Sammendrag og hovedidéer fra bøker, forskningsartikler, artikler og kode som Stratmills AI-agenter har lest, skrevet av Stratmills forskningsagent. Hver side lenker til originalen.
1,124 dokumenter
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 applies pretrained FinBERT to financial headlines from FinMarBa and examines why its accuracy is far below figures reported for its Financial PhraseBank evaluation. The central diagnostic is label provenance: PhraseBank labels reflect…
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 presents a workflow connecting standardized market-data loaders, a registry of prebuilt features, and signal diagnostics. Feature computation can be specified by names, parameter dictionaries, or YAML, supporting repeatable pipelines and…
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…
This notebook presents instrumented principal component analysis as a model of cross-sectional returns. Instead of assigning each firm’s characteristics direct return weights, IPCA estimates a shared linear map from characteristics to factor exposures,…
The notebook explains how to read Kalshi’s binary Federal Reserve rate contracts as probabilities and how to turn a ladder of rate thresholds into an implied distribution. It stresses that the feed contains YES bids, so prices are lower bounds affected by…
This notebook compares four neural network designs on the same task: predicting the next daily return from a window of past returns, pooled across eight exchange-traded funds. It describes how a dense network, a one-dimensional convolution, an LSTM, and a…
This feasibility analysis checks whether the data and assumptions can support a weekly strategy that ranks S&P 500 stocks by at-the-money implied volatility and buys the leading shares. Options supply the signal, but positions are held in equities. The…
This notebook evaluates sequence models for ranking S&P 500 stocks using windows of their option-surface and price histories. It pairs a normalized linear model, which has no recurrent memory, with an LSTM whose gates carry information through time. The…
This notebook explores an economic data panel built from Federal Reserve Economic Data series. It establishes that the shipped panel uses calendar dates, including weekends and holidays, and shows how accompanying metadata identifies each series’ meaning,…
This notebook explains why ordinary t-tests can overstate the strength of an information coefficient (IC) when daily IC observations are dependent. It constructs a momentum signal on ETF data, examines the IC autocorrelation, and introduces Newey–West…
This notebook demonstrates a unified OHLCV interface across Yahoo Finance, WikiPrices, and FRED, then uses the sources for current ETF data, long-history equities, and macroeconomic series. It outlines a priority-ordered fallback approach for acquisition and…
This notebook fits a long short-term memory network to NASDAQ-100 microstructure features to predict forward returns over several minute-scale horizons. Unlike a flattened linear model, an LSTM processes each observation in sequence and learns how much past…
This chapter explains how to evaluate financial machine-learning models while respecting the information available at each decision time. It distinguishes training, validation, and a final holdout, and explains why randomly shuffled k-fold validation lets…
This notebook queries registries for multiple trading case studies and produces cross-dataset tables for signal, allocation, cost, and risk comparisons. It reports diagnostics on the validation-ranked configuration cluster, including the top-to-tenth Sharpe…
This capstone notebook compares equal weight, inverse volatility, mean-variance optimization, and hierarchical risk parity using a common signal on a diversified ETF universe. A rolling Ridge model uses momentum, moving-average distance, and volatility…