KAVA EQUITIES 2020 PERFRMANCE
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🤖POWEBIKE by Nikola
This repository contains examples of interactive Python Jupyter Notebook to provide window into the abilityt use modern scripting language to do stock market analysis on performance and using automation to create real time tools to gain an advantage for trading equities and options market. Each notebook has interactive Jupyter Notebook demo that allows you to play with the respective strategies, algorithms configurations and immediately see the results, charts and predictions right in your browser.
EDUCATION ONLY / INVESTMENT DISCLAIMER
Futures, options, and forex are leveraged instruments, and you can blow out your account. The content on this repo is for educational purposes. I am not your investment advisor and aren't trying to sell you securities or equities. You're responsible for your trading-- both gains and losses. There is a very high degree of risk involved in trading.
Past results are not indicative of future returns. I and all individuals affiliated with this site assume no responsibilities for your trading and investment results. The indicators, strategies, columns, articles and all other features are for educational purposes only and should not be construed as investment advice. input→output so proceed with caution. And best of luck
Currently thrre is two notebooks.
2020 Performance This is great beginner into into using python for listing equities and using python libaries such as Panadas to performe mathemtical calculations and other popular libaries to graph stock
2021 and beyond 20x Trends (WIP) This is my 2020x investment thesis on emeging hypergrowth industries and ways to create high new porfolio for 20x in this decade
🤖 Linear Regression 📗Math | Linear Regression - theory and links for further readings ⚙️Code | Linear Regression - implementation example ▶️Demo | Univariate Linear Regression - predict
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▶️Demo | Multivariate Linear Regression - predict
country happinessscore by
▶️Demo | Non-linear Regression - use linear regression with polynomial and sinusoid features to predict non-linear dependencies
Machine Learning Map
Make sure that you have Python installed on your machine.
You might want to use venv standard Python library
to create virtual environments and have Python,
pip and all dependent packages to be installed and
served from the local project directory to avoid messing with system wide packages and their
Install all dependencies that are required for the project by running:
pip install -r requirements.txt
Launching Jupyter Locally
All demos in the project may be run directly in your browser without installing Jupyter locally. But if you want to launch Jupyter Notebook locally you may do it by running the following command from the root folder of the project:
After this Jupyter Notebook will be accessible by
Launching Jupyter Remotely
Each algorithm section contains demo links to Jupyter NBViewer. This is fast online previewer for Jupyter notebooks where you may see demo code, charts and data right in your browser without installing anything locally. In case if you want to change the code and experiment with demo notebook you need to launch the notebook in Binder. You may do it by simply clicking the "Execute on Binder" link in top right corner of the NBViewer.
git add -A git commit -m "solar pv" git push origin main
The list of datasets that is being used for Jupyter Notebook demos may be found in data folder.