Beating Stock Market

Beating Stock Market

Note from Towards Data Science’s editors: While we allow independent authors to publish articles in accordance with our rules and guidelines, we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our Reader Terms for details. Summary This is a personal project in which I have tried to develop a trading application using machine learning tools.

Note from Towards Data Science’s editors:_ While we allow independent authors to publish articles in accordance with our [rules and guidelines_](https://towardsdatascience.com/questions-96667b06af5), we do not endorse each author’s contribution. You should not rely on an author’s works without seeking professional advice. See our [Reader Terms_](https://towardsdatascience.com/readers-terms-b5d780a700a4) for details._

Summary

This is a personal project in which I have tried to develop a trading application using machine learning tools. Starting with data modelling along with a categorisation based on distribution and machine learning techniques, I have developed a trading strategy for beginner investors to generate low-risk profit with the help of this application.

Introduction

The market analysis is both interesting and complex as it can be seen in the following link [1]. Nevertheless, there are several works carried out with machine-learning which try to shed light on this field.

In this piece of work, I have created an application consisting of two main points:

  1. A screen where stock market index may be analysed over different temporal horizons. Here it can be found a candlestick chart; a chart to analyse technical indicators [2]; a line chart which shows the percentage of price change between days, as well as a box-plot representing this last chart in order to understand that distribution.
  2. A screen where the analysis of the trading strategy which I have developed (Strategyone) can be done. This strategy is divided into two different parts: the first one consisting of the prediction of stock market index movements by means of machine learning, whereas the second one involves the comparison between the current data vectors prediction and what happened in the past. The chosen temporal horizons range from 7, 14, 21 to 28 days.

This last section is explained thoroughly in “How to beat the market” and “Trading strategy”

Data has been obtained through the Alpha Vantage API [3], while a list of the stock market index from the Finnhub API [4].

fintech data-science trading machine-learning stock-market

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