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datamodel_super.py
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datamodel_super.py
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import warnings
warnings.filterwarnings('ignore')
import os
import pickle as pkl
import numpy as np
from sklearn.preprocessing import scale, robust_scale, normalize
from statsmodels.tsa.statespace.sarimax import SARIMAX
import statsmodels.api as sm
from statsmodels.tsa.statespace.kalman_filter import KalmanFilter
import talib as ta
try:
from tradesys import *
import tradesys.ml as ml
except ImportError:
import sys
sys.path.insert(0, '/home/peter/code/projects/tradesys/')
from tradesys import *
import tradesys.ml as ml
class DataModel(ml.BaseDataModel):
def input(self, timewin):
# take the raw timewin and construct a DataMatrix from it
self.datalen = timewin.shape[1]
self.d_open = timewin[0, :]
self.d_high = timewin[1, :]
self.d_low = timewin[2, :]
self.d_close = timewin[3, :]
self.d_volume = timewin[4, :]
self.d_year = timewin[5, :]
self.d_mon = timewin[6, :]
self.d_day = timewin[7, :]
self.d_hour = timewin[8, :]
self.d_min = timewin[9, :]
self.d_sec = timewin[10, :]
self.datamatrix = DataMatrix()
self.dts = []
for i in range(self.datalen):
d = DateTime(int(self.d_year[i]), int(self.d_mon[i]), int(self.d_day[i]), int(self.d_hour[i]), int(self.d_min[i]), int(self.d_sec[i]))
self.dts.append(d)
self.datamatrix.add_row(d, float(self.d_open[i]), float(self.d_high[i]), float(self.d_low[i]), float(self.d_close[i]), int(self.d_volume[i]))
self.prs = []
self.frs = []
def process(self):
# The raw data (price range) + time of day (fixed range)
self.prs.append(self.d_open)
self.prs.append(self.d_high)
self.prs.append(self.d_low)
self.prs.append(self.d_close)
t = ((self.d_hour * 60 + self.d_min) - 240) / 960
self.frs.append(t)
self.prs.append(ta.MA(self.d_open, timeperiod=7))
self.prs.append(ta.MA(self.d_high, timeperiod=7))
self.prs.append(ta.MA(self.d_low, timeperiod=7))
self.prs.append(ta.MA(self.d_close, timeperiod=7))
"""
# ARIMA
arima = sm.tsa.statespace.SARIMAX(self.d_close, order=(7, 1, 7), seasonal_order=(0, 0, 0, 0),
enforce_stationarity=False, enforce_invertibility=False, ).fit()
arima.summary()
self.prs.append(arima.predict())
# SARIMA
sarima = sm.tsa.statespace.SARIMAX(self.d_close, order=(7, 1, 7), seasonal_order=(7, 1, 7, 1),
enforce_stationarity=False, enforce_invertibility=False).fit()
sarima.summary()
self.prs.append(sarima.predict())
# ARIMAX
arimax = sm.tsa.statespace.SARIMAX(self.d_close, order=(7, 1, 7), seasonal_order=(0, 0, 0, 0), # exog = exog_train,
enforce_stationarity=False, enforce_invertibility=False, ).fit()
arimax.summary()
self.prs.append(arimax.predict())
# SARIMAX
sarimax = sm.tsa.statespace.SARIMAX(self.d_close, order=(7, 1, 7), seasonal_order=(1, 0, 5, 1), # exog = exog_train,
