日内 VWAP 均线
计算当日成交量加权平均价（VWAP），常用于日内交易判断买卖点：from alphafeed import AlphaFeed
af = AlphaFeed(api_key="your-api-key")
df = af.klines.intraday("600519.SH", to_dataframe=True)
df["vwap"] = (df["amount"].cumsum() / (df["volume"].cumsum() * 100)).round(2)
print(df[["trade_time", "close", "volume", "vwap"]].tail(5).to_string(index=False))
trade_time close volume vwap
2026-06-18 14:56:00 1223.50 273 1221.07
2026-06-18 14:57:00 1223.88 261 1221.08
2026-06-18 14:58:00 1223.26 2 1221.08
2026-06-18 14:59:00 1223.26 0 1221.08
2026-06-18 15:00:00 1215.00 1788 1220.89
MA 均线 + MACD 指标
计算均线和 MACD 技术指标：from alphafeed import AlphaFeed
af = AlphaFeed(api_key="your-api-key")
df = af.klines.get("600519.SH", period="1d", count=60, to_dataframe=True)
df["ma5"] = df["close"].rolling(5).mean().round(2)
df["ma20"] = df["close"].rolling(20).mean().round(2)
ema12 = df["close"].ewm(span=12).mean()
ema26 = df["close"].ewm(span=26).mean()
df["dif"] = (ema12 - ema26).round(2)
df["dea"] = df["dif"].ewm(span=9).mean().round(2)
df["macd"] = ((df["dif"] - df["dea"]) * 2).round(2)
print(df[["trade_date", "close", "ma5", "ma20", "dif", "dea", "macd"]].tail(5).to_string(index=False))
trade_date close ma5 ma20 dif dea macd
2026-06-12 1291.91 1273.15 1291.66 -22.42 -25.61 6.38
2026-06-15 1271.10 1274.78 1289.06 -21.83 -24.85 6.04
2026-06-16 1255.67 1274.71 1285.63 -22.33 -24.35 4.04
2026-06-17 1240.00 1267.54 1281.88 -23.71 -24.22 1.02
2026-06-18 1215.00 1254.74 1277.08 -26.49 -24.68 -3.62
涨跌停检测
通过标的信息获取精确涨跌停价（精度 1e-3），再结合五档盘口确认封板状态：from alphafeed import AlphaFeed
af = AlphaFeed(api_key="your-api-key")
# 1. 获取全 A 行情
quotes_df = af.quotes.get(universes=["CN_Stock"], to_dataframe=True)
# 2. 获取标的信息（含今日涨跌停价，SDK 自动分批）
insts = af.instruments.batch(quotes_df["symbol"].tolist())
inst_map = {x["symbol"]: x for x in insts if x.get("ext", {}).get("limit_up") is not None}
# 3. 价格初筛（允许 1e-3 误差）
def match_limit(row, key):
inst = inst_map.get(row["symbol"])
if not inst:
return False
price = inst["ext"].get(key)
return price is not None and abs(row["last_price"] - price) < 1e-3
quotes_df["is_limit_up"] = quotes_df.apply(lambda r: match_limit(r, "limit_up"), axis=1)
quotes_df["is_limit_down"] = quotes_df.apply(lambda r: match_limit(r, "limit_down"), axis=1)
up_candidates = quotes_df[quotes_df["is_limit_up"]]
down_candidates = quotes_df[quotes_df["is_limit_down"]]
# 4. 盘口确认：卖1量为0=涨停封板，买1量为0=跌停封板（SDK 自动分批）
depths_up = af.depth.batch(up_candidates["symbol"].tolist())
confirmed_up = [sym for sym, d in depths_up.items() if d["ask_volumes"][0] == 0]
depths_down = af.depth.batch(down_candidates["symbol"].tolist())
confirmed_down = [sym for sym, d in depths_down.items() if d["bid_volumes"][0] == 0]
up_final = up_candidates[up_candidates["symbol"].isin(confirmed_up)]
down_final = down_candidates[down_candidates["symbol"].isin(confirmed_down)]
print(f"涨停封板: {len(up_final)} 只")
print(up_final[["symbol", "ext.name", "last_price"]].head(5).to_string(index=False))
print(f"\n跌停封板: {len(down_final)} 只")
print(down_final[["symbol", "ext.name", "last_price"]].head(5).to_string(index=False))
涨停封板: 100 只
symbol ext.name last_price
301580.SZ 爱迪特 63.17
002159.SZ 三特索道 14.37
002859.SZ 洁美科技 99.73
000889.SZ 中嘉博创 4.02
603956.SH 威派格 4.98
跌停封板: 34 只
symbol ext.name last_price
002323.SZ *ST雅博 1.30
600539.SH 狮头股份 14.44
002568.SZ 百润股份 16.54
600537.SH *ST亿晶 2.84
601010.SH ST文峰 1.47
批量盘口数据
from alphafeed import AlphaFeed
af = AlphaFeed(api_key="your-api-key")
result = af.depth.batch(["000001.SZ", "600000.SH"])
for sym, depth in result.items():
print(f"--- {sym} ---")
for i in range(5):
bid = f"买{i+1}: {depth['bid_prices'][i]:>8.2f} x {depth['bid_volumes'][i]:<6}"
ask = f"卖{i+1}: {depth['ask_prices'][i]:>8.2f} x {depth['ask_volumes'][i]}"
print(f" {bid} | {ask}")
--- 000001.SZ ---
买1: 10.52 x 19374 | 卖1: 10.53 x 2
买2: 10.51 x 16237 | 卖2: 10.54 x 430
买3: 10.50 x 28122 | 卖3: 10.55 x 75
买4: 10.49 x 4656 | 卖4: 10.56 x 2771
买5: 10.48 x 3764 | 卖5: 10.57 x 2566
--- 600000.SH ---
买1: 9.08 x 4661 | 卖1: 9.09 x 7087
买2: 9.07 x 5576 | 卖2: 9.10 x 1052
买3: 9.06 x 6301 | 卖3: 9.11 x 2108
买4: 9.05 x 10340 | 卖4: 9.12 x 1537
买5: 9.04 x 1074 | 卖5: 9.13 x 3112
多标的日内分时对比
from alphafeed import AlphaFeed
af = AlphaFeed(api_key="your-api-key")
dfs = af.klines.intraday_batch(["600519.SH", "000001.SZ"], to_dataframe=True)
for sym, df in dfs.items():
print(f"{sym} ({df['name'].iloc[0]}): {len(df)} 条分钟线")
600519.SH (贵州茅台): 241 条分钟线
000001.SZ (平安银行): 241 条分钟线