def AllUser(*_):
os.system('cls||clear')
global mornitorFilePath
global transacFilePath
for user in configJson:
if os.name == 'nt':
print('[For Dev Testing...]')
Realtime(user, sendNotify=False)
else:
try:
Realtime(user)
except Exception as e:
print('\nError To Record ! : {} then skip\n'.format(e))
print('!!!! ==========================')
exc_type, exc_obj, exc_tb = sys.exc_info()
fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]
print('Error Type {}\nFile {}\n Line {}'.format(
exc_type, fname, exc_tb.tb_lineno))
print('!!!! ==========================')
while isInternetConnect and not os.name == 'nt':
try:
if os.path.exists(mornitorFilePath):
gSheet.updateFromCSV(mornitorFilePath, 'Mornitor')
if os.path.exists(transacFilePath):
gSheet.updateFromCSV(transacFilePath, 'Transaction')
except:
pass
else:
break
time.sleep(10)
def AllUser(*_):
os.system('cls||clear')
global mornitorFilePath
global transacFilePath
for user in configJson:
if os.name == 'nt':
print('[For Dev Testing...]')
MornitoringUser(user, sendNotify=False)
else:
try:
MornitoringUser(user)
except Exception as e:
print('\nError To Record ! : {} then skip\n'.format(e))
continue
while isInternetConnect and not os.name == 'nt':
try:
#print('Uploading mornitoring data...')
if os.path.exists(mornitorFilePath):
gSheet.updateFromCSV(mornitorFilePath, 'Mornitor')
#print('Upload mornitoring data finish')
#print('Uploading Transaction data...')
if os.path.exists(transacFilePath):
gSheet.updateFromCSV(transacFilePath, 'Transaction')
#print('Upload Transaction data finish')
except:
pass
else:
break
time.sleep(10)
def getHistDataframe(*_):
print('load history data from google sheet...')
sheetData = gSheet.getAllDataS('History')
print('row count {}'.format(len(sheetData)))
if sheetData == []:
allHistPath = dataPath + '/cryptoHist.csv'
gSheet.updateFromCSV(allHistPath, 'History')
df = pd.read_csv(allHistPath)
else:
df = pd.DataFrame.from_records(sheetData)
return df
def updateGSheetHistory(limit=45000):
ticker = kbApi.getTicker()
symbols = kbApi.getSymbol()
df = pd.DataFrame()
df = df.append(getHistDataframe())
date = dt.now().strftime('%Y-%m-%d')
hour = int(dt.now().strftime('%H'))
epoch = float(time.time())
minute = int(dt.now().strftime('%M'))
second = int(dt.now().strftime('%S'))
date_time = str(dt.now().strftime('%Y-%m-%d %H:%M:%S'))
#backup hist
backupPath = dataPath + '/hist_backup/cryptoHist_{}_{}.csv'.format(
date.replace('-', '_'), 0)
if hour >= 8 and hour <= 16:
backupPath = dataPath + '/hist_backup/cryptoHist_{}_{}.csv'.format(
date.replace('-', '_'), 1)
elif hour > 16:
backupPath = dataPath + '/hist_backup/cryptoHist_{}_{}.csv'.format(
date.replace('-', '_'), 2)
df.to_csv(backupPath, index=False)
#spare backup path
backupPath = dataPath + '/hist_backup/cryptoHist_{}_{}.csv'.format(
date.replace('-', '_'), 3)
# append backup
backupList = os.listdir(dataPath + '/hist_backup')
backupList.sort()
if len(backupList) > 10:
backupList = backupList[-10:]
for f in backupList:
filePath = dataPath + '/hist_backup/{}'.format(f)
print('Read [ {} ]'.format(filePath))
try:
df = df.append(pd.read_csv(filePath).sort_values(
['dateTime'], ascending=[True]).tail(5000),
ignore_index=True)
except:
print('Can\'t Read {} Column DateTime..'.format(filePath))
else:
pass
os.system('cls||clear')
for data in symbols:
sym = data['symbol']
if not sym in ticker:
continue
print('{} {} Baht'.format(sym, ticker[sym]['last']))
rowData = {
'epoch': epoch,
'date': date,
'hour': hour,
'minute': minute,
'second': second,
'symbol': sym,
'dateTime': date_time
}
for colName in ticker[sym]:
rowData[colName] = [ticker[sym][colName]]
df = df.append(pd.DataFrame(rowData), ignore_index=True)
# delete duplicate
df.drop_duplicates(['symbol', 'date', 'hour', 'minute'],
keep='last',
inplace=True)
#cleanup & sort
epoch_limit = time.time() - (((5 * 24) * 60) * 60)
df.dropna(subset=['epoch', 'dateTime'], inplace=True)
df['epoch'] = pd.to_numeric(df['epoch'], errors='coerce')
df['dateTime'] = df['dateTime'].astype(str)
df = df[df['dateTime'] != 'nan']
#df.sort_values(['epoch'], ascending=[True])
df.sort_values(['epoch', 'dateTime'], ascending=[True, True])
df.sort_index(inplace=True)
df = df.drop(df[(df['date'].str.isdigit() == True)].index)
df = df.drop(df[(df['dateTime'].str.isdigit() == True)].index)
df = df.drop(df[(df['epoch'] < epoch_limit)].index)
#limit row
df = df.tail(limit)
print('Save Historical Data...')
