Stock Prediction Using Evolution Strategy

EasyChair Preprint 4765

Abstract

In Day Trading, there are lot of things you have to understand. There are all these fancy terms, technical indicators and background knowledge that you have to understand to trade effectively. You have to understand order types, indicators, things related to account activities. We have taken this complex topic and made it even more complex by adding this wonderful world of programming into it.

Stock Trading has ended up playing a significant function for the financial specialists of various organizations and the day traders. Understanding the reasons for market fluctuations in their beginning phases is the place where the brokers and investors slack. The fundamental goal to put resources into the market is to get capital returns and benefits. We will be building up a bot that that can take trading strategies and execute them in an automate fashion with minimal or limited interaction. We have to be explicit with how we trade in order to make the bot understand how we want it to trade and define the type of orders, the quantity, the profit margins and when to exit the trade. One of the most important objectives of the trading bot and what it has to do to function the way we want it to do would be to stream quotes, i.e., get price information which would further help in calculating indicators, placing orders and organizing our data. This bot also has to check for

any fluctuations in the market as referenced before, study that data and cautiously contribute on just those stocks which give at least 1 percent return using several algorithms like sentimental analysis and evolutionary strategies from reinforcement learning.

In this project, we use ZerodhaApi which will act as a data set to provide a huge pool of data to the bot to make it understand and asses the market better.

Keyphrases: Day Trading, Day trading bot, Machine Learning Algorithms, Reinforcement Learning, Sentimental Analysis, Stock Prediction, Zerodha api

@booklet{EasyChair:4765,
  author    = {Archit Madan and Ashish Bali and Aayush Upadhyay and Piyush Sah and Vibha Nehra},
  title     = {Stock Prediction Using Evolution Strategy},
  howpublished = {EasyChair Preprint 4765},
  year      = {EasyChair, 2020}}