def trainAgent(trainDf, backTestDf, genPath, saveAgentDir, saveAgentName):
startTime = datetime.now()
print("Start train time: {}".format(startTime))
openerPriceDiffGenerator = MultiScalerDiffGenerator(featureList=hkFeatList).loadGenerator("{}".format(genPath))
buyerPriceDiffGenerator = MultiScalerDiffGenerator(featureList=hkFeatList).loadGenerator("{}".format(genPath))
sellerPriceDiffGenerator = MultiScalerDiffGenerator(featureList=hkFeatList).loadGenerator("{}".format(genPath))
trainEnv = CompositeEnv(trainDf, openerPriceDiffGenerator, buyerPriceDiffGenerator, sellerPriceDiffGenerator,
startDeposit=300, lotSize=0.1, lotCoef=100000, spread=18, spreadCoef=0.00001,
stoplossPuncts=100, takeprofitPuncts=300,
renderFlag=True, renderDir=saveDir, renderName="trainDealsPlot")
backTestEnv = CompositeEnv(backTestDf, openerPriceDiffGenerator, buyerPriceDiffGenerator, sellerPriceDiffGenerator,
startDeposit=300, lotSize=0.1, lotCoef=100000, spread=18, spreadCoef=0.00001,
stoplossPuncts=100, takeprofitPuncts=300,
renderFlag=True, renderDir=saveDir, renderName="backTestDealsPlot")
# get size of state and action from environment
openerAgent = DQNAgent(trainEnv.observation_space["opener"], trainEnv.action_space["opener"].n,
memorySize=2500, batch_size=100, train_start=200, epsilon_min=0.05, epsilon=1,
epsilon_decay=0.9997, learning_rate=0.0002)
buyerAgent = DQNAgent(trainEnv.observation_space["buyer"], trainEnv.action_space["buyer"].n,
memorySize=5000, batch_size=200, train_start=300, epsilon_min=0.05, epsilon=1,
epsilon_decay=1.0, learning_rate=0.0002)
sellerAgent = DQNAgent(trainEnv.observation_space["seller"], trainEnv.action_space["seller"].n,
memorySize=5000, batch_size=200, train_start=300, epsilon_min=0.05, epsilon=1,
epsilon_decay=1.0, learning_rate=0.0002)
agent = CompositeAgent(openerAgent, buyerAgent, sellerAgent)
lastSaveEp = agent.fit_agent(env=trainEnv, backTestEnv=backTestEnv, nEpisodes=4, nWarmUp=0,
uniformEps=False, synEps=True, plotScores=False, saveBest=True, saveFreq=1,
saveDir = saveAgentDir, saveName = saveAgentName)
endTime = datetime.now()
print("Training finished. Total time: {}".format(endTime - startTime))
return lastSaveEp
def collectStatistics(symbol, nExperiment, trainDf, backTestDf, testDf, startDeposit=300, lotSize=0.1, lotCoef=100000, spread=18, spreadCoef=0.00001):
reset_keras()
openerPriceDiffGenerator = MultiScalerDiffGenerator(featureList=priceFeatList).loadGenerator("./MSDiffGen.pkl")
buyerPriceDiffGenerator = MultiScalerDiffGenerator(featureList=priceFeatList).loadGenerator("./MSDiffGen.pkl")
sellerPriceDiffGenerator = MultiScalerDiffGenerator(featureList=priceFeatList).loadGenerator("./MSDiffGen.pkl")
trainEnv = CompositeEnv(trainDf, openerPriceDiffGenerator, buyerPriceDiffGenerator, sellerPriceDiffGenerator,
startDeposit=startDeposit, lotSize=lotSize, lotCoef=lotCoef, spread=spread, spreadCoef=spreadCoef, renderFlag=True)
backTestEnv = CompositeEnv(backTestDf, openerPriceDiffGenerator, buyerPriceDiffGenerator, sellerPriceDiffGenerator,
startDeposit=startDeposit, lotSize=lotSize, lotCoef=lotCoef, spread=spread, spreadCoef=spreadCoef, renderFlag=True)
forwardTestEnv = CompositeEnv(testDf, openerPriceDiffGenerator, buyerPriceDiffGenerator, sellerPriceDiffGenerator,
startDeposit=startDeposit, lotSize=lotSize, lotCoef=lotCoef, spread=spread, spreadCoef=spreadCoef, renderFlag=True)
