def generate_feature(data):
high = data.High.values
low = data.Low.values
close = data.Close.values
feature_df = pd.DataFrame(index=data.index)
feature_df["ADX"] = ADX = talib.ADX(high, low, close, timeperiod=14)
feature_df["ADXR"] = ADXR = talib.ADXR(high, low, close, timeperiod=14)
feature_df["APO"] = APO = talib.APO(close, fastperiod=12, slowperiod=26, matype=0)
feature_df["AROONOSC"] = AROONOSC = talib.AROONOSC(high, low, timeperiod=14)
feature_df["CCI"] = CCI = talib.CCI(high, low, close, timeperiod=14)
feature_df["CMO"] = CMO = talib.CMO(close, timeperiod=14)
feature_df["DX"] = DX = talib.DX(high, low, close, timeperiod=14)
feature_df["MINUS_DI"] = MINUS_DI = talib.MINUS_DI(high, low, close, timeperiod=14)
feature_df["MINUS_DM"] = MINUS_DM = talib.MINUS_DM(high, low, timeperiod=14)
feature_df["MOM"] = MOM = talib.MOM(close, timeperiod=10)
feature_df["PLUS_DI"] = PLUS_DI = talib.PLUS_DI(high, low, close, timeperiod=14)
feature_df["PLUS_DM"] = PLUS_DM = talib.PLUS_DM(high, low, timeperiod=14)
feature_df["PPO"] = PPO = talib.PPO(close, fastperiod=12, slowperiod=26, matype=0)
feature_df["ROC"] = ROC = talib.ROC(close, timeperiod=10)
feature_df["ROCP"] = ROCP = talib.ROCP(close, timeperiod=10)
feature_df["ROCR100"] = ROCR100 = talib.ROCR100(close, timeperiod=10)
feature_df["RSI"] = RSI = talib.RSI(close, timeperiod=14)
feature_df["ULTOSC"] = ULTOSC = talib.ULTOSC(high, low, close, timeperiod1=7, timeperiod2=14, timeperiod3=28)
feature_df["WILLR"] = WILLR = talib.WILLR(high, low, close, timeperiod=14)
feature_df = feature_df.fillna(0.0)
matrix = np.stack((
ADX, ADXR, APO, AROONOSC, CCI, CMO, DX, MINUS_DI, ROCR100, ROC,
MINUS_DM, MOM, PLUS_DI, PLUS_DM, PPO, ROCP, WILLR, ULTOSC, RSI))
matrix = np.nan_to_num(matrix)
matrix = matrix.transpose()
return feature_df, matrix
def ppo(candles: np.ndarray,
fastperiod=12,
slowperiod=26,
matype=0,
sequential=False) -> Union[float, np.ndarray]:
"""
PPO - Percentage Price Oscillator
:param candles: np.ndarray
:param fastperiod: int - default: 12
:param slowperiod: int - default: 26
:param matype: int - default: 0
:param sequential: bool - default=False
:return: float | np.ndarray
"""
if not sequential and len(candles) > 240:
candles = candles[-240:]
res = talib.PPO(candles[:, 2],
fastperiod=fastperiod,
slowperiod=slowperiod,
matype=matype)
return res if sequential else res[-1]
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 ppo(prices_df: pd.DataFrame,
fast=12,
slow=26,
signal=9) -> Tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame]:
prices_arr = prices_df.to_numpy()
ppos, pposignals = np.empty_like(prices_arr), np.empty_like(prices_arr)
for col in range(prices_arr.shape[1]):
ppos[:, col] = talib.PPO(prices_arr[:, col],
fastperiod=fast,
slowperiod=slow,
matype=MA_Type.EMA)
try:
pposignals[:, col] = talib.EMA(ppo, timeperiod=signal)
except:
pposignals[:, col] = np.nan
ppohists = ppos - pposignals
return (pd.DataFrame(ppos,
dtype=prices_arr.dtype,
index=prices_df.index,
columns=prices_df.columns),
pd.DataFrame(pposignals,
dtype=prices_arr.dtype,
index=prices_df.index,
columns=prices_df.columns),
pd.DataFrame(ppohists,
dtype=prices_arr.dtype,
index=prices_df.index,
columns=prices_df.columns))
def test_ppo(self):
"""
Test Percentage Price Oscillator.
"""
q = qufilab.ppo(self.close)
t = talib.PPO(self.close, matype=1)
np.testing.assert_allclose(q, t, rtol=self.tolerance)
def ppo(Close, fast=12, slow=26):
tmp_ppo = ta.PPO(Close, fastperiod=fast, slowperiod=slow, matype=0)
ppo = pd.Series(index=Close.index, data=[0] * len(Close))
ppo.loc[tmp_ppo > 0] = 1
ppo.loc[tmp_ppo <= 0] = -1
return ppo
def ppo(close_ts, fastperiod=12, slowperiod=26, matype=0):
import talib
close_np = close_ts.cpu().detach().numpy()
close_df = pd.DataFrame(close_np)
PPO = close_df.apply(lambda x: talib.PPO(x, fastperiod=12, slowperiod=26, matype=0))
PPO_ts = torch.tensor(PPO.values, dtype=close_ts.dtype, device=close_ts.device)
return PPO_ts
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 ppo(candles: np.ndarray,
fast_period: int = 12,
slow_period: int = 26,
matype: int = 0,
source_type: str = "close",
sequential: bool = False) -> Union[float, np.ndarray]:
"""
PPO - Percentage Price Oscillator
:param candles: np.ndarray
:param fast_period: int - default: 12
:param slow_period: int - default: 26
:param matype: int - default: 0
:param source_type: str - default: "close"
:param sequential: bool - default=False
:return: float | np.ndarray
"""
candles = slice_candles(candles, sequential)
source = get_candle_source(candles, source_type=source_type)
res = talib.PPO(source,
fastperiod=fast_period,
slowperiod=slow_period,
matype=matype)
return res if sequential else res[-1]
def run_indicators(symbol, file):
try:
if os.path.exists(file):
# read file
df = np.genfromtxt(file, delimiter=',')
# run talib indicators using dataframe
# create additional columns in the datafarame
closePrice = df[:, 4]
openPrice = df[:, 1]
highPrice = df[:, 2]
lowPrice = df[:, 3]
volume = df[:, 5]
sma = talib.SMA(closePrice)
atr = talib.ATR(highPrice, lowPrice, closePrice)
natr = talib.NATR(highPrice, lowPrice, closePrice)
adx = talib.ADX(highPrice, lowPrice, closePrice)
mfi = talib.MFI(highPrice, lowPrice, closePrice, volume)
ppo = talib.PPO(closePrice, fastperiod=12, slowperiod=26, matype=0)
rsi = talib.RSI(closePrice)
slowk, slowd = talib.STOCH(highPrice,
lowPrice,
closePrice,
fastk_period=14,
slowk_period=3,
slowd_period=3)
macd, macdsignal, macdhist = talib.MACD(closePrice,
fastperiod=12,
slowperiod=26,
signalperiod=9)
# get ticker current price
client = Client(os.environ.get('BINANCE_API_KEY'),
os.environ.get('BINANCE_SECRET_KEY'),
{"timeout": 100})
closing_price = client.get_symbol_ticker(symbol=symbol)
# create crypto object
crypto = {}
crypto['ticker'] = symbol
crypto['price'] = closing_price['price']
crypto['SMA14'] = sma[-1]
crypto['ATR'] = atr[-1]
crypto['NATR'] = natr[-1]
crypto['ADX'] = adx[-1]
crypto['MFI'] = mfi[-1]
crypto['RSI'] = rsi[-1]
crypto['STO'] = slowk[-1], slowd[-1]
crypto['MACD'] = macd[-1]
crypto['PPO'] = ppo[-1]
else:
crypto = {}
crypto['ticker'] = symbol
return crypto
except Exception as e:
print(str(e))
def ppo(self, n, array=False):
"""
PPO.
"""
result = talib.PPO(self.close, n)
if array:
return result
return result[-1]
def ppo(self, n: int, array: bool = False) -> Union[float, np.ndarray]:
"""
PPO.
