Predicting S&P500 volatility to classify the market in Python
Model the volatility of the S&P500 to classify the market into three different segments to enhance algorithmic trading strategies. Source
Prediction
Predicting Market Crashes with Topological Data Analysis
Swiss researchers at the Ecole Polytechnique fédérale de Lausanne (EPFL) predicted market crashes using topological data analysis (TDA). Post Doc Guillaume Tauzin with fellow researchers introduced giotto-tda, a Python library that integrates topological data analysis with ...
The 6 fundamental factors and their underlying indicators to predict stock return
How to evaluate a company fundamental performance and compare it with others Source.
How AI, alternative data and insights from psychology resulted in an innovative stock market indicator
A short walkthrough on how the combination of three exciting fields; behavioral finance, alternative data, and machine learning leads to new insights in to the stock market Source
Time Series Forecasting with Deep Learning and Attention Mechanism
An overview of the architecture and the implementation details of the most important Deep Learning algorithms for Time Series Forecasting Source
Here’s How I Use Natural Language Processing In Stock Price Analysis
Using NLP and Granger causality to analyze the relationship between the sentiment of a written article and a stock’s price Source
A Technical Guide on RNN/LSTM/GRU for Stock Price Prediction
These artificial intelligence products are powered by Recursive Neural Network (RNN), Long Short-Term Model (LSTM) and Gated recurrent unit (GRU), of an important branch of deep learning that deliver superior predictions for sequential data such ...
Fibonacci retracements in Python
Let’s see how to calculate this powerful technical indicator in Python Source
Pragmatic Deep Learning Model for Forex Forecasting
Using LSTM and TensorFlow on the GBPUSD Time Series for multi-step prediction Source
Quit Trying to Predict The Market
Why 99.99% of Machine Learning algorithms never truly work Source