mjclass
AI Researcher & Data Scientist. Blockchain. Financial Engineering. Curious by nature.
Paris, France
mjclass's Stars
loveunk/Deep-learning-books
Books for machine learning, deep learning, math, NLP, CV, RL, etc
jankrepl/deepdow
Portfolio optimization with deep learning.
instabotai/instabotai
Instagram AI bot with face detection. It works without instagram api, need only login and password.
InstaPy/InstaPy
📷 Instagram Bot - Tool for automated Instagram interactions
bhavinjawade/Advanced-Data-Structures-with-Python
Python implementations of Advanced Data Structures and Algorithms. With each code, there is an associated markdown for explanation and applications of that algorithm or data structure.
krother/advanced_python
Examples of advanced Python programming techniques
Roibal/Cryptocurrency-Trading-Bots-Python-Beginner-Advance
Crypto Trading Bots in Python - Triangular Arbitrage, Beginner & Advanced Cryptocurrency Trading Bots Written in Python
yangyutu/EfficientPython
Efficient python for data science, machine learning, and software engineering
microsoft/nlp-recipes
Natural Language Processing Best Practices & Examples
deshpandenu/Time-Series-Forecasting-of-Amazon-Stock-Prices-using-Neural-Networks-LSTM-and-GAN-
Project analyzes Amazon Stock data using Python. Feature Extraction is performed and ARIMA and Fourier series models are made. LSTM is used with multiple features to predict stock prices and then sentimental analysis is performed using news and reddit sentiments. GANs are used to predict stock data too where Amazon data is taken from an API as Generator and CNNs are used as discriminator.
Azure/DeepLearningForTimeSeriesForecasting
A tutorial demonstrating how to implement deep learning models for time series forecasting
quantopian/research_public
Quantitative research and educational materials
borisbanushev/stockpredictionai
In this noteboook I will create a complete process for predicting stock price movements. Follow along and we will achieve some pretty good results. For that purpose we will use a Generative Adversarial Network (GAN) with LSTM, a type of Recurrent Neural Network, as generator, and a Convolutional Neural Network, CNN, as a discriminator. We use LSTM for the obvious reason that we are trying to predict time series data. Why we use GAN and specifically CNN as a discriminator? That is a good question: there are special sections on that later.
robertmartin8/PyPortfolioOpt
Financial portfolio optimisation in python, including classical efficient frontier, Black-Litterman, Hierarchical Risk Parity
robcarver17/pysystemtrade
Systematic Trading in python
EliteQuant/EliteQuant
A list of online resources for quantitative modeling, trading, portfolio management
yangyutu/EssentialMath
bentrevett/pytorch-sentiment-analysis
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
yangyutu/CommonAlgorithms
Alro10/deep-learning-time-series
List of papers, code and experiments using deep learning for time series forecasting
hudson-and-thames/mlfinlab
MlFinLab helps portfolio managers and traders who want to leverage the power of machine learning by providing reproducible, interpretable, and easy to use tools.