xksh Goto Github PK

followers: 0.0 following: 9.0 repos: 63.0 gists: 0.0

Type: User

Company: Tongji university;ENSAE Paristech;Groupe BPCE;INSEE

Bio: Condition de l'homme: inconstance, ennui, inquiétude.

xksh's Projects

15-minute-apps

15 minute (small) desktop apps built with PyQt

abu

阿布量化交易系统(股票，期权，期货，比特币，机器学习) 基于python的开源量化交易，量化投资架构

awesome-computer-vision

A curated list of awesome computer vision resources

awesome-nlp

:book: A curated list of resources dedicated to Natural Language Processing (NLP)

awesome-rl

Reinforcement learning resources curated

bitcoin-arbitrage

Bitcoin arbitrage - opportunity detector

breeds-of-dogs

Using the self-organizing map to explore the modality data of dogs. A clustering of dog breeds is built.

bridge.jl

A statistical toolbox for diffusion processes and stochastic differential equations. Named after the Brownian Bridge.

bytenet

A tensorflow implementation of French-to-English machine translation using DeepMind's ByteNet .

carte-kohonen

Some interesting phenomenons found by self-organizing map

convolutional-neural-stock-market-technical-analyser

Uses Deep Convolutional Neural Networks (CNNs) to model the stock market using technical analysis. Predicts the future trend of stock selections.

deep-learning-in-python

Hands-on, practical knowledge of how to use neural networks and deep learning with Keras 2.0

This project uses reinforcement learning on stock market and agent tries to learn trading. The goal is to check if the agent can learn to read tape. The project is dedicated to hero in life great Jesse Livermore.

evolutionary-trading-strategies

This code illustrates the use of genetic programming to evolve financial trading strategies for a single equity stock. Individuals (strategies) are considered as functions of historical price data, outputting a position allocation. Strategy fitness evaluation is computed by simulating the strategy over historical financial data. Because financial investment requires a fundamental tradeoff between risk and return, strategies are evaluated on multi-objective fitness functions depending on profit and maximum drawdown of the strategy and ranging from very risk-prone to very risk-averse. The population of individual strategies is evolved using tournament selection, single-point crossover, and random mutation as evolutionary operators. Strategies with the best fitness at any stage in the evolutionary process are recorded in a ‘hall-of-fame’. At the end of the evolutionary process, strategies in the ‘hall-of-fame’ are evaluated over a set of test data and selected based on a train-test criterion which penalizes strategies that do not generalize well.

xksh Goto Github PK

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