class SpeechNoise(db.EmbeddedDocument):
start_time = db.DateTimeField()
right_silence = db.ListField()
left_silence = db.ListField()
right_noise = db.ListField()
left_noise = db.ListField()
def create(self, property, result):
self.__setattr__(property, result)
return self
class D2VModel(db.Document):
serialized_model = db.FileField()
last_run = db.DateTimeField()
vectors = db.DictField()
# [
# [a1, a2],
# [b1, b2]
# ]
vectors_2d = db.ListField(db.ListField(db.FloatField()))
@property
def model(self):
"""
load serialized model
:return:
"""
if not self.serialized_model:
return None
bytes_io = io.BytesIO(self.serialized_model)
model = pickle.load(bytes_io)
return model
@model.setter
def model(self, model):
# instead of a file
bytes_io = io.BytesIO()
# save model to bytesio
model.save(bytes_io)
# getvalue has the actual bytes
self.serialized_model = bytes_io.getvalue()
# close buffer
bytes_io.close()
# update timestamp and save
self.touch()
@staticmethod
def get():
d2v_model = D2VModel.objects.first()
if not d2v_model:
d2v_model = D2VModel()
d2v_model.touch()
d2v_model.save()
return d2v_model
def touch(self):
self.last_run = datetime.datetime.now()
class Patient(db.Document):
ssnid = db.StringField(max_length=9, unique=True)
patientId = db.IntField()
name = db.StringField(required=True)
age = db.IntField(max_length=3)
address = db.StringField()
dateOfJoining = db.StringField()
roomType = db.StringField()
city = db.StringField()
state = db.StringField()
pharmacy = db.ListField(db.StringField())
diagnostics = db.ListField(db.StringField())
status = db.StringField(default="active")
class Feed(db.Document):
title = db.StringField()
url = db.StringField(required=True)
articles = db.ListField(db.ReferenceField("Article"))
users = db.ListField(db.ReferenceField("User"))
def as_dict(self):
return dict(title=self.title, url=self.url)
def __repr__(self):
return '<Feed %r (%s)>' % (self.title, self.url)
class Post(UserMixin, db.Document):
meta = {'collection': 'posts'}
title = db.StringField(max_length=30)
date = db.DateTimeField()
content = db.StringField(max_length=1000)
author = db.StringField(max_length=30)
tags = db.ListField()
owner = db.LazyReferenceField('User')
class ClubEvent(BaseDocument):
name = db.StringField(required=True)
image = db.ImageField(size=(500, 500), thumbnail_size=(20, 20))
tags = db.ListField(db.StringField())
presenter = db.EmbeddedDocumentField(Presenter)
start = db.DateTimeField(requried=True)
end = db.DateTimeField(requried=True)
description = db.StringField()
location = db.StringField()
meta = {"ordering": ["start"]}
class Subscriber(BaseDocument):
cellphone = db.StringField()
email = db.EmailField()
vaccine_site = db.ListField(db.ReferenceField(Maps))
def to_mongo(self, *args, **kwargs):
data = super().to_mongo(*args, **kwargs)
data["vaccine_site"] = [s.name for s in self.vaccine_site]
return data
class User(db.Document):
uid = db.UUIDField(required=True, primary_key=True)
player_ids = db.ListField(db.UUIDField(required=True))
read = db.MapField(field=db.BooleanField(default=False, required=True))
notifications = db.ListField(db.ReferenceField(GenericNotification, reverse_delete_rule=mongoengine.PULL))
def read_notifications(self):
for notification in self.notifications:
self.read[str(notification.id)] = True
self.update(read=self.read)
def serialize_notifications(self):
notifications = self.notifications[-PER_PAGE:]
utils.populate_name_cache(notifications)
serialized_notifications = []
for notification in notifications:
serialized_notification = notification.serialize()
read = str(notification.nid) in self.read
