Artificial Intelligence for Trading (Training)

Colleagues, the Artificial Intelligence for Trading program involves real-world projects designed by industry experts, covering topics from asset management to trading signal generation. Master AI algorithms for trading, and build your career-ready portfolio. Learn the basics of quantitative analysis, including data processing, trading signal generation, and portfolio management. Use Python to work with historical stock data, develop trading strategies, and construct a multi-factor model with optimization. Training modules - each with a hands-on project - include: 1) Basic Quantitative Trading - market mechanics and how to generate signals with stock data. Work on developing a momentum-trading strategy in your first project. (Project: Trading with Momentum); 2) Advanced Quantitative Trading - quant workflow for signal generation, and apply advanced quantitative methods commonly used in trading (Project: Breakout Strategy); 3) Stocks, Indices,and ETFs - portfolio optimization, and financial securities formed by stocks, including market indices, vanilla ETFs, and Smart Beta ETFs (Project: Smart Beta and Portfolio Optimization); 4) Factor Investing and Alpha Research - alpha and risk factors, and construct a portfolio with advanced optimization techniques (Project: Alpha Research and Factor Modeling); 5) Sentiment Analysis with Natural Language Processing - fundamentals of text processing, and analyze corporate filings to generate sentiment-based trading signals (Project: Sentiment Analysis Using NLP); 6) Advanced Natural Language Processing with Deep Learning - apply deep learning in quantitative analysis and use recurrent neural networks and long short-term memory to generate trading signals (Project: Deep Neural Network with News Data); 7) Combining Multiple Signals - advanced techniques to select and combine the factors you’ve generated from both traditional and alternative data (Project: Combine Signals for Enhanced Alpha); and 8) Simulating Trades with Historical Data - refine trading signals by running rigorous back tests. Track your P&L while your algorithm buys and sells (Project: Backtesting).

Enroll today (eams & execs welcome): https://tinyurl.com/hw9tnjjf 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy

Deep Neural Networks with PyTorch (IBM)

AI colleagues, the Deep Neural Networks with PyTorch from IBM equips you  to develop deep learning models using  Pytorch. The course will start with Pytorch's  tensors and Automatic differentiation package. Then each section will cover different models starting off with fundamentals such as Linear Regression, and logistic/softmax regression. Followed by  Feedforward deep neural networks, the role of different activation functions, normalization and dropout layers. Then Convolutional Neural Networks and Transfer learning will be covered. Finally, several other Deep learning methods will be covered. After completing this program you will be able to explain and apply their knowledge of Deep Neural Networks and related machine learning methods, know how to use Python libraries such as PyTorch  for Deep Learning applications and build Deep Neural Networks using PyTorch. Skill-based training modules cover: 1) Tensor and Datasets, 2) Differentiation in PyTorch, 3) Simple Datasets, 4) Linear Regression, 2) Gradient Descent, 3) Prediction in One Dimension, 4) PyTorch Linear Regression Training Slope and Bia, 5) Training Parameters in PyTorch, 6) Multiple Input Output Linear Regression, 7) Multiple Output Linear Regression, 8) Linear Classifier, 9) Logistic Regression: Prediction, 10) Bernoulli Distribution and Maximum Likelihood Estimation, 11) Softmax Regression, 12) Deep Neural Networks, 13) Convolutional Neural Network, and 14) TorchVision Models.

Enroll today (eams & execs welcome): https://tinyurl.com/4uj75b2z 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy