fly2fire / machine-learning-for-trading

Implementation of various techniques in ML and application in the context of financial markets.

Machine Learning for Trading

Implementation of various techniques in machine learning and application in the context of stock trading.

Contents

• 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

Dependencies

• ml4t-libraries.txt