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twstock.py
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| import pandas as pd | |
| import requests | |
| import numpy as np | |
| from io import StringIO | |
| import time | |
| import matplotlib.pyplot as plt | |
| import html5lib | |
| from datetime import datetime, timedelta | |
| import calendar | |
| import datetime as dt | |
| from functools import reduce | |
| import json | |
| import os | |
| from collections import Counter | |
| # 小程式 | |
| def revr(data, nmbr): | |
| cols = data.columns.tolist() | |
| cols = cols[nmbr:] + cols[:nmbr] | |
| data = data[cols] | |
| return data | |
| def add_months(sourcedate, months): | |
| sourcedate = datetime.strptime(sourcedate, "%Y%m%d").date() if type(sourcedate)==str else sourcedate | |
| month = sourcedate.month - 1 + months | |
| year = sourcedate.year + month // 12 | |
| month = month % 12 + 1 | |
| day = min(sourcedate.day, calendar.monthrange(year,month)[1]) | |
| return dt.date(year, month, day) | |
| def fullpathname(outdir, outname): | |
| if not os.path.exists(outdir): | |
| os.mkdir(outdir) | |
| return os.path.join(outdir, outname) | |
| # class TwFinanceStatement: | |
| class TwFinanceRatio: | |
| # 對照表 | |
| m_mapping = {'上市':'sii','上櫃':'otc'} | |
| base_url = 'https://mops.twse.com.tw/mops/web/' | |
| p_mapping = { | |
| # '損益表':base_url+'t163sb04','資產負債表':base_url+'t163sb05', | |
| '營益分析':base_url+'t163sb06' | |
| ,'財務結構分析':base_url+'t51sb02'} | |
| old_p_mapping = { | |
| # '損益表':base_url+'t51sb08','資產負債表':base_url+'t51sb07', | |
| '營益分析':base_url+'t51sb06' | |
| ,'財務結構分析':base_url+'ajax_t51sb02'} | |
| def __init__(self, mkt_type, year, season, purpose, \ | |
| m_mapping=m_mapping, p_mapping=p_mapping, old_p_mapping=old_p_mapping, smp=True): | |
| year = year if year < 1000 else year-1911 | |
| self.year = year #yyy | |
| self.season = '0'+str(season) if type(season)==int else season | |
| self.mkt_type = m_mapping[mkt_type] if mkt_type in m_mapping else None | |
| self.purpose = purpose | |
| p_mapping = p_mapping if year>=102 else old_p_mapping | |
| self.url = p_mapping[self.purpose] if self.purpose in p_mapping else None | |
| self.smp = smp | |
| def add_raw(self): | |
| form = {'encodeURIComponent':1, | |
| 'step':1, | |
| 'firstin':1, | |
| 'off':1, | |
| 'TYPEK':self.mkt_type, | |
| 'year':str(self.year), | |
| 'season':self.season | |
| } | |
| form['ifrs']='Y' if self.year>=102 else 'N' | |
| headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_1) \ | |
| AppleWebKit/537.36 (KHTML, like Gecko) Chrome/39.0.2171.95 Safari/537.36'} | |
| r = requests.post(self.url, form, headers=headers) | |
| r.encoding = 'utf8' | |
| dfs = pd.read_html(StringIO(r.text)) | |
| self.raw_data = dfs | |
| def unify(self): | |
| if self.purpose=='營益分析': | |
| dfs = [i for i in self.raw_data if i.shape[0]>5] | |
| dfs = [i for i in dfs if i.shape[1]>5] | |
| data = dfs[0] | |
| data.columns = ['公司代號','公司名稱','營業收入(百萬元)','毛利率(%)','營業利益率(%)','稅前純益率(%)','稅後純益率(%)'] | |
| data = data[data['公司代號']!='公司代號'] | |
| data = data.reset_index().drop('index', axis=1) | |
| data['財報年度'] = self.year+1911 #yyyy | |
| data['季'] = self.season | |
| data = revr(data,-2) | |
| self.data = data | |
| elif self.purpose=='財務結構分析': | |
| dfs = [i for i in self.raw_data if i.shape[0]>5] | |
| dfs = [i for i in dfs if i.shape[1]>5] | |
| data = dfs[0] | |
| data.columns = data.columns.get_level_values(1) | |
| data = data[data['公司代號']!='公司代號'] | |
| data = data.reset_index().drop('index', axis=1) | |
| data.rename(columns={'股東權益報酬率(%)':'權益報酬率(%)' | |
| ,'不動產、廠房及設備週轉率(次)':'固定資產週轉率(次)' | |
| ,'負債佔資產比率(%)':'負債比率(%)' | |
| ,'公司簡稱':'公司名稱'}, inplace=True) | |
| data['財報年度'] = self.year+1911 #yyyy | |
| # data['季'] = self.season | |
| data = revr(data,-1) | |
| if self.smp==True : | |
| clms = list(data.columns) | |
| clms = [i for i in clms if i not in \ | |
| ['平均收現日數','平均售貨日數','平均銷貨日數' | |
| ,'長期資金佔不動產、廠房及設備比率(%)','純益率(%)','長期資金佔固定資產比率(%)' | |
| ,'應收款項收現日數','稅前純益佔實收資本比率(%)','營業利益佔實收資本比率(%)']] | |
| data = data[clms] | |
| self.data = data | |
| def siiPrice(str_date): | |
| url = f'https://www.twse.com.tw/exchangeReport/MI_INDEX?response=csv&date={str_date}&type=ALL' | |
| r = requests.get(url) | |
| if r.text=='': | |
| return None | |
| df = pd.read_csv(StringIO(r.text.replace("=", "")), | |
| header=["證券代號" in l for l in r.text.split("\n")].index(True)-1) | |
| df.drop(['Unnamed: 16','最後揭示買價', '最後揭示買量', '最後揭示賣價', '最後揭示賣量'], axis=1, inplace=True) | |
| for i in ['成交股數', '成交筆數', '成交金額', '開盤價', '最高價', '最低價', '收盤價', '漲跌價差', '本益比']: | |
| df[i] = df[i].apply(lambda x: pd.to_numeric(x.replace(",", ""), errors='coerce') if type(x)!=float else x) | |
| df['股價日期'] = datetime(int(str_date[:4]),int(str_date[4:6]),int(str_date[6:]),0,0,0) | |
| df = revr(df,-1) | |
| return df | |
| def otcPrice(str_date): | |
| url = f'https://www.tpex.org.tw/web/stock/aftertrading/otc_quotes_no1430/stk_wn1430_result.php?l=zh-tw&o=htm&d={str_date}&se=AL&s=0,asc,0' | |
| r = requests.get(url) | |
| if r.text=='': | |
| return None | |
| df = pd.read_html(StringIO(r.text))[0] | |
| df.columns = df.columns.get_level_values(1) | |
| df.drop(['次日漲停價','次日跌停價'], axis=1, inplace=True) | |
| spl = str_date.split('/') | |
| df['股價日期'] = datetime(int(spl[0])+1911,int(spl[1]),int(spl[2]),0,0,0) | |
| df = revr(df,-1) | |
| return df | |
| def TwPrice(dt, mkt): | |
| # 對照表 | |
| mkt_mapping = {'上市':'sii','上櫃':'otc'} | |
| dt_mapping = {} | |
| if '/' not in dt: | |
| dt_mapping['sii_date']=dt | |
| dt_mapping['otc_date']=f'{int(dt[:-4])-1911}/{dt[-4:-2]}/{dt[-2:]}' | |
| else: | |
| dt_mapping['sii_date']=f'{int(dt[:-6])+1911}{dt[-5:-3]}{dt[-2:]}' | |
| dt_mapping['otc_date']=dt | |
| mkt = mkt_mapping[mkt] | |
| dt = dt_mapping[f'{mkt}_date'] | |
| price = siiPrice(dt) if mkt=='sii' else otcPrice(dt) | |
| return price | |
| class TwRevenue: | |
| m_mapping = {'上市':'sii','上櫃':'otc'} | |
| base_url = 'https://mops.twse.com.tw/nas/t21/{}/t21sc03_{}_{}_0.html' | |
| def __init__(self, mkt_type, year, month, url=base_url, m_mapping=m_mapping): | |
| self.mkt_type = m_mapping[mkt_type] if mkt_type in m_mapping else None | |
| year = year if year < 1000 else year-1911 | |
| self.year = year #yyy | |
| self.month = month | |
| self.url = url.format(self.mkt_type, self.year, self.month) | |
| def add_raw(self): | |
| headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'} | |
| r = requests.get(self.url, headers=headers) | |
| r.encoding = 'big5' | |
| dfs = pd.read_html(StringIO(r.text)) | |
| self.raw_data = dfs | |
| def unify(self, smp=True): | |
| data = pd.concat([data for data in self.raw_data if data.shape[1] <= 11 and data.shape[1] > 5]\ | |
| ,axis=0,ignore_index=True) | |
| if 'levels' in dir(data.columns): | |
| data.columns = data.columns.get_level_values(1) | |
| else: | |
| data = data[list(range(0,10))] | |
| column_index = data.index[(data[0] == '公司代號')][0] | |
| data.columns = data.iloc[column_index] | |
| data['當月營收'] = pd.to_numeric(data['當月營收'], 'coerce') | |
| data = data[~data['當月營收'].isnull()] | |
| data = data[~data['公司代號'].str.contains('合計')] | |
| if '備註' not in data.columns: | |
| data['備註'] = '-' | |
| data.drop_duplicates(subset='公司代號', keep='first', inplace=True) | |
| # 置換例外資料 | |
| if (self.year,self.month)==(102,1): | |
| data.replace(to_replace='不適用', value=np.NaN, inplace=True) | |
| data = data.drop('備註', axis=1) | |
| data['年'] = self.year | |
| data['月'] = self.month | |
| if smp==True: | |
| clms = ['年','月','公司代號','公司名稱','當月營收','去年當月營收','去年同月增減(%)'] | |
| else: | |
| clms = ['年','月','公司代號','公司名稱','當月營收','去年當月營收','去年同月增減(%)'\ | |
| ,'上月營收','上月比較增減(%)','前期比較增減(%)','去年累計營收','當月累計營收'] | |
| data = data[clms] | |
| data = data.rename(columns={'當月營收':f'{self.month}月營收' | |
| ,'去年當月營收':f'去年{self.month}月營收' | |
| ,'去年同月增減(%)':f'{self.month}月營收年成長(%)'}) | |
| self.data = data | |
| def join_tej(d, fileName='tej.csv'): | |
| tej = pd.read_csv(fileName) | |
| tej = tej[['ID', 'NAME', 'MKT', 'INDUSTRY_NAME_1', 'INDUSTRY_NAME_2']] | |
| tej['ID'] = tej['ID'].astype(str) | |
| d = pd.merge(d, tej, how='inner', left_on='公司代號', right_on='ID') | |
| d = d.drop(['ID', 'NAME'], axis=1) | |
| d.rename(columns = {'MKT':'市場' | |
| ,'INDUSTRY_NAME_1':'主產業' | |
| ,'INDUSTRY_NAME_2':'次產業'} | |
| , inplace=True) | |
| d['市場'] = d['市場'].apply(lambda x:'上市' if x=='TSE' else '上櫃') | |
| cols = list(d.columns) | |
| cols = cols[:2]+cols[-3:]+cols[2:-3] | |
| d = d[cols] | |
| pe1 = d.groupby('主產業')['本益比'].median().reset_index().rename(columns={'本益比':'PE1 中位數'}) | |
| pe2 = d.groupby('次產業')['本益比'].median().reset_index().rename(columns={'本益比':'PE2 中位數'}) | |
| d = pd.merge(pd.merge(d,pe1),pe2) | |
| for i in ['PE1 中位數', 'PE2 中位數']: | |
| d[i] = d[i].replace(0, 1e-4) | |
| return d | |
| def join_tej_drv(d, fileName='tej_drv.csv'): | |
| tej = pd.read_csv(fileName) | |
| tej = tej[['ID', 'NAME', 'MKT', 'INDUSTRY_NAME_1', 'INDUSTRY_NAME_2', 'INDUSTRY_SELF_DIFINED']] | |
| tej['ID'] = tej['ID'].astype(str) | |
| d = pd.merge(d, tej, how='inner', left_on='公司代號', right_on='ID') | |
| d = d.drop(['ID', 'NAME'], axis=1) | |
| d.rename(columns = {'MKT':'市場' | |
| ,'INDUSTRY_NAME_1':'主產業' | |
| ,'INDUSTRY_NAME_2':'次產業' | |
| ,'INDUSTRY_SELF_DIFINED':'自定產業'}, inplace=True) | |
| d['市場'] = d['市場'].apply(lambda x:'上市' if x=='TSE' else '上櫃') | |
| cols = list(d.columns) | |
| cols = cols[:2]+cols[-4:]+cols[2:-4] | |
| d = d[cols] | |
| pe1 = d.groupby('主產業')['本益比'].median().reset_index().rename(columns={'本益比':'PE1 中位數'}) | |
| pe2 = d.groupby('次產業')['本益比'].median().reset_index().rename(columns={'本益比':'PE2 中位數'}) | |
| pe3 = d.groupby('自定產業')['本益比'].median().reset_index().rename(columns={'本益比':'PE3 中位數'}) | |
| d = reduce(lambda x,y:pd.merge(x,y), (d,pe1,pe2,pe3)) | |
| for i in ['PE1 中位數', 'PE2 中位數', 'PE3 中位數']: | |
| d[i] = d[i].replace(0, 1e-4) | |
| return d | |
| # 營益分析 inner join 財務結構分析 | |
| def genRatio(y): | |
| # financeStatement - 年度財務比率數值 | |
| x = TwFinanceRatio('上市', y, 4, '營益分析') | |
| x.add_raw() | |
| x.unify() | |
| incm = x.data | |
| x = TwFinanceRatio('上市', y, None, '財務結構分析') | |
| x.add_raw() | |
| x.unify() | |
| baln = x.data | |
| sii = pd.merge(incm[[i for i in incm.columns if i!='季']], baln, how='inner', on=['財報年度', '公司代號', '公司名稱']) | |
| print(f'{y} stage = siiRatio', end=' ') | |
| x = TwFinanceRatio('上櫃', y, 4, '營益分析') | |
| x.add_raw() | |
| x.unify() | |
| incm = x.data | |
| x = TwFinanceRatio('上櫃', y, None, '財務結構分析') | |
| x.add_raw() | |
| x.unify() | |
| baln = x.data | |
| otc = pd.merge(incm[[i for i in incm.columns if i!='季']], baln, how='inner', on=['財報年度', '公司代號', '公司名稱']) | |
| print('otcRatio') | |
| ratio = pd.concat([sii,otc], axis=0, ignore_index=True) | |
| return ratio | |
| # 近3月營收 YOY(%) | |
| def last3MthRvnuGrw(y,last_m=3): | |
| str_last_m = '0'+str(last_m) if last_m<=9 else str(last_m) | |
| last_ymd = f'{y}{str_last_m}01' | |
| YmList = [add_months(last_ymd, i).strftime("%Y%m") for i in range(0,-3,-1)] | |
| r_container = {} | |
| for seq,ym in enumerate(YmList): | |
| y = int(ym[:4]) | |
| m = ym[4:] | |
| m = int(m[1]) if m[0]=='0' else int(m) | |
| x = TwRevenue('上市', y, m) | |
| x.add_raw() | |
| x.unify() | |
| sii_r = x.data | |
| if seq==2: | |
| print('siiRvnu', end=' ') | |
| x = TwRevenue('上櫃', y, m) | |
| x.add_raw() | |
| x.unify() | |
| otc_r = x.data | |
| if seq==2: | |
| print('otcRvnu') | |
| r = pd.concat([sii_r,otc_r], axis=0, ignore_index=True) | |
| r = r.drop(['年','月'], axis=1) | |
| r_container[(y,m)]=r | |
| m_list = [i[1] for i in r_container.keys()] | |
| rvnu = reduce(lambda x,y:pd.merge(x,y,on=['公司代號','公司名稱'],how='inner'), list(r_container.values())) | |
| rvnu = reduce(lambda x,y:pd.merge(x,y,on=['公司代號','公司名稱'],how='inner'), list(r_container.values())) | |
| rvnu['近3月營收 平均YOY(%)'] = round((rvnu[f'{m_list[0]}月營收年成長(%)']\ | |
| +rvnu[f'{m_list[1]}月營收年成長(%)']+rvnu[f'{m_list[2]}月營收年成長(%)'])/3,2) | |
| rvnu['近3月累計營收'] = rvnu[f'{m_list[0]}月營收']+rvnu[f'{m_list[1]}月營收']+rvnu[f'{m_list[2]}月營收'] | |
| rvnu['去年近3月累計營收'] = rvnu[f'去年{m_list[0]}月營收']\ | |
| +rvnu[f'去年{m_list[1]}月營收']+rvnu[f'去年{m_list[2]}月營收'] | |
| rvnu['近3月累計營收 YOY(%)'] = round((rvnu['近3月累計營收']/rvnu['去年近3月累計營收']-1)*100,2) | |
| rvnu = rvnu[['公司代號','公司名稱','近3月營收 平均YOY(%)','近3月累計營收 YOY(%)']] | |
| return rvnu | |
| # 取得該月首交易日 | |
| def FirstTxnDate(eg_yr, mkt, mh=4, dt=1, ft=siiPrice): | |
| cn_yr = eg_yr-1911 | |
| mh = str(mh) if mh>=10 else '0'+str(mh) | |
| dt = str(dt) if dt>=10 else '0'+str(dt) | |
| test_ymd = f'{eg_yr}{mh}{dt}' | |
| d_test = ft(test_ymd) | |
| while d_test is None: | |
| d_plus_one = datetime.strptime(test_ymd, "%Y%m%d").date()+ timedelta(days=1) | |
| test_ymd = datetime.strftime(d_plus_one, "%Y%m%d") | |
| d_test = siiPrice(test_ymd) | |
| eg_yr,mh,dt = test_ymd[:4],test_ymd[4:6],test_ymd[6:] | |
| if mkt=='sii': | |
| return f'{eg_yr}{mh}{dt}' | |
| elif mkt=='otc': | |
| return f'{cn_yr}/{mh}/{dt}' | |
| else: | |
| return None | |
| # 取得該月末交易日 | |
| def LastTxnDate(eg_yr, mkt, mh=12, dt=31, ft=siiPrice): | |
| cn_yr = eg_yr-1911 | |
| mh = str(mh) if mh>=10 else '0'+str(mh) | |
| dt = str(dt) if dt>=10 else '0'+str(dt) | |
| test_ymd = f'{eg_yr}{mh}{dt}' | |
| d_test = ft(test_ymd) | |
| while d_test is None: | |
| d_plus_one = datetime.strptime(test_ymd, "%Y%m%d").date()+ timedelta(days=-1) | |
| test_ymd = datetime.strftime(d_plus_one, "%Y%m%d") | |
| d_test = siiPrice(test_ymd) | |
| eg_yr,mh,dt = test_ymd[:4],test_ymd[4:6],test_ymd[6:] | |
| if mkt=='sii': | |
| return f'{eg_yr}{mh}{dt}' | |
| elif mkt=='otc': | |
| return f'{cn_yr}/{mh}/{dt}' | |
| else: | |
| return None | |
| # 取得該交易日價格 | |
| def allPrice(dt): | |
| sii_p = TwPrice(dt, '上市') | |
| sii_p = sii_p[['股價日期', '證券代號', '證券名稱', '收盤價']] | |
| sii_p = sii_p.rename(columns = {'證券代號':'公司代號', '證券名稱':'公司名稱'}) | |
| otc_p = TwPrice(dt, '上櫃') | |
| otc_p = otc_p[['股價日期', '代號', '名稱', '收盤']] | |
| otc_p = otc_p.rename(columns = {'代號':'公司代號', '名稱':'公司名稱', '收盤':'收盤價'}) | |
| price = pd.concat([sii_p,otc_p]) | |
| return price | |
| ############### (財務比率 left join 近3月營收 left join 測試日收盤價格) inner join TEJ ############### | |
| def gen(y, fileName=None, tej=True, tej_drv=False): | |
| # financeRatio - 年度財務比率數值 | |
| if fileName==None: | |
| ratio = genRatio(y) | |
| else: | |
| ratio = pd.read_csv(fileName) | |
| ratio = ratio[ratio.財報年度==y] | |
| # revenue - 隔年1～3月的營收 | |
| rvnu = last3MthRvnuGrw(y+1) # 隔年 | |
| # price - 財報+隔年1～3月的營收 揭露後首個交易日的收盤價 | |
| dt = FirstTxnDate(eg_yr=y+1, mkt='sii', mh=4, dt=11) # 隔年 | |
| price = allPrice(dt) | |
| price = price.drop('股價日期', axis=1).rename(columns = {'收盤價':'年報公佈後收盤價'}) | |
| # merge & process data | |
| for i in [ratio,rvnu,price]: | |
| i['公司代號'] = i['公司代號'].astype(str) | |
| data = reduce(lambda x,y:pd.merge(x,y,on=['公司代號','公司名稱'],how='left'), [ratio,rvnu,price]) | |
| # data['每股盈餘(元)'] = data['每股盈餘(元)'].fillna(1e-4) | |
| data = data.fillna(1e-4) | |
| def flt(x): | |
| try: | |
| return float(x) | |
| except ValueError: | |
| # return 0 | |
| return 1e-4 | |
| for i in list(filter(lambda x: x not in ['財報年度', '公司代號', '公司名稱', '股價日期'], data.columns)): | |
| data[i] = data[i].apply(flt) | |
| data['本益比'] = round(data['年報公佈後收盤價'] / data['每股盈餘(元)'], 2) | |
| cols = list(data.columns) | |
| cols = cols[1:3]+cols[:1]+cols[3:] | |
| data = data[cols] | |
| # tej | |
| if tej==True: | |
| data = join_tej(data) | |
| elif tej_drv==True: | |
| data = join_tej_drv(data) | |
| return data | |
| ######################################################################################################### | |
| # 計算 YOY | |
| def drv(d_current, d_last, spc_cmpr=None): | |
| cmpr = ['營業收入(百萬元)', '毛利率(%)', '營業利益率(%)', \ | |
| '稅後純益率(%)', '資產報酬率(%)', '權益報酬率(%)', '現金再投資比率(%)'] \ | |
| if spc_cmpr==None else spc_cmpr | |
| # 營業收入分開處理 | |
| names_mapping = {} | |
| for c in [c for c in cmpr if '營業收入' not in c]: | |
| names_mapping[c] = f'{c[:-3]} YOY(%)' | |
| orgn_col = ['公司代號']+cmpr | |
| drv_col = ['公司代號']+['營業收入 YOY(%)']+list(names_mapping.values()) | |
| # 計算差異 | |
| d_cmpr = pd.merge(d_current[orgn_col], d_last[orgn_col], how='left', on=['公司代號']) | |
| d_cmpr['營業收入 YOY(%)'] = round(((d_cmpr['營業收入(百萬元)_x'] / d_cmpr['營業收入(百萬元)_y'])-1)*100, 2) | |
| for c in names_mapping: | |
| d_cmpr[names_mapping[c]] = d_cmpr[f'{c}_x'] - d_cmpr[f'{c}_y'] | |
| d_cmpr = d_cmpr[drv_col] | |
| x = pd.merge(d_current, d_cmpr, how='inner', on=['公司代號']) | |
| x = x.fillna(1e-4) | |
| return x | |
| # 推算還原價格 | |
| # etf 股利 | |
| class twEtfDiv: | |
| def __init__(self, year, start='0101', end='1231'): | |
| year = year if year > 1000 else year+1911 | |
| self.year = year #yyyy | |
| self.start = start | |
| self.end = end | |
| self.url = f"https://www.twse.com.tw/exchangeReport/TWT49U?response=json&strDate={year}{start}&endDate={year}{end}" | |
| self.headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'} | |
| def add_raw(self): | |
| r = requests.get(self.url, headers=self.headers) | |
| d = json.loads(r.text) | |
| data = pd.DataFrame(data=d['data'], columns=d['fields']) | |
| data = data[data.股票代號.str.startswith('0')] | |
