def get_volume(volumeNum, date, data=pd.DataFrame(), getMax=False):
Temp_index = 0
Temp_volume2 = 0
avgDay = 5
if data.empty == False:
for index, row in data.iterrows():
Temp_volume = get_stock_history.get_stock_price(
str(index),
tools.DateTime2String(date),
get_stock_history.stock_data_kind.Volume,
isSMA=True)
while (Temp_volume == None):
if get_stock_history.check_no_use_stock(index):
break
date = date + timedelta(days=-1) #加一天
Temp_volume = get_stock_history.get_stock_price(
str(index),
tools.DateTime2String(date),
get_stock_history.stock_data_kind.Volume,
isSMA=True)
if Temp_volume != None and Temp_volume >= volumeNum:
if (mypick.check_volum_Max.isChecked()):
if Temp_volume > Temp_volume2:
if (Temp_index != 0):
data = data.drop(index=Temp_index)
Temp_index = index
Temp_volume2 = Temp_volume
else:
data = data.drop(index=index)
else:
pass
else:
data = data.drop(index=index)
else:
print("get_volume:輸入的data是空的")
return data
def get_AVG_value(
time, volume, days,
data=pd.DataFrame()): #time = 取得資料的時間 volume = 平均成交金額 days = 平均天數
print('get_AVG_value: start')
Volume_Time = time
if type(Volume_Time) == str:
Volume_Time = datetime.strptime(time, "%Y-%m-%d")
All_monthRP = data
if All_monthRP.empty == True:
All_monthRP = get_allstock_monthly_report(Volume_Time)
Volume_data = pd.DataFrame(columns=['公司代號', 'volume'])
for i in range(0, len(All_monthRP)):
Temp_AvgVolume = 0
AvgDays = days
NoDataDays = 10
Temp_Volume_Time = Volume_Time
while AvgDays > 0:
if NoDataDays == 0:
break
Temp_Volume = get_stock_price(
str(All_monthRP.iloc[i].name),
tools.DateTime2String(Temp_Volume_Time),
stock_data_kind.Volume)
if Temp_Volume == None:
if Temp_Volume_Time == Volume_Time:
break
NoDataDays = NoDataDays - 1
Temp_Volume_Time = Temp_Volume_Time + timedelta(days=-1)
continue
Temp_AvgVolume = Temp_AvgVolume + Temp_Volume
AvgDays = AvgDays - 1
NoDataDays = 10
Temp_Volume_Time = Temp_Volume_Time + timedelta(days=-1)
Temp_AvgVolume = Temp_AvgVolume / days
if Temp_AvgVolume >= volume:
Temp_number = int(All_monthRP.iloc[i].name)
Volume_data.loc[(len(Volume_data) + 1)] = {
'公司代號': Temp_number,
'volume': Temp_AvgVolume
}
print('get_AVG_value: ' + str(All_monthRP.iloc[i].name) + '/' +
str(Temp_AvgVolume))
Volume_data['公司代號'] = Volume_data['公司代號'].astype('int')
Volume_data['volume'] = Volume_data['volume'].astype('int')
Volume_data.set_index('公司代號', inplace=True)
print('get_AVG_value: end')
return Volume_data
def avg_stock_price(date,vData = pd.DataFrame()):
All_price = 0 #
Count = 0
Result_avg_price = 0
if(vData.empty == True):
return 0,0
for value in range(0,len(vData)):
Nnumber = str(vData.iloc[value].name)
Temp_stock_price = get_stock_history.get_stock_price(Nnumber,tools.DateTime2String(date),
get_stock_history.stock_data_kind.AdjClose)
if Temp_stock_price != None:
All_price = All_price + Temp_stock_price
Count = Count + 1
if All_price == 0 or Count == 0:
return 0,0
Result_avg_price = All_price / Count
return Result_avg_price,Count
def get_stock_price(number, date, kind, isSMA=False): #取得某股票某天的ＡＤＪ價格
global Holiday_trigger
if check_no_use_stock(number) == True:
print('get_stock_price: ' + str(number) + ' in no use')
return None
stock_data = get_stock_history(number,
date,
reGetInfo=False,
UpdateInfo=False)
if stock_data.empty == True:
return None
result = stock_data[kind.value]
if kind == stock_data_kind.Volume:
result = tools.smooth_Data(result, 5)
result = stock_data[stock_data.index == date]
if result.empty == True:
if Holiday_trigger == True:
return None
if type(date) != str:
date = tools.DateTime2String(date)
if datetime.strptime(date, "%Y-%m-%d").isoweekday() in [1, 2, 3, 4, 5]:
stock_data = get_stock_history(number,
date,
reGetInfo=False,
UpdateInfo=False) #只會重新抓硬碟資料
result = stock_data[stock_data.index == date]
if result.empty == True:
print('get_stock_price: ' + '星期' +
str(datetime.strptime(date, "%Y-%m-%d").isoweekday()))
print('get_stock_price: ' + str(number) + '--' + date +
' is no data. Its holiday?')
