6.3k star terminal fishing tool! Help you cross the stock market!


[introduction]: gametone terminal is a great stock and cryptocurrency market terminal.

brief introduction

Gamestonk terminal is a great stock and cryptocurrency market terminal, which was originally developed for entertainment. Gamestonk terminal provides a modern integrated environment based on Python for investment research, on which ordinary investors can use the most advanced data analysis and machine learning technology for research and analysis.

As a modern environment based on python, gametone terminal allows the use of different extended tool libraries, such as pandas, numpy, SciPy and jumper for data analysis; Python, tensorflow, sklearn, flair for machine learning; Beautiful Soup for data acquisition.

The project address is:


(it is recommended that the full screen and horizontal screen be used for better experience)


The project was originally written and tested based on Python 3.6.8, and now supports Python 3.6, 3.7, and 3.8.

The official recommendation is to use ancconda’s Python distribution, anaconda3 latest or miniconda3 latest. Although the python dependency of machine learning is optional, several functions in the project use machine learning, so if the machine learning related functions are eventually used, the python distribution of ancconda will have a better user experience.

  1. Install ancconda to confirm whether ancconda is installed. Use the following command. If the version number can be output correctly, it means that ancconda is installed:
conda -V
  1. You can name the environment as you want, but it is recommended to use some simple and intuitive expressions, such as GST. After all, you need to use it frequently in the process of creating an environment. The name is too long to be convenient
conda create -n gst python=3.6.8
  1. Activate virtual environment
conda activate gst

Use the following command to disable the environment:

conda deactivate gst
  1. Pull source code
//   Based on httpgit   clone   https://github.com/DidierRLopes/GamestonkTerminal.git//   Based on sshgit   clone   [email protected] :DidierRLopes/GamestonkTerminal.git
  1. Install the Poetry Library
conda install poetry
  1. Install the dependency of poetry
poetry install
  1. Start execution
python terminal.py


First load the stock code of interest:

load -t GME

To view the historical data of the stock:


6.3k star terminal fishing tool! Help you cross the stock market!

Load and set the start time to segment the historical data

load -t GME -s 2020-06-04

In the technical analysis menu, enter:


Run SMA (simple moving average) with the following command:


Check the settings available on the SMA command:

sma -h

Change the length window to 10:

sma -h

To export historical data to a file or standard output:

export -f GNUS_data -F csv

Use support line and resistance line to display the historical data of the past 6 months:


6.3k star terminal fishing tool! Help you cross the stock market!

major function

Gametone terminal mainly includes the following functions:

  • Stock search, including map, sectors and other commands, respectively represents the stock map of the S & P 500 index and displays the industry performance.
  • Market analysis, including watchlist, popular and other commands, respectively shows the user’s attention list and the popular stock market.
  • Web research, view the analysis and research results of other websites, including macroaxis, Yahoo and other commands www.macroaxis.com 、 www.finance.yahoo.com .
  • Fundamental analysis, including commands such as income and assets, represents the company’s income statement and assets respectively.
  • Technical analysis, including EMA, SMA and other commands, respectively represent the exponential moving average and simple moving average.
  • Due diligence, including orders such as news and EST, represents the latest news, quarterly and annual earnings forecast of the company respectively.
  • Technical prediction, including linear, RNN and other commands, respectively represents linear regression and recurrent neural network.

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