AI for Trading (AIE301m)
Vietnamese title: Ứng dụng TTNT cho giao dịch
Credits: 3
Prerequisites: PFP191, AIL303m

Description:
Artificial intelligence (AI) has transformed industries around the world. The aim of this unit is to introduce the current and future applications of AI in trading strategy. Students will be guided to discover the structure used in machine learning (ML), deep learning (DL), and reinforcement learning (RL) techniques, the required steps, and the optimized methods in the AI-driven trading field. Students also learn how to build some ML models on Keras and Tensorflow.

Learning outcomes:
Understand the fundamentals of trading.
Define quantitative trading and the main types of quantitative trading strategies.
Basic steps in exchange arbitrage, statistical arbitrage, index arbitrage and the application of machine learning to financial use cases.
Design basic quantitative trading strategies.
Use Keras and Tensorflow to build machine learning models.
Build a pair trading strategy prediction model, a momentum-based trading model, and back test them.
Understand the structure and techniques used in reinforcement learning (RL) strategies.
Understand the benefits of using RL vs. other learning methods.
Describe the steps required to develop, test, and optimize an RL trading strategy.

Student tasks:
'- Students must attend at least 80% of contact slots in order to be accepted to the final examination.
- Student is responsible to do all exercises, assignments and labs given by instructor in class or at home and submit on time
- Use laptop in class only for learning purpose
- Promptly access to the FU FLM at https://flm.fpt.edu.vn/ for up-to-date course information

Tools:
No

Materials:
description: Spec: Machine Learning for Trading Specialization
author: Google Cloud
publisher: Coursera
note: https://www.coursera.org/specializations/machine-learning-trading
description: MOOC 1: Introduction to Trading, Machine Learning & GCP
author: Google Cloud
publisher: Coursera
note: https://www.coursera.org/learn/introduction-trading-machine-learning-gcp?specialization=machine-learning-trading
description: MOOC 2: Using Machine Learning in Trading and Finance
author: Google Cloud
publisher: Coursera
note: https://www.coursera.org/learn/machine-learning-trading-finance?specialization=machine-learning-trading
description: MOOC 3: Reinforcement Learning for Trading Strategies
author: Google Cloud
publisher: Coursera
note: https://www.coursera.org/learn/trading-strategies-reinforcement-learning?specialization=machine-learning-trading
description: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
author: Aurélien Géron
publisher: O'Reilly Media
published_date: 5/9/2017 12:00:00 AM
edition: 1st edition
isbn: 978-1491962299
note: http://ds.libol.fpt.edu.vn/
description: Course AI For Everyone
author: Deeplearning.AI
publisher: Coursera
note: https://www.coursera.org/learn/ai-for-everyone

Assessments:
category: Assignment 1
type: on-going
weight: 5.0
duration: 60'
question_type: CLO1 - 3
no_question: Group presentation (50%) and Personal task (50%)
knowledge_and_skill: 1
grading_guide: algorithms and labs 1-3
note: in class, by group and personal presentations
category: Assignment 2
type: on-going
weight: 5.0
duration: 60'
question_type: CLO5
no_question: Group presentation (50%) and Personal task (50%)
knowledge_and_skill: 1
grading_guide: algorithms and labs 4-6
note: in class, by group and personal presentations
category: Assignment 3
type: on-going
weight: 5.0
duration: 60'
question_type: CLO5 - 6
no_question: Group presentation (50%) and Personal task (50%)
knowledge_and_skill: 1
grading_guide: algorithms and labs 7-9
note: in class, by group and personal presentations
category: Assignment 4
type: on-going
weight: 5.0
duration: 60'
question_type: CLO9
no_question: Group presentation (50%) and Personal task (50%)
knowledge_and_skill: 1
grading_guide: algorithms and labs 10-12
note: in class, by group and personal presentations
category: Progress test 1
type: on-going
weight: 10.0
duration: 20'-30'
question_type: CLO1, CLO2, CLO3
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 25
grading_guide: concepts (Introduction to Trading, Machine Learning & GCP), slides 1.1-1.4
note: in class, by instructor
category: Progress test 2
type: on-going
weight: 10.0
duration: 20'-30'
question_type: CLO4, CLO5, CLO6
no_question: Multiple choicesMarked by Computer or a suitable format
knowledge_and_skill: 25
grading_guide: concepts (Using Machine Learning in Trading and Finance), slides 2.1-2.4
note: in class, by instructor
category: Progress test 3
type: on-going
weight: 10.0
duration: 20'-30'
question_type: CLO7, CLO8, CLO9
no_question: Multiple choicesMarked by Computer or a suitable format
knowledge_and_skill: 25
grading_guide: concepts (Reinforcement Learning for Trading Strategies), slides 3.1-2.3
note: in class, by instructor
category: Final exam
type: Final exam
weight: 50.0
duration: 60'
question_type: CLO1 - 9
no_question: Multiple choices
Marked by Computer or a suitable format
knowledge_and_skill: 50
grading_guide: concepts, algorithms;
all studied slides and labs
note: by exam board,
using computer

