About this Course

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Shareable Certificate
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100% online
Start instantly and learn at your own schedule.
Flexible deadlines
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Intermediate Level
  • Basic competency in Python, familiarity with the Scikit Learn, Statsmodels and Pandas library. 
  • Familiarity with statistics, financial markets, ML
Approx. 19 hours to complete
English

Skills you will gain

Algorithmic TradingPython ProgrammingMachine Learning
Shareable Certificate
Earn a Certificate upon completion
100% online
Start instantly and learn at your own schedule.
Flexible deadlines
Reset deadlines in accordance to your schedule.
Intermediate Level
  • Basic competency in Python, familiarity with the Scikit Learn, Statsmodels and Pandas library. 
  • Familiarity with statistics, financial markets, ML
Approx. 19 hours to complete
English

Offered by

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New York Institute of Finance

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Google Cloud

Syllabus - What you will learn from this course

Week
1

Week 1

1 hour to complete

Introduction to Quantitative Trading and TensorFlow

1 hour to complete
4 videos (Total 23 min), 1 reading, 1 quiz
4 videos
Basic Trading Strategy Entries and Exits Endogenous Exogenous7m
Basic Trading Strategy Building a Trading Model2m
Advanced Concepts in Trading Strategies6m
1 reading
Welcome to Using Machine Learning in Trading and Finance10m
1 practice exercise
Understand Quantitative Strategies
4 hours to complete

Introduction to TensorFlow

4 hours to complete
11 videos (Total 50 min)
11 videos
Introduction to TensorFlow6m
TensorFlow API Hierarchy4m
Components of tensorflow Tensors and Variables8m
Getting Started with Google Cloud Platform and Qwiklabs3m
Lab Intro Writing low-level TensorFlow programs43s
Working in-memory and with files3m
Training on Large Datasets with tf.data API4m
Getting the data ready for model training6m
Embeddings8m
Lab Intro Manipulating data with TensorFlow Dataset API34s
Week
2

Week 2

3 hours to complete

Training neural networks with Tensorflow 2 and Keras

3 hours to complete
12 videos (Total 53 min)
12 videos
Activation functions8m
Activation functions: Pitfalls to avoid in Backpropagation 5m
Neural Networks with Keras Sequential API7m
Serving models in the cloud3m
Lab Intro : Keras Sequential API21s
Neural Networks with Keras Functional API9m
Regularization: The Basics4m
Regularization: L1, L2, and Early Stopping5m
Regularization: Dropout5m
Lab Intro: Keras Functional API38s
Recap57s
Week
3

Week 3

6 hours to complete

Build a Momentum-based Trading System

6 hours to complete
12 videos (Total 68 min), 1 reading, 2 quizzes
12 videos
Introduction to Hurst8m
Building a Momentum Trading Model7m
Define the Problem9m
Collect the Data2m
Creating Features3m
Split the Data3m
Selecting a Machine Learning Algorithm3m
Backtest on Unseen Data1m
Understanding the Code: Simple ML Strategies to Generate Trading Signal9m
Lab Intro: Momentum Trading43s
Momentum Trading Lab Solution7m
1 reading
Hurst Exponent and Trading Signals Derived from Market Time Series10m
Week
4

Week 4

5 hours to complete

Build a Pair Trading Strategy Prediction Model

5 hours to complete
11 videos (Total 74 min)
11 videos
Picking Pairs4m
Picking Pairs with Clustering8m
How to implement a Pair Trading Strategy9m
Evaluate Results of a Pair Trade6m
Backtesting and Avoiding Overfitting6m
Next Steps: Imrovements to your Pair Strategy5m
Lab Intro: Pairs Trading30s
Lab Solution: Pairs Trading7m
Kalman Filter Introduction11m
Kalman Filter Trading Applications6m
1 practice exercise
Pairs Trading Strategy concepts

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About the Machine Learning for Trading Specialization

Machine Learning for Trading

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