Agent skill

Miniqmt

by LeoYeAI in LeoYeAI/openclaw-master-skills

miniQMT 极简量化交易终端 - 支持外接Python获取行情数据和程序化交易,基于xtquant SDK. An agent skill from LeoYeAI/openclaw-master-skills.

MITAuto-check passedData & Analytics

Install Miniqmt

skills CLI
$ npx skills add LeoYeAI/openclaw-master-skills --skill miniqmt -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install LeoYeAI/openclaw-master-skills miniqmt --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/LeoYeAI/openclaw-master-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/miniqmt .claude/skills/miniqmt && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
miniqmt
GitHub stars
2.2k
Token cost
~4k tokens
SKILL.md length
467 words
Files
10
Skills in repo
1,235
Repo updated
First seen
Licence
MIT

At a glance

miniQMT 极简量化交易终端 - 支持外接Python获取行情数据和程序化交易,基于xtquant SDK. An agent skill from LeoYeAI/openclaw-master-skills.

  • Works in 7 steps: 启动 miniQMT → 安装 xtquant → 使用Python连接行情数据 → …
  • Data & Analytics work in your project
  • SKILL.md covers miniQMT 概述, 架构, 如何获取 miniQMT and 使用流程, plus 11 more sections
  • Runs Python scripts from its folder; calls pip

What it does

Miniqmt is an agent skill from LeoYeAI/openclaw-master-skills. miniQMT 极简量化交易终端 - 支持外接Python获取行情数据和程序化交易,基于xtquant SDK。

Its SKILL.md is about 4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 10 other files (for example `QUICK_REFERENCE.md`, `README.md` and `_meta.json`).

It sits in Data & Analytics. It works with Python. The repository describes itself as: 🧠 Curated collection of 1209+ best OpenClaw skills — weekly updated by MyClaw.ai. The licence is MIT.

When your agent uses it

  • Data & Analytics work in your project

Example prompts

  • “/miniqmt”

Requirements

  • Python 3

Workflow steps

7 steps, taken from the step headings in SKILL.md.

  1. 启动 miniQMT
  2. 安装 xtquant
  3. 使用Python连接行情数据
  4. 使用Python连接交易服务
  5. 数据校验与错误处理
  6. 多步组合分析
  7. 构建动态监控与日志

What it can do on your machine

Read from SKILL.md and the folder at commit e5199b5. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • pip

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    Links to these hosts (documentation or services it may open):

    • dict.thinktrader.net
    • space.bilibili.com

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Miniqmt loads about 4k tokens when it runs. Until then it costs about 16 tokens; SKILL.md has 467 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~16
When it runs · the whole SKILL.md, loaded when a task matches
~4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from LeoYeAI/openclaw-master-skills at commit e5199b5, republished under its MIT licence (© LeoYeAI). 467 words, ~4,046 tokens.

Download SKILL.mdSave it as .claude/skills/miniqmt/SKILL.md (or your agent's skills folder). This skill also uses 9 other files; get the full folder from GitHub.
name
miniqmt
description
miniQMT 极简量化交易终端 - 支持外接Python获取行情数据和程序化交易,基于xtquant SDK。
version
1.2.0
homepage
http://dict.thinktrader.net/nativeApi/start_now.html

miniQMT(迅投极简量化交易终端)

miniQMT 是迅投科技开发的轻量级量化交易终端,专为外接Python设计。它作为本地Windows服务运行,通过 XtQuant Python SDK(xtdata + xttrade)提供行情数据和交易功能。

⚠️ 需要券商开通miniQMT权限。联系您的证券公司开通。多家国内券商支持(国金、华鑫、中泰、东方财富、国信、方正等)。

miniQMT 概述

  • 轻量级QMT客户端,在Windows上作为后台服务运行
  • 为外部Python程序提供行情数据服务 + 交易服务
  • Python脚本通过 xtquant SDK经本地TCP连接(xtdata获取行情,xttrade执行交易)
  • 支持品种:A股、ETF、可转债、期货、期权、融资融券
  • 部分券商提供免费的 Level 2数据

架构

Python脚本(任意IDE: VS Code, PyCharm, Jupyter等)
    ↓ xtquant SDK(pip install xtquant)
    ├── xtdata  ──TCP──→ miniQMT(行情数据服务)
    └── xttrade ──TCP──→ miniQMT(交易服务)
                              ↓
                    券商交易系统

