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Bear Market Probability Model# Bear Market Probability Model: A Multi-Factor Risk Assessment Framework The Bear Market Probability Model represents a comprehensive quantitative framework for assessing systemic market risk through the integration of 13 distinct risk factors across four analytical categories: macroeconomic indicators, technical analysis factors, market sentiment measures, and market breadth metrics. This indicator synthesizes established financial research methodologies to provide real-time probabilistic assessments of impending bear market conditions, offering institutional-grade risk management capabilities to retail and professional traders alike. ## Theoretical Foundation ### Historical Context of Bear Market Prediction Bear market prediction has been a central focus of financial research since the seminal work of Dow (1901) and the subsequent development of technical analysis theory. The challenge of predicting market downturns gained renewed academic attention following the market crashes of 1929, 1987, 2000, and 2008, leading to the development of sophisticated multi-factor models. Fama and French (1989) demonstrated that certain financial variables possess predictive power for stock returns, particularly during market stress periods. Their three-factor model laid the groundwork for multi-dimensional risk assessment, which this indicator extends through the incorporation of real-time market microstructure data. ### Methodological Framework The model employs a weighted composite scoring methodology based on the theoretical framework established by Campbell and Shiller (1998) for market valuation assessment, extended through the incorporation of high-frequency sentiment and technical indicators as proposed by Baker and Wurgler (2006) in their seminal work on investor sentiment. The mathematical foundation follows the general form: Bear Market Probability = Σ(Wi × Ci) / ΣWi × 100 Where: - Wi = Category weight (i = 1,2,3,4) - Ci = Normalized category score - Categories: Macroeconomic, Technical, Sentiment, Breadth ## Component Analysis ### 1. Macroeconomic Risk Factors #### Yield Curve Analysis The inclusion of yield curve inversion as a primary predictor follows extensive research by Estrella and Mishkin (1998), who demonstrated that the term spread between 3-month and 10-year Treasury securities has historically preceded all major recessions since 1969. The model incorporates both the 2Y-10Y and 3M-10Y spreads to capture different aspects of monetary policy expectations. Implementation: - 2Y-10Y Spread: Captures market expectations of monetary policy trajectory - 3M-10Y Spread: Traditional recession predictor with 12-18 month lead time Scientific Basis: Harvey (1988) and subsequent research by Ang, Piazzesi, and Wei (2006) established the theoretical foundation linking yield curve inversions to economic contractions through the expectations hypothesis of the term structure. #### Credit Risk Premium Assessment High-yield credit spreads serve as a real-time gauge of systemic risk, following the methodology established by Gilchrist and Zakrajšek (2012) in their excess bond premium research. The model incorporates the ICE BofA High Yield Master II Option-Adjusted Spread as a proxy for credit market stress. Threshold Calibration: - Normal conditions: < 350 basis points - Elevated risk: 350-500 basis points - Severe stress: > 500 basis points #### Currency and Commodity Stress Indicators The US Dollar Index (DXY) momentum serves as a risk-off indicator, while the Gold-to-Oil ratio captures commodity market stress dynamics. This approach follows the methodology of Akram (2009) and Beckmann, Berger, and Czudaj (2015) in analyzing commodity-currency relationships during market stress. ### 2. Technical Analysis Factors #### Multi-Timeframe Moving Average Analysis The technical component incorporates the well-established moving average convergence methodology, drawing from the work of Brock, Lakonishok, and LeBaron (1992), who provided empirical evidence for the profitability of technical trading rules. Implementation: - Price relative to 50-day and 200-day simple moving averages - Moving average convergence/divergence analysis - Multi-timeframe MACD assessment (daily and weekly) #### Momentum and Volatility Analysis The model integrates Relative Strength Index (RSI) analysis following Wilder's (1978) original methodology, combined with maximum drawdown analysis based on the work of Magdon-Ismail and Atiya (2004) on optimal drawdown measurement. ### 3. Market Sentiment Factors #### Volatility Index Analysis The VIX component follows the established research of Whaley (2009) and subsequent work by Bekaert and Hoerova (2014) on VIX as a predictor of market stress. The model incorporates both absolute VIX levels and relative VIX spikes compared to the 20-day moving average. Calibration: - Low volatility: VIX < 20 - Elevated concern: VIX 20-25 - High fear: VIX > 25 - Panic conditions: VIX > 30 #### Put-Call Ratio Analysis Options flow analysis through put-call ratios provides insight into sophisticated investor positioning, following the methodology established by Pan and Poteshman (2006) in their analysis of informed trading in options markets. ### 4. Market Breadth Factors #### Advance-Decline Analysis Market breadth assessment follows the classic work of Fosback (1976) and subsequent research by Brown and Cliff (2004) on market breadth as a predictor of future returns. Components: - Daily advance-decline ratio - Advance-decline line momentum - McClellan Oscillator (Ema19 - Ema39 of A-D difference) #### New Highs-New Lows Analysis The new highs-new lows ratio serves as a market leadership indicator, based on the research of Zweig (1986) and validated in academic literature by Zarowin (1990). ## Dynamic Threshold Methodology The model incorporates adaptive thresholds based on rolling volatility and trend analysis, following the methodology established by Pagan and Sossounov (2003) for business cycle dating. This approach allows the model to adjust sensitivity based on prevailing market conditions. Dynamic Threshold Calculation: - Warning Level: Base threshold ± (Volatility × 1.0) - Danger Level: Base threshold ± (Volatility × 1.5) - Bounds: ±10-20 points from base threshold ## Professional Implementation ### Institutional Usage Patterns Professional risk managers typically employ multi-factor bear market models in several contexts: #### 1. Portfolio Risk Management - Tactical Asset Allocation: Reducing equity exposure when probability exceeds 60-70% - Hedging Strategies: Implementing protective puts or VIX calls when warning thresholds are breached - Sector Rotation: Shifting from