def log_account(df_account_value, df_actions):
now = datetime.datetime.now().strftime("%Y%m%d-%Hh%M")
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 = backtest_stats(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_model(model, environment, name):
df_account_value, df_actions = Agent.DRL_prediction(
model=model, environment=environment)
df_account_value.to_csv("./" + config.RESULTS_DIR + "/df_account_value_" +
name + ".csv")
df_actions.to_csv("./" + config.RESULTS_DIR + "/df_actions_" + name +
".csv")
print("==============Get Backtest Results===========")
perf_stats_all = backtest_stats(account_value=df_account_value)
perf_stats_all = pd.DataFrame(perf_stats_all)
perf_stats_all.to_csv("./" + config.RESULTS_DIR + "/perf_stats_all_" +
name + '.csv')
# <a id='6'></a>
# # Part 7: Backtest Our Strategy
# Backtesting plays a key role in evaluating the performance of a trading strategy. Automated backtesting tool is preferred because it reduces the human error. We usually use the Quantopian pyfolio package to backtest our trading strategies. It is easy to use and consists of various individual plots that provide a comprehensive image of the performance of a trading strategy.
# <a id='6.1'></a>
# ## 7.1 BackTestStats
# pass in df_account_value, this information is stored in env class
#
# In[197]:
print("==============Get Backtest Results===========")
perf_stats_all = backtest_stats(account_value=df_account_value, value_col_name = 'total_assets')
# <a id='6.2'></a>
# ## 7.2 BackTestPlot
# In[198]:
print("==============Compare to DJIA===========")
get_ipython().run_line_magic('matplotlib', 'inline')
# S&P 500: ^GSPC
# Dow Jones Index: ^DJI
# NASDAQ 100: ^NDX
backtest_plot(df_account_value,
baseline_ticker = '^DJI',
'''Create Trade Env'''
e_trade_gym = StockTradingEnv(df=trade, turbulence_threshold=380, **env_kwargs)
############# TRADE #############
df_account_value, df_actions = DRLAgent.DRL_prediction(model=trained_a2c,
environment=e_trade_gym)
df_account_value.shape
df_account_value.tail()
############# BACKTEST #############
print("==============Get Backtest Results===========")
now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')
perf_stats_all = backtest_stats(account_value=df_account_value)
perf_stats_all = pd.DataFrame(perf_stats_all)
#baseline stats
print("==============Get Baseline Stats===========")
baseline_df = get_baseline(ticker="^DJI", start='2019-01-01', end='2021-01-01')
stats = backtest_