Nvidia Stock Prediction

No description available

No description available

量化交易管理系统

量化交易 Trading Options Using Monte-Carlo Simulated Black-Scholes Pricing Model

Create a Model to predict movement of S&P 500 Index based on Quantitative, Qualitative and Public sentiment factors. • Natural language processing (NLP) is used to derive a variable for Public sentiment. • A Final Model is created using ML techniques like Decision Trees, SVMs, Random Forests, Neural Networks.

一个面向对象的，可扩展的，易于开发和部署的量化交易程序

量化交易Agent

Quantitative trading Implement a portfolio management system and create investment models

A repository to test some ideas based on Ernest P Chan's book

This repo is a platform for quantitative trading basing on MatLab.

UESTC-互联网与计算智能

量化交易

No description available

虚拟货币(BTC、ETH、DOGE)炒币量化系统项目。币安交易所-量化交易-网格策略实践。火币、OKEX热门交易所未来都支持。最简单收益最靠谱的项目，包教包会。币安智能交易量化项目，

No description available

Binance 量化合约

This is the final team project for the Quantitative Trading course,2019 Spring,by PB17000-015/047/112

QuantitativeTrading(generated_by_Claude)

Quantitative Trading Strategy Visualization

No description available

This repository provides a framework for Quantitative Trading strategies, focusing on the development of Alpha Algorithms. It allows users to backtest and simulate trading strategies with historical market data and execute those strategies in real-time. Works with any timeframe.

Codes from QF621 - Quantitative Trading Strategies' Projects (SMU MSc in Quantitative Finance Term 5)

可擴展的數量化交易模型框架（scalable quantitative trading model frame），分別使用 Julia 和 Python 程式設計語言（computer programming language）各自獨立實現 Julia , Pyhton 竝立兩套方案序列遍歷（sequence traversal），借用 Julia 和 Python 的第三方擴展包（third-party extensions ( libraries or modules )）實現 Julia , Python 竝立兩套方案參數優化算法（parameters optimization）。

FluentMoney

基于springcloud的量化交易平台开发

No description available

量化相关代码

This includes self-learnt materials as well as data that has been scraped off the internet for the purposes of backtesting. It is still in development but not shown everything on github just yet.

No description available