mirror of
https://ghfast.top/https://github.com/aeroxw/easy_tdx_max.git
synced 2026-09-12 16:54:20 +08:00
feat: add examples (01-08), fix index bars parsing, add ruff hook
- Add example scripts for all API categories (connection, market info, kline, minute, transaction, finance, block, fund flow) - Fix GetIndexBarsCmd: index bar records have 4 extra bytes (advance/ decline counts) that were not consumed, causing pos drift and corrupted dates/volumes for all records after the first - Fix price_limits.py example (SecurityQuote has no name attr) - Fix finance_info.py display (scientific notation -> formatted numbers) - Add PostToolUse ruff hook (scripts/ruff_hook.py) Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
This commit is contained in:
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co-authored by
Claude Opus 4.7
Claude Opus 4.7
parent
ace1099ab0
commit
7fd6e610cf
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"""演示：异步客户端连接与基本用法。"""
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import asyncio
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from xmtdx import AsyncTdxClient, Market, KlineCategory
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async def main():
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# 手动指定服务器
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async with AsyncTdxClient("180.153.18.170") as c:
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count = await c.get_security_count(Market.SH)
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print(f"沪市证券总数: {count}")
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# 自动优选服务器
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async with AsyncTdxClient.from_best_host() as c:
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bars = await c.get_security_bars(
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Market.SH, "600000", KlineCategory.DAY, 0, 5
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)
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for bar in bars:
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print(f"{bar.year}-{bar.month:02d}-{bar.day:02d} "
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f"开:{bar.open:.2f} 高:{bar.high:.2f} "
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f"低:{bar.low:.2f} 收:{bar.close:.2f}")
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asyncio.run(main())
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"""演示：自动从候选服务器中选延迟最低的建立连接。"""
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from xmtdx import TdxClient, Market
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# 方式一：手动指定服务器
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with TdxClient("180.153.18.170") as c:
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print(f"已连接到 {c._host}:{c._port}")
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# 方式二：自动优选最低延迟服务器
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with TdxClient.from_best_host() as c:
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print(f"已自动选择最优服务器: {c._host}:{c._port}")
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count = c.get_security_count(Market.SH)
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print(f"沪市证券总数: {count}")
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"""演示：测量多台通达信服务器延迟并排序。"""
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import pandas as pd
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from xmtdx import TdxClient
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results = TdxClient.ping_all()
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df = pd.DataFrame(results, columns=["服务器", "延迟(s)"])
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df["延迟(ms)"] = df["延迟(s)"] * 1000
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print(df[["服务器", "延迟(ms)"]].to_string(index=False))
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"""演示：获取全市场涨跌统计概况。"""
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import pandas as pd
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from xmtdx import TdxClient
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with TdxClient.from_best_host() as c:
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stat = c.get_market_stat()
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df = pd.DataFrame([{
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"上涨": stat.up_count,
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"下跌": stat.down_count,
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"平盘": stat.neutral_count,
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"停牌(估算)": stat.suspended_count,
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"总计": stat.total_count,
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"成交额(亿)": round(stat.total_amount / 1e8, 2),
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"总市值(万亿)": round(stat.total_market_cap / 1e12, 4),
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"涨停": stat.limit_up_count,
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"跌停": stat.limit_down_count,
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}])
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print(df.T.to_string(header=False))
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"""演示：获取市场证券总数。"""
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from xmtdx import TdxClient, Market
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with TdxClient.from_best_host() as c:
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sh_count = c.get_security_count(Market.SH)
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sz_count = c.get_security_count(Market.SZ)
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print(f"沪市证券总数: {sh_count}")
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print(f"深市证券总数: {sz_count}")
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"""演示：获取市场证券列表（分页）。
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展示 SecurityInfo 全部字段的中文映射与表结构。
