MTF Levels [OmegaTools]📖 Introduction
The Ω Levels Indicator is a complete market structure and level-mapping framework designed to help traders identify key zones where price is likely to react.
It blends classic technical anchors (VWAP, pivots, means, standard deviations) with modern statistical pattern recognition to dynamically project areas of manipulation, extension, and equilibrium.
At its core, Ω Levels creates an evolving map of market balance vs. imbalance, showing traders where liquidity is most likely to build and where price could pivot or accelerate.
But what makes it truly unique is the Pivot Forecaster — an embedded predictive engine that applies machine-learning inspired logic to recognize conditions that historically precede market turning points.
🔎 Key Features
Customizable Levels Framework
Define up to three levels (manipulation, extensions, VWAP, pivots, stdev bands, or prior extremes).
Choose mean references such as Open, VWAP, Pivot Mean, or Previous Session Mean.
Style controls (solid, dotted, dashed) and fill modes (internal, external, ranges) allow you to adapt the chart to your visual workflow.
Dynamic Zone Highlighting
Automatic fills between internal/external levels, or between specific level pairs (1–2, 1–3, 2–3).
Makes it easy to visualize value areas, expansions, and compression zones at a glance.
Multi-Timeframe Anchoring
Works on any timeframe, but calculations can be anchored to a higher timeframe (e.g., show daily VWAP & pivots on a 15m chart).
This allows traders to align intraday execution with higher timeframe context.
Pivot Forecaster (Machine Learning / Pattern Recognition)
This is the advanced predictive component.
The algorithm collects historical conditions observed around pivot highs and lows (volume state, ATR state, % candle expansion, oscillator conditions).
It then builds statistical “profiles” of typical pivot behavior and compares them in real-time against current market conditions.
When conditions match the “signature” of a pivot, the indicator highlights a Forecast Pivot High or Forecast Pivot Low (displayed as small diamond markers).
This functions as a pattern-recognition system, effectively learning from past pivots to anticipate where the next turning point is more likely to occur.
⚡ How Traders Can Use It
Intraday Execution: Use VWAP, manipulation, and extension levels to frame trades around liquidity zones.
Swing Context: Overlay higher timeframe pivots and means to guide medium-term positioning.
Fade Setups: Forecasted pivots often coincide with exhaustion zones where fading momentum carries edge.
Breakout Validation: When price breaks a structural level but the forecaster does not confirm a pivot, continuation probability is higher.
Risk Management: Levels provide natural stop/target placements, while pivot forecasts serve as warning signals for potential reversals.
⚙️ Settings Overview
Timeframe: Choose the anchor timeframe for calculations (default: Daily).
Means: Two selectable mean references (Open, VWAP, Pivot Point, Previous Mean).
Levels: Three levels can be customized (Manipulation, Extension, 1–2 StDev, Pivot Point, VWAP, Previous Extremes).
Fill Modes: Highlight zones between internal/external levels or custom ranges.
Visual Customization: Colors, line styles, fill opacity, and toggle for old levels.
Pivot Forecaster: Fully automated — no settings required, it adapts to instrument and timeframe.
🧭 Best Practices
Align Levels With Market Profile: Treat the levels as dynamic S/R zones and watch how price interacts with them.
Use Forecaster as Confirmation: The diamonds are not standalone signals; they are context filters that help you decide whether a move has higher reversal odds.
Higher Timeframe Anchoring: On intraday charts, set the timeframe to Daily or Weekly to trade with institutional levels.
Combine With ATR: Pair with the Ω ATR Indicator to size positions according to volatility while Ω Levels provides the structural roadmap.
📌 Summary
The Ω Levels Indicator is more than a level plotter — it’s a market map + predictive engine.
By combining traditional levels with an intelligent pivot forecaster, it gives traders both the static structure of where price should react, and the dynamic signal of where it is likely to react next.
This dual-layer approach — structural + predictive — makes it an invaluable tool for discretionary intraday traders, swing traders, and anyone who wants to anticipate price behavior instead of just reacting to it.
Forecasting
Implied Volatility RangeThe Implied Volatility Range is a forward-looking tool that transforms option market data into probability ranges for future prices. Based on the lognormal distribution of asset prices assumed in modern option pricing models, it converts the implied volatility curve into a volatility cone with dynamic labels that show the market’s expectations for the price distribution at a specific point in time. At the selected future date, it displays projected price levels and their percentage change from today’s close across 1, 2, and 3 standard deviation (σ) ranges:
1σ range = ~68.2% probability the price will remain within this range.
2σ range = ~95.4% probability the price will remain within this range.
3σ range = ~99.7% probability the price will remain within this range.
What makes this indicator especially useful is its ability to incorporate implied volatility skew. When only ATM IV (%) is entered, the indicator displays the standard Black–Scholes lognormal distribution. By adding High IV (%) and Low IV (%) values tied to strikes above and below the current price, the indicator interpolates between these inputs to approximate the implied volatility skew. This adjustment produces a market-implied probability distribution that indicates whether the option market is leaning bullish or bearish, based on the data entered in the menu:
ATM IV (%) = Implied volatility at the current spot price (at-the-money).
High IV (%) = Implied volatility at a strike above the current spot price.
High Strike = Strike price corresponding to the High IV input (OTM call).
Low IV (%) = Implied volatility at a strike below the current spot price.
Low Strike = Strike price corresponding to the Low IV input (OTM put).
Expiration (Day, Month, Year) = Option expiration date for the projection.
Once these inputs are entered, the indicator calculates implied probability ranges and, if both High IV and Low IV values are provided, adjusts for skew to approximate the option market’s distribution. If no implied volatility data is supplied, the indicator defaults to a lognormal distribution based on historical volatility, using past realized volatility over the same forward horizon. This keeps the tool functional even without implied volatility inputs, though in that case the output represents only an approximation of ATM IV, not the actual market view.
In summary, the Implied Volatility Range is a powerful tool that translates implied volatility inputs into a clear and practical estimate of the market’s expectations for future prices. It allows traders to visualize the probability of price ranges while also highlighting directional bias, a dimension often difficult to interpret from traditional implied volatility charts. It should be emphasized, however, that this tool reflects only the market’s expectations at a specific point in time, which may change as new information and trading activity reshape implied volatility.
Piano Frequency LevelsPiano Frequency Levels
This indicator applies the mathematical principles of musical harmony to market analysis, creating support and resistance levels based on authentic piano frequency ratios. Drawing from centuries-old musical theory, it maps the precise mathematical relationships between piano keys to price levels.
How It Works: The indicator uses the exact frequency ratios from equal temperament tuning - the same mathematical system that makes pianos sound harmonious. Each level represents an actual piano key frequency, scaled proportionally to your chosen anchor price.
