DUCKQUANT / THE QUANT & AI DIRECTORY
14,444 resources, ranked by in-directory PageRank
Showing 4,033–4,068 of 14,444 resources
Divides net advances by the number of issues traded before accumulating, which keeps the line comparable across decades as listing counts change.
Cumulative Advance/Decline Line: Recompute History Without Looking Ahead — https://thefintechbuilder.com/market-breadth-and-internals/advance-decline-breadth/cumulative-advance-decline-line/
Build a seeded, calendar-complete, revision-aware cumulative breadth line that refuses to publish incomplete evidence.
Advance/Decline Ratio: Advancing Issues per Declining Issue — https://thefintechbuilder.com/market-breadth-and-internals/advance-decline-breadth/advance-decline-ratio/
Describe trend, momentum, volatility, volume, and cycles. Representative subjects: SMA, EMA, RSI, MACD, ADX, ATR.
Explain where returns and risk came from. Representative subjects: Time- and money-weighted return, Brinson attribution, factor attribution, contribution to return, tracking error, information ratio.
Describe systematic return drivers. Representative subjects: CAPM beta, Fama-French factors, momentum, quality, value, size.
Measure ordinary and extreme portfolio loss. Representative subjects: Historical/parametric/Monte Carlo VaR, expected shortfall, drawdown, marginal/component VaR, stress tests, scenario analysis.
Convert forecasts and constraints into holdings. Representative subjects: Mean-variance, minimum variance, maximum Sharpe, risk parity, HRP, Black-Litterman.
Explain how orders become prices. Representative subjects: Spread estimators, trade signing, order-flow imbalance, Kyle lambda, PIN/VPIN, queue imbalance.
MACD (Moving Average Convergence Divergence) — https://thefintechbuilder.com/technical-indicators/trend-systems/macd/
Hull MA — https://thefintechbuilder.com/technical-indicators/trend-smoothing/hull-ma/
Triple Exponential Moving Average (TEMA) — https://thefintechbuilder.com/technical-indicators/trend-smoothing/tema/
Double Exponential Moving Average (DEMA) — https://thefintechbuilder.com/technical-indicators/trend-smoothing/dema/
Wilder RMA (SMA-Seeded 1/n Smoothing) — https://thefintechbuilder.com/technical-indicators/trend-smoothing/wilder-rma/
Weighted Moving Average (WMA) — https://thefintechbuilder.com/technical-indicators/trend-smoothing/wma/
Moving average convergence/divergence: the gap between a fast and a slow EMA, its own EMA as a signal line, and the difference between them…
Hull moving average: a weighted combination of two WMAs de-lagged against each other, then re-smoothed over sqrt(window).
Triple exponential moving average: 3 × EMA − 3 × EMA(EMA) + EMA(EMA(EMA)).
Double exponential moving average: 2 × EMA − EMA(EMA). Subtracting the second smoothing pass cancels most of the lag a single EMA introduces, at…
Derive, implement, and verify an SMA-seeded Wilder moving average without hiding initialization or state.
Prove who is acting and assign the right assurance to financial interactions. Representative subjects: Identity proofing, document and biometric matching, adaptive authentication, federation assurance,…
Explore this resource at its source website for documentation and current information.
Quantitative Portfolio Management: The Art and Science of Statistical Arbitrage (2021)
Protect financial applications, services, data, and operations across the whole cyber-risk lifecycle. Representative subjects: Threat modeling, access control, API security, data and key protection,…
Turn assumptions into integrated, controlled forecasts. Representative subjects: Revenue and margin drivers, working capital, debt and tax schedules, three-statement models, scenarios, consensus.
Restate Basic EPS after stock splits and share consolidations with exact factors, basis-aware event selection, reporting cutoffs, and reproducible controls.
Detect suspicious actors and transactions. Representative subjects: Rules engines, anomaly detection, device/entity linkage, graph fraud, velocity checks, account takeover.
Parse, normalize, reconcile, and validate reported data. Representative subjects: XBRL mapping, statement linkage, periodization, TTM/LTM construction, fiscal calendarization, restatement detection.
Learn nonlinear predictive relationships responsibly. Representative subjects: Regularized regression, trees/boosting, random forests, Gaussian processes, sequence models, transformers.
Price currencies and manage multi-currency exposure. Representative subjects: Cross-rate triangulation, forward points, covered interest parity, carry, purchasing-power parity, currency hedging.
Handle term structures, rolls, storage, and seasonality. Representative subjects: Continuous contracts, carry, convenience yield, curve signals, calendar spreads, roll yield.
Price nonlinear claims and estimate sensitivities. Representative subjects: Binomial/trinomial trees, Black-Scholes-Merton, Greeks, implied volatility, local/stochastic volatility, Monte Carlo pricing.
Analyze tokens, chains, AMMs, and on-chain market structure. Representative subjects: AMM invariant curves, impermanent loss, on-chain flows, wallet clustering, staking yield, liquidation engines.
Determine whether an algorithm works out of sample. Representative subjects: Walk-forward testing, purged cross-validation, embargo, bootstrap confidence intervals, reality checks, sensitivity analysis.
Search feasible decisions and quantify uncertainty. Representative subjects: Linear/quadratic/mixed-integer optimization, dynamic programming, stochastic optimization, Monte Carlo, bootstrap, scenario generation.
