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Fair Value Lead-Lag Model [BackQuant]Fair Value Lead-Lag Model A cross-asset model that estimates where price "should" be relative to a chosen reference series, then tracks the deviation as a normalized oscillator. It helps you answer two questions: 1) is the asset rich or cheap vs its driver, and 2) is the driver leading or lagging price over the next N bars. Concept in one paragraph Many assets co-move with a macro or sector driver. Think BTC vs DXY, gold vs real yields, a stock vs its sector ETF. This tool builds a rolling fair value of the charted asset from a reference series and shows how far price is above or below that fair value in standard deviation units. You can shift the reference forward or backward to test who leads whom, then use the deviation and its bands to structure mean-reversion or trend-following ideas. What the model does Reference mapping : Pulls a reference symbol at a chosen timeframe, with an optional lead or lag in bars to test causality. Fair value engine : Converts the reference into a synthetic fair value of the chart using one of four methods: Ratio : price/ref with a rolling average ratio. Good when the relationship is proportional. Spread : price minus ref with a rolling average spread. Good when the relationship is additive. Z-Score : normalizes both series, aligns on standardized units, then re-projects to price space. Good when scale drifts. Beta-Adjusted : rolling regression style. Uses covariance and variance to compute beta, then builds a fair value = mean(price) + beta * (ref − mean(ref)). Deviation and bands : Computes a z-scored deviation of price vs fair value and plots sigma bands (±1, ±2, ±3) around the fair value line on the chart. Correlation context : Shows rolling correlation so you can judge if deviations are meaningful or just noise when co-movement is weak. Visuals : Fair value line on price chart with sigma envelopes. Deviation as a column oscillator and optional line. Threshold shading beyond user-set upper and lower levels. Summary table with reference, deviation, status, correlation, and method. Why this is useful Mean reversion framework : When correlation is healthy and deviation stretches beyond your sigma threshold, probability favors reversion toward fair value. This is classic pairs logic adapted to a driver and a target. Trend confirmation : If price rides the fair value line and deviation stays modest while correlation is positive, it supports trend persistence. Pullbacks to negative deviation in an uptrend can be buyable. Lead-lag discovery : Shift the reference forward by +N bars. If correlation improves, the reference tends to lead. Shift backward for the reverse. Use the best setting for planning early entries or hedges. Regime detection : Large persistent deviations with falling correlation hint at regime change. The relationship you relied on may be breaking down, so reduce confidence or switch methods. How to use it step by step Pick a sensible reference : Choose a macro, index, currency, or sector driver that logically explains the asset’s moves. Example: gold with DXY, a semiconductor stock with SOXX. Test lead-lag : Nudge Lead/Lag Periods to small positive values like +1 to +5 to see if the reference leads. If correlation improves, keep that offset. If correlation worsens, try a small negative value or zero. Select a method : Start with Beta-Adjusted when the relationship is approximately linear with drift. Use Ratio if the assets usually move in proportional terms. Use Spread when they trade around a level difference. Use Z-Score when scales wander or volatility regimes shift. Tune windows : Rolling Window controls how quickly fair value adapts. Shorter equals faster but noisier. Normalization Period controls how deviations are standardized. Longer equals stabler sigma sizing. Correlation Length controls how co-movement is measured. Keep it near the fair value window. Trade the edges : Mean reversion idea : Wait for deviation beyond your Upper or Lower Threshold with positive correlation. Fade back toward fair value. Exit at the fair value line or the next inner sigma band. Trend idea : In an uptrend, buy pullbacks when deviation dips negative but correlation remains healthy. In a downtrend, sell bounces when deviation spikes positive. Read the table : Deviation shows how many sigmas you are from fair value. Status tells you overvalued or undervalued. Correlation color hints confidence. Method tells you the projection style used. Reading the display Fair value line on price chart: the model’s estimate of where price should trade given the