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Lot Size Calculator (Dynamic) with Manual Pip ValueDevoleper: Sheikh Rakib This TradingView indicator helps you calculate the correct lot size based on your risk amount in USD and stop loss (SL) in pips. It dynamically detects pip value per lot depending on the trading instrument (e.g., Forex majors, minors, gold, crypto), and also allows manual override if needed. ✅ Key Features: 📏 Input SL in pips and risk amount in USD ⚙️ Automatically detects pip size and pip value per lot 🧮 Calculates lot size based on your inputs ✍️ Manual pip value override option if auto-detection is incorrect 🖥️ Clean, organized info panel displayed on chart 💹 Works with Forex, Gold (XAUUSD), Silver (XAGUSD), BTC, ETH, and more 📘 Usage Tips: Set your SL in pips and how much you want to risk per trade (USD) If the pip value is not calculated correctly (rare for exotic pairs), enable and set your own value using the “Manual Pip Value” input Recommended for scalpers, day traders, and swing traders who want to manage risk smartly Built with risk management in mind — because consistent trading starts with proper lot sizing.
Penunjuk Pine Script®
oleh Greenbd
Thors Economic NewsThe Live Economic Calendar indicator seamlessly integrates with external news sources to provide real-Time, upcoming, and past financial news directly on your Tradingview chart. By having a clear understanding of when news are planned to be released, as well as their respective impact, analysts can prepare their weeks and days in advance. These injections of volatility can be harnessed by analysts to support their thesis, or may want to be avoided to ensure higher probability market conditions. Fundamentals and news releases transcend the boundaries of technical analysis, as their effects are difficult to predict or estimate. Designed for both novice and experienced traders, the Live Economic Calendar indicator enhances your analysis by keeping you informed of the latest and upcoming market-moving news.
Penunjuk Pine Script®
oleh cegb001
Custom Ichimoku Cloud with Signals📊 OVERVIEW This indicator generates trading signals based on Ichimoku Cloud breakouts and breakdowns. It identifies when price decisively moves through the cloud boundaries, filtering out false signals from consolidation periods. 📈 KEY FEATURES - Transition-based signals only when price breaks through cloud - Candle body must completely clear cloud (no touching) - Alternating signal system prevents consecutive duplicate signals - Built-in alerts for automated notifications - Standard Ichimoku components included ⚙️ HOW IT WORKS BUY SIGNAL: Triggered when candle body moves completely above cloud after being inside/below SELL SIGNAL: Triggered when candle body moves completely below cloud after being inside/above 🎯 USE CASES - Trend continuation trading - Breakout trading strategies - Cloud support/resistance analysis - Multi-timeframe analysis 📝 PARAMETERS - Adjustable Ichimoku periods (Conversion, Base, Lagging Span B) - Customizable lookback period for transition detection - Visual signal markers with alerts ⚠️ DISCLAIMER This indicator is for educational purposes. Past performance doesn't guarantee future results. Always use proper risk management and combine with other analysis methods. ⚠️ DISCLAIMER & RISK WARNING This indicator is provided for informational and educational purposes only and should not be considered as financial advice. TRADING RISKS: - Trading involves substantial risk of loss and is not suitable for all investors - Past performance is not indicative of future results - You can lose more than your initial investment - Never trade with money you cannot afford to lose NO GUARANTEES: - This indicator does not guarantee profits or predict market movements with certainty - Signals are based on mathematical calculations and may produce false signals - Market conditions can change, making any strategy ineffective - Success depends on multiple factors beyond this indicator USER RESPONSIBILITY: - You are solely responsible for your trading decisions - Always conduct your own research and analysis - Consider consulting with a qualified financial advisor - Use proper risk management and position sizing - Test thoroughly on demo accounts before live trading TECHNICAL LIMITATIONS: - Indicator may be subject to repainting in real-time conditions - Historical results do not represent actual trading - Signals are for analysis only, not automatic trade execution - Performance varies across different timeframes and instruments By using this indicator, you acknowledge that you understand these risks and accept full responsibility for your trading outcomes. The author assumes no liability for any losses incurred. NOT FINANCIAL ADVICE - FOR EDUCATIONAL PURPOSES ONLY
Penunjuk Pine Script®
oleh viprasol
Liquidity Grab Entry Signals [Daily Enhanced]Liquidity Grab Entry Signals is a powerful tool designed to detect intraday reversal opportunities around daily high/low liquidity zones. Core features: – Plots current daily high/low levels – Identifies price interaction with these key zones – Confirms rejection via strong engulfing candles – Plots real-time long/short entry signals directly on chart – Includes alerts for both long and short setups This script is ideal for scalpers and intraday traders looking to exploit stop hunts, liquidity sweeps, and false breakouts. Optimized for instruments like US30, NAS100, Gold, BTC and more. Customize the sensitivity buffer to suit your asset and timeframe. Use this in combination with VWAP, FVG or Smart Money concepts for enhanced confirmation. --- Built for: 1s–15m charts Includes: Alerts + Custom Settings Type: Non-repainting Trade with clarity around the most manipulated price levels of the day.
Penunjuk Pine Script®
oleh ChartEdgePro
Leader-Lagger DashboardSummary: The ultimate frustration for a trader: being right on the idea, but wrong on the asset. You correctly predict a market move, develop a solid bullish or bearish thesis, but the instrument you choose fails to follow through. Meanwhile, a correlated asset makes the exact move you anticipated, leaving you with a losing trade or a missed opportunity. This common pitfall is precisely what the Leader/Lagger Dashboard is designed to solve. The Solution: Instant Clarity on Relative Strength The Leader/Lagger Dashboard provides a clear, real-time verdict on the relative strength between two correlated assets, such as ES (S&P 500 futures) and NQ (Nasdaq 100 futures). By instantly identifying the Leader (the stronger asset) and the Lagger (the weaker asset), it empowers you to focus your capital on the instrument with the highest probability of performing in line with your market view. As shown in the example image, if your idea is to short the market, choosing the "Weak" asset (ES) results in a winning trade, while shorting the "Strong" asset (NQ) would have failed. This tool helps you make that critical distinction before you enter. How It Works The engine at the core of this dashboard analyzes the price action of two assets on a higher timeframe (defaulting to 90 minutes). It measures how the current bar's high and low are performing relative to the previous bar's range for each asset. By comparing these normalized values, it generates a score to determine which asset is exhibiting stronger momentum (the Leader) and which is showing weakness (the Lagger). A tie-breaking mechanism using a lower timeframe ensures you always have a decisive verdict. How to Use It The principle is simple: Go long the leader, and short the lagger. If you are Bullish: Look for the asset marked "Strong." This is the instrument most likely to lead the upward move. If you are Bearish: Look for the asset marked "Weak." This is the instrument most likely to lead the downward move. By aligning your trade execution with the market's internal momentum, you dramatically increase your odds of success and avoid the frustration of trading against underlying strength or weakness. Key Features Instant Verdict: A simple on-chart table displays a "Strong" or "Weak" verdict for each asset. Focus on the Leader: Easily identify which asset is leading the move to align your trades with momentum. Avoid the Lagger: Steer clear of the weaker asset that might chop around or reverse, even if your directional bias is correct. Fully Customizable: Change the two assets to any symbols you trade (e.g., GOLD vs. SILVER, EURUSD vs. GBPUSD). Adjustable Display: Control the table's position and font size to perfectly fit your chart layout. The table is designed to be visible on lower timeframes (5-minutes and under) to assist with day trading execution. This tool is designed to be a crucial part of your decision-making process, providing an objective layer of confirmation for your trading ideas. so Stop guessing and start trading the right asset. As always, use this indicator in conjunction with your own complete analysis and risk management strategy.