如何获取 miniQMT

  1. 在支持QMT的券商开立证券账户
  2. 申请miniQMT权限(部分券商要求最低资产,如5万-10万元)
  3. 从券商处下载安装QMT客户端
  4. 以miniQMT模式(极简模式)启动并登录

使用流程

1. 启动 miniQMT

以极简模式启动QMT客户端并登录。miniQMT界面非常简洁——只有一个登录窗口。

2. 安装 xtquant
bash
pip install xtquant
3. 使用Python连接行情数据
python
from xtquant import xtdata

# 连接本地miniQMT行情数据服务
xtdata.connect()

# 下载历史数据(首次访问前必须下载)
xtdata.download_history_data('000001.SZ', '1d', start_time='20240101', end_time='20240630')

# 获取K线数据(返回以股票代码为键的DataFrame字典)
data = xtdata.get_market_data_ex(
    [], ['000001.SZ'], period='1d',
    start_time='20240101', end_time='20240630',
    dividend_type='front'  # 前复权
)
print(data['000001.SZ'].tail())
4. 使用Python连接交易服务
python
from xtquant import xtconstant
from xtquant.xttrader import XtQuantTrader, XtQuantTraderCallback
from xtquant.xttype import StockAccount

# path必须指向QMT安装目录下的userdata_mini文件夹
path = r'D:\券商QMT\userdata_mini'
# session_id对每个策略/脚本必须唯一
session_id = 123456
xt_trader = XtQuantTrader(path, session_id)

# 注册回调接收实时推送通知
class MyCallback(XtQuantTraderCallback):
    def on_disconnected(self):
        print('已断开连接 — 需要重新连接')
    def on_stock_order(self, order):
        print(f'Order update: {order.stock_code} status={order.order_status} msg={order.status_msg}')
    def on_stock_trade(self, trade):
        print(f'Trade filled: {trade.stock_code} {trade.traded_volume}@{trade.traded_price}')
    def on_order_error(self, order_error):
        print(f'Order error: {order_error.error_msg}')

xt_trader.register_callback(MyCallback())
xt_trader.start()
connect_result = xt_trader.connect()  # 收益率 0 on success, non-zero on failure

account = StockAccount('your_account')
xt_trader.subscribe(account)  # 订阅账户推送通知

# 下买入单
order_id = xt_trader.order_stock(
    account, '000001.SZ', xtconstant.STOCK_BUY, 100,
    xtconstant.FIX_PRICE, 11.50, 'my_strategy', 'test_order'
)
# order_id > 0 表示成功,-1 表示失败

miniQMT 与完整版 QMT 对比

特性miniQMTQMT(完整版)
Python外接Python(任意版本)内置Python(版本受限)
IDE任意(VS Code, PyCharm, Jupyter等)仅内置编辑器
第三方库所有pip包(pandas, numpy等)仅内置库
界面极简(仅登录窗口)完整交易UI + 图表
行情数据通过xtdata API内置 + xtdata API
交易通过xttrade API内置 + xttrade API
资源占用轻量(~50 MB内存)较重(完整GUI,~500 MB+)
调试完整IDE调试支持有限
使用场景自动化策略、外部集成可视化分析 + 手动交易
连接方式一次性连接,无自动重连持久连接

数据能力(通过xtdata)

类别详情
K-linetick, 1m, 5m, 15m, 30m, 1h, 1d, 1w, 1mon — supports adjustment (forward / backward / proportional)
TickReal-time tick data with 5-level bid/ask, volume, turnover, trade count
Level 2l2quote (real-time snapshot), l2order (order-by-order), l2transaction (trade-by-trade), l2quoteaux (aggregate buy/sell), l2orderqueue (order queue), l2thousand (1000-level order book), fullspeedorderbook (full-speed 20-level)
FinancialsBalance sheet, income statement, cash flow statement, per-share metrics, share structure, top 10 shareholders / free-float holders, shareholder count
ReferenceTrading calendar, holidays, sector lists, index constituents & weights, ex-dividend data, contract info
Real-timeSingle-stock subscription (subscribe_quote), market-wide push (subscribe_whole_quote)
SpecialConvertible bond info, IPO subscription data, ETF creation/redemption lists, announcements & news, consecutive limit-up tracking, snapshot indicators (volume ratio / price velocity), high-frequency IOPV
数据访问模式
download_history_data() → get_market_data_ex()  # Historical data: download to local cache first, then read from cache
subscribe_quote()       → callback               # Real-time data: subscribe and receive via callback
get_full_tick()                                   # Snapshot data: get latest tick for the entire market

