In this video, watch Siraj Raval build a cryptocurrency trading bot called GradientTrader, and he shows you the tools used to build it.It uses a graphical interface that lets you back-test on historical data, simulate paper trading, and implement a custom trading strategy for the real markets. The technique I used was a cutting edge Deep Reinforcement Learning strategy called Multi Agent Actor Critic.

In this follow up video to “How To Build An AI Startup With PyTorch,” the great Siraj Raval explores how to make money with TensorFlow 2.0.

From the video description:

I’ve built an app called NeuralFund that uses Tensorflow 2.0 to make automated investment decisions. I used Tensorflow 2.0 to train a transformer network on time series data that i downloaded using the Yahoo Finance API. Then, I used Tensorflow Serving + Flask to create a simple web app around it. I’ll explain what the important parts you should know in Tensorflow 2.0 are, then I’ll guide you through my code & thought process of building an AI startup using it. Enjoy!

By the way, the code for this video is available on GitHub.

In this documentary from 2010, vpro explores the people at the intersection of advanced mathematics and finance: quants. Quants are the math wizards and computer programmers in the engine room of our global financial system who designed the financial products that almost crashed Wall street ten years ago. The credit crunch showed how the global financial system became increasingly dependent on mathematical models trying to quantify human/economic behavior.

Siraj Raval explores the world of automated training with reinforcement learning with a few lines of Python code!

In this video, he demonstrates how a popular reinforcement learning technique called “Q learning” allows an agent to approximate prices for stocks in a portfolio. The literature of reinforcement learning is incredibly rich. There are so many concepts, like TD-Learning and Actor-Critic for example, that have real-world potential.