2026-03-19 金融工程 AI 投研应用系列之四： OpenClaw 投研实践——从部署到应用 相关研究报告 报告摘要 OpenClaw 作为开源 AI 智能体框架，正在从对话工具向执行工具 演进。本文从投研应用角度出发，介绍了 OpenClaw 的部署方案、数 据源接入以及投研应用场景实践。 OpenClaw 部署与使用 对比纯本地、WSL2+云端模型、纯云端三类方案的适用场景与优 劣势，并以 WSL2+云端模型为例，完整演示环境搭建、OpenClaw 安 装、大模型配置及飞书集成流程。 介绍了 Tushare、AkShare 等数据 Skill 及其他金融数据库的配置 方法。 OpenClaw 投研应用 持仓监控报告推送：实现自动化数据采集、分析与报告推送。 量化策略回测与优化：通过行业动量+拥挤度轮动策略测试 OpenClaw 的量化回测与参数优化能力。从测试体验来看，当前大模型 自动生成的脚本仍可能存在逻辑错误或数据处理偏差，需人工反复调 试与修正，仍需进一步提升。 前沿因子挖掘：构建因子发现 Agent，实现论文自动抓取与潜在 因子思路提炼，总结因子逻辑、构建方法与评估可实现性。随着大模 型能 ...

Core Viewpoint - The development of AI has significantly enhanced the efficiency of quantitative work, evolving through three stages: initial challenges with data processing, rapid coding assistance, and the introduction of OpenClaw for streamlined strategy construction and execution [1][2][3][4]. Group 1: AI's Impact on Quantitative Research - AI has opened new investment strategy methodologies, including machine learning, while also improving traditional multi-factor and fundamental quantitative frameworks [1]. - The first stage of AI in quantitative research faced challenges due to data hallucinations from large models, making precise data processing difficult [1]. - In the second stage, AI's coding capabilities evolved rapidly, allowing quantitative researchers to quickly generate code and optimize existing code, significantly enhancing work efficiency [2]. - The third stage introduced OpenClaw, which autonomously handles the quantitative work environment and data extraction, potentially allowing users to construct and backtest strategies with minimal coding knowledge [3][4]. Group 2: OpenClaw Deployment and Functionality - OpenClaw can be deployed on cloud servers or local machines, with considerations for hardware capabilities and security [6][7]. - The integration of data APIs into OpenClaw represents a significant efficiency boost, although challenges remain with the quality and cost of data sources [8]. - OpenClaw autonomously installs necessary libraries and configurations for quantitative analysis, streamlining the setup process [9][12]. Group 3: Strategy Construction and Testing - OpenClaw can facilitate simple quantitative strategy testing based on user ideas, such as backtesting a strategy involving stocks that experience consecutive price increases [15][16]. - The performance of various strategies, including those based on consecutive price increases and decreases, has been analyzed, revealing different profitability characteristics [18][22]. - OpenClaw successfully generated a comprehensive factor table for the CSI 500 index, demonstrating its capability in multi-factor quantitative stock selection [30][31]. Group 4: Machine Learning Strategy Development - OpenClaw has been utilized to develop machine learning strategies, such as a GRU model, showcasing its potential for automating complex quantitative tasks [40][44]. - The feature engineering process for the GRU model was effectively executed, resulting in a substantial dataset for training [45]. Group 5: Challenges and Limitations - Despite advancements, OpenClaw still faces issues such as slow response times, misunderstanding of commands, and occasional failures in executing tasks correctly [48][53]. - The need for improved data extraction efficiency and error correction during calculations has been identified as critical for enhancing user experience [39][54].