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Course Skill Level:


Course Duration:

5 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Developers with basic Python skills

Who should attend & recommended skills

  • This course is geared for developers who want to explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras.
  • Skill-level: Foundation-level Machine Learning for Algorithmic Trading skills for Intermediate skilled team members. This is not a basic class.
  • Python: Basic (1-2 years’ experience)

About this course

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, stats models, 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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our Machine Learning for Algorithmic Trading expert instructor, students will learn about and explore:
  • Implementing machine learning algorithms to build, train, and validate algorithmic models
  • Creating your own algorithmic design process to apply probabilistic machine learning approaches to trading decisions
  • Developing neural networks for algorithmic trading to perform time series forecasting and smart analytics
  • Implementing machine learning techniques to solve investment and trading problems
  • Leveraging market, fundamental, and alternative data to research alpha factors
  • Designing and fine-tuning supervised, unsupervised, and reinforcement learning models
  • Optimizing portfolio risk and performance using pandas, NumPy, and scikit-learn
  • Integrating machine learning models into a live trading strategy on Quantopian
  • Evaluating strategies using reliable back testing methodologies for time series
  • Designing and evaluate deep neural networks using Keras, PyTorch, and TensorFlow
  • Working with reinforcement learning for trading strategies in the OpenAI Gym

Course breakdown / modules

  • 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

  • How to work with market data
  • How to work with fundamental data
  • Efficient data storage with pandas

  • The alternative data revolution
  • Evaluating alternative datasets
  • The market for alternative data
  • Working with alternative data

  • Engineering alpha factors
  • Seeking signals – how to use zipline
  • Separating signal and noise – how to use alphalens
  • Alpha factor resources

  • 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

  • Learning from data
  • The machine learning workflow

  • 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

  • Analytical tools for diagnostics and feature extraction
  • Univariate time series models
  • Multivariate time series models

  • How Bayesian machine learning works
  • Probabilistic programming with PyMC3

  • Decision trees
  • Random forests

  • Adaptive boosting
  • Gradient boosting machines
  • Fast scalable GBM implementations
  • How to interpret GBM results

  • Dimensionality reduction
  • Clustering

  • 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

  • Learning latent topics: goals and approaches
  • Latent semantic indexing
  • Probabilistic latent semantic analysis
  • Latent Dirichlet allocation

  • How word embeddings encode semantics
  • Word vectors from SEC filings using gensim
  • Sentiment analysis with Doc2vec
  • Bonus – Word2vec for translation

  • 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

  • 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

  • Recurrent Neural Networks
  • How RNNs work
  • How to build and train RNNs using Python

  • Autoencoders and Generative Adversarial Nets
  • How autoencoders work
  • Designing and training autoencoders using Python
  • How GANs work

  • 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

  • Key takeaways and lessons learned
  • ML for trading in practice
  • Conclusion