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"""
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import pandas as pd
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from xmtdx import TdxClient, Market
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with TdxClient.from_best_host() as c:
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stocks = c.get_security_list(Market.SH, start=0)
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# 表结构说明
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print("=" * 70)
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print("SecurityInfo 表结构（字段中英文对照）")
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print("=" * 70)
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schema = pd.DataFrame([
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{"英文字段": "market", "中文含义": "市场", "类型": "Market", "说明": "SZ=深圳 SH=上海 BJ=北京"},
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{"英文字段": "code", "中文含义": "证券代码", "类型": "str", "说明": "6位代码，如 600000"},
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{"英文字段": "name", "中文含义": "证券名称", "类型": "str", "说明": "GBK 解码"},
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{"英文字段": "volunit", "中文含义": "成交量单位", "类型": "int", "说明": "1手 = volunit 股"},
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{"英文字段": "decimal_point", "中文含义": "价格小数位", "类型": "int", "说明": "通常为 2"},
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{"英文字段": "pre_close", "中文含义": "昨收价", "类型": "float", "说明": "通达信自定义浮点"},
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{"英文字段": "industry_tdx", "中文含义": "通达信行业", "类型": "str", "说明": "需 get_security_list_all()"},
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{"英文字段": "industry_sw", "中文含义": "申万行业", "类型": "str", "说明": "需 get_security_list_all()"},
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])
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print(schema.to_string(index=False))
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# 全字段中文 DataFrame
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print(f"\n沪市第 1 页，共 {len(stocks)} 只:")
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df = pd.DataFrame([{
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"市场": s.market.name,
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"代码": s.code,
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"名称": s.name,
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"成交量单位(股/手)": s.volunit,
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"价格小数位": s.decimal_point,
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"昨收价": s.pre_close,
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"通达信行业": s.industry_tdx or "",
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"申万行业": s.industry_sw or "",
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} for s in stocks])
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print(df.head(20).to_string(index=False))
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"""演示：获取沪深 A 股完整列表（含行业映射）。
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展示 SecurityInfo 全部字段（含扩展行业字段）的中文映射。
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注意：此方法需要拉取 tdxhy.cfg 并遍历全部证券，耗时较长。
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"""
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import logging
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import pandas as pd
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from xmtdx import TdxClient
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# 启用日志，查看分页进度
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logging.basicConfig(level=logging.INFO, format="%(message)s")
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# timeout 调大到 30 秒，避免全量拉取时分页请求超时
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with TdxClient.from_best_host(timeout=30.0) as c:
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all_stocks = c.get_security_list_all()
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# 表结构说明
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print("=" * 70)
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print("SecurityInfo 表结构（字段中英文对照）")
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print("=" * 70)
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schema = pd.DataFrame([
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{"英文字段": "market", "中文含义": "市场", "类型": "Market", "说明": "SZ=深圳 SH=上海 BJ=北京"},
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{"英文字段": "code", "中文含义": "证券代码", "类型": "str", "说明": "6位代码，如 600000"},
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{"英文字段": "name", "中文含义": "证券名称", "类型": "str", "说明": "GBK 解码"},
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{"英文字段": "volunit", "中文含义": "成交量单位", "类型": "int", "说明": "1手 = volunit 股"},
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{"英文字段": "decimal_point", "中文含义": "价格小数位", "类型": "int", "说明": "通常为 2"},
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{"英文字段": "pre_close", "中文含义": "昨收价", "类型": "float", "说明": "通达信自定义浮点"},
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{"英文字段": "industry_tdx", "中文含义": "通达信行业", "类型": "str", "说明": "如 T1001，来自 tdxhy.cfg"},
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{"英文字段": "industry_sw", "中文含义": "申万行业", "类型": "str", "说明": "如 X500102，来自 tdxhy.cfg"},
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])
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print(schema.to_string(index=False))
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# 全字段中文 DataFrame
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print(f"\n沪深 A 股总数: {len(all_stocks)}")
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df = pd.DataFrame([{
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"市场": s.market.name,
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"代码": s.code,
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"名称": s.name,
