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.
742 documents
This notebook applies a previously selected S&P 500 equity and options strategy to predictions for a separate 2021 holdout period. It carries the selected configuration forward unchanged, including its allocator, concentration rules, risk overlay, and…
This analysis explains how to read information coefficients for a catalog of CME futures prediction models. It defines each date’s IC as the rank correlation between predicted and realized returns across products, then averages those daily values across…
This notebook runs three otherwise identical research agents on a recession question, either by replaying a saved capture or by running live models and search in parallel. It records each agent’s probability, confidence, evidence-gathering activity, and…
This notebook brings an ETF case study’s registered backtests together into an uncertainty-aware strategy assessment. It reads performance measures with block-bootstrap confidence intervals and uses paired bootstraps to compare successive pipeline stages,…
This notebook explains how a macro panel stamped with the period it measures can leak future information into a trading backtest. It estimates publication dates by adding the period length to the stamped date and then applying a conservative release lag…
This notebook implements DeePM, an end-to-end portfolio policy for a diversified ETF universe. Its model combines temporal features, asset metadata, cross-sectional attention, and a macro graph prior that permits attention within asset classes and across…
This notebook turns financial headlines into stock-level signals and evaluates them against forward returns. It embeds headlines, measures news surprise as semantic distance from a rolling embedding baseline, and combines surprise with sentiment direction to…
This document explains how to evaluate a previously selected US equities strategy on holdout data while keeping its configuration fixed. It derives the correct holdout prediction set from the selected model’s training identity and checkpoint, then applies…
The document explains how to create currency-pair features from three fitted time-series models: a state-space filter that estimates a slowly changing price level, a two-state hidden Markov model for dollar volatility regimes, and an ARIMA model whose…
This notebook measures how trading costs affect strategies already selected through signal, allocation, and risk-overlay stages. It fixes one validation-selected configuration per label before varying costs, so the resulting curves isolate the effect of the…
This notebook asks whether sequence models can improve on hand-built summaries of perpetual funding premium history. It compares NLinear, a simple linear model that reads an ordered window, with a two-layer LSTM. Both use the same feature order, walk-forward…
This document explains why stop-losses, trailing stops, and time exits cannot be evaluated in a case study whose backtest holds weights across a month and observes only the realized monthly forward return. Such rules depend on the price path between entry…
This document describes a strategy assessment process for registered NASDAQ-100 microstructure backtests. It reads existing runs rather than training models or rerunning backtests, traces selected strategy lineages, compares candidates with an equal-weight…
This document describes building a cross-sectional feature matrix for S&P 500 stocks by combining adjusted share-price histories with summarized option implied-volatility surfaces. It organizes features by role, input, lookback, and observability delay.…
This notebook runs or replays a panel of identical research agents answering a shared forecasting question, then inspects their forecasts, search activity, and conversation traces. Separate per-agent tracing preserves attribution during parallel calls. The…
This notebook defines monthly total-return labels for a cross-sectional US firm-characteristics study and checks how those outcomes align with the provider’s rows. The panel pairs characteristics from the prior month with the return earned in the month…
This notebook shows how explicit execution costs can erode a hypothetical intraday strategy’s gross returns. It anchors the crossing spread to the median volume-weighted quoted spread across NASDAQ-100 constituents, then builds crossing, worked-order, and…
This notebook explains how a temporal convolutional network (TCN) can forecast returns from ordered financial data. Causal convolutions ensure that an output at a given time depends only on current and earlier inputs. Dilations expand the receptive field…
This document presents an event-driven method for holding a limited number of intraday positions. Predictions are aligned to price bars using the latest available score, subject to an optional freshness limit. Entry signals use a rolling quantile computed…
This case study turns stored model predictions for US stocks into long-short, equal-weight portfolios. It sweeps multiple entry schemes, each selecting the highest-ranked names for long positions and the lowest-ranked names for short positions, to examine…
This document describes several ways to convert prediction scores into trading signals: fixed cutoffs, rolling per-asset percentiles, and cross-sectional percentiles. Fixed thresholds use a chosen score level; rolling thresholds adapt to an asset’s recent…
This notebook reviews a selected foreign-exchange strategy and its holdout lineage using registered research artifacts. It reproduces the choice from a frozen validation candidate set, ranking eligible solvent backtests by validation Sharpe with a…
This document describes utilities for comparing registered trading strategies and assembling structured case-study assessments. A central ranking method restricts return series to their exact shared timestamps before calculating portfolio metrics, so each…
This notebook compares sklearn HistGradientBoosting, XGBoost, LightGBM, and CatBoost on an ETF return-prediction task. It measures cross-sectional information coefficient, training time, and process memory across CPU and, where supported, GPU runs, using…