Key Features:
• Piano-Based Ratios: Uses authentic 12-tone equal temperament frequency relationships (1.05946 ratio between semitones)
• Directional Intelligence: Automatically creates ascending levels from lows (resistance) or descending levels from highs (support)
• Musical Note Labels: Optional display of actual piano key names (C4, D#5, F6, etc.) alongside price levels
• Black Key Subdivisions: Toggle authentic sharp/flat keys between natural notes for additional precision
• Octave Color Coding: Each musical octave displays in a different color for easy visual identification
• Anchor Reference: Bright green line clearly marks your C-note reference point
Musical Foundation: Every level corresponds to an actual piano key. The anchor point represents "C" (the musical root), with levels progressing through the natural musical sequence: C, D, E, F, G, A, B, then repeating in higher octaves. This creates proportional spacing that mirrors the harmonic relationships musicians have used for centuries.
Usage:
1. Set your anchor to a significant market high or low
2. Choose your desired number of levels (typically 12-24 for 1-2 octaves)
3. Enable "Add Black Keys" for additional intermediate levels
4. Enable "Show Note Names" to see which piano key each level represents
The Theory: Musical harmony is based on precise mathematical ratios that create pleasing relationships between frequencies. These same mathematical principles may manifest in market movements, as price action often exhibits proportional relationships similar to musical intervals.
Unique Advantages:
• Based on established mathematical principles rather than arbitrary ratios
• Provides both major levels (white keys) and intermediate levels (black keys)
• Automatically adapts direction based on anchor type (high vs low)
• Maintains authentic musical relationships across all timeframes and price ranges
Important Note: This indicator presents a theoretical framework for market analysis. Like all technical analysis tools, it should be used in conjunction with other forms of analysis and proper risk management. The musical ratios provide a unique perspective on potential support and resistance levels, but past performance does not guarantee future results.
Transform your charts into a musical instrument and discover the hidden harmonies in market movements.
Expected Value Monte CarloI created this indicator after noticing that there was no Expected Value indicator here on TradingView.
The EVMC provides statistical Expected Value to what might happen in the future regarding the asset you are analyzing.
It uses 2 quantitative methods:
Historical Backtest to ground your analysis in long-term, factual data.
Monte Carlo Simulation to project a cone of probable future outcomes based on recent market behavior.
This gives you a data-driven edge to quantify risk, and make more informed trading decisions.
The indicator includes:
Dual analysis: Combines historical probability with forward-looking simulation.
Quantified projections: Provides the Expected Value ($ and %), Win Rate, and Sharpe Ratio for both methods.
Asset-aware: Automatically adjusts its calculations for Stocks (252 trading days) and Crypto (365 days) for mathematical accuracy.
The projection cone shows the mean expected path and the +/- 1 standard deviation range of outcomes.
No repainting
Calculation:
1. Historical Expected Value:
This is a systematic backtest over thousands of bars. It calculates the return Rᵢ for N past trades (buy-and-hold). The Historical EV is the simple average of these returns, giving a baseline performance measure.
Historical EV % = (Σ Rᵢ) / N
2. Monte Carlo Projection:
This projection uses the Geometric Brownian Motion (GBM) model to simulate thousands of future price paths based on the market's recent behavior.
It first measures the drift (μ), or recent trend, and volatility (σ), or recent risk, from the Projection Lookback period. It then projects a final return for each simulation using the core GBM formula:
Projected Return = exp( (μ - σ²/2)T + σ√T * Z ) - 1
(Where T is the time horizon and Z is a random variable for the simulation.)
The purple line on the chart is the average of all simulated outcomes (the Monte Carlo EV). The cone represents one standard deviation of those outcomes.
The dashed lines represent one standard deviation (+/- 1σ) from the average, forming a cone of probable outcomes. Roughly 68% of the simulated paths ended within this cone.
This projection answers the question: "If the recent trend and volatility continue, where is the price most likely to go?"
Here's how to read the indicator
Expected Value ($/%): Is my average trade profitable?
Win Rate: How often can I expect to be right?
Sharpe Ratio: Am I being adequately compensated for the risk I'm taking?
User Guide
Max trade duration (bars): This is your analysis timeframe. Are you interested in the probable outcome over the next month (21 bars), quarter (63 bars), or year (252 bars)?
Position size ($): Set this to your typical trade size to see the Expected Value in real dollar terms.
Projection lookback (bars): This is the most important input for the Monte Carlo model. A short lookback (e.g., 50) makes the projection highly sensitive to recent momentum. Use this to identify potential recency bias. A long lookback (e.g., 252) provides a more stable, long-term projection of trend and volatility.
Historical Lookback (bars): For the historical backtest, more data is always better. Use the maximum that your TradingView plan allows for the most statistically significant results.
Use TP/SL for Historical EV: Check this box to see how the historical performance would have changed if you had used a simple Take Profit and Stop Loss, rather than just holding for the full duration.
I hope you find this indicator useful and please let me know if you have any suggestions. 😊
Stop Loss Advisor📊 Stop Loss Advisor - Advanced Risk Management Tool
A sophisticated Pine Script v5 indicator designed to suggest optimal stop loss distances based on market volatility, combining ATR and Standard Deviation analysis for precise risk management.
🎯 What Makes This Different from Bollinger Bands?
While Bollinger Bands focus on mean reversion and overbought/oversold conditions using a moving average center line, this indicator is specifically designed for risk management . It creates dynamic bands around the current price to suggest where NOT to place your stop loss, preventing you from being stopped out by normal market noise.
⚡ Key Features
Dynamic ATR Calculation - Fully customizable ATR periods with adaptive volatility filtering
Standard Deviation Integration - Optional StdDev component for enhanced statistical accuracy
Multiple Combination Modes - Average, Maximum, ATR Weighted, or StdDev Weighted
Flexible Price Sources - Choose from Close, HL2, HLC3, or OHLC4
Automatic Pip Calculation - Works across all instruments with automatic pip value detection
Smart Alerts System - Get notified when suggested stop loss exceeds your base risk tolerance
Real-time Information Table - Displays current values and risk status
Visual Labels - Shows exact pip distances directly on chart
Band Smoothing - Prevents erratic movements with customizable averaging
📈 How It Works
ATR Analysis : Calculates Average True Range to measure current market volatility
Statistical Enhancement : Optionally combines with Standard Deviation for more robust calculations
Dynamic Bands : Creates upper and lower bands that expand/contract with volatility
Pip Conversion : Automatically converts distances to pips for easy interpretation
Risk Assessment : Compares suggested distances with your base stop loss tolerance
🔧 Customization Options
ATR Settings:
Customizable ATR period (default: 14)
Adjustable multiplier with 0.1 step precision
Optional volatility filtering for enhanced sensitivity
Standard Deviation (Optional):
Independent period and multiplier settings
Multiple price source options
Four combination modes with ATR
Visual Customization:
Fully customizable colors for all elements
Multiple line styles (solid, dashed, dotted)
Optional band filling with transparency control
Show/hide ATR line overlay
Configurable band smoothing
💡 Perfect For
Forex Traders - Especially effective on major pairs and XAUUSD
Risk Managers - Calculate optimal stop distances before entering trades
Scalpers - Avoid being stopped out by normal market fluctuations
Swing Traders - Adapt stop losses to current volatility conditions
📊 Indicator Values
The information table displays:
Current ATR Value (in pips)
Suggested Long Stop Loss (distance in pips)
Suggested Short Stop Loss (distance in pips)
Risk Status - "SAFE" or "HIGH RISK" based on your base tolerance
Standard Deviation Value (when enabled)
Combination Method (when using both ATR and StdDev)
⚠️ Important Notes
This indicator suggests minimum stop loss distances, not entry/exit signals
Always combine with your trading strategy and risk management rules
Do not use as a standalone trading system
Backtesting recommended before live implementation
Default settings work well for most scenarios, but optimization is encouraged
🎨 Default Configuration
ATR Period: 14
ATR Multiplier: 2.0
Price Source: Close
Base Stop Loss: 20 pips
Band Smoothing: 3 periods
Standard Deviation: Optional (20 period, 2.0 multiplier)
🚀 Getting Started
Add the indicator to your chart
Set your base stop loss tolerance in the settings
Choose your preferred price source and ATR parameters
Enable Standard Deviation for enhanced accuracy (optional)
Monitor the information table for real-time risk assessment
Use the suggested distances as minimum stop loss levels
Pro Tip: In low volatility markets, the bands will contract suggesting tighter stops. In high volatility periods, they expand warning you to use wider stops to avoid being stopped out by normal price action.
