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Document jupyter with docker usage

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Matthias 2020-09-20 15:12:30 +02:00
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# Analyzing bot data with Jupyter notebooks
You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/`.
You can analyze the results of backtests and trading history easily using Jupyter notebooks. Sample notebooks are located at `user_data/notebooks/` after initializing the user directory with `freqtrade create-userdir --userdir user_data`.
## Pro tips
## Quick start with docker
Freqtrade provides a docker-compose file which starts up a jupyter lab server.
You can run this server using the following command: `docker-compose -f docker/docker-compose-jupyter.yml up`
This will create a dockercontainer running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
Please use the link that's printed in the console after startup for simplified login.
For more information, Please visit the [Data analysis with Docker](docker_quickstart.md#data-analayis-using-docker-compose) section.
### Pro tips
* See [jupyter.org](https://jupyter.org/documentation) for usage instructions.
* Don't forget to start a Jupyter notebook server from within your conda or venv environment or use [nb_conda_kernels](https://github.com/Anaconda-Platform/nb_conda_kernels)*
* Copy the example notebook before use so your changes don't get clobbered with the next freqtrade update.
* Copy the example notebook before use so your changes don't get overwritten with the next freqtrade update.
### Using virtual environment with system-wide Jupyter installation
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!!! Note
This section is provided for completeness, the Freqtrade Team won't provide full support for problems with this setup and will recommend to install Jupyter in the virtual environment directly, as that is the easiest way to get jupyter notebooks up and running. For help with this setup please refer to the [Project Jupyter](https://jupyter.org/) [documentation](https://jupyter.org/documentation) or [help channels](https://jupyter.org/community).
## Fine print
Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.
!!! Warning
Some tasks don't work especially well in notebooks. For example, anything using asynchronous execution is a problem for Jupyter. Also, freqtrade's primary entry point is the shell cli, so using pure python in a notebook bypasses arguments that provide required objects and parameters to helper functions. You may need to set those values or create expected objects manually.
## Recommended workflow

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```
You can then run `docker-compose build` to build the docker image, and run it using the commands described above.
## Data analayis using docker compose
Freqtrade provides a docker-compose file which starts up a jupyter lab server.
You can run this server using the following command:
``` bash
docker-compose -f docker/docker-compose-jupyter.yml up
```
This will create a dockercontainer running jupyter lab, which will be accessible using `https://127.0.0.1:8888/lab`.
Please use the link that's printed in the console after startup for simplified login.
Since part of this image is built on your machine, it is recommended to rebuild the image from time to time to keep freqtrade (and dependencies) uptodate.
``` bash
docker-compose -f docker/docker-compose-jupyter.yml build --no-cache
```