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 document summarizes research on forecasting multiple future steps from limit order book data. Rather than predicting only one future point, the proposed approach uses sequence-to-sequence encoder-decoder networks with attention to generate a path of…
The document describes a tick-data compressor that stores changes in bid, ask, and time rather than repeating full tick records. Small price and time changes can fit into a compact representation, while larger differences use additional bytes. It also offers…
This indicator method turns a stair-step moving average into the center of an oscillator. It updates the trend center when a triangular moving average moves beyond a configurable percentage threshold; otherwise, the prior center is retained. A short simple…
The document summarizes a study that develops a probabilistic classifier to identify high-frequency trading activity from intraday order data. Using French BEDOFIH market records, the researchers engineered features describing orders, including their prices,…
The document describes three high-frequency trading approaches through an example in which an institution splits a large stock order into smaller child orders. Liquidity rebate trading detects likely follow-on orders and provides liquidity to earn exchange…
These reading notes survey high-frequency trading from market structure through strategy and infrastructure. They describe electronic order books, the roles of investors, market makers, arbitrageurs, and directional predictors, and how market makers earn…
This presentation interprets findings from a 2021 survey of Chinese quantitative investment institutions and discusses how the sector was developing at that time. It covers strategy mixes, research organization, talent, artificial intelligence, alternative…
This research reproduction describes factors based on the share of a Chinese stock’s trading volume occurring in the opening call auction and near the close. The opening-auction factor, OCVP, is smoothed with a simple moving average and an exponentially…
This brief description presents a higher-timeframe variant of the ColorMFI_X20_Cloud indicator. Its key feature is a configurable timeframe input, with a four-hour period shown as the default. The indicator can therefore calculate using a period different…
The example outlines a limit-order market-making loop. It computes a midpoint from the best bid and ask, adjusts a reservation price using a forecast and an inventory-related risk term, then places bid and ask quotes around that price. It rounds quotes to…
This FORTS Expert Advisor places limit orders around the best bid and ask when its spread condition is met. The spread input is measured in minimum price steps, so the threshold is converted using the instrument’s tick size. Once an order is accepted and a…
This article explains how market orders and limit orders interact in an electronic market. Limit orders state a desired price and add available liquidity to the limit order book; market orders seek immediate execution against that liquidity and consume it.…
This research surveys how several forms of Chinese Level 2 market data can support equity signals: minute bars, order-book snapshots and queues, and transaction-level records. It describes factors based on intraday return shape, downside variation,…
The article argues that individual investors should not expect consumer AI tools to compete with professional high-frequency trading. It points to differences in computing location, market data access, and technical resources, and describes an alleged…
The document presents volatility of volatility (VoV) as a proxy for uncertainty about an asset’s probability distribution, distinct from ordinary risk. It argues that investors tend to avoid stocks with greater ambiguity and may favor stocks whose prospects…
This document summarizes a securities research report on constructing factors from high-frequency data, with a focus on combining intraday and day-level information. It frames the choice of calculation method around whether price and volume signals retain…
This Chinese-language post discusses connecting BigQuant research with Guojin Securities’ QMT platform for automated live trading. Its concrete example is a stock strategy that first processes daily data to select a watchlist, then monitors those names and…
This talk overview explains four broad approaches to quantitative trading: market making, statistical arbitrage, price prediction, and microstructure trading. Market makers post bids and offers to supply liquidity and seek to earn the spread, while managing…
HftBacktest uses Numba-compiled classes and strategy functions, so importing the library and compiling a strategy can add startup time before a backtest begins. The document describes enabling Numba’s cache option on a strategy function so compiled code can…
This article contrasts retail investors' execution environment with that of quantitative firms. It describes exchange co-location and direct connectivity as ways to reduce signal and order latency, then discusses how automated systems may react quickly to…
The article discusses two reported measures intended to reduce speed advantages for quantitative firms in China’s A-share market: removing servers located inside exchange facilities and adding latency equivalent to a stated 200-kilometer separation. It…
The article argues that no single programming language is best for every algorithmic trading system. It recommends starting with system requirements and strategy characteristics, then selecting tools for separate components such as historical research,…
This research overview outlines a framework for constructing high-frequency equity factors from Level 2 market data. It recommends reducing tick or intraday observations into daily measures, then transforming those measures over time with operations such as…
This weekly Chinese equity research note reviews long-short screens built from high-frequency and technical factors. The listed signals include return skewness, downside volatility share, opening-period buying interest and large-order flows, reversal,…