Amid the AI boom in the stock market, many retail investors turn to AI tools, but most are paid plugins or designed for U.S. markets; this article curates ten AI‑driven skills that cover real‑time data, technical analysis, quantitative backtesting, monitoring, and risk alerts specifically for Chinese investors.
The article presents FinAgent, a multi‑agent orchestration framework that maps traditional algorithmic trading components to autonomous agents, validates it on hourly stock and minute‑level Bitcoin back‑tests, and reports superior risk control, auditability, and scalability compared with standard benchmarks.
The article reviews the 3S‑Trader framework, a training‑free multi‑LLM system that uses scoring, strategy, and selection modules to construct weekly stock portfolios, and shows that it outperforms rule‑based and deep‑learning baselines on DJIA and sector datasets with strong risk‑adjusted returns.
AlphaQuanter tackles the three major limitations of existing LLM trading agents by introducing a single‑agent framework that dynamically orchestrates market tools, learns transparent decision policies via reinforcement learning, and achieves state‑of‑the‑art performance on key financial metrics across extensive stock‑level experiments.
This article presents three algorithmic challenges: counting pattern occurrences on an M×N canvas, determining the number of leaf nodes in a binary tree represented by an array, and maximizing stock trade profit under constraints on negative returns and total gain, each with input specifications, sample cases, and solution outlines.
A Python enthusiast reproduces a Tsinghua University quantitative trading strategy, swapping K‑Nearest Neighbors for logistic regression, fetches three years of Moutai stock data, engineers features, trains and evaluates the model, and finds logistic regression slightly underperforms the original KNN benchmark.
This article walks through designing and implementing a Java-based stock trading monitoring system, covering strategy overview, architecture with SpringBoot, data collection, notification services, code structure, deployment steps, and sample outputs, enabling readers to build low‑frequency grid and intraday T‑strategies themselves.
This article introduces five open-source stock trading tools—funds, ZVT, QUANTAXIS, StockAnalysisSystem, and match-trade—detailing their authors, star counts, features, and repository links, offering developers practical resources to build or enhance their own trading applications.
