We cover the whole process: From Market Predictability and GPU-Accelerated AI to Cloud Deployment
Register at:
https://attendee.gotowebinar.com/register/6147605264199768928
This free three-part webinar series develops a complete algorithmic-trading workflow with Python, Google Colab, and PyTorch — from testing market predictability to training neural networks on GPUs and deploying a compact trading system.
🔎 Session 1 — From Efficient Markets to Trading Signals
We start with the core question: Can markets be predicted at all? Using random walks and the Efficient Market Hypothesis as benchmarks, we explore autocorrelation, lagged returns, simple predictive models, vectorized backtesting, transaction costs, and robustness.
🧠 Session 2 — GPU-Accelerated Deep Learning with PyTorch
We move from linear models to neural networks and use Colab GPUs for training. Topics include financial features and targets, PyTorch datasets and training loops, model validation, and the crucial distinction between prediction accuracy and trading profitability.
🚀 Session 3 — From Notebook to Trading System
Finally, we turn the trained model into a compact trading application. We cover simulated live market data, signal generation, positions and P&L, logging, SQLite-based persistence, monitoring, operational safeguards, and the transition from GPU-based research to CPU-based deployment.
📓 Three Comprehensive Notebooks
Each session is built around one self-contained, executable Google Colab notebook, creating a coherent progression from research to deployment.
⚙️ Technologies & Concepts
Python · Google Colab · PyTorch · GPU Computing · Financial Time Series · Backtesting · SQLite · Logging · Monitoring
🔎 Discover → 🧠 Learn → 🚀 Deploy
3 live webinars · 75–90 minutes each
1 comprehensive Colab notebook per session
