. -This tool streamlines risk placement so you can focus on execution while keeping dollar risk consistent across markets.
Pine Script® göstergesi
smg387 tarafından
3-Daumen-RegelThis indicator evaluates three key market conditions and summarizes them in a compact table using simple thumbs-up / thumbs-down signals. It’s designed specifically for daily timeframes and helps you quickly assess whether a market is showing technical strength or weakness. The Three Checks Price Above the 200-Day SMA Indicates the long-term trend direction. A thumbs-up means the price is trading above the 200-day moving average. Positive Performance During the First 5 Trading Days of the Year (YTD Start) Measures early-year strength. If not enough bars are available, a warning is shown. Price Above the YTD Level Compares the current price to the first trading day’s close of the year. Color Coding for Instant Clarity Green: Condition met Red: Condition not met This creates a compact “thumbs check” that gives you a quick read on the market’s technical health. Note The indicator is intended for daily charts. A message appears if a different timeframe is used.
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Yazici-Investment-Management tarafından
Expected Move BandsExpected move is the amount that an asset is predicted to increase or decrease from its current price, based on the current levels of volatility. In this model, we assume asset price follows a log-normal distribution and the log return follows a normal distribution. Note: Normal distribution is just an assumption, it's not the real distribution of return Settings: "Estimation Period Selection" is for selecting the period we want to construct the prediction interval. For "Current Bar", the interval is calculated based on the data of the previous bar close. Therefore changes in the current price will have little effect on the range. What current bar means is that the estimated range is for when this bar close. E.g., If the Timeframe on 4 hours and 1 hour has passed, the interval is for how much time this bar has left, in this case, 3 hours. For "Future Bars", the interval is calculated based on the current close. Therefore the range will be very much affected by the change in the current price. If the current price moves up, the range will also move up, vice versa. Future Bars is estimating the range for the period at least one bar ahead. There are also other source selections based on high low. Time setting is used when "Future Bars" is chosen for the period. The value in time means how many bars ahead of the current bar the range is estimating. When time = 1, it means the interval is constructing for 1 bar head. E.g., If the timeframe is on 4 hours, then it's estimating the next 4 hours range no matter how much time has passed in the current bar. Note: It's probably better to use "probability cone" for visual presentation when time > 1 Volatility Models : Sample SD: traditional sample standard deviation, most commonly used, use (n-1) period to adjust the bias Parkinson: Uses High/ Low to estimate volatility, assumes continuous no gap, zero mean no drift, 5 times more efficient than Close to Close Garman Klass: Uses OHLC volatility, zero drift, no jumps, about 7 times more efficient Yangzhang Garman Klass Extension: Added jump calculation in Garman Klass, has the same value as Garman Klass on markets with no gaps. about 8 x efficient Rogers: Uses OHLC, Assume non-zero mean volatility, handles drift, does not handle jump 8 x efficient EWMA: Exponentially Weighted Volatility. Weight recently volatility more, more reactive volatility better in taking account of volatility autocorrelation and cluster. YangZhang: Uses OHLC, combines Rogers and Garmand Klass, handles both drift and jump, 14 times efficient, alpha is the constant to weight rogers volatility to minimize variance. Median absolute deviation: It's a more direct way of measuring volatility. It measures volatility without using Standard deviation. The MAD used here is adjusted to be an unbiased estimator. Volatility Period is the sample size for variance estimation. A longer period makes the estimation range more stable less reactive to recent price. Distribution is more significant on a larger sample size. A short period makes the range more responsive