| Name | zipline-reloaded JSON |
| Version |
3.1
JSON |
| download |
| home_page | None |
| Summary | A Pythonic backtester for trading algorithms |
| upload_time | 2024-09-26 14:08:23 |
| maintainer | Stefan Jansen |
| docs_url | None |
| author | Quantopian Inc |
| requires_python | >=3.9 |
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For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. "Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. 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You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions: (a) You must give any other recipients of the Work or Derivative Works a copy of this License; and (b) You must cause any modified files to carry prominent notices stating that You changed the files; and (c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and (d) If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. 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We also recommend that a file or class name and description of purpose be included on the same "printed page" as the copyright notice for easier identification within third-party archives. Copyright 2018 Quantopian, Inc. Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. |
| keywords |
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| requirements |
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<p align="center">
<a href="https://zipline.ml4trading.io">
<img src="https://i.imgur.com/DDetr8I.png" width="25%">
</a>
</p>
# Backtest your Trading Strategies
| Version Info | [](https://pypi.python.org/pypi/zipline-reloaded) [](https://anaconda.org/ml4t/zipline-reloaded)  [](https://anaconda.org/conda-forge/zipline-reloaded) |
| ------------------- | ---------- |
| **Test** **Status** | [](https://github.com/stefan-jansen/zipline-reloaded/actions/workflows/unit_tests.yml) [](https://github.com/stefan-jansen/zipline-reloaded/actions/workflows/build_wheels.yml) [](https://codecov.io/gh/stefan-jansen/zipline-reloaded) |
| **Community** | [](https://exchange.ml4trading.io) [](https://ml4trading.io) [](https://twitter.com/ml4trading) |
Zipline is a Pythonic event-driven system for backtesting, developed and used as the backtesting and live-trading engine by [crowd-sourced investment fund Quantopian](https://www.bizjournals.com/boston/news/2020/11/10/quantopian-shuts-down-cofounders-head-elsewhere.html). Since it closed late 2020, the domain that had hosted these docs expired. The library is used extensively in the book [Machine Larning for Algorithmic Trading](https://ml4trading.io)
by [Stefan Jansen](https://www.linkedin.com/in/applied-ai/) who is trying to keep the library up to date and available to his readers and the wider Python algotrading community.
- [Join our Community!](https://exchange.ml4trading.io)
- [Documentation](https://zipline.ml4trading.io)
## Features
- **Ease of Use:** Zipline tries to get out of your way so that you can focus on algorithm development. See below for a code example.
- **Batteries Included:** many common statistics like moving average and linear regression can be readily accessed from within a user-written algorithm.
- **PyData Integration:** Input of historical data and output of performance statistics are based on Pandas DataFrames to integrate nicely into the existing PyData ecosystem.
- **Statistics and Machine Learning Libraries:** You can use libraries like matplotlib, scipy, statsmodels, and scikit-klearn to support development, analysis, and visualization of state-of-the-art trading systems.
> **Note:** Release 3.05 makes Zipline compatible with Numpy 2.0, which requires Pandas 2.2.2 or higher. If you are using an older version of Pandas, you will need to upgrade it. Other packages may also still take more time to catch up with the latest Numpy release.
> **Note:** Release 3.0 updates Zipline to use [pandas](https://pandas.pydata.org/pandas-docs/stable/whatsnew/v2.0.0.html) >= 2.0 and [SQLAlchemy](https://docs.sqlalchemy.org/en/20/) > 2.0. These are major version updates that may break existing code; please review the linked docs.
> **Note:** Release 2.4 updates Zipline to use [exchange_calendars](https://github.com/gerrymanoim/exchange_calendars) >= 4.2. This is a major version update and may break existing code (which we have tried to avoid but cannot guarantee). Please review the changes [here](https://github.com/gerrymanoim/exchange_calendars/issues/61).
