Arificial Intelligence and Machine Learning for Finance and Banks
One of the most popular packages used for interactive visualizations is Plotly. Plotly is built on top of python and enables data scientists to produce professional and great-looking plots with less-code. It became popular because ...
Insights from Chaos Theory Applied to Trading. A Study in Python. Source
I have tested in real-time the implementation coded with Python of a famous mathematical technics to predict market movement (Bollinger Band) to check the gap between theory and reality. And here is the result. Source
A case study with technical analysis, feature selection, accuracy score & bias-variance trade-off Source
A stock price is a given for every share issued by a publicly traded company. The price is a reflection of the company’s value — what the public is willing to pay for a piece of the company. It can and will rise ...
This blog will teach you how to pull the necessary data and create a model that forecasts the price of the S&P 500! Source
To begin, let’s first understand the goal of this article, which is to provide the average retail investor with a quick and easy way to pull live data, use that data to highlight key indicators ...
Probabilistic Machine Learning comes hand in hand with Stock Trading: Probabilistic Machine Learning uses past instances to predict probabilities of certain events happening in future instances. This can be directly applied to stock trading, to ...
In our daily lives we interact with chatbot customer services, e-mail spam detections, voice recognition, language translation, or stock market predictions. These artificial intelligence products are powered by Recursive Neural Network (RNN), Long Short-Term Model ...
Forecasting Stock Prices with Neural Networks containing Multivariable Inputs from Technical Analysis Source