Stock Indicators for Python: QuickStart Guide

A beginner's guide to setting up and using the Stock Indicators for Python library for financial market analysis.

Tip

TLDR, for the impatient, run these commands to fast-forward this tutorial:

git clone https://github.com/facioquo/stock-indicators-python-quickstart.git
cd stock-indicators-python-quickstart
python -m venv .venv
source .venv/bin/activate  # On Windows use: .venv\Scripts\activate
pip install stock-indicators
python main.py

or follow step-by-step instructions below

Install prerequisite software

Required software versions:

• Python ≥ 3.8
• .NET SDK ≥ 6.0
• Git (any recent version)
• VS Code (recommended)

VS Code Extensions:

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

# Verify installations
python --version   # Should be ≥ 3.8
dotnet --version   # Should be ≥ 6.0

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):

   # create environment
   python -m venv .venv
   
   # then activate it
   source .venv/bin/activate  # On Windows use: .venv\Scripts\activate

   You can also use VS Code 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

   # install the package
   pip install stock-indicators

   I'm using v1.3.1, the latest version. You should see it installed in .venv/Lib/site-packages.

   # verify the install
   pip freeze --local

   ...
   clr-loader==0.2.7
   pycparser==2.22
   pythonnet==3.0.5
   stock-indicators==1.3.1
   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.

   # Get price history data from CSV file
   with open("quotes.csv", "r", newline="", encoding="utf-8") as file:
      rows = list(csv.reader(file))
   
   # Convert rows into Quote objects that stock-indicators understands
   # CSV returns strings, but Quote needs numbers for prices and volume
   quotes = []
   for row in rows[1:]:  # skip 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 results
   print("Date        SMA")
   for r in islice(results, 0, 30):  # show first 30 days
      sma = f"{r.sma:.3f}" if r.sma else ""
      print(f"{r.date:%Y-%m-%d}  {sma}")

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  213.872
    2017-01-10  214.102
    2017-01-11  214.200
    2017-01-12  214.226
    2017-01-13  214.196
    2017-01-17  214.156
    2017-01-18  214.210
    2017-01-19  213.986
    2017-01-20  214.024
    ...

    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. We're showing 3 decimal places here.

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

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Common issues

• Import errors: Ensure you've activated the virtual environment
• Runtime errors: Verify .NET SDK installation
• .NET loading issues: On Linux/macOS, you may need additional dependencies

Next steps

• Explore all available indicators
• Learn about data sources
• Join our community discussions

Getting help

Having trouble? Try these resources:

• Documentation
• GitHub Discussions
• Stack Overflow

Share your work

Built something cool? Share it with the community!

— @DaveSkender, January 2025