Holiday_trigger = True
return None
else:
return None
result = result[kind.value]
close = result[date]
Holiday_trigger = False
return close
def backtest_KD_pick(mainParament):
userInfo = get_user_info.data_user_info(mainParament.money_start,
mainParament.date_start,
mainParament.date_end)
Temp_result_pick = pd.DataFrame(columns=['date', '選股數量'])
Temp_table = get_stock_history.get_stock_history(2330,
userInfo.start_day,
reGetInfo=False,
UpdateInfo=False)
All_stock_signal = dict()
for key, value in get_stock_info.ts.codes.items():
if value.market == "上市" and len(value.code) == 4:
if get_stock_history.check_no_use_stock(value.code) == True:
print('get_stock_price: ' + str(value.code) + ' in no use')
continue
table = get_stock_history.get_stock_history(value.code,
userInfo.start_day,
reGetInfo=False,
UpdateInfo=False)
table_K, table_D = talib.STOCH(table['High'],
table['Low'],
table['Close'],
fastk_period=50,
slowk_period=20,
slowk_matype=0,
slowd_period=20,
slowd_matype=0)
table_sma10 = talib.SMA(np.array(table['Close']), 10)
table_sma240 = talib.SMA(np.array(table['Close']), 240)
signal_buy = (table_K > table_D)
signal_sell = (table_K < table_D)
signal_sma10 = table.Close < table_sma10
signal_sma240 = table.Close > table_sma240
signal = signal_buy.copy()
signal = (signal_sma10 & signal_sma240 & signal_buy)
signal[signal_sell] = -1
All_stock_signal[value.code] = signal
ROE_record_day = datetime.datetime(2000, 1, 1)
buy_data = pd.DataFrame(columns=['Date', '公司代號']).set_index('Date')
sell_data = pd.DataFrame(columns=['Date', '公司代號']).set_index('Date')
ROE_data = {}
jump_day = 240
for index, row in Temp_table.iterrows():
while userInfo.now_day < index:
userInfo.add_one_day()
if jump_day > 0:
jump_day = jump_day - 1
continue
if ROE_record_day <= index:
ROE_data = {}
ROE_record_day = tools.changeDateMonth(index, 1)
BOOK_data = get_stock_history.get_allstock_financial_statement(
tools.changeDateMonth(index, -3), get_stock_history.FS_type.BS)
CPL_data = get_stock_history.get_allstock_financial_statement(
tools.changeDateMonth(index, -3),
get_stock_history.FS_type.CPL)
ROE_data["ROE_data_1"] = pd.DataFrame({
'ROE_data_1':
(CPL_data["本期綜合損益總額（稅後）"] / BOOK_data['權益總額']) * 100
})
BOOK_data = get_stock_history.get_allstock_financial_statement(
tools.changeDateMonth(index, -6), get_stock_history.FS_type.BS)
CPL_data = get_stock_history.get_allstock_financial_statement(
tools.changeDateMonth(index, -6),
get_stock_history.FS_type.CPL)
ROE_data["ROE_data_2"] = pd.DataFrame({
'ROE_data_2':
(CPL_data["本期綜合損益總額（稅後）"] / BOOK_data['權益總額']) * 100
})
BOOK_data = get_stock_history.get_allstock_financial_statement(
tools.changeDateMonth(index, -9), get_stock_history.FS_type.BS)
CPL_data = get_stock_history.get_allstock_financial_statement(
tools.changeDateMonth(index, -9),
get_stock_history.FS_type.CPL)
ROE_data["ROE_data_3"] = pd.DataFrame({
'ROE_data_3':
(CPL_data["本期綜合損益總額（稅後）"] / BOOK_data['權益總額']) * 100
})
BOOK_data = get_stock_history.get_allstock_financial_statement(
tools.changeDateMonth(index, -12),
get_stock_history.FS_type.BS)
CPL_data = get_stock_history.get_allstock_financial_statement(
tools.changeDateMonth(index, -12),
get_stock_history.FS_type.CPL)
ROE_data["ROE_data_4"] = pd.DataFrame({
