Predicting Cryptocurrency Using AI

Image sources: Quantum Ai Trading

Local Chapter Bahrain Chapter

Coordinated byBahrain ,

Status: Completed

Project Duration: 10 Jun 2023 - 10 Jul 2023

Open Source resources available from this project

Project background.

Virtual currencies are financial assets and exchange currencies, making accurate price prediction essential to understanding the patterns of cryptocurrency behaviours. The cryptocurrency market has become increasingly volatile and unpredictable in recent years, leading many traders and investors to seek out new tools and strategies for predicting price movements and identifying profitable opportunities. Machine learning models offer a powerful solution to this challenge, leveraging vast amounts of historical data and sophisticated algorithms to analyze trends, identify patterns, and generate accurate predictions of future market behavior. By using these models to analyze cryptocurrency market data, traders and investors can gain valuable insights into the underlying dynamics of the market, and make more informed and profitable decisions about when to buy, sell, or hold their assets.

The problem.

Bitcoin is highly volatile, with a 7-year daily return rate standard deviation of 3.85%. Given the extremes in price fluctuations, the function of cryptocurrencies as a valuable commodity and a method of transaction has been criticized. Time series prediction models can help explore data variables that contribute to the behavior and risk assessment of peer-to-peer electronic payment systems.

Project goals.

To develop and compare algorithmic models with high prediction accuracy for determining the value of cryptocurrencies and explaining variables that influence the price.

Project plan.

  • Week 1

    Establishing teams for data processing, exploratory data analysis, research and presentation teams, deployment team and machine learning modelling teams, and assigning project managers.

  • Week 2

    Data processing and EDA, start research paper and presentation

  • Week 3

    ML Models (i.e. SVM, RF, LSTM, CNN, time-series algorithms)

  • Week 4

    Model evaluation, deployment, research and presentation

Learning outcomes.

Understanding cryptocurrencies; applying traditional and state of the art ML Models to historical crytocurrency data to make quantifiable predictions.

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