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"成交量单位(股/手)": s.volunit,
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"价格小数位": s.decimal_point,
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"昨收价": s.pre_close,
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"通达信行业": s.industry_tdx or "",
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"申万行业": s.industry_sw or "",
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} for s in all_stocks])
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print(df.head(20).to_string(index=False))
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"""演示：批量获取实时五档行情。最多支持 80 只/次。"""
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import pandas as pd
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from xmtdx import TdxClient, Market
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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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quotes = c.get_security_quotes(stocks)
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df = pd.DataFrame([{
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"代码": q.code,
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"现价": q.price,
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"涨跌幅%": (q.price - q.pre_close) / q.pre_close * 100,
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"今开": q.open,
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"最高": q.high,
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"最低": q.low,
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"昨收": q.pre_close,
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"成交量(手)": q.vol,
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"成交额": q.amount,
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} for q in quotes])
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print(df.to_string(index=False))
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"""演示：获取指数 K 线数据。
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常用指数代码:
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上证指数: Market.SH, "000001"
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深证成指: Market.SZ, "399001"
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创业板指: Market.SZ, "399006"
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"""
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import pandas as pd
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from xmtdx import TdxClient, Market, KlineCategory
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with TdxClient.from_best_host() as c:
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bars = c.get_index_bars(Market.SH, "999999", KlineCategory.DAY, 0, 10)
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df = pd.DataFrame([{
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"日期": f"{b.year}-{b.month:02d}-{b.day:02d}",
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"开盘": b.open,
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"最高": b.high,
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"最低": b.low,
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"收盘": b.close,
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"成交量": b.vol,
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"成交额": b.amount,
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} for b in reversed(bars)])
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print("上证指数 日K线:")
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fmt = {"成交量": lambda x: f"{x:,.0f}", "成交额": lambda x: f"{x:,.0f}"}
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print(df.to_string(index=False, formatters=fmt))
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"""演示：获取个股 K 线数据。
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K 线类别:
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KlineCategory.MIN_1 / MIN_5 / MIN_15 / MIN_30 / MIN_60
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KlineCategory.DAY / WEEK / MONTH / YEAR
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"""
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import pandas as pd
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from xmtdx import TdxClient, Market, KlineCategory
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with TdxClient.from_best_host() as c:
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bars = c.get_security_bars(Market.SZ, "002176", KlineCategory.DAY, 0, 100)
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df = pd.DataFrame([{
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"日期": f"{b.year}-{b.month:02d}-{b.day:02d}",
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"开盘": b.open,
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"最高": b.high,
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"最低": b.low,
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"收盘": b.close,
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"成交量": b.vol,
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"成交额": b.amount,
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} for b in reversed(bars)])
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print("上证指数 日K线:")
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print(df.to_string(index=False))
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"""演示：获取历史某日分时数据。date 参数为 YYYYMMDD 格式的整数。"""
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import pandas as pd
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from xmtdx import TdxClient, Market
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with TdxClient.from_best_host() as c:
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date = 20250110
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bars = c.get_history_minute_time_data(Market.SH, "600000", date)
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df = pd.DataFrame([{
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"序号": i + 1,
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"价格": bar.price,
