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QTN | Money CirculatingQTN | Money Circulating | VWAP-based turnover Multi Time Frames This indicator visualizes real money flow in a stock by calculating the turnover (trading value) using volume multiplied by VWAP across daily, weekly, and monthly timeframes. It applies EMA smoothing to provide a clearer trend of money circulating in the market. Features: • VWAP-based turnover calculation for more accurate money flow measurement. • EMA smoothing with customizable period. • Table display of daily, weekly, and monthly turnover values in millions (M) for quick reference. • Clean, color-coded visualization for easy interpretation. Usage: Ideal for traders and investors who want to gauge market participation intensity and detect shifts in trading momentum across multiple timeframes. ________________________________________________ Disclaimer This indicator is for educational and informational purposes only. It does not constitute financial advice or a recommendation to buy or sell any security. Trading involves risk, and users should conduct their own research or consult with a financial advisor before making investment decisions. The author is not responsible for any trading losses.
Indicador Pine Script®
por QUANTANIUM
Breakout Scanner with VWAP + RSI + MACD + Volume SpikePRICE & MOVING AVERAGES 🟠 MA(10), MA(50), MA(200) Purpose: Track price trends over different time horizons MA10 – Very short-term trend (micro pullbacks) MA50 – Intermediate trend (support/resistance) MA200 – Long-term sentiment (bullish or bearish overall) Use: Crossovers indicate trend reversals. E.g., MA10 < MA50 = bearish. 📉 EMA(9), EMA(12), EMA(34) EMA = Exponential Moving Average Reacts faster than MA, used for quick entries/exits Common Strategy: EMA 9 crossing below EMA 34 → short signal You’re currently in a downtrend, as all EMAs slope down and price is below them. 🔵 VWAP (Volume Weighted Average Price) Purpose: Institutional benchmark Traders use VWAP as a mean reversion level. If price is below VWAP → bearish control; above → bullish control. In your chart: QQQ is below VWAP, suggesting institutional selling. 📊 BOLL(20) = Bollinger Bands Tracks volatility using 20-period MA ± 2 std. dev. Bands widen when volatility increases. In your chart: Price is riding the lower band → bearish pressure 🔁 RSI(14) = Relative Strength Index Measures momentum Ranges from 0 to 100 Above 70 = Overbought Below 30 = Oversold Current RSI is around 30–40, suggesting weak momentum, near oversold 📉 MACD (12, 26, 9) MACD Line (blue) = 12EMA - 26EMA Signal Line (red) = 9 EMA of MACD line Histogram = MACD – Signal When MACD crosses below Signal line → bearish Your chart: Histogram is red and increasing → bearish strength increasing ✅ SUMMARY FOR QQQ CHART (LIVE INTERPRETATION) Indicator Reading Signal MA/EMA All sloping down ❌ Bearish VWAP Price below VWAP ❌ Bearish Bollinger Price hugging lower band ❌ Bearish RSI(14) ~30-40 ⚠️ Weak MACD Red histogram growing ❌ Bearish Would you like me to generate a script-based trade signal system combining EMA + RSI + MACD for QQQ intraday calls/puts?
Indicador Pine Script®
por karothu
RSI TrendSignal🔍 **Smart RSI System – Free & Open Source** A powerful RSI-based indicator designed for traders who want clarity, simplicity, and filtered signals that *actually mean something*. --- ### 🎯 Key Features: ✅ Classic RSI with custom smoothing ✅ Optional Bollinger Bands over RSI ✅ Built-in Divergence Detection (Regular Bullish/Bearish) ✅ Dynamic Buy/Sell Conditions based on RSI + MA cross ✅ STAR signals for high-conviction entries (Overbought/Oversold + strength filter) ✅ ATR-based strength filter and custom visualizations ✅ Works great on **crypto**, **forex**, or **indices** ✅ Fully open-source and beginner-friendly! --- ### 📊 Recommended Timeframes: 15min, 1H, 4H, Daily – test and adjust settings for your style. --- ### ⚙️ How to Use: 1. Watch for **Buy/Sell** shapes when RSI confirms crossover with smoothed MA. 2. **STAR signals** are stronger – when RSI is above 70 or below 30 with momentum separation. 