Very basic processing and analysis of a pkl file containing roughly monthly price data of a set of indices.
Simple Qt-based desktop app that can display multiple tickers' histories.
To run: python main.py
Requires an ASGI server (I used uvicorn, as per FastAPI docs).
To run: uvicorn ticker_sandbox_rest:app --reload
This creates a swagger interactive doc page at http://127.0.0.1:8000/docs.
Opens the pkl file and extracts most of the data.
get_ticker(symbol: str): If the symbol is present in the given pkl file, returns a Ticker object which contains all data relative to the requested symbol.get_symbols(): returns a list of all ticker symbols available in the dataset.
Interacts with a Sqlite db file, creating it if necessary, stores ticker data, and provides some analysis capability.
insert_ticker(ticker: Ticker): Ingests a Ticker object into the database.insert_spot(date: str, ticker: Ticker, spot: Spot): Adds a new spot value for a given ticker.get_last_index_value_at_date(date: str): Gets the index value (the weighted value of all assets tracked in the database) at a given date (or at the nearest earlier date with data available, if the given date cannot be found).get_sector_value_data(): returns a list of tuples in the format(sector, date, average price for the sector)get_relative_sector_distribution(date: str): Returns a dictionary in the format{sector: relative weight}, for a given date (or nearest earlier date). Relative weight is the number of assets in that sector divided by the total number of assets in the index.get_ticker_history(symbol: str): Returns the full (date, price) history for a given ticker.get_all_symbols(): Returns the full list of symbols available in the database.
database = StorageProvider("my_database_file")
index_value = database.get_last_index_value_at_date('2018-01-31')
sector_value_data = database.get_sector_value_data()
sector_distribution = database.get_relative_sector_distribution('2018-12-31')