def main(train_stock, val_stock, window_size, batch_size, ep_count,
strategy="t-dqn", model_name="model_debug", pretrained=False,
debug=False):
""" Trains the stock trading bot using Deep Q-Learning.
Please see https://arxiv.org/abs/1312.5602 for more details.
Args: [python train.py --help]
"""
import tensorflow as tf
print("#TF Version: ",tf.__version__)
from tensorflow.python.client import device_lib
print("Using device: ")
print(device_lib.list_local_devices())
agent = Agent(window_size, strategy=strategy, pretrained=pretrained, model_name=model_name)
train_data = get_stock_data(train_stock)
val_data = get_stock_data(val_stock)
initial_offset = val_data["Close"][1] - val_data["Close"][0]
for episode in range(1, ep_count + 1):
train_result = train_model(agent, episode, train_data, ep_count=ep_count,
batch_size=batch_size, window_size=window_size)
val_result, _ , actionCollection = evaluate_model(agent, val_data, window_size, debug)
show_train_result(train_result, val_result, initial_offset)
def main(train_stock,
val_stock,
window_size,
batch_size,
ep_count,
strategy="t-dqn",
model_name="model_debug",
pretrained=False,
debug=False):
""" Trains the stock trading bot using Deep Q-Learning.
Please see https://arxiv.org/abs/1312.5602 for more details.
Args: [python train.py --help]
"""
agent = Agent(window_size,
strategy=strategy,
pretrained=pretrained,
model_name=model_name)
train_data = get_stock_data(train_stock)
val_data = get_stock_data(val_stock)
initial_offset = val_data[1] - val_data[0]
for episode in range(1, ep_count + 1):
train_result = train_model(agent,
episode,
train_data,
ep_count=ep_count,
batch_size=batch_size,
window_size=window_size)
val_result, _ = evaluate_model(agent, val_data, window_size, debug)
show_train_result(train_result, val_result, initial_offset)
def train(train_stock,
val_stock,
window_size,
batch_size,
ep_count,
strategy="t-dqn",
model_name="model_double-dqn_GOOG_50",
pretrained=True,
debug=False):
"""
Trains the stock trading bot using Deep Q-Learning.
Please see https://arxiv.org/abs/1312.5602 for more details.
Args: [python train.py --help]
"""
print("Started the model training for the {}".format(symbol))
agent = Agent(window_size,
strategy=strategy,
pretrained=pretrained,
model_name=model_name)
train_data = train_stock
val_data = val_stock
initial_offset = np.array(val_data)[1] - np.array(val_data)[0]
for episode in range(1, ep_count + 1):
train_result = train_model(agent,
episode,
train_data,
ep_count=ep_count,
batch_size=batch_size,
window_size=window_size)
val_result, _ = evaluate_model(agent, val_data, window_size, debug)
show_train_result(train_result, val_result, initial_offset)
print("Training the model completed!!")
def main(train_stock,
val_stock,
window_size,
batch_size,
ep_count,
strategy="t-dqn",
model_name="model_debug",
pretrained=False,
debug=False):
agent = Agent(window_size,
strategy=strategy,
pretrained=pretrained,
model_name=model_name)
train_data = get_stock_data(train_stock)
val_data = get_stock_data(val_stock)
initial_offset = val_data[1] - val_data[0]
for episode in range(1, ep_count + 1):
train_result = train_model(agent,
episode,
train_data,
ep_count=ep_count,
batch_size=batch_size,
window_size=window_size)
val_result, _ = evaluate_model(agent, val_data, window_size, debug)
show_train_result(train_result, val_result, initial_offset)
def main(train_stock,
val_stock,
window_size,
batch_size,
ep_count,
strategy="t-dqn",
model_name="model_debug",
pretrained=False,
debug=False):
"""Trains the stock trading bot using Deep Q-Learning.
Please see https://arxiv.org/abs/1312.5602 for more details.
Args: [python train.py --help]
"""
train_data = filter_data_by_feature_columns(get_stock_data(train_stock))
val_data = filter_data_by_feature_columns(get_stock_data(val_stock))
agent = Agent(window_size * train_data.shape[1],
strategy=strategy,
pretrained=pretrained,
model_name=model_name)
for episode in range(1, ep_count + 1):
train_result = train_model(agent,
episode,
train_data,
ep_count=ep_count,
batch_size=batch_size,
window_size=window_size)
val_result, _ = evaluate_model(agent, val_data, window_size, debug)
show_train_result(train_result, val_result)
def main(train_stock,
window_size,
batch_size,
ep_count,
strategy="dqn",
model_name="dqn",
pretrained=False,
debug=False):
""" Trains the stock trading bot using Deep Q-Learning.
Please see https://arxiv.org/abs/1312.5602 for more details.
Args: [python train.py --help]
"""
train_data = get_stock_data(train_stock)
#val_data = get_stock_data(val_stock)
agent = Agent(window_size,
current_price=train_data[0],
strategy=strategy,
pretrained=pretrained,
model_name=model_name)
#initial_offset = val_data[1] - val_data[0]
cash_in_hand = []
total_profit = []
for episode in range(1, ep_count + 1):
train_result = train_model(agent,
episode,
train_data,
ep_count=ep_count,
batch_size=batch_size,
window_size=window_size)
#val_result, _ = evaluate_model(agent, val_data, window_size, debug)
if episode % 100 == 0:
agent.save(episode)
#show_train_result(train_result)
total_profit.append(train_result[2])
cash_in_hand.append(agent.cash_in_hand)
agent.reset()
plt.plot(np.array(cash_in_hand))
plt.xlabel('cash in hand for each episode')
plt.ylabel('Amount')
plt.show()
plt.plot(np.array(total_profit))
plt.xlabel('total profit for each episode')
plt.ylabel('Amount')
plt.show()
def main(train_stock,
val_stock,
economy,
window_size,
batch_size,
ep_count,
strategy="dqn",
model_name="model_debug",
pretrained=False,
debug=False):
""" Trains the stock trading bot using Deep Q-Learning.
Please see https://arxiv.org/abs/1312.5602 for more details.
Args: [python train.py --help]
"""
print("initialize agent")
agent = Agent(window_size,
strategy=strategy,
pretrained=pretrained,
model_name=model_name)
print('get stock data')
train_data = get_stock_data(train_stock)
print('get economy leading')
economy_data = get_economy_data(economy)
print('get val data')
val_data = get_stock_data(val_stock)
# 첫 째날과 둘 째 날의 종가의 차
initial_offset = val_data[0][1] - val_data[0][0]
last_checkpoint = 0
for episode in range(1, ep_count + 1):
print('train episode : ', episode)
train_result, is_earlystopping = train_model(
agent,
episode,
train_data,
economy_data,
ep_count=ep_count,
batch_size=batch_size,
window_size=window_size,
last_checkpoint=last_checkpoint)
val_result, _ = evaluate_model(agent, val_data, economy_data,
window_size, debug)
show_train_result(train_result, val_result, initial_offset)
if is_earlystopping == False:
last_checkpoint = episode
def main(window_size, batch_size, ep_count, model_name, pretrained, debug):
""" Trains the stock trading bot using Deep Q-Learning.
Please see https://arxiv.org/abs/1312.5602 for more details.
Args: [python train.py --help]
"""
agent = Agent(window_size, pretrained=pretrained, model_name=model_name)
train_data = get_stock_data('data/training.csv')
val_data = get_stock_data('data/test.csv')
initial_offset = val_data[1] - val_data[0]
for episode in range(1, ep_count + 1):
train_result = train_model(agent,
episode,
train_data,
ep_count=ep_count,
batch_size=batch_size,
window_size=window_size)
val_result, _ = evaluate_model(agent, val_data, window_size, debug)
show_train_result(train_result, val_result, initial_offset)
agent.soft_save()
def main(train_stock,
val_stock,
window_size,
batch_size,
ep_count,
strategy="t-dqn",
model_name="model_debug",
pretrained=False,
debug=False):
""" Trains the stock trading bot using Deep Q-Learning.
Please see https://arxiv.org/abs/1312.5602 for more details.
Args: [python train.py --help]
"""
agent = Agent(window_size,
strategy=strategy,
pretrained=pretrained,
model_name=model_name)
train_data = get_stock_data(train_stock)
val_data = get_stock_data(val_stock)
final_rewards = []
train_roi = []
valid_roi = []
train_loss = []
rewards = []
initial_offset = val_data[1] - val_data[0]
for episode in range(1, ep_count + 1):
train_result, rewards = train_model(agent,
episode,
train_data,
ep_count=ep_count,
batch_size=batch_size,
window_size=window_size)
final_rewards.extend(rewards)
train_roi.append(train_result[2])
train_loss.append(train_result[3])
val_result, _ = evaluate_model(agent, val_data, window_size, debug)
valid_roi.append(val_result)
show_train_result(train_result, val_result, initial_offset)
gs = gridspec.GridSpec(2, 2)
fig = plt.figure(figsize=(20, 9))
# To be shifted to Axis 1
ax1 = fig.add_subplot(gs[0, 0])
ax1.plot(range(len(train_loss)), train_loss, color='purple', label='loss')
ax1.legend(loc=0,
ncol=2,
prop={'size': 20},
fancybox=True,
borderaxespad=0.)
ax1.set_xlabel('Epochs', size=20)
ax1.set_ylabel('Train Loss', size=20)
ax1.set_title('Loss w.r.t. Epochs', size=20)
# To be shifted to Axis 2
ax2 = fig.add_subplot(gs[0, 1])
ax2.plot(range(len(train_roi)), train_roi, color='crimson', label='train')
ax2.plot(range(len(valid_roi)), valid_roi, color='olive', label='val')
ax2.legend(loc=0,
ncol=2,
prop={'size': 20},
fancybox=True,
borderaxespad=0.)
ax2.set_ylabel('Return of Investment($)', size=20)
ax2.set_xlabel('Epochs', size=20)
ax2.set_title('Train and Valid ROI w.r.t. Epochs', size=20)
# To be shifted to Axis 3
ax3 = fig.add_subplot(gs[1, :])
ax3.plot(range(len(final_rewards)),
final_rewards,
color='red',
label='Reward of Rainbow DQN')
ax3.set_xlabel('Episodes', size=20)
ax3.set_ylabel('Rewards', size=20)
ax3.set_title('Reward w.r.t. episodes', size=20)
ax3.legend(loc=0,
ncol=2,
prop={'size': 20},
fancybox=True,
borderaxespad=0.)
plt.show()