## Installation
Zipline supports Python >= 3.9 and is compatible with current versions of the relevant [NumFOCUS](https://numfocus.org/sponsored-projects?_sft_project_category=python-interface) libraries, including [pandas](https://pandas.pydata.org/) and [scikit-learn](https://scikit-learn.org/stable/index.html).
### Using `pip`
If your system meets the pre-requisites described in the [installation instructions](https://zipline.ml4trading.io/install.html), you can install Zipline using `pip` by running:
```bash
pip install zipline-reloaded
```
### Using `conda`
If you are using the [Anaconda](https://www.anaconda.com/products/individual) or [miniconda](https://docs.conda.io/en/latest/miniconda.html) distributions, you install `zipline-reloaded` from the channel `conda-forge` like so:
```bash
conda install -c conda-forge zipline-reloaded
```
You can also [enable](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-channels.html) `conda-forge` by listing it in your `.condarc`.
In case you are installing `zipline-reloaded` alongside other packages and encounter [conflict errors](https://github.com/conda/conda/issues/9707), consider using [mamba](https://github.com/mamba-org/mamba) instead.
See the [installation](https://zipline.ml4trading.io/install.html) section of the docs for more detailed instructions and the corresponding [conda-forge site](https://github.com/conda-forge/zipline-reloaded-feedstock).
## Quickstart
See our [getting started tutorial](https://zipline.ml4trading.io/beginner-tutorial).
The following code implements a simple dual moving average algorithm.
```python
from zipline.api import order_target, record, symbol
def initialize(context):
context.i = 0
context.asset = symbol('AAPL')
def handle_data(context, data):
# Skip first 300 days to get full windows
context.i += 1
if context.i < 300:
return
# Compute averages
# data.history() has to be called with the same params
# from above and returns a pandas dataframe.
short_mavg = data.history(context.asset, 'price', bar_count=100, frequency="1d").mean()
long_mavg = data.history(context.asset, 'price', bar_count=300, frequency="1d").mean()
# Trading logic
if short_mavg > long_mavg:
# order_target orders as many shares as needed to
# achieve the desired number of shares.
order_target(context.asset, 100)
elif short_mavg < long_mavg:
order_target(context.asset, 0)
# Save values for later inspection
record(AAPL=data.current(context.asset, 'price'),
short_mavg=short_mavg,
long_mavg=long_mavg)
```
You can then run this algorithm using the Zipline CLI. But first, you need to download some market data with historical prices and trading volumes.
This will download asset pricing data from [NASDAQ](https://data.nasdaq.com/databases/WIKIP) (formerly [Quandl](https://www.nasdaq.com/about/press-center/nasdaq-acquires-quandl-advance-use-alternative-data)).
> This requires an API key, which you can get for free by signing up at [NASDAQ Data Link](https://data.nasdaq.com).
```bash
$ export QUANDL_API_KEY="your_key_here"
$ zipline ingest -b quandl
````
The following will
- stream the through the algorithm over the specified time range.
- save the resulting performance DataFrame as `dma.pickle`, which you can load and analyze from Python using, e.g., [pyfolio-reloaded](https://github.com/stefan-jansen/pyfolio-reloaded).
```bash
$ zipline run -f dual_moving_average.py --start 2014-1-1 --end 2018-1-1 -o dma.pickle --no-benchmark
```
You can find other examples in the [zipline/examples](https://github.com/stefan-jansen/zipline-reloaded/tree/main/src/zipline/examples) directory.
## Questions, suggestions, bugs?
If you find a bug or have other questions about the library, feel free to [open an issue](https://github.com/stefan-jansen/zipline/issues/new) and fill out the template.
