def adosc(high,
low,
close,
volume,
open_=None,
fast=None,
slow=None,
talib=None,
offset=None,
**kwargs):
"""Indicator: Accumulation/Distribution Oscillator"""
# Validate Arguments
fast = int(fast) if fast and fast > 0 else 3
slow = int(slow) if slow and slow > 0 else 10
_length = max(fast, slow)
high = verify_series(high, _length)
low = verify_series(low, _length)
close = verify_series(close, _length)
volume = verify_series(volume, _length)
offset = get_offset(offset)
if "length" in kwargs: kwargs.pop("length")
mode_tal = bool(talib) if isinstance(talib, bool) else True
if high is None or low is None or close is None or volume is None: return
# Calculate Result
if Imports["talib"] and mode_tal:
from talib import ADOSC
adosc = ADOSC(high, low, close, volume, fast, slow)
else:
ad_ = ad(high=high, low=low, close=close, volume=volume, open_=open_)
fast_ad = ema(close=ad_, length=fast, **kwargs)
slow_ad = ema(close=ad_, length=slow, **kwargs)
adosc = fast_ad - slow_ad
# Offset
if offset != 0:
adosc = adosc.shift(offset)
# Handle fills
if "fillna" in kwargs:
adosc.fillna(kwargs["fillna"], inplace=True)
if "fill_method" in kwargs:
adosc.fillna(method=kwargs["fill_method"], inplace=True)
# Name and Categorize it
adosc.name = f"ADOSC_{fast}_{slow}"
adosc.category = "volume"
return adosc
def adosc_talib(my_close, my_high, my_low, my_volume, fastperiod, slowperiod):
x = ADOSC(my_high.values,
my_low.values,
my_close.values,
my_volume.values,
fastperiod=fastperiod,
slowperiod=slowperiod)
return x
def getTechnicalInd(data, window=10):
close = data['Close']
high = data['High']
low = data['Low']
volume = data['Volume']
#madev = roc = willr=rsi=apo=atr=obv=adosc=slowk=fastk=slowd=fastd=vpos=vneg={}
window = 10
for i in range(0, window):
# momentum indicators
# rate of change: (price/prevPrice - 1)*100
data["roc{0}".format(i)] = ROC(close.shift(i), timeperiod=10)
# williams oscillator
data["willr{0}".format(i)] = WILLR(high.shift(i),
low.shift(i),
close.shift(i),
timeperiod=14)
# relative strength index
data["rsi{0}".format(i)] = RSI(close.shift(i), timeperiod=14)
# absolute price oscillator: slowMA(price) - fastMA(price)
data["apo{0}".format(i)] = APO(close.shift(i),
fastperiod=12,
slowperiod=26,
matype=0)
# volatility indicator
data["atr{0}".format(i)] = ATR(high.shift(i),
low.shift(i),
close.shift(i),
timeperiod=14) # average true range
# Volume indicator
# on balance volume
data["obv{0}".format(i)] = OBV(close.shift(i), volume.shift(i))
# chaikin A/D oscillator
data["adosc{0}".format(i)] = ADOSC(high.shift(i),
low.shift(i),
close.shift(i),
volume.shift(i),
fastperiod=3,
slowperiod=10)
# Stochastic Oscillator Slow %k: (C-L)/(H-L)*100
data["slowk{0}".format(i)], data["slowd{0}".format(i)] = STOCH(
high.shift(i),
low.shift(i),
close.shift(i),
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
# STOCHF - Stochastic Fast
data["fastk{0}".format(i)], data["fastd{0}".format(i)] = STOCHF(
high.shift(i),
low.shift(i),
close.shift(i),
fastk_period=5,
fastd_period=3,
fastd_matype=0)
#vortex
data["vortex_indicator_pos{0}".format(i)] = vortex_indicator_pos(
high.shift(i), low.shift(i), close.shift(i), n=14, fillna=False)
data["vortex_indicator_neg{0}".format(i)] = vortex_indicator_neg(
high.shift(i), low.shift(i), close.shift(i), n=14, fillna=False)
# returns
for i in range(1, window):
# overlap studies
data["madev{0}".format(i)] = close - close.shift(1).rolling(
window=i).mean()
data["return{0}".format(i)] = close - close.shift(i)
# std
for i in range(2, window):
data["std{0}".format(i)] = close.rolling(
i).std() #Standard deviation for a period of 5 days
return data #madev, roc,willr,rsi,apo,atr,obv,adosc,slowk,slowd,fastk,fastd,vpos, vneg, returns, stds
def setvolume(open, high, low, close, volume):
#volume
df["obv"] = OBV(close, volume)
#df["ad"] = AD(high, low, close, volume)
df["ad"] = ADOSC(high, low, close, volume, fastperiod=3, slowperiod=10)
corrected_data=corrected_data[['Open','High','Low','Close','Volume']]
corrected_data=corrected_data[~((corrected_data['High']==0) |
(corrected_data['Open']==0) |
(corrected_data['Close']==0) |
(corrected_data['Low']==0) |
(corrected_data['Volume']==0))]
print(corrected_data.shape)
print('<<< Extract Technical Indicators....')
corrected_data['SMA_10']=corrected_data['Close'].rolling(window=10).mean()
corrected_data['WMA_10']=WMA(corrected_data['Close'], timeperiod=10)
corrected_data['rsi']=RSI(corrected_data['Close'], timeperiod=10)
corrected_data['stoc_k'], corrected_data['stoc_d'] = STOCH(corrected_data['High'],corrected_data['Low'],corrected_data['Close'],10,6,0,6)
corrected_data['mom']=MOM(corrected_data['Close'], timeperiod=10)
corrected_data['macd'],_,_=MACD(corrected_data['Close'], fastperiod=12, slowperiod=26, signalperiod=10)
corrected_data['adosc']=ADOSC(corrected_data['High'], corrected_data['Low'], corrected_data['Close'], corrected_data['Volume'], fastperiod=3, slowperiod=10)
corrected_data['cci']=CCI(corrected_data['High'], corrected_data['Low'], corrected_data['Close'],timeperiod=10)
corrected_data['willr']=WILLR(corrected_data['High'], corrected_data['Low'], corrected_data['Close'],timeperiod=10)
null_value_checks(corrected_data)
corrected_data=corrected_data[~corrected_data['macd'].isnull()]
null_value_checks(corrected_data)
corrected_data_ta=corrected_data
print('<<< Completed extraction. Reading Forex Data....')
exchange_rate=pd.read_csv('./Data/GBP_USD.csv')
print(exchange_rate.head(2))
exchange_rate['Match_Date']=pd.to_datetime(exchange_rate['Date'],format='%b %d, %Y')
print('Shape of Exchange Rate: ', exchange_rate.shape)
null_value_checks(exchange_rate)
exchange_rate=exchange_rate[::-1]
slowperiod=26,
signalperiod=9)
# print('-----------------------------------MACD-----------------------------------')
# print(macd[34:37])
# print(macdsignal[34:37])
# print(macdhist[34:37])
#
rsi = RSI(fechamento, timeperiod=14)
# print('-----------------------------------RSI-----------------------------------')
# print(rsi[34:37])
# Momentum Indicators # Volume Indicators
mfi = MFI(maximo, minimo, fechamento, volume, timeperiod=14)
# print('-----------------------------------MFI-----------------------------------')
# print(mfi[34:37])
#Volume Indicators
ad = AD(maximo, minimo, fechamento, volume)
adosc = ADOSC(maximo, minimo, fechamento, volume, fastperiod=3, slowperiod=10)
obv = OBV(fechamento, volume)
# dataFrameConcatened = pd.concat(fechamento, rsi)
dataFrameConcatened = np.concatenate((abertura, rsi), axis=0)
print(dataFrameConcatened)
ax = plt.gca()
# brsr6CSV.plot(kind='line',x='DATA',y='ABERTURA',ax=ax)
brsr6CSV.plot(kind='line', x='DATA', y='FECHAMENTO', color='red', ax=ax)
plt.plot(upBR, label='BB UP')
plt.plot(lowBR, label='BB Low')
plt.show()