📝 Version History & Updates
This indicator is actively maintained and updated based on user feedback. Future enhancements may include multi-timeframe analysis, trend-based asymmetric bands, and additional statistical measures.
Transform your risk management approach with data-driven stop loss suggestions that adapt to real market conditions!
The Maker StrategyDESCRIPTION
The Maker Strategy is a trend-following system built around exponential moving averages (EMAs). By analyzing the alignment of multiple EMAs, the strategy identifies strong bullish or bearish momentum and generates precise entry signals. This method is designed to capture sustained trends while filtering out sideways or noisy market conditions.
USER INPUTS :
• EMA 1 Length (Default: 30)
• EMA 2 Length (Default: 35)
• EMA 3 Length (Default: 40)
• EMA 4 Length (Default: 45)
• EMA 5 Length (Default: 50)
• EMA 6 Length (Default: 60)
LONG CONDITION :
A long signal is triggered when all EMAs are perfectly aligned in ascending order:
EMA1 > EMA2 > EMA3 > EMA4 > EMA5 > EMA6
SHORT CONDITION :
A short signal is triggered when all EMAs are perfectly aligned in descending order:
EMA1 < EMA2 < EMA3 < EMA4 < EMA5 < EMA6
WHY IT IS UNIQUE:
Unlike traditional EMA crossover systems that rely on just 2 or 3 moving averages, The Maker Strategy uses 6 EMAs in sequence. This ensures that trades are only taken when there is clear and strong market momentum. The approach minimizes false signals in ranging markets and focuses on capturing trends with higher probability setups.
HOW USER CAN BENEFIT FROM IT :
• Clear entry alerts for both long and short positions.
• Visual confirmation through candle coloring and EMA band fills.
• Works on multiple timeframes and instruments (stocks, forex, crypto, indices).
• Helps traders stay on the right side of the trend while avoiding whipsaws.
• A simple yet effective tool for those who want a disciplined, rules-based strategy.
Adaptive Log Trend ChannelOne-line Summary / 一句话简介
EN: Adaptive log-scale trend channel using Pearson-optimized regression and deviation bands.
中文：基于皮尔逊优化回归的自适应对数趋势通道，带标准差波动带。
Full Description / 完整介绍
What it does / 功能
EN: This indicator fits a log-linear regression to price and builds a trend channel with ±k·σ deviation bands. It automatically selects the period with the highest Pearson correlation (R), ensuring the channel best matches the dominant market trend.
中文：该指标通过价格的对数线性回归拟合趋势，并在中线上下绘制 ±k·σ 偏差通道。它会自动选择皮尔逊相关系数 (R) 最高的周期，从而保证通道与主要趋势最贴合。
Why it’s useful / 适用价值
EN:
Naturally fits assets with multiplicative growth (crypto, tech stocks).
Adapts dynamically to different market regimes.
Provides CAGR estimates on Daily/Weekly charts for trend strength evaluation.
中文：
自然适用于呈现乘法增长的资产（如加密货币与科技股）。
可动态适应不同的市场阶段。
在日线/周线图上提供 趋势年化收益率 (CAGR)，帮助评估趋势强度。
How it works / 工作原理
EN:
Computes log(price) → regression slope & intercept.
Draws a midline (log regression projection).
Upper & lower bands = ±k·σ in log space.
Info panel shows: Auto-Selected Period, Trend Strength (or Pearson’s R), and CAGR.
中文：
对价格取对数 → 计算回归斜率与截距。
绘制 中线（对数回归投影）。
上下轨 = 对数空间中的 ±k·σ。
信息面板显示：自动选择周期、趋势强度（或皮尔逊 R 值）、以及 CAGR 年化收益率。
Key Settings / 主要参数
EN:
Long-Term Mode: Uses extended periods (300–1200).
Deviation Multiplier (k): Controls channel width (default 2.0).
Styles: Colors, line type, and extension.
Panel Options: Toggle auto-period, Pearson’s R, and CAGR.
中文：
长期模式：采用更长周期 (300–1200)。
偏差倍数 (k)：控制通道宽度（默认 2.0）。
样式：可设置颜色、线型、延长方式。
信息面板：可开关自动周期、皮尔逊 R、CAGR。
Notes / 注意事项
EN:
CAGR is only available on Daily/Weekly timeframes.
Regression-based tools may repaint as new bars form; treat it as context, not signals.
中文：
CAGR 仅在日线与周线周期可用。
回归类指标在新K线形成时可能重绘，仅用于趋势参考而非交易信号。
🕊️ Shemitah + Jubilee Cycle OverlayShemitah + Jubilee Cycle Overlay
This indicator overlays significant biblical cycle events—Shemitah and Jubilee years—onto your chart, providing a unique perspective on historical and potential future market cycles. The Shemitah cycle occurs every 7 years, while the Jubilee cycle spans 49 years, both starting from a user-defined year (defaulting to 1917). Key features include:
Highlight Shemitah Years: Shades the chart background in orange during Shemitah years, with customizable color and transparency.
Mark Jubilee Years: Draws bold blue vertical lines on the chart for Jubilee years, with optional labels.
Event Markers: Places vertical lines and labels (e.g., "Shemitah" or "Jubilee") on a specified month and day (defaulting to September 10th) for each cycle event.
Countdown Display: Shows the next upcoming Shemitah and Jubilee years on the chart for easy reference.
Customizable Inputs: Adjust the start year, cycle lengths, event date, colors, and visibility of highlights and labels to suit your analysis.
Ideal for traders and analysts exploring long-term economic or market patterns influenced by these traditional cycles, this overlay combines historical context with visual clarity. Use it on daily or higher timeframes for best results. The best markets to overlay this indicator on include major stock indices (e.g., S&P 500, Dow Jones), precious metals (e.g., Gold, Silver), cryptocurrencies (e.g., Bitcoin, Ethereum), and debt markets (e.g., U.S. Treasury Bonds, Corporate Bond ETFs), as these markets often reflect macroeconomic shifts, debt cycles, and speculative behavior that may correlate with Shemitah and Jubilee cycles.
