Quant Trading R&D Environment

Stock Data DB

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

database

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 create to 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 sync updates 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

datafeed.py

The objects in datafeed are used to retreive quote data. As of right now it only handles historical intraday quotes.

Analysis Tools

Common indicator calculations as well as Machine-learning predictors