Algorithmic trading encompasses any systematized, rule-based approach to trading that reduces or eliminates human discretion in execution. This ranges from simple: 'buy when the 50-day moving average crosses above the 200-day moving average' — to complex: deep learning models that process satellite imagery, earnings call audio, and alternative data simultaneously to generate multi-factor signals. The common thread is that trading decisions follow explicit, pre-coded rules derived from quantitative research, rather than human judgment in the moment of execution.

The spectrum by speed and complexity: high-frequency trading (HFT) operates on microsecond timescales, exploiting market microstructure — bid/ask spreads, order book dynamics, latency advantages. Statistical arbitrage runs on seconds-to-minutes, exploiting price discrepancies between correlated securities. Systematic trend-following (CTA strategies) operates on days-to-weeks, capturing momentum across futures markets. Quantitative fundamental strategies (like the frameworks underpinning AIQ's factor models) operate on weeks-to-months, systematically implementing factor-based stock selection that fundamental investors do discretionally.

An algorithmic trading system has several components: data pipeline (ingesting, cleaning, and storing market data, fundamentals, and alternative data), signal generation (computing indicators, factors, or model outputs that identify trading opportunities), portfolio construction (translating signals into target positions, applying constraints and risk management), execution (converting position targets into orders, minimizing market impact and slippage), and performance monitoring (tracking live performance vs. backtest expectations, detecting degradation).

Data quality is the most underestimated challenge. Stock price data requires adjustment for splits, dividends, and delisting survivorship bias. Point-in-time fundamental data (using only information available at the time of trading, not retroactively restated figures) is essential to avoid lookahead bias. Alternative data (satellite imagery, credit card transactions, mobile app analytics) requires careful legal review, data provider reliability assessment, and signal-noise analysis before incorporation. Many algorithmic strategies fail not because of flawed logic but because of inconsistent data quality that corrupts the historical analysis.

Institutional algorithmic trading (hedge funds, prop trading firms, bank trading desks) has access to co-located servers adjacent to exchange matching engines, custom-built execution infrastructure, proprietary alternative data sets, and teams of PhDs and engineers. Retail algorithmic traders operate at a structural disadvantage on speed and data access — competing with HFT on microsecond latency is not viable for individuals.

However, retail algorithmic traders have structural advantages in specific niches: they can trade much smaller positions (no market impact), access micro-cap and small-cap stocks that institutional size precludes, move quickly into or out of positions that would require weeks for large funds, and avoid the career-risk herding that affects institutional managers. Systematic approaches at the retail level — rule-based selection, automated rebalancing, defined exit rules — capture most of the behavioral benefits of algorithmic trading (discipline, consistency, removing emotional decision-making) without requiring institutional infrastructure.