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
If you just want to run the example code without building it yourself, fork this repo and skip the Write the code section (steps 5-9) entirely.
My baseline environment and the tools that I've already installed:
- Windows 11 OS
- Download and install Git for Windows for git and bash terminal CLI
Note
Don't sweat the OS. These instructions are the same for Mac and Linux users; however, you'll have to download a different version of tools installers from the links provided. Overall, Python and our library are designed to work everywhere -- on Windows, Linux, and Mac operating systems.
-
I installed
v3.12.2
, the latest LTS version, using administrative privileges, for all users, and chose to add Python to my environment PATH variables. We supportv3.8
or newer.# test with bash terminal command python --version > Python 3.12.2
-
I installed
v8.0.202
, the latest LTS version. We supportv6
or newer. We do not support Mono.# test with bash terminal command dotnet --version > 8.0.202
I also installed these recommended extensions:
- Python Extension Pack (includes primary Python extension)
- Pylance
- Python Debugger
-
Create a new project folder.
-
Initialize git in this folder with
git init
bash command. Also add a Python flavored.gitignore
file; I found this one in the gitignore templates repo. This step is optional and is only needed if you intend to store your work in a git repository. -
Initialize Python workspace with a virtual environment (a cached instance):
# git bash commands # create environment python -m venv .venv # then activate it .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. -
Install the
stock-indicators
package from PyPI# bash terminal command pip install stock-indicators
I'm using
v1.2.1
, the latest version. To verify, you should see these subfolders under.venv/Lib/site-packages
:- clr_loader
- pycparser
- pythonnet
- stock_indicators
- and others
It's time to start writing some code.
-
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. -
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
-
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 thestock-indicators
library. For a quickstart that uses pandas.DataFrame, see our online ReplIt code example for the Williams Fractal indicator. -
Calculate an indicator from the
quotes
# calculate 5-period simple moving average results = indicators.get_sma(quotes, 5)
-
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 ''}")
-
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
python main.py
from your bash terminal.# console output 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.
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