Qian Capital (钱资本)'s Projects
This repository contains three ways to obtain arbitrage which are Dual Listing, Options and Statistical Arbitrage. These are projects in collaboration with Optiver and have been peer-reviewed by staff members of Optiver.
Performance analysis of predictive (alpha) stock factors
A bot that makes profit of the best crypto Arbitrage opportunities on the market
High performance datastore for time series and tick data
ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem.
:mag_right: :chart_with_upwards_trend: :snake: :moneybag: Backtest trading strategies in Python.
Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more
Application of reinforcement learning to the development of execution algorithms in the field of finance. These algorithms break CARE orders into CHILD orders and exectute them in the market.
This project is based upon the paper: Frazzini, A. & Pedersen, L. (2014). Betting against beta.
A profitable triangular arbitrage cycles finder.
Details on how to get Binance public data
Detect in-market cryptocurrency arbitrage
C++ Implied Volatility calculator, which uses the Black-Scholes-Merton model to calculate the Implied Volatility of European call and put options.
Blackbird Bitcoin Arbitrage: a long/short market-neutral strategy
🚀 💸 Easily build, backtest and deploy your algo in just a few lines of code. Trade stocks, cryptos, and forex across exchanges w/ one package.
Triangle arbitrage trading bot for Binance
low-cost, high-efficiency, easy-to-implement
Implementation of a Wasserstein Generative Adversarial Network with Gradient Penalty to enforce lipchitz constraint. The WGAN utilizes the wasserstein loss or critic as its loss function instead of the vanilla GAN loss. It has shown to perform better as is often used as a solution to mode collapse, a common issue in GANs where the generator produce
Automatic Cryptocurrency Trading Bot using Triangular or Exchange Arbitrages
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.
Deep Reinforcement Learning toolkit: record and replay cryptocurrency limit order book data & train a DDQN agent
The algorithm to calculate Triangular Arbitrage with depth on Centralised exchanges.
Crypto Trading Bots in Python - Triangular Arbitrage, Beginner & Advanced Cryptocurrency Trading Bots Written in Python
Master Thesis: Limit order placement with Reinforcement Learning
Portfolio optimization and back-testing.
Tradingview Lightweight Charts wrapper for Plotly Dash apps
The official Python client library for Databento.