As per result of training, I was able to find the ideal performance for each indicator and have published the results in the repository.
The setup.py initializes what the ideal period for each indicator is. These periods are results from running data worth 24 years across different asset groups.
Historical data will be called for each indicator and each strategy from now on. Previously this caused problems because of yfinance policy of rate limiting. Using cache and incremental delays, we were able to solve this problem - a workaround if you want to call it that.
We also included a suggestion heap that will suggest the bot buy based on buy_weight - sell_weight. This is secondary with primary still being the buy heap. Current training based on this updated indicator is being done so please note that the indicator ranking on the website is not accurate for at least until January 1, 2025 which is the approximate date that the training should be done based on updated codebase.
Improvements to be made:
Sharding. Currently, there will be 3 minute delays on rank to give time for trading to catch up but sharding will improve performance speed of trading client.