def getMinDate(self, historyData):
df = historyData.copy()
########
from monlan.mods.VSASpread import VSASpread
from monlan.mods.HeikenAshiMod import HeikenAshiMod
from monlan.mods.EnergyMod import EnergyMod
historyMod = VSASpread()
df = historyMod.modHistory(df)
historyMod = HeikenAshiMod()
df = historyMod.modHistory(df)
historyMod = EnergyMod()
df = historyMod.modHistory(df, featList=["open", "close", "low", "high"])
########
for i in range(self.nDiffs):
for feat in self.featureList:
notShifted = df[feat]
shiftedData = df[feat].shift(periods=1)
df[feat] = notShifted - shiftedData
iter = next(df.iterrows())
df = df.drop(iter[0])
iter = df.iterrows()
obs = next(iter)
#acceleration
#for i in range(self.nPoints + self.nPoints):
#
for i in range(self.nPoints - 1):
obs = next(iter)
return obs[0]
def getManyPointsFeat(self, datetimeStr, historyData):
obsList = None
if self.fitMode:
obsList = self.fitData.loc[:str(datetimeStr)].tail(self.nPoints).copy()
else:
df = historyData.copy()
########
df = df[:str(datetimeStr)].tail(500)
from monlan.mods.VSASpread import VSASpread
from monlan.mods.HeikenAshiMod import HeikenAshiMod
from monlan.mods.EnergyMod import EnergyMod
historyMod = VSASpread()
df = historyMod.modHistory(df)
historyMod = HeikenAshiMod()
df = historyMod.modHistory(df)
historyMod = EnergyMod()
df = historyMod.modHistory(df, featList=["open", "close", "low", "high"])
########
for i in range(self.nDiffs):
for feat in self.featureList:
notShifted = df[feat]
shiftedData = df[feat].shift(periods=1)
df[feat] = notShifted - shiftedData
iter = next(df.iterrows())
df = df.drop(iter[0])
obsList = df.loc[:str(datetimeStr)].tail(self.nPoints).copy()
obsListTransformed = {}
for obs in obsList.iterrows():
for feat in self.featureList:
data = obs[1][feat]
data = data.reshape(-1, 1)
data = self.scalerDict[feat].transform(data)
obs[1][feat] = data
obsListTransformed[obs[0]] = obs[1]
obsList = pd.DataFrame(obsListTransformed.values(), index=list(obsListTransformed.keys()))
obs = []
if self.flatStack:
localObs = np.zeros((self.nPoints,))
# for feat in self.featureList:
# localObs = np.vstack([localObs, vectorPrices[feat]])
# obs = np.array(localObs[1:])
else:
localObs = np.zeros((self.nPoints,))
for feat in self.featureList:
localObs = np.vstack([localObs, obsList[feat].values])
obs.append(localObs[1:])
obs = np.array(obs)
obs = np.reshape(obs, (obs.shape[1], obs.shape[2], 1))
return obs
"open", "close", "low", "high", "spread", "vsa_spread", "tick_volume",
"hkopen", "hkclose", "enopen", "enclose", "enlow", "enhigh"
]
#terminal = MT5Terminal(login=123456, server="broker-server", password="******")
#dataUpdater = SymbolDataUpdater("../data/raw/")
dataManager = SymbolDataManager("../data/raw/")
#dataUpdater.fullUpdate(terminal, symbol, timeframe, startDate="2008-01-01 00:00:00")
df = dataManager.getData(symbol, timeframe)
df = df.tail(400000)
########
historyMod = VSASpread()
modDf = historyMod.modHistory(df)
historyMod = HeikenAshiMod()
modDf = historyMod.modHistory(modDf)
historyMod = EnergyMod()
modDf = historyMod.modHistory(modDf, featList=["open", "close", "low", "high"])
########
priceDiffGenerator = MultiScalerDiffGenerator(featureList=hkFeatList,
nDiffs=1,
nPoints=256,
flatStack=False,
fitOnStep=False)
priceDiffGenerator.setFitMode(True)
priceDiffGenerator = priceDiffGenerator.globalFit(modDf)
priceDiffGenerator.saveGenerator("../models/MSDiffGen.pkl")
###############################
def getManyPointsFeat(self, datetimeStr, historyData):
obsList = None
if self.fitMode:
obsList = self.fitData.loc[:datetimeStr].tail(self.nPoints).copy()
else:
df = historyData.copy()
########
df = df[:str(datetimeStr)].tail(500)
from monlan.mods.VSASpread import VSASpread
from monlan.mods.HeikenAshiMod import HeikenAshiMod
from monlan.mods.EnergyMod import EnergyMod
historyMod = VSASpread()
df = historyMod.modHistory(df)
historyMod = HeikenAshiMod()
df = historyMod.modHistory(df)
historyMod = EnergyMod()
df = historyMod.modHistory(
df, featList=["open", "close", "low", "high"])
########
for i in range(self.nDiffs):
for feat in self.featureList:
notShifted = df[feat]
shiftedData = df[feat].shift(periods=1)
df[feat] = notShifted - shiftedData
iter = next(df.iterrows())
df = df.drop(iter[0])
obsList = df.loc[:str(datetimeStr)].tail(self.nPoints).copy()
obsListTransformed = {}
for obs in obsList.iterrows():
for feat in self.featureList:
data = obs[1][feat]
data = data.reshape(-1, 1)
data = self.scalerDict[feat].transform(data)
obs[1][feat] = data
obsListTransformed[obs[0]] = obs[1]
obsList = pd.DataFrame(obsListTransformed.values(),
index=list(obsListTransformed.keys()))
#################################
# Scale each feature series to (0, 1) individually
# to remove big picture properties from current window of local features
for feat in self.featureList:
tmp = obsList[feat].values.reshape((-1, 1))
tmp = MinMaxScaler(feature_range=(0.0, 1.0)).fit_transform(tmp)
#tmp = StandardScaler().fit_transform(tmp)
tmp = tmp.reshape((-1, ))
obsList[feat] = tmp
#################################
obs = []
if self.flatStack:
localObs = np.zeros((self.nPoints, ))
#for feat in self.featureList:
# localObs = np.vstack([localObs, vectorPrices[feat]])
#obs = np.array(localObs[1:])
else:
localObs = np.zeros((self.nPoints, ))
for feat in self.featureList:
localObs = np.vstack([localObs, obsList[feat].values])
obs.append(localObs[1:])
obs = np.array(obs)
#2DConv features shape
obs = np.reshape(obs, (obs.shape[1], obs.shape[2], 1))
#1DConv features shape
#obs = np.reshape(obs, (obs.shape[1], obs.shape[2]))
#obs = obs.T
##################
return obs