คลังความรู้

The document examines the ADIA Lab causal discovery challenge as supervised inference within a known synthetic data-generating family. The task assigns variables to eight roles around a treatment and outcome, using many labeled dataset and graph pairs. It…

This module builds a mean forecast ensemble from registered prediction sets, with particular attention to reproducibility and avoiding selection bias. It resolves qualifying model configurations from the registry rather than maintaining a hand-written member…

This notebook explains a local linear trend state-space model for extracting financial features from price levels. The hidden state contains a price level and its slope; observations are noisy closes. At each step, the filter predicts the state, measures the…

This notebook separates continuous price variation from discrete jumps in intraday equity returns. It estimates daily realized variance from squared log returns and uses bipower variation as a jump-robust estimate of the continuous component; their…

This notebook uses principal component analysis on a liquid subset of US equities to extract latent factors and form long-short eigenportfolios. It explains how standardized PCA vectors are converted to raw-return weights and normalized by gross exposure,…

This notebook expresses a four-phase forecasting workflow using plain Python orchestration, CrewAI, and LangGraph. It compares where state is held, how failures are inspected or recovered, what dependencies each approach adds, and how tracing works. The…

This notebook examines linear-model regularization for a universe of currency pairs whose returns are linked because each pair is a quote between shared currencies. It distinguishes feature collinearity, which ridge, lasso, and elastic net can address, from…

This notebook studies whether an extreme perpetual futures premium affects subsequent BTC returns or premium reversion. It defines treatment as a high-premium state and compares a broad market outcome, which may be influenced by many forces, with premium…

This chapter surveys ways to turn fitted statistical procedures into features for trading models. It covers diagnostics and stationarity, structural breaks, fractional differencing, Kalman filtering, spectral and path-signature methods, ARIMA and GARCH…

This notebook builds forward share-return labels for a strategy that reads listed options data to rank equities and then holds the shares. It creates an adjusted price series that accounts for corporate actions and identifies companies by persistent security…

This notebook frames a parent order as a finite-horizon control problem. At each interval, tabular Q-learning chooses a multiplier on a VWAP-based trade slice, conditioning on time and remaining inventory plus the most recently observed spread and volatility…

This notebook uses historical US equity returns to build a correlation network and explore portfolio construction. It selects a liquid universe using estimation-window data, converts pairwise correlations into distances, and extracts a minimum spanning tree…

This notebook describes a one-feature-at-a-time screen for candidate signals across 20 currency pairs. It computes each feature’s daily cross-sectional rank agreement with the next-session return using only walk-forward validation windows. The evaluation…

This notebook presents principal component analysis as an unconditioned latent-factor baseline for forecasting stock returns in an option analytics study. PCA uses the historical return panel to estimate common movements and each stock’s exposure to them,…

This notebook compares exhaustive grid search with Optuna’s TPE sampler for tuning a LightGBM return-prediction model. It first gives both methods the same trial budget over the same discrete parameter grid, where enumeration covers every combination and…

This notebook evaluates a cross-sectional momentum factor using information coefficients, quantile returns, top-to-bottom spreads, and classification diagnostics. It introduces Spearman IC as the date-by-date rank correlation between a signal and subsequent…

This notebook explains pairwise and cross-sectional features using ETF panels. It compares Engle–Granger and Johansen cointegration tests, distinguishing a stationary spread from simple co-movement. Its energy fund and crude oil fund example fails both…

This case study examines whether information from listed options can help select S&P 500 stocks. It combines daily equity prices with option-implied volatility levels, skew, term structure, and variance risk premium measures across 633 stocks from 2017 to…

This configuration specifies a historical macroeconomic dataset from FRED for regime filtering and cross-asset analysis. It organizes series by daily, weekly, monthly, and quarterly frequency, including Treasury yields, the federal funds rate, the VIX, labor…

This module defines a reader-facing workflow for CME futures research, covering model requests, prediction and backtest execution, candidate pools, and final selection. It reads the configured label sweep from a shared setup declaration, preserves its order,…

This notebook uses maximum favorable excursion (MFE) and maximum adverse excursion (MAE) to ground triple-barrier label widths in observed price paths. For a position entered at a bar’s close, it measures the best favorable and worst adverse movement over a…

This analysis asks whether the data can support a cross-sectional crypto perpetual strategy before fitting a model or making forecasts. The proposed approach ranks contracts by premium at each funding settlement, then buys one side of the ranking and sells…

This notebook demonstrates an operational cycle for an ETF equity strategy: refresh market data, compute financial features, retrain a Ridge model, generate current cross-sectional predictions, replay them through a backtest engine, and stage a top-ranked…

The document describes an FX portfolio allocation sweep designed to isolate position sizing from signal selection. It starts from frozen equal-weight baseline candidates ranked by validation backtest performance, then advances configurations while preserving…