to recent price. Might be better for high volatility clusters. Standard deviations: Standard Deviation One shows the estimated range where the closing price will be about 68% of the time. Standard Deviation two shows the estimated range where the closing price will be about 95% of the time. Standard Deviation three shows the estimated range where the closing price will be about 99.7% of the time. Note: All these probabilities are based on the normal distribution assumption for returns. It's the estimated probability, not the actual probability. Manually Entered Standard Deviation shows the range of any entered standard deviation. The probability of that range will be presented on the panel. People usually assume the mean of returns to be zero. To be more accurate, we can consider the drift in price from calculating the geometric mean of returns. Drift happens in the long run, so short lookback periods are not recommended. Assuming zero mean is recommended when time is not greater than 1. When we are estimating the future range for time > 1, we typically assume constant volatility and the returns to be independent and identically distributed. We scale the volatility in term of time to get future range. However, when there's autocorrelation in returns( when returns are not independent), the assumption fails to take account of this effect. Volatility scaled with autocorrelation is required when returns are not iid. We use an AR(1) model to scale the first-order autocorrelation to adjust the effect. Returns typically don't have significant autocorrelation. Adjustment for autocorrelation is not usually needed. A long length is recommended in Autocorrelation calculation. Note: The significance of autocorrelation can be checked on an ACF indicator. ACF The multimeframe option enables people to use higher period expected move on the lower time frame. People should only use time frame higher than the current time frame for the input. An error warning will appear when input Tf is lower. The input format is multiplier * time unit. E.g. : 1D Unit: M for months, W for Weeks, D for Days, integers with no unit for minutes (E.g. 240 = 240 minutes). S for Seconds. Smoothing option is using a filter to smooth out the range. The filter used here is John Ehler's supersmoother. It's an advance smoothing technique that gets rid of aliasing noise. It affects is similar to a simple moving average with half the lookback length but smoother and has less lag. Note: The range here after smooth no long represent the probability Panel positions can be adjusted in the settings. X position adjusts the horizontal position of the panel. Higher X moves panel to the right and lower X moves panel to the left. Y position adjusts the vertical position of the panel. Higher Y moves panel up and lower Y moves panel down. Step line display changes the style of the bands from line to step line. Step line is recommended because it gets rid of the directional bias of slope of expected move when displaying the bands. Warnings: People should not blindly trust the probability. They should be aware of the risk evolves by using the normal distribution assumption. The real return has skewness and high kurtosis. While skewness is not very significant, the high kurtosis should be noticed. The Real returns have much fatter tails than the normal distribution, which also makes the peak higher. This property makes the tail ranges such as range more than 2SD highly underestimate the actual range and the body such as 1 SD slightly overestimate the actual range. For ranges more than 2SD, people shouldn't trust them. They should beware of extreme events in the tails. Different volatility models provide different properties if people are interested in the accuracy and the fit of expected move, they can try expected move occurrence indicator. (The result also demonstrate the previous point about the drawback of using normal distribution assumption). Expected move Occurrence Test The prediction interval is only for the closing price, not wicks. It only estimates the probability of the price closing at this level, not in between. E.g., If 1 SD range is 100 - 200, the price can go to 80 or 230 intrabar, but if the bar close within 100 - 200 in the end. It's still considered a 68% one standard deviation move.