3. Divergences (optional) can confirm reversals. 4. Use ATR plot or your own trailing stop logic for exit strategy. --- 🔔 Alerts are built-in and ready to use. 📌 You can connect them to bots, webhooks, or Telegram (see alert templates in the script). --- 🧠 **Built by a trader, for traders.** Use this as a base and build your own version – or just trade it as is. --- --- 💬 **Feedback / Questions / Want to talk?** Feel free to message me on Telegram: 👉 (t.me/Ario_pinescript_pogramer) This is a clean version of RSI TrendSignal with improved alerts. It uses RSI cross with a smoothed moving average to generate filtered buy/sell signals. No external links or bots. Fully compliant with TradingView rules. 📺 Demo & Tutorial coming soon on my YouTube channel – stay tuned
Indicador Pine Script®
por setbet215
Combined ATPC & MACD DivergenceTrend Optimizer + Divergence Finder in One Unified Tool 🔍 Overview: This powerful dual-system indicator merges two proven analytical engines: ✅ The Algorganic Typical Price Channel (ATPC) — a custom trend oscillator that highlights mean-reversion and directional bias. ✅ A refined MACD system with divergence detection, enhanced with an adjusted Donchian midline for real-time trend strength filtering. Together, they provide a high-confidence, multi-signal system ideal for swing trading, scalping, or confirming reversals with context. ⚙️ Core Components & Logic 🧠 1. ATPC Engine (Trend Commodity Index) A momentum and volatility-normalized oscillator based on the typical price (H+L+C)/3: TrendCI Line (Blue) – Main trend signal based on smoothed CCI logic. TrendLine2 (Orange) – A slower smoothing of TrendCI for crossovers. Key Zones (customizable): 🔴 Ultra Overbought: +73 🟣 Overbought: +58 🟣 Oversold: -58 🔴 Ultra Oversold: -73 Trade Logic: ✅ Buy Signal: TrendCI crosses above TrendLine2 while in oversold zone ❌ Sell Signal: TrendCI crosses below TrendLine2 while in overbought zone Additional visual feedback: Histogram Bars show strength and direction of momentum shift Green/Red Circles highlight potential long/short setups 📉 2. MACD System + Divergence Finder Classic MACD enhanced with a Donchian Midline overlay to filter trend bias. 🔷 MACD Line and 🟠 Signal Line show crossover momentum 🟩/🟥 Histogram shows distance from the signal line 🟪 Adjusted Donchian Midline dynamically adapts to range-bound vs trending environments Background Color provides real-time trend state: ✅ Green = Bullish Trend ❌ Red = Bearish Trend No color = Neutral / Choppy MACD Boundaries (user-defined): Overbought: +1.0 Oversold: -1.0 🔀 3. Divergence Detection Spot hidden power shifts before price reacts: 🔼 Positive Divergence – Price makes lower lows, but MACD histogram rises 🔽 Negative Divergence – Price makes higher highs, but MACD histogram weakens These are visually marked with: Green “+Div” label (bullish reversal cue) Red “–Div” label (bearish exhaustion signal) 🎯 How to Use It For Trend Traders: Stay in sync with macro trend using MACD histogram + background Use ATPC crossovers for precision entries Avoid signals during neutral background (chop filter) For Reversal Traders: Look for bullish +Div with ATPC buy signal in oversold zone Look for bearish –Div with ATPC sell signal in overbought zone Mid-Donchian line can act as confluence or breakout trigger For Scalpers & Intraday Traders: Combine with VWAP, liquidity zones, or order flow levels ATPC crossovers + MACD histogram zero-line flip = potential scalp entry Use histogram slope and divergence to avoid false momentum traps 🧩 Customizable Inputs 🎛️ ATPC: Channel & Smoothing lengths, overbought/oversold thresholds 🎛️ MACD: Fast/slow EMAs, signal smoothing, Donchian period, bounds 🎨 Fully theme-compatible with adjustable colors and line styles 🔔 Alerts (Add Your Own) While this version doesn’t contain built-in alerts, you can easily add alerts based on: buySignal or sellSignal from ATPC logic Histogram cross zero or trend flip MACD Divergence event 📜 “This indicator doesn't just show signals—it tells a story about who’s in control of the market, and when that control might be slipping.”
Indicador Pine Script®
por herose4the
Supply/Demand Zones + Engulfment-based ExecutionSupply/Demand Zones + Engulfment-Based Execution Strategy Overview This strategy combines institutional trading concepts—supply/demand zones and engulfing candle patterns—to generate high-probability long and short trade setups. The system uses aggregated price action to identify potential reversal zones and confirms entries with engulfing candle patterns, ensuring trades are only taken when market structure shows commitment in the direction of the trade. Core Concepts • Supply & Demand Zones: These are automatically detected by analyzing aggregated bullish and bearish candle structures over user-defined intervals. Supply zones are formed after bearish continuation patterns; demand zones appear after bullish continuation patterns. • Engulfing Entries: Once price enters a zone, the strategy waits for a bullish engulfing pattern (in a demand zone) or a bearish engulfing pattern (in a supply zone) before executing a trade. This adds confirmation and reduces false signals. • Risk Management: Stop-loss is placed at the low (for long trades) or high (for short trades) of the engulfed candle. Take-profit can be calculated using a fixed R-multiple (risk-to-reward ratio) or a user-defined target price. Key Features Fully customizable aggregation factor for zone detection Visual zone boxes, entry/SL/TP boxes, and engulfing pattern labels Optional removal of mitigated zones for cleaner charting Configurable trade mode (Long only, Short only, or Both) Support for trading sessions and date filtering Alerts for price entering supply or demand zones How to Use Select Aggregation Factor: Choose how many candles to group together for identifying key zones (e.g., 4x timeframe). Enable Zones: Turn on supply and/or demand zones as needed. Set Execution Parameters: – Choose R-multiple (e.g., 2:1 risk-reward) – Or use a fixed take-profit price Define Trade Time Window: – Set the date and time ranges to restrict execution – Use Start Hour and End Hour to limit trades to specific sessions (e.g., London/New York) Run on Desired Timeframe: Typically used on 15m–4H charts, depending on your strategy and the asset’s volatility. Ideal For • Traders using Smart Money Concepts (SMC) • Those who value high-confluence entries • Intraday to swing traders looking for structure-based automation ⚠️ Important Notes • The strategy requires engulfing confirmation within the zone to enter a position. • This script does not repaint and executes trades on a bar close basis. • Backtest results may vary based on session filters and aggregation factor. © Attribution This strategy was developed by The_Forex_Steward and is licensed under the Mozilla Public License 2.0. You are free to use, modify, and distribute it under the terms of that license.
Estratégia Pine Script®
por The_Forex_Steward
EMA 12/21 Crossover with ATR-based SL/TP📈 Ultimate Scalper v2 Strategy Type: Trend-Pullback Scalping Indicators Used: EMA (12/21), MACD Histogram, ADX, ATR Platform: TradingView (Pine Script v5) Author: 🎯 Strategy Overview The Ultimate Scalper v2 is a scalping strategy that catches pullbacks within short-term trends using a dynamic combination of 12/21 EMA bands, MACD Histogram crossovers, and ADX for trend confirmation. It uses ATR-based stop-loss and take-profit levels, making it suitable for volatility-sensitive environments. 🧠 Logic Breakdown 🔍 Trend Detection Uses the 12 EMA and 21 EMA to identify the short-term trend: Uptrend: EMA 12 > EMA 21 and ADX > threshold Downtrend: EMA 12 < EMA 21 and ADX > threshold The ADX (default: 25) filters out low-momentum environments. 📉 Pullback Identification Once a trend is detected: A pullback is flagged when the MACD Histogram moves against the trend (below 0 in uptrend, above 0 in downtrend). An entry signal is triggered when the histogram crosses back through zero (indicating momentum is resuming in the trend direction). 🟢 Entry Conditions Long Entry: EMA 12 > EMA 21 ADX > threshold MACD Histogram was below 0 and crosses above 0 Short Entry: EMA 12 < EMA 21 ADX > threshold MACD Histogram was above 0 and crosses below 0 ❌ Exit Logic (ATR-based) The strategy calculates stop-loss and take-profit levels using ATR at the time of entry: Stop-Loss: Entry Price −/+ ATR × Multiplier Take-Profit: Entry Price ± ATR × 2 × Multiplier Default ATR Multiplier: 1.0 ⚙️ Customizable Inputs ADX Threshold: Minimum trend strength for trades (default: 25) ATR Multiplier: Controls SL/TP distance (default: 1.0) 📊 Visuals EMA 12 and EMA 21 band can be added manually for visual reference. Entry and exit signals are plotted via TradingView’s built-in backtesting engine. ⚠️ Disclaimer This is a backtesting strategy, not financial advice. Performance varies across markets and timeframes. Always combine with additional confluence or risk management when going live.
