V-PCap's repositories
django-crypto-trading-bot
Multi-Account simple trading bot
freqtrade
Free, open source crypto trading bot
tribeca
A high frequency, market making cryptocurrency trading platform in node.js
Krypto-trading-bot
Self-hosted crypto trading bot (automated high frequency market making) written in C++
awesome-quant
A curated list of insanely awesome libraries, packages and resources for Quants (Quantitative Finance)
algotrading
Algorithmic trading framework for cryptocurrencies.
CryptoList
Curated collection of blockchain & cryptocurrency resources.
Blockchain
Compilation of useful documents and scientific papers about Blockchain & cryptocurrencies.
catalyst
An Algorithmic Trading Library for Crypto-Assets in Python
cointrol
฿ Bitcoin trading bot with a real-time dashboard for Bitstamp.
blackbird
Blackbird Bitcoin Arbitrage: a long/short market-neutral strategy
bitcoin-arbitrage
Bitcoin arbitrage - opportunity detector
Crypto-Liquid-Market-Maker
Deployment of a market maker trading strategy through the GDAX.com API
gekko
A bitcoin trading bot written in node - https://gekko.wizb.it/
peregrine
Detects arbitrage opportunities across 131 cryptocurrency exchanges in 50 countries
theo
Ethereum recon and exploitation tool.
crypto-arbitrage-framework
A cryptocurrency arbitrage framework implemented with ccxt and cplex. It can be used to monitor multiple exchanges, find a multi-lateral arbitrage path which maximizes rate of return, calculate the optimal trading amount for each pair in the path given flexible constraints, and execute trades with multi-threading implemenation.
CryptoBot
High(ish) frequency trading bot for cryptocurrencies, using Machine Learning for future price predictions
pytrader
cryptocurrency trading robot
btctrading
Time Series Forecast with Bitcoin value, to detect upward/down trends with Machine Learning Algorithms
bitpredict
Machine learning for high frequency bitcoin price prediction
SGX-Full-OrderBook-Tick-Data-Trading-Strategy
Providing the solutions for high-frequency trading (HFT) strategies using data science approaches (Machine Learning) on Full Orderbook Tick Data.