À propos de ce cours

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Dates limites flexibles
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Niveau intermédiaire
  • Basic competency in Python, familiarity with the Scikit Learn, Statsmodels and Pandas library. 
  • Familiarity with statistics, financial markets, ML
Approx. 19 heures pour terminer
Anglais
Sous-titres : Anglais

Compétences que vous acquerrez

Algorithmic TradingPython ProgrammingMachine Learning
Certificat partageable
Obtenez un Certificat lorsque vous terminez
100 % en ligne
Commencez dès maintenant et apprenez aux horaires qui vous conviennent.
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
  • Basic competency in Python, familiarity with the Scikit Learn, Statsmodels and Pandas library. 
  • Familiarity with statistics, financial markets, ML
Approx. 19 heures pour terminer
Anglais
Sous-titres : Anglais

Offert par

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New York Institute of Finance

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Google Cloud

Programme du cours : ce que vous apprendrez dans ce cours

Semaine
1

Semaine 1

1 heure pour terminer

Introduction to Quantitative Trading and TensorFlow

1 heure pour terminer
4 vidéos (Total 23 min), 1 lecture, 1 quiz
4 vidéos
Basic Trading Strategy Entries and Exits Endogenous Exogenous7 min
Basic Trading Strategy Building a Trading Model2 min
Advanced Concepts in Trading Strategies6 min
1 lecture
Welcome to Using Machine Learning in Trading and Finance10 min
1 exercice pour s'entraîner
Understand Quantitative Strategies
4 heures pour terminer

Introduction to TensorFlow

4 heures pour terminer
11 vidéos (Total 50 min)
11 vidéos
Introduction to TensorFlow6 min
TensorFlow API Hierarchy4 min
Components of tensorflow Tensors and Variables8 min
Getting Started with Google Cloud Platform and Qwiklabs3 min
Lab Intro Writing low-level TensorFlow programs43s
Working in-memory and with files3 min
Training on Large Datasets with tf.data API4 min
Getting the data ready for model training6 min
Embeddings8 min
Lab Intro Manipulating data with TensorFlow Dataset API34s
Semaine
2

Semaine 2

3 heures pour terminer

Training neural networks with Tensorflow 2 and Keras

3 heures pour terminer
12 vidéos (Total 53 min)
12 vidéos
Activation functions8 min
Activation functions: Pitfalls to avoid in Backpropagation 5 min
Neural Networks with Keras Sequential API7 min
Serving models in the cloud3 min
Lab Intro : Keras Sequential API21s
Neural Networks with Keras Functional API9 min
Regularization: The Basics4 min
Regularization: L1, L2, and Early Stopping5 min
Regularization: Dropout5 min
Lab Intro: Keras Functional API38s
Recap57s
Semaine
3

Semaine 3

6 heures pour terminer

Build a Momentum-based Trading System

6 heures pour terminer
12 vidéos (Total 68 min), 1 lecture, 2 quiz
12 vidéos
Introduction to Hurst8 min
Building a Momentum Trading Model7 min
Define the Problem9 min
Collect the Data2 min
Creating Features3 min
Split the Data3 min
Selecting a Machine Learning Algorithm3 min
Backtest on Unseen Data1 min
Understanding the Code: Simple ML Strategies to Generate Trading Signal9 min
Lab Intro: Momentum Trading43s
Momentum Trading Lab Solution7 min
1 lecture
Hurst Exponent and Trading Signals Derived from Market Time Series10 min
Semaine
4

Semaine 4

5 heures pour terminer

Build a Pair Trading Strategy Prediction Model

5 heures pour terminer
11 vidéos (Total 74 min)
11 vidéos
Picking Pairs4 min
Picking Pairs with Clustering8 min
How to implement a Pair Trading Strategy9 min
Evaluate Results of a Pair Trade6 min
Backtesting and Avoiding Overfitting6 min
Next Steps: Imrovements to your Pair Strategy5 min
Lab Intro: Pairs Trading30s
Lab Solution: Pairs Trading7 min
Kalman Filter Introduction11 min
Kalman Filter Trading Applications6 min
1 exercice pour s'entraîner
Pairs Trading Strategy concepts

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À propos du Spécialisation Machine Learning for Trading

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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