A step-by-step guide to test alpha performance of a quant algorithmic trading model
How to Build Quant Algorithmic Trading Model in Python Source
Identifying Candlestick Patterns using Deep Learning
Training a neural network on candlestick charts and then using it to identify patterns on it Source
A Step-by-step Implementation of a Trading Strategy in Python using ARIMA + GARCH models
Make use of a completely functional ARIMA+GARCH python implementation and test it over different markets using a simple framework for visualization and comparisons. Source
Practical Machine Learning with Scikit-Learn
EDA, feature engineering and preprocessing, pipelines Churn prediction is a common task in predictive analytics. In this article, we will try to predict whether a customer will leave the credit card services of a bank. ...
Farewell RNNs, Welcome TCNs
How Temporal Convolutional Networks are moving in favor of Sequence Modeling — Stock Trend Prediction. Source
How to send Stock Updates with Python
As a way to identify new companies to invest in, I like to watch out for the biggest market movers each day. However, there are thousands of stocks out there, and checking the news for ...
Stock Price Analysis with Pandas and Altair
Practical guide for pandas and Altairs Stock price analysis is an example of time series analysis which is one of the primary areas in predictive analytics. Time series data contains measurements or observations attached to ...
How to Get Started with Algorithmic Trading in Python
Algorithmic or Quantitative trading can be defined as the process of designing and developing statistical and mathematical trading strategies. It is an extremely sophisticated area of finance.Source
A Substitute for Fibonacci in Trading. Thinking Outside the Box
Presenting the Lucas Series as a Complement to Trading. Source
Day-Trading with AI: When to Hold, When to Fold, and When to Not Play!
Market Clustering with Transdimensional Machine Learning The ability to characterize market day-trading with TML enables tailoring strategies to specific market “personalities”. These “personalities” render it seemingly impossible to trade profitably approximately 40 percent of trading ...