KalmanFilter

שלשום

Kalman Filter Library for Trading Applications

Description:
A comprehensive implementation of the Kalman Filter algorithm specifically designed for financial market analysis and trading. This library provides adaptive state estimation and noise filtering capabilities for price series data.

Key Features:
• Basic Kalman Filter implementation with state and covariance tracking
• Adaptive measurement noise calculation
• Enhanced trading signals with velocity estimation
• Built-in variance calculation methods
• Trading-specific signal generation

Main Functions:
1. initialize(): Sets up initial state and covariance with noise parameters
2. update(): Core Kalman Filter update function
3. calculate_measurement_noise(): Adaptive noise calculation
4. update_trading(): Enhanced version with velocity tracking and signal generation

Usage Example:

//version=5
indicator("Kalman Filter Example")
import Rocky-Studio/KalmanFilter/1 as kf
[state, covariance] = kf.initialize(close, 0.01, 0.1)
[state, velocity, signal] = kf.update_trading(state, 0, covariance, close, 20)

Parameter Recommendations:
• process_noise: 0.01-0.1 (lower for smoother output)
• measurement_noise: 0.1-1.0 (higher for noisier data)
• volatility_window: 20-50 (adjust based on timeframe)

Applications:
• Price trend filtering
• Noise reduction in technical indicators
• Signal generation for trading strategies
• Adaptive volatility estimation
• Momentum tracking

Created by: Rocky-Studio
Version: 1.0
License: Mozilla Public License 2.0

For questions or support:
Contact through TradingView: Rocky-Studio

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הערות שחרור

Description:
A comprehensive Kalman Filter implementation for financial markets, featuring both classic Kalman filtering and the advanced Model 4 from Eric Benhamou's "Trading Without Hiccups" (IFTA Journal 2018). This library provides sophisticated noise reduction and trend detection capabilities, with special emphasis on mean-reversion trading applications.

Key Features:
• Classic Kalman Filter implementation with adaptive noise estimation
• Model 4 implementation (Benhamou 2018) for mean-reversion trading
• Velocity-based signal generation
• Adaptive measurement noise calculation
• Matrix operations optimized for Pine Script
• Comprehensive initialization functions

The library includes:
1. Basic Kalman Filter functions:
- initialize(): Set up initial filter parameters
- update(): Standard Kalman filter update
- update_trading(): Enhanced update with velocity tracking

2. Advanced Model 4 functions:
- model4_initialize(): Initialize Model 4 parameters
- model4_update(): Update state with mean-reversion modeling
- model4_calculate_measurement_noise(): Adaptive noise estimation

3. Helper functions:
- calculate_measurement_noise(): Variance estimation
- model4_default_process_noise(): Default noise matrix generation

Based on academic research and optimized for trading applications. Perfect for developing sophisticated trading strategies with noise-resistant signal generation.

Reference: Benhamou, E. (2018). "Trading Without Hiccups", IFTA Journal 2018.