知识库

This document explains how wavelet decompositions and rolling spectral estimates describe financial returns at different time scales. Wavelets divide returns into components spanning progressively slower periods, making it possible to see which scales…

This document compares alternative position-sizing methods for CME futures strategies selected from a baseline ranked by equal-weight validation Sharpe. Equal weighting treats every selected product alike, although futures contracts can have very different…

This notebook turns model prediction sets into comparable S&P 500 options backtests. On each weekly decision date, it ranks predicted returns, selects the highest-ranked symbols in the liquid universe, and sells equally weighted at-the-money straddles.…

This notebook uses double machine learning to estimate whether futures carry has an effect on subsequent returns after adjusting for volatility, momentum, and cross-sectional carry rank. It distinguishes causal explanation from predictive performance: a…

This document explains how to compare an incumbent and a candidate model in shadow evaluation before allocating capital. It fixes their roles and promotion thresholds in advance, then assesses the candidate on five dimensions: improvement in annualized…

This notebook demonstrates how to configure Feast with Parquet sources, entities, feature views, timestamps, and a time-to-live policy, then retrieve features for training examples and a live-style as-of query. It compares Feast’s output feature by feature…

The notebook introduces a supervised autoencoder for equity factor modeling. Unlike unsupervised autoencoders that learn a bottleneck by reconstructing the cross-section, this model also includes forward returns in its training objective. The target…

This exploratory notebook describes the structure and interpretation of AlgoSeek NASDAQ-100 minute bars. It organizes the dataset’s precomputed fields into quote prices and sizes, trade prices, spreads, volume, trade-location buckets, tick direction,…

The notebook explains how to separate continuous price variation from jumps in intraday equity returns. It estimates daily realized variance from squared returns and uses bipower variation as a jump-robust estimate of the continuous component; their…

This notebook applies a neural stochastic discount-factor model to CME futures. In asset-pricing terms, a stochastic discount factor is a random variable whose product with each asset's return has the same expected value under no arbitrage. The model…

This configuration note distinguishes settings that appear in a backtest identity from settings that actually affect simulated trading costs. In the described case study, all registered runs use a vectorized, return-to-expiry path. The configured…

The document demonstrates a broker wrapper that checks orders and portfolio state before forwarding trades. Its controls include per-order share and value caps, position exposure limits, order-rate limits, asset allow and block lists, and an emergency halt.…

This analysis checks whether settlement data can support a weekly, cross-sectional CME futures strategy before any model is fit. It tests whether enough products are quoted on rebalance dates to fill the intended long and short portfolios, estimates spread…

This notebook presents a cross-case-study inventory of engineered financial features across nine asset classes. It counts features and name-based families from each available feature panel, compares feature-space sizes, and summarizes the strongest…

This chapter surveys financial text representations, from dictionaries and word counts through TF-IDF, static embeddings, recurrent networks, and Transformers. It explains the trade-offs: simpler methods are fast, interpretable, and adaptable to finance,…

This notebook builds a financial retrieval-augmented generation evaluation harness with separate measures for retrieval coverage, answer faithfulness, appropriate abstention, and security behavior. It uses six hand-written fixtures, with context drawn in…

This case study evaluates monthly long-short decile strategies built from US firm characteristics, comparing linear models, gradient boosting, and latent-factor approaches under point-in-time accounting lags, walk-forward validation, and era-dependent…

This notebook uses Optuna to tune LightGBM return models while considering both cross-sectional information coefficient (IC) and prediction turnover. A single-objective search maximizes IC for comparison; an NSGA-II multi-objective search identifies…

This notebook compares embedded, server-based, general-purpose, and trading-focused databases on financial time-series tasks: bulk writes, full scans, time-window queries, OHLCV aggregation, and trade-quote alignment. Its main lesson is that storage layout…

This educational treatment compares parametric stochastic models and bootstrap methods for generating synthetic financial paths. It covers geometric Brownian motion, jump diffusion, mean reversion, Heston stochastic volatility, and GARCH, alongside…

This analysis compares three ways to convert signed model return forecasts into binary positions: a fixed zero cutoff, a trailing percentile for each symbol, and a cross-sectional percentile across symbols. It applies the rules to registered validation…

This notebook turns 21-day forward ETF returns into an up-or-down target and evaluates L1- and L2-regularized logistic regression on chronological walk-forward folds. It scales features using each fold’s training data, then assesses predictions with…

This notebook configures Feast as a feature store for financial and model-based data, then compares its historical and point-in-time retrievals with a transparent, hand-built join. It declares entities, timestamped sources, feature views, and a time to live,…

This notebook compares gradient boosting objectives on monthly US stock returns while holding features, target, and walk-forward folds fixed. It contrasts squared error with absolute error and Huber loss, which reduces the influence of large residuals. The…