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️Omega RatioThe Omega Ratio is a risk-return performance measure of an investment asset, portfolio, or strategy. It is defined as the probability-weighted ratio, of gains versus losses for some threshold return target. The ratio is an alternative for the widely used Sharpe ratio and is based on information the Sharpe ratio discards. █ OVERVIEW As we have mentioned many times, stock market returns are usually not normally distributed. Therefore the models that assume a normal distribution of returns may provide us with misleading information. The Omega Ratio improves upon the common normality assumption among other risk-return ratios by taking into account the distribution as a whole. █ CONCEPTS Two distributions with the same mean and variance, would according to the most commonly used Sharpe Ratio suggest that the underlying assets of the distribution offer the same risk-return ratio. But as we have mentioned in our Moments indicator, variance and standard deviation are not a sufficient measure of risk in the stock market since other shape features of a distribution like skewness and excess kurtosis come into play. Omega Ratio tackles this problem by employing all four Moments of the distribution and therefore taking into account the differences in the shape features of the distributions. Another important feature of the Omega Ratio is that it does not require any estimation but is rather calculated directly from the observed data. This gives it an advantage over standard statistical estimators that require estimation of parameters and are therefore sampling uncertainty in its calculations. █ WAYS TO USE THIS INDICATOR Omega calculates a probability-adjusted ratio of gains to losses, relative to the Minimum Acceptable Return (MAR). This means that at a given MAR using the simple rule of preferring more to less, an asset with a higher value of Omega is preferable to one with a lower value. The indicator displays the values of Omega at increasing levels of MARs and creating the so-called Omega Curve. Knowing this one can compare Omega Curves of different assets and decide which is preferable given the MAR of your strategy. The indicator plots two Omega Curves. One for the on chart symbol and another for the off chart symbol that u can use for comparison. When comparing curves of different assets make sure their trading days are the same in order to ensure the same period for the Omega calculations. Value interpretation: Omega<1 will indicate that the risk outweighs the reward and therefore there are more excess negative returns than positive. Omega>1 will indicate that the reward outweighs the risk and that there are more excess positive returns than negative. Omega=1 will indicate that the minimum acceptable return equals the mean return of an asset. And that the probability of gain is equal to the probability of loss. █ FEATURES • "Low-Risk security" lets you select the security that you want to use as a benchmark for Omega calculations. • "Omega Period" is the size of the sample that is used for the calculations. • “Increments” is the number of Minimal Acceptable Return levels the calculation is carried on. • “Other Symbol” lets you select the source of the second curve. • “Color Settings” you can set the color for each curve.
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Linear Moments█ OVERVIEW The Linear Moments indicator, also known as L-moments, is a statistical tool used to estimate the properties of a probability distribution. It is an alternative to conventional moments and is more robust to outliers and extreme values. █ CONCEPTS █ Four moments of a distribution We have mentioned the concept of the Moments of a distribution in one of our previous posts. The method of Linear Moments allows us to calculate more robust measures that describe the shape features of a distribution and are anallougous to those of conventional moments. L-moments therefore provide estimates of the location, scale, skewness, and kurtosis of a probability distribution. The first L-moment, λ₁, is equivalent to the sample mean and represents the location of the distribution. The second L-moment, λ₂, is a measure of the dispersion of the distribution, similar to the sample standard deviation. The third and fourth L-moments, λ₃ and λ₄, respectively, are the measures of skewness and kurtosis of the distribution. Higher order L-moments can also be calculated to provide more detailed information about the shape of the distribution. One advantage of using L-moments over conventional moments is that they are less affected by outliers and extreme values. This is because L-moments are based on order statistics, which are more resistant to the influence of outliers. By contrast, conventional moments are based on the deviations of each data point from the sample mean, and outliers can have a disproportionate effect on these deviations, leading to skewed or biased estimates of the distribution parameters. █ Order Statistics L-moments are statistical measures that are based on linear combinations of order statistics, which are the sorted values in a dataset. This approach makes L-moments more resistant to the influence of outliers and extreme values. However, the computation of L-moments requires sorting the