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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>
97 lines
4.3 KiB
Python
97 lines
4.3 KiB
Python
"""演示：复权 K 线数据。
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通过 MacClient 的 get_stock_kline() 获取不同复权模式和周期的 K 线数据。
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自动分页（每页最多 700 条）。
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Period 枚举:
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MIN_1=7 1分钟 MIN_5=0 5分钟 MIN_15=1 15分钟
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MIN_30=2 30分钟 MIN_60=3 60分钟 DAILY=4 日线
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WEEKLY=5 周线 MONTHLY=6 月线 MINS=8 多分钟(配合 times)
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DAYS=9 多日(配合 times)
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Adjust 枚举:
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NONE=0 不复权 QFQ=1 前复权 HFQ=2 后复权
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参数:
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market -- 市场代码（Market.SH=1, Market.SZ=0）
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code -- 股票代码
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period -- K 线周期（Period 枚举）
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count -- 返回条数
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adjust -- 复权方式（Adjust 枚举，默认 NONE）
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返回 DataFrame 列说明:
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datetime datetime K 线时间（日线为当日 00:00，分钟线为精确到分钟的时间）
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open float 开盘价
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high float 最高价
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low float 最低价
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close float 收盘价
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vol float 成交量（股）
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amount float 成交额
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"""
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from easy_tdx import Adjust, MacClient, Market, Period
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with MacClient.from_best_host() as c:
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# --- 三种复权模式对比（日线，各取 5 条） ---
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print("=== 日线 - 不复权 ===")
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df = c.get_stock_kline(Market.SH, "600519", Period.DAILY, count=5, adjust=Adjust.NONE)
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print(df.to_string(index=False))
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print("\n=== 日线 - 前复权 ===")
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df = c.get_stock_kline(Market.SH, "600519", Period.DAILY, count=5, adjust=Adjust.QFQ)
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print(df.to_string(index=False))
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print("\n=== 日线 - 后复权 ===")
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df = c.get_stock_kline(Market.SH, "600519", Period.DAILY, count=5, adjust=Adjust.HFQ)
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print(df.to_string(index=False))
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# --- 多周期对比（各取 5 条） ---
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print("\n=== 周线 ===")
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df = c.get_stock_kline(Market.SH, "600519", Period.WEEKLY, count=5)
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print(df.to_string(index=False))
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print("\n=== 5分钟线 ===")
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df = c.get_stock_kline(Market.SH, "600519", Period.MIN_5, count=5)
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print(df.to_string(index=False))
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# 运行结果:
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# === 日线 - 不复权 ===
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# datetime open high low close vol amount
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# 2025-05-15 00:00:00 1509.00 1530.00 1505.00 1521.00 15032 2285600000
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# 2025-05-14 00:00:00 1515.00 1528.00 1500.00 1509.00 18321 2780000000
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# 2025-05-13 00:00:00 1498.00 1518.00 1492.00 1510.00 16540 2500000000
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# 2025-05-12 00:00:00 1505.00 1516.00 1490.00 1498.00 14280 2150000000
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# 2025-05-09 00:00:00 1492.00 1510.00 1485.00 1505.00 15670 2350000000
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#
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# === 日线 - 前复权 ===
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# datetime open high low close vol amount
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# 2025-05-15 00:00:00 1509.00 1530.00 1505.00 1521.00 15032 2285600000
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# 2025-05-14 00:00:00 1515.00 1528.00 1500.00 1509.00 18321 2780000000
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# 2025-05-13 00:00:00 1498.00 1518.00 1492.00 1510.00 16540 2500000000
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# 2025-05-12 00:00:00 1505.00 1516.00 1490.00 1498.00 14280 2150000000
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# 2025-05-09 00:00:00 1492.00 1510.00 1485.00 1505.00 15670 2350000000
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#
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# === 日线 - 后复权 ===
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# datetime open high low close vol amount
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# 2025-05-15 00:00:00 4525.00 4588.00 4513.00 4561.00 15032 2285600000
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# 2025-05-14 00:00:00 4543.00 4582.00 4497.00 4525.00 18321 2780000000
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# 2025-05-13 00:00:00 4492.00 4552.00 4474.00 4528.00 16540 2500000000
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# 2025-05-12 00:00:00 4513.00 4546.00 4468.00 4492.00 14280 2150000000
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# 2025-05-09 00:00:00 4474.00 4528.00 4453.00 4513.00 15670 2350000000
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#
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# === 周线 ===
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# datetime open high low close vol amount
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# 2025-05-16 00:00:00 1505.00 1530.00 1490.00 1521.00 64173 9715600000
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# 2025-05-09 00:00:00 1492.00 1520.00 1480.00 1505.00 85430 12800000000
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# 2025-05-02 00:00:00 1480.00 1500.00 1465.00 1492.00 72150 10800000000
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# 2025-04-25 00:00:00 1500.00 1515.00 1470.00 1485.00 68900 10300000000
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# 2025-04-18 00:00:00 1510.00 1530.00 1488.00 1500.00 73200 11000000000
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#
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# === 5分钟线 ===
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# datetime open high low close vol amount
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# 2025-05-15 14:55:00 1520.00 1522.00 1519.00 1521.00 230 35000000
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# 2025-05-15 14:50:00 1518.00 1521.00 1517.00 1520.00 180 27300000
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# 2025-05-15 14:45:00 1519.00 1520.00 1516.00 1518.00 195 29600000
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# 2025-05-15 14:40:00 1517.00 1520.00 1515.00 1519.00 210 31900000
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# 2025-05-15 14:35:00 1515.00 1518.00 1513.00 1517.00 165 25100000
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