def test_bop(self):
"""
Test Balance of Power (BOP).
"""
q = qufilab.bop(self.high, self.low, self.open, self.close)
t = talib.BOP(self.open, self.high, self.low, self.close)
np.testing.assert_allclose(q, t, rtol=self.tolerance)
def bop(self, opens, highs, lows, closes):
"""
Balance Of Power (Momentum Indicators)
Inputs:
prices: ['open', 'high', 'low', 'close']
Outputs:
real
:param open:
:param high:
:param low:
:param close:
:return:
"""
# nejdříve vyřízneme potřebnou délku
# a převedeme na desetinné číslo.
open = float(opens[-1])
high = float(highs[-1])
low = float(lows[-1])
close = float(closes[-1])
# převedeme na datová pole
open = numpy.asarray((open, ))
high = numpy.asarray((high, ))
low = numpy.asarray((low, ))
close = numpy.asarray((close, ))
# Výsledek.
result = talib.BOP(open, high, low, close)
cutresult = float(result[0])
return cutresult
def bop(
client,
symbol,
timeframe="6m",
highcol="high",
lowcol="low",
closecol="close",
volumecol="volume",
):
"""This will return a dataframe of
Balance of power
for the given symbol across the given timeframe
Args:
client (pyEX.Client): Client
symbol (string): Ticker
timeframe (string): timeframe to use, for pyEX.chart
highcol (string): column to use to calculate
lowcol (string): column to use to calculate
closecol (string): column to use to calculate
volumecol (string): column to use to calculate
Returns:
DataFrame: result
"""
df = client.chartDF(symbol, timeframe)
x = t.BOP(df[highcol].values, df[lowcol].values, df[closecol].values,
df[volumecol].values)
return pd.DataFrame({
highcol: df[highcol].values,
lowcol: df[lowcol].values,
closecol: df[closecol].values,
volumecol: df[volumecol].values,
"bop": x,
})
def momentum(self): adx = talib.ADX(self.high,self.low,self.close,self.period) adxr = talib.ADXR(self.high,self.low,self.close,self.period) apo = talib.APO(self.high,self.low,self.close,self.period) aroondown, aroonup = talib.AROON(self.high, self.low, period) aroonosc = talib.AROONOSC(self.high,self.low,self.period) bop = talib.BOP(self.opens,self.high,self.low,self.close) cci = talib.CCI(self.high,self.low,self.close,self.period) cmo = talib.CMO(self.close,self.period) dx = talib.DX(self.high,self.low,self.close,self.period) macd, macdsignal, macdhist = talib.MACD(self.close, fastperiod=period, slowperiod=period*5, signalperiod=period*2) macd1, macdsignal1, macdhist1 = talib.MACDEXT(self.close, fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0) macd2, macdsignal2, macdhist2 = talib.MACDFIX(self.close, signalperiod=9) mfi = talib.MFI(self.high, self.low, self.close, self.volume, timeperiod=14) minus_di = talib.MINUS_DI(self.high, self.low, self.close, timeperiod=14) minus_dm = talib.MINUS_DM(self.high, self.low, timeperiod=14) mom = talib.MOM(self.close, timeperiod=10) plus_di = talib.PLUS_DI(self.high, self.low, self.close, timeperiod=14) plus_dm = talib.PLUS_DM(self.high, self.low, timeperiod=14) ppo = talib.PPO(self.close, fastperiod=12, slowperiod=26, matype=0) roc = talib.ROC(self.close, timeperiod=10) rocp = talib.ROCP(self.close, timeperiod=10) rocr = talib.ROCR(self.close, timeperiod=10) rocr100 = talib.ROCR100(self.close, timeperiod=10) rsi = talib.RSI(self.close, timeperiod=14) slowk, slowd = talib.STOCH(self.high, self.low, self.close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0) fastk, fastd = talib.STOCHF(self.high, self.low, self.close, fastk_period=5, fastd_period=3, fastd_matype=0) fastk1, fastd1 = talib.STOCHRSI(self.close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0) trix = talib.TRIX(self.close, timeperiod=30) ultosc = talib.ULTOSC(self.high, self.low, self.close, timeperiod1=7, timeperiod2=14, timeperiod3=28) willr = talib.WILLR(self.high, self.low, self.close, timeperiod=14)
def add_indicators(df):
high = df["HA_High"].values
close = df["HA_Close"].values
low = df["HA_Low"].values
_open = df["HA_Open"].values
volume = df["volume"].values.astype('uint32')
df["APO"] = talib.APO(close, fastperiod=9, slowperiod=21, matype=0)
df["APO"] = talib.APO(close, fastperiod=9, slowperiod=21, matype=0)
df["aroondown"], df["aroonup"] = talib.AROON(high, low, timeperiod=14)
df["BOP"] = talib.BOP(_open, high, low, close)
df["CCI"] = talib.CCI(high, low, close, timeperiod=10)
df["DX"] = talib.DX(high, low, close, timeperiod=10)
df["MOM"] = talib.MOM(close, timeperiod=10)
df["slowk"], df["slowd"] = talib.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df["OBV"] = talib.OBV(close, np.asarray(volume, dtype='float'))
df["ADOSC"] = talib.ADOSC(high,
low,
close,
np.asarray(volume, dtype='float'),
fastperiod=3,
slowperiod=10)
df["upperband"], df["middleband"], df["lowerband"] = talib.BBANDS(
close, timeperiod=5, nbdevup=2, nbdevdn=2, matype=0)
return df
def logic (ohlc, exchange="NSE"):
inputs = {"open":np.array(ohlc.open.values),
"high": np.array(ohlc.high.values),
"low": np.array(ohlc.low.values),
"close": np.array(ohlc.close.values),
"volume": np.array(ohlc.volume.values).astype(float)}
try:
sma8 = talib.SMA(inputs["close"], 8)
sma21 = talib.SMA(inputs["close"], 21)
rsi = talib.RSI(inputs["close"], timeperiod=15)
sma_rsi = talib.SMA(rsi,8)
#natr = talib.NATR(inputs["high"],inputs["low"],inputs["close"], timeperiod=14)
macd, macdsignal, macdhist = talib.MACDEXT(inputs["close"], fastperiod=12, fastmatype=0, slowperiod=26, slowmatype=0, signalperiod=9, signalmatype=0)
ultosc = talib.ULTOSC(inputs["high"],inputs["low"],inputs["close"],timeperiod1=7, timeperiod2=14, timeperiod3=28)
slowk, slowd = talib.STOCH(inputs["high"],inputs["low"],inputs["close"], 5, 3, 0, 3, 0)
#obv = talib.OBV(inputs["close"], inputs["volume"])
bop = talib.SMA(talib.BOP(inputs["open"], inputs["high"],inputs["low"],inputs["close"]),5)
data = pd.DataFrame({ "sma_rsi":sma_rsi,"slowk": slowk, "slowd": slowd,"rsi":rsi, "ultosc": ultosc,
"macd": macd, "macdsignal": macdsignal,"macdhist":macdhist,# "obv":obv, "natr": natr,
"bop":bop, "sma8":sma8, "sma21":sma21,
})
except Exception as e:
print(item + str(e) + " can't process")
return data
def extract_features(data):
high = data['High']
low = data['Low']
close = data['Close']
volume = data['Volume']
open_ = data['Open']
data['ADX'] = ta.ADX(high, low, close, timeperiod=19)
data['CCI'] = ta.CCI(high, low, close, timeperiod=19)
data['CMO'] = ta.CMO(close, timeperiod=14)
data['MACD'], X, Y = ta.MACD(close, fastperiod=10, slowperiod=30, signalperiod=9)
data['MFI'] = ta.MFI(high, low, close, volume, timeperiod=19)
data['MOM'] = ta.MOM(close, timeperiod=9)