# get size of state and action from environment
openerAgent = DQNAgent(trainEnv.observation_space["opener"], trainEnv.action_space["opener"].n,
memorySize=2500, batch_size=100, train_start=200, epsilon_min=0.05, epsilon=1,
epsilon_decay=0.9995, learning_rate=0.0005)
buyerAgent = DQNAgent(trainEnv.observation_space["buyer"], trainEnv.action_space["buyer"].n,
memorySize=5000, batch_size=200, train_start=300, epsilon_min=0.05, epsilon=1,
epsilon_decay=1.0, learning_rate=0.0005)
sellerAgent = DQNAgent(trainEnv.observation_space["seller"], trainEnv.action_space["seller"].n,
memorySize=5000, batch_size=200, train_start=300, epsilon_min=0.05, epsilon=1,
epsilon_decay=1.0, learning_rate=0.0005)
agent = CompositeAgent(openerAgent, buyerAgent, sellerAgent)
continueFit = False
trainDealsStatistics = []
testDealsStatistics = []
backDealsStatistics = []
for i in range(20):
agent.fit_agent(env=trainEnv, backTestEnv=None, nEpisodes=1, nWarmUp=0,
uniformEps=False, synEps=True, plotScores=True, saveBest=False, saveFreq=111, continueFit=continueFit)
continueFit = True
trainDealsStatistics.append( agent.use_agent(trainEnv) )
testDealsStatistics.append( agent.use_agent(forwardTestEnv) )
backDealsStatistics.append( agent.use_agent(backTestEnv) )
with open("./" + "trainDealsStatistics_{}_{}.pkl".format(symbol, nExperiment), mode="wb") as dealsFile:
joblib.dump(trainDealsStatistics, dealsFile)
with open("./" + "testDealsStatistics_{}_{}.pkl".format(symbol, nExperiment), mode="wb") as dealsFile:
joblib.dump(testDealsStatistics, dealsFile)
with open("./" + "backDealsStatistics_{}_{}.pkl".format(symbol, nExperiment), mode="wb") as dealsFile:
joblib.dump(backDealsStatistics, dealsFile)
pass
def trainAgent(trainDf, startDeposit=300, lotSize=0.1, lotCoef=100000, spread=18, spreadCoef=0.00001 ):
openerPriceDiffGenerator = W2VDiffGenerator(featureList=priceFeatList).loadGenerator("./w2vPriceDiffGen.pkl")
buyerPriceDiffGenerator = W2VDiffGenerator(featureList=priceFeatList).loadGenerator("./w2vPriceDiffGen.pkl")
sellerPriceDiffGenerator = W2VDiffGenerator(featureList=priceFeatList).loadGenerator("./w2vPriceDiffGen.pkl")
openerVolumeDiffGenerator = W2VDiffGenerator(featureList=volumeFeatList).loadGenerator("./w2vVolumeDiffGen.pkl")
buyerVolumeDiffGenerator = W2VDiffGenerator(featureList=volumeFeatList).loadGenerator("./w2vVolumeDiffGen.pkl")
sellerVolumeDiffGenerator = W2VDiffGenerator(featureList=volumeFeatList).loadGenerator("./w2vVolumeDiffGen.pkl")
openerCompositeGenerator = W2VCompositeGenerator( [openerPriceDiffGenerator,openerVolumeDiffGenerator], flatStack=False)
buyerCompositeGenerator = W2VCompositeGenerator( [buyerPriceDiffGenerator,buyerVolumeDiffGenerator], flatStack=False)
sellerCompositeGenerator = W2VCompositeGenerator( [sellerPriceDiffGenerator,sellerVolumeDiffGenerator], flatStack=False)
trainEnv = CompositeEnv(trainDf, openerCompositeGenerator, buyerCompositeGenerator, sellerCompositeGenerator,
startDeposit=startDeposit, lotSize=lotSize, lotCoef=lotCoef, spread = spread, spreadCoef = spreadCoef, renderFlag=True)
openerAgent = DQNAgent(trainEnv.observation_space["opener"], trainEnv.action_space["opener"].n,
memorySize=500, batch_size=100, train_start=200, epsilon_min=0.1, epsilon=1, epsilon_decay=0.9994)
buyerAgent = DQNAgent(trainEnv.observation_space["buyer"], trainEnv.action_space["buyer"].n,
