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BB 100 with Barcolors6/19/15 I added confirmation highlight bars to the code. In other words, if a candle bounced off the lower Bollinger band, it needed one more close above the previous candle to confirm a higher probability that a change in investor sentiment has reversed. Same is true for upper Bollinger band bounces. I also added confirmation highlight bars to the 100 sma (the basis). The idea is that lower and upper bands are potential points of support and resistance. The same is true of the basis if a trend is to continue. 6/28/15 I added a plotshape to identify closes above/below TLine. One thing this system points out is it operates best in a trend reversal. Consolidations will whipsaw the indicator too much. I have found that when this happens, if using daily candles, switch to hourly, 30 min, etc., to catch a better signal. Nothing moves in a straight line. As with any indicator, it is a tool to be used in conjunction with the art AND science of trading. As always, try the indicator for a time so that you are comfortable enough to use real money. This is designed to be used with "BB 25 with Barcolors".
Pine Script® 인디케이터
rmwaddelljr의
BB 100 with Barcolors6/19/15 I added confirmation highlight bars to the code. In other words, if a candle bounced off the lower Bollinger band, it needed one more close above the previous candle to confirm a higher probability that a change in investor sentiment has reversed. Same is true for upper Bollinger band bounces. I also added confirmation highlight bars to the 100 sma (the basis). The idea is that lower and upper bands are potential points of support and resistance. The same is true of the basis if a trend is to continue. Nothing moves in a straight line. As with any indicator, it is a tool to be used in conjunction with the art AND science of trading. As always, try the indicator for a time so that you are comfortable enough to use real money. This is designed to be used with "BB 25 with Barcolors".
Pine Script® 인디케이터
rmwaddelljr의
PnL Bubble [%] | Fractalyst1. What's the indicator purpose? The PnL Bubble indicator transforms your strategy's trade PnL percentages into an interactive bubble chart with professional-grade statistics and performance analytics. It helps traders quickly assess system profitability, understand win/loss distribution patterns, identify outliers, and make data-driven strategy improvements. How does it work? Think of this indicator as a visual report card for your trading performance. Here's what it does: What You See Colorful Bubbles: Each bubble represents one of your trades Blue/Cyan bubbles = Winning trades (you made money) Red bubbles = Losing trades (you lost money) Bigger bubbles = Bigger wins or losses Smaller bubbles = Smaller wins or losses How It Organizes Your Trades: Like a Photo Album: Instead of showing all your trades at once (which would be messy), it shows them in "pages" of 500 trades each: Page 1: Your first 500 trades Page 2: Trades 501-1000 Page 3: Trades 1001-1500, etc. What the Numbers Tell You: Average Win: How much money you typically make on winning trades Average Loss: How much money you typically lose on losing trades Expected Value (EV): Whether your trading system makes money over time Positive EV = Your system is profitable long-term Negative EV = Your system loses money long-term Payoff Ratio (R): How your average win compares to your average loss R > 1 = Your wins are bigger than your losses R < 1 = Your losses are bigger than your wins Why This Matters: At a Glance: You can instantly see if you're a profitable trader or not Pattern Recognition: Spot if you have more big wins than big losses Performance Tracking: Watch how your trading improves over time Realistic Expectations: Understand what "average" performance looks like for your system The Cool Visual Effects: Animation: The bubbles glow and shimmer to make the chart more engaging Highlighting: Your biggest wins and losses get extra attention with special effects Tooltips: hover any bubble to see details about that specific trade. What are the underlying calculations? The indicator processes trade PnL data using a dual-matrix architecture for optimal performance: Dual-Matrix System: • Display Matrix (display_matrix): Bounded to 500 trades for rendering performance • Statistics Matrix (stats_matrix): Unbounded storage for complete statistical accuracy Trade Classification & Aggregation: // Separate wins, losses, and break-even trades if val > 0.0 pos_sum += val // Sum winning trades pos_count += 1 // Count winning trades else if val < 0.0 neg_sum += val // Sum losing trades neg_count += 1 // Count losing trades else zero_count += 1 // Count break-even trades Statistical Averages: avg_win = pos_count > 0 ? pos_sum / pos_count : na avg_loss = neg_count > 0 ? math.abs(neg_sum) / neg_count : na Win/Loss Rates: total_obs = pos_count + neg_count + zero_count win_rate = pos_count / total_obs loss_rate = neg_count / total_obs Expected Value (EV): ev_value = (avg_win × win_rate) - (avg_loss × loss_rate) Payoff Ratio (R): R = avg_win ÷ |avg_loss| Contribution Analysis: ev_pos_contrib = avg_win × win_rate // Positive EV contribution ev_neg_contrib = avg_loss × loss_rate // Negative EV contribution How to integrate with any trading strategy? Equity Change Tracking Method: //@version=6 strategy("Your Strategy with Equity Change Export", overlay=true) float prev_trade_equity = na float equity_change_pct = na if barstate.isconfirmed and na(prev_trade_equity) prev_trade_equity := strategy.equity trade_just_closed = strategy.closedtrades != strategy.closedtrades if trade_just_closed and not na(prev_trade_equity) current_equity = strategy.equity equity_change_pct := ((current_equity - prev_trade_equity) / prev_trade_equity) * 100 prev_trade_equity := current_equity else equity_change_pct := na plot(equity_change_pct, "Equity Change %", display=display.data_window) Integration Steps: 1. Add equity tracking code to your strategy 2. Load both strategy and PnL Bubble indicator on the same chart 3. In bubble indicator settings, select your strategy's equity tracking output as data source 4. Configure visualization preferences (colors, effects, page navigation) How does the pagination system work? The indicator uses an intelligent pagination system to handle large trade datasets efficiently: Page Organization: • Page 1: Trades 1-500 (most recent) • Page 2: Trades 501-1000 • Page 3: Trades 1001-1500 • Page N: Trades to Example: With 1,500 trades total (3 pages available): • User selects Page 1: Shows trades 1-500 • User selects Page 4: Automatically falls back to Page 3 (trades 1001-1500) 5. Understanding the Visual Elements Bubble Visualization: • Color Coding: Cyan/blue gradients for wins, red gradients for losses • Size Mapping: Bubble size proportional to trade magnitude (larger = bigger P&L) • Priority Rendering: Largest trades displayed first to ensure visibility • Gradient Effects: Color intensity increases with trade magnitude within each category Interactive Tooltips: Each bubble displays quantitative trade information: tooltip_text = outcome + " | PnL: " + pnl_str + "\nDate: " + date_str + " " + time_str + "\nTrade #" + str.tostring(trade_number) + " (Page " + str.tostring(active_page) + ")" + "\nRank: " + str.tostring(rank) + " of " + str.tostring(n_display_rows) + "\nPercentile: " + str.tostring(percentile, "#.#") + "%" + "\nMagnitude: " + str.tostring(magnitude_pct, "#.#") + "%" Example Tooltip: Win | PnL: +2.45% Date: 2024.03.15 14:30 Trade #1,247 (Page 3) Rank: 5 of 347 Percentile: 98.6% Magnitude: 85.2% Reference Lines & Statistics: • Average Win Line: Horizontal reference showing typical winning trade size • Average Loss Line: Horizontal reference showing typical losing trade size • Zero Line: Threshold separating wins from losses • Statistical Labels: EV, R-Ratio, and contribution analysis displayed on chart What do the statistical metrics mean? Expected Value (EV): Represents the mathematical expectation per trade in percentage terms EV = (Average Win × Win Rate) - (Average Loss × Loss Rate) Interpretation: • EV > 0: Profitable system with positive mathematical expectation • EV = 0: Break-even system, profitability depends on execution • EV < 0: Unprofitable system with negative mathematical expectation Example: EV = +0.34% means you expect +0.34% profit per trade on average Payoff Ratio (R): Quantifies the risk-reward relationship of your trading system R = Average Win ÷ |Average Loss| Interpretation: • R > 1.0: Wins are larger than losses on average (favorable risk-reward) • R = 1.0: Wins and losses are equal in magnitude • R < 1.0: Losses are larger than wins on average (unfavorable risk-reward) Example: R = 1.5 means your average win is 50% larger than your average loss Contribution Analysis (Σ): Breaks down the components of expected value Positive Contribution (Σ+) = Average Win × Win Rate Negative Contribution (Σ-) = Average Loss × Loss Rate Purpose: • Shows how much wins contribute to overall expectancy • Shows how much losses detract from overall expectancy • Net EV = Σ+ - Σ- (Expected Value per trade) Example: Σ+: 1.23% means wins contribute +1.23% to expectancy Example: Σ-: -0.89% means losses drag expectancy by -0.89% Win/Loss Rates: Win Rate = Count(Wins) ÷ Total Trades Loss Rate = Count(Losses) ÷ Total Trades Shows the probability of winning vs losing trades Higher win rates don't guarantee profitability if average losses exceed average wins 7. Demo Mode & Synthetic Data Generation When using built-in sources (close, open, etc.), the indicator generates realistic demo trades for testing: if isBuiltInSource(source_data) // Generate random trade outcomes with realistic distribution u_sign = prand(float(time), float(bar_index)) if u_sign < 0.5 v_push := -1.0 // Loss trade else // Skewed distribution favoring smaller wins (realistic) u_mag = prand(float(time) + 9876.543, float(bar_index) + 321.0) k = 8.0 // Skewness factor t = math.pow(u_mag, k) v_push := 2.5 + t * 8.0 // Win trade Demo Characteristics: • Realistic win/loss distribution mimicking actual trading