data['ROCR'] = ta.ROCR(close, timeperiod=12)
data['RSI'] = ta.RSI(close, timeperiod=19)
data['STOCHSLOWK'], data['STOCHSLOWD'] = ta.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
data['TRIX'] = ta.TRIX(close, timeperiod=30)
data['WILLR'] = ta.WILLR(high, low, close, timeperiod=14)
data['OBV'] = ta.OBV(close, volume)
data['TSF'] = ta.TSF(close, timeperiod=14)
data['NATR'] = ta.NATR(high, low, close)#, timeperiod=14)
data['ULTOSC'] = ta.ULTOSC(high, low, close)
data['AROONOSC'] = ta.AROONOSC(high, low, timeperiod=14)
data['BOP'] = ta.BOP(open_, high, low, close)
data['LINEARREG'] = ta.LINEARREG(close)
data['AP0'] = ta.APO(close, fastperiod=9, slowperiod=23, matype=1)
data['TEMA'] = ta.TRIMA(close, 29)
return data
def _set_df(self, df):
for c in AssetColumns:
if str(c) not in df.columns:
raise RuntimeError(
f"{c} column doesn't exist in the dataframe")
df['datetime'] = pd.to_datetime(df['timestamp'], unit='s')
df['month'] = df['datetime'].dt.month
df['weekday'] = df['datetime'].dt.weekday
df['hour'] = df['datetime'].dt.hour
df = df.drop(['datetime', 'timestamp', 'index'], 1)
df['sma'] = talib.SMA(df['close'], self.window)
df['rsi'] = talib.RSI(df['close'], self.window)
df['mom'] = talib.MOM(df['close'], self.window)
df['bop'] = talib.BOP(df['open'], df['high'], df['low'], df['close'])
df['aroonosc'] = talib.AROONOSC(df['high'], df['low'], self.window)
self.min = df.min()
self.max = df.max()
self.df = df
self.row_count = len(self.df.index)
if self.row_count < EP_LEN:
raise RuntimeError(f"not enought rows")
# add balance, asset_held, net_worth at each timestamp
n_features = (len(df.columns) * self.window) + 3
self.observation_space = spaces.Box(-np.inf,
np.inf,
shape=((n_features, )),
dtype=np.float32)
def _compute_states(self):
start = self.current_idx - self.lookback
end = self.current_idx
df = self._data.iloc[start:end, :]
states = {}
states['roc1'] = ta.ROC(df.close, timeperiod=5).iloc[-1]
states['roc2'] = ta.ROC(df.close, timeperiod=20).iloc[-1]
states['rsi1'] = ta.RSI(df.close, timeperiod=14).iloc[-1]
states['rsi2'] = ta.RSI(df.close, timeperiod=28).iloc[-1]
states['cci1'] = ta.CCI(df.high, df.low, df.close,
timeperiod=14).iloc[-1]
states['cci2'] = ta.CCI(df.high, df.low, df.close,
timeperiod=28).iloc[-1]
states['adx1'] = ta.ADX(df.high, df.low, df.close,
timeperiod=14).iloc[-1]
states['adx2'] = ta.ADX(df.high, df.low, df.close,
timeperiod=28).iloc[-1]
states['atr'] = ta.ATR(df.high, df.low, df.close,
timeperiod=14).iloc[-1]
states['aroon1'] = ta.AROONOSC(df.high, df.low, timeperiod=14).iloc[-1]
states['aroon2'] = ta.AROONOSC(df.high, df.low, timeperiod=28).iloc[-1]
states['bop'] = ta.BOP(df.open, df.high, df.low, df.close).iloc[-1]
states['cmo'] = ta.CMO(df.close, timeperiod=14).iloc[-1]
states['close'] = df.close.iloc[-1]
states['date'] = df.index[-1]
states['position'] = self.position
states['entry_price'] = self.entry_price
states['entry_date'] = self.entry_date
return states
def getMomentumIndicators(df):
high = df['High']
low = df['Low']
close = df['Close']
open = df['Open']
volume = df['Volume']
df['ADX'] = ta.ADX(high, low, close, timeperiod=14)
df['SMA'] = ta.ADXR(high, low, close, timeperiod=14)
df['APO'] = ta.APO(close, fastperiod=12, slowperiod=26, matype=0)
