Developing trading strategies through reinforcement
Developing trading algorithms
Algorithmic trading
Python programming
Machine learning
Applied Machine Learning for Finance
finance
trade
Investments
What you will learn in the course
Courses for which the course is suitable
Hedge fund trader
Analyst
Day trader
Investment Manager
Investment Portfolio Manager
Machine Learning Expert
Developer of quantitative trading strategies
Trading algorithm developer
Internship – 3-part course series
General description
This 3-course specialization series from Google Cloud and the New York Institute of Finance (NYIF) is designed for finance professionals.
The courses are designed for hedge fund traders, analysts, day traders, and investment managers.
The goal is to acquire in-depth knowledge about building effective trading strategies using machine learning (ML) and Python.
This program is also designed for machine learning experts who want to apply their skills to quantitative trading strategies.
Course objectives
At the end of the internship, you will understand how to use Google Cloud capabilities to develop and run deep learning and reinforcement learning models.
You will learn how to create trading strategies that can update and train themselves.
As a challenge, you are invited to apply the concepts of reinforcement learning to commerce use cases.
Prerequisites
Intermediate level understanding of the basics of machine learning.
Advanced Python programming skills and familiarity with relevant machine learning libraries, such as Scikit-Learn, StatsModels, and Pandas.
Solid background in ML and statistics (including regression, classification, and an introduction to statistical concepts).
Basic knowledge of financial markets (stocks, bonds, derivatives, market structure and coverage).
Experience with SQL is recommended.
Hands-on Learning Project
The three courses will show you how to create a variety of quantitative and algorithmic trading strategies using Python.
At the end of the internship, you will be able to create and improve quantitative trading strategies using machine learning.
You will also learn how to use deep learning and reinforcement learning strategies to create algorithms that can update and train themselves.
Details of the courses that make up the specialization
Introduction to Commerce, Machine Learning, and GCP
Course 1
9 hours
4.0 (844 ratings)
Course Details
What you’ll learn
Understanding the basics of trading, including concepts such as trend, returns, stop losses, and uncertainty.
Definition of quantitative trading and the main types of quantitative trading strategies.
Understanding the basic steps in foreign exchange arbitrage, statistical arbitrage, and index arbitrage.
Understanding of the application of machine learning in finance use cases.
The skills you will acquire
Category: Machine Learning Applied to Finance
Category: Financial
Category: Commerce
Category: Investments
Using machine learning in commerce and finance
Course 2
18 hours
3.9 (363 ratings)
Course Details
What you’ll learn
Designing basic quantitative trading strategies
Using Keras and Tensorflow to build machine learning models
Building a predictive model for a pair trading strategy and retesting it.
Building a momentum-based trading model and retesting it.
The skills you will acquire
Category: Algorithmic Trading
Category: Python Programming
Category: Machine Learning
Strong learning for trading strategies
Course 3
12 hours
3.5 (230 ratings)
Course Details
What you’ll learn
Understanding the structure and techniques used in robust learning (RL) strategies.
Understanding the benefits of using RL compared to other learning methods.
Description of the steps required to develop and test a trading strategy using RL.
Description of the methods used to optimize a RL trading strategy.
The skills you will acquire
Category: Developing a Strong Learning Model
Category: Optimization of trading algorithms with strong learning
