def main():
df = pd.read_csv('dataset/btc_indexed2.csv')
env = trading_env.make(
env_id='training_v1',
obs_data_len=50,
step_len=1,
df=df,
fee=0.003,
max_position=5,
deal_col_name='close',
return_transaction=True,
sample_days=30,
normalize_reward=True,
feature_names=['open', 'high', 'low', 'close', 'volume'])
env = wrapper.LogPriceFilterWrapper(env)
logger.configure()
pi = train(env, num_timesteps=1024 * 1024)
print("training done.")
print(pi)
df2 = pd.read_csv('dataset/btc_indexed2_test.csv')
env2 = trading_env.make(
env_id='training_v1',
obs_data_len=50,
step_len=1,
df=df,
fee=0.003,
max_position=5,
deal_col_name='close',
return_transaction=True,
sample_days=30,
normalize_reward=False,
feature_names=['open', 'high', 'low', 'close', 'volume'])
env2 = wrapper.LogPriceFilterWrapper(env2)
evaluation(env2, pi, 50)
def make_env():
df = pd.read_csv('dataset/btc_indexed2_test.csv')
env = trading_env.make(
env_id='training_v1',
obs_data_len=1,
step_len=1,
df=df,
fee=0.003,
max_position=5,
deal_col_name='close',
return_transaction=True,
sample_days=30,
normalize_reward=False,
feature_names=['open', 'high', 'low', 'close', 'volume'])
env = wrapper.LogPriceFilterWrapper(env)
return env
def _thunk():
env = trading_env.make(custom_args=args,
env_id='training_v1',
obs_data_len=obs_data_len,
step_len=step_len,
df=df,
fee=0.0,
max_position=5,
deal_col_name='Price',
feature_names=[
'Price', 'Volume', 'Ask_price', 'Bid_price',
'Ask_deal_vol', 'Bid_deal_vol',
'Bid/Ask_deal', 'Updown'
])
return env
def main():
# mode = args.mode
mode = 'test' #'train'
# codes = args.codes
codes = ["AU88", "RB88", "CU88", "AL88"]
# codes = ["T9999"]
# market = args.market
market = 'future'
# episode = args.episode
episode = 2000
training_data_ratio = 0.5
# training_data_ratio = args.training_data_ratio
model_name = os.path.basename(__file__).split('.')[0]
df = pd.read_csv('TXF1_5m.csv', parse_dates=[['Date', 'Time']])
df = df.iloc[-10001:]
df.reset_index(drop=True, inplace=True)
df = df.rename(columns={'Date_Time': 'datetime'})
df['serial_number'] = df.index
# df['serial_number'] = df['serial_number'] - df['serial_number'].iloc[0]
env = trading_env.make(
env_id='training_v1',
obs_data_len=1,
step_len=1,
df=df,
fee=2,
max_position=1,
deal_col_name='Price',
feature_names=[
'Price',
'Volume', # 'datetime' ,
'Open',
'serial_number',
'High',
'Low'
],
fluc_div=100.0)
# env = Market(codes, start_date="2012-01-01", end_date="2018-01-01", **{
# "market": market,
# # "use_sequence": True,
# "logger": generate_market_logger(model_name),
# "training_data_ratio": training_data_ratio,
# })
algorithm = Algorithm(
tf.Session(config=config), env, 1, 5, **{
"mode":
mode,
"episodes":
episode,
"enable_saver":
True,
"learning_rate":
0.003,
"batch_size":
1320,
"enable_summary_writer":
True,
"logger":
generate_algorithm_logger(model_name),
"save_path":
os.path.join(CHECKPOINTS_DIR, "RL", model_name, market, "model"),
"summary_path":
os.path.join(CHECKPOINTS_DIR, "RL", model_name, market, "summary"),
})
algorithm.run()
algorithm.eval()
if __name__ == '__main__':
"""
df = pd.read_csv('../../dataset/kaggle_data/coinbaseUSD_1-min_data_2014-12-01_to_2018-01-08.csv')
# Need serial number and datetime to be the column name.
df.reset_index(inplace=True)
df.rename(columns={'index':'serial_number', 'Timestamp':'datetime'}, inplace=True)
env = trading_env.make(env_id='training_v1', obs_data_len=1, step_len=1,
df=df, fee=0.1, max_position=5, deal_col_name='Close',
return_transaction=False, feature_names=['Open', 'High', 'Low', 'Close'])
"""
df = pd.read_csv('../../dataset/btc_indexed2.csv')
env = trading_env.make(env_id='training_v1', obs_data_len=1, step_len=1,
df=df, fee=0.1, max_position=5, deal_col_name='close',
return_transaction=False, sample_days=7,
feature_names=['low', 'high', 'open', 'close', 'volume'])
env.reset()
# exploration strategy
exp_schedule = LinearExploration(env, config.eps_begin,
config.eps_end, config.eps_nsteps)
# learning rate schedule
lr_schedule = LinearSchedule(config.lr_begin, config.lr_end,
config.lr_nsteps)
# train model
model = Linear(env, config)
model.run(exp_schedule, lr_schedule)
#agent = MAAgent()
#agent = RSIAgent()
#agent = BBANDAgent()
agent = RandomAgent()
# make env
df = pd.read_csv('dataset/btc_test.csv')
print(df.describe())
env = trading_env.make(
env_id='training_v1',
obs_data_len=5,
step_len=1,
df=df,
fee=0.003,
max_position=5,
deal_col_name='close',
return_transaction=True,
sample_days=30,
normalize_price=True,
reward_delta=False,
feature_names=['low', 'high', 'open', 'close', 'volume'])
#env.render()
"""
df = pd.read_csv('dataset/btc_indexed2.csv')
print(df.describe())
env = trading_env.make(env_id='training_v1', obs_data_len=1, step_len=1,
df=df, fee=0.003, max_position=5, sample_days = 30,
return_transaction=False, deal_col_name='close',
feature_names=['low', 'high',
import numpy as np
import pandas as pd
import trading_env
from datetime import datetime
st = datetime.now()
## need to refactor the testcase
df = pd.read_csv('trading_env/test/data/SGXTWsample.csv', index_col=0, parse_dates=['datetime'])
env = trading_env.make(env_id='training_v1', obs_data_len=256, step_len=128,
df=df, fee=0.1, max_position=5, deal_col_name='Price',
feature_names=['Price', 'Volume',
'Ask_price','Bid_price',
'Ask_deal_vol','Bid_deal_vol',
'Bid/Ask_deal', 'Updown'],
fluc_div=100.0)
env.reset()
print(env.df_sample['datetime'].iloc[0].date())
for i in range(500):
#print(i)
state, reward, done, info = env.step(np.random.randint(3))
#print(state, reward)
#env.render()
if done:
break
print(datetime.now() - st)
df = pd.read_csv(
'dataset/kaggle_data/coinbaseUSD_1-min_data_2014-12-01_to_2018-01-08.csv')
# Need serial number and datetime to be the column name.
df.reset_index(inplace=True)
df.rename(columns={
'index': 'serial_number',
'Timestamp': 'datetime'
},
inplace=True)
env = trading_env.make(env_id='training_v1',
obs_data_len=1,
step_len=128,
df=df,
fee=0.1,
max_position=5,
deal_col_name='Close',
return_transaction=False,
feature_names=['Open', 'High', 'Low', 'Close'])
env.reset()
env.render()
state, reward, done, info = env.step(random.randrange(3))
print('********')
print(state)
print('********')
print(reward)
print('********')
from datetime import datetime
st = datetime.now()
## need to refactor the testcase
df = pd.read_csv('trading_env/test/data/SGXTWsample.csv',
index_col=0,
parse_dates=['datetime'])
env = trading_env.make(env_id='training_v1',
obs_data_len=256,
step_len=128,
df=df,
fee=0.1,
max_position=5,
deal_col_name='Price',
feature_names=[
'Price', 'Volume', 'Ask_price', 'Bid_price',
'Ask_deal_vol', 'Bid_deal_vol', 'Bid/Ask_deal',
'Updown'
],
fluc_div=100.0)
env.reset()
print(env.df_sample['datetime'].iloc[0].date())
for i in range(500):
#print(i)
state, reward, done, info = env.step(np.random.randint(3))
#print(state, reward)
#env.render()
if done:
import random
import numpy as np
import pandas as pd
import trading_env
#df = pd.read_hdf('dataset/SGXTW.h5', 'STW')
#df = pd.read_hdf('dataset/SGXTWsample.h5', 'STW')
df = pd.read_csv('dataset/btc_indexed2.csv')
print(df.describe())
env = trading_env.make(
env_id='training_v1',
obs_data_len=50,
step_len=1,
df=df,
fee=0.1,
max_position=5,
deal_col_name='close',
feature_names=['low', 'high', 'open', 'close', 'volume', 'datetime'])
env.reset()
env.render()
state, reward, done, info = env.step(random.randrange(3))
### randow choice action and show the transaction detail
for i in range(200):
print(i)
state, reward, done, info = env.step(random.randrange(3))
print(state, reward)
env.render()
try:
df = pd.read_hdf('dataset/BITCOIN.h5', 'STW')
except OSError as e:
df = get_market_data('bitcoin')
df = df.iloc[::-1]
df = df.reset_index(drop=True)
df['serial_number'] = 0
print(df.head())
print(df.tail())
env = trading_env.make(env_id='training_v1',
obs_data_len=1,
step_len=7,
df=df,
fee=0.1,
max_position=1,
deal_col_name='Close**',
feature_names=['Close**', 'Volume', 'Market Cap'])
env.reset()
env.render()
state, reward, done, info = env.step(random.randrange(3))
### randow choice action and show the transaction detail
for i in range(500):
print(i)
state, reward, done, info = env.step(random.randrange(3))
print(state, reward)
env.render()
"""
"""
Use a different architecture for the Atari game. Please report the final result.
Feel free to change the configuration. If so, please report your hyperparameters.
"""
if __name__ == '__main__':
# make env
df = pd.read_csv('dataset/btc_indexed2_train.csv')
print(df.describe())
test_env = trading_env.make(
env_id='training_v1',
obs_data_len=1,
step_len=1,
df=pd.read_csv('dataset/btc_indexed2_test.csv'),
fee=0.003,
max_position=5,
deal_col_name='close',
return_transaction=True,
sample_days=30,
normalize_price=True,
feature_names=['low', 'high', 'open', 'close', 'volume'])
env = trading_env.make(
env_id='training_v1',
obs_data_len=1,
step_len=1,
df=df,
fee=0.003,
max_position=5,
deal_col_name='close',
return_transaction=True,
