Let us help you find the training program you are looking for.

If you can't find what you are looking for, contact us, we'll help you find it. We have over 800 training programs to choose from.

Machine Learning for Algorithmic Trading

  • Course Code: Data Science - Machine Learning for Algorithmic Trading
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 5 Days Audience: This course is geared for those who wants to Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras.

Course Snapshot 

  • Duration: 5 days 
  • Skill-level: Foundation-level Machine Learning for Algorithmic Tradingskills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras. 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

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. 

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 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Implement machine learning techniques to solve investment and trading problems 
  • Leverage market, fundamental, and alternative data to research alpha factors 
  • Design and fine-tune supervised, unsupervised, and reinforcement learning models 
  • Optimize portfolio risk and performance using pandas, NumPy, and scikit-learn 
  • Integrate machine learning models into a live trading strategy on Quantopian 
  • Evaluate strategies using reliable backtesting methodologies for time series 
  • Design and evaluate deep neural networks using Keras, PyTorch, and TensorFlow 
  • Work with reinforcement learning for trading strategies in the OpenAI Gym 

Audience & Pre-Requisites 

This course is designed for developers wants to Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras 

Pre-Requisites:  Students should have familiar with  

  • Basics of Python and ML 
  • Knowledge of Python is assumed. 

Course Agenda / Topics 

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

    Course Inquiry

    Fill in the details below and we will get back to you as quickly as we can.

    Interested in any of these related courses?