90 lines
3.0 KiB
Python
90 lines
3.0 KiB
Python
import os
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import re
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import akshare as ak
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import pandas as pd
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from loguru import logger
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# index_hist_df = ak.index_zh_a_hist(
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# symbol="000001", # 指数代码,如上证指数
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# period="daily", # K线周期: daily(日K)
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# start_date="19700101", # 开始日期
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# end_date="22220101", # 结束日期
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# )
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def get_all_stock_index():
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index_choice = ["沪深重要指数", "上证系列指数", "深证系列指数", "中证系列指数"]
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index_df_list = []
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for source in index_choice:
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logger.info(f"正在获取 {source}...")
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index_df = ak.stock_zh_index_spot_em(symbol=source)
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index_df["symbol"] = source
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index_df_list.append(index_df)
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logger.info(f"{source}: {index_df.shape[0]}")
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df = pd.concat(index_df_list)
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return df
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def get_index_fund_info():
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# 读取指数数据和基金数据
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index_df = pd.read_csv(
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"/Users/aszer/Documents/vscode/etf/data/index_all_stock.csv",
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encoding="utf-8-sig",
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)
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fund_df = pd.read_csv(
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"/Users/aszer/Documents/vscode/etf/data/fund_info.csv", encoding="utf-8-sig"
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)
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# 构建指数名称集合去重，加快后续匹配
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index_name_set = set(index_df["名称"].astype(str).unique())
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# 对每个基金名称，查找其是否包含某一指数名称，允许多对多匹配
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records = []
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for fund_idx, fund_row in fund_df.iterrows():
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fund_name = str(fund_row["基金名称"])
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fund_code = str(fund_row["基金代码"])
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funf_fee = fund_row["手续费"]
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matched_index_list = [
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idx_name
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for idx_name in index_name_set
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if idx_name.lower() in fund_name.lower()
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]
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for idx_name in matched_index_list:
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# 找到指数的相关代码
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index_row = index_df[index_df["名称"] == idx_name]
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index_code = None
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if not index_row.empty:
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index_code = str(index_row.iloc[0]["代码"])
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records.append(
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{
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"指数代码": index_code,
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"指数名称": idx_name,
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"基金代码": fund_code,
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"基金名称": fund_name,
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"手续费": funf_fee,
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}
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)
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index_fund_df = pd.DataFrame(records)
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index_fund_df = index_fund_df.sort_values("指数代码").reset_index(drop=True)
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return index_fund_df
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if __name__ == "__main__":
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# df = get_all_stock_index()
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# df.to_csv("index_all_stock.csv", index=False, encoding="utf-8-sig")
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# res = ak.fund_etf_spot_em()
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# print(res)
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# df = get_index_fund_info()
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# df.to_csv("index_fund_info.csv", index=False, encoding="utf-8-sig")
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import akshare as ak
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import pandas as pd
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for symbol in ["NVDA", "AAPL", "MSFT", "AMZN", "TSLA", "META", "GOOGL"]:
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stock_us_daily_df = ak.stock_us_daily(symbol=symbol, adjust="qfq")
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stock_us_daily_df.to_csv(
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f"{symbol}_stock_us_daily.csv", index=False, encoding="utf-8-sig"
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)
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