enforce_stationarity=False, enforce_invertibility=False).fit()
sarimax.summary()
self.prs.append(sarimax.predict())
"""
# In[381]:
bb_upperband, bb_middleband, bb_lowerband = ta.BBANDS(self.d_close, timeperiod=10, nbdevup=2, nbdevdn=2, matype=0)
self.prs.append(bb_upperband)
self.prs.append(bb_middleband)
self.prs.append(bb_lowerband)
# In[382]:
bb_upperband, bb_middleband, bb_lowerband = ta.BBANDS(self.d_close, timeperiod=20, nbdevup=2, nbdevdn=2, matype=0)
self.prs.append(bb_upperband)
self.prs.append(bb_middleband)
self.prs.append(bb_lowerband)
# In[383]:
bb_upperband, bb_middleband, bb_lowerband = ta.BBANDS(self.d_close, timeperiod=5, nbdevup=3, nbdevdn=3, matype=0)
self.prs.append(bb_upperband)
self.prs.append(bb_middleband)
self.prs.append(bb_lowerband)
# In[384]:
bb_upperband, bb_middleband, bb_lowerband = ta.BBANDS(self.d_close, timeperiod=14, nbdevup=3, nbdevdn=3, matype=0)
self.prs.append(bb_upperband)
self.prs.append(bb_middleband)
self.prs.append(bb_lowerband)
# In[385]:
Double_EMA = ta.DEMA(self.d_close, timeperiod=60)
self.prs.append(Double_EMA)
# In[386]:
EMA = ta.EMA(self.d_close, timeperiod=60)
self.prs.append(EMA)
# In[387]:
EMA = ta.EMA(self.d_close, timeperiod=10)
self.prs.append(EMA)
# In[388]:
KAMA = ta.KAMA(self.d_close, timeperiod=60)
self.prs.append(KAMA)
# In[389]:
KAMA = ta.KAMA(self.d_close, timeperiod=10)
self.prs.append(KAMA)
# In[390]:
MA = ta.MA(self.d_close, timeperiod=15, matype=0)
self.prs.append(MA)
# In[391]:
MA = ta.MA(self.d_close, timeperiod=60, matype=0)
self.prs.append(MA)
# In[399]:
TEMA = ta.TEMA(self.d_close, timeperiod=60)
self.prs.append(TEMA)
# In[400]:
TRIMA = ta.TRIMA(self.d_close, timeperiod=60)
self.prs.append(TRIMA)
# In[401]:
WMA = ta.WMA(self.d_close, timeperiod=7)
self.prs.append(WMA)
# In[404]:
TEMA = ta.TEMA(self.d_close, timeperiod=7)
self.prs.append(TEMA)
# In[405]:
TRIMA = ta.TRIMA(self.d_close, timeperiod=7)
self.prs.append(TRIMA)
# In[406]:
WMA = ta.WMA(self.d_close, timeperiod=7)
self.prs.append(WMA)
# In[394]:
MIDPOINT = ta.MIDPOINT(self.d_close, timeperiod=7)
self.prs.append(MIDPOINT)
# In[395]:
MIDPRICE = ta.MIDPRICE(self.d_high, self.d_low, timeperiod=7)
self.prs.append(MIDPRICE)
# In[396]:
SAR = ta.SAR(self.d_high, self.d_low, acceleration=.05, maximum=.5)
self.prs.append(SAR)
# In[397]:
SAREXT = ta.SAREXT(self.d_high, self.d_low, startvalue=0, offsetonreverse=0, accelerationinitlong=.05,
accelerationlong=.05,
accelerationmaxlong=.5, accelerationinitshort=.05, accelerationshort=.05,
accelerationmaxshort=.5)
self.frs.append(SAREXT)
# In[398]:
T3 = ta.T3(self.d_close, timeperiod=10, vfactor=.14)
self.prs.append(T3)
# In[409]:
T3 = ta.T3(self.d_close, timeperiod=20, vfactor=.07)
self.prs.append(T3)
# In[402]:
ADX = ta.ADX(self.d_high, self.d_low, self.d_close, timeperiod=7)