Raw data
{
"_id": null,
"home_page": null,
"name": "zipline-reloaded",
"maintainer": "Stefan Jansen",
"docs_url": null,
"requires_python": ">=3.9",
"maintainer_email": "pm@ml4trading.io",
"keywords": null,
"author": "Quantopian Inc",
"author_email": "pm@ml4trading.io",
"download_url": "https://files.pythonhosted.org/packages/78/34/52df3fea6315e902c021005968533394f55fede8597aacf63eaa76bc83b5/zipline_reloaded-3.1.tar.gz",
"platform": null,
"description": "<p align=\"center\">\n<a href=\"https://zipline.ml4trading.io\">\n<img src=\"https://i.imgur.com/DDetr8I.png\" width=\"25%\">\n</a>\n</p>\n\n# Backtest your Trading Strategies\n\n| Version Info | [](https://pypi.python.org/pypi/zipline-reloaded) [](https://anaconda.org/ml4t/zipline-reloaded)  [](https://anaconda.org/conda-forge/zipline-reloaded) |\n| ------------------- | ---------- |\n| **Test** **Status** | [](https://github.com/stefan-jansen/zipline-reloaded/actions/workflows/unit_tests.yml) [](https://github.com/stefan-jansen/zipline-reloaded/actions/workflows/build_wheels.yml) [](https://codecov.io/gh/stefan-jansen/zipline-reloaded) |\n| **Community** | [](https://exchange.ml4trading.io) [](https://ml4trading.io) [](https://twitter.com/ml4trading) |\n\nZipline is a Pythonic event-driven system for backtesting, developed and used as the backtesting and live-trading engine by [crowd-sourced investment fund Quantopian](https://www.bizjournals.com/boston/news/2020/11/10/quantopian-shuts-down-cofounders-head-elsewhere.html). Since it closed late 2020, the domain that had hosted these docs expired. The library is used extensively in the book [Machine Larning for Algorithmic Trading](https://ml4trading.io)\nby [Stefan Jansen](https://www.linkedin.com/in/applied-ai/) who is trying to keep the library up to date and available to his readers and the wider Python algotrading community.\n- [Join our Community!](https://exchange.ml4trading.io)\n- [Documentation](https://zipline.ml4trading.io)\n\n## Features\n\n- **Ease of Use:** Zipline tries to get out of your way so that you can focus on algorithm development. See below for a code example.\n- **Batteries Included:** many common statistics like moving average and linear regression can be readily accessed from within a user-written algorithm.\n- **PyData Integration:** Input of historical data and output of performance statistics are based on Pandas DataFrames to integrate nicely into the existing PyData ecosystem.\n- **Statistics and Machine Learning Libraries:** You can use libraries like matplotlib, scipy, statsmodels, and scikit-klearn to support development, analysis, and visualization of state-of-the-art trading systems.\n\n> **Note:** Release 3.05 makes Zipline compatible with Numpy 2.0, which requires Pandas 2.2.2 or higher. If you are using an older version of Pandas, you will need to upgrade it. Other packages may also still take more time to catch up with the latest Numpy release.\n\n> **Note:** Release 3.0 updates Zipline to use [pandas](https://pandas.pydata.org/pandas-docs/stable/whatsnew/v2.0.0.html) >= 2.0 and [SQLAlchemy](https://docs.sqlalchemy.org/en/20/) > 2.0. These are major version updates that may break existing code; please review the linked docs.\n\n> **Note:** Release 2.4 updates Zipline to use [exchange_calendars](https://github.com/gerrymanoim/exchange_calendars) >= 4.2. This is a major version update and may break existing code (which we have tried to avoid but cannot guarantee). Please review the changes [here](https://github.com/gerrymanoim/exchange_calendars/issues/61).\n\n## Installation\n\nZipline supports Python >= 3.9 and is compatible with current versions of the relevant [NumFOCUS](https://numfocus.org/sponsored-projects?_sft_project_category=python-interface) libraries, including [pandas](https://pandas.pydata.org/) and [scikit-learn](https://scikit-learn.org/stable/index.html).\n\n### Using `pip`\n\nIf your system meets the pre-requisites described in the [installation instructions](https://zipline.ml4trading.io/install.html), you can install Zipline using `pip` by running:\n\n```bash\npip install zipline-reloaded\n```\n\n### Using `conda`\n\nIf you are using the [Anaconda](https://www.anaconda.com/products/individual) or [miniconda](https://docs.conda.io/en/latest/miniconda.html) distributions, you install `zipline-reloaded` from the channel `conda-forge` like so:\n\n```bash\nconda install -c conda-forge zipline-reloaded\n```\n\nYou can also [enable](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-channels.html) `conda-forge` by listing it in your `.condarc`.