miniQMT
VerifiedminiQMT — programming languages tool with Python support.
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Content available in Chinese
# 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
- 在支持QMT的券商开立证券账户
- 申请miniQMT权限(部分券商要求最低资产,如5万-10万元)
- 从券商处下载安装QMT客户端
- 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 | miniQMT | QMT(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-line | tick, 1m, 5m, 15m, 30m, 1h, 1d, 1w, 1mon — supports adjustment (forward / backward / proportional) | | Tick | Real-time tick data with 5-level bid/ask, volume, turnover, trade count | | Level 2 | l2quote (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) | | Financials | Balance sheet, income statement, cash flow statement, per-share metrics, share structure, top 10 shareholders / free-float holders, shareholder count | | Reference | Trading calendar, holidays, sector lists, index constituents & weights, ex-dividend data, contract info | | Real-time | Single-stock subscription (`subscribe_quote`), market-wide push (`subscribe_whole_quote`) | | Special | Convertible 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 | |---|---| | Stocks | Buy/sell (sync and async), limit/market/best price orders | | ETF | Buy/sell, creation/redemption | | Convertible bonds | Buy/sell | | Futures | Open long/close long/open short/close short | | Options | Buy/sell open/close, covered open/close, exercise, lock/unlock | | Margin trading | Margin buy, short sell, buy to cover, direct return, sell to repay, direct repayment, special margin/short | | IPO | New share/bond subscription, query subscription quota | | Cancel | Cancel by order_id or broker contract number (sync and async) | | Query | Assets, orders, trades, positions, futures position summary | | Credit query | Credit assets, liability contracts, margin-eligible securities, available-to-short data, collateral | | Bank-broker transfer | Bank to securities, securities to bank (sync and async) | | Smart algorithms | VWAP and other algorithmic execution | | Securities lending | Query available securities, apply for lending, manage contracts |
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` | | ETF | `510300.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") ```
---
社区与支持
由 大佬量化 (BossQuant) 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
Use Cases
- Interface with miniQMT quantitative trading platform via Python
- Execute trades and manage portfolios through miniQMT API
- Build automated trading strategies using the miniQMT framework
- Monitor market data and positions through miniQMT connections
- Develop and backtest quantitative trading algorithms with miniQMT
Pros & Cons
Pros
- +Compatible with multiple platforms including claude-code, openclaw
- +Well-documented with detailed usage instructions and examples
- +Open source with permissive licensing
Cons
- -Documentation primarily in Chinese — may need translation for English users
- -No built-in analytics or usage metrics dashboard
FAQ
What does miniQMT do?
What platforms support miniQMT?
What are the use cases for miniQMT?
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