patterns • Skewed distribution favoring smaller wins over large wins • Deterministic randomness for consistent demo results • Includes jitter effects to prevent visual overlap 8. Performance Limitations & Optimizations Display Constraints: points_count = 500 // Maximum 500 dots per page for optimal performance Pine Script v6 Limits: • Label Count: Maximum 500 labels per indicator • Line Count: Maximum 100 lines per indicator • Box Count: Maximum 50 boxes per indicator • Matrix Size: Efficient memory management with dual-matrix system Optimization Strategies: • Pagination System: Handle unlimited trades through 500-trade pages • Priority Rendering: Largest trades displayed first for maximum visibility • Dual-Matrix Architecture: Separate display (bounded) from statistics (unbounded) • Smart Fallback: Automatic page clamping prevents empty displays Impact & Workarounds: • Visual Limitation: Only 500 trades visible per page • Statistical Accuracy: Complete dataset used for all calculations • Navigation: Use page input to browse through entire trade history • Performance: Smooth operation even with thousands of trades 9. Statistical Accuracy Guarantees Data Integrity: • Complete Dataset: Statistics matrix stores ALL trades without limit • Proper Aggregation: Separate tracking of wins, losses, and break-even trades • Mathematical Precision: Pine Script v6's enhanced floating-point calculations • Dual-Matrix System: Display limitations don't affect statistical accuracy Calculation Validation: // Verified formulas match standard trading mathematics avg_win = pos_sum / pos_count // Standard average calculation win_rate = pos_count / total_obs // Standard probability calculation ev_value = (avg_win * win_rate) - (avg_loss * loss_rate) // Standard EV formula Accuracy Features: • Mathematical Correctness: Formulas follow established trading statistics • Data Preservation: Complete dataset maintained for all calculations • Precision Handling: Proper rounding and boundary condition management • Real-Time Updates: Statistics recalculated on every new trade 10. Advanced Technical Features Real-Time Animation Engine: // Shimmer effects with sine wave modulation offset = math.sin(shimmer_t + phase) * amp // Dynamic transparency with organic flicker new_transp = math.min(flicker_limit, math.max(-flicker_limit, cur_transp + dir * flicker_step)) • Sine Wave Shimmer: Dynamic glowing effects on bubbles • Organic Flicker: Random transparency variations for natural feel • Extreme Value Highlighting: Special visual treatment for outliers • Smooth Animations: Tick-based updates for fluid motion Magnitude-Based Priority Rendering: // Sort trades by magnitude for optimal visual hierarchy sort_indices_by_magnitude(values_mat) • Largest First: Most important trades always visible • Intelligent Sorting: Custom bubble sort algorithm for trade prioritization • Performance Optimized: Efficient sorting for real-time updates • Visual Hierarchy: Ensures critical trades never get hidden Professional Tooltip System: • Quantitative Data: Pure numerical information without interpretative language • Contextual Ranking: Shows trade position within page dataset • Percentile Analysis: Performance ranking as percentage • Magnitude Scaling: Relative size compared to page maximum • Professional Format: Clean, data-focused presentation 11. Quick Start Guide Step 1: Add Indicator • Search for "PnL Bubble | Fractalyst" in TradingView indicators • Add to your chart (works on any timeframe) Step 2: Configure Data Source • Demo Mode: Leave source as "close" to see synthetic trading data • Strategy Mode: Select your strategy's PnL% output as data source Step 3: Customize Visualization • Colors: Set positive (cyan), negative (red), and neutral colors • Page Navigation: Use "Trade Page" input to browse trade history • Visual Effects: Built-in shimmer and animation effects are enabled by default Step 4: Analyze Performance • Study bubble patterns for win/loss distribution • Review statistical metrics: EV, R-Ratio, Win Rate • Use tooltips for detailed trade analysis • Navigate pages to explore full trade history Step 5: Optimize Strategy • Identify outlier trades (largest bubbles) • Analyze risk-reward profile through R-Ratio • Monitor Expected Value for system profitability • Use contribution analysis to understand win/loss impact 12. Why Choose PnL Bubble Indicator? Unique Advantages: • Advanced Pagination: Handle unlimited trades with smart fallback system • Dual-Matrix Architecture: Perfect balance of performance and accuracy • Professional Statistics: Institution-grade metrics with complete data integrity • Real-Time Animation: Dynamic visual effects for engaging analysis • Quantitative Tooltips: Pure numerical data without subjective interpretations • Priority Rendering: Intelligent magnitude-based display ensures critical trades are always visible Technical Excellence: • Built with Pine Script v6 for maximum performance and modern features • Optimized algorithms for smooth operation with large datasets • Complete statistical accuracy despite display optimizations • Professional-grade calculations matching institutional trading analytics Practical Benefits: • Instantly identify system profitability through visual patterns • Spot outlier trades and risk management issues • Understand true risk-reward profile of your strategies • Make data-driven decisions for strategy optimization • Professional presentation suitable for performance reporting Disclaimer & Risk Considerations: Important: Historical performance metrics, including positive Expected Value (EV), do not guarantee future trading success. Statistical measures are derived from finite sample data and subject to inherent limitations: • Sample Bias: Historical data may not represent future market conditions or regime changes • Ergodicity Assumption: Markets are non-stationary; past statistical relationships may break down • Survivorship Bias: Strategies showing positive historical EV may fail during different market cycles • Parameter Instability: Optimal parameters identified in backtesting often degrade in forward testing • Transaction Cost Evolution: Slippage, spreads, and commission structures change over time • Behavioral Factors: Live trading introduces psychological elements absent in backtesting • Black Swan Events: Extreme market events can invalidate statistical assumptions instantaneously
Pine Script® 인디케이터
Fractalyst의
Support and Resistance levels from Options DataINTRODUCTION This script is designed to visualize key support and resistance levels derived from options data on TradingView charts. It overlays lines, labels, and boxes to highlight levels such as Put Walls (gamma support), Call Walls (gamma resistance), Gamma Flip points, Vanna levels, and more. These levels are intended to help traders identify potential areas of price magnetism, reversal, or breakout based on options market dynamics. All calculations and visualizations are based on user-provided data pasted into the input field, as Pine Script cannot directly fetch external options data due to platform limitations (explained below). For convenience, my website allows users to interact with a bot that will generate the string for up to 30 tickers at once getting nearly real-time data on demand (data is cached for 15min). With the output string pasted into this indicator, it's a bliss to shuffle through your portfolio and see those levels for each ticker. The script is open-source under TradingView's terms, allowing users to study, modify, and improve it. It draws inspiration from common options-derived metrics like gamma exposure and vanna, which are widely discussed in financial literature. No external code is copied without rights; all logic is original or based on standard mathematical formulas. How the Options Levels Are Calculated The levels displayed by this script are not computed within Pine Script itself—instead, they rely on pre-calculated values provided by the user (via a pasted data string). These values are derived from options chain data fetched from financial APIs (e.g., using libraries like yfinance in Python). Here's a step-by-step overview of how these levels are generally calculated externally before being input into the script: Fetching Options Data: Historical and current options chain data for a ticker (e.g., strikes, open interest, volume, implied volatility, expirations) is retrieved for near-term expirations (e.g., up to 90 days). Current stock price is obtained from recent history. Gamma Support (Put Wall) and Resistance (Call Wall): Gamma Calculation: For each option, gamma (the rate of change of delta) is computed using the Black-Scholes formula: gamma = N'(d1) / (S * sigma * sqrt(T)) where S is the stock price, K is the strike, T is time to expiration (in years), sigma is implied volatility, r is the risk-free rate (e.g., 0.0445), and N'(d1) is the normal probability density function. Weighted gamma is multiplied by open interest and aggregated by strike. The Put Wall is the strike below the current price with the highest weighted gamma from puts (acting as support). The Call Wall is the strike above the current price with the highest weighted gamma from calls (acting as resistance). Short-term versions focus on strikes closer to the money (e.g., within 10-15% of the price). Gamma Flip Level: Net dealer gamma exposure (GEX) is calculated across all strikes: GEX = sum (gamma * OI * 100 * S^2 * sign * decay) where sign is +1 for calls/-1 for puts, and decay is 1 / sqrt(T). The flip point is the price where net GEX changes sign (from positive to negative or vice versa), interpolated between strikes. Vanna Levels: Vanna (sensitivity of delta to volatility) is calculated: vanna = -N'(d1) * d2 / sigma where d2 = d1 - sigma * sqrt(T). Weighted by open interest, the highest positive and negative vanna strikes are identified. Other Levels: S1/R1: Significant strikes with high combined open interest and volume (80% OI + 20% volume), below/above price for support/resistance. Implied Move: ATM implied volatility scaled by S * sigma * sqrt(d/365) (e.g., for 7 days). Call/Put Ratio: Total call contracts divided by put contracts (OI + volume). IV Percentage: Average ATM implied volatility. Options Activity Level: Average contracts per unique strike, binned into levels (0-4). Stop Loss: Dynamically set below the lowest support (e.g., Put Wall, Gamma Flip), adjusted by IV (tighter in low IV). Fib Target: 1.618 extension from Put Wall to Call Wall range. Previous day levels are stored for comparison (e.g., to detect Call Wall movement >2.5% for alerts). Effect as Support and Resistance in Technical Trading Options levels like gamma walls influence price action due to market maker hedging: Put Wall (Gamma Support): High put gamma below price creates a "magnet" effect—market makers buy stock as price falls, providing support. Traders might look for bounces here as entry points for longs. Call Wall (Gamma Resistance): High call gamma above price leads to selling pressure from hedging, acting as resistance. Rejections here could signal trims, sells or even shorts. Gamma Flip: Where gamma exposure flips sign, often a volatility pivot—crossing it can accelerate moves (bullish above, bearish below). Vanna Levels: Positive/negative vanna indicate volatility sensitivity; crosses may signal regime shifts. Implied Move: Shows expected range; prices outside suggest overextension. S1/R1 and Fib Target: Volume/OI clusters act as classic S/R; Fib extensions project upside targets post-breakout. In trading, these are not guarantees—combine with TA (e.g., volume, trends). High activity levels imply stronger effects; low CP ratio suggests bearish sentiment. Alerts trigger on proximities/crosses for awareness, not advice. Limitations of the TradingView Platform for Data Pulling TradingView's Pine Script is sandboxed for security and performance: No direct internet access or API calls (e.g., can't fetch yfinance data in-script). Limited to chart data/symbol info; no real-time options chains. Inputs are static per load; updates require manual pasting. Caching isn't persistent across sessions. This prevents dynamic data pulling, ensuring scripts remain lightweight but requiring external tools for fresh data. Creative Solution for On-Demand Data Pulling To overcome these limitations, users can use external tools or scripts (e.g., Python-based) to fetch and compute levels on demand. The tool processes tickers, generates a formatted string (e.g., "TICKER:level1,level2,...;TIMESTAMP:unix;"), and users paste it into the script's input. This keeps data fresh without violating platform rules, as computation happens off-platform. For example, run a local script to query APIs and output the string—adaptable for any ticker. Script Functionality Breakdown Inputs: Custom data string (parsed for levels/timestamp); toggles for short-term/previous/Vanna/stop loss; style options (colors, transparency). Parsing: Extracts levels for the chart symbol; gets timestamp for "updated ago" display. Drawing: Lines/labels for levels; boxes for gamma zones/implied move; clears old elements on updates. Info Panel: Top-right summary with metrics (CP ratio, IV, distances, activity); emojis for quick status. Alerts: Conditions for proximities, crosses, bounces (e.g., 0.5% bounce from Put Wall). Performance: Uses vars for persistence; efficient for real-time. This script is educational—test thoroughly. Not financial advice; past performance isn't indicative of future results. Feedback welcome via TradingView comments.
Pine Script® 인디케이터
marsrides의
1
Multi SMA + Golden/Death + Heatmap + BB**Multi SMA (50/100/200) + Golden/Death + Candle Heatmap + BB** A practical trend toolkit that blends classic 50/100/200 SMAs with clear crossover labels, special 🚀 Golden / 💀 Death Cross markers, and a readable candle heatmap based on a dynamic regression midline and volatility bands. Optional Bollinger Bands are included for context. * See trend direction at a glance with SMAs. * Get minimal, de-cluttered labels on important crosses (50↔100, 50↔200, 100↔200). * Highlight big regime shifts with special Golden/Death tags. * Read momentum and volatility with the candle heatmap. * Add Bollinger Bands if you want classic mean-reversion context. Designed to be lightweight, non-repainting on confirmed bars, and flexible across timeframes. # What This Indicator Does (plain English) * **Tracks trend** using **SMA 50/100/200** and lets you optionally compute each SMA on a higher or different timeframe (HTF-safe, no lookahead). * **Prints labels** when SMAs cross each other (up or down). You can force signals only after bar close to avoid repaint. * **Marks Golden/Death Crosses** (50 over/under 200) with special labels so major regime changes stand out. * **Colors candles** with a **heatmap** built from a regression midline and volatility bands—greenish above, reddish below, with a smooth gradient. * **Optionally shows Bollinger Bands** (basis SMA + stdev bands) and fills the area between them. * **Includes alert conditions** for Golden and Death Cross so you can automate notifications. --- # Settings — Simple Explanations ## Source * **Source**: Price source used to calculate SMAs and Bollinger basis. Default: `close`. ## SMA 50 * **Show 50**: Turn the SMA(50) line on/off. * **Length 50**: How many bars to average. Lower = faster but noisier. * **Color 50** / **Width 50**: Visual style. * **Timeframe 50**: Optional alternate timeframe for SMA(50). Leave empty to use the chart timeframe. ## SMA 100 * **Show 100**: Turn the SMA(100) line on/off. * **Length 100**: Bars used for the mid-term trend. * **Color 100** / **Width 100**: Visual style. * **Timeframe 100**: Optional alternate timeframe for SMA(100). ## SMA 200 * **Show 200**: Turn the SMA(200) line on/off. * **Length 200**: Bars used for the long-term trend. * **Color 200** / **Width 200**: Visual style. * **Timeframe 200**: Optional alternate timeframe for SMA(200). ## Signals (crossover labels) * **Show crossover signals**: Prints triangle labels on SMA crosses (50↔100, 50↔200, 100↔200). * **Wait for bar close (confirmed)**: If ON, signals only appear after the candle closes (reduces repaint). * **Min bars between same-pair signals**: Minimum spacing to avoid duplicate labels from the same SMA pair too often. * **Trend filter (buy: 50>100>200, sell: 50<100<200)**: Only show bullish labels when SMAs are stacked bullish (50 above 100 above 200), and only show bearish labels when stacked bearish. ### Label Offset * **Offset mode**: Choose how to push labels away from price: * **Percent**: Offset is a % of price. * **ATR x**: Offset is ATR(14) × multiplier. * **Percent of price (%)**: Used when mode = Percent. * **ATR multiplier (for ‘ATR x’)**: Used when mode = ATR x. ### Label Colors * **Bull color** / **Bear color**: Background of triangle labels. * **Bull label text color** / **Bear label text color**: Text color inside the triangles. ## Golden / Death Cross * **Show 🚀 Golden Cross (50↑200)**: Show a special “Golden” label when SMA50 crosses above SMA200. * **Golden label color** / **Golden text color**: Styling for Golden label. * **Show 💀 Death Cross (50↓200)**: Show a special “Death” label when SMA50 crosses below SMA200. * **Death label color** / **Death text color**: Styling for Death label. ## Candle Heatmap * **Enable heatmap candle colors**: Turns the heatmap on/off. * **Length**: Lookback for the regression midline and volatility measure. * **Deviation Multiplier**: Band width around the midline (bigger = wider). * **Volatility basis**: * **RMA Range** (smoothed high-low range) * **Stdev** (standard deviation of close) * **Upper/Middle/Lower color**: Gradient colors for the heatmap. * **Heatmap transparency (0..100)**: 0 = solid, 100 = invisible. * **Force override base candles**: Repaint base candles so heatmap stays visible even if your chart has custom coloring. ## Bollinger Bands (optional) * **Show Bollinger Bands**: Toggle the overlay on/off. * **Length**: Basis SMA length. * **StdDev Multiplier**: Distance of bands from the basis in standard deviations. * **Basis color** / **Band color**: Line colors for basis and bands. * **Bands fill transparency**: Opacity of the fill between upper/lower bands. --- # Features & How It Works ## 1) HTF-Safe SMAs Each SMA can be calculated on the chart timeframe or a higher/different timeframe you choose. The script pulls HTF values **without lookahead** (non-repainting on confirmed bars). ## 2) Crossover Labels (Three Pairs) * **50↔100**, **50↔200**, **100↔200**: * **Triangle Up** label when the first SMA crosses **above** the second. * **Triangle Down** label when it crosses **below**. * Optional **Trend Filter** ensures only signals aligned with the overall stack (50>100>200 for bullish, 50<100<200 for bearish). * **Debounce** spacing avoids repeated labels for the same pair too close together. ## 3) Golden / Death Cross Highlights * **🚀 Golden Cross**: SMA50 crosses **above** SMA200 (often a longer-term bullish regime shift). * **💀 Death Cross**: SMA50 crosses **below** SMA200 (often a longer-term bearish regime shift). * Separate styling so they stand out from regular cross labels. ## 4) Candle Heatmap * Builds a **regression midline** with **volatility bands**; colors candles by their position inside that channel. * Smooth gradient: lower side → reddish, mid → yellowish, upper side → greenish. * Helps you see momentum and “where price sits” relative to a dynamic channel. ## 5) Bollinger Bands (Optional) * Classic **basis SMA** ± **StdDev** bands. * Light visual context for mean-reversion and volatility expansion. ## 6) Alerts * **Golden Cross**: `🚀 GOLDEN CROSS: SMA 50 crossed ABOVE SMA 200` * **Death Cross**: `💀 DEATH CROSS: SMA 50 crossed BELOW SMA 200` Add these to your alerts to get notified automatically. --- # Tips & Notes * For fewer false positives, keep **“Wait for bar close”** ON, especially on lower timeframes. * Use the **Trend Filter** to align signals with the broader stack and cut noise. * For HTF context, set **Timeframe 50/100/200** to higher frames (e.g., H1/H4/D) while you trade on a lower frame. * Heatmap “Length” and “Deviation Multiplier” control smoothness and channel width—tune for your asset’s volatility.
