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Getting started

Installation

Install backtide's newest release easily via pip:

pip install -U backtide

or via conda:

conda install -c conda-forge backtide


Latest source

Sometimes, new features and bug fixes are already implemented in the development branch, but waiting for the next release to be made available. If you can't wait for that, it's possible to install the package directly from git.

pip install git+https://github.com/tvdboom/backtide.git@development#egg=backtide

Don't forget to include #egg=backtide to explicitly name the project, this way pip can track metadata for it without having to have run the setup.py script.


Optional dependencies

Some specific functionalities or configuration options require the installation of additional libraries. Install all optional dependencies with:

pip install -U backtide[full]


Contributing

If you are planning to contribute to the project, you'll need the development dependencies. Install them with:

pip install -U backtide[dev]

Click here for a complete list of package files for all versions published on PyPI.



Usage

There are three ways to use backtide:


Via the application

Backtide ships with an interactive Streamlit application. Launch it from the terminal with:

backtide launch

The app provides a graphical interface for configuring experiments, visualizing results and managing stored data. Read more in the user guide.


Via the CLI

The backtide command-line interface lets you download data, run backtests and manage storage directly from the terminal.

backtide download AAPL MSFT --interval 1d
backtide run experiment.toml

Run backtide --help to see all available commands.


Via Python

Import backtide in any Python script or notebook for full programmatic control.

>>> from backtide.backtest import run_experiment
>>> from backtide.data import resolve_profiles, download_bars
>>> from backtide.storage import query_bars
>>> from backtide.strategies import MultiBollingerRotation

>>> profiles = resolve_profiles(["AAPL", "MSFT"], "stocks", ["1h", "1d"])
>>> result = download_bars(profiles)

>>> data = query_bars("AAPL")
>>> print(data.head())

  symbol interval provider  ...  adj_close       volume  n_trades
0   AAPL       1d    yahoo  ...   0.098207  469033600.0      None
1   AAPL       1d    yahoo  ...   0.093083  175884800.0      None
2   AAPL       1d    yahoo  ...   0.086251  105728000.0      None
3   AAPL       1d    yahoo  ...   0.088386   86441600.0      None
4   AAPL       1d    yahoo  ...   0.090949   73449600.0      None

[5 rows x 13 columns]

>>> results = run_experiment(
...     name="test experiment",
...     symbols=["AAPL", "MSFT"],
...     interval="1d",
...     strategies=[MultiBollingerRotation()],
... )
>>> print(results)

ExperimentResult(id="d94eed792875472b", name="test experiment", status="Success", n_strategies=1)