Gamestonk Terminal provides a modern Python-based integrated environment for investment research, that allows the average joe retail trader to leverage state-of-the-art Data Science and Machine Learning technologies.
As a modern Python-based environment, GamestonkTerminal opens access to numerous Python data libraries in Data Science (Pandas, Numpy, Scipy, Jupyter), Machine Learning (Pytorch, Tensorflow, Sklearn, Flair), and Data Acquisition (Beautiful Soup, and numerous third-party APIs).
This project was originally written and tested with Python 3.6.8. It should now support Python 3.6, 3.7, and 3.8.
Our current recommendation is to use this project with Anaconda's Python distribution - either full Anaconda3 Latest or Miniconda3 Latest. Several features in this project utilize Machine Learning. Machine Learning Python dependencies are optional. If you decided to add Machine Learning features at a later point, you will likely have better user experience with Anaconda's Python distribution.
- Install Anaconda
Confirm that you have it with:
conda -V. The output should be something along the lines of:
- Create Environment
You can name the environment whatever you want. Although you could use names such as:
diamondhands, we recommend something simple and intuitive like
gst. This is because this name will be used from now onwards.
conda create -n gst python=3.6.8
- Activate the virtual environment
conda activate gst
Note: At the end, you can deactivate it with:
- Fork the Project
- Via HTTPS:
git clone https://github.com/DidierRLopes/GamestonkTerminal.git
- via SSH:
git clone [email protected]:DidierRLopes/GamestonkTerminal.git
Navigate into the folder with:
- Install poetry
conda install poetry
- Install poetry dependencies
This is a library for package management, and ensures a smoother experience than:
pip install -r requirements.txt
- You're ready to Gamestonk it!
Advanced User Install - Python 3.8
Note that the
conda deactivate ->
conda activate in the middle is on purpose, this is sometimes required to avoid issues with poetry
conda create -n gst python=3.8.8 conda activate gst conda install poetry conda deactivate conda activate gst poetry install conda install -c conda-forge fbprophet numpy=1.19.5 hdf5=1.10.5 poetry install -E prediction
Advanced User Install - Machine Learning
If you are an advanced user and use other Python distributions, we have several requirements.txt documents that you can pick from to download project dependencies.
Note: The libraries specified in the requirements.txt file have been tested and work for the purpose of this project, however, these may be older versions. Hence, it is recommended for the user to set up a virtual python environment prior to installing these. This allows to keep dependencies required by different projects in separate places.
If you would like to use optional Machine Learning features:
- Update your feature_flags.py with:
ENABLE_PREDICT = os.getenv("GTFF_ENABLE_PREDICT") or True
- Install optional ML features dependencies:
poetry install -E prediction
If you run into issues installing or
Cannot convert a symbolic Tensor... at runtime, try this:
conda install -c conda-forge fbprophet numpy=1.19.5 hdf5=1.10.5 poetry install poetry install -E prediction
If you would like to set up a docker image:
- Build the docker:
docker build .
- Run it:
docker run -it gamestonkterminal:dev
Note: The problem with docker is that it won't output matplotlib figures.
*Commands that may help you in case of an error:
python -m pip install --upgrade pip
pip install pystan --upgrade
poetry update --lock
If you run into trouble with poetry and the advice above did not help, your best bet is to try
poetry update --lock
conda activate gst, then try again
Delete the poetry cache, then try again
Platform Location Linux "~/.cache/pypoetry" Mac "~/Library/Caches/pypoetry" Windows "%localappdata%/pypoetry/cache"
Track down the offensive package and purge it from your anaconda
<environment_name>folder, then try again (removing through conda can sometimes leave locks behind)
Platform Location Linux/Mac "~/anaconda3/envs" or "~/opt/anaconda3/envs" Windows "%userprofile%/anaconda3/envs"
Completely nuke your conda environment folder and make a new environment from scratch
Reboot your computer and try again
Submit a ticket on github
The project is build around several different API calls, whether it is to access historical data or financials.
These are the ones where a key is necessary:
- Alpha Vantage: https://www.alphavantage.co
- Quandl: https://www.quandl.com/tools/api
- Reddit: https://www.reddit.com/prefs/apps
- Twitter: https://developer.twitter.com
- Polygon: https://polygon.io
- Financial Modeling Prep: https://financialmodelingprep.com/developer
- FRED: https://fred.stlouisfed.org/docs/api/api_key.html
- News API: https://newsapi.org
When these are obtained, don't forget to update config_terminal.py.
Alternatively, you can also set them to the following environment variables: GT_API_KEY_ALPHAVANTAGE, GT_API_KEY_FINANCIALMODELINGPREP, GT_API_KEY_QUANDL, GT_API_REDDIT_CLIENT_ID, GT_API_REDDIT_CLIENT_SECRET, GT_API_REDDIT_USERNAME, GT_API_REDDIT_USER_AGENT, GT_API_REDDIT_PASSWORD, GT_API_TWITTER_KEY, GT_API_TWITTER_SECRET_KEY, GT_API_TWITTER_BEARER_TOKEN, GT_API_POLYGON_KEY, GT_FRED_API_KEY, GT_API_NEWS_TOKEN.
Note that it is not necessary to have a valid Alpha Vantage key to get daily OHLC values.
Start by loading a ticker of interest:
load -t GME
The menu will expand to all its menus since a ticker has been loaded.
View the historical data of this stock:
Slice the historical data by loading ticker and setting a starting point, e.g.
load -t GME -s 2020-06-04
Enter in technical analysis menu with
and run a SMA with:
However, imagine that you wanted to change the length of the window because you don't want to go long but do a swing, and therefore a smaller window is necessary. Check what settings are available on the SMA command:
Once that has been seen, set the parameters that you want after flagging them. In this case, to change length window to 10, we would have to do:
sma -l 10