Assignments as part of CS 7646 at GeorgiaTech under Dr. Tucker Balch in Fall 2017
Course Page : http://quantsoftware.gatech.edu/CS7646_Fall_2017
Udacity course by Tucker Balch : https://www.udacity.com/course/machine-learning-for-trading--ud501
This course is composed of three mini-courses:
Mini-course 1: Manipulating Financial Data in Python
Mini-course 2: Computational Investing
Mini-course 3: Machine Learning Algorithms for Trading
Completed 8 projects in total:
Title : Assess portfolio
Goal : To evaluate and compare different portfolios by computing certain metrics based on available historical data, and plot a comparison graph
Link : http://quantsoftware.gatech.edu/Assess_portfolio
Title : Optimize portfolio
Goal : To find how much of a portfolio's funds should be allocated to each stock so as to optimize it's performance by considering 'minimum volatility' as the optimizer metric
Link : http://quantsoftware.gatech.edu/Optimize_something
Title : Market simulator
Goal : To 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
Link : http://quantsoftware.gatech.edu/Marketsim
Title : Defeat learners
Goal : To generate data that will work better for one learner than another. The two learners are:
(1) A decision tree learner with leaf_size = 1 (DTLearner). Note that for testing purposes we will use our implementation of DTLearner
(2) The LinRegLearner provided as part of the repo.
Data generation should use a random number generator as part of its data generation process. Generators will be passed a random number seed. Whenever the seed is the same return exactly the same data set. Different seeds should result in different data sets.
Link : http://quantsoftware.gatech.edu/Defeat_learners
Title : Assess learners
Goal : To implement and evaluate three learning algorithms as Python classes: A "classic" Decision Tree learner, a Random Tree learner, and a Bootstrap Aggregating learner (Assume data to be static, and consider this to be a regression problem)
Link : http://quantsoftware.gatech.edu/Assess_learners
Title : Qlearning robot
Goal : To implement the Q-Learning and Dyna-Q solutions to the reinforcement learning problem, and apply them to a navigation problem in this project
Link : http://quantsoftware.gatech.edu/Qlearning_robot
Title : Manual strategy
Goal : To develop a trading strategy using your intuition and Technical Analysis, and test it against a stock using the market simulator built in project 3
Link : http://quantsoftware.gatech.edu/Manual_strategy
Title : Strategy learner
Goal : To design a learning trading agent and perform following tasks: - Devise numerical/technical indicators to evaluate the state of a stock on each day - Build a strategy learner based on one of the learners described above that uses the indicators - Test/debug the strategy learner on specific symbol/time period problems - Write a report describing your learning strategy