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pricing_tableThis script helps you evaluate the fair value of an option. It poses the question "if I bought or sold an option under these circumstances in the past, would it have expired in the money, or worthless? What would be its expected value, at expiration, if I opened a position at N standard deviations, given the volatility forecast, with M days to expiration at the close of every previous trading day?" The default (and only) "hv" volatility forecast is based on the assumption that today's volatility will hold for the next M days. To use this script, only one step is mandatory. You must first select days to expiration. The script will not do anything until this value is changed from the default (-1). These should be CALENDAR days. The script will convert to these to business days for forecasting and valuation, as trading in most contracts occurs over ~250 business days per year. Adjust any other variables as desired: model: the volatility forecasting model window: the number of periods for a lagged model (e.g. hv) filter: a filter to remove forecasts from the sample filter type: "none" (do not use the filter), "less than" (keep forecasts when filter < volatility), "greater than" (keep forecasts when filter > volatility) filter value: a whole number percentage. see example below discount rate: to discount the expected value to present value precision: number of decimals in output trim outliers: omit upper N % of (generally itm) contracts The theoretical values are based on history. For example, suppose days to expiration is 30. On every bar, the 30 days ago N deviation forecast value is compared to the present price. If the price is above the forecast value, the contract has expired in the money; otherwise, it has expired worthless. The theoretical value is the average of every such sample. The itm probabilities are calculated the same way. The default (and only) volatility model is a 20 period EWMA derived historical (realized) volatility. Feel free to extend the script by adding your own. The filter parameters can be used to remove some forecasts from the sample. Example A: filter: filter type: none filter value: Default: the filter is not used; all forecasts are included in the the sample. Example B: filter: model filter type: less than filter value: 50 If the model is "hv", this will remove all forecasts when the historical volatility is greater than fifty. Example C: filter: rank filter type: greater than filter value: 75 If the model volatility is in the top 25% of the previous year's range, the forecast will be included in the sample apart from "model" there are some common volatility indexes to choose from, such as Nasdaq (VXN), crude oil (OVX), emerging markets (VXFXI), S&P; (VIX) etc. Refer to the middle-right table to see the current forecast value, its rank among the last 252 days, and the number of business days until expiration. NOTE: This script is meant for the daily chart only.
Penunjuk Pine Script®
oleh taylor_o
4
RSI-VWAP Indicator %█ OVERALL Simple and effective script that, as you already know, uses vwap as source of the rsi, and with good results as long as the market has no long-term downtrend. RsiVwap = rsi (vwap (close), Length) The default settings are for BTC in a 30 minute time frame. For other pairs and time frames you just have to play with the settings. █ FEATURES • The option to start trading from a certain date has been added. • To make the profit more progressive, a percentage of your equity is used for entries and a percentage of your position is used for closings. • The option to trade in Spot mode has been added, since, for the TradingView backtest, the money is infinite and if you do not limit it somehow, it would offer you much better profits than the live trading. QuantityOnLong = Spot ? (EquityPercent / 100) * ((strategy.equity / close) - strategy.position_size) : (EquityPercent / 100) * (strategy.equity / close) • The option to stop the system when the drawdown exceeds the fixed limit has been added. Drawdown, as you already know, is a very important measure of risk in trading systems. The maximum drawdown will tell us what the maximum loss of a trading system has been during a period. This maximum loss is determined by: strategy.risk.max_drawdown(Risk, strategy.percent_of_equity) • Leverage plotted on labels added. █ ALERTS To enjoy the benefits of automatic trading, TradingView alerts can be used as direct buy-sell orders on spot, or long-close orders with leverage. Currently there are Chrome extensions that act as a bridge between TradingView and your Exchange or Broker. This is an example of syntax for this type of extensions. Copy and paste a message like this into the alert window: {{strategy.order.action}} @ {{strategy.order.price}} | e = {{exchange}} a = account s = {{ticker}} b = {{strategy.order.action}} {{strategy.order.alert_message}} █ NOTE Certain Risks of Live Algorithmic Trading You Should Know: • Backtesting cannot assure actual results. • The relevant market might fail or behave unexpectedly. • Your broker may experience failures in its infrastructure, fail to execute your orders in a correct or timely fashion or reject your orders. • The system you use for generating trading orders, communicating those orders to your broker, and receiving queries and trading results from your broker may fail. • Time lag at various point in live trading might cause unexpected behavior. • The systems of third parties in addition to those of the provider from which we obtain various services, your broker, and the applicable securities market may fail or malfunction. █ THANKS Thanks to TradingView, its Pine code, its community and especially those Pine wizards who post their ideas that helps us to learn. If the world is heading toward a equitable new world economic order, let's get rich first ... Happy trading!
Strategi Pine Script®
oleh UnknownUnicorn2151907
34
Percentage Change Comparison [BVCC]This script allows you to input 2 different coins and plot % changes against each other. Look Back is adjustable to account for different time frame windows. Default is 1, so each line will be graphed on a 1:1 ratio with the candle period selected on the chart. raising this number to 24 will plot the change across every 24 candles and so on. It's pretty interesting to move the input dialogue window out of the way and change this number, watching how the % gain comparisons change in real time. Default coins to compare are set to BTCUSD and ETHUSD @ coinbase.
Penunjuk Pine Script®
oleh Fifth_Horizon
GMO (Gyroscopic Momentum Oscillator) GMO Overview This indicator fuses multiple advanced concepts to give traders a comprehensive view of market momentum, volatility, and potential turning points. It leverages the Gyroscopic Momentum Oscillator (GMO) foundation and layers on IQR-based bands, dynamic ATR-adjusted OB/OS levels, torque filtering, and divergence detection. The outcome is a versatile tool that can assist in identifying both short-term squeezes and long-term reversal zones while detecting subtle shifts in momentum acceleration. Key Components: Gyroscopic Momentum Oscillator (GMO) – A physics-inspired metric capturing trend stability and momentum by treating price dynamics as “angle,” “angular velocity,” and “inertia.” IQR Bands – Highlight statistically typical oscillation ranges, providing insight into short-term squeezes and potential near-term trend shifts. ATR-Adjusted OB/OS Levels – Dynamic thresholds for overbought/oversold conditions, adapting to volatility, aiding in identifying long-term potential reversal zones. Torque Filtering & Scaling – Smooths and thresholds torque (the rate of change of momentum) and visually scales it for clarity, indicating sudden force changes that may precede volatility adjustments. Divergence Detection – Highlights potential reversal cues by comparing oscillator swings against price swings, revealing regular and hidden bullish/bearish divergences. Conceptual Insights IQR Bands (Short-Term Squeeze & Trend Direction): Short-Term Momentum and Squeeze: The IQR (Interquartile Range) bands show where the oscillator tends to “live” statistically. When the GMO line hovers within compressed IQR bands, it can signal a momentum squeeze phase. Exiting these tight ranges often correlates with short-term breakout opportunities. Trend Reversals: If the oscillator pushes beyond these IQR ranges, it may indicate an emerging short-term trend change. Traders can watch for GMO escaping the IQR “comfort zone” to anticipate a new directional move. Dynamic OB/OS Levels (Long-Term Reversal Zones): ATR-Based Adaptive Thresholds: Instead of static overbought/oversold lines, this tool uses ATR to adjust OB/OS boundaries. In calm markets, these lines remain closer to ±90. As volatility rises, they approach ±100, reflecting greater permissible swings. Long-Term Trend Reversal Potential: If GMO hits these dynamically adjusted OB/OS extremes, it suggests conditions ripe for possible long-term trend reversals. Traders seeking major inflection points may find these adaptive levels more reliable than fixed thresholds. Torque (Sudden Force & Directional Shifts): Momentum Acceleration Insight: Torque represents the second derivative of momentum, highlighting how quickly momentum is changing. High positive torque suggests a rapidly strengthening bullish force, while high negative torque warns of sudden bearish pressure. Early Warning & Stability/Volatility Adjustments: By monitoring torque spikes, traders can anticipate momentum shifts before price fully confirms them. This can signal imminent changes in stability or increased volatility phases. Indicator Parameters and Usage GMO-Related Inputs: lenPivot (Default 100): Length for calculating the pivot line (slow market axis). lenSmoothAngle (Default 200): Smooths the angle measure, reducing noise. lenATR (Default 14): ATR period for scaling factor, linking price changes to volatility. useVolatility (Default true): If true, volatility (ATR) influences inertia, adjusting momentum calculations. useVolume (Default false): If true, volume affects inertia, adding a liquidity dimension to momentum. lenVolSmoothing (Default 50): Smooths volume calculations if useVolume is enabled. lenMomentumSmooth (Default 20): EMA smoothing of GMO for a cleaner oscillator line. normalizeRange (Default true): Normalizes GMO to a fixed range for consistent interpretation. lenNorm (Default 100): Length for