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Algorithmic Trading

Overview

Design, test, and deploy data-driven trading strategies that operate with discipline and speed. This topic connects statistics, ML, market microstructure, and robust software to achieve performance, reliability, and controlled risk from research to live execution.

Status: 🟧 Intermediate–Advanced

Who should learn this?
✅ Aspiring/active quant researchers
✅ Software/ML engineers moving into trading
✅ Traders automating discretionary ideas

Prerequisites

  • Python and data tooling (pandas, NumPy)
  • Probability, statistics, linear algebra, time series basics
  • Markets/microstructure basics, order types, venue mechanics
  • Git, testing, containers; basic Linux

Learning Objectives

  • Build a research→backtest→paper-trade→live pipeline
  • Implement robust backtests with realistic costs, latency, and slippage
  • Engineer alpha signals and features; avoid leakage and overfitting
  • Design execution (TWAP/VWAP/POV), risk controls, and monitoring

Key Concepts

  • Market microstructure – Ticks, spreads, queue priority, auctions
  • Alpha & features – Stationarity, factor design, target construction
  • Backtesting – Event-driven engines, costs, latency, walk-forward
  • Execution – Scheduling, child orders, impact, venue selection
  • Risk – Exposure limits, VaR/ES, drawdown, kill switches

Applications in Algorithmic Trading

  • Live deployment – Event-driven services with order and market data gateways
  • Throughput/latency – Efficient data pipelines and execution adapters
  • Risk & monitoring – Real-time limits, circuit breakers, alerting

🧠 Study Materials

📚 Books

📘 Beginner

Title Author(s) Description Link
Python for Data Analysis Wes McKinney Core pandas workflows for research https://wesmckinney.com/book/

📗 Intermediate

Title Author(s) Description Link
Machine Learning for Algorithmic Trading Stefan Jansen End-to-end ML and backtesting https://www.packtpub.com/product/machine-learning-for-algorithmic-trading-second-edition/9781839217715
Algorithmic Trading Ernest P. Chan Practical systems and pitfalls https://www.wiley.com/en-us/Algorithmic+Trading%3A+Winning+Strategies+and+Their+Rationale-p-9781118460146

📙 Advanced

Title Author(s) Description Link
Advances in Financial Machine Learning Marcos López de Prado Features, CV, backtest rigor, HRP https://www.wiley.com/en-us/Advances+in+Financial+Machine+Learning-p-9781119482086

🎓 Courses

📘 Beginner

Course Title Provider Level Description
Intro to Trading, ML & Python Various (Coursera/edX) Beginner High-level overview, basic strategies

📗 Intermediate

Course Title Provider Level Description
Financial Engineering & Risk Management Columbia (Coursera) Intermediate Risk, derivatives, data workflows

📙 Advanced

Course Title Provider Level Description
Quant Research/Execution Topics University/Industry Advanced Microstructure, execution, robust testing

🏅 Certifications & Developer Programs

Credential Provider Description
CQF Fitch/CQF Applied quantitative finance
FRM GARP Risk-focused credential
CFA CFA Institute Broader investment knowledge

🛠️ Tools & Libraries

  • Research – pandas, NumPy, scikit-learn, statsmodels
  • Backtesting – backtrader, Zipline, Qlib
  • Execution – Broker APIs (IBKR, CCXT), FIX gateways
  • Infra – Docker, GitHub Actions, Prometheus/Grafana

🧪 Hands-On Projects

  • Implement a mean-reversion signal and walk-forward backtest
  • Build a cost/latency-aware execution simulator
  • Deploy a paper-trading bot with risk limits and alerts

✅ Assessment

  • Explain leakage and how to prevent it in pipelines
  • Add realistic costs and slippage to an event-driven backtest
  • Compare execution schedules given liquidity constraints

❓ FAQs

Q: Should I learn this before deep ML?
A: Learn solid backtesting, data hygiene, and risk first; then add ML.

Q: Do I need expensive data?
A: Start with free/retail data; focus on methodology before breadth.

🔗 Next Steps