Algorithmic Trading Basics
AI-Powered Investing • Advanced Level
Algorithmic trading uses computer programs to execute orders at speeds and frequencies beyond human capability. By codifying trading rules and leveraging AI tools, retail investors can access systematic strategies once reserved for institutions, eliminating emotional biases and operating 24/7 across global markets.
1What Is Algorithmic Trading?
Definition
Automated execution of pre-defined trading strategies using software programs that follow specific rules and conditions.
Evolution
From simple rule-based systems (e.g., "if price crosses moving average, then buy") to AI-driven decision engines.
Retail Benefits
- • Eliminates emotional biases
- • Executes complex multi-asset strategies
- • Operates 24/7 in global markets
Key Insight
Even simple algorithms can outperform discretionary trading when well-tested and disciplined. The key is consistency and removing emotional decision-making from the trading process.
2Common Strategy Types
Different algorithmic strategies suit various market conditions, timeframes, and retail investor capabilities.
| Strategy Type | Mechanism | Timeframe | Retail Fit |
|---|---|---|---|
Trend-Following | Buy when price breaks above moving average | Intraday to swing | High |
Mean Reversion | Fade extreme price moves back toward mean | Intraday | Medium |
Statistical Arbitrage | Exploit price inefficiencies between correlated assets | Seconds to minutes | Low (requires data depth) |
Liquidity Seeking | Use VWAP/TWAP to minimize market impact | Intraday | High |
Momentum | Rank stocks by recent performance, allocate to top decile | Daily to weekly | Medium |
Machine Learning | Predict returns using features and models | Varies | High (with platform support) |
3Key Components of an Algo System
A robust algorithmic trading system requires several interconnected components working together seamlessly.
Strategy Logic
Encoded rules or ML model predictions determine trade signals based on market conditions and indicators.
Data Feed
Real-time market data, fundamental/alternative data sources, and news feeds provide decision inputs.
Execution Engine
Routes orders via broker APIs, managing order types, timing, and execution quality.
Risk & Compliance Module
Enforces position limits, stop-losses, leverage constraints, and regulatory compliance.
Performance Analytics
Tracks P&L, slippage, execution quality, and comprehensive strategy performance metrics.
Modular Design Tip
Use modular architecture to swap components (e.g., testing new signal models) without rewriting the entire system.
4Order Types & Execution Algorithms
Basic Order Types
Market Orders
Pros: Immediate execution guaranteed
Cons: Variable fill price, potential slippage
Limit Orders
Pros: Price control, execution at specified price or better
Cons: No execution guarantee
Algorithmic Execution Strategies
VWAP (Volume-Weighted Average Price)
Slices orders to match market volume profile, minimizing market impact by trading when volume is naturally high.
TWAP (Time-Weighted Average Price)
Spreads orders evenly over a specified time window, providing consistent execution pace regardless of volume.
Implementation Shortfall
Minimizes the difference between decision price and actual execution price by balancing market impact and timing risk.
Smart Order Routing
Splits and routes orders across multiple venues (exchanges, dark pools, ECNs) to capture best prices and available liquidity, optimizing for both price improvement and execution speed.
5Backtesting & Simulation
Robust historical testing is crucial before deploying live algorithms.
Data Quality
Use clean, timestamped tick or minute-level data; adjust for corporate actions and survivorship bias.
Avoiding Overfitting
Keep parameter sets lean; use walk-forward optimization and cross-validation techniques.
Performance Metrics
Sharpe Ratio, Sortino Ratio, Maximum Drawdown, Calmar Ratio for comprehensive evaluation.
Transaction Costs
Model realistic fees, latency, and fill rates to estimate accurate net returns.
Always maintain a paper-trading environment to validate live performance under real market conditions.
6Risk Controls & Monitoring
Maintaining safeguards prevents catastrophic losses in automated systems.
Real-Time Checks
Position limits, aggregate exposure caps, maximum daily loss triggers for immediate protection.
Stop-Loss & Take-Profit
Automated exits to lock in gains or cut losses based on predefined risk parameters.
Kill Switches
Instant shutdown capability for abnormal behavior or connectivity issues.
Alerting & Logging
Detailed logs of signals, orders, executions; alerts for system errors or limit breaches.
Platforms & Tools for Retail Traders
Brokerage APIs
- • Alpaca - Commission-free API trading
- • Interactive Brokers - Professional tools
- • Robinhood - Simple retail access
Quant Platforms
- • QuantConnect - Cloud-based backtesting
- • Zipline - Python algorithmic trading
- • Backtrader - Flexible backtesting framework
Data Providers
- • Polygon.io - Real-time market data
- • Tiingo - Financial data API
- • Quandl - Alternative datasets
AI/ML Frameworks
- • TensorFlow - Deep learning models
- • PyTorch - Research-oriented ML
- • scikit-learn - Classical ML algorithms
Getting Started Tip
Start with a cloud-based quant platform to leverage built-in data, backtesting, and managed infrastructure. This reduces setup complexity and lets you focus on strategy development rather than technical implementation.