Rationale for Including Debt Markets
U.S. Treasury Bonds:
Treasury securities (e.g., 10-year or 30-year bonds) are sensitive to interest rate changes and government debt levels. The Shemitah cycle (every 7 years) has been historically linked to debt-related economic adjustments, while the Jubilee cycle (every 49 years) aligns with broader debt forgiveness or restructuring concepts, making bonds a prime candidate.
Corporate Bond ETFs (e.g., LQD, HYG):
Corporate bond ETFs represent corporate debt and are influenced by credit cycles and economic resets. The indicator could highlight periods of potential default risk or yield shifts tied to Shemitah or Jubilee years.
Relevance to Biblical Context:
The Jubilee year, as described in the Bible (Leviticus 25), involves the release of debts and return of land, directly tying it to debt markets. The Shemitah year also includes a release of debts every seven years, making debt instruments a natural fit for this indicator’s thematic analysis.
OG OHLC MarkerDraws, OHLC for Previous day and Today with options to add alerts when any PD Array is swept
Magic Phoenix By Azam JaniThe indicator is best used for swing entries on lower timeframes (e.g., 5m/1m charts with 15m HTF signals) and can trigger alerts for automation.
Shashwat Khurana's Pivot + Mean Reversion + RSI (Signals Only)Show BUY labels below bars when a bullish reversal is detected.
Show SELL labels above bars when a bearish reversal is detected.
Uses pivot levels, mean reversion, big candle, RSI, and volume filters.
Moon Phase & Celestial Events TrackerMoon Phase & Celestial Events Tracker
Overview
A comprehensive astronomical and celestial event indicator that tracks and projects major cosmic events from 2011 to 2040. This indicator overlays important astronomical phenomena directly on your charts, allowing traders and researchers to analyze potential correlations between celestial events and market movements.
Key Features
Eclipse Tracking 🌑
Blood Moons (Total Lunar Eclipses) including 2014-2015 tetrad
Partial Lunar Eclipses with distinctive yellow markers
Solar Eclipses: Total, Annular, Partial, and Hybrid types with unique symbols
Optional eclipse season background highlighting
Moon Cycles 🌕
Supermoons at perigee (closest Earth approach)
Regular moon phases: New, First Quarter, Full, Last Quarter
Adjustable phase marking with day-offset capability
Mercury Retrograde ☿
Start and end dates clearly marked
Optional period highlighting for entire retrograde duration
Complete cycle tracking through 2040
Seasonal Transitions ✨
Spring Equinox, Summer Solstice, Autumn Equinox, Winter Solstice
Precise astronomical season changes
Future Projections 📊
Event forecasting up to 5 years ahead
Customizable projection range (30-1825 days)
Selective projection by event type
Adjustable visual styles and transparency
Interpretation Guide
Blood Moons
Total lunar eclipses where Earth's atmosphere creates the red appearance. In financial astrology, these are often watched as potential reversal or volatility periods, though correlations vary significantly.
Eclipse Seasons
Twice-yearly windows when Sun-Earth-Moon alignment allows eclipses. Some market practitioners note increased volatility during these periods, though empirical evidence remains debated.
Mercury Retrograde
The apparent backward motion of Mercury occurs 3-4 times yearly. In trading folklore, it's associated with communication issues, technical problems, and false signals. Many practitioners suggest extra caution with new positions during these periods.
Supermoons
Full or new moons at closest Earth approach. Some traders track these for potential short-term highs/lows, particularly in commodities and currencies, though effects are subtle if present.
Seasonal Markers
Astronomical season changes have been incorporated into various market timing systems, with some analysts noting clustering of trend changes around these dates.
Use Cases
Historical pattern analysis
Event-based research
Educational astronomy tracking
Market cycle studies
Long-term planning and observation
Technical Details ⚙️
Data Coverage: 2011-2040 (30 years of precise astronomical events)
Compatibility: All timeframes with smart filtering (Weekly/Monthly show only major events)
Performance: Lightweight with efficient calculations and minimal chart impact
Data Source: Based on NASA ephemeris data for precise event timing
Customization Options 🎨
Individual colors for each event type
Transparency controls for projections
Event visibility toggles
Optional date labels on events
Alert Options 🔔
Set custom alerts for any tracked event including all eclipse types, moon phases, Mercury retrograde start/end, and seasonal transitions.
⚠️ Important Note
This indicator displays astronomical events for research and educational purposes. Any perceived correlations with market movements should be thoroughly backtested. Financial astrology interpretations are included for historical context only and should not be considered trading advice. Always use proper risk management and multiple forms of analysis in trading decisions.
Best Suited For
Market researchers and analysts
Students of market cycles
Those interested in astronomical timing
Educational and observational purposes
Long-term pattern analysis
ASPO - Adaptive Statistical Position OscillatorASPO - Advanced Statistical Price Position Oscillator - Time-Weighted
Based on a time-weighted statistical model, this indicator quantifies price deviation from its recent mean. It uses a Z-Score to normalize price position and calculates the statistical probability of its occurrence, helping traders identify over-extended market conditions and mean-reversion opportunities with greater sensitivity.
- Time-Weighted Model: Reacts more quickly to recent price changes by using a Weighted Moving Average (WMA) and a weighted standard deviation.
- Statistical Foundation: Utilizes Z-Score standardization and a probability calculation to provide an objective measure of risk and price extremity.
- Dynamic Adaptation: Automatically adjusts its calculation period and sensitivity based on market volatility, making it versatile across different market conditions.
- Intelligent Visuals: Dynamic line thickness and gradient color-coding intuitively display the intensity of price deviations.
- Multi-Dimensional Analysis: Combines the main line's position (Z-Score), a momentum histogram, and real-time probability for a comprehensive view.
1. Time-Weighted Statistical Model (Z-Score Calculation)
- Weighted Mean (μ_w): Instead of a simple average, the indicator uses a Weighted Moving Average (ta.wma) to calculate the price mean, giving more weight to recent data points.
- Weighted Standard Deviation (σ_w): A custom weighted_std function calculates the standard deviation, also prioritizing recent prices. This ensures that the measure of dispersion is more responsive to the latest market behavior.
- Z-Score: The core of the indicator is the Z-Score, calculated as Z = (Price - μ_w) / σ_w. This value represents how many weighted standard deviations the current price is from its weighted mean. A higher absolute Z-Score indicates a more statistically significant price deviation.
2. Probability Calculation
- The indicator uses an approximation of the Normal Cumulative Distribution Function (normal_cdf_approx) to calculate the probability of a Z-Score occurring.
- The final price_probability is a two-tailed probability, calculated as 2 * (1 - CDF(|Z-Score|)). This value quantifies the statistical rarity of the current price deviation. For example, a probability of 0.05 (or 5%) means that a deviation of this magnitude or greater is expected to occur only 5% of the time, signaling a potential market extreme.