reference, updated each bar. Sigma bands around fair value: a quick sense of residual volatility. Reversions often target inner bands first. Deviation oscillator : above zero means rich vs fair value, below zero means cheap. Color bins intensify with distance. Correlation line (optional): scale is folded to match thresholds. Higher values increase trust in deviations. Parameter tips Start with Rolling Window 20 to 30, Normalization Period 100, Correlation Length 50. Upper and Lower Threshold at ±2.0 are classic. Tighten to ±1.5 for more signals or widen to ±2.5 to focus on outliers. When correlation drifts below about 0.3, treat deviations with caution. Consider switching method or reference. If the fair value line whipsaws, increase Rolling Window or move to Beta-Adjusted which tends to be smoother. Playbook examples Pairs-style reversion : Asset is +2.3 sigma rich vs reference, correlation 0.65, trend flat. Short the deviation back toward fair value. Cover near the fair value line or +1 sigma. Pro-trend pullback : Uptrend with correlation 0.7. Deviation dips to −1.2 sigma while price sits near the −1 sigma band. Buy the dip, target the fair value line, trail if the line is rising. Lead-lag timing : Reference leads by +3 bars with improved correlation. Use reference swings as early cues to anticipate deviation turns on the target. Caveats The model assumes a stable relationship over the chosen windows. Structural breaks, policy shocks, and index rebalances can invalidate recent history. Correlation is descriptive, not causal. A strong correlation does not guarantee future convergence. Do not force trades when the reference has low liquidity or mismatched hours. Use a reference timeframe that captures real overlap. Bottom line This tool turns a loose cross-asset intuition into a quantified, visual fair value map. It gives you a consistent way to find rich or cheap conditions, time mean-reversion toward a statistically grounded target, and confirm or fade trends when the driver agrees.
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BackQuant의
Relative Performance Tracker [QuantAlgo]🟢 Overview The Relative Performance Tracker is a multi-asset comparison tool designed to monitor and rank up to 30 different tickers simultaneously based on their relative price performance. This indicator enables traders and investors to quickly identify market leaders and laggards across their watchlist, facilitating rotation strategies, strength-based trading decisions, and cross-asset momentum analysis. 🟢 Key Features 1. Multi-Asset Monitoring Track up to 30 tickers across any market (stocks, crypto, forex, commodities, indices) Individual enable/disable toggles for each ticker to customize your watchlist Universal compatibility with any TradingView symbol format (EXCHANGE:TICKER) 2. Ranking Tables (Up to 3 Tables) Each ticker's percentage change over your chosen lookback period, calculated as: (Current Price - Past Price) / Past Price × 100 Automatic sorting from strongest to weakest performers Rank: Position from 1-30 (1 = strongest performer) Ticker: Symbol name with color-coded background (green for gains, red for losses) % Change: Exact percentage with color intensity matching magnitude For example, Rank #1 has the highest gain among all enabled tickers, Rank #30 has the lowest (or most negative) return. 3. Histogram Visualization Adjustable bar count: Display anywhere from 1 to 30 top-ranked tickers (user customizable) Bar height = magnitude of percentage change. Bars extend upward for gains, downward for losses. Taller bars = larger moves. Green bars for positive returns, red for negative returns. 4. Customizable Color Schemes Classic: Traditional green/red for intuitive interpretation Aqua: Blue/orange combination for reduced eye strain Cosmic: Vibrant aqua/purple optimized for dark mode Custom: Full personalization of positive and negative colors 5. Built-In Ranking Alerts Six alert conditions detect when rankings change: Top 1 Changed: New #1 leader emerges Top 3/5/10/15/20 Changed: Shifts within those tiers 🟢 Practical Applications → Momentum Trading: Focus on top-ranked assets (Rank 1-10) that show strongest relative strength for trend-following strategies → Market Breadth Analysis: Monitor how many tickers are above vs. below zero on the histogram to gauge overall market health → Divergence Spotting: Identify when previously leading assets lose momentum (drop out of top ranks) as potential trend reversal signals → Multi-Timeframe Analysis: Use different lookback periods on different charts to align short-term and long-term relative strength → Customized Focus: Adjust histogram bars to show only top 5-10 strongest movers for concentrated analysis, or expand to 20-30 for comprehensive overview