normalization window, ensuring GMO’s scale adapts to recent extremes. IQR Bands Settings: iqrLength (Default 14): Period to compute the oscillator’s statistical IQR. iqrMult (Default 1.5): Multiplier to define the upper and lower IQR-based bands. ATR-Adjusted OB/OS Settings: baseOBLevel (Fixed at 90) and baseOSLevel (Fixed at 90): Base lines for OB/OS. atrPeriodForOBOS (Default 50): ATR length for adjusting OB/OS thresholds dynamically. atrScaling (Default 0.2): Controls how strongly volatility affects OB/OS lines. Torque Filtering & Visualization: torqueSmoothLength (Default 10): EMA length to smooth raw torque values. atrPeriodForTorque (Default 14): ATR period to determine torque threshold. atrTorqueScaling (Default 0.5): Scales ATR for determining torque’s “significant” threshold. torqueScaleFactor (Default 10.0): Multiplies the torque values for better visual prominence on the chart. Divergence Inputs: showDivergences (Default true): Toggles divergence signals. lbR, lbL (Defaults 5): Pivot lookback periods to identify swing highs and lows. rangeUpper, rangeLower: Bar constraints to validate potential divergences. plotBull, plotHiddenBull, plotBear, plotHiddenBear: Toggles for each divergence type. Visual Elements on the Chart GMO Line (Blue) & Zero Line (Gray): GMO line oscillates around zero. Positive territory hints bullish momentum, negative suggests bearish. IQR Bands (Teal Lines & Yellow Fill): Upper/lower bands form a statistical “normal range” for GMO. The median line (purple) provides a central reference. Contraction near these bands indicates a short-term squeeze, expansions beyond them can signal emerging short-term trend changes. Dynamic OB/OS (Red & Green Lines): Red line near +90 to +100: Overbought zone (dynamic). Green line near -90 to -100: Oversold zone (dynamic). Movement into these zones may mark significant, longer-term reversal potential. Torque Histogram (Colored Bars): Plotted below GMO. Green bars = torque above positive threshold (bullish acceleration). Red bars = torque below negative threshold (bearish acceleration). Gray bars = neutral range. This provides early warnings of momentum shifts before price responds fully. Precession (Orange Line): Scaled for visibility, adds context to long-term angular shifts in the oscillator. Divergence Signals (Shapes): Circles and offset lines highlight regular or hidden bullish/bearish divergences, offering potential reversal signals. Practical Interpretation & Strategy Short-Term Opportunities (IQR Focus): If GMO compresses within IQR bands, the market might be “winding up.” A break above/below these bands can signal a short-term trade opportunity. Long-Term Reversal Zones (Dynamic OB/OS): When GMO approaches these dynamically adjusted extremes, conditions may be ripe for a major trend shift. This is particularly useful for swing or position traders looking for significant turnarounds. Monitoring Torque for Acceleration Cues: Torque spikes can precede price action, serving as an early catalyst signal. If torque turns strongly positive, anticipate bullish acceleration; strongly negative torque may warn of upcoming bearish pressure. Confirm with Divergences: Divergences between price and GMO reinforce potential reversal or continuation signals identified by IQR, OB/OS, or torque. Use them to increase confidence in setups. Tips and Best Practices Combine with Price & Volume Action: While the indicator is powerful, always confirm signals with actual price structure, volume patterns, or other trend-following tools. Adjust Lengths & Periods as Needed: Shorter lengths = more responsiveness but more noise. Longer lengths = smoother signals but greater lag. Tune parameters to match your trading style and timeframe. Use ATR and Volume Settings Wisely: If markets are highly volatile, consider useVolatility to refine momentum readings. If liquidity is key, enable useVolume. Scaling Torque: If torque bars are hard to read, increase torqueScaleFactor further. The scaling doesn’t affect logic—only visibility. Conclusion The “GMO + IQR Bands + ATR-Adjusted OB/OS + Torque Filtering (Scaled)” indicator presents a holistic framework for understanding market momentum across multiple timescales and conditions. By interpreting short-term squeezes via IQR bands, long-term reversal zones via adaptive OB/OS, and subtle acceleration changes through torque, traders can gain advanced insights into when to anticipate breakouts, manage risk around potential reversals, and fine-tune timing for entries and exits. This integrated approach helps navigate complex market dynamics, making it a valuable addition to any technical analysis toolkit.
Penunjuk Pine Script®
oleh TEDCORP2
3
base16Library "base16" Base16 Syntax Theme Collection. dark/light Pairs placed into 2 matched groups. included is tool for assembling your own themes, as well as all themes String names to create your own Input menus / add to your own theme matrix, and theme selectors addToMatrix(_mtx, _title, _choices, _theme)   To create a theme matrix with string index, use a color matrix global add theme name to string array of theme titles and last input a theme from above, or create your own theme arrays.   Parameters:      _mtx : (color ) matrix for storage      _title : (string ) Name of theme being added      _choices : (string ) name index      _theme : (color ) colors being added   Returns: void addToMatrix(_mtx, _theme)   Add theme to color matrix Non-indexed   Parameters:      _mtx : (color ) matrix for storage      _theme : (color ) colors being added dark()   Dark Themne Selection (With light Equivalent in same location)   Returns: Color matrix of dark themes light()   light Themne Selection (With dark Equivalent in same location)   Returns: Color matrix of light themes selectTheme(_mtx, _themes, _theme)   Get a Theme By Name   Parameters:      _mtx : (Matrix color) Name of Theme      _themes : (Array string) Array with Names of Themes      _theme : (string ) Name of Theme to select selectTheme(_mtx, _theme)   Get a Theme By Number   Parameters:      _mtx : (Matrix color) Name of Theme      _theme : (int ) Number of Theme to select /// all themes included: 3024 apathy apprentice ashes atelier_cave_light atelier_cave atelier_dune_light atelier_dune atelier_estuary_light atelier_estuary atelier_forest_light atelier_forest atelier_heath_light atelier_heath atelier_lakeside_light atelier_lakeside atelier_plateau_light atelier_plateau atelier_savanna_light atelier_savanna atelier_seaside_light atelier_seaside atelier_sulphurpool_light atelier_sulphurpool atlas ayu_dark ayu_light ayu_mirage bespin black_metal_bathory black_metal_burzum black_metal_dark_funeral black_metal_gorgoroth black_metal_immortal black_metal_khold black_metal_marduk black_metal_mayhem black_metal_nile black_metal_venom black_metal blue_forest blueish brewer bright brogrammer brush_trees_dark brush_trees catppuccin chalk circus classic_dark classic_light codeschool clrs cupcake cupertino da_one_black da_one_gray da_one_ocean da_one_paper da_one_sea da_one_white danqing_light danqing darcula darkmoss darktooth dark_violet decaf default_dark default_light dirtysea dracula edge_dark edge_light eighties embers emil equilibrium_dark equilibrium_gray_dark equilibrium_gray_light equilibrium_light espresso eva_dim eva everforest flat framer fruit_soda gigavolt github google_dark google_light gotham grayscale_dark grayscale_light green_screen gruber gruvbox_dark_hard gruvbox_dark_medium gruvbox_dark_pale gruvbox_dark_soft gruvbox_light_hard gruvbox_light_medium gruvbox_light_soft gruvbox_material_dark_hard gruvbox_material_dark_medium gruvbox_material_dark_soft gruvbox_material_light_hard gruvbox_material_light_medium gruvbox_material_light_soft hardcore harmonic16_dark harmonic16_light heetch_light heetch_dark helios hopscotch horizon_dark horizon_light horizon_terminal_dark horizon_terminal_light humanoid_dark humanoid_light ia_dark ia_light icy_dark ir_black isotope kanagawa katy kimber lime macintosh marrakesh materia material_darker material_lighter material_palenight material_vivid material mellow_purple mexico_light mocha monokai Nebula nord nova ocean oceanicnext one_light onedark outrun_dark pandora papercolor_dark papercolor_light paraiso pasque phd pico pinky pop porple primer_dark_dimmed primer_dark primer_light purpledream qualia railscasts rebecca rose_pine_dawn rose_pine_moon rose_pine sagelight sakura sandcastle seti_ui shades_of_purple shadesmear_dark shadesmear_light shapeshifter silk_dark silk_light snazzy solar_flare_light solar_flare solarized_dark solarized_light spaceduck spacemacs stella still_alive summercamp summerfruit_dark summerfruit_light synth_midnight_terminal_dark synth_midnight_terminal_light tango tender tokyo_city_dark tokyo_city_light tokyo_city_terminal_dark tokyo_city_terminal_light tokyo_night_dark tokyo_night_light tokyo_night_storm tokyo_night_terminal_dark tokyo_night_terminal_light tokyo_night_terminal_storm tokyodark_terminal tokyodark tomorrow_night_eighties tomorrow_night tomorrow london_tube twilight unikitty_dark unikitty_light unikitty_reversible uwunicorn vice vulcan windows_10_light windows_10 windows_95_light windows_95 windows_high_contrast_light windows_high_contrast windows_nt_light windows_nt woodland xcode_dusk zenburn
Perpustakaan Pine Script®
oleh kaigouthro
3
Overnight Gap Dominance Indicator (OGDI)The Overnight Gap Dominance Indicator (OGDI) measures the relative volatility of overnight price gaps versus intraday price movements for a given security, such as SPY or SPX. It uses a rolling standard deviation of absolute overnight percentage changes divided by the standard deviation of absolute intraday percentage changes over a customizable window. This helps traders identify periods where overnight gaps predominate, suggesting potential opportunities for strategies leveraging extended market moves. Instructions A pply the indicator to your TradingView chart for the desired security (e.g., SPY or SPX). Adjust the "Rolling Window" input to set the lookback period (default: 60 bars). Modify the "1DTE Threshold" and "2DTE+ Threshold" inputs to tailor the levels at which you switch from 0DTE to 1DTE or multi-DTE strategies (default: 0.5 and 0.6). Observe the OGDI line: values above the 1DTE threshold suggest favoring 1DTE strategies, while values above the 2DTE+ threshold indicate multi-DTE strategies may be more effective. Use in conjunction with low VIX environments and uptrend legs for optimal results.
