Algorithmic Trading Basics: Introduction to Systematic Strategies

Introduction

Algorithmic trading, also called systematic trading, is the use of coded rules to generate buy and sell decisions and to execute trades automatically. It replaces discretionary judgment with repeatable, testable rules that you can refine, test, and deploy at scale.

Why does this matter to you as an experienced investor? Because algorithmic approaches let you remove emotion, apply consistent risk management, and explore strategies that would be impractical to implement manually. What makes a good algorithmic strategy, and how do you avoid the common traps that inflate historical performance?

This article walks through the core concepts of algorithmic trading, simple strategy blueprints, backtesting fundamentals, realistic performance evaluation, and accessible tools you can use today to prototype and validate automated trading ideas. By the end you'll know how to design a simple strategy, test it properly, and understand the limitations you must manage.

• Algorithmic trading replaces discretion with coded rules, enabling repeatability and scale.
• Robust backtesting requires clean data, realistic costs, out-of-sample testing, and attention to bias.
• Simple strategies like moving average crossovers or mean reversion can be effective when executed with sound risk and trade management.
• Key metrics include CAGR, volatility, max drawdown, and risk-adjusted returns such as Sharpe and MAR ratios.
• Use Python and platforms like QuantConnect, Backtrader, or broker APIs with careful simulation of slippage and latency.

Understanding Systematic Strategies

Systematic strategies are rule-based and usually split into three components: signal generation, position sizing, and execution. Signal generation defines the conditions for entry and exit. Position sizing determines allocation and risk. Execution handles order placement and fills.

You should think in terms of signal, portfolio construction, and trade execution as separate modules. That separation makes it easier to test changes to one component without invalidating others.

Types of systematic strategies

Common families of strategies include momentum, mean reversion, statistical arbitrage, and market making. Momentum strategies buy assets that have outperformed and sell laggards. Mean reversion strategies assume prices revert to a local mean after extreme moves. Statistical arbitrage exploits temporary mispricings between correlated instruments.

For example, a momentum approach might buy $AAPL after a 3-month outperformance relative to $SPY. A mean reversion trade could short $NVDA after a 4-day move above 2 standard deviations of a short-term moving average.

Simple Strategy Examples

Concrete blueprints help you move from concept to code. Below are two compact strategies you can prototype quickly. Each example states the signal, sizing, and execution assumptions.

Example 1: SMA Crossover on $SPY

1. Signal: Go long when the 50-day simple moving average crosses above the 200-day SMA. Exit when the 50-day crosses below the 200-day.
2. Sizing: Fixed fraction of portfolio, for example 20% capital per trade. No leverage in the base case.
3. Execution: Market order at next open. Assume slippage of 0.01% and commission of $0.005 per share for simulation.

Backtest this from 2005 to present on $SPY. Track annualized return, volatility, max drawdown, and Sharpe ratio. In many regimes this strategy captures long-term trends but suffers whipsaws around range-bound markets.

Example 2: Mean Reversion on $AAPL Intraday

1. Signal: On an intraday 5-minute chart, compute 20-bar exponential moving average EMA20. If price falls more than 0.8% below EMA20 and intraday volume spikes above 1.5x average, enter a long mean reversion trade. Exit at either a 0.5% profit target or after 20 bars.
2. Sizing: Risk 0.25% of portfolio per trade with a hard stop loss at 1% below entry.
3. Execution: Use limit orders at mid-price where possible. Assume 1 tick slippage and a commission schedule consistent with your broker.

Intraday strategies require accounting for bid-ask spreads, latency, and order book dynamics. Fill assumptions are critical. You also need minute or sub-minute data, which brings higher data costs and quality risks.

Backtesting Fundamentals

Backtesting is the process of simulating a strategy on historical data. Sound backtesting separates signal discovery from evaluation. If you relax the discipline, you'll fall prey to overfitting and inflated expectations.

Data quality and adjustments

Use clean, adjusted price series for equities that account for dividends and splits. For backtesting execution you need raw bid/ask or trade prints if you simulate intraday fills. Avoid survivorship bias by using historical constituent lists and delisted symbols when testing beyond major indices.

Data vendors can vary in quality. Validate data by checking for gaps, duplicate timestamps, and unrealistic returns. For example, a single incorrect trade print can create massive artificial gains in a high-frequency test.

Biases to avoid

Common biases include lookahead bias, survivorship bias, and multiple-testing bias. Lookahead bias occurs when future information leaks into signals. Survivorship bias arises if you test on companies that survived to the present without including delisted firms. Multiple-testing bias is the illusion of performance after you try many parameter combinations until one looks good.

Fix these by implementing strict train-test splits, using walk-forward optimization, and logging the number of hypotheses you test. If you tried 1,000 parameter sets, your in-sample p-values need to be adjusted accordingly.

Realistic trading costs

Always model commissions, slippage, and market impact. For small retail-size trades slippage might be low, but for larger orders or liquidity-poor securities impact can dominate returns. Use empirical slippage models tied to ADV, order size, and spread.

For instance, a strategy that trades 1% of ADV on $AAPL will face materially different impact than one trading 0.01% of ADV. Simulate fills conservatively and stress test returns under double and triple cost scenarios.

