This repository contains implementations of various trading strategies, each designed to generate buy or sell signals based on specific market conditions. The optimal parameter values for each strategy have been carefully tuned to maximize profit potential while minimizing risk.
- Parameters:
window=100, risk_tolerance=0.04, z_threshold=1.5 - Description: Identifies assets that have deviated significantly from their historical mean prices, anticipating a reversion to the mean.
- Parameters:
window=50, risk_tolerance=0.04 - Description: Utilizes the Aroon indicator to identify trend changes and generate buy or sell signals accordingly.
- Parameters:
window=90, risk_tolerance=0.02, short_window=14, long_window=50 - Description: Generates signals based on the crossover of short-term and long-term moving averages, indicating potential changes in trend direction.
- Parameters:
window=120, risk_tolerance=0.1, upper_threshold=70, lower_threshold=30, rsi_period=14 - Description: Uses the Relative Strength Index (RSI) to identify overbought and oversold conditions, generating buy or sell signals accordingly.
- Parameters:
short_window=10, long_window=50, risk_tolerance=0.01 - Description: Similar to the Simple Moving Average Crossover strategy, but utilizes exponential moving averages for smoother signal generation.
- Parameters:
window=100, risk_tolerance=0.04, std=1.5 - Description: Uses Bollinger Bands to identify potential buying or selling opportunities based on price volatility.
- Parameters:
window=5, atr_multiplier=2.5, risk_tolerance=0.03 - Description: Utilizes the Average True Range (ATR) indicator to determine market volatility and adjust risk parameters accordingly.
In order to run the trading strategy:
python main.py --symbol MSFT --strategy mean-reversion --window 100 --cash_at_risk 0.30 --risk_tolerance 0.04
In order to run the same trading strategy unit test (pass symbol as an environmental variable):
SYMBOL=MSFT python tests/test_MeanReversion.py