Estratégia Pine Script®
por RUmbro16
Candle Setup🧠 This indicator is based on a strategy concept by Arshia from the LEEMEENAL group. The SMA Shadow Strategy is a visual candlestick-based setup designed to identify potential reversal points by analyzing the relationship between the candle's shadows (wicks) and a Simple Moving Average (SMA). 📊 How It Works This strategy focuses on candles with significant upper or lower shadows relative to their body size, suggesting potential rejection zones. The conditions are split between red (bearish) and green (bullish) candles: 🔴 Red Candle Setup: The SMA line is inside or slightly above the upper shadow. The upper shadow is significantly larger than the body (customizable ratio). The lower shadow is smaller than the body. These conditions hint at strong rejection from above, often signaling a potential short opportunity or resistance confirmation. 🟢 Green Candle Setup: The SMA line is inside or slightly below the lower shadow. The lower shadow is significantly larger than the body (customizable ratio). The upper shadow is smaller than the body. This setup suggests a price rejection from below, indicating a potential long opportunity or support confirmation. ⚙️ Customizable Conditions Users can enable or disable each of the setup rules independently for both red and green candles: Enable SMA shadow alignment. Adjust the shadow-to-body ratio separately for red and green candles. Toggle shadow logic (e.g., lower/upper shadows relative to body). 🛎 Alerts Alerts are included for both red and green signal conditions, making it easier to integrate into automated workflows or notification systems. If you found this indicator helpful, feel free to share or give credit to Arshia - LEEMEENAL Group. 🙏
Indicador Pine Script®
por YasinAbbasiiii
Color Bar Based on Closing Range, ATR, and VolumeTight Closes with Volume Highlight is a bar coloring script that helps you spot price consolidation and potential breakout zones. It highlights bars with two key conditions: Tight Closing Ranges based on ATR to detect compression. Volume and Price Strength using dynamic volume comparisons to flag accumulation or interest. Customize the bar colors to suit your chart style and use this tool to visually scan for high-potential setups with less effort.
Indicador Pine Script®
por vssebuyungo
Demo StrategyGBPUSD – Potential Reversal from Key Support After a series of bearish candles, GBPUSD has approached a strong support zone around 1.2700. Recent price action shows signs of exhaustion from sellers, with long lower wicks and increased buying volume—indicating a potential short-term reversal. On the 4H chart, the pair is forming a minor double bottom near support. A confirmed breakout above 1.2780 could open the door for a bullish correction toward 1.2850–1.2880. Strategy: Wait for a pullback entry around 1.2725–1.2740 with a tight stop below 1.2700. First target at 1.2850, extended target near 1.2880. Risk Note: The overall trend is still bearish. This setup is a counter-trend trade based on technical signals, so proper risk management is essential.
Estratégia Pine Script®
por taikhuong199x
Risk-Adjusted Momentum Oscillator# Risk-Adjusted Momentum Oscillator (RAMO): Momentum Analysis with Integrated Risk Assessment ## 1. Introduction Momentum indicators have been fundamental tools in technical analysis since the pioneering work of Wilder (1978) and continue to play crucial roles in systematic trading strategies (Jegadeesh & Titman, 1993). However, traditional momentum oscillators suffer from a critical limitation: they fail to account for the risk context in which momentum signals occur. This oversight can lead to significant drawdowns during periods of market stress, as documented extensively in the behavioral finance literature (Kahneman & Tversky, 1979; Shefrin & Statman, 1985). The Risk-Adjusted Momentum Oscillator addresses this gap by incorporating real-time drawdown metrics into momentum calculations, creating a self-regulating system that automatically adjusts signal sensitivity based on current risk conditions. This approach aligns with modern portfolio theory's emphasis on risk-adjusted returns (Markowitz, 1952) and reflects the sophisticated risk management practices employed by institutional investors (Ang, 2014). ## 2. Theoretical Foundation ### 2.1 Momentum Theory and Market Anomalies The momentum effect, first systematically documented by Jegadeesh & Titman (1993), represents one of the most robust anomalies in financial markets. Subsequent research has confirmed momentum's persistence across various asset classes, time horizons, and geographic markets (Fama & French, 1996; Asness, Moskowitz & Pedersen, 2013). However, momentum strategies are characterized by significant time-varying risk, with particularly severe drawdowns during market reversals (Barroso & Santa-Clara, 2015). ### 2.2 Drawdown Analysis and Risk Management Maximum drawdown, defined as the peak-to-trough decline in portfolio value, serves as a critical risk metric in professional portfolio management (Calmar, 1991). Research by Chekhlov, Uryasev & Zabarankin (2005) demonstrates that drawdown-based risk measures provide superior downside protection compared to traditional volatility metrics. The integration of drawdown analysis into momentum calculations represents a natural evolution toward more sophisticated risk-aware indicators. ### 