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.
1,116 documents
The study examines how reaction speed and trading urgency affect automated strategies in simulated markets with one exchange, a public limit order book, and continuous double-auction matching. Reaction speed means the time a strategy needs to calculate a…
This study examines how CME market-data packets and matching-engine transactions arrive, and uses those measurements to derive design guidance for high-frequency trading receivers. Its evidence comes from more than a year of observations of the Nasdaq-100…
This work examines ways to reduce latency in performance-sensitive C++ applications, with high-frequency trading as its main setting. It describes a programming repository with benchmarked examples, an optimized market-neutral statistical arbitrage pairs…
PulseReddit aligns large-scale Reddit discussions with high-frequency cryptocurrency market statistics to study whether social information can support short-term trading. The paper evaluates LLM-based multi-agent systems that incorporate this data and…
This paper describes a Long Short-Term Memory model that predicts cryptocurrency price direction over a fixed short-term horizon using trade-by-trade data from a lookback period. Its workflow includes feature design and hyperparameter search, with training…
This study investigates recurring patterns in volatility and trading volume for Bitcoin and Ether across Coinbase Pro, Binance, and Uniswap V2. It examines variation by weekday, hour of day, and intervals within each hour. The authors report systematic…
The document presents a high-frequency market-making framework that combines tick-level market data with periodic prediction signals. It applies deep reinforcement learning to use both fast order-level information and less frequent forecasts when setting…
This paper develops an autoregressive model that generates tokenized limit order book messages, which a simulator uses to update the book state. Its architecture uses simplified structured state-space layers to process long sequences of messages and states.…
The paper models high-frequency order flow as a source of intraday price changes and applies Sequence Generative Adversarial Networks to generate synthetic order sequences. It uses those sequences to simulate price paths, then compares the generated behavior…
The paper proposes a futures pair-trading strategy that uses path signatures as time-series features. It decomposes the signature into two indicators: a segmented signature measure of path interactivity and a covariation-of-increments measure of direction.…
The paper explores reinforcement learning for statistical arbitrage in high-frequency trading. It describes an agent learning through interaction with its trading environment and focuses on deep Q-learning as a way to adapt decisions to short-lived market…
The document examines whether order-book imbalance (OBI) better tracks near-term returns after filtering orders by lifetime, number of modifications, and when modifications occur. It compares signals from the full stream of orders with signals built from the…
This study uses deep learning on a high-frequency database of US equity quotes and trades to model how order-book supply and demand relate to later price changes. It tests predictions of price-move direction using histories of prices and order flow,…
The document models the bid and ask queues in a liquid market where orders arrive at high frequency. It derives a functional central limit theorem for their joint dynamics and shows that, as order arrival frequency increases, the book can be approximated by…
The paper models high-frequency equity returns by separating price changes per trade from the process governing when trades arrive. For a highly liquid near-month E-mini S&P 500 futures contract, it reports that trade-time returns are approximately Gaussian…
The document presents a high-frequency price model designed for irregular observation times and discrete price changes. It uses a zero-inflated Skellam distribution within a score-driven volatility framework, with a moving-average component intended to…
This paper develops a state-space approach to modeling mean-reverting spreads used in statistical arbitrage, including pairs trading. It treats observed spreads as noisy readings of hidden states and uses real-time estimates of those states to identify…
This paper develops state-dependent Hawkes processes, in which event counts and a changing market state affect one another. The state determines the excitation kernels governing self- and cross-excitation, while events in the counting process can trigger…
The paper studies estimation of mean-reverting price dynamics from high-frequency observations, where market microstructure noise can bias parameter estimates. It proposes a maximum-likelihood estimator for an Ornstein–Uhlenbeck process that accounts for…
The paper builds a microscopic account of financial price motion from observations of high-frequency traders in a foreign exchange market. It tracks individual trader trajectories and reports a collective trend-following pattern that conventional order-book…
The document describes an end-to-end deep reinforcement learning approach to active high-frequency stock trading. Agents use Proximal Policy Optimization to trade one unit of Intel stock, with states built from different sets of limit order book features.…
The study proposes an automated high-frequency Bitcoin trading framework that combines a long short-term memory (LSTM) price model with proximal policy optimization (PPO). It represents prices as the agent's states, trades as actions, and returns as rewards.…
The document surveys how market design has changed as trading technologies and participants have evolved. It moves from floor-based trading through electronic order books and high-frequency trading, then considers batch auctions, dark pools, blockchain-based…
This study models minute-level Bitcoin return volatility with generalized autoregressive conditional heteroskedasticity methods. It combines historical returns and volatility, as represented by variants of the GARCH family, with the Mixture of Distribution…