交易能力(通过xttrade)

类别操作
StocksBuy/sell (sync and async), limit/market/best price orders
ETFBuy/sell, creation/redemption
Convertible bondsBuy/sell
FuturesOpen long/close long/open short/close short
OptionsBuy/sell open/close, covered open/close, exercise, lock/unlock
Margin tradingMargin buy, short sell, buy to cover, direct return, sell to repay, direct repayment, special margin/short
IPONew share/bond subscription, query subscription quota
CancelCancel by order_id or broker contract number (sync and async)
QueryAssets, orders, trades, positions, futures position summary
Credit queryCredit assets, liability contracts, margin-eligible securities, available-to-short data, collateral
Bank-broker transferBank to securities, securities to bank (sync and async)
Smart algorithmsVWAP and other algorithmic execution
Securities lendingQuery available securities, apply for lending, manage contracts
Show full SKILL.md (146 more words)Show less
账户类型
python
StockAccount('id')            # 普通股票账户
StockAccount('id', 'CREDIT')  # 信用账户(融资融券)
StockAccount('id', 'FUTURE')  # 期货账户
关键交易回调
回调函数触发时机
on_stock_order(order)Order status change (submitted, partially filled, fully filled, cancelled, rejected)
on_stock_trade(trade)Trade execution report
on_stock_position(position)Position change
on_stock_asset(asset)Asset/fund change
on_order_error(error)Order placement failure
on_cancel_error(error)Order cancellation failure
on_disconnected()Disconnected from miniQMT
订单状态码
值状态
48未报
50已报
54已撤
55部分成交
56已成
57废单

常见券商路径

python
# 国金证券
path = r'D:\国金证券QMT交易端\userdata_mini'
# 华鑫证券
path = r'D:\华鑫证券\userdata_mini'
# 中泰证券
path = r'D:\中泰证券\userdata_mini'
# 东方财富
path = r'D:\东方财富证券QMT交易端\userdata_mini'

股票代码格式

市场示例
上海A股600000.SH
深圳A股000001.SZ
北交所430047.BJ
指数000001.SH(上证综指), 399001.SZ(深证成指)
中金所期货IF2401.IF
上期所期货ag2407.SF
期权10004358.SHO
ETF510300.SH
可转债113050.SH

完整示例:行情数据 + 交易策略

python
from xtquant import xtdata, xtconstant
from xtquant.xttrader import XtQuantTrader, XtQuantTraderCallback
from xtquant.xttype import StockAccount

# === 回调类定义 ===
class MyCallback(XtQuantTraderCallback):
    def on_disconnected(self):
        print('已断开连接')
    def on_stock_trade(self, trade):
        print(f'Trade filled: {trade.stock_code} {trade.traded_volume}@{trade.traded_price}')
    def on_order_error(self, order_error):
        print(f'Error: {order_error.error_msg}')

# === 1. 连接行情数据服务 ===
xtdata.connect()

# === 2. 下载并获取历史数据 ===
stock = '000001.SZ'
xtdata.download_history_data(stock, '1d', start_time='20240101', end_time='20240630')
data = xtdata.get_market_data_ex(
    [], [stock], period='1d',
    start_time='20240101', end_time='20240630',
    dividend_type='front'  # 前复权
)
df = data[stock]

# === 3. 计算简单均线交叉信号 ===
df['ma5'] = df['close'].rolling(5).mean()    # 5日均线
df['ma20'] = df['close'].rolling(20).mean()  # 20日均线
latest = df.iloc[-1]   # 最新K线
prev = df.iloc[-2]     # 前一根K线

