Glossary
Quant terms used on FinLab AI
Understand the numbers before they impress you. Fourteen terms, plain language.
Terms are ordered to mirror the FinLab AI backtest report — 1–2 sentences each, no jargon stacking.
- CAGR (Compound Annual Growth Rate)
- Total strategy return expressed as a single compounded annual rate. A 5-year cumulative return of 100% works out to a CAGR of about 14.9%. 完整說明與台股實例 →
- Excess return per unit of risk (volatility). > 1 is solid, > 2 is excellent; the absolute number depends on the risk-free rate assumption used in the calculation.
- Max Drawdown
- The largest peak-to-trough decline in equity over the period. -30% means at the worst point your capital was down by a third — the key metric for whether you can sleep at night.
- Backtest
- Simulating a strategy on historical data. A great backtest doesn't promise future returns, but a bad backtest almost always means a worse future.
- Win Rate
- The share of trades that ended profitably. High win rate doesn't equal high returns — a strategy that loses big and wins small can have a great win rate and still lose money. 完整說明與台股實例 →
- Turnover
- How frequently the portfolio is rotated. 200% annual turnover means the whole portfolio is swapped roughly twice a year; high turnover eats into returns through fees and slippage. 完整說明與台股實例 →
- Slippage
- The gap between your intended price and the price you actually fill at. FinLab backtests build in fees and transaction tax but no slippage assumption; raise fee_ratio to stress-test costs so you don't overestimate strategy performance. 完整說明與台股實例 →
- Alpha
- Return earned above what the broader market explains. Alpha = 5% means the strategy beat the market-driven return by 5 percentage points. 完整說明與台股實例 →
- Beta
- How tightly a strategy moves with the market. Beta = 1 tracks the market, < 1 is more defensive, > 1 is more aggressive. 完整說明與台股實例 →
- F-Score (Piotroski F-Score)
- A 0–9 score across nine financial-health checks. Higher scores indicate cleaner, more conservative financials — a common quality filter for value strategies.
- Momentum
- The empirical tendency for recent winners to keep winning. Momentum strategies typically buy stocks that have outperformed over 3–12 months, rebalanced monthly or quarterly.
- Mean Reversion
- The empirical tendency for stretched moves to retrace. Mean-reversion strategies fade recent oversold names and tend to complement momentum strategies.
- Quant Trading
- Generating trades from statistical models and code rather than discretionary judgment. FinLab AI focuses on transparent, reproducible quant research.
- Factor / Indicator
- A quantifiable signal used to select stocks — e.g. P/E, YoY revenue growth, RSI. FinLab AI ships 900+ U.S. and Taiwan factors you can combine.
- Look-ahead Bias (Point-in-Time)
- Using information in a backtest that wasn't actually available at the time — e.g. fundamentals not yet aligned to their filing date, or values that were only revised later. It inflates backtested performance and can't be reproduced in live trading. Using point-in-time data aligned to the filing date avoids it; FinLab aligns its financial data to the filing date by default. 完整說明與台股實例 →