def send_notifications(self):
for symbol, strategy in self.orders.items():
for strat, order in self.orders[symbol].items():
for index, row in order.iterrows():
t = parser.parse(row['TIME']) - datetime.timedelta(hours=2)
if (datetime.datetime.now() - t).total_seconds() < 60 * 60:
value = "Type: " + row['TYPE'] + " -- SL:" + str(row['SL']) + " -- TP:" + str(
row['TP']) + " -- OPEN_AT:" + \
str(row['OPEN_AT']) + ' -- TIME ' + str(row['TIME'])
notify("[Strategy: " + strat + " ] " + value, symbol)
def analyse_high_and_low(self, symbol):
# LOWINPIPS, HIGHINPIPS
df = self.prices[symbol].copy()
df['AVG_LOW'] = df['LOWINPIPS'].rolling(window=5).mean()
if df['AVG_LOW'].iloc[-1] < df['LOWINPIPS'].iloc[-1] and get_pips(
symbol, df['LOWINPIPS'].iloc[-1]) > 15:
notify(symbol, "LOW Signal")
df['AVG_HIGH'] = df['HIGHINPIPS'].rolling(window=5).mean()
if df['AVG_HIGH'].iloc[-1] < df['HIGHINPIPS'].iloc[-1] and get_pips(
symbol, df['HIGHINPIPS'].iloc[-1]) > 15:
notify(symbol, "HIGH Signal")
def notify_buy_sell(self, symbol, avg):
return
df = self.prices[symbol].copy()
df['AVG'] = df['CLOSE'] - df['CLOSE'].ewm(span=avg).mean()
last = df['AVG'].iloc[-1]
penu = df['AVG'].iloc[-2]
if symbol in SELL:
if last < 0 < penu:
notify("You might Want to SELL " + symbol + " now", symbol)
if symbol in BUY:
if penu < 0 < last:
notify("You might Want to BUY " + symbol + " now", symbol)
def notify_trends(self, symbol, avg=20, long=10):
def consecutive_mean(x):
pos = 0
neg = 0
for j in x:
if j >= 0:
pos += 1
else:
neg += 1
if pos == len(x):
return 1
if neg == len(x):
return -1
return 0
df = self.prices[symbol].copy()
# APPLY AVG
df['AVG'] = df['CLOSE'] - df['CLOSE'].ewm(span=avg).mean()
df['consecutive'] = df['AVG'].rolling(
window=long).apply(lambda x: consecutive_mean(x))
trending = ""
if df['consecutive'].iloc[-1] == 1:
trending = 1
if df['consecutive'].iloc[-1] == -1:
trending = -1
if trending != "":
for i in range(1, 30, 1):
df['target' + str(i)] = -df['CLOSE'].diff(-i)
df['target' +
str(i)] = df['target' +
str(i)].apply(lambda x: get_pips(symbol, x))
df = df[df['consecutive'] == trending]
to_send = []
for i in range(1, 30, 1):
to_send.append('Ahead: ' + str(i) + 'pips: ' +
str(df['target' + str(i)].mean()))
to_send = "\n".join(to_send)
if trending == 1:
trending = "UP"
else:
trending = "DOWN"
notify("TRENDING: " + trending + "\n" + to_send, symbol)
def read_objects(self, path, symbol):
old_objects = self.objects.get(symbol, None)
self.objects[symbol] = pd.read_csv(path)
if old_objects is None:
return
old_rows = old_objects.shape[0]
new_rows = self.objects[symbol].shape[0]
if old_rows != new_rows:
return
for i in range(old_rows):
row_old = old_objects.iloc[i]
row_new = self.objects[symbol].iloc[i]
old_diff = row_old['dist']
new_diff = row_new['dist']
if old_diff * new_diff < 0:
now = "Now is Up"
if new_diff < 0:
now = "Now is Down"
msg = "Possible (False) Breakout " + row_new[
'Type'] + " - " + now + "\n"
msg += "Name Line is: " + row_new['name']
notify(msg, symbol)
def send_notifications(self):
if self.last_notification is None:
self.last_notification = datetime.datetime.now()
elif (datetime.datetime.now() -
self.last_notification).total_seconds() < 60 * 5:
return
else:
self.last_notification = datetime.datetime.now()
symbols1 = {*self.objects}
ok = False