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QuantAlgo의
2
Portfolio Strategy TesterThe Portfolio Strategy Tester is an institutional-grade backtesting framework that evaluates the performance of trend-following strategies on multi-asset portfolios. It enables users to construct custom portfolios of up to 30 assets and apply moving average crossover strategies across individual holdings. The model features a clear, color-coded table that provides a side-by-side comparison between the buy-and-hold portfolio and the portfolio using the risk management strategy, offering a comprehensive assessment of both approaches relative to the benchmark. Portfolios are constructed by entering each ticker symbol in the menu, assigning its respective weight, and reviewing the total sum of individual weights displayed at the top left of the table. For strategy selection, users can choose between Exponential Moving Average (EMA), Simple Moving Average (SMA), Wilder’s Moving Average (RMA), Weighted Moving Average (WMA), Moving Average Convergence Divergence (MACD), and Volume-Weighted Moving Average (VWMA). Moving average lengths are defined in the menu and apply only to strategy-enabled assets. To accurately replicate real-world portfolio conditions, users can choose between daily, weekly, monthly, or quarterly rebalancing frequencies and decide whether cash is held or redistributed. Daily rebalancing maintains constant portfolio weights, while longer intervals allow natural drift. When cash positions are not allowed, capital from bearish assets is automatically redistributed proportionally among bullish assets, ensuring the portfolio remains fully invested at all times. The table displays a comprehensive set of widely used institutional-grade performance metrics: CAGR = Compounded annual growth rate of returns. Volatility = Annualized standard deviation of returns. Sharpe = CAGR per unit of annualized standard deviation. Sortino = CAGR per unit of annualized downside deviation. Calmar = CAGR relative to maximum drawdown. Max DD = Largest peak-to-trough decline in value. Beta (β) = Sensitivity of returns relative to benchmark returns. Alpha (α) = Excess annualized risk-adjusted returns relative to benchmark. Upside = Ratio of average return to benchmark return on up days. Downside = Ratio of average return to benchmark return on down days. Tracking = Annualized standard deviation of returns versus benchmark. Turnover = Average sum of absolute changes in weights per year. Cumulative returns are displayed on each label as the total percentage gain from the selected start date, with green indicating positive returns and red indicating negative returns. In the table, baseline metrics serve as the benchmark reference and are always gray. For portfolio metrics, green indicates outperformance relative to the baseline, while red indicates underperformance relative to the baseline. For strategy metrics, green indicates outperformance relative to both the baseline and the portfolio, red indicates underperformance relative to both, and gray indicates underperformance relative to either the baseline or portfolio. Metrics such as Volatility, Tracking Error, and Turnover ratio are always displayed in gray as they serve as descriptive measures. In summary, the Portfolio Strategy Tester is a comprehensive backtesting tool designed to help investors evaluate different trend-following strategies on custom portfolios. It enables real-world simulation of both active and passive investment approaches and provides a full set of standard institutional-grade performance metrics to support data-driven comparisons. While results are based on historical performance, the model serves as a powerful portfolio management and research framework for developing, validating, and refining systematic investment strategies.
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QuantitativeAlpha의
Volume Profile, Pivot Anchored by DGT - reviewedVolume Profile, Pivot Anchored by DGT - reviewed This indicator, “Volume Profile, Pivot Anchored”, builds a volume profile between swing highs and lows (pivot points) to show where trading activity is concentrated. It highlights: Value Area (VAH / VAL) and Point of Control (POC) Volume distribution by price level Pivot-based labels showing price, % change, and volume Optional colored candles based on volume strength relative to the average Essentially, it visualizes how volume is distributed between market pivots to reveal key price zones and volume imbalances.