'ROE_data_4':
(CPL_data["本期綜合損益總額（稅後）"] / BOOK_data['權益總額']) * 100
})
mask = tools.MixDataFrames(ROE_data)
ROE_data_result = (mask["ROE_data_1"] + mask["ROE_data_2"] +
mask["ROE_data_3"] + mask["ROE_data_4"]) / 4
ROE_data_result = ROE_data_result.dropna()
ROE_data = mask["ROE_data_1"] > ROE_data_result
buy_numbers = []
sell_numbers = []
for i, value in All_stock_signal.items():
try:
if value[index] == True:
if ROE_data.index.__contains__(int(i)):
if ROE_data[int(i)] == True:
buy_numbers.append(i)
elif value[index] < 0:
sell_numbers.append(i)
except:
#print('error:' + str(index) + ' at ' + str(i))
continue
#出場訊號篩選--------------------------------------
if len(userInfo.handle_stock) > 0:
Temp_data = userInfo.handle_stock
for key, value in list(Temp_data.items()):
if sell_numbers.__contains__(key):
userInfo.sell_stock(key, value.amount)
#入場訊號篩選--------------------------------------
if len(buy_numbers) > 0:
Temp_buy = pd.DataFrame(columns={'公司代號', 'volume'})
for number in buy_numbers:
volume = get_stock_history.get_stock_price(
number, tools.DateTime2String(userInfo.now_day),
get_stock_history.stock_data_kind.Volume)
Temp_buy = Temp_buy.append({
'公司代號': number,
'volume': volume
},
ignore_index=True)
Temp_buy = Temp_buy.sort_values(by='volume',
ascending=False).set_index('公司代號')
userInfo.buy_all_stock(Temp_buy)
if len(buy_numbers) != 0:
buy_data = buy_data.append({
'Date': index,
'公司代號': buy_numbers
},
ignore_index=True)
if len(sell_numbers) != 0:
sell_data = sell_data.append({
'Date': index,
'公司代號': sell_numbers
},
ignore_index=True)
#更新資訊--------------------------------------
userInfo.Record_userInfo()
userInfo.Recod_tradeInfo()
Temp_result_pick = Temp_result_pick.append(
{
'date': userInfo.now_day,
'選股數量': len(buy_numbers)
},
ignore_index=True)
buy_data = buy_data.set_index('Date')
buy_data.to_csv('buy.csv')
sell_data = sell_data.set_index('Date')
sell_data.to_csv('sell.csv')
#最後總結算----------------------------
Temp_result_pick.set_index('date', inplace=True)
Temp_alldata = tools.MixDataFrames(
{
'draw': userInfo.Temp_result_draw,
'pick': Temp_result_pick
}, 'date')
draw_figur.draw_backtest(userInfo.Temp_result_draw.set_index('date'))
Temp_alldata = tools.MixDataFrames(
{
'all': Temp_alldata,
'userinfo': userInfo.Temp_result_All
}, 'date')
userInfo.Temp_result_draw.set_index('date').to_csv('backtestdata.csv')
userInfo.Temp_trade_info.set_index('date').to_csv('backtesttrade.csv')
Temp_alldata.set_index('date').to_csv('backtestAll.csv')
def backtest_KD_pick(mainParament):
userInfo = get_user_info.data_user_info(mainParament.money_start,mainParament.date_start,mainParament.date_end)
Temp_result_pick = pd.DataFrame(columns=['date','選股數量'])
Temp_table = get_stock_history.get_stock_history(2330,"2005-01-01",reGetInfo = False,UpdateInfo = False)
All_stock_signal = dict()
ROE_record_day = datetime.strptime("2000-01-01","%Y-%m-%d")
buy_data = pd.DataFrame(columns = ['Date','code']).set_index('Date')
sell_data = pd.DataFrame(columns = ['Date','code']).set_index('Date')
ROE_data = {}
add_one_day = userInfo.add_one_day
changeDateMonth = tools.changeDateMonth
get_ROE_range = get_stock_history.get_ROE_range
MixDataFrames = tools.MixDataFrames
sell_stock = userInfo.sell_stock
