def main(args):
mode = args.mode
# mode = 'test'
# codes = args.codes
codes = ["600036"]
# codes = ["AU88", "RB88", "CU88", "AL88"]
# codes = ["T9999"]
market = args.market
# market = 'future'
# episode = args.episode
episode = 200
# training_data_ratio = 0.5
training_data_ratio = args.training_data_ratio
model_name = os.path.basename(__file__).split('.')[0]
env = Market(
codes,
start_date="2008-01-01",
end_date="2019-07-19",
**{
"market": market,
# "use_sequence": True,
# "mix_index_state": True,
"logger": generate_market_logger(model_name),
"training_data_ratio": training_data_ratio,
})
algorithm = Algorithm(
tf.Session(config=config), env, env.trader.action_space, env.data_dim,
**{
"mode":
mode,
"episodes":
episode,
"enable_saver":
True,
"learning_rate":
0.003,
"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()
algorithm.plot()
def __init__(self, session, env, **options):
self.session = session
self.env = env
self.total_step = 0
try:
self.learning_rate_decay = options['learning_rate_decay']
except KeyError:
self.learning_rate_decay = 0.99
try:
self.learning_rate = options['learning_rate']
except KeyError:
self.learning_rate = 0.001
try:
self.batch_size = options['batch_size']
except KeyError:
self.batch_size = 32
try:
self.logger = options['logger']
except KeyError:
self.logger = generate_algorithm_logger('model')
try:
self.enable_saver = options["enable_saver"]
except KeyError:
self.enable_saver = False
try:
self.enable_summary_writer = options['enable_summary_writer']
except KeyError:
self.enable_summary_writer = False
try:
self.save_path = options["save_path"]
except KeyError:
self.save_path = None
try:
self.summary_path = options["summary_path"]
except KeyError:
self.summary_path = None
try:
self.mode = options['mode']
except KeyError:
self.mode = 'train'
def __init__(self, session, env, **options):
self.session = session
self.env = env
self.total_step = 0
try:
self.learning_rate = options['learning_rate']
except KeyError:
self.learning_rate = 0.001
try:
self.batch_size = options['batch_size']
except KeyError:
self.batch_size = 32
try:
self.logger = options['logger']
except KeyError:
self.logger = generate_algorithm_logger('model')
try:
self.enable_saver = options["enable_saver"]
except KeyError:
self.enable_saver = False
try:
self.enable_summary_writer = options['enable_summary_writer']
except KeyError:
self.enable_summary_writer = False
try:
self.save_path = options["save_path"] # lstm中,在Algorithm的初始化的时候传入了"save_path":os.path.join(CHECKPOINTS_DIR, "SL", model_name, market, "model"
except KeyError:
self.save_path = None
try:
self.summary_path = options["summary_path"]
except KeyError:
self.summary_path = None
try:
self.mode = options['mode']
except KeyError:
self.mode = 'train'
def main(args):
mode = args.mode
#mode = 'test'
codes = args.codes
#codes = ["600036"]
#codes = ["eos_usdt"]
# codes = ["600036", "601998"]
# codes = ["AU88", "RB88", "CU88", "AL88"]
# codes = ["T9999"]
market = 'k15m'
#market = args.market
# market = 'future'
# episode = args.episode
episode = 1000
training_data_ratio = 0.95
# training_data_ratio = args.training_data_ratio
#pdb.set_trace()
model_name = os.path.basename(__file__).split('.')[0]
#env = Market(codes, start_date="2018-06-04", end_date="2018-06-12", **{
env = Market(codes, start_date=args.start, end_date=args.end, **{
"market": market,
"mix_index_state": False,
"logger": generate_market_logger(model_name),
"training_data_ratio": training_data_ratio,
})
algorithm = Algorithm(tf.Session(config=config), env, env.trader.action_space, env.data_dim, **{
"mode": mode,
"episodes": episode,
"enable_saver": True,
"learning_rate": 0.003,
"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()
algorithm.plot()
def generator(code, start_date, end_date, market="stock", mode='trade'):
training_data_ratio = 0.8
episode = 500
env = Market(
code,
start_date=start_date,
end_date=end_date,
**{
"market": market,
# "use_sequence": True,
"logger": generate_market_logger(model_name),
"training_data_ratio": training_data_ratio,
})
return Algorithm(
tf.Session(config=config), env, env.trader.action_space,
env.data_dim, **{
"mode":
mode,
"episodes":
episode,
"enable_saver":
True,
"learning_rate":
0.003,
"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"),
})
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()