3. Dynamic Parameter Adjustment
- Volatility Measurement: The system measures market volatility using the standard deviation of price changes (ta.stdev(ta.change(src))) over a specific lookback period.
- Volatility Percentile: It then calculates the percentile rank (ta.percentrank) of the current volatility relative to its history. This contextualizes whether the market is in a high-volatility or low-volatility state.
- Adaptive Adjustment:
- If volatility is high (e.g., >75th percentile), the indicator can shorten its distribution_period and increase its position_sensitivity. This makes it more responsive to fast-moving markets.
- If volatility is low (e.g., <25th percentile), it can lengthen the period and decrease sensitivity, making it more stable in calmer markets. This adaptive mechanism helps maintain the indicator's relevance across different market regimes.
4. Momentum and Cycle Analysis (Histogram)
- The indicator does not use a Hilbert Transform. Instead, it analyzes momentum cycles by calculating a histogram: Histogram = (Z-Score - EMA(Z-Score)) * Sensitivity.
- This histogram represents the rate of change of the Z-Score. A positive and rising histogram indicates accelerating upward deviation, while a negative and falling histogram indicates accelerating downward deviation. Divergences between the price and the histogram can signal a potential exhaustion of the current deviation trend, often preceding a reversal.
- Reversal Signals: Look for the main line in extreme zones (e.g., Z-Score > 2 or < -2), probability below a threshold (e.g., 5%), and divergence or contraction in the momentum histogram.
- Trend Filtering: The main line's direction indicates the trend of price deviation, while the histogram confirms its momentum.
- Risk Management: Enter a high-alert state when probability drops below 5%; consider risk control when |Z-Score| > 2.
- Gray, thin line: Price is within a normal statistical range (~1 sigma, ~68% probability).
- Orange/Yellow, thick line: Price is moderately deviated (1 to 2 sigma).
- Cyan/Purple, thick line: Price is extremely deviated (>2 sigma, typically <5% probability).
- Distribution Period: 50 (for weighted calculation)
- Position Sensitivity: 2.5
- Volatility Lookback: 10
- Probability Threshold: 0.03
Suitable for all financial markets and timeframes, especially in markets that exhibit mean-reverting tendencies.
This indicator is a technical analysis tool and does not constitute investment advice. Always use in conjunction with other analysis methods and a strict risk management strategy.
Copyright (c) 2025 | Pine Script v6 Compatible
---
高级统计价格位置振荡器 (ASPO) - 时间加权版
基于时间加权统计学模型，该指标量化了当前价格与其近期均值的偏离程度。它使用Z分数对价格位置进行标准化，并计算其出现的统计概率，帮助交易者更灵敏地识别市场过度延伸和均值回归的机会。
- 时间加权模型：通过使用加权移动平均（WMA）和加权标准差，对近期价格变化反应更迅速。
- 统计学基础：利用Z分数标准化和概率计算，为风险和价格极端性提供了客观的衡量标准。
- 动态自适应：根据市场波动率自动调整其计算周期和敏感度，使其在不同市场条件下都具有通用性。
- 智能视觉：动态线条粗细和渐变颜色编码，直观地展示价格偏离的强度。
- 多维分析：结合了主线位置（Z分数）、动能柱和实时概率，提供了全面的市场视角。
1. 时间加权统计模型 (Z分数计算)
- 加权均值 (μ_w)：指标使用加权移动平均 (ta.wma) 而非简单平均来计算价格均值，赋予近期数据点更高的权重。
- 加权标准差 (σ_w)：通过一个自定义的 weighted_std 函数计算标准差，同样优先考虑近期价格。这确保了离散度的衡量对最新的市场行为更敏感。
- Z分数：指标的核心是Z分数，计算公式为 Z = (价格 - μ_w) / σ_w。该值表示当前价格偏离其加权均值的加权标准差倍数。Z分数的绝对值越高，表示价格偏离在统计上越显著。
2. 概率计算
- 指标使用正态累积分布函数 (normal_cdf_approx) 的近似值来计算特定Z分数出现的概率。
- 最终的 price_probability 是一个双尾概率，计算公式为 2 * (1 - CDF(|Z分数|))。该值量化了当前价格偏离的统计稀有性。例如，0.05（或5%）的概率意味着这种幅度或更大的偏离预计只在5%的时间内发生，这预示着一个潜在的市场极端。
3. 动态参数调整
- 波动率测量：系统通过计算特定回溯期内价格变化的标准差 (ta.stdev(ta.change(src))) 来测量市场波动率。
- 波动率百分位：然后，它计算当前波动率相对于其历史的百分位排名 (ta.percentrank)。这将当前市场背景定义为高波动率或低波动率状态。
- 自适应调整：
- 如果波动率高（例如，>75百分位），指标可以缩短其 distribution_period（分布周期）并增加其 position_sensitivity（位置敏感度），使其对快速变化的市场反应更灵敏。
- 如果波动率低（例如，<25百分位），它可以延长周期并降低敏感度，使其在较平静的市场中更稳定。这种自适应机制有助于保持指标在不同市场制度下的有效性。
4. 动能与周期分析 (动能柱)
- 该指标不使用希尔伯特变换。相反，它通过计算一个动能柱来分析动量周期：动能柱 = (Z分数 - Z分数的EMA) * 敏感度。
- 该动能柱代表Z分数的变化率。一个正向且不断增长的动能柱表示向上的偏离正在加速，而一个负向且不断下降的动能柱表示向下的偏离正在加速。价格与动能柱之间的背离可以预示当前偏离趋势的衰竭，通常发生在反转之前。
- 反转信号：寻找主线进入极端区域（如Z分数 > 2 或 < -2）、概率低于阈值（如5%）以及动能柱出现背离或收缩。
- 趋势过滤：主线的方向指示价格偏离的趋势，而动能柱确认其动量。
- 风险管理：当概率降至5%以下时进入高度警惕状态；当|Z分数| > 2时考虑风险控制。
- 灰色细线：价格处于正常统计范围内（约1个标准差，约68%概率）。
- 橙色/黄色粗线：价格中度偏离（1到2个标准差）。
- 青色/紫色粗线：价格极端偏离（>2个标准差，通常概率<5%）。
- 分布周期：50（用于加权计算）
- 位置敏感度：2.5
- 波动率回溯期：10
- 概率阈值：0.03
适用于所有金融市场和时间框架，尤其是在表现出均值回归特性的市场中。
本指标为技术分析辅助工具，不构成任何投资建议。请务必结合其他分析方法和严格的风险管理策略使用。
版权所有 (c) 2025 | Pine Script v6 兼容
Measured Move Volume XIndicator Description
The "Measured Move Volume X" indicator, developed for TradingView using Pine Script version 6, projects potential price targets based on the measured move concept, where the magnitude of a prior price leg (Leg A) is used to forecast a subsequent move. It overlays translucent boxes on the chart to visualize bullish (green) or bearish (red) price projections, extending them to the right for a user-specified number of bars. The indicator integrates volume analysis (relative to a simple moving average), RSI for momentum, and VWAP for price-volume weighting, combining these into a confidence score to filter entry signals, displayed as triangles on breakouts. Horizontal key level lines (large, medium, small) are drawn at significant price points derived from the measured moves, with customizable thresholds, colors, and styles. Exhaustion hints, shown as orange labels near box extremes, indicate potential reversal points. Anomalous candles, marked with diamond shapes, are identified based on volume spikes and body-to-range ratios. Optional higher timeframe candle coloring enhances context. The indicator is fully customizable through input groups for lookback periods, transparency, and signal weights, making it adaptable to various assets and timeframes.