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gcarbis의
3
Nq/ES daily CME risk intervalReverse engineering the risk interval for CME (Chicago Mercantile Exchange) products based on margin requirements involves understanding the relationship between margin requirements, volatility, and the risk interval (price movement assumed for margin calculation) The CME uses a methodology called SPAN (Standard Portfolio Analysis of Risk) to calculate margins. At a high level, the initial margin is derived from: Initial Margin = Risk Interval × Contract Size × Volatility Adjustment Factor Where: Risk Interval: The price movement range used in the margin calculation. Contract Size: The unit size of the futures contract. Volatility Adjustment Factor: A measure of how much price fluctuation is expected, often tied to historical volatility. To calculate an approximate of the daily CME risk interval, we need: Initial Margin Requirement: Available on the CME Group website or broker platforms. Contract Size: The size of one futures contract (e.g., for the S&P 500 E-mini, it is $50 × index points). Volatility Adjustment Factor: This is derived from historical volatility or CME's implied volatility estimates. As we do not have access to CME calculations , the volatility adjustment factor can be estimated using historical volatility: We calculate the standard deviation of daily returns over a specific period (e.g., 20 or 30 or 60 days). Key Considerations The exact formulas and parameters used by CME for CME's implied volatility estimates are proprietary, so this calculation based on standard deviation of daily returns is an approximation. How to use: Input the maintenance margin obtained from the CME website. Adjust volatility period calculation. The indicator displays the range high and low for the trading day. 1.Lines can be used as targets intraday 2.Market tends to snap back in between the lines and close the day in the range
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DrodzCip의
2
PPI Inflation Monitor (Change YoY & MoM)📊 PPI Inflation Monitor - Leading Inflation Indicator The Producer Price Index (PPI) measures wholesale/producer-level prices and serves as a critical leading indicator for consumer inflation trends. This tool helps you anticipate CPI movements and identify corporate margin pressures before they show up in earnings. 🎯 KEY FEATURES: - Dual Perspective Analysis: - Year-over-Year (YoY): Histogram bars showing annual producer price inflation - Month-over-Month (MoM): Line overlay showing monthly wholesale price changes - Visual Reference System: - Dashed line at 2% (typical target for producer price inflation) - Dotted line at 0.17% (equivalent monthly target) - Color-coded bars: Red above target, Green below target - Real-Time Data Table: - Current PPI Index value - YoY inflation rate with color coding - MoM inflation rate with color coding - Deviation from target level - Automated Alerts: - YoY crosses above/below target - MoM crosses above/below target - Early warning system for inflation trends 📈 WHY PPI IS YOUR EARLY WARNING SYSTEM: PPI typically leads CPI by 1-3 months because: - Producers face cost increases first - These costs are eventually passed to consumers - Shows whether companies can maintain pricing power Rising PPI with stable CPI = Margin compression → Bearish for stocks Rising PPI followed by rising CPI = Broad inflation → Fed hawkishness incoming Falling PPI = Disinflationary trend starting → Positive for risk assets 🔍 TRADING APPLICATIONS: 1. Lead Time Advantage: Position before CPI confirms PPI trends 2. Sector Rotation: High PPI = favor companies with pricing power 3. Margin Analysis: PPI-CPI divergence = margin pressure/expansion signals 4. Fed Anticipation: PPI acceleration = Fed likely to turn hawkish soon 💡 STRATEGIC USE CASES: - Value vs. Growth: Rising PPI favors value stocks with pricing power - Commodities: PPI often correlates with commodity price trends - Small Caps: More vulnerable to input cost increases (high PPI = cautious) - Corporate Earnings: Anticipate margin pressure before quarterly reports 🔄 COMBINE WITH: - CPI: Confirm if producer costs reach consumers - PCE: Validate Fed's preferred inflation metric response - Fed Funds Rate: Assess if Fed is behind/ahead of curve 📊 DATA SOURCE: Official PPI data from FRED (Federal Reserve Economic Data), updated monthly when new data releases occur. 🎨 CUSTOMIZATION: Fully customizable: - Toggle YoY/MoM displays - Adjust reference target levels - Customize colors - Show/hide absolute PPI values Perfect for: Macro traders, fundamental analysts, earnings traders, and investors seeking early inflation signals before they appear in consumer prices. ⚡ Remember: PPI leads CPI. Use this advantage to position ahead of the crowd.
