def display_IntraDay_Data(self, company_name, interval_time):
try:
collection = ''
if interval_time == '1sec':
collection = 'IntraDay1 secs'
elif interval_time == '1min':
collection = 'IntraDay1 min'
elif interval_time == '5min':
collection = 'IntraDay5 mins'
elif interval_time == '15min':
collection = 'IntraDay15 mins'
elif interval_time == '30min':
collection = 'IntraDay30 mins'
elif interval_time == '1hr':
collection = 'IntraDay1 hour'
elif interval_time == '4hr':
collection = 'IntraDay4 hours'
client = mongodb.MongoClient(mongodb.MongoIP, mongodb.MonngoPort)
db = client[str(mongodb.MongoStore)]
coll_names = db.list_collection_names()
value = mongodb.ReadValue(collection, company_name)['Data']
collectionP = collection + "Predict"
if coll_names.__contains__(collectionP):
valueP = mongodb.ReadValue(collectionP, company_name)['Data']
df = pd.DataFrame(eval(value))
dfP = pd.DataFrame(eval(valueP))
# date_filter = (df['date'].max()).split(' ')[0]
# dt = parser.parse(date_filter).date()
df.rename(columns={
0: "date",
1: "open",
2: "high",
3: "low",
4: "close",
5: "volume"
},
inplace=True)
# df.rename(columns={0: "open", 1: "high", 2: "low", 3: "close",
# 4: "volume",5:""},inplace=True)
# df['date'] = pd.to_datetime(df['date'])
# df['_date'] = [d.date() for d in df['date']]
# df['_time'] = [t.time() for t in df['date']]
# df['date'] = df['date'].astype(str)
# df['_date'] = df['_date'].astype(str)
# df['_time'] = df['_time'].astype(str)
# p_df = self.temp(company_name, date_filter)
# final_df = pd.merge(df, p_df, how='left', left_on='date', right_on='date')
# final_df = display_data().functionCalling(final_df)
# final_df = final_df.fillna('')
df = pd.concat([dfP, df], axis=1)
return df.to_dict(orient='list')
# return temp_list
except Exception as e:
print(sys.exc_info())
return str(e)
def next_day_prediction(self):
collectionname = 'IntraDay'
for com in self.CompanyP:
value = mongodb.ReadValue(collectionname, com)['Data']
df = pd.DataFrame(eval(value))
# print(df)
next_date = DataFeed().getNextDate((df['date'].max()).split(' ')[0])
df.rename(columns={"1. open": "open", "2. high": "high", "3. low": "low", "4. close": "close",
"5. volume": "volume"}, inplace=True)
if 'volume' in df.columns:
del df['volume']
dataframe = df.reset_index(drop=True)
dates = dataframe['date'].copy()
del dataframe['date']
seedValue = dataframe.tail(1)
dataframe, scaler = stocksLSTM.ScaleDataSet(dataframe)
dataframe = stocksLSTM.prepareDataSet(dataframe)
model, details = stocksLSTM.trainModel(dataframe)
seedValue, _ = stocksLSTM.ScaleDataSet(seedValue, scaler)
p_df = stocksLSTM.predictfulDay(model, details, seedValue)
p_df = stocksLSTM.deScaleData(p_df, scaler)
rng = pd.date_range(str(next_date) + ' ' + '09:35:00', periods=100, freq='5min')
ts = pd.Series(rng)
p_df['date'] = ts
p_df['date'] = p_df['date'].astype(str)
# print(p_df)
mongodb.UpdateValue('FuturePrediction', com, p_df.to_dict(orient='list'))
def temp(self, company_name, filter_date):
try:
collectionname = 'PredictionStore'
value = mongodb.ReadValue(collectionname, company_name + ' ' +
str(filter_date))['Data']
df = pd.DataFrame(eval(value))
except:
df = pd.DataFrame(
[], columns=['date', 'highP', 'lowP', 'openP', 'closeP'])
return df
def display_Daily_Data(self, company_name):
try:
collection = 'Daily'
value = mongodb.ReadValue(collection, company_name)['Data']
df = pd.DataFrame(eval(value))
df.rename(columns={
"1. open": "open",
"2. high": "high",
"3. low": "low",
"4. close": "close"
},
inplace=True)
df = df.tail(30)
return df.to_dict(orient='list')
except Exception as e:
return 'False'
def predict(self, company):
try:
collectionname = 'FuturePrediction'
value = mongodb.ReadValue(collectionname, company)['Data']
df = pd.DataFrame(eval(value))
df.rename(columns={
"openP": "open",
"highP": "high",
"lowP": "low",
"closeP": "close",
"volumeP": "volume"
},
inplace=True)
return df.head().to_dict(orient='list')
except Exception as e:
print(str(e))
return str(e)
def same_day_prediction(self):
collectionname = 'IntraDay'
for com in self.CompanyP:
value = mongodb.ReadValue(collectionname, com)['Data']
df = pd.DataFrame(eval(value))
# print(df)
next_date = DataFeed().getNextDate(
(df['date'].max()).split(' ')[0])
df.rename(columns={
"1. open": "open",
"2. high": "high",
"3. low": "low",
"4. close": "close",
"5. volume": "volume"
},
inplace=True)
if 'volume' in df.columns:
del df['volume']
dataframe = df.reset_index(drop=True)
dates = dataframe['date'].copy()
del dataframe['date']
testEnd = dataframe.iloc[312:].copy()
trainStart = dataframe.drop(dataframe.index[312:])
trainStart, scaler = stocksLSTM.ScaleDataSet(trainStart)
testEnd, _ = stocksLSTM.ScaleDataSet(testEnd, scaler)
# testEnd = testEnd.shift(-1)
# testEnd = testEnd.dropna()
# testEnd.reset_index(drop=True, inplace=True)
trainStart = stocksLSTM.prepareDataSet(trainStart)
model, details = stocksLSTM.trainModel(trainStart)
presults = stocksLSTM.predict(model, testEnd)
presults = stocksLSTM.deScaleData(presults, scaler)
ndates = pd.DataFrame(dates[312:], columns=['date'])
# ndates = ndates.shift(-1)
# ndates = ndates.dropna()
ndates.reset_index(drop=True, inplace=True)
presults = pd.concat([presults, ndates], axis=1)
date_filter = (presults['date'].max()).split(' ')[0]
mongodb.UpdateValue('PredictionStore',
com + ' ' + str(date_filter),
presults.to_dict(orient='list'))
def display_Daily_Data(self, company_name):
try:
collection = 'Daily'
value = mongodb.ReadValue(collection, company_name)['Data']
df = pd.DataFrame(eval(value))
df.rename(columns={
"1. open": "open",
"2. high": "high",
"3. low": "low",
"4. close": "close",
"5. volume": "volume"
},
inplace=True)
df = display_data().functionCalling(df)
return df.dropna().to_dict(orient='list')
except Exception as e:
print(str(e))
return str(e)
def getData_intraDay(stockname='aapl'):
value = mongodb.ReadValue('IntraDay', 'aapl')['Data']
df = pd.DataFrame(eval(value), )
# print(df)
df.rename(columns={
"1. open": "open",
"2. high": "high",
"3. low": "low",
"4. close": "close",
"5. volume": "volume"
},
inplace=True)
#
# if 'volume' in df.columns:
# del df['volume']
dataframe = df.reset_index(drop=True)
return dataframe
def selected_predict(self, company_name, start_date, end_date,
nopredictions):
try:
collectionname = 'IntraDay'
t1 = start_date.split(' ')[1]
t2 = end_date.split(' ')[1]
FMT = '%H:%M:%S'
tdelta = datetime.strptime(t2, FMT) - datetime.strptime(t1, FMT)
total_minutes = int(tdelta.total_seconds() / 60)
value = mongodb.ReadValue(collectionname, company_name)['Data']
df = pd.DataFrame(eval(value))
df.rename(columns={
"1. open": "open",
"2. high": "high",
"3. low": "low",
"4. close": "close",
"5. volume": "volume"
},
inplace=True)
if 'volume' in df.columns:
del df['volume']
test_df = df[(df['date'] >= start_date) & (df['date'] <= end_date)]
max_date = (test_df['date'].max()).split(' ')[0]
max_time = (test_df['date'].max()).split(' ')[1]
testEnd = test_df
trainStart = df.tail(312)
# dates = df['date'].copy()
del testEnd['date']
del trainStart['date']
trainStart, scaler = stocksLSTM.ScaleDataSet(trainStart)
testEnd, _ = stocksLSTM.ScaleDataSet(testEnd, scaler)
trainStart = stocksLSTM.prepareDataSet(trainStart)
model, details = stocksLSTM.trainModel(trainStart)
presults = stocksLSTM.predict(model, testEnd)
presults = stocksLSTM.deScaleData(presults, scaler)
temp_time = datetime.strptime(max_time, "%H:%M:%S")
time = temp_time.time()
gen_date = None
rng = None
global frequency
global points
if nopredictions == 5:
frequency = '5min'
points = total_minutes / 5
elif nopredictions == 15:
frequency = '15min'
points = total_minutes / 15
elif nopredictions == 30:
frequency = '30min'
points = total_minutes / 30
elif nopredictions == 60:
frequency = '60min'
points = total_minutes / 60
if time.hour == 16:
gen_date = self.getNextDate(max_date)
rng = pd.date_range(str(gen_date) + ' ' + '09:35:00',
periods=points,
freq=frequency)
else:
rng = pd.date_range(str(max_date) + ' ' + str(max_time),
periods=points,
freq=frequency)
ts = pd.Series(rng)
presults['date'] = ts
presults['date'] = presults['date'].astype(str)
presults.rename(columns={
"openP": "open",
"highP": "high",
"lowP": "low",
"closeP": "close"
},
inplace=True)
return presults.to_dict(orient='list')
except Exception as e:
print(str(e))
return 'False'
import pandas as pd
import mongodb
value = mongodb.ReadValue('Daily', 'aapl')['Data']
df = pd.DataFrame(eval(value))
d = pd.DataFrame()
d = df.iloc[1]