"""
result = talib.PPO(self.close, n)
if array:
return result
return result[-1]
def PPO(close, fastperiod=12, slowperiod=26, matype=0):
# Percentage Price Oscillator
param = {
'fastperiod': fastperiod,
'slowperiod': slowperiod,
'matype': matype,
}
return talib.PPO(close, **param)
def PPO(self, fastperiod=12, slowperiod=26, matype=0):
real_data = np.array([self.df.close], dtype='f8')
ppo = talib.PPO(real_data[0], fastperiod=fastperiod, slowperiod=slowperiod, matype=matype)
# return go.Scatter(
# x=self.df.index,
# y=ppo,
# name='PPO'
# )
return ppo
def get_po(price_arr, cur_pos, period=10):
if cur_pos <= period:
s = 0
else:
s = cur_pos - period
tmp_arr = price_arr[s:cur_pos]
prices = np.array(tmp_arr, dtype=float)
return ta.PPO(prices)[-1]
def onMarketDataUpdate(self, market, code, md):
stock_info = self.stock_info[code]
# The following time is not allowed to trade. Only trade from 9:30 am to 12:00 am, and from 13:00 to 16:00
time_info = md.timestamp.split('_')
if int(time_info[1][:4]) not in (range(930, 1201) + range(1300, 1601)) or \
(stock_info[MA] and md.lastPrice < 1 < stock_info[MA]):
return
# For open price record last day close price
if time_info[0] != stock_info[LAST_DATE]:
stock_info[LAST_DATE] = time_info[0]
if len(stock_info[CLOSE_PRICE]) > max(
self.rsi_period, self.ma_period, self.ppo_slow_period) + 1:
stock_info[CLOSE_PRICE].pop(0)
if stock_info[LAST_PRICE]:
stock_info[CLOSE_PRICE].append(stock_info[LAST_PRICE])
if len(stock_info[CLOSE_PRICE]) < max(
self.rsi_period, self.ma_period, self.ppo_slow_period):
stock_info[LAST_PRICE] = md.lastPrice
self.stock_info[code].update(stock_info)
return
stock_info[MA] = talib.MA(numpy.array(stock_info[CLOSE_PRICE]),
self.ma_period)[-1]
stock_info[STD] = talib.STDDEV(
numpy.array(stock_info[CLOSE_PRICE]), self.ma_period)[-1]
stock_info[PPO] = talib.PPO(numpy.array(stock_info[CLOSE_PRICE]),
fastperiod=self.ppo_fast_period,
slowperiod=self.ppo_slow_period)[-1]
if stock_info[
HOLD_VOLUME] and stock_info[PPO] < self.ppo_sell_threshold:
self.short_security(md.timestamp, code, md.bidPrice1)
stock_info[RSI] = talib.RSI(numpy.array(stock_info[CLOSE_PRICE] +
[md.lastPrice]),
timeperiod=self.rsi_period)[-1]
if stock_info[RSI] < self.rsi_buy_bound:
std_factor = 2
else:
std_factor = 1.2
if md.askPrice1 + std_factor * stock_info[STD] < stock_info[MA] and \
stock_info[PPO] > self.ppo_buy_threshold:
self.long_security(md.timestamp, code, md.askPrice1)
elif stock_info[HOLD_VOLUME] and md.bidPrice1 >= stock_info[MA]:
self.short_security(md.timestamp, code, md.bidPrice1)
stock_info[LAST_PRICE] = md.lastPrice
self.stock_info[code].update(stock_info)
def ppo(df, n=12, m=26, normalize=False, col='Close'):
c = df[col].as_matrix()
_ppo = pd.Series(ta.PPO(c, fastperiod=n, slowperiod=m, matype=1),
index=df.index,
name="PPO_" + str(n) + "_" + str(m))
if normalize:
_ppo = z_score(_ppo)
return _ppo
def _get_indicators(security, open_name, close_name, high_name, low_name,
volume_name):
"""
expand the features of the data through technical analysis across 26 different signals
:param security: data which features are going to be expanded
:param open_name: open price column name
:param close_name: close price column name
:param high_name: high price column name
:param low_name: low price column name
:param volume_name: traded volumn column name
:return: expanded and extracted data
