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README.md

KAVA EQUITIES 2020 PERFRMANCE

Binder

You might be interested in 🤖 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

Available Notebooks

Currently thrre is two notebooks.

  1. 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

  2. 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

Machine Learning Map

Machine Learning Map

Prerequisites

Installing Python

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 versions.

Installing Dependencies

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:

jupyter notebook

After this Jupyter Notebook will be accessible by http://localhost:8888.

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

Datasets

The list of datasets that is being used for Jupyter Notebook demos may be found in data folder.

Supporting the project

❤️GitHub or ❤️Twitter.

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