order statistics, which can lead to a higher computational complexity. To address this issue, we have implemented an Online Sorting Algorithm that efficiently obtains the sorted dataset of order statistics, reducing the time complexity of the indicator. The Online Sorting Algorithm is an efficient method for sorting large datasets that can be updated incrementally, making it well-suited for use in trading applications where data is often streamed in real-time. By using this algorithm to compute L-moments, we can obtain robust estimates of distribution parameters while minimizing the computational resources required. █ Bias and efficiency of an estimator One of the key advantages of L-moments over conventional moments is that they approach their asymptotic normal closer than conventional moments. This means that as the sample size increases, the L-moments provide more accurate estimates of the distribution parameters. Asymptotic normality is a statistical property that describes the behavior of an estimator as the sample size increases. As the sample size gets larger, the distribution of the estimator approaches a normal distribution, which is a bell-shaped curve. The mean and variance of the estimator are also related to the true mean and variance of the population, and these relationships become more accurate as the sample size increases. The concept of asymptotic normality is important because it allows us to make inferences about the population based on the properties of the sample. If an estimator is asymptotically normal, we can use the properties of the normal distribution to calculate the probability of observing a particular value of the estimator, given the sample size and other relevant parameters. In the case of L-moments, the fact that they approach their asymptotic normal more closely than conventional moments means that they provide more accurate estimates of the distribution parameters as the sample size increases. This is especially useful in situations where the sample size is small, such as when working with financial data. By using L-moments to estimate the properties of a distribution, traders can make more informed decisions about their investments and manage their risk more effectively. Below we can see the empirical dsitributions of the Variance and L-scale estimators. We ran 10000 simulations with a sample size of 100. Here we can clearly see how the L-moment estimator approaches the normal distribution more closely and how such an estimator can be more representative of the underlying population. █ WAYS TO USE THIS INDICATOR The Linear Moments indicator can be used to estimate the L-moments of a dataset and provide insights into the underlying probability distribution. By analyzing the L-moments, traders can make inferences about the shape of the distribution, such as whether it is symmetric or skewed, and the degree of its spread and peakedness. This information can be useful in predicting future market movements and developing trading strategies. One can also compare the L-moments of the dataset at hand with the L-moments of certain commonly used probability distributions. Finance is especially known for the use of certain fat tailed distributions such as Laplace or Student-t. We have built in the theoretical values of L-kurtosis for certain common distributions. In this way a person can compare our observed L-kurtosis with the one of the selected theoretical distribution. █ FEATURES Source Settings Source - Select the source you wish the indicator to calculate on Source Selection - Selec whether you wish to calculate on the source value or its log return Moments Settings Moments Selection - Select the L-moment you wish to be displayed Lookback - Determine the sample size you wish the L-moments to be calculated with Theoretical Distribution - This setting is only for investingating the kurtosis of our dataset. One can compare our observed kurtosis with the kurtosis of a selected theoretical distribution.
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Historical Volatility EstimatorsHistorical volatility is a statistical measure of the dispersion of returns for a given security or market index over a given period. This indicator provides different historical volatility model estimators with percentile gradient coloring and volatility stats panel. █ OVERVIEW There are multiple ways to estimate historical volatility. Other than the traditional close-to-close estimator. This indicator provides different range-based volatility estimators that take high low open into account for volatility calculation and volatility estimators that use other statistics measurements instead of standard deviation. The gradient coloring and stats panel provides an overview of how high or low the current volatility is compared to its historical values. █ CONCEPTS We have mentioned the concepts of historical volatility in our previous indicators, Historical Volatility, Historical  Volatility  Rank, and Historical  Volatility  Percentile. You can check the definition of these scripts. The basic calculation is just the sample standard deviation of log return scaled with the square root of time. The main focus of this script is the difference between volatility models. Close-to-Close HV Estimator: Close-to-Close is the traditional historical volatility calculation. It uses sample standard deviation. Note: the TradingView build in historical volatility value is a bit off because it uses population standard deviation instead of sample deviation. N – 1 should be used here to get rid of the sampling bias. Pros:   • Close-to-Close HV estimators are the most commonly used estimators in finance. The calculation is straightforward and easy to understand. When people reference historical volatility, most of the time they are talking about the close to close estimator. Cons:   • The Close-to-close estimator only calculates volatility based on the closing price. It does not take account into intraday volatility drift such as high, low. It also does not take account into the jump when open and close prices are not the same.   • Close-to-Close weights past volatility equally during the lookback period, while there are other ways to weight the historical data.   • Close-to-Close is calculated based on standard deviation so it is vulnerable to returns that are not normally distributed and have fat tails. Mean and Median absolute   deviation makes the historical volatility more stable with extreme values. Parkinson Hv Estimator:   • Parkinson was one of the first to come up with improvements to historical volatility calculation. • Parkinson suggests using the High and Low of each bar can represent volatility better as it takes into account intraday volatility. So Parkinson HV is also known as Parkinson High Low HV. • It is about 5.2 times more efficient than Close-to-Close estimator. But it does not take account into jumps and drift. Therefore, it underestimates volatility. Note: By Dividing the Parkinson Volatility by Close-to-Close volatility you can get a similar result to Variance Ratio Test. It is called the Parkinson number. It can be used to test if the market follows a random walk. (It is mentioned in Nassim Taleb's Dynamic Hedging book but it seems like he made a mistake and wrote the ratio wrongly.) Garman-Klass Estimator:   • Garman Klass expanded on Parkinson’s Estimator. Instead of Parkinson’s estimator using high and low, Garman Klass’s method uses open, close, high, and low to find the minimum variance method.   • The estimator is about 7.4 more efficient than the traditional estimator. But like Parkinson HV, it ignores jumps and drifts. Therefore, it underestimates volatility. Rogers-Satchell Estimator:   • Rogers and Satchell found some drawbacks in Garman-Klass’s estimator. The Garman-Klass assumes price as Brownian motion with zero drift.   • The Rogers Satchell Estimator calculates based on open, close, high, and low. And it can also handle drift in the financial series.   • Rogers-Satchell HV is more efficient than Garman-Klass HV when there’s drift in the data. However, it is a little bit less efficient when drift is zero. The estimator doesn’t handle jumps, therefore it still underestimates volatility. Garman-Klass Yang-Zhang extension:   • Yang Zhang expanded Garman Klass HV so that it can handle jumps. However, unlike the Rogers-Satchell estimator, this estimator cannot handle drift. It is about 8 times more efficient than the traditional estimator.   • The Garman-Klass Yang-Zhang extension HV has the same value as Garman-Klass when there’s no gap in the data such as in cryptocurrencies. Yang-Zhang Estimator:   • The Yang Zhang Estimator combines Garman-Klass and Rogers-Satchell Estimator so that it is based on Open, close, high, and low and it can also handle non-zero drift. It also expands the calculation so that the estimator can also handle overnight jumps in the data.   • This estimator is the most powerful estimator among the range-based estimators. It has the minimum variance error among them, and it is 14 times more efficient than the close-to-close estimator. When the overnight and daily volatility are correlated, it might underestimate volatility a little.   • 1.34 is the optimal value for alpha according to their paper. The alpha constant in the calculation can be adjusted in the settings. Note: There are already some volatility estimators coded on TradingView. Some of them are right, some of them are wrong. But for Yang Zhang Estimator I have not seen a correct version on TV. EWMA Estimator: • EWMA stands for Exponentially Weighted Moving Average. The Close-to-Close and all other estimators here are all equally weighted.   • EWMA weighs more recent volatility more and older volatility less. The benefit of this is that volatility is usually autocorrelated. The autocorrelation has close to exponential decay as you can see using an Autocorrelation Function indicator on absolute or squared returns. The autocorrelation causes volatility clustering which values the recent volatility more. Therefore, exponentially weighted volatility can suit the property of volatility well.   • RiskMetrics uses 0.94 for lambda which equals 30 lookback period. In this indicator Lambda is coded to adjust with the lookback. It's also easy for EWMA to forecast one period volatility ahead.   • However, EWMA volatility is not often used because there are better options to weight volatility such as ARCH and GARCH. Adjusted Mean Absolute Deviation Estimator:   • This estimator does not use standard deviation to calculate volatility. It uses the distance log return is from its moving average as volatility.   • It’s a simple way to calculate volatility and it’s effective. The difference is the estimator does not have to square the log returns to get the volatility. The paper suggests this estimator has more predictive power.   • The mean absolute deviation here is adjusted to get rid of the bias. It scales the value so that it can be comparable to the other historical volatility estimators.   • In Nassim Taleb’s paper, he mentions people sometimes confuse MAD with standard deviation for volatility measurements. And he suggests people use mean absolute deviation instead of standard deviation when we talk about volatility. Adjusted Median Absolute Deviation Estimator:   • This is another estimator that does not use standard deviation to measure volatility.   • Using the median gives a more robust estimator when there are extreme values in the returns. It works better in fat-tailed distribution.   • The median absolute deviation is adjusted by maximum likelihood estimation so that its value is scaled to be comparable to other volatility estimators. █ FEATURES   • You can select the volatility estimator models in the Volatility Model input   • Historical Volatility is annualized. You can type in the numbers of trading days in a year in the Annual input based on the asset you are trading.   • Alpha is used to adjust the Yang Zhang volatility estimator value.   • Percentile Length is used to Adjust Percentile coloring lookbacks.   • The gradient coloring will be based on the percentile value (0- 100). The higher the percentile value, the warmer the color will be, which indicates high volatility. The lower the percentile value, the colder the color will be, which indicates low volatility.   • When percentile coloring is off, it won’t show the gradient color.   • You can also use invert color to make the high volatility a cold color and a low volatility high color. Volatility has some mean reversion properties. Therefore when volatility is very low, and color is close to aqua, you would expect it to expand soon. When volatility is very high, and close to red, you would it expect it to contract and cool down.   • When the background signal is on, it gives a signal when HVP is very low. Warning there might be a volatility expansion soon. • You can choose the plot style, such as lines, columns, areas in the plotstyle input.   • When the show information panel is on, a small panel will display on the right.   • The information panel displays the historical volatility model name, the 50th percentile of HV, and HV percentile. 50 the percentile of HV also means the median of HV. You can compare the value with the current HV value to see how much it is above or below so that you can get an idea of how high or low HV is. HV Percentile value is from 0 to 100. It tells us the percentage of periods over the entire lookback that historical volatility traded below the current level. Higher HVP, higher HV compared to its historical data. The gradient color is also based on this value. █ HOW TO USE If you haven’t used the hvp indicator, we suggest you use the HVP indicator first. This indicator is more like historical volatility with HVP coloring. So it displays HVP values in the color and panel, but it’s not range bound like the HVP and it displays HV values. The user can have a quick understanding of how high or low the current volatility is compared to its historical value based on the gradient color. They can also time the market better based on volatility mean reversion. High volatility means volatility contracts soon (Move about to End, Market will cooldown), low volatility means volatility expansion soon (Market About to Move). █ FINAL THOUGHTS HV vs ATR The above volatility estimator concepts are a display of history in the quantitative finance realm of the research of historical volatility estimations. It's a timeline of range based from the Parkinson Volatility to Yang Zhang volatility. We hope these descriptions make more people know that even though ATR is the most popular volatility indicator in technical analysis, it's not the best estimator. Almost no one in quant finance uses ATR to measure volatility (otherwise these papers will be based on how to improve ATR measurements instead of HV). As you can see, there are much more advanced volatility estimators that also take account into open, close, high, and low. HV values are based on log returns with some calculation adjustment. It can also be scaled in terms of price just like ATR. And for profit-taking ranges, ATR is not based on probabilities. Historical volatility can be used in a probability distribution function to calculated the probability of the ranges such as the Expected Move indicator. Other Estimators There are also other more advanced historical volatility estimators. There are high frequency sampled HV that uses intraday data to calculate volatility. We will publish the high frequency volatility estimator in the future. There's also ARCH and GARCH models that takes volatility clustering into account. GARCH models require maximum likelihood estimation which needs a solver to find the best weights for each component. This is currently not possible on TV due to large computational power requirements. All the other indicators claims to be GARCH are all wrong.