self.frs.append(ADX)
# In[403]:
ADXR = ta.ADXR(self.d_high, self.d_low, self.d_close, timeperiod=7)
self.frs.append(ADXR)
# In[410]:
APO = ta.APO(self.d_close, fastperiod=6, slowperiod=13, matype=0)
self.frs.append(APO)
# In[411]:
AROONDOWN, AROONUP = ta.AROON(self.d_high, self.d_low, timeperiod=7)
self.frs.append(AROONDOWN)
self.frs.append(AROONUP)
# In[412]:
AROONDOWN, AROONUP = ta.AROON(self.d_high, self.d_low, timeperiod=28)
self.frs.append(AROONDOWN)
self.frs.append(AROONUP)
# In[413]:
AROONOSC = ta.AROONOSC(self.d_high, self.d_low, timeperiod=7)
self.frs.append(AROONOSC)
# In[414]:
AROONOSC = ta.AROONOSC(self.d_high, self.d_low, timeperiod=28)
self.frs.append(AROONOSC)
# In[415]:
CCI = ta.CCI(self.d_high, self.d_low, self.d_close, timeperiod=7)
self.frs.append(CCI)
# In[416]:
CCI = ta.CCI(self.d_high, self.d_low, self.d_close, timeperiod=28)
self.frs.append(CCI)
# In[417]:
CMO = ta.CMO(self.d_close, timeperiod=14)
self.frs.append(CMO)
# In[418]:
CMO = ta.CMO(self.d_close, timeperiod=7)
self.frs.append(CMO)
# In[419]:
CMO = ta.CMO(self.d_close, timeperiod=28)
self.frs.append(CMO)
# In[420]:
DX = ta.DX(self.d_high, self.d_low, self.d_close, timeperiod=14)
self.frs.append(CMO)
# In[421]:
DX = ta.DX(self.d_high, self.d_low, self.d_close, timeperiod=7)
self.frs.append(DX)
# In[422]:
DX = ta.DX(self.d_high, self.d_low, self.d_close, timeperiod=28)
self.frs.append(DX)
# In[423]:
macd, macdsignal, macdhist = ta.MACD(self.d_close, fastperiod=6, slowperiod=13, signalperiod=5)
self.frs.append(macd)
self.frs.append(macdsignal)
self.frs.append(macdhist)
# In[424]:
macd, macdsignal, macdhist = ta.MACDEXT(self.d_close, fastperiod=6, fastmatype=0, slowperiod=13, slowmatype=0,
signalperiod=5, signalmatype=0)
self.frs.append(macd)
self.frs.append(macdsignal)
self.frs.append(macdhist)
# In[425]:
macd, macdsignal, macdhist = ta.MACDFIX(self.d_close, signalperiod=5)
self.frs.append(macd)
self.frs.append(macdsignal)
self.frs.append(macdhist)
# In[426]:
macd, macdsignal, macdhist = ta.MACD(self.d_close, fastperiod=20, slowperiod=40, signalperiod=9)
self.frs.append(macd)
self.frs.append(macdsignal)
self.frs.append(macdhist)
# In[431]:
MINUS_DI = ta.MINUS_DI(self.d_high, self.d_low, self.d_close, timeperiod=7)
self.frs.append(MINUS_DI)
# In[432]:
MINUS_DM = ta.MINUS_DM(self.d_high, self.d_low, timeperiod=14)
self.frs.append(MINUS_DM)
# In[433]:
MOM = ta.MOM(self.d_close, timeperiod=10)
self.frs.append(MOM)
# In[434]:
MOM = ta.MOM(self.d_close, timeperiod=5)
self.frs.append(MOM)
# In[438]:
PLUS_DI = ta.PLUS_DI(self.d_high, self.d_low, self.d_close, timeperiod=7)
self.frs.append(PLUS_DI)
# In[439]:
PLUS_DM = ta.PLUS_DM(self.d_high, self.d_low, timeperiod=7)
self.frs.append(PLUS_DM)
# In[440]:
PPO = ta.PPO(self.d_close, fastperiod=6, slowperiod=13, matype=0)