allHistPath = dataPath + '/cryptoHist.csv'
df = df[list(rowData)]
df.to_csv(allHistPath, index=False)
df.tail(5000).to_csv(backupPath, index=False)
while isInternetConnect():
try:
if not os.name == 'nt': #for raspi
print('uploading history data...')
gSheet.updateFromCSV(allHistPath, 'History')
print('upload history data finish')
except:
pass
time.sleep(10)
if gSheet.getAllDataS('History') != []:
break
def getSignalAllPreset():
rec_date = dt.datetime.now().strftime('%Y-%m-%d %H:00:00')
minute = int(dt.datetime.now().strftime('%M'))
if minute >= 30 and minute < 60:
rec_date = dt.datetime.now().strftime('%Y-%m-%d %H:30:00')
"""
elif minute >= 30 and minute < 45:
rec_date = dt.datetime.now().strftime('%Y-%m-%d %H:30:00')
elif minute >= 45 and minute < 59:
rec_date = dt.datetime.now().strftime('%Y-%m-%d %H:45:00')
"""
signal_df = pd.DataFrame()
# Clear Directory
imgPath = dataPath + '/analysis_img/'
oldImgFiles = os.listdir(imgPath)
for f in oldImgFiles:
os.remove(imgPath + f)
count = 0
for file in histFileList:
quote = file.split('.')[0]
count += 1
os.system('cls||clear')
print('{}/{} {}'.format(count, len(histFileList), quote))
for ps in presetJson:
try:
df = getAnalysis(histPath + os.sep + file,
ps,
saveImage=False,
showImage=False)
df['Preset'] = ps
df['Symbol'] = quote
df['Rec_Date'] = rec_date
# Condition List
entry_condition_list = [
df['SMA_S'][0] > df['SMA_L'][0], #0
df['%K'][0] > df['%D'][0], #1
df['GL_Ratio'][0] > df['GL_Ratio_Slow'][0] #2
]
exit_condition_list = [
df['SMA_S'][0] < df['SMA_L'][0],
df['GL_Ratio'][0] < df['GL_Ratio_Slow'][0]
]
# Condition Setting
filter_condition = (
#df['SMA_L'][0] >= df['Close'].mean() and
df['Volume'][0] >= df['Volume'][1]
and df['GL_Ratio_Slow'][0] > 1
#True
)
entry_condition = (entry_condition_list[0]
and entry_condition_list[1]
and entry_condition_list[2])
exit_condition = (exit_condition_list[0]
and exit_condition_list[1])
# Trade Entry
if filter_condition and entry_condition:
print('Preset : {} | Entry : {}'.format(ps, file))
df['Signal'] = 'Entry'
signal_df = signal_df.append(df.iloc[0])
getAnalysis(histPath + os.sep + file,
ps,
saveImage=True,
showImage=False)
# Trade Exit
elif filter_condition and exit_condition:
print('Preset : {} | Exit : {}'.format(ps, file))
df['Signal'] = 'Exit'
signal_df = signal_df.append(df.iloc[0])
getAnalysis(histPath + os.sep + file,
ps,
saveImage=True,
showImage=False)
else:
df['Signal'] = ''
signal_df = signal_df.append(df.iloc[0])
except:
pass
signal_df = signal_df.sort_values(
['Signal', 'Preset', 'Value_M', 'GL_Ratio'],
ascending=[True, True, False, False])
csvPath = dataPath + os.sep + 'signal.csv'
if not os.path.exists(csvPath):
signal_df.to_csv(csvPath, index=False)
# New Signal DataFrame (All Signal Record)
new_signal_df = pd.read_csv(csvPath)
new_signal_df = new_signal_df[new_signal_df['Rec_Date'] != rec_date]
new_signal_df = new_signal_df.append(signal_df)
new_signal_df = new_signal_df.sort_values(['Date', 'Rec_Date', 'Signal'],
ascending=[True, True, True])
new_signal_df.drop_duplicates(['Rec_Date', 'Preset', 'Symbol'],
keep='last',
inplace=True,
ignore_index=False)
new_signal_df = new_signal_df.tail(5000)
new_signal_df.to_csv(csvPath, index=False)
if not os.name == 'nt':
# Update G Sheet
gsheet_csvPath = dataPath + os.sep + 'signal_gsheet.csv'
new_signal_df.to_csv(gsheet_csvPath, index=False)
gSheet.updateFromCSV(gsheet_csvPath, 'SignalRecord')