| self.raw_data = data | |
| def unify(self): | |
| self.raw_data['詳細資料'] = self.raw_data['詳細資料'].apply(lambda x:x.split("'")[1][9:]) | |
| self.raw_data.drop(['漲停價格','跌停價格','開盤競價基準','最近一次申報資料 季別/日期',\ | |
| '最近一次申報每股 (單位)淨值','最近一次申報每股 (單位)盈餘'], axis=1, inplace=True) | |
| self.raw_data['資料日期'] = self.raw_data['詳細資料'].apply(lambda x:x[-8:]) | |
| self.raw_data = self.raw_data[self.raw_data['除權息前收盤價']!=self.raw_data['減除股利參考價']] | |
| def stockDividend(dtl): | |
| headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'} | |
| basic_url = 'https://www.twse.com.tw/zh/' | |
| url = basic_url+dtl | |
| r = requests.get(url, headers=headers) | |
| d = pd.read_html(StringIO(r.text))[0] | |
| tgt = d.iloc[4][1].split(' ')[0] | |
| time.sleep(3) | |
| return tgt | |
| def cashDividend(dtl): | |
| headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'} | |
| basic_url = 'https://www.twse.com.tw/zh/' | |
| url = basic_url+dtl | |
| r = requests.get(url, headers=headers) | |
| d = pd.read_html(StringIO(r.text))[0] | |
| tgt = d.iloc[2][1].split(' ')[0] | |
| time.sleep(3) | |
| return tgt | |
| x1 = self.raw_data[self.raw_data['權/息']=='息'] | |
| x1['現金股利'] = x1['權值+息值'] | |
| x1['股票股利'] = 0 | |
| x2 = self.raw_data[self.raw_data['權/息']=='權'] | |
| x2['現金股利'] = 0 | |
| x2['股票股利'] = x2['詳細資料'].apply(stockDividend) | |
| x12 = self.raw_data[self.raw_data['權/息']=='權息'] | |
| x12['現金股利']=x12['詳細資料'].apply(cashDividend) | |
| x12['股票股利']=x12['詳細資料'].apply(stockDividend) | |
| d_all = pd.concat([x1,x2,x12],ignore_index=True) | |
| d_all = d_all[['資料日期', '股票代號', '股票名稱', '現金股利', '股票股利', '詳細資料']] | |
| self.data = d_all | |
| # 公司股利 | |
| class twDividend: | |
| m_mapping = {'上市':'sii','上櫃':'otc'} | |
| def __init__(self, mkt_type, year, m_mapping=m_mapping): | |
| year = year if year <= 1000 else year-1911 | |
| self.mkt_type = m_mapping[mkt_type] | |
| self.year = str(year) #yyy | |
| self.url = f"https://mops.twse.com.tw/mops/web/ajax_t108sb27" | |
| self.headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'} | |
| def add_raw(self): | |
| form = { | |
| "step": "1", | |
| "firstin": "1", | |
| "TYPEK": self.mkt_type, | |
| "year": self.year, | |
| "type": "2" | |
| } | |
| r = requests.post(self.url, data=form, headers=self.headers) | |
| self.raw_data = pd.read_html(StringIO(r.text))[0] | |
| def unify(self): | |
| d = self.raw_data | |
| d.columns = d.columns.get_level_values(-1) | |
| d = d[d['公司代號']!='公司代號'] | |
| d['資料日期'] = d[['除權交易日' , '除息交易日']].apply(lambda x:x[0] if x[0] is not np.nan else x[1], axis=1) | |
| for i in ['盈餘分配之股東現金股利(元/股)' , '法定盈餘公積、資本公積發放之現金(元/股)', \ | |
| '盈餘轉增資配股(元/股)' , '法定盈餘公積、資本公積轉增資配股(元/股)']: | |
| d[i] = d[i].replace(to_replace='-', value=np.nan) | |
| d['現金股利'] = d[['盈餘分配之股東現金股利(元/股)' , '法定盈餘公積、資本公積發放之現金(元/股)']].apply\ | |
| (lambda x:float(x[0])+float(x[1]) if x[0] is not np.nan and x[1] is not np.nan else np.nan, axis=1) | |
| d['股票股利'] = d[['盈餘轉增資配股(元/股)' , '法定盈餘公積、資本公積轉增資配股(元/股)']].apply\ | |
| (lambda x:(float(x[0])+float(x[1]))*100 if x[0] is not np.nan and x[1] is not np.nan else np.nan, axis=1) | |
| d = d[['資料日期', '公司代號', '公司名稱', '現金股利', '股票股利']] | |
| d = d.fillna(0) | |
| d = d[(d['現金股利']!=0) | (d['股票股利']!=0)] | |
| d = d.drop_duplicates() | |
| def f(x): | |
| x = x.split('/') | |
| return str(int(x[0])+1911)+x[1]+x[2] | |
| d['資料日期'] = d['資料日期'].apply(f) | |
| d['MKT'] = '上市' if self.mkt_type=='sii' else '上櫃' | |
| cols = list(d.columns) | |
| d = d[cols[:1]+cols[-1:]+cols[1:-1]] | |
| self.data = d | |
| # 減資換發新股 | |
| class twCptRdc: | |
| m_mapping = {'上市':'sii','上櫃':'otc'} | |
| def __init__(self, mkt_type, year, start='0101', end='1231', m_mapping=m_mapping): | |
| eg_year = year if year > 1000 else year+1911 #yyyy | |
| cn_year = year if year < 1000 else year-1911 | |
| self.mkt_type = m_mapping[mkt_type] | |
| if self.mkt_type=='sii': | |
| self.year = eg_year | |
| self.full_start = f'{eg_year}{start}' | |
| self.full_end = f'{eg_year}{end}' | |
| self.url = f'https://www.twse.com.tw/exchangeReport/TWTAUU?response=json&strDate={self.full_start}&endDate={self.full_end}' | |
| elif self.mkt_type=='otc': | |
| self.year = cn_year | |
| self.full_start = f'{cn_year}/{start[:2]}/{start[2:]}' | |
| self.full_end = f'{cn_year}/{end[:2]}/{end[2:]}' | |