Penunjuk Pine Script®
oleh traderofoptions
SmartPlusSmartPlus Overview The SmartPlus indicator is a complete framework for intraday traders. It combines key market reference points (VWAP, moving averages, and the first 15-minute high/low range) with predictive levels based on historical daily moves. Together, these elements allow traders to build directional bias, spot breakouts, and manage risk throughout the session. Key Features 1. VWAP (Volume-Weighted Average Price) - Plots the intraday VWAP in real time. - VWAP acts as a central “fair value” reference point for institutional order flow. - Price trading above VWAP generally suggests bullish bias, while below VWAP leans bearish. 2. Exponential Moving Averages (EMAs) - Two configurable EMAs are included: - Fast EMA (default: 21 periods) - Slow EMA (default: 34 periods) - Each EMA is plotted with a single, user-selectable color for clarity. - Crossovers or alignment between price, VWAP, and EMAs help define market structure. 3. Smart Bar Coloring - Candles automatically change color when conditions align: - Bull Zone: Price above VWAP, Fast EMA, and Slow EMA. - Bear Zone: Price below VWAP, Fast EMA, and Slow EMA. - Fluorescent bar coloring helps highlight momentum zones visually without additional analysis. 4. First 15-Minute High/Low/Mid (Automatic) - Automatically detects the first 15 minutes of each new trading day (no manual input required). - Plots horizontal lines for: - First 15-Minute High (green) - First 15-Minute Low (red) - Midpoint of that range (gray) - Once the initial 15-minute window ends, these levels remain projected throughout the session as breakout or support/resistance zones. - Alerts trigger when price breaks above the high or below the low after the window. 5. Daily Support/Resistance Forecast - Uses a rolling lookback of recent daily ranges (default: 126 days). - Tracks average up moves and down moves from the daily open. - Optionally incorporates standard deviation for wider confidence bands. - Plots forecast levels above/below the current day’s open for reference. Trading Logic (How to Use) - Bullish Bias: - Price is above VWAP, above both EMAs, and ideally above the first 15-minute high. - This setup suggests trend continuation or breakout opportunities on the long side. - Bearish Bias: - Price is below VWAP, below both EMAs, and ideally below the first 15-minute low. - This setup suggests downward pressure or breakout opportunities on the short side. - Neutral / Caution Zone: - Price caught between VWAP, EMAs, or inside the 15-minute range often signals indecision. - Best to wait for confirmation or breakout before committing to trades. Expectations After Using It - The script provides context and structure, not trading signals. - It highlights where price is relative to meaningful market levels so traders can act with greater confidence. - Combining VWAP, EMAs, and the 15-minute breakout framework helps traders stay aligned with the market’s natural rhythm. Disclaimer This script is a tool for market analysis and educational purposes only. It does not constitute financial advice, trading recommendations, or guaranteed profitability. Markets are inherently risky, and past patterns do not ensure future results. Always combine this tool with sound risk management, personal research, and professional guidance before making any trading decisions.
Penunjuk Pine Script®
oleh moneypots
1
Golden Launch Pad🔰 Golden Launch Pad This indicator identifies high-probability bullish setups by analyzing the relationship between multiple moving averages (MAs). A “Golden Launch Pad” is formed when the following five conditions are met simultaneously: 📌 Launch Pad Criteria (all must be true): MAs Are Tightly Grouped The selected MAs must be close together, measured using the Z-score spread — the difference between the highest and lowest Z-scores of the MAs. Z-scores are calculated relative to the average and standard deviation of price over a user-defined window. This normalizes MA distance based on volatility, making the signal adaptive across different assets. MAs Are Bullishly Stacked The MAs must be in strict ascending order: MA1 > MA2 > MA3 > ... > MA(n). This ensures the short-term trend leads the longer-term trend — a classic sign of bullish structure. All MAs Have Positive Slope Each MA must be rising, based on a lookback period that is a percentage of its length (e.g. 30% of the MA’s bars). This confirms momentum and avoids signals during sideways or weakening trends. Price Is Above the Fastest MA The current close must be higher than the first (fastest) moving average. This adds a momentum filter and reduces false positives. Price Is Near the MA Cluster The current price must be close to the average of all selected MAs. Proximity is measured in standard deviations (e.g. within 1.0), ensuring the price hasn't already made a large move away from the setup zone. ⚙️ Customization Options: Use 2 to 6 MAs for the stack Choose from SMA, EMA, WMA, VWMA for each MA Adjustable Z-score window and spread threshold Dynamic slope lookback based on MA length Volatility-adjusted price proximity filter 🧠 Use Case: This indicator helps traders visually and systematically detect strong continuation setups — often appearing before breakouts or sustained uptrends. It works well on intraday, swing, and positional timeframes across all asset classes. For best results, combine with volume, breakout structure, or multi-timeframe confirmation.
Penunjuk Pine Script®
oleh QuesoHouseKey
X EMA EQThe X EMA EQ is a versatile technical analysis tool designed to overlay price action with customizable Exponential Moving Averages (EMAs) and real-time equilibrium levels. Ideal for intraday traders, it blends trend-following and mean-reversion concepts to highlight both directional bias and potential value zones. 🔹 Key Features: 1. Dual EMA Visualization Plot up to two user-defined EMAs (default: 20 and 50 periods). Independently toggle and style each EMA to suit your strategy. Helps track short- and mid-term trend dynamics with clarity. 2. Running Equilibrium Bands Displays a real-time dynamic price range based on the highest high and lowest low over a user-defined rolling window (default: 15 minutes). Includes upper/lower quartile lines and a central midpoint, giving structure to intraday price movement. Useful for identifying compression, breakouts, and fair value zones. 3. Linear Regression Overlay (Optional) Apply a smoothed linear regression curve across the same time window. Highlights directional momentum and price mean trajectory. Valuable for assessing slope bias and trend strength over the equilibrium period. 4. Intraday Timeframe Optimization Designed specifically for intraday charts with minute-based resolutions (30 seconds to 60 minutes). Auto-adjusts logic based on the current chart’s timeframe. 5. Clean Visual Design Minimalist and translucent color schemes ensure readability without clutter. All components are independently toggleable for full customization. ⚙️ Settings Overview: EMA Settings: Enable/disable each EMA, set lengths and colors. Time & Price Settings: Define the running equilibrium period (in minutes), control visibility of bands and regression line, and adjust styling. X EMA EQ offers a compact yet powerful visual framework for traders seeking to align with short-term trend structure while keeping an eye on evolving price balance zones.
Penunjuk Pine Script®
oleh KinetiCapital
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
FEDFUNDS Rate Divergence Oscillator [BackQuant]FEDFUNDS Rate Divergence Oscillator 1. Concept and Rationale The United States Federal Funds Rate is the anchor around which global dollar liquidity and risk-free yield expectations revolve. When the Fed hikes, borrowing costs rise, liquidity tightens and most risk assets encounter head-winds. When it cuts, liquidity expands, speculative appetite often recovers. Bitcoin, a 24-hour permissionless asset sometimes described as “digital gold with venture-capital-like convexity,” is particularly sensitive to macro-liquidity swings. The FED Divergence Oscillator quantifies the behavioural gap between short-term monetary policy (proxied by the effective Fed Funds Rate) and Bitcoin’s own percentage price change. By converting each series into identical rate-of-change units, subtracting them, then optionally smoothing the result, the script produces a single bounded-yet-dynamic line that tells you, at a glance, whether Bitcoin is outperforming or underperforming the policy backdrop—and by how much. 2. Data Pipeline • Fed Funds Rate – Pulled directly from the FRED database via the ticker “FRED:FEDFUNDS,” sampled at daily frequency to synchronise with crypto closes. • Bitcoin Price – By default the script forces a daily timeframe so that both series share time alignment, although you can disable that and plot the oscillator on intraday charts if you prefer. • User Source Flexibility – The BTC series is not hard-wired; you can select any exchange-specific symbol or even swap BTC for another crypto or risk asset whose interaction with the Fed rate you wish to study. 3. Math under the Hood (1) Rate of Change (ROC) – Both the Fed rate and BTC close are converted to percent return over a user-chosen lookback (default 30 bars). This means a cut from 5.25 percent to 5.00 percent feeds in as –4.76 percent, while a climb from 25 000 to 30 000 USD in BTC over the same window converts to +20 percent. (2) Divergence Construction – The script subtracts the Fed ROC from the BTC ROC. Positive values show BTC appreciating faster than policy is tightening (or falling slower than the rate is cutting); negative values show the opposite. (3) Optional Smoothing – Macro series are noisy. Toggle “Apply Smoothing” to calm the line with your preferred moving-average flavour: SMA, EMA, DEMA, TEMA, RMA, WMA or Hull. The default EMA-25 removes day-to-day whips while keeping turning points alive. (4) Dynamic Colour Mapping – Rather than using a single hue, the oscillator line employs a gradient where deep greens represent strong bullish divergence and dark reds flag sharp bearish divergence. This heat-map approach lets you gauge intensity without squinting at numbers. (5) Threshold Grid – Five horizontal guides create a structured regime map:  • Lower Extreme (–50 pct) and Upper Extreme (+50 pct) identify panic capitulations and euphoria blow-offs.  • Oversold (–20 pct) and Overbought (+20 pct) act as early warning alarms.  • Zero Line demarcates neutral alignment. 4. Chart Furniture and User Interface • Oscillator fill with a secondary DEMA-30 “shader” offers depth perception: fat ribbons often precede high-volatility macro shifts. • Optional bar-colouring paints candles green when the oscillator is above zero and red below, handy for visual correlation. • Background tints when the line breaches extreme zones, making macro inflection weeks pop out in the replay bar. • Everything—line width, thresholds, colours—can be customised so the indicator blends into any template. 5. Interpretation Guide Macro Liquidity Pulse  • When the oscillator spends weeks above +20 while the Fed is still raising rates, Bitcoin is signalling liquidity tolerance or an anticipatory pivot view. That condition often marks the embryonic phase of major bull cycles (e.g., March 2020 rebound).  • Sustained prints below –20 while the Fed is already dovish indicate risk aversion or idiosyncratic crypto stress—think exchange scandals or broad flight to safety. Regime Transition Signals  • Bullish cross through zero after a long sub-zero stint shows Bitcoin regaining upward escape velocity versus policy.  • Bearish cross under zero during a hiking cycle tells you monetary tightening has finally started to bite. Momentum Exhaustion and Mean-Reversion  • Touches of +50 (or –50) come rarely; they are statistically stretched events. Fade strategies either taking profits or hedging have historically enjoyed positive expectancy.  • Inside-bar candlestick patterns or lower-timeframe bearish engulfings simultaneously with an extreme overbought print make high-probability short scalp setups, especially near weekly resistance. The same logic mirrors for oversold. Pair Trading / Relative Value  • Combine the oscillator with spreads like BTC versus Nasdaq 100. When both the FED Divergence oscillator and the BTC–NDQ relative-strength line roll south together, the cross-asset confirmation amplifies conviction in a mean-reversion short.  • Swap BTC for miners, altcoins or high-beta equities to test who is the divergence leader. Event-Driven Tactics  • FOMC days: plot the oscillator on an hourly chart (disable ‘Force Daily TF’). Watch for micro-structural spikes that resolve in the first hour after the statement; rapid flips across zero can front-run post-FOMC swings.  • CPI and NFP prints: extremes reached into the release often mean positioning is one-sided. A reversion toward neutral in the first 24 hours is common. 6. Alerts Suite Pre-bundled conditions let you automate workflows: • Bullish / Bearish zero crosses – queue spot or futures entries. • Standard OB / OS – notify for first contact with actionable zones. • Extreme OB / OS – prime time to review hedges, take profits or build contrarian swing positions. 7. Parameter Playground • Shorten ROC Lookback to 14 for tactical traders; lengthen to 90 for macro investors. • Raise extreme thresholds (for example ±80) when plotting on altcoins that exhibit higher volatility than BTC. • Try HMA smoothing for responsive yet smooth curves on intraday charts. • Colour-blind users can easily swap bull and bear palette selections for preferred contrasts. 8. Limitations and Best Practices • The Fed Funds series is step-wise; it only changes on meeting days. Rapid BTC oscillations in between may dominate the calculation. Keep that perspective when interpreting very high-frequency signals. • Divergence does not equal causation. Crypto-native catalysts (ETF approvals, hack headlines) can overwhelm macro links temporarily. • Use in conjunction with classical confirmation tools—order-flow footprints, market-profile ledges, or simple price action to avoid “pure-indicator” traps. 9. Final Thoughts The FEDFUNDS Rate Divergence Oscillator distills an entire macro narrative monetary policy versus risk sentiment into a single colourful heartbeat. It will not magically predict every pivot, yet it excels at framing market context, spotting stretches and timing regime changes. Treat it as a strategic compass rather than a tactical sniper scope, combine it with sound risk management and multi-factor confirmation, and you will possess a robust edge anchored in the world’s most influential interest-rate benchmark. Trade consciously, stay adaptive, and let the policy-price tension guide your roadmap.
Penunjuk Pine Script®
oleh BackQuant
1
Alternate Hourly HighlightAlternate Hourly Highlight This indicator automatically highlights every alternate one-hour window on your chart, making it easy to visually identify and separate each trading hour. The background alternates color every hour, helping traders spot hourly cycles, session changes, or develop time-based trading strategies. Works on any timeframe. No inputs required—just add to your chart and go! Especially useful for intraday traders who analyze price action, volatility, or volume by the hour. For custom colors or session windows, feel free to modify the script!
Penunjuk Pine Script®
oleh ratheeshinv
NY HIGH LOW BREAKNY HIGH LOW BREAK: A New York Session Breakout Strategy The "NY HIGH LOW BREAK" indicator is a powerful TradingView script designed to identify and capitalize on breakout opportunities during the New York trading session. This strategy focuses on the initial price action of the New York market open, looking for clear breaches of the high or low established within the first 30 minutes. It's particularly suited for intraday traders who seek to capture momentum-driven moves. Strategy Logic The core of the "NY HIGH LOW BREAK" strategy revolves around these key components: New York Session Opening Range Identification: The script first identifies the opening range of the New York session. This is defined by the high and low prices established during the first 30 minutes of the New York trading session (from 7:01 AM GMT-4 to 7:31 AM GMT-4). These crucial levels are then extended forward on the chart as horizontal lines, serving as potential support and resistance zones. Breakout Signal Generation: Long Signal: A buy signal is generated when the price breaks above the high of the New York opening range. Specifically, it looks for a candle whose open and close are both above the highLinePrice, and importantly, the previous candle's open was below and close was above the highLinePrice. This indicates a strong upward momentum confirming the breakout. Short Signal: Conversely, a sell signal is generated when the price breaks below the low of the New York opening range. It looks for a candle whose open and close are both below the lowLinePrice, and the previous candle's open was above and close was below the lowLinePrice. This suggests strong downward momentum confirming the breakdown. Supertrend Filter (Implicit/Future Enhancement): While the supertrend and direction variables are present in the code, they are not actively used in the current signal generation logic. This suggests a potential future enhancement where the Supertrend indicator could be incorporated as a trend filter to confirm breakout directions, adding an extra layer of confluence to the signals. For example, only taking long breakouts when Supertrend indicates an uptrend, and short breakouts when Supertrend indicates a downtrend. Second Candle Confirmation (Possible Future Enhancement): The close_sec_candle function and openSEC, closeSEC variables indicate an attempt to capture the open and close of a "second candle" (30 minutes after the initial New York open). Currently, closeSEC is used in a specific condition for signal_way but not directly in the primary longSignal or shortSignal logic. This also suggests a potential future refinement where the price action of this second candle could be used for further confirmation or specific entry criteria. Time-Based Filtering: Signals are only considered valid within a specific trading window from 8:00 AM GMT-4 to 8:00 AM GMT-4 + 16 * 30 minutes (which is 480 minutes, or 8 hours) on 1-minute and 5-minute timeframes. This ensures that trades are taken during the most active and volatile periods of the New York session, avoiding late-session chop. The script also highlights the New York session and lunch hours using background colors, providing visual context to the trading day. Key Features Automated New York Open Range Detection: The script automatically identifies and plots the high and low of the first 30 minutes of the New York trading session. Clear Breakout Signals: Visually distinct "BUY" and "SELL" labels appear on the chart when a breakout occurs, making it easy to spot trading opportunities. Timeframe Adaptability: While optimized for 1-minute and 5-minute timeframes for signal generation, the opening range lines can be displayed on various timeframes. Customizable Risk-to-Reward (RR): The rr input allows users to define their preferred risk-to-reward ratio for potential trades, although it's not directly implemented in the current signal or trade management logic. This could be used by traders for manual trade management. Visual Session and Lunch Highlights: The script colors the background to clearly delineate the New York trading session and the lunch break, helping traders understand the market context. How to Use Apply the Indicator: Add the "NY HIGH LOW BREAK" indicator to your chart on TradingView. Select a Relevant Timeframe: For optimal signal generation, use 1-minute or 5-minute timeframes. Observe the Opening Range: The green and red lines represent the high and low of the first 30 minutes of the New York session. Look for Breakouts: Wait for price to decisively break above the green line (for a buy) or below the red line (for a sell). Confirm Signals: The "BUY" or "SELL" labels will appear on the chart when the breakout conditions are met within the active trading window. Implement Your Risk Management: Use your preferred risk management techniques, including stop-loss and take-profit levels, in conjunction with the signals generated. The rr input can guide your manual risk-to-reward calculations. Potential Enhancements & Considerations Supertrend Confirmation: Integrating the supertrend variable to filter signals would significantly enhance the strategy's robustness by aligning trades with the prevailing trend. Stop-Loss and Take-Profit Automation: The rr input currently serves as a manual guide. Future versions could integrate automated stop-loss and take-profit placement based on this ratio, potentially using ATR for dynamic sizing. Volume Confirmation: Adding a volume filter to confirm breakouts would ensure that only high-conviction moves are traded. Backtesting and Optimization: Thorough backtesting across various assets and market conditions is crucial to determine the optimal settings and profitability of this strategy. Session Times: The current session times are hardcoded. Making these user-definable inputs would allow for greater flexibility across different time zones and trading preferences. The "NY HIGH LOW BREAK" is a straightforward yet effective strategy for capturing initial New York session momentum. By focusing on clear breakout levels, it aims to provide timely and actionable trading signals for intraday traders.
Penunjuk Pine Script®
oleh hassansadeghirashed
Rifle UnifiedThis script is designed for use on 30-second charts of Dow Jones-related symbols (YM, MYM, US30). It provides automated buy and sell signals using a combination of price action, RSI (Relative Strength Index), and volume analysis. The script is intended for both live trading signals and backtesting, with configurable risk management and debugging features. Core Functionality 1. Signal Generation Logic Trigger: The algorithm looks for a sharp price move (drop or rise) of a user-defined threshold (default: 80 points) within a specified lookback window (default: 20 minutes). Levels: It monitors for price drops below specific numerical levels ending in 23, 43, or 73 (e.g., 42223, 42273). RSI Condition: When price falls below one of these levels and the RSI is below 30, the setup is considered active. Buy Signal: A buy is triggered if, after setup: Price rises back above the level, The RSI rate of change (ROC) indicates exhaustion of the drop, The current bar shows positive momentum. 2. Trade Management Stop Loss & Take Profit: Configurable fixed or trailing stop loss and take profit levels are plotted and managed automatically. Exit Signals: The script signals exit based on price action relative to these risk management levels. 3. Filters & Enhancements Parabolic Move Filter: Prevents entries during extreme price moves. Dead Cat Bounce Filter: Avoids false signals after sharp reversals. Volume Filter: Optionally requires volume conditions for trade entries (especially for shorts). Multiple Confirmation Layers : Includes checks for 5-minute RSI, momentum, and price retracement. User Inputs & Customization Trade Direction: Toggle between LONG and SHORT signal generation. Trigger Settings: Adjust thresholds for price moves, lookback windows, RSI ROC, and volume requirements. Trade Settings: Set take profit, stop loss, and trailing stop behavior. Debug & Visualization: Enable or disable various plots, labels, and debug tables for in-depth analysis. Backtesting: Integrated backtester with summary and detailed statistics tables. Technical Features Uses External Libraries: Relies on RifleShooterLib for core logic and BackTestLib for backtesting and statistics. Multi-timeframe Analysis: Incorporates both 30-second and 5-minute RSI calculations. Chart Annotations: Plots entry/exit points, risk levels, and debug information directly on the chart. Alert Conditions: Built-in alert triggers for key events (initial move, stall, entry). Intended Use Markets: Dow Jones symbols (YM, MYM, US30, or US30 CFD). Timeframe: 30-second chart. Purpose: Automated signal generation for discretionary or algorithmic trading, with robust risk management and backtesting support. Notable Customization & Extension Points Momentum Calculation: Plans to replace the current momentum measure with "sqz momentum". Displacement Logic: Future update to use "FVG concept" for displacement. High-Contrast RSI: Optional visual enhancements for RSI extremes. Time-based Stop: Consideration for adding a time-based stop mechanism. This script is highly modular, with extensive user controls, and is suitable for both live trading and historical analysis of Dow Jones index movements
Penunjuk Pine Script®
oleh BlueMagicTrading
Momentum Regression [BackQuant]Momentum Regression The Momentum Regression is an advanced statistical indicator built to empower quants, strategists, and technically inclined traders with a robust visual and quantitative framework for analyzing momentum effects in financial markets. Unlike traditional momentum indicators that rely on raw price movements or moving averages, this tool leverages a volatility-adjusted linear regression model (y ~ x) to uncover and validate momentum behavior over a user-defined lookback window. Purpose & Design Philosophy Momentum is a core anomaly in quantitative finance — an effect where assets that have performed well (or poorly) continue to do so over short to medium-term horizons. However, this effect can be noisy, regime-dependent, and sometimes spurious. The Momentum Regression is designed as a pre-strategy analytical tool to help you filter and verify whether statistically meaningful and tradable momentum exists in a given asset. Its architecture includes: Volatility normalization to account for differences in scale and distribution. Regression analysis to model the relationship between past and present standardized returns. Deviation bands to highlight overbought/oversold zones around the predicted trendline. Statistical summary tables to assess the reliability of the detected momentum. Core Concepts and Calculations The model uses the following: Independent variable (x): The volatility-adjusted return over the chosen momentum period. Dependent variable (y): The 1-bar lagged log return, also adjusted for volatility. A simple linear regression is performed over a large lookback window (default: 1000 bars), which reveals the slope and intercept of the momentum line. These values are then used to construct: A predicted momentum trendline across time. Upper and lower deviation bands , representing ±n standard deviations of the regression residuals (errors). These visual elements help traders judge how far current returns deviate from the modeled momentum trend, similar to Bollinger Bands but derived from a regression model rather than a moving average. Key Metrics Provided On each update, the indicator dynamically displays: Momentum Slope (β₁): Indicates trend direction and strength. A higher absolute value implies a stronger effect. Intercept (β₀): The predicted return when x = 0. Pearson’s R: Correlation coefficient between x and y. R² (Coefficient of Determination): Indicates how well the regression line explains the variance in y. Standard Error of Residuals: Measures dispersion around the trendline. t-Statistic of β₁: Used to evaluate statistical significance of the momentum slope. These statistics are presented in a top-right summary table for immediate interpretation. A bottom-right signal table also summarizes key takeaways with visual indicators. Features and Inputs ✅ Volatility-Adjusted Momentum : Reduces distortions from noisy price spikes. ✅ Custom Lookback Control : Set the number of bars to analyze regression. ✅ Extendable Trendlines : For continuous visualization into the future. ✅ Deviation Bands : Optional ±σ multipliers to detect abnormal price action. ✅ Contextual Tables : Help determine strength, direction, and significance of momentum. ✅ Separate Pane Design : Cleanly isolates statistical momentum from price chart. How It Helps Traders 📉 Quantitative Strategy Validation: Use the regression results to confirm whether a momentum-based strategy is worth pursuing on a specific asset or timeframe. 🔍 Regime Detection: Track when momentum breaks down or reverses. Slope changes, drops in R², or weak t-stats can signal regime shifts. 📊 Trade Filtering: Avoid false positives by entering trades only when momentum is both statistically significant and directionally favorable. 📈 Backtest Preparation: Before running costly simulations, use this tool to pre-screen assets for exploitable return structures. When to Use It Before building or deploying a momentum strategy : Test if momentum exists and is statistically reliable. During market transitions : Detect early signs of fading strength or reversal. As part of an edge-stacking framework : Combine with other filters such as volatility compression, volume surges, or macro filters. Conclusion The Momentum Regression indicator offers a powerful fusion of statistical analysis and visual interpretation. By combining volatility-adjusted returns with real-time linear regression modeling, it helps quantify and qualify one of the most studied and traded anomalies in finance: momentum.
