f i0dZddlZddlZddlZ ddejfd Z dd ed edejfdZ dejdejfdZ e dkrMe d dZ e e Z ee edddd dZe eZ ee dSdS)u Yang-Zhang-s-Realized-Volatility-Automated-Estimation-in-Python https://github.com/hugogobato/Yang-Zhang-s-Realized-Volatility-Automated-Estimation-in-Python 论文地址：https://www.jstor.org/stable/10.1086/209650 N0000012021-10-20 09:30:002024-11-01 15:00:001hfqreturncddlm}||||||}|dddddd d ||ddk}tj|d|d<|dd |S) u 从东方财富网获取股票的分钟级历史行情数据,并进行数据清洗和格式化为计算 yz 已实现波动率所需的数据格式 https://quote.eastmoney.com/concept/sh603777.html?from=classic :param symbol: 股票代码,如"000001" :type symbol: str :param start_date: 开始日期时间,格式"YYYY-MM-DD HH:MM:SS" :type start_date: str :param end_date: 结束日期时间,格式"YYYY-MM-DD HH:MM:SS" :type end_date: str :param period: 时间周期,可选{'1','5','15','30','60'}分钟 :type period: str :param adjust: 复权方式,可选{'','qfq'(前复权),'hfq'(后复权)} :type adjust: str :return: 整理后的分钟行情数据,包含Date(索引),Open,High,Low,Close列 :rtype: pandas.DataFrame r)stock_zh_a_hist_min_emsymbol start_dateend_dateperiodadjustDateOpenHighLowClose)u时间u开盘u最高u最低u收盘Tcolumnsinplacekeysr)#akshare.stock_feature.stock_hist_emr renamepd to_datetime set_index)r r rrrr temp_dfs 9/usr/local/lib/python3.11/dist-packages/akshare/cal/rv.pyrv_from_stock_zh_a_hist_min_emr" s.KJJJJJ$$ G NN      gfo*+GnWV_55GFO 64000 NIF20085r rcddlm}|||}|dddddd d tj|d|d<|dd |S) u 从新浪财经获取期货的分钟级历史行情数据,并进行数据清洗和格式化 https://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html#titlePos_3 :param symbol: 期货合约代码,如"IF2008"代表沪深300期货2020年8月合约 :type symbol: str :param period: 时间周期,可选{'1','5','15','30','60'}分钟 :type period: str :return: 整理后的分钟行情数据,包含Date(索引),Open,High,Low,Close列 :rtype: pandas.DataFrame r)futures_zh_minute_sinar rrrrrr)datetimeopenhighlowcloseTrr)akshare.futures.futures_zh_sinar'rrrr)r rr'r s r!rv_from_futures_zh_minute_sinar/=sGFFFFF$$F6BBBG NN      nWV_55GFO 64000 Nr#datac tjdtjtj|ddd|ddd|d<tjtj|ddd|ddd|d<tjtj|d dd|ddd|d <tjtj|ddd|ddt |dz |d <|dd}|d|d|dz z|d |d |dz zz|d <|d tjd  }|d dgtjd  }tt |t |z }dd|dz|dz z zz }tj d|z |z|d z|d|zz}tj|}|ddid|dddg|_tj|ddjj|d<|S)u" 波动率-已实现波动率-Yang-Zhang 已实现波动率(Yang-Zhang Realized Volatility) https://github.com/hugogobato/Yang-Zhang-s-Realized-Volatility-Automated-Estimation-in-Python 论文地址：https://www.jstor.org/stable/10.1086/209650 基于以下公式计算: RV^2 = Vo + k*Vc + (1-k)*Vrs 其中: - Vo: 隔夜波动率, Vo = 1/(n-1)*sum(Oi-Obar)^2 Oi为标准化开盘价, Obar为标准化开盘价均值 - Vc: 收盘波动率, Vc = 1/(n-1)*sum(ci-Cbar)^2 ci为标准化收盘价, Cbar为标准化收盘价均值 - k: 权重系数, k = 0.34/(1.34+(n+1)/(n-1)) n为样本数量 - Vrs: Rogers-Satchell波动率代理, Vrs = ui(ui-ci)+di(di-ci) ui = ln(Hi/Oi), ci = ln(Ci/Oi), di = ln(Li/Oi), oi = ln(Oi/Ci-1) Hi/Li/Ci/Oi分别为最高价/最低价/收盘价/开盘价 :param data: 包含 OHLC(开高低收) 价格的 pandas.DataFrame :type data: pandas.DataFrame :return: 包含 Yang-Zhang 实现波动率的 pandas.DataFrame :rtype: pandas.DataFrame 要求输入数据包含以下列: - Open: 开盘价 - High: 最高价 - Low: 最低价 - Close: 收盘价 # yang_zhang_rv formula is give as: # RV^2 = Vo + k*Vc + (1-k)*Vrs # where Vo = 1/(n-1)*sum(Oi-Obar)^2 # with oi = normalized opening price at time t and Obar = mean of normalized opening prices # Vc = = 1/(n-1)*sum(ci-Cbar)^2 # with ci = normalized close price at time t and Cbar = mean of normalized close prices # k = 0.34/(1.34+(n+1)/(n-1)) # with n = total number of days or time periods considered # Vrs (Rogers & Satchell RV proxy) = ui(ui-ci)+di(di-ci) # with ui = ln(Hi/Oi), ci = ln(Ci/Oi), di=(Li/Oi), oi = ln(Oi/Ci-1) # where Hi = high price at time t and Li = low price at time t ignorerNruircirdioiRS1D)freqg(\?gq= ףp?ryz_rvTr)rdatervcoerce)errors)warningsfilterwarningsnplogdividelengroupbyrGroupermeandropnavarintsqrt DataFramer reset_indexrrdtr<)r0rs_var vc_and_vonk yang_zhang_rvyang_zhang_rv_dfs r!volatility_yz_rvrV\sV H%%% $v,qrr"2DL4DEEFFDJ $w-"3T&\!""5EFFGGDJ $u+abb/4<3CDDEEDJ $v,qrr"2DM/CIIPQM/4RSSTTDJ 8DdtDzDJ67$t* T T$Z;DJ$Z    5 5 5 6 6 ; ; = = D D F FFdD\"**2:4+@+@+@AAEEGGNNPPI CIIF #$$A A!a%(()AGQUf,y>4STATTUUM|M22QL$???   ... &~!~    V r#__main__r()r0r )rrrrr)r$r%)__doc__r@numpyrBpandasrrMr"strr/rV__name__ futures_dfvolatility_yz_rv_dfprintstock_dfr#r!rcsp  $ "   -- \ ----b+. $'\>A2<ABLAAAAH z//xLLLJ** ;;; E --(& H+*999 E r#