memorySize=1000, batch_size=200, train_start=300, epsilon_min=0.1, epsilon=1, epsilon_decay=0.9999)
sellerAgent = DQNAgent(trainEnv.observation_space["seller"], trainEnv.action_space["seller"].n,
memorySize=1000, batch_size=200, train_start=300, epsilon_min=0.1, epsilon=1, epsilon_decay=0.9999)
agent = CompositeAgent(openerAgent, buyerAgent, sellerAgent)
agent.fit_agent(env=trainEnv, nEpisodes=15, nWarmUp = 0, uniformEps = False, plotScores=True, saveBest=True, saveFreq=2)
backTestEnv = CompositeEnv(backTestDf, openerPriceDiffGenerator, buyerPriceDiffGenerator, sellerPriceDiffGenerator,
startDeposit=300, lotSize=0.1, lotCoef=100000, spread=18, spreadCoef=0.00001, renderFlag=True)
# get size of state and action from environment
openerAgent = DQNAgent(trainEnv.observation_space["opener"], trainEnv.action_space["opener"].n,
memorySize=2500, batch_size=100, train_start=200, epsilon_min=0.05, epsilon=1,
epsilon_decay=0.9999, learning_rate=0.0001)
buyerAgent = DQNAgent(trainEnv.observation_space["buyer"], trainEnv.action_space["buyer"].n,
memorySize=5000, batch_size=200, train_start=300, epsilon_min=0.05, epsilon=1,
epsilon_decay=1.0, learning_rate=0.0001)
sellerAgent = DQNAgent(trainEnv.observation_space["seller"], trainEnv.action_space["seller"].n,
memorySize=5000, batch_size=200, train_start=300, epsilon_min=0.05, epsilon=1,
epsilon_decay=1.0, learning_rate=0.0001)
agent = CompositeAgent(openerAgent, buyerAgent, sellerAgent)
#agent = agent.load_agent("./", "best_composite")
#agent = agent.load_agent("./", "checkpoint_composite")
lastSaveEp = agent.fit_agent(env=trainEnv, backTestEnv=backTestEnv, nEpisodes=25, nWarmUp=0,
uniformEps=False, synEps=True, plotScores=True, saveBest=True, saveFreq=1)
endTime = datetime.now()
print("Training finished. Total time: {}".format(endTime - startTime))
###############################
# use agent
###############################
testDf = modDf.tail(7200).tail(2200)
#testDf = modDf.tail(6032).head(5032)
#testDf = modDf.tail(6032).tail(1032)
openerPriceDiffGenerator = CompositeGenerator().loadGenerator("./CompositeMSDiffGen.pkl")
buyerPriceDiffGenerator = CompositeGenerator().loadGenerator("./CompositeMSDiffGen.pkl")
sellerPriceDiffGenerator = CompositeGenerator().loadGenerator("./CompositeMSDiffGen.pkl")
train_start=1200,
epsilon_decay=0.999)
buyerAgent = DQNAgent(trainEnv.observation_space["buyer"][0],
trainEnv.action_space["buyer"].n,
memorySize=2000,
batch_size=1000,
train_start=1200,
epsilon_decay=0.999)
sellerAgent = DQNAgent(trainEnv.observation_space["seller"][0],
trainEnv.action_space["seller"].n,
memorySize=2000,
batch_size=1000,
train_start=1200,
epsilon_decay=0.999)
agent = CompositeAgent(openerAgent, buyerAgent, sellerAgent)
agent.fit_agent(env=trainEnv, nEpisodes=10000, plotScores=True,
saveFreq=10) #TODO: add save best only
agent.save_agent("./", "test_composite_agent")
print("agent saved")
###############################
# use agent
###############################
"""testDF = df[int(len(df)*0.9):]
featureFactory = ScalerGenerator(featureList=obsFeatList, fitOnStep=True)
featureFactory.globalFit(df[:int(len(df)*0.9)])
testEnv = FeatureGenEnv(testDF, featureFactory, startDeposit=10, lotSize=0.01, lotCoef=10, renderFlag=True)
state_size = testEnv.observation_space.shape[0]
action_size = testEnv.action_space.n
print("loading agent")
