Howtrader: A crypto quant framework for developing, backtesting, and executing your own trading strategies. Seamlessly integrates with TradingView and other third-party signals. Simply send a post request to automate trading and order placement. Supports Binance and Okex exchanges.

quant strategy backtesting from pobo financial

Your AI's shortcut to mass-produce alpha-generating quant strategies.

Quant is a python-based system for stock trading strategy backtesting

qmt、okx、joinquant、ML、AI、Qlib Quant Strategy 量化策略

Codera Quant is a Java framework for algorithmic trading strategies development, execution and backtesting via Interactive Brokers TWS API or other brokers API

QUANTAXIS 策略文档中心

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Webull OpenAPI aims to provide quantitative trading investors with convenient, fast and secure services. Webull aims to help every quant traders achieve flexible and changeable trading or market strategies.

Implementation of "Advances in Financial Machine Learning" quant trading strategies in python (building upon Freqtrade / FreqAI open source project)

I will upload and update my quant strategies here

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Production DeFi strategy framework for Quants

基于FMZ平台和python 目前已复现4个策略 自己开发了1个基于HMM的策略，趋势预测正确率可达85%

多因子选股量化交易策略, 基于中证500指数股票2017至2022年年分钟交易数据构建.

My quant portfolio leverages quantitative finance and data-driven insights to optimize investment strategies. Using advanced models, statistical analysis, and machine learning, I develop systematic trading strategies to capitalize on market inefficiencies and generate alpha.

AI-Agent Alpha Quant Strategy: A-Share Sentiment Index — Daily 0–100 sentiment score from Tushare market microstructure signals + AI commentary for risk warnings and position management.

Quant trading framework by OctoBot. Write, backtest & automate Python trading strategies like TradingView Pine Script. Work in progress.

A quantitative trading strategy backtester with an interactive dashboard. Enables users to implement, test, and visualise trading strategies using historical market data, featuring customisable parameters and key performance metrics. Developed with Python and Polars.

Quant_Strategy

This is a Python-based repository for a multi-strategy quant system. It provides a framework for implementing and testing various quantitative trading strategies on financial time series data, and can be used to generate buy/sell signals based on the outputs of those strategies.

quant_rv is a quantitative ETF trading strategy based on realized volatility, written in R

quant trading strategy research

The strategy module for using VeighNa (vnpy) quant trading platform on the crypto market.

Quantitave research and Engineering on various promisory strategies, existing quant problems and opportunities using Mathematics and Statistics with end-end Data Science workflows

Quantitative Country ETF Trading Strategy

Quant \ Trading \ Momentum Strategy

Firstly, multiple effective factors are discovered through IC value, IR value, and correlation analysis and back-testing. Then, XGBoost classification model is adopted to predict whether the stock is profitable in the next month, and the positions are adjusted monthly. The idea of mean-variance analysis is adopted for risk control, and the volatility of the statistical benchmark index (HS300 Index) is used as a threshold for risk control. Back-testing results: the annual return rate is 11.54%, and the maximum drawdown is 17.91%.

tbg-Quant strategies examples