Penunjuk Pine Script®
oleh BackQuant
3
Aetherium Institutional Market Resonance EngineAetherium Institutional Market Resonance Engine (AIMRE) A Three-Pillar Framework for Decoding Institutional Activity 🎓 THEORETICAL FOUNDATION The Aetherium Institutional Market Resonance Engine (AIMRE) is a multi-faceted analysis system designed to move beyond conventional indicators and decode the market's underlying structure as dictated by institutional capital flow. Its philosophy is built on a singular premise: significant market moves are preceded by a convergence of context , location , and timing . Aetherium quantifies these three dimensions through a revolutionary three-pillar architecture. This system is not a simple combination of indicators; it is an integrated engine where each pillar's analysis feeds into a central logic core. A signal is only generated when all three pillars achieve a state of resonance, indicating a high-probability alignment between market organization, key liquidity levels, and cyclical momentum. ⚡ THE THREE-PILLAR ARCHITECTURE 1. 🌌 PILLAR I: THE COHERENCE ENGINE (THE 'CONTEXT') Purpose: To measure the degree of organization within the market. This pillar answers the question: " Is the market acting with a unified purpose, or is it chaotic and random? " Conceptual Framework: Institutional campaigns (accumulation or distribution) create a non-random, organized market environment. Retail-driven or directionless markets are characterized by "noise" and chaos. The Coherence Engine acts as a filter to ensure we only engage when institutional players are actively steering the market. Formulaic Concept: Coherence = f(Dominance, Synchronization) Dominance Factor: Calculates the absolute difference between smoothed buying pressure (volume-weighted bullish candles) and smoothed selling pressure (volume-weighted bearish candles), normalized by total pressure. A high value signifies a clear winner between buyers and sellers. Synchronization Factor: Measures the correlation between the streams of buying and selling pressure over the analysis window. A high positive correlation indicates synchronized, directional activity, while a negative correlation suggests choppy, conflicting action. The final Coherence score (0-100) represents the percentage of market organization. A high score is a prerequisite for any signal, filtering out unpredictable market conditions. 2. 💎 PILLAR II: HARMONIC LIQUIDITY MATRIX (THE 'LOCATION') Purpose: To identify and map high-impact institutional footprints. This pillar answers the question: " Where have institutions previously committed significant capital? " Conceptual Framework: Large institutional orders leave indelible marks on the market in the form of anomalous volume spikes at specific price levels. These are not random occurrences but are areas of intense historical interest. The Harmonic Liquidity Matrix finds these footprints and consolidates them into actionable support and resistance zones called "Harmonic Nodes." Algorithmic Process: Footprint Identification: The engine scans the historical lookback period for candles where volume > average_volume * Institutional_Volume_Filter. This identifies statistically significant volume events. Node Creation: A raw node is created at the mean price of the identified candle. Dynamic Clustering: The engine uses an ATR-based proximity algorithm. If a new footprint is identified within Node_Clustering_Distance (ATR) of an existing Harmonic Node, it is merged. The node's price is volume-weighted, and its magnitude is increased. This prevents chart clutter and consolidates nearby institutional orders into a single, more significant level. Node Decay: Nodes that are older than the Institutional_Liquidity_Scanback period are automatically removed from the chart, ensuring the analysis remains relevant to recent market dynamics. 3. 🌊 PILLAR III: CYCLICAL RESONANCE MATRIX (THE 'TIMING') Purpose: To identify the market's dominant rhythm and its current phase. This pillar answers the question: " Is the market's immediate energy flowing up or down? " Conceptual Framework: Markets move in waves and cycles of varying lengths. Trading in harmony with the current cyclical phase dramatically increases the probability of success. Aetherium employs a simplified wavelet analysis concept to decompose price action into short, medium, and long-term cycles. Algorithmic Process: Cycle Decomposition: The engine calculates three oscillators based on the difference between pairs of Exponential Moving Averages (e.g., EMA8-EMA13 for short cycle, EMA21-EMA34 for medium cycle). Energy Measurement: The 'energy' of each cycle is determined by its recent volatility (standard deviation). The cycle with the highest energy is designated as the "Dominant Cycle." Phase Analysis: The engine determines if the dominant cycles are in a bullish phase (rising from a trough) or a bearish phase (falling from a peak). Cycle Sync: The highest conviction timing signals occur when multiple cycles (e.g., short and medium) are synchronized in the same direction, indicating broad-based momentum. 🔧 COMPREHENSIVE INPUT SYSTEM Pillar I: Market Coherence Engine Coherence Analysis Window (10-50, Default: 21): The lookback period for the Coherence Engine. Lower Values (10-15): Highly responsive to rapid shifts in market control. Ideal for scalping but can be sensitive to noise. Balanced (20-30): Excellent for day trading, capturing the ebb and flow of institutional sessions. Higher Values (35-50): Smoother, more stable reading. Best for swing trading and identifying long-term institutional campaigns. Coherence Activation Level (50-90%, Default: 70%): The minimum market organization required to enable signal generation. Strict (80-90%): Only allows signals in extremely clear, powerful trends. Fewer, but potentially higher quality signals. Standard (65-75%): A robust filter that effectively removes choppy conditions while capturing most valid institutional moves. Lenient (50-60%): Allows signals in less-organized markets. Can be useful in ranging markets but may increase false signals. Pillar II: Harmonic Liquidity Matrix Institutional Liquidity Scanback (100-400, Default: 200): How far back the engine looks for institutional footprints. Short (100-150): Focuses on recent institutional activity, providing highly relevant, immediate levels. Long (300-400): Identifies major, long-term structural levels. These nodes are often extremely powerful but may be less frequent. Institutional Volume Filter (1.3-3.0, Default: 1.8): The multiplier for detecting a volume spike. High (2.5-3.0): Only registers climactic, undeniable institutional volume. Fewer, but more significant nodes. Low (1.3-1.7): More sensitive, identifying smaller but still relevant institutional interest. Node Clustering Distance (0.2-0.8 ATR, Default: 0.4): The ATR-based distance for merging nearby nodes. High (0.6-0.8): Creates wider, more consolidated zones of liquidity. Low (0.2-0.3): Creates more numerous, precise, and distinct levels. Pillar III: Cyclical Resonance Matrix Cycle Resonance Analysis (30-100, Default: 50): The lookback for determining cycle energy and dominance. Short (30-40): Tunes the engine to faster, shorter-term market rhythms. Best for scalping. Long (70-100): Aligns the timing component with the larger primary trend. Best for swing trading. Institutional Signal Architecture Signal Quality Mode (Professional, Elite, Supreme): Controls the strictness of the three-pillar confluence. Professional: Loosest setting. May generate signals if two of the three pillars are in strong alignment. Increases signal frequency. Elite: Balanced setting. Requires a clear, unambiguous resonance of all three pillars. The recommended default. Supreme: Most stringent. Requires perfect alignment of all three pillars, with each pillar exhibiting exceptionally strong readings (e.g., coherence > 85%). The highest conviction signals. Signal Spacing Control (5-25, Default: 10): The minimum bars between signals to prevent clutter and redundant alerts. 🎨 ADVANCED VISUAL SYSTEM The visual architecture of Aetherium is designed not merely for aesthetics, but to provide an intuitive, at-a-glance understanding of the complex data being processed. Harmonic Liquidity Nodes: The core visual element. Displayed as multi-layered, semi-transparent horizontal boxes. Magnitude Visualization: The height and opacity of a node's "glow" are proportional to its volume magnitude. More significant nodes appear brighter and larger, instantly drawing the eye to key levels. Color Coding: Standard nodes are blue/purple, while exceptionally high-magnitude nodes are highlighted in an accent color to denote critical importance. 🌌 Quantum Resonance Field: A dynamic background gradient that visualizes the overall market environment. Color: Shifts from cool blues/purples (low coherence) to energetic greens/cyans (high coherence and organization), providing instant context. Intensity: The brightness and opacity of the field are influenced by total market energy (a composite of coherence, momentum, and volume), making powerful market states visually apparent. 💎 Crystalline Lattice Matrix: A geometric web of lines projected from a central moving average. Mathematical Basis: Levels are projected using multiples of the Golden Ratio (Phi ≈ 1.618) and the ATR. This visualizes the natural harmonic and fractal structure of the market. It is not arbitrary but is based on mathematical principles of market geometry. 🧠 Synaptic Flow Network: A dynamic particle system visualizing the engine's "thought process." Node Density & Activation: The number of particles and their brightness/color are tied directly to the Market Coherence score. In high-coherence states, the network becomes a dense, bright, and organized web. In chaotic states, it becomes sparse and dim. ⚡ Institutional Energy Waves: Flowing sine waves that visualize market volatility and rhythm. Amplitude & Speed: The height and speed of the waves are directly influenced by the ATR and volume, providing a feel for market energy. 