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ai-for-trading

Udacity nanodegree: AI for Trading

ai-for-trading-1

📈This repo contains detailed notes and multiple projects implemented in Python related to AI and Finance. Follow the blog here: https://purvasingh.medium.com

artificial-intelligence-for-trading

Content for Udacity's AI in Trading NanoDegree.

chatbot

cs-notes

:books: 技术面试必备基础知识、Leetcode、计算机操作系统、计算机网络、系统设计

data-analysis

Data Science Using Python

deep-learning-resources

由淺入深的深度學習資源 Collection of deep learning materials for everyone

deep-learning-with-python-notebooks

Jupyter notebooks for the code samples of the book "Deep Learning with Python"

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

deeplearningzerotoall

TensorFlow Basic Tutorial Labs

finance

Here you can find all the quantitative finance algorithms that I've worked on and refined over the past year!

fyp_dev

ho-steven.github.io

leetcode-go

✅ Solutions to LeetCode by Go, 100% test coverage, runtime beats 100% / LeetCode 题解

machine-learning-for-trading

Code for Machine Learning for Algorithmic Trading, 2nd edition.

pythoncode-tutorials

The Python Code Tutorials

pytorch-tutorial

PyTorch Tutorial for Deep Learning Researchers

pytorchzerotoall

Simple PyTorch Tutorials Zero to ALL!

quantstats

Portfolio analytics for quants, written in Python

simplestockanalysispython

Teaches step-by-step to analysis stock data in python.

sp500-analyzer

stock_market_data_analysis

Scrape, analyze & visualize stock market data for the S&P500 using Python. Build a basic trading strategy using machine learning to assess company performance and determine buy, sell, hold. Read me & instructions available in Spanish. This is a working repo, with plans to expand the project from technical analysis to fundamental analysis.

stock_market_prediction_with_snp500

This project would demonstrate the following capabilities: 1. Extraction Loading and Transformation of S&P 500 data and company fundamentals. 2. Exploratory and Time Series Data Analysis on top of the stock data. 3. Stock Screener based on fundamentals. 4. Stock Price Prediction using multiple and/or an ensemble of machine learning models.

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