課程信息

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第 2 門課程(共 3 門)

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中級

完成時間大約為13 小時

建議:16 hours/week...

英語(English)

字幕:英語(English)

您將獲得的技能

Algorithmic TradingPython ProgrammingMachine Learning

100% 在線

立即開始,按照自己的計劃學習。

第 2 門課程(共 3 門)

可靈活調整截止日期

根據您的日程表重置截止日期。

中級

完成時間大約為13 小時

建議:16 hours/week...

英語(English)

字幕:英語(English)

教學大綱 - 您將從這門課程中學到什麼

1

1

完成時間為 3 小時

Introduction to Quantitative Trading and TensorFlow

完成時間為 3 小時
10 個視頻 (總計 46 分鐘), 1 個閱讀材料, 2 個測驗
10 個視頻
Basic Trading Strategy Entries and Exits Endogenous Exogenous7分鐘
Basic Trading Strategy Building a Trading Model2分鐘
Advanced Concepts in Trading Strategies6分鐘
Introduction to TensorFlow1分鐘
Estimator API3分鐘
Predicting real estate house values using simple data set5分鐘
Estimator API Lab Introduction39
Getting Started with Google Cloud Platform and Qwiklabs3分鐘
Estimator API Lab Solution10分鐘
1 個閱讀材料
Welcome to Using Machine Learning in Trading and Finance10分鐘
1 個練習
Understand Quantitative Strategies
2

2

完成時間為 2 小時

Build a Pair Trading Strategy Prediction Model

完成時間為 2 小時
9 個視頻 (總計 56 分鐘)
9 個視頻
Picking Pairs4分鐘
Picking Pairs with Clustering8分鐘
How to Implement a Pair Strategy9分鐘
Evaluate Results of a Pair Trade6分鐘
Backtesting and Avoiding Overfitting6分鐘
Next Steps: Improvements to Your Pairs Strategy5分鐘
Pairs Trading Lab Introduction30
Pairs Trading Lab Solution7分鐘
1 個練習
Pairs Trading Strategy and Backtesting
3

3

完成時間為 2 小時

Build a Momentum-based Trading System

完成時間為 2 小時
13 個視頻 (總計 77 分鐘)
13 個視頻
Building a Momentum Trading Model7分鐘
Define the Problem9分鐘
Collect the Data2分鐘
Creating Features3分鐘
Split the Data3分鐘
Selecting a Machine Learning Algorithm3分鐘
Backtest on Unseen Data1分鐘
Understanding the Code: Simple ML Strategies to Generate Trading Signal9分鐘
Kalman Filter Introduction11分鐘
Kalman Filter Trading Applications6分鐘
Momentum Trading Lab Introduction43
Momentum Trading Lab Solution7分鐘

審閱

來自USING MACHINE LEARNING IN TRADING AND FINANCE的熱門評論
查看所有評論

提供方

纽约金融学院 徽標

纽约金融学院

Google 云端平台 徽標

Google 云端平台

關於 Machine Learning for Trading 專項課程

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)....
Machine Learning for Trading

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