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Phillipklh의
PCE Inflation Monitor (Change YoY & MoM)📊 PCE Inflation Monitor - The Fed's Most Important Metric Personal Consumption Expenditures (PCE) is the Federal Reserve's preferred inflation measure and THE metric they target for their 2% inflation goal. If you want to predict Fed policy, you need to watch PCE. 🎯 KEY FEATURES: - Dual Perspective Analysis: - Year-over-Year (YoY): Histogram bars showing annual PCE inflation - Month-over-Month (MoM): Line overlay showing monthly consumption price changes - Visual Reference System: - Dashed line at 2% (Fed's official PCE inflation target) - Dotted line at 0.17% (equivalent monthly target) - Color-coded bars: Red above Fed target, Green below target - Real-Time Data Table: - Current PCE Index value - YoY inflation rate vs. Fed's 2% target - MoM inflation rate with color coding - Exact deviation from Fed target (critical for policy predictions) - Automated Alerts: - PCE crosses Fed's 2% target (major policy signal!) - MoM crosses monthly target - Stay informed of Fed-relevant inflation changes 📈 WHY PCE IS DIFFERENT (AND MORE IMPORTANT): PCE vs. CPI differences: - Flexible basket: PCE adjusts for substitution (beef → chicken if prices rise) - Broader coverage: Includes healthcare paid by insurance/government - Lower readings: Typically 0.2-0.4% below CPI - Fed's choice: Explicitly stated as their target metric Most importantly: When Powell speaks about "our 2% target," he means PCE, not CPI! 🔍 TRADING IMPLICATIONS: PCE Above 2% (Red Zone): → Fed under pressure to maintain/raise rates → Hawkish policy stance likely → Negative for growth stocks, crypto → Positive for USD, bearish for gold PCE Below 2% (Green Zone): → Fed has flexibility to cut rates → Dovish policy stance possible → Positive for risk assets, growth stocks → Negative for USD, bullish for commodities PCE Approaching 2% from Above: → Fed "mission accomplished" narrative → Rate cut cycle becomes possible → Major bullish signal for equities/crypto 💡 ADVANCED STRATEGIES: 1. Fed Meeting Preparation: Check PCE before FOMC meetings for policy clues 2. Dot Plot Predictions: PCE trend determines Fed's rate forecast updates 3. Pivot Timing: When PCE MoM turns negative, Fed pivot becomes realistic 4. Press Conference Analysis: Compare Powell's comments to PCE deviation 🎯 KEY LEVELS TO WATCH: - 2.0% YoY: Fed's official target - crossing this level is major news - 2.5% YoY: "Uncomfortably high" - Fed forced to stay restrictive - 3.0% YoY: "Crisis mode" - Fed turns very hawkish - 1.5% YoY: "Below target" - Rate cuts become likely 🔄 COMBINE WITH: - CPI: Public perception vs. Fed's metric (often diverge) - Core PCE: Even more important (excludes food/energy volatility) - Fed Funds Rate: Is Fed responding appropriately to PCE? 📊 DATA SOURCE: Official PCE data from FRED (Federal Reserve Economic Data), updated monthly typically in the last week of each month (after CPI/PPI releases). 🎨 CUSTOMIZATION: Fully customizable: - Toggle YoY/MoM displays - Adjust Fed target if needed - Customize colors - Show/hide absolute PCE values Perfect for: Fed watchers, macro traders, policy analysts, and serious investors who want to predict monetary policy changes before they happen. ⚠️ CRITICAL INSIGHT: While media focuses on CPI, the Fed focuses on PCE. Trade what the Fed trades, not what the headlines say. 🎓 Pro Tip: Fed members often mention "Core PCE" (excluding food/energy). Consider adding that indicator alongside this one for complete Fed policy analysis.
Pine Script® 인디케이터
Phillipklh의
CPI Inflation Monitor (Change YoY & MoM)📊 CPI Inflation Monitor - Complete Macro Analysis Tool This indicator provides a comprehensive view of Consumer Price Index (CPI) inflation trends, essential for understanding monetary policy, market conditions, and making informed trading decisions. 🎯 KEY FEATURES: - Dual Perspective Analysis: - Year-over-Year (YoY): Histogram bars showing annual inflation rate - Month-over-Month (MoM): Line overlay showing monthly price changes - Visual Reference System: - Dashed line at 2% (Fed's official inflation target for YoY) - Dotted line at 0.17% (equivalent monthly target for MoM) - Color-coded bars: Red above target, Green below target - Real-Time Data Table: - Current CPI Index value - YoY inflation rate with color coding - MoM inflation rate with color coding - Deviation from Fed target - Automated Alerts: - YoY crosses above/below 2% target - MoM crosses above/below 0.17% target - Perfect for staying informed without constant monitoring 📈 WHY THIS MATTERS FOR TRADERS: CPI is the most widely reported inflation metric and directly influences: - Federal Reserve interest rate decisions - Bond yields and currency valuations - Stock market sentiment (especially growth vs. value rotation) - Cryptocurrency and risk asset performance Rising inflation (red bars) typically leads to: → Higher interest rates → Negative for growth stocks, crypto → Stronger USD → Pressure on commodities Falling inflation (green bars) typically leads to: → Rate cut expectations → Positive for growth stocks, crypto → Weaker USD → Support for commodities 🔍 HOW TO USE: 1. Strategic Positioning: Use YoY trend (thick bars) for long-term asset allocation 2. Tactical Timing: Use MoM trend (thin line) to identify turning points early 3. Divergence Trading: When MoM falls but YoY remains high, anticipate trend reversal 4. Fed Policy Prediction: Distance from 2% target indicates Fed's likely hawkishness 💡 PRO TIPS: - Multiple months of MoM above 0.3% = Accelerating inflation → Fed turns hawkish - MoM turning negative while YoY still elevated = Peak inflation → Position for pivot - Compare with PPI and PCE indicators for complete inflation picture - Use alerts to catch important threshold crossings automatically 📊 DATA SOURCE: Official CPI data from FRED (Federal Reserve Economic Data), updated monthly mid-month when new data releases occur. 🎨 CUSTOMIZATION: Fully customizable through settings: - Toggle YoY/MoM displays - Adjust target levels - Customize colors for visual preference - Show/hide absolute CPI values Perfect for: Macro traders, swing traders, long-term investors, and anyone wanting to understand the inflation environment affecting their portfolio. Note: This indicator works on any chart timeframe as it loads external monthly economic data.