📊 INSTITUTIONAL CONTROL MATRIX (DASHBOARD) The dashboard is the central command console, providing a real-time, quantitative summary of each pillar's status. Header: Displays the script title and version. Coherence Engine Section: State: Displays a qualitative assessment of market organization: ◉ PHASE LOCK (High Coherence), ◎ ORGANIZING (Moderate Coherence), or ○ CHAOTIC (Low Coherence). Color-coded for immediate recognition. Power: Shows the precise Coherence percentage and a directional arrow (↗ or ↘) indicating if organization is increasing or decreasing. Liquidity Matrix Section: Nodes: Displays the total number of active Harmonic Liquidity Nodes currently being tracked. Target: Shows the price level of the nearest significant Harmonic Node to the current price, representing the most immediate institutional level of interest. Cycle Matrix Section: Cycle: Identifies the currently dominant market cycle (e.g., "MID ") based on cycle energy. Sync: Indicates the alignment of the cyclical forces: ▲ BULLISH , ▼ BEARISH , or ◆ DIVERGENT . This is the core timing confirmation. Signal Status Section: A unified status bar that provides the final verdict of the engine. It will display "QUANTUM SCAN" during neutral periods, or announce the tier and direction of an active signal (e.g., "◉ TIER 1 BUY ◉" ), highlighted with the appropriate color. 🎯 SIGNAL GENERATION LOGIC Aetherium's signal logic is built on the principle of strict, non-negotiable confluence. Condition 1: Context (Coherence Filter): The Market Coherence must be above the Coherence Activation Level. No signals can be generated in a chaotic market. Condition 2: Location (Liquidity Node Interaction): Price must be actively interacting with a significant Harmonic Liquidity Node. For a Buy Signal: Price must be rejecting the Node from below (testing it as support). For a Sell Signal: Price must be rejecting the Node from above (testing it as resistance). Condition 3: Timing (Cycle Alignment): The Cyclical Resonance Matrix must confirm that the dominant cycles are synchronized with the intended trade direction. Signal Tiering: The Signal Quality Mode input determines how strictly these three conditions must be met. 'Supreme' mode, for example, might require not only that the conditions are met, but that the Market Coherence is exceptionally high and the interaction with the Node is accompanied by a significant volume spike. Signal Spacing: A final filter ensures that signals are spaced by a minimum number of bars, preventing over-alerting in a single move. 🚀 ADVANCED TRADING STRATEGIES The Primary Confluence Strategy: The intended use of the system. Wait for a Tier 1 (Elite/Supreme) or Tier 2 (Professional/Elite) signal to appear on the chart. This represents the alignment of all three pillars. Enter after the signal bar closes, with a stop-loss placed logically on the other side of the Harmonic Node that triggered the signal. The Coherence Context Strategy: Use the Coherence Engine as a standalone market filter. When Coherence is high (>70%), favor trend-following strategies. When Coherence is low (<50%), avoid new directional trades or favor range-bound strategies. A sharp drop in Coherence during a trend can be an early warning of a trend's exhaustion. Node-to-Node Trading: In a high-coherence environment, use the Harmonic Liquidity Nodes as both entry points and profit targets. For example, after a BUY signal is generated at one Node, the next Node above it becomes a logical first profit target. ⚖️ RESPONSIBLE USAGE AND LIMITATIONS Decision Support, Not a Crystal Ball: Aetherium is an advanced decision-support tool. It is designed to identify high-probability conditions based on a model of institutional behavior. It does not predict the future. Risk Management is Paramount: No indicator can replace a sound risk management plan. Always use appropriate position sizing and stop-losses. The signals provided are probabilistic, not certainties. Past Performance Disclaimer: The market models used in this script are based on historical data. While robust, there is no guarantee that these patterns will persist in the future. Market conditions can and do change. Not a "Set and Forget" System: The indicator performs best when its user understands the concepts behind the three pillars. Use the dashboard and visual cues to build a comprehensive view of the market before acting on a signal. Backtesting is Essential: Before applying this tool to live trading, it is crucial to backtest and forward-test it on your preferred instruments and timeframes to understand its unique behavior and characteristics. 🔮 CONCLUSION The Aetherium Institutional Market Resonance Engine represents a paradigm shift from single-variable analysis to a holistic, multi-pillar framework. By quantifying the abstract concepts of market context, location, and timing into a unified, logical system, it provides traders with an unprecedented lens into the mechanics of institutional market operations. It is not merely an indicator, but a complete analytical engine designed to foster a deeper understanding of market dynamics. By focusing on the core principles of institutional order flow, Aetherium empowers traders to filter out market noise, identify key structural levels, and time their entries in harmony with the market's underlying rhythm. "In all chaos there is a cosmos, in all disorder a secret order." - Carl Jung — Dskyz, Trade with insight. Trade with confluence. Trade with Aetherium.
Penunjuk Pine Script®
oleh DskyzInvestments
18
Red Report Filter x 'Bull_Trap_9'Hello Traders! This one is my favorite. This is indicator / filter: '2 of 2.' '1 of 2' is the, 'Closed Market Filter,' I posted before this that you may like. Again, I prefer 'Filter' over 'Indicator' because this Pine Script code does not interact with the actual price data. It makes handling high impact reports effortless. As you all know; if you're on a Prop and breach a 'Red,' you lose your account. This will filter up to 5 reports. More than enough unless you're on EURUSD! It offers both 'Red' and 'Orange' report control. The default window times of 15 / 6 are programmed for red events. You can always alter the base code for your desired, 'Before / After.' Click the tooltip for more info. How to use: You do need to update the inputs daily with the current report times before each open. I trade YM / US markets. Those reports are very repetitive on their delivery times, so I usually leave a 10:00 setting in slot 1. I then toggle it 'On' or 'Off' per demand. Just open the dialogue box and it is pretty self explanatory. I used task scheduler for a lot of years, but that wasn't very reliable, modest work to set up daily and a lot of times I may not hear it or it malfunctions because of a Windows update. TradingView has the little icon that floats from the bottom right, but who really looks for that. Any audio alert is subject to fail for a number of reasons. This filter REDS the screen in your face. Leaves no doubt about what's coming. I know there may be other apps and options out there, but this filter is integral to the TradingView chart itself embedded through Pine Script. It is right there, a click away, easy to input data, and as long as your chart is active and working, the filter will fire. I did not build an alert condition into this, but I'm sure that could be an option if you want to program in audio as well. Please Note: Only when the price candles push into the filter zone, will the filter start to display. Run a test a minute from the current price candle and you can see how it functions. I appreciate your interest.
Penunjuk Pine Script®
oleh Bull_Trap_9
Intraday & Annual CAPM AlphaIntraday & Annual CAPM Alpha This TradingView™ Pine v6 indicator computes and plots a stock’s CAPM α (alpha) on both intraday and daily/annualized timeframes, allowing you to monitor relative performance against a chosen benchmark (e.g. SPX, NDX). ⸻ Key Outputs 1. Intraday α per Bar (blue line) • Calculates α from a rolling-window linear regression of the last N bars’ returns (default 60). • Expressed as “extra return per bar” vs. the benchmark. 2. Intraday α Daily-Equivalent (stepped blue line) • Scales the per-bar α to a full trading day (390 minutes), showing “if this pace held all day, outperformance (%)”. 3. Annualized α (yellow line) • Performs the same CAPM regression on daily returns over a D-day lookback (default 252), then annualizes α by multiplying by 252. • Indicates longer-term relative strength/weakness vs. the benchmark. ⸻ Inputs • Benchmark Symbol: Choose any index or ETF (e.g. “SPX”, “NDX”). • Intraday Lookback Bars: Number of bars for intraday α regression (default 60). • Daily Lookback Days: Number of trading days for daily CAPM regression (default 252). • Use Log Returns?: Toggle between arithmetic vs. log returns. ⸻ How to Use • Short-Term Signals: • Watch the blue α/bar line on 1–15 min charts. A cross from negative to positive suggests intraday outperformance; a reversal warns of weakening momentum. • The blue daily-equivalent α gives a smoother view—e.g. > +1% signals strong intraday bias, < –1% signals underperformance. • Long-Term Trends: • On daily charts, focus on the yellow annualized α. A sustained positive α implies this stock has historically beaten the benchmark; sustained negative α implies the opposite. • Combining Timeframes: • Use intraday α for timing entries/exits within the session, and annualized α to confirm whether you want a bullish or bearish bias over days to weeks. ⸻ Install & Configure 1. Copy the Pine v6 script into the TradingView Pine Editor. 2. Set your favorite benchmark, lookback periods, and returns type. 3. Add to your chart to start visualizing real-time CAPM α signals! Feel free to adjust the lookback windows and threshold levels to suit your trading style.
