Technical Trading Rule Python is an open source library for popular technical analysis function for financial time series data.
To install the current release:
$ pip install ttrpy
Load historical stock data into Pandas from existing csv file or directly from the web.
>>> import pandas as pd
>>> df = pd.read_csv("weekly_MSFT.csv").sort_values(by="timestamp").reset_index(drop=True)
>>> df.tail(3) timestamp open high low close volume
1098 2019-01-25 106.75 107.88 ... ... ...
1099 2019-02-01 106.26 106.48 ... ... ...
1100 2019-02-07 102.87 107.27 ... ... ...
Let's say we are interested in the overall long-term trend for MSFT, we can use the simple moving average function from the trend package.
>>> from ttrpy.trend.sma import sma
>>> df = sma(df, "close", "sma", 200)
>>> df.tail(3) timestamp open high low close volume sma
1098 2019-01-25 106.75 107.88 ... ... ... 71.080175
1099 2019-02-01 106.26 106.48 ... ... ... 71.392625
1100 2019-02-07 102.87 107.27 ... ... ... 71.707025
As easy as that!
If you want to contribute to ttrpy, be sure to review the contribution guidelines. This project adheres to ttrpy's code of conduct. By participating, you are expected to uphold this code.
To get the local repository set up for development,
$ pip install pipenv
$ pip install --dev
If you find a bug, kindly open an issue here.
If you would like to request a new feature, feel free to do so by opening an issue here.
To fix a bug or enhance an existing module, follow these steps:
- Fork the repository
- Create a new branch (
git checkout - b branch-name) - Make the appropriate changes in the files
- Add changes to reflect the changes made
- Commit your changes (
git commit -m 'commit message') - Push to the branch (
git push origin branch-name) - Create a Pull Request
We use SemVer for versioning. For the versions available, see the tags on this repository
This project is licensed under Apache License 2.0 - see the LICENSE file for details.
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