| self.url = f'https://www.tpex.org.tw/web/stock/exright/revivt/revivt_result.php?l=zh-tw&d={self.full_start}&ed={self.full_end}&s=0,asc,0&o=csv' | |
| self.headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'} | |
| def add_raw(self): | |
| if self.mkt_type=='sii': | |
| r = requests.get(self.url, headers=self.headers) | |
| d = json.loads(r.text) | |
| data = pd.DataFrame(data=d['data'], columns=d['fields']) | |
| data = data.rename(columns={'停止買賣前收盤價格':'停止買賣前價格'}) | |
| self.raw_data = data | |
| elif self.mkt_type=='otc': | |
| r = requests.get(self.url, headers=self.headers) | |
| d = pd.read_csv(StringIO(r.text), header = ["股票代號" in l for l in r.text.split("\n")].index(True)) | |
| d = d[~d.股票代號.isnull()] | |
| d.股票代號 = d.股票代號.astype(int) | |
| d.股票代號 = d.股票代號.astype(str) | |
| data = d.rename(columns={'恢復買賣日期 ':'恢復買賣日期' | |
| ,'最後交易日之收盤價格':'停止買賣前價格' | |
| ,'減資恢復買賣開始日參考價格':'恢復買賣參考價' | |
| ,'開始交易基準價':'開盤競價基準'}) | |
| data['恢復買賣日期'] = data['恢復買賣日期'].apply(lambda x:f'{x[:3]}/{x[3:5]}/{x[5:]}') | |
| self.raw_data = data | |
| def unify(self): | |
| data = self.raw_data | |
| data = data[data['停止買賣前價格']!=data['恢復買賣參考價']] | |
| data.drop(['漲停價格','跌停價格','開盤競價基準','除權參考價'], axis=1, inplace=True) | |
| if self.mkt_type=='sii': | |
| data['詳細資料'] = data['詳細資料'].apply(lambda x:x.split("'")[1][9:]) | |
| def rdc(x): | |
| p1, p2, reason, dtl = x[0], x[1], x[2], x[3] | |
| if reason=='彌補虧損': | |
| exchange_ratio,refund = float(p1)/float(p2)*1000,0 | |
| else: | |
| stockId = dtl.split('STK_NO=')[1].rstrip() | |
| headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36'} | |
| basic_url = 'https://www.twse.com.tw/zh/' | |
| url = basic_url+dtl | |
| r = requests.get(url, headers=headers) | |
| d = pd.read_html(StringIO(r.text))[0] | |
| exchange_ratio = float(d.iloc[3][1].split(' ')[0]) | |
| refund = float(d.iloc[4][1].split(' ')[0]) | |
| time.sleep(4) | |
| return exchange_ratio,refund | |
| tmpDtl = data[['停止買賣前價格','恢復買賣參考價','減資原因','詳細資料']].apply(rdc, axis=1) | |
| elif self.mkt_type=='otc': | |
| # 因較難取得詳細資料簡化計算 | |
| def rdc(x): | |
| p1, p2 = x[0], x[1] | |
| exchange_ratio,refund = float(p1)/float(p2)*1000,0 | |
| return exchange_ratio,refund | |
| tmpDtl = data[['停止買賣前價格','恢復買賣參考價']].apply(rdc, axis=1) | |
| data['每千股換發股數'] = tmpDtl.apply(lambda x:x[0]) | |
| data['每股退還金額'] = tmpDtl.apply(lambda x:x[1]) | |
| cols = ['恢復買賣日期', '股票代號', '名稱', '停止買賣前價格', '恢復買賣參考價', '減資原因', '每千股換發股數', '每股退還金額'] | |
| data = data[cols] | |
| data = data.rename(columns={'恢復買賣日期':'資料日期'}) | |
| for i in ['每千股換發股數', '每股退還金額']: | |
| data[i] = data[i].apply(lambda x:round(x,2)) | |
| for i in ['停止買賣前價格', '恢復買賣參考價', '每千股換發股數', '每股退還金額']: | |
| data[i] = data[i].astype(float) | |
| def f(x): | |
| x = x.split('/') | |
| return str(int(x[0])+1911)+x[1]+x[2] | |
| data['資料日期'] = data['資料日期'].apply(f) | |
| data['MKT'] = '上市' if self.mkt_type=='sii' else '上櫃' | |
| cols = list(data.columns) | |
| data = data[cols[:1]+cols[-1:]+cols[1:-1]] | |
| self.data = data | |
| # data - 回測日價格 - 還原價格 - 年報酬 - 同期台灣50之報酬 | |
| class RvnCmpt: | |
| def __init__(self,data,data_next,start_date='0401',end_date='0401'): | |
| self.data=data | |
| self.data_next=data_next | |
| self.stm_year=list(self.data.財報年度)[0] | |
| self.test_start_year=self.stm_year+1 | |
| self.test_end_year=self.stm_year+2 if self.data_next is not None else self.test_start_year | |
| self.start_date=start_date | |
| self.end_date=end_date | |
| self.start_ymd=f'{self.test_start_year}{self.start_date}' | |
| self.end_ymd=f'{self.test_end_year}{self.end_date}' | |
| def addPrice(self): | |
| if self.data_next is not None: | |
| data = pd.merge(self.data, self.data_next[['公司代號','年報公佈後收盤價']], how='inner', on='公司代號') | |
| data = data[data['年報公佈後收盤價_y']!=0] | |
| data.rename(columns = {'年報公佈後收盤價_x':'年報公佈後收盤價' | |
| ,'年報公佈後收盤價_y':'次年年報公佈後收盤價'}, inplace=True) | |
| else: | |
| # get price that day | |
| dt = self.end_ymd # yyyymmdd | |
| sii_dt = dt | |
| otc_dt = f'{(int(dt[:4])-1911)}/{dt[4:6]}/{dt[6:]}' | |
| # scraping | |
| sii_price = siiPrice(sii_dt) | |
| sii_price = sii_price[['證券代號', '證券名稱', '收盤價']] | |
| sii_price = sii_price.rename(columns = {'證券代號':'公司代號', '證券名稱':'公司名稱'}) | |
| otc_price = otcPrice(otc_dt) | |
| otc_price = otc_price[['代號', '名稱', '收盤']] | |