2.3 Adaptive Smoothing and Market Regimes The concept of adaptive smoothing in technical analysis draws from the broader literature on regime-switching models in finance (Hamilton, 1989). Perry Kaufman's Adaptive Moving Average (1995) pioneered the application of efficiency ratios to adjust indicator responsiveness based on market conditions. RAMO extends this concept by incorporating volatility-based adaptive smoothing, allowing the indicator to respond more quickly during high-volatility periods while maintaining stability during quiet markets. ## 3. Methodology ### 3.1 Core Algorithm Design The RAMO algorithm consists of several interconnected components: #### 3.1.1 Risk-Adjusted Momentum Calculation The fundamental innovation of RAMO lies in its risk adjustment mechanism: Risk_Factor = 1 - (Current_Drawdown / Maximum_Drawdown × Scaling_Factor) Risk_Adjusted_Momentum = Raw_Momentum × max(Risk_Factor, 0.05) This formulation ensures that momentum signals are dampened during periods of high drawdown relative to historical maximums, implementing an automatic risk management overlay as advocated by modern portfolio theory (Markowitz, 1952). #### 3.1.2 Multi-Algorithm Momentum Framework RAMO supports three distinct momentum calculation methods: 1. Rate of Change: Traditional percentage-based momentum (Pring, 2002) 2. Price Momentum: Absolute price differences 3. Log Returns: Logarithmic returns preferred for volatile assets (Campbell, Lo & MacKinlay, 1997) This multi-algorithm approach accommodates different asset characteristics and volatility profiles, addressing the heterogeneity documented in cross-sectional momentum studies (Asness et al., 2013). ### 3.2 Leading Indicator Components #### 3.2.1 Momentum Acceleration Analysis The momentum acceleration component calculates the second derivative of momentum, providing early signals of trend changes: Momentum_Acceleration = EMA(Momentum_t - Momentum_{t-n}, n) This approach draws from the physics concept of acceleration and has been applied successfully in financial time series analysis (Treadway, 1969). #### 3.2.2 Linear Regression Prediction RAMO incorporates linear regression-based prediction to project momentum values forward: Predicted_Momentum = LinReg_Value + (LinReg_Slope × Forward_Offset) This predictive component aligns with the literature on technical analysis forecasting (Lo, Mamaysky & Wang, 2000) and provides leading signals for trend changes. #### 3.2.3 Volume-Based Exhaustion Detection The exhaustion detection algorithm identifies potential reversal points by analyzing the relationship between momentum extremes and volume patterns: Exhaustion = |Momentum| > Threshold AND Volume < SMA(Volume, 20) This approach reflects the established principle that sustainable price movements require volume confirmation (Granville, 1963; Arms, 1989). ### 3.3 Statistical Normalization and Robustness RAMO employs Z-score normalization with outlier protection to ensure statistical robustness: Z_Score = (Value - Mean) / Standard_Deviation Normalized_Value = max(-3.5, min(3.5, Z_Score)) This normalization approach follows best practices in quantitative finance for handling extreme observations (Taleb, 2007) and ensures consistent signal interpretation across different market conditions. ### 3.4 Adaptive Threshold Calculation Dynamic thresholds are calculated using Bollinger Band methodology (Bollinger, 1992): Upper_Threshold = Mean + (Multiplier × Standard_Deviation) Lower_Threshold = Mean - (Multiplier × Standard_Deviation) This adaptive approach ensures that signal thresholds adjust to changing market volatility, addressing the critique of fixed thresholds in technical analysis (Taylor & Allen, 1992). ## 4. Implementation Details ### 4.1 Adaptive Smoothing Algorithm The adaptive smoothing mechanism adjusts the exponential moving average alpha parameter based on market volatility: Volatility_Percentile = Percentrank(Volatility, 100) Adaptive_Alpha = Min_Alpha + ((Max_Alpha - Min_Alpha) × Volatility_Percentile / 100) This approach ensures faster response during volatile periods while maintaining smoothness during stable conditions, implementing the adaptive efficiency concept pioneered by Kaufman (1995). ### 4.2 Risk Environment Classification RAMO classifies market conditions into three risk environments: - Low Risk: Current_DD < 30% × Max_DD - Medium Risk: 30% × Max_DD ≤ Current_DD < 70% × Max_DD - High Risk: Current_DD ≥ 70% × Max_DD This classification system enables conditional signal generation, with long signals filtered during high-risk periods—a approach consistent with institutional risk management practices (Ang, 2014). ## 5. Signal Generation and Interpretation ### 5.1 Entry Signal Logic RAMO generates enhanced entry signals through multiple confirmation layers: 1. Primary Signal: Crossover between indicator and signal line 2. Risk Filter: Confirmation of favorable risk environment for long positions 3. Leading Component: Early warning signals via acceleration analysis 4. Exhaustion Filter: Volume-based reversal detection This multi-layered approach addresses the false signal problem common in traditional technical indicators (Brock, Lakonishok & LeBaron, 1992). ### 5.2 Divergence Analysis RAMO incorporates both traditional and leading divergence detection: - Traditional Divergence: Price and indicator divergence over 3-5 periods - Slope Divergence: Momentum slope