"""
open_price = security[open_name].values
close_price = security[close_name].values
low_price = security[low_name].values
high_price = security[high_name].values
volume = security[volume_name].values if volume_name else None
security['MOM'] = talib.MOM(close_price)
security['HT_DCPERIOD'] = talib.HT_DCPERIOD(close_price)
security['HT_DCPHASE'] = talib.HT_DCPHASE(close_price)
security['SINE'], security['LEADSINE'] = talib.HT_SINE(close_price)
security['INPHASE'], security['QUADRATURE'] = talib.HT_PHASOR(
close_price)
security['ADXR'] = talib.ADXR(high_price, low_price, close_price)
security['APO'] = talib.APO(close_price)
security['AROON_UP'], _ = talib.AROON(high_price, low_price)
security['CCI'] = talib.CCI(high_price, low_price, close_price)
security['PLUS_DI'] = talib.PLUS_DI(high_price, low_price, close_price)
security['PPO'] = talib.PPO(close_price)
security['MACD'], security['MACD_SIG'], security[
'MACD_HIST'] = talib.MACD(close_price)
security['CMO'] = talib.CMO(close_price)
security['ROCP'] = talib.ROCP(close_price)
security['FASTK'], security['FASTD'] = talib.STOCHF(
high_price, low_price, close_price)
security['TRIX'] = talib.TRIX(close_price)
security['ULTOSC'] = talib.ULTOSC(high_price, low_price, close_price)
security['WILLR'] = talib.WILLR(high_price, low_price, close_price)
security['NATR'] = talib.NATR(high_price, low_price, close_price)
security['RSI'] = talib.RSI(close_price)
security['EMA'] = talib.EMA(close_price)
security['SAREXT'] = talib.SAREXT(high_price, low_price)
# security['TEMA'] = talib.EMA(close_price)
security['RR'] = security[close_name] / security[close_name].shift(
1).fillna(1)
security['LOG_RR'] = np.log(security['RR'])
if volume_name:
security['MFI'] = talib.MFI(high_price, low_price, close_price,
volume)
# security['AD'] = talib.AD(high_price, low_price, close_price, volume)
# security['OBV'] = talib.OBV(close_price, volume)
security[volume_name] = np.log(security[volume_name])
security.drop([open_name, close_name, high_name, low_name], axis=1)
security = security.dropna().astype(np.float32)
return security
def generate_feature(data):
high = data.High.values
low = data.Low.values
close = data.Close.values
# feature_df = pd.DataFrame(index=data.index)
feature_df = data.copy()
feature_df["ADX"] = ADX = talib.ADX(high, low, close, timeperiod=14)
feature_df["ADXR"] = ADXR = talib.ADXR(high, low, close, timeperiod=14)
feature_df["APO"] = APO = talib.APO(close,
fastperiod=12,
slowperiod=26,
matype=0)
feature_df["AROONOSC"] = AROONOSC = talib.AROONOSC(high,
low,
timeperiod=14)
feature_df["CCI"] = CCI = talib.CCI(high, low, close, timeperiod=14)
feature_df["CMO"] = CMO = talib.CMO(close, timeperiod=14)
feature_df["DX"] = DX = talib.DX(high, low, close, timeperiod=14)
feature_df["MINUS_DI"] = MINUS_DI = talib.MINUS_DI(high,
low,
close,
timeperiod=14)
feature_df["MINUS_DM"] = MINUS_DM = talib.MINUS_DM(high,
low,
timeperiod=14)
feature_df["MOM"] = MOM = talib.MOM(close, timeperiod=10)
feature_df["PLUS_DI"] = PLUS_DI = talib.PLUS_DI(high,
low,
close,
timeperiod=14)
feature_df["PLUS_DM"] = PLUS_DM = talib.PLUS_DM(high, low, timeperiod=14)
feature_df["PPO"] = PPO = talib.PPO(close,
fastperiod=12,
slowperiod=26,
matype=0)
feature_df["ROC"] = ROC = talib.ROC(close, timeperiod=10)
feature_df["ROCP"] = ROCP = talib.ROCP(close, timeperiod=10)
