23 open-source projects similar to seanmcowen/financeandpython.com-clusteringindustries, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best FinanceAndPython.com ClusteringIndustries alternative.
This project is a collection of predictive models and quantitative tools for stock price forecasting. It implements a variety of machine learning architectures, including generative adversarial networks, long short-term memory networks, and language models for financial analysis. The system distinguishes itself by combining time-series forecasting with natural language processing to convert financial news into numerical sentiment scores. It also incorporates synthetic market data generation and automated hyperparameter optimization using Bayesian and reinforcement learning methods to reduce p
Motivation: If I, asset manager x, can find funds similar to my own which are not currently captured by Morningstar Category relationships, I can target those funds for competition.
Automated unsupervised machine learning Principal Component Analysis (PCA) on the Dow Jones Industrial Average index and it's respective 30 stocks to construct an optimized diversified intelligent portfolio.
Solution of the given task of predicting the buying and selling volume of the corporate bonds by treating it as a time series problem. The details of the solution and the techniques implemented can be found in Documentation.pdf, and bonds.ipynb and bonds_ts.ipynb respectively.
Educational notebooks on quantitative finance, algorithmic trading, financial modelling and investment strategy
Jonathan Larkin, August 2017
Extracting sentiment from financial statements using neural networks
Variational Reccurrent Autoencoder for Embedding stocks to vectors based on the price history Often, the stock price of the companies in the same industry will move in tandem with each other. This is because market conditions generally affect the companies in the same industry the same way. With…
We use an adversarial expert based online learning algorithm to learn the optimal parameters required to maximise wealth trading zero-cost portfolio strategies. The learning algorithm is used to determine the relative population dynamics of technical trading strategies that can survive…
This repository contains a comprehensive guide to various classification algorithms in machine learning. This guide covers both theoretical concepts and practical implementation examples. The algorithms discussed include Logistic Regression, K-Nearest Neighbors (KNN), Support Vector Machines…
It is Based on Anamoly Detection and by Using Deep Learning Model SOM which is an Unsupervised Learning Method to find patterns followed by the fraudsters.
A comparison between 5 models for performing anomaly detection on financial risk measures and returns. These experiments were conducted as part of the degree project Anomaly Detection for Portfolio Risk Management, which can be found in SimonWesterlindMasters_Thesis.pdf or on Diva. 1. Install…