| otc_price = otc_price.rename(columns = {'代號':'公司代號', '名稱':'公司名稱', '收盤':'收盤價'}) | |
| p = pd.concat([sii_price,otc_price]) | |
| p = p[(~p.公司名稱.str.contains('筆')) & (p.收盤價!='----')] | |
| p = p[['公司代號', '收盤價']] | |
| p.收盤價 = p.收盤價.apply(float) | |
| # combine | |
| data = pd.merge(self.data,p,how='inner',on='公司代號') | |
| data = data[data['收盤價']!=0] | |
| data = data.rename(columns = {'收盤價':'測試日收盤價'}) | |
| self.data=data | |
| def adjDiv(self): | |
| if self.data_next is not None: | |
| original_price_name = '次年年報公佈後收盤價' | |
| div = pd.concat([pd.read_csv(f'./div/div_{self.test_start_year}.csv')\ | |
| ,pd.read_csv(f'./div/div_{self.test_end_year}.csv')], ignore_index=True) | |
| div = div[(div['資料日期']>=int(f'{self.test_start_year}{self.start_date}'))\ | |
| &(div['資料日期']<int(f'{self.test_end_year}{self.end_date}'))] | |
| else: | |
| original_price_name = '測試日收盤價' | |
| div = pd.read_csv(f'./div/div_{self.test_start_year}.csv') | |
| div = div[(div['資料日期']>=int(f'{self.test_start_year}{self.start_date}'))\ | |
| &(div['資料日期']<int(f'{self.test_end_year}{self.end_date}'))] | |
| div = div.rename(columns={'股票代號':'公司代號'}) | |
| div = div[['公司代號', '現金股利', '股票股利']] | |
| div.公司代號 = div.公司代號.astype(str) | |
| self.div = div | |
| self.data = pd.merge(self.data,div,how='left',on='公司代號') | |
| self.data['現金股利'] = self.data['現金股利'].fillna(0) | |
| self.data['股票股利'] = self.data['股票股利'].fillna(0) | |
| self.data['還原股利後價格'] = self.data[[original_price_name, '現金股利', '股票股利']].apply\ | |
| (lambda x:(x[0]*(1000+x[2])/1000)+x[1], axis=1) | |
| data = self.data.drop_duplicates() | |
| # address companines issueing multiple dividents | |
| mtp_div_companies = [k for (k,v) in Counter(data.公司代號).items() if v > 1] | |
| if mtp_div_companies!=[]: | |
| def recalculate_div(companyId,data): | |
| data_exclude = data[data.公司代號!=companyId] | |
| tgt = data[data.公司代號==companyId] | |
| tgt = tgt.reset_index().drop('index', axis=1) | |
| div_cols = ['公司代號','現金股利','股票股利'] | |
| recalculate_cols = ['還原股利後價格'] | |
| same_cols = ['公司代號']+list(filter(lambda x:x not in div_cols+recalculate_cols, list(tgt.columns))) | |
| tgt_div = tgt[div_cols] | |
| tgt_div = tgt_div.groupby('公司代號').sum().reset_index() | |
| tgt_same = tgt.loc[[0]][same_cols] | |
| tgt = pd.merge(tgt_same,tgt_div,how='left',on='公司代號') | |
| p = '次年年報公佈後收盤價' if '測試日收盤價' not in same_cols else '測試日收盤價' | |
| tgt['還原股利後價格'] = tgt[[p, '現金股利', '股票股利']].apply\ | |
| (lambda x:(x[0]*(1000+x[2])/1000)+x[1], axis=1) | |
| data = pd.concat([data_exclude,tgt],ignore_index=True) | |
| return data | |
| for i in mtp_div_companies: | |
| data = recalculate_div(i,data) | |
| self.data = data | |
| def adjRdc(self): | |
| original_price_name = '還原股利後價格' | |
| if self.data_next is not None: | |
| rdc = pd.concat([pd.read_csv(f'./rdc/rdc_{self.test_start_year}.csv')\ | |
| ,pd.read_csv(f'./rdc/rdc_{self.test_end_year}.csv')], ignore_index=True) | |
| rdc = rdc[(rdc['資料日期']>=int(f'{self.test_start_year}{self.start_date}'))\ | |
| &(rdc['資料日期']<int(f'{self.test_end_year}{self.end_date}'))] | |
| else: | |
| rdc = pd.read_csv(f'./rdc/rdc_{self.test_start_year}.csv') | |
| rdc = rdc[(rdc['資料日期']>=int(f'{self.test_start_year}{self.start_date}'))\ | |
| &(rdc['資料日期']<int(f'{self.test_end_year}{self.end_date}'))] | |
| rdc = rdc.rename(columns={'股票代號':'公司代號'}) | |
| rdc = rdc[['公司代號', '每千股換發股數', '每股退還金額']] | |
| rdc.公司代號 = rdc.公司代號.astype(str) | |
| self.rdc = rdc | |
| self.data = pd.merge(self.data,rdc,how='left',on='公司代號') | |
| self.data['每千股換發股數'] = self.data['每千股換發股數'].fillna(0) | |
| self.data['每股退還金額'] = self.data['每股退還金額'].fillna(0) | |
| self.data['還原股利及減資價格'] = self.data[[original_price_name, '每千股換發股數', '每股退還金額']].apply\ | |
| (lambda x:x[0] if x[1]==0 else x[0]*(x[1]/1000)+x[2], axis=1) | |
| self.data = self.data.drop_duplicates() | |
| def getRvn(self): | |
| self.data = self.data.drop('還原股利後價格', axis=1) | |
| self.data = self.data.rename(columns={'還原股利及減資價格':'還原股價'}) | |
| self.data['年報酬(%)'] = round(((self.data['還原股價']/self.data['年報公佈後收盤價'])-1)*100,2) | |
| self.data['還原股價'] = self.data['還原股價'].apply(lambda x:round(x,2)) | |
| def get_tw50(self): | |
| etf = pd.read_csv(f'./etf/etf_{self.test_start_year}.csv') | |
| etf = etf[(etf['資料日期']>=int(f'{self.test_start_year}{self.start_date}'))\ | |
