Sonnet of Intraday Stock Volatility: Metrics and Decomposition.
This article discusses several measures of high-frequency stock volatility within a day, and summarizes its various decomposition methods from a unified perspective, providing a reference for volatility-related investment practices.
Ke Yun has special flowers in recent years, often growing into new branches.
The anomaly of low volatility (low risk) is a well-known anomaly prevalent in the global secondary market, and the idiosyncratic risk (idiosyncratic volatility) factor calculated based on idiosyncratic rate of return is an outstanding representative among them. After excluding the impact of systemic risk factors, the residual return rate of individual stocks is considered to better represent risk, and the factors constructed from this also have better performance. Under the implicit factor framework, this article introduces the theoretical basis of idiosyncratic volatility factors, empirical evidence in the A-share market, and subsequent improvement ideas.
In the summer of 2023, I started my full-time quantitative trading career.
In quantitative trading, few problems are more frustrating than a strategy going live and then performing far worse than expected. The goal of this article is to give readers a first-pass understanding of what “backtest overfitting” means, why it happens, and what can be done about it.
To do a good job, you must first sharpen your tools. This article ranks the tools I use in daily life by a mix of practicality and novelty.
The scope here includes online services, executable programs, plugins, and other user-facing tools. It does not include libraries accessed through APIs or programming languages themselves.
HOUYI is positioned as a general-purpose aiming assistant for electronic shooting games. It currently supports Apex Legends, abbreviated below as apex. Its runtime framework is a two-stage pipeline of detection and tracking: first, object detection is used to identify enemies on the screen, and then the crosshair position is computed and corrected automatically. The mission of HOUYI is to use computer technology to resist the alienation imposed by electronic shooting games.
Don’t Panic
There is, or will soon be, a problem in this society: most people produce data most of the time, are governed by data at the same time, and yet know almost nothing about it. Humanity has produced a large number of articles in response, but most of them begin with combinations of mathematical symbols, which is strange, because the things that are ignorant are not those symbols.
The purpose of this article is to give you everything you need to enter the age of big data, even if you have never heard of calculus. It may look long, and it certainly contains some false or at least imprecise statements, but it surpasses more advanced and old-fashioned academic works in two extremely important ways.
First, it is completely free. Second, at the beginning, in large friendly letters, it says “Don’t Panic”. Its content also lets readers skip difficult formulas and directly obtain the high-level picture. I would call this approach math-free.
In Risk of Rain 2, a character’s DPS can be decomposed into the product of several factors, such as base damage, attack speed, and critical strike chance. As a result, linearly stacking items that increase attack speed, critical chance, and similar stats can produce exponential DPS growth. Similarly, survivability can be decomposed into total health multiplied by armor. Because item resources are limited, marginal returns are huge, and the costs of improving different stats are inconsistent, it is worth computing the optimal build in advance under an item-count constraint so that DPS and effective health are maximized. This matters most for the Engineer, because turrets deal damage in a pure stationary-output style.
This article first proves the convergence of the k-means algorithm in a relatively concise and clear way compared with many existing materials online. The second half discusses two variants produced by combining k-means with ideas from GMM and RPCL. This article is not intended as introductory reading for someone encountering k-means for the first time.
This article trains an agent in Python to move from the start to the goal without falling off the cliff. It implements and compares the Sarsa and Q-learning algorithms in reinforcement learning, and discusses the factors that shape the final solution.