def update_output_div(input_value, date):
"""
根據所選策略、所選時間，用simulation函式來產生 dataframe equality 和標題
並且顯示於網頁中的 table 上
"""
# 還沒選擇
if date is None or input_value is None:
return html.H4(children='請選擇上方的策略與日期')
try:
# 監測開始
print('start simulation')
date = datetime.datetime.strptime(date.split()[0], '%Y-%m-%d')
data = Data()
# 根據所選策略、所選時間，用simulation函式來產生 dataframe equality 和標題
df, eq, s = simulation(strategies[input_value], data, date.date())
print('end simulation')
df.index.name = '股票代號'
# 產生 html
return html.Div(children=[
html.H3(s),
html.Br(),
dcc.Graph(
id='example-graph-2',
# 畫圖
figure={
'data': [
{
'x': eq.index,
'y': eq,
'type': 'line',
'name': 'SF'
},
],
'layout': {}
}
# 用 generate_table 將 dataframe 轉成 HTML table
),
generate_table(df.reset_index().round(2), len(df))
])
except:
errorlog = "Unexpected error: " + str(sys.exc_info())
return html.H4(children='遇到了一些問題喔！' + errorlog)
if __name__ == '__main__':
SumROE_DF_All = pd.DataFrame()
for RSV_Ndays in range (50, 201, 50):
for StockHold_Days in range (30, 61, 30):
# Input:
StartDate = PickDate('2015_01_01')
EndDate = PickDate('2018_12_31')
print('RSV_Ndays: ' + str(RSV_Ndays))
print('StockHold_Days: ' + str(StockHold_Days))
# Evaluation:
# equality是累計總報酬率 2018-11-30 0.859861
# transections是買賣個股的價格 1701 2018-05-03 20.90 ... 2018-05-31 20.55
equality, transections = backtest(StartDate, EndDate, StockHold_Days, RSV_Ndays, Data())
# Output:
dicData = dic_ROE(equality, RSV_Ndays, StockHold_Days)
SumROE_DF_All = SumROE_DF_All.append(dicData, ignore_index = True)
SumROE_DF_All_Sort = SumROE_DF_All.sort_values( by = 'SumROE', ascending= True)
print(SumROE_DF_All_Sort)
'''
# 測試最佳 RSV_Ndays 50 - 200天
for RSV_Ndays in range(50 , 201, 50):
for Sold_NDays in range (30, 61,30):
print('RSV '+str(RSV_Ndays)+' days')
print('持股天數 '+str(Sold_NDays)+' days')
data = Data()
RSV_Ratio = RSV_Ratio / 100
for StockHold_Days in range(15, 121, 15):
# Inpu
StartDate = PickDate('2018_01_01')
EndDate = PickDate('2018_12_30')
print('-' * 10 + ' RSV & Hold Days ' + '-' * 10)
print('RSV_Ndays: ' + str(RSV_Ndays))
print('RSV_Ratio: ' + str(RSV_Ratio))
print('StockHold_Days: ' + str(StockHold_Days))
# Evaluation:
# equality是累計總報酬率 2018-11-30 0.859861
# transections是買賣個股的價格 1701 2018-05-03 20.90 ... 2018-05-31 20.55
equality, transections, sheet_name = backtest(
StartDate, EndDate, StockHold_Days, RSV_Ndays, RSV_Ratio,
lstStockSorted, PickStockNumber, Data(), PathExcelFile)
# Output:
# Write All ROE to Excel into each page and All_ROE page
dftotal_ROE = pd.DataFrame()
dicData = dic_ROE(equality, RSV_Ndays, RSV_Ratio,
StockHold_Days)
dftotal_ROE = dftotal_ROE.append(dicData, ignore_index=True) #
AddDataToExcelSheet(PathExcelFile, sheet_name, dftotal_ROE)
AddDataToExcelSheet(PathExcelFile, 'All_ROE', dftotal_ROE)
#input('Check')
SumROE_DF_All = SumROE_DF_All.append(dicData,
ignore_index=True)
dfSumROE_All_Sorted = SumROE_DF_All.sort_values(by='SumROE',
ascending=True)
PathExcelFileName = datetime.datetime.now().strftime('%Y%m%d_%H%M')+'.xlsx'
PathExcelFile = os.getcwd()+'\\'+PathExcelFileName
for RSV_Ndays in range (50, 251, 100):
for StockHold_Days in range (15, 16, 15):
# Inpu
StartDate = PickDate('2018_11_01')
EndDate = PickDate('2018_12_30')
print('-'*10 + ' RSV & Hold Days ' +'-'*10)
print('RSV_Ndays: ' + str(RSV_Ndays))
print('StockHold_Days: ' + str(StockHold_Days))
# Evaluation:
# equality是累計總報酬率 2018-11-30 0.859861
# transections是買賣個股的價格 1701 2018-05-03 20.90 ... 2018-05-31 20.55
equality, transections = backtest(StartDate, EndDate, StockHold_Days, RSV_Ndays, lstStockSorted, PickStockNumber, Data(), PathExcelFile)
# Output:
dicData = dic_ROE(equality, RSV_Ndays, StockHold_Days)