growth to defensive sectors during elevated risk periods #### 2. Risk Budgeting - Value-at-Risk Adjustment: Incorporating bear market probability into VaR calculations - Stress Testing: Using probability levels to calibrate stress test scenarios - Capital Requirements: Adjusting regulatory capital based on systemic risk assessment #### 3. Client Communication - Risk Reporting: Quantifying market risk for client presentations - Investment Committee Decisions: Providing objective risk metrics for strategic decisions - Performance Attribution: Explaining defensive positioning during market stress ### Implementation Framework Professional traders typically implement such models through: #### Signal Hierarchy: 1. Probability < 30%: Normal risk positioning 2. Probability 30-50%: Increased hedging, reduced leverage 3. Probability 50-70%: Defensive positioning, cash building 4. Probability > 70%: Maximum defensive posture, short exposure consideration #### Risk Management Integration: - Position Sizing: Inverse relationship between probability and position size - Stop-Loss Adjustment: Tighter stops during elevated risk periods - Correlation Monitoring: Increased attention to cross-asset correlations ## Strengths and Advantages ### 1. Comprehensive Coverage The model's primary strength lies in its multi-dimensional approach, avoiding the single-factor bias that has historically plagued market timing models. By incorporating macroeconomic, technical, sentiment, and breadth factors, the model provides robust risk assessment across different market regimes. ### 2. Dynamic Adaptability The adaptive threshold mechanism allows the model to adjust sensitivity based on prevailing volatility conditions, reducing false signals during low-volatility periods and maintaining sensitivity during high-volatility regimes. ### 3. Real-Time Processing Unlike traditional academic models that rely on monthly or quarterly data, this indicator processes daily market data, providing timely risk assessment for active portfolio management. ### 4. Transparency and Interpretability The component-based structure allows users to understand which factors are driving risk assessment, enabling informed decision-making about model signals. ### 5. Historical Validation Each component has been validated in academic literature, providing theoretical foundation for the model's predictive power. ## Limitations and Weaknesses ### 1. Data Dependencies The model's effectiveness depends heavily on the availability and quality of real-time economic data. Federal Reserve Economic Data (FRED) updates may have lags that could impact model responsiveness during rapidly evolving market conditions. ### 2. Regime Change Sensitivity Like most quantitative models, the indicator may struggle during unprecedented market conditions or structural regime changes where historical relationships break down (Taleb, 2007). ### 3. False Signal Risk Multi-factor models inherently face the challenge of balancing sensitivity with specificity. The model may generate false positive signals during normal market volatility periods. ### 4. Currency and Geographic Bias The model focuses primarily on US market indicators, potentially limiting its effectiveness for global portfolio management or non-USD denominated assets. ### 5. Correlation Breakdown During extreme market stress, correlations between risk factors may increase dramatically, reducing the model's diversification benefits (Forbes and Rigobon, 2002). ## References Akram, Q. F. (2009). Commodity prices, interest rates and the dollar. Energy Economics, 31(6), 838-851. Ang, A., Piazzesi, M., & Wei, M. (2006). What does the yield curve tell us about GDP growth? Journal of Econometrics, 131(1-2), 359-403. Baker, M., & Wurgler, J. (2006). Investor sentiment and the cross‐section of stock returns. The Journal of Finance, 61(4), 1645-1680. Baker, S. R., Bloom, N., & Davis, S. J. (2016). Measuring economic policy uncertainty. The Quarterly Journal of Economics, 131(4), 1593-1636. Barber, B. M., & Odean, T. (2001). Boys will be boys: Gender, overconfidence, and common stock investment. The Quarterly Journal of Economics, 116(1), 261-292. Beckmann, J., Berger, T., & Czudaj, R. (2015). Does gold act as a hedge or a safe haven for stocks? A smooth transition approach. Economic Modelling, 48, 16-24. Bekaert, G., & Hoerova, M. (2014). The VIX, the variance premium and stock market volatility. Journal of Econometrics, 183(2), 181-192. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. The Journal of Finance, 47(5), 1731-1764. Brown, G. W., & Cliff, M. T. (2004). Investor sentiment and the near-term stock market. Journal of Empirical Finance, 11(1), 1-27. Campbell, J. Y., & Shiller, R. J. (1998). Valuation ratios and the long-run stock market outlook. The Journal of Portfolio Management, 24(2), 11-26. Dow, C. H. (1901). Scientific stock speculation. The Magazine of Wall Street. Estrella, A., & Mishkin, F. S. (1998). Predicting US recessions: Financial variables as leading indicators. Review of Economics and Statistics, 80(1), 45-61. Fama, E. F., & French, K. R. (1989). Business conditions and expected returns on stocks and bonds. Journal of Financial Economics, 25(1), 23-49. Forbes, K. J., & Rigobon, R. (2002). No contagion, only interdependence: measuring stock market comovements. The Journal of Finance, 57(5), 2223-2261. Fosback, N. G. (1976). Stock market logic: A sophisticated approach to profits on Wall Street. The Institute for Econometric Research. Gilchrist, S., & Zakrajšek, E. (2012). Credit spreads and business cycle fluctuations. American Economic Review, 102(4), 1692-1720. Harvey, C. R. (1988). The real term structure and consumption growth. Journal of Financial Economics, 22(2), 305-333. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. Magdon-Ismail, M., & Atiya, A. F. (2004). Maximum drawdown. Risk, 17(10), 99-102. Nickerson, R. S. (1998). Confirmation bias: A ubiquitous phenomenon in many guises. Review of General Psychology, 2(2), 175-220. Pagan, A. R., & Sossounov, K. A. (2003). A simple framework for analysing bull and bear markets. Journal of Applied Econometrics, 18(1), 23-46. Pan, J., & Poteshman, A. M. (2006). The information in option volume for future stock prices. The Review of Financial Studies, 19(3), 871-908. Taleb, N. N. (2007). The black swan: The impact of the highly improbable. Random House. Whaley, R. E. (2009). Understanding the VIX. The Journal of Portfolio Management, 35(3), 98-105. Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research. Zarowin, P. (1990). Size, seasonality, and stock market overreaction. Journal of Financial and Quantitative Analysis, 25(1), 113-125. Zweig, M. E. (1986). Winning on Wall Street. Warner Books.