Adjustment Tips for Optimization
To optimize the "Measured Move Volume X" indicator for specific assets or timeframes, adjust the following input parameters:
Leg A Lookback (default: 14 bars): Increase to 20-30 for volatile markets (e.g., cryptocurrencies) to capture larger price swings; decrease to 5-10 for intraday charts (e.g., stocks) for faster signals.
Extend Box to the Right (default: 30 bars): Extend to 50+ for daily or weekly charts to project further targets; shorten to 10-20 for lower timeframes to reduce clutter.
Volume SMA Length (default: 20) and Relative Volume Threshold (default: 1.5): Lower the threshold to 1.2-1.3 for low-volume assets (e.g., commodities) to detect subtler spikes; raise to 2.0+ for high-volume equities to filter noise. Match SMA length to RSI length for consistency.
RSI Parameters (default: length 14, overbought 70, oversold 30): Set overbought to 80 and oversold to 20 in trending markets to reduce premature exit signals; shorten length to 7-10 for scalping.
Key Level Thresholds (default: large 10%, medium 5%, small 5%): Increase thresholds (e.g., large to 15%) for volatile assets to focus on significant moves; disable medium or small lines to declutter charts.
Confidence Score Weights (default: volume 0.5, VWAP 0.3, RSI 0.2): Increase volume weight (e.g., 0.7) for volume-driven markets like futures; emphasize RSI (e.g., 0.4) for momentum-focused strategies.
Anomaly Detection (default: volume multiplier 1.5, small body ratio 0.2, large body ratio 0.75): Adjust the volume multiplier higher for stricter anomaly detection in noisy markets; fine-tune body-to-range ratios based on asset-specific candle patterns.
Use TradingView’s replay feature to test adjustments on historical data, ensuring settings suit the chosen market and timeframe.
Tips for Using the Indicator
Interpreting Signals: Green upward triangles indicate bullish breakout entries when price exceeds the prior high with a confidence score ≥40; red downward triangles signal bearish breakouts. Use these to identify potential entry points aligned with the projected box targets.
Box Projections: Bullish boxes project upward targets (top of box) equal to the prior leg’s height added to the breakout price; bearish boxes project downward. Monitor price action near box edges for target completion or reversal.
Exhaustion Hints: Orange labels near box tops (bullish) or bottoms (bearish) suggest potential exhaustion when price deviates within the set percentage (default: 5%) and RSI or volume conditions are met. Use these as cues to watch for reversals.
Key Level Lines: Large, medium, and small lines mark significant price levels from box tops/bottoms. Use these as potential support/resistance zones, especially when drawn with high volume (colored differently).
Anomaly Candles: Orange diamonds highlight candles with unusual volume/body characteristics, indicating potential reversals or pauses. Combine with box levels for context.
Higher Timeframe Coloring: Enable to color bars based on higher timeframe candle closures (e.g., 1, 2, 5, or 15 minutes) for added trend context.
Customization: Toggle "Only Show Bullish Moves" to focus on bullish setups. Adjust transparency and line styles for visual clarity. Test settings to balance signal frequency and chart readability.
Inputs: Organized into groups (e.g., "Measured Move Settings") using input.int, input.float, input.color, and input.bool for user customization, with tooltips for clarity.
Calculations: Computes relative volume (ta.sma(volume, volLookback)), VWAP (ta.vwap(hlc3)), RSI (ta.rsi(close, rsiLength)), and prior leg extremes (ta.highest/lowest) using prior bar data ( ) to prevent repainting.
Boxes and Lines: Creates boxes (box.new) for bullish/bearish projections and lines (line.new) for key levels. The f_addLine function manages line arrays (array.new_line), capping at maxLinesCount to avoid clutter.
Confidence Score: Combines volume, VWAP distance, and RSI into a weighted score (confScore), filtering entries (≥40). Rounded for display.
Exhaustion Hints: Functions like f_plotBullExitHint assess price deviation, RSI, and volume decrease, using label.new for dynamic orange labels.
Entry Signals and Plots: plotshape displays triangles for breakouts; plot and hline show VWAP and RSI levels; request.security handles higher timeframe coloring.
Anomaly Detection: Identifies candles with small-body high-volume or large-body average-volume patterns via ratios, plotted as diamonds.
DZ/SZ - HFM by MamaRight-Empty Wick Zones (MTF) draws Supply/Demand zones from the remaining wick of adjacent opposite-color candles (Classic & Non-classic rules). Zones extend right only through empty space and stop at the first touching candle. Multi-TF scan (H1/H4/1D/1W/1M) with TF-colored boxes and labels showing Demand/Supply + H/L.
Demand (red → green, adjacent):
Classic: if the red candle’s lower wick is longer than the green’s → zone = (the “excess” red wick).
Non-classic: if the red’s lower wick is shorter or equal → zone = (use the longer green wick).
Supply (green → red, adjacent):
Classic: if the green candle’s upper wick is longer than the red’s → zone = (the “excess” green wick).
Non-classic: if the green’s upper wick is shorter or equal → zone = (use the longer red wick).
After a zone is created, the box extends right and terminates at the very first bar whose price range (body or wick) overlaps the zone → ensures the plotted area is genuinely right-empty.
What you see
Zone boxes with distinct colors per timeframe (e.g., H1/H4/1D/1W/1M).
Optional labels on each box: H4 Demand / H1 Supply, plus H/L prices of the zone.
Labels can sit at the left edge or follow the right edge of the box.
Inputs
Toggles: Demand Classic / Demand Non-classic / Supply Classic / Supply Non-classic.
Timeframes to scan: H1, H4, 1D, 1W, 1M.
Min zone thickness (price): minimum height of a zone (in price units).
Initial right extension (bars): initial box length; the script auto-cuts at the first touch.
Show labels / place labels at the right edge.
How to use (suggestion)
Use higher TF (e.g., 1D) for bias and lower TFs (H1/H4) for execution zones.
Keep only the rule set (Classic/Non-classic) that matches your playbook.
Treat zones as areas of interest—wait for your own confirmations (e.g., swing rejection, wick re-entry, structure shift, volume cues) and manage risk accordingly.
Notes
Because zones are sourced from higher TFs via request.security, the drawing can update intrabar; a zone is final once the source TF bar closes.
Min zone thickness uses price units (e.g., on XAUUSD, 1.00 ≈ $1).
This tool is an analytical aid, not financial advice or an entry/exit signal.
อินดิเคเตอร์ DZ/SZ - HFM by Mama ใช้หา Demand/Supply zone จาก “ไส้ที่เหลือ” ของ คู่แท่งสีตรงข้ามที่ติดกัน แล้ววาดเป็นกล่อง ยืดไปทางขวาเฉพาะช่วงที่ว่าง และ หยุดตรงแท่งแรกที่เข้ามาแตะโซน รองรับหลาย Timeframe (H1/H4/1D/1W/1M) พร้อมสีแยก TF และป้ายกำกับ Demand/Supply + H/L ของโซน
รายละเอียดการทำงาน (ไทย)
แนวคิดหลัก
Demand: เลือกคู่ แดง→เขียว ที่ “ติดกัน”
Classic: ถ้า ไส้ล่าง ของแท่งแดงยาวกว่าแท่งเขียว → โซน =
Non-classic: ถ้า ไส้ล่าง ของแท่งแดงสั้นกว่าหรือเท่าเขียว → โซน =
Supply: เลือกคู่ เขียว→แดง ที่ “ติดกัน”
Classic: ถ้า ไส้บน ของแท่งเขียวยาวกว่าแท่งแดง → โซน =
Non-classic: ถ้า ไส้บน ของแท่งเขียวสั้นกว่าหรือเท่าแดง → โซน =
เมื่อสร้างโซนแล้ว กล่องจะ ยืดทางขวา ไปเรื่อย ๆ และ หยุดทันทีเมื่อมีแท่งแรกที่ช่วงราคา (ไส้หรือตัวแท่ง) ทับซ้อนกับโซน ⇒ ได้ “พื้นที่ขวาว่าง” ตามโจทย์
สิ่งที่แสดงบนกราฟ
กล่องโซนสีตาม Timeframe (เช่น H1=ฟ้า, H4=เขียว, 1D=ส้ม, 1W=ม่วง, 1M=เทา)
Label ที่มุมกล่อง: H4 Demand / H1 Supply + ราคาของ High/Low ของโซน
(เลือกวาง ซ้าย หรือ ขอบขวา ของกล่องได้ในตั้งค่า)
ตัวเลือกสำคัญใน Settings
เปิด/ปิด: Demand Classic / Demand Non-classic / Supply Classic / Supply Non-classic
เลือก TF ที่จะสแกน: H1, H4, 1D, 1W, 1M
Min zone thickness (price): กำหนด “ความหนา” ขั้นต่ำของโซน (หน่วยเป็นราคา เช่น XAUUSD = ดอลลาร์)
Initial right extension (bars): ความยาวยืดเริ่มต้น (อินดี้จะตัดให้สั้นลงเองเมื่อมีแท่งมาแตะ)
แสดง Label บนโซน และ วาง Label ที่ขอบขวากล่อง
วิธีใช้แนะนำ
เลือก TF ที่ต้องการ (เช่น ให้ H1/H4 เป็นโซนเทรดละเอียด และ 1D ใช้กรองทิศ)
เปิดเฉพาะโหมด (Classic/Non-classic) ที่ตรงกับแนวคิดการเทรดของคุณ
ใช้โซนเป็นบริเวณ “สนใจ” แล้วรอพฤติกรรมราคา/สัญญาณยืนยันเสริม (เช่น สวิงกลับ, rejection wick, โวลลุ่ม, หรือโครงสร้างจบคลื่น)
หมายเหตุสำคัญ
อินดี้ใช้ข้อมูลข้าม TF; สัญญาณจาก TF สูง อาจเปลี่ยนระหว่างแท่งยังไม่ปิด (ลักษณะ intrabar update) โซนจะ “นิ่ง” เมื่อแท่งของ TF ต้นทาง ปิดแล้ว
หน่วยของ Min zone thickness เป็น หน่วยราคา ไม่ใช่ pips (XAUUSD: 1.00 = $1)
อินดี้ไม่ได้ให้สัญญาณเข้า–ออกอัตโนมัติ ควรใช้ร่วมกับแผนเทรดและการจัดการความเสี่ยง
Short Sellingell signal when RSI < 40, MACD crosses zero or signal line downward in negative zone, close below 50 EMA, candle bearish.
Strong sell signal confirmed on 5-minute higher timeframe with same conditions.
Square off half/full signals as defined.
Target lines drawn bold based on previous swing lows and extended as described.
Blue candle color when RSI below 30.
One sell and one full square off per cycle, blocking repeated sells until full square off.
BLITZ PE ANAYLYZERFollowing script is designed specifically to meet the requirement of accessing the PE ratio, comparing it to it's historical averages, median and expected values that are possible.
Following is the method to use the indicator:
1) User must select the look back years which is by default set to 3 years as per the text book reference from the book "The Intelligent Investor" by Sir Benjamin Graham
2) The red or green histogram represents the deviation of the current PE to the average PE. If the histograms are green in color, it represent buy opportunity because the current PE is lower than that of the average PE values, the % deviation of the current PE from the average value is mentioned in the black color table and a negative value represents under evaluations as compared to the historical PE ratio
3) The black color line is the SMA of the PE ratio.
4) Another plots exists for plotting the current PE which is red or green depending upon its deviation from the average PE values & another plot exists for median PE ratio which is light blue when healthy and purple when not healthy.
5) Using the inflation data and the EPS growth of the company the black table also displays the expected value of the PE ratio for the stock.
Value Investing IndicatorThis is based on PeterNagy Indicator. I just update it from v.4 to v.6 and modify. Open for tweak
Gann Percentage SureshCalculates the Gann percentages from covid lows to find the future Supports and resistance Levels
SPPO - Statistical Price Position OscillatorSPPO - Statistical Price Position Oscillator - Time-Weighted
Based on a time-weighted statistical model, this indicator quantifies price deviation from its recent mean. It uses a Z-Score to normalize price position and calculates the statistical probability of its occurrence, helping traders identify over-extended market conditions and mean-reversion opportunities with greater sensitivity.
- Time-Weighted Model: Reacts more quickly to recent price changes by using a Weighted Moving Average (WMA) and a weighted standard deviation.
- Statistical Foundation: Utilizes Z-Score standardization and a probability calculation to provide an objective measure of risk and price extremity.
- Dynamic Adaptation: Automatically adjusts its calculation period and sensitivity based on market volatility, making it versatile across different market conditions.
- Intelligent Visuals: Dynamic line thickness and gradient color-coding intuitively display the intensity of price deviations.
- Multi-Dimensional Analysis: Combines the main line's position (Z-Score), a momentum histogram, and real-time probability for a comprehensive view.
1. Time-Weighted Statistical Model (Z-Score Calculation)
- Weighted Mean (μ_w): Instead of a simple average, the indicator uses a Weighted Moving Average (ta.wma) to calculate the price mean, giving more weight to recent data points.
- Weighted Standard Deviation (σ_w): A custom weighted_std function calculates the standard deviation, also prioritizing recent prices. This ensures that the measure of dispersion is more responsive to the latest market behavior.
- Z-Score: The core of the indicator is the Z-Score, calculated as Z = (Price - μ_w) / σ_w. This value represents how many weighted standard deviations the current price is from its weighted mean. A higher absolute Z-Score indicates a more statistically significant price deviation.