agent = DQNAgent(state_size, action_size).load_agent(".", "test_agent")
print("start using agent")
buyerAgent = DQNAgent(trainEnv.observation_space["buyer"],
trainEnv.action_space["buyer"].n,
memorySize=2000,
batch_size=200,
train_start=300,
epsilon_decay=0.999)
sellerAgent = DQNAgent(trainEnv.observation_space["seller"],
trainEnv.action_space["seller"].n,
memorySize=2000,
batch_size=200,
train_start=300,
epsilon_decay=0.999)
agent = CompositeAgent(openerAgent, buyerAgent, sellerAgent)
agent.fit_agent(env=trainEnv,
nEpisodes=30,
plotScores=True,
saveBest=True,
saveFreq=2)
###############################
# use agent
###############################
testDF = df[int(len(df) * 0.6):]
testEnv = CompositeEnv(testDF,
openerFeatureFactory,
buyerFeatureFactory,
sellerFatureFactory,
startDeposit=10,
lotSize=0.01,
lotCoef=10,
renderFlag=True)
def trainAgent(trainDf,
backTestDf,
startDeposit=300,
lotSize=0.1,
lotCoef=100000,
spread=18,
spreadCoef=0.00001):
openerPriceDiffGenerator = MultiScalerDiffGenerator(
featureList=priceFeatList).loadGenerator("./MSDiffGen.pkl")
buyerPriceDiffGenerator = MultiScalerDiffGenerator(
featureList=priceFeatList).loadGenerator("./MSDiffGen.pkl")
sellerPriceDiffGenerator = MultiScalerDiffGenerator(
featureList=priceFeatList).loadGenerator("./MSDiffGen.pkl")
trainEnv = CompositeEnv(trainDf,
openerPriceDiffGenerator,
buyerPriceDiffGenerator,
sellerPriceDiffGenerator,
startDeposit=startDeposit,
lotSize=lotSize,
lotCoef=lotCoef,
spread=spread,
spreadCoef=spreadCoef,
renderFlag=True)
backTestEnv = CompositeEnv(backTestDf,
openerPriceDiffGenerator,
buyerPriceDiffGenerator,
sellerPriceDiffGenerator,
startDeposit=startDeposit,
lotSize=lotSize,
lotCoef=lotCoef,
spread=spread,
spreadCoef=spreadCoef,
renderFlag=True)
# get size of state and action from environment
openerAgent = DQNAgent(trainEnv.observation_space["opener"],
trainEnv.action_space["opener"].n,
memorySize=2500,
batch_size=100,
train_start=200,
epsilon_min=0.05,
epsilon=1,
epsilon_decay=0.9999,
learning_rate=0.0002)
buyerAgent = DQNAgent(trainEnv.observation_space["buyer"],
trainEnv.action_space["buyer"].n,
memorySize=5000,
batch_size=200,
train_start=300,
epsilon_min=0.05,
epsilon=1,
epsilon_decay=1.0,
learning_rate=0.0002)
sellerAgent = DQNAgent(trainEnv.observation_space["seller"],
trainEnv.action_space["seller"].n,
memorySize=5000,
batch_size=200,
train_start=300,
epsilon_min=0.05,
epsilon=1,
epsilon_decay=1.0,
learning_rate=0.0002)
agent = CompositeAgent(openerAgent, buyerAgent, sellerAgent)
lastSaveEp = agent.fit_agent(env=trainEnv,
backTestEnv=backTestEnv,
nEpisodes=3,
nWarmUp=0,
uniformEps=False,
synEps=True,
plotScores=True,
saveBest=False,
saveFreq=1)
return lastSaveEp
sellerCompositeGenerator = W2VCompositeGenerator( [sellerPriceDiffGenerator, sellerVolumeDiffGenerator], flatStack=False)
#trainDf = df[:int(len(df)*0.9)]
trainDf = df.tail(2044).head(1022)
#trainDf = trainDf[:int(len(trainDf)*0.5)]
trainEnv = CompositeEnv(trainDf, openerCompositeGenerator, buyerCompositeGenerator, sellerCompositeGenerator,
startDeposit=300, lotSize=0.1, lotCoef=100000, renderFlag=True)
# get size of state and action from environment
openerAgent = DQNAgent(trainEnv.observation_space["opener"], trainEnv.action_space["opener"].n,
memorySize=500, batch_size=100, train_start=200, epsilon_min=0.05, epsilon=1, epsilon_decay=0.9994)
buyerAgent = DQNAgent(trainEnv.observation_space["buyer"], trainEnv.action_space["buyer"].n,
memorySize=1000, batch_size=200, train_start=300, epsilon_min=0.05, epsilon=1, epsilon_decay=0.9999)
sellerAgent = DQNAgent(trainEnv.observation_space["seller"], trainEnv.action_space["seller"].n,
memorySize=1000, batch_size=200, train_start=300, epsilon_min=0.05, epsilon=1, epsilon_decay=0.9999)
agent = CompositeAgent(openerAgent, buyerAgent, sellerAgent)
agent.fit_agent(env=trainEnv, nEpisodes=15, nWarmUp = 0, uniformEps = False, plotScores=True, saveBest=True, saveFreq=2)
###############################
# use agent
###############################
#testDf = df.copy()#[int(len(df)*0.9):]
testDf = df.tail(2044).tail(1022) #check scaling at test env
#testDf = testDf[int(len(testDf)*0.5):]
openerPriceDiffGenerator = W2VDiffGenerator(featureList=hkFeatList).loadGenerator("./w2vPriceDiffGen.pkl")
buyerPriceDiffGenerator = W2VDiffGenerator(featureList=hkFeatList).loadGenerator("./w2vPriceDiffGen.pkl")
sellerPriceDiffGenerator = W2VDiffGenerator(featureList=hkFeatList).loadGenerator("./w2vPriceDiffGen.pkl")
openerVolumeDiffGenerator = W2VDiffGenerator(featureList=volumeFeatList).loadGenerator("./w2vVolumeDiffGen.pkl")
buyerVolumeDiffGenerator = W2VDiffGenerator(featureList=volumeFeatList).loadGenerator("./w2vVolumeDiffGen.pkl")
sellerVolumeDiffGenerator = W2VDiffGenerator(featureList=volumeFeatList).loadGenerator("./w2vVolumeDiffGen.pkl")
def trainAgent(trainDf, backTestDf):
openerPriceDiffGenerator = MultiScalerDiffGenerator(
featureList=hkFeatList).loadGenerator("./MSDiffGen.pkl")
buyerPriceDiffGenerator = MultiScalerDiffGenerator(
featureList=hkFeatList).loadGenerator("./MSDiffGen.pkl")
sellerPriceDiffGenerator = MultiScalerDiffGenerator(
featureList=hkFeatList).loadGenerator("./MSDiffGen.pkl")
trainEnv = CompositeEnv(trainDf,
openerPriceDiffGenerator,
buyerPriceDiffGenerator,
sellerPriceDiffGenerator,
startDeposit=300,
lotSize=0.1,
lotCoef=100000,
spread=18,
spreadCoef=0.00001,
renderFlag=True)
backTestEnv = CompositeEnv(backTestDf,
openerPriceDiffGenerator,
buyerPriceDiffGenerator,
sellerPriceDiffGenerator,
startDeposit=300,
lotSize=0.1,
lotCoef=100000,
spread=18,
spreadCoef=0.00001,
renderFlag=True)
# get size of state and action from environment
openerAgent = DQNAgent(trainEnv.observation_space["opener"],
trainEnv.action_space["opener"].n,
memorySize=2500,
batch_size=100,
train_start=200,
epsilon_min=0.05,
epsilon=1,
epsilon_decay=0.9994,
learning_rate=0.001)
buyerAgent = DQNAgent(trainEnv.observation_space["buyer"],
trainEnv.action_space["buyer"].n,
memorySize=5000,
batch_size=200,
train_start=300,
epsilon_min=0.05,
epsilon=1,
epsilon_decay=0.9999,
learning_rate=0.001)
sellerAgent = DQNAgent(trainEnv.observation_space["seller"],
trainEnv.action_space["seller"].n,
memorySize=5000,
batch_size=200,
train_start=300,
epsilon_min=0.05,
epsilon=1,
epsilon_decay=0.9999,
learning_rate=0.001)
agent = CompositeAgent(openerAgent, buyerAgent, sellerAgent)
# agent = agent.load_agent("./", "best_composite")
# agent = agent.load_agent("./", "checkpoint_composite")
lastSaveEp = agent.fit_agent(env=trainEnv,
backTestEnv=backTestEnv,
nEpisodes=5,
nWarmUp=0,
uniformEps=False,
synEps=True,
plotScores=True,
saveBest=True,
saveFreq=1)
endTime = datetime.now()
print("Training finished. Total time: {}".format(endTime - startTime))
return lastSaveEp