A simple framework for fast and dirty backtesting
fastbt is a simple and dirty way to do backtests based on end of day data, especially for day trading. The main purpose is to provide a simple framework to weed out bad strategies so that you could test and improve your better strategies further.
It is based on the assumption that you enter into a position based on some pre-defined rules for a defined period and exit either at the end of the period or when stop loss is triggered. See the rationale for this approach and the built-in assumptions. fastbt is rule-based and not event-based.
If your strategy gets you good results, then check them with a full featured backtesting framework such as zipline or backtrader to verify your results. If your strategy fails, then it would most probably fail in other environments.
This is alpha
Most of the modules are stand alone and you could use them as a single file. See embedding for more details
- Create your strategies in Microsoft Excel
- Backtest as functions so you can parallelize
- Try different simulations
- Run from your own datasource or a database connection.
- Run backtest based on rules
- Add any column you want to your datasource as formulas
fastbt requires python >=3.6 and can be installed via pip
pip install fastbt
Fastbt assumes your data have the following columns (rename them in case of other names)
from fastbt.rapid import * backtest(data=data)
would return a dataframe with all the trades.
And if you want to see some metrics
You now ran a backtest without a strategy! By default, the strategy buys the top 5 stocks with the lowest price at open price on each period and sells them at the close price at the end of the period.
You can either specify the strategy by way of rules (the recommended way) or create your strategy as a function in python and pass it as a parameter
If you want to connect to a database, then
from sqlalchemy import create_engine engine = create_engine('sqlite:///data.db') backtest(connection=engine, tablename='data')
And to SELL instead of BUY
Let's implement a simple strategy.
BUY the top 5 stocks with highest last week returns
Assuming we have a weeklyret column,
backtest(data=data, order='B', sort_by='weeklyret', sort_mode=False)
We used sort_mode=False to sort them in descending order.
If you want to test this strategy on a weekly basis, just pass a dataframe with weekly frequency.
See the Introduction notebook in the examples directory for an in depth introduction.
Since fastbt is a thin wrapper around existing packages, the following files can be used as standalone without installing the fastbt package
Copy these files and just use them in your own modules.
- New methods added to
- mtm - to calculate mtm for open positions
- clear - to clear the existing entries
- helper attributes for positions
order_fill_pricemethod added to utils to simulate order quantity
- Simple bug fixes added
OptionExpiryclass added to calculate option payoffs based on expiry
- Brokers module deprecation warning added
- Options module revamped
- More helper functions added to utils
- Tradebook class enhanced
- A Meta class added for event based simulation
- Backtest from different formats added
- Rolling function added
- First release on PyPI
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Hashes for fastbt-0.6.0-py2.py3-none-any.whl