04-25-2020: Update for xubuntu 20.04; minor fixes for latest libraries; test everything against Python3.8
02-16-2020: Download latest time series for every S&P 500 component (see sp500-components-timeseries example)
02-16-2020: Update your entire ticker symbol cache to latest time series (see update-cache-symbols example)
01-31-2020: Add basic multi symbol support (see trade-two-symbols example)
12-27-2019: Integration with Quantopian's pyfolio and empyrical libraries (see pyfolio-empyrical example)
12-24-2019: Use trade_log to create trade_state column in daily_balance. No longer need to manually set trade_state
12-20-2019: Add capability to merge like trades (all buys or all sells) that occur on same day (see merge-trades example)
12-13-2019: Add calendar columns to a dataframe (see sell-in-may-and-go-away example)
11-12-2019: Add prettier_graphs (see double-7s example)
11-01-2019: Add monthly and holding_period tables (see buy-and-hold example)
10-18-2019: Add capability to maintain a certain value, shares, or percent in a security (see percent-allocate example)
07-31-2019: First major update since original release - Add scale-in and scale-out capability
A backtester and spreadsheet library for security analysis.
Why another python backtesting library? How is pinkfish different? Simple, I couldn't find a python backtesting library that allowed me to backtest intraday strategies with daily data. Even simple strategies like 'buying on the close' on the SAME day a 'new 20 day high is set' were not allowed. Additionally, I was put off by the complexity of some of the libraries available, and wanted something simple, that doesn't get in the way, and just allows me to test my trading ideas. One user commented that Pinkfish is "very lightweight and to the point". I didn't set out to write a new backtesting library, but I had to. Daily data is free; minute and tick data are typically not. Using minute and tick data can require hours to run vs seconds for daily data.
Some of the key features of pinkfish:
- leverages pandas for dataframe, spreadsheet like features
- leverages matplotlib for making financial graphs
- uses ta-lib to easily implement technical indicators
- uses daily data (vs minute or tick data) for intraday trading
- uses free daily data from yahoo finance
- simple to use python API
- create spreadsheets within Jupyter Notebook by utilizing pandas dataframes and itable formatting
Follow the installation instructions located at: https://fja05680.github.io/pinkfish/
- spreadsheet - make a read only spreadsheet within ipython
- golden-cross - an example illustrating the classic long term trading algorithm