def cycleTrain(symbol, timeframe, dataUpdater, saveDir, genPath,
saveAgentName):
while True:
dataUpdater.partialUpdate(terminal, symbol, timeframe)
df = SymbolDataManager().getData(symbol, timeframe)
preprocDf = preprocData(df)
createGenerator(preprocDf,
featureList=hkFeatList,
saveDir=saveDir,
saveName=genName)
trainDf = preprocDf.tail(2032).tail(1032)
backTestDf = preprocDf.tail(2032).head(1032)
lastSaveEp = trainAgent(trainDf=trainDf,
backTestDf=backTestDf,
genPath=genPath,
saveAgentDir=saveDir,
saveAgentName=saveAgentName)
##############
reset_keras()
##############
#####
# break
#####
sumReward = evaluateAgent(backTestDf=backTestDf,
genPath=genPath,
agentDir=saveDir,
agentName=saveAgentName)
if lastSaveEp > 0 and sumReward > -110:
print("Backtest's succesfull. Stop Training.")
break
else:
print("Backtest failed. Retrain.")
#############
#weighted train/back reward
"""backReward = evaluateAgent(backTestDf=backTestDf, genPath=genPath, agentDir=saveDir, agentName=saveAgentName)
trainReward = evaluateAgent(backTestDf=trainDf, genPath=genPath, agentDir=saveDir, agentName=saveAgentName)
backW = 0.8 #1.0 - (len(backTestDf) / (len(backTestDf) + len(trainDf)))
trainW = 0.2 #1.0 - (len(trainDf) / (len(backTestDf) + len(trainDf)))
weightedReward = (backW * backReward + trainW * trainReward) / 2
if lastSaveEp > 0 and weightedReward > 0:
print("Backtest's succesfull. Stop Training.")
break
else:
print("Backtest failed. Retrain.")"""
#############
reset_keras()
from avera.datamanagement.SymbolDataManager import SymbolDataManager symbolDM = SymbolDataManager() readedData = symbolDM.getData(symbol="TEST", timeFrame="H1") print(readedData.tail(2)) readedData = symbolDM.getData(symbol="TEST", timeFrame="H1", normalizeNames=True) print(readedData.tail(2)) readedData = symbolDM.getData(symbol="TEST", timeFrame="H1", normalizeNames=True, normalizeDateTime=True) print(readedData.tail(2))
from avera.mods.EnergyMod import EnergyMod
from avera.mods.VSASpread import VSASpread
import matplotlib.pyplot as plt
from datetime import datetime
startTime = datetime.now()
print("start time: {}".format(startTime))
symbol = "EURUSD_i"
timeframe = "H1"
hkFeatList = ["open", "close", "low", "high", "vsa_spread", "tick_volume",
"hkopen", "hkclose", "enopen", "enclose", "enlow", "enhigh"]
#terminal = MT5Terminal()
dataUpdater = SymbolDataUpdater("../../data/raw/")
dataManager = SymbolDataManager("../../data/raw/")
#dataUpdater.fullUpdate(terminal, symbol, timeframe, startDate="2015-01-01 00:00:00")
df = SymbolDataManager().getData(symbol, timeframe)
########
historyMod = VSASpread()
modDf = historyMod.modHistory(df)
historyMod = HeikenAshiMod()
modDf = historyMod.modHistory(modDf)
historyMod = EnergyMod()
modDf = historyMod.modHistory(modDf, featList=["open", "close", "low", "high"])
########
genList = []
for i in range(2):
from avera.feature_generators.DiffGenerator import DiffGenerator
from avera.feature_generators.FeatureScaler import FeatureScaler
from avera.datamanagement.SymbolDataManager import SymbolDataManager
from matplotlib import pyplot as plt
from dateutil import parser
featureList = ["open", "close"]
df = SymbolDataManager().getData("TEST",
"H1",
normalizeDateTime=True,
normalizeNames=True)
df.set_index("datetime", drop=True, inplace=True)
featureScaler = DiffGenerator(featureList=featureList,
nDiffs=1,
nPoints=4,
flatStack=True)
featureScaler = featureScaler.globalFit(df)
print(df.tail(1))
lastDt = list(df.tail(1).index)[0]
print(lastDt)
print(df.loc[lastDt])
print(featureScaler.getFeatByDatetime(lastDt, df))
featureScaler = FeatureScaler()
df = featureScaler.extractFeature(df, featureList)
print(df.loc[lastDt])
from avera.envs.RealEnv import RealEnv
from avera.terminal.MT5Terminal import MT5Terminal
#from avera.feature_generators.FeatureScaler import FeatureScaler
from avera.feature_generators.ScalerGenerator import ScalerGenerator
from avera.feature_generators.DiffGenerator import DiffGenerator
from avera.feature_generators.CompositeGenerator import CompositeGenerator
from avera.datamanagement.SymbolDataManager import SymbolDataManager
from avera.datamanagement.SymbolDataUpdater import SymbolDataUpdater
symbol = "EURUSD_i"
timeframe = "H1"
obsFeatList = ["open", "close"]
#terminal = MT5Terminal()
#dataUpdater = SymbolDataUpdater()
dataManager = SymbolDataManager()
#dataUpdater.fullUpdate(terminal, symbol, timeframe, startDate="2015-01-01 00:00:00")
df = SymbolDataManager().getData(
symbol, timeframe) #, normalizeNames=True, normalizeDateTime=True)
df = df.tail(1000)
openerScaleGen = ScalerGenerator(featureList=obsFeatList,
