Implementation of various techniques in machine learning and application in the context of stock trading.
- assess_portfolio
- Computes the daily portfolio value over given date range, and a set of statistics describing performance of the overall portfolio
- assess_learners
- Implements and evalutes performance of three learning algorithms: Decision Tree, Random Tree, and Bootstrap Aggregation
- defeat_learners
- A set of simple data-generation functions used to show bias in two ML models.
- manual_strategy
- Development of a trading strategy using intuition and technical analysis, and testing against a stock using market simulator
- marketsim
- 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
- optimize_something
- Implements a function that can find the optimal allocations for a given set of stocks, optimizing for maximum Sharpe Ratio
- qlearning_robot
- Implements the Q-Learning and Dyna-Q solutions to the reinforcement learning problem, and applies them to a navigation problem
- strategy_learner
- Design of a learning trading agent capable of using technical indicators and a Random Forest learner to learn a profitable trading strategy
- ml4t-libraries.txt