Penunjuk Pine Script®
oleh ShellGas
Adaptive Signal Oscillator (ASO)📘 Adaptive Signal Oscillator (ASO) A fully dynamic, self-calibrating oscillator that adapts to any asset or timeframe by optimizing for real-time signal stability and volatility structure — without relying on static parameters or hardcoded thresholds. 🔍 Overview The Adaptive Signal Oscillator (ASO) is a next-generation technical analysis tool designed to provide context-aware long/short signals across crypto, equities, or forex markets. Unlike traditional oscillators (RSI, Stochastics, MACD), ASO requires no manual tuning of lookback periods or overbought/oversold zones — it self-optimizes based on current market behavior. 🧠 How It Works ✅ 1. Dynamic Lookback Optimization ASO evaluates a range of lookback lengths between user-defined minLen and maxLen. For each length, it calculates the standard deviation of returns and finds the one with the least volatility change (i.e., the most stable structure). This length is dynamically assigned as bestLen, recalculated on every bar. ✅ 2. Multi-Layer Signal Composition Four independent signal layers are computed using bestLen: RSI Layer: Measures relative price strength via a custom dynamic RSI. Z-Score Layer: Standardized deviation of price from its mean. Volatility Layer: Standard deviation of log or percent returns. Price Position Layer: Current price percentile within the lookback window. Each of these layers is transformed into a percentile score scaled to the range . ✅ 3. Volatility-Based Weighting The standard deviation (volatility) of each signal layer is computed. Less volatile layers are weighted more heavily, ensuring the final composite signal prioritizes stable, consistent inputs. Weights are normalized and combined to form a composite score, representing a dynamically blended, noise-weighted signal across the four layers. ✅ 4. Optional Adaptive Smoothing A boolean toggle lets users apply smoothing to the final score. The smoothing window scales proportionally to bestLen, preserving adaptiveness even during trend transitions. ✅ 5. Percentile-Based Thresholding Rather than using arbitrary fixed thresholds, ASO converts the composite score into a ranked percentile. Long/short signals are then generated based on user-defined percentile bands, adapting naturally to each asset’s behavior. 📈 Interpreting ASO Score > Threshold → Strong long signal (highlighted in aqua). Score < Threshold → Strong short signal (highlighted in fuchsia). Crossing h_thresh (e.g., 0) → Neutral-to-bias change; useful for early trend cues. The background and label update in real time to reflect the current regime and bestLen. ⚙️ Inputs minLen, maxLen, step: Define the search range for optimal lookback length. retMethod: Choose between log or percent return calculations. threshHigh, threshLow: Define signal zones using percentiles. smooth: Enable dynamic score smoothing. h_thresh: Midline crossover zone for directional context. ⚠️ Disclaimer This tool is designed for exploratory and educational purposes only. It does not offer financial advice or trading recommendations. Past performance is not indicative of future results. Always consult a licensed financial advisor before making investment decisions.
Penunjuk Pine Script®
oleh RWCS_LTD
Chebyshev-Gauss Moving AverageThis indicator applies the principles of Chebyshev-Gauss Quadrature to create a novel type of moving average. Inspired by reading rohangautam.github.io What is Chebyshev-Gauss Quadrature? It's a numerical method to approximate the integral of a function f(x) that is weighted by 1/sqrt(1-x^2) over the interval . The approximation is a simple sum: ∫ f(x)/sqrt(1-x^2) dx ≈ (π/n) * Σ f(xᵢ) where xᵢ are special points called Chebyshev nodes. How is this applied to a Moving Average? A moving average can be seen as the "mean value" of the price over a lookback window. The mean value of a function with the Chebyshev weight is calculated as: Mean = / The math simplifies beautifully, resulting in the mean being the simple arithmetic average of the function evaluated at the Chebyshev nodes: Mean = (1/n) * Σ f(xᵢ) What's unique about this MA? The Chebyshev nodes xᵢ are not evenly spaced. They are clustered towards the ends of the interval . We map this interval to our lookback period. This means the moving average samples prices more intensely from the beginning and the end of the lookback window, and less intensely from the middle. This gives it a unique character, responding quickly to recent changes while also having a long "memory" of the start of the trend.
Penunjuk Pine Script®
oleh uncleric0
PCA Regime-Adjusted MomentumSummary The PCA Regime-Adjusted Momentum (PCA-RAM) is an advanced market analysis tool designed to provide nuanced insights into market momentum and structural stability. It moves beyond traditional indicators by using Principal Component Analysis (PCA) to deconstruct market data into its most essential patterns. The indicator provides two key pieces of information: A smoothed momentum signal based on the market's dominant underlying trend. A dynamic regime filter that gauges the stability and clarity of the market's structure, advising you when to trust or fade the momentum signals. This allows traders to not only identify potential shifts in momentum but also to understand the context and confidence behind those signals. Core Concepts & Methodology The strength of this indicator lies in its sound, data-driven methodology. 1. Principal Component Analysis (PCA) At its core, the indicator analyzes a rolling window (default 50 periods) of standardized market data (Open, High, Low, Close, and Volume). PCA is a powerful statistical technique that distills this complex, 5-dimensional data into its fundamental, uncorrelated components of variance. We focus on the First Principal Component (PC1), which represents the single most dominant pattern or "theme" driving the market's behavior in the lookback window. 2. The Momentum Signal Instead of just looking at price, we project the current market data onto this dominant underlying pattern (PC1). This gives us a raw "projection score" that measures how strongly the current bar aligns with the historically dominant market structure. This raw score is then smoothed using two an exponential moving averages (a fast and a slow line) to create a clear, actionable momentum signal, similar in concept to a MACD. 3. The Dynamic Regime Filter This is arguably the indicator's most powerful feature. It answers the question: "How clear is the current market picture?" It calculates the Market Concentration Ratio, which is the percentage of total market variance explained by PC1 alone. A high ratio indicates that the market is moving in a simple, one-dimensional way (e.g., a clear, strong trend). A low ratio indicates the market is complex, multi-dimensional, and choppy, with no single dominant theme. Crucially, this filter is dynamic. It compares the current concentration ratio to its own recent average, allowing it to adapt to any asset or timeframe. It automatically learns what "normal" and "choppy" look like for the specific chart you are viewing. How to Interpret the Indicator The indicator is displayed in a separate pane with two key visual elements: The Momentum Lines (White & Gold) White Line: The "Fast Line," representing the current momentum. Gold Line: The "Slow Line," acting as the trend confirmation. Bullish Signal: A crossover of the White Line above the Gold Line suggests a shift to positive momentum. Bearish Signal: A crossover of the White Line below the Gold Line suggests a shift to negative momentum. The Regime Filter (Purple & Dark Red Background) This is your confidence gauge. Navy Blue Background (High Concentration): The market structure is stable, simple, and trending. Momentum signals are more reliable and should be given higher priority. Dark Red Background (Low Concentration): The market structure is complex, choppy, or directionless. Momentum signals are unreliable and prone to failure or "whipsaws." This is a signal to be cautious, tighten stops, or potentially stay out of the market. Potential Trading Strategies This tool is versatile and can be used in several ways: 1. Primary Signal Strategy Condition: Wait for the background to turn Purple, confirming a stable, high-confidence regime. Entry: Take the next crossover signal from the momentum lines (White over Gold for long, White under Gold for short). Exit/Filter: Consider exiting positions or ignoring new signals when the background turns Navy. 2. As a Confirmation or Filter for Your Existing Strategy Do you have a trend-following system? Only enable its long and short signals when the PCA-RAM background is Purple. Do you have a range-trading or mean-reversion system? It might be most effective when the PCA-RAM background is Navy, indicating a lack of a clear trend. 3. Advanced Divergence Analysis Look for classic divergences between price and the momentum lines. For example, if the price is making a new high, but the Gold Line is making a lower high, it may indicate underlying weakness in the trend, even on a Purple background. This divergence signal is more powerful because it shows that the new price high is not being confirmed by the market's dominant underlying pattern.
Penunjuk Pine Script®
oleh marcosag90
2
Pearson vs Approx. Spearman CorrelationThis indicator displays the rolling Pearson and approximate Spearman correlation between the chart's asset and a second user-defined asset, based on log returns over a customizable window. Features: - Pearson correlation of log returns (standard linear dependency measure) - Approximate Spearman correlation, using percentile ranks to better capture nonlinear and monotonic relationships / Horizontal lines showing: Maximum and minimum correlation values over a statistical window 1st quartile (25%) and 3rd quartile (75%) — helpful for identifying statistically high or low regimes This script is useful for identifying dynamic co-movements, regime changes, or correlation breakdowns between assets — applicable in risk management, portfolio construction, and pairs trading strategies.
Penunjuk Pine Script®
oleh Bbbruz