Ultimate Machine Learning RSI (Deep Learning Edition)

26. Okt.

This script represents an advanced implementation of a Machine Learning-based Relative Strength Index (RSI) indicator in Pine Script, incorporating several sophisticated techniques to create a more adaptive, intelligent, and responsive RSI.

Key Components and Features:

Lookback Period: The period over which the indicator "learns" from past data, set to 1000 bars by default.

Momentum and Volatility Weighting: These factors control how much the momentum and volatility of the market influence the learning and signal generation.
RSI Length Range: The minimum and maximum values for the RSI length, allowing the algorithm to adjust the RSI length dynamically.

Learning Rate: Controls how quickly the system adapts to new data. An adaptive learning rate can change based on market volatility.

Memory Factor: Influences how much the system "remembers" previous performance when making adjustments.

Monte Carlo Simulations: Used for probabilistic modeling to create a more robust signal.
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Price Change: Tracks the difference between the current close and the previous close.
Momentum: A measure of the rate of change in the price over the lookback period.
Volatility: Calculated using the standard deviation of the close prices.
ATR (Average True Range): Tracks the volatility of the market over a short period to influence decisions.
Monte Carlo Simulation:

Probabilistic Signal: This uses multiple random simulations (Monte Carlo) to generate potential future signals. These simulations are weighted by the momentum and volatility of the market. A cluster factor further enhances the simulation based on volatility regimes.
Z-Score for Extreme Conditions:

Z-Score: Measures how extreme current price movements are compared to the historical average, providing context for identifying overbought and oversold conditions.
Dynamic Learning Rate:

The learning rate adjusts based on the volatility of the market, becoming more responsive in high-volatility periods and slower in low-volatility markets. This prevents the system from overreacting to noise but ensures responsiveness to significant shifts.
Recursive Learning and Feedback:

Error Calculation: The system calculates the difference between the true RSI and the predicted RSI, creating an error that is fed back into the system to adjust the RSI length and other parameters dynamically.
RSI Length Adjustment: Based on the error, the RSI length is adjusted, ensuring that the system evolves over time to better reflect market conditions.
Adaptive Smoothing:

In periods of high volatility, the indicator applies a Triple Exponential Moving Average (TEMA) for faster adaptation, while in quieter markets, it uses an Exponential Moving Average (EMA) for smoother adjustments.
Recursive Memory Feedback:

The system maintains a memory of past RSI values, which helps refine the output further. The memory factor influences how much weight is given to past performance versus the current adaptive signal.
Volatility-Based Reinforcement: Higher market volatility increases the impact of this memory feedback, making the model more reactive in volatile conditions.
Multi-Factor Dynamic Thresholds:

Dynamic Overbought/Oversold: Instead of fixed RSI levels (70/30), the thresholds adjust dynamically based on the Z-Score, making the system more sensitive to extreme market conditions.
Combined Multi-Factor Signal:

The final output signal is the result of combining the true RSI, adaptive RSI, and the probabilistic signal generated from the Monte Carlo simulations. This creates a robust, multi-factor signal that incorporates various market conditions and machine learning techniques.
Visual Representation:
The final combined signal is plotted in blue on the chart, along with reference lines at 55 (overbought), 10 (oversold), and 35 (neutral).
Alerts are set up to trigger when the combined signal crosses above the dynamic overbought level or below the dynamic oversold level.
Conclusion:
This "Ultimate Machine Learning RSI" script leverages multiple machine learning techniques—probabilistic modeling, adaptive learning, recursive feedback, and dynamic thresholds—to create an advanced, highly responsive RSI indicator. The result is an RSI that continuously learns from market conditions, adjusts itself in real-time, and provides a more nuanced and robust signal compared to traditional fixed-length RSI. This indicator pushes the boundaries of what's possible with Pine Script and introduces cutting-edge techniques for technical analysis.