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Working in a hands-on learning environment, led by our Machine Learning for Algorithmic Trading expert instructor, students will learn about and explore: Implement machine learning algorithms to build, train, and validate algorithmic models.Create your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions.Develop neural networks for algorithmic trading to perform time series forecasting and smart analytics.

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Last Updated

January 9, 2021

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Certification

Course Description

The explosive growth of digital data has boosted the demand for expertise in trading strategies that use machine learning (ML). This course enables you to use a broad range of supervised and unsupervised algorithms to extract signals from a wide variety of data sources and create powerful investment strategies. This course shows how to access market, fundamental, and alternative data via API or web scraping and offers a framework to evaluate alternative data. You’ll practice the ML workflow from model design, loss metric definition, and parameter tuning to performance evaluation in a time series context. You will understand ML algorithms such as Bayesian and ensemble methods and manifold learning, and will know how to train and tune these models using pandas, statsmodels, sklearn, PyMC3, xgboost, lightgbm, and catboost. This course also teaches you how to extract features from text data using spaCy, classify news and assign sentiment scores, and to use gensim to model topics and learn word embeddings from financial reports. You will also build and evaluate neural networks, including RNNs and CNNs, using Keras and PyTorch to exploit unstructured data for sophisticated strategies. Finally, you will apply transfer learning to satellite images to predict economic activity and use reinforcement learning to build agents that learn to trade in the OpenAI Gym.

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Course Curriculum

    • Machine Learning for Trading 00:00:00
    • How to read this course 00:00:00
    • The rise of ML in the investment industry 00:00:00
    • Design and execution of a trading strategy 00:00:00
    • ML and algorithmic trading strategies 00:00:00
    • Market and Fundamental Data 00:00:00
    • How to work with market data 00:00:00
    • How to work with fundamental data 00:00:00
    • Efficient data storage with pandas 00:00:00
    • Alternative Data for Finance 00:00:00
    • The alternative data revolution 00:00:00
    • Evaluating alternative datasets 00:00:00
    • The market for alternative data 00:00:00
    • Working with alternative data 00:00:00
    • Alpha Factor Research 00:00:00
    • Engineering alpha factors 00:00:00
    • Seeking signals – how to use zipline 00:00:00
    • Separating signal and noise – how to use alphalens 00:00:00
    • Alpha factor resources 00:00:00
    • Strategy Evaluation 00:00:00
    • How to build and test a portfolio with zipline 00:00:00
    • How to measure performance with pyfolio 00:00:00
    • How to avoid the pitfalls of backtesting 00:00:00
    • How to manage portfolio risk and return 00:00:00
    • The Machine Learning Process 00:00:00
    • Learning from data 00:00:00
    • The machine learning workflow 00:00:00
    • Linear Models 00:00:00
    • Linear regression for inference and prediction 00:00:00
    • The multiple linear regression model 00:00:00
    • How to build a linear factor model 00:00:00
    • Shrinkage methods – regularization for linear regression 00:00:00
    • How to use linear regression to predict returns 00:00:00
    • Linear classification 00:00:00
    • Time Series Models 00:00:00
    • Analytical tools for diagnostics and feature extraction 00:00:00
    • Univariate time series models 00:00:00
    • Multivariate time series models 00:00:00
    • Bayesian Machine Learning 00:00:00
    • How Bayesian machine learning works 00:00:00
    • Probabilistic programming with PyMC3 00:00:00
    • Decision Trees and Random Forests 00:00:00
    • Decision trees 00:00:00
    • Random forests 00:00:00
    • Gradient Boosting Machines 00:00:00
    • Adaptive boosting 00:00:00
    • Gradient boosting machines 00:00:00
    • Fast scalable GBM implementations 00:00:00
    • How to interpret GBM results 00:00:00
    • Unsupervised Learning 00:00:00
    • Dimensionality reduction 00:00:00
    • Clustering 00:00:00
    • Working with Text Data 00:00:00
    • How to extract features from text data 00:00:00
    • From text to tokens – the NLP pipeline 00:00:00
    • From tokens to numbers – the document-term matrix 00:00:00
    • Text classification and sentiment analysis 00:00:00
    • Topic Modeling 00:00:00
    • Learning latent topics: goals and approaches 00:00:00
    • Latent semantic indexing 00:00:00
    • Probabilistic latent semantic analysis 00:00:00
    • Latent Dirichlet allocation 00:00:00
    • Word Embeddings 00:00:00
    • How word embeddings encode semantics 00:00:00
    • Word vectors from SEC filings using gensim 00:00:00
    • Sentiment analysis with Doc2vec 00:00:00
    • Bonus – Word2vec for translation 00:00:00
    • Deep Learning 00:00:00
    • Deep learning and AI 00:00:00
    • How to design a neural network 00:00:00
    • How to build a neural network using Python 00:00:00
    • How to train a neural network 00:00:00
    • How to use DL libraries 00:00:00
    • How to optimize neural network architectures 00:00:00
    • Convolutional Neural Networks 00:00:00
    • How ConvNets work 00:00:00
    • How to design and train a CNN using Python 00:00:00
    • How to detect objects 00:00:00
    • Recent developments 00:00:00
    • Recurrent Neural Networks 00:00:00
    • How RNNs work 00:00:00
    • How to build and train RNNs using Python 00:00:00
    • Autoencoders and Generative Adversarial Nets 00:00:00
    • How autoencoders work 00:00:00
    • Designing and training autoencoders using Python 00:00:00
    • How GANs work 00:00:00
    • Reinforcement Learning 00:00:00
    • Key elements of RL 00:00:00
    • How to solve RL problems 00:00:00
    • Dynamic programming – Value and Policy iteration 00:00:00
    • Q-learning 00:00:00
    • Deep reinforcement learning 00:00:00
    • Reinforcement learning for trading 00:00:00
    • Next Steps 00:00:00
    • Key takeaways and lessons learned 00:00:00
    • ML for trading in practice 00:00:00
    • Conclusion 00:00:00

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