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ai.py
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import numpy as np
np.random.seed(7)
import tensorflow as tf
import datetime
import time
import threading
import math
import random
random.seed(7)
import os
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from keras.models import *
from keras.layers import *
from keras import backend as K
from enum import Enum
from time import sleep
THREADS = 8
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
np.set_printoptions(linewidth = 500)
art = """
.d8888b. 888 d88888888888
d88P Y88b 888 d88888 888
888 888 888 d88P888 888
888 888d888888 88888888b. 888888 .d88b. d88P 888 888
888 888P" 888 888888 "88b888 d88""88b d88P 888 888
888 888888 888 888888 888888 888 888 d88P 888 888
Y88b d88P888 Y88b 888888 d88PY88b. Y88..88P d8888888888 888
"Y8888P" 888 "Y8888888888P" "Y888 "Y88P" d88P 8888888888
888888 d88P
Y8b d88P888 d88P
"Y88P" 888 d88P by UmeW
****** Deep AC3 Trader ******
"""
# HyperParams
LOSS_V = .5 # v loss coefficient
LOSS_ENTROPY = 0.1 # entropy coefficient
LEARNING_RATE = 1e-4
EPS_START = 0.5
EPS_END = 0.1
EPS_SLOPE = 600
N_STEP_RETURN = 8
MIN_BATCH = 32
NUM_HISTORY = 300
NUM_STATE = 1 * NUM_HISTORY + 1 + 1 + 1 + 1# Scrapped data + (Shares bought?) + (Budget?)
NUM_DENSE = 120
NUM_DENSE2 = 30
GAMMA = 0.99
GAMMA_N = GAMMA ** N_STEP_RETURN
CAN_SHORT = False
NUM_ACTIONS = 3 # Buy = 0 , Sell = 1 , Hold = 2
# States Var
mdPrice = []
mdPriceMin = []
mdPriceMax = []
mdBSRatio = []
mdVolume = []
mdVar = [0] * THREADS
mdMean = [0] * THREADS
mdTimeMax = [0] * THREADS
class Action(Enum):
BUY = 0
SELL = 1
HOLD = 2
aHistory = [[] for i in range(THREADS)]
stopSignal = False
testFile = open("result2.test", "a")
def loadData():
j = 0
for j in range(0, 8):
with open('training2/training_'+ str(j) +'.data', 'r') as f:
buf = f.readlines()
mdPrice.append([])
mdPriceMin.append([])
mdPriceMax.append([])
mdBSRatio.append([])
mdVolume.append([])
esp = 0
esp2 = 0
for line in buf: # we should test if everything good at import
dat = line.split(' ')
#>>> t = "2017-12-08 23:22:00 16066.530120481928 16060 16072 38 225691"
#['2017-12-08', '23:22:00', '16066.530120481928', '16060', '16072', '38', '225691']
mdPrice[j].append(float(dat[2]))
esp += float(dat[2])
esp2 += float(dat[2]) ** 2
mdPriceMin[j].append(float(dat[3]))
mdPriceMax[j].append(float(dat[4]))
mdBSRatio[j].append(float(dat[5]))
mdVolume[j].append(float(dat[6]))
mdTimeMax[j] = int(len(buf))
esp = esp / mdTimeMax[j]
esp2 = esp2 / mdTimeMax[j]
mdVar[j] = math.sqrt(esp2 - (esp ** 2))
mdMean[j] = esp
#print(mdVar[j])
class Brain():
def __init__(self):
g = tf.Graph()
SESSION = tf.Session(graph=g)
self.session = SESSION
with g.as_default():
tf.set_random_seed(7)
K.set_session(self.session)
K.manual_variable_initialization(True)
self.model = self.BuildModel()
self.graph = self.BuildGraph()
self.session.run(tf.global_variables_initializer())
self.default_graph = tf.get_default_graph()
#self.default_graph.finalize()
self.buffer = [[], [], [], [], []]
self.lock = threading.Lock()
def BuildModel(self):
l_input = Input(batch_shape=(None, NUM_STATE))
#l_predense = Dense(NUM_DENSE, activation='relu', kernel_regularizer=regularizers.l2(0.01))(l_input)
#l_dense = Dense(NUM_DENSE, activation='relu', kernel_regularizer=regularizers.l2(0.01))(l_predense)
l_predense = Dense(NUM_DENSE, activation='tanh')(l_input)
l_dense = Dense(NUM_DENSE, activation='tanh')(l_predense)
out_actions = Dense(NUM_ACTIONS, activation='softmax')(l_dense)
out_value = Dense(1, activation='linear')(l_dense)
model = Model(inputs=[l_input], outputs=[out_actions, out_value])
model._make_predict_function()
self.intermediateModel = Model(inputs=[l_input], outputs=[l_dense])
self.intermediateModel._make_predict_function()
return model
def BuildGraph(self):
s_t = tf.placeholder(tf.float64, shape=(None, NUM_STATE))
r_t = tf.placeholder(tf.float64, shape=(None, 1)) # r + gamma vs'
a_t = tf.placeholder(tf.float64, shape=(None, NUM_ACTIONS))
p_t, v_t = self.model(s_t)
advantage = r_t - v_t
log_prob = tf.log(tf.reduce_sum(p_t * a_t, axis=1, keep_dims=True) + 1e-10)
loss_policy = - log_prob * tf.stop_gradient(advantage)
loss_value = LOSS_V * tf.square(advantage)
entropy = LOSS_ENTROPY * tf.reduce_sum(p_t * tf.log(p_t + 1e-10), axis=1, keep_dims=True)
loss_total = tf.reduce_mean(loss_policy + loss_value + entropy)
#loss_total = tf.reduce_mean(entropy)
self.loss = loss_total
optimizer = tf.train.RMSPropOptimizer(LEARNING_RATE, decay=.99)
minimize = optimizer.minimize(loss_total)
return s_t, a_t, r_t, minimize
def getPrediction(self, s):
with self.default_graph.as_default():
#print(self.intermediateModel.predict(s))
p, v = self.model.predict(s)
#print(p)
#s_t, a_t, r_t, minimize = self.graph
#k = self.session.run(self.entropy, feed_dict={s_t: s})
#print(k)
return p, v
def getValue(self, s):
with self.default_graph.as_default():
p, v = self.model.predict(s)
return v
def getPolicy(self, s):
with self.default_graph.as_default():
p, v = self.model.predict(s)
return p
def pushTraining(self, action, reward, oldStep, newStep, threadId):
with self.lock:
act = np.zeros(NUM_ACTIONS)
act[action] = 1
self.buffer[0].append(act)
self.buffer[1].append(reward)
self.buffer[2].append(oldStep)
if newStep is None:
self.buffer[3].append(np.zeros(NUM_STATE))
self.buffer[4].append(0)
else:
self.buffer[3].append(newStep)
self.buffer[4].append(1)
def optimize(self):
if len(self.buffer[0]) > MIN_BATCH :
batch = []
with self.lock:
batch = self.buffer
self.buffer = [[], [], [], [], []]
#print(self.threadC)
s_t, a_t, r_t, minimize = self.graph
a = np.vstack(batch[0])
r = np.vstack(batch[1])
s = np.vstack(batch[2])
newStates = np.vstack(batch[3])
newStatesMask = np.vstack(batch[4])
newStatesValue = self.getValue(newStates)
rew = r + newStatesValue * GAMMA_N * newStatesMask
#x = np.hstack([s,a,r,newStatesValue,newStatesMask,rew])
#print(x)
#print(len(s))
#if len(s) > 5*MIN_BATCH: print("Optimizer alert! Minimizing batch of %d" % len(s))
#print("*************************************")
#print(s)
self.session.run(minimize, feed_dict={s_t: s, a_t: a, r_t: rew})
#for i in range(0,100):
# self.session.run(minimize, feed_dict={s_t: s, a_t: a, r_t: rew})
# k = self.session.run(self.loss, feed_dict={s_t: s, a_t: a, r_t: rew})
# print(k)
class Optimizer(threading.Thread):
def __init__(self):
threading.Thread.__init__(self)
def run(self):
while not stopSignal:
sleep(0.001)
brain.optimize()
class Actor(threading.Thread):
def __init__(self, idt, isTest):
threading.Thread.__init__(self)
self.id = idt
self.isTest = isTest
self.steps = 0
self.simCount = 0
#print("Actor " + str(idt) +" created")
def run(self):
#print("Actor " + str(self.id) +" started")
if self.isTest :
self.startSimulation()
else:
while not stopSignal:
sleep(0)
self.startSimulation()
self.simCount += 1
#print(str(self.id) + " : " + str(self.steps))
def normalizeBin(self, value):
#if value > 0:
# return [1]
#else:
# return [-1]
if value > 255 :
return [0.9]*8
r = value
ret = [0.1] * 8
i = 0
while r > 0:
x = (r % 2)*(0.8) + 0.1
ret[i] = x
r = int(r/2)
i +=1
return ret
def startSimulation(self):
if self.isTest:
self.budget = 300
elif self.simCount < 100:
self.budget = random.randint(1000, 5000) *( self.id + 1)
elif self.simCount < 200:
self.budget = random.randint(9 *(100 - self.simCount) + 1000, 45 * (100 - self.simCount) + 5000) *( self.id + 1)
else:
self.budget = 300
self.budget = random.randint(50000, 100000) *( self.id + 1)
self.initbud = self.budget
self.shares = 0
self.mem = [] # (a, t, st, r)
self.r = 0
t = NUM_HISTORY - 1
self.timeMax = mdTimeMax[self.id % 8] #random.randint(NUM_HISTORY , mdTimeMax[self.id % 8])
if self.id == 12:
t = self.simCount % ( mdTimeMax[self.id % 8] - NUM_HISTORY - 50) + NUM_HISTORY - 1
self.timeMax = t + 50
totalSteps = self.timeMax - t
self.R = 0
#print("t " + str(t) +" timemax " + str(mdTimeMax))
actions = []
self.badActions = 0
kill = False
while t < self.timeMax - 1 and not kill:
#st = ([[self.budget] + [self.shares] + mdPrice[t + 1 - NUM_HISTORY: t + 1] + mdPriceMin[t + 1 - NUM_HISTORY: t + 1] +
#mdPriceMax[t + 1 - NUM_HISTORY: t + 1] + mdBSRatio[t + 1 - NUM_HISTORY: t + 1] + mdVolume[t + 1 - NUM_HISTORY: t + 1]])
if self.budget >= mdPrice[self.id % 8][t] :
canBuy = [1]
else :
canBuy = [-1]
priceA = mdPrice[self.id % 8 ][t + 1 - NUM_HISTORY: t + 1]
priceA = [(x - mdMean[self.id % 8 ]) / mdVar[self.id % 8 ] for x in priceA]
st = [[(self.shares * mdPrice[self.id % 8 ][t])/mdVar[self.id % 8] ] + canBuy + [(self.budget )/ mdVar[self.id % 8], mdMean[self.id % 8 ]/mdVar[self.id % 8] ] + priceA]
state = np.array(st)
#if self.isTest : print(state)
a, v = self.getActionValue(state, t)
if self.id == 12:
#if self.isTest: print("self.budget: " + str(self.budget) + " - p: " + str(mdPrice[self.id % 8 ][t]))
if self.budget > mdPrice[self.id % 8 ][t] and mdPrice[self.id % 8 ][t] < mdMean[self.id % 8 ]:
a = 0
elif self.shares > 0 and mdPrice[self.id % 8 ][t] > mdMean[self.id % 8 ]:
a = 1
else :
a = 2
if self.isTest and False:
print("time: " + str(t) + " | price: " + str(mdPrice[self.id][t]))
print("budget: " + str(self.budget) + "| shares:" + str(self.shares))
print("action: " + Action(a).name)
testFile.write(str(mdPrice[self.id][t])+ " "+ str(a)+ "\n")
r = self.act(state, t, a)
self.R += r
# print("reward: " + str(r))
self.pushTraining(a,t,st,r)
#if(t%3 == 0):
while (len(brain.buffer[0]) > MIN_BATCH and not stopSignal) :
sleep(0)
t += 1
self.steps += 1
actions.append(a)
if(self.budget <= 0 and self.shares == 0):
kill = True
ratioComplete = (t*100)/(self.timeMax - 1)
aHistory[self.id].append(actions)
badActionPct = self.badActions
print("Actor " + str(self.id) +" FINISH -- REWARD: " + str(self.R)+ " -- Bad: " + str(badActionPct) + " -- SimC: " + str(self.simCount)+ " -- Comp:%" + str(ratioComplete) )
def pushTraining(self,a,t,st,r):
# a debug
self.mem.append((a,t,st,r))
self.r = (self.r + GAMMA_N * r) / GAMMA
#print("selfR: " + str(self.r))
if t == self.timeMax - 2:
while len(self.mem) > 0:
brain.pushTraining(self.mem[0][0], self.r, self.mem[0][2], None, self.id)
self.r = (self.r - self.mem[0][3])/GAMMA
self.mem.pop(0)
elif len(self.mem) == N_STEP_RETURN :
brain.pushTraining(self.mem[0][0], self.r, self.mem[0][2], st, self.id)
self.r = self.r - self.mem[0][3]
self.mem.pop(0)
#print("NselfR: " + str(self.r))
#print("\n\n")
def getActionValue(self, state, time):
eps = self.getEps(self.steps)
a, v = brain.getPrediction(state)
#print("TIME: " + str(time) + " - EPS: " + str(eps) + " - VAL: " + str(v))
if random.random() < eps:
return random.randint(0, NUM_ACTIONS - 1), v
else:
return np.argmax(a), v
def act(self, state, time, action):
action = Action(action)
oldPortfolio = self.budget + self.shares * mdPrice[self.id % 8][time]
oldBud = self.budget
#print("oldportfolioValue: " + str(oldPortfolio))
if action == Action.BUY and self.budget >= mdPrice[self.id % 8][time]:
self.shares += 1
self.budget -= mdPrice[self.id % 8][time]
elif action == Action.BUY and self.budget < mdPrice[self.id % 8][time]:
self.budget -= mdPrice[self.id % 8][time] * 3
self.badActions += 1
elif action == Action.SELL and (self.shares > 0 or CAN_SHORT):
self.budget += mdPrice[self.id % 8][time] * self.shares
self.shares = 0
elif action == Action.SELL and (self.shares <= 0 and not CAN_SHORT):
self.budget -= mdPrice[self.id % 8 ][time] * 3
self.badActions += 1
#elif action == Action.HOLD and self.shares == 0:
# self.budget -= mdPrice[self.id % 8][time] * 10
# self.badActions += 1
newPortfolio = self.budget + self.shares * mdPrice[self.id % 8][time + 1]
#print("newportfolioValue: " + str(newPortfolio))
#return newPortfolio - oldPortfolio
#return (self.budget - oldBud)/(mdVar[self.id % 8])
return newPortfolio - oldPortfolio
def getEps(self, time):
if self.isTest :
return 0.0
else:
EPS_STEP = EPS_SLOPE *(self.timeMax - NUM_HISTORY)
#print("EPS STEP : " + str(EPS_STEP))
if time >= EPS_STEP:
return EPS_END
else:
slope = (EPS_END - EPS_START) / (EPS_STEP)
eps = slope * time + EPS_START
return eps
print(art)
loadData()
def start():
global brain
global stopSignal
stopSignal = False
brain = Brain()
# Start Actors
actors = [Actor(i,False) for i in range(THREADS)]
for t in actors: t.start()
# Start Critics
opt = Optimizer()
opt.start()
sleep(3600)
stopSignal = True
sleep(5)
#mdTimeMax = 2 * mdTimeMax
#Test Strategy
#print("**TRAINING COMPLETE*********")
testers = [Actor(i,True) for i in range(8)]
results = [0] * 8
for t in testers: t.start()
for t in testers:
t.join()
results[t.id] = str(int(t.R))
return " ".join(results)
if False :
HP_LOSS_V = [0.5]
HP_LOSS_ENTROPY = [0.01,0.001,0.1,0.5,1,10]
HP_LEARNING_RATE = [1e-4,5e-4,1e-3,1e-2]
HP_EPS_START = [0.5,0.6,0.7,0.4,0.3]
HP_EPS_END = [0.15,0.05,0.25,0.1]
HP_EPS_SLOPE = [10, 15, 5]
HP_N_STEP_RETURN = [8]
HP_MIN_BATCH = [32,64,512]
HP_NUM_HISTORY = [1,2,3]
HP_GAMMA = [0.99]
for loss_v in HP_LOSS_V:
LOSS_V = loss_v
for eps_start in HP_EPS_START:
EPS_START = eps_start
for eps_end in HP_EPS_END:
EPS_END = eps_end
for eps_slope in HP_EPS_SLOPE:
EPS_SLOPE = eps_slope
for n_step_return in HP_N_STEP_RETURN:
N_STEP_RETURN = n_step_return
for min_batch in HP_MIN_BATCH:
MIN_BATCH = min_batch
for num_history in HP_NUM_HISTORY:
NUM_STATE = 1 * NUM_HISTORY + 1 + 1 + 1
HP_NUM_DENSE = [30, 10, 100]
for num_dense in HP_NUM_DENSE:
NUM_DENSE = num_dense
for loss_entropy in HP_LOSS_ENTROPY:
LOSS_ENTROPY = loss_entropy
for learning_rate in HP_LEARNING_RATE:
LEARNING_RATE = learning_rate
for gamma in HP_GAMMA:
GAMMA = gamma
GAMMA_N = GAMMA ** N_STEP_RETURN
result = start()
strin = ("loss_v: " + str(loss_v) +
" | loss_entropy: " + str(loss_entropy) +
" | learning_rate: " + str(learning_rate) +
" | eps_start: " + str(eps_start) +
" | eps_end: " + str(eps_end) +
" | eps_slope: " + str(eps_slope) +
" | n_step_return: " + str(n_step_return) +
" | min_batch: " + str(min_batch) +
" | num_history: " + str(num_history) +
" | num_dense: " + str(num_dense) +
" | result: " + str(result) + "\n"
)
print(strin)
else :
print(start())
testFile.close
plt.ion()
fig = plt.figure()
lines = []
prices = []
for i in range(THREADS):
x = np.arange(NUM_HISTORY - 1, mdTimeMax[i%8], 1)
priceA = mdPrice[i % 8 ][NUM_HISTORY - 1: mdTimeMax[i % 8] ]
priceA = np.array([(x - mdMean[i % 8 ])/ mdVar[i % 8 ] for x in priceA])
prices.append(priceA)
ax = fig.add_subplot(int(THREADS/2), 2, i + 1)
acts = aHistory[i][0]
fill = [-1]*(mdTimeMax[i] - NUM_HISTORY- len(acts) + 1)
actions = np.array( acts + fill ) + 1
beee,line = ax.plot(x, priceA, 'b-', x, actions, 'ro')
lines.append(line)
plt.title(str(i))
fig.canvas.draw()
k = 0
while k < 1000:
for i in range(THREADS):
if k < len(aHistory[i]):
acts = aHistory[i][k]
fill = [-1]*(mdTimeMax[i] - NUM_HISTORY - len(acts) + 1)
actions = np.array( acts + fill ) + 1
else:
acts = aHistory[i][-1]
fill = [-1]*(mdTimeMax[i] - NUM_HISTORY - len(acts) + 1)
actions = np.array( acts + fill ) + 1
lines[i].set_ydata(actions)
k += 1
t = time.time()
while time.time() < t + 1 :
fig.canvas.flush_events()
sleep(0.001)
#for phase in np.linspace(0, 10*np.pi, 500):
# line1.set_ydata(np.sin(x + phase))
# t = 0
# while t < 1000:
# fig.canvas.flush_events()
# sleep(0.001)
# t+=1