Pine Script® 인디케이터
Phillipklh의
Whale HunterThis script searches for gaps (above selected price gap percent), and mark it with a box (that can be extend right) also it takes the buy price of Gap-Up and sell price of a Gap-Down and tries to cluster them into price ranges in a table (the space between price clusters is configurable), so you can see what are the most likely price range that the whales are buying and selling that makes the price move in a way that causes a X% gap, so this indicator will show you where the whales are buying and selling
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shay_private의
Simplified Percentile ClusteringSimplified Percentile Clustering (SPC) is a clustering system for trend regime analysis. Instead of relying on heavy iterative algorithms such as k-means, SPC takes a deterministic approach: it uses percentiles and running averages to form cluster centers directly from the data, producing smooth, interpretable market state segmentation that updates live with every bar. Most clustering algorithms are designed for offline datasets, they require recomputation, multiple iterations, and fixed sample sizes. SPC borrows from both statistical normalization and distance-based clustering theory , but simplifies them. Percentiles ensure that cluster centers are resistant to outliers , while the running mean provides a stable mid-point reference. Unlike iterative methods, SPC’s centers evolve smoothly with time, ideal for charts that must update in real time without sudden reclassification noise. SPC provides a simple yet powerful clustering heuristic that: Runs continuously in a charting environment, Remains interpretable and reproducible, And allows traders to see how close the current market state is to transitioning between regimes. Clustering by Percentiles Traditional clustering methods find centers through iteration. SPC defines them deterministically using three simple statistics within a moving window: Lower percentile (p_low) → captures the lower basin of feature values. Upper percentile (p_high) → captures the upper basin. Mean (mid) → represents the central tendency. From these, SPC computes stable “centers”: // K = 2 → two regimes (e.g., bullish / bearish) = // K = 3 → adds a neutral zone = These centers move gradually with the market, forming live regime boundaries without ever needing convergence steps. Two clusters capture directional bias; three clusters add a neutral ‘range’ state. Multi-Feature Fusion While SPC can cluster a single feature such as RSI, CCI, Fisher Transform, DMI, Z-Score, or the price-to-MA ratio (MAR), its real strength lies in feature fusion. Each feature adds a unique lens to the clustering system. By toggling features on or off, traders can test how each dimension contributes to the regime structure. In “Clusters” mode, SPC measures how far the current bar is from each cluster center across all enabled features, averages these distances, and assigns the bar to the nearest combined center. This effectively creates a multi-dimensional regime map , where each feature contributes equally to defining the overall market state. The fusion distance is computed as: dist := (rsi_d * on_off(use_rsi) + cci_d * on_off(use_cci) + fis_d * on_off(use_fis) + dmi_d * on_off(use_dmi) + zsc_d * on_off(use_zsc) + mar_d * on_off(use_mar)) / (on_off(use_rsi) + on_off(use_cci) + on_off(use_fis) + on_off(use_dmi) + on_off(use_zsc) + on_off(use_mar)) Because each feature can be standardized (Z-Score), the distances remain comparable across different scales. Fusion mode combines multiple standardized features into a single smooth regime signal. Visualizing Proximity - The Transition Gradient Most indicators show binary or discrete conditions (e.g., bullish/bearish). SPC goes further, it quantifies how close the current value is to flipping into the next cluster. It measures the distances to the two nearest cluster centers and interpolates between them: rel_pos = min_dist / (min_dist + second_min_dist) real_clust = cluster_val + (second_val - cluster_val) * rel_pos This real_clust output forms a continuous line that moves smoothly between clusters: Near 0.0 → firmly within the current regime Around 0.5 → balanced between clusters (transition zone) Near 1.0 → about to flip into the next regime Smooth interpolation reveals when the market is close to a regime change. How to Tune the Parameters SPC includes intuitive parameters to adapt sensitivity and stability: K Clusters (2–3): Defines the number of regimes. K = 2 for trend/range distinction, K = 3 for trend/neutral transitions. Lookback: Determines the number of past bars used for percentile and mean calculations. Higher = smoother, more stable clusters. Lower = faster reaction to new trends. Lower / Upper Percentiles: Define what counts as “low” and “high” states. Adjust to widen or tighten cluster ranges. Shorter lookbacks react quickly to shifts; longer lookbacks smooth the clusters. Visual Interpretation In “Clusters” mode, SPC plots: A colored histogram for each cluster (red, orange, green depending on K) Horizontal guide lines separating cluster levels Smooth proximity transitions between states Each bar’s color also changes based on its assigned cluster, allowing quick recognition of when the market transitions between regimes. Cluster bands visualize regime structure