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.
765 documents
The article outlines a MetaTrader 5 approach that selects currency pairs using Pearson correlation and cointegration tests, then combines a negatively correlated pair with a trading system framed around Nash equilibrium. It explains Nash equilibrium as a…
This notebook explains a local linear trend state-space model for extracting financial features from price levels. The hidden state contains a price level and its slope; observations are noisy closes. At each step, the filter predicts the state, measures the…
This notebook explains pairwise and cross-sectional features using ETF panels. It compares Engle–Granger and Johansen cointegration tests, distinguishing a stationary spread from simple co-movement. Its energy fund and crude oil fund example fails both…
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 notebook explains Kalman filtering as a way to extract features from financial prices. A local linear trend state tracks both a latent price level and its slope, while an observation model accounts for noisy closes. Each update produces a filtered…
This notebook develops features that require multiple asset series. It compares Engle-Granger and Johansen tests for cointegration, explains why co-movement alone does not imply a stationary spread, and estimates hedge ratios both with a full-sample…
The document formulates pairs trading as a dynamic portfolio optimization problem. The spread between two related securities follows a Gaussian mean-reverting process, while its drift rate changes according to an unobserved finite-state continuous-time…
The document introduces pair trading as a market-neutral strategy that takes simultaneous long and short positions in two correlated assets. It describes the broad workflow: identify cointegrated pairs, construct trading signals from relative price…
This paper extends the Robust Positive Expectation theorem from a single stock to a pair of stocks. The original result concerns combining specially designed linear feedback controllers, one long and one short, to produce a gain-loss function with robustly…
This paper formulates a pairs-trading strategy for two stocks whose prices follow general geometric Brownian motions, rather than assuming their price difference is mean-reverting. The trader compares the stocks’ relative strength, opens a position by…
This paper studies when to close a stock pairs trade, which holds one stock long and another short. It formulates the exit decision as an optimal selling and repurchasing problem under trading constraints. The model assumes that the two stock prices follow a…
This work frames pair trading as a hierarchical decision problem with two linked stages: selecting an asset pair over a longer horizon and executing trades over shorter horizons under partial observability. Because feedback arrives late and may be ambiguous,…
The paper develops a framework that learns conditional latent factors to identify related equities, detect relative mispricing, and form a trading policy that accounts for trading costs. Its attention factors are built from embeddings of firm…
The paper presents an optimal-execution framework for statistical arbitrage when predictive signals depend on the path of market data. It represents both the alpha signal and trading speed as linear functions of a truncated signature of a time-augmented…
This paper models the price spread between two assets as a hidden state that follows a mean-reverting process. The model allows spread innovations to be non-Gaussian and heteroskedastic, while making mean reversion nonlinear. It uses the filtered estimate of…
This case study tests whether a cointegration-based pairs strategy for PepsiCo and Coca-Cola is statistically sound and economically viable. It estimates the spread's mean-reversion behavior using earlier data, holds those statistical estimates fixed, and…
The study examines Asian American Depositary Receipts in markets whose trading hours do not align with those of the underlying foreign shares. It separates ADR returns into components earned during U.S. market hours and overnight, and compares each ADR with…
The study examines pairs-trading extensions across Brent, WTI, and Shanghai crude oil futures. It reports that the three price series are cointegrated and models their resulting spread as mean-reverting with regimes governed by a hidden Markov chain. Online…
This study tests deep reinforcement learning as an execution overlay for pair trading in volatile cryptocurrency markets. Its framework first filters and ranks candidate pairs, then uses a Proximal Policy Optimization agent with a Long Short-Term Memory…
The document describes a data-driven method for finding statistical arbitrage strategies that seek to remain profitable under uncertainty about the market model. It uses deep neural networks to consider many securities together, avoiding the need to first…
This paper studies optimal trading when a signal follows a mean-reverting Ornstein-Uhlenbeck process whose parameters change according to hidden regimes. It combines recurrent neural networks with reinforcement learning to infer information about the…
This paper presents an optimization method for constructing a mean-reverting portfolio from a larger set of assets. It seeks portfolios whose spread is well described by an Ornstein–Uhlenbeck process, with process parameters estimated by maximum likelihood.…
This paper studies an investment and consumption problem in a pairs trading market where the spread between risky assets follows an Ornstein–Uhlenbeck process. It focuses on optimal strategies under power utility, bringing together portfolio decisions and…
This study develops a mean-reversion pairs strategy for US equities. Rather than selecting pairs through customary pair-trading practice, it uses maximum likelihood estimation to form static portfolios that fit an Ornstein-Uhlenbeck process. The estimated…