This project implements a Mean Reversion trading strategy using Python. Mean Reversion is a popular quantitative trading strategy that assumes asset prices will revert to their historical mean over time. This project is designed to help beginners and enthusiasts understand and implement a basic Mean Reversion strategy in a systematic way.
The Mean Reversion strategy is based on the idea that asset prices tend to fluctuate around a historical average. When prices deviate significantly from this average, they are expected to revert back, providing trading opportunities.
- Identify Asset: Choose an asset with a historical tendency to revert to the mean.
- Calculate Indicators: Use statistical measures like moving averages to determine the historical mean and standard deviation.
- Generate Signals:
- Buy Signal: Triggered when the asset price falls below a certain threshold (e.g., two standard deviations below the mean).
- Sell Signal: Triggered when the asset price rises above a certain threshold (e.g., two standard deviations above the mean).
- Execute Trades: Enter long positions on buy signals and short positions on sell signals.
- Monitor and Exit: Close positions when the price reverts to the mean or predefined exit criteria are met.