Azure Crypto Currency Trading Simulator

Completion Date:

07-01-2022

Project Overview

Every minute prices are fluctuating on financial exchanges all over the world. Many savvy traders make use of technical analysis techniques to assist them in gaining an edge in extracting profits from those markets. This project focuses on the Crypto Currency market, specifically Bitcoin, Ethereum, Ripple, NEO, Stellar, Omise, Tron, ZCash and EOS. It constructs a data warehouse that is capable of allowing traders to back test and improve their trading strategies in a risk-less environment. After building and loading a Data Warehouse in Azure, the KPIs will test the effectiveness of a trading strategy that uses 9,12 and 26 Day Moving Average Convergence Divergences to produce buy signals, sells if the price increases by 4%, sets a stop loss using a 14 Day Average True Range and sells at the close of trading if the 4% increase is not reached or the stop loss is not hit.

Role:

I was the lead ETL and Data Warehouse developer.

Problem Statement

The interest in trading Crypto Currencies has taken the world by storm but most traders don\'t make out to well. It\'s estimated that 95% of traders lose money trading virtual currency and 80% quit trading within the first or second year. Building a data warehouse that is capable of back testing trading strategies can provide traders, at all levels of competency, a way to test their theories and intuitions in a risk-less environment before betting the farm.

Executive Summary

Data Dictionary

Key Takeaways

Next Steps

1. Add Technical Indicators that make use of the volume data to improve the trading strategy. One example is the volume weighted average price. This would make use of the volume data that our current strategy doesn’t take into account.
2. Use the Data from the Data Warehouse in Machine Learning and Deep Learning Algorithms to generate buy and sell recommendations.