libfinance.api.get_price 源代码
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
from typing import List, Union
import datetime
import pandas as pd
from libfinance.client import get_client
from libfinance.utils.decorators import export_as_api, ttl_cache, compatible_with_parm
from libfinance.utils.datetime_func import convert_dateteime_to_timestamp
[文档]@export_as_api
def history_bars(
order_book_ids: list,
bar_count: int,
datetime: str,
frequency: str="1d",
fields: List[str]=None,
skip_suspended: bool=True,
include_now: bool=True,
adjust_type: str="none",
adjust_orig:datetime.datetime = None) -> pd.DataFrame:
"""获取指定合约的历史 k 线行情，支持任意日频率xd(1d,5d)和任意分钟频率xm(1m,3m,5m,15m)的历史数据。
:param order_book_ids: 多个标的合约代码
:param bar_count: 获取的历史数据数量，必填项
:param frequency: 获取数据什么样的频率进行。'1d'或'1m'分别表示每日和每分钟，必填项
:param fields: 返回数据字段。必填项。见下方列表。
:param skip_suspended: 是否跳过停牌数据
:param include_now: 是否包含当前数据
:param adjust_type: 复权类型，默认为前复权 pre；可选 pre, none, post
========================= ===================================================
fields 字段名
========================= ===================================================
datetime 时间戳
open 开盘价
high 最高价
low 最低价
close 收盘价
volume 成交量
total_turnover 成交额
open_interest 持仓量（期货专用）
basis_spread 期现差（股指期货专用）
settlement 结算价（期货日线专用）
prev_settlement 结算价（期货日线专用）
========================= ===================================================
Example1::
获取中国平安和万科 2020-04-20之前10天的交易数据
.. code-block:: python3
import pandas as pd
from libfinance import history_bars
#
>>> dt = "2020-04-20"
>>> data = history_bars(order_book_ids=["000001.XSHE", "000002.XSHE"], datetime=dt, bar_count=10)
>>> print(data)
open high low close volume
order_book_id datetime
000001.XSHE 2020-04-07 12.89 12.94 12.81 12.88 87031371.0
2020-04-08 12.88 12.92 12.72 12.78 52871614.0
2020-04-09 12.88 12.89 12.72 12.74 40855377.0
2020-04-10 12.76 12.98 12.65 12.79 66667495.0
2020-04-13 12.67 12.71 12.47 12.59 44621440.0
2020-04-14 12.65 12.86 12.57 12.86 68608687.0
2020-04-15 12.86 12.93 12.78 12.87 65639640.0
2020-04-16 12.79 12.79 12.54 12.68 78915498.0
2020-04-17 12.77 13.04 12.65 12.89 133116477.0
2020-04-20 12.86 13.05 12.77 12.99 81845583.0
000002.XSHE 2020-04-07 27.34 27.42 26.80 27.07 67154006.0
2020-04-08 26.90 27.25 26.75 26.96 41251395.0
2020-04-09 27.10 27.16 26.60 26.69 38726254.0
2020-04-10 26.84 27.34 26.59 26.88 62460322.0
2020-04-13 26.74 27.13 26.61 27.04 43264902.0
2020-04-14 27.10 27.75 27.02 27.35 64241868.0
2020-04-15 27.20 27.23 26.55 26.70 70359257.0
2020-04-16 26.52 26.76 26.40 26.58 50238931.0
2020-04-17 26.78 27.03 26.55 26.72 83813322.0
2020-04-20 26.78 26.81 26.05 26.58 85012343.0
"""
dt = convert_dateteime_to_timestamp(datetime)
return get_client().history_bars(order_book_ids=order_book_ids,
bar_count=bar_count,
frequency=frequency,
fields=fields,
dt=dt,
skip_suspended=skip_suspended,
include_now=include_now,
adjust_type=adjust_type,
adjust_orig=adjust_orig)
[文档]@export_as_api
def get_price(
order_book_ids: list,
start_date: str,
end_date: str,
frequency: str="1d",
fields: List[str]=None,
skip_suspended: bool=True,
include_now: bool=True,
adjust_type: str="none",
adjust_orig:datetime.datetime = None) -> pd.DataFrame:
"""获取指定合约的历史 k 线行情，支持任意日频率xd(1d,5d)和任意分钟频率xm(1m,3m,5m,15m)的历史数据。
:param order_book_ids: 多个标的合约代码, 必填项
:param start_date: 开始日期，必填项
:param end_date: 结束日期，必填项
:param frequency: 获取数据什么样的频率进行。'1d'或'1m'分别表示每日和每分钟
:param fields: 返回数据字段
:param skip_suspended: 是否跳过停牌数据
:param include_now: 是否包含当前数据
:param adjust_type: 复权类型，默认为前复权 pre；可选 pre, none, post
========================= ===================================================
fields 字段名
========================= ===================================================
datetime 时间戳
open 开盘价
high 最高价
low 最低价
close 收盘价
volume 成交量
total_turnover 成交额
open_interest 持仓量（期货专用）
basis_spread 期现差（股指期货专用）
settlement 结算价（期货日线专用）
prev_settlement 结算价（期货日线专用）
========================= ===================================================
Example1::
获取中国平安和浦发银行 2024-03-01至2024-03-11之间的交易数据
.. code-block:: python3
import pandas as pd
from libfinance import get_price
>>> data = get_price(order_book_ids=["000001.XSHE","600000.XSHG"], start_date="2024-03-01", end_date="2024-03-11")
>>> print(data)
open high low close volume
order_book_id datetime
000001.XSHE 2024-03-01 10.59 10.60 10.43 10.49 182810290.0
2024-03-04 10.45 10.50 10.32 10.33 165592954.0
2024-03-05 10.30 10.47 10.26 10.43 181731907.0
2024-03-06 10.40 10.45 10.33 10.33 134564016.0
2024-03-07 10.33 10.64 10.33 10.38 201616589.0
2024-03-08 10.35 10.44 10.30 10.38 111397428.0
2024-03-11 10.38 10.47 10.34 10.47 121067298.0
600000.XSHG 2024-03-01 7.13 7.16 7.10 7.11 29431801.0
2024-03-04 7.12 7.12 7.05 7.07 27855963.0
2024-03-05 7.05 7.18 7.04 7.16 41756232.0
2024-03-06 7.17 7.22 7.12 7.12 25918749.0
2024-03-07 7.12 7.20 7.11 7.14 24690348.0
2024-03-08 7.12 7.17 7.11 7.12 19861794.0
2024-03-11 7.13 7.17 7.06 7.11 26195498.0
"""
start_date = convert_dateteime_to_timestamp(start_date)
end_date = convert_dateteime_to_timestamp(end_date)
return get_client().get_price(order_book_ids=order_book_ids,
start_date=start_date,
end_date=end_date,
frequency=frequency,
fields=fields,
skip_suspended=skip_suspended,
include_now=include_now,
adjust_type=adjust_type,
adjust_orig=adjust_orig)