Schedule:
session: Course introduction:
- Introduce the materials of the courses and how to asses these materials
- Introduce the assignments and how to pass
- Introduce CMS and the way to log on to submit the assignments http://cms.btec.fpt.edu.vn and class forum with enroll key.
learning_outcomes: IT
materials: mat1
session: 1.1 Introduction to Trading with Machine learning on Google Cloud
1.1.1 Machine Learning in Finance and your first model using Google Cloud
learning_outcomes: T
materials: mat2
session: 1.1.2 Why Google Cloud Platform?
learning_outcomes: T
session: 1.1.3 Learn ML interactively with Jupyter Notebooks on Cloud AI Platform
learning_outcomes: T
session: 1.1.4 Trading Fundamentals: Quant Theory, Arbitrage, and Back-testing
learning_outcomes: T
session: 1.1.4 (Cont.) Trading Fundamentals: Quant Theory, Arbitrage, and Back-testing
learning_outcomes: T
session: 1.2 Supervised Learning with BigQuery ML
1.2.1 Introduction to Supervised Learning
learning_outcomes: T
session: Lab 1: BigWuery Machine Learning model types: Reading "Staying current with BigQuery ML model types
learning_outcomes: T
session: Lab 2: Building a Regression Model in BigQuery for AAPL Stock Data
learning_outcomes: T
session: 1.3 Time Series and ARIMA Modeling
1.3.1 Time Series
learning_outcomes: T
session: 1.3.2 Comparing Regression to ARIMA
learning_outcomes: T
session: Lab 3: Building an ARIMA Model for a Financial Dataset
learning_outcomes: T
session: 1.4 Introduction to Neural networks and Deep learning
1.4.1 Deep Learning
learning_outcomes: T
session: 1.4.2 Model Generalization
learning_outcomes: T
session: Assignment 1: Example BigQuery ML DNN code
learning_outcomes: U
session: Review Assignment 1
learning_outcomes: U
session: Progress Test 1
learning_outcomes: U
session: Review Progress Test 1
learning_outcomes: U
session: 2.1 Introduction to Quantitative trading and TensorFlow
2.1.1 Introduction to TensorFlow, Trading, ML
learning_outcomes: T
session: 2.1.2 Understand Quantitive Trading Strategies
learning_outcomes: T
session: 2.1.3 Introduction to TensorFlow
learning_outcomes: T
session: 2.1.3 (Cont.) Introduction to TensorFlow
learning_outcomes: T
session: Lab 4: Writing low-level TensorFlow Programs
learning_outcomes: T
session: Lab 5: Manipulating data with TensorFlow Dataset API
learning_outcomes: T
session: 2.2 Traning neural networks with TensorFlow 2 and Keras
2.2.1 Overview of Neural Networks and Introduction to Keras APIs
learning_outcomes: T
session: 2.2.1 (Cont.) Overview of Neural Networks and Introduction to Keras APIs
learning_outcomes: T
session: Lab 6: Introducing the Keras Sequential API
learning_outcomes: T
session: Assignment 2: The Keras Functional API
learning_outcomes: U
session: Review Assignment 2
learning_outcomes: U
session: 2.3 Build a Momentum-based Trading System
2.3.1 Identfy momentum-based factors
learning_outcomes: T
session: 2.3.2 Build a trading model that uses momentum factors
learning_outcomes: T
session: 2.3.2 (Cont.) Build a trading model that uses momentum factors
learning_outcomes: T
session: Lab 7: Reading "Hurst Exponent and Trading Signals Derived from Market Time Series
learning_outcomes: T
session: Lab 8: Momentum Strategies
learning_outcomes: T
session: 2.4 Build a Pair Strategy Prediction Model
2.4.1 Picking Pairs
learning_outcomes: T
session: 2.4.2 Trading Strategy
learning_outcomes: T
session: 2.4.3 Backtesting and Avoiding Overfitting
learning_outcomes: T
session: 2.4.4 Optimize momentum trading model to minimize costs
learning_outcomes: T
session: Lab 9: Estimate parameters using Kalman Filters
learning_outcomes: T
session: Assignment 3: Pairs Trading Strategy
learning_outcomes: U
session: Review Assignment 3
learning_outcomes: U
session: Progress Test 2
learning_outcomes: U
session: Review Progress Test 2
learning_outcomes: U
session: 3.1 Introduction to Course and Reinforcement Learning
3.1.1. Introduction to Course
learning_outcomes: T
session: 3.1.2 Working with MDPs
learning_outcomes: T
session: 3.1.3 Online Learning
learning_outcomes: T
session: Lab 10: Reading "Idiosyncrasies and challenges of data driven learning in electronic trading
learning_outcomes: T
session: Lab 11: Early Reinforcement Learning
learning_outcomes: T
session: 3.2 Neural network based reinforcement learning
3.2.1 Q-Networks
learning_outcomes: T
session: 3.2.2 Policy gradients
learning_outcomes: T
session: 3.2.3 What is LSTM and How to apply it
learning_outcomes: T
session: Lab 12: Policy Gradients and Actor-to-Critic
learning_outcomes: T
session: 3.3 Portfolio optimization
3.3.1 Investment and Trading portfolio optimization
learning_outcomes: T
session: 3.3.2 Auto ML
learning_outcomes: T
session: Assignment 4: Reinforcement Learning DQN
learning_outcomes: U
session: Review Assignment 4
learning_outcomes: U
session: Progress Test 3
learning_outcomes: U
session: Review Progress Test 3
learning_outcomes: U
session: Summarize All Slides and Labs
learning_outcomes: T
session: Summarize All Assignments and Tests
learning_outcomes: T