Pine Script®指標
由EdgeTools提供
10
Multi-Session ORBThe Multi-Session ORB Indicator is a customizable Pine Script (version 6) tool designed for TradingView to plot Opening Range Breakout (ORB) levels across four major trading sessions: Sydney, Tokyo, London, and New York. It allows traders to define specific ORB durations and session times in Central Daylight Time (CDT), making it adaptable to various trading strategies. Key Features: 1. Customizable ORB Duration: Users can set the ORB duration (default: 15 minutes) via the inputMax parameter, determining the time window for calculating the high and low of each session’s opening range. 2. Flexible Session Times: The indicator supports user-defined session and ORB times for: ◦ Sydney: Default ORB (17:00–17:15 CDT), Session (17:00–01:00 CDT) ◦ Tokyo: Default ORB (19:00–19:15 CDT), Session (19:00–04:00 CDT) ◦ London: Default ORB (02:00–02:15 CDT), Session (02:00–11:00 CDT) ◦ New York: Default ORB (08:30–08:45 CDT), Session (08:30–16:00 CDT) 3. Session-Specific ORB Levels: For each session, the indicator calculates and tracks the high and low prices during the specified ORB period. These levels are updated dynamically if new highs or lows occur within the ORB timeframe. 4. Visual Representation: ◦ ORB high and low lines are plotted only during their respective session times, ensuring clarity. ◦ Each session’s lines are color-coded for easy identification: ▪ Sydney: Light Yellow (high), Dark Yellow (low) ▪ Tokyo: Light Pink (high), Dark Pink (low) ▪ London: Light Blue (high), Dark Blue (low) ▪ New York: Light Purple (high), Dark Purple (low) ◦ Lines are drawn with a linewidth of 2 and disappear when the session ends or if the timeframe is not intraday (or exceeds the ORB duration). 5. Intraday Compatibility: The indicator is optimized for intraday timeframes (e.g., 1-minute to 15-minute charts) and only displays when the chart’s timeframe multiplier is less than or equal to the ORB duration. How It Works: • Session Detection: The script uses the time() function to check if the current bar falls within the user-defined ORB or session time windows, accounting for all days of the week. • ORB Logic: At the start of each session’s ORB period, the script initializes the high and low based on the first bar’s prices. It then updates these levels if subsequent bars within the ORB period exceed the current high or fall below the current low. • Plotting: ORB levels are plotted as horizontal lines during the respective session, with visibility controlled to avoid clutter outside session times or on incompatible timeframes. Use Case: Traders can use this indicator to identify key breakout levels for each trading session, facilitating strategies based on price action around the opening range. The flexibility to adjust ORB and session times makes it suitable for various markets (e.g., forex, stocks, or futures) and time zones. Limitations: • The indicator is designed for intraday timeframes and may not display on higher timeframes (e.g., daily or weekly) or if the timeframe multiplier exceeds the ORB duration. • Time inputs are in CDT, requiring users to adjust for their local timezone or market requirements. • If you need to use this for GC/CL/SPY/QQQ you have to adjust the times by one hour. This indicator is ideal for traders focusing on session-based breakout strategies, offering clear visualization and customization for global market sessions.
Pine Script®指標
由Eskemo_Bob提供
Bitcoin Open Interest [SAKANE]Bitcoin Open Interest — Unveiling the True Flow of Capital PurposeVisualize and compare Bitcoin open interest (OI) from CME and Binance, the leading derivatives exchanges, in a single intuitive chart, providing traders with clear insights into crypto market capital dynamics. Background & MotivationIn the 24/7 crypto market, price movements alone reveal only part of the story. Open interest (OI)—the total outstanding futures contracts—offers critical clues to the market’s next move. Yet, accessing and interpreting OI data is challenging: CME Constraints: Commitment of Traders (COT) reports are weekly, and standalone BTC1! or BTC2! OI is noisy due to contract rollovers, obscuring true OI changes. Existing Tool Limitations: Most OI indicators are fixed to either USD or BTC, limiting flexible analysis. This indicator overcomes these hurdles, enabling seamless comparison of CME and Binance OI to track the market’s “capital center of gravity” in real time. Key Features Synthetic CME OI: Combines BTC1! and BTC2! to deliver high-accuracy OI, eliminating rollover noise. Multi-Timeframe Analysis: Displays daily CME OI as pseudo-candlestick (OHLC) on any timeframe (e.g., 4H), allowing intuitive capital flow tracking across timeframes. CME/Binance One-Click Toggle: Instantly compare institutional-driven CME and retail-driven Binance OI. USD/BTC Flexibility: Switch between BTC (real demand) and USD (margin) perspectives for OI analysis. Robust Design: Concise, global-scope code ensures stability and adaptability to TradingView updates. Insights & Use Cases Holistic Market Sentiment: Analyze capital flows by region and exchange for a multidimensional view. Signal Detection: E.g., a sharp drop in CME OI during a sell-off may signal institutional withdrawal. Retail Trends: A surge in Binance OI suggests retail-driven inflows. Event-Driven Insights: E.g., during a hypothetical April 2025 “Trump Tariff Shock,” instantly identify which exchange drives capital shifts. Unique ValueUnlike price-centric indicators, this tool focuses on capital flow (OI). It’s the only indicator offering one-click multi-timeframe and multi-exchange OI comparison, empowering traders to uncover the market’s “true intent” and gain a strategic edge. ConclusionBitcoin Open Interest makes the market’s hidden capital movements accessible to all. By capturing market dynamics and pinpointing the “leading forces” during events, it sets a new standard for traders seeking a revolutionary perspective.