buy_all_stock = userInfo.buy_all_stock
for index,row in Temp_table.iterrows():
if index < userInfo.now_day:
continue
while userInfo.now_day != index:
if add_one_day() == False:
break
has_trade = False
#ROE篩選
if ROE_record_day <= index:
ROE_data = {}
ROE_record_day = changeDateMonth(index,1)
ROE_data["ROE_data_1"] = pd.DataFrame(get_ROE_range(changeDateMonth(index,-3),0,999))
ROE_data["ROE_data_1"].rename(columns={'ROE':'ROE_data_1'},inplace=True)
ROE_data["ROE_data_2"] = pd.DataFrame(get_ROE_range(changeDateMonth(index,-6),0,999))
ROE_data["ROE_data_2"].rename(columns={'ROE':'ROE_data_2'},inplace=True)
ROE_data["ROE_data_3"] = pd.DataFrame(get_ROE_range(changeDateMonth(index,-9),0,999))
ROE_data["ROE_data_3"].rename(columns={'ROE':'ROE_data_3'},inplace=True)
ROE_data["ROE_data_4"] = pd.DataFrame(get_ROE_range(changeDateMonth(index,-12),0,999))
ROE_data["ROE_data_4"].rename(columns={'ROE':'ROE_data_4'},inplace=True)
mask = MixDataFrames(ROE_data)
ROE_data_result =(mask["ROE_data_1"]+mask["ROE_data_2"]+mask["ROE_data_3"]+mask["ROE_data_4"])/4
ROE_data_result = ROE_data_result.dropna()
ROE_data_mask = mask["ROE_data_1"]> ROE_data_result
ROE_data = ROE_data_mask[ROE_data_mask]
for key,value in ROE_data.iteritems():#先算出股票的買賣訊號
if get_stock_history.check_no_use_stock(key) == True:
print('get_stock_price: ' + str(key) + ' in no use')
continue
if All_stock_signal.__contains__(key):
continue
table = get_stock_history.get_stock_history(key,"2005-01-01",reGetInfo = False,UpdateInfo = False)
table_K,table_D = talib.STOCH(table['High'],table['Low'],table['Close'],fastk_period=50, slowk_period=20, slowk_matype=0, slowd_period=20, slowd_matype=0)
table_sma10 = talib.SMA(np.array(table['Close']), 10)
table_sma240 = talib.SMA(np.array(table['Close']), 240)
signal_buy = (table_K > table_D)
signal_sell = (table_K < table_D)
signal_sma10 = table.Close < table_sma10
signal_sma240 = table.Close > table_sma240
signal = signal_buy.copy()
signal = (signal_sma10 & signal_sma240 & signal_buy)
signal[signal_sell] = -1
All_stock_signal[key] = signal
#找出買入訊號跟賣出訊號-------------------------
buy_numbers = []
sell_numbers = []
for i,value in All_stock_signal.items():
try:
if value[index] == True:
if ROE_data.index.__contains__(int(i)):
if ROE_data[int(i)] == True:
buy_numbers.append(i)
elif value[index] < 0 :
sell_numbers.append(i)
except:
#print('error:' + str(index) + ' at ' + str(i))
continue
#出場訊號篩選--------------------------------------
if len(userInfo.handle_stock) > 0:
Temp_data = userInfo.handle_stock
for key,value in list(Temp_data.items()):
if sell_numbers.__contains__(int(key)):
sell_stock(key,value.amount)
has_trade = True
#入場訊號篩選--------------------------------------
if len(buy_numbers) > 0 :
Temp_buy = pd.DataFrame(columns={'code','volume'})
for number in buy_numbers:
volume = get_stock_history.get_stock_price(number,tools.DateTime2String(userInfo.now_day),get_stock_history.stock_data_kind.Volume)
Temp_buy = Temp_buy.append({'code':str(number),'volume':volume},ignore_index = True)
Temp_buy = Temp_buy.sort_values(by='volume', ascending=False).set_index('code')
buy_all_stock(Temp_buy)
has_trade = True
#更新資訊--------------------------------------
if has_trade:
if len(buy_numbers) != 0:
buy_data = buy_data.append({'Date':index,'code':buy_numbers},ignore_index = True)
if len(sell_numbers) != 0:
sell_data = sell_data.append({'Date':index,'code':sell_numbers},ignore_index = True)
userInfo.Record_userInfo()
userInfo.Recod_tradeInfo()
Temp_result_pick = Temp_result_pick.append({'date':userInfo.now_day,
'選股數量':len(buy_numbers)},ignore_index = True)
buy_data = buy_data.set_index('Date')
buy_data.to_csv('buy.csv')
sell_data = sell_data.set_index('Date')
sell_data.to_csv('sell.csv')
#最後總結算----------------------------
Temp_result_pick.set_index('date',inplace=True)
Temp_alldata = tools.MixDataFrames({'draw':userInfo.Temp_result_draw,'pick':Temp_result_pick},'date')
Temp_alldata = tools.MixDataFrames({'all':Temp_alldata,'userinfo':userInfo.Temp_result_All},'date')
userInfo.Temp_result_draw.set_index('date').to_csv('backtestdata.csv')
userInfo.Temp_trade_info.set_index('date').to_csv('backtesttrade.csv')
Temp_alldata.set_index('date').to_csv('backtestAll.csv')
return userInfo.Temp_result_draw.set_index('date')
def get_stock_AD_index(date): #取得上漲和下跌家數
print('get_stock_AD_index')
ADindex_result = pd.DataFrame(
columns=['Date', '上漲', '下跌']).set_index('Date')
if type(date) == str:
date = datetime.strptime(date, "%Y-%m-%d")
time = date
str_date = tools.DateTime2String(time)
time_yesterday = tools.backWorkDays(time, 1)
while (get_stock_price(2330, time_yesterday,
stock_data_kind.AdjClose) == None):
time_yesterday = tools.backWorkDays(time_yesterday, 1) #加一天
str_yesterday = tools.DateTime2String(time_yesterday)
fileName = filePath + '/' + fileName_index + '/' + 'AD_index'
if fileName in load_memery:
ADindex_result = load_memery[fileName]
#=========test
ADindex_result = load_other_file(fileName, 'AD_index')
if ADindex_result.empty == True:
if os.path.isfile(fileName + '.csv') == True:
ADindex_result = pd.read_csv(fileName + '.csv',
index_col='Date',
parse_dates=['Date'])
load_memery[fileName] = ADindex_result
else:
print('no csv file')
#=========
up = 0
down = 0
if ADindex_result.empty == False and (ADindex_result.index
== time).__contains__(True):
return ADindex_result[ADindex_result.index == time]
for key, value in get_stock_info.ts.codes.items():
if value.market == "上市" and len(value.code) == 4:
if check_no_use_stock(value.code) == True:
print('get_stock_price: ' + str(value.code) + ' in no use')
continue
m_history = get_stock_history(value.code,
str_yesterday,
reGetInfo=False,
UpdateInfo=False)['Close']
try:
if m_history[str_yesterday] > m_history[str_date]:
down = down + 1
elif m_history[str_yesterday] < m_history[str_date]:
up = up + 1
except:
print("get " + str(value.code) + " info fail!")
m_temp = get_stock_history(2330,
str_yesterday,
reGetInfo=False,
UpdateInfo=False)['Close']
if (m_temp.index == time).__contains__(True) != True:
return pd.DataFrame()
ADindex_result_new = pd.DataFrame({
'Date': [time],
'上漲': [up],
'下跌': [down]
}).set_index('Date')
ADindex_result = ADindex_result.append(ADindex_result_new)
ADindex_result = ADindex_result.sort_index()
update_stock_info.saveTable('AD_index', ADindex_result)
ADindex_result.to_csv(fileName + '.csv')
load_memery[fileName] = ADindex_result
df = ADindex_result[ADindex_result.index == time]
return df