def __init__(self, sess, name, N_S, N_A, globalAC):
self.SESS = sess
self.N_S = N_S
self.N_A = N_A
self.env = StockEnv()
self.name = name
self.AC = A3CNet(self.SESS, self.name, self.N_S, self.N_A, globalAC)
def main(train=False):
data = np.loadtxt('./data.csv', delimiter=',', skiprows=1)
data = data[230:-1] #delete the first day data
angent = DQN_Trade()
for i in range(0, 10):
iters = len(data) / 240
for iter_step in range(0, iters):
#print iter_step
iter_data = data[iter_step * 240:iter_step * 240 + 240]
env = StockEnv(iter_data)
s = env.reset()
while True:
action = angent.egreedy_action(s)
s_, reward, done = env.gostep(action)
print(action)
angent.precive(s, action, reward, s_, done)
s = s_
if done:
break
angent.save_model(step=i)
def main(train = False):
data = np.loadtxt('./data.csv',delimiter = ',',skiprows=1)
data = data[230:-1] #delete the first day data
angent = DQN_Trade()
for i in range(0,10):
iters =len(data)/240
for iter_step in range(0,iters):
#print iter_step
iter_data =data[iter_step*240:iter_step*240+240]
env =StockEnv(iter_data)
s = env.reset()
while True:
action = angent.egreedy_action(s)
s_,reward,done =env.gostep(action)
print action
angent.precive(s,action,reward,s_,done)
s= s_
if done:
break
angent.save_model(step=i)
def run_stock():
n_epoch = 100
mean = []
for x in range(n_epoch):
env = Env(STOCK.Baidu)
env.set_count(999) # from 1000 to 2000
for i in range(1000):
action = random.random() * 2 - 1 # [-1, 1]
env.step(action)
mean.append(env.asset - 10000)
# while True:
# action = random.random() * 2 - 1 # [-1, 1]
# observation_, reward, done = env.step(action)
#
# if done:
# mean.append(env.asset - 10000)
# break
print(np.mean(mean), np.var(mean))
# end of game
print('game over')
def test(test_data, model, tickers, randomize, num_rand_stocks=0):
"""
the test function: test agent on test_data
if randomize == True, we know we have randomized stocks in training,
so we test on num_rand_stocks randomly selected stocks from test_data
if otherwise, we used the entire test_data set
:param test_data: the testing set
:param model: the trained model
:param tickers: stocks tickers corresponding to test_data, including "CASH"
:param randomize: boolean indicating whether we have randomized stocks
:param num_rand_stocks: number of stocks randomized in training
"""
if randomize:
# the last element of tickers is "CASH", we don't include "CASH" in randomization
rand_stock_indices = np.random.choice(len(tickers) - 1,
num_rand_stocks,
replace=False)
# get randomly selected stock names
episode_tickers = [tickers[index] for index in rand_stock_indices]
episode_tickers.append("CASH")
rand_stock_indices = tf.reshape(rand_stock_indices,
(len(rand_stock_indices), 1))
# saving the randomization to a new variable so we don't mess w/ test_data
episode_input = tf.gather_nd(test_data, rand_stock_indices)
else:
episode_input = test_data
episode_tickers = tickers
env = StockEnv(episode_input, episode_tickers, is_testing=True)
states, actions, rewards = env.generate_episode(model)
min_testing_episode_len = 20
while len(rewards) < min_testing_episode_len:
print("test episode not long enough")
states, actions, rewards = env.generate_episode(model)
print(f'final portfolio total value: {rewards[-1]}')
class Worker(object):
def __init__(self, name, globalAC):
self.env = StockEnv()
self.name = name
self.AC = ACNet(name, globalAC)
def work(self):
global GLOBAL_RUNNING_R, GLOBAL_EP
total_step = 1
buffer_s, buffer_a, buffer_r = [], [], []
while not COORD.should_stop() and GLOBAL_EP < MAX_GLOBAL_EP:
s = self.env.reset()
ep_r = 0
for ep_t in range(MAX_EP_STEP):
if self.name == 'W_0':
self.env.render()
a = self.AC.choose_action(s)
s_, r, done = self.env.step(a)
if ep_t == MAX_EP_STEP - 1: done = True
ep_r += r
buffer_s.append(s)
buffer_a.append(a)
buffer_r.append(r)
if total_step % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net
if done:
v_s_ = 0 # terminal
else:
v_s_ = SESS.run(self.AC.v,
{self.AC.s: s_[np.newaxis, :]})[0, 0]
buffer_v_target = []
for r in buffer_r[::-1]: # reverse buffer r
v_s_ = r + GAMMA * v_s_
buffer_v_target.append(v_s_)
buffer_v_target.reverse()
buffer_s, buffer_a, buffer_v_target = np.vstack(
buffer_s), np.vstack(buffer_a), np.vstack(
buffer_v_target)
feed_dict = {
self.AC.s: buffer_s,
self.AC.a_his: buffer_a,
self.AC.v_target: buffer_v_target,
}
test = self.AC.update_global(feed_dict)
buffer_s, buffer_a, buffer_r = [], [], []
self.AC.pull_global()
s = s_
total_step += 1
if done:
if len(GLOBAL_RUNNING_R
) == 0: # record running episode reward
GLOBAL_RUNNING_R.append(ep_r)
else:
GLOBAL_RUNNING_R.append(0.9 * GLOBAL_RUNNING_R[-1] +
0.1 * ep_r)
print(
self.name,
"Ep:",
GLOBAL_EP,
"| Ep_r: %i" % GLOBAL_RUNNING_R[-1],
'| Var:',
test,
)
GLOBAL_EP += 1
break
MAX_GLOBAL_EP = 2000
MAX_EP_STEP = 300
UPDATE_GLOBAL_ITER = 5
N_WORKERS = multiprocessing.cpu_count()
LR_A = 1e-4 # learning rate for actor
LR_C = 2e-4 # learning rate for critic
GAMMA = 0.9 # reward discount
# MODE = ['easy', 'hard']
# n_model = 1
GLOBAL_NET_SCOPE = 'Global_Net'
ENTROPY_BETA = 0.01
GLOBAL_RUNNING_R = []
GLOBAL_EP = 0
env = StockEnv()
N_S = env.reset().shape[0]
N_A = 1
A_BOUND = env.action_bound[1]
del env
class ACNet(object):
def __init__(self, scope, globalAC=None):
if scope == GLOBAL_NET_SCOPE: # get global network
with tf.variable_scope(scope):
self.s = tf.placeholder(tf.float32, [None, N_S], 'S')
self._build_net()
self.a_params = tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES, scope=scope + '/actor')
self.portfolio[i] -= 1
elif a == 1 and self.balance - p - TRANS_FEE >= 0: # buy
self.balance -= (p + TRANS_FEE)
self.portfolio[i] += 1
# else is hold so you do nothing
def update_value(self, prices):
total = 0
for i, p in enumerate(prices):
total += self.portfolio[i] * p
self.value.append(total + self.balance)
### Begin Simulation ###
env = StockEnv(NUM_SECTORS)
agent = q_agent(len(env.sectors))
fig = plt.figure()
ax = fig.add_subplot(111)
fig.suptitle('Hard Coded Agent')
for episode in range(NUM_EPISODES):
# reset the environment and initialize the portfolio value
agent.reset()
p0 = env.reset()
agent.update_value(p0)
for t in range(MAX_T):
# select the next action
action = agent.select_action(p0)
# execute the next action and get next state and reward
def train(train_data,
model,
tickers,
randomize,
num_rand_stocks=0,
episode_max_days=200):
"""
the train function, train the model for an entire epoch
:param train_data: the preprocessed training data, of shape [num_stocks, num_days, datum_size]
:param model: the model to be trained
:param tickers: stock tickers corresponding to train_data, including "CASH"
:param randomize: boolean indicating whether we have randomized stocks
:param num_rand_stocks: number of stocks randomized in training
:param episode_max_days: the maximum number of days of trading actions in an episode
:return losses and rewards
"""
num_days = train_data.shape[1]
loss_list = []
offset = model.past_num - 1 # extra days of price history needed at beginning
start = 0 # start of price history slice (inclusive)
end = start + episode_max_days + offset # end of price history slice (exclusive)
num_episodes = (num_days - offset) // episode_max_days
# a list of total cash value
rewards_list = []
for episode in range(num_episodes):
print(f"Training episode {episode+1} of {num_episodes}")
if randomize:
# the last element of tickers is "CASH", we don't include "CASH" in randomization
rand_stock_indices = np.random.choice(len(tickers) - 1,
num_rand_stocks,
replace=False)
# get randomly selected stock names
episode_tickers = [tickers[index] for index in rand_stock_indices]
episode_tickers.append("CASH")
rand_stock_indices = tf.reshape(rand_stock_indices,
(len(rand_stock_indices), 1))
episode_input = tf.gather_nd(train_data, rand_stock_indices)
else:
episode_input = train_data
episode_tickers = tickers
# Slice of pricing history to generate this episode on
episode_input = episode_input[:, start:end, :]
start += episode_max_days
end += episode_max_days
# (REMOVED) randomize starting date in each episode
# rand_start = randint(0, int(episode_max_days / 5))
# episode_input = episode_input[:, rand_start:episode_max_days,:]
# Hyperparameters to be adjusted below:
env = StockEnv(episode_input,
episode_tickers,
interest_annual=0.1,
borrow_interest_annual=0.2,
transaction_penalty=0.0001)
with tf.GradientTape() as tape:
states, actions, rewards = env.generate_episode(model)
rewards_list.extend(rewards)
discounted_rewards = discount(rewards)
model.remember(states, actions, discounted_rewards)
repl_states, repl_actions, repl_discounted_rewards = model.experience_replay(
)
model_loss = model.loss(repl_states, repl_actions,
repl_discounted_rewards)
gradients = tape.gradient(model_loss, model.trainable_variables)
model.optimizer.apply_gradients(
zip(gradients, model.trainable_variables))
loss_list.append(model_loss.numpy()) # reward at end of batch
return list(loss_list), rewards_list
class Worker(object):
GAMMA = 0.9
GLOBAL_RUNNING_R = []
GLOBAL_EP = 0
def __init__(self, sess, name, N_S, N_A, globalAC):
self.SESS = sess
self.N_S = N_S
self.N_A = N_A
self.env = StockEnv()
self.name = name
self.AC = A3CNet(self.SESS, self.name, self.N_S, self.N_A, globalAC)
# self.saver = tf.train.Saver()
def _record_global_reward_and_print(self, global_runing_rs, ep_r,
global_ep, total_step):
global_runing_rs.append(ep_r)
try:
print(self.name, "Ep:", global_ep,
"| Ep_r: %i" % global_runing_rs[-1], "| total step:",
total_step)
except Exception as e:
print(e)
def train(self):
buffer_s, buffer_a, buffer_r = [], [], []
s = self.env.reset()
ep_r = 0
total_step = 1
def reset():
nonlocal ep_r, total_step
self.env.reset()
ep_r = 0
total_step = 1
while not COORD.should_stop() and self.GLOBAL_EP < MAX_GLOBAL_EP:
# s = self.env.reset()
# ep_r = 0
# total_step = 1
reset()
while total_step < MAX_TOTAL_STEP:
try:
s = self.env.get_state()
a, p = self.AC.choose_action(s)
s_, r, done = self.env.step(a)
if done:
r = -2
ep_r += r
buffer_s.append(s)
buffer_a.append(a)
buffer_r.append(r)
if total_step % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net
self.AC.update(done, s_, buffer_r, buffer_s, buffer_a)
buffer_s, buffer_a, buffer_r = [], [], []
if done:
self._record_global_reward_and_print(
self.GLOBAL_RUNNING_R, ep_r, self.GLOBAL_EP,
total_step)
self.GLOBAL_EP += 1
reset()
# s = s_
total_step += 1
if self.name == 'W_0':
self.env.render()
time.sleep(0.05)
logger.debug([
"s ", s, " v ",
self.AC.get_v(s), " a ", a, " p ", p, " ep_r ",
ep_r, " total ", self.env.total, " acct ",
self.env.acct
])
except Exception as e:
print(e)
try:
print(self.name, " not done,may be donkey!", " total_step:",
total_step)
except Exception as e:
print(e)
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from stock_env import StockEnv
from actor_critic_eligibility_trace import ActorCriticEligibilityTrace
test = True
env = StockEnv(test=test, random_flips=False)
env_passive = StockEnv(test=test)
ac = ActorCriticEligibilityTrace(env)
if not test:
ac.train()
else:
df = pd.read_csv("csvs/norm_all_stocks_5yr.csv")
stock_df = df[df.Name == env.test_stock_name]
policy_mlp = ac.policy_mlp
policy_mlp.load_state_dict(torch.load("policy_mlp.pth"))
value_mlp = ac.value_mlp
value_mlp.load_state_dict(torch.load("value_mlp.pth"))
obss = []
actions = []
rewards = []
obs = env.reset()
while True:
obss.append(obs)
def __init__(self, name, globalAC):
self.env = StockEnv()
self.name = name
self.AC = ACNet(name,self.env.get_state().shape[0], 4, globalAC)
class Worker(object):
def __init__(self, name, globalAC):
self.env = StockEnv()
self.name = name
self.AC = ACNet(name,self.env.get_state().shape[0], 4, globalAC)
def _update_global_reward(self, ep_r):
global GLOBAL_RUNNING_R, GLOBAL_EP
if len(GLOBAL_RUNNING_R) == 0: # record running episode reward
GLOBAL_RUNNING_R.append(ep_r)
else:
GLOBAL_RUNNING_R.append(0.99 * GLOBAL_RUNNING_R[-1] + 0.01 * ep_r)
logger.debug(
[self.name,
"Ep:",
GLOBAL_EP,
"| Ep_r: %i" % GLOBAL_RUNNING_R[-1]]
)
GLOBAL_EP += 1
def _update_globa_acnet(self, done, s_, buffer_s, buffer_a, buffer_r):
if done:
v_s_ = 0 # terminal
else:
v_s_ = SESS.run(self.AC.v, {self.AC.s: s_[np.newaxis, :]})[0, 0]
buffer_v_target = []
for r in buffer_r[::-1]: # reverse buffer r
v_s_ = r + GAMMA * v_s_
buffer_v_target.append(v_s_)
buffer_v_target.reverse()
buffer_s, buffer_a, buffer_v_target = np.vstack(
buffer_s), np.array(buffer_a), np.vstack(buffer_v_target)
feed_dict = {
self.AC.s: buffer_s,
self.AC.a_his: buffer_a,
self.AC.v_target: buffer_v_target,
}
self.AC.update_global(feed_dict)
def work(self):
total_step = 1
buffer_s, buffer_a, buffer_r = [], [], []
self.env.reset()
if self.name == 'W_0':
self.env.render()
while not COORD.should_stop():
ep_r = 0
while True:
s = self.env._get_state()
a, p = self.AC.choose_action(s)
s_, r, done = self.env.step(a)
if done:
r = -0.5
ep_r += r
buffer_s.append(s)
buffer_a.append(a)
buffer_r.append(r)
if total_step % UPDATE_GLOBAL_ITER == 0 or done:
self._update_globa_acnet(done, s_, buffer_s, buffer_a,
buffer_r)
buffer_s, buffer_a, buffer_r = [], [], []
self.AC.pull_global()
# s = s_
total_step += 1
if done:
self._update_global_reward(ep_r)
break
if self.name == 'W_0':
logger.debug(["s", s, " a:", a, " p:", p, " r:", r, " total_step:", total_step, 'total', self.env.total])
time.sleep(0.5)
def train(self):
global GLOBAL_RUNNING_R, GLOBAL_EP
total_step = 1
buffer_s, buffer_a, buffer_r = [], [], []
while not COORD.should_stop() and GLOBAL_EP < MAX_GLOBAL_EP:
s = self.env.reset()
ep_r = 0
while True:
# if self.name == 'W_0':
# self.env.render()
a, p = self.AC.choose_action(s)
s_, r, done = self.env.step(a)
if done: r = -0.5
ep_r += r
buffer_s.append(s)
buffer_a.append(a)
buffer_r.append(r)
if total_step % UPDATE_GLOBAL_ITER == 0 or done: # update global and assign to local net
self._update_globa_acnet(done, s_, buffer_s, buffer_a,
buffer_r)
buffer_s, buffer_a, buffer_r = [], [], []
self.AC.pull_global()
if done:
self._update_global_reward(ep_r)
logger.debug(["s", s, " a:", a, " p:", p, " r:", r, " total_step:", total_step, 'total', self.env.total])
break
s = s_
total_step += 1
LOG_DIR = './log'
N_WORKERS = multiprocessing.cpu_count()
MAX_GLOBAL_EP = 50
GLOBAL_NET_SCOPE = 'Global_Net'
UPDATE_GLOBAL_ITER = 50
# GAMMA = 0.9
GAMMA = 0.8
# ENTROPY_BETA = 0.001
ENTROPY_BETA = 0.1
LR_A = 0.001 # learning rate for actor
# LR_C = 0.001 # learning rate for critic
LR_C = 0.01 # learning rate for critic
GLOBAL_RUNNING_R = []
GLOBAL_EP = 0
env = StockEnv()
N_S = env.get_state().shape[0]
N_A = 4
logger = Logger('A3C')
class ACNet(object):
def __init__(self, scope, N_S, A_S, globalAC=None):
if scope == GLOBAL_NET_SCOPE: # get global network
with tf.variable_scope(scope):
self.s = tf.placeholder(tf.float32, [None, N_S], 'S')
self.a_params, self.c_params = self._build_net(scope)[-2:]
else: # local net, calculate losses
with tf.variable_scope(scope):
self.s = tf.placeholder(tf.float32, [None, N_S], 'S')
from stock_env import StockEnv
from stock_env import STOCK
from stock_env_discrete import StockEnv as StockEnvD
from stock_env_discrete import STOCK as STOCKD
from sklearn.externals import joblib
import warnings
if __name__ == '__main__':
warnings.filterwarnings("ignore") # ignore warnings
stockHMM = StockHMM(STOCK.Baidu)
# load model
stockHMM.model = joblib.load("BaiDuHMM.pkl")
print('Continuous: ')
env = StockEnv(STOCK.Baidu)
# [1000, 2000)
env.set_count(999)
for x in range(1000):
p_states = stockHMM.predict(x + 1000)
# order: 2 3 4 1 0
my_action = p_states[
2] + p_states[3] * 0.5 - p_states[1] * 0.5 - p_states[0]
env.step(my_action)
print(env.asset - 10000)
print('Discrete: ')
env = StockEnvD(STOCKD.Baidu)
# [1000, 2000)
env.set_count(999)
def __init__(self, name, globalAC):
self.env = StockEnv()
self.name = name
self.AC = ACNet(name, globalAC)
from rl.agents.dqn import DQNAgent from rl.policy import LinearAnnealedPolicy, BoltzmannQPolicy, EpsGreedyQPolicy from rl.memory import SequentialMemory from rl.core import Processor from rl.callbacks import FileLogger, ModelIntervalCheckpoint from stock_env import StockEnv import models INPUT_SHAPE = (30, 180) WINDOW_LENGTH = 4 model = models.build_combined_model() # Get the environment and extract the number of actions. env = StockEnv() nb_actions = 3 # Next, we build our model. We use the same model that was described by Mnih et al. (2015). input_shape = (WINDOW_LENGTH, ) + INPUT_SHAPE # Finally, we configure and compile our agent. You can use every built-in Keras optimizer and # even the metrics! memory = SequentialMemory(limit=10000000, window_length=WINDOW_LENGTH) # Select a policy. We use eps-greedy action selection, which means that a random action is selected # with probability eps. We anneal eps from 1.0 to 0.1 over the course of 1M steps. This is done so that # the agent initially explores the environment (high eps) and then gradually sticks to what it knows # (low eps). We also set a dedicated eps value that is used during testing. Note that we set it to 0.05 # so that the agent still performs some random actions. This ensures that the agent cannot get stuck. policy = LinearAnnealedPolicy(EpsGreedyQPolicy(),
loss = torch.mean((Qpred - Qs) ** 2)
# print(f"loss:{loss.data[0]}")
loss.backward()
optim.step()
'''
035720 카카오
005930 삼성전자
000660 SK하이닉스
000120 CJ대한통운
285130 SK케미칼
008970 동양철관
'''
# env = StockEnv("000660")
env = StockEnv("000660")
Replay = namedtuple("Replay",
["state", "action", "new_state", "reward", "done"])
predDQN = DQN(env.num_state(), env.num_action(), 40)
targetDQN = DQN(env.num_state(), env.num_action(), 40)
# optim = torch.optim.Adam(predDQN.parameters(), lr=0.1)
optim = torch.optim.SGD(predDQN.parameters(), lr=0.1)
replay_buffer = deque()
BUFFER_SIZE = 5000
num_episods = 30000
gamma = 0.9
reward_history = []
duration_history = []
from stock_env import StockEnv
env = StockEnv()
if __name__ == '__main__':
env.render()
# print(env.step(1))
s,r,done = env.step(1)
print(s)
print(s.shape)
print(r)
print("=====================")
s,r,done = env.step(0)
print(s)
print(r)
print("====================")
s,r,done = env.step(2)
print(s)
print(r)
self.env.acct
])
except Exception as e:
print(e)
try:
print(self.name, " not done,may be donkey!", " total_step:",
total_step)
except Exception as e:
print(e)
if __name__ == "__main__":
GLOBAL_NET_SCOPE = 'Global_Net'
N_A = 4
N_S = StockEnv().get_state().shape[0]
SESS = tf.Session()
with tf.device("/cpu:0"):
GLOBAL_AC = A3CNet(SESS, GLOBAL_NET_SCOPE, N_S,
N_A) # we only need its params
workers = []
# Create worker
for i in range(N_WORKERS):
i_name = 'W_%i' % i # worker name
workers.append(Worker(SESS, i_name, N_S, N_A, GLOBAL_AC))
COORD = tf.train.Coordinator()
SESS.run(tf.global_variables_initializer())