Quant funds use algorithms, statistical models, and machine learning to trade at scale. High-frequency trading exploits tiny price discrepancies in microseconds. Factor models drive systematic strategies across thousands of securities.
Answer: Quant strategies use systematic, rules-based models rather than human judgement. Quant strategies are fully systematic — rules-based, emotionless, and scalable across thousands of securities.
Answer: Tiny price inefficiencies over microseconds using speed advantages. HFT firms co-locate servers near exchanges to execute millions of trades per second, profiting from tiny spreads.
Answer: The model works perfectly on historical data but fails in live trading. Overfitting means a model was tuned to historical noise rather than real signal — it fails on new out-of-sample data.
Answer: Testing a trading strategy against historical data to evaluate performance. Backtesting simulates a strategy on historical data — but past performance doesn't guarantee future results, especially if overfitted.
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