Python RecurrentNetwork._setParameters方法代码示例

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import _setParameters [as 别名]
def main():
    inData=createDataset()
    env = MarketEnvironment(inData)
    task = MaximizeReturnTask(env)
    numIn=min(env.worldState.shape)

    net=RecurrentNetwork()
    net.addInputModule(BiasUnit(name='bias'))
    #net.addOutputModule(TanhLayer(1, name='out'))
    net.addOutputModule((SignLayer(1,name='out')))
    net.addRecurrentConnection(FullConnection(net['out'], net['out'], name='c3'))
    net.addInputModule(LinearLayer(numIn,name='in'))
    net.addConnection(FullConnection(net['in'],net['out'],name='c1'))
    net.addConnection((FullConnection(net['bias'],net['out'],name='c2')))
    net.sortModules()
    # remove bias (set weight to 0)
    #initialParams=append(array([0.0]),net._params[1:])
    #net._setParameters(initialParams)
    #net._setParameters([ 0.0,-0.05861005,1.64281513,0.98302613])
    #net._setParameters([0., 1.77132063, 1.3843613, 4.73725269])
    #net._setParameters([ 0.0, -0.95173719, 1.92989266, 0.06837472])
    net._setParameters([ 0.0, 1.29560957, -1.14727503, -1.80005888, 0.66351325, 1.19240189])

    ts=env.ts
    learner = RRL(numIn+2,ts) # ENAC() #Q_LinFA(2,1)
    agent = LearningAgent(net,learner)
    exp = ContinuousExperiment(task,agent)

    print(net._params)
    exp.doInteractionsAndLearn(len(ts)-1)
    print(net._params)

    outData=DataFrame(inData['RETURNS']/100)
    outData['ts']=[i/100 for i in ts]
    outData['cum_log_ts']=cumsum([log(1+i) for i in outData['ts']])

    outData['Action_Hist']=env.actionHistory
    outData['trading rets']=pE.calculateTradingReturn(outData['Action_Hist'],outData['ts'])
    outData['cum_log_rets']=cumsum([log(1+x) for x in outData['trading rets']])

    paramHist=learner.paramHistory
    plt.figure(0)
    for i in range(len(net._params)):
        plt.plot(paramHist[i])
    plt.draw()

    print(pE.percentOfOutperformedMonths(outData['trading rets'],outData['ts']))


    #ax1.plot(sign(actionHist),'r')
    plt.figure(1)
    outData['cum_log_ts'].plot(secondary_y=True)
    outData['cum_log_rets'].plot(secondary_y=True)
    outData['Action_Hist'].plot()
    plt.draw()
    plt.show()

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import _setParameters [as 别名]
from pybrain.rl.environments.timeseries.timeseries import MonthlySnPEnvironment
from pybrain.rl.learners.directsearch.rrl import RRL

from pybrain.structure import RecurrentNetwork
from pybrain.structure import LinearLayer, SigmoidLayer, TanhLayer, BiasUnit
from pybrain.structure import FullConnection
from pybrain.rl.agents import LearningAgent
from pybrain.rl.experiments import EpisodicExperiment

from numpy import sign, round
from matplotlib import pyplot

net= RecurrentNetwork()
#Single linear layer with bias unit, and single tanh layer. the linear layer is whats optimised
net.addInputModule(BiasUnit(name='bias'))
net.addOutputModule(TanhLayer(1, name='out'))
net.addRecurrentConnection(FullConnection(net['out'], net['out'], name='c3'))
net.addInputModule(LinearLayer(1,name='in'))
net.addConnection(FullConnection(net['in'],net['out'],name='c1'))
net.addConnection((FullConnection(net['bias'],net['out'],name='c2')))
net.sortModules()
net._setParameters([-8.79227886e-02, -8.29319017e+02, 1.25946474e+00])
print(net._params)
env=MonthlySnPEnvironment()
task=MaximizeReturnTask(env)
learner = RRL() # ENAC() #Q_LinFA(2,1)
agent = LearningAgent(net,learner)
exp=EpisodicExperiment(task,agent)

exp.doEpisodes(10)

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import _setParameters [as 别名]
"""
This script aims to create a trading model that trades on a simple AR(1) process
"""

##building the Recurrent Network

net= RecurrentNetwork()
#Single linear layer with bias unit, and single tanh layer. the linear layer is whats optimised
net.addInputModule(BiasUnit(name='bias'))
net.addOutputModule(TanhLayer(1, name='out'))
net.addRecurrentConnection(FullConnection(net['out'], net['out'], name='c3'))
net.addInputModule(LinearLayer(1,name='in'))
net.addConnection(FullConnection(net['in'],net['out'],name='c1'))
net.addConnection((FullConnection(net['bias'],net['out'],name='c2')))
net.sortModules()
net._setParameters([-0.0, 1.8, 1.6])
print(net._params)
#print(net.activate(0.5))
#print(net.activate(0.6))
#net.activate(2)


env=AR1Environment(2000)
task=MaximizeReturnTask(env)#MaximizeReturnTask(env)

learner = RRL() # ENAC() #Q_LinFA(2,1)
agent = LearningAgent(net,learner)
exp = ContinuousExperiment(task,agent)

ts=env.ts.tolist()
exp.doInteractionsAndLearn(1999)

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import _setParameters [as 别名]
def main():
    inData=createDataset()
    env = MarketEnvironment(inData)
    task = MaximizeReturnTask(env)
    numIn=min(env.worldState.shape)

    net=RecurrentNetwork()
    net.addInputModule(BiasUnit(name='bias'))
    net.addOutputModule((SignLayer(1,name='out')))
    net.addRecurrentConnection(FullConnection(net['out'], net['out'], name='c3'))
    net.addInputModule(LinearLayer(numIn,name='in'))
    net.addConnection(FullConnection(net['in'],net['out'],name='c1'))
    net.addConnection((FullConnection(net['bias'],net['out'],name='c2')))
    net.sortModules()
    ###net._setParameters([1.89523389,  2.41243781, -0.37355216, 0.60550426, 1.29560957, -1.14727503, -1.80005888, 0.66351325, 1.91905451])
    ###net._setParameters([ 1.07300605, 2.37801446, -0.28118081, -0.78715898, 0.13367809, 0.31757825,-1.23956247, 1.90411791, 0.95458375])
    ##net._setParameters([1.35840492,1.87785682, -0.15779415, -0.79786631, 0.13380422, 0.0067797, -1.28202562, 2.38574234, 0.909462])
    ###net._setParameters([ 0.36062235, 1.70329005, 2.24180157, 0.34832656, 0.31775365, -0.60400026, -0.44850303, 1.50005529, -0.99986366])
    net._setParameters([ 1.15741417, 1.70427034, 1.05050831, -0.47303435, -0.87220272, -1.44743793,  0.93697461, 2.77489952, 0.27374758])
    ts=env.ts
    learner = RRL(numIn+2,ts) # ENAC() #Q_LinFA(2,1)
    agent = LearningAgent(net,learner)
    exp = ContinuousExperiment(task,agent)


    # in sample learning
    in_sample_len=500
    print("Before in sample {}".format(net._params))
    for i in range(100):
        exp.doInteractionsAndLearn(in_sample_len)
        learner.reset()
        agent.reset()
        env.reset()

    # ouy of sample, online learning
    print("Before oos {}".format(net._params))
    exp.doInteractionsAndLearn(len(ts)-1)
    print("After oos {}".format(net._params))

    #performance evaluation
    dfIndex=inData['RETURNS'].index
    rf=0#inData['Fed Fund Target']
    outDataOOS=pE.outData(ts,env.actionHistory,dfIndex,startIndex=in_sample_len)
    sharpe_oos=pE.annualisedSharpe(outDataOOS['trading rets'],rf)
    drawDown_oos=pE.maximumDrawdown(outDataOOS['trading rets'])
    numOutperformedMonths_oos=pE.percentOfOutperformedMonths(outDataOOS['trading rets'],outDataOOS['ts'])
    foo=outDataOOS['cum_log_rets'][-1]
    bar=math.exp(foo)
    traderReturn=math.exp(outDataOOS['cum_log_rets'][-1])-1
    benchmarkReturn=math.exp(outDataOOS['cum_log_ts'].values[-1])-1
    print( "oos sharpe: {}, \noos drawdown: {} \noos percent outperformed months {}\noos trader return {}".format(sharpe_oos, drawDown_oos, numOutperformedMonths_oos,traderReturn))

    paramHist=learner.paramHistory
    inData.rename(columns={'RETURNS': 'r(t-1)'},inplace=True)
    lbs=insert(inData.columns.values,0,'Bias')
    lbs=append(lbs,'F(t-1)')
    plt.figure(0)
    for i in range(len(net._params)):
        if i<7:
            plt.plot(paramHist[i],label=lbs[i])
        else:
            plt.plot(paramHist[i],'--',label=lbs[i])
    plt.legend(loc='upper center', bbox_to_anchor=(0.5, 1.1),ncol=3)
    plt.draw()



    fix, axes = plt.subplots(nrows=2,ncols=1)
    plotFrame=outDataOOS[['cum_log_ts','cum_log_rets']]
    plotFrame.columns=['Buy and Hold','Trading Agent']
    plotFrame.plot(ax=axes[0])
    outDataOOS['Action_Hist'].plot(ax=axes[1],color='r')


    plt.draw()
    plt.show()

# 需要导入模块: from pybrain.structure import RecurrentNetwork [as 别名]
# 或者: from pybrain.structure.RecurrentNetwork import _setParameters [as 别名]
from pybrain.rl.agents import LearningAgent
from pybrain.rl.experiments import ContinuousExperiment

from numpy import sign, round
from matplotlib import pyplot

net= RecurrentNetwork()
#Single linear layer with bias unit, and single tanh layer. the linear layer is whats optimised
net.addInputModule(BiasUnit(name='bias'))
net.addOutputModule(TanhLayer(1, name='out'))
net.addRecurrentConnection(FullConnection(net['out'], net['out'], name='c3'))
net.addInputModule(LinearLayer(1,name='in'))
net.addConnection(FullConnection(net['in'],net['out'],name='c1'))
net.addConnection((FullConnection(net['bias'],net['out'],name='c2')))
net.sortModules()
net._setParameters([0, 10, 1.259])
print(net._params)
env=MonthlySnPEnvironment()
task=MaximizeReturnTask(env)
learner = RRL() # ENAC() #Q_LinFA(2,1)
agent = LearningAgent(net,learner)
exp = ContinuousExperiment(task,agent)

ts=env.ts.tolist()
exp.doInteractionsAndLearn(795)
print(net._params)
actionHist=sign(env.actionHistory)/20
pyplot.plot(ts[0])
pyplot.plot(actionHist)
pyplot.show()