df['AROONDOWN'], df['AROOONUP'] = ta.AROON(high, low, timeperiod=14)
df['AROONOSC'] = ta.AROONOSC(high, low, timeperiod=14)
df['BOP'] = ta.BOP(open, high, low, close)
df['CCI'] = ta.CCI(high, low, close, timeperiod=14)
df['CMO'] = ta.CMO(close, timeperiod=14)
df['DX'] = ta.DX(high, low, close, timeperiod=14)
df['MACD'], df['MACDSIGNAL'], df['MACDHIST'] = ta.MACD(close, fastperiod=12, slowperiod=26, signalperiod=9)
df['MFI'] = ta.MFI(high, low, close, volume, timeperiod=14)
df['MINUS_DI'] = ta.MINUS_DI(high, low, close, timeperiod=14)
df['MINUS_DM']= ta.MINUS_DM(high, low, timeperiod=14)
df['MOM'] = ta.MOM(close, timeperiod=10)
df['PLUS_DM'] =ta.PLUS_DM(high, low, timeperiod=14)
df['PPO'] = ta.PPO(close, fastperiod=12, slowperiod=26, matype=0)
df['ROC'] = ta.ROC(close, timeperiod=10)
df['ROCP'] = ta.ROCP(close, timeperiod=10)
df['ROCR'] = ta.ROCR(close, timeperiod=10)
df['ROCR100'] = ta.ROCR100(close, timeperiod=10)
df['RSI'] = ta.RSI(close, timeperiod=14)
df['SLOWK'], df['SLOWD'] = ta.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
df['FASTK'], df['FASTD'] = ta.STOCHF(high, low, close, fastk_period=5, fastd_period=3, fastd_matype=0)
df['FASTK2'], df['FASTD2'] = ta.STOCHRSI(close, timeperiod=14, fastk_period=5, fastd_period=3, fastd_matype=0)
df['TRIX'] = ta.TRIX(close, timeperiod=30)
df['ULTOSC'] = ta.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
df['WILLR'] = ta.WILLR(high, low, close, timeperiod=14)
def bop(self, array: bool = False):
"""
BOP.
"""
result = talib.BOP(self.open, self.high, self.low, self.close)
if array:
return result
return result[-1]
def BOP(self):
real_data = np.array([self.df.open, self.df.high, self.df.low, self.df.close], dtype='f8')
bop = talib.BOP(real_data[0], real_data[1], real_data[2], real_data[3])
# return go.Scatter(
# x=self.df.index,
# y=bop,
# name='BOP'
# )
return bop
def bop(self, array: bool = False) -> Union[float, np.ndarray]:
"""
BOP.
"""
result = talib.BOP(self.open, self.high, self.low, self.close)
if array:
return result
return result[-1]
def BOP(open, high, low, close):
''' Balance Of Power 均势
分组: Momentum Indicator 动量指标
简介:
real = BOP(open, high, low, close)
'''
return talib.BOP(open, high, low, close)
def _assemble_state(self):
'''Here we can add other things such as indicators and times'''
self._get_last_N_timebars()
bars = [self.last5m, self.last1h, self.last1d]
state = []
candles = {
j: {k: np.array([])
for k in ['open', 'high', 'low', 'close']}
for j in range(len(bars))
}
for j, bar in enumerate(bars):
for col in ['open', 'high', 'low', 'close']:
candles[j][col] = np.asarray(bar[col])
state += (list(np.asarray(bar[col]))[-10:])
self.state = np.array([])
self.state = np.append(self.state, state)
self.state = np.append(self.state, self.position)
np.append(self.state, np.sign(self.pnl_sum))
self.state = np.append(self.state, self._time_of_day)
self.state = np.append(self.state, self._day_of_week)
for c in candles:
try:
sma1 = talib.SMA(candles[c]['close'], self.lkbk - 1)[-1]
sma2 = talib.SMA(candles[c]['close'], self.lkbk - 8)[-1]
self.state = np.append(self.state, (sma1 - sma2) / sma2)
self.state = np.append(self.state, sma1)
self.state = np.append(
self.state,
talib.RSI(candles[c]['close'], self.lkbk - 1)[-1])