for symbol in symbols1:
old_shape = self.shapes.get(symbol, 0)
if old_shape != self.prices[symbol].shape[0]:
self.shapes[symbol] = self.prices[symbol].shape[0]
else:
continue
trend_bearish = self.get_trend_line(symbol, bearish=True)
trend_bullish = self.get_trend_line(symbol, bearish=False)
line_bullish = self.get_lines(symbol, bearish=False)
line_bearish = self.get_lines(symbol, bearish=True)
pin_bar_up = self.get_pin_bar_up(symbol)
pin_bar_down = self.get_pin_bar_down(symbol)
ok = True
self.send_actual_notification_threshold(trend_bullish, symbol)
self.send_actual_notification_threshold(trend_bearish, symbol)
self.send_actual_notification_threshold(line_bullish, symbol)
self.send_actual_notification_threshold(line_bearish, symbol)
rsi = self.prices[symbol]["RSI"].iloc[-1]
dist_bb_25_up = int(
get_pips(symbol,
self.prices[symbol]["DIST_BB_25_UP"].iloc[-1]))
dist_bb_25_down = int(
get_pips(symbol,
self.prices[symbol]["DIST_BB_25_DOWN"].iloc[-1]))
if rsi < 27 or rsi > 73:
notify(symbol, "RSI: " + str(rsi))
if pin_bar_down:
notify(symbol, 'PIN_BAR_DOWN (BUY)')
if pin_bar_up:
notify(symbol, 'PIN_BAR_UP (SELL)')
#
# if dist_bb_25_up > -5:
# notify(symbol, "Bollinger UP: " + str(dist_bb_25_up))
#
# if dist_bb_25_down < 5:
# notify(symbol, "Bollinger DOWN: " + str(dist_bb_25_down))
# self.analyse_high_and_low(symbol)
# self.notify_trends(symbol, avg=25, long=20)
#self.notify_break(symbol, avg=25, long=20)
#self.notify_buy_sell(symbol, avg=25)
if ok:
correlation(self.prices)
def notify_break(self, symbol, avg=20, long=10):
def consecutive_mean(x):
pos = 0
neg = 0
for j in x:
if j >= 0:
pos += 1
else:
neg += 1
if pos == len(x):
return 1
if neg == len(x):
return -1
return 0
df = self.prices[symbol].copy()
# APPLY AVG
df['AVG'] = df['CLOSE'] - df['CLOSE'].ewm(span=avg).mean()
df['consecutive'] = df['AVG'].rolling(
window=long).apply(lambda x: consecutive_mean(x))
trending = ""
if df['consecutive'].iloc[-1] == 0 and df['consecutive'].iloc[-2] == 1:
trending = "Possible retracement DOWN"
if df['consecutive'].iloc[-1] == 0 and df['consecutive'].iloc[-2] == -1:
trending = "Possible retracement UP"
if trending != "":
for i in range(1, 30, 1):
df['target' + str(i)] = -df['CLOSE'].diff(-i)
df['target' +
str(i)] = df['target' +
str(i)].apply(lambda x: get_pips(symbol, x))
df['consecutive_'] = df['consecutive'].shift(1)
if 'UP' in trending:
df = df[(df['consecutive'] == 0) & (df['consecutive_'] == -1)]
else:
df = df[(df['consecutive'] == 0) & (df['consecutive_'] == 1)]
to_send = []
for i in range(1, 30, 1):
to_send.append('Ahead: ' + str(i) + 'pips: ' +
str(df['target' + str(i)].mean()))
to_send = "\n".join(to_send)
notify("BREAKING AVG20: " + trending + "\n" + to_send, symbol)
# Look for pullback
tot = 0
pos = 0
neg = 0
for dist in list(df['AVG'].tail(60)):
if dist >= 0:
pos += 1
if dist <= 0:
neg += 1
tot += 1
pos = pos / tot
neg = neg / tot
if pos >= 0.8:
l = list(df['AVG'].tail(3))
if l[2] > 0 > l[1] and l[0] < 0:
notify("Possible Pullback (BUY)", symbol)
if neg >= 0.8:
l = list(df['AVG'].tail(3))
if l[2] < 0 and l[1] > 0 and l[0] > 0:
notify("Possible Pullback (SELL)", symbol)
def send_actual_notification_threshold(self, objs, symbol, threshold=50):
for obj in objs:
if abs(obj['dist']) < threshold:
notify(symbol, "TREND LINE " + str(obj))