Glossary

Alpha as risk-adjusted excess return: definition, the beta-adjustment math, and why most claimed alpha disappears once you adjust for the right factors.

Beta as factor sensitivity: what it measures, why a beta of 1 doesn't mean 'tracks the market', and the rolling-vs-static distinction to watch.

Drawdown explained: peak-to-trough decline, why max drawdown alone is misleading, and the recovery math that actually matters.

Expected shortfall: the average loss given a VaR breach. Why regulators are migrating from VaR and what ES catches that VaR misses.

What the Kelly criterion is, when full Kelly blows up, and why most working quants size at half- or quarter-Kelly.

Sharpe ratio defined, when it lies (skew, fat tails, autocorrelation), and how to read a Sharpe number you didn't compute yourself.

Sharpe vs Sortino: when the gap between the two tells you something real about a strategy's tail behaviour — and when it's just noise from a small sample.

Sortino ratio: same numerator as Sharpe, denominator only counts downside volatility. When it's the right number to look at.

Value at Risk: the loss threshold you'll exceed with probability α. Why historical VaR is brittle and what it doesn't tell you about the tail.

Volatility as the standard deviation of returns: realized vs implied, the annualization gotcha, and why volatility-of-volatility matters.

Look-ahead bias: when a backtest accidentally uses data the strategy wouldn't have had at decision time. The most common variants and how to catch them.

Monte Carlo simulation in trading: when it's the right tool, when it's overkill, and the seed-discipline gotcha that ruins most published examples.

Overfitting in trading-strategy backtests: how multiple-testing inflates apparent edges and the diagnostics that catch it.

Probability of Backtest Overfitting (PBO): Bailey, Borwein, López de Prado and Zhu, Journal of Computational Finance 20(4), 2017, SSRN 2326253, via CSCV.

Survivorship bias in backtests: why dropped tickers, delisted funds, and dead share classes systematically inflate historical returns.

Walk-forward optimization: rolling-window train/test that mimics live deployment. Why anchored vs sliding matters and the gotchas in window sizing.

Bid-ask spread defined: quoted vs effective vs realized spread, why the touch isn't the cost you actually pay, and how to measure each.

Latency arbitrage: cross-venue price discrepancies exploited by being faster than the slowest replicator. Why the game is mostly won at the cable layer.

Maker-taker fee model: makers get a rebate, takers pay. Why the model exists, what it incentivizes, and how to size up real net cost.

Order book imbalance: definition, the depth-weighting choice that changes everything, and why it predicts short-horizon price moves more than fundamentals.

Slippage as the gap between expected and executed price: the components (spread, market impact, latency), and how to model each in a backtest.

Agent skill testing: the regression-test discipline for LLM-driven agents. What to test, how to score, and the difference between pass-rate and capability.

The agent-cost envelope: the loop of (calls × tokens × retries × model_price) that determines the dollar cost of an LLM-driven trading agent per decision.

FAQPage schema explained: the Schema.org JSON-LD type that makes FAQ content machine-readable for search and LLM crawlers, plus when to apply or skip it.

Schema.org HowTo: the structured-data type for step-by-step procedural content. The fields that matter for agent ingestion vs the ones search ignores.

How to detect LLM hallucinations in financial outputs: citation grounding, verifiable-claim checks, and cross-model agreement that flag fabricated data.

Model Context Protocol: Anthropic's open standard for letting LLMs discover and call tools — the interface, why it matters, and finance MCP server checks.

Model drift: when an LLM's behavior changes between calls, versions, or weeks. The monitoring stack that catches it before production breaks.

Prompt injection: when untrusted text in a prompt overrides system instructions. The attack patterns and the structural defenses that work in production.

Regulatory cost as a function of jurisdiction, model class, and end-use: the FTC vs NLT distinction and the documentation burden by regime.