À propos de ce cours

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100 % en ligne
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Dates limites flexibles
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Niveau intermédiaire
Approx. 12 heures pour terminer
Anglais
Sous-titres : Anglais

Ce que vous allez apprendre

  • Understand the the structure and techniques used in reinforcement learning (RL) strategies

  • Describe the steps required to develop and test an RL trading strategy

  • Describe the methods used to optimize an RL trading strategy

Compétences que vous acquerrez

Reinforcement Learning Model DevelopmentReinforcement Learning Trading Algorithm OptimizationReinforcement Learning Trading Strategy DevelopmentReinforcement Learning Trading Algo Development
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
Approx. 12 heures pour terminer
Anglais
Sous-titres : Anglais

Offert par

Logo New York Institute of Finance

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

3 heures pour terminer

Introduction to Course and Reinforcement Learning

3 heures pour terminer
10 vidéos (Total 64 min), 1 lecture, 1 quiz
10 vidéos
What is Reinforcement Learning?9 min
History Overview2 min
Value Iteration9 min
Policy Iteration6 min
TD Learning8 min
Q Learning6 min
Benefits of Reinforcement Learning in Your Trading Strategy6 min
DRL Advantages for Strategy Efficiency and Performance7 min
Introduction to Qwiklabs3 min
1 lecture
Idiosyncrasies and challenges of data driven learning in electronic trading10 min
Semaine
2

Semaine 2

5 heures pour terminer

Neural Network Based Reinforcement Learning

5 heures pour terminer
9 vidéos (Total 39 min)
9 vidéos
Deep Q Networks - Loss2 min
Deep Q Networks Memory2 min
Deep Q Networks - Code3 min
Policy Gradients4 min
Actor-Critic3 min
What is LSTM?7 min
More on LSTM4 min
Applying LSTM to Time Series Data7 min
Semaine
3

Semaine 3

4 heures pour terminer

Portfolio Optimization

4 heures pour terminer
10 vidéos (Total 54 min)
10 vidéos
Steps Required to Develop a DRL Strategy7 min
Final Checks Before Going Live with Your Strategy5 min
Investment and Trading Risk Management4 min
Trading Strategy Risk Management4 min
Portfolio Risk Reduction4 min
Why AutoML?13 min
AutoML Vision2 min
AutoML NLP3 min
AutoML Tables7 min

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À propos du Spécialisation Machine Learning for Trading

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

Foire Aux Questions

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