versus price direction - Acceleration Divergence: Changes in momentum acceleration This comprehensive divergence analysis framework draws from Elliott Wave theory (Prechter & Frost, 1978) and momentum divergence literature (Murphy, 1999). ## 6. Empirical Advantages and Applications ### 6.1 Risk-Adjusted Performance The risk adjustment mechanism addresses the fundamental criticism of momentum strategies: their tendency to experience severe drawdowns during market reversals (Daniel & Moskowitz, 2016). By automatically reducing position sizing during high-drawdown periods, RAMO implements a form of dynamic hedging consistent with portfolio insurance concepts (Leland, 1980). ### 6.2 Regime Awareness RAMO's adaptive components enable regime-aware signal generation, addressing the regime-switching behavior documented in financial markets (Hamilton, 1989; Guidolin, 2011). The indicator automatically adjusts its parameters based on market volatility and risk conditions, providing more reliable signals across different market environments. ### 6.3 Institutional Applications The sophisticated risk management overlay makes RAMO particularly suitable for institutional applications where drawdown control is paramount. The indicator's design philosophy aligns with the risk budgeting approaches used by hedge funds and institutional investors (Roncalli, 2013). ## 7. Limitations and Future Research ### 7.1 Parameter Sensitivity Like all technical indicators, RAMO's performance depends on parameter selection. While default parameters are optimized for broad market applications, asset-specific calibration may enhance performance. Future research should examine optimal parameter selection across different asset classes and market conditions. ### 7.2 Market Microstructure Considerations RAMO's effectiveness may vary across different market microstructure environments. High-frequency trading and algorithmic market making have fundamentally altered market dynamics (Aldridge, 2013), potentially affecting momentum indicator performance. ### 7.3 Transaction Cost Integration Future enhancements could incorporate transaction cost analysis to provide net-return-based signals, addressing the implementation shortfall documented in practical momentum strategy applications (Korajczyk & Sadka, 2004). ## References Aldridge, I. (2013). *High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems*. 2nd ed. Hoboken, NJ: John Wiley & Sons. Ang, A. (2014). *Asset Management: A Systematic Approach to Factor Investing*. New York: Oxford University Press. Arms, R. W. (1989). *The Arms Index (TRIN): An Introduction to the Volume Analysis of Stock and Bond Markets*. Homewood, IL: Dow Jones-Irwin. Asness, C. S., Moskowitz, T. J., & Pedersen, L. H. (2013). Value and momentum everywhere. *Journal of Finance*, 68(3), 929-985. Barroso, P., & Santa-Clara, P. (2015). Momentum has its moments. *Journal of Financial Economics*, 116(1), 111-120. Bollinger, J. (1992). *Bollinger on Bollinger Bands*. New York: McGraw-Hill. Brock, W., Lakonishok, J., & LeBaron, B. (1992). Simple technical trading rules and the stochastic properties of stock returns. *Journal of Finance*, 47(5), 1731-1764. Calmar, T. (1991). The Calmar ratio: A smoother tool. *Futures*, 20(1), 40. Campbell, J. Y., Lo, A. W., & MacKinlay, A. C. (1997). *The Econometrics of Financial Markets*. Princeton, NJ: Princeton University Press. Chekhlov, A., Uryasev, S., & Zabarankin, M. (2005). Drawdown measure in portfolio optimization. *International Journal of Theoretical and Applied Finance*, 8(1), 13-58. Daniel, K., & Moskowitz, T. J. (2016). Momentum crashes. *Journal of Financial Economics*, 122(2), 221-247. Fama, E. F., & French, K. R. (1996). Multifactor explanations of asset pricing anomalies. *Journal of Finance*, 51(1), 55-84. Granville, J. E. (1963). *Granville's New Key to Stock Market Profits*. Englewood Cliffs, NJ: Prentice-Hall. Guidolin, M. (2011). Markov switching models in empirical finance. In D. N. Drukker (Ed.), *Missing Data Methods: Time-Series Methods and Applications* (pp. 1-86). Bingley: Emerald Group Publishing. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. *Econometrica*, 57(2), 357-384. Jegadeesh, N., & Titman, S. (1993). Returns to buying winners and selling losers: Implications for stock market efficiency. *Journal of Finance*, 48(1), 65-91. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. *Econometrica*, 47(2), 263-291. Kaufman, P. J. (1995). *Smarter Trading: Improving Performance in Changing Markets*. New York: McGraw-Hill. Korajczyk, R. A., & Sadka, R. (2004). Are momentum profits robust to trading costs? *Journal of Finance*, 59(3), 1039-1082. Leland, H. E. (1980). Who should buy portfolio insurance? *Journal of Finance*, 35(2), 581-594. Lo, A. W., Mamaysky, H., & Wang, J. (2000). Foundations of technical analysis: Computational algorithms, statistical inference, and empirical implementation. *Journal of Finance*, 55(4), 1705-1765. Markowitz, H. (1952). Portfolio selection. *Journal of Finance*, 7(1), 77-91. Murphy, J. J. (1999). *Technical Analysis of the Financial Markets: A Comprehensive Guide to Trading Methods and Applications*. New York: New York Institute of Finance. Prechter, R. R., & Frost, A. J. (1978). *Elliott Wave Principle: Key to Market Behavior*. Gainesville, GA: New Classics Library. Pring, M. J. (2002). *Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points*. 4th ed. New York: McGraw-Hill. Roncalli, T. (2013). *Introduction to Risk Parity and Budgeting*. Boca Raton, FL: CRC Press. Shefrin, H., & Statman, M. (1985). The disposition to sell winners too early and ride losers too long: Theory and evidence. *Journal of Finance*, 40(3), 777-790. Taleb, N. N. (2007). *The Black Swan: The Impact of the Highly Improbable*. New York: Random House. Taylor, M. P., & Allen, H. (1992). The use of technical analysis in the foreign exchange market. *Journal of International Money and Finance*, 11(3), 304-314. Treadway, A. B. (1969). On rational entrepreneurial behavior and the demand for investment. *Review of Economic Studies*, 36(2), 227-239. Wilder, J. W. (1978). *New Concepts in Technical Trading Systems*. Greensboro, NC: Trend Research.
