PROTECTED SOURCE SCRIPT
Perfect MACD
Overview
This script is an advanced implementation of the Moving Average Convergence Divergence (MACD) indicator, enhanced with adaptive features, reinforcement learning concepts, and improved trend detection mechanisms. It aims to provide a more responsive and dynamic tool for technical analysis by adjusting its parameters based on current market conditions.
Key Components
Inputs and Parameters:
Lookback Periods: Defines the periods for volatility calculations.
Weighting Factors: Base values for momentum, volatility, and volume influences.
MACD Parameters: Initial fast and slow lengths, and signal smoothing period.
Reinforcement Learning Parameters: Learning rate, memory factor, reward threshold, and decay factor.
Simulation Parameters: Number of simulations and history depth for Monte Carlo simulation.
Feature Extraction:
Rate of Change (ROC): Measures the percentage change in price over a specified period.
Average True Range (ATR): Calculates market volatility.
Trend Detection using ADX:
Average Directional Index (ADX): Assesses the strength of a trend.
Volume Analysis: Compares short-term and long-term volume to detect high-volume periods.
Market State Determination: Categorizes the market as "Trending Strong," "Trending," "High Volume," or "Range Bound" based on ADX and volume.
Adaptive Weight Adjustments:
Modifies weighting factors for momentum, volatility, and volume based on the current market state to enhance responsiveness.
Adaptive Learning Rate:
Adjusts the learning rate dynamically based on the ratio of short-term to long-term volatility.
Reinforcement Learning:
Error Calculation: Computes the difference between the predicted and actual MACD values.
Reward System: Applies a reward mechanism to fine-tune MACD lengths, aiming to minimize prediction error.
Monte Carlo Simulation:
Simulates future returns using historical data to predict potential price movements.
Uses the Box-Muller transform to generate random values assuming a normal distribution.
MACD Length Adjustment:
Adjusts the fast and slow lengths of the MACD based on simulated returns and reinforcement learning outcomes.
MACD Calculation:
Calculates both the standard MACD and the adaptive MACD using the adjusted lengths.
Signal Reinforcement:
Enhances the MACD signal by incorporating volatility and volume-based reinforcements.
Applies an adaptive memory factor to balance the influence of new information against historical data.
Plotting:
Plots the adaptive MACD line, signal line, and histogram.
Uses background colors to indicate the current market state for visual emphasis.
Usage
Customization: Users can adjust input parameters to suit different assets and timeframes.
Interpretation:
Adaptive MACD Line and Signal Line: Used similarly to the traditional MACD but adapt to market conditions.
Histogram: Represents the difference between the MACD line and the signal line, indicating momentum.
Background Colors: Provide a visual cue of the market state, helping users make informed decisions.
This script is an advanced implementation of the Moving Average Convergence Divergence (MACD) indicator, enhanced with adaptive features, reinforcement learning concepts, and improved trend detection mechanisms. It aims to provide a more responsive and dynamic tool for technical analysis by adjusting its parameters based on current market conditions.
Key Components
Inputs and Parameters:
Lookback Periods: Defines the periods for volatility calculations.
Weighting Factors: Base values for momentum, volatility, and volume influences.
MACD Parameters: Initial fast and slow lengths, and signal smoothing period.
Reinforcement Learning Parameters: Learning rate, memory factor, reward threshold, and decay factor.
Simulation Parameters: Number of simulations and history depth for Monte Carlo simulation.
Feature Extraction:
Rate of Change (ROC): Measures the percentage change in price over a specified period.
Average True Range (ATR): Calculates market volatility.
Trend Detection using ADX:
Average Directional Index (ADX): Assesses the strength of a trend.
Volume Analysis: Compares short-term and long-term volume to detect high-volume periods.
Market State Determination: Categorizes the market as "Trending Strong," "Trending," "High Volume," or "Range Bound" based on ADX and volume.
Adaptive Weight Adjustments:
Modifies weighting factors for momentum, volatility, and volume based on the current market state to enhance responsiveness.
Adaptive Learning Rate:
Adjusts the learning rate dynamically based on the ratio of short-term to long-term volatility.
Reinforcement Learning:
Error Calculation: Computes the difference between the predicted and actual MACD values.
Reward System: Applies a reward mechanism to fine-tune MACD lengths, aiming to minimize prediction error.
Monte Carlo Simulation:
Simulates future returns using historical data to predict potential price movements.
Uses the Box-Muller transform to generate random values assuming a normal distribution.
MACD Length Adjustment:
Adjusts the fast and slow lengths of the MACD based on simulated returns and reinforcement learning outcomes.
MACD Calculation:
Calculates both the standard MACD and the adaptive MACD using the adjusted lengths.
Signal Reinforcement:
Enhances the MACD signal by incorporating volatility and volume-based reinforcements.
Applies an adaptive memory factor to balance the influence of new information against historical data.
Plotting:
Plots the adaptive MACD line, signal line, and histogram.
Uses background colors to indicate the current market state for visual emphasis.
Usage
Customization: Users can adjust input parameters to suit different assets and timeframes.
Interpretation:
Adaptive MACD Line and Signal Line: Used similarly to the traditional MACD but adapt to market conditions.
Histogram: Represents the difference between the MACD line and the signal line, indicating momentum.
Background Colors: Provide a visual cue of the market state, helping users make informed decisions.
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