Category: Quant Finance
Pricing Options and Implied Volatility with Python In 2012, my first options trade lost $9,000. 12 months later I was making $1,100 per week
Real-world datasets are important when building and testing machine learning models. You can test your model by establishing a benchmark or identifying its flaws
Analyzing Stock Data Near Events with Pandas Stock returns can be heavily impacted by certain events. Sometimes these events are unexpected or a surprise
Introduction to Atomic Simulations by Metropolis Monte Carlo In this lecture, we review the theory behind Metropolis Monte Carlo modeling and apply these concepts
Swap curve fitting in TensorFlow Quant Finance (Jupyter notebook)
Algorithmic Trading Using Python Learn how to perform algorithmic trading using Python in this complete course. Algorithmic trading means using computers to make investment
How To Create A Fully Automated AI Based Trading System With Python End-to-end project: get the data, train the model, place the order, get
Fast Implied Volatilities in the NAG Library Mark 27.1 of the NAG Library contains a new routine, s30acf, for computing the implied volatility of a