QuantWave: intraday-GARCH-strategy-in-Python

The script implements an intraday trading strategy, combining GARCH for daily volatility forecasting and intraday technical indicators. It generates signals based on prediction premiums, integrating daily and intraday data. The strategy's cumulative returns are visualized for performance evaluation.

source: Wikipedia

Overview:

This Python script implements an intraday trading strategy, merging GARCH daily volatility forecasting with intraday technical indicators. The strategy generates signals based on prediction premiums, combining daily and intraday data for enhanced decision-making.

Files:

1. intraday_trading_strategy.py: Python script containing the intraday trading strategy implementation.

2. Data Files:

   • daily_data.csv: Simulated daily financial data.
   • 5min_data.csv: Simulated 5-minute intraday data.

Dependencies:

• pandas, matplotlib, arch, pandas_ta, numpy

Instructions:

1. Ensure Python and required libraries are installed.
2. Adjust the data_folder variable in the script to the correct path.
3. Run the script (intraday_trading_strategy.py).

Output:

• The script generates visualizations of daily signals and cumulative returns of the intraday trading strategy.

GARCH:

• The strategy combines GARCH for volatility forecasting with intraday technical indicators for robust decision-making.
• Results may vary based on market conditions and simulated data.
• A GARCH (Generalized Autoregressive Conditional Heteroskedasticity) trading strategy involves using statistical models to predict future volatility in financial markets. Here's a brief overview:

1. GARCH Models:

   • Model volatility as a dynamic, time-varying process.
   • Uses past volatility to forecast future volatility.

2. Model Estimation:

   • Parameters are calibrated using historical price or return data.

3. Volatility Forecasting:

   • GARCH model provides estimates of future volatility.

4. Trading Signals:

   • Triggers based on forecasted volatility surpassing predefined thresholds.
   • Signals may prompt position adjustments or risk hedging.

5. Risk Management:

   • Adjusts position sizes based on forecasted volatility.
   • Considers transaction costs and adapts to changing market conditions.

6. Implementation Considerations:

   • Accounts for transaction costs and market frictions.
   • Requires regular updates as new data becomes available.

7. Limitations:

   • Forecasting accuracy may be affected by unexpected events.
   • Assumes stationarity in the underlying time series.

8. Backtesting and Evaluation:

   • Tests the strategy using historical data.
   • Evaluates performance with metrics like risk-adjusted returns.

Remember, while GARCH models provide insights into volatility, no model ensures future success. Traders should incorporate GARCH within a comprehensive risk management framework and stay informed about market dynamics.

Author:

Mayank Bharwal

Date:

7th January,2024

Feel free to reach out for any inquiries or improvements. Happy trading!