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MACD+ADX Enhanced IndicatorThe MACD+ADX Enhanced Indicator is a Pine Script v5 trading indicator that combines MACD, ADX, trend confirmation, and OBV signals to identify buy and sell opportunities. It dynamically adjusts MACD parameters based on VIX volatility, making it adaptable to varying market conditions. The indicator displays buy (green triangles/bars) and sell (red triangles/bars) signals on the chart, along with an SMA trend line, while MACD and ADX are shown in separate panels.Blessing every one can retire before 40 years old, and everyone should be retire 40 years old, don't work as a machine
Pine Script®指標
由Charity_Quant提供
S&P 500 Top 25 - EPS AnalysisEarnings Surprise Analysis Framework for S&P 500 Components: A Technical Implementation The "S&P 500 Top 25 - EPS Analysis" indicator represents a sophisticated technical implementation designed to analyze earnings surprises among major market constituents. Earnings surprises, defined as the deviation between actual reported earnings per share (EPS) and analyst estimates, have been consistently documented as significant market-moving events with substantial implications for price discovery and asset valuation (Ball and Brown, 1968; Livnat and Mendenhall, 2006). This implementation provides a comprehensive framework for quantifying and visualizing these deviations across multiple timeframes. The methodology employs a parameterized approach that allows for dynamic analysis of up to 25 top market capitalization components of the S&P 500 index. As noted by Bartov et al. (2002), large-cap stocks typically demonstrate different earnings response coefficients compared to their smaller counterparts, justifying the focus on market leaders. The technical infrastructure leverages the TradingView Pine Script language (version 6) to construct a real-time analytical framework that processes both actual and estimated EPS data through the platform's request.earnings() function, consistent with approaches described by Pine (2022) in financial indicator development documentation. At its core, the indicator calculates three primary metrics: actual EPS, estimated EPS, and earnings surprise (both absolute and percentage values). This calculation methodology aligns with standardized approaches in financial literature (Skinner and Sloan, 2002; Ke and Yu, 2006), where percentage surprise is computed as: (Actual EPS - Estimated EPS) / |Estimated EPS| × 100. The implementation rigorously handles potential division-by-zero scenarios and missing data points through conditional logic gates, ensuring robust performance across varying market conditions. The visual representation system employs a multi-layered approach consistent with best practices in financial data visualization (Few, 2009; Tufte, 2001). The indicator presents time-series plots of the four key metrics (actual EPS, estimated EPS, absolute surprise, and percentage surprise) with customizable color-coding that defaults to industry-standard conventions: green for actual figures, blue for estimates, red for absolute surprises, and orange for percentage deviations. As demonstrated by Padilla et al. (2018), appropriate color mapping significantly enhances the interpretability of financial data visualizations, particularly for identifying anomalies and trends. The implementation includes an advanced background coloring system that highlights periods of significant earnings surprises (exceeding ±3%), a threshold identified by Kinney et al. (2002) as statistically significant for market reactions. Additionally, the indicator features a dynamic information panel displaying current values, historical maximums and minimums, and sample counts, providing important context for statistical validity assessment. From an architectural perspective, the implementation employs a modular design that separates data acquisition, processing, and visualization components. This separation of concerns facilitates maintenance and extensibility, aligning with software engineering best practices for financial applications (Johnson et al., 2020). The indicator processes individual ticker data independently before aggregating results, mitigating potential issues with missing or irregular data reports. Applications of this indicator extend beyond merely observational analysis. As demonstrated by Chan et al. (1996) and more recently by Chordia and Shivakumar (2006), earnings surprises can be successfully incorporated into systematic trading strategies. The indicator's ability to track surprise percentages across multiple companies simultaneously provides a foundation for sector-wide analysis and potentially improves portfolio management during earnings seasons, when market volatility typically increases (Patell and Wolfson, 1984). References: Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of Accounting Research, 6(2), 159-178. Bartov, E., Givoly, D., & Hayn, C. (2002). The rewards to meeting or beating earnings expectations. Journal of Accounting and Economics, 33(2), 173-204. Bernard, V. L., & Thomas, J. K. (1989). Post-earnings-announcement drift: Delayed price response or risk premium? Journal of Accounting Research, 27, 1-36. Chan, L. K., Jegadeesh, N., & Lakonishok, J. (1996). Momentum