feature_df["ROCR100"] = ROCR100 = talib.ROCR100(close, timeperiod=10)
feature_df["RSI"] = RSI = talib.RSI(close, timeperiod=14)
feature_df["ULTOSC"] = ULTOSC = talib.ULTOSC(high,
low,
close,
timeperiod1=7,
timeperiod2=14,
timeperiod3=28)
feature_df["WILLR"] = WILLR = talib.WILLR(high, low, close, timeperiod=14)
feature_df = feature_df.fillna(0.0)
# Exclude columns you don't want
feature_df = feature_df[feature_df.columns[
~feature_df.columns.isin(['Open', 'High', 'Low', 'Close'])]]
matrix = feature_df.values
return feature_df, matrix
def indicators(data):
"""
:param data: data
:return: np array of each indicator
"""
cmo = talib.CMO(np.array(data), timeperiod=10).reshape(-1, 1)
roc = talib.ROC(np.array(data), timeperiod=5).reshape(-1, 1)
rsi = talib.RSI(np.array(data), timeperiod=5).reshape(-1, 1)
wma = talib.WMA(np.array(data), timeperiod=20).reshape(-1, 1)
ppo = talib.PPO(np.array(data), fastperiod=10, slowperiod=20, matype=0).reshape(-1, 1)
return cmo, roc, rsi, wma, ppo
def ppo(self,
fast_period: int,
slow_period: int,
matype: int = 0,
array: bool = False) -> Union[float, np.ndarray]:
"""
PPO.
"""
result = talib.PPO(self.close, fast_period, slow_period, matype)
if array:
return result
return result[-1]
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 PPO(close, fastperiod=12, slowperiod=26, matype=0):
''' Percentage Price Oscillator 价格震荡百分比指数
分组: Momentum Indicator 动量指标
简介: 价格震荡百分比指标（PPO）是一个和MACD指标非常接近的指标。
PPO标准设定和MACD设定非常相似：12,26,9和PPO，
和MACD一样说明了两条移动平均线的差距，
但是它们有一个差别是PPO是用百分比说明。
real = PPO(close, fastperiod=12, slowperiod=26, matype=0)
'''
return talib.PPO(close, fastperiod, slowperiod, matype)
def test_ppo(self):
result = pandas_ta.ppo(self.close)
self.assertIsInstance(result, DataFrame)
self.assertEqual(result.name, 'PPO_12_26_9')
try:
expected = tal.PPO(self.close)
pdt.assert_series_equal(result['PPO_12_26_9'], expected, check_names=False)
except AssertionError as ae:
try:
corr = pandas_ta.utils.df_error_analysis(result['PPO_12_26_9'], expected, col=CORRELATION)
self.assertGreater(corr, CORRELATION_THRESHOLD)
except Exception as ex:
error_analysis(result['PPO_12_26_9'], CORRELATION, ex)
def OnBar(self, bar):
if self.bar_count >= 41:
close = np.array(self.array.close)
signal = ta.PPO(close, fastperiod=12, slowperiod=26, matype=0)
if signal[-1] > 0 and self.pos == 0:
self.buy(99999, 5000)
elif signal[-1] < 0 and self.pos > 0:
self.sell(1, self.pos)
elif signal[-1] > 0 and self.pos < 0:
self.buy_to_cover(99999, -self.pos)
elif signal[-1] < 0 and self.pos == 0:
self.sell_short(1, 5000)
self.strategy_output(self.pos)
def generate_tech_data_default(stock,
open_name,
close_name,
high_name,
low_name,
volume_name='vol'):
open_price = stock[open_name].values
close_price = stock[close_name].values
low_price = stock[low_name].values
high_price = stock[high_name].values
volume = stock[volume_name].values
data = stock.copy()
data['MOM'] = talib.MOM(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['ADXR'] = talib.ADXR(high_price, low_price, close_price)
data['APO'] = talib.APO(close_price)
data['AROON_UP'], _ = talib.AROON(high_price, low_price)
data['CCI'] = talib.CCI(high_price, low_price, close_price)
data['PLUS_DI'] = talib.PLUS_DI(high_price, low_price, close_price)
data['PPO'] = talib.PPO(close_price)
data['macd'], data['macd_sig'], data['macd_hist'] = talib.MACD(close_price)
data['CMO'] = talib.CMO(close_price)
