def get_monthRP(date_end, date_start,
Number): #end = 後面時間 start = 前面時間 Number = 股票號碼
date_end_str = str(date_end.year()) + '-' + str(
date_end.month()) + '-' + str(date_end.day())
m_date_end = datetime.strptime(date_end_str, "%Y-%m-%d")
date_start_str = str(date_start.year()) + '-' + str(
date_start.month()) + '-' + str(date_start.day())
m_date_start = datetime.strptime(date_start_str, "%Y-%m-%d")
stockNum = Number
data_result = None
while (m_date_start <= m_date_end):
try:
monthRP_temp = get_stock_history.get_stock_monthly_report(
stockNum, m_date_start)
except:
print(str(m_date_start) + "月營收未出喔")
m_date_start = tools.changeDateMonth(m_date_start, +1)
continue
monthRP_temp.insert(0, '日期', m_date_start)
monthRP_temp['當月營收'] = int(monthRP_temp['當月營收']) / 1000
data_result = pd.concat([data_result, monthRP_temp])
m_date_start = tools.changeDateMonth(m_date_start, +1)
if m_date_start > m_date_end and m_date_end.year == m_date_start.year:
try:
monthRP_temp = get_stock_history.get_stock_monthly_report(
stockNum, m_date_start)
monthRP_temp.insert(0, '日期', m_date_start)
monthRP_temp['當月營收'] = int(monthRP_temp['當月營收']) / 1000
data_result = pd.concat([data_result, monthRP_temp])
except:
print(str(m_date_start) + "月營收未出喔")
data_result.set_index('日期', inplace=True)
return data_result
def get_ROE_range(
time, ROE_start, ROE_end,
data=pd.DataFrame()): #time = 取得資料的時間 ROE_start = ROE最小值 ROE_end ROE最大值
print('get_ROE_rang: start')
ROE_data = pd.DataFrame(columns=['公司代號', 'ROE'])
#股東權益＝資產 － 負債 （資產負債表中）
#稅後淨利 = (本期綜合損益)
if ROE_start == ROE_end == 0:
return ROE_data
if ROE_end < 0 or ROE_start < 0 or ROE_end < ROE_start:
print("ROE range number wrong!")
return ROE_data
ROE_date = time
All_ROE = data
if type(time) == str:
ROE_date = datetime.strptime(time, "%Y-%m-%d")
if ROE_date.month in [1, 2, 3]:
Use_ROE_date = datetime(ROE_date.year - 1, 12, 1)
else:
Use_ROE_date = datetime(
ROE_date.year,
tools.changeDateMonth(ROE_date, -3).month,
tools.check_monthDate(
tools.changeDateMonth(ROE_date, -3).month, ROE_date.day))
BOOK_data = get_allstock_financial_statement(Use_ROE_date, FS_type.BS)
CPL_data = get_allstock_financial_statement(Use_ROE_date, FS_type.CPL)
if All_ROE.empty == True:
All_ROE = BOOK_data
for index, row in All_ROE.iterrows():
if check_no_use_stock(index):
continue
Temp_Book = int(BOOK_data.at[index, '權益總額'])
Temp_CPL = int(CPL_data.at[index, "本期綜合損益總額（稅後）"])
Temp_ROE = round((Temp_CPL / Temp_Book), 4) * 100
if Temp_ROE < 0:
continue
print('get_ROE_range:' + str(index) + '--' + str(Temp_CPL) + '/' +
str(Temp_Book) + '= ' + str(Temp_ROE))
if (Temp_ROE > ROE_start) and (Temp_ROE < ROE_end):
Temp_number = int(index)
ROE_data = ROE_data.append({
'公司代號': Temp_number,
'ROE': Temp_ROE
},
ignore_index=True)
ROE_data['公司代號'] = ROE_data['公司代號'].astype('int')
ROE_data.set_index('公司代號', inplace=True)
print('get_ROE_rang: end')
return ROE_data
def get_monthRP_up(time, avgNum,
upNum): #time = 取得資料的時間 avgNum = 平滑曲線月份 upNum = 連續成長月份
print('get_monthRP_up: start:' + str(time))
fileName = str(time.year) + str(time.month) + str(avgNum) + str(upNum)
if fileName in load_memery:
print('get_monthRP_up: end')
return load_memery[fileName]
data = {}
for i in range(avgNum + upNum):
temp_now = tools.changeDateMonth(time, -(i + 1))
data['%d-%d-01' %
(temp_now.year,
temp_now.month)] = get_allstock_monthly_report(temp_now)
result = pd.DataFrame({k: result['當月營收']
for k, result in data.items()}).transpose()
