Autocorrelation Price Forecasting [ScrimpleAI]

Discover how to predict future price movements using autocorrelation and linear regression models to identify potential trading opportunities.

An advanced model to predict future price movements using autocorrelation and linear regression. This script helps identify recurring market cycles and calculates potential gains, with clear visual signals for quick and informed decisions.

Main Function
This script leverages an autocorrelation model to estimate the future price of an asset based on historical price relationships. It also integrates linear regression on percentage returns to provide more accurate predictions of price movements.

Key Features
1. Customizable Inputs:
- Analysis Length: number of historical bars used for autocorrelation calculation. Adjustable between 1 and 200.
- Forecast Colors: customize colors for bullish and bearish signals.

2. Price Autocorrelation: uses the ta.correlation function to measure price autocorrelation, detecting significant cycles when the value exceeds a defined threshold (signal_threshold = 0.50).

3. Linear Regression on Returns: calculates percentage returns and applies linear regression to identify the future projected price value.

4. Hypothetical Gain Assessment: evaluates potential profit by comparing the estimated future price with the current price.

5. Visual Alerts:
- Labels: hypothetical gains or losses are displayed as labels above or below the bars.
- Dynamic Coloring: bullish (green) and bearish (red) signals are highlighted directly on the chart.
- Forecast Line: A continuous line is plotted to represent the estimated future price values.

Practical Applications

• Short-term Trading: identify repetitive market cycles to anticipate future movements.
  
• Visual Decision-making: colored signals and labels make it easier to visualize potential profit or loss for each trade.
  
• Advanced Customization: adjust the data length and colors to tailor the indicator to your strategies.
  

💡What do you think about this model?
If you already use autocorrelation-based analysis or want to try predictive strategies, leave a comment with your feedback!