self.state = np.append(
self.state,
talib.MOM(candles[c]['close'], self.lkbk - 1)[-1])
#self.state = np.append(self.state,talib.MACD(candles[c]['close'],fastperiod=11, slowperiod=22, signalperiod=9)[0][0])
self.state = np.append(
self.state,
talib.BOP(candles[c]['open'], candles[c]['high'],
candles[c]['low'], candles[c]['close'])[-1])
#self.state = np.append(self.state,talib.ADXR(candles[c]['high'],
# candles[c]['low'],
# candles[c]['close'],
# self.lkbk-3)[-1])
#self.state = np.append(self.state,talib.STOCH(candles[c]['high'],
# candles[c]['low'],
# candles[c]['close'],5,3,0,3,0)[-1][0])
self.state = np.append(
self.state,
talib.AROONOSC(candles[c]['high'], candles[c]['low'],
self.lkbk - 3)[-1])
except:
print(traceback.format_exc())
#print('-->',self.state)
self.state = (np.array(self.state) - np.mean(self.state)) / np.std(
self.state)
def bop(self, sym, frequency):
if not self.kbars_ready(sym, frequency):
return []
opens = self.open(sym, frequency)
highs = self.high(sym, frequency)
lows = self.low(sym, frequency)
closes = self.close(sym, frequency)
bop = ta.BOP(opens, highs, lows, closes)
return bop
def get_momentum_studies(open, low, high, close, volume, df):
# Momentum studies
# https://mrjbq7.github.io/ta-lib/func_groups/momentum_indicators.html
df['MACD'], df['MACD_SIGN'], df['MACD_HIST'] = talib.MACD(
close, fastperiod=12, slowperiod=26, signalperiod=9)
df['STOCH-SLOW-K'], df['STOCH-SLOW-D'] = talib.STOCH(high,
low,
close,
fastk_period=5,
slowk_period=3,
slowk_matype=0,
slowd_period=3,
slowd_matype=0)
df['STOCH-FAST-K'], df['STOCH-FAST-D'] = talib.STOCHF(high,
low,
close,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['STOCH-RSI-K'], df['STOCH-RSI-D'] = talib.STOCHRSI(close,
timeperiod=14,
fastk_period=5,
fastd_period=3,
fastd_matype=0)
df['AROON-DOWN'], df['AROON-UP'] = talib.AROON(high,
low,
timeperiod=14)
df["MINUS_DI"] = talib.MINUS_DI(high, low, close, timeperiod=14)
df["MINUS_DM"] = talib.MINUS_DM(high, low, timeperiod=14)
df["PLUS_DI"] = talib.PLUS_DI(high, low, close, timeperiod=14)
df["PLUS_DM"] = talib.PLUS_DM(high, low, timeperiod=14)
df["MOM"] = talib.MOM(close, timeperiod=10)
df["MFI"] = talib.MFI(high, low, close, volume, timeperiod=14)
df["ADX"] = talib.ADX(high, low, close, timeperiod=14)
df["ADXR"] = talib.ADXR(high, low, close, timeperiod=14)
df["APO"] = talib.APO(close, fastperiod=12, slowperiod=26, matype=0)
df["AROONOSC"] = talib.AROONOSC(high, low, timeperiod=14)
df["BOP"] = talib.BOP(open, high, low, close)
df["CCI"] = talib.CCI(high, low, close, timeperiod=14)
df["CMO"] = talib.CMO(close, timeperiod=14)
df["DX"] = talib.DX(high, low, close, timeperiod=14)
df["PPO"] = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0)
df["ROC"] = talib.ROC(close, timeperiod=10)
df["RSI"] = talib.RSI(close, timeperiod=14)
df["TRIX"] = talib.TRIX(close, timeperiod=30)
df["ULT"] = talib.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
df["WILLR"] = talib.WILLR(high, low, close, timeperiod=14)
def handle_data(context):
# 获取历史数据, 取后bop_window根bar
hist = context.data.get_price(context.security,
count=context.user_data.bop_window * 10,
frequency=context.frequency)
if len(hist.index) < context.user_data.bop_window * 10:
context.log.warn("bar的数量不足, 等待下一根bar...")
