Ths Advanced Analysis

用户："帮我分析一下宁德时代"
Claude："好的，请问你主要想看哪个方向？
  1. 今日实时行情 + 资金流向
  2. 分钟K线（盘中走势）
  3. 近期日K线趋势
  4. 和比亚迪、亿纬锂能等对比
  5. 用问财筛选相关概念股"

pip install --upgrade thsdk

from thsdk import THS

with THS() as ths:
    ...

with THS() as ths:
    resp = ths.search_symbols("宁德时代")
    # resp.data → [{'THSCODE': 'USZA300750', 'Name': '宁德时代', 'Code': '300750', 'MarketDisplay': '深A'}, ...]

from datetime import datetime
from zoneinfo import ZoneInfo

tz = ZoneInfo('Asia/Shanghai')

with THS() as ths:
    # 方式1：按条数（最常用）
    resp = ths.klines("USZA300750", interval="5m", count=78)

    # 方式2：按时间范围
    resp = ths.klines(
        "USZA300750",
        interval="day",
        start_time=datetime(2025, 1, 1, tzinfo=tz),
        end_time=datetime(2025, 3, 1, tzinfo=tz)
    )

    # 复权：前复权 forward / 后复权 backward / 不复权 ""（默认）
    resp = ths.klines("USHA600519", interval="day", count=250, adjust="forward")

    df = resp.df  # 列: 时间, 开盘价, 最高价, 最低价, 收盘价, 成交量 等
    # 分钟K线的"时间"已自动转为 datetime；日K的"时间"为 datetime(YYYYMMDD)

with THS() as ths:
    resp = ths.klines("USZA300750", interval="5m", count=78)
    df = resp.df
    df['ma5'] = df['收盘价'].rolling(5).mean()
    df['ma20'] = df['收盘价'].rolling(20).mean()
    # 成交量异动（超均量2倍）
    df['vol_avg'] = df['成交量'].rolling(20).mean()
    df['vol_spike'] = df['成交量'] > df['vol_avg'] * 2
    # 支撑/压力位
    support = df['最低价'].tail(20).min()
    resistance = df['最高价'].tail(20).max()

with THS() as ths:
    resp = ths.intraday_data("USZA300750")
    df = resp.df  # 列: 时间(datetime), 价格, 成交量, 均价 等

with THS() as ths:
    resp = ths.min_snapshot("USZA300750", date="20250301")
    df = resp.df

with THS() as ths:
    resp = ths.depth("USZA300750")              # 单只
    resp = ths.depth(["USZA300750", "USHA600519"])  # 多只
    df = resp.df  # 含 买1~5价/量, 卖1~5价/量

with THS() as ths:
    resp = ths.tick_level1("USZA300750")
    df = resp.df

with THS() as ths:
    resp = ths.tick_super_level1("USZA300750")                      # 实时
    resp = ths.tick_super_level1("USZA300750", date="20250301")     # 历史（近一年）
    df = resp.df

with THS() as ths:
    resp = ths.big_order_flow("USZA300750")
    df = resp.df
    # 含字段：主动买入特大单量/金额/笔数、主动卖出特大单量/金额/笔数、
    #         主动买入大单量/金额/笔数、资金流入/流出 等

with THS() as ths:
    resp = ths.call_auction_anomaly("USHA")   # 沪市
    resp = ths.call_auction_anomaly("USZA")   # 深市
    df = resp.df
    # 异动类型1 已自动映射中文：
    # 涨停试盘 / 跌停试盘 / 涨停撤单 / 竞价抢筹 / 竞价砸盘
    # 大幅高开 / 大幅低开 / 急速上涨 / 急速下跌
    # 买一剩余大 / 卖一剩余大 / 大买单试盘 / 大卖单试盘

with THS() as ths:
    resp = ths.call_auction("USZA300750")
    df = resp.df

with THS() as ths:
    resp = ths.ths_industry()   # 同花顺行业（含 URFI 前缀的 link_code）
    df = resp.df  # 含板块名称、代码(link_code)、涨幅、成交量、上涨/下跌家数 等

with THS() as ths:
    resp = ths.ths_concept()
    df = resp.df  # 含概念名称、link_code、涨幅、领涨股 等

with THS() as ths:
    # 先获取行业/概念列表，找到 link_code（格式 URFIXXXXXX）
    industry_resp = ths.ths_industry()
    target_row = [r for r in industry_resp.data if '新能源' in str(r.get('名称', ''))][0]
    link_code = target_row.get('代码') or target_row.get('link_code')

    resp = ths.block_constituents(link_code)
    df = resp.df  # 含成分股代码、名称等

with THS() as ths:
    # query_key: "基础数据"（涨幅/成交/市值）或 "扩展"（涨速/主力净流入）
    resp = ths.market_data_block("URFI881273", "基础数据")
    df = resp.df
    # 含: 价格, 涨幅, 成交量, 板块总市值, 板块流通市值, 上涨家数, 下跌家数, 领涨股

with THS() as ths:
    # 单只
    resp = ths.market_data_index("USHI000001", "基础数据")
    # 多只（必须同市场：同为 USHI 或同为 USZI）
    resp = ths.market_data_index(["USHI000001", "USHI000300", "USHI000905"])
    df = resp.df  # 含: 价格, 涨幅, 涨跌, 成交量, 总金额, 最高价, 最低价