nPoints=50,
flatStack=True,
fitOnStep=False)
openerDiffGen = DiffGenerator(featureList=obsFeatList,
nDiffs=1,
nPoints=50,
flatStack=True,
fitOnStep=False)
from avera.datamanagement.SymbolDataManager import SymbolDataManager
from datetime import datetime
from avera_core.CloserFeatureGenerator import CloserFeatureGenerator
from avera.utils.save_load import *
from tqdm import tqdm
startTime = datetime.now()
print("start time: {}".format(startTime))
symbol = "EURUSD_i"
timeframe = "M10"
dataManager = SymbolDataManager("../data/raw/")
df = dataManager.getData(symbol, timeframe)
df = df.tail(50000)
print(df.shape)
print(df.head(2))
print(df.tail(2))
df.set_index("datetime", drop=True, inplace=True)
featGen = CloserFeatureGenerator(featureList=["open", "close"],
nFeatRows=1,
nPoints=110,
nLevels=5,
flatStack=True,
fitOnStep=True)
featGen = featGen.globalFit(df)
startTime = datetime.now()
from avera.envs.CompositeEnv import CompositeEnv
#from avera.feature_generators.FeatureScaler import FeatureScaler
from avera.feature_generators.W2VCompositeGenerator import W2VCompositeGenerator
from avera.feature_generators.W2VScaleGenerator import W2VScaleGenerator
from avera.feature_generators.W2VDiffGenerator import W2VDiffGenerator
from avera.datamanagement.SymbolDataManager import SymbolDataManager
from avera.mods.EnergyMod import EnergyMod
symbol = "EURUSD_i"
timeframe = "H1"
energyFeatList = ["enopen", "enclose", "enlow", "enhigh"]
volumeFeatList = ["tick_volume"]
#terminal = MT5Terminal()
#dataUpdater = SymbolDataUpdater()
dataManager = SymbolDataManager()
#dataUpdater.fullUpdate(terminal, symbol, timeframe, startDate="2015-01-01 00:00:00")
df = SymbolDataManager().getData(symbol, timeframe)
########
#df = df.tail(1000)
print("applying energy mod...")
enMod = EnergyMod()
df = enMod.modHistory(df, ["open", "close", "low", "high"])
#enMod.checkQuality(df)
########
"""energyScaleGenerator = W2VScaleGenerator(featureList=energyFeatList, nPoints = 21, flatStack = False, fitOnStep = False,
nIntervals = 10000, w2vSize=32, window=20, iter=100, min_count=0, sample=0.0, sg=0)
energyDiffGenerator = W2VDiffGenerator(featureList=energyFeatList, nDiffs=1, nPoints = 21, flatStack = False, fitOnStep = False,
nIntervals = 10000, w2vSize=32, window=20, iter=100, min_count=0, sample=0.0, sg=0)"""
hkFeatList = [
"open", "close", "low", "high", "vsa_spread", "tick_volume", "hkopen",
"hkclose", "enopen", "enclose", "enlow", "enhigh"
]
saveDir = "../models/"
genName = "MSDiffGen"
genPath = saveDir + genName + ".pkl"
agentName = "best_composite"
timeConstraint = timedelta(days=5, hours=12, minutes=0)
terminal = MT5Terminal(login=123456,
server="broker-server",
password="******")
#terminal = MT5Terminal(login=123456, server="broker-server", password="******")
dataUpdater = SymbolDataUpdater()
dataManager = SymbolDataManager()
dataUpdater.fullUpdate(terminal,
symbol,
timeframe,
startDate="2008-01-01 00:00:00")
while True:
#################
"""symbolList = ["EURUSD_i", "USDCHF_i", "USDCAD_i", "AUDUSD_i"]
for sym in symbolList:
print("Partial update: {}".format(sym))
#dataUpdater.fullUpdate(terminal, sym, timeframe, startDate="2015-01-01 00:00:00")
dataUpdater.partialUpdate(terminal, sym, timeframe)
symbol = selectSymbol(symbolList, dataManager, period=18)
print("Selected symbol: {}".format(symbol))"""
#################
from avera.feature_generators.FeatureDiff import FeatureDiff
from avera.feature_generators.FeatureScaler import FeatureScaler
from avera.datamanagement.SymbolDataManager import SymbolDataManager
from matplotlib import pyplot as plt
df = SymbolDataManager().getData("EURUSD_i", "H1")
df = FeatureDiff().extractFeature(df, featList=["open"], nDiffs=1)
df = FeatureScaler().extractFeature(df, featList=["open"])
print(df)
print(df["open"].mean())
print(df["open"].std())
y = df["open"].values
x = [x for x in range(y.shape[0])]
plt.plot(x, y)
plt.show()
from avera.datamanagement.SymbolDataManager import SymbolDataManager
from avera.envs.SimpleEnv import SimpleEnv
df = SymbolDataManager().getData("TEST", "H1")
#featureScaler = FeatureScaler()
#df = featureScaler.extractFeature(df)
simpleEnv = SimpleEnv(df)
obs = simpleEnv.reset()
print(str(obs[0]) + " | " + str(obs[1]))
#check buy
obs, reward, done, info = simpleEnv.step(0)
print(str(obs[0]) + " | " + str(obs[1]))
print(reward)
#check hold
obs, reward, done, info = simpleEnv.step(1)
print(str(obs[0]) + " | " + str(obs[1]))
print(reward)
#check sell
obs, reward, done, info = simpleEnv.step(2)
print(str(obs[0]) + " | " + str(obs[1]))
print(reward)