Mulakan

Euclidean

Analog Flow [KedArc Quant]Overview AnalogFlow is an advanced analogue based market projection engine that reconstructs future price tendencies by matching current price behavior to historical analogues in the same instrument. Instead of using traditional indicators such as moving averages, RSI, or regression, AnalogFlow applies pattern vector similarity analysis - a data driven technique that identifies historically similar sequences and aggregates their subsequent movements into a smooth, forward looking curve. Think of it as a market memory system: If the current pattern looks like one we have seen before, how did price move afterward? Why AnalogFlow Is Unique 1. Pattern centric - it does not rely on any standard indicator formula; it directly analyzes price movement vectors. 2. Adaptive - it learns from the same instrument's past behavior, making it self calibrating to volatility and regime shifts. 3. Non repainting - the projection is generated on the latest completed bar and remains fixed until new data is available. 4. Noise resistant - the EMA Blend engine smooths the projected trajectory, reducing random variance between analogues. Inputs and Configuration Pattern Bars Number of bars in the reference pattern window: 40 Projection Bars Number of bars forward to project: 30 Search Depth Number of bars back to look for matching analogues: 600 Distance Metric Comparison method: Euclidean, Manhattan, or Cosine (default Euclidean) Matches Number of top analogues to blend (1-5): Top 3 Build Mode Projection type: Cumulative, MeanStep, or EMA Blend (default EMA Blend) EMA Blend Length Smoothness of the projected path: 15 Normalize Pattern Enable Z score normalization for shape matching: true Dissimilarity Mode If true, finds inverse analogues for mean reversion analysis: false Line Color and Width Style settings for projection curve: Blue, width 2 How It Works with Past Data 1. The system builds a memory bank of patterns from the last N bars based on the scanDepth value. 2. It compares the latest Pattern Bars segment to each historical segment. 3. It selects the Top K most similar or dissimilar analogues. 4. For each analogue, it retrieves what happened after that pattern historically. 5. It averages or smooths those forward moves into a single composite forecast curve. 6. The forecast (blue line) is drawn ahead of the current candle using line.new with no repainting. Output Explained Blue Path The weighted mean future trajectory based on historical analogues. Smoother when EMA Blend mode is enabled. Flat Section Indicates low directional consensus or equilibrium across analogues. Upward or Downward Slope Represents historical tendency toward continuation or reversal following similar conditions. Recommended Timeframes Scalping / Short Term 1m - 5m : Short winLen (20-30), small ahead (10-15) Swing Trading 15m - 1h : Balanced settings (winLen 40-60, ahead 20-30) Positional / Multi Day 4h - 1D : Large windows (winLen 80-120, ahead 30-50) Instrument Compatibility Works seamlessly on: Stocks and ETFs Indices Cryptocurrency Commodities (Gold, Crude, etc.) Futures and F&O (both intraday and positional) Forex No symbol specific calibration needed. It self adapts to volatility. How Traders Can Use It Forecast Context Identify likely short term price path or drift direction. Reversal Detection Flip seekOpp to true for mean reversion pattern analysis. Scenario Comparison Observe whether the current regime tends to continue or stall. Momentum Confirmation Combine with trend tools such as EMA or MACD for directional bias. Backtesting Support Compare projected path versus realized price to evaluate reliability. FAQ Q1. Does AnalogFlow repaint? No. It calculates only once per completed bar and projects forward. The future path remains static until a new bar closes. Q2. Is it a neural network or AI model? Not in the machine learning sense. It is a deterministic analogue matching engine using statistical distance metrics. Q3. Why does the projection sometimes flatten? That means similar historical setups had no clear consensus in direction (neutral expectation). Q4. Can I use it for live trading signals? AnalogFlow is not a signal generator. It provides probabilistic context for upcoming movement. Q5. Does higher scanDepth improve accuracy? Up to a point. More depth gives more analogues, but too much can dilute recency. Try 400 to 800. Glossary Analogue A past pattern similar to the current price behavior. Distance Metric Mathematical formula for pattern similarity. Step Vector Difference between consecutive closing prices. EMA Blend Exponential smoothing of the projected path. Cumulative Mode Adds sequential historical deltas directly. Z Score Normalization Rescaling to mean 0 and variance 1 for shape comparison. Summary AnalogFlow converts the market's historical echoes into a structured, statistically weighted forward projection. It gives traders a contextual roadmap, not a signal, showing how similar past setups evolved and allowing better informed entries, exits, and scenario planning across all asset classes. Disclaimer This script is provided for educational purposes only. Past performance does not guarantee future results. Trading involves risk, and users should exercise caution and proper risk management when applying this strategy.
