如何使用Python計算還原股價
最近在開發量化投資系統,要做量化投資最重要的就是對股票進行回測,而回測計算報酬率的時候,必須計算還原股價 ,估計公司沒有除權息調整時的股價,所以這篇文章就來教大家如何用Python計算還原股價。
資料的方面我們使用FindMind所提供的開源數據集,來做計算~
一. 取得股票各年股利分配情況
首先先取得股票的各年股利分配情況的資料,那我們這邊使用台積電(2330),作為範例,參數中的token需要到FindMind的官網去 申請帳號取得。
import requests import pandas as pd def get_dividend_data(): url = "https://api.finmindtrade.com/api/v4/data" parameter = { "dataset": "TaiwanStockDividend", "data_id": "2330", "start_date": "2000-01-01", "token":'your token' } dividend_data = requests.get(url, params=parameter) dividend_data = dividend_data.json() dividend_data = pd.DataFrame(dividend_data['data']) return dividend_data
二. 取得股票的股價資料
一樣也是從FindMind官網去取得資料,和取得股利資料一樣。
def get_stock_data(): url = "https://api.finmindtrade.com/api/v4/data" parameter = { "dataset": "TaiwanStockPrice", "data_id": "2330", "start_date": "2006-01-01", "token":'your_token' } resp = requests.get(url, params=parameter) stock_data = resp.json() stock_data = pd.DataFrame(stock_data["data"]) return stock_data
三. 股利資料欄位介紹以及計算調整股價
首先我們計算還原股價會用到的是以下四個欄位:
- CashExDividendTradingDate : 除息交易日
- StockExDividendTradingDate : 除權交易日
- CashEarningsDistribution : 現金股利
- StockEarningsDistribution + StockStatutorySurplus : 股票股利
計算調整股價公式:
主要分成兩部分:
a. 計算除權因子: 在發放股票股利那天以前(包含當天)的股價都要乘上除權因子去調整股價。
除權因子=1/(1+股票股利/10)
除權因子=1/(1+股票股利/10)
b. 計算除息因子:
這裡有人可能會直接用收盤價扣現金股利,但這樣會導致股價調整前和調整後日收益率改變,但用下面的方法就可以讓日收益率不變, 因為我們是去計算發放現金股利後與原本股價的改變比例,而這裡t-1代表的是昨日的價格,在發放現金股利那天以前的股價 都要乘上除息因子去調整股價。
程式計算說明
首先將股利資料分別分成現金股利以及股票股利,並且將這兩個資料合併到股票價格資料上,因為股利資料會有先公布的資料, 也就是未來的資料,所以合併的同時也可以使未來的資料刪掉。
dividend_data = get_dividend_data() stock_data = get_stock_data() # 將資料分成現金股利和股票股利 stock_dividend_data = dividend_data[['StockExDividendTradingDate', 'StockEarningsDistribution', 'StockStatutorySurplus']] stock_dividend_data = stock_dividend_data.rename(columns={"StockExDividendTradingDate":'date'}) cash_dividend_data = dividend_data[['CashExDividendTradingDate', 'CashEarningsDistribution']] cash_dividend_data = cash_dividend_data.rename(columns={"CashExDividendTradingDate":'date'}) # 順便也將未來要發股利的去掉了 stock_data = stock_data.merge(stock_dividend_data, on='date', how='left') stock_data = stock_data.merge(cash_dividend_data, on='date', how='left')
然後在按照公式分別計算除權因子和除息因子
# 計算除權因子 stock_dividend_factor = 1 / (1 + (stock_data.loc[stock_data['StockEarningsDistribution'].notnull(), 'StockEarningsDistribution'] + stock_data.loc[stock_data['StockEarningsDistribution'].notnull(), 'StockStatutorySurplus']) / 10) stock_data.loc[stock_dividend_factor.index-1, 'stock_dividend_factor'] = stock_dividend_factor.values # 計算除息因子 stock_data['previous_close_price'] = stock_data['close'].shift(1) cash_dividend_factor = (stock_data.loc[stock_data['CashEarningsDistribution'].notnull(), 'previous_close_price'] - stock_data.loc[stock_data['CashEarningsDistribution'].notnull(), 'CashEarningsDistribution']) / stock_data.loc[stock_data['CashEarningsDistribution'].notnull(), 'previous_close_price'] stock_data.loc[cash_dividend_factor.index-1, 'cash_dividend_factor'] = cash_dividend_factor.values cash_dividend_index = stock_data.loc[stock_data['CashEarningsDistribution'].notnull(), 'cash_dividend_factor'].index
然後將每一期的除息因子都做累積乘積,這樣就可以將每一期調整的程度反映到過去的股價上,算完除息因子後乘上除權因子 就可以得到總因子,再乘上原始股價就可以得到調整股價,因為有些是在除權後的股價,因此就是填充為原始股價。
# 累積總因子 cum_prod_cash_dividend = stock_data.loc[stock_data['cash_dividend_factor'].notnull(), 'cash_dividend_factor'][::-1].cumprod()[::-1] stock_data.loc[cash_dividend_factor.index-1, 'cash_dividend_factor'] = cum_prod_cash_dividend.values stock_data['cash_dividend_factor'] = stock_data['cash_dividend_factor'].fillna(method='backfill') stock_data['stock_dividend_factor'] = stock_data['stock_dividend_factor'].fillna(method='backfill') stock_data['cash_dividend_factor'] = stock_data['cash_dividend_factor'].fillna(method='backfill') stock_data['stock_dividend_factor'] = stock_data['stock_dividend_factor'].fillna(method='backfill').fillna(1) stock_data['total_factor'] = stock_data['cash_dividend_factor'] * stock_data['stock_dividend_factor'] stock_data['adjust_price'] = stock_data['close'] * stock_data['total_factor'] # 還沒到除息日的股價用現在的股價填充 stock_data.loc[stock_data['adjust_price'].isnull(), 'adjust_price'] = stock_data.loc[stock_data['adjust_price'].isnull(), 'close']
那以下就是完整的程式碼,如果有什麼問題歡迎在下面留言,喜歡我的作品可以幫我拍拍手喔!!!