Pine Script® 인디케이터
a_jabbaroff의
Markov Chain [3D] | FractalystWhat exactly is a Markov Chain? This indicator uses a Markov Chain model to analyze, quantify, and visualize the transitions between market regimes (Bull, Bear, Neutral) on your chart. It dynamically detects these regimes in real-time, calculates transition probabilities, and displays them as animated 3D spheres and arrows, giving traders intuitive insight into current and future market conditions. How does a Markov Chain work, and how should I read this spheres-and-arrows diagram? Think of three weather modes: Sunny, Rainy, Cloudy. Each sphere is one mode. The loop on a sphere means “stay the same next step” (e.g., Sunny again tomorrow). The arrows leaving a sphere show where things usually go next if they change (e.g., Sunny moving to Cloudy). Some paths matter more than others. A more prominent loop means the current mode tends to persist. A more prominent outgoing arrow means a change to that destination is the usual next step. Direction isn’t symmetric: moving Sunny→Cloudy can behave differently than Cloudy→Sunny. Now relabel the spheres to markets: Bull, Bear, Neutral. Spheres: market regimes (uptrend, downtrend, range). Self‑loop: tendency for the current regime to continue on the next bar. Arrows: the most common next regime if a switch happens. How to read: Start at the sphere that matches current bar state. If the loop stands out, expect continuation. If one outgoing path stands out, that switch is the typical next step. Opposite directions can differ (Bear→Neutral doesn’t have to match Neutral→Bear). What states and transitions are shown? The three market states visualized are: Bullish (Bull): Upward or strong-market regime. Bearish (Bear): Downward or weak-market regime. Neutral: Sideways or range-bound regime. Bidirectional animated arrows and probability labels show how likely the market is to move from one regime to another (e.g., Bull → Bear or Neutral → Bull). How does the regime detection system work? You can use either built-in price returns (based on adaptive Z-score normalization) or supply three custom indicators (such as volume, oscillators, etc.). Values are statistically normalized (Z-scored) over a configurable lookback period. The normalized outputs are classified into Bull, Bear, or Neutral zones. If using three indicators, their regime signals are averaged and smoothed for robustness. How are transition probabilities calculated? On every confirmed bar, the algorithm tracks the sequence of detected market states, then builds a rolling window of transitions. The code maintains a transition count matrix for all regime pairs (e.g., Bull → Bear). Transition probabilities are extracted for each possible state change using Laplace smoothing for numerical stability, and frequently updated in real-time. What is unique about the visualization? 3D animated spheres represent each regime and change visually when active. Animated, bidirectional arrows reveal transition probabilities and allow you to see both dominant and less likely regime flows. Particles (moving dots) animate along the arrows, enhancing the perception of regime flow direction and speed. All elements dynamically update with each new price bar, providing a live market map in an intuitive, engaging format. Can I use custom indicators for regime classification? Yes! Enable the "Custom Indicators" switch and select any three chart series as inputs. These will be normalized and combined (each with equal weight), broadening the regime classification beyond just price-based movement. What does the “Lookback Period” control? Lookback Period (default: 100) sets how much historical data builds the probability matrix. Shorter periods adapt faster to regime changes but may be noisier. Longer periods are more stable but slower to adapt. How is this different from a Hidden Markov Model (HMM)? It sets the window for both regime detection and probability calculations. Lower values make the system more reactive, but potentially noisier. Higher values smooth estimates and make the system more robust. How is this Markov Chain different from a Hidden Markov Model (HMM)? Markov Chain (as here): All market regimes (Bull, Bear, Neutral) are directly observable on the chart. The transition matrix is built from actual detected regimes, keeping the model simple and interpretable. Hidden Markov Model: The actual regimes are unobservable ("hidden") and must be inferred from market output or indicator "emissions" using statistical learning algorithms. HMMs are more complex, can capture more subtle structure, but are harder to visualize and require additional machine learning steps for training. A standard Markov Chain models transitions between observable states using a simple transition matrix, while a Hidden Markov Model assumes the true states are hidden (latent) and must be inferred from observable “emissions” like price or volume data. In practical terms, a Markov Chain is transparent and easier to implement and interpret; an HMM is more expressive but requires statistical inference to estimate hidden states from data. Markov Chain: states are observable; you directly count or estimate transition probabilities between visible states. This makes it simpler, faster, and easier to validate and tune. HMM: states are hidden; you only observe emissions generated by those latent states. Learning involves machine learning/statistical algorithms (commonly Baum–Welch/EM for training and Viterbi for decoding) to infer both the transition dynamics and the most likely hidden state sequence from data. How does the indicator avoid “repainting” or look-ahead bias? All regime changes and matrix updates happen only on confirmed (closed) bars, so no future data is leaked, ensuring reliable real-time operation. Are there practical tuning tips? Tune the Lookback Period for your asset/timeframe: shorter for fast markets, longer for stability. Use custom indicators if your asset has unique regime drivers. Watch for rapid changes in transition probabilities as early warning of a possible regime shift. Who is this indicator for? Quants and quantitative researchers exploring probabilistic market modeling, especially those interested in regime-switching dynamics and Markov models. Programmers and system developers who need a probabilistic regime filter for systematic and algorithmic backtesting: The Markov Chain indicator is ideally suited for programmatic integration via its bias output (1 = Bull, 0 = Neutral, -1 = Bear). Although the visualization is engaging, the core output is designed for automated, rules-based workflows—not for discretionary/manual trading decisions. Developers can connect the indicator’s output directly to their Pine Script logic (using input.source()), allowing rapid and robust backtesting of regime-based strategies. It acts as a plug-and-play regime filter: simply plug the bias output into your entry/exit logic, and you have a scientifically robust, probabilistically-derived signal for filtering, timing, position sizing, or risk regimes. The MC's output is intentionally "trinary" (1/0/-1), focusing on clear regime states for unambiguous decision-making in code. If you require nuanced, multi-probability or soft-label state vectors, consider expanding the indicator or stacking it with a probability-weighted logic layer in your scripting. Because it avoids subjectivity, this approach is optimal for systematic quants, algo developers building backtested, repeatable strategies based on probabilistic regime analysis. What's the mathematical foundation behind this? The mathematical foundation behind this Markov Chain indicator—and probabilistic regime detection in finance—draws from two principal models: the (standard) Markov Chain and the Hidden Markov Model (HMM). How to use this indicator programmatically? The Markov Chain indicator automatically exports a bias value (+1 for Bullish, -1 for Bearish, 0 for Neutral) as a plot visible in the Data Window. This allows you to integrate its regime signal into your own scripts and strategies for backtesting, automation, or live trading. Step-by-Step Integration with Pine Script (input.source) Add the Markov Chain indicator to your chart. This must be done first, since your custom script will "pull" the bias signal from the indicator's plot. In your strategy, create an input using input.source() Example: //@version=5 