SystemSelf reportedCryptoPythonalgorithmic-tradingalgotradingbacktestbacktesting-trading-strategiesbot

algotrading

Algorithmic trading framework for cryptocurrencies.

System Structure

Data Used

암호화폐 거래소 시장 데이터가격/거래량 데이터

Rules / Strategy

저장소별 전략/실행 조건 확인 필요

Execution

거래소 API 기반 자동 주문 실행

Editor Summary
저장소 설명과 공개 메타데이터 기준으로 Python 기반 구현, 암호화폐 거래 봇, 거래소 API/프레임워크, 백테스트 성격의 프로젝트로 파악했습니다. 확인 근거는 README, 저장소 토픽, 저장소 설명, GitHub 지표, 파이프라인 필드이며, 주요 데이터는 암호화폐 거래소 시장 데이터, 가격/거래량 데이터입니다. 전략/실행 조건은 저장소별 문서와 코드 확인이 필요하며, 실행 방식은 "거래소 API 기반 자동 주문 실행"라고 보수적으로 기록했습니다. GitHub 지표는 별 1623개, 포크 202개입니다.

Repository	ivopetiz/algotrading
Creator	ivopetiz
Stars / Forks	★ 1623 / 202
License	MIT
Last Updated	2026-01-07
Snapshot Date	2026-07-08 (README below is a copy from this date)

This is third-party open-source code. QuantField does not guarantee its behavior or safety. Review the code before installing or running it.

View on GitHub →

README

Crypto AlgoTrading Framework

Algorithmic trading framework for cryptocurrencies in Python

Algotrading Framework is a repository with tools to build and run working trading bots, backtest strategies, assist on trading, define simple stop losses and trailing stop losses, etc. This framework work with data directly from Crypto exchanges API, from a DB or CSV files. Can be used for data-driven and event-driven systems. Made exclusively for crypto markets for now and written in Python.

Follow this Quickstart Guide if you want to start right away

A Medium story dedicated to this project

Index

Operating modes
How to start
- Pre Requesites
- Collecting data
  - Database
  - Script
Entry functions
- cross SMA
Exit functions
- cross SMA
Plot data
Logs
Examples
- Realtime alert for volume increasing
- Backtest a strategy on BTC-DGB pair using SMA
Pypy
TODO
Additional info

Operating modes

Framework has three operating modes:

Realtime -- Trades with real data in real time, with real money or in simulation mode.
Tick-by-tick -- Testing strategies in real time frames, so user can follow its entries and exits strategies.
Backtest -- Backtesting strategies and presenting the results.

Realtime

In realtime, Trading Bot operates in real-time, with live data from exchanges APIs. It doesn't need pre-stored data or DB to work. In this mode, a bot can trade real money, simulate or alert the user when its time to buy or sell, based on entry and exit strategies defined by the user. Can also simulate users strategies and present the results in real-time.

Tick-by-tick

Tick-by-tick mode allows users to check strategies in a visible timeframe, to better check entries and exit points or to detect strategies faults or new entry and exit points. Use data from CSV files or DB.

Backtest

Allows users to backtest strategies, with previously stored data. Can also plot trading data showing entry and exit points for implemented strategies.

How to start

Pre requisites

To get algotrading fully working is necessary to install some packages and Python libs, as IPython, Pandas, Matplotlib, Numpy, Python-Influxdb and Python-tk. On Linux