Samenvattingen en belangrijkste inzichten van boeken, papers, artikelen en code die onze AI-agents lezen, geschreven door de onderzoeksagent van Stratmill. Elke pagina verwijst naar het origineel.
1,124 documenten
This notebook aggregates existing walk-forward model results across financial case studies to compare LSTM, NLinear, TSMixer, TCN, and PatchTST with linear, gradient-boosted, and tabular neural-network baselines. Its main measure is average daily Spearman…
This notebook examines whether published investment factors offer credible return premia and diversify one another. It draws on long-history and cross-asset series from AQR alongside Fama-French equity factors, using serial-correlation-aware statistics and a…
This shared analysis module describes ways to estimate trading frictions across asset classes. It includes high-low and return-autocovariance estimators for bid-ask spreads, rolling average volume measures, and regression approaches for calibrating…
This notebook treats total value locked as alternative data and evaluates the hypothesis that DeFi capital conditions can help predict ether returns. It defines TVL as the dollar value of crypto assets deposited in protocols, then emphasizes that it is a…
This notebook examines why a model’s information coefficient does not determine the Sharpe ratio of a strategy built from its predictions. Across a set of case studies, it compares signal rankings with backtest outcomes and considers how entry rules,…
This notebook implements a daily educational adaptation of an adversarial stochastic discount factor model. A portfolio-weight network constructs a factor from next-day excess returns using characteristics and market state available at the prior close. An…
This analysis assesses whether selected trading signals appear reliable by comparing information coefficient (IC) evidence with the Sharpe ratios produced after predictions are converted into positions. It aggregates model-family results across asset…
This notebook aggregates previously run walk-forward results to compare LSTM, NLinear, TSMixer, TCN, and PatchTST across case studies, alongside linear, gradient-boosted, and TabM baselines. It reports average daily Spearman information coefficients with HAC…
This feasibility analysis asks whether the data and assumptions for a weekly, delta-hedged short-straddle strategy on S&P 500 constituents are plausible before fitting a model. It explains how calls and puts at the same strike and expiration form a straddle,…
This exploratory notebook profiles hourly CME futures data across products and asset classes, using the E-mini S&P 500 as an example. It explains the hierarchy from product to expiring contract to continuous series, summarizes coverage windows, and checks…
This notebook compares portfolio weighting rules for selling straddles on selected S&P 500 symbols. It holds the chosen symbols constant and varies allocation, using equal weight as the baseline. The methods include weighting by predicted score, inverse…
This notebook tests how a selected US firm characteristics strategy responds when its transaction cost assumption changes. It holds the strategy configuration fixed and reruns validation backtests across a declared grid of commission and slippage levels,…
The notebook previews S&P 100 10-K and 8-K filings intended as inputs to a financial knowledge graph. It describes schema checks for required fields, unique company and accession keys, consistent form labels, year alignment, and text-length metadata. Annual…
This document describes a reader-facing interface for examining backtest results stored in a registry. It supports summaries of run counts, inspection of specifications and individual runs, ranking results, comparing allocation methods, viewing concentration…
This notebook runs a fixed equity-characteristics strategy on a reserved holdout period using predictions and a portfolio allocator selected earlier. It keeps the configuration, position sizing, concentration, rebalance cadence, and cost assumption…
This environment models a market maker that adjusts quote center and spread in response to inventory, volatility, and a discrete action choice. Synthetic prices use conditional volatility generated with a GARCH process, while order imbalance evolves over…
This notebook analyzes timestamped financial event edges from a knowledge graph, distinguishing when an event happened, when it became public, and when the extraction pipeline created the edge. It calculates disclosure and extraction lags, builds snapshots…
This notebook develops a unit-aware framework for estimating trading costs across equities, crypto perpetuals, futures, ETFs, and foreign exchange. It distinguishes share volume, contract volume, base-asset volume, and price-update counts, converting…
This chapter review describes a hypothesis-driven process for defining trading strategies before backtesting. It connects documented data assumptions and immutable configuration to exploratory analysis, event studies, and a structured strategy term sheet.…
This notebook explains how futures contract specifications affect backtest accounting. It shows how the contract multiplier converts price moves into dollar P&L, how price times multiplier determines notional value for sizing, and why per-contract…
This notebook trains a vanilla autoencoder on hourly returns for a basket of crypto perpetual markets. Returns are standardized using the training period, and a neural network compresses the multi-asset input into a two-dimensional latent representation…
This notebook compares reference ETF portfolios under historical crises, hand-defined simultaneous asset shocks, and Monte Carlo loss scenarios. Historical windows include major equity and rate-driven selloffs, with portfolio returns compounded over explicit…
This case study assesses a weekly S&P 500 options short straddle using registered backtests. It selects a configuration from a nominated liquid universe, ranks candidates on validation, and evaluates the chosen configuration on holdout data without using…
The document describes a shared analysis workflow for comparing model configurations across case studies. It first selects one configuration per case study, then derives fold-level and cross-study summaries from that selection so later analyses use a…