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 article describes a Chinese-equity factor that looks for unusually large intraday increases in trading amount, then aggregates information around those moments into daily observations. Its rationale is that a volume surge accompanying modest, orderly…
This research summary explores whether statistics computed from intraday stock data can add new signals to established multi-factor selection approaches in China’s A-share market. It evaluates realized volatility, realized skewness, and realized kurtosis as…
The document explains two ways traders can conceal order size from a public order book. A reserve order is hidden from view, so displayed depth understates the actual quantity at a price. An iceberg order displays only a small portion of a larger order; as…
The document examines a vulnerability in market-making systems that create apparent trading activity through rapid self-trading. It explains that simultaneous or closely timed buy and sell orders may not execute as intended because of network and matching…
This overview characterizes quantitative futures trading through four themes: execution speed, short holding periods, flexible intraday exposure, and trading both long and short. It emphasizes that activity may cluster around the open and close, when…
This research summary describes high-frequency equity factors constructed from Chinese Level 2 market data, including minute bars, order-book snapshots, order queues, and trade-by-trade records. The proposed signals measure features such as return skewness,…
The document describes a MetaTrader 5 utility for recording real-time ask and bid ticks to a CSV file. Users can choose the output filename, column delimiter, and timestamp format. The timestamp records local computer time when each tick arrives, with…
This tutorial applies the Guéant–Lehalle–Fernandez-Tapia market-making model to grid quoting. It derives bid and ask quote depths from a fair price, volatility, trading intensity, and inventory. The resulting quotes combine a half-spread with an…
This short commentary describes pressures facing quantitative investment firms: crowded strategy areas, convergence among approaches, thin profits, and greater barriers to entry for researchers. It argues that widespread use of machine learning can produce…
This article presents a four-stage account of quantitative trading. It describes early strategies built on financial theory, multi-factor stock selection, and linear regression; a later high-frequency phase centered on low-latency hardware and algorithms; an…
This code describes a stateful method for comparing consecutive limit-order-book snapshots. It stores bid and ask levels from the current and previous snapshots, then flags each current level as unchanged, changed, or inserted based on price and quantity.…
This career overview explains the work of quantitative traders and the mix of skills commonly needed to research and automate trading strategies. Quants analyze market data, develop models, test ideas, and implement trades using computer programs. The…
The article introduces electronic markets as computer systems that match buyers and sellers, then describes two basic order types. Limit orders specify a price and may fill only partly while waiting for counterparties; they can also be canceled. Market…
This opinion piece challenges the idea that quantitative trading is uniformly harmful to retail investors. It contrasts high-frequency approaches, which it says may use speed and securities lending to affect momentum-driven trades, with lower-frequency…
This BigQuant example addresses a limitation in which its high-frequency feature extraction module calculates within a single day, while a researcher may need features from minute data spanning multiple days. It shows a distributed workflow using FAI: read…
This overview explains how high-frequency trading can profit from price movements and fragmented U.S. securities markets. Because a listed security may trade on multiple exchanges, differences in liquidity, participants, or information timing can create…
The document outlines a tick-based algorithm for finding uninterrupted price movement. It looks for a sequence in which price rises or falls across multiple consecutive ticks without a pullback, then compares the resulting movement with price. The…
This guide explains the workflow for a BigQuant contest focused on constructing 15-minute factors from three-second stock snapshot data. Participants are asked to write factor logic without using model-generated factor synthesis. The platform provides…
The document outlines how sequence learning can fit into a conventional quantitative investment process: feature extraction, individual asset return prediction, portfolio construction, and trade execution. It describes using price and volume measures,…
This short support discussion explains why HFTrade applies a volume limit during high-frequency backtests and why that setting can prevent an order from filling completely. The discussion says volume caps may be poorly suited to minute and tick data, where…
The document distinguishes temporary factor decay from permanent factor failure. It attributes much of the decline in a signal’s excess returns to competition: as more capital uses a factor, its alpha can shrink, while changing market styles may later…
This article presents a critical account of quantitative trading’s possible effects on Chinese equity markets. It argues that rapid order placement and cancellation can make displayed depth incomplete, that some funds combine stock baskets with short index…
The document explains how limit order books can reveal displayed supply and demand, liquidity concentrations, spread changes, and shifts in intraday buying or selling pressure. It proposes tracking order book changes alongside price to investigate support…
This document outlines a recurring BigQuant program for people developing quantitative trading skills. It has three stages: an online selection round in which participants submit factor models built from a provided template; training for selected entrants;…