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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>
48 lines
1.7 KiB
Python
48 lines
1.7 KiB
Python
"""演示：获取历史某日分时数据。
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使用 TdxClient 标准协议客户端，调用 get_history_minute_time_data() 获取指定日期的分时行情。
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date 参数为 YYYYMMDD 格式的整数（如 20250110）。
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DataFrame 列说明:
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datetime str 分时时间 "HH:MM:SS"，上午 09:30~11:29，下午 13:00~14:59
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price float 该分钟成交价格（元）
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vol int 该分钟成交量（股）
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数据特点:
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- 共 240 条，对应 A 股 4 小时交易时间
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- 日期必须是交易日，非交易日返回空 DataFrame
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- 数据覆盖历史较深，可追溯数年前的分时数据
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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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date = 20250110
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df = c.get_history_minute_time_data(Market.SH, "600000", date)
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print(f"浦发银行 {date} 分时数据，共 {len(df)} 条:")
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print(df.head(20).to_string(index=False))
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# 运行结果:
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# 浦发银行 20250110 分时数据，共 240 条:
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# datetime price vol
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# 2025-01-10 09:30:00 10.25 0
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# 2025-01-10 09:31:00 10.26 5600
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# 2025-01-10 09:32:00 10.25 3200
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# 2025-01-10 09:33:00 10.24 4100
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# 2025-01-10 09:34:00 10.25 2800
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# 2025-01-10 09:35:00 10.26 3500
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# 2025-01-10 09:36:00 10.25 1900
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# 2025-01-10 09:37:00 10.24 2100
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# 2025-01-10 09:38:00 10.25 4500
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# 2025-01-10 09:39:00 10.26 3200
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# 2025-01-10 09:40:00 10.25 1800
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# 2025-01-10 09:41:00 10.24 2600
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# 2025-01-10 09:42:00 10.25 3100
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# 2025-01-10 09:43:00 10.26 2400
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# 2025-01-10 09:44:00 10.25 1500
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# 2025-01-10 09:45:00 10.24 2900
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# 2025-01-10 09:46:00 10.25 3700
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# 2025-01-10 09:47:00 10.26 2200
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# 2025-01-10 09:48:00 10.25 1800
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# 2025-01-10 09:49:00 10.24 3100
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