Algorithmic trading, or algo trading, refers to the use of computer programs and algorithms to execute financial market trades. These programs follow predefined criteria, such as price, timing, and volume, to make trading decisions and execute orders. The goal is to achieve efficiency, consistency, and speed while minimizing human error.
Algorithms start by collecting vast amounts of historical and real-time market data. This data includes stock prices, trading volumes, economic indicators, and even sentiment analysis from news and social media.
Based on the analysis, traders develop strategies that are codified into algorithms. These strategies can range from simple moving averages to complex statistical models. The key is to identify patterns or inefficiencies that the algorithm can exploit.
Before deploying in live markets, algorithms are rigorously tested using historical data. Backtesting helps ensure that the strategy would have been profitable in the past, providing a level of confidence for future performance.
Once live, the algorithm continuously monitors the market, scanning for conditions that match its criteria. When it identifies a potential trade, it executes orders at high speeds, often within milliseconds, to capitalize on short-lived market opportunities.
Even after deployment, algorithms require constant monitoring. Market conditions can change, and a previously profitable strategy might need adjustments. Continuous optimization ensures that the algorithm remains effective.
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