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
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.
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 supportv3.8
or newer.
# test with bash terminal command
python --version
> Python 3.12.4
I installed
v8.0.303
, the latest LTS version. We supportv6
or newer. We do not support Mono.
# test with bash terminal command
dotnet --version
> 8.0.303
I also installed these recommended extensions:
Create a new project folder.
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.
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.
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
...
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 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.
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