and transitions at a glance. Practical Applications Identify market regimes (bullish, neutral, bearish) in real time Detect early transition phases before a trend flip occurs Fuse multiple indicators into a single consistent signal Engineer interpretable features for machine-learning research Build adaptive filters or hybrid signals based on cluster proximity Final Notes Simplified Percentile Clustering (SPC) provides a balance between mathematical rigor and visual intuition. It replaces complex iterative algorithms with a clear, deterministic logic that any trader can understand, and yet retains the multidimensional insight of a fusion-based clustering system. Use SPC to study how different indicators align, how regimes evolve, and how transitions emerge in real time. It’s not about predicting; it’s about seeing the structure of the market unfold. Disclaimer This indicator is intended for educational and analytical use. It does not generate buy or sell signals. Historical regime transitions are not indicative of future performance. Always validate insights with independent analysis before making trading decisions.
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InvestorUnknown의
7
ETH OHLC by tncylyvETH OHLC Projection Levels 📜 Indicator Description This indicator projects key potential price levels for Ethereum (ETH) based on its historical price behavior. Using the opening price of a user-selected timeframe (4H, 1D, or 1W) as a baseline, it calculates and displays statistically-derived levels for potential "Manipulation" and "Distribution" phases of price action. These projections are designed to provide traders with potential zones of interest for support, resistance, stop-loss placement, and take-profit targets for the current trading period. ________________________________________ 🧠 Core Concepts Explained The indicator is built on two key concepts derived from candlestick analysis: • Manipulation: This represents the initial price movement that occurs against the candle's eventual primary direction. o For a bullish candle, it's the extent of the lower wick (the move from Open down to Low). o For a bearish candle, it's the extent of the upper wick (the move from Open up to High). o The "M" levels on the chart project the average (mean and median) historical size of this manipulation wick, suggesting potential areas for liquidity grabs or stop hunts. • Distribution: This represents the primary price movement in the direction of the candle's trend. o For a bullish candle, it's the total move from Open to High. o For a bearish candle, it's the total move from Open to Low. o The "D" levels project the average (mean and median) historical range of this price expansion, suggesting potential targets for the period. ________________________________________ 📊 Data & Methodology It is important to note that the statistical ratios used for the projections are not calculated in real-time by the indicator itself. These values have been pre-calculated through an extensive historical analysis performed in Python. The analysis used the complete historical ETH/USD price data from the Coinbase exchange to determine the mean and median ratios for both manipulation and distribution across the different timeframes. The resulting fixed values are then hard-coded into the script to ensure performance and consistency. ________________________________________ ⚙️ How to Use It At the beginning of each new period (e.g., at the start of a new day on the 1D timeframe), the indicator will draw a new set of horizontal lines and zones based on that period's opening price. • The central dotted line represents the Opening Price for the selected timeframe. • Manipulation Levels (+M / -M): These inner levels can be interpreted as potential reversal zones. Price may test these areas to trigger stops before moving in the primary direction for the session. • Distribution Levels (+D / -D): These outer levels can be used as potential take-profit targets, representing the average historical price extension for a period. • Mean vs. Median Zones: The script plots levels based on both the historical mean (average) and median (middle value). The shaded area between them creates a zone rather than a single price line, offering a more practical range for analysis. ________________________________________ 🛠️ Settings and Features • Projection Timeframe: Select the primary timeframe for the analysis (4H, 1D, or 1W). The historical data used for projections is specific to the chosen timeframe. • Historical Periods to Show: Adjust how many past periods of data you want to see on your chart. A value of 1 will only show the projections for the current, active period. • Timezone (UTC-4): The 4H calculations are based on a fixed UTC-4 timezone to align with specific, high-volume market sessions (e.g., New York open). This is not changeable to ensure data consistency. • Visual Customization: You have full control over the appearance of the indicator. o Toggle the visibility, colors, and line styles for the Open price line and each of the Manipulation/Distribution levels using their respective checkboxes and inputs. o Enable or disable the shaded fills between the mean and median levels. o Tip: To quickly hide all price labels at once, edit the "Label Color" setting and set its opacity to 100% (fully transparent).