strategy("MC Bias Strategy Example") mcBias = input.source(close, "MC Bias Source") After saving, go to your script’s settings. For the “MC Bias Source” input, select the plot/output of the Markov Chain indicator (typically its bias plot). Use the bias in your trading logic Example (long only on Bull, flat otherwise): if mcBias == 1 strategy.entry("Long", strategy.long) else strategy.close("Long") For more advanced workflows, combine mcBias with additional filters or trailing stops. How does this work behind-the-scenes? TradingView’s input.source() lets you use any plot from another indicator as a real-time, “live” data feed in your own script (source). The selected bias signal is available to your Pine code as a variable, enabling logical decisions based on regime (trend-following, mean-reversion, etc.). This enables powerful strategy modularity : decouple regime detection from entry/exit logic, allowing fast experimentation without rewriting core signal code. Integrating 45+ Indicators with Your Markov Chain — How & Why The Enhanced Custom Indicators Export script exports a massive suite of over 45 technical indicators—ranging from classic momentum (RSI, MACD, Stochastic, etc.) to trend, volume, volatility, and oscillator tools—all pre-calculated, centered/scaled, and available as plots. // Enhanced Custom Indicators Export - 45 Technical Indicators // Comprehensive technical analysis suite for advanced market regime detection //@version=6 indicator('Enhanced Custom Indicators Export | Fractalyst', shorttitle='Enhanced CI Export', overlay=false, scale=scale.right, max_labels_count=500, max_lines_count=500) // |----- Input Parameters -----| // momentum_group = "Momentum Indicators" trend_group = "Trend Indicators" volume_group = "Volume Indicators" volatility_group = "Volatility Indicators" oscillator_group = "Oscillator Indicators" display_group = "Display Settings" // Common lengths length_14 = input.int(14, "Standard Length (14)", minval=1, maxval=100, group=momentum_group) length_20 = input.int(20, "Medium Length (20)", minval=1, maxval=200, group=trend_group) length_50 = input.int(50, "Long Length (50)", minval=1, maxval=200, group=trend_group) // Display options show_table = input.bool(true, "Show Values Table", group=display_group) table_size = input.string("Small", "Table Size", options= , group=display_group) // |----- MOMENTUM INDICATORS (15 indicators) -----| // // 1. RSI (Relative Strength Index) rsi_14 = ta.rsi(close, length_14) rsi_centered = rsi_14 - 50 // 2. Stochastic Oscillator stoch_k = ta.stoch(close, high, low, length_14) stoch_d = ta.sma(stoch_k, 3) stoch_centered = stoch_k - 50 // 3. Williams %R williams_r = ta.stoch(close, high, low, length_14) - 100 // 4. MACD (Moving Average Convergence Divergence) = ta.macd(close, 12, 26, 9) // 5. Momentum (Rate of Change) momentum = ta.mom(close, length_14) momentum_pct = (momentum / close ) * 100 // 6. Rate of Change (ROC) roc = ta.roc(close, length_14) // 7. Commodity Channel Index (CCI) cci = ta.cci(close, length_20) // 8. Money Flow Index (MFI) mfi = ta.mfi(close, length_14) mfi_centered = mfi - 50 // 9. Awesome Oscillator (AO) ao = ta.sma(hl2, 5) - ta.sma(hl2, 34) // 10. Accelerator Oscillator (AC) ac = ao - ta.sma(ao, 5) // 11. Chande Momentum Oscillator (CMO) cmo = ta.cmo(close, length_14) // 12. Detrended Price Oscillator (DPO) dpo = close - ta.sma(close, length_20) // 13. Price Oscillator (PPO) ppo = ta.sma(close, 12) - ta.sma(close, 26) ppo_pct = (ppo / ta.sma(close, 26)) * 100 // 14. TRIX trix_ema1 = ta.ema(close, length_14) trix_ema2 = ta.ema(trix_ema1, length_14) trix_ema3 = ta.ema(trix_ema2, length_14) trix = ta.roc(trix_ema3, 1) * 10000 // 15. Klinger Oscillator klinger = ta.ema(volume * (high + low + close) / 3, 34) - ta.ema(volume * (high + low + close) / 3, 55) // 16. Fisher Transform fisher_hl2 = 0.5 * (hl2 - ta.lowest(hl2, 10)) / (ta.highest(hl2, 10) - ta.lowest(hl2, 10)) - 0.25 fisher = 0.5 * math.log((1 + fisher_hl2) / (1 - fisher_hl2)) // 17. Stochastic RSI stoch_rsi = ta.stoch(rsi_14, rsi_14, rsi_14, length_14) stoch_rsi_centered = stoch_rsi - 50 // 18. Relative Vigor Index (RVI) rvi_num = ta.swma(close - open) rvi_den = ta.swma(high - low) rvi = rvi_den != 0 ? rvi_num / rvi_den : 0 // 19. Balance of Power (BOP) bop = (close - open) / (high - low) // |----- TREND INDICATORS (10 indicators) -----| // // 20. Simple Moving Average Momentum sma_20 = ta.sma(close, length_20) sma_momentum = ((close - sma_20) / sma_20) * 100 // 21. Exponential Moving Average Momentum ema_20 = ta.ema(close, length_20) ema_momentum = ((close - ema_20) / ema_20) * 100 // 22. Parabolic SAR sar = ta.sar(0.02, 0.02, 0.2) sar_trend = close > sar ? 1 : -1 // 23. Linear Regression Slope lr_slope = ta.linreg(close, length_20, 0) - ta.linreg(close, length_20, 1) // 24. Moving Average Convergence (MAC) mac = ta.sma(close, 10) - ta.sma(close, 30) // 25. Trend Intensity Index (TII) tii_sum = 0.0 for i = 1 to length_20 tii_sum += close > close ? 1 : 0 tii = (tii_sum / length_20) * 100 // 26. Ichimoku Cloud Components ichimoku_tenkan = (ta.highest(high, 9) + ta.lowest(low, 9)) / 2 ichimoku_kijun = (ta.highest(high, 26) + ta.lowest(low, 26)) / 2 ichimoku_signal = ichimoku_tenkan > ichimoku_kijun ? 1 : -1 // 27. MESA Adaptive Moving Average (MAMA) mama_alpha = 2.0 / (length_20 + 1) mama = ta.ema(close, length_20) mama_momentum = ((close - mama) / mama) * 100 // 28. Zero Lag Exponential Moving Average (ZLEMA) zlema_lag = math.round((length_20 - 1) / 2) zlema_data = close + (close - close ) zlema = ta.ema(zlema_data, length_20) zlema_momentum = ((close - zlema) / zlema) * 100 // |----- VOLUME INDICATORS (6 indicators) -----| // // 29. On-Balance Volume (OBV) obv = ta.obv // 30. Volume Rate of Change (VROC) vroc = ta.roc(volume, length_14) // 31. Price Volume Trend (PVT) pvt = ta.pvt // 32. Negative Volume Index (NVI) nvi = 0.0 nvi := volume < volume ? nvi + ((close - close ) / close ) * nvi : nvi // 33. Positive Volume Index (PVI) pvi = 0.0 pvi := volume > volume ? pvi + ((close - close ) / close ) * pvi : pvi // 34. Volume Oscillator vol_osc = ta.sma(volume, 5) - ta.sma(volume, 10) // 35. Ease of Movement (EOM) eom_distance = high - low eom_box_height = volume / 1000000 eom = eom_box_height != 0 ? eom_distance / eom_box_height : 0 eom_sma = ta.sma(eom, length_14) // 36. Force Index force_index = volume * (close - close ) force_index_sma = ta.sma(force_index, length_14) // |----- VOLATILITY INDICATORS (10 indicators) -----| // // 37. Average True Range (ATR) atr = ta.atr(length_14) atr_pct = (atr / close) * 100 // 38. Bollinger Bands Position bb_basis = ta.sma(close, length_20) bb_dev = 2.0 * ta.stdev(close, length_20) bb_upper = bb_basis + bb_dev bb_lower = bb_basis - bb_dev bb_position = bb_dev != 0 ? (close - bb_basis) / bb_dev : 0 bb_width = bb_dev != 0 ? (bb_upper - bb_lower) / bb_basis * 100 : 0 // 39. Keltner Channels Position kc_basis = ta.ema(close, length_20) kc_range = ta.ema(ta.tr, length_20) kc_upper = kc_basis + (2.0 * kc_range) kc_lower = kc_basis - (2.0 * kc_range) kc_position = kc_range != 0 ? (close - kc_basis) / kc_range : 0 // 40. Donchian Channels Position dc_upper = ta.highest(high, length_20) dc_lower = ta.lowest(low, length_20) dc_basis = (dc_upper + dc_lower) / 2 dc_position = (dc_upper - dc_lower) != 0 ? (close - dc_basis) / (dc_upper - dc_lower) : 0 // 41. Standard Deviation std_dev = ta.stdev(close, length_20) std_dev_pct = (std_dev / close) * 100 // 42. Relative Volatility Index (RVI) rvi_up = ta.stdev(close > close ? close : 0, length_14) rvi_down = ta.stdev(close < close ? close : 0, length_14) rvi_total = rvi_up + rvi_down rvi_volatility = rvi_total != 0 ? (rvi_up / rvi_total) * 100 : 50 // 43. Historical Volatility hv_returns = math.log(close / close ) hv = ta.stdev(hv_returns, length_20) * math.sqrt(252) * 100 // 44. Garman-Klass Volatility gk_vol = math.log(high/low) * math.log(high/low) - (2*math.log(2)-1) * math.log(close/open) * math.log(close/open) gk_volatility = math.sqrt(ta.sma(gk_vol, length_20)) * 100 // 45. Parkinson Volatility park_vol = math.log(high/low) * math.log(high/low) parkinson = math.sqrt(ta.sma(park_vol, length_20) / (4 * math.log(2))) * 100 // 46. Rogers-Satchell Volatility rs_vol = math.log(high/close) * math.log(high/open) + math.log(low/close) * math.log(low/open) rogers_satchell = math.sqrt(ta.sma(rs_vol, length_20)) * 100 // |----- OSCILLATOR INDICATORS (5 indicators) -----| // // 