\n\nIn case you are installing `zipline-reloaded` alongside other packages and encounter [conflict errors](https://github.com/conda/conda/issues/9707), consider using [mamba](https://github.com/mamba-org/mamba) instead.\n\nSee the [installation](https://zipline.ml4trading.io/install.html) section of the docs for more detailed instructions and the corresponding [conda-forge site](https://github.com/conda-forge/zipline-reloaded-feedstock).\n\n## Quickstart\n\nSee our [getting started tutorial](https://zipline.ml4trading.io/beginner-tutorial).\n\nThe following code implements a simple dual moving average algorithm.\n\n```python\nfrom zipline.api import order_target, record, symbol\n\n\ndef initialize(context):\n context.i = 0\n context.asset = symbol('AAPL')\n\n\ndef handle_data(context, data):\n # Skip first 300 days to get full windows\n context.i += 1\n if context.i < 300:\n return\n\n # Compute averages\n # data.history() has to be called with the same params\n # from above and returns a pandas dataframe.\n short_mavg = data.history(context.asset, 'price', bar_count=100, frequency=\"1d\").mean()\n long_mavg = data.history(context.asset, 'price', bar_count=300, frequency=\"1d\").mean()\n\n # Trading logic\n if short_mavg > long_mavg:\n # order_target orders as many shares as needed to\n # achieve the desired number of shares.\n order_target(context.asset, 100)\n elif short_mavg < long_mavg:\n order_target(context.asset, 0)\n\n # Save values for later inspection\n record(AAPL=data.current(context.asset, 'price'),\n short_mavg=short_mavg,\n long_mavg=long_mavg)\n```\n\nYou can then run this algorithm using the Zipline CLI. But first, you need to download some market data with historical prices and trading volumes.\n\nThis will download asset pricing data from [NASDAQ](https://data.nasdaq.com/databases/WIKIP) (formerly [Quandl](https://www.nasdaq.com/about/press-center/nasdaq-acquires-quandl-advance-use-alternative-data)).\n\n> This requires an API key, which you can get for free by signing up at [NASDAQ Data Link](https://data.nasdaq.com).\n\n```bash\n$ export QUANDL_API_KEY=\"your_key_here\"\n$ zipline ingest -b quandl\n````\n\nThe following will \n- stream the through the algorithm over the specified time range. \n- save the resulting performance DataFrame as `dma.pickle`, which you can load and analyze from Python using, e.g., [pyfolio-reloaded](https://github.com/stefan-jansen/pyfolio-reloaded).\n\n```bash\n$ zipline run -f dual_moving_average.py --start 2014-1-1 --end 2018-1-1 -o dma.pickle --no-benchmark\n```\n\nYou can find other examples in the [zipline/examples](https://github.com/stefan-jansen/zipline-reloaded/tree/main/src/zipline/examples) directory.\n\n## Questions, suggestions, bugs?\n\nIf you find a bug or have other questions about the library, feel free to [open an issue](https://github.com/stefan-jansen/zipline/issues/new) and fill out the template.\n",
"bugtrack_url": null,
"license": " Apache License Version 2.0, January 2004 http://www.apache.org/licenses/ TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION 1. Definitions. \"License\" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document. \"Licensor\" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License. \"Legal Entity\" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, \"control\" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity. \"You\" (or \"Your\") shall mean an individual or Legal Entity exercising permissions granted by this License. \"Source\" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files. \"Object\" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types. \"Work\" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below). \"Derivative Works\" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof. \"Contribution\" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, \"submitted\" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as \"Not a Contribution.\" \"Contributor\" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work. 2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form. 3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed. 4. Redistribution. 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