result.index = pd.to_datetime(result.index)
result = result.sort_index()
method2 = result.rolling(avgNum, min_periods=avgNum).mean()
method2 = (method2 > method2.shift()).iloc[-upNum:].sum()
final_result = method2[method2 >= upNum]
final_result = pd.DataFrame(final_result)
try:
final_result = final_result.drop('全部國內上市公司合計', axis=0)
except:
final_result = final_result
final_result = final_result.rename(index=int)
final_result.index.name = '公司代號'
load_memery[str(time.year) + str(time.month) + str(avgNum) +
str(upNum)] = final_result
print('get_monthRP_up: end')
return final_result
def button_pick_click():
volume_date = tools.QtDate2DateTime(myshow.date_endDate.date())
GPM = mypick.input_GPM.toPlainText()
OPR = mypick.input_OPR.toPlainText()
EPS = mypick.input_EPS.toPlainText()
RPS = mypick.input_RPS.toPlainText()
resultAllFS = pd.DataFrame()
resultAllFS = get_financial_statement(volume_date, GPM, OPR, EPS, RPS)
if volume_date.isoweekday() == 6:
volume_date = volume_date + timedelta(days=-1) #加一天
elif volume_date.isoweekday() == 7:
volume_date = volume_date + timedelta(days=-2) #加2天
else:
pass
price_data = get_stock_history.get_price_range(
volume_date, int(mypick.input_price_high.toPlainText()),
int(mypick.input_price_low.toPlainText()), resultAllFS)
if price_data.empty == False:
resultAllFS = tools.MixDataFrames({
'pick': resultAllFS,
'recordHigh': price_data
})
resultAllFS = resultAllFS.dropna(axis=0, how='any')
resultAllFS = get_volume(int(mypick.input_volum.toPlainText()),
tools.changeDateMonth(volume_date, 0),
resultAllFS)
mypick.treeView_pick.setModel(
creat_treeView_model(mypick.treeView_pick,
pick_titalList)) #設定treeView功能
set_treeView2(mypick.treeView_pick.model(), resultAllFS)
def get_allstock_financial_statement(start, type): #爬某季所有股票歷史財報
print("get_allstock_financial_statement:" + str(type))
for i in range(12):
try:
season = int(((start.month - 1) / 3) + 1)
if season == 4:
season = 3
if start < datetime(start.year, 11, 14):
season = 2
start = tools.changeDateMonth(start, -6)
else:
start = tools.changeDateMonth(start, -3)
elif season == 3:
season = 2
if start < datetime(start.year, 8, 14):
season = 1
start = tools.changeDateMonth(start, -6)
else:
start = tools.changeDateMonth(start, -3)
elif season == 2:
season = 1
if start < datetime(start.year, 5, 15):
season = 4
start = tools.changeDateMonth(start, -6)
else:
start = tools.changeDateMonth(start, -3)
elif season == 1:
season = 4
if start < datetime(start.year, 3, 31):
season = 3
start = tools.changeDateMonth(start, -6)
else:
start = tools.changeDateMonth(start, -3)
fileName = filePath + '/' + fileName_season + '/' + str(
start.year) + "-season" + str(
season) + "-" + type.value + ".csv"
if fileName in load_memery:
return load_memery[fileName]
if os.path.isfile(fileName) == True:
print("已經有" + str(start.month) + "月財務報告")
break
financial_statement(start.year, season, type)
print("下載" + str(start.month) + "月財務報告ＯＫ")
break
except:
print(str(start.month) + "月財務報告未出跳下一個月")
start = tools.changeDateMonth(start, -1)
continue
stock = pd.read_csv(fileName, index_col='公司代號', parse_dates=['公司代號'])
load_memery[fileName] = stock
return stock
def get_price_range(time, high, low,
data=pd.DataFrame()): #time = 取得資料的時間 high = 最高價 low = 最低價
print('get_price_rang: start')
price_data = pd.DataFrame(columns=['公司代號', 'price'])
if high == low == 0:
return price_data
if high < low or high < 0 or low < 0:
print("price range number wrong!")