47. Elder Ray Index elder_bull = high - ta.ema(close, 13) elder_bear = low - ta.ema(close, 13) elder_power = elder_bull + elder_bear // 48. Schaff Trend Cycle (STC) stc_macd = ta.ema(close, 23) - ta.ema(close, 50) stc_k = ta.stoch(stc_macd, stc_macd, stc_macd, 10) stc_d = ta.ema(stc_k, 3) stc = ta.stoch(stc_d, stc_d, stc_d, 10) // 49. Coppock Curve coppock_roc1 = ta.roc(close, 14) coppock_roc2 = ta.roc(close, 11) coppock = ta.wma(coppock_roc1 + coppock_roc2, 10) // 50. Know Sure Thing (KST) kst_roc1 = ta.roc(close, 10) kst_roc2 = ta.roc(close, 15) kst_roc3 = ta.roc(close, 20) kst_roc4 = ta.roc(close, 30) kst = ta.sma(kst_roc1, 10) + 2*ta.sma(kst_roc2, 10) + 3*ta.sma(kst_roc3, 10) + 4*ta.sma(kst_roc4, 15) // 51. Percentage Price Oscillator (PPO) ppo_line = ((ta.ema(close, 12) - ta.ema(close, 26)) / ta.ema(close, 26)) * 100 ppo_signal = ta.ema(ppo_line, 9) ppo_histogram = ppo_line - ppo_signal // |----- PLOT MAIN INDICATORS -----| // // Plot key momentum indicators plot(rsi_centered, title="01_RSI_Centered", color=color.purple, linewidth=1) plot(stoch_centered, title="02_Stoch_Centered", color=color.blue, linewidth=1) plot(williams_r, title="03_Williams_R", color=color.red, linewidth=1) plot(macd_histogram, title="04_MACD_Histogram", color=color.orange, linewidth=1) plot(cci, title="05_CCI", color=color.green, linewidth=1) // Plot trend indicators plot(sma_momentum, title="06_SMA_Momentum", color=color.navy, linewidth=1) plot(ema_momentum, title="07_EMA_Momentum", color=color.maroon, linewidth=1) plot(sar_trend, title="08_SAR_Trend", color=color.teal, linewidth=1) plot(lr_slope, title="09_LR_Slope", color=color.lime, linewidth=1) plot(mac, title="10_MAC", color=color.fuchsia, linewidth=1) // Plot volatility indicators plot(atr_pct, title="11_ATR_Pct", color=color.yellow, linewidth=1) plot(bb_position, title="12_BB_Position", color=color.aqua, linewidth=1) plot(kc_position, title="13_KC_Position", color=color.olive, linewidth=1) plot(std_dev_pct, title="14_StdDev_Pct", color=color.silver, linewidth=1) plot(bb_width, title="15_BB_Width", color=color.gray, linewidth=1) // Plot volume indicators plot(vroc, title="16_VROC", color=color.blue, linewidth=1) plot(eom_sma, title="17_EOM", color=color.red, linewidth=1) plot(vol_osc, title="18_Vol_Osc", color=color.green, linewidth=1) plot(force_index_sma, title="19_Force_Index", color=color.orange, linewidth=1) plot(obv, title="20_OBV", color=color.purple, linewidth=1) // Plot additional oscillators plot(ao, title="21_Awesome_Osc", color=color.navy, linewidth=1) plot(cmo, title="22_CMO", color=color.maroon, linewidth=1) plot(dpo, title="23_DPO", color=color.teal, linewidth=1) plot(trix, title="24_TRIX", color=color.lime, linewidth=1) plot(fisher, title="25_Fisher", color=color.fuchsia, linewidth=1) // Plot more momentum indicators plot(mfi_centered, title="26_MFI_Centered", color=color.yellow, linewidth=1) plot(ac, title="27_AC", color=color.aqua, linewidth=1) plot(ppo_pct, title="28_PPO_Pct", color=color.olive, linewidth=1) plot(stoch_rsi_centered, title="29_StochRSI_Centered", color=color.silver, linewidth=1) plot(klinger, title="30_Klinger", color=color.gray, linewidth=1) // Plot trend continuation plot(tii, title="31_TII", color=color.blue, linewidth=1) plot(ichimoku_signal, title="32_Ichimoku_Signal", color=color.red, linewidth=1) plot(mama_momentum, title="33_MAMA_Momentum", color=color.green, linewidth=1) plot(zlema_momentum, title="34_ZLEMA_Momentum", color=color.orange, linewidth=1) plot(bop, title="35_BOP", color=color.purple, linewidth=1) // Plot volume continuation plot(nvi, title="36_NVI", color=color.navy, linewidth=1) plot(pvi, title="37_PVI", color=color.maroon, linewidth=1) plot(momentum_pct, title="38_Momentum_Pct", color=color.teal, linewidth=1) plot(roc, title="39_ROC", color=color.lime, linewidth=1) plot(rvi, title="40_RVI", color=color.fuchsia, linewidth=1) // Plot volatility continuation plot(dc_position, title="41_DC_Position", color=color.yellow, linewidth=1) plot(rvi_volatility, title="42_RVI_Volatility", color=color.aqua, linewidth=1) plot(hv, title="43_Historical_Vol", color=color.olive, linewidth=1) plot(gk_volatility, title="44_GK_Volatility", color=color.silver, linewidth=1) plot(parkinson, title="45_Parkinson_Vol", color=color.gray, linewidth=1) // Plot final oscillators plot(rogers_satchell, title="46_RS_Volatility", color=color.blue, linewidth=1) plot(elder_power, title="47_Elder_Power", color=color.red, linewidth=1) plot(stc, title="48_STC", color=color.green, linewidth=1) plot(coppock, title="49_Coppock", color=color.orange, linewidth=1) plot(kst, title="50_KST", color=color.purple, linewidth=1) // Plot final indicators plot(ppo_histogram, title="51_PPO_Histogram", color=color.navy, linewidth=1) plot(pvt, title="52_PVT", color=color.maroon, linewidth=1) // |----- Reference Lines -----| // hline(0, "Zero Line", color=color.gray, linestyle=hline.style_dashed, linewidth=1) hline(50, "Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(-50, "Lower Midline", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(25, "Upper Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1) hline(-25, "Lower Threshold", color=color.gray, linestyle=hline.style_dotted, linewidth=1) // |----- Enhanced Information Table -----| // if show_table and barstate.islast table_position = position.top_right table_text_size = table_size == "Tiny" ? size.tiny : table_size == "Small" ? size.small : size.normal var table info_table = table.new(table_position, 3, 18, bgcolor=color.new(color.white, 85), border_width=1, border_color=color.gray) // Headers table.cell(info_table, 0, 0, 'Category', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) table.cell(info_table, 1, 0, 'Indicator', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) table.cell(info_table, 2, 0, 'Value', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.blue, 70)) // Key Momentum Indicators table.cell(info_table, 0, 1, 'MOMENTUM', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90)) table.cell(info_table, 1, 1, 'RSI Centered', text_color=color.purple, text_size=table_text_size) table.cell(info_table, 2, 1, str.tostring(rsi_centered, '0.00'), text_color=color.purple, text_size=table_text_size) table.cell(info_table, 0, 2, '', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 1, 2, 'Stoch Centered', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 2, str.tostring(stoch_centered, '0.00'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 3, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 3, 'Williams %R', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 3, str.tostring(williams_r, '0.00'), text_color=color.red, text_size=table_text_size) table.cell(info_table, 0, 4, '', text_color=color.orange, text_size=table_text_size) table.cell(info_table, 1, 4, 'MACD Histogram', text_color=color.orange, text_size=table_text_size) table.cell(info_table, 2, 4, str.tostring(macd_histogram, '0.000'), text_color=color.orange, text_size=table_text_size) table.cell(info_table, 0, 5, '', text_color=color.green, text_size=table_text_size) table.cell(info_table, 1, 5, 'CCI', text_color=color.green, text_size=table_text_size) table.cell(info_table, 2, 5, str.tostring(cci, '0.00'), text_color=color.green, text_size=table_text_size) // Key Trend Indicators table.cell(info_table, 0, 6, 'TREND', text_color=color.navy, text_size=table_text_size, bgcolor=color.new(color.navy, 90)) table.cell(info_table, 1, 6, 'SMA Momentum %', text_color=color.navy, text_size=table_text_size) table.cell(info_table, 2, 6, str.tostring(sma_momentum, '0.00'), text_color=color.navy, text_size=table_text_size) table.cell(info_table, 0, 7, '', text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 1, 7, 'EMA Momentum %', text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 2, 7, str.tostring(ema_momentum, '0.00'), text_color=color.maroon, text_size=table_text_size) table.cell(info_table, 0, 8, '', text_color=color.teal, text_size=table_text_size) table.cell(info_table, 1, 8, 'SAR Trend', text_color=color.teal, text_size=table_text_size) table.cell(info_table, 2, 8, str.tostring(sar_trend, '0'), text_color=color.teal, text_size=table_text_size) table.cell(info_table, 0, 9, '', text_color=color.lime, text_size=table_text_size) table.cell(info_table, 1, 9, 'Linear Regression', text_color=color.lime, text_size=table_text_size) table.cell(info_table, 2, 