return
# 历史收盘价
hist_open = np.array(hist["open"])
hist_high = np.array(hist["high"])
hist_low = np.array(hist["low"])
hist_close = np.array(hist["close"])
# 初始化买入/卖出信号
long_signal_triggered = False
short_signal_triggered = False
# 计算AD值
bop = talib.BOP(hist_open, hist_high, hist_low, hist_close)
# 产生买入/卖出信号
if bop[-1] > 0:
long_signal_triggered = True
elif bop[-1] < 0:
short_signal_triggered = True
# 有卖出信号，且持有仓位，则市价单全仓卖出
if short_signal_triggered:
if context.account.huobi_cny_btc >= HUOBI_CNY_BTC_MIN_ORDER_QUANTITY:
context.log.info("正在卖出 %s" % context.security)
context.log.info("卖出数量为 %s" % context.account.huobi_cny_btc)
context.order.sell_limit(context.security,
quantity=str(
context.account.huobi_cny_btc),
price=str(hist_close[-1] * 0.98))
else:
context.log.info("仓位不足，无法卖出")
# 有买入信号，且持有现金，则市价单全仓买入
elif long_signal_triggered:
if context.account.huobi_cny_cash >= HUOBI_CNY_BTC_MIN_ORDER_CASH_AMOUNT:
context.log.info("正在买入 %s" % context.security)
context.log.info("下单金额为 %s 元" % context.account.huobi_cny_cash)
context.order.buy_limit(
context.security,
quantity=str(context.account.huobi_cny_cash / hist_close[-1] *
0.98),
price=str(hist_close[-1] * 1.02))
else:
context.log.info("现金不足，无法下单")
else:
context.log.info("无交易信号，进入下一根bar")
def bop(candles: np.ndarray, sequential: bool = False) -> Union[float, np.ndarray]:
"""
BOP - Balance Of Power
:param candles: np.ndarray
:param sequential: bool - default=False
:return: float | np.ndarray
"""
candles = slice_candles(candles, sequential)
res = talib.BOP(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
return res if sequential else res[-1]
def test_bop(self):
result = pandas_ta.bop(self.open, self.high, self.low, self.close)
self.assertIsInstance(result, Series)
self.assertEqual(result.name, 'BOP')
try:
expected = tal.BOP(self.open, self.high, self.low, self.close)
pdt.assert_series_equal(result, expected, check_names=False)
except AssertionError as ae:
try:
corr = pandas_ta.utils.df_error_analysis(result, expected, col=CORRELATION)
self.assertGreater(corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result, CORRELATION, ex)
def generateIndicatorBOP(dictDataSpec):
"""
https://www.investopedia.com/articles/technical/02/041002.asp
"""
df = dictDataSpec['df']
sReal = pd.Series(talib.BOP(df.Open.values, df.High.values, df.Low.values, df.Close.values), index=df.index)
sIndicator = sReal.apply(lambda x: np.nan)
print sReal.describe()
ThresholdUpper = 0.8
ThresholdLower = -0.8
sIndicator[(sReal > ThresholdUpper)&(sReal.shift(1) < ThresholdUpper)] = 1
sIndicator[(sReal < ThresholdLower)&(sReal.shift(1) > ThresholdLower)] = -1
return sIndicator
def add_BOP(self, type='histogram', color='tertiary', **kwargs):
"""Balance of Power."""
if not self.has_OHLC:
raise Exception()
utils.kwargs_check(kwargs, VALID_TA_KWARGS)
if 'kind' in kwargs:
type = kwargs['kind']
name = 'BOP'
self.sec[name] = dict(type=type, color=color)
self.ind[name] = talib.BOP(self.df[self.op].values,
self.df[self.hi].values,
self.df[self.lo].values,
self.df[self.cl].values)
def get_additional_factors(open, high, low, close, volume):
# Overlap Studies Functions
mat = get_all_factors(open, high, low, close, volume)
mat = np.column_stack((mat, talib.HT_TRENDLINE(close))) ## close
mat = np.column_stack((mat, talib.KAMA(close, timeperiod=30))) ##close
#Momentum Indicator Functions
mat = np.column_stack((mat, talib.ADX(high, low, close, timeperiod=14)))