Penunjuk Pine Script®
oleh traderprimez
Trading Holidays and Expiry CalendarTrading Holiday and Expiry Calendar This indicator displays calendar for current and next 2 months. Calendar marks ‘CRITICAL DAYS’ on the calendar. ‘CRITICAL DAYS’: Trading Days Trading Holidays Weekends Expiry Days Out of these ‘Expiry Days’ are marked based on User input and rest of the days are derived and marked automatically. Why this indicator: Most of the Pine Script developers find it tedious (rather difficult or impossible) to find future holidays and expiry days. This indicator exactly does that based on a simple input parameter Use cases: Calendar view of 3 months Trading days along with identification off critical activity days Pine Script developers can extract code/functions from the indicator and use it for building indicators and strategies Chart Snapshot
Penunjuk Pine Script®
oleh Sharad_Gaikwad
3
Correlation HeatMap [TradingFinder] Sessions Data Science Stats🔵 Introduction n financial markets, correlation describes the statistical relationship between the price movements of two assets and how they interact over time. It plays a key role in both trading and investing by helping analyze asset behavior, manage portfolio risk, and understand intermarket dynamics. The Correlation Heatmap is a visual tool that shows how the correlation between multiple assets and a central reference asset (the Main Symbol) changes over time. It supports four market types forex, stocks, crypto, and a custom mode making it adaptable to different trading environments. The heatmap uses a color-coded grid where warmer tones represent stronger negative correlations and cooler tones indicate stronger positive ones. This intuitive color system allows traders to quickly identify when assets move together or diverge, offering real-time insights that go beyond traditional correlation tables. 🟣 How to Interpret the Heatmap Visually ? Each cell represents the correlation between the main symbol and one compared asset at a specific time. Warm colors (e.g. red, orange) suggest strong negative correlation as one asset rises, the other tends to fall. Cool colors (e.g. blue, green) suggest strong positive correlation both assets tend to move in the same direction. Lighter shades indicate weaker correlations, while darker shades indicate stronger correlations. The heatmap updates over time, allowing users to detect changes in correlation during market events or trading sessions. One of the standout features of this indicator is its ability to overlay global market sessions such as Tokyo, London, New York, or major equity opens directly onto the heatmap timeline. This alignment lets traders observe how correlation structures respond to real-world session changes. For example, they can spot when assets shift from being inversely correlated to moving together as a new session opens, potentially signaling new momentum or macro flow. The customizable symbol setup (including up to 20 compared assets) makes it ideal not only for forex and crypto traders but also for multi-asset and sector-based stock analysis. 🟣 Use Cases and Advantages Analyze sector rotation in equities by tracking correlation to major indices like SPX or DJI. Monitor altcoin behavior relative to Bitcoin to find early entry opportunities in crypto markets. Detect changes in currency alignment with DXY across trading sessions in forex. Identify correlation breakdowns during market volatility, signaling possible new trends. Use correlation shifts as confirmation for trade setups or to hedge multi-asset exposure 🔵 How to Use Correlation is one of the core concepts in financial analysis and allows traders to understand how assets behave in relation to one another. The Correlation Heatmap extends this idea by going beyond a simple number or static matrix. Instead, it presents a dynamic visual map of how correlations shift over time. In this indicator, a Main Symbol is selected as the reference point for analysis. In standard modes such as forex, stocks, or crypto, the symbol currently shown on the main chart is automatically used as the main symbol. This allows users to begin correlation analysis right away without adjusting any settings. The horizontal axis of the heatmap shows time, while the vertical axis lists the selected assets. Each cell on the heatmap shows the correlation between that asset and the main symbol at a given moment. This approach is especially useful for intermarket analysis. In forex, for example, tracking how currency pairs like OANDA:EURUSD EURUSD, FX:GBPUSD GBPUSD, and PEPPERSTONE:AUDUSD AUDUSD correlate with TVC:DXY DXY can give insight into broader capital flow. If these pairs start showing increasing positive correlation with DXY say, shifting from blue to light green it could signal the start of a new phase or reversal. Conversely, if negative correlation fades gradually, it may suggest weakening relationships and more independent or volatile movement. In the crypto market, watching how altcoins correlate with Bitcoin can help identify ideal entry points in secondary assets. In the stock market, analyzing how companies within the same sector move in relation to a major index like SP:SPX SPX or DJ:DJI DJI is also a highly effective technique for both technical and fundamental analysts. This indicator not only visualizes correlation but also displays major market sessions. When enabled, this feature helps traders observe how correlation behavior changes at the start of each session, whether it's Tokyo, London, New York, or the opening of stock exchanges. Many key shifts, breakouts, or reversals tend to happen around these times, and the heatmap makes them easy to spot. Another important feature is the market selection mode. Users can switch between forex, crypto, stocks, or custom markets and see correlation behavior specific to each one. In custom mode, users can manually select any combination of symbols for more advanced or personalized analysis. This makes the heatmap valuable not only for forex traders but also for stock traders, crypto analysts, and multi-asset strategists. Finally, the heatmap's color-coded design helps users make sense of the data quickly. Warm colors such as red and orange reflect stronger negative correlations, while cool colors like blue and green represent stronger positive relationships. This simplicity and clarity make the tool accessible to both beginners and experienced traders. 🔵 Settings Correlation Period: Allows you to set how many historical bars are used for calculating correlation. A higher number means a smoother, slower-moving heatmap, while a lower number makes it more responsive to recent changes. Select Market: Lets you choose between Forex, Stock, Crypto, or Custom. In the first three options, the chart’s active symbol is automatically used as the Main Symbol. In Custom mode, you can manually define the Main Symbol and up to 20 Compared Symbols. Show Open Session: Enables the display of major trading sessions such as Tokyo, London, New York, or equity market opening hours directly on the timeline. This helps you connect correlation shifts with real-world market activity. Market Mode: Lets you select whether the displayed sessions relate to the forex or stock market. 🔵 Conclusion The Correlation Heatmap is a robust and flexible tool for analyzing the relationship between assets across different markets. By tracking how correlations change in real time, traders can better identify alignment or divergence between symbols and gain valuable insights into market structure. Support for multiple asset classes, session overlays, and intuitive visual cues make this one of the most effective tools for intermarket analysis. Whether you’re looking to manage portfolio risk, validate entry points, or simply understand capital flow across markets, this heatmap provides a clear and actionable perspective that you can rely on.