# === 4. 连接交易服务 ===
path = r'D:\券商QMT\userdata_mini'
xt_trader = XtQuantTrader(path, 123456)
xt_trader.register_callback(MyCallback())
xt_trader.start()
if xt_trader.connect() != 0:
    print('连接失败!')
    exit()

account = StockAccount('your_account')
xt_trader.subscribe(account)  # 订阅账户推送通知

# === 5. 执行交易信号 ===
if prev['ma5'] <= prev['ma20'] and latest['ma5'] > latest['ma20']:
    # 金叉信号:5日均线上穿20日均线,买入
    order_id = xt_trader.order_stock(
        account, stock, xtconstant.STOCK_BUY, 100,
        xtconstant.LATEST_PRICE, 0, 'ma_cross', 'golden_cross'
    )
    print(f'Golden cross buy — {stock}, order_id={order_id}')
elif prev['ma5'] >= prev['ma20'] and latest['ma5'] < latest['ma20']:
    # 死叉信号:5日均线下穿20日均线,卖出
    order_id = xt_trader.order_stock(
        account, stock, xtconstant.STOCK_SELL, 100,
        xtconstant.LATEST_PRICE, 0, 'ma_cross', 'death_cross'
    )
    print(f'Death cross sell — {stock}, order_id={order_id}')

# === 6. 查询结果 ===
asset = xt_trader.query_stock_asset(account)
print(f'Available cash: {asset.cash}, Total assets: {asset.total_asset}')

positions = xt_trader.query_stock_positions(account)
for pos in positions:
    print(f'{pos.stock_code}: {pos.volume} shares, available={pos.can_use_volume}, cost={pos.open_price}')

完整示例:实时行情监控

python
from xtquant import xtdata
import threading

def on_tick(datas):
    """Tick数据回调函数"""
    for code, tick in datas.items():
        print(f'{code}: latest={tick["lastPrice"]}, volume={tick["volume"]}')

# 连接行情数据服务
xtdata.connect()

# 在单独线程中运行订阅(xtdata.run()会阻塞当前线程)
def run_data():
    xtdata.subscribe_quote('000001.SZ', period='tick', callback=on_tick)
    xtdata.subscribe_quote('600000.SH', period='tick', callback=on_tick)
    xtdata.run()  # 阻塞线程,持续接收数据

t = threading.Thread(target=run_data, daemon=True)
t.start()

# 主线程可以执行交易或其他操作
# ...

使用技巧

  • miniQMT 仅支持Windows — 如果TCP可达,Python脚本可以在同一台或不同机器上运行。
  • Python脚本运行期间,miniQMT必须保持登录状态。
  • connect() 是一次性连接 — 断开后不会自动重连,需要自行实现重连逻辑。
  • session_id 对每个策略必须唯一 — 不同Python脚本必须使用不同的session_id。
  • 实时订阅时,xtdata.run() 会阻塞线程 — 请在单独线程中运行,主线程用于交易。
  • 下载的数据会本地缓存 — 后续读取速度极快。
  • 在推送回调(on_stock_order等)中,使用异步查询方法(如 query_stock_orders_async)避免死锁。或启用 set_relaxed_response_order_enabled(True)。
  • 部分券商提供miniQMT免费Level 2数据 — 请咨询您的券商。
  • 文档:http://dict.thinktrader.net/nativeApi/start_now.html

进阶示例

网格交易策略
python
from xtquant import xtdata, xtconstant
from xtquant.xttrader import XtQuantTrader, XtQuantTraderCallback
from xtquant.xttype import StockAccount
import threading

class GridCallback(XtQuantTraderCallback):
    def on_stock_trade(self, trade):
        print(f'Trade filled: {trade.stock_code} {trade.traded_volume}@{trade.traded_price}')
    def on_order_error(self, error):
        print(f'Error: {error.error_msg}')

# 初始化交易
path = r'D:\券商QMT\userdata_mini'
xt_trader = XtQuantTrader(path, 100001)
xt_trader.register_callback(GridCallback())
xt_trader.start()
xt_trader.connect()
account = StockAccount('your_account')
xt_trader.subscribe(account)

# 网格参数
stock = '000001.SZ'
grid_base = 11.0       # 基准价格
grid_step = 0.2        # 网格间距
grid_shares = 100      # 每格交易股数
grid_levels = 5        # 上下各5档
last_grid = 0          # 当前网格层级

xtdata.connect()

def on_tick(datas):
    global last_grid
    for code, tick in datas.items():
        price = tick['lastPrice']
        # 计算价格对应的当前网格层级
        current_grid = int((price - grid_base) / grid_step)

        if current_grid < last_grid:
            # 价格下穿网格线,买入
            for _ in range(last_grid - current_grid):
                xt_trader.order_stock(
                    account, code, xtconstant.STOCK_BUY, grid_shares,
                    xtconstant.LATEST_PRICE, 0, 'grid', f'网格买入_level{current_grid}'
                )
            last_grid = current_grid

        elif current_grid > last_grid:
            # 价格上穿网格线,卖出
            for _ in range(current_grid - last_grid):
                xt_trader.order_stock(
                    account, code, xtconstant.STOCK_SELL, grid_shares,
                    xtconstant.LATEST_PRICE, 0, 'grid', f'网格卖出_level{current_grid}'
                )
            last_grid = current_grid

# 启动行情数据订阅
def run_data():
    xtdata.subscribe_quote(stock, period='tick', callback=on_tick)
    xtdata.run()

t = threading.Thread(target=run_data, daemon=True)
t.start()
xt_trader.run_forever()
可转债T+0日内交易
python
from xtquant import xtdata, xtconstant
from xtquant.xttrader import XtQuantTrader, XtQuantTraderCallback
from xtquant.xttype import StockAccount
import threading

class CBCallback(XtQuantTraderCallback):
    def on_stock_trade(self, trade):
        print(f'Trade filled: {trade.stock_code} {trade.traded_volume}@{trade.traded_price}')

path = r'D:\券商QMT\userdata_mini'
xt_trader = XtQuantTrader(path, 100002)
xt_trader.register_callback(CBCallback())
xt_trader.start()
xt_trader.connect()
account = StockAccount('your_account')
xt_trader.subscribe(account)

# 可转债代码(可转债支持T+0交易)
cb_code = '113050.SH'
buy_threshold = -0.5   # 跌幅超过0.5%买入
sell_threshold = 0.5   # 涨幅超过0.5%卖出
position = 0

xtdata.connect()

def on_tick(datas):
    global position
    for code, tick in datas.items():
        price = tick['lastPrice']
        pre_close = tick['lastClose']
        if pre_close == 0:
            continue
        pct_change = (price - pre_close) / pre_close * 100

        # 跌幅达到阈值,买入10手
        if pct_change <= buy_threshold and position == 0:
            xt_trader.order_stock(
                account, code, xtconstant.STOCK_BUY, 10,
                xtconstant.LATEST_PRICE, 0, 'cb_t0', '可转债T0买入'
            )
            position = 10

        # 涨幅达到阈值,卖出
        elif pct_change >= sell_threshold and position > 0:
            xt_trader.order_stock(
                account, code, xtconstant.STOCK_SELL, position,
                xtconstant.LATEST_PRICE, 0, 'cb_t0', '可转债T0卖出'
            )
            position = 0

def run_data():
    xtdata.subscribe_quote(cb_code, period='tick', callback=on_tick)
    xtdata.run()

t = threading.Thread(target=run_data, daemon=True)
t.start()
xt_trader.run_forever()
定时打新申购
python
from xtquant import xtdata, xtconstant
from xtquant.xttrader import XtQuantTrader, XtQuantTraderCallback
from xtquant.xttype import StockAccount
import datetime
import time

class IPOCallback(XtQuantTraderCallback):
    def on_stock_order(self, order):
        print(f'IPO subscription: {order.stock_code} status={order.order_status} {order.status_msg}')

path = r'D:\券商QMT\userdata_mini'
xt_trader = XtQuantTrader(path, 100003)
xt_trader.register_callback(IPOCallback())
xt_trader.start()
xt_trader.connect()
account = StockAccount('your_account')
xt_trader.subscribe(account)

# 查询新股申购额度
limits = xt_trader.query_new_purchase_limit(account)
print(f"Subscription quota: {limits}")

# 查询今日新股数据
ipo_data = xt_trader.query_ipo_data()
if ipo_data:
    for code, info in ipo_data.items():
        print(f"New stock: {code} {info['name']} issue price={info['issuePrice']} max subscription={info['maxPurchaseNum']}")
        # 以最大允许量申购
        max_vol = info['maxPurchaseNum']
        if max_vol > 0:
            order_id = xt_trader.order_stock(
                account, code, xtconstant.STOCK_BUY, max_vol,
                xtconstant.FIX_PRICE, info['issuePrice'], 'ipo', '新股申购'
            )
            print(f"  Subscription submitted: order_id={order_id}")
else:
    print("今日无新股可申购")


🤖 AI Agent 高阶使用指南

对于 AI Agent,在使用该量化/数据工具时应遵循以下高阶策略和最佳实践,以确保任务的高效完成:

