Stock Indicators for Python: QuickStart guide

These are the detailed steps to setup a Python project and to run your first finanical market price analysis with the Stock Indicators for Python PyPI library package. This guide is partly derived from the more detailed Visual Studio Code Python Tutorial.

Tip

TLDR, if you just want to quickly run this example, use these CLI commands:

git clone https://github.com/facioquo/stock-indicators-python-quickstart.git
cd stock-indicators-python-quickstart
python -m venv .venv
sh .venv/Scripts/activate
pip install stock-indicators
python main.py

or follow step-by-step instructions below

Install prerequisite software

These are the tools that I've already installed and will use in this guide.

Note

Don't sweat the OS. These instructions were written for Windows 11, but are the same for Mac and Linux OS; however, you may need different tool editions.

Install Git

• Download and install Git for Windows for git and bash terminal CLI

Install Python v3

• Download and install Python

  I installed v3.12.4, the latest LTS version, using administrative privileges, for all users, and chose to add Python to my environment PATH variables. We support v3.8 or newer.

  # test with bash terminal command
  python --version
  > Python 3.12.4

Install the .NET SDK

• Download and install .NET SDK

  I installed v8.0.303, the latest LTS version. We support v6 or newer. We do not support Mono.

  # test with bash terminal command
  dotnet --version
  > 8.0.303

Install the Visual Studio Code IDE

• Download and install VS Code

I also installed these recommended extensions:

• Python Extension Pack (includes primary Python extension)
• Pylance
• Python Debugger

Setup your project

1. Create a new project folder.

2. Optional: initialize a git repository in this folder with git init bash command and add a Python flavored .gitignore file. I found this one in the gitignore templates repo.

3. Initialize Python workspace with a virtual environment (a cached instance):

   # git bash commands
   # create environment
   python -m venv .venv
   
   # then activate it
   sh .venv/Scripts/activate

   You can also use VSCode command: Python: Create Environment ... and then Python: Select Interpreter to pick your just created venv instance. When done correctly, you should have a .venv folder in the root of your project folder. There are other ways to initialize in a global environment; however, this is the recommended approach from the Python tutorial I'd mentioned above.

4. Install the stock-indicators package from PyPI

   # bash terminal command
   pip install stock-indicators

   I'm using v1.3.0, the latest version. You should see it installed in .venv/Lib/site-packages.

   # test with bash terminal command
   pip freeze --local

   ...
   clr-loader==0.2.6
   pycparser==2.22
   pythonnet==3.0.3
   stock-indicators==1.3.0
   typing_extensions==4.12.2
   ...

Write the code

It's time to start writing some code.

5. To start, add a quotes.csv file containing historical financial market prices in OHLCV format. Use the one I put in this repo. You can worry about all the available stock quote sources later.

6. Create a main.py file and import the utilities we'll be using at the top of it.

   import csv
   from datetime import datetime
   from itertools import islice
   from stock_indicators import indicators, Quote

7. Import the data from the CSV file and convert it into an iterable list of the Quote class.

   # import each row of the csv file into a raw iterable string list
   with open('quotes.csv', 'r', newline='', encoding="utf-8") as file:
      rows = list(csv.reader(file))
      file.close()
   
   # parse each row into proper `Quote` format
   quotes = []
   for row in rows[1:]: # skipping CSV file header row
      quotes.append(Quote(
         datetime.strptime(row[0], '%Y-%m-%d'), # date
         row[1], # open
         row[2], # high
         row[3], # low
         row[4], # close
         row[5], # volume
      ))

   These quotes can now be used by the stock-indicators library. For a quickstart that uses pandas.DataFrame, see our online ReplIt code example for the Williams Fractal indicator.

8. Calculate an indicator from the quotes

   # calculate 5-period simple moving average
   results = indicators.get_sma(quotes, 5)

9. Configure results for console output

   # show the first 30 periods, for brevity
   print("Date        SMA")
   for r in islice(results, 0, 30):
      print(f"{r.date:%Y-%m-%d}  {r.sma or ''}")

Run the code

10. Click the Run Python File in Terminal (►) play button in the top-right side of the VS Code editor to run the code, or execute from the commandline in your bash terminal. The SMA indicator output will print to the console.

# from CLI (optional)
python main.py

Date        SMA
2017-01-03
2017-01-04
2017-01-05
2017-01-06
2017-01-09  213.87199999999999
2017-01-10  214.102
2017-01-11  214.2
2017-01-12  214.22599999999997
2017-01-13  214.196
2017-01-17  214.156
2017-01-18  214.20999999999998
2017-01-19  213.98600000000002
2017-01-20  214.02400000000003
...

The small deviations shown in these raw results are normal for double floating point precision data types. They're not programming errors. Developers will usually truncate or round to fewer significant digits when displaying.

You've done it! That's the end of this QuickStart guide.

Still having trouble getting started?

Ask a question in our open community help and support discussions.

And if you end up building something wonderful, come back and share it with us. We love 💖 to see all the creative ways people are using the library.

Good luck 🍀 and have fun in building your systems!

-- @DaveSkender, July 2024