Hussam1

Hussam1's Stars

• grelade/time-series-seasonality

  accompanying notebook

  Language:Jupyter Notebook1

• Farmhouse121/Adventures-in-Financial-Data-Science

  Here I am collecting the scripts I have used to prepare my book "Adventures in Financial Data Science" and to support my other writing, such as that on Medium. Those for the book are mostly originally written for the RATS time-series analysis system, which is commercial software, so I have begun porting the code to Python Notebooks written for Google's colab https://colab.research.google.com. This is mostly what I will post here. The figures for the book will be generated by notebooks titled "Section n.n.n Section Title" etc.

  Language:Jupyter Notebook6216

• davidthemathman/gaussian_mixture_wind_distribution

  Show the improvement of a mixed Gaussian at modelling wind data compared to a standard Weibull approach.

  Language:Jupyter Notebook1

• davidthemathman/vae_for_time_series

  Tensorflow implementation of time series generation using a variational autoencoder

  Language:Jupyter Notebook203

• Nixtla/statsforecast

  Lightning ⚡️ fast forecasting with statistical and econometric models.

  Language:Python4k289

• 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.

  4.4k1.7k

• AnalyzerREST/python-tradingview-ta

  Unofficial TradingView technical analysis API wrapper.

  Language:Python1.1k257

• fmzquant/strategies

  quantitative trading with Javascript, Python, C++, PineScript, Blockly, MyLanguage(麦语言)

  4.2k1.4k

• ProfSynapse/open-interpreter

  OpenAI's Code Interpreter in your terminal, running locally

  253

• mpkrass7/solid-octo-robot

  Streamlit app showing state to state migration

  Language:Python1727

• M-ballabio1/MeHEDI-app

  Web application for the data-driven management of a small healthcare facility. Proof of Concept of a wider platform project in the Patient Satisfaction area

  Language:Python2514

• functime-org/functime

  Time-series machine learning at scale. Built with Polars for embarrassingly parallel feature extraction and forecasts on panel data.

  Language:Python1.1k58

• giswqs/earthengine-py-notebooks

  A collection of 360+ Jupyter Python notebook examples for using Google Earth Engine with interactive mapping

  Language:Jupyter Notebook1.4k407

• dipanjanS/practical-machine-learning-with-python

  Master the essential skills needed to recognize and solve complex real-world problems with Machine Learning and Deep Learning by leveraging the highly popular Python Machine Learning Eco-system.

  Language:Jupyter Notebook2.3k1.6k

• toobrien/acsil

  sierra chart custom studies

  Language:C++33

• IntelligenzaArtificiale/Free-Auto-GPT

  Free Auto GPT with NO paids API is a repository that offers a simple version of Auto GPT, an autonomous AI agent capable of performing tasks independently. Unlike other versions, our implementation does not rely on any paid OpenAI API, making it accessible to anyone.

  Language:Python2.5k385

• Significant-Gravitas/AutoGPT

  AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

  Language:Python169k44.5k

• ranaroussi/quantstats

  Portfolio analytics for quants, written in Python

  Language:Python5.1k874

• Sinaptik-AI/pandas-ai

  Chat with your database (SQL, CSV, pandas, polars, mongodb, noSQL, etc). PandasAI makes data analysis conversational using LLMs (GPT 3.5 / 4, Anthropic, VertexAI) and RAG.

  Language:Python13.7k1.3k

• AbdelTID/Holiday-Plotly-Dash-Challenge

  Dash-app : Analyze customer churn behavior, as well as use a classification model to predict which customers are at risk of churning

  Language:CSS64

• PacktPublishing/Modern-Time-Series-Forecasting-with-Python

  Modern Time Series Forecasting with Python, published by Packt

  Language:Jupyter Notebook463230

• smazzanti/are_you_still_using_elbow_method

  Language:Jupyter Notebook14840

• modin-project/modin

  Modin: Scale your Pandas workflows by changing a single line of code

  Language:Python9.9k655

• erykml/nvidia_articles

  Code used for articles published at Nvidia's Developer Blog

  Language:Jupyter Notebook108

• pola-rs/polars

  Dataframes powered by a multithreaded, vectorized query engine, written in Rust

  Language:Rust30.9k2k

• opengeos/streamlit-geospatial

  A multi-page streamlit app for geospatial

  Language:Python893399

• prodipta/techchart

  R package for technical-analysis feature extraction - See package vignette for more details

  Language:R2314

• Orosenthal/SPY-correlation-window

  cross-correlation between the last N (user input) trading days and the historical data

  Language:Jupyter Notebook31

• Dragonlogesh/Awesome-Trading-Algorithm

  This algorithm takes a daily position on the SPY ETF by indirectly predicting the change in price through the put/call ratio. I do this by implementing machine learning techniques on Sofien Kaabar's put/call ratio trading strategy.

  Language:Jupyter Notebook95

• Matteo-Ferrara/gex-tracker

  Dealers' gamma exposure (GEX) tracker

  Language:Python11340