1. 数据校验与错误处理

在获取数据或执行操作后,AI 应当主动检查返回的结果格式是否符合预期,以及是否存在缺失值(NaN)或空数据。

  • 示例策略:在通过 API 获取数据框(DataFrame)后,使用 if df.empty: 进行校验;捕获 Exception 以防网络或接口错误导致进程崩溃。
2. 多步组合分析

AI 经常需要进行宏观经济分析或跨市场对比。应善于将当前接口与其他数据源或工具组合使用。

  • 示例策略:先获取板块或指数的宏观数据,再筛选成分股,最后对具体标的进行深入的财务或技术面分析,形成完整的决策链条。
3. 构建动态监控与日志

对于交易和策略类任务,AI 可以定期拉取数据并建立监控机制。

  • 示例策略:使用循环或定时任务检查特定标的的异动(如涨跌停、放量),并在发现满足条件的信号时输出结构化日志或触发预警。

社区与支持

由 大佬量化 维护 — 量化交易教学与策略研发团队。

微信客服: bossquant1 · Bilibili · 搜索 大佬量化 — 微信公众号 / Bilibili / 抖音

© LeoYeAI, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 9 other files in skills/miniqmt of LeoYeAI/openclaw-master-skills.

  • SKILL.md
  • QUICK_REFERENCE.md
  • README.md
  • _meta.json
  • demo_project/README.md
  • demo_project/demo.py
  • metadata.json
  • requirements.txt
  • xtdata.md
  • xttrader.md

Open the folder on GitHubat commit e5199b5

Compare with similar skills

Miniqmt next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

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Excel and CSV Data Analysisbytedance/deer-flow84k4 repos~2.2kAutomated safety check: PassMIT
StatsmodelszLanqing/codex-claude-academic-skills4.7k15 repos~4.9kAutomated safety check: PassBSD-3-Clause
Scientific Figure MakingChenLiu-1996/figures4papers8.3k—~557Automated safety check: PassCustom licence

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Works with

Questions about Miniqmt

What does Miniqmt do?

miniQMT 极简量化交易终端 - 支持外接Python获取行情数据和程序化交易,基于xtquant SDK. An agent skill from LeoYeAI/openclaw-master-skills. Miniqmt is an agent skill from LeoYeAI/openclaw-master-skills.

When should I use Miniqmt?

Miniqmt fits situations like: data & Analytics work in your project.

How do I install Miniqmt in Claude Code?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill miniqmt -a claude-code`. Or copy the skill folder (skills/miniqmt in LeoYeAI/openclaw-master-skills) into .claude/skills/miniqmt in your project. Claude Code loads it when a task matches its description.

How do I install Miniqmt in Codex?

Run `npx skills add LeoYeAI/openclaw-master-skills --skill miniqmt -a codex`. Or copy the skill folder (skills/miniqmt in LeoYeAI/openclaw-master-skills) into .agents/skills/miniqmt in your project. Codex loads it when a task matches its description.

Can I use Miniqmt in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add LeoYeAI/openclaw-master-skills --skill miniqmt -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/miniqmt, .gemini/skills/miniqmt, .github/skills/miniqmt and .opencode/skills/miniqmt in your project.

What does Miniqmt need to run?

Going by SKILL.md and its folder, Miniqmt needs Python for the scripts in its folder and the command-line tools its instructions call (pip). Our summary lists: Python 3.

Does Miniqmt access the network?

SKILL.md names 2 domains. As links in the text: dict.thinktrader.net and space.bilibili.com. This is read from the text; nothing was executed.

Is Miniqmt safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Miniqmt use?

Miniqmt is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Miniqmt use?

About 4k tokens (SKILL.md is roughly 16k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Miniqmt?

Skills that share tags, products or a category with Miniqmt: Scikit Learn (zLanqing/codex-claude-academic-skills, 4.7k stars), TimesFM Forecasting (google-research/timesfm, 34k stars), Excel and CSV Data Analysis (bytedance/deer-flow, 84k stars) and Statsmodels (zLanqing/codex-claude-academic-skills, 4.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Miniqmt?

LeoYeAI (a GitHub user) maintains it in LeoYeAI/openclaw-master-skills, which has 2,161 GitHub stars. The repository holds 1,235 skills in this directory. The repository was last updated on July 20, 2026.

Source: LeoYeAI/openclaw-master-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.