Knowledge library

This notebook introduces EdgarTools for exploring SEC filings by company and filing type. It demonstrates looking up issuers using stable CIK identifiers, retrieving annual and quarterly reports, extracting XBRL financial statements, and inspecting insider…

This notebook describes an LSTM sequence model for predicting stock returns from ordered windows of recent features. Each example uses 60 consecutive sessions for one stock; windows that cross listing gaps, halts, or other interruptions are excluded so that…

This analysis checks whether daily option data can support a weekly strategy that sells at-the-money straddles on S&P 500 constituents, delta hedges shares, and holds contracts to expiration. It explains how calls and puts form a straddle, how implied and…

This reference summarizes listed contract months for 35 CME futures products across equity indexes, Treasuries, energy, metals, currencies, interest rates, agriculture, livestock, and crypto. It explains the exchange’s month-code system and distinguishes…

This document describes a pipeline for downloading official Binance USD-M perpetual funding records, normalizing them, and caching them in a columnar file. Monthly archives are fetched concurrently for the requested symbols and date range. The records are…

This notebook applies TabM, a weight-sharing neural ensemble, to a US equities panel represented as one feature row per stock and session. A shared two-layer backbone feeds multiple members, each with its own scaling vector and output layer; averaging their…

This analysis selects one configuration from a frozen pool of crypto perpetual-futures candidates spanning four prediction labels and multiple strategy stages. It chooses the highest validation Sharpe and treats label, split, and execution details as part of…

This notebook defines forward-return targets for ranking US stocks and explains how to make their horizons correspond to actual trading sessions. It uses split- and dividend-adjusted prices for returns, but uses the contemporaneous printed close and dollar…

This notebook compares gradient boosting configurations for crypto perpetual futures using features built around the premium of perpetual contracts over spot. It varies tree capacity and loss function, including squared, absolute, and Huber losses, and…

This notebook develops model-based features for crypto perpetual futures: per-asset conditional volatility forecasts from GJR-GARCH and a market-wide stressed-regime probability from a two-state Gaussian hidden Markov model fitted to funding settlements.…

This case study lays out a research pipeline for crypto perpetual futures, treating funding payments exchanged between long and short positions at regular settlements as a potential return source. It describes data and model stages from label construction…

This analysis compares feature triage results across nine trading case studies and asks whether features that pass screening predict stronger strategies. Its screening combines Benjamini–Hochberg false-discovery control with a second route based on a case…

This notebook describes DeePM, an end-to-end ETF portfolio policy designed to improve performance across varying market conditions. Its components include volatility-normalized return and trend features, an LSTM-based model, variable selection, conditioning…

This case study applies causal estimation to ETF momentum and asks whether its effect on forward returns varies with market volatility. It uses a continuous momentum treatment, a 21-day forward-return outcome, volatility and yield-curve controls, and a…

This notebook introduces NLinear as a simple sequence-model baseline for predicting returns across US equities. Each example uses a window of 60 consecutive sessions for one stock, with features arranged in time order. For each feature, the model subtracts…

This notebook uses maximum favorable and adverse excursion analysis to inform triple-barrier widths from observed price paths. For a long entry at a bar’s close, it measures the best high and worst low over the following holding window; short positions…

This notebook evaluates NLinear, a minimal sequence model that subtracts the last observed level from each lookback window and maps the remaining values to a forecast. Its simplicity makes it a useful benchmark for more complex sequence architectures. The…

The document describes a walk-forward study of TabM neural networks predicting monthly returns from firm characteristics. Because the shared training runner fixes the objective to squared error for regression and cross-entropy for classification, the…

The document explains how four market-impact models estimate the adverse per-share price move associated with an order: no impact, linear impact, square-root impact, and a configurable power law. It clarifies the sign convention, the roles of participation,…

The document turns a cross-asset momentum hypothesis into a monthly exploratory test using a 100-ETF universe. It constructs adjusted month-end prices, measures 12-to-1-month momentum, then ranks eligible ETFs into five equal-weight groups and compares their…

This notebook presents a family-level view of five latent-factor approaches applied to an equity option analytics case study. PCA estimates common movements directly from the return panel; IPCA maps characteristics to exposures linearly; a conditional…

This notebook examines why two backtesting engines can produce different results from identical signals. It holds the strategy and tradable universe fixed, makes portfolio weights deterministic when predictions tie, and exposes execution settings such as…

This chapter treats a backtest as an attempt to falsify a strategy through explicit assumptions about signal timing, execution, rebalancing, sizing, costs, constraints, and benchmarks. It compares vectorized and event-driven simulation by their treatment of…

This source describes safeguards for reconstructing a model request from its immutable training specification and validating the resulting run. It checks that the request uses a supported identity version, canonical execution tier, matching model family and…