strategies. The Journal of Finance, 51(5), 1681-1713. Chordia, T., & Shivakumar, L. (2006). Earnings and price momentum. Journal of Financial Economics, 80(3), 627-656. Few, S. (2009). Now you see it: Simple visualization techniques for quantitative analysis. Analytics Press. Gu, S., Kelly, B., & Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273. Johnson, J. A., Scharfstein, B. S., & Cook, R. G. (2020). Financial software development: Best practices and architectures. Wiley Finance. Ke, B., & Yu, Y. (2006). The effect of issuing biased earnings forecasts on analysts' access to management and survival. Journal of Accounting Research, 44(5), 965-999. Kinney, W., Burgstahler, D., & Martin, R. (2002). Earnings surprise "materiality" as measured by stock returns. Journal of Accounting Research, 40(5), 1297-1329. Livnat, J., & Mendenhall, R. R. (2006). Comparing the post-earnings announcement drift for surprises calculated from analyst and time series forecasts. Journal of Accounting Research, 44(1), 177-205. Padilla, L., Kay, M., & Hullman, J. (2018). Uncertainty visualization. Handbook of Human-Computer Interaction. Patell, J. M., & Wolfson, M. A. (1984). The intraday speed of adjustment of stock prices to earnings and dividend announcements. Journal of Financial Economics, 13(2), 223-252. Skinner, D. J., & Sloan, R. G. (2002). Earnings surprises, growth expectations, and stock returns or don't let an earnings torpedo sink your portfolio. Review of Accounting Studies, 7(2-3), 289-312. Tufte, E. R. (2001). The visual display of quantitative information (Vol. 2). Graphics Press.
Pine Script®指標
由EdgeTools提供
Climax Detector (Buy & Sell)This indicator identifies potential Buying Climax (BC) and Selling Climax (SC) events based on volume spikes relative to historical averages. • Buying Climax (BC): • Detected when a green candle forms with volume significantly higher than the average (default: 2×). • Often signals the end of an uptrend or distribution phase. • Selling Climax (SC): • Detected when a red candle forms with very high volume (default: 2× average). • Often occurs at the end of a downtrend, suggesting panic selling and potential accumulation. How it works: • Calculates a moving average of volume over a user-defined period (default: 20 candles) • Flags a climax when current volume exceeds the defined multiplier (default: 2.0×) • Marks: • BC with an orange triangle above the bar • SC with a fuchsia triangle below the bar Customizable Settings: • Volume spike sensitivity • Lookback period for average volume Use Cases: • Spot possible trend exhaustion • Confirm Wyckoff phases • Combine with support/resistance for reversal entries Disclaimer: This tool is designed to assist in identifying high-probability exhaustion zones but should be used alongside other confirmations or strategies.
Pine Script®指標
由peacefulFerret86228提供
6 Exponential Moving Averages 2 SMA6 EMA Trend Indicator This indicator plots 6 Exponential Moving Averages (EMAs) with customizable periods to help traders visually analyze short-, medium-, and long-term trend alignments. Ideal for identifying trend strength, pullback zones, and dynamic support/resistance. Features: • 6 fully adjustable EMA inputs • Clear color-coded visualization • Works on all timeframes • Effective for trend trading and scalping Use it to confirm trend direction, spot EMA crossovers, or align multiple EMAs for high-probability entries.
Pine Script®指標
由yashdarji提供
All‑MA Crossover + analyzer + risk Management [quantotc]🔍 Overview All‑MA Trend Analyzer + Risk Management is a full-featured, multi-purpose trend and crossover system that lets you compare 8 different moving average types, visualize their alignment across timeframes, and apply robust risk management strategies — all in one powerful tool. 🧠 What Makes This Indicator Unique? 🔄 8 Moving Average Types — Easily switch between SMA, EMA, WMA, VWMA, HMA, RMA, SMMA, and TMA. 🟢 Signal Clarity — Buy/Sell labels appear on fast/slow MA crossovers. 📊 Dual Analysis Tables Top-right: Multi-timeframe crossover trends (15m, 1h, 4h, Daily) Bottom-right: MA type trends on current timeframe (Bull/Bear) ⚙️ Risk Management Supports fixed SL/TP or trailing stop-loss Works in % or Points Visual SL/TP/TSL exit labels with separate alerts 🎯 How to Use Select your desired MA Type (e.g., TMA, VWMA, etc.) Adjust Fast/Slow Lengths depending on your strategy Enable Long/Short entries as needed Choose SL/TP Mode: Points or Percentage Enable Trailing Stop for dynamic protection Each feature is grouped and labeled with tooltips in the settings panel for clarity. 🖼 Visual Aids A TMA Bull signal Table-based trend analysis Buy label clarity Sell label clarity Exit label on Take Profit Exit label on Stop Loss Trailing Stop Loss Exit 🚨 Alerts Included BUY / SELL TAKE PROFIT STOPLOSS TRAILING STOPLOSS Each is customizable in the settings. 👤 Developer Info Developer: quantotc Website: quantotc.com YouTube:https://youtube.com/@quantotc Tags: multi timeframe, crossover, risk management, all MA, trailing stop, bullish bearish, trend table, strategy builder ⚠️ Disclaimer This script is for educational purposes only. No guarantee of profitability. Always backtest and use proper risk management.
