Algorithmic Trading Strategies
Overview
Algorithmic trading uses computer programs to execute trades based on predefined rules — from simple moving average crossovers to complex machine learning models. The key advantage is removing emotion, achieving consistent execution speed, and backtesting strategies across decades of data before risking real capital.
Key Concepts
Strategy types: trend-following, mean-reversion, momentum, statistical. Backtesting and walk-forward analysis. Execution algorithms (TWAP, VWAP, iceberg orders). Risk management automation (position sizing, max drawdown kills). Slippage and latency considerations. Overfitting and curve-fitting traps. Paper trading vs live deployment.
Entry Signals
Defined by the algorithm's rule set — examples: MA crossover, Bollinger breakout, RSI oversold + volume spike, or ML model output exceeding confidence threshold. All entries are systematic with no manual override.
Exit Signals
Time-based stops, trailing stops, profit targets, or opposing signal. Max drawdown circuit breaker. Correlation with benchmark exceeding threshold (strategy no longer edge-positive). Regime detection flagging unfavorable market conditions.
Best Timeframes
Any — from tick data for HFT to daily bars for swing strategies. The timeframe is dictated by the strategy's edge and the market's microstructure.
Pro Tips
Backtesting is necessary but not sufficient — always walk-forward test and paper trade before going live. Account for slippage, commissions, and latency in all backtests. Diversify across uncorrelated strategies rather than putting all capital into one system.
More Topics in This Category
Statistical Arbitrage
Statistical arbitrage (stat arb) exploits temporary mispricings between correlated assets using quantitative models. Pairs trading — the simplest form — goes long the underperformer and short the outperformer when their price ratio deviates beyond historical norms, profiting as the spread reverts to the mean.
Backtesting & Monte Carlo Simulation
Backtesting applies a trading strategy to historical data to evaluate how it would have performed, while Monte Carlo simulation randomises the order of those historical trades across thousands of iterations to understand the range of possible outcomes. Together, these techniques provide both an estimate of expected performance and a probabilistic view of risk, drawdown, and return distributions.
Machine Learning in Trading
Machine learning applies statistical algorithms to financial market data to identify patterns, generate predictions, and optimise trading decisions without being explicitly programmed for each scenario. From simple linear regression to deep neural networks, machine learning models can process vast datasets, detect non-linear relationships, and adapt to changing market conditions — though they also carry significant risks of overfitting and data-snooping bias.
Grid Trading
Grid trading places buy and sell limit orders at regular price intervals above and below the current market price, creating a 'grid' that profits from range-bound oscillations. Each buy order has a corresponding sell order at a higher grid level, generating profit from each completed round trip. It works best in sideways markets and can be automated easily.