Algorithmic Trading
Trading strategy based on coded instructions executed automatically by algorithms.
Beginner-friendly explanation
Algorithmic trading means using a computer program to automatically buy or sell on the markets. The program follows a set of simple rules (e.g., "buy if price rises by 3% and volume increases") and executes trades without your input.
Example: You set up a bot to buy Ethereum when the short moving average crosses above the long one. The bot monitors the market for you and acts when the condition is met.
Intermediate-level insight
Algorithmic trading involves coding a strategy in computer language so the system can analyze data and execute trades based on precise conditions. This may include: technical indicators (RSI, MACD, Bollinger), time filters (specific hours, volatility), combined signals (multi-asset, multi-timeframe). Algorithms can be backtested on historical data to assess their effectiveness before going live.
Example: An algorithm combines RSI < 30, bullish MACD crossover, and above-average volume. It is tested on the last 6 months of Bitcoin history to check potential profitability.
Advanced perspective
Algorithmic trading is an advanced branch of quantitative trading. It relies on mathematical modeling of strategies that are then coded, optimized, and executed automatically. Algorithmic systems can: use deterministic, adaptive, or AI-based algorithms, exploit micro-market variations (HFT) or inter-exchange arbitrage, dynamically adjust parameters based on volatility, liquidity, or external signals. They are subject to strict requirements: minimal latency, model robustness, refined risk control, full logging and traceability.
Example: A quantitative fund designs a multi-factor algorithm combining momentum, sector trend, and on-chain signals. The code runs on a dedicated server with latency below 5 ms and built-in risk management.
Tools & Automation
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