- Trading Quantitative Trading is an application of Technical Analysis tools, such as Backtesting to evaluate investments by analyzing statistical trends gathered from trading activity, such as price movement and volume.
- Backtesting is accomplished by reconstructing, with historical data, trades that would have occurred in the past using rules defined by a given strategy. The result offers statistics to gauge the effectiveness of the strategy.
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Table of contents
Trade involves the transfer of goods and services from one person or entity to another, often in exchange for money. Economists refer to a system or network that allows trade as a market.
A cryptocurrency is a digital or virtual currency that is secured by cryptography, which makes it nearly impossible to counterfeit or double-spend. Many cryptocurrencies are decentralized networks based on blockchain technology—a distributed ledger enforced by a disparate network of computers.
A startup company wants to make it simple for everyone to enter the world of cryptocurrencies and general stock market trade. It also wants to give investors a reliable source of investment while lowering the risk associated with trading cryptocurrencies.
Although the past performance of any financial market is never a reliable indicator of the future, it is important to run backtests that simulate current and past particular situations as well as their trend over time. Having a clear understanding of the financial system, and stock market trading, and recognizing the complex data engineering systems involved in the crypto and general stock market trading systems are essential.
The data used for generating these back testing results comes from several historical data of stocks and crypto currency trades. These datasets are publicly available and can be found at yahoo finance and binance. You can read a brief description of what a K-line or candlestick data is here.
The front end application can be found here in the frontend folder.
The back end application can be found here in the backend folder.
All the notebooks that are used in this project including EDA and machine learning model generations are found here in the Notebooks folder.
All the scripts and modules used for this project relating to interactions with kafka, airflow, and other frameworks along with default parameters and values used will be found here, in the scripts folder.
All the unit and integration tests are found here in the tests folder.
The detailed use and implementation of the pipelines using Apache Airflow, pipeline summary and interaction, kafka clusters, interaction with the topics on the kafka clusters, front-end images and usage can all be found in this screenshots folder as image files.
- First clone this repo to your local machine using the command below
git clone git@github.com:Group5-week9-10ac/Crypto-Trading-Engineering.git
cd backtesting
pip install -r requirements.txt
- start using the application by first running the front and back ends.
- You can run the front-end by running the following command on the terminal
- A more detailed instruction regarding the front-end can be found at presentation/readme.md file.
cd presentation
npm run start
This project was bootstrapped with Create React App.
In the project directory, you can run:
Runs the app in the development mode.
Open http://localhost:3000 to view it in your browser.
The page will reload when you make changes.
You may also see any lint errors in the console.
Launches the test runner in the interactive watch mode.
See the section about running tests for more information.
Builds the app for production to the build folder.
It correctly bundles React in production mode and optimizes the build for the best performance.
The build is minified and the filenames include the hashes.
Your app is ready to be deployed!
See the section about deployment for more information.
You can learn more in the Create React App documentation.
To learn React, check out the React documentation.
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- You can run the back-end by running the following command on the terminal
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