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.
55 documents
This deployment demonstration retrains a Ridge model on historical daily data for a cross-section of major FX pairs, fetches current bars through an Interactive Brokers paper session, ranks the pairs, and builds a long basket. It walks through connecting to…
This document describes a foreign exchange OHLCV dataset covering G10 majors and crosses, with daily and four-hour bars from OANDA. It outlines how the data can be downloaded, loaded, filtered by pair or date range, and explored through coverage summaries…
This document develops a daily feature panel for ranking twenty currency pairs at the New York 5 PM close. It distinguishes rankable signals, such as standardized multi-horizon returns and channel position, from market-state measures such as volatility,…
This notebook examines linear-model regularization for predicting returns across currency pairs. It distinguishes collinearity among input features from dependence among the assets: currency pairs share underlying currencies, so their returns and rankings…
This notebook converts FX pair predictions into baseline trading results. At each decision time it ranks pairs, takes equal-sized long and short sleeves, and runs the selected prediction configurations and checkpoints through an existing backtest engine.…
This notebook describes how to create holdout predictions for an FX strategy after its configuration has already been selected using validation results. It resolves the highest-Sharpe validation backtest among admitted candidates that remained solvent, then…
This notebook reconstructs the selected FX pairs configuration from a frozen validation candidate set, then assesses its validation and holdout evidence. Selection uses the highest eligible validation Sharpe, with a deterministic backtest identity…
This document presents a reusable diagnostic survey for financial datasets, covering index integrity, duplicates, missingness, outliers, calendar gaps, and domain-specific anomalies. It checks time types, ordering and uniqueness, including per-symbol…
This notebook measures how changing proportional transaction costs affects one validation-selected FX strategy per return label. It first selects a parent from signal, allocation, and risk-overlay candidates, then varies only the aggregate cost per traded…
This exploratory analysis profiles a four-hour OANDA panel of 20 currency pairs and explains how to interpret its data. It distinguishes direct, indirect, and cross pairs by the dollar’s position, showing why direct quotes must be inverted before combining…
This notebook sets up an LSTM forecasting run for an FX-pairs case study. The model carries a hidden state through a lookback sequence, and the notebook resolves architecture and training settings through the shared configuration and study-planning tools. It…
The document describes a validation-stage comparison of fixed and trailing stop rules for FX strategies. For each return label, it selects a parent strategy from a sealed cohort of signal and allocation results, then varies the declared position-level risk…
This notebook examines linear-model regularization for a universe of currency pairs whose returns are linked because each pair is a quote between shared currencies. It distinguishes feature collinearity, which ridge, lasso, and elastic net can address, from…
This notebook describes a one-feature-at-a-time screen for candidate signals across 20 currency pairs. It computes each feature’s daily cross-sectional rank agreement with the next-session return using only walk-forward validation windows. The evaluation…
The document describes an FX portfolio allocation sweep designed to isolate position sizing from signal selection. It starts from frozen equal-weight baseline candidates ranked by validation backtest performance, then advances configurations while preserving…
This notebook refits the FX configuration already selected by validation and registers its predictions for a later holdout backtest. It resolves the selection from solvent validation backtests, constrained to a frozen candidate set, then rebuilds the…
This notebook turns currency-pair prediction rankings into a traded baseline. At each decision time, it forms equal-sized long and short sleeves from the highest- and lowest-ranked pairs, then evaluates them with an FX backtest engine. Equal weighting is…
This study compares gradient-boosted trees with a penalized linear model for ranking currency pairs by future returns. Its motivation is that trees can represent conditional relationships, such as using momentum in one market regime and carry in another,…
This exploratory analysis profiles a four-hour OANDA dataset covering twenty currency pairs. It explains that the volume field counts quote updates at one venue, not consolidated traded size, and therefore cannot support a market-wide liquidity ranking. It…
This notebook describes how to prepare and register NLinear forecasts for FX labels. NLinear subtracts the last observed level from each fixed-length input history, focusing the model on changes within the lookback. A shared sequence eligibility process…
This analysis compares registered double machine learning treatment effects from nine trading case studies. It loads current results from each study’s registry, checks registry integrity and duplicate labels, and makes coverage explicit. Effects and…
This notebook builds fitted-model features from foreign-exchange price histories, complementing indicators calculated directly from past prices. It describes three approaches: a state-space filter that estimates a slowly changing price level and related…
This case study configures an LSTM model for forecasting FX-pair labels. It contrasts the recurrent model’s carried hidden state with fixed lookback transformations and convolutional receptive fields, while deferring comparisons with other models to a…
This notebook measures how a validation-selected FX strategy responds to changes in proportional transaction costs. For each prediction label, it selects a parent strategy from the signal, allocation, and risk-overlay results, then reruns that strategy…