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# Analyzing bot data with Jupyter notebooks
# 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/` after initializing the user directory with `freqtrade create-userdir --userdir user_data`.
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`.
## Quick start with docker
@ -41,32 +41,35 @@ ipython kernel install --user --name=freqtrade
!!! 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
## Recommended workflow
| Task | Tool |
--- | ---
Bot operations | CLI
| Task | Tool |
--- | ---
Bot operations | CLI
Repetitive tasks | Shell scripts
Data analysis & visualization | Notebook
Data analysis & visualization | Notebook
1. Use the CLI to
* download historical data
* run a backtest
* run with real-time data
* export results
* export results
1. Collect these actions in shell scripts
* save complicated commands with arguments
* execute multi-step operations
* execute multi-step operations
* automate testing strategies and preparing data for analysis
1. Use a notebook to
* visualize data
* munge and plot to generate insights
* mangle and plot to generate insights
## Example utility snippets
## Example utility snippets
### Change directory to root
### Change directory to root
Jupyter notebooks execute from the notebook directory. The following snippet searches for the project root, so relative paths remain consistent.