# 默认只返回 100 根
df = tf.klines.get("600000.SH", period="1d", as_dataframe=True)
print(len(df))  # 100

# 设置 count 获取更多
df = tf.klines.get("600000.SH", period="1d", count=10000, as_dataframe=True)
print(len(df))  # 取决于该标的可用数据量

# 比例前复权（StockHubX 默认）
df_ratio = tf.klines.get("600000.SH", period="1d", count=1000, adjust="forward", as_dataframe=True)

# 差值前复权（东方财富/同花顺默认，价格与这些软件一致）
df_additive = tf.klines.get("600000.SH", period="1d", count=1000, adjust="forward_additive", as_dataframe=True)

# 不复权
df_raw = tf.klines.get("600000.SH", period="1d", count=1000, adjust="none", as_dataframe=True)

# 比例后复权
df_back = tf.klines.get("600000.SH", period="1d", count=1000, adjust="backward", as_dataframe=True)

# 差值后复权
df_back_add = tf.klines.get("600000.SH", period="1d", count=1000, adjust="backward_additive", as_dataframe=True)

factors = tf.klines.ex_factors(["600000.SH"], as_dataframe=True)
print(factors)

# ❌ 不推荐：逐只调用，100 只标的 = 100 次请求
for symbol in symbols:
    df = tf.klines.get(symbol, period="1d", count=1000, as_dataframe=True)

# ✅ 推荐：批量接口，100 只标的 = 1 次请求
dfs = tf.klines.batch(
    symbols,
    period="1d",
    count=1000,
    as_dataframe=True,
    show_progress=True,
)
# dfs 是 dict[str, DataFrame]
print(dfs["600000.SH"].tail())

# ✅ 批量获取日内分钟 K 线
dfs = tf.klines.intraday_batch(symbols, as_dataframe=True, show_progress=True)

# 列出所有标的池
universes = tf.universes.list()
for u in universes:
    print(f"{u['id']}: {u['name']} ({u['symbol_count']} 只)")

# 获取某个标的池的全部标的
a_shares = tf.universes.get("CN_Equity_A")
print(f"A 股共 {len(a_shares['symbols'])} 只")