mat = np.column_stack((mat, talib.ADXR(high, low, close, timeperiod=14)))
mat = np.column_stack(
(mat, talib.APO(close, fastperiod=12, slowperiod=26, matype=0)))
mat = np.column_stack((mat, talib.AROONOSC(high, low, timeperiod=14)))
mat = np.column_stack((mat, talib.BOP(open, high, low, close)))
mat = np.column_stack((mat, talib.MOM(close, timeperiod=10)))
#Volume Indicator Functions
mat = np.column_stack((mat, talib.AD(high, low, close, volume)))
mat = np.column_stack(
(mat, talib.ADOSC(high,
low,
close,
volume,
fastperiod=3,
slowperiod=10)))
mat = np.column_stack((mat, talib.OBV(close, volume)))
#Volatility Indicator Functions
mat = np.column_stack((mat, talib.NATR(high, low, close, timeperiod=14)))
mat = np.column_stack((mat, talib.TRANGE(high, low, close)))
#Price Transform Functions
mat = np.column_stack((mat, talib.AVGPRICE(open, high, low, close)))
mat = np.column_stack((mat, talib.MEDPRICE(high, low)))
mat = np.column_stack((mat, talib.TYPPRICE(high, low, close)))
mat = np.column_stack((mat, talib.WCLPRICE(high, low, close)))
#Cycle Indicator Functions
mat = np.column_stack((mat, talib.HT_DCPERIOD(close)))
mat = np.column_stack((mat, talib.HT_DCPHASE(close)))
mat = np.column_stack((mat, talib.HT_TRENDMODE(close)))
# 20
return mat
def init_state(indata, test=False):
openn = indata['open'].values
close = indata['close'].values
high = indata['high'].values
low = indata['low'].values
volume = indata['volume'].values
diff = np.diff(close)
diff = np.insert(diff, 0, 0)
sma30 = talib.SMA(close, 30)
sma60 = talib.SMA(close, timeperiod=60)
rsi = talib.RSI(close, timeperiod=14)
atr = talib.ATR(high, low, close, timeperiod=14)
trange = talib.TRANGE(high, low, close)
macd, macdsignal, macdhist = talib.MACD(close, 12, 26, 9)
upper, middle, lower = talib.BBANDS(close, 20, 2, 2)
ema = talib.EMA(close, 30)
ma = talib.MA(close, 30)
wma = talib.WMA(close, timeperiod=30)
tema = talib.TEMA(close, 30)
obv = talib.OBV(close, np.asarray(volume, dtype='float'))
adx = talib.ADX(high, low, close, 14)
apo = talib.APO(close, 12, 2, 0)
bop = talib.BOP(openn, high, low, close)
mom = talib.MOM(close,10)
ppo = talib.PPO(close, 12, 26, 0)
slowk, slowd = talib.STOCH(high, low, close, fastk_period=5, slowk_period=3, slowk_matype=0, slowd_period=3, slowd_matype=0)
ad = talib.AD(high, low, close, np.asarray(volume, dtype='float'))
wcl = talib.WCLPRICE(high, low, close)
#--- Preprocess data
xdata = np.column_stack((close, diff, sma30, sma60, rsi, atr, macd, macdsignal, macdhist, lower, middle, upper, ema, ma, wma, adx, apo, bop, mom, ppo, slowk, slowd, trange, wcl))
xdata = np.nan_to_num(xdata)
if test == False:
scaler = preprocessing.StandardScaler()
xdata = np.expand_dims(scaler.fit_transform(xdata), axis=1)
joblib.dump(scaler, 'data/scaler.pkl')
elif test == True:
scaler = joblib.load('data/scaler.pkl')
xdata = np.expand_dims(scaler.fit_transform(xdata), axis=1)
state = xdata[0:1, 0:1, :]
return state, xdata, close
def bop(candles: np.ndarray, sequential=False) -> Union[float, np.ndarray]:
"""
BOP - Balance Of Power
:param candles: np.ndarray
:param sequential: bool - default=False
:return: float | np.ndarray
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
res = talib.BOP(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
if sequential:
return res
else:
return None if np.isnan(res[-1]) else res[-1]
def add_BOP(self, type="histogram", color="tertiary", **kwargs):
"""Balance of Power."""
