lgh0504 Goto Github PK

cnn-ta

Algorithmic Financial Trading with Deep Convolutional Neural Networks: Time Series to Image Conversion Approach: A novel algorithmic trading model CNN-TA using a 2-D convolutional neural network based on image processing properties.

cointanalysis

Python library for cointegration analysis. It carries out cointegration test and evaluates spread between cointegrated time-series based on scikit-learn API.

convex-optimization-boyd

This is CVXPY implementation of convex optimization additional problems

covpred

Covariance prediction via convex optimization

crypto-arbitrager

crypto asset arbitrage opportunity watcher, market maker, hedge and arbitrage

cryptocurrency_arbitrage

Automated Trading program that detects pairwise and triangular arbitrage opportunities on altcoin/bitcoin exchanges

cuda-learning

Collaborative learning on CUDA GPU programming for mathematical finance

cuda-premium-calculator

Implementation on GPU with CUDA the Longstaff-Schwartz algorithm to compute American stock option prices.

cvx101-hw-with-python

Home work in python using cvxpy to Stephen Boyd's Convex Optimization class (CVX101 Stanford)

dask-lightgbm

data

Data and code behind the stories and interactives at FiveThirtyEight

data_integration_celery

通过celery定期执行更相关任务，将万得wind，同花顺ifind，东方财富choice、Tushrae、JQDataSDK、pytdx、CMC等数据终端的数据进行整合，清洗，一致化，供其他系统数据分析使用

dataminingcase

数据挖掘（实战代码/欢迎讨论/大量注释/机器学习）. 你将习得，如：数据的处理、LightGBM、GridSearchCV寻找最优参、StratifiedKFold分层5折切分、画AUC图、输出预测名单等。

deep-learing-from-scratch

This book is an introductory book in the true sense of deep learning. It analyzes the principles and related technologies of deep learning in a simple way. Using Python 3 in the book, try not to rely on external libraries or tools to lead the reader to create a classic deep learning network from scratch, so that readers can gradually understand deep learning in the process. The book not only introduces the basic knowledge of deep learning and neural network concepts, features, but also provides an in-depth explanation of error back propagation method, convolutional neural network, etc. Practical practical skills, automatic driving, image generation, reinforcement learning, etc., and why the deepening layer can improve the "why" of recognition accuracy. This book is suitable for deep learning beginners to read, but also can be used as a college textbook.

deep-reinforcement-learning-algorithms

25 projects in the framework of Deep Reinforcement Learning algorithms: DQN, PPO, DDPG, TD3, SAC, A2C and others. Each project is provided with a detailed training log.

deep-reinforcement-learning-for-stock-trading

An implementation of Deep Reinforcement Learning agents (DDQN, Dueling, Distributional, A2C, etc.) for stock trading

deep-reinforcement-learning-with-pytorch

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

deep_rl_trader

Trading Environment(OpenAI Gym) + DDQN (Keras-RL)

deeplob-deep-convolutional-neural-networks-for-limit-order-books

This jupyter notebook is used to demonstrate our recent work, "DeepLOB: Deep Convolutional Neural Networks for Limit Order Books", published in IEEE Transactions on Singal Processing. We use FI-2010 dataset and present how model architecture is constructed here. The FI-2010 is publicly avilable and interested readers can check out their paper.

deeptrade

A LSTM model using Risk Estimation loss function for stock trades in market

derivativesforexcel

Bare bones derivative pricing formula for excel using the methodology of Steve Dalton from "Excel add-in development in C/C++"

dmm-net

DMM-Net: Deep Multi-modal Learning for Time Series Classification

docker_practice

Learn and understand Docker technologies, with real DevOps practice!

dotnet.jl

Julia ❤️ .NET

elitequant_python

Python quantitative trading and investment platform; Python3 based multi-threading, concurrent high-frequency trading platform that provides consistent backtest and live trading solutions. It follows modern design patterns such as event-driven, server/client architect, and loosely-coupled robust distributed system. It follows the same structure and performance metrix as other EliteQuant product line, which makes it easier to share with traders using other languages.

enterprise-components

Enterprise Components for kdb+

event-driven-backtesting

The strategy-backtesting repository will hold the event driven python backtester. This program will test algorithmic strategies and provide interaction to a fake Interactive Brokers portfolio.

event-driven-research

This repo containing code relevant to my original research about event driven strategy.

exceldna

Excel-DNA - Free and easy .NET for Excel. This repository contains the core Excel-DNA library.

excelwrappercpp-cli

Better Wrapper for Excel using C++/CLI. Uses managed classes. Made to feel like C#/VBA's Excel.