Ai Quant Trader

基于AKShare的AI量化交易助手，实现策略生成、参数优化、自动交易、止盈止损及实时选股模拟。

Security Scan

The code and docs largely match an AI-driven simulated quant-trading skill, but there are a few incoherent or risky bits (a hard-coded Windows Admin OpenClaw install script, filesystem writes that can overwrite user skills, and diagnostic code that prints environment info) that you should review before installing or running.

Purpose & Capability

The skill's name, description, SKILL.md, and Python modules (data_provider, strategy_gen, broker, auto_trader, risk_manager, stock_screener) are consistent with an AKShare-based simulated quant trading assistant and require akshare/pandas/numpy as declared. However, the presence of register_with_openclaw.py (which copies files into a user's OpenClaw workspace) implies installer-like behavior beyond a pure 'instruction-only' skill; that is plausible for an OpenClaw integration but is more intrusive than the simple description suggests.

Instruction Scope

SKILL.md instructions are focused on simulation, strategy generation, and using AKShare; it asks the user to pip install akshare/pandas/numpy. The runtime code will create and read files under a user_data directory and caches. check_env.py prints system paths and working directory (revealing environment info) — harmless if run locally but not needed for core functionality. SKILL.md does not explicitly instruct running the register script, but that script exists and would alter user skill directories if executed.

Install Mechanism

There is no declared automated install spec, but the repository includes register_with_openclaw.py which, if run, copies files into a hard-coded Windows path (C:/Users/Administrator/.openclaw/workspace/skills). This is an ad-hoc install mechanism that will write files to the host, create/overwrite skill directories, and create an enabled skill_config.json. The copy/backup behavior is potentially destructive if paths are wrong or if you run it as an administrator.

Credentials

The registry metadata declares no required env vars or credentials (appropriate for a simulated trader). The code uses AKShare which performs external network requests for market data (expected). check_env.py prints environment details (python path, working dir, sys.path) which is not required for normal operation and could leak environment information if you share its output.

Persistence & Privilege

always is false, and the skill requires no cloud credentials — good. But register_with_openclaw.py will create files inside the user's OpenClaw skills directory and write an enabled skill_config.json (registered_at, dependencies, enabled=true). Running that script grants the skill persistent presence in the user's OpenClaw installation and may overwrite or move existing skill directories (it moves existing target to a backup name). This file-system-level persistence is significant and should be executed only after review.

What to consider before installing

What to consider before installing or running this skill: - Functional fit: The code and docs align with an AKShare-based simulated quant-trader; akshare/pandas/numpy are reasonable dependencies for that purpose. - Review the register script before running: register_with_openclaw.py copies files into a hard-coded Windows Administrator OpenClaw path (C:/Users/Administrator/.openclaw/...). If you run it it will create/overwrite skill files and write an enabled skill_config.json. Edit the script to point to the correct OpenClaw workspace for your account (use environment variables like %USERPROFILE% or HOME) or avoid running it and install manually. - Backup first: If you plan to use the register script, back up your existing OpenClaw skills directory. The script can move/overwrite existing skill directories (it does a move to a backup name but that can still change state). - Run in a sandbox/test account: Because the package creates files and caches under the skill directory (user_data, data_cache, etc.), test it in an isolated environment or throwaway VM/container before running on your main workstation. - Inspect outputs of check_env.py: It prints Python executable, working dir, and sys.path — useful for debugging but avoid sharing its output publicly as it reveals environment details. - Network behavior: The skill uses AKShare to fetch market data (expected). If you must avoid external network calls, do not run modules that call akshare. - No secrets requested: The skill does not request API keys or other credentials, which is coherent for a simulated system. However, if you later link a real broker, that would change the risk profile — treat broker integrations as sensitive. - If you are unsure: Ask the skill author for a non-admin installation method or an OpenClaw-market-style packaging option. If you want, I can point out the exact lines in register_with_openclaw.py to change to make it safer (use relative paths, use current user profile, avoid auto-enabling).

AI量化交易助手技能

技能概述

基于AKShare的AI驱动量化交易模拟系统，支持AI策略生成、自动交易、风险控制和实时选股。

核心功能

1. 模拟实时交易（支持蜻蜓点金手续费规则）
2. AI大模型生成交易策略
3. AI优化策略参数
4. 自动策略执行
5. 止盈止损管理
6. 策略胜率统计
7. AI实时选股

安装依赖

pip install akshare pandas numpy

简化版使用（无需安装依赖）

如果Python环境有问题，可以使用简化对话版：

1. 直接通过OpenClaw对话使用所有功能
2. AI会模拟执行筛选、分析、策略生成
3. 查看 SIMPLIFIED_SKILL.md 了解详情

命令列表

交易命令

• /交易 设置本金 [金额] - 设置初始资金（默认100000）
• /交易 买入 [股票代码] [数量] - 买入股票
• /交易 卖出 [股票代码] [数量] - 卖出股票
• /持仓 - 查看当前持仓

策略命令

• /策略 生成 [描述] - AI生成交易策略
• /策略 优化 [策略名] - 优化策略参数
• /策略 列表 - 查看所有策略
• /策略 回测 [策略名] - 回测策略表现

自动交易

• /自动 启用 [策略名] [股票代码] - 启用自动交易
• /自动 暂停 [股票代码] - 暂停自动交易
• /自动 列表 - 查看自动交易状态

风控命令

• /风控 设置止损 [股票代码] [百分比] - 设置止损（如5%）
• /风控 设置止盈 [股票代码] [百分比] - 设置止盈（如10%）
• /风控 移动止盈 [股票代码] [回撤百分比] - 设置移动止盈

选股统计

• /选股 今日推荐 - AI推荐今日股票
• /选股 筛选 [条件] - 按条件筛选股票
• /统计 [策略名] - 查看策略统计

文件结构

ai-quant-trader/
├── SKILL.md              # 技能文档
├── main.py              # 主程序
├── broker.py           # 模拟交易引擎
├── strategy_gen.py     # AI策略生成器
├── auto_trader.py      # 自动交易执行
├── risk_manager.py     # 风控管理
├── stock_screener.py   # 选股引擎
└── data_provider.py    # 数据提供

使用示例

用户：/交易 设置本金 100000
AI：✅ 已设置初始资金：100,000元

用户：/选股 今日推荐
AI：📈 今日AI推荐：
    1. 600519 贵州茅台 (MACD金叉，趋势向上)
    2. 000858 五粮液 (RSI超卖反弹)

用户：/策略 生成 "一个基于MACD金叉的短线策略"
AI：🤖 已生成策略"MACD短线策略"，代码已保存

用户：/自动 启用 MACD短线策略 600519
AI：✅ 已为600519启用MACD短线策略，开始自动监控

注意事项

1. 所有交易均为模拟，不涉及真实资金
2. AI策略仅供参考，投资有风险
3. 数据来自AKShare，可能有延迟
4. 建议先小额测试，熟悉系统后再增加资金