Penunjuk Pine Script®
oleh TFlab
1
Daily Manipulation Probability Dashboard📜 Summary Tired of getting stopped out on a "Judas Swing" just before the price moves in your intended direction? This indicator is designed to give you a statistical edge by quantifying the daily manipulation move. The Daily Manipulation Probability Dashboard analyzes thousands of historical trading days to reveal the probability of the initial "stop-hunt" or "fakeout" move reaching certain percentage levels. It presents this data in a clean, intuitive dashboard right on your chart, helping you make more data-driven decisions about stop-loss placement and entry timing. 🧠 The Core Concept The logic is simple but powerful. For every trading day, we measure two things: Amplitude Above Open (AAO): The distance price travels up from the daily open (High - Open). Amplitude Below Open (ABO): The distance price travels down from the daily open (Open - Low). The indicator defines the "Manipulation" as the smaller of these two moves. The idea is that this smaller move often acts as a liquidity grab to trap traders before the day's primary, larger move ("Distribution") begins. This tool focuses exclusively on providing deep statistical insight into this crucial manipulation phase. 🛠️ How to Use This Tool This dashboard is designed to be a practical part of your daily analysis and trade planning. 1. Smarter Stop-Loss Placement This is the primary use case. The "Prob. (%)" column tells you the historical chance of the manipulation move being at least a certain size. Example: If the table shows that for EURUSD, the ≥ 0.25% level has a probability of 30%, you can flip this information: there is a 70% probability that the daily manipulation move will be less than 0.25%. Action: Placing your stop-loss just beyond a level with a low probability gives you a statistically sound buffer against typical stop-hunts. 2. Entry Timing and Patience The live arrow (→) shows you where the current day's manipulation falls. Example: If the arrow is pointing at ≥ 0.10% and you know there is a high probability (e.g., 60%) of the manipulation reaching ≥ 0.20%, you might wait for a deeper pullback before entering, anticipating that the "Judas Swing" hasn't completed yet. 3. Assessing Daily Character Quickly see if the current day's action is unusual. If the manipulation move is already in a very low probability zone (e.g., > 1.00%), it might indicate that your Bias is wrong, or signal a high-volatility day or a potential trend reversal. 📊 Understanding the Dashboard Ticker: The top-right shows the current symbol you are analyzing. → (Arrow): Points to the row that corresponds to the current, live day's manipulation amplitude. Manip. Level: The percentage threshold being analyzed (e.g., ≥ 0.20%). Days Analyzed: The raw count of historical days where the manipulation move met or exceeded this level. Prob. (%): The key statistic. The cumulative probability of the manipulation move being at least the size of the level. ⚙️ Settings Position: Choose where you want the dashboard to appear on your chart. Text Size: Adjust the font size for readability. Max Historical Days to Analyze: Set the number of past daily candles to include in the statistical analysis. A larger number provides a more robust sample size. I believe this tool provides a unique, data-driven edge for intraday traders across all markets (Forex, Crypto, Stocks, Indices). Your feedback and suggestions are highly welcome! - @traderprimez
Penunjuk Pine Script®
oleh traderprimez
6
JADUGAAR_GORACHAND_V21. What is a Trendline? A trendline is a straight line drawn on a chart that connects two or more price points. It helps visualize the direction and strength of a trend — uptrend, downtrend, or sideways. 🔼 2. Uptrend Line An uptrend line connects higher lows. It acts as a support level, suggesting that buyers are in control. Price tends to bounce upward off this line during a bullish trend. 🔽 3. Downtrend Line A downtrend line connects lower highs. It acts as a resistance level, indicating that sellers dominate. Price tends to fall after touching this line in a bearish trend. 🔄 4. Trendline Breaks When price breaks a trendline, it may signal a potential trend reversal or trend weakening. Traders often use this for entry or exit signals. 📊 5. Trendline Validity A trendline is more reliable when: It touches 3 or more points It's drawn over a longer time frame There's strong volume on the breakout
Penunjuk Pine Script®
oleh socialtraders_bharat
Sat Stacking Strategies Simulation (SSSS)Sat Stacking Strategies Simulation (SSSS) This indicator simulates and compares different Bitcoin stacking strategies over time, allowing you to visualize performance, cost basis, and stacking behavior directly on your chart. Core Features: Three Stacking Strategies • Trend-Based – Stack only when price is above/below a long-term SMA. • Stack the Dip – Buy during sharp pullbacks or oversold conditions. • Price Zone – Stack only in “cheap”, “fair”, or “expensive” zones based on a simulated Short-Term Holder (STH) cost basis. Always Stack Benchmark Compare your chosen strategy against a simple “Always Stack” approach for a real-world DCA reference. Performance Metrics Table Track: • Total Fiat Added • Total BTC Accumulated • Current Value • Average Cost per BTC • PnL % • CAGR • Sharpe Ratio & Stdev • Stack Events & Time Underwater Advanced Options • Simulate cash-secured puts on unused fiat. • Simulate covered calls on BTC holdings. • Roll over unused stacking amounts for future buys. This tool is designed for Bitcoiners, stackers, and DCA enthusiasts who want to backtest and visualize their stacking plan—whether you keep it simple or go full quant. Sometimes the best alpha is just showing up every week with your wallet open… and occasionally wearing a helmet. 🪖💰
Penunjuk Pine Script®
oleh piriya33
1
Dark Pool Block Trades - Institutional Volume📊 Dark Pool Block Trades - Institutional Volume Visualize where institutional money positions before major price moves occur. This indicator reveals hidden dark pool block trades that often precede significant price movements - because when smart money deploys millions and billions in strategic accumulation or distribution, retail traders need to see where it's happening. 🎯 WHY DARK POOL DATA MATTERS: Institutions don't move large capital randomly. Dark pool block trades represent strategic positioning by sophisticated money managers with superior research and conviction. These trades create hidden support/resistance levels that often predict future price action. The key principle: Follow institutional flow, don't fight it. When institutions get involved, they create high-probability trading opportunities. 💰 HOW INSTITUTIONS INFLUENCE PRICE: - Large block trades establish hidden accumulation/distribution zones - Smart money builds positions BEFORE retail awareness increases - Institutional activity creates "footprints" at key technical levels - These trades often signal conviction plays ahead of major moves - Institutions typically add to winning positions throughout trends 🔍 WHAT THIS INDICATOR SHOWS: - Visual overlay of dark pool block trades directly on price charts - Track institutional positioning across major stocks and ETFs - Identify accumulation/distribution zones before they become obvious to retail - Spot high-conviction institutional trades in real-time visualization - Customizable block trade size filters and timeframe selection - Historical institutional activity up to 5 years or custom ranges 💡 THE TRADING ADVANTAGE: Instead of guessing price direction, see where institutions are already positioning. When large block trades appear in dark pools, you're witnessing strategic institutional commitment that frequently leads to significant price movements. ⚡ HOW IT WORKS: This Pine Script displays institutional dark pool transactions as visual markers on your charts. The script comes with sample data for immediate use. For expanded ticker coverage and real-time updates, external data services are available. 🎯 IDEAL FOR: - Swing traders following institutional footprints - Traders seeking setups backed by smart money conviction - Position traders looking for accumulation zones - Anyone wanting to align with institutional flow rather than fight it 🔄 SAMPLE DATA INCLUDED: Pre-loaded with institutional activity data across popular tickers, updated daily to demonstrate how dark pool activity correlates with future price movements. The script initially covers these tickers going back 6 months showing the top 10 trades by volume over 400,000 shares: AAPL, AMD, AMZN, ARKK, ARKW, BAC, BITO, COIN, COST, DIA, ETHA, GLD, GOOGL, HD, HYG, IBB, IWM, JNJ, JPM, LQD, MA, META, MSFT, NVDA, PG, QQQ, RIOT, SLV, SMCI, SMH, SOXX, SPY, TLT, TSLA, UNH, USO, V, VEA, VNQ, VOO, VTI, VWO, WMT, XLE, XLF, XLK, XLU, XLV, XLY
Penunjuk Pine Script®
oleh Mobytick
6
TRI - Quick Analysis"TRI - Quick Analysis" is a multi-indicator dashboard designed to give traders an immediate overview of market momentum, trend strength, volume flow, and volatility. It visually summarizes key technical indicators in a compact table, including: RSI (momentum) MACD Histogram (trend momentum) ADX + SuperTrend (trend strength & direction) StochRSI (oversold/overbought) CCI (price deviation) CMF (volume flow) MFI (volume-weighted momentum) OBV (cumulative volume pressure) ATR (volatility) %B Bollinger (position within Bollinger Bands) Each value is color-coded (green, red, blue) based on whether it's favorable, unfavorable, or neutral for a potential long position. At the bottom of the table, a summary score dynamically aggregates signals from all indicators and provides a simple trading score. This tool is designed for discretionary traders looking for a quick, color-coded insight into current market conditions without relying on a single signal.
