This wrapper is an enhancement of the Python library backtesting.py, which is very popular for backtesting of trading strategies.

Currently part of backtesting_ms. Will extract and deploy to PyPi as a library

Issues addressed:

• backtesting.py relies on hardcoding of strategy configuration, such as upper and lower bands, and also the optimisation criteria, etc., meaning much code has to be written to define strategies.
• The default strategies, and many circulated online, are not going to find alpha (an opportunity to exploit market inefficiency); because they are utilised by too many traders.

Solution:

• The wrapper assembles strategy details according to content of either a config file, or a database. (WIP: This can then be edited via a UI.)
• There is a factory to fetch the strategy class.
• Walkforward testing of all strategies.
• Strategies, with optimisation functionality, are required that compare data sources of world events, including news events and social media sentiment, to market movements to find patterns that reflect correlation. market_sentiment_ms
• Optimsiation can be ehanced with machine learning stonk-value-forecasting-model