Penunjuk Pine Script®
oleh kedarcquant
AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend)The AiTrend Pattern Matrix for kNN Forecasting (AiBitcoinTrend) is a cutting-edge indicator that combines advanced mathematical modeling, AI-driven analytics, and segment-based pattern recognition to forecast price movements with precision. This tool is designed to provide traders with deep insights into market dynamics by leveraging multivariate pattern detection and sophisticated predictive algorithms. 👽 Core Features Segment-Based Pattern Recognition At its heart, the indicator divides price data into discrete segments, capturing key elements like candle bodies, high-low ranges, and wicks. These segments are normalized using ATR-based volatility adjustments to ensure robustness across varying market conditions. AI-Powered k-Nearest Neighbors (kNN) Prediction The predictive engine uses the kNN algorithm to identify the closest historical patterns in a multivariate dictionary. By calculating the distance between current and historical segments, the algorithm determines the most likely outcomes, weighting predictions based on either proximity (distance) or averages. Dynamic Dictionary of Historical Patterns The indicator maintains a rolling dictionary of historical patterns, storing multivariate data for: Candle body ranges, High-low ranges, Wick highs and lows. This dynamic approach ensures the model adapts continuously to evolving market conditions. Volatility-Normalized Forecasting Using ATR bands, the indicator normalizes patterns, reducing noise and enhancing the reliability of predictions in high-volatility environments. AI-Driven Trend Detection The indicator not only predicts price levels but also identifies market regimes by comparing current conditions to historically significant highs, lows, and midpoints. This allows for clear visualizations of trend shifts and momentum changes. 👽 Deep Dive into the Core Mathematics 👾 Segment-Based Multivariate Pattern Analysis The indicator analyzes price data by dividing each bar into distinct segments, isolating key components such as: Body Ranges: Differences between the open and close prices. High-Low Ranges: Capturing the full volatility of a bar. Wick Extremes: Quantifying deviations beyond the body, both above and below. Each segment contributes uniquely to the predictive model, ensuring a rich, multidimensional understanding of price action. These segments are stored in a rolling dictionary of patterns, enabling the indicator to reference historical behavior dynamically. 👾 Volatility Normalization Using ATR To ensure robustness across varying market conditions, the indicator normalizes patterns using Average True Range (ATR). This process scales each component to account for the prevailing market volatility, allowing the algorithm to compare patterns on a level playing field regardless of differing price scales or fluctuations. 👾 k-Nearest Neighbors (kNN) Algorithm The AI core employs the kNN algorithm, a machine-learning technique that evaluates the similarity between the current pattern and a library of historical patterns. Euclidean Distance Calculation: The indicator computes the multivariate distance across four distinct dimensions: body range, high-low range, wick low, and wick high. This ensures a comprehensive and precise comparison between patterns. Weighting Schemes: The contribution of each pattern to the forecast is either weighted by its proximity (distance) or averaged, based on user settings. 👾 Prediction Horizon and Refinement The indicator forecasts future price movements (Y_hat) by predicting logarithmic changes in the price and projecting them forward using exponential scaling. This forecast is smoothed using a user-defined EMA filter to reduce noise and enhance actionable clarity. 👽 AI-Driven Pattern Recognition Dynamic Dictionary of Patterns: The indicator maintains a rolling dictionary of N multivariate patterns, continuously updated to reflect the latest market data. This ensures it adapts seamlessly to changing market conditions. Nearest Neighbor Matching: At each bar, the algorithm identifies the most similar historical pattern. The prediction is based on the aggregated outcomes of the closest neighbors, providing confidence levels and directional bias. Multivariate Synthesis: By combining multiple dimensions of price action into a unified prediction, the indicator achieves a level of depth and accuracy unattainable by single-variable models. Visual Outputs Forecast Line (Y_hat_line): A smoothed projection of the expected price trend, based on the weighted contribution of similar historical patterns. Trend Regime Bands: Dynamic high, low, and midlines highlight the current market regime, providing actionable insights into momentum and range. Historical Pattern Matching: The nearest historical pattern is displayed, allowing traders to visualize similarities 👽 Applications Trend Identification: Detect and follow emerging trends early using dynamic trend regime analysis. Reversal Signals: Anticipate market reversals with high-confidence predictions based on historically similar scenarios. Range and Momentum Trading: Leverage multivariate analysis to understand price ranges and momentum, making it suitable for both breakout and mean-reversion strategies. Disclaimer: This information is for entertainment purposes only and does not constitute financial advice. Please consult with a qualified financial advisor before making any investment decisions.
Penunjuk Pine Script®
oleh aibitcointrend
8