Indicador Pine Script®
por EdgeTools
Options Risk Manager v2.2.0 - Priority 7 CompleteScript Description for TradingView Publication Options Risk Manager v2.2.0 - Priority 7 Complete What does this script do? Options Risk Manager v2.2.0 is a comprehensive position management system designed specifically for options traders. The indicator calculates precise stop loss levels, risk/reward targets, and position sizing based on user-defined risk parameters. It provides real-time profit/loss tracking, options Greeks monitoring, and automated alert systems for critical price levels. The script displays entry points, stop losses, and profit targets directly on the chart while continuously calculating position metrics including dollar risk, account exposure, and probability of success. Version 2.2.0 introduces Priority 7 advanced alerts with dynamic risk warnings and multi-condition notifications. How does it do it? The script performs several key calculations: 1. Risk-Based Stop Loss Calculation - Determines stop loss levels based on percentage of entry price, automatically adjusting for calls versus puts. Put positions place stops above entry, while calls place stops below. 2. Position Sizing Algorithm - Calculates optimal contract quantities using account size, risk percentage, and stop distance to ensure consistent risk per trade regardless of underlying price. 3. Options-Specific P&L Tracking - Incorporates Delta, Gamma, Vega, and Theta to provide accurate profit/loss calculations for options positions, including time decay effects. 4. Three-Phase Trade Management - Implements systematic position management through Entry Phase (initial risk), Profit Phase (approaching target), and Trailing Phase (EMA-based exit management). 5. Multi-Level Alert System - Monitors price action, Greeks thresholds, time decay acceleration, and account risk levels to generate context-aware notifications. How to use it? Initial Setup: 1. Apply indicator to any optionable security 2. Toggle "In Position" ON when entering a trade 3. Set Direction (Call/Put) and Side (Long/Short) 4. Enter the underlying price at position entry 5. Specify number of contracts and risk percentage Position Management: Blue line shows entry price Red line indicates stop loss level Orange line displays risk/reward target Purple EMA line activates after target hit Monitor real-time P&L in trade panels Alert Configuration: Enable Advanced Alerts in settings Set profit/loss notification thresholds Configure Greek-based warnings Activate time decay alerts for expiration Risk Parameters: Risk % determines stop distance from entry Account Value sets position sizing limits Contract Multiplier (standard = 100) R:R Ratio defines profit targets What makes it unique? Options Risk Manager addresses the specific challenges of options trading that generic indicators miss. The script accounts for the inverse relationship in put options (profiting from price declines), incorporates Greeks for accurate P&L calculations, and provides options-specific limit orders for TradeStation integration. The three-phase management system removes emotional decision-making by defining clear rules for position management. Phase transitions occur automatically based on price action, shifting from initial risk management to profit protection to trend-following modes. Version 2.2.0's Priority 7 alert system provides intelligent notifications that include live metrics, risk warnings, and market context rather than simple price crosses. Key Features Summary Options-Specific Calculations - Proper handling of calls/puts with inverse relationships Risk-Based Position Sizing - Consistent risk regardless of underlying price Greeks Integration - Delta, Gamma, Vega, Theta for accurate tracking Phase Management System - Systematic three-stage position handling Advanced Alert System - Context-aware notifications with metrics TradeStation Integration - Option limit orders for execution Visual Risk Display - Clear chart overlays for all levels Probability Calculator - Win/loss probability with expected value Multi-Account Support - Scales from small to large accounts Important Notes This indicator requires manual input of option prices and Greeks (available from your broker's option chain). It functions as a risk management overlay and does not generate entry signals. The calculations assume standard options contracts of 100 shares. Designed for TradeStation platform with full functionality. Basic features available on other platforms without options data integration. Always verify calculations with your broker's risk system before placing trades.
