Kokkuvõtted ja põhiideed raamatutest, teadustöödest, artiklitest ja koodist, mida meie AI-agendid loevad. Need on koostanud Stratmilli uurimisagent. Igal lehel on link originaalile.
3,481 dokumenti
This article discusses recruiting and interviewing in China’s quantitative trading industry. It argues that candidates need not come from finance: people trained in fields such as chemistry, mathematics, statistics, and computer science may bring useful…
The post outlines a proposed equity workflow that links market breadth, sector selection, stock screening, and portfolio exposure. It would compare broad-market indices with industry or concept indices using beta, select sectors with higher beta, then choose…
The note answers how to prevent a backtest from buying stocks that cannot realistically be traded because they are suspended or locked at a one-price limit. It says BigQuant's backtesting module handles these cases by automatically canceling an order when…
This daily report summarizes an RSRS-based market timing model for major Chinese equity indices on April 7, 2020. The model retained bullish views on the Shanghai Composite, CSI 500, and ChiNext, while taking a more cautious stance on the SSE 50 and CSI 300.…
This brokerage report reviews Chinese equity style behavior during the week of April 13–17, 2020, then outlines a style outlook using macroeconomic conditions, historical calendar patterns, cross-sectional dispersion, fund flows, valuations, and macro event…
The article describes inferring a fund’s constituents and weights from its total value time series. It compares Extended Linear Cloning (ELC), which uses regression coefficients as asset importance scores and retains the highest scoring candidates, with…
This brief BigQuant support note explains a common cause of a “no data left after dropna” error when backtesting a strategy on the SSE 50 universe. The error can occur because the strategy’s filters leave no eligible stocks, rather than because the backtest…
This study constructs equity selection factors from trade by trade data to measure aggressive buying. It aggregates transactions into minute level buying and selling amounts, then derives buying share and buying intensity measures. The proposed factors can…
This overview explains how AI quantitative investment platforms combine machine learning, deep learning, and natural language processing with financial data analysis. It describes a general workflow in which platforms collect and process market and financial…
The document briefly explains a variation of the Schaff Trend Cycle indicator. It says the indicator is calculated from the difference between two RSI oscillators that use different lookback periods, and identifies the item as a colored version of that…
This study proposes a passive index that selects between the A-share and H-share of the same company according to which listing is cheaper after currency adjustment. It examines price gaps across dual-listed Chinese companies, using panel regression to…
This report examines shifts in China’s A-share market and argues that both industry fundamentals and market style help explain relative sector returns. It describes a stepwise regression that attributes sector performance to beta, company size, cyclicality,…
This research note evaluates 31 quality factors for stock selection in Hong Kong equities and compares their effectiveness with results in mainland A-shares. Twelve factors are reported as notably effective, particularly measures tied to operating cash flow…
This Chinese-language weekly report presents a cautious near-term market view after large-weight industries weakened relative to smaller industries. It describes an industry allocation signal favoring market-cap-weighted exposure, which would reduce exposure…
This report is presented as the opening installment in a series on fundamental quantitative stock selection, focused on the lithium battery industry chain. Its stated aim is to combine fundamental information with traditional multi-factor selection,…
This overview introduces statistical learning methods for prediction and data analysis. It covers linear and logistic regression, linear and quadratic discriminant analysis, resampling, predictor selection, regularization, dimension reduction, nonlinear…
This forum post describes a long-short stock routine that first attempts to close existing positions, then buys the three highest-ranked stocks and sells short the three lowest-ranked. It checks whether each security can be traded before sending an order,…
The document introduces Altman's Z-score as a composite financial-risk measure built from five ratios: working capital, retained earnings, earnings before interest and taxes, market equity relative to liabilities, and sales, each scaled against assets except…
This research review examines momentum in China’s A-share market as institutional ownership and analyst attention have grown. It compares traditional lookback returns with measures that account for price paths, remove limit-up days, use earnings announcement…
This research summary examines how earnings forecasts can improve equity valuation and stock selection in China’s A-share market. It argues that historical return on equity alone has limited selection value there, while substituting forecast earnings for…
This research note describes extending mean-variance portfolio allocation with higher-moment information, especially co-skewness and co-kurtosis among assets. Mean and covariance alone can miss differences in joint tail behavior: portfolios with similar…
This article presents six proposed bullish chart setups: a pullback that holds above a limit-up level and a moving average, rising volume after a contraction, a two-stage expansion from a price low, a large down day on reduced volume, a sequence of pullbacks…
This report summary lays out a framework for analyzing government interest-rate bonds through supply and demand, return decomposition, duration timing, and portfolio allocation. It attributes supply partly to fiscal conditions and economic pressure, and…
This submission describes a composite stock factor combining three ranked measures: three-year net profit growth, the inverse of summed turnover over five periods, and the inverse of five-period trading-amount variability. The intended profile is high growth…