Algorithmic trading, sometimes called algo trading or automated trading, is the practice of using computer programs to execute trades based on a predefined set of rules. These rules can be as simple as moving average crossovers or as complex as machine learning models that process hundreds of input features. The defining characteristic is that the trading decision is made by code, not by a human reacting to a chart in real time.
How algorithmic trading works
An algorithmic trading system typically consists of several components. The signal generator analyzes market data and identifies trading opportunities based on the strategy's logic. The risk manager evaluates whether the proposed trade fits within the portfolio's risk constraints. The execution engine routes orders to the market, handling order type selection, timing, and size. The monitoring layer tracks performance and alerts the trader to any anomalies.
The strategy itself can operate on any time frame. High-frequency strategies hold positions for milliseconds or seconds and rely on speed advantages. Intraday strategies hold positions for minutes to hours and exploit patterns within a single trading session. Swing and position strategies hold for days to weeks, using algorithms to time entries and exits more precisely than manual trading would allow.
Why algorithmic trading matters
Algorithms eliminate emotional decision-making, which is one of the biggest sources of loss for discretionary traders. Fear, greed, and hesitation cause traders to deviate from their plans, cut winners short, and hold losers too long. An algorithm follows its rules consistently regardless of market conditions or recent performance.
Algorithms also enable strategies that would be impractical to execute manually. A strategy that monitors 500 stocks simultaneously for specific patterns, or one that requires sub-second reaction to price changes, can only be implemented through automation. This expands the universe of tradeable strategies far beyond what manual trading allows.
Backtesting as the foundation
Before deploying an algorithmic strategy with real capital, it must be backtested against historical data. Backtesting reveals whether the strategy's logic produces a genuine edge or whether its apparent profitability is an artifact of hindsight. The quality of the backtest depends critically on the realism of the simulation, including accurate fill modeling, slippage, and transaction costs.
Practical example
A trader develops an algorithm that buys stocks when their 10-day RSI drops below 30 and sells when it rises above 70. The algorithm monitors a universe of 200 stocks in real time, calculates RSI on every price update, and submits orders automatically when conditions are met. Position size is determined by a volatility-adjusted formula that limits each trade to 1% portfolio risk. The entire process from signal to execution happens without human intervention.
How Tektii helps
Tektii is built specifically for algorithmic trading workflows. Strategies are packaged as Docker containers that connect to the backtesting engine via WebSocket, meaning traders can write algorithms in any programming language and use any libraries they prefer. The platform provides tick-level market data, realistic execution modeling, and comprehensive performance analytics so that traders can develop, test, and refine their algorithms with confidence before deploying capital.