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 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]
"""
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]
"""
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,
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 alpaca_trading_bot(stock_name, window_size=10, model_name='model_debug'):
# Alpaca API
api = tradeapi.REST()
# Create Agent Object
agent = Agent(window_size, pretrained=True, model_name=model_name)
# Get Ticker from last intraday times from Polygon
file = open('ticker.csv', 'w')
file.write('Adj Close\n')
# Get date for ticker
date = api.get_barset(timeframe='15Min',
symbols=stock_name,
limit=1000,
end=datetime.datetime.now())
# Write ticker csv
for minutes in date.get(stock_name):
file.write(str(minutes.c))
file.write('\n')
file.close()
data = get_stock_data('ticker.csv')
# Call actual buy/sell/hold decisions and print result forever
decisions(agent, data, window_size, debug, stock_name, api)
def main(eval_stock, window_size, model_name, debug, manual_run):
""" Evaluates the stock trading bot.
Please see https://arxiv.org/abs/1312.5602 for more details.
Args: [python eval.py --help]
"""
if type(eval_stock) == str:
data = get_stock_data(eval_stock)
elif type(eval_stock) == list:
data = eval_stock
initial_offset = data[1] - data[0]
# Single Model Evaluation
if model_name is not None:
agent = Agent(window_size, pretrained=True, model_name=model_name, manual = manual_run)
profit, _ = evaluate_model(agent, data, window_size, debug)
show_eval_result(model_name, profit, initial_offset)
# Multiple Model Evaluation
else:
for model in os.listdir("models"):
if os.path.isfile(os.path.join("models", model)):
agent = Agent(window_size, pretrained=True, model_name=model)
profit = evaluate_model(agent, data, window_size, debug)
show_eval_result(model, profit, initial_offset)
del agent
def main(eval_stock, window_size, model_name, debug):
""" Evaluates the stock trading bot.
Please see https://arxiv.org/abs/1312.5602 for more details.
Args: [python eval.py --help]
"""
data = filter_data_by_feature_columns(get_stock_data(eval_stock))
# Single Model Evaluation
if model_name is not None:
agent = Agent(window_size * data.shape[1],
pretrained=True,
model_name=model_name)
profit, history = evaluate_model(agent, data, window_size, debug)
first_buy = history[0][0]
show_eval_result(model_name, profit, first_buy)
# Multiple Model Evaluation
else:
for model in os.listdir("models"):
if os.path.isfile(os.path.join("models", model)):
agent = Agent(window_size * data.shape[1],
pretrained=True,
model_name=model)
profit = evaluate_model(agent, data, window_size, debug)
show_eval_result(model, profit)
del agent
def alpaca_trading_bot(stock_name, window_size=10, model_name='model_debug'):
# Alpaca API
api = tradeapi.REST()
# Create Agent Object
agent = Agent(window_size, pretrained=True, model_name=model_name)
# Get Ticker from last intraday times from Polygon
file = open('ticker.csv', 'w')
file.write('Adj Close\n')
# Check for connection errors and retry 30 times
cnt = 0
while cnt <= 30:
try:
date = api.get_barset(timeframe='minute', symbols=stock_name, limit=1000, end=datetime.datetime.now())
break
except:
logging.warning("Error retrieving initial 1000 prices, retrying in 30s (" + str(cnt) + "/30)")
time.sleep(30)
cnt += 1
continue
# Write ticker csv
for minutes in date.get(stock_name):
file.write(str(minutes.c))
file.write('\n')
file.close()
data = get_stock_data('ticker.csv')
# Call actual buy/sell/hold decisions and print result forever
decisions(agent, data, window_size, debug, stock_name, api)
def main(train_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) 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)
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(eval_stock, window_size, model_name, debug):
""" Evaluates the stock trading bot.
Please see https://arxiv.org/abs/1312.5602 for more details.
Args: [python eval.py --help]
"""
data = get_stock_data(eval_stock)
initial_offset = data[1] - data[0]
# Single Model Evaluation
if model_name is not None:
agent = Agent(window_size, pretrained=True, model_name=model_name)
profit, _ = evaluate_model(agent, data, window_size, debug)
show_eval_result(model_name, profit, initial_offset)
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(eval_stock, window_size, model_name, debug):
data = get_stock_data(eval_stock)
initial_offset = data[1] - data[0]
# Single Model Evaluation
if model_name is not None:
agent = Agent(window_size, pretrained=True, model_name=model_name)
profit, _ = evaluate_model(agent, data, window_size, debug)
show_eval_result(model_name, profit, initial_offset)
# Multiple Model Evaluation
else:
for model in os.listdir("models"):
if os.path.isfile(os.path.join("models", model)):
agent = Agent(window_size, pretrained=True, model_name=model)
profit = evaluate_model(agent, data, window_size, debug)
show_eval_result(model, profit, initial_offset)
del agent
def fn(ticker,strategy,ep_count,year):
stock = '{}'.format(ticker)
model_name = '{}_{}_{}'.format(ticker,strategy,ep_count)
test_stock = 'data/Nifty50/Split/{}_{}.csv'.format(ticker,year)
window_size = 10
debug = True
tf.keras.backend.clear_session()
K.clear_session()
agent = Agent(window_size, pretrained=True, model_name=model_name)
# read csv into dataframe
df = pd.read_csv(test_stock)
# df = df.iloc[:55]
# filter out the desired features
df = df[['Date', 'Adj Close']]
# rename feature column names
df = df.rename(columns={'Adj Close': 'actual', 'Date': 'date'})
# convert dates from object to DateTime type
dates = df['date']
dates = pd.to_datetime(dates, infer_datetime_format=True)
df['date'] = dates
df.head()
coloredlogs.install(level='DEBUG')
switch_k_backend_device()
test_data = get_stock_data(test_stock)
initial_offset = test_data[1] - test_data[0]
test_result, history = evaluate_model(agent, test_data, window_size, debug)
show_eval_result(model_name, test_result, initial_offset)
def visualize(df, history, title="trading session"):
# add history to dataframe
position = [history[0][0]] + [x[0] for x in history]
actions = ['HOLD'] + [x[1] for x in history]
df['position'] = position
df['action'] = actions
# specify y-axis scale for stock prices
scale = alt.Scale(domain=(min(min(df['actual']), min(df['position'])) - 50, max(max(df['actual']), max(df['position'])) + 50), clamp=True)
# plot a line chart for stock positions
actual = alt.Chart(df).mark_line(
color='green',
opacity=0.5
).encode(
x='date:T',
y=alt.Y('position', axis=alt.Axis(format='$.2f', title='Price'), scale=scale)
).interactive(
bind_y=False
)
# plot the BUY and SELL actions as points
points = alt.Chart(df).transform_filter(
alt.datum.action != 'HOLD'
).mark_point(
filled=True
).encode(
x=alt.X('date:T', axis=alt.Axis(title='Date')),
y=alt.Y('position', axis=alt.Axis(format='$.2f', title='Price'), scale=scale),
color='action'
).interactive(bind_y=False)
# merge the two charts
chart = alt.layer(actual, points, title=title).properties(height=300, width=1000)
return chart
chart = visualize(df, history, title=test_stock)
cap = [0]
inv = 0
ret = 0
b = 0
for i in range(len(df)):
if df.iloc[i]['action']=='BUY':
cap.append(cap[-1]+df.iloc[i]['actual'])
inv+=df.iloc[i]['actual']
b+=1
if df.iloc[i]['action']=='SELL' and b>0:
cap.append(cap[-1]-df.iloc[i]['actual'])
ret += df.iloc[i]['actual']
b-=1
req_cap = max(cap)
prof = ret+(df['action'].value_counts().get('BUY',0)-df['action'].value_counts().get('SELL',0))*df.iloc[-1]['actual']-inv
return pd.DataFrame([[ticker,strategy,ep_count,year,inv,ret,req_cap, prof, (prof/req_cap)*100]],columns=['ticker','strategy','ep_count','year','investment','returns','required capital','profit','profit percentage'])
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()
