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nautilus_trader

A high-performance algorithmic trading platform and event-driven backtester

neofinrl

A suite of universal environments for DRL in quant finance.

nni

An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.

numerai

model for prediction challenge at http://numer.ai

oneworld

https://www.kaggle.com/c/jane-street-market-prediction/overview “Buy low, sell high.” It sounds so easy…. In reality, trading for profit has always been a difficult problem to solve, even more so in today’s fast-moving and complex financial markets. Electronic trading allows for thousands of transactions to occur within a fraction of a second, resulting in nearly unlimited opportunities to potentially find and take advantage of price differences in real time. In a perfectly efficient market, buyers and sellers would have all the agency and information needed to make rational trading decisions. As a result, products would always remain at their “fair values” and never be undervalued or overpriced. However, financial markets are not perfectly efficient in the real world. Developing trading strategies to identify and take advantage of inefficiencies is challenging. Even if a strategy is profitable now, it may not be in the future, and market volatility makes it impossible to predict the profitability of any given trade with certainty. As a result, it can be hard to distinguish good luck from having made a good trading decision. In the first three months of this challenge, you will build your own quantitative trading model to maximize returns using market data from a major global stock exchange. Next, you’ll test the predictiveness of your models against future market returns and receive feedback on the leaderboard. Your challenge will be to use the historical data, mathematical tools, and technological tools at your disposal to create a model that gets as close to certainty as possible. You will be presented with a number of potential trading opportunities, which your model must choose whether to accept or reject. In general, if one is able to generate a highly predictive model which selects the right trades to execute, they would also be playing an important role in sending the market signals that push prices closer to “fair” values. That is, a better model will mean the market will be more efficient going forward. However, developing good models will be challenging for many reasons, including a very low signal-to-noise ratio, potential redundancy, strong feature correlation, and difficulty of coming up with a proper mathematical formulation.

open-source-fpga-bitcoin-miner

A completely open source implementation of a Bitcoin Miner for Altera and Xilinx FPGAs. This project hopes to promote the free and open development of FPGA based mining solutions and secure the future of the Bitcoin project as a whole. A binary release is currently available for the Terasic DE2-115 Development Board, and there are compile-able projects for numerous boards.

openaigymrltrader

Reinforcement Learning agent for trading based on custom OpenAI Gym Environment.

optimize-trading-strategy-using-freqtrade

Optimize Trading Strategies Using Freqtrade

optionpayoffs

Visualizes the profit and loss graph of various option strategies

optionpricing

python期权定价;波动率交易策略；期权交易策略

optiver-realized-volatility-prediction

optiver-volatility-prediction

Optiver wants us to predict the realized volatility of a set of stocks on given time IDs using the information collected over a 10mins time window. A classic tabular time-series data, with RMSPE to optimize. Hosted on Kaggle by Optiver.

pair-trading

Financial analysis and demonstration of the classic algorithmic trading method, pair trading. This analysis compares the portfolio's growth with the underlying assets value and volatility over time.

pairs-trading-with-machine-learning

Pair Trading Strategy using Machine Learning written in Python

pairs-trading-with-ml

pandas-polygon

AFML HFT

passivbot_futures

pd-njode

Official implementation of the paper: Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs

penv

An example for making highly customized environments in TensorTrade.

personae

📈 Personae is a repo of implements and environment of Deep Reinforcement Learning & Supervised Learning for Quantitative Trading.

pgportfolio

PGPortfolio: Policy Gradient Portfolio, the source code of "A Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem"(https://arxiv.org/pdf/1706.10059.pdf).

plutus

An automated bitcoin wallet collider that brute forces random wallet addresses

poboquant

quant strategy backtesting from pobo financial

portfolio-management-list

:notebook: List of portfolio management resources, using Reinforcement Learning.

portfolio-rebalance

Small project for rebalancing an investment portfolio by allocating funds to realize specified targets

portfolio_optimization

Portfolio optimization using Riskfolio-Lib

ppo-bihyb

Implementation of our NeurIPS 2021 paper "A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs".

predictions

pvaluation

Quantamental finance research with python

py-finance-cook