Introduction to systematic and algorithmic trading. Learn about backtesting frameworks, strategy development, common quant strategies, and the path from backtest to live.

What Is Algorithmic Trading?

Algorithmic trading (also called algo trading, automated trading, or systematic trading) uses computer programs to execute trades based on predefined rules. Instead of a human watching charts and clicking buttons, software monitors the market and places trades when conditions are met.

The Spectrum of Automation

Algo trading exists on a spectrum:

Fully Manual

You watch the charts, identify setups, place orders, and manage trades yourself. No automation.

Semi-Automated

You identify the setup but use automation for execution. Examples:

• Automated stop loss and take profit orders
• Alerts that notify you when conditions are met
• Scripts that calculate position size automatically

Fully Automated

The algorithm handles everything: signal generation, entry, exit, risk management, and position sizing. You monitor but don't intervene.

Most traders start semi-automated and gradually increase automation as they gain confidence.

Why Consider Algorithmic Trading?

Advantages

• No emotions: The algorithm follows rules without fear, greed, or fatigue
• Speed: Can analyze multiple instruments and timeframes simultaneously
• Consistency: Executes the same strategy identically every time
• Backtestable: You can test the strategy on historical data before risking money
• Scalable: Can manage multiple accounts or strategies simultaneously
• 24/7 operation: Doesn't need sleep or breaks

Disadvantages

• Technical complexity: Requires programming knowledge (Python, C#, etc.)
• Overfitting risk: Easy to create strategies that work on historical data but fail live
• Technology risk: Server outages, connectivity issues, bugs can cause losses
• Market adaptation: Markets change, and algos need periodic updating
• Latency: Retail infrastructure is slower than institutional, limiting some strategy types

Common Misconceptions

"I need a PhD in mathematics": False. Many profitable algos use simple rules (moving average crossovers, breakout strategies) with disciplined risk management. Complexity is not correlated with profitability.

"Algos guarantee profit": False. An algo is only as good as the strategy it implements. A bad strategy automated is still a bad strategy.

"I need expensive infrastructure": False. A basic cloud server ($20-50/month) and free data sources are sufficient for most retail algo strategies. You're not competing with high-frequency trading firms.

Getting Started

The best path into algorithmic trading:

1. Master manual trading first: You need to understand markets, price action, and risk management before automating
2. Learn basic programming: Python is the most popular choice — start with fundamentals
3. Automate your existing strategy: Turn your discretionary trading rules into code
4. Backtest rigorously: Test on historical data before going live
5. Paper trade the algorithm: Run it live on a demo account for months
6. Deploy with small size: Start with minimum position sizes on a live account

For Prop Firm Traders

Many prop firms allow algorithmic trading (check your specific firm's rules). Benefits include:

• Consistent execution across multiple funded accounts
• Eliminating the psychological pressure of manual trading
• Scaling strategies across multiple firms simultaneously
• Ensuring compliance with trading rules (daily loss limits, max positions) programmatically

Key takeaways

• Algorithmic trading uses computer programs to execute trades based on predefined rules
• Algos can eliminate emotional decision-making and execute faster than humans
• You don't need a PhD — many successful algos use simple rules with disciplined risk management
• Start with automating your existing discretionary strategy before building complex systems

Continue to lesson 2

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What's in the full course

1. 1Introduction to Algo TradingReading
2. 2Backtesting Frameworks and Methodology🔒
3. 3Strategy Development Process🔒
4. 4Common Quantitative Strategies🔒
5. 5Risk Management for Algorithms🔒
6. 6From Backtest to Live Trading🔒