Quant Strategies
This package contains some quant indicators and strategies. Custom indicators are in Indicators.py and Strategies are found in Strategies.py.
Writing Indicators and Strategies
Indicators and strategies go in their respective files. tester.py should be used to see the graph of a strategy and make sure it works for one stock before adding more.
If you're making an indicator and wouldn't like to run a strategy, you should comment out the final lines in tester.py that calculate the sharpe ratio and RoMaD to avoid a crash.
Most strategies currently written output all trades to log.txt.
Dataset
The main dataset used is 1/1/2016-1/1/2020 OCHLV data from Yahoo Finance for several outperforming stocks (GOOG, AMZN, MSFT) and many stocks that were delisted from the S&P 500 and underperformed afterwords.
For the backtest in main.py, Roughly 70% of tickers are chosen randomly and fed to the strategy in a random order.
Measuring Strategy Quality
When backtesting a strategy, the Sharpe ratio and RoMaD are used. The numbers to beat are 0.94 and 0.65 respectively, which are the numbers for buying and holding the S&P 500 during 2016-2020.
Requirements
Backtrader >= 1.9.76.123