Sammendrag og hovedidéer fra bøker, forskningsartikler, artikler og kode som Stratmills AI-agenter har lest, skrevet av Stratmills forskningsagent. Hver side lenker til originalen.
3,481 dokumenter
This research summary organizes equity factor allocation around two macro conditions: economic growth or recession, and tighter or looser liquidity. Their combinations define four regimes in a factor investment clock. The proposed framework treats corporate…
The document frames a multi-strategy allocation problem: how to adjust the capital assigned to several simulated strategies, including custom and subscribed strategies, as their performance changes. It proposes considering changes in the slope of each…
The document presents a SQL example for constructing a turnover-related stock factor and neutralizing it for industry membership and company size. It calculates the logarithm of total market capitalization, a 20-day average of turnover, and a neutralized…
The article argues for buying strength after price momentum is confirmed rather than waiting for a pullback to make a stock seem cheaper. It describes looking for a large bullish candle on increased volume or a breakout from a trading range, with particular…
This report summary describes a Chinese equity strategy that rotates among industry and theme ETFs, then uses the portfolio in absolute-return constructions. It selects 14 industry indices with matching ETFs from a broader set of industries, aiming to…
This research note outlines a stock-selection strategy for constituents of the CSI 500 index. It uses the number of news items about each stock as a proxy for investor attention, drawing on news-volume data collected by web crawler from 2010 onward. Stocks…
The document explains alpha as a signal or mathematical expression that can be converted into portfolio weights, then outlines a research cycle from investment hypothesis and data choice through backtesting and review. It discusses potential signal sources,…
This project report describes using a model with the BigQuant dai function’s syntax and formulas as context, then prompting it to build increasingly complex quantitative factors. The author says ordinary prompts and iterative adjustment moved the generated…
The document reviews a study that decomposes the S&P 500 dividend yield into expected long-run real dividend growth, a subjective risk premium, and a residual attributed to differences between investors’ subjective growth expectations and rational…
This post describes a beginner’s adaptation of a template strategy on the BigQuant platform. Its stated backtest setup holds one equally weighted stock for five days over a specified historical period. The author reports using filters to exclude…
This discussion clarifies how a quantitative platform’s automatic labeling feature maps a continuous score into twenty discrete classes. The questioner initially expects each class to contain an equal share of observations ranked by return, which would make…
The article describes a data-driven way to group stocks for portfolio diversification. Instead of relying only on fixed business-sector labels, it trains models to predict future pairwise return correlations, then clusters stocks using the predicted…
This Chinese-language platform support post asks when the position and fund data files refresh and whether users can trigger an update. The author describes a timing problem: a stock bought near the prior day’s close may appear unavailable in the last…
This report outlines a framework for classifying and evaluating bond funds, with emphasis on medium- and long-term pure bond funds. It groups funds by investment objective, such as seeking returns above the bond market or favoring particular bond types,…
This assignment response outlines quantitative investing as a workflow: analyze price and other information for relationships, express findings as indicators, factors, or models, turn them into entry and exit rules, and evaluate them with historical data,…
This short forum post considers whether combining quantitative stock selection with discretionary choices improves results. The author argues that adding human stock picks to a quantitative selection process may perform worse than either approach alone, and…
This stock-selection screen combines a 14-period RSI below 65 with three consecutive sessions in which each close is below its open. It also requires relative volume to be above 1.5 and below 6. The article presents the conditions as a way to find weak…
The article describes several ways quantitative trading may shape Chinese A-share activity. It presents broad index direction as a dominant signal, with overnight futures and macro news used to anticipate the market and influence broad buying or selling. It…
The article examines how large datasets and complex models can make factor discovery more productive while increasing the risk of overfitting. It contrasts hypothesis-led research, where an economic or behavioral rationale is tested, with data-led mining,…
This tutorial explains attention and Transformer components before outlining a time-series stock ranking model. It adds Time2Vec features to factor sequences so the model can represent both periodic and linear time patterns, then applies self-attention,…
This brief guide explains how to exclude delisted stocks from both the training and validation data flows in a visual AI stock-selection strategy. It instructs users to insert a shared “remove delisted stocks” module before the missing-data handling module…
This MATLAB note introduces Monte Carlo estimation through repeated random sampling. It uses the Monty Hall problem to illustrate estimating a win probability by switching doors, and compares simulated frequencies with the theoretical probability. It then…
This brief troubleshooting note links a Python-style NoneType error from a BigQuant experiment to a likely missing data record. It recommends printing the security identifier and date associated with the failure, then checking whether data exists for that…
This short BigQuant help entry answers where to find indicators that classify trading activity as super-large, large, medium, or small orders. It directs users to search for large-order measures in the platform’s A-share data section. For users seeking to…