if not self.has_OHLC:
raise Exception()
utils.kwargs_check(kwargs, VALID_TA_KWARGS)
if "kind" in kwargs:
type = kwargs["kind"]
name = "BOP"
self.sec[name] = dict(type=type, color=color)
self.ind[name] = talib.BOP(
self.df[self.op].values,
self.df[self.hi].values,
self.df[self.lo].values,
self.df[self.cl].values,
)
def bop(data,
high_col='High',
low_col='Low',
close_col='Close',
vol_col='Volume',
open_col='Open',
fillna=False):
high = data[high_col]
low = data[low_col]
close = data[close_col]
open_ = data[open_col]
real = talib.BOP(open_, high, low, close)
if fillna:
real = real.replace([np.inf, -np.inf], np.nan).fillna(0)
return pd.Series(real, name='bop')
def generate_tech_data(stock, open_name, close_name, high_name, low_name):
open_price = stock[open_name].values
close_price = stock[close_name].values
low_price = stock[low_name].values
high_price = stock[high_name].values
data = pd.DataFrame(stock)
data['MOM'] = talib.MOM(close_price)
data['SMA'] = talib.SMA(close_price)
data['HT_DCPERIOD'] = talib.HT_DCPERIOD(close_price)
data['HT_DCPHASE'] = talib.HT_DCPHASE(close_price)
data['sine'], data['leadsine'] = talib.HT_SINE(close_price)
data['inphase'], data['quadrature'] = talib.HT_PHASOR(close_price)
data['HT_TRENDMODE'] = talib.HT_TRENDMODE(close_price)
data['SAREXT'] = talib.SAREXT(high_price, low_price)
data['ADX'] = talib.ADX(high_price, low_price, close_price)
data['ADXR'] = talib.ADX(high_price, low_price, close_price)
data['APO'] = talib.APO(close_price)
data['AROON_UP'], data['AROON_DOWN'] = talib.AROON(high_price, low_price)
data['AROONOSC'] = talib.AROONOSC(high_price, low_price)
data['BOP'] = talib.BOP(open_price, high_price, low_price, close_price)
data['CCI'] = talib.CCI(high_price, low_price, close_price)
data['PLUS_DI'] = talib.PLUS_DI(high_price, low_price, close_price)
data['PLUS_DM'] = talib.PLUS_DM(high_price, low_price)
data['PPO'] = talib.PPO(close_price)
data['macd'], data['macd_sig'], data['macd_hist'] = talib.MACD(close_price)
data['RSI'] = talib.RSI(close_price)
data['CMO'] = talib.CMO(close_price)
data['ROC'] = talib.ROC(close_price)
data['ROCP'] = talib.ROCP(close_price)
data['ROCR'] = talib.ROCR(close_price)
data['slowk'], data['slowd'] = talib.STOCH(high_price, low_price,
close_price)
data['fastk'], data['fastd'] = talib.STOCHF(high_price, low_price,
close_price)
data['TRIX'] = talib.TRIX(close_price)
data['ULTOSC'] = talib.ULTOSC(high_price, low_price, close_price)
data['WILLR'] = talib.WILLR(high_price, low_price, close_price)
data['NATR'] = talib.NATR(high_price, low_price, close_price)
data['TRANGE'] = talib.TRANGE(high_price, low_price, close_price)
data = data.drop([open_name, close_name, high_name, low_name], axis=1)
data = data.dropna()
return data
def bop(candles: np.ndarray,
sequential: bool = False) -> Union[float, np.ndarray]:
"""
BOP - Balance Of Power
:param candles: np.ndarray
:param sequential: bool - default=False
:return: float | np.ndarray
"""
warmup_candles_num = get_config('env.data.warmup_candles_num', 240)
if not sequential and len(candles) > warmup_candles_num:
candles = candles[-warmup_candles_num:]
res = talib.BOP(candles[:, 1], candles[:, 3], candles[:, 4], candles[:, 2])
if sequential:
return res
else:
return None if np.isnan(res[-1]) else res[-1]
def test_BOP(self):
self.env.add_operator('bop', {
'operator': OperatorBOP,
})
string = 'bop(open, high, low, close)'
gene = self.env.parse_string(string)
df = gene.eval(self.env, self.dates[14], self.dates[15])
ser0, ser1 = df.iloc[0], df.iloc[1]
o = self.env.get_data_value('open').values
h = self.env.get_data_value('high').values
l = self.env.get_data_value('low').values
c = self.env.get_data_value('close').values
res0, res1 = [], []
for i in ser0.index:
res = talib.BOP(o[:15 + 1, i], h[:15 + 1, i], l[:15 + 1, i],
c[:15 + 1, i])
val0, val1 = res[-2], res[-1]
res0.append(ser0[i] == val0)
res1.append(ser1[i] == val1)
self.assertTrue(all(res0) and all(res1))