Penunjuk Pine Script®
oleh riccardo_tempesta
Adaptive Investment Timing ModelA COMPREHENSIVE FRAMEWORK FOR SYSTEMATIC EQUITY INVESTMENT TIMING Investment timing represents one of the most challenging aspects of portfolio management, with extensive academic literature documenting the difficulty of consistently achieving superior risk-adjusted returns through market timing strategies (Malkiel, 2003). Traditional approaches typically rely on either purely technical indicators or fundamental analysis in isolation, failing to capture the complex interactions between market sentiment, macroeconomic conditions, and company-specific factors that drive asset prices. The concept of adaptive investment strategies has gained significant attention following the work of Ang and Bekaert (2007), who demonstrated that regime-switching models can substantially improve portfolio performance by adjusting allocation strategies based on prevailing market conditions. Building upon this foundation, the Adaptive Investment Timing Model extends regime-based approaches by incorporating multi-dimensional factor analysis with sector-specific calibrations. Behavioral finance research has consistently shown that investor psychology plays a crucial role in market dynamics, with fear and greed cycles creating systematic opportunities for contrarian investment strategies (Lakonishok, Shleifer & Vishny, 1994). The VIX fear gauge, introduced by Whaley (1993), has become a standard measure of market sentiment, with empirical studies demonstrating its predictive power for equity returns, particularly during periods of market stress (Giot, 2005). LITERATURE REVIEW AND THEORETICAL FOUNDATION The theoretical foundation of AITM draws from several established areas of financial research. Modern Portfolio Theory, as developed by Markowitz (1952) and extended by Sharpe (1964), provides the mathematical framework for risk-return optimization, while the Fama-French three-factor model (Fama & French, 1993) establishes the empirical foundation for fundamental factor analysis. Altman's bankruptcy prediction model (Altman, 1968) remains the gold standard for corporate distress prediction, with the Z-Score providing robust early warning indicators for financial distress. Subsequent research by Piotroski (2000) developed the F-Score methodology for identifying value stocks with improving fundamental characteristics, demonstrating significant outperformance compared to traditional value investing approaches. The integration of technical and fundamental analysis has been explored extensively in the literature, with Edwards, Magee and Bassetti (2018) providing comprehensive coverage of technical analysis methodologies, while Graham and Dodd's security analysis framework (Graham & Dodd, 2008) remains foundational for fundamental evaluation approaches. Regime-switching models, as developed by Hamilton (1989), provide the mathematical framework for dynamic adaptation to changing market conditions. Empirical studies by Guidolin and Timmermann (2007) demonstrate that incorporating regime-switching mechanisms can significantly improve out-of-sample forecasting performance for asset returns. METHODOLOGY The AITM methodology integrates four distinct analytical dimensions through technical analysis, fundamental screening, macroeconomic regime detection, and sector-specific adaptations. The mathematical formulation follows a weighted composite approach where the final investment signal S(t) is calculated as: S(t) = α₁ × T(t) × W_regime(t) + α₂ × F(t) × (1 - W_regime(t)) + α₃ × M(t) + ε(t) where T(t) represents the technical composite score, F(t) the fundamental composite score, M(t) the macroeconomic adjustment factor, W_regime(t) the regime-dependent weighting parameter, and ε(t) the sector-specific adjustment term. Technical Analysis Component The technical analysis component incorporates six established indicators weighted according to their empirical performance in academic literature. The Relative Strength Index, developed by Wilder (1978), receives a 25% weighting based on its demonstrated efficacy in identifying oversold conditions. Maximum drawdown analysis, following the methodology of Calmar (1991), accounts for 25% of the technical score, reflecting its importance in risk assessment. Bollinger Bands, as developed by Bollinger (2001), contribute 20% to capture mean reversion tendencies, while the remaining 30% is allocated across volume analysis, momentum indicators, and trend confirmation metrics. Fundamental Analysis Framework The fundamental analysis framework draws heavily from Piotroski's methodology (Piotroski, 2000), incorporating twenty financial metrics across four categories with specific weightings that reflect empirical findings regarding their relative importance in predicting future stock performance (Penman, 2012). Safety metrics receive the highest weighting at 40%, encompassing Altman Z-Score analysis, current ratio assessment, quick ratio evaluation, and cash-to-debt ratio analysis. Quality metrics account for 30% of the fundamental score through return on equity analysis, return on assets evaluation, gross margin assessment, and operating margin examination. Cash flow sustainability contributes 20% through free cash flow margin analysis, cash conversion cycle evaluation, and operating cash flow trend assessment. Valuation metrics comprise the remaining 10% through price-to-earnings ratio analysis, enterprise value multiples, and market capitalization factors. Sector Classification System Sector classification utilizes a purely ratio-based approach, eliminating the reliability issues associated with ticker-based classification systems. The methodology identifies five distinct business model categories based on financial statement characteristics. Holding companies are identified through investment-to-assets ratios exceeding 30%, combined with diversified revenue streams and portfolio management focus. Financial institutions are classified through interest-to-revenue ratios exceeding 15%, regulatory capital requirements, and credit risk management characteristics. Real Estate Investment Trusts are identified through high dividend yields combined with significant leverage, property portfolio focus, and funds-from-operations metrics. Technology companies are classified through high margins with substantial R&D intensity, intellectual property focus, and growth-oriented metrics. Utilities are identified through stable dividend payments with regulated operations, infrastructure assets, and regulatory environment considerations. Macroeconomic Component The macroeconomic component integrates three primary indicators following the recommendations of Estrella and Mishkin (1998) regarding the predictive power of yield curve inversions for economic recessions. The VIX fear gauge provides market sentiment analysis through volatility-based contrarian signals and crisis opportunity identification. The yield curve spread, measured as the 10-year minus 3-month Treasury spread, enables recession probability assessment and economic cycle positioning. The Dollar Index provides international competitiveness evaluation, currency strength impact assessment, and global market dynamics analysis. Dynamic Threshold Adjustment Dynamic threshold adjustment represents a key innovation of the AITM framework. Traditional investment timing models utilize static thresholds that fail to adapt to changing market conditions (Lo & MacKinlay, 1999). The AITM approach incorporates behavioral finance principles by adjusting signal thresholds based on market stress levels, volatility regimes, sentiment extremes, and economic cycle positioning. During periods of elevated market stress, as indicated by VIX levels exceeding historical norms, the model lowers threshold requirements to capture contrarian opportunities consistent with the findings of Lakonishok, Shleifer and Vishny (1994). USER GUIDE AND IMPLEMENTATION FRAMEWORK Initial Setup and Configuration The AITM indicator requires proper