2. Probability Calculation
- The indicator uses an approximation of the Normal Cumulative Distribution Function (normal_cdf_approx) to calculate the probability of a Z-Score occurring.
- The final price_probability is a two-tailed probability, calculated as 2 * (1 - CDF(|Z-Score|)). This value quantifies the statistical rarity of the current price deviation. For example, a probability of 0.05 (or 5%) means that a deviation of this magnitude or greater is expected to occur only 5% of the time, signaling a potential market extreme.
3. Dynamic Parameter Adjustment
- Volatility Measurement: The system measures market volatility using the standard deviation of price changes (ta.stdev(ta.change(src))) over a specific lookback period.
- Volatility Percentile: It then calculates the percentile rank (ta.percentrank) of the current volatility relative to its history. This contextualizes whether the market is in a high-volatility or low-volatility state.
- Adaptive Adjustment:
- If volatility is high (e.g., >75th percentile), the indicator can shorten its distribution_period and increase its position_sensitivity. This makes it more responsive to fast-moving markets.
- If volatility is low (e.g., <25th percentile), it can lengthen the period and decrease sensitivity, making it more stable in calmer markets. This adaptive mechanism helps maintain the indicator's relevance across different market regimes.
4. Momentum and Cycle Analysis (Histogram)
- The indicator does not use a Hilbert Transform. Instead, it analyzes momentum cycles by calculating a histogram: Histogram = (Z-Score - EMA(Z-Score)) * Sensitivity.
- This histogram represents the rate of change of the Z-Score. A positive and rising histogram indicates accelerating upward deviation, while a negative and falling histogram indicates accelerating downward deviation. Divergences between the price and the histogram can signal a potential exhaustion of the current deviation trend, often preceding a reversal.
- Reversal Signals: Look for the main line in extreme zones (e.g., Z-Score > 2 or < -2), probability below a threshold (e.g., 5%), and divergence or contraction in the momentum histogram.
- Trend Filtering: The main line's direction indicates the trend of price deviation, while the histogram confirms its momentum.
- Risk Management: Enter a high-alert state when probability drops below 5%; consider risk control when |Z-Score| > 2.
- Gray, thin line: Price is within a normal statistical range (~1 sigma, ~68% probability).
- Orange/Yellow, thick line: Price is moderately deviated (1 to 2 sigma).
- Cyan/Purple, thick line: Price is extremely deviated (>2 sigma, typically <5% probability).
- Distribution Period: 50 (for weighted calculation)
- Position Sensitivity: 2.5
- Volatility Lookback: 10
- Probability Threshold: 0.03
Suitable for all financial markets and timeframes, especially in markets that exhibit mean-reverting tendencies.
This indicator is a technical analysis tool and does not constitute investment advice. Always use in conjunction with other analysis methods and a strict risk management strategy.
Copyright (c) 2025 | Pine Script v6 Compatible
---
统计价格位置振荡器 (SPPO) - 时间加权版
基于时间加权统计学模型，该指标量化了当前价格与其近期均值的偏离程度。它使用Z分数对价格位置进行标准化，并计算其出现的统计概率，帮助交易者更灵敏地识别市场过度延伸和均值回归的机会。
- 时间加权模型：通过使用加权移动平均（WMA）和加权标准差，对近期价格变化反应更迅速。
- 统计学基础：利用Z分数标准化和概率计算，为风险和价格极端性提供了客观的衡量标准。
- 动态自适应：根据市场波动率自动调整其计算周期和敏感度，使其在不同市场条件下都具有通用性。
- 智能视觉：动态线条粗细和渐变颜色编码，直观地展示价格偏离的强度。
- 多维分析：结合了主线位置（Z分数）、动能柱和实时概率，提供了全面的市场视角。
1. 时间加权统计模型 (Z分数计算)
- 加权均值 (μ_w)：指标使用加权移动平均 (ta.wma) 而非简单平均来计算价格均值，赋予近期数据点更高的权重。
- 加权标准差 (σ_w)：通过一个自定义的 weighted_std 函数计算标准差，同样优先考虑近期价格。这确保了离散度的衡量对最新的市场行为更敏感。
- Z分数：指标的核心是Z分数，计算公式为 Z = (价格 - μ_w) / σ_w。该值表示当前价格偏离其加权均值的加权标准差倍数。Z分数的绝对值越高，表示价格偏离在统计上越显著。
2. 概率计算
- 指标使用正态累积分布函数 (normal_cdf_approx) 的近似值来计算特定Z分数出现的概率。
- 最终的 price_probability 是一个双尾概率，计算公式为 2 * (1 - CDF(|Z分数|))。该值量化了当前价格偏离的统计稀有性。例如，0.05（或5%）的概率意味着这种幅度或更大的偏离预计只在5%的时间内发生，这预示着一个潜在的市场极端。
3. 动态参数调整
- 波动率测量：系统通过计算特定回溯期内价格变化的标准差 (ta.stdev(ta.change(src))) 来测量市场波动率。
- 波动率百分位：然后，它计算当前波动率相对于其历史的百分位排名 (ta.percentrank)。这将当前市场背景定义为高波动率或低波动率状态。
- 自适应调整：
- 如果波动率高（例如，>75百分位），指标可以缩短其 distribution_period（分布周期）并增加其 position_sensitivity（位置敏感度），使其对快速变化的市场反应更灵敏。
- 如果波动率低（例如，<25百分位），它可以延长周期并降低敏感度，使其在较平静的市场中更稳定。这种自适应机制有助于保持指标在不同市场制度下的有效性。
4. 动能与周期分析 (动能柱)
- 该指标不使用希尔伯特变换。相反，它通过计算一个动能柱来分析动量周期：动能柱 = (Z分数 - Z分数的EMA) * 敏感度。
- 该动能柱代表Z分数的变化率。一个正向且不断增长的动能柱表示向上的偏离正在加速，而一个负向且不断下降的动能柱表示向下的偏离正在加速。价格与动能柱之间的背离可以预示当前偏离趋势的衰竭，通常发生在反转之前。
- 反转信号：寻找主线进入极端区域（如Z分数 > 2 或 < -2）、概率低于阈值（如5%）以及动能柱出现背离或收缩。
- 趋势过滤：主线的方向指示价格偏离的趋势，而动能柱确认其动量。
- 风险管理：当概率降至5%以下时进入高度警惕状态；当|Z分数| > 2时考虑风险控制。
- 灰色细线：价格处于正常统计范围内（约1个标准差，约68%概率）。
- 橙色/黄色粗线：价格中度偏离（1到2个标准差）。
- 青色/紫色粗线：价格极端偏离（>2个标准差，通常概率<5%）。
- 分布周期：50（用于加权计算）
- 位置敏感度：2.5
- 波动率回溯期：10
- 概率阈值：0.03
适用于所有金融市场和时间框架，尤其是在表现出均值回归特性的市场中。
本指标为技术分析辅助工具，不构成任何投资建议。请务必结合其他分析方法和严格的风险管理策略使用。
版权所有 (c) 2025 | Pine Script v6 兼容
