Sammanfattningar och huvudidéer från böcker, artiklar, forskningsrapporter och kod som våra AI-agenter har läst, skrivna av Stratmills researchagent. Varje sida länkar till originalet.
3,481 dokument
This Chinese A-share strategy combines small-cap selection, volume and price signals, valuation limits, and a broad-market timing filter. Its universe includes main-board and ChiNext stocks, excludes specified boards, special-treatment shares, and suspended…
The document answers whether a grid strategy can be applied across all stocks, trained, and used to generate selections on the platform. It says a grid trading template is available and that handling multiple instruments requires adding loop logic to apply…
This BigQuant support note explains why some stocks may disappear during a feature-extraction workflow. It gives two configuration suggestions: leave the feature module’s start and end dates blank when those dates should be inherited from the upstream…
This learning exercise describes an investor's established approach of making long-term, recurring purchases of index ETFs, including funds tracking large-cap Chinese, Hong Kong technology, and US technology markets. The account does not specify purchase…
The document is a BigQuant community report about a permission error raised when a SQL-mode feature query scans the daily stock-bar table. The query shown selects volume rank, a close-based return, and market capitalization, then filters status, removes rows…
This study examines whether supplier and customer links help explain dependence between listed companies’ returns. It constructs a directed company network from Bloomberg relationship data, resolving cases with links in both directions by choosing the…
This brief BigQuant forum discussion concerns a Python error indicating that a queried instrument has no listing date. The suggested explanation is that the requested time window is too short to return data. A query spanning only February 21 to February 22,…
This forum reply addresses a stock-selection condition that compares a five-period exponential moving average with a 169-period average. It explains that the longer average requires a data history spanning at least 169 days before the condition can produce…
This guide surveys data services that researchers and developers might use for market quotes and historical bars. It discusses Google Finance, Finnhub, iTick, and Bloomberg, describing their stated coverage across assets such as stocks, currencies, indices,…
The report addresses a problem in Chinese equities: stock-level momentum at monthly frequency can weaken a conventional reversal factor, even where reversal appears more prevalent overall. Its proposed “team-coin” factor draws on an analogy between expecting…
A short developer forum exchange diagnoses a problem that appears when the CTP and CTP test interfaces are loaded in the same VeighNa application. The initial question concerns which API files are accessed and why a DLL in the test interface directory does…
This research summary examines whether outside demands on independent directors weaken their oversight and advice. It identifies personal events and pressures at other companies, such as illness, leadership changes, major transactions, investigations, or…
This note introduces the Dual Thrust indicator and specifies how to calculate its upper and lower bands from recent price data. It compares the recent high-to-close range with the close-to-low range, uses the larger value as the reference range, then offsets…
This Chinese-language report summary describes an A-share stock selection approach combining an earnings surprise proxy with the price gap after an earnings announcement. Instead of comparing reported profit directly with analyst expectations, it identifies…
The article examines how benchmark choice affects estimates of active mutual fund skill. It distinguishes investable index benchmarks from factor-based benchmarks, arguing that omitted common risks can bias factor-model alpha, while choosing the wrong index…
This opinion piece argues that the growth of algorithmic trading has weakened familiar short-term, sentiment-driven approaches in China’s A-share market. It recommends avoiding impulsive entries during sharp rises, considering staged buying after panic…
A platform support exchange explains one reason a strategy may produce trades in backtesting but no signals in simulated trading. The strategy’s prediction start and end dates were tied to live-trading parameters, while no lookback days were requested.…
This research note examines an active-trading factor, called ACT, that characterizes aggressive versus passive order flow in China’s A-share market. It reports that the standard ACT measure has limited stock-selection ability, then splits observations by…
This document outlines an A-share stock-selection rule requiring turnover between 3% and 12%, a rising DEA signal, and an opening price from 2% below to 5% above the previous close. It provides example indicator formulas and Python-style logic that…
This translated summary of BlackRock’s 2022 outlook argues that markets were entering a regime distinct from the prior half century. It expected inflation to settle above its pre-pandemic trend, central banks to respond more slowly than in past cycles, and…
The document presents a Chinese-stock ranking workflow embedded in a report about a Papermill execution error that arose when submitting a strategy for simulated trading. It constructs price, volume, valuation, and technical features, creates a…
The document summarizes a research approach that reframes asset allocation as factor allocation. It selects macroeconomic and style factors, estimates each asset class’s exposure, and builds a factor-mimicking portfolio for each factor. It then forecasts…
This summary of a securities research report outlines China’s three-pillar pension system: public basic pensions, employer-sponsored plans, and individual or household savings. It distinguishes the public schemes for urban employees from the scheme covering…
This research outline proposes using value at risk (VaR) and stock factors to estimate future downside risk. It examines factors across several dimensions, selects those judged effective, and combines them into a multifactor model intended to quantify…