self.frs.append(PPO)
# In[441]:
ROC = ta.ROC(self.d_close, timeperiod=5)
self.frs.append(ROC)
# In[442]:
ROC = ta.ROC(self.d_close, timeperiod=20)
self.frs.append(ROC)
# In[445]:
ROCP = ta.ROCR(self.d_close, timeperiod=5)
self.frs.append(ROCP)
# In[446]:
ROCR100 = ta.ROCR100(self.d_close, timeperiod=5)
self.frs.append(ROCR100)
# In[447]:
ROCP = ta.ROCR(self.d_close, timeperiod=20)
self.frs.append(ROCP)
# In[448]:
ROCR100 = ta.ROCR100(self.d_close, timeperiod=20)
self.frs.append(ROCR100)
# In[449]:
RSI = ta.RSI(self.d_close, timeperiod=7)
self.frs.append(RSI)
# In[450]:
RSI = ta.RSI(self.d_close, timeperiod=20)
self.frs.append(RSI)
# In[455]:
SLOWK, SLOWD = ta.STOCH(self.d_high, self.d_low, self.d_close, fastk_period=10, slowk_period=5, slowk_matype=0, slowd_period=5,
slowd_matype=0)
self.frs.append(SLOWK)
self.frs.append(SLOWD)
# In[456]:
FASTK, FASTD = ta.STOCHF(self.d_high, self.d_low, self.d_close, fastk_period=10, fastd_period=5, fastd_matype=0)
self.frs.append(FASTK)
self.frs.append(FASTD)
# In[460]:
TRIX = ta.TRIX(self.d_close, timeperiod=10)
self.frs.append(TRIX)
# In[461]:
ULTOSC = ta.ULTOSC(self.d_high, self.d_low, self.d_close, timeperiod1=14, timeperiod2=28, timeperiod3=60)
self.frs.append(ULTOSC)
# In[462]:
WILLR = ta.WILLR(self.d_high, self.d_low, self.d_close, timeperiod=7)
self.frs.append(WILLR)
# In[463]:
WILLR = ta.WILLR(self.d_high, self.d_low, self.d_close, timeperiod=28)
self.frs.append(WILLR)
# In[467]:
ATR = ta.ATR(self.d_high, self.d_low, self.d_close, timeperiod=7)
self.frs.append(ATR)
# In[468]:
ATR = ta.ATR(self.d_high, self.d_low, self.d_close, timeperiod=28)
self.frs.append(ATR)
# In[472]:
NATR = ta.NATR(self.d_high, self.d_low, self.d_close, timeperiod=7)
self.frs.append(NATR)
# In[479]:
BETA = ta.BETA(self.d_high, self.d_low, timeperiod=10)
self.frs.append(BETA)
# In[480]:
BETA = ta.BETA(self.d_high, self.d_low, timeperiod=20)
self.frs.append(BETA)
# In[483]:
CORREL = ta.CORREL(self.d_high, self.d_low, timeperiod=15)
self.frs.append(CORREL)
# In[484]:
LINEARREG = ta.LINEARREG(self.d_close, timeperiod=7)
self.prs.append(LINEARREG)
# In[485]:
LINEARREG_ANGLE = ta.LINEARREG_ANGLE(self.d_close, timeperiod=7)
self.frs.append(LINEARREG_ANGLE)
# In[486]:
LINEARREG_INTERCEPT = ta.LINEARREG_INTERCEPT(self.d_close, timeperiod=7)
self.prs.append(LINEARREG_INTERCEPT)
# In[487]:
LINEARREG_SLOPE = ta.LINEARREG_SLOPE(self.d_close, timeperiod=7)
self.frs.append(LINEARREG_SLOPE)
# In[488]:
STDDEV = ta.STDDEV(self.d_close, timeperiod=10, nbdev=2)
self.frs.append(STDDEV)
# In[495]:
TSF = ta.TSF(self.d_close, timeperiod=7)
self.prs.append(TSF)
# In[496]:
VAR = ta.VAR(self.d_close, timeperiod=10, nbdev=1)
self.frs.append(VAR)
# In[498]:
MAX = ta.MAX(self.d_close, timeperiod=15)
self.prs.append(MAX)
# In[500]:
MIN = ta.MIN(self.d_close, timeperiod=15)