d = df.iloc[2]
d = df.iloc[3]
print(d)
df = pd.DataFrame()
def same_day_prediction(self):
collectionname = ['IntraDay5 mins', 'IntraDay4 hours']
for col in collectionname:
for com in self.CompanyP:
value = mongodb.ReadValue(col, com)['Data']
df = pd.DataFrame(eval(value))
# print(df)
# df = {"date": ['20190502 13:30:00', '20190502 16:00:00'], "open": [209.95, 208.65],
# "high": [212.65, 210.29], "low": [208.13, 208.41], "close": [208.63, 209.17],
# "volume": [149612, 100915]}
# next_date = DataFeed().getNextDate((df['date'].max()).split(' ')[0])
df.rename(columns={
0: "date",
1: "open",
2: "high",
3: "low",
4: "close",
5: "volume"
},
inplace=True)
if 'volume' in df.columns:
del df['volume']
dataframe = df.reset_index(drop=True)
dates = dataframe['date'].copy()
signals = dataframe['signal'].copy()
del dataframe['date']
del dataframe['signal']
dfleng = len(dataframe.index) - 1
testEnd = dataframe.iloc[dfleng:].copy()
trainStart = dataframe.drop(dataframe.index[dfleng:])
trainStart, scaler = stocksLSTM.ScaleDataSet(trainStart)
testEnd, _ = stocksLSTM.ScaleDataSet(testEnd, scaler)
# testEnd = testEnd.shift(-1)
# testEnd = testEnd.dropna()
# testEnd.reset_index(drop=True, inplace=True)
trainStart = stocksLSTM.prepareDataSet(trainStart)
model, details = stocksLSTM.trainModel(trainStart)
presults = stocksLSTM.predict(model, testEnd)
presults = stocksLSTM.deScaleData(presults, scaler)
ndates = pd.DataFrame(dates[dfleng:], columns=['date'])
nsignals = pd.DataFrame(signals[dfleng:], columns=['signal'])
# ndates = ndates.shift(-1)
# ndates = ndates.dropna()
ndates.reset_index(drop=True, inplace=True)
nsignals.reset_index(drop=True, inplace=True)
presults = pd.concat([presults, ndates], axis=1)
presults = pd.concat([presults, nsignals], axis=1)
print(presults)
presults = pd.DataFrame(presults)
presults.rename(columns={
"date": "dateP",
"open": "openP",
"high": "highP",
"low": "lowP",
"close": "closeP",
"signal": "signalP"
},
inplace=True)
presults1 = self.strategy(presults)
df.reset_index(drop=True, inplace=True)
presults = pd.concat([presults, df], axis=1)
print(presults1)
date_filter = (presults['date'].max()).split(' ')[0]
# mongodb.UpdateValue(col+"Predict", com + ' ' + str(date_filter), presults1.to_dict(orient='list'))
mongodb.UpdateValue(col + "Predict", com,
presults1.to_dict(orient='list'))
def display_IntraDay_Data(self, company_name, barSize, counter):
try:
collection = ''
if barSize == '5 secs':
collection = 'IntraDay_5s'
elif barSize == '15 secs':
collection = 'IntraDay_15s'
elif barSize == '30 secs':
collection = 'IntraDay_30s'
elif barSize == '1 min':
collection = 'IntraDay_1m'
elif barSize == '2 min':
collection = 'IntraDay_2m'
elif barSize == '3 min':
collection = 'IntraDay_3m'
elif barSize == '5 min':
collection = 'IntraDay_5m'
elif barSize == '15 min':
collection = 'IntraDay_15m'
elif barSize == '30 min':
collection = 'IntraDay_30m'
elif barSize == '1 hour':
collection = 'IntraDay_1h'
elif barSize == '1 day':
collection = 'IntraDay'
value = mongodb.ReadValue(collection, company_name)['Data']
df = pd.DataFrame(eval(value))
# data = df[(df['date'] > start_date) & (df['date'] < end_date)]
date_filter = (df['date'].max()).split(' ')[0]
dt = parser.parse(date_filter).date()
df.rename(columns={
"1. open": "open",
"2. high": "high",
"3. low": "low",
"4. close": "close"
},
inplace=True)
df['date'] = pd.to_datetime(df['date'])
df['_date'] = [d.date() for d in df['date']]
df['_time'] = [t.time() for t in df['date']]
df['date'] = df['date'].astype(str)
df['_date'] = df['_date'].astype(str)
df['_time'] = df['_time'].astype(str)
filter_date = dt - timedelta(days=int(counter))
p_df = self.temp(company_name, filter_date)
# p_df = pd.DataFrame(eval(value))
# p_df.rename(columns={"date": "dateP"}, inplace=True)
final_df = pd.merge(df,
p_df,
how='left',
left_on='date',
right_on='date')
# print(final_df)
final_df = final_df.fillna('')
# temp_list = [data.to_dict(orient='list'), p_df.to_dict(orient='list')]
#
# final_df = pd.concat([data,p_df])
# return str(final_df.to_dict(orient='list'))
final_df = final_df[final_df['date'].str.contains(
str(filter_date))]
return final_df.to_dict(orient='list')
# return temp_list
except Exception as e:
return 'False'