Statistical Inference With Python — Stock Visuals
I am attending online classes, while I know most of you are chilling with your friends, living your college life. So I was working on a python project with my friend Shivam Bhatt. Ok, enough of casual talking let’s dive deep into the project.
Stock Visuals is python based GUI application that retrieves data from various sources using rest APIs. Retrieved data can be plotted to visualise stock movements. Stock visualiser provides some technical indicators which help in stock price prediction. Indicators are plotted on a graph using Matplotlib (python graphical library). Data is manipulated to get useful information using NumPy and pandas. It predicts the future price of a stock from statistical inferences of data. These inferences are done using technical analysis. Stock Visual analyse data with two indicators.
Support Resistance Indicator
Past data of stock consists of various critical points. These critical points can be categorised as maxima, minima or points of inflexion. These maxims/minimal can act as support or resistance.
Support is a price level where a downward trend will pause and bounce back to change its direction.
Resistance is a price level where an upward trend will pause and bounce back to change its direction.
Below snapshot from the project explains this concept:
Williams %r
It is a momentum indicator that moves between 0 and -100 and measures overbought and oversold levels. A reading above -20 depicts overbought and a reading below -80 depicts oversold.
The formula for Williams %r:
Below snapshot from the project explains this concept:
Modules used:
- Tkinter
- PIL
- pandas
- NumPy
- yfinance
- matplotlib
- webbrowser
- pandas_ta
- datetime
- mplfinance.original_flavor
- plotly.graph_objects
We are open to contributions. We want to contribute more to this open-source community. You can check out our GitHub repo for the contribution guide. Don’t forget to star and fork the repo ❤️
Contribution:
- Kanav Arora — https://linktr.ee/kanavarora
- Shivam Bhatt — https://linktr.ee/_Shivam_