Pine Script®指標
由quantotc提供
ForexDada Live Trade SummaryThis indicator is designed for manual traders following Ali Jahanzaib's VSA strategies, specifically focusing on Signs of Strength (SOS) and Signs of Weakness (SOW) setups. 🔧 Key Features: Track daily trade stats: number of trades, SL hits/pips, TP hits/pips. Manually record the current active trade, including: Trade type (SOS or SOW) Setup subtype (e.g., Shakeout Candle, Upthrust Candle, TBR, EVR) Trade result (TP/SL) Pips gained/lost Entry timeframe (manually entered, does not depend on chart) A clean, compact table in the top-right corner with optional coloring for visual clarity. Helps review trading performance and setups visually without external notes. 📌 Note: This tool does not track trades automatically — it’s built for manual logging and journaling directly on the chart.
Pine Script®指標
由forexdadaofficial提供
Liquidity and S&R Zones╔══════════════════════════════════════════════════════════════════════╗ ║ Description ║ ╚══════════════════════════════════════════════════════════════════════╝ This indicator identifies liquidity zones and support/resistance (S&R) levels using pivot points and volume analysis. Liquidity zones highlight areas of high trading activity, while S&R levels mark key price levels where price may reverse or break. Breakouts are confirmed with a volume oscillator and visualized with shapes. Alerts are provided for significant S&R breakouts. ╔══════════════════════════════════════════════════════════════════════╗ ║ User Guide ║ ╚══════════════════════════════════════════════════════════════════════╝ #### Overview This indicator detects liquidity zones and support/resistance (S&R) levels using pivot points and volume analysis. Liquidity zones highlight areas of high trading activity, often targeted by institutional traders. S&R levels indicate key price levels where price may reverse or break, with breakouts confirmed by a volume oscillator. The indicator is designed for traders seeking to trade breakouts or reversals at critical levels. #### Features - **Liquidity Zones**: Identifies pivot highs/lows with high-volume confirmation. - **Support/Resistance Levels**: Plots dynamic S&R lines based on pivot points. - **Breakout Signals**: Displays shapes for price crossing S&R levels with volume confirmation. - **Volume Oscillator**: Uses short/long EMA difference to confirm breakouts. - **Alerts**: Notifies users of support/resistance breakouts. #### Input Parameters - **Liquidity Settings**: - *Liquidity Lookback Period*: Bars for average volume (default: 50). - *Liquidity Volume Threshold Multiplier*: Volume multiplier for liquidity zones (default: 1.5). - *Liquidity Pivot Lookback*: Bars for pivot detection (default: 5). - **S&R Settings**: - *Show Breaks*: Toggle breakout shapes (default: true). - *Left/Right Bars*: Bars for S&R pivot detection (default: 15). - *S&R Volume Threshold*: Minimum oscillator value for breakouts (default: 20). - **Style Settings**: Predefined colors for liquidity and S&R visualization. #### Usage 1. Apply the indicator to a chart (e.g., 1H, 4H, or D timeframes recommended). 2. Adjust input parameters to suit the instrument and timeframe: - Increase `liqLookback` for smoother volume averages on lower timeframes. - Adjust `leftBars` and `rightBars` for more/less sensitive S&R levels. - Set `srVolumeThresh` based on typical oscillator values (plot `osc` to calibrate). 3. Monitor liquidity zones (red/green/yellow crosses) and S&R lines (red/green). 4. Watch for breakout signals (shapes) when price crosses S&R levels with volume confirmation. 5. Set up alerts for "Support Broken" or "Resistance Broken" to receive notifications. #### Recommended Settings - **Timeframes**: 1H, 4H, or D for reliable signals. - **Instruments**: Assets with good volume (e.g., crypto, forex, indices). - **Liquidity**: Increase `liqVolumeThreshold` (e.g., 2.0) for stricter zones. - **S&R**: Use `leftBars = rightBars = 10` for faster markets. #### Cautions - Ensure sufficient chart history for pivot and volume calculations. - High `liqLookback` or `leftBars` may delay signals on lower timeframes. - Volume oscillator requires accurate volume data; test on reliable instruments. - Backtest breakout signals, as false breakouts can occur in choppy markets. #### Customization Ideas - Add Fibonacci levels to complement S&R zones. - Integrate with trend indicators (e.g., EMA) to filter breakouts. - Visualize volume oscillator as a histogram for calibration. - Extend liquidity zones with boxes to highlight price ranges. #### Notes - Combine with other analysis for a complete trading system. - Test thoroughly in a demo account before live trading. - Contact the author for support or feature requests. Happy trading, and may your trades align with the market’s key levels! 🚀