9, str.tostring(lr_slope, '0.000'), text_color=color.lime, text_size=table_text_size) // Key Volatility Indicators table.cell(info_table, 0, 10, 'VOLATILITY', text_color=color.yellow, text_size=table_text_size, bgcolor=color.new(color.yellow, 90)) table.cell(info_table, 1, 10, 'ATR %', text_color=color.yellow, text_size=table_text_size) table.cell(info_table, 2, 10, str.tostring(atr_pct, '0.00'), text_color=color.yellow, text_size=table_text_size) table.cell(info_table, 0, 11, '', text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 1, 11, 'BB Position', text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 2, 11, str.tostring(bb_position, '0.00'), text_color=color.aqua, text_size=table_text_size) table.cell(info_table, 0, 12, '', text_color=color.olive, text_size=table_text_size) table.cell(info_table, 1, 12, 'KC Position', text_color=color.olive, text_size=table_text_size) table.cell(info_table, 2, 12, str.tostring(kc_position, '0.00'), text_color=color.olive, text_size=table_text_size) // Key Volume Indicators table.cell(info_table, 0, 13, 'VOLUME', text_color=color.blue, text_size=table_text_size, bgcolor=color.new(color.blue, 90)) table.cell(info_table, 1, 13, 'Volume ROC', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 13, str.tostring(vroc, '0.00'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 14, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 14, 'EOM', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 14, str.tostring(eom_sma, '0.000'), text_color=color.red, text_size=table_text_size) // Key Oscillators table.cell(info_table, 0, 15, 'OSCILLATORS', text_color=color.purple, text_size=table_text_size, bgcolor=color.new(color.purple, 90)) table.cell(info_table, 1, 15, 'Awesome Osc', text_color=color.blue, text_size=table_text_size) table.cell(info_table, 2, 15, str.tostring(ao, '0.000'), text_color=color.blue, text_size=table_text_size) table.cell(info_table, 0, 16, '', text_color=color.red, text_size=table_text_size) table.cell(info_table, 1, 16, 'Fisher Transform', text_color=color.red, text_size=table_text_size) table.cell(info_table, 2, 16, str.tostring(fisher, '0.000'), text_color=color.red, text_size=table_text_size) // Summary Statistics table.cell(info_table, 0, 17, 'SUMMARY', text_color=color.black, text_size=table_text_size, bgcolor=color.new(color.gray, 70)) table.cell(info_table, 1, 17, 'Total Indicators: 52', text_color=color.black, text_size=table_text_size) regime_color = rsi_centered > 10 ? color.green : rsi_centered < -10 ? color.red : color.gray regime_text = rsi_centered > 10 ? "BULLISH" : rsi_centered < -10 ? "BEARISH" : "NEUTRAL" table.cell(info_table, 2, 17, regime_text, text_color=regime_color, text_size=table_text_size) This makes it the perfect “indicator backbone” for quantitative and systematic traders who want to prototype, combine, and test new regime detection models—especially in combination with the Markov Chain indicator. How to use this script with the Markov Chain for research and backtesting: Add the Enhanced Indicator Export to your chart. Every calculated indicator is available as an individual data stream. Connect the indicator(s) you want as custom input(s) to the Markov Chain’s “Custom Indicators” option. In the Markov Chain indicator’s settings, turn ON the custom indicator mode. For each of the three custom indicator inputs, select the exported plot from the Enhanced Export script—the menu lists all 45+ signals by name. This creates a powerful, modular regime-detection engine where you can mix-and-match momentum, trend, volume, or custom combinations for advanced filtering. Backtest regime logic directly. Once you’ve connected your chosen indicators, the Markov Chain script performs regime detection (Bull/Neutral/Bear) based on your selected features—not just price returns. The regime detection is robust, automatically normalized (using Z-score), and outputs bias (1, -1, 0) for plug-and-play integration. Export the regime bias for programmatic use. As described above, use input.source() in your Pine Script strategy or system and link the bias output. You can now filter signals, control trade direction/size, or design pairs-trading that respect true, indicator-driven market regimes. With this framework, you’re not limited to static or simplistic regime filters. You can rigorously define, test, and refine what “market regime” means for your strategies—using the technical features that matter most to you. Optimize your signal generation by backtesting across a universe of meaningful indicator blends. Enhance risk management with objective, real-time regime boundaries. Accelerate your research: iterate quickly, swap indicator components, and see results with minimal code changes. Automate multi-asset or pairs-trading by integrating regime context directly into strategy logic. Add both scripts to your chart, connect your preferred features, and start investigating your best regime-based trades—entirely within the TradingView ecosystem. References & Further Reading Ang, A., & Bekaert, G. (2002). “Regime Switches in Interest Rates.” Journal of Business & Economic Statistics, 20(2), 163–182. Hamilton, J. D. (1989). “A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle.” Econometrica, 57(2), 357–384. Markov, A. A. (1906). "Extension of the Limit Theorems of Probability Theory to a Sum of Variables Connected in a Chain." The Notes of the Imperial Academy of Sciences of St. Petersburg. Guidolin, M., & Timmermann, A. (2007). “Asset Allocation under Multivariate Regime Switching.” Journal of Economic Dynamics and Control, 31(11), 3503–3544. Murphy, J. J. (1999). Technical Analysis of the Financial Markets. New York Institute of Finance. Brock, W., Lakonishok, J., & LeBaron, B. (1992). “Simple Technical Trading Rules and the Stochastic Properties of Stock Returns.” Journal of Finance, 47(5), 1731–1764. Zucchini, W., MacDonald, I. L., & Langrock, R. (2017). Hidden Markov Models for Time Series: An Introduction Using R (2nd ed.). Chapman and Hall/CRC. On Quantitative Finance and Markov Models: Lo, A. W., & Hasanhodzic, J. (2009). The Heretics of Finance: Conversations with Leading Practitioners of Technical Analysis. Bloomberg Press. Patterson, S. (2016). The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution. Penguin Press. TradingView Pine Script Documentation: www.tradingview.com TradingView Blog: “Use an Input From Another Indicator With Your Strategy” www.tradingview.com GeeksforGeeks: “What is the Difference Between Markov Chains and Hidden Markov Models?” www.geeksforgeeks.org What makes this indicator original and unique? - On‑chart, real‑time Markov. The chain is drawn directly on your chart. You see the current regime, its tendency to stay (self‑loop), and the usual next step (arrows) as bars confirm. - Source‑agnostic by design. The engine runs on any series you select via input.source() — price, your own oscillator, a composite score, anything you compute in the script. - Automatic normalization + regime mapping. Different inputs live on different scales. The script standardizes your chosen source and maps it into clear regimes (e.g., Bull / Bear / Neutral) without you micromanaging thresholds each time. - Rolling, bar‑by‑bar learning. Transition tendencies are computed from a rolling window of confirmed bars. What you see is exactly what the market did in that window. - Fast experimentation. Switch the source, adjust the window, and the Markov view updates instantly. It’s a rapid way to test ideas and feel regime persistence/switch behavior. Integrate your own signals (using input.source()) - In settings, choose the Source . This is powered by input.source() . - Feed it price, an indicator you compute inside the script, or a custom composite series. - The script will automatically normalize that series and process it through the Markov engine, mapping it to regimes and updating the on‑chart spheres/arrows in real time. Credits: Deep gratitude to @RicardoSantos for both the foundational Markov chain processing engine and inspiring open-source contributions, which made advanced probabilistic market modeling accessible to the TradingView community. Special thanks to @Alien_Algorithms for the innovative and visually stunning 3D sphere logic that powers the indicator’s animated, regime-based visualization. Disclaimer This tool summarizes recent behavior. It is not financial advice and not a guarantee of future results.