return price_data
price_time = time
All_price = data
if type(time) == str:
price_time = datetime.strptime(time, "%Y-%m-%d")
if price_time.month in [1, 2, 3]:
Use_price_time = datetime(price_time.year - 1, 12, 1)
else:
Use_price_time = datetime(
price_time.year,
tools.changeDateMonth(price_time, -3).month,
tools.check_monthDate(
tools.changeDateMonth(price_time, -3).month, price_time.day))
if All_price.empty == True:
return price_data
for index, row in All_price.iterrows():
if check_no_use_stock(index):
continue
Temp_price = get_stock_price(index, Use_price_time,
stock_data_kind.AdjClose)
if Temp_price == None:
continue
if (Temp_price > low) and (Temp_price < high):
Temp_number = int(index)
price_data = price_data.append(
{
'公司代號': Temp_number,
'price': Temp_price
}, ignore_index=True)
price_data['公司代號'] = price_data['公司代號'].astype('int')
price_data.set_index('公司代號', inplace=True)
print('get_price_rang: end')
return price_data
def get_allstock_monthly_report(start): #爬某月所有股票月營收
if start.day < 15: #還沒超過15號，拿前一個月
print("get_allstock_monthly_report:未到25號取上個月報表")
start = tools.changeDateMonth(start, -1)
year = start.year
fileName = filePath + '/' + fileName_monthRP + '/' + str(
start.year) + '-' + str(start.month) + 'monthly_report.csv'
if fileName in load_memery:
return load_memery[fileName]
if os.path.isfile(fileName) == False:
# 假如是西元，轉成民國
if year > 1990:
year -= 1911
url = 'https://mops.twse.com.tw/nas/t21/sii/t21sc03_' + str(
year) + '_' + str(start.month) + '_0.html'
if year <= 98:
url = 'https://mops.twse.com.tw/nas/t21/sii/t21sc03_' + str(
year) + '_' + str(start.month) + '.html'
# 下載該年月的網站，並用pandas轉換成 dataframe
r = requests.get(url, headers=tools.get_random_Header())
r.encoding = 'big5-hkscs'
dfs = pd.read_html(StringIO(r.text), encoding='big-5')
df = pd.concat(
[df for df in dfs if df.shape[1] <= 11 and df.shape[1] > 5])
if 'levels' in dir(df.columns):
df.columns = df.columns.get_level_values(1)
else:
df = df[list(range(0, 10))]
column_index = df.index[(df[0] == '公司代號')][0]
df.columns = df.iloc[column_index]
df['當月營收'] = pd.to_numeric(df['當月營收'], 'coerce')
df = df[~df['當月營收'].isnull()]
df = df[df['公司代號'] != '合計']
df.to_csv(fileName, index=False)
# 偽停頓
time.sleep(1.5)
df = pd.read_csv(fileName, index_col='公司代號', parse_dates=['公司代號'])
load_memery[fileName] = df
return df
def get_financial_statement(date, GPM='0', OPR='0', EPS='0', RPS='0'):
FS_type = get_stock_history.FS_type.PLA
resultAllFS1 = []
resultAllFS2 = []
resultAllFS3 = []
this = pd.DataFrame()
FS_type = get_stock_history.FS_type.PLA
volume_date = date
for i in range(12):
try:
this = get_stock_history.get_allstock_financial_statement(
date, FS_type)
print(str(date.month) + "月財務報告ＯＫ")
break
except:
print(str(date.month) + "月財務報告未出跳下一個月")
date = tools.changeDateMonth(date, -1)
continue
this1 = this["毛利率(%)"] > float(GPM)
this2 = this["營業利益率(%)"] > float(OPR)
resultAllFS1 = this[this1 & this2]
FS_type = get_stock_history.FS_type.BS
this = get_stock_history.get_allstock_financial_statement(date, FS_type)
this1 = this["每股參考淨值"] > float(RPS)
resultAllFS2 = this[this1]
FS_type = get_stock_history.FS_type.CPL
this = get_stock_history.get_allstock_financial_statement(date, FS_type)
this1 = this["基本每股盈餘（元）"] > float(EPS)
resultAllFS3 = this[this1]
resultAllFS_temp = pd.merge(resultAllFS1,
resultAllFS2,
left_index=True,
right_index=True)
resultAllFS = pd.merge(resultAllFS3,
resultAllFS_temp,