self.prs.append(MIN)
intrabar_percent = """((Price('high') - Price('close')) /
(Price('high') - Price('low') + F(0.000000001)) * F(100.0))
"""
some_percent = """ (PRICE('high') - PRICE('low')) / IFTHENELSE( ABS(PRICE('open') - (PRICE('close'))) != F(0.0),
ABS(PRICE('open') - (PRICE('close'))),
F(1.0) ) """
prev_opposite_bar = """(
((PRICE('close', bars_back=1) > PRICE('open', bars_back=1)) and (PRICE('close') < PRICE('open')))
or
((PRICE('close', bars_back=1) < PRICE('open', bars_back=1)) and (PRICE('close') > PRICE('open')))
)"""
prev_same_bar = """(
((PRICE('close', bars_back=1) > PRICE('open', bars_back=1)) and (PRICE('close') > PRICE('open')))
or
((PRICE('close', bars_back=1) < PRICE('open', bars_back=1)) and (PRICE('close') < PRICE('open')))
)"""
new_day = """NEW_DAY"""
at_open = """TIME_IS(09:31)"""
in_regular_hours = """TIME_IS_BETWEEN(09:31, 16:00)"""
pmove = """(((PRICE('close', data={0}) -
VALUE_AT_TIME(PRICE('open', data={0}), ['open_time',09:31],
days_ago=['days_ago', 0], reset_overnight=true)) /
VALUE_AT_TIME(PRICE('open', data={0}), ['open_time',09:31],
days_ago=['days_ago', 0], reset_overnight=true))*F(100.0))""".format(0)
pmove2 = """(((PRICE('close', data={0}) -
VALUE_AT_TIME(PRICE('open', data={0}), ['open_time',04:00],
days_ago=['days_ago', 0], reset_overnight=true)) /
VALUE_AT_TIME(PRICE('open', data={0}), ['open_time',04:00],
days_ago=['days_ago', 0], reset_overnight=true))*F(100.0))""".format(0)
strend_up = "SUPERTREND_UP(1.5, 3, 3)"
strend_down = "SUPERTREND_DOWN(1.5, 3, 3)"
up_bars_in_row = "TIMES_IN_ROW(PRICE('open') < PRICE('close'))"
down_bars_in_row = "TIMES_IN_ROW(PRICE('open') > PRICE('close'))"
candles = [['CDL_2CROWS', '2 Crows'],
['CDL_3BLACKCROWS', '3 Black Crows'],
['CDL_3INSIDE', '3 Inside'],
['CDL_3LINESTRIKE', '3 Line Strike'],
['CDL_3OUTSIDE', '3 Outside'],
['CDL_3STARSINSOUTH', '3 Stars In South'],
['CDL_3WHITESOLDIERS', '3 White Soldiers'],
['CDL_ADVANCEBLOCK', 'Advance Block'],
['CDL_BELTHOLD', 'Belt Hold'],
['CDL_BREAKAWAY', 'Break Away'],
['CDL_CLOSINGMARUBOZU', 'Closing Maribozu'],
['CDL_CONCEALBABYSWALL', 'Conceal Babys Wall'],
['CDL_COUNTERATTACK', 'Counter Attack'],
['CDL_DOJI', 'Doji'],
['CDL_DOJISTAR', 'Doji Star'],
['CDL_DRAGONFLYDOJI', 'Dragonfly Doji'],
['CDL_ENGULFING', 'Engulfing Pattern'],
['CDL_GAPSIDESIDEWHITE', 'Gap Side By Side White'],
['CDL_GRAVESTONEDOJI', 'Gravestone Doji'],
['CDL_HAMMER', 'Hammer'],
['CDL_HANGINGMAN', 'Hanging Man'],
['CDL_HARAMI', 'Harami Pattern'],
['CDL_HARAMICROSS', 'Harami Cross Pattern'],
['CDL_HIGHWAVE', 'High-Wave Candle'],
['CDL_HIKKAKE', 'Hikkake Pattern'],
['CDL_HIKKAKEMOD', 'Modified Hikkake Pattern'],
['CDL_HOMINGPIGEON', 'Homing Pigeon'],
['CDL_IDENTICAL3CROWS', 'Identical 3 Crows'],
['CDL_INNECK', 'In Neck Pattern'],
['CDL_INVERTEDHAMMER', 'Inverted Hammer'],
