用于交易的机器学习

你将学到什么

课程概况

This Specialization 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. Alternatively, this specialization can be for machine learning professionals who seek to apply their craft to quantitative trading strategies.

The courses will teach you how to create various trading strategies using Python. By the end of the Specialization, you will be able to create quantitative trading strategies that you can train and implement. You will also learn how to use reinforcement learning strategies to create algorithms that can update and train themselves.

To be successful in this Specialization, you should have a basic competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL will be helpful. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and a basic knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).

包含课程

课程1
Introduction to Trading, Machine Learning & GCP

This course is for finance professionals, investment management professionals, and traders. Alternatively, this course can be for machine learning professionals who seek to apply their craft to trading strategies. At the end of the course you will be able to do the following:

- Understand the fundamentals of trading, including the concept of trend, returns, stop-loss and volatility
- Understand the differences between supervised/unsupervised and regression/classification machine learning models
- Identify the profit source and structure of basic quantitative trading strategies
- Gauge how well the model generalizes its learning
- Explain the differences between regression and forecasting
- Identify the steps needed to create development and implementation backtesters
- Use Google Cloud Platform to build basic machine learning models in Jupyter Notebooks

To be successful in this course, you should have a basic competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL will be helpful. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).

课程2
Using Machine Learning in Trading and Finance

This course is for finance professionals, investment management professionals, and traders. Alternatively, this Specialization can be for machine learning professionals who seek to apply their craft to trading strategies. At the end of the course you will be able to do the following:

- Design basic quantitative trading strategies
- Use Keras and Tensorflow to build machine learning models
- Build a pair trading strategy prediction model and back test it
- Build a momentum-based trading model and back test it

To be successful in this course, you should have a basic competency in Python programming and familiarity with the Scikit Learn, Statsmodels and Pandas library. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).

课程3
Reinforcement Learning for Trading Strategies

This course is for finance professionals, investment management professionals, and traders. Alternatively, this Specialization can be for machine learning professionals who seek to apply their craft to trading strategies. At the end of the course you will be able to do the following:

- Understand what reinforcement learning is and how trading is an RL problem
- Build Trading Strategies Using Reinforcement Learning (RL)
- Understand the benefits of using RL vs. other learning methods
- Differentiate between actor-based policies and value-based policies
- Incorporate RL into a momentum trading strategy

To be successful in this course, you should have a basic competency in Python programming and familiarity with the Scikit Learn, Statsmodels and Pandas library.You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).

课程项目

The three courses will show you how to create various quantitative and algorithmic trading strategies using Python. By the end of the specialization, you will be able to create quantitative trading strategies that you can train, test and implement in live markets. You will also learn how to use reinforcement learning strategies to create algorithms that can update and train themselves