Pine Script®指標
由KoKalito提供
Future OI AnalysisThis indicator appears to be a Futures Open Interest (OI) Analysis Tool designed to help traders understand market sentiment (long/short bias), cost of carry dynamics, and rollover behaviour. Below is a comprehensive description along with the formulas used: To analyze futures market trends using: Price change Open Interest (OI) data Cost of Carry (COC) Rollover percentage Column Breakdown and Formulas: Date : Trading date. Close : Close price of the spot on that day. OI Cur (Current Open Interest) : Total open interest of the current (near) month contract. OI Next (Next Month Open Interest) : Open interest of the next month contract. COI (Cumulative Open Interest) : OI Cur + OI Next COI Chg = ((Current COI - Previous COI) / Previous COI) × 100 Price Cng = ((Current Close - Previous Close) / Previous Close) × 100 OI BuildUP (Interpreted sentiment based on price & OI change): Long Buildup : Price ↑ & OI ↑ Short Buildup : Price ↓ & OI ↑ Short Covering : Price ↑ & OI ↓ Long Unwinding : Price ↓ & OI ↓
Pine Script®指標
由subhajitbhattacharya提供
13
PoiBox# PoiBox: Advanced Market Structure and POI Visualization Tool PoiBox is a comprehensive market structure analysis tool designed to identify high-probability trading zones through advanced internal market structure (IDM) detection and points of interest (POI) calculation. ## How It Works The indicator uses a multi-step approach to analyze price action: 1. **Market Structure Identification**: The script identifies significant highs and lows within your selected time range to determine the overall market structure direction (up or down). 2. **IDM Pattern Detection**: It then analyzes internal market structure patterns within this range, focusing on significant price movements that create trading opportunities. 3. **POI Calculation**: Using adaptive ATR measurements across multiple timeframes, the indicator calculates precise POI zones where price is likely to react. These zones are calibrated based on the volatility profile of each identified structure. 4. **Timeframe Correlation**: The script automatically determines which timeframe best matches each structure's size, providing valuable context for your trading decisions. 5. **Technical Implementation**: The indicator uses a sophisticated algorithm to analyze price swings, identify pivot points, and calculate market structure connections. It maintains a database of significant highs/lows and uses these to determine trend direction and potential reversal zones. ## Display Modes PoiBox offers three powerful display options: - **Main BOS**: Shows only the most significant breakout structure with its associated POI zone - **Leg**: Displays the largest price leg within the selected range along with percentage-based POI zones - **All IDMs**: Reveals all detected internal market structures and their POI zones ## Advanced Features - **QM Mode**: Visualizes important market structure relationships with dashed lines connecting significant highs and lows - **Trick Display**: Identifies nested market structures (tricks) within larger patterns, perfect for precision entries - **Customizable POI Labels**: Control which price labels appear to maintain chart clarity - **Extensive Color Settings**: Fully customizable colors for all visual elements - **Safety Functions**: Includes built-in buffer management and error prevention algorithms to ensure stable performance across all timeframes and market conditions ## Trading Examples **Downtrend Example:** When PoiBox identifies a downtrend structure (Higher High → High → Low → Lower Low), it creates POI zones based on the market structure. As shown in the chart, these zones provide excellent entry opportunities when price returns to test previous structure. In this example, entering at the red POI zone with a stop above the zone and target at the QM level resulted in a 3.45 risk/reward trade. **How to Read QM Lines:** The dashed lines connecting High → Low → Higher High → Lower Low reveal the market's true structure. These connections help you anticipate where price might head next. When price breaks below a significant Low and creates a Lower Low, it confirms the downtrend continuation and provides a trading opportunity when price retests the