Algorithmic Trading with Python and Google Colab (3)

From Notebook to Trading System — Session 3 🚀 Close the loop from research to operations — load the exact frozen Session 2 model, replay 499 test-period bars through a deterministic inference pipeline, and run a monitored, risk-guarded simulation with a full SQLite audit trail. 📂 GitHub repo: https://github.com/yhilpisch/algocolab 🎁 Use our community discount code SPECIAL5 when enrolling in any of our programs (5% off). In this final session of our free 3-part webinar series "Algorithmic Trading with Python and Google Colab", Dr. Yves J. Hilpisch demonstrates the operational gap between a research notebook and a trading system: checkpoint loading, batch/replay parity, event replay, failure injection, and drawdown-based risk halts. 📐 Topics in this session: • Replay contract: EUR/USD daily data, frozen 20-member ensemble, fixed 0.50 rule, $100,000 initial capital, 0.5 bp one-way costs • Loading checkpoint, scaler, feature order, threshold, and model identity • Pinning the replay code to the Session 2 commit in the run manifest • Batch/replay inference parity within a stated floating-point tolerance • Replaying test-period events through the same feature and decision interfaces • Persisting ticks, signals, orders, and portfolio states in SQLite • Failure injection: stale data, price jumps, drawdown breaches • Flatten-and-halt on a 10% drawdown breach, with re-entry prevented while halted • Monitoring, deployment, broker integration, and shadow-mode extensions 🔗 Resources: 📑 Slides: https://tpq.io/tpq_colab_trading_3.pdf 📂 Code & notebooks: https://github.com/yhilpisch/algocolab 📊 TDS: https://thedatascientist.dev 📈 CPF: https://python-for-finance.com 🤖 TAE: https://theaiengineer.dev ₿ TCE: https://thecryptoengineer.dev 📌 Disclaimer: This content is provided solely for illustration and educational purposes. It does not constitute financial, investment, legal, or professional advice, nor an offer or solicitation to buy or sell any security, financial instrument, or trading strategy. Past performance, whether simulated, backtested, or real, is no guarantee of future results. Algorithmic trading involves substantial risk, including the possible loss of principal capital. No warranties or guarantees of any kind are given. Use at your own risk. #AlgorithmicTrading #Python #PyTorch #QuantFinance #Finance #Deployment #Simulation #SQLite #GoogleColab #RiskManagement #Backtesting #EURUSD #Trading #ThePythonQuants