Autocorrelation Price Forecasting Backtesting [ScrimpleAI]

This script presents an innovative trading backtesting strategy designed to leverage autocorrelation models and linear regression on historical price returns. The goal is to forecast future price movements, identify recurring market cycles, and optimize trading decisions.

Main Functionality
This backtesting script is built to simulate trades by integrating historical autocorrelation with dynamic price forecasting. It incorporates risk management, stop-loss features, and an advanced backtesting date range, providing traders with maximum flexibility for evaluating strategies.

Key Features
1. Customizable Date Range for Backtesting
Allows users to define the exact date period for backtesting their strategies, ensuring they can fine-tune results for specific historical scenarios.
- Inputs: Start and End dates (day, month, year).

2. Autocorrelation Price Forecasting
- Detects cycles in market movements using the `ta.correlation` function.
- Highlights significant cycles when the autocorrelation exceeds a threshold value (default: 0.50).
- Stores projected values based on autocorrelation and linear regression of percentage returns for enhanced forecasting accuracy.

3. Forecast Threshold and Profit Assessment
- Evaluates hypothetical gains by comparing forecasted future prices to the current price.
- Customizable threshold gains to determine minimum profitability requirements for opening trades.

4. Strategy Side
- Long or Short Mode: Users can choose to test either long or short strategies to align with their trading approach.

5. Risk and Trade Management
- Order Sizing: Adjust position size as a percentage of the portfolio.
- Stop-Loss Integration: Dynamically calculates stop-loss based on user-defined percentages.
- Take Profit Target: Automatically sets take-profit levels based on forecasted gains.

6. Visual Alerts
- Provides clear visual signals of long and short entries on the chart, including labels and dynamic coloring.
- Forecasted prices are displayed directly on the chart as a continuous line, enhancing decision-making clarity.

Practical Applications
1. Cycle Detection: Utilize autocorrelation to identify repetitive market behaviors and cycles.
2. Forecasting for Backtesting: Simulate trades and assess the profitability of various strategies based on future price predictions.
3. Risk Management: Test different stop-loss and take-profit configurations.
4. Custom Period Analysis: Evaluate strategy performance in specific historical market conditions using the date range filter.

Core Logic Walkthrough
1. Autocorrelation for Cycle Detection:
- Historical prices are analyzed for recurring patterns using the `ta.correlation` function.
- If a significant cycle is detected (above the `signal_threshold`), the `linreg_values` (linear regression of returns) are stored for price projection.

2. Future Price Estimation: Forecasted price is calculated based on linear regression values and current price movements.

3. Trade Entry Logic

Long Trades
- Triggered if the hypothetical gain exceeds the threshold gain.
- Sets a take-profit level based on the projected future price.
- Includes an optional stop-loss based on user-defined percentages.

Short Trades
- Triggered if the hypothetical gain is less than the negative of the threshold gain.
- Configures take-profit and stop-loss levels for bearish trades.

4. Risk Management
- Position Sizing: Automatically calculates the order size as a percentage of the portfolio.
- Stop-Loss: Dynamically adjusts stop-loss levels to minimize risk.

5. Date Range Filtering: Ensures trades are executed only within the defined backtesting period.

Example Use Case: Backtesting with Autocorrelation
- A trader analyzes a 6-month period using 50 historical bars for autocorrelation.
- Sets a threshold gain of 10% and enables a stop-loss at 5%.
- Evaluates the effectiveness of a long-only strategy in this period to assess its profitability and risk-adjusted performance.

If you find this strategy useful or have ideas for improvements, leave a comment! What new features would you like to see in this strategy?