Local stock quote storage for backtesting and algorithm training. The database
serves as a local cache for stock data. When data is requested from the
StockDBManager it will be served from the local database if available, or
rom an external source otherwise. All requested data is stored locally for
faster retrieval during subsequent requests. The quant module is used to calculate
lots of common indicators and stores them to the database. This is useful for generating
large datasets for testing/ML applications, as well as for speeding up backtesting
the database module contains definitions for all database-access related functionalityit may be run as a script to perform several database administration functions
Getting started:
- Configure database settings in config.py
- Use
python database.py createto create the database on local machine - Add stocks to the database with
python database.py add <symbol>. Once a stock is added,The quotes database is populated with historical quotes for the stock. python database.py syncupdates quotess for all stocks in the database and should be used daily to keep the database up to date.- Quotes are retreived through the interfaces in
datafeed.py
The objects in datafeed are used to retreive quote data. As of right now it only handles historical intraday quotes.
Common indicator calculations as well as Machine-learning predictors