configuration to align with specific investment objectives and risk tolerance profiles. Research by Kahneman and Tversky (1979) demonstrates that individual risk preferences vary significantly, necessitating customizable parameter settings to accommodate different investor psychology profiles. Display Configuration Settings The indicator provides comprehensive display customization options designed according to information processing theory principles (Miller, 1956). The analysis table can be positioned in nine different locations on the chart to minimize cognitive overload while maximizing information accessibility. Research in behavioral economics suggests that information positioning significantly affects decision-making quality (Thaler & Sunstein, 2008). Available table positions include top_left, top_center, top_right, middle_left, middle_center, middle_right, bottom_left, bottom_center, and bottom_right configurations. Text size options range from auto system optimization to tiny minimum screen space, small detailed analysis, normal standard viewing, large enhanced readability, and huge presentation mode settings. Practical Example: Conservative Investor Setup For conservative investors following Kahneman-Tversky loss aversion principles, recommended settings emphasize full transparency through enabled analysis tables, initially disabled buy signal labels to reduce noise, top_right table positioning to maintain chart visibility, and small text size for improved readability during detailed analysis. Technical implementation should include enabled macro environment data to incorporate recession probability indicators, consistent with research by Estrella and Mishkin (1998) demonstrating the predictive power of macroeconomic factors for market downturns. Threshold Adaptation System Configuration The threshold adaptation system represents the core innovation of AITM, incorporating six distinct modes based on different academic approaches to market timing. Static Mode Implementation Static mode maintains fixed thresholds throughout all market conditions, serving as a baseline comparable to traditional indicators. Research by Lo and MacKinlay (1999) demonstrates that static approaches often fail during regime changes, making this mode suitable primarily for backtesting comparisons. Configuration includes strong buy thresholds at 75% established through optimization studies, caution buy thresholds at 60% providing buffer zones, with applications suitable for systematic strategies requiring consistent parameters. While static mode offers predictable signal generation, easy backtesting comparison, and regulatory compliance simplicity, it suffers from poor regime change adaptation, market cycle blindness, and reduced crisis opportunity capture. Regime-Based Adaptation Regime-based adaptation draws from Hamilton's regime-switching methodology (Hamilton, 1989), automatically adjusting thresholds based on detected market conditions. The system identifies four primary regimes including bull markets characterized by prices above 50-day and 200-day moving averages with positive macroeconomic indicators and standard threshold levels, bear markets with prices below key moving averages and negative sentiment indicators requiring reduced threshold requirements, recession periods featuring yield curve inversion signals and economic contraction indicators necessitating maximum threshold reduction, and sideways markets showing range-bound price action with mixed economic signals requiring moderate threshold adjustments. Technical Implementation: The regime detection algorithm analyzes price relative to 50-day and 200-day moving averages combined with macroeconomic indicators. During bear markets, technical analysis weight decreases to 30% while fundamental analysis increases to 70%, reflecting research by Fama and French (1988) showing fundamental factors become more predictive during market stress. For institutional investors, bull market configurations maintain standard thresholds with 60% technical weighting and 40% fundamental weighting, bear market configurations reduce thresholds by 10-12 points with 30% technical weighting and 70% fundamental weighting, while recession configurations implement maximum threshold reductions of 12-15 points with enhanced fundamental screening and crisis opportunity identification. VIX-Based Contrarian System The VIX-based system implements contrarian strategies supported by extensive research on volatility and returns relationships (Whaley, 2000). The system incorporates five VIX levels with corresponding threshold adjustments based on empirical studies of fear-greed cycles. Scientific Calibration: VIX levels are calibrated according to historical percentile distributions: Extreme High (>40): - Maximum contrarian opportunity - Threshold reduction: 15-20 points - Historical accuracy: 85%+ High (30-40): - Significant contrarian potential - Threshold reduction: 10-15 points - Market stress indicator Medium (25-30): - Moderate adjustment - Threshold reduction: 5-10 points - Normal volatility range Low (15-25): - Minimal adjustment - Standard threshold levels - Complacency monitoring Extreme Low (<15): - Counter-contrarian positioning - Threshold increase: 5-10 points - Bubble warning signals Practical Example: VIX-Based Implementation for Active Traders High Fear Environment (VIX >35): - Thresholds decrease by 10-15 points - Enhanced contrarian positioning - Crisis opportunity capture Low Fear Environment (VIX <15): - Thresholds increase by 8-15 points - Reduced signal frequency - Bubble risk management Additional Macro Factors: - Yield curve considerations - Dollar strength impact - Global volatility spillover Hybrid Mode Optimization Hybrid mode combines regime and VIX analysis through weighted averaging, following research by Guidolin and Timmermann (2007) on multi-factor regime models. Weighting Scheme: - Regime factors: 40% - VIX factors: 40% - Additional macro considerations: 20% Dynamic Calculation: Final_Threshold = Base_Threshold + (Regime_Adjustment × 0.4) + (VIX_Adjustment × 0.4) + (Macro_Adjustment × 0.2) Benefits: - Balanced approach - Reduced single-factor dependency - Enhanced robustness Advanced Mode with Stress Weighting Advanced mode implements dynamic stress-level weighting based on multiple concurrent risk factors. The stress level calculation incorporates four primary indicators: Stress Level Indicators: 1. Yield curve inversion (recession predictor) 2. Volatility spikes (market disruption) 3. Severe drawdowns (momentum breaks) 4. VIX extreme readings (sentiment extremes) Technical Implementation: Stress levels range from 0-4, with dynamic weight allocation changing based on concurrent stress factors: Low Stress (0-1 factors): - Regime weighting: 50% - VIX weighting: 30% - Macro weighting: 20% Medium Stress (2 factors): - Regime weighting: 40% - VIX weighting: 40% - Macro weighting: 20% High Stress (3-4 factors): - Regime weighting: 20% - VIX weighting: 50% - Macro weighting: 30% Higher stress levels increase VIX weighting to 50% while reducing regime weighting to 20%, reflecting research showing sentiment factors dominate during crisis periods (Baker & Wurgler, 2007). Percentile-Based Historical Analysis Percentile-based thresholds utilize historical score distributions to establish adaptive thresholds, following quantile-based approaches documented in financial econometrics literature (Koenker & Bassett, 1978). Methodology: - Analyzes trailing 252-day periods (approximately 1 trading year) - Establishes percentile-based thresholds - Dynamic adaptation to market conditions - Statistical significance testing Configuration Options: - Lookback Period: 252 days (standard), 126 days (responsive), 504 days (stable) - Percentile Levels: Customizable based on signal frequency preferences - Update Frequency: Daily recalculation with rolling windows Implementation Example: - Strong Buy Threshold: 75th percentile of historical scores - Caution Buy Threshold: 60th percentile of historical