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"成交量": bar.vol,
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} for i, bar in enumerate(bars)])
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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.to_string(index=False))
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"""演示：获取今日分时数据（240 条）。"""
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import pandas as pd
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from xmtdx import TdxClient, Market
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with TdxClient.from_best_host() as c:
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bars = c.get_minute_time_data(Market.SH, "600000")
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df = pd.DataFrame([{
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"序号": i + 1,
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"价格": bar.price,
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"成交量": bar.vol,
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} for i, bar in enumerate(bars)])
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print(f"浦发银行今日分时，共 {len(df)} 条:")
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print(df.to_string(index=False))
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"""演示：获取历史逐笔成交数据。date 参数为 YYYYMMDD 格式的整数。"""
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import pandas as pd
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from xmtdx import TdxClient, Market
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with TdxClient.from_best_host() as c:
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date = 20250110
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records = c.get_history_transaction_data(Market.SH, "600000", date, 0, 20)
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df = pd.DataFrame([{
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"时间": f"{r.hour:02d}:{r.minute:02d}",
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"成交价": r.price,
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"成交量": r.vol,
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"方向": "买" if r.buyorsell == 0 else "卖",
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} for r in records])
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print(f"浦发银行 {date} 最近 {len(df)} 笔成交:")
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print(df.to_string(index=False))
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"""演示：获取当日逐笔成交数据。"""
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import pandas as pd
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from xmtdx import TdxClient, Market
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with TdxClient.from_best_host() as c:
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records = c.get_transaction_data(Market.SH, "600000", 0, 20)
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df = pd.DataFrame([{
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"时间": f"{r.hour:02d}:{r.minute:02d}",
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"成交价": r.price,
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"成交量": r.vol,
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"方向": "买" if r.buyorsell == 0 else "卖",
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} for r in records])
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print(f"浦发银行最近 {len(df)} 笔成交:")
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print(df.to_string(index=False))
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@@ -0,0 +1,78 @@
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"""演示：获取公司信息目录与各个分类的详细内容。"""
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import pandas as pd
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from xmtdx import TdxClient, Market
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CODE = "600519"
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NAME = "贵州茅台"
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MARKET = Market.SH
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# 要展示的分类，按需注释/取消注释
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SHOW_CATEGORIES = [
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"最新提示",
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"公司概况",
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"财务分析",
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"股本结构",
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"股东研究",
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"机构持股",
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"分红融资",
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"高管治理",
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"资金动向",
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"资本运作",
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"热点题材",
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"公司公告",
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"公司报道",
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"经营分析",
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"行业分析",
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"研报评级",
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]
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def show_categories(categories):
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"""显示公司信息目录。"""
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df = pd.DataFrame([{
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"目录名": cat.name,
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"文件名": cat.filename,
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"起始偏移": cat.start,
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"内容长度": cat.length,
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} for cat in categories])
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print(f"{NAME} 公司信息目录:")
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print(df.to_string(index=False))
