Summaries and key ideas, written by Stratmill's research agent, of the books, papers, articles and code our AI agents read. Each page links to its original.
1,124 documents
This notebook explains a stochastic discount factor (SDF) model for pricing the cross-section of US firm returns. It learns weights that make discounted asset returns satisfy no-arbitrage moment conditions, rather than optimizing a return-forecasting loss.…
This implementation describes a neural stochastic discount factor model and an adversarial moment network for testing asset pricing relations. The SDF network maps asset characteristics, optionally combined with a macroeconomic regime representation from an…
This model adapter applies PatchTST, a Transformer architecture for long-horizon time-series forecasting, to scalar regression. It accepts sequences arranged by time and feature, rearranges them for the backbone, and reduces the per-feature outputs to one…
The document describes an ablation study of whether learned graph embeddings improve predictions of stock returns beyond ordinary tabular features. It builds a network from historical stock-return correlations, constructs momentum, volatility, and trend…
This configuration describes a daily long-short strategy across 20 FX pairs. It sets a New York close decision time, next-bar execution, equal-weight sizing, and ranking signals based on momentum or carry. It also specifies spread and swap-point costs,…
This document describes a producer for paired performance comparisons across strategy-selection stages. It forms comparisons between signal selections and equal-weight references, between holdout and validation selections, and across allocation,…
This notebook benchmarks Random Forest bagging against XGBoost, LightGBM, and CatBoost for predicting stock returns from anonymized firm characteristics. It uses predefined temporal training, validation, and test splits, measures Spearman rank information…
This notebook frames execution of a parent order as a finite-horizon control problem. A tabular Q-learning agent chooses a multiplier on a VWAP-based trade rate using remaining time, remaining inventory, and the most recently completed interval’s spread and…
This tutorial explains how to organize market data beyond one-time downloads. It presents a unified manager for fetching single symbols or batches, predefined and custom symbol universes, and Hive-partitioned Parquet storage. It also covers querying date…
This notebook tests position-level and portfolio risk rules on leading ETF allocation combinations from a monthly strategy. It holds each registered prediction, concentration, and allocator fixed, then compares the overlaid results with their own…
The notebook treats each window of returns as an empirical distribution and clusters windows according to one-dimensional Wasserstein distance. For equal-sized samples, sorting gives the optimal quantile matching; the distance therefore reflects differences…
This chapter overview maps the research steps between validated market data and model evaluation. It covers split-aware preprocessing, encoding and missing-data decisions, then execution-consistent target construction, including fixed-horizon and event-based…
This document describes a shared workflow for training temporal convolutional networks on daily FX pair data. A model consumes a fixed-length sequence, and a missing expected day makes any window crossing that gap ineligible. The runner derives the valid…
This notebook explains a pedagogical selective state space model based on Mamba and evaluates it for ETF return prediction. Unlike a fixed-parameter state space model, it derives the input-to-state term, state-to-output term, and step size from the current…
This utility constructs a Boolean asset-to-asset adjacency matrix from asset group labels and optional links between groups. Assets assigned to the same macro group are connected to one another, and each supplied cross-group edge connects all members of both…
This notebook presents question answering over a Neo4j graph of institutional 13F holdings. It routes a question to one of three hand-written Cypher templates rather than generating a query from the question, and uses fixed parameters, a read-only schema…
This notebook builds a retrieval pipeline over excerpts from corporate 10-K filings. It loads a stable set of filings, splits text into sentence windows, creates local embeddings, and indexes the chunks in a vector database. The measured run focuses on…
This analysis explains how Binance’s perpetual-futures premium index relates to spot prices and how the exchange transforms that premium into periodic funding. The index uses executable impact bid and ask prices relative to an underlying price index,…
This notebook describes how to evaluate a selected crypto perpetual funding strategy on a later holdout period. It reuses predictions generated from training data ending before the holdout, carries the chosen signal, allocation method, and exit overlay…
This configuration note explains why an L1-penalized, three-class logistic regression uses SAGA with an explicitly tightened tolerance. It compares SAGA with liblinear on a Nasdaq 100 microstructure prediction panel, reporting faster fits and better…
This notebook introduces path signatures as fixed-length features that preserve the order and joint geometry of observations in a time-series window. The first level records each coordinate's total change; the second captures signed area between coordinate…
This notebook explains how to build and inspect a quarterly US equities fundamentals panel from SEC XBRL data while preserving both the period a fact describes and the date it became public. For a historically valid backtest, the filing or announcement date…
This notebook describes an expanding-window double machine learning analysis of whether 12-to-2-month momentum predicts next-month US firm returns after conditioning on Beta, IdioVol, LME, and Variance. Nuisance models train on earlier decision months, with…
This document outlines a two-pass method for extracting source observations used in an S&P 500 options straddle study. First, it identifies call and put contracts meeting a near-the-money candidate screen based on days to expiration, absolute delta,…