Indicador Pine Script®
por patrickamp
1min&5min EMA + CE indicatorCombined indicator for EMA cross-pullback strategy. In addition to that - there are buttons to turn on/off ce signal appearance limitations on time after 5min cross and ema direction match between 1min and 5min ema. You can set a single alert for both the 5-minute EMA cross and the 1-minute CE signals simultaneously. I find it max easy to use by adjusting the Visibility settings - switch it on only for 1min and 5min charts - it will not appear on htf charts. By doing this, you no longer need to switch on/off indicators(in this case only 1) to get clear htf charts.
Indicador Pine Script®
por MHesse11
abusuhil bullish breakAbusuhil Bullish Break is a price action-based confirmation tool that identifies a bullish reversal pattern consisting of: Two consecutive bearish candles followed by A strong bullish candle that closes above the high of both. The script includes: Optional dual MACD filter (current timeframe + higher timeframe) Configurable stop-loss and multiple take-profit levels Visual lines for targets and stop Custom styling for all elements It’s a clean, logic-driven entry confirmation tool for intraday and swing trading. ⚠️ Open-source and fully customizable. مؤشر Abusuhil Bullish Break هو أداة تأكيد لانعكاسات الاتجاه الصاعد بناءً على حركة السعر (Price Action)، ويكتشف نموذجًا يتكون من: شمعتين هابطتين متتاليتين تتبعهما شمعة صاعدة قوية تغلق فوق أعلى الشمعتين السابقتين يحتوي المؤشر على: فلتر MACD مزدوج اختياري (للفريم الحالي وفريم أعلى) إعدادات مخصصة للوقف والأهداف المتعددة خطوط مرئية احترافية للأهداف والوقف تحكم كامل في الألوان والنمط والعرض مناسب للتداول اللحظي والسوينج. ✅ مفتوح المصدر وقابل للتعديل بالكامل.
Indicador Pine Script®
por abusuhil
HP Strategy (Hannah's Precise) - with AlertsThis script is for a custom indicator called HP Strategy (Hannah’s Precise). It identifies high-probability buy and sell setups based on: Order block detection (based on specific bullish/bearish candle formations) EMA confluences (price bouncing off 20, 50, 100 EMAs) Hull Moving Average (HMA100) for trend direction A custom Trama line for added confirmation The strategy ensures only buys above HMA100 and EMA100, and only sells below them When all these conditions align with a valid order block, the indicator shows a BUY or SELL label and optionally triggers an alert.
Indicador Pine Script®
por hannahmohamed
Holy Grail Setup with Confidence OpacityVersion 1 Triggers Raschke's Holy Grail set up. More translucent=less confidence, more opaque=more confidence. Uses Raschke's default parameters 20 EMA + ADX > 30 + pullback and reversal ADX stands for Average Directional Index, a technical indicator developed by Welles Wilder to quantify trend strength — not direction, just strength. It's a core component of Linda Raschke’s Holy Grail strategy, where the goal is to only trade pullbacks during strong trends. ADX ranges from 0 to 100: Below 20: Weak or no trend (range-bound market) 25–30 and above: Strong trend 50+: Very strong trend (often near trend exhaustion) In the Holy Grail setup, Raschke recommends only taking trades when ADX > 30, to ensure that: The market is trending Pullbacks are more likely to resolve in the direction of the trend
Indicador Pine Script®
por joshuabort
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