> ## Documentation Index > Fetch the complete documentation index at: https://docs.alphafeed.org/llms.txt > Use this file to discover all available pages before exploring further. # 快速开始 > 安装和使用 AlphaFeed Python SDK ## 安装 ```bash theme={null} pip install alphafeed ``` 支持 Python 3.9+，推荐 3.10 或更高版本。内置 pandas 和 tqdm 支持。 ## 初始化 ```python theme={null} from alphafeed import AlphaFeed af = AlphaFeed(api_key="your-api-key") ``` 也可以通过环境变量设置： ```bash theme={null} export ALPHAFEED_API_KEY="your-api-key" ``` ```python theme={null} from alphafeed import AlphaFeed af = AlphaFeed() # 自动读取 ALPHAFEED_API_KEY ``` ## 标的代码格式 标的代码统一使用 `{代码}.{交易所后缀}` 格式： | 后缀 | 交易所 | 示例 | | ----- | ------- | ----------- | | `.SH` | 上海证券交易所 | `600519.SH` | | `.SZ` | 深圳证券交易所 | `000001.SZ` | | `.BJ` | 北京证券交易所 | `920047.BJ` | | `.US` | 美股 | `AAPL.US` | | `.HK` | 港股 | `00700.HK` | *** ## 获取日K线 支持周期：`1d`（日）、`1w`（周）、`1M`（月）、`1Q`（季）、`1Y`（年） ```python theme={null} df = af.klines.get("600519.SH", period="1d", count=5, to_dataframe=True) print(df[["symbol", "name", "trade_date", "open", "high", "low", "close", "volume"]]) ``` ``` symbol name trade_date open high low close volume 600519.SH 贵州茅台 2026-06-12 1271.18 1295.00 1265.01 1291.91 50495 600519.SH 贵州茅台 2026-06-15 1292.70 1292.70 1270.10 1271.10 41586 600519.SH 贵州茅台 2026-06-16 1267.01 1267.88 1255.00 1255.67 34970 600519.SH 贵州茅台 2026-06-17 1258.00 1259.77 1238.56 1240.00 44803 600519.SH 贵州茅台 2026-06-18 1235.00 1238.87 1211.22 1215.00 57472 ``` ## 获取分钟K线 支持周期：`1m`（1分钟）、`5m`（5分钟）、`15m`（15分钟）、`30m`（30分钟）、`60m`（60分钟） ```python theme={null} df = af.klines.get("600519.SH", period="5m", count=5, to_dataframe=True) print(df[["symbol", "name", "trade_time", "open", "high", "low", "close", "volume"]]) ``` ``` symbol name trade_time open high low close volume 600519.SH 贵州茅台 2026-06-18 14:40:00 1214.44 1215.82 1212.26 1215.82 1151 600519.SH 贵州茅台 2026-06-18 14:45:00 1215.02 1217.00 1215.00 1216.99 997 600519.SH 贵州茅台 2026-06-18 14:50:00 1216.52 1217.39 1215.99 1217.38 827 600519.SH 贵州茅台 2026-06-18 14:55:00 1217.40 1228.37 1217.40 1222.55 2355 600519.SH 贵州茅台 2026-06-18 15:00:00 1223.02 1223.99 1215.00 1215.00 2324 ``` ## 复权方式 K 线接口支持 `adjust` 参数： | 值 | 说明 | | ------------------- | -------------- | | `forward` | 前复权 - 比例复权（默认） | | `backward` | 后复权 - 比例复权 | | `forward_additive` | 前复权 - 差值复权 | | `backward_additive` | 后复权 - 差值复权 | | `none` | 不复权 | 比例复权使用乘法因子还原价格，适合计算收益率；差值复权使用加减法还原价格，适合观察绝对价差。 ```python theme={null} df = af.klines.get("600519.SH", adjust="none", to_dataframe=True) # 不复权 df = af.klines.get("600519.SH", adjust="forward", to_dataframe=True) # 前复权-比例（默认） df = af.klines.get("600519.SH", adjust="backward", to_dataframe=True) # 后复权-比例 df = af.klines.get("600519.SH", adjust="forward_additive", to_dataframe=True) # 前复权-差值 df = af.klines.get("600519.SH", adjust="backward_additive", to_dataframe=True) # 后复权-差值 ``` ## 时间区间查询 通过 `start_time` 和 `end_time` 指定时间范围（毫秒时间戳），配合 `count` 控制返回条数。 ### 指定日期区间 ```python theme={null} import datetime from alphafeed import AlphaFeed af = AlphaFeed(api_key="your-api-key") start = int(datetime.datetime(2026, 6, 1).timestamp() * 1000) end = int(datetime.datetime(2026, 6, 20).timestamp() * 1000) df = af.klines.get("000001.SZ", period="1d", start_time=start, end_time=end, to_dataframe=True) print(f"2026-06-01 ~ 2026-06-20 共 {len(df)} 个交易日") print(df[["trade_date", "open", "close", "volume"]].tail(5).to_string(index=False)) ``` ``` 2026-06-01 ~ 2026-06-20 共 14 个交易日 trade_date open close volume 2026-06-12 11.00 11.24 2032355 2026-06-15 11.21 11.06 1541305 2026-06-16 11.05 10.94 942708 2026-06-17 10.97 10.78 965828 2026-06-18 10.74 10.52 1426893 ``` ### 获取某日期之前的 N 根 K 线 使用 `end_time` + `count` 组合，获取截止到某个时间点的最近 N 根 K 线： ```python theme={null} import datetime from alphafeed import AlphaFeed af = AlphaFeed(api_key="your-api-key") end = int(datetime.datetime(2026, 6, 15).timestamp() * 1000) df = af.klines.get("600519.SH", period="1d", count=5, end_time=end, to_dataframe=True) print(df[["trade_date", "open", "high", "low", "close", "volume"]].to_string(index=False)) ``` ``` trade_date open high low close volume 2026-06-09 1262.99 1263.00 1252.55 1256.00 27860 2026-06-10 1252.08 1282.00 1250.21 1275.88 39244 2026-06-11 1272.12 1282.88 1266.91 1279.00 25352 2026-06-12 1271.18 1295.00 1265.01 1291.91 50495 2026-06-15 1292.70 1292.70 1270.10 1271.10 41586 ``` ### 分钟级时间区间 对分钟 K 线同样适用，适合截取盘中某一时段的数据： ```python theme={null} import datetime from alphafeed import AlphaFeed af = AlphaFeed(api_key="your-api-key") start = int(datetime.datetime(2026, 6, 23, 9, 30).timestamp() * 1000) end = int(datetime.datetime(2026, 6, 23, 10, 0).timestamp() * 1000) df = af.klines.get("000001.SZ", period="5m", start_time=start, end_time=end, to_dataframe=True) print(df[["trade_time", "open", "high", "low", "close", "volume"]].to_string(index=False)) ``` ``` trade_time open high low close volume 2026-06-23 09:35:00 10.65 10.83 10.63 10.81 143966 2026-06-23 09:40:00 10.82 10.87 10.81 10.84 83205 2026-06-23 09:45:00 10.85 10.88 10.81 10.87 86994 2026-06-23 09:50:00 10.88 10.90 10.87 10.88 95711 2026-06-23 09:55:00 10.88 10.91 10.86 10.88 70423 2026-06-23 10:00:00 10.89 10.90 10.85 10.85 32324 ``` ## 批量获取K线 一次请求获取多只标的的K线数据： ```python theme={null} symbols = ["600519.SH", "000001.SZ"] dfs = af.klines.batch(symbols, period="1d", count=3, to_dataframe=True, show_progress=True) for sym, df in dfs.items(): print(f"--- {sym} ({df['name'].iloc[0]}) ---") print(df[["trade_date", "open", "close", "volume"]].to_string(index=False)) ``` ``` --- 600519.SH (贵州茅台) --- trade_date open close volume 2026-06-16 1267.01 1255.67 34970 2026-06-17 1258.00 1240.00 44803 2026-06-18 1235.00 1215.00 57472 --- 000001.SZ (平安银行) --- trade_date open close volume 2026-06-16 11.05 10.94 942708 2026-06-17 10.97 10.78 965828 2026-06-18 10.74 10.52 1426893 ``` ### 批量 + 时间区间 `klines.batch` 同样支持 `start_time` 和 `end_time`： ```python theme={null} import datetime from alphafeed import AlphaFeed af = AlphaFeed(api_key="your-api-key") start = int(datetime.datetime(2026, 6, 16).timestamp() * 1000) end = int(datetime.datetime(2026, 6, 20).timestamp() * 1000) dfs = af.klines.batch(["600519.SH", "000001.SZ"], period="1d", start_time=start, end_time=end, to_dataframe=True) for sym, df in dfs.items(): print(f"--- {sym} ({df['name'].iloc[0]}) ---") print(df[["trade_date", "close", "volume"]].to_string(index=False)) ``` ``` --- 600519.SH (贵州茅台) --- trade_date close volume 2026-06-16 1255.67 34970 2026-06-17 