Pine Script®指標
由amusedMagpie26867提供
GCM Centre Line Candle MarkerGCM Centre Line Candle Marker (GCM-CLCM) - Descriptive Notes Indicator Overview: The "GCM Centre Line Candle Marker" is a versatile TradingView overlay indicator designed to enhance chart analysis by drawing short horizontal lines at user-defined "centre" points of candles. These lines provide a quick visual reference to key price levels within each candle, such as midpoints, open, close, or typical prices. The indicator offers extensive customization for line appearance, positioning, and conditional display, including an option to highlight only bullish engulfing patterns. Key Features: 1. Customizable Line Position: o Users can choose from various methods to calculate the "centre" price for the line:  (High + Low) / 2 (Default)  (Open + Close) / 2  Close  Open  (Open + High + Low + Close) / 4 (HLCO/4)  (Open + High + Close) / 3 (Typical Price HLC/3 variation)  (Open + Close + Low) / 3 (Typical Price OCL/3 variation) 2. Line Appearance Customization: o Visibility: Toggle lines on/off. o Style: Solid, dotted, or dashed lines. o Width: Adjustable line thickness (1 to 5). o Length: Defines how many candles forward the line extends (1 to 10). o Color: Lines are colored based on candle type (bullish/bearish), with user-selectable base colors. o Dynamic Opacity: Line opacity is dynamically adjusted based on the candle's size relative to recent candles. Larger candles produce more opaque lines (up to the user-defined maximum opacity), while smaller candles result in more transparent lines. This helps significant candles stand out. 3. Price Labels: o Show Labels: Option to display price labels at the end of each center line. o Label Background Color: Customizable. o Dynamic Text Color: Label text color can change based on the movement of the center price:  Green: Current center price is higher than the previous.  Red: Current center price is lower than the previous.  Gray: No change or first label. o Static Text Color: Alternatively, a fixed color can be used for all labels. 4. Conditional Drawing - Bullish Engulfing Filter: o Users can enable an option to Only Show Bullish Engulfing Candles. When active, center lines will only be drawn for candles that meet bullish engulfing criteria (current bull candle's body engulfs the previous bear candle's body). 5. Performance Management: o Max Lines to Show: Limits the number of historical lines displayed on the chart to maintain clarity and performance. Older lines are automatically removed as new ones are drawn. 6. Alert Condition: o Includes a built-in alert: Big Bullish Candle. This alert triggers when a bullish candle's range (high - low) is greater than the 20-period simple moving average (SMA) of candle ranges. How It Works: • For each new candle, the script calculates the "center" price based on the user's Line Position selection. • If showLines is enabled and (if applicable) the bullish engulfing condition is met, a new line is drawn from the current candle's bar_index at the calculated _center price, extending lineLength candles forward. • The line's color is determined by whether the candle is bullish (close > open) or bearish (close < open). • Opacity is calculated dynamically: scaledOpacity = int((100 - maxUserOpacity) * (1 - dynamicFactor) + maxUserOpacity), where dynamicFactor is candleSize / maxSize (current candle size relative to the max size in the last 20 candles). This means maxUserOpacity is the least transparent the line will be (for the largest candles), and smaller candles will have lines approaching full transparency. • Optional price labels are added at the end of these lines. • The script manages an array of drawn lines, removing the oldest ones if the maxLines limit is exceeded. Potential Use Cases: • Visualizing Intra-Candle Levels: Quickly see midpoints or other key price points without manual drawing. • Short-Term Reference Points: The extended lines can act as very short-term dynamic support/resistance or points of interest. • Pattern Recognition: Highlight bullish engulfing patterns or simply emphasize candles based on their calculated center. • Volatility Indication: The dynamic opacity can subtly indicate periods of larger or smaller candle ranges. • Confirmation Tool: Use in conjunction with other indicators or trading strategies. User Input Groups: • Line Settings: Controls all aspects of the line's appearance and calculation. • Label Settings: Manages the display and appearance of price labels. • Other Settings: Contains options for line management and conditional filtering (like Bullish Engulfing). This indicator provides a clean and customizable way to mark significant price levels within candles, aiding traders in their technical analysis.
Pine Script®指標
由uniGram提供
Swing-Based Volatility IndexSwing-Based Volatility Index This indicator helps traders quickly determine whether the market has moved enough over the past few hours to justify scalping. It measures the percentage price swing (high to low) over a configurable time window (e.g., last 4–8 hours) and compares it to a minimum threshold (e.g., 1%). ✅ If the percent move exceeds the threshold → Market is volatile enough to scalp (green background). 🚫 If it's below the threshold → Market is too quiet (red background). Features: Adjustable lookback period in hours Custom threshold for volatility sensitivity Automatically adapts to the current chart timeframe This tool is ideal for scalpers and short-term traders who want to avoid entering trades in low-volatility environments.
Pine Script®指標
由crodl提供
Multi-EnvelopeRMA Multi-Envelope Indicator The RMA Multi-Envelope Indicator is a technical analysis tool designed for TradingView, utilizing Pine Script v6. It creates eight customizable envelope bands around a 200-period Running Moving Average (RMA) on a 5-minute timeframe, based on current market measurements. Each band has independent upper and lower percentage deviations, preset to: Band 1 (0.42%, 0.46%), Band 2 (0.78%, 0.69%), Band 3 (1.01%, 1.03%), Band 4 (1.36%, 1.39%), Band 5 (1.80%, 1.62%), Band 6 (2.15%, 2.13%), Band 7 (2.93%, 2.81%), and Band 8 (4.65%, 4.18%). Users can adjust the timeframe, moving average type (RMA, SMA, or EMA), length, and colors for the basis line and bands via hex codes (e.g., #FF6D00 for the basis and Band 8) with semi-transparent color.rgb fills. Ideal for identifying support/resistance, overbought/oversold conditions, or trend boundaries on a 5-minute chart.
Pine Script®指標
由Servantthought提供
Quarter ICT Theo TradeQuarter ICT | Theo Trade The "Multi-Level Yearly Divisions" indicator is a visual tool designed for TradingView charts. Its primary purpose is to help traders and analysts visualize and analyze price action within a structured, hierarchical breakdown of the year. It divides each year into progressively smaller, equal time segments, allowing for detailed observation of how markets behave during specific portions of the year, quarters, and even finer sub-divisions. Yearly Detection: It first identifies the start of each new year on the chart. Four Levels of Division: Level 0: Marks the beginning of the year with a distinct line. Level 1 (Quarters): Divides the entire year into four equal parts (quarters). Level 2: Each quarter is then further divided into four equal smaller segments. Level 3: Each of these Level 2 segments is again divided into four equal parts. Level 4: Finally, each Level 3 segment is divided into four more equal parts.