| &(etf['資料日期']<int(f'{self.test_end_year}{self.end_date}'))] | |
| def index(dt): | |
| d_test = siiPrice(dt) | |
| while d_test is None: | |
| dt = str(int(dt)+1) | |
| d_test = siiPrice(dt) | |
| d = d_test | |
| d = d[d['證券代號']=='0050'] | |
| d = list(d.收盤價)[0] | |
| return d | |
| ix1 = index(self.start_ymd) | |
| tw50_div = etf[etf['股票代號']=='0050'] | |
| tw50_div = list(tw50_div.現金股利)[0] | |
| ix2 = index(self.end_ymd) | |
| rev = round(((ix2+tw50_div)/ix1 -1)*100, 2) | |
| self.tw50 = ix1,ix2,rev | |
| def cmpr_tw50(self): | |
| self.data['勝過台灣50'] = self.data['年報酬(%)'].apply(lambda x:'Y' if x>self.tw50[2] else 'N') | |
| self.exc_pcnt = round(len([i for i in list(self.data['勝過台灣50'])\ | |
| if i!='N']) / len(list(self.data['勝過台灣50'])) * 100, 2) | |
| @staticmethod | |
| def back_testing(x,f): | |
| print(f'財報年度 = {x.stm_year}, 回測期間 = {x.start_ymd}~{x.end_ymd}') | |
| data = f(x.data) | |
| # # address companines issueing multiple dividents | |
| # data = data.drop_duplicates() | |
| # mtp_div_companies = [k for (k,v) in Counter(data.公司代號).items() if v > 1] | |
| # if mtp_div_companies!=[]: | |
| # def recalculate_div(companyId,data,tw_50_rvn = x.tw50[2]): | |
| # data_exclude = data[data.公司代號!=companyId] | |
| # x = data[data.公司代號==companyId] | |
| # x = x.reset_index().drop('index', axis=1) | |
| # div_cols = ['公司代號','現金股利','股票股利'] | |
| # recalculate_cols = ['還原股價','年報酬(%)','勝過台灣50'] | |
| # same_cols = ['公司代號']+list(filter(lambda x:x not in div_cols+recalculate_cols, list(x.columns))) | |
| # x_div = x[div_cols] | |
| # x_div = x_div.groupby('公司代號').sum().reset_index() | |
| # x = x.loc[[0]] | |
| # x_same = x[same_cols] | |
| # x = pd.merge(x_same,x_div,how='left',on='公司代號') | |
| # p = '次年年報公佈後收盤價' if '測試日收盤價' not in same_cols else '測試日收盤價' | |
| # x['還原股價'] = x[[p, '現金股利', '股票股利']].apply\ | |
| # (lambda x:(x[0]*(1000+x[2])/1000)+x[1], axis=1) | |
| # x['年報酬(%)'] = round(((x['還原股價']/x['年報公佈後收盤價'])-1)*100,2) | |
| # x['勝過台灣50'] = x['年報酬(%)'].apply(lambda x:'Y' if x>tw_50_rvn else 'N') | |
| # data = pd.concat([data_exclude,x],ignore_index=True) | |
| # return data | |
| # for i in mtp_div_companies: | |
| # data = recalculate_div(i,data) | |
| rev = np.array(data['年報酬(%)']) | |
| rev = rev[~np.isnan(rev)] | |
| rev = np.array(list(filter(lambda x:x!=np.inf, rev))) | |
| r = round(rev.mean(), 2) | |
| exc_rev = r - x.tw50[2] | |
| exc_pcnt = round(len([i for i in list(data['勝過台灣50'])\ | |
| if i!='N']) / len(list(data['勝過台灣50'])) * 100, 2) | |
| print(f'台灣50指數 {x.tw50[0]} -> {x.tw50[1]}, 年報酬 = {x.tw50[2]}%, 報酬>台灣50個股比例 = {x.exc_pcnt}%') | |
| print(f'篩選股數 = {data.shape[0]}') | |
| print(f'年報酬 = {r}%, 超額報酬 = {exc_rev}%, 報酬>台灣50個股比例 = {exc_pcnt}%\n') | |
| return data,r | |
| @staticmethod | |
| def SearchBestCondition(drv_ds, f, graph=True): | |
| prm_list = [] | |
| y_list = list(drv_ds.keys()) | |
| for y in y_list: | |
| if y!=y_list[-1]: | |
| x = RvnCmpt(drv_ds[y],drv_ds[y+1]) | |
| else: # 不完整年度處理 | |
| dt = LastTxnDate(y+1, 'sii')[-4:] # 隔年 | |
| x = RvnCmpt(drv_ds[y],None,end_date=dt) | |
| x.addPrice() | |
| x.adjDiv() | |
| x.adjRdc() | |
| x.getRvn() | |
| x.get_tw50() | |
| x.cmpr_tw50() | |
| data,r = RvnCmpt.back_testing(x,f) | |
| prm_list.append([data,x.tw50[2],r]) | |
| data_array,tw50_array,slct_array = np.array(prm_list)[:,0],np.array(prm_list)[:,1],np.array(prm_list)[:,2] | |
| if graph==True: | |
| plt.figure(figsize=(9,4)) | |
| y_array = np.array(list(range(y_list[1],y_list[-1]+2))) | |
| plt.plot(y_array,tw50_array,label='tw50') | |
| plt.plot(y_array,slct_array,label='自選etf') | |
| plt.title('tw50 v.s 自選etf') | |
| plt.xlabel('Year', fontsize=12) | |
| plt.ylabel('報酬率', fontsize=12) | |
| plt.legend() | |
| def hstRevn(t): | |
| return round((reduce(lambda x,y:(100+x)*(100+y), t)/100**len(t)-1)*100, 2) | |
| print('歷史績效:') | |
| print(f'近三年報酬 tw50={hstRevn(tw50_array[-3:])}% v.s 自選etf={hstRevn(slct_array[-3:])}%') | |
| print(f'近五年報酬 tw50={hstRevn(tw50_array[-5:])}% v.s 自選etf={hstRevn(slct_array[-5:])}%') | |
| print(f'近八年報酬 tw50={hstRevn(tw50_array)}% v.s 自選etf={hstRevn(slct_array)}%') | |
| return data_array,tw50_array,slct_array | |
| @staticmethod | |
| def select(d, cdts): | |
| for i in cdts: | |
| d = d[i] | |
| return d |
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