このコースについて

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100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
  • Basic competency in Python, familiarity with the Scikit Learn, Statsmodels and Pandas library. 
  • Familiarity with statistics, financial markets, ML
約19時間で修了
英語
字幕:英語

習得するスキル

Algorithmic TradingPython ProgrammingMachine Learning
共有できる証明書
修了時に証明書を取得
100%オンライン
自分のスケジュールですぐに学習を始めてください。
柔軟性のある期限
スケジュールに従って期限をリセットします。
中級レベル
  • Basic competency in Python, familiarity with the Scikit Learn, Statsmodels and Pandas library. 
  • Familiarity with statistics, financial markets, ML
約19時間で修了
英語
字幕:英語

提供:

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ニューヨーク金融金融研究所

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Google Cloud

シラバス - 本コースの学習内容

1

1

1時間で修了

Introduction to Quantitative Trading and TensorFlow

1時間で修了
4件のビデオ (合計23分), 1 reading, 1 quiz
4件のビデオ
Basic Trading Strategy Entries and Exits Endogenous Exogenous7 分
Basic Trading Strategy Building a Trading Model2 分
Advanced Concepts in Trading Strategies6 分
1件の学習用教材
Welcome to Using Machine Learning in Trading and Finance10 分
1の練習問題
Understand Quantitative Strategies
4時間で修了

Introduction to TensorFlow

4時間で修了
11件のビデオ (合計50分)
11件のビデオ
Introduction to TensorFlow6 分
TensorFlow API Hierarchy4 分
Components of tensorflow Tensors and Variables8 分
Getting Started with Google Cloud Platform and Qwiklabs3 分
Lab Intro Writing low-level TensorFlow programs43
Working in-memory and with files3 分
Training on Large Datasets with tf.data API4 分
Getting the data ready for model training6 分
Embeddings8 分
Lab Intro Manipulating data with TensorFlow Dataset API34
2

2

3時間で修了

Training neural networks with Tensorflow 2 and Keras

3時間で修了
12件のビデオ (合計53分)
12件のビデオ
Activation functions8 分
Activation functions: Pitfalls to avoid in Backpropagation 5 分
Neural Networks with Keras Sequential API7 分
Serving models in the cloud3 分
Lab Intro : Keras Sequential API21
Neural Networks with Keras Functional API9 分
Regularization: The Basics4 分
Regularization: L1, L2, and Early Stopping5 分
Regularization: Dropout5 分
Lab Intro: Keras Functional API38
Recap57
3

3

6時間で修了

Build a Momentum-based Trading System

6時間で修了
12件のビデオ (合計68分), 1 reading, 2 quizzes
12件のビデオ
Introduction to Hurst8 分
Building a Momentum Trading Model7 分
Define the Problem9 分
Collect the Data2 分
Creating Features3 分
Split the Data3 分
Selecting a Machine Learning Algorithm3 分
Backtest on Unseen Data1 分
Understanding the Code: Simple ML Strategies to Generate Trading Signal9 分
Lab Intro: Momentum Trading43
Momentum Trading Lab Solution7 分
1件の学習用教材
Hurst Exponent and Trading Signals Derived from Market Time Series10 分
4

4

5時間で修了

Build a Pair Trading Strategy Prediction Model

5時間で修了
11件のビデオ (合計74分)
11件のビデオ
Picking Pairs4 分
Picking Pairs with Clustering8 分
How to implement a Pair Trading Strategy9 分
Evaluate Results of a Pair Trade6 分
Backtesting and Avoiding Overfitting6 分
Next Steps: Imrovements to your Pair Strategy5 分
Lab Intro: Pairs Trading30
Lab Solution: Pairs Trading7 分
Kalman Filter Introduction11 分
Kalman Filter Trading Applications6 分
1の練習問題
Pairs Trading Strategy concepts

レビュー

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Machine Learning for Trading専門講座について

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....
Machine Learning for Trading

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