Pine Script® 인디케이터
Fractalyst의
5
Real Woodies CCIAs always, this is not financial advice and use at your own risk. Trading is risky and can cost you significant sums of money if you are not careful. Make sure you always have a proper entry and exit plan that includes defining your risk before you enter a trade. Ken Wood is a semi-famous trader that grew in popularity in the 1990s and early 2000s due to the establishment of one of the earliest trading forums online. This forum grew into "Woodie's CCI Club" due to Wood's love of his modified Commodity Channel Index (CCI) that he used extensively. From what I can tell, the website is still active and still follows the same core principles it did in the early days, the CCI is used for entries, range bars are used to help trader's cut down on the noise, and the optional addition of Woodie's Pivot Points can be used as further confirmation of support and resistance. This is my take on his famous "Woodie's CCI" that has become standard on many charting packages through the years, including a TradingView sponsored version as one of the many stock indicators provided by TradingView. Woodie has updated his CCI through the years to include several very cool additions outside of the standard CCI. I will have to say, I am a bit biased, but I think this is hands down one of the best indicators I have ever used, and I am far too young to have been part of the original CCI Club. Being a daytrader primarily, this fits right in my timeframe wheel house. Woodie designed this indicator to work on a day-trading time scale and he frequently uses this to trade futures and commodity contracts on the 30 minute, often even down to the one minute timeframe. This makes it unique in that it is probably one of the only daytrading-designed indicators out there that I am aware of that was not a popular indicator, like the MACD or RSI, that was just adopted by daytraders. The CCI was originally created by Donald Lambert in 1980. Over time, it has become an extremely popular house-hold indicator, like the Stochastics, RSI, or MACD. However, like the RSI and Stochastics, there are extensive debates on how the CCI is actually meant to be used. Some trade it like a reversal indicator, where values greater than 100 or less than -100 are considered overbought or oversold, respectively. Others trade it like a typical zero-line cross indicator, where once the value goes above or below the zero-line, a trade should be considered in that direction. Lastly, some treat it as strictly a momentum indicator, where values greater than 100 or less than -100 are seen as strong momentum moves and when these values are reached, a new strong trend is establishing in the direction of the move. The CCI itself is nothing fancy, it just visualizes the distance of the closing price away from a user-defined SMA value and plots it as a line. However, Woodie's CCI takes this simple concept and adds to it with an indicator with 5 pieces to it designed to help the trader enter into the highest probability setups. Bear with me, it initially looks super complicated, but I promise it is pretty straight-forward and a fun indicator to use. 1) The CCI Histogram. This is your standard CCI value that you would find on the normal CCI. Woodie's CCI uses a value of 14 for most trades and a value of 20 when the timeframe is equal to or greater than 30minutes. I personally use this as a 20-period CCI on all time frames, simply for the fact that the 20 SMA is a very popular moving average and I want to know what the crowd is doing. This is your coloured histogram with 4 colours. A gray colouring is for any bars above or below the zero line for 1-4 bars. A yellow bar is a "trend bar", where the long period CCI has been above/below the zero line for 5 consecutive bars, indicating that a trend in the current direction has been established. Blue bars above and red bars below are simply 6+n number of bars above or below the zero line confirming trend. These are used for the Zero-Line Reject Trade (explained below). The CCI Histogram has a matching long-period CCI line that is painted the same colour as the histogram, it is the same thing but is used just to outline the Histogram a bit better. 2) The CCI Turbo line. This is a sped-up 6 period CCI. This is to be used for the Zero-Line Reject trades, trendline breaks, and to identify shorter term overbought/oversold conditions against the main trend. This is coloured as the white line. 3) The Least Squares Moving Average Baseline (LSMA) Zero Line. You will notice that the Zero Line of the indicator is either green or red. This is based on when price is above or below the 25-period LSMA on the chart. The LSMA is a 25 period linear regression moving average and is one of the best moving averages out there because it is more immune to noise than a typical MA. Statistically, an LSMA is designed to find the line of best fit across the lookback periods and identify whether price is advancing, declining, or flat, without the whipsaw that other MAs can be privy to. The zero line of the indicator will turn green when the close candle is over the LSMA or red when it is below the LSMA. This is meant to be a confirmation tool only and the CCI Histogram and Turbo Histogram can cross this zero line without any corresponding change in the colour of the zero line on that immediate candle. 4) The +100 and -100 lines are used in two ways. First, they can be used by the CCI Histogram and CCI Turbo as a sort of minor price resistance and if the CCI values cannot get through these, it is considered weakness in that trade direction until they do so. You will notice that both of these lines are multi-coloured. They have been plotted with the ChopZone Indicator, another TradingView built-in indicator. The ChopZone is a trend identification tool that uses the slope and the direction of a 34-period EMA to identify when price is trending or range bound. While there are ~10 different colours, the main two a trader needs to pay attention to are the turquoise/cyan blue, which indicates price is in an uptrend, and dark red, which indicates price is in a downtrend based on the slope and direction of the 34 EMA. All other colours indicate "chop". These colours are used solely for the Zero-Line Reject and pattern trades discussed below. They are plotted both above and below so you can easily see the colouring no matter what side of the zero line the CCI is on. 5) The +200 and -200 lines are also used in two ways. First, they are considered overbought/oversold levels where if price exceeds these lines then it has moved an extreme amount away from the average and is likely to experience a pullback shortly. This is more useful for the CCI Histogram than the Turbo CCI, in all honesty. You will also notice that these are coloured either red, green, or yellow. This is the Sidewinder indicator portion. The documentation on this is extremely sparse, only pointing to a "relationship between the LSMA and the 34 EMA" (see here: tlc.thinkorswim.com). Since I am not a member of Woodie's CCI Club and never intend to be I took some liberty here and decided that the most likely relationship here was the slope of both moving averages. Therefore, the Sidewinder will be green when both the LSMA and the 34 EMA are rising, red when both are falling, and yellow when they are not in agreement with one another (i.e. one rising/flat while the other is flat/falling). I am a big fan of Dr. Alexander Elder as those who follow me know, so consider this like Woodie's version of the Elder Impulse System. I will fully admit that this version of the Sidewinder is a guess and may not represent the real Sidewinder indicator, but it is next to impossible to find any information on this, so I apologize, but my version does do something useful anyways. This is also to be used only with the Zero-Line Reject trades. They are plotted both above and below so you can easily see the colouring no matter what side of the zero line the CCI is on. How to Trade It According to Woodie's CCI Club: Now that I have all of my components and history out of the way, this is what you all care about. I will only provide a brief overview of the trades in this system, but there are quite a few more detailed descriptions listed in the Woodie's CCI Club pamphlet. I have had little success trading the "patterns" but they do exist and do work on occasion. I just prefer to trade with the flow of the markets rather than getting overly scalpy. If you are interested in these patterns, see the pamphlet here (www.trading-attitude.com), hop into the forums and see for yourself, or check out a couple of the YouTube videos. 1) Zero line cross. As simple as any other momentum oscillator out there. When the long period CCI crosses above or below the zero line open a trade in that direction. Extra confirmation can be had when the CCI Turbo has already broken the +100/-100 line "resistance or support". Trend traders may wish to wait until the yellow "trend confirmation bar" has been printed. 2) Zero Line Reject. This is when the CCI Turbo heads back down to the zero line and then bounces back in the same direction of the prevailing trend. These are fantastic continuation trades if you missed the initial entry either on the zero line cross or on the trend bar establishment. ZLR trades are only viable when you have the ChopZone indicator showing a trend (turquoise/cyan for uptrend, dark red for downtrend), the LSMA line is green for an uptrend or red for a downtrend, and the SideWinder is either green confirming the uptrend or red confirming the downtrend. 3) Hook From Extreme. This is the exact same as the Zero Line Reject trade, however, the CCI Turbo now goes to the +100/-100 line (whichever is opposite the currently established trend) and then hooks back into the established trend direction. Ideally the HFE trade needs to have the Long CCI Histogram above/below the corresponding 100 level and the CCI Turbo both breaks the 100 level on the trend side and when it does break it has increased ~20 points from the previous value (i.e. CCI Histogram = +150 with LSMA, CZ, and SW all matching up and trend bars printed on CCI Histogram, CCI Turbo went to -120 and bounced to +80 on last 2 bars, current bar closes with CCI Turbo closing at +110). 4) Trend Line Break. Either the CCI Turbo or CCI Histogram, whichever you prefer (I find the Turbo a bit more accurate since its a faster value) creates a series of higher highs/lows you can draw a trend line linking them. When the line breaks the trendline that is your signal to take a counter trade position. For example, if the CCI Turbo is making consistently higher lows and then breaks the trendline through the zero line, you can then go short. This is a good continuation trade. 5) The Tony Trade. Consider this like a combination zero line reject, trend line break, and weak zero line cross all in one. The idea is that the SW, CZ, and LSMA values are all established in one direction. The CCI Histogram should be in an established trend and then cross the zero line but never break the 100 level on the new side as long as it has not printed more than 9 bars on the new side. If the CCI Histogram prints 9 or less bars on the new side and then breaks the trendline and crosses back to the original trend side, that is your signal to take a reversal trade. This is best used in the Elder Triple Screen method (discussed in final section) as a failed dip or rip. 6) The GB100 Trade. This is a similar trade as the Tony Trade, however, the CCI Histogram can break the 100 level on the new side but has to have made less than 6 bars on the new side. A trendline break is not necessary here either, it is more of a "pop and drop" or "momentum failure" trade trying in the new direction. 7) The Famir Trade. This is a failed CCI Long Histogram ZLR trade and is quite complicated. I have never traded this but it is in the pamphlet. Essentially you have a typical ZLR reject (i.e. all components saying it is likely a long/short continuation trade), but the ZLR only stays around the 50 level, goes back to the trend side, fails there as well immediately after 1 bar and then rebreaks to the new side. This is important to be considered with the LSMA value matching the side of the trade, so if the Famir says to go long, you need the LSMA indicator to also say to go long. 8) The Vegas Trade. This is essentially a trend-reversal trade that takes into account the LSMA and a cup and handle formation on the CCI Long Histogram after it has reached an extreme value (+200/-200). You will see the CCI Histogram hit the extreme value, head towards the zero line, and then sort of round out back in the direction of the extreme price. The low point where it reversed back in the direction of the extreme can be considered support or resistance on the CCI and once the CCI Long Histogram breaks this level again, with LSMA confirmation, you can take a counter trend trade with a stop under/over the highest/lowest point of the last 2 bars as you want to be out quickly if you are wrong without much damage but can get a huge win if you are right and add later to the position once a new trade has formed. 9) The Ghost Trade. This is nothing more than a(n) (inverse) head and shoulders pattern created on the CCI. Draw a trend line connecting the head and shoulders and trade a reversal trade once the CCI Long Histogram breaks the trend line. Same deal as the Vegas Trade, stop over/under the most recent 2 bar high/low and add later if it is a winner but cut quickly if it is a loser. Like I said, this is a complicated system and could quite literally take years to master if you wanted to go into the patterns and master them. I prefer to trade it in a much simpler format, using the Elder Triple Screen System. First, since I am a day trader, I look to use the 20 period Woodie's on the hourly and look at the CZ, SW, and LSMA values to make sure they all match the direction of the CCI Long Histogram (a trend establishment is not necessary here). It shows you the hourly trend as your "tide". I then drill down to the 15 minute time frame and use the Turbo CCI break in the opposite direction of the trend as my "wave" and to indicate when there is a dip or rip against the main trend. Lastly, I drill down to a 3 minute time frame and enter when the CCI Long Histogram turns back to match the main trend ("ripple") as long as the CCI Turbo has broken the 100 level in the matched direction. Enjoy, and please read the pamphlet if you have any questions about the patterns as they are not how I use these and will not be able to answer those questions.
Pine Script® 인디케이터
christoefert의
11