data['ROCP'] = talib.ROCP(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['MFI'] = talib.MFI(high_price, low_price, close_price, volume)
data['RSI'] = talib.RSI(close_price)
data['AD'] = talib.AD(high_price, low_price, close_price, volume)
data['OBV'] = talib.OBV(close_price, volume)
data['EMA'] = talib.EMA(close_price)
data['SAREXT'] = talib.SAREXT(high_price, low_price)
data['TEMA'] = talib.EMA(close_price)
#data = data.drop([open_name, close_name, high_name, low_name, volume_name, 'amount', 'count'], axis=1)
data = drop_columns(data, [
open_name, close_name, high_name, low_name, volume_name, 'amount',
'count'
])
data = data.dropna().astype(np.float32)
return data
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 test_PPO(self):
self.env.add_operator('ppo', {
'operator': OperatorPPO,
})
string = 'ppo(12, 26, open)'
gene = self.env.parse_string(string)
self.assertRaises(IndexError, gene.eval, self.env, self.dates[24],
self.dates[-1])
df = gene.eval(self.env, self.dates[25], self.dates[26])
ser0, ser1 = df.iloc[0], df.iloc[1]
o = self.env.get_data_value('open').values
res0, res1 = [], []
for i in ser0.index:
res = talib.PPO(o[:26 + 1, i], fastperiod=12, slowperiod=26)
val0, val1 = res[-2], res[-1]
res0.append(ser0[i] == val0)
res1.append(ser1[i] == val1)
self.assertTrue(all(res0) and all(res1))
def add_PPO(self,
fastperiod=12,
slowperiod=26,
matype=0,
type='line',
color='secondary',
**kwargs):
"""Percent Price Oscillator."""
if not self.has_close:
raise Exception()
utils.kwargs_check(kwargs, VALID_TA_KWARGS)
if 'kind' in kwargs:
kwargs['type'] = kwargs['kind']
name = 'PPO({},{})'.format(str(fastperiod), str(slowperiod))
self.ind[name] = talib.PPO(self.df[self.cl].values, fastperiod, slowperiod,
matype)
def onMarketDataUpdate(self, market, code, md):
# The following time is not allowed to trade. Only trade from 9:30 am to 12:00 am, and from 13:00 to 16:00
time_info = md.timestamp.split('_')
if int(time_info[1][:4]) not in (range(930, 1201) + range(1300, 1601)) or md.lastPrice < 1 < self.mean_average:
return
# For open price record last day close price
if time_info[0] != self.last_date:
self.last_date = time_info[0]
if len(self.close_price) > max(self.rsi_period, self.ma_period, self.ppo_slow_period) + 1:
self.close_price.pop(0)
if self.last_price:
self.close_price.append(self.last_price)
if len(self.close_price) < max(self.rsi_period, self.ma_period):
self.last_price = md.lastPrice
return
self.mean_average = talib.MA(numpy.array(self.close_price), self.ma_period)[-1]
self.standard_dev = talib.STDDEV(numpy.array(self.close_price), self.ma_period)[-1]
self.ppo = talib.PPO(numpy.array(self.close_price), fastperiod=self.ppo_fast_period,
slowperiod=self.ppo_slow_period)
if self.buy_volume and self.ppo[-1] < self.ppo_sell_threshold:
self.short_security(md.timestamp, code, md.bidPrice1)
self.rsix = talib.RSI(numpy.array(self.close_price + [md.lastPrice]), timeperiod=self.rsi_period)
if self.rsix[-1] < self.rsi_buy_bound:
self.std_factor = 2
else:
self.std_factor = 1.2
if (md.askPrice1 + self.std_factor * self.standard_dev < self.mean_average and
self.ppo[-1] > self.ppo_buy_threshold):
self.long_security(md.timestamp, code, md.askPrice1)
elif self.buy_volume and md.bidPrice1 >= self.mean_average:
self.short_security(md.timestamp, code, md.bidPrice1)
self.last_price = md.lastPrice