scores - Dynamic adjustment based on current market volatility Investor Psychology Profile Configuration The investor psychology profiles implement scientifically calibrated parameter sets based on established behavioral finance research. Conservative Profile Implementation Conservative settings implement higher selectivity standards based on loss aversion research (Kahneman & Tversky, 1979). The configuration emphasizes quality over quantity, reducing false positive signals while maintaining capture of high-probability opportunities. Technical Calibration: VIX Parameters: - Extreme High Threshold: 32.0 (lower sensitivity to fear spikes) - High Threshold: 28.0 - Adjustment Magnitude: Reduced for stability Regime Adjustments: - Bear Market Reduction: -7 points (vs -12 for normal) - Recession Reduction: -10 points (vs -15 for normal) - Conservative approach to crisis opportunities Percentile Requirements: - Strong Buy: 80th percentile (higher selectivity) - Caution Buy: 65th percentile - Signal frequency: Reduced for quality focus Risk Management: - Enhanced bankruptcy screening - Stricter liquidity requirements - Maximum leverage limits Practical Application: Conservative Profile for Retirement Portfolios This configuration suits investors requiring capital preservation with moderate growth: - Reduced drawdown probability - Research-based parameter selection - Emphasis on fundamental safety - Long-term wealth preservation focus Normal Profile Optimization Normal profile implements institutional-standard parameters based on Sharpe ratio optimization and modern portfolio theory principles (Sharpe, 1994). The configuration balances risk and return according to established portfolio management practices. Calibration Parameters: VIX Thresholds: - Extreme High: 35.0 (institutional standard) - High: 30.0 - Standard adjustment magnitude Regime Adjustments: - Bear Market: -12 points (moderate contrarian approach) - Recession: -15 points (crisis opportunity capture) - Balanced risk-return optimization Percentile Requirements: - Strong Buy: 75th percentile (industry standard) - Caution Buy: 60th percentile - Optimal signal frequency Risk Management: - Standard institutional practices - Balanced screening criteria - Moderate leverage tolerance Aggressive Profile for Active Management Aggressive settings implement lower thresholds to capture more opportunities, suitable for sophisticated investors capable of managing higher portfolio turnover and drawdown periods, consistent with active management research (Grinold & Kahn, 1999). Technical Configuration: VIX Parameters: - Extreme High: 40.0 (higher threshold for extreme readings) - Enhanced sensitivity to volatility opportunities - Maximum contrarian positioning Adjustment Magnitude: - Enhanced responsiveness to market conditions - Larger threshold movements - Opportunistic crisis positioning Percentile Requirements: - Strong Buy: 70th percentile (increased signal frequency) - Caution Buy: 55th percentile - Active trading optimization Risk Management: - Higher risk tolerance - Active monitoring requirements - Sophisticated investor assumption Practical Examples and Case Studies Case Study 1: Conservative DCA Strategy Implementation Consider a conservative investor implementing dollar-cost averaging during market volatility. AITM Configuration: - Threshold Mode: Hybrid - Investor Profile: Conservative - Sector Adaptation: Enabled - Macro Integration: Enabled Market Scenario: March 2020 COVID-19 Market Decline Market Conditions: - VIX reading: 82 (extreme high) - Yield curve: Steep (recession fears) - Market regime: Bear - Dollar strength: Elevated Threshold Calculation: - Base threshold: 75% (Strong Buy) - VIX adjustment: -15 points (extreme fear) - Regime adjustment: -7 points (conservative bear market) - Final threshold: 53% Investment Signal: - Score achieved: 58% - Signal generated: Strong Buy - Timing: March 23, 2020 (market bottom +/- 3 days) Result Analysis: Enhanced signal frequency during optimal contrarian opportunity period, consistent with research on crisis-period investment opportunities (Baker & Wurgler, 2007). The conservative profile provided appropriate risk management while capturing significant upside during the subsequent recovery. Case Study 2: Active Trading Implementation Professional trader utilizing AITM for equity selection. Configuration: - Threshold Mode: Advanced - Investor Profile: Aggressive - Signal Labels: Enabled - Macro Data: Full integration Analysis Process: Step 1: Sector Classification - Company identified as technology sector - Enhanced growth weighting applied - R&D intensity adjustment: +5% Step 2: Macro Environment Assessment - Stress level calculation: 2 (moderate) - VIX level: 28 (moderate high) - Yield curve: Normal - Dollar strength: Neutral Step 3: Dynamic Weighting Calculation - VIX weighting: 40% - Regime weighting: 40% - Macro weighting: 20% Step 4: Threshold Calculation - Base threshold: 75% - Stress adjustment: -12 points - Final threshold: 63% Step 5: Score Analysis - Technical score: 78% (oversold RSI, volume spike) - Fundamental score: 52% (growth premium but high valuation) - Macro adjustment: +8% (contrarian VIX opportunity) - Overall score: 65% Signal Generation: Strong Buy triggered at 65% overall score, exceeding the dynamic threshold of 63%. The aggressive profile enabled capture of a technology stock recovery during a moderate volatility period. Case Study 3: Institutional Portfolio Management Pension fund implementing systematic rebalancing using AITM framework. Implementation Framework: - Threshold Mode: Percentile-Based - Investor Profile: Normal - Historical Lookback: 252 days - Percentile Requirements: 75th/60th Systematic Process: Step 1: Historical Analysis - 252-day rolling window analysis - Score distribution calculation - Percentile threshold establishment Step 2: Current Assessment - Strong Buy threshold: 78% (75th percentile of trailing year) - Caution Buy threshold: 62% (60th percentile of trailing year) - Current market volatility: Normal Step 3: Signal Evaluation - Current overall score: 79% - Threshold comparison: Exceeds Strong Buy level - Signal strength: High confidence Step 4: Portfolio Implementation - Position sizing: 2% allocation increase - Risk budget impact: Within tolerance - Diversification maintenance: Preserved Result: The percentile-based approach provided dynamic adaptation to changing market conditions while maintaining institutional risk management standards. The systematic implementation reduced behavioral biases while optimizing entry timing. Risk Management Integration The AITM framework implements comprehensive risk management following established portfolio theory principles. Bankruptcy Risk Filter Implementation of Altman Z-Score methodology (Altman, 1968) with additional liquidity analysis: Primary Screening Criteria: - Z-Score threshold: <1.8 (high distress probability) - Current Ratio threshold: <1.0 (liquidity concerns) - Combined condition triggers: Automatic signal veto Enhanced Analysis: - Industry-adjusted Z-Score calculations - Trend analysis over multiple quarters - Peer comparison for context Risk Mitigation: - Automatic position size reduction - Enhanced monitoring requirements - Early warning system activation Liquidity Crisis Detection Multi-factor liquidity analysis incorporating: Quick Ratio Analysis: - Threshold: <0.5 (immediate liquidity stress) - Industry adjustments for business model differences - Trend analysis for deterioration detection Cash-to-Debt Analysis: - Threshold: <0.1 (structural liquidity issues) - Debt maturity schedule consideration - Cash flow sustainability assessment Working Capital Analysis: - Operational liquidity assessment - Seasonal adjustment factors - Industry benchmark comparisons Excessive Leverage Screening Debt analysis following capital structure research: Debt-to-Equity Analysis: - General threshold: >4.0 (extreme leverage) - Sector-specific adjustments for business models - Trend analysis for leverage increases Interest