1240.00 44803 2026-06-18 1215.00 57472 --- 000001.SZ (平安银行) --- trade_date close volume 2026-06-16 10.94 942708 2026-06-17 10.78 965828 2026-06-18 10.52 1426893 ``` ## 获取实时行情 ### 按标的代码查询 ```python theme={null} df = af.quotes.get(symbols=["600519.SH", "000001.SZ"], to_dataframe=True) print(df[["symbol", "last_price", "prev_close", "volume", "ext.name", "ext.change_pct"]]) ``` ``` symbol last_price prev_close volume ext.name ext.change_pct 000001.SZ 10.52 10.78 1426893 平安银行 -0.024119 600519.SH 1215.00 1240.00 57472 贵州茅台 -0.020161 ``` ### 按标的池查询（全量行情） 支持的标的池： | 标的池 ID | 说明 | | ---------- | -------- | | `CN_Stock` | A 股（沪深京） | | `CN_ETF` | ETF | | `US_Stock` | 美股 | | `HK_Stock` | 港股 | ```python theme={null} df = af.quotes.get(universes=["CN_Stock"], to_dataframe=True) print(df) ``` ``` symbol region ... ext.amplitude ext.turnover_rate 0 920985.BJ CN ... 0.043893 0.015161 1 002414.SZ CN ... 0.027106 0.015167 2 600589.SH CN ... 0.047852 0.076141 3 688543.SH CN ... 0.043046 0.052545 4 300988.SZ CN ... 0.044633 0.043186 ... ... ... ... ... ... 5496 002546.SZ CN ... 0.022026 0.014857 5497 920363.BJ CN ... 0.038917 0.027170 5498 688590.SH CN ... 0.033557 0.051160 5499 001317.SZ CN ... 0.053210 0.043987 5500 300731.SZ CN ... 0.080393 0.090136 [5501 rows x 18 columns] ``` ## 获取日内分时 ### 单只标的 支持周期：`1m`（默认）、`5m`、`15m`、`30m`、`60m` ```python theme={null} df = af.klines.intraday("600519.SH", count=5, to_dataframe=True) print(df[["symbol", "name", "trade_time", "close", "volume"]]) ``` ``` symbol name trade_time close volume 600519.SH 贵州茅台 2026-06-18 14:56:00 1223.50 273 600519.SH 贵州茅台 2026-06-18 14:57:00 1223.88 261 600519.SH 贵州茅台 2026-06-18 14:58:00 1223.26 2 600519.SH 贵州茅台 2026-06-18 14:59:00 1223.26 0 600519.SH 贵州茅台 2026-06-18 15:00:00 1215.00 1788 ``` ### 批量日内分时 ```python theme={null} dfs = af.klines.intraday_batch(["600519.SH", "000001.SZ"], to_dataframe=True) for sym, df in dfs.items(): print(f"{sym}: {len(df)} 条分钟线") ``` ## 获取标的信息 ```python theme={null} insts = af.instruments.get(["600519.SH", "000001.SZ"]) for i in insts: print(f"{i['symbol']:>10s} {i['name']:<6s} 交易所={i['exchange']} 类型={i['type']} 上市={i['ext'].get('listing_date','')}") ``` ``` 600519.SH 贵州茅台 交易所=SH 类型=stock 上市=2001-08-27 000001.SZ 平安银行 交易所=SZ 类型=stock 上市=1991-04-03 ``` ## 获取除权因子 ```python theme={null} df = af.klines.ex_factors(["600519.SH"], to_dataframe=True) print(df[["symbol", "trade_date", "ex_factor"]].tail(5)) ``` ``` symbol trade_date ex_factor 600519.SH 2023-12-20 1.011540 600519.SH 2024-06-19 1.020716 600519.SH 2024-12-20 1.015637 600519.SH 2025-06-26 1.019649 600519.SH 2025-12-19 1.017003 ``` ## 获取五档盘口 ### 单只标的 ```python theme={null} depth = af.depth.get("600519.SH") print(f"标的: {depth['symbol']} 地区: {depth['region']}") for i in range(5): bid = f"买{i+1}: {depth['bid_prices'][i]:>10.2f} x {depth['bid_volumes'][i]}" ask = f"卖{i+1}: {depth['ask_prices'][i]:>10.2f} x {depth['ask_volumes'][i]}" print(f" {bid} | {ask}") ``` ``` 标的: 600519.SH 地区: CN 买1: 1215.00 x 123 | 卖1: 1215.28 x 1 买2: 1214.95 x 2 | 卖2: 1215.96 x 1 买3: 1214.88 x 1 | 卖3: 1216.00 x 2 买4: 1214.48 x 1 | 卖4: 1218.00 x 1 买5: 1214.40 x 1 | 卖5: 1218.89 x 1 ``` ### 批量盘口 ```python theme={null} 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 ```