Pine Script®指標
由fkgazm提供
multi-tf standard devs [keypoems]Multi-Timeframe Standard Deviations Levels A visual map of “how far is too far” across any three higher time-frames. 1. What it does This script plots dynamic price “rails” built from standard deviation (StDev)—the same math that underpins the bell curve—on up to three higher-time-frames (HTFs) at once. • It measures the volatility of intraday open-to-close increments, reaching back as far as 5000 bars (≈ 20 years on daily data). • Each HTF can be extended to the next session or truncated at session close for tidy dashboards. • Lines can be mirrored so you see symmetric positive/negative bands, and optional background fills shade the “probability cone.” Because ≈ 68 % of moves live inside ±1 StDev, ≈ 95 % inside ±2, and ≈ 99.7 % inside ±3, the plot instantly shows when price is statistically stretched or compressed. 3. Key settings Higher Time-Frame #1-3 Turn each HTF on/off, pick the interval (anything from 1 min to 1 year), and decide whether lines should extend into the next period. Show levels for last X days Keep your chart clean by limiting how many historical sessions are displayed (1-50). Based on last X periods Length of the StDev sample. Long look-backs (e.g. 5 000) iron-out day-to-day noise; short look-backs make the bands flex with recent volatility. Fib Settings Toggle each multiple, line thickness/style/colour, label size, whether to print the numeric level, the live price, the HTF label, and whether to tint the background (choose your own opacity). 4. Under-the-hood notes StDev is calculated on (close – open) / open rather than absolute prices, making the band width scale-agnostic. Watch for tests of ±1: Momentum traders ride the breakout with a target at the next band. Mean-reversion traders wait for the first stall candle and trade back to zero line or VWAP. Bottom line: Multi-Timeframe Standard-Deviations turns raw volatility math into an intuitive “price terrain map,” helping you instantly judge whether a move is ordinary, stretched, or extreme—across the time-frames that matter to you. Original code by fadizeidan and stats by NQStats's ProbableChris.
Pine Script®指標
由keypoems提供
2
Swing High/Low by %REnglish Description Swing High/Low by %R This indicator identifies potential swing high and swing low points by combining William %R overbought/oversold turning points with classic swing price structures. Swing High: Detected when William %R turns down from overbought territory and the price forms a local high (higher than both neighboring bars). Swing Low: Detected when William %R turns up from oversold territory and the price forms a local low (lower than both neighboring bars). This tool is designed to help traders spot possible market reversals and better time their entries and exits. Customizable parameters: Williams %R period Overbought & Oversold thresholds The indicator plots clear signals above/below price bars for easy visualization. For educational purposes. Please use with proper risk management! คำอธิบายภาษาไทย Swing High/Low by %R อินดิเคเตอร์นี้ใช้ระบุจุด Swing High และ Swing Low ที่มีโอกาสเป็นจุดกลับตัวของตลาด โดยอาศัยสัญญาณจาก William %R ที่พลิกกลับตัวบริเวณ overbought/oversold ร่วมกับโครงสร้างราคาแบบ swing Swing High: เกิดเมื่อ William %R พลิกกลับลงจากเขต Overbought และราคาแท่งกลางสูงกว่าทั้งสองแท่งข้างเคียง Swing Low: เกิดเมื่อ William %R พลิกกลับขึ้นจากเขต Oversold และราคาแท่งกลางต่ำกว่าทั้งสองแท่งข้างเคียง ช่วยให้เทรดเดอร์สามารถมองเห็นโอกาสในการกลับตัวของราคา และใช้ประกอบการวางแผนจังหวะเข้าหรือออกจากตลาดได้อย่างแม่นยำมากขึ้น ตั้งค่าได้: ระยะเวลา Williams %R ค่าขอบเขต Overbought & Oversold อินดิเคเตอร์จะแสดงสัญลักษณ์อย่างชัดเจนบนกราฟเพื่อความสะดวกในการใช้งาน ควรใช้ร่วมกับการบริหารความเสี่ยง
Pine Script®指標
由August_Supertrader提供
3
5:30 AM IST Close + Offset Lines + TablesDescription: This script captures the 5:30 AM IST close price and plots it on the chart along with dynamic offset levels above and below (±5, ±20, ±40, ±60, ±80 points). It also displays these levels in neatly organized tables at the top-right and bottom-right corners for quick reference. 🔹 Timezone: Asia/Kolkata (IST) 🔹 Useful for: Intraday traders who reference early morning levels 🔹 Visual aids: Orange line for 5:30 AM close Green lines for points above Red lines for points below Tables summarizing all levels This tool helps identify key early-morning reference zones that can act as support/resistance or breakout targets.
Pine Script®指標
由sayedquadri190提供
Multi-Timeframe Session HighlighterWhat is the Multi-Timeframe Session Highlighter? It’s a simple Pine Script indicator that paints two special candles on your chart, no matter what timeframe you’re looking at. Think of it as a highlighter pen for session starts and ends—can be used for session-based strategies or just keeping an eye on key turning points. How it works: Green Bar (Session Open): Marks the exact bar when your chosen higher-timeframe session kicks off. If you select “4H,” on the indicator, you’ll see green on every 4-hour open, even if you’re staring at a 15-minute chart. Red Bar (Session Close): Highlights the very last lower-timeframe candle immediately before that session wraps up. So on a 1H chart with “Daily” selected, you’ll get a red band on the 23:00 hour before the new daily bar at midnight. Customizable: Pick your own colors and transparency level to match your chart theme. Getting started: Add the indicator to your chart. In the inputs, select the session timeframe (for example, “240” for 4H or “D” for daily). Choose your favorite green and red shades. That’s it.
