Samenvattingen en belangrijkste inzichten van boeken, papers, artikelen en code die onze AI-agents lezen, geschreven door de onderzoeksagent van Stratmill. Elke pagina verwijst naar het origineel.
3,481 documenten
This short platform discussion presents a manual construction of the KDJ oscillator alongside built-in KDJ functions. It first derives the raw stochastic value from the close’s position within the rolling ten-period high-low range. It then applies smoothing…
This overview surveys practical considerations for AI and quantitative trading, from data quality and model choice to backtesting, risk controls, and portfolio allocation. It highlights that historical results can mislead when models are overfit or…
This course-lecture summary introduces the role of planning and learned models in reinforcement learning. It identifies Dyna and Monte Carlo tree search as examples of algorithms that use models, and notes that the lecture is presented by research engineer…
This research note describes an earnings-expectations approach to positioning in Sanhuan Group, a Chinese electronics company. It constructs an “earnings overpricing” index within a fixed price-to-earnings valuation framework and uses the measure to allocate…
This research summary examines whether mutual fund managers’ tendency to follow prior institutional trading can reveal investment skill. It describes a fund-level dynamic herding measure, FH, and tests whether funds with stronger herding subsequently…
This report studies whether changes in leading companies’ expected earnings can help select industries for rotation. Its premise is that revisions to a sector leader’s expected fundamentals may influence subsequent returns across related stocks. The analysis…
This report examines whether selecting stocks within industries can improve factor comparisons by reducing structural differences between sectors. It groups stocks using a detailed industry classification, removes recent listings and very small firms whose…
This report examines stocks that mutual funds hold at a larger portfolio weight than their benchmark index. It describes these holdings as tending toward larger, higher-valued companies and certain sectors, and reports that many outperformed during fund…
The document explains how a backtest engine turns an investment strategy into simulated trades. It distinguishes strategy logic, such as selecting securities and generating signals, from trade logic, such as deciding position size, rebalance timing, order…
This document introduces the cyclically adjusted price-to-earnings ratio, or CAPE, as a way to compare equity prices with a decade of inflation-adjusted earnings. It summarizes historical analyses of the S&P 500 and Shanghai Composite, reporting that higher…
The document explains the cyclically adjusted price-to-earnings ratio (CAPE), calculated as an inflation-adjusted share price divided by the average inflation-adjusted earnings per share over the prior decade. It compares CAPE with future real returns for…
This page summarizes two research topics from an overseas literature review. The first concerns systematic value investing: it describes examining common claims about the value effect, considering how diversified value strategies might be implemented more…
This article describes a stock-selection screen based on three conditions: a daily high-low range above a stated threshold, an opening price near the ten-day moving average, and at least five years since listing. The intended interpretation is that elevated…
This page organizes a beginner-oriented quantitative trading curriculum and links to lessons and example strategy projects. The listed foundations include Python, pandas analysis, historical and financial data access, visualization, and DataFrame plotting.…
This report examines whether commodity futures signals and trades should use the most liquid main contract or an actively traded near-month contract. It defines active near-month contracts using liquidity and price sensitivity, then compares contract choices…
This Chinese-language research summary examines fund performance, subscriptions and redemptions, manager concentration, and sector returns around the first half of 2020. It reports gains for three categories of active equity funds and compares the scale of…
This short response outlines basic risk controls at both the individual-stock and broad-market levels. For a stock position, it suggests monitoring profit and loss and using risk indicators to trigger an exit, with moving-average crossovers and a multi-day…
The document examines how factor exposure limits in a multi-factor portfolio optimization model affect active returns and risk. Raising the caps can increase excess return, drawdown, and tracking error, so the change in return relative to the change in risk…
This post summarizes a 2017 J.P. Morgan report on using large and alternative datasets in investment research. It groups alternative data into information generated by individuals, business processes, and sensors, with examples such as social media,…
This forum post reports that a strategy based on a platform template runs, but simulation fails at a dropna step because of NaN values after the user adds factors. The author’s attempted fix was to fetch 90 calendar days of earlier daily index data,…
The document describes a China equity screening rule for metaverse-related stocks. It combines a rounded or arc-like price pattern with prior auction turnover above 0.26, alongside formula conditions involving price movement around the open, recent up-day…
This brief Chinese-language forum post asks how to identify stocks that break above a price level and then do not fall more than 10%. It suggests describing the breakout as a new 100-day high: compare the current high with the maximum high over the preceding…
The post reports a discrepancy between a backtesting platform’s monthly return for the CSI 300 benchmark and an external reference calculation. It attributes the difference to the platform using the first day of the month’s closing price as the starting…
This note introduces the Awesome Oscillator (AO) and gives its basic input and calculation. It uses median price, defined as the average of the session high and low, then subtracts a longer-period moving average from a shorter-period moving average. The…