left_index=True,
right_index=True)
return resultAllFS
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 button_monthRP_Up_click(): #月營收逐步升高篩選
date = tools.QtDate2DateTime(myshow.date_endDate.date())
date_5 = date + timedelta(days=-14) #加一天
if date.isoweekday() == 6:
date = date + timedelta(days=-1) #加一天
elif date.isoweekday() == 7:
date = date + timedelta(days=-2) #加2天
else:
pass
GPM = mypick.input_GPM.toPlainText()
OPR = mypick.input_OPR.toPlainText()
EPS = mypick.input_EPS.toPlainText()
RPS = mypick.input_RPS.toPlainText()
FS_data = pd.DataFrame()
result_data = pd.DataFrame()
BOOK_data = pd.DataFrame()
PER_data = pd.DataFrame()
yield_data = pd.DataFrame()
FS_data = get_financial_statement(date, GPM, OPR, EPS, RPS)
result_data = get_stock_history.get_monthRP_up(
tools.changeDateMonth(date, 0),
int(mypick.input_monthRP_smoothAVG.toPlainText()),
int(mypick.input_monthRP_UpMpnth.toPlainText()))
BOOK_data = get_stock_history.get_PBR_range(
tools.changeDateMonth(date, 0),
float(mypick.input_PBR_low.toPlainText()),
float(mypick.input_PBR_high.toPlainText()))
PER_data = get_stock_history.get_PER_range(
tools.changeDateMonth(date, 0),
float(mypick.input_PER_low.toPlainText()),
float(mypick.input_PER_high.toPlainText()))
ROE_data = get_stock_history.get_ROE_range(
tools.changeDateMonth(date, 0),
float(mypick.input_ROE_low.toPlainText()),
float(mypick.input_ROE_high.toPlainText()))
yield_data = get_stock_history.get_yield_range(
tools.changeDateMonth(date, 0),
float(mypick.input_yiled_high.toPlainText()),
float(mypick.input_yiled_low.toPlainText()))
pick_data = FS_data
if result_data.empty == False:
pick_data = pd.merge(pick_data,
result_data,
left_index=True,
right_index=True,
how='left')
pick_data = pick_data.dropna(axis=0, how='any')
if BOOK_data.empty == False:
pick_data = pd.merge(pick_data,
BOOK_data,
left_index=True,
right_index=True,
how='left')
pick_data = pick_data.dropna(axis=0, how='any')
if PER_data.empty == False:
pick_data = pd.merge(pick_data,
PER_data,
left_index=True,
right_index=True,
how='left')
pick_data = pick_data.dropna(axis=0, how='any')
if ROE_data.empty == False:
pick_data = pd.merge(pick_data,
ROE_data,
left_index=True,
right_index=True,
how='left')
pick_data = pick_data.dropna(axis=0, how='any')
if yield_data.empty == False:
pick_data = pd.merge(pick_data,
yield_data,
left_index=True,
right_index=True,
how='left')
pick_data = pick_data.dropna(axis=0, how='any')
price_data = get_stock_history.get_price_range(
date, int(mypick.input_price_high.toPlainText()),
int(mypick.input_price_low.toPlainText()), pick_data)
if price_data.empty == False:
pick_data = tools.MixDataFrames({
'pick': pick_data,
'price': price_data
})
pick_data = pick_data.dropna(axis=0, how='any')
record_data = get_stock_history.get_RecordHigh_range(
date, int(mypick.input_flash_Day.toPlainText()),
int(mypick.input_record_Day.toPlainText()), pick_data)
if record_data.empty == False:
pick_data = tools.MixDataFrames({
'pick': pick_data,
'recordHigh': record_data
})
pick_data = pick_data.dropna(axis=0, how='any')
pick_data = get_volume(int(mypick.input_volum.toPlainText()),
tools.changeDateMonth(date_5, 0), pick_data)
pick_data = pick_data.dropna(axis=0, how='any')
print("總挑選數量:" + str(len(pick_data)))
mypick.treeView_pick.setModel(
creat_treeView_model(mypick.treeView_pick,
pick_titalList)) #設定treeView功能
set_treeView2(mypick.treeView_pick.model(), pick_data)