Pine Script®指標
由shwdow_nine提供
Realtime ATR-Based Stop Loss Numerical OverlayRealtime ATR-Based Stop Loss Numerical Overlay A simple, effective tool for dynamic risk management based on ATR (Average True Range) without adding cluttered and distracting lines all over your chart. 📌 Description This script plots a real-time stop loss level using the Average True Range (ATR) on your chart, helping you set consistent, volatility-based stops. It supports both: ✅ Current chart timeframe ✅ Custom fixed timeframe inputs (1m, 5m, 15m, 1h, etc.) The stop level is calculated as: Stop = ATR × Multiplier and updates in real-time. An overlay table displays on the bottom-right of your chart with the calculated stop value in a clean, simple way. ⚙️ Settings ATR Timeframe Source: Choose between using the current chart's timeframe or a fixed one (e.g. 5, 15, 60, D, etc). ATR Length: Period used to calculate the ATR (default is 14). Stop Loss Multiplier: Multiplies the ATR value to define your stop (e.g., 1.5 × ATR). Wait for Timeframe Closes: If enabled, the ATR value waits for the selected timeframe’s candle to close before updating. If unselected, it will update in real time. 🛠️ How to Use Add this script to your chart from your indicators list. Configure your desired timeframe, ATR length, and multiplier in the settings panel. Use the value shown in the table overlay as your suggested stop loss distance from entry. Adjust your position sizing accordingly to fit your risk tolerance. This tool is especially useful for traders looking for adaptive risk management that evolves with market volatility — whether scalping intraday or swing trading. 💡 Pro Tip The ATR stop can also be used to dynamically trail your stop behind price movement.
Pine Script®指標
由HyprZero提供
Adaptive Momentum Flow (AMF)Overview The Adaptive Momentum Flow (AMF) indicator is a powerful, multi-faceted tool designed to provide a comprehensive and adaptive view of market momentum and trend strength. Unlike traditional oscillators with fixed settings, AMF dynamically adjusts its calculations based on market volatility , ensuring its signals remain relevant across varying market conditions. By combining advanced Double Exponential Moving Averages (DEMA) with a powerful volume analysis component and a customizable scoring system, AMF offers a unique perspective on price action and underlying buying/selling pressure. Key Features & How It Works 1. Adaptive DEMA Trend Strength: At its core, AMF utilizes three DEMA lines (Fast, Medium, Slow) to assess the current trend's alignment and strength. The indicator dynamically adjusts the lengths of these DEMA lines based on real-time market volatility, measured by Average True Range (ATR). This means AMF becomes more responsive in volatile markets and smoother in calmer periods. A "Volatility Sensitivity" input allows you to fine-tune how aggressively the indicator adapts to these changes. 2. Volume Analysis (Buying/Selling Pressure): AMF incorporates a dedicated volume analysis module to gauge whether volume is predominantly supporting upward or downward price movements. This helps identify periods of significant buying or selling pressure. This volume analysis component is smoothed with an adjustable Moving Average (SMA, EMA, WMA, or DEMA) and contributes to the overall momentum score, adding a crucial layer of volume-driven confirmation to the analysis. 3. Comprehensive Scoring System: The indicator generates a normalized "Oscillator Score" that ranges from -100 to 100. This score is a weighted sum of: Price's relationship to the Fast DEMA. The Fast DEMA's relationship to the Medium DEMA. The Medium DEMA's relationship to the Slow DEMA. The smoothed value from the volume analysis. Each component's influence on the final score can be individually adjusted via input weights, allowing for deep customization. Signal Line & Crossovers: A smoothed "Signal Line" provides additional confirmation for momentum shifts. Crossovers between the main AMF line and its Signal Line can indicate potential changes in market direction. Overbought/Oversold Levels: Adjustable Overbought (default 70) and Oversold (default -70) levels visually highlight extreme momentum conditions. These zones are enhanced with a color fill effect (bright red for overbought, bright cyan for oversold), making it easy to spot when the market is entering potentially exhausted states. Crucially, these extreme zones can often be further validated by combining them with volatility bands (like Bollinger Bands or Keltner Channels as shown in the chart above) or other confluence indicators, offering stronger signals for potential reversals or exhaustion. Benefits for Traders Reduced Lag: DEMA's inherent design helps minimize lag compared to traditional moving averages, providing more timely signals. Adaptive Intelligence: Automatically adjusts to market volatility, ensuring the indicator's sensitivity is appropriate for current conditions. Holistic Momentum View: Combines price-based trend alignment with volume-based pressure for a more robust assessment of market flow. Clear Visual Cues: Intuitive plots, signal line, and vibrant overbought/oversold zone fills make interpretation straightforward. Customizable: Extensive input options allow traders to tailor the indicator to their specific trading style, asset, and timeframe. How to Use Trend Confirmation: Look for the AMF line and its Signal Line to align with the price trend. Momentum Shifts: Crossovers between the AMF line and its Signal Line can indicate shifts in momentum. Extreme Conditions: Pay attention when the AMF line enters the neon-highlighted overbought or oversold zones, signaling potential reversals or pauses in the current momentum. Always consider confirming these signals with other analysis tools, such as price action, chart patterns, support/resistance levels, or volatility indicators. Customization: Experiment with the "Volatility Sensitivity," DEMA multipliers, and scoring weights to find the optimal settings for your trading strategy.
