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.
5,922 documents
This configuration defines a Qlib workflow for training a Temporal Fusion Transformer model on Alpha158 features for CSI 300 stocks. It sets Chinese market data from 2008 through mid-2020, using 2008–2014 for training, 2015–2016 for validation, and 2017–2020…
This Qlib documentation describes visual reports for evaluating intraday portfolios and prediction models. Portfolio reports display benchmark and portfolio cumulative returns, returns with and without transaction costs, turnover, drawdowns, and cumulative…
This research outline proposes allocating among equity industries by tracking the behavior of different market participants. It motivates industry rotation with the observation that returns can diverge substantially across sectors and styles, so broad asset…
The document describes a commodity futures strategy that ranks 28 markets by changes in Twitter-derived sentiment. It calculates daily sentiment from keyword-matched posts using a financial sentiment dictionary, then forms equal-weighted long and short…
This forum post presents a workflow for combining predictions from three model outputs. It merges the datasets on instrument and date, preserves columns that are not already present, renames each model’s prediction column, and computes their arithmetic mean…
Staggered Orders is a grid-like trading mode intended for sideways markets. A trader sets upper and lower price bounds, along with a spread and increment; the system then calculates the buy and sell orders needed to cover that range and uses available funds.…
This reading list summarizes three studies on portfolio construction. One develops a finite-horizon allocation framework using nominal assets, with closed-form optimal strategies and utility. It describes how hedging demand depends on the investor’s horizon,…
The document describes a U.S. equity strategy based on balance-sheet accruals, the noncash component of reported earnings. It estimates accruals from annual changes in current assets, cash, current liabilities, short-term debt, income taxes payable, and…
The document describes Temporal Routing Adaptor (TRA), a way to extend a stock prediction model so it can learn from different patterns in market data. It notes that momentum and reversal behavior may coexist, which challenges the assumption that…
The document answers how to allocate weights across strategies in a multi-strategy backtest. Its proposed workflow is to extract each strategy’s daily return series and use an optimization package to find portfolio weights. This frames the task as portfolio…
Qlib separates forecasting signals from portfolio construction. A strategy turns prediction scores into trading decisions, while a weight-based base class lets users specify target holdings and delegates order generation to the framework. The documented…
This example describes a concentrated long-only stock portfolio built through a sequence of AI agents. A research agent ranks companies for understandable businesses, cash generation, and attractive prices. A second agent challenges each idea by examining…
This short forum exchange explains how to configure BigQuant’s trading engine to rebalance on a weekly or monthly schedule. For weekly scheduling, it specifies the weekly trading-day mode and a day value of 5; for monthly scheduling, it specifies the monthly…
This post describes a Chinese equity screen combining three conditions: a positive MACD reading, membership in selected beverage and alcohol import-export industry classifications, and a daily percentage change below a stated ceiling. Its example…
The document summary highlights two applications of machine learning in quantitative investing. First, it describes forecasting volatility to inform how capital is allocated among strategies, based on the claim that many strategies’ profitability is closely…
This sample describes a high-dividend stock-selection model for Chinese equities. The process excludes special-treatment stocks, suspended securities, and Beijing Stock Exchange listings. It then screens for larger companies by market-capitalization rank,…
This sample strategy selects Chinese equities using a dividend yield ranking alongside size and valuation filters. It first removes special-treatment stocks, suspended shares, and Beijing Stock Exchange listings. From the remaining universe, it favors…
This report surveys several approaches to allocating across asset classes: macro and cycle-based fundamentals, mean-variance optimization, Kelly-CVaR, Black-Litterman, and risk parity. It describes a macro model that separates directional forecasts from…
This Chinese-language research digest summarizes two separate topics. The first reviews the United States target-date fund market, covering market share and flows, relative performance among fund series, and glide paths. It discusses glide-path averages and…
This report describes a Chinese equity index-enhancement strategy built from a composite stock-selection signal and portfolio constraints. It combines factors spanning company size, valuation, growth, profitability, technical behavior, liquidity, and…
This research report describes a Chinese equity fund approach that first selects industries through fundamental analysis, then applies a multi-factor model to stocks within those industries. Industry research estimates long-term growth across more granular…
This research note reviews the growth and allocation case for quantitative funds in China, focusing on index enhancement and equity long-short strategies. It reports that in the first half of 2021, CSI 500 enhancement strategies outperformed selected active…
This guide describes NautilusTrader’s system for turning completed backtests into interactive or static performance reports. Users can select charts and themes, include run metadata and performance statistics, and inspect equity, drawdown, monthly and yearly…
This note surveys seven pitfalls in quantitative investing: survivorship bias, look-ahead bias, storytelling, data mining, signal decay and trading costs, outliers, and asymmetric long-short payoffs. It explains how current index constituents can distort…