['CDL_KICKING', 'Kicking'],
['CDL_KICKINGBYLENGTH', 'Kicking By Length'],
['CDL_LADDERBOTTOM', 'Ladder Bottom'],
['CDL_LONGLEGGEDDOJI', 'Long Legged Doji'],
['CDL_LONGLINE', 'Long Line'],
['CDL_MARUBOZU', 'Maribozu'],
['CDL_MATCHINGLOW', 'Matching Low'],
['CDL_ONNECK', 'On Neck Pattern'],
['CDL_PIERCING', 'Piercing Pattern'],
['CDL_RICKSHAWMAN', 'Rickshaw Man'],
['CDL_RISEFALL3METHODS', 'Rising Falling 3 Methods'],
['CDL_SEPARATINGLINES', 'Separating Lines'],
['CDL_SHOOTINGSTAR', 'Shooting Star'],
['CDL_SHORTLINE', 'Short Line'],
['CDL_SPINNINGTOP', 'Spinning Top'],
['CDL_STALLEDPATTERN', 'Stalled Pattern'],
['CDL_STICKSANDWICH', 'Stick Sandwich'],
['CDL_TAKURI', 'Takuri'],
['CDL_TASUKIGAP', 'Tasuki Gap'],
['CDL_THRUSTING', 'Thrusting Pattern'],
['CDL_TRISTAR', 'Tristar Pattern'],
['CDL_UNIQUE3RIVER', 'Unique 3 River'],
['CDL_UPSIDEGAP2CROWS', 'Upside Gap 2 Crows'],
['CDL_XSIDEGAP3METHODS', 'Side Gap 3 Methods']]
signals = [(intrabar_percent, 100.0),
(some_percent, 1.0),
(prev_opposite_bar, 1.0),
(prev_same_bar, 1.0),
(at_open, 1.0),
(new_day, 1.0),
(in_regular_hours, 1.0),
(pmove, 1.0),
(pmove2, 1.0),
(strend_up, 1.0),
(strend_down, 1.0),
(up_bars_in_row, 5.0),
(down_bars_in_row, 5.0), ]
signals += [(x[0], 1.0) for x in candles]
# All TradeSys signal tree based indicators
for s, mx in signals:
a = np.array(get_stree_log(s, self.datamatrix), dtype=np.float32)
a = a / mx
self.frs.append(a)
# Stack and scale all data
pr_data = np.vstack(self.prs)
# lag the data
b = pr_data[:, 1:] - pr_data[:, 0:-1]
b = np.hstack([np.zeros(b.shape[0]).reshape(-1,1), b])
pr_data = b
# scale the price data and all price range indicators along with it
#mnp, mxp = np.min(pr_data[3]), np.max(pr_data[3])
#pr_data = (((pr_data - mnp) / abs(mxp - mnp)) - 0.5) * 2
#self.lag_close_minmax = [mnp, mxp]
#pr_data = pr_data * 25.0
pr_data = scale(pr_data, axis=1)
self.minmaxes = []
# scale each row of the fixed range indicators separately
#for i, r in enumerate(self.frs):
# a = r.copy()
# a[np.isnan(a)] = 0
# mn, mx = np.min(a), np.max(a)
# self.minmaxes.append( [mn, mx] )
# if mx - mn > 0:
# self.frs[i] = (((r - mn) / abs(mx - mn)) - 0.5) * 2
# else:
# self.frs[i] = np.zeros_like(self.frs[i])
fr_data = np.vstack(self.frs)
b = fr_data[:, 1:] - fr_data[:, 0:-1]
b = np.hstack([np.zeros(b.shape[0]).reshape(-1,1), b])
fr_data = b
fr_data = scale(fr_data, axis=1)
self.data = np.vstack([pr_data, fr_data])
def cut_nans(self):
# find the real start of the data (without NaN)
nc = 0
for cl in range(self.data.shape[1]):
a = self.data[:, cl]
if len(a[np.isnan(a)]) > 0:
nc = cl
# cut off the parts that contain NaN
self.data = self.data[:, nc + 1:]
return nc+1
def cut_nans_fast(self, cutidx):
self.data = self.data[:, cutidx:]
def output(self, winlen):
x = self.data[:, -winlen:]
return x#scale(x)