Performance Evaluation and Risk Management

Beyond raw returns, evaluate strategies on risk-adjusted metrics and behavior under stress. Key metrics include annualized return, volatility, maximum drawdown, Sharpe ratio, Sortino ratio, and Calmar ratio. Also inspect the distribution of drawdowns and monthly returns.

Walk-forward testing and robustness

Walk-forward testing partitions historical data into rolling in-sample and out-of-sample windows. You optimize on the in-sample window and validate on the next out-of-sample period, then roll forward. This approach better approximates live performance than a static split.

You should also run sensitivity analysis. Vary parameters by small amounts to see if performance degrades gracefully. If tiny parameter changes create massive disparities, the strategy is likely overfit.

Position sizing and portfolio-level risks

Position sizing is where theory meets risk management. Use volatility parity, Kelly fraction with conservative shrinkage, or fixed fractional sizing. Correlation across strategies matters. Two uncorrelated profitable strategies can produce a smoother equity curve than one strategy alone.

Implement stop-loss rules, tranche sizing, and maximum drawdown alerts. At the end of the day you need rules that limit ruin while preserving edge.

Tools and Platforms for Development

There are mature open-source and commercial tools you can use to prototype and deploy strategies. Choose tools based on your time horizon, data needs, and preferred language.

Python ecosystem

Python with pandas, NumPy, and SciPy is the de facto standard for prototyping. Backtesting libraries include Backtrader, Zipline, and vectorbt. For execution and live trading, connect to brokers through APIs like Interactive Brokers, Alpaca, or REST gateways provided by platforms such as QuantConnect.

QuantConnect offers cloud backtesting and live execution with data and multi-asset support. If you prefer local control, Backtrader is flexible for custom strategies but requires you to manage data and execution connectors.

Commercial platforms and broker APIs

Platforms like Tradestation and Bloomberg offer integrated data, backtesting, and execution but carry higher costs. For retail-friendly deployment, Alpaca and Interactive Brokers provide APIs for automated trading with reasonable commissions. Always test on paper or a sandbox account before moving to live funds.

Real-World Examples and Numbers

Concrete scenarios make abstract issues tangible. Below are simplified backtest summaries to illustrate the impact of costs and bias.

Example: SMA crossover on $SPY, 2005-2023

• Gross annualized return, naive fills: 9.2%.
• Net annualized return after 0.02% round-trip slippage and $0.005/share: 7.8%.
• Max drawdown: 28%. Sharpe ratio: 0.78 net.

When you double slippage assumptions the net return can fall below a buy-and-hold benchmark. This example shows how modest costs and execution assumptions matter for low-turnover strategies.

Example: Intraday mean reversion on $AAPL, 2020

• Trial with ideal fills: annualized net profit 18% with high turnover.
• After realistic fills including 1 tick slippage and 0.1% market impact, net profit drops to 4% and Sharpe falls sharply.

Intraday edge often erodes when you model real-world frictions. That is why latency, routing, and execution strategy are as important as signal quality for short-horizon systems.

Common Mistakes to Avoid

• Overfitting: Tuning too many parameters to historical data. Avoid by limiting degrees of freedom and using walk-forward testing.
• Ignoring transaction costs: Simulate realistic commissions, slippage, and market impact to avoid inflated returns.
• Data bias: Using survivorship-free, adjusted historical data prevents overly optimistic results.
• Poor risk controls: Not defining position sizing, stop losses, or diversification rules can lead to catastrophic drawdowns.
• Insufficient out-of-sample validation: Relying only on in-sample performance will mislead you about future viability.

FAQ

Q: How much capital do I need to start algorithmic trading?

A: It depends on the strategy and asset class. For simple equity strategies trading liquid ETFs you can start with a few thousand dollars, but meaningful statistical confidence and ability to manage costs often require larger capital. Always begin with paper trading and scale gradually.

Q: Can retail investors compete with institutional quant funds?

A: Yes, in many niches. Retail traders can develop viable strategies in areas where speed and capital are less decisive, such as daily rebalancing, niche mean reversion, or cross-asset signals. Institutions dominate areas requiring low latency and deep capital, but retail edge can still exist if you focus on sound research and disciplined execution.

Q: How do I know if a backtest is overfit?

A: Signs include extreme sensitivity to parameter changes, very low out-of-sample performance relative to in-sample, and large improvements after testing many combinations. Use cross-validation, walk-forward testing, and penalize complexity to detect overfitting.

Q: What metrics should I prioritize when evaluating a strategy?

A: Prioritize risk-adjusted metrics. Look at CAGR, volatility, max drawdown, Sharpe ratio, Sortino ratio, and percent of winning months. Also examine the strategy's return distribution and correlation with benchmarks to understand portfolio effects.

Bottom Line

Algorithmic and systematic trading give you a framework to turn investment hypotheses into testable, repeatable strategies. The value comes from disciplined signal design, rigorous backtesting, and realistic simulation of costs and market behavior.

If you want to get started, pick a simple strategy, gather clean data, and run a disciplined backtest with in-sample and out-of-sample splits. Use robust metrics, stress-test cost assumptions, and start live with small capital or paper trading before scaling.

Algorithmic trading is a craft that combines data, statistics, and sound risk management. Keep learning, iterate slowly, and remember that robustness beats complexity when you move from backtest to live trading.