implementing machine learning based trading strategies including the algorithmic steps from information gathering to market orders. The focus is on how to apply probabilistic machine learning approaches to trading decisions. We consider statistical approaches like linear regression, Q-Learning, KNN and regression trees and how to apply them to actual stock trading situations.

projects includes:

1. assess protofolio
   portfolio analysis. Use pandas for reading in data, calculating various statistics and plotting a comparison graph.
2. assess learner
   implement and evaluate three learning algorithms as Python classes: A "classic" Decision Tree learner, a Random Tree learner, and a Bootstrap Aggregating learner.
3. defeat learner
   test the strengths and weaknesses of various learners.
4. marketism
   create a market simulator that accepts trading orders and keeps track of a portfolio's value over time and then assesses the performance of that portfolio.
5. qlearning robot
   implement the Q-Learning and Dyna-Q solutions to the reinforcement learning problem. You will apply them to a navigation problem in this project.
6. strategy learner
   design a learning trading agent.