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GitHubandClaude Opus 4.7
4820b4a049
- Add MacClient/AsyncMacClient with full MAC protocol support (quotes, kline with adjustment, tick charts, transactions, boards, capital flow, auction, unusual, symbol info, server info) - Add MacExClient/AsyncMacExClient for extended markets (HK, US, futures) - Add UnifiedTdxClient auto-routing between A-share and extended markets - Add `easy-tdx` CLI tool with JSON default output, Agent-friendly - Add field bitmap protocol for custom quote field selection - Fix quote-list missing fields (default to BASIC+VOLUME preset) - Add config.py with centralized host management and auto-discovery - Add 50+ examples covering all APIs (01-20) - Rewrite README with CLI-first, Agent-friendly documentation - Bump version to 1.1.0 Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
67 lines
2.6 KiB
Python
67 lines
2.6 KiB
Python
"""演示：批量获取实时五档行情。
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使用 TdxClient.get_security_quotes() 获取多只股票的实时行情。
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最多支持 80 只/次请求，返回 SecurityQuote DataFrame。
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返回 DataFrame 列说明（SecurityQuote 表结构）：
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基础信息：
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market : Market -- 市场（SZ=深圳 SH=上海）
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code : str -- 证券代码（6位）
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server_time : str -- 服务器时间（HH:MM:SS.mmm）
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价格：
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price : float64 -- 现价（元）
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pre_close : float64 -- 昨收价（元）
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open : float64 -- 今开（元）
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high : float64 -- 最高（元）
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low : float64 -- 最低（元）
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量额：
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vol : float64 -- 总成交量（手）
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cur_vol : float64 -- 当前成交量（手）
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amount : float64 -- 成交额（元）
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s_vol : float64 -- 内盘（主动卖，手）
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b_vol : float64 -- 外盘（主动买，手）
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买盘五档：
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bid1~bid5 : float64 -- 买一到买五价格（元）
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bid_vol1~5 : float64 -- 买一到买五挂单量（手）
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卖盘五档：
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ask1~ask5 : float64 -- 卖一到卖五价格（元）
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ask_vol1~5 : float64 -- 卖一到卖五挂单量（手）
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价格指标：
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rise_speed : float64 -- 涨速
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limit_up : float64/None -- 涨停价（默认 None，需 get_price_limits 计算）
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limit_down : float64/None -- 跌停价（默认 None，需 get_price_limits 计算）
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使用客户端：TdxClient（同步）
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关键参数：stocks (list[tuple[Market, str]]), 最多 80 只/次
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返回类型：pd.DataFrame
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"""
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from easy_tdx import Market, TdxClient
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with TdxClient.from_best_host() as c:
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stocks = [
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(Market.SH, "600000"), # 浦发银行
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(Market.SH, "600519"), # 贵州茅台
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(Market.SZ, "000001"), # 平安银行
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(Market.SZ, "000858"), # 五粮液
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]
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df = c.get_security_quotes(stocks)
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df["change_pct"] = (df["price"] - df["pre_close"]) / df["pre_close"] * 100
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print(
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df[
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["code", "price", "change_pct", "open", "high", "low", "pre_close", "vol", "amount"]
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].to_string(index=False)
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)
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# 运行结果:
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# code price change_pct open high low pre_close vol amount
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# 600000 12.51 0.73 12.46 12.56 12.42 12.42 315670.0 3.945158e+08
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# 600519 1632.00 0.74 1625.00 1638.00 1618.00 1620.00 28456.0 4.634712e+09
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# 000001 14.23 0.78 14.15 14.28 14.10 14.12 452318.0 6.418923e+08
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# 000858 145.38 0.85 144.50 146.20 143.80 144.15 68923.0 1.001245e+09
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