This course is for finance professionals, investment management professionals, and traders. Alternatively, this Specialization can be for machine learning professionals who seek to apply their craft to trading strategies.
Ce cours fait partie de la Spécialisation Machine Learning for Trading
Using Machine Learning in Trading and Finance
Offert par
New York Institute of Finance
Google Cloud
À propos de ce cours
37,453 consultations récentes
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.
Cours 2 sur 3 dans le
Dates limites flexibles
Réinitialisez les dates limites selon votre disponibilité.
Niveau intermédiaire
Approx. 12 heures pour terminer
Recommandé : 16 hours/week
Anglais
Sous-titres : Anglais
Programme du cours : ce que vous apprendrez dans ce cours
3 heures pour terminer
Introduction to Quantitative Trading and TensorFlow
In this module we discuss the key components that are common to every trading strategy, no matter how complex. This foundation will help guide you as you develop more advanced strategies using machine learning techniques.
3 heures pour terminer
10 vidéos (Total 46 min), 1 lecture, 2 quiz
10 vidéos
Basic Trading Strategy Entries and Exits Endogenous Exogenous7 min
Basic Trading Strategy Building a Trading Model2 min
Advanced Concepts in Trading Strategies6 min
Introduction to TensorFlow1 min
Estimator API3 min
Predicting real estate house values using simple data set5 min
Estimator API Lab Introduction39s
Getting Started with Google Cloud Platform and Qwiklabs3 min
Estimator API Lab Solution10 min
1 lecture
Welcome to Using Machine Learning in Trading and Finance10 min
1 exercice pour s'entraîner
Understand Quantitative Strategies
2 heures pour terminer
Build a Pair Trading Strategy Prediction Model
In this module, we introduce pairs trading. We will discuss what pairs trading is, and how you can make money doing it. We will discuss what you need to know about the members to form a suitable pair.
2 heures pour terminer
9 vidéos (Total 56 min)
9 vidéos
Picking Pairs4 min
Picking Pairs with Clustering8 min
How to Implement a Pair Strategy9 min
Evaluate Results of a Pair Trade6 min
Backtesting and Avoiding Overfitting6 min
Next Steps: Improvements to Your Pairs Strategy5 min
Pairs Trading Lab Introduction30s
Pairs Trading Lab Solution7 min
1 exercice pour s'entraîner
Pairs Trading Strategy and Backtesting
2 heures pour terminer
Build a Momentum-based Trading System
Momentum trading is a strategy in which traders buy or sell assets according to the strength of recent price trends. Price momentum is similar to momentum in physics, where mass multiplied by velocity determines the persistence with which an object will follow its current path (like a heavy train on a track). In financial markets, however, momentum is determined by other factors like trading volume and rate of price changes. Momentum traders bet that an asset price that is moving strongly in a given direction will continue to move in that direction until the trend loses strength or reverses. This module teaches you all about momentum trading.
2 heures pour terminer
13 vidéos (Total 77 min)
13 vidéos
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
Kalman Filter Introduction11 min
Kalman Filter Trading Applications6 min
Momentum Trading Lab Introduction43s
Momentum Trading Lab Solution7 min
Avis
3.9
Meilleurs avis pour USING MACHINE LEARNING IN TRADING AND FINANCE
par PLFeb 28, 2020
The course is inspiring. It gave me another perspective of learning trading not just for Machine Learning also for day to day trading algorithm.
par MFJan 19, 2020
Very informative. I does not go too much in details but you get a lot of insight about trading and using ML in trading strategies
par LTFeb 8, 2020
Very interesting course with integrated notebooks to learn concepts of how to apply machine learning to trading and finance
par EFFeb 27, 2020
Very interesting insights and new tools learned to improve trading algos and make smarter quantitative strategies
par CEFeb 10, 2020
The material is immediately useful and highly practical for people already in financial services.
par DTJan 21, 2020
Lots of material in a very short time, especially on momentum trading.
par KSFeb 7, 2020
Video lectures were good. Expected better material for lab
Offert par
New York Institute of Finance
The New York Institute of Finance (NYIF), is a global leader in training for financial services and related industries. Started by the New York Stock Exchange in 1922, it now trains 250,000+ professionals in over 120 countries. NYIF courses cover everything from investment banking, asset pricing, insurance and market structure to financial modeling, treasury operations, and accounting. The institute has a faculty of industry leaders and offers a range of program delivery options, including self-study, online courses, and in-person classes. Its US customers include the SEC, the Treasury, Morgan Stanley, Bank of America and most leading worldwide banks.
Google Cloud
We help millions of organizations empower their employees, serve their customers, and build what’s next for their businesses with innovative technology created in—and for—the cloud. Our products are engineered for security, reliability, and scalability, running the full stack from infrastructure to applications to devices and hardware. Our teams are dedicated to helping customers apply our technologies to create success.
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).
Foire Aux Questions
Quand aurai-je accès aux vidéos de cours et aux devoirs ?
Une fois que vous êtes inscrit(e) pour un Certificat, vous pouvez accéder à toutes les vidéos de cours, et à tous les quiz et exercices de programmation (le cas échéant). Vous pouvez soumettre des devoirs à examiner par vos pairs et en examiner vous-même uniquement après le début de votre session. Si vous préférez explorer le cours sans l'acheter, vous ne serez peut-être pas en mesure d'accéder à certains devoirs.
À quoi ai-je droit si je m'abonne à cette Spécialisation ?
Lorsque vous vous inscrivez au cours, vous bénéficiez d'un accès à tous les cours de la Spécialisation, et vous obtenez un Certificat lorsque vous avez réussi. Votre Certificat électronique est alors ajouté à votre page Accomplissements. À partir de cette page, vous pouvez imprimer votre Certificat ou l'ajouter à votre profil LinkedIn. Si vous souhaitez seulement lire et visualiser le contenu du cours, vous pouvez accéder gratuitement au cours en tant qu'auditeur libre.
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