Automated Stock and Crypto Trading using Machine Learning

  • Introduction

    Volitility in trading is often seen as a negative thing that often keeps traders up at night, as price swings wildly, fortunes made earlier can be lost in just moments. While volitility may appear as the villian of the trading world, but what if you could ride the waves of these intraday price fluctuation. Suddenly this opens up a new opportunity for traders to capitalize on.

    About the project

    There are 2 main methods to analyse a particular stock, namely fundamental analysis and technical analysis. The latter is considered to play a bigger role in the intra-day price swings that this project aims to take advantage of for profits. Technical analysis is all about gathering data from price action charts (such as candlestick charts) and finding patterns in them that might indicate the future price action of a stock.

    Componets of the Project

    1. Data Gathering and Preprocessing: Acquiring high-frequency stock data and preprocessing it to ensure accuracy and consistency throughout the trading session.
    2. Feature Engineering: Constructing a set of relevant features from the raw data is crucial for model accuracy. These features include technical indicators and classifiers based on research papers.
    3. Machine Learning Model: Employing machine learning techniques to build a model capable of recognizing and predicting intraday price fluctuations.
    4. Trade Execution: Implementing a trading strategy that leverages the model’s predictions to initiate buy or short sell orders during intraday waves.
    5. Risk Management: Incorporating risk management mechanisms to safeguard trading capital and optimize profit extraction.
    6. Performance Evaluation: Continuously monitoring and evaluating the trading system’s performance, assessing its ability to capture and capitalize on intraday waves, and adjusting strategies as needed.

Project timeline