serialized_notification['read'] = read
serialized_notifications.append(serialized_notification)
return serialized_notifications[::-1]
class Hacker(BaseDocument): # Stored in the "user" collection
meta = {"indexes": ["email"]}
private_fields = ["id", "email_token_hash"]
first_name = db.StringField()
last_name = db.StringField()
birthday = db.DateTimeField()
country = db.StringField()
phone_number = db.StringField()
isaccepted = db.BooleanField(default=False)
can_share_info = db.BooleanField(default=False)
rsvp_status = db.BooleanField(default=False)
beginner = db.BooleanField(default=False)
ethnicity = db.StringField()
pronouns = db.StringField()
edu_info = db.EmbeddedDocumentField(Education_Info)
resume = db.ReferenceField(Resume)
socials = db.EmbeddedDocumentField(Socials)
why_attend = db.StringField()
what_learn = db.ListField()
in_person = db.BooleanField(default=False)
dietary_restrictions = db.StringField()
email = db.EmailField(unique=True, required=True)
date = db.DateTimeField(default=datetime.utcnow)
mlh = db.EmbeddedDocumentField(MLH_Authorizations)
def encode_email_token(self) -> str:
"""Encode the email token"""
email_token = encode_jwt(sub=self.email)
conf = app.config["BCRYPT_LOG_ROUNDS"]
email_token_hash = bcrypt.generate_password_hash(email_token, conf)
self.modify(set__email_token_hash=email_token_hash)
self.save()
return email_token
@staticmethod
def decode_email_token(email_token: str) -> str:
"""Decodes the email token"""
return decode_jwt(email_token)["sub"]
@classmethod
def pre_delete(cls, sender, document, **kwargs):
if document.resume:
document.resume.delete()
class Article(db.Document):
feed = db.ReferenceField(Feed, required=True)
source_id = db.StringField(required=True)
url = db.StringField()
title = db.StringField()
top_image = db.StringField()
movies = db.ListField()
keywords = db.ListField()
tags = db.ListField()
authors = db.ListField()
publish_date = db.DateTimeField()
summary = db.StringField()
html = db.StringField()
meta_data = db.DictField()
language = db.StringField()
text = db.StringField()
active = db.BooleanField(default=True)
vector = db.ListField(db.FloatField())
vector_2d = db.ListField(db.FloatField())
def as_dict(self):
return dict(feed=self.feed.url,
source_id=self.source_id,
url=self.url,
title=self.title,
top_image=self.top_image,
movies=self.movies,
keywords=self.keywords,
tags=self.tags,
authors=self.authors,
publish_date='%s' % self.publish_date,
summary=self.summary,
html=self.html,
meta_data=self.meta_data,
language=self.language,
text=self.text,
vector=self.vector,
vector_2d=self.vector_2d)
@staticmethod
def update_vectors(model, vectors_2d=[]):
# https://stackoverflow.com/questions/30943076/mongoengine-bulk-update-without-objects-update
bulk_operations = []
keys = model.docvecs.doctags.keys()
vectors = model.docvecs.vectors_docs
for key, vector, vector_2d in zip(keys, vectors, vectors_2d):
bulk_operations.append(
UpdateOne({'_id': key}, {
'$set':
dict(vector=vector.tolist(), vector_2d=vector_2d)
}))
if bulk_operations:
collection = Article._get_collection() \
.bulk_write(bulk_operations, ordered=False)
class LabInvoice(db.Document):
date = db.DateTimeField(default=datetime.datetime.utcnow())
items = db.ListField(db.ObjectIdField())
total = db.FloatField()
class PharmacyInvoice(db.Document):
date = db.DateTimeField(default=datetime.datetime.utcnow())
items = db.ListField(db.DictField())
total = db.FloatField()
class Calculation(UserMixin, db.Document):
meta = {'collection': 'entries'}
keep = db.BooleanField(default=True)
date_stamp = db.StringField(max_length=40)
