def test_mavg2(self):
target = "MiddleBand"
result = bollinger_mavg(close=self._df["Close"],
window=20,
fillna=False)
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def BB_MAV(df, col_name, intervals):
"""
Bollinger Bands Moving Average.
Reference: https://www.investopedia.com/ask/answers/112814/how-do-i-create-trading-strategy-bollinger-bands-and-moving-averages.asp
"""
from ta.volatility import bollinger_mavg
from tqdm.auto import tqdm
for interval in tqdm(intervals):
df['bb_' + str(interval)] = bollinger_mavg(df[col_name],
n=interval,
fillna=True)
def calculate_Volitality_Indicators():
JSON_sent = request.get_json()
df = pd.DataFrame(JSON_sent[0])
_, atr, bbsma, bbupper, bblower, keltnerC = JSON_sent
# Average True Range
if atr['displayATR']:
indicator_ATR = average_true_range(high=df['high'],
low=df['low'],
close=df['close'],
n=atr['nForATR'])
df['atr'] = indicator_ATR
# # Bollinger Band SMA
if bbsma['displayBBSMA']:
indicator_BBSMA = bollinger_mavg(close=df['close'],
n=bbsma['nForBBSMA'])
df['bbsma'] = indicator_BBSMA
# # Bollinger Band Upper
if bbupper['displayBBUpper']:
indicator_BBUpper = bollinger_hband(close=df['close'],
n=bbupper['nForBBUpper'],
ndev=bbupper['ndevBBUpper'])
df['BBupper'] = indicator_BBUpper
# # Bollinger Band Lower
if bblower['displayBBLower']:
indicator_BBLower = bollinger_lband(close=df['close'],
n=bblower['nForBBLower'],
ndev=bblower['ndevBBLower'])
df['BBlower'] = indicator_BBLower
# # Keltner Channel Central
if keltnerC['displayKeltnerC']:
indicator_keltnerC = keltner_channel_mband(high=df['high'],
low=df['low'],
close=df['close'],
n=keltnerC['nForKeltnerC'])
df['keltnerC'] = indicator_keltnerC
df.fillna(0, inplace=True)
return (json.dumps(df.to_dict('records')))
def test_mavg2(self):
target = 'MiddleBand'
result = bollinger_mavg(close=self._df['Close'], n=20, fillna=False)
pd.testing.assert_series_equal(self._df[target].tail(),
result.tail(),
check_names=False)
def Bollinger(df0, n=160):
df = df0
df['bollinger_center'] = bollinger_mavg(df['close'], n, fillna=False)
df['bollinger_upper'] = bollinger_hband(df['close'], n, fillna=False)
df['bollinger_lower'] = bollinger_lband(df['close'], n, fillna=False)
return df
def squeeze(history, sqz, symbol, t):
bbh = ta.bollinger_hband(history[symbol]['close'].dropna(), n=20, ndev=2)
bbl = ta.bollinger_lband(history[symbol]['close'].dropna(), n=20, ndev=2)
bba = ta.bollinger_mavg(history[symbol]['close'].dropna(), n=20)
kca = ta.keltner_channel_central(history[symbol]['high'],
history[symbol]['low'],
history[symbol]['close'],
n=20)
kcl = ta.keltner_channel_lband(history[symbol]['high'],
history[symbol]['low'],
history[symbol]['close'],
n=20)
kch = ta.keltner_channel_hband(history[symbol]['high'],
history[symbol]['low'],
history[symbol]['close'],
n=20)
mom = momentum.ao(history[symbol]['high'], history[symbol]['low'])
#print(bbl[-1], kcl[-1], bbh[-1], kch[-1])
sqzon = (bbl[-1] > kcl[-1]) and (bbh[-1] < kch[-1])
sqzoff = bbl[-1] < kcl[-1] and bbh[-1] > kch[-1]
nosqz = sqzon == False and sqzoff == False
"""
value = (Highest[lengthKC](high)+Lowest[lengthKC](low)+average[lengthKC](close))/3
val = linearregression[lengthKC](close-value)
val = (max(history[symbol]['high'][-20:-1]) + min(history[symbol]['low'][-20:-1]) + history[symbol]['close'].mean())/3.0
v = lr(history[symbol]['close'] - val)
"""
flag = -1
#print("hi")
if sqz[symbol][0] and sqzon:
pass
if sqz[symbol][0] and sqzoff:
sqz[symbol][0] = False
sqz[symbol][1] = True
flag = 0
if sqz[symbol][1] and sqzoff:
pass
if sqz[symbol][1] and sqzon:
sqz[symbol][1] = False
sqz[symbol][0] = True
flag = 1
if sqz[symbol][2] and sqzon:
sqz[symbol][2] = False
sqz[symbol][0] = True
flag = 1
if sqz[symbol][2] and sqzoff:
sqz[symbol][2] = False
sqz[symbol][1] = True
#print("sqeeze is OFF")
#print(time.time())
flag = -1
if flag == -1:
#print('No Change')
pass
if flag == 0:
send_message(symbol, t + " pop : " + str(mom[-1]))
if flag == 1:
send_message(symbol, "Squeeze On " + t)
def __init__(self, N_day):
super().__init__(indicator=lambda x: bollinger_mavg(x, N_day))
self.lookback_window = N_day