Quantitative
Data-driven approach using statistics and algorithms.
Beginner-friendly explanation
Quantitative trading uses math and computers to make decisions. Instead of following instinct, it follows numbers and rules. This kind of strategy is often automated. Example: A bot that buys Bitcoin every time the price drops 5%, using a simple rule, is doing quantitative trading.
Intermediate-level insight
A quantitative strategy is based on statistical models, often tested via backtesting. It may include volatility filters, momentum conditions, or combined signals. It requires mathematical rigor and solid data handling. Example: An RSI + MACD strategy coded in PineScript, tested on 3 years of data, with automated stop management.
Advanced perspective
Quantitative trading uses algorithmic models based on multivariate analysis, machine learning, or neural networks. Strategies are optimized via metrics like Sharpe Ratio, max drawdown, or risk-adjusted win rate. Quant funds often use high-frequency databases and tools like Python, R, or Matlab. Example: A hedge fund builds a high-frequency market-making strategy on ETH/USDC using an LSTM prediction model trained on 10 million ticks.
Trading Strategies
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