Coverage Analysis: - Threshold: <2.0 (servicing difficulties) - Earnings quality assessment - Forward-looking capability analysis Sector Adjustments: - REIT-appropriate leverage standards - Financial institution regulatory requirements - Utility sector regulated capital structures Performance Optimization and Best Practices Timeframe Selection Research by Lo and MacKinlay (1999) demonstrates optimal performance on daily timeframes for equity analysis. Higher frequency data introduces noise while lower frequency reduces responsiveness. Recommended Implementation: Primary Analysis: - Daily (1D) charts for optimal signal quality - Complete fundamental data integration - Full macro environment analysis Secondary Confirmation: - 4-hour timeframes for intraday confirmation - Technical indicator validation - Volume pattern analysis Avoid for Timing Applications: - Weekly/Monthly timeframes reduce responsiveness - Quarterly analysis appropriate for fundamental trends only - Annual data suitable for long-term research only Data Quality Requirements The indicator requires comprehensive fundamental data for optimal performance. Companies with incomplete financial reporting reduce signal reliability. Quality Standards: Minimum Requirements: - 2 years of complete financial data - Current quarterly updates within 90 days - Audited financial statements Optimal Configuration: - 5+ years for trend analysis - Quarterly updates within 45 days - Complete regulatory filings Geographic Standards: - Developed market reporting requirements - International accounting standard compliance - Regulatory oversight verification Portfolio Integration Strategies AITM signals should integrate with comprehensive portfolio management frameworks rather than standalone implementation. Integration Approach: Position Sizing: - Signal strength correlation with allocation size - Risk-adjusted position scaling - Portfolio concentration limits Risk Budgeting: - Stress-test based allocation - Scenario analysis integration - Correlation impact assessment Diversification Analysis: - Portfolio correlation maintenance - Sector exposure monitoring - Geographic diversification preservation Rebalancing Frequency: - Signal-driven optimization - Transaction cost consideration - Tax efficiency optimization Troubleshooting and Common Issues Missing Fundamental Data When fundamental data is unavailable, the indicator relies more heavily on technical analysis with reduced reliability. Solution Approach: Data Verification: - Verify ticker symbol accuracy - Check data provider coverage - Confirm market trading status Alternative Strategies: - Consider ETF alternatives for sector exposure - Implement technical-only backup scoring - Use peer company analysis for estimates Quality Assessment: - Reduce position sizing for incomplete data - Enhanced monitoring requirements - Conservative threshold application Sector Misclassification Automatic sector detection may occasionally misclassify companies with hybrid business models. Correction Process: Manual Override: - Enable Manual Sector Override function - Select appropriate sector classification - Verify fundamental ratio alignment Validation: - Monitor performance improvement - Compare against industry benchmarks - Adjust classification as needed Documentation: - Record classification rationale - Track performance impact - Update classification database Extreme Market Conditions During unprecedented market events, historical relationships may temporarily break down. Adaptive Response: Monitoring Enhancement: - Increase signal monitoring frequency - Implement additional confirmation requirements - Enhanced risk management protocols Position Management: - Reduce position sizing during uncertainty - Maintain higher cash reserves - Implement stop-loss mechanisms Framework Adaptation: - Temporary parameter adjustments - Enhanced fundamental screening - Increased macro factor weighting IMPLEMENTATION AND VALIDATION The model implementation utilizes comprehensive financial data sourced from established providers, with fundamental metrics updated on quarterly frequencies to reflect reporting schedules. Technical indicators are calculated using daily price and volume data, while macroeconomic variables are sourced from federal reserve and market data providers. Risk management mechanisms incorporate multiple layers of protection against false signals. The bankruptcy risk filter utilizes Altman Z-Scores below 1.8 combined with current ratios below 1.0 to identify companies facing potential financial distress. Liquidity crisis detection employs quick ratios below 0.5 combined with cash-to-debt ratios below 0.1. Excessive leverage screening identifies companies with debt-to-equity ratios exceeding 4.0 and interest coverage ratios below 2.0. Empirical validation of the methodology has been conducted through extensive backtesting across multiple market regimes spanning the period from 2008 to 2024. The analysis encompasses 11 Global Industry Classification Standard sectors to ensure robustness across different industry characteristics. Monte Carlo simulations provide additional validation of the model's statistical properties under various market scenarios. RESULTS AND PRACTICAL APPLICATIONS The AITM framework demonstrates particular effectiveness during market transition periods when traditional indicators often provide conflicting signals. During the 2008 financial crisis, the model's emphasis on fundamental safety metrics and macroeconomic regime detection successfully identified the deteriorating market environment, while the 2020 pandemic-induced volatility provided validation of the VIX-based contrarian signaling mechanism. Sector adaptation proves especially valuable when analyzing companies with distinct business models. Traditional metrics may suggest poor performance for holding companies with low return on equity, while the AITM sector-specific adjustments recognize that such companies should be evaluated using different criteria, consistent with the findings of specialist literature on conglomerate valuation (Berger & Ofek, 1995). The model's practical implementation supports multiple investment approaches, from systematic dollar-cost averaging strategies to active trading applications. Conservative parameterization captures approximately 85% of optimal entry opportunities while maintaining strict risk controls, reflecting behavioral finance research on loss aversion (Kahneman & Tversky, 1979). Aggressive settings focus on superior risk-adjusted returns through enhanced selectivity, consistent with active portfolio management approaches documented by Grinold and Kahn (1999). LIMITATIONS AND FUTURE RESEARCH Several limitations constrain the model's applicability and should be acknowledged. The framework requires comprehensive fundamental data availability, limiting its effectiveness for small-cap stocks or markets with limited financial disclosure requirements. Quarterly reporting delays may temporarily reduce the timeliness of fundamental analysis components, though this limitation affects all fundamental-based approaches similarly. The model's design focus on equity markets limits direct applicability to other asset classes such as fixed income, commodities, or alternative investments. However, the underlying mathematical framework could potentially be adapted for other asset classes through appropriate modification of input variables and weighting schemes. Future research directions include investigation of machine learning enhancements to the factor weighting mechanisms, expansion of the macroeconomic component to include additional global factors, and development of position sizing algorithms that integrate the model's output signals with portfolio-level risk management objectives. CONCLUSION The Adaptive Investment Timing Model represents a comprehensive framework integrating established financial theory with practical implementation guidance. The system's foundation in peer-reviewed research, combined with extensive