    # 扩展（含量比、振幅等）
    resp = ths.market_data_index("USHI000001", "扩展")

import pandas as pd
from collections import defaultdict
from thsdk import THS

stock_names = ["贵州茅台", "五粮液", "泸州老窖"]

with THS() as ths:
    # Step 1: 批量解析代码
    stock_codes = []
    for name in stock_names:
        resp = ths.search_symbols(name)
        a_shares = [s for s in resp.data
                    if any(m in s.get('MarketDisplay', '') for m in ['沪A', '深A'])]
        if a_shares:
            stock_codes.append({'name': name, 'code': a_shares[0]['THSCODE']})

    # Step 2: 按市场分组（market_data_cn 要求同市场）
    by_market = defaultdict(list)
    for s in stock_codes:
        by_market[s['code'][:4]].append(s)

    # Step 3: 批量获取行情
    rows = []
    for market, stocks in by_market.items():
        codes = [s['code'] for s in stocks]
        resp = ths.market_data_cn(codes, "汇总")
        for i, row in enumerate(resp.data):
            row['股票名称'] = stocks[i]['name']
            rows.append(row)
    quote_df = pd.DataFrame(rows)

    # Step 4: 批量K线
    klines_data = {}
    for s in stock_codes:
        resp = ths.klines(s['code'], interval="day", count=30, adjust="forward")
        klines_data[s['name']] = resp.df

# Step 5: 归一化
for name, df in klines_data.items():
    df['归一化'] = df['收盘价'] / df['收盘价'].iloc[0] * 100

# Step 6: 相关性（量化场景）
returns = pd.DataFrame({
    name: df['收盘价'].pct_change()
    for name, df in klines_data.items()
})
corr_matrix = returns.corr()

with THS() as ths:
    resp = ths.wencai_nlp("连续3日主力净流入，换手率大于5%，非ST")
    df = resp.df  # 每行一只股票，列为查询涉及的字段

"今日涨停，非ST"
"连续2日涨停，非一字板，非ST"
"今日涨停原因类别，涨停封单额，封单量"
"竞价涨幅大于3%，竞价量大于昨日成交量5%，非ST"
"主力净流入由大到小排名前20，非ST"
"近10日区间主力资金流向大于5000万，市值大于100亿，日成交额大于30亿"

"今日申万行业涨跌幅排名"
"今日概念板块涨幅排名前20"
"人工智能概念股，今日涨跌幅，成交额，主力净流入"
"半导体行业股票，涨幅，换手率，市值"
"今日涨幅最大的5个概念板块，涨幅，成分股数量"

"连续3年ROE大于15%，非ST，上市大于3年"
"净利润增长率大于30%，营业收入增长率大于20%，非ST"
"市盈率小于15，股息率大于3%，市净率小于2，非ST"
"市净率小于1，非ST，流通市值大于20亿"          # 破净股
"连续5年分红，股息率大于4%，资产负债率小于60%"

"均线多头排列，MACD金叉，换手率大于3%，非ST"
"5日均线上穿20日均线，成交量放大，涨幅大于1%"
"均线粘合，平台突破，成交量大于5日均量1.5倍"
"仙人指路，非ST，非停牌"
"250日新高，非ST，沪深A，上市超过250天"

# 短线强势选股
"均线多头排列，MACD金叉，DIFF上穿中轴，换手率大于1%且小于10%，30日内有2个交易日涨幅大于4%，非ST"

# 竞价选股（隔日打板）
"昨日非一字板涨停，今日竞价涨幅大于等于0%且小于等于9.9%，今日隔夜买单额小于10亿，非ST，非科创板"

# 连板选股
"最近5日有过涨停，最近5日没有跌停，今日成交量大于5日平均成交量，今日竞价涨幅在2%到3%之间，非北交所非科创板非ST"

"涨停原因归类前20"                       # 今日涨停题材分布
"今日龙虎榜"                             # 龙虎榜数据
"今日大宗交易"                           # 大宗交易
"今日融资融券余额最大的前20只股票"
"近一周北向资金净买入前20"

with THS() as ths:
    resp = ths.wencai_nlp("连续3日主力净流入，换手率大于5%，非ST，市值大于30亿")
    if not resp:
        print(f"查询失败: {resp.error}")
    else:
        df = resp.df
        # 字段名来自查询语句，常见列：股票代码、股票简称、涨幅、成交额、主力净流入 等

        # 补全 ths_code（供后续调用 klines/market_data_cn）
        def to_ths_code(code_str):
            code_str = str(code_str).zfill(6)
            if code_str.startswith('6'):   return f"USHA{code_str}"
            if code_str.startswith(('0','3')): return f"USZA{code_str}"
            if code_str.startswith('8'):   return f"USTM{code_str}"
            return None

        df['ths_code'] = df.get('股票代码', df.get('代码', pd.Series())).apply(to_ths_code)

with THS() as ths:
    resp = ths.corporate_action("USHA600519")
    df = resp.df

with THS() as ths:
    resp = ths.ipo_today()   # 今日上市新股
    resp = ths.ipo_wait()    # 待申购打新

with THS() as ths:
    resp = ths.wencai_nlp("今日申万行业涨跌幅排名")
    resp = ths.wencai_nlp("今日概念板块涨跌幅排名前20")
    resp = ths.wencai_nlp("换手率大于10%且涨幅大于5%的股票")
    df_list = resp.data

with THS() as ths:
    resp = ths.klines("USZA300750", interval="5m", count=60)
    if not resp:                  # resp.success == False
        print(f"调用失败: {resp.error}")
    elif resp.df.empty:
        print("数据为空，可能是非交易时间")
    else:
        df = resp.df