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6
BTC OHLC by tncylyvBTC OHLC Projection Levels 📜 Indicator Description This indicator projects key potential price levels for Bitcoin (BTC) based on historical price behavior. Using the opening price of a user-selected timeframe (4H, 1D, or 1W) as a baseline, it calculates and displays statistically-derived levels for potential "Manipulation" and "Distribution" phases of price action. These projections are designed to provide traders with potential zones of interest for support, resistance, stop-loss placement, and take-profit targets for the current trading period. ________________________________________ 🧠 Core Concepts Explained The indicator is built on two key concepts derived from candlestick analysis: • Manipulation: This represents the initial price movement that occurs against the candle's eventual primary direction. o For a bullish candle, it's the extent of the lower wick (the move from Open down to Low). o For a bearish candle, it's the extent of the upper wick (the move from Open up to High). o The "M" levels on the chart project the average (mean and median) historical size of this manipulation wick, suggesting potential areas for liquidity grabs or stop hunts. • Distribution: This represents the primary price movement in the direction of the candle's trend. o For a bullish candle, it's the total move from Open to High. o For a bearish candle, it's the total move from Open to Low. o The "D" levels project the average (mean and median) historical range of this price expansion, suggesting potential targets for the period. ________________________________________ 📊 Data & Methodology It is important to note that the statistical ratios used for the projections are not calculated in real-time by the indicator itself. These values have been pre-calculated through an extensive historical analysis performed in Python. The analysis used the complete historical BTC/USD price data from the Coinbase exchange to determine the mean and median ratios for both manipulation and distribution across the different timeframes. The resulting fixed values are then hard-coded into the script to ensure performance and consistency. ________________________________________ ⚙️ How to Use It At the beginning of each new period (e.g., at the start of a new day on the 1D timeframe), the indicator will draw a new set of horizontal lines and zones based on that period's opening price. • The central dotted line represents the Opening Price for the selected timeframe. • Manipulation Levels (+M / -M): These inner levels can be interpreted as potential reversal zones. Price may test these areas to trigger stops before moving in the primary direction for the session. • Distribution Levels (+D / -D): These outer levels can be used as potential take-profit targets, representing the average historical price extension for a period. • Mean vs. Median Zones: The script plots levels based on both the historical mean (average) and median (middle value). The shaded area between them creates a zone rather than a single price line, offering a more practical range for analysis. ________________________________________ 🛠️ Settings and Features • Projection Timeframe: Select the primary timeframe for the analysis (4H, 1D, or 1W). The historical data used for projections is specific to the chosen timeframe. • Historical Periods to Show: Adjust how many past periods of data you want to see on your chart. A value of 1 will only show the projections for the current, active period. • Timezone (UTC-4): The 4H calculations are based on a fixed UTC-4 timezone to align with specific, high-volume market sessions (e.g., New York open). This is not changeable to ensure data consistency. • Visual Customization: You have full control over the appearance of the indicator. o Toggle the visibility, colors, and line styles for the Open price line and each of the Manipulation/Distribution levels using their respective checkboxes and inputs. o Enable or disable the shaded fills between the mean and median levels. o Tip: To quickly hide all price labels at once, edit the "Label Color" setting and set its opacity to 100% (fully transparent).
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