broken structure. **POI Zone Interpretation:** - Red zones indicate bearish POI areas (ideal for short entries) - Green zones indicate bullish POI areas (ideal for long entries) - Yellow zones highlight the identified market structure ## Practical Application Example In the GBP/USD example shown in the chart: 1. PoiBox identified a downtrend structure with Higher High → High → Low → Lower Low 2. The yellow box shows the main market structure area 3. The red POI zone appeared when price returned to test previous structure 4. Entry was taken at the POI zone with stop loss above structure 5. Target was placed at the QM level, resulting in a 3.45 risk/reward ratio trade 6. The dashed QM lines showed the overall market flow and direction This demonstrates how PoiBox automatically identifies optimal entry and exit points based on market structure, without requiring manual analysis of each price swing. ## Mathematical Approach PoiBox uses several mathematical concepts to determine market structure and calculate POI zones: 1. **Adaptive ATR Integration**: The script analyzes ATR (Average True Range) across multiple timeframes (M1, M5, M15, H1, H4, D1, W1, MN1) to determine the appropriate volatility context for each structure. 2. **Height-to-ATR Ratio**: The indicator calculates the ratio between structure height and the closest matching ATR value to determine the structure's timeframe context. 3. **Dynamic POI Calculation**: POI values are calculated using the formula: `POI = factor * (atr_trigger + atr_double_trigger)` where `factor` is derived from the structure's height-to-ATR ratio. 4. **Self-Adjusting Limits**: If the calculated POI value exceeds certain thresholds relative to structure height, the script automatically applies proportional adjustments to maintain optimal zone sizing. ## What Makes PoiBox Unique While many indicators use common concepts like support/resistance or trend analysis, PoiBox stands apart through its: 1. **Adaptive POI Calculation**: Unlike static indicators, PoiBox automatically calibrates POI zones based on each market structure's volatility profile by analyzing ATR across multiple timeframes. 2. **Smart Timeframe Detection**: The indicator automatically determines the most relevant timeframe for each structure, eliminating guesswork and helping you align your trading with the appropriate market cycles. 3. **QM Visualization System**: Our proprietary QM visualization method reveals hidden market structure relationships that standard indicators cannot detect, giving you an edge in anticipating price movements. 4. **Nested Pattern Recognition**: The "Trick" detection feature identifies high-probability setups where smaller patterns form within larger ones, creating precise entry opportunities missed by conventional tools. 5. **Self-Adjusting Analysis**: PoiBox dynamically adapts to changing market conditions without requiring manual parameter adjustments, saving you time and increasing accuracy. These innovations combine to create a truly original trading system that transforms complex market structure concepts into clear, actionable signals. ## How To Use 1. Define your analysis area using the time range selectors (X1 and X2) 2. Choose your preferred display mode based on your trading style 3. Enable QM Mode for additional market structure context if needed 4. Use the POI zones as potential entry and exit areas for your trades 5. Reference the automatically detected timeframe indicators to align your trading with the appropriate timeframe ### Settings Explanation **Display Settings:** - Display Mode: Choose between Main BOS, Leg, or All IDMs visualization - QM Mode: Enable to see market structure connections with dashed lines **Trick Settings:** - Trick Display: Show the main trick or all nested patterns - Trick POI: Control which POI zones appear for trick patterns **Label Settings:** - Leg POI %: Customize percentage-based POI zones in Leg mode - POI Labels: Control which price labels appear on your chart **Time Range:** - X1 and X2: Define the analysis area for market structure detection **Colors:** - TF Color: Color for timeframe labels - H/L Color: Color for high/low labels - QM Lines: Color for market structure connection lines - Trick Color: Color for nested pattern visualization This indicator is designed for traders who understand market structure concepts and want a powerful tool that automatically identifies high-probability trading zones based on structural price patterns and volatility-adjusted measurements.
Pine Script®指標
由Atabaki提供