Pine Script®指標
由B3AR_Trades提供
Beta Tracker [theUltimator5]This script calculates the Pearson correlation coefficient between the charted symbol and a dynamic composite of up to four other user-defined tickers. The goal is to track how closely the current asset’s normalized price behavior aligns with, or diverges from, the selected group (or basket) How can this indicator be valuable? You can compare the correlation of your current symbol against a basket of other tickers to see if it is moving independently, or being pulled with the basket.... or is it moving against the basket. It can be used to help identify 'swap' baskets of stocks or other tickers that tend to generally move together and visually show when your current ticker diverges from the basket. It can be used to track beta (or negative beta) with the market or with a specific ticker. This is best used as a supplement to other trading signals to give a more complete picture of the external forces potentially pulling or pushing the price action of the ticker. 🛠️ How It Works The current symbol and each selected comparison ticker are normalized over a custom lookback window, allowing fair pattern-based comparison regardless of price scale. The normalized values from 1 to 4 selected tickers are averaged into a composite, which represents the group’s collective movement. A Pearson correlation coefficient is computed over a separate correlation lookback period, measuring the relationship between the current asset and the composite. The result is plotted as a dynamic line, with color gradients: Blue = strongly correlated (near +1) Orange = strongly inverse correlation (near –1) Intermediate values fade proportionally A highlighted background appears when the correlation drops below a user-defined threshold (e.g. –0.7), helping identify strong negative beta periods visually. A toggleable info table displays which tickers are currently being compared, along with customizable screen positioning. ⚙️ User Inputs Ticker 1–4: Symbols to compare the current asset against (blank = ignored) Normalization Lookback: Period to normalize each series Correlation Lookback: Period over which correlation is calculated Negative Correlation Highlight: Toggle for background alert and threshold level Comparison Table: Toggle and position controls for an on-screen summary of selected tickers imgur.com ⚠️ Notes The script uses request.security() to pull data from external symbols; these must be available for the selected chart timeframe. A minimum of one valid ticker must be provided for the script to calculate a composite and render correlation.
Pine Script®指標
由TheUltimator5提供
Bullish Volume AnomalyAnomaly is designed to spot hidden bullish accumulation before price actually breaks out, by blending a trend-aware volume measure with a volatility-adjusted price channel. Here’s how it works: First, it runs a simple ATR-based zigzag to identify the current swing direction. Volume is then signed (+ for up-trends, – for down-trends) and cumulatively summed. By converting that cumulative signed volume into a z-score over the past 480 bars, we get a sense of when buying or selling pressure is unusually strong relative to its own history. At the same time, price itself is normalized into a z-score over the same 480-bar window, and its change over that period is also tracked. These two measures—volume z-score (s) and price z-score (p)—are compared, and the indicator looks for moments when s outpaces p by at least two standard deviations (s – p > 2), while price momentum change remains low (c < 1) and the net volume is positive (s > 0). That combination flags instances where heavy buying is taking place but price hasn’t yet reacted. To define a dynamic trading zone, it plots a 288-bar EMA of price as the middle band (t2), and builds upper and lower bands around it using the average close-to-open range multiplied by a user-set factor. The lower band (t1) sits beneath the EMA by that volatility-based margin. A signal fires only when the bar’s high stays below t1—meaning price is still “sleeping” under the lower volatility boundary even as bullish volume builds up. Together, these filters home in on anomalies: strong, trend-aligned volume surges that outstrip price movement, occurring while price sits below its lower volatility band. In practice, that often marks early accumulation before a breakout. You can tweak the ATR length and multiplier for the zigzag, as well as the channel period and range factor, to suit different markets or timeframes.
Pine Script®指標
由mertenes3提供
Footprint BoxesThe Footprint Boxes indicator takes each higher-timeframe candle and builds a mini “footprint” map of where buying and selling happened within that bar’s range. You choose how many price bins to split the candle into and which lower timeframe to sample. For each small interval it grabs the signed volume (positive when the close is above the open, negative when below) and distributes that volume evenly across every bin touched by the price move in that interval. Once the bar closes , the script finds the true high and low of all the lower-timeframe candles that make up the parent bar, divides that span into your chosen number of bins, and sums up the signed volume in each bin. It then draws a row of colored boxes beside the bar: green-tinted boxes for net buying and red-tinted ones for net selling, with shade intensity proportional to the percentage of total volume in that bin . Each box is labeled with its percentage of the bar’s total volume delta. Finally, it draws one extra box with a bold white border showing the bar’s overall delta (net buying minus selling) as an absolute number. This gives you both a granular view of intra-bar activity and a quick glance at whether the buyers or sellers dominated the entire candle.
Pine Script®指標
由mertenes3提供
MA OrderlinessMA Orderliness measures how well a series of simple moving averages (SMAs) are stacked in the expected order for a trending market and turns that measurement into a normalized oscillator. You choose how many MAs to include and the shortest and longest lengths. The script generates a family of evenly spaced SMAs between those lengths, then compares each pair: shorter MAs should lie above longer ones in an uptrend and below in a downtrend. When any pair is out of order, a “violation” score is accumulated, but violations between nearby MAs count more heavily than those between MAs that are far apart. All weights are summed, and the total weighted violations are converted into a score from –1 (completely reversed) to +1 (perfectly ordered). This orderliness score is plotted as a line oscillator. A fixed horizontal line at +1 marks perfect order, and another at –1 marks perfect reversal. To smooth the raw oscillator and generate trading signals, the script also plots a simple moving average of the orderliness score over a user-defined period. When the unsmoothed score crosses above its moving average, a bullish crossover alert fires. When it crosses below, a bearish crossover alert fires. Everything is calculated on each bar so you can see the oscillator evolve in real time. You can customize the number of MAs, their minimum and maximum lengths, and the length of the signal-line SMA to suit different timeframes or markets.
Pine Script®指標
由mertenes3提供
1
Normalized DXY+Custom USD Index (DXY+) – Normalized Dollar Strength with Bitcoin, Gold, and Yuan. This custom USD strength index replicates the structure of the official U.S. Dollar Index (DXY), while expanding it to include modern financial assets such as Bitcoin (BTC), Ethereum (ETH), gold (XAU), and the Chinese yuan (CNY). Weights for the core fiat currencies (EUR, JPY, GBP, CAD, SEK, CHF) follow the official ICE DXY methodology. Additional components are weighted proportionally based on their estimated global economic influence. The index is normalized from its initial valid data point, meaning it starts at 100 on the first day all asset inputs are available. From that point forward, it tracks the relative strength of the U.S. dollar against this expanded basket. This provides a more comprehensive and modernized view of the dollar's strength—not only against traditional fiat currencies, but also in the context of rising decentralized assets and non-Western trade power.
