Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/diogomatoschaves/MyCryptoBot

Automated, open source crypto trading and backtesting platform
https://github.com/diogomatoschaves/MyCryptoBot

algorithmic-trading algotrading backtesting backtesting-trading-strategies binance binance-bot binance-trading-bots crypto crypto-trading crypto-trading-bot cryptocurrency open-source quantitative-analysis quantitative-trading trading trading-bot trading-bot-for-binance trading-platform trading-strategies trading-systems

Last synced: 2 months ago
JSON representation

Automated, open source crypto trading and backtesting platform

Awesome Lists containing this project

README 

        
# MyCryptoBot



![build_badge](https://github.com/diogomatoschaves/MyCryptoBot/workflows/test-build-data-app/badge.svg)

![build_badge](https://github.com/diogomatoschaves/MyCryptoBot/workflows/test-build-model-app/badge.svg)

![build_badge](https://github.com/diogomatoschaves/MyCryptoBot/workflows/test-build-execution-app/badge.svg)

[![codecov](https://codecov.io/gh/diogomatoschaves/MyCryptoBot/branch/master/graph/badge.svg?token=M1Z50IJEJ3)](https://codecov.io/gh/diogomatoschaves/MyCryptoBot)





  

   




Welcome to the MyCryptoBot repository, a free, open source, and extensible trading platform that utilizes a 

microservices architecture. The platform empowers you to run your own trading bots and strategies locally 

or in the cloud. At present, the app is integrated with Binance, supporting both the 

[testnet](https://testnet.binancefuture.com) and [live](https://www.binance.com/en/futures) *Futures* accounts.



For those looking to validate trading strategies against historical data, **[stratestic](https://github.com/diogomatoschaves/stratestic)** 

offers a comprehensive backtesting framework and integrates seamleassly with this trading platform.



*Please check the **[disclaimer](DISCLAIMER.md)** before using this repository.*



## Installation & Usage



The detailed steps for installing and using this application locally and remotely on Heroku are provided in the

[INSTALLATION.md](INSTALLATION.md) file. However, we recommend that you understand the app's structure before 

diving into the installation steps.



## Introduction



The primary goal of this repository is to provide a platform that enables you to deploy your strategies, 

run them locally or on a remote server, and manage all your trading activities with a user-friendly web UI. 

The app comprises three micro-services that are interconnected via a database and served by a frontend web app.

There is a ready-made functionality to deploy the services to Heroku, which will require an account and associated 

monthly costs.



The app's architecture consists of the following:



![MyCryptoBot Architecture](shared/utils/drawings/MyCryptoBot%20architecture.png)



### Data Service



This service acts as the data warehouse of the app. It acquires data from external sources, such as exchanges, 

preprocesses it, and stores it in a database. When a new trading bot is initiated through the user interface, 

a new data pipeline is initiated, fetching live data from the various sources. Whenever a new candle is completed, 

a request is sent to the **Model** app for processing the new data according to the strategy.



This service can also be used to retrieve historical data for any symbol.



```python

from data.sources.binance import get_historical_data

data = get_historical_data("BTCUSDT", "1h", "2021-01-01", "2021-01-31", save_file=True)

```



### Model Service



The Model service is responsible for processing the data and generating a signal (Buy or Sell) according to the 

selected strategy. Whenever a request is received to generate a new signal, the data is loaded, processed, 

and a signal is produced, which is then sent to the **Order Execution** service with an order to be executed.



This is also the module where you can add a new strategy. Check [ADD_NEW_STRATEGY.md](ADD_NEW_STRATEGY.md) for more 

details on how to do this.



### Order Execution Service



The Order Execution service acts as a layer to handle the communication between the app and the exchange. 

It receives buy and sell orders from the **Model** service with a signal, assesses whether an action is required 

(if the received signal differs from the current one), and responds accordingly.



### Web App



The user interface enables the user to start and stop trading bots, view open positions, executed trades, 

statistics, and more. The following are brief descriptions of the various views of the app.



#### Dashboard





  




This serves as the main view of the application, presenting a comprehensive overview of the equity evolution, 

trading bot data, open positions, and associated trades over time. Summary statistics are displayed to provide 

key insights. Users can easily navigate to different sections of the app by clicking on the corresponding section 

or utilizing the left-hand side menu.



#### Trading Bots





  




This page provides an overview of all existing trading bots along with their current status. 

Users have the ability to edit, start, and stop these bots directly from this view. 

Clicking on a specific trading bot leads to a detailed page, showcasing the equity evolution, 

statistics, and a comprehensive list of all trades associated with that bot.





  




Additionally, users can create a new trading bot by selecting the `New Trading Bot` button. 

Simply fill in all the required details, and the new bot will be created and initiated automatically.





  




#### Positions





  




This page displays the currently open positions linked to the trading bots.



#### Trades





  




This page shows all the trades executed by any of the trading bots. It is possible to filter by live or testnet trades.



## Software Requirements



- [Poetry](https://python-poetry.org/docs/) (For creating an environment for local development.)

- [Docker](https://docs.docker.com/get-docker/) (For running the app locally.)

- [Heroku CLI](https://devcenter.heroku.com/articles/heroku-cli) (For deploying the app to a Heroku.)

- [AWS S3](https://signin.aws.amazon.com/) (For storing machine learning models in the cloud.)