flo7up / relataly-public-python-tutorials
Beginner-friendly collection of Python notebooks for various use cases of machine learning, deep learning, and analytics. For each notebook there is a separate tutorial on the relataly.com blog.
☆150Updated last year
Alternatives and similar repositories for relataly-public-python-tutorials
Users that are interested in relataly-public-python-tutorials are comparing it to the libraries listed below
Sorting:
- Jupyter Notebooks Collection for Learning Time Series Models☆71Updated 5 years ago
- This repository is the result of our work for the course CSCI-SHU 360 Machine Learning☆59Updated 4 years ago
- Comparing Long Term Short Memory (LSTM) & Gated Re-current Unit (GRU) during forecasting of oil price .Exploring multivariate relationsh…☆46Updated 3 years ago
- Forecasting crude oil price based on only historical price data utilizing time-series forecasting and ensemble modeling.☆13Updated 2 years ago
- CNNpred: CNN-based stock market prediction using a diverse set of variables☆71Updated 4 years ago
- Compilation of technical analysis tools (EMA, Bollinger bands), fundamental analysis, machine learning models (LSTM, Random forest, ARIMA…☆13Updated 3 years ago
- This project seeks to utilize Deep Learning models, Long-Short Term Memory (LSTM) Neural Networks to predict stock prices.☆84Updated 3 years ago
- This notebook provides some skills to perform financial analysis on economical data.☆23Updated 3 years ago
- By combining GARCH(1,1) and LSTM model implementing predictions.☆57Updated 6 years ago
- Time Series forecasting using Seasonal ARIMA & Prophet. Applied statistical tests like Augmented Dickey–Fuller test to check stationary o…☆26Updated 3 years ago
- Stock markets are an essential component of the economy. Their prediction naturally arouses afascination in the academic and financial w…☆21Updated 3 years ago
- Jupyter notebooks and data files of the new EDHEC specialization on quantitative finance (completed Aug 2022)☆41Updated 2 years ago
- detecting regime of financial market☆36Updated 2 years ago
- ☆78Updated 4 years ago
- Modern Time Series Forecasting with Python 2E, Published by Packt☆132Updated last week
- Investment Funnel 📈 is an open-source python platform designed for an easy development and backtesting of outperforming investment strat…☆62Updated last week
- This repository displays my work in finance and economics datascience for future employers and collaborators.☆10Updated 2 years ago
- Using machine learning to predict/forecast the future trend of stock prices.☆45Updated 3 years ago
- The repository contains the code for project for DS 5500 course at Northeastern.☆36Updated 5 years ago
- kennedyCzar / STOCK-RETURN-PREDICTION-USING-KNN-SVM-GUASSIAN-PROCESS-ADABOOST-TREE-REGRESSION-AND-QDAForecast stock prices using machine learning approach. A time series analysis. Employ the Use of Predictive Modeling in Machine Learning …☆133Updated 2 years ago
- A network tries to predict movements in stock prices based on a picture of a time series stock price.☆40Updated 4 years ago
- A Streamlit based application to predict future Stock Price and pipeline to let anyone train their own multiple Machine Learning models o…☆90Updated 9 months ago
- Transformer and MultiTransformer layers for stock volatility forecasting purposes☆66Updated 3 years ago
- This notebook is devoted to exploring some aspects of the Capital Asset Pricing Model (CAPM) using Python☆18Updated 5 years ago
- This repository represents work in progress for the Worldquant University Capstone Project titled: Asset Portfolio Management using Deep …☆80Updated 2 years ago
- A Python implementation of a Hybrid LSTM-GARCH model for volatility forecasting☆35Updated 2 years ago
- A stock price prediction model based on ARMA and GARCH☆23Updated 10 months ago
- Hidden Markov Model (HMM) based stock forecasting☆100Updated 7 years ago
- ☆102Updated 3 years ago
- An investment portfolio of stocks is created using Long Short-Term Memory (LSTM) stock price prediction and optimized weights. The perfor…☆34Updated last year