Pine Script®指標
由travisryno提供
HGDA Hany Ghazy Digital Analytics area zone'sIndicator Name: HGDA Hany Ghazy Digital Analytics area zones Description: This indicator plots several key price zones based on the highest high and lowest low over a user-defined lookback period. The plotted zones represent dynamic support and resistance levels calculated using specific ratios of the price range (High - Low), as follows: - Zone 1 (Light Red): Represents an upper resistance zone. - Zone 2 (Medium Green): Represents a medium support zone. - Zone 3 (Dark Red): Represents a lower resistance zone. - Zone 4 (Dark Green): Represents a strong support zone. Additionally, the indicator plots a yellow "Zero" line representing the midpoint price of the selected period, serving as a balance point for price action. This indicator is ideal for identifying the overall market trend, as prices typically move from the upper resistance zones (light red) downwards to the end of the wave in the lower zones (dark green). This helps traders better understand wave nature and direction. Usage: - The colored zones assist in identifying potential reversal or continuation areas. - These zones can be used to plan entries, exits, and risk management. - Default lookback period is 20 bars, adjustable in the settings to suit the timeframe. Notes: - This indicator relies on historical price data and does not guarantee market predictions. - It is recommended to combine it with other indicators and analytical tools for improved trading decisions. --- Developed by Hany Ghazy Digital Analytics (HGDA).
Pine Script®指標
由Hany_Elghazy提供
Momentum Long + Short Strategy (BTC 3H)Momentum Long + Short Strategy (BTC 3H) 🔍 How It Works, Step by Step Detect the Trend (📈/📉) Calculate two moving averages (100-period and 500-period), either EMA or SMA. For longs, we require MA100 > MA500 (uptrend). For shorts, we block entries if MA100 exceeds MA500 by more than a set percentage (to avoid fading a powerful uptrend). Apply Momentum Filters (⚡️) RSI Filter: Measures recent strength—only allow longs when RSI crosses above its smoothed average, and shorts when RSI dips below the oversold threshold. ADX Filter: Gauges trend strength—ensures we only enter when a meaningful trend exists (optional). ATR Filter: Confirms volatility—avoids choppy, low-volatility conditions by requiring ATR to exceed its smoothed value (optional). Confirm Entry Conditions (✅) Long Entry: Price is above both MAs Trend alignment & optional filters pass ✅ Short Entry: Price is below both MAs and below the lower Bollinger Band RSI is sufficiently oversold Trend-blocker & ATR filter pass ✅ Position Sizing & Risk (💰) Each trade uses 100 % of account equity by default. One pyramid addition allowed, so you can scale in if the move continues. Commission and slippage assumptions built in for realistic backtests. Stops & Exits (🛑) Long Stop-Loss: e.g. 3 % below entry. Long Auto-Exit: If price falls back under the 500-period MA. Short Stop-Loss: e.g. 3 % above entry. Short Take-Profit: e.g. 4 % below entry. 🎨 Why It’s Powerful & Customizable Modular Filters: Turn on/off RSI, ADX, ATR filters to suit different market regimes. Adjustable Thresholds: Fine-tune stop-loss %, take-profit %, RSI lengths, MA gaps and more. Multi-Timeframe Potential: Although coded for 3 h BTC, you can adapt it to stocks, forex or other cryptos—just recalibrate! Backtest Fine-Tuned: Default settings were optimized via backtesting on historical BTC data—but they’re not guarantees of future performance. ⚠️ Warning & Disclaimer This strategy is for educational purposes only and designed for a toy fund. Crypto markets are highly volatile—you can lose 100 % of your capital. It is not a predictive “holy grail” but a rules-based framework using past data. The parameters have been fine-tuned on historical data and are not valid for future trades without fresh calibration. Always practice with paper-trading first, use proper risk management, and do your own research before risking real money. 🚨🔒 Good luck exploring and experimenting! 🚀📊
Pine Script®策略
由noobztrader101提供
MFI + RSI + EMA Dynamic SignalsThe MFI + RSI + EMA Dynamic Signals is a designed to combine with widened criteria to capture more trading opportunities, it balances momentum, trend, and flexibility, making it suitable for trading on timeframes like 15-minute to 4-hour charts. How It Works The indicator uses three technical components with relaxed criteria to produce signals: Money Flow Index (MFI) for Momentum Extremes: The MFI, calculated over a 14-period length, measures buying and selling pressure using price and volume. A buy signal can trigger when MFI crosses above the oversold level (default: 30, widened from 20), indicating potential buying pressure, while a sell signal can occur when MFI crosses below the overbought level (default: 70, widened from 80), suggesting selling pressure. Relative Strength Index (RSI) for Momentum Confirmation: The RSI, calculated over a 14-period length, confirms momentum strength. Bullish momentum is confirmed when RSI is above a buy threshold (default: 45, relaxed from 50), and bearish momentum when below a sell threshold (default: 55, relaxed from 50), allowing more signals near neutral momentum levels. Exponential Moving Average (EMA) for Trend Sensitivity: The indicator uses a fast EMA (default: 9 periods) and a slow EMA (default: 21 periods) to detect trend direction and crossovers. Signals can trigger when the fast EMA crosses the slow EMA, or when the fast EMA is within a proximity threshold (default: 0.5%) of the slow EMA, capturing early trend changes and increasing signal frequency. Signal Generation Signals are generated using the previous bar’s values to prevent repainting, with widened criteria for more frequent triggers: Buy Signal: Either the MFI crosses above the oversold level or the fast EMA crosses above the slow EMA, and either RSI confirms bullish momentum (above 45) or the EMAs are near a crossover (within 0.5%). Displayed as a green upward triangle below the bar. Sell Signal: Either the MFI crosses below the overbought level or the fast EMA crosses below the slow EMA, and either RSI confirms bearish momentum (below 55) or the EMAs are near a crossover (within 0.5%). Displayed as a red downward triangle above the bar.
Pine Script®指標
由Raisontgh提供