def train_one():
"""
train an agent
"""
print("==============Start Fetching Data===========")
df = YahooDownloader(start_date=config.START_DATE,
end_date=config.END_DATE,
ticker_list=config.DOW_30_TICKER).fetch_data()
print("==============Start Feature Engineering===========")
df = FeatureEngineer(df,
feature_number=5,
use_technical_indicator=True,
use_turbulence=True).preprocess_data()
train = data_split(df, config.START_DATE, config.START_TRADE_DATE)
trade = data_split(df, config.START_TRADE_DATE, config.END_DATE)
env_setup = EnvSetup(stock_dim=len(train.tic.unique()))
env_train = env_setup.create_env_training(data=train,
env_class=StockEnvTrain)
agent = DRLAgent(env=env_train)
print("==============Model Training===========")
now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')
a2c_params_tuning = {
'n_steps': 5,
'ent_coef': 0.005,
'learning_rate': 0.0007,
'verbose': 0,
'timesteps': 100000
}
model_a2c = agent.train_A2C(model_name="A2C_{}".format(now),
model_params=a2c_params_tuning)
print("==============Start Trading===========")
env_trade, obs_trade = env_setup.create_env_trading(
data=trade, env_class=StockEnvTrade, turbulence_threshold=250)
df_account_value = DRLAgent.DRL_prediction(model=model_a2c,
test_data=trade,
test_env=env_trade,
test_obs=obs_trade)
df_account_value.to_csv("./" + config.RESULTS_DIR + "/" + now + '.csv')
print("==============Get Backtest Results===========")
perf_stats_all = BackTestStats(df_account_value)
perf_stats_all = pd.DataFrame(perf_stats_all)
perf_stats_all.to_csv("./" + config.RESULTS_DIR + "/perf_stats_all_" +
now + '.csv')
def train_one():
"""
train an agent
"""
print("==============Start Fetching Data===========")
df = YahooDownloader(start_date=config.START_DATE,
end_date=config.END_DATE,
ticker_list=config.SP_500_TICKER).fetch_data()
print("==============Start Feature Engineering===========")
df = FeatureEngineer(df, use_technical_indicator=True,
use_turbulence=True).preprocess_data()
# Training & Trade data split
train = data_split(df, config.START_DATE, config.START_TRADE_DATE)
trade = data_split(df, config.START_TRADE_DATE, config.END_DATE)
# data normalization
#feaures_list = list(train.columns)
#feaures_list.remove('date')
#feaures_list.remove('tic')
#feaures_list.remove('close')
#print(feaures_list)
#data_normaliser = preprocessing.StandardScaler()
#train[feaures_list] = data_normaliser.fit_transform(train[feaures_list])
#trade[feaures_list] = data_normaliser.fit_transform(trade[feaures_list])
# calculate state action space
stock_dimension = len(train.tic.unique())
state_space = 1 + 2 * stock_dimension + len(
config.TECHNICAL_INDICATORS_LIST) * stock_dimension
env_setup = EnvSetup(stock_dim=stock_dimension,
state_space=state_space,
hmax=100,
initial_amount=3000,
transaction_cost_pct=0.001)
env_train = env_setup.create_env_training(data=train,
env_class=StockEnvTrain)
agent = DRLAgent(env=env_train)
print("==============Model Training===========")
now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')
a2c_params_tuning = {
'n_steps': 5,
'ent_coef': 0.005,
'learning_rate': 0.0007,
'verbose': 0,
'timesteps': 80000
}
model = agent.train_A2C(model_name="A2C_{}".format(now),
model_params=a2c_params_tuning)
print("==============Start Trading===========")
env_trade, obs_trade = env_setup.create_env_trading(
data=trade, env_class=StockEnvTrade, turbulence_threshold=250)
df_account_value, df_actions = DRLAgent.DRL_prediction(model=model,
test_data=trade,
test_env=env_trade,
test_obs=obs_trade)
df_account_value.to_csv("./" + config.RESULTS_DIR + "/df_account_value_" +
now + '.csv')
df_actions.to_csv("./" + config.RESULTS_DIR + "/df_actions_" + now +
'.csv')
print("==============Get Backtest Results===========")
perf_stats_all = BackTestStats(df_account_value)
perf_stats_all = pd.DataFrame(perf_stats_all)
perf_stats_all.to_csv("./" + config.RESULTS_DIR + "/perf_stats_all_" +
now + '.csv')
def test(training_data, trading_data):
# params
stock_dimension = len(training_data.tic.unique())
state_space = 1 + 2 * stock_dimension + len(
config.TECHNICAL_INDICATORS_LIST) * stock_dimension
hmax = 100
starting_capital = 1000000
transaction_cost_pct = 0.001
reward_scaling = 1e-4
technical_indicator_list = config.TECHNICAL_INDICATORS_LIST
env_setup = EnvSetup(stock_dim=stock_dimension,
state_space=state_space,
hmax=100,
initial_amount=1000000,
transaction_cost_pct=0.001)
# pre-make training environment
# env_train = StockEnvTrain(params)
# transition to make
# https://medium.com/@apoddar573/making-your-own-custom-environment-in-gym-c3b65ff8cdaa
env_train = gym.make('multi-stock-train-v0',
df=training_data,
stock_dim=stock_dimension,
hmax=hmax,
initial_amount=starting_capital,
transaction_cost_pct=transaction_cost_pct,
reward_scaling=reward_scaling,
state_space=state_space,
action_space=stock_dimension,
tech_indicator_list=technical_indicator_list,
turbulence_threshold=250,
day=0)
# --------------- Training
log_dir = "me/tmp/"
os.makedirs(log_dir, exist_ok=True)
env_train = Monitor(env_train, log_dir)
callback = SaveOnBestTrainingRewardCallback(check_freq=5000,
log_dir=log_dir)
agent = DRLAgent(env=env_train)
print("==============Model Training===========")
now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')
# a2c_params_tuning = {'n_steps': 512,
# 'ent_coef': 0.005,
# 'learning_rate': 0.0002,
# 'verbose': 0,
# 'timesteps': 150000}
a2c_params_tuning = {
"n_steps": 32,
"gamma": 0.999304473794672,
"gae_lambda": 0.994452346235796,
"learning_rate": 0.00010054610987642753,
"ent_coef": 0.00215496380633495,
"max_grad_norm": 2.217146296318495,
'verbose': 0,
'timesteps': 2e5, # 2e5
"policy_kwargs": {
"net_arch": 'tiny',
"activation_fn": 'tanh',
"ortho_init": False,
}
}
model_a2c = agent.train_A2C(
model_name="A2C_full_train_tuned{}".format(now),
model_params=a2c_params_tuning,
save=True,
callback=callback)
print("============End Model Training=========")
# model_a2c = A2C.load(os.path.abspath('./me/tmp/best_model.zip'))
account_value, actions = get_trade_results(env_setup, model_a2c)