import requests import pandas as pd def get_dividend_data(): url = "https://api.finmindtrade.com/api/v4/data" parameter = { "dataset": "TaiwanStockDividend", "data_id": "2330", "start_date": "2000-01-01", "token":'your token' } dividend_data = requests.get(url, params=parameter) dividend_data = dividend_data.json() dividend_data = pd.DataFrame(dividend_data['data']) return dividend_data def get_stock_data(): url = "https://api.finmindtrade.com/api/v4/data" parameter = { "dataset": "TaiwanStockPrice", "data_id": "2330", "start_date": "2006-01-01", "token":'your token' } resp = requests.get(url, params=parameter) stock_data = resp.json() stock_data = pd.DataFrame(stock_data["data"]) return stock_data def cal_adjusted_price(stock_data, dividend_data): stock_dividend_data = dividend_data[['StockExDividendTradingDate', 'StockEarningsDistribution', 'StockStatutorySurplus']] stock_dividend_data = stock_dividend_data.rename(columns={"StockExDividendTradingDate":'date'}) cash_dividend_data = dividend_data[['CashExDividendTradingDate', 'CashEarningsDistribution']] cash_dividend_data = cash_dividend_data.rename(columns={"CashExDividendTradingDate":'date'}) # 順便也將未來要發股利的去掉了 stock_data = stock_data.merge(stock_dividend_data, on='date', how='left') stock_data = stock_data.merge(cash_dividend_data, on='date', how='left') # 計算除權因子 stock_dividend_factor = 1 / (1 + (stock_data.loc[stock_data['StockEarningsDistribution'].notnull(), 'StockEarningsDistribution'] + stock_data.loc[stock_data['StockEarningsDistribution'].notnull(), 'StockStatutorySurplus']) / 10) stock_data.loc[stock_data['StockEarningsDistribution'].notnull(), 'stock_dividend_factor'] = stock_dividend_factor # 計算除息因子 stock_data['previous_close_price'] = stock_data['close'].shift(1) cash_dividend_factor = (stock_data.loc[stock_data['CashEarningsDistribution'].notnull(), 'previous_close_price'] - stock_data.loc[stock_data['CashEarningsDistribution'].notnull(), 'CashEarningsDistribution']) / stock_data.loc[stock_data['CashEarningsDistribution'].notnull(), 'previous_close_price'] stock_data.loc[stock_data['CashEarningsDistribution'].notnull(), 'cash_dividend_factor'] = cash_dividend_factor cash_dividend_index = stock_data.loc[stock_data['CashEarningsDistribution'].notnull(), 'cash_dividend_factor'].index # 累積總因子 cum_prod_cash_dividend = stock_data.loc[stock_data['cash_dividend_factor'].notnull(), 'cash_dividend_factor'][::-1].cumprod()[::-1] cum_prod_cash_dividend.index = cash_dividend_index stock_data.loc[stock_data['CashEarningsDistribution'].notnull(), 'cash_dividend_factor'] = cum_prod_cash_dividend stock_data['cash_dividend_factor'] = stock_data['cash_dividend_factor'].fillna(method='backfill') stock_data['stock_dividend_factor'] = stock_data['stock_dividend_factor'].fillna(method='backfill') stock_data['cash_dividend_factor'] = stock_data['cash_dividend_factor'].fillna(method='backfill') stock_data['stock_dividend_factor'] = stock_data['stock_dividend_factor'].fillna(method='backfill').fillna(1) stock_data['total_factor'] = stock_data['cash_dividend_factor'] * stock_data['stock_dividend_factor'] stock_data['adjust_price'] = stock_data['close'] * stock_data['total_factor'] # 還沒到除息日的股價用現在的股價填充 stock_data.loc[stock_data['adjust_price'].isnull(), 'adjust_price'] = stock_data.loc[stock_data['adjust_price'].isnull(), 'close'] return stock_data if __name__ == "__main__": dividend_data = get_dividend_data() stock_data = get_stock_data() stock_data = cal_adjusted_price(stock_data, dividend_data)
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