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Jack Farmer and Ram Seshadri

This 3-course Specialization from Google Cloud and New York Institute of Finance (NYIF) is for finance professionals, including but not limited to hedge fund traders, analysts, day traders, those involved in investment management or portfolio management, and anyone interested in gaining greater knowledge of how to construct effective trading strategies using Machine Learning (ML) and Python. Alternatively, this program can be for Machine Learning professionals who seek to apply their craft to quantitative trading strategies. By the end of the Specialization, you'll understand how to use the capabilities of Google Cloud to develop and deploy serverless, scalable, deep learning, and reinforcement learning models to create trading strategies that can update and train themselves. As a challenge, you're invited to apply the concepts of Reinforcement Learning to use cases in Trading. This program is intended for those who have an understanding of the foundations of Machine Learning at an intermediate level. To successfully complete the exercises within the program, you should have advanced competency in Python programming and familiarity with pertinent libraries for Machine Learning, such as Scikit-Learn, StatsModels, and Pandas; a solid background in ML and statistics (including regression, classification, and basic statistical concepts) and basic knowledge of financial markets (equities, bonds, derivatives, market structure, and hedging). Experience with SQL is recommended.

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What's inside

Three courses

Introduction to Trading, Machine Learning & GCP

(0 hours)
In this course, you'll learn the basics of trading, including trend, returns, stop-loss, and volatility. You'll also learn how to identify profit sources and structure basic quantitative trading strategies. By the end, you'll be able to use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks.

Using Machine Learning in Trading and Finance

(0 hours)
This course provides a foundation for developing advanced trading strategies using machine learning techniques. You'll review key components common to every trading strategy and be introduced to quantitative, pairs, and momentum trading. By the end, you'll be able to design basic quantitative trading strategies, build machine learning models using Keras and TensorFlow, and build and backtest pair and momentum-based trading models.

Reinforcement Learning for Trading Strategies

(0 hours)
In this final course, you will learn about reinforcement learning (RL) and its benefits in trading strategies. You will explore how RL integrates with neural networks, particularly LSTMs, for time series data. By the end, you will build trading strategies using RL, distinguish between actor-based and value-based policies, and apply RL to a momentum trading strategy.

Learning objectives

  • Understand the structure and techniques used in machine learning, deep learning, and reinforcement learning (rl) strategies.
  • Describe the steps required to develop and test an ml-driven trading strategy.
  • Describe the methods used to optimize an ml-driven trading strategy.
  • Use keras and tensorflow to build machine learning models.

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