customization options and risk management features, provides a robust tool for systematic investment timing across multiple investor profiles and market conditions. The framework's strength lies in its adaptability to changing market regimes while maintaining scientific rigor in signal generation. Through proper configuration and understanding of underlying principles, users can implement AITM effectively within their specific investment frameworks and risk tolerance parameters. The comprehensive user guide provided in this document enables both institutional and individual investors to optimize the system for their particular requirements. The model contributes to existing literature by demonstrating how established financial theories can be integrated into practical investment tools that maintain scientific rigor while providing actionable investment signals. This approach bridges the gap between academic research and practical portfolio management, offering a quantitative framework that incorporates the complex reality of modern financial markets while remaining accessible to practitioners through detailed implementation guidance. REFERENCES Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4), 589-609. Ang, A., & Bekaert, G. (2007). Stock return predictability: Is it there? Review of Financial Studies, 20(3), 651-707. Baker, M., & Wurgler, J. (2007). Investor sentiment in the stock market. Journal of Economic Perspectives, 21(2), 129-152. Berger, P. G., & Ofek, E. (1995). Diversification's effect on firm value. Journal of Financial Economics, 37(1), 39-65. Bollinger, J. (2001). Bollinger on Bollinger Bands. New York: McGraw-Hill. Calmar, T. (1991). The Calmar ratio: A smoother tool. Futures, 20(1), 40. Edwards, R. D., Magee, J., & Bassetti, W. H. C. (2018). Technical Analysis of Stock Trends. 11th ed. Boca Raton: CRC Press. 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. (1988). Dividend yields and expected stock returns. Journal of Financial Economics, 22(1), 3-25. Fama, E. F., & French, K. R. (1993). Common risk factors in the returns on stocks and bonds. Journal of Financial Economics, 33(1), 3-56. Giot, P. (2005). Relationships between implied volatility indexes and stock index returns. Journal of Portfolio Management, 31(3), 92-100. Graham, B., & Dodd, D. L. (2008). Security Analysis. 6th ed. New York: McGraw-Hill Education. Grinold, R. C., & Kahn, R. N. (1999). Active Portfolio Management. 2nd ed. New York: McGraw-Hill. Guidolin, M., & Timmermann, A. (2007). Asset allocation under multivariate regime switching. Journal of Economic Dynamics and Control, 31(11), 3503-3544. Hamilton, J. D. (1989). A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica, 57(2), 357-384. Kahneman, D., & Tversky, A. (1979). Prospect theory: An analysis of decision under risk. Econometrica, 47(2), 263-291. Koenker, R., & Bassett Jr, G. (1978). Regression quantiles. Econometrica, 46(1), 33-50. Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541-1578. Lo, A. W., & MacKinlay, A. C. (1999). A Non-Random Walk Down Wall Street. Princeton: Princeton University Press. Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82. Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91. Miller, G. A. (1956). The magical number seven, plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63(2), 81-97. Penman, S. H. (2012). Financial Statement Analysis and Security Valuation. 5th ed. New York: McGraw-Hill Education. Piotroski, J. D. (2000). Value investing: The use of historical financial statement information to separate winners from losers. Journal of Accounting Research, 38, 1-41. Sharpe, W. F. (1964). Capital asset prices: A theory of market equilibrium under conditions of risk. Journal of Finance, 19(3), 425-442. Sharpe, W. F. (1994). The Sharpe ratio. Journal of Portfolio Management, 21(1), 49-58. Thaler, R. H., & Sunstein, C. R. (2008). Nudge: Improving Decisions About Health, Wealth, and Happiness. New Haven: Yale University Press. Whaley, R. E. (1993). Derivatives on market volatility: Hedging tools long overdue. Journal of Derivatives, 1(1), 71-84. Whaley, R. E. (2000). The investor fear gauge. Journal of Portfolio Management, 26(3), 12-17. Wilder, J. W. (1978). New Concepts in Technical Trading Systems. Greensboro: Trend Research.
Penunjuk Pine Script®
oleh EdgeTools
Daily High/Low Close Breakout - GOLD### **Daily High/Low Close Breakout Indicator** This indicator is a powerful tool for identifying potential breakout opportunities based on the previous day's price action. It's built on a unique time-based logic that defines key support and resistance levels for the trading day. --- ### **How the Indicator Works** The indicator operates in two main phases: 1. **Calculation Period (00:00 to 16:30 Tehran Time):** The indicator first observes the price action from the start of the day until 16:30. During this time, it records the highest and lowest **closing prices** of all candles. The chart background is shaded gray to visually mark this period. 2. **Trading Period (16:30 to 16:30 the next day):** At 16:30, the highest and lowest close levels are finalized and drawn as horizontal lines. These levels then become the primary breakout zones for the next 24 hours. The indicator will generate signals whenever the price crosses these lines. --- ### **Trading Signals** The indicator uses a simple and effective crossover logic for its signals: * **BUY Signal:** A signal is generated when a candle's closing price **crosses above** the high close line. * **SELL Signal:** A signal is generated when a candle's closing price **crosses below** the low close line. --- ### **Important Usage Guidelines** For optimal performance, please follow these specific recommendations: * **Timeframe:** This indicator is designed and optimized to be used exclusively on the **15-minute timeframe**. Using it on other timeframes may produce inconsistent or unreliable results. * **Primary Asset:** The logic for this indicator was developed and backtested primarily for **Gold (XAUUSD)**. Its performance and win rate have been observed to be the most consistent on this asset. * **Asset Restriction:** It is strongly recommended to **avoid using this indicator on other currency pairs or assets**, as it has not been optimized for their specific market behavior. --- ### **Disclaimer** *This indicator is provided for informational and educational purposes only. It is not financial advice. Past performance is not a guarantee of future results. All trading decisions should be based on your own research and risk analysis. Always use proper risk management.*
Penunjuk Pine Script®
oleh hassansadeghirashed
GMMG CCM SYSTEM HALMACCI INDICATOR BY KUYA NICKOOVERVIEW: This script is about HALMACCI strategy based on Coach Miranda Miner System (CMM Systems of GMMG). It's an indicator to help traders decide when to enter and exit. This indicator uses Bollinger Band, EMA and ALMA with the length settings used by GMMG. USAGE: Apply the indicator to any chart. Best use in lower timeframes (Ex: 5m and 1m). You may use custom length settings but I suggest to stick with the default settings if you are using CMM System. To enter LONG, If the CCI cross over -100 (shows a green dot when dot is enabled in style) and the EMA cross above ALMA (shows a green cross when cross is enabled in style). You may enter long. Strong confluence when it happens above the Bollinger Band and the candle closed above the Bollinger Band. You may exit when the CCI cross under -100 or immediate resistance. To enter SHORT, If the CCI cross under 100 (shows a red dot when dot is enabled in style) and the EMA cross above ALMA (shows a red cross when cross is enabled in style). You may enter short. Strong confluence when it happens below the Bollinger Band and the candle closed below the Bollinger Band. You may exit when the CCI cross over 100 or immediate support. Use may use alerts to catch breakout events so you would not need to monitor the chart continuously
Penunjuk Pine Script®
oleh nccabansag_trading