SumROE_DF_All = SumROE_DF_All.append(dicData, ignore_index = True)
dfSumROE_All_Sort = SumROE_DF_All.sort_values( by = 'SumROE', ascending= True)
print('-'*10 + ' ROE Result ' +'-'*10)
print(dfSumROE_All_Sort)
# Write to Excel
sheet_name = 'ALL_ROE'
AddDataToExcelSheet(PathExcelFile, sheet_name, dfSumROE_All_Sort)
'''
SumROE_DF_All = pd.DataFrame()
for RSV_Ndays in range(50, 101, 50):
for StockHold_Days in range(15, 16, 15):
# Inpu
StartDate = PickDate('2018_10_01')
EndDate = PickDate('2018_12_30')
print('-' * 10 + ' RSV & Hold Days ' + '-' * 10)
print('RSV_Ndays: ' + str(RSV_Ndays))
print('StockHold_Days: ' + str(StockHold_Days))
# Evaluation:
# equality是累計總報酬率 2018-11-30 0.859861
# transections是買賣個股的價格 1701 2018-05-03 20.90 ... 2018-05-31 20.55
equality, transections, intReturnProfit = backtest(
intInvestAmount, intNetProfit, StartDate, EndDate,
StockHold_Days, RSV_Ndays, lstStockSorted, PickStockNumber,
Data(), PathExcelFile)
# Output:
dicData = dic_ROE(equality, RSV_Ndays, StockHold_Days)
SumROE_DF_All = SumROE_DF_All.append(dicData, ignore_index=True)
# Put ReturnAmount as InvestAmount
intNetProfit = intNetProfit + intReturnProfit
dfSumROE_All_Sort = SumROE_DF_All.sort_values(by='SumROE', ascending=True)
print('-' * 10 + ' ROE Result ' + '-' * 10)
print(dfSumROE_All_Sort)
# Write to Excel
sheet_name = 'ALL_ROE'
AddDataToExcelSheet(PathExcelFile, sheet_name, dfSumROE_All_Sort)
'''
if __name__ == '__main__':
SumROE_DF_All = pd.DataFrame()
for RSV_Ndays in range(50, 201, 50):
for StockHold_Days in range(30, 61, 30):
# Input:
StartDate = PickDate('2015_01_01')
EndDate = PickDate('2018_12_31')
print('RSV_Ndays: ' + str(RSV_Ndays))
print('StockHold_Days: ' + str(StockHold_Days))
# Evaluation:
# equality是累計總報酬率 2018-11-30 0.859861
# transections是買賣個股的價格 1701 2018-05-03 20.90 ... 2018-05-31 20.55
equality, transections = backtest(StartDate, EndDate,
StockHold_Days, RSV_Ndays,
Data())
# Output:
dicData = dic_ROE(equality, RSV_Ndays, StockHold_Days)
SumROE_DF_All = SumROE_DF_All.append(dicData, ignore_index=True)
SumROE_DF_All_Sort = SumROE_DF_All.sort_values(by='SumROE', ascending=True)
print(SumROE_DF_All_Sort)
'''
# 測試最佳 RSV_Ndays 50 - 200天
for RSV_Ndays in range(50 , 201, 50):
for Sold_NDays in range (30, 61,30):
print('RSV '+str(RSV_Ndays)+' days')
print('持股天數 '+str(Sold_NDays)+' days')
data = Data()
stocks = mystrategy2(data, RSV_Ndays)
#equality是累計總報酬率 2018-11-30 0.859861
import numpy as np
import pandas as pd
from finlab.data import Data
import datetime
if __name__ == '__main__':
data = Data()
ClosePrice = data.get('¦¬½L»ù', -2)
print(ClosePrice)
import datetime
import pandas as pd
from finlab.data import Data
from finlab.backtest import portfolio
from finlab.backtest_test import backtest
from strategies.MFS_new_strategy import strategy
data = Data()
starttime = datetime.datetime.now()
# 回測檢查報酬率
print("========Backtesting Begin========")
#設定pd讓輸出不要省略
#顯示所有列
pd.set_option('display.max_columns', None)
#顯示所有行
pd.set_option('display.max_rows', None)
#設置value的顯示長度为100，默認为50
pd.set_option('max_colwidth',100)
y1 = 2021
m1 = 10
d1 = 12
y2 = 2021
m2 = 10
d2 = 17
gap = 0
#!/usr/bin/env python
# coding: utf-8
# # 神經網路實做
# ## 拿取加權指數資料
# In[1]:
from finlab.data import Data
data = Data()
twii = data.get("發行量加權股價指數")
twii = twii['台股指數'].resample("15T").first().dropna()
twii.head()
# ## 製作features
# In[2]:
import talib
import numpy as np
import pandas as pd
sma = talib.SMA(twii, timeperiod=120)
wma = talib.WMA(twii, timeperiod=120)
mom = talib.MOM(twii, timeperiod=120)
k, d = talib.STOCH(twii,
twii,
twii,
def get_price(name="發行量加權股價指數", freq="15T"):
data = Data()
twii = data.get(name)
twii = twii['台股指數']
twii = twii.resample(freq).first().dropna()
return twii