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def show_category_content(client, categories, category_name, max_chars=500):
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"""获取并展示指定分类的内容。"""
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cat = next((c for c in categories if c.name == category_name), None)
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if not cat:
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print(f" 未找到分类: {category_name}")
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return
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content = client.get_company_info_content(
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MARKET, CODE, cat.filename, cat.start, cat.length
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)
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text = content.strip()
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if len(text) > max_chars:
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text = text[:max_chars] + f"\n... (共 {len(content.strip())} 字，仅显示前 {max_chars} 字)"
|
||||
print(f"\n{'='*60}")
|
||||
print(f"【{cat.name}】 (共 {cat.length} 字节)")
|
||||
print(f"{'='*60}")
|
||||
print(text)
|
||||
|
||||
|
||||
def show_all_categories(client, categories):
|
||||
"""依次展示所有 SHOW_CATEGORIES 中列出的分类内容。"""
|
||||
for name in SHOW_CATEGORIES:
|
||||
show_category_content(client, categories, name)
|
||||
|
||||
|
||||
with TdxClient.from_best_host() as c:
|
||||
categories = c.get_company_info_category(MARKET, CODE)
|
||||
|
||||
# 1. 显示目录
|
||||
show_categories(categories)
|
||||
|
||||
# 2. 显示所有分类内容（每个分类默认只显示前500字）
|
||||
show_all_categories(c, categories)
|
||||
|
||||
# 3. 也可以单独获取某个分类的完整内容，例如：
|
||||
# show_category_content(c, categories, "公司概况", max_chars=99999)
|
||||
@@ -0,0 +1,21 @@
|
||||
"""演示：获取最新财务数据。"""
|
||||
|
||||
import pandas as pd
|
||||
from xmtdx import TdxClient, Market
|
||||
|
||||
with TdxClient.from_best_host() as c:
|
||||
info = c.get_finance_info(Market.SH, "600519")
|
||||
df = pd.DataFrame([
|
||||
{"项目": "总股本(万股)", "数值": info.zong_guben},
|
||||
{"项目": "流通股本(万股)", "数值": info.liutong_guben},
|
||||
{"项目": "每股净资产", "数值": info.meigujing_zichan},
|
||||
{"项目": "净利润(元)", "数值": info.jing_lirun},
|
||||
{"项目": "主营收入(元)", "数值": info.zhuying_shouru},
|
||||
{"项目": "主营利润(元)", "数值": info.zhuying_lirun},
|
||||
{"项目": "净资产(元)", "数值": info.jing_zichan},
|
||||
{"项目": "总资产(元)", "数值": info.zong_zichan},
|
||||
{"项目": "股东人数", "数值": info.gudong_renshu},
|
||||
{"项目": "上市日期", "数值": info.ipo_date},
|
||||
])
|
||||
print("贵州茅台 最新财务数据:")
|
||||
print(df.to_string(index=False, formatters={"数值": lambda x: f"{x:,.0f}"}))
|
||||
@@ -0,0 +1,23 @@
|
||||
"""演示：计算个股涨跌停价格。"""
|
||||
|
||||
import pandas as pd
|
||||
from xmtdx import TdxClient, Market
|
||||
|
||||
CODE = "600519"
|
||||
NAME = "贵州茅台"
|
||||
|
||||
with TdxClient.from_best_host() as c:
|
||||
quotes = c.get_security_quotes([(Market.SH, CODE)])
|
||||
if quotes:
|
||||
q = quotes[0]
|
||||
limit_up, limit_down = c.get_price_limits(
|
||||
Market.SH, CODE, NAME, q.pre_close
|
||||
)
|
||||
df = pd.DataFrame([{
|
||||
"代码": CODE,
|
||||
"名称": NAME,
|
||||
"昨收": q.pre_close,
|
||||
"涨停价": limit_up,
|
||||
"跌停价": limit_down,
|
||||
}])
|
||||
print(df.to_string(index=False))
|
||||
@@ -0,0 +1,17 @@
|
||||
"""演示：获取除权除息历史记录。"""
|
||||
|
||||
import pandas as pd
|
||||
from xmtdx import TdxClient, Market, XDXR_CATEGORY_NAMES
|
||||
|
||||
with TdxClient.from_best_host() as c:
|
||||
records = c.get_xdxr_info(Market.SH, "600519")
|
||||
df = pd.DataFrame([{
|
||||
"日期": f"{r.year}-{r.month:02d}-{r.day:02d}",
|
||||
"类型": XDXR_CATEGORY_NAMES.get(r.category, f"未知({r.category})"),
|
||||
"每股分红(元)": r.fenhong,
|
||||
"送转股比例": r.songzhuangu,
|
||||
"配股价": r.peigujia,
|
||||
"配股比例": r.peigu,
|
||||
} for r in records])
|
||||
print(f"贵州茅台 除权除息记录，共 {len(df)} 条:")
|
||||
print(df.tail(10).to_string(index=False))
|
||||
@@ -0,0 +1,21 @@
|
||||
"""演示：获取板块信息（行业、概念、风格）。
|
||||
|
||||
常用板块文件:
|
||||
'block_zs.dat' - 行业/指数板块
|
||||
'block_gn.dat' - 概念板块
|
||||
'block_fg.dat' - 风格板块
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
from xmtdx import TdxClient
|
||||
|
||||
with TdxClient.from_best_host() as c:
|
||||
blocks = c.get_block_info("block_gn.dat")
|
||||
df = pd.DataFrame([{
|
||||
"板块名称": b.name,
|
||||
"分类": b.category,
|
||||
"成分股数": b.count,
|
||||
"代码(前5)": ", ".join(b.codes[:5]),
|
||||
} for b in blocks])
|
||||
print(f"概念板块，共 {len(df)} 个:")
|
||||
print(df.head(20).to_string(index=False))
|
||||
@@ -0,0 +1,19 @@
|
||||
"""演示：获取个股当日资金流向（基于 L1 逐笔数据统计）。
|
||||
|
||||
资金分为四级: 超大(>100万)、大(20-100万)、中(4-20万)、小(<4万)。
|
||||
"""
|
||||
|
||||
import pandas as pd
|
||||
from xmtdx import TdxClient, Market
|
||||
|
||||
with TdxClient.from_best_host() as c:
|
||||
flow = c.get_fund_flow(Market.SH, "600519")
|
||||
df = pd.DataFrame([
|
||||
{"级别": "超大单", "流入(亿)": flow.super_in / 1e8, "流出(亿)": flow.super_out / 1e8},
|
||||
{"级别": "大单", "流入(亿)": flow.large_in / 1e8, "流出(亿)": flow.large_out / 1e8},
|
||||
{"级别": "中单", "流入(亿)": flow.medium_in / 1e8, "流出(亿)": flow.medium_out / 1e8},
|
||||
{"级别": "小单", "流入(亿)": flow.small_in / 1e8, "流出(亿)": flow.small_out / 1e8},
|
||||
])
|
||||
df["净流入(亿)"] = df["流入(亿)"] - df["流出(亿)"]
|
||||
print("贵州茅台 当日资金流向:")
|
||||
print(df.to_string(index=False))
|
||||
@@ -0,0 +1,15 @@
|
||||
"""演示：获取个股历史日线资金流向序列。"""
|
||||
|
||||
import pandas as pd
|
||||
from xmtdx import TdxClient, Market
|
||||
|
||||
with TdxClient.from_best_host() as c:
|
||||
flows = c.get_history_fund_flow(Market.SH, "600519", 0, 10)
|
||||
df = pd.DataFrame([{
|
||||
"日期": f"{f.year}-{f.month:02d}-{f.day:02d}",
|
||||
"超大单净流入(亿)": (f.super_in - f.super_out) / 1e8,
|
||||
"大单净流入(亿)": (f.large_in - f.large_out) / 1e8,
|
||||
"主力净流入(亿)": f.main_net_inflow / 1e8,
|
||||
} for f in flows])
|
||||
print(f"贵州茅台 历史资金流向，共 {len(df)} 天:")
|
||||
print(df.to_string(index=False))
|
||||
Reference in New Issue
Block a user