知微
fa45d8aa5f
- health_checklist.json: 192.168.1.122→node122
- ocr_client.py: docstring IP→node122
- docs/market-data-requirements.md: IP→node122
- 所有API调用通过ProxyHandler({})绕过系统代理
Privoxy对node122:18003返回500，直连正常
37 lines
1.2 KiB
Python
37 lines
1.2 KiB
Python
#!/usr/bin/env python
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# -*- coding:utf-8 -*-
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"""
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Date: 2020/4/21 15:34
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Desc: 高频数据-标普 500 指数
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https://github.com/FutureSharks/financial-data
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long history data for S&P 500 index daily
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http://www.econ.yale.edu/~shiller/data.htm
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"""
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import pandas as pd
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def hf_sp_500(year: str = "2017") -> pd.DataFrame:
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"""
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S&P 500 minute data from 2012-2018
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:param year: from 2012-2018
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:type year: str
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:return: specific year dataframe
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:rtype: pandas.DataFrame
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"""
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url = f"https://github.com/FutureSharks/financial-data/raw/master/pyfinancialdata/data/stocks/histdata/SPXUSD/DAT_ASCII_SPXUSD_M1_{year}.csv"
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temp_df = pd.read_table(url, header=None, sep=";")
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temp_df.columns = ["date", "open", "high", "low", "close", "price"]
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temp_df["date"] = pd.to_datetime(temp_df["date"]).dt.date
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temp_df["open"] = pd.to_numeric(temp_df["open"])
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temp_df["high"] = pd.to_numeric(temp_df["high"])
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temp_df["low"] = pd.to_numeric(temp_df["low"])
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temp_df["close"] = pd.to_numeric(temp_df["close"])
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temp_df["price"] = pd.to_numeric(temp_df["price"])
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return temp_df
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if __name__ == "__main__":
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hf_sp_500_df = hf_sp_500(year="2017")
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print(hf_sp_500_df)
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