type_info = db.StringField()
buy = db.FloatField()
sell = db.FloatField()
av_length = db.IntField()
ticker = db.StringField()
period = db.StringField()
interval = db.StringField()
money = db.FloatField()
trade_cost = db.FloatField()
owner = db.LazyReferenceField('User')
final_money = db.FloatField()
final_owned = db.FloatField()
final_liquid = db.FloatField()
td = db.ListField()
tt = db.ListField()
ty = db.ListField()
tp = db.ListField()
tv = db.ListField()
graph_x = db.ListField()
graph_y = db.ListField()
chart_x = db.ListField()
chart_y = db.ListField()
model = db.LazyReferenceField('Model')
user_model = db.LazyReferenceField('UserModel')
def static_range(self):
stock = yf.Ticker(self.ticker)
df = stock.history(period=self.period, interval=self.interval)
owned = 0
count = 0
trade_money = self.money
trade_cost = self.trade_cost
buy = self.buy
sell = self.sell
td = [] #trade date
tt = [] #trade time
ty = [] #trade type
tp = [] #trade price
tv = [] #trade volume
store_chart_x = []
store_chart_y = []
for index, row in df.iterrows():
count += 1
available_money = trade_money - trade_cost
available_volume = row['Volume']/40
low = round(row['Low'], 4)
high = round(row['High'], 4)
date = index.to_pydatetime().strftime("%d-%m-%Y")
time = index.to_pydatetime().strftime("%H:%M")
etime = index.to_pydatetime() + timedelta(minutes=2)
endtime = etime.strftime("%H:%M")
if low != high:
if float(row['Open']) > float(row['Close']):
store_chart_y.append(float(high))
store_chart_x.append(date[6:10]+'-'+date[3:5]+'-'+date[0:2] + 'T' + time + ':00')
store_chart_y.append(float(low))
store_chart_x.append(date[6:10] + '-' + date[3:5] + '-' + date[0:2] + 'T' + endtime + ':00')
elif float(row['Close']) > float(row['Open']):
store_chart_y.append(float(low))
store_chart_x.append(date[6:10] + '-' + date[3:5] + '-' + date[0:2] + 'T' + time + ':00')
store_chart_y.append(float(high))
store_chart_x.append(date[6:10] + '-' + date[3:5] + '-' + date[0:2] + 'T' + endtime + ':00')
elif low == high:
store_chart_y.append(low)
store_chart_x.append(date[6:10] + '-' + date[3:5] + '-' + date[0:2] + 'T' + time + ':00')
lvalue = (low * available_volume)
hvalue = (high * available_volume)
if available_money > 0 and low <= buy and lvalue > trade_cost:
if lvalue>available_money>0:
owned += (available_money/low)
trade_money = 0
td.append(date)
tt.append(time)
ty.append('b')
tp.append(low)
tv.append(available_volume)
elif available_money>lvalue>0:
owned += (lvalue/low)
trade_money -= trade_cost
trade_money -= lvalue
td.append(date)
tt.append(time)
ty.append('b')
tp.append(low)
tv.append(available_volume)
if owned > 0 and high >= sell and hvalue > trade_cost:
if available_volume > owned:
trade_money += (owned*high)
trade_money -= trade_cost
owned = 0
td.append(date)
tt.append(time)
ty.append('s')
tp.append(high)
tv.append(available_volume)
elif owned > available_volume:
trade_money += (available_volume*high)
trade_money -= trade_cost
owned -= available_volume
td.append(date)
tt.append(time)
ty.append('s')
tp.append(high)
tv.append(available_volume)
if owned > 0:
liquid_money = (trade_money - trade_cost) + df.Low.ix[count-1]*owned
else:
liquid_money = trade_money
self.chart_x = store_chart_x
self.chart_y = store_chart_y
self.final_money = round(trade_money, 2)
self.final_owned = round(owned)
self.final_liquid = round(liquid_money, 2)
self.td = td
self.tt = tt
self.ty = ty
self.tp = tp
self.tv = tv
self.save()
return self.id
def moving_average(self):
stock = yf.Ticker(self.ticker)
df = stock.history(period=self.period, interval='1d')
owned = 0
count = 0
