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Cryptocurrency Data Analysis

This project is all about the visualization and quant strategy testing for the cryptocurrency market. It allows you to follow the market as closely as possible in real-time, while giving you full control over incoming financial data.

Getting Started

Its very simple to get started. Just clone/download this repository onto your computer.

How to run?

System pre-requesits:

  • pipenv,
  • Python version 3+,
  • Have the dependecies installed (outlined below)

Now, once you got the pre-requisits, run the following in inside the folder

  • pipenv shell to start the virtual enviroment,
  • install the dependecies into your dev list, from the Pipfile.lock file, using pipenv install --ignore-pipfile --dev,
  • that is it!

Dependencies Overview:

This project uses

  • pandas
  • numpy
  • matplotlib
  • binance

All of which can be found in the Pipfile.lock file.

Algo-Trading Method:

This project works in the following way:

#1. First, you get the Historical data for a particular period in time from available API Available Exchanges:

  • Binance

#2. Parse the gathered data into the strategy simulator, for constructing a table (dataframe) with necessary details, to be used for further analysis.

#3. Pass the strategy specific table (dataframe), into the risk_management() method, to calculate the necessary risk assessment for the respective strategy table (dataframe).

#4. After the strategy has been risk analysed thoroughly, the metrics + the strategy ID are saved/appended to a list of risk_metrics which can then be saved into a target file.

#5. The strategy signals are saved into a JSON file for future further analysis.

Current Testing Methods

  • x2 SMA Cross
  • x2 EMA Cross

Project Structure

hist_data/ -> contains a snapshot of historical data in JSON format from a target exchange for a particular trading market pair/ticker.

instance/ -> contains local files that are to be filled out and used on a local machine, such as: secret keys, api keys, etc.

other/ -> contains random code used thorughout the project that may or may not be useful in the future.

results/ -> contains the strategy processed output data in JSON format from strategy/, genereated from any target hist_data/ JSON file.

strategy/ -> contains all target and/or experimental strategies used in the project, for testing and validation.

out.json -> contains the strategy processed output data in JSON format from strategy/, genereated from any target hist_data/ JSON file.

main.py -> contains all of the main logic to orchistrate the project from one single file, with stuff like: variables, main, etc.

MarektOnClosePortfolio.py -> contains the backtesting for a strategy dataframe (df) for tracing and tracking the performance of the stratey using real trading values and variables.

Procedural Examples:

This is a flow diagram with represntaition of how the poroject flows and works: