Огляди й ключові ідеї книжок, наукових праць, статей і коду, які читають наші ШІ-агенти. Їх підготував дослідницький агент Stratmill. На кожній сторінці є посилання на оригінал.
Документів: 1,124
This notebook explores ARIMA as a feature generator for models that may use its output alongside other predictors. It explains how ACF and PACF plots can suggest model orders, how information criteria can compare candidate orders on training data, and why…
This notebook applies a previously selected NASDAQ-100 trading configuration to predictions generated for an untouched holdout window. The strategy, portfolio concentration, allocation method, rebalance schedule, risk overlay, and trading cost assumption are…
This notebook explains why a daily constant-maturity options series is not the return history of a single tradeable contract. Selecting a new near-the-money straddle each day can change the strike, expiration, or both. In particular, moving to a later…
This reference describes academic factor-return datasets from the Fama-French library and AQR for use in strategy analysis and factor modeling. Fama-French offerings include market, size, value, profitability, investment, and momentum series, alongside…
This document outlines a shared data system for quantitative trading research, cataloging datasets across equities, options, futures, crypto, foreign exchange, factors, macroeconomics, filings, positioning, news, and prediction markets. It describes the…
This case study describes producing an out-of-sample prediction set for an already selected S&P 500 options model. The holdout configuration is fixed using validation results, then fitted again on data ending before the holdout window. A label buffer…
This case study compares predictive models for monthly cross-asset rotation across ETFs spanning equities, fixed income, commodities, currencies, and real estate. Its central lesson is that information coefficient (IC) and trading performance can rank models…
This research-agent record considers whether the Federal Reserve will raise the upper bound of its target rate during 2026. It contains a market price, search traces, and agent probability estimates. The first rationale favors a hike, citing inflation risks…
This notebook explains how double machine learning (DML) estimates whether FX momentum affects future returns after adjusting for configured confounders. It distinguishes this intervention question from prediction: predictive models are compared by…
This notebook presents lightweight falsification diagnostics for feature triage, explicitly distinguishing mechanism consistency from causal identification. It first scans ETF features across forward-return horizons with multiple-testing correction, then…
This notebook surveys supervised-learning labels using ETF price data. It covers fixed-horizon forward returns for regression or direction classification, time-series rolling percentiles, cross-sectional percentile labels, triple-barrier labels with fixed or…
This notebook aggregates registry results for OLS, Ridge, Lasso, and ElasticNet across nine case studies. It selects complete validation results at each study's primary label, compares mean daily cross-sectional rank information coefficients and HAC…
This notebook trains Skip-gram Word2Vec on labeled financial news sentences and explains how its vectors represent words that occur in similar contexts. It describes the roles of vector size, context window, minimum frequency, prediction mode, negative…
This notebook compares learned and analytical hedges for a short European call when rebalancing is discrete and trading incurs proportional costs. It defines the self-financing terminal P&L from hedge gains, turnover costs, and the option payoff, then trains…
This notebook evaluates ETF features one at a time by calculating a date-by-date rank information coefficient between feature values and subsequent monthly returns. It screens financial and model-derived features over walk-forward validation dates, then…
This notebook demonstrates position-level exits and portfolio-level controls through constructed examples. Static rules include stop losses, profit targets and time exits; dynamic rules include trailing stops that follow prior highs, tightening trails, and…
This notebook evaluates candidate features for ranking US equities by subsequent returns. It computes daily cross-sectional information coefficients, summarizes their average with uncertainty estimates that account for serial dependence, and adjusts…
This notebook shows how to align macroeconomic observations with the dates traders could actually have known them. It distinguishes the period a value measures from its publication date, estimates release dates from period length and agency lag schedules,…
This notebook explains PCA as a baseline latent-factor model for forecasting ETF returns. It decomposes a training panel of forward returns into leading directions and estimates each fund’s exposure and factor premia. The estimator ignores the available…
This notebook demonstrates fine-tuning three transformer checkpoints for three-class financial sentence sentiment and evaluating them with accuracy, macro F1, and confusion matrices. It uses a stratified train, validation, and test split so class imbalance…
This notebook estimates the adjusted effect of a continuous ETF momentum measure on forward returns using double machine learning. It contrasts an unadjusted regression with DML estimates that control for recent and longer-term volatility, market regime, and…
This guide describes a daily US equities dataset from NASDAQ Data Link’s Wiki Prices, with adjusted OHLCV data and coverage from 1962 through March 2018. It explains how to obtain the archive with an API key, load it, filter by symbol or date, inspect its…
This utility measures the absolute relative change in an entered options straddle's premium over specified session horizons. It builds a valid straddle premium from paired call and put quotes with positive bids and asks above bids, then follows the exact…
This notebook presents a deterministic method for checking whether backtest and live trading pipelines behave alike. It compares successive stages: features computed from the same bars, predictions from those features, signals given the same position state,…