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Single AHR DCA (HM) — AHR Pane (customized quantile)Customized note The log-regression window LR length controls how long a long-term fair value path is estimated from historical data. The AHR window AHR window length controls over which historical regime you measure whether the coin is “cheap / expensive”. When you choose a log-regression window of length L (years) and an AHR window of length A (years), you can intuitively read the indicator as: “Within the last A years of this regime, relative to the long-term trend estimated over the same A years, the current price is cheap / neutral / expensive.” Guidelines: In general, set the AHR window equal to or slightly longer than the LR window: If the AHR window is much longer than LR, you mix different baselines (different LR regimes) into one distribution. If the AHR window is much shorter than LR, quantiles mostly reflect a very local slice of history. For BTC / ETH and other BTC-like assets, you can use relatively long horizons (e.g. LR ≈ 3–5 years, AHR window ≈ 3–8 years). For major altcoins (BNB / SOL / XRP and similar high-beta assets), it is recommended to use equal or slightly shorter horizons, e.g. LR ≈ 2–3 years, AHR window ≈ 2–3 years. 1. Price series & windows Working timeframe: daily (1D). Let the daily close of the current symbol on day t be P_t . Main length parameters: HM window: L_HM = maLen (default 200 days) Log-regression window: L_LR = lrLen (default 1095 days ≈ 3 years) AHR window (regime window): W = windowLen (default 1095 days ≈ 3 years) 2. Harmonic moving average (HM) On a window of length L_HM, define the harmonic mean: HM_t = ^(-1) Here eps = 1e-10 is used to avoid division by zero. Intuition: HM is more sensitive to low prices – an extremely low price inside the window will drag HM down significantly. 3. Log-regression baseline (LR) On a window of length L_LR, perform a linear regression on log price: Over the last L_LR bars, build the series x_k = log( max(P_k, eps) ), for k = t-L_LR+1 ... t, and fit x_k ≈ a + b * k. The fitted value at the current index t is log_P_hat_t = a + b * t. Exponentiate to get the log-regression baseline: LR_t = exp( log_P_hat_t ). Interpretation: LR_t is the long-term trend / fair value path of the current regime over the past L_LR days. 4. HM-based AHR (valuation ratio) At each time t, build an HM-based AHR (valuation multiple): AHR_t = ( P_t / HM_t ) * ( P_t / LR_t ) Interpretation: P_t / HM_t : deviation of price from the mid-term HM (e.g. 200-day harmonic mean). P_t / LR_t : deviation of price from the long-term log-regression trend. Multiplying them means: if price is above both HM and LR, “expensiveness” is amplified; if price is below both, “cheapness” is amplified. Typical reading: AHR_t < 1 : price is below both mid-term mean and long-term trend → statistically cheaper. AHR_t > 1 : price is above both mid-term mean and long-term trend → statistically more expensive. 5. Empirical quantile thresholds (Opp / Risk) On each new day, whenever AHR_t is valid, add it into a rolling array: A_t_window = { AHR_{t-W+1}, ..., AHR_t } (at most W = windowLen elements) On this empirical distribution, define two quantiles: Opportunity quantile: q_opp (default 15%) Risk quantile: q_risk (default 65%) Using standard percentile computation (order statistics + linear interpolation), we get: Opp threshold: theta_opp = Percentile( A_t_window, q_opp ) Risk threshold: theta_risk = Percentile( A_t_window, q_risk ) We also compute the percentile rank of the current AHR inside the same history: q_now = PercentileRank( A_t_window, AHR_t ) ∈ This yields three valuation zones: Opportunity zone: AHR_t <= theta_opp (corresponds to roughly the cheapest ~q_opp% of historical states in the last W days.) Neutral zone: theta_opp < AHR_t < theta_risk Risk zone: AHR_t >= theta_risk (corresponds to roughly the most expensive ~(100 - q_risk)% of historical states in the last W days.) All quantiles are purely empirical and symbol-specific: they are computed only from the current asset’s own history, without reusing BTC thresholds or assuming cross-asset similarity. 6. DCA simulation (lightweight, rolling window) Given: a daily budget B (input: budgetPerDay), and a DCA simulation window H (input: dcaWindowLen, default 900 days ≈ 2.5 years), The script applies the following rule on each new day t: If thresholds are unavailable or AHR_t > theta_risk → classify as Risk zone → buy = 0 If AHR_t <= theta_opp → classify as Opportunity zone → buy = 2B (double size) Otherwise (Neutral zone) → buy = B (normal DCA) Daily invested cash: C_t ∈ {0, B, 2B} Daily bought quantity: DeltaQ_t = C_t / P_t The script keeps rolling sums over the last H days: Cumulative position: Q_H = sum_{k=t-H+1..t} DeltaQ_k Cumulative invested cash: C_H = sum_{k=t-H+1..t} C_k Current portfolio value: PortVal_t = Q_H * P_t Cumulative P&L: PnL_t = PortVal_t - C_H Active days: number of days in the last H with C_k > 0. These results are only used to visualize how this AHR-quantile-driven DCA rule would have behaved over the recent regime, and do not constitute financial advice.
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Mebane Faber GTAA 5In 2007, Mebane Faber published research that challenged the conventional wisdom of buy-and-hold investing. His paper, titled "A Quantitative Approach to Tactical Asset Allocation" and published in the Journal of Wealth Management, demonstrated that a simple timing mechanism could reduce portfolio volatility and drawdowns while maintaining competitive returns (Faber, 2007). This indicator implements his Global Tactical Asset Allocation strategy, known as GTAA5, following the original methodology. The core insight of Faber's research stems from a century of market data. By analyzing asset class performance from 1901 onwards, Faber found that a ten-month simple moving average served as an effective trend filter across major asset classes. When an asset trades above its ten-month moving average, it tends to continue its upward trajectory; when it falls below, significant drawdowns often follow (Faber, 2007, pp. 12-16). This observation aligns with momentum research by Jegadeesh and Titman (1993), who documented that intermediate-term momentum persists across equity markets. The GTAA5 strategy allocates capital equally across five diversified asset classes: domestic equities (SPY), international developed markets (EFA), aggregate bonds (AGG), commodities (DBC), and real estate investment trusts (VNQ). Each asset receives a twenty percent allocation when trading above its ten-month moving average. When an asset falls below this threshold, its allocation moves to short-term treasury bills (SHY), creating a dynamic cash position that scales with market risk (Cambria Investment Management, 2013). The strategy's historical performance during market crises illustrates its function. During the 2008 financial crisis, traditional sixty-forty portfolios experienced drawdowns exceeding forty percent. The GTAA5 strategy limited losses to approximately twelve percent by reducing equity exposure as prices declined below their moving averages (Faber, 2013). This asymmetric return profile represents the strategy's primary characteristic. This implementation uses monthly closing prices retrieved via request.security() to calculate the ten-month simple moving average. This distinction matters, as approximations using daily data (such as a 200-day moving average) can generate different signals during volatile periods. Monthly data ensures the indicator produces signals consistent with published academic research. The indicator provides position monitoring, automatic rebalancing detection on either the first or last trading day of each month, and share calculations based on user-defined capital. A dashboard displays current trend status for each asset class, target versus actual weightings, and trade instructions for rebalancing. Performance metrics including annualized volatility and Sharpe ratio provide ongoing risk assessment. Several limitations warrant acknowledgment. First, the strategy rebalances monthly, meaning it cannot respond to intra-month market crashes. Second, transaction costs and taxes from monthly rebalancing may reduce net returns for taxable accounts. Third, the ten-month lookback period, while historically robust, offers no guarantee of future effectiveness. As Ilmanen (2011) notes in "Expected Returns", all timing strategies face the risk of regime change, where historical relationships break down. This indicator serves educational purposes and portfolio monitoring. It does not constitute financial advice. References: Cambria Investment Management (2013). Global Tactical Asset Allocation: An Introduction to the Approach. Research Report, Los Angeles. Faber, M.T. (2007). A Quantitative Approach to Tactical Asset Allocation. Journal of Wealth Management, Spring 2007, pp. 9-79. Faber, M.T. (2013). Global Asset Allocation: A Survey of the World's Top Asset Allocation Strategies. Cambria Investment Management, Los Angeles. Ilmanen, A. (2011). Expected Returns: An Investor's Guide to Harvesting Market Rewards. John Wiley and Sons, Chichester. Jegadeesh, N. and Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1), pp. 65-91.
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