Quantitative Trading with Python

What you will achieve

Backtest with commission/slippage/stop-loss in Backtrader, render perf reports, run live via broker API

Syllabus

• 1
  Quant Concepts & Workflow

  Idea → backtest → live trading

• 2
  Historical Data & Cleaning

  yfinance, broker APIs, gap filling

• 3
  TA-Lib Indicators

  150+ indicators, custom indicators

• 4
  Backtrader Framework

  Strategy, Cerebro, Broker, Sizer

• 5
  Backtest Pitfalls

  Look-ahead, survivorship, overfitting

• 6
  Commission, Slippage, Stop

  Real-world cost models

• 7
  Performance Metrics

  Sharpe, Sortino, MDD, win/loss

• 8
  Optimization & Overfit

  Walk-forward, cross-validation

• 9
  Turtle Quant Implementation

  Code GBP range-orders from Turtle course and backtest

• 10
  Live Deployment

  Shioaji / Fubon / Interactive Brokers

• 11
  Cloud Hosting & Monitoring

  GCP VM, systemd, LINE alerts

Highlights

• Pairs with Turtle Trading: from manual to automated
• Repo includes 5 ready backtests
• Graduate with a cloud-resident trading bot

Instructor

12 years of cross-market trading experience — Taiwan stocks, TXO options, forex margin, crypto spot and futures. Formerly with an asset management firm, now trading personal accounts systematically with a 12~30% annualized target. My teaching style starts with risk awareness and money management before discussing strategy. I firmly believe "only those who survive get to profit" — so every lesson dedicates significant time to stop-loss and hedging.