trade_money = self.money
trade_cost = self.trade_cost
bd = self.buy
sd = self.sell
ma_length = self.av_length
ma_conv = ma_length - 1
ma_sum = 0
td = [] # trade date
tt = [] # trade time
ty = [] # trade type
tp = [] # trade price
tv = [] # trade volume
store_ma = []
store_ma_x = []
store_chart_x = []
store_chart_y = []
for i in range(0, df.shape[0] - ma_conv):
for j in range(0, ma_length):
ma_sum += df.iloc[i + j, 3]
df.loc[df.index[i + ma_conv], 'SMA_' + str(ma_length)] = np.round((ma_sum / ma_length), 4)
ma_sum = 0
for index, row in df.iloc[ma_length:].iterrows():
count += 1
available_money = trade_money - trade_cost
available_volume = row['Volume'] / 40
low = round(row['Low'], 4)
high = round(row['High'], 4)
date = index.to_pydatetime().strftime("%d-%m-%Y")
time = index.to_pydatetime().strftime("%H:%M")
if low != high:
if float(row['Open']) > float(row['Close']):
store_chart_y.append(float(high))
store_chart_x.append(date[6:10]+'-'+date[3:5]+'-'+date[0:2]+'T09:30:00')
store_chart_y.append(float(low))
store_chart_x.append(date[6:10] + '-' + date[3:5] + '-' + date[0:2] + 'T16:00:00')
elif float(row['Close']) > float(row['Open']):
store_chart_y.append(float(low))
store_chart_x.append(date[6:10] + '-' + date[3:5] + '-' + date[0:2] + 'T09:30:00')
store_chart_y.append(float(high))
store_chart_x.append(date[6:10] + '-' + date[3:5] + '-' + date[0:2] + 'T16:00:00')
elif low == high:
store_chart_y.append(low)
store_chart_x.append(date[6:10] + '-' + date[3:5] + '-' + date[0:2] + 'T12:45:00')
lvalue = (low * available_volume)
hvalue = (high * available_volume)
buy = row['SMA_' + str(ma_length)] - bd
sell = row['SMA_' + str(ma_length)] + sd
store_ma.append(float(row['SMA_' + str(ma_length)]))
store_ma_x.append(date[6:10] + '-' + date[3:5] + '-' + date[0:2] + 'T' + time + ':00')
if available_money > 0 and low <= buy and lvalue > trade_cost:
if lvalue > available_money > 0:
owned += (available_money / low)
trade_money = 0
td.append(date)
tt.append(time)
ty.append('b')
tp.append(low)
tv.append(available_volume)
elif available_money > lvalue > 0:
owned += (lvalue / low)
trade_money -= trade_cost
trade_money -= lvalue
td.append(date)
tt.append(time)
ty.append('b')
tp.append(low)
tv.append(available_volume)
if owned > 0 and high >= sell and hvalue > trade_cost:
if available_volume > owned:
trade_money += (owned * high)
trade_money -= trade_cost
owned = 0
td.append(date)
tt.append(time)
ty.append('s')
tp.append(high)
tv.append(available_volume)
elif owned > available_volume:
trade_money += (available_volume * high)
trade_money -= trade_cost
owned -= available_volume
td.append(date)
tt.append(time)
ty.append('s')
tp.append(high)
tv.append(available_volume)
if owned > 0:
liquid_money = (trade_money - trade_cost) + df.Low.ix[count - 1] * owned
else:
liquid_money = trade_money
self.chart_x = store_chart_x
self.chart_y = store_chart_y
self.graph_y = store_ma
self.graph_x = store_ma_x
self.final_money = round(trade_money, 2)
self.final_owned = round(owned)
self.final_liquid = round(liquid_money, 2)
self.td = td
self.tt = tt
self.ty = ty
self.tp = tp
self.tv = tv
self.save()
return self.id
def weighted_moving_average(self):
stock = yf.Ticker(self.ticker)
df = stock.history(period=self.period, interval='1d')
owned = 0
count = 0
trade_money = self.money
trade_cost = self.trade_cost
bd = self.buy
sd = self.sell
wma_length = self.av_length
wma_conv = wma_length - 1
wma_sum = 0
weight = 0
weight_sum = 0
for k in range(1, wma_length + 1):
weight_sum += k
td = [] # trade date
tt = [] # trade time
ty = [] # trade type
tp = [] # trade price
tv = [] # trade volume
store_wma = []
store_wma_x = []
store_chart_y = []
store_chart_x = []
for i in range(0, df.shape[0] - wma_conv):
for j in range(0, wma_length):
weight += 1
wma_sum += (df.iloc[i + j, 3]*weight)
weight = 0
df.loc[df.index[i + wma_conv], 'WMA_' + str(wma_length)] = np.round((wma_sum / weight_sum), 4)
wma_sum = 0
for index, row in df.iloc[wma_length:].iterrows():
count += 1
available_money = trade_money - trade_cost
available_volume = row['Volume'] / 40
low = round(row['Low'], 4)
high = round(row['High'], 4)
date = index.to_pydatetime().strftime("%d-%m-%Y")
time = index.to_pydatetime().strftime("%H:%M")
if low != high:
if float(row['Open']) > float(row['Close']):
store_chart_y.append(float(high))
store_chart_x.append(date[6:10]+'-'+date[3:5]+'-'+date[0:2]+'T09:30:00')
store_chart_y.append(float(low))
store_chart_x.append(date[6:10] + '-' + date[3:5] + '-' + date[0:2] + 'T16:00:00')
elif float(row['Close']) > float(row['Open']):
store_chart_y.append(float(low))
store_chart_x.append(date[6:10] + '-' + date[3:5] + '-' + date[0:2] + 'T09:30:00')
store_chart_y.append(float(high))
store_chart_x.append(date[6:10] + '-' + date[3:5] + '-' + date[0:2] + 'T16:00:00')
elif low == high:
store_chart_y.append(low)
store_chart_x.append(date[6:10] + '-' + date[3:5] + '-' + date[0:2] + 'T12:45:00')
lvalue = (low * available_volume)
hvalue = (high * available_volume)
buy = row['WMA_' + str(wma_length)] - bd
sell = row['WMA_' + str(wma_length)] + sd
store_wma.append(float(row['WMA_' + str(wma_length)]))
store_wma_x.append(date[6:10]+'-'+date[3:5]+'-'+date[0:2]+'T'+time+':00')
if available_money > 0 and low <= buy and lvalue > trade_cost:
if lvalue > available_money > 0:
owned += (available_money / low)
trade_money = 0
td.append(date)
tt.append(time)
ty.append('b')
tp.append(low)
tv.append(available_volume)
elif available_money > lvalue > 0:
owned += (lvalue / low)
trade_money -= trade_cost
trade_money -= lvalue
td.append(date)
tt.append(time)
ty.append('b')
tp.append(low)
tv.append(available_volume)
if owned > 0 and high >= sell and hvalue > trade_cost:
if available_volume > owned:
trade_money += (owned * high)
trade_money -= trade_cost
owned = 0
td.append(date)
tt.append(time)
ty.append('s')
tp.append(high)
tv.append(available_volume)
elif owned > available_volume:
trade_money += (available_volume * high)
trade_money -= trade_cost
owned -= available_volume
td.append(date)
tt.append(time)
ty.append('s')
tp.append(high)
tv.append(available_volume)
if owned > 0:
liquid_money = (trade_money - trade_cost) + df.Low.ix[count - 1] * owned
else:
liquid_money = trade_money
self.chart_x = store_chart_x
self.chart_y = store_chart_y
self.graph_x = store_wma_x
self.graph_y = store_wma
self.final_money = round(trade_money, 2)
self.final_owned = round(owned)
self.final_liquid = round(liquid_money, 2)
self.td = td
self.tt = tt
self.ty = ty
self.tp = tp
self.tv = tv
self.save()
return self.id
class Ternary(db.EmbeddedDocument):
start_time = db.DateTimeField()
d1 = db.ListField()
d2 = db.ListField()
d3 = db.ListField()
d4 = db.ListField()
d5 = db.ListField()
d6 = db.ListField()
d7 = db.ListField()
d8 = db.ListField()
d9 = db.ListField()
d10 = db.ListField()
d11 = db.ListField()
d12 = db.ListField()
d13 = db.ListField()
d14 = db.ListField()
d15 = db.ListField()
d16 = db.ListField()
d17 = db.ListField()
d18 = db.ListField()
d19 = db.ListField()
d20 = db.ListField()
d21 = db.ListField()
d22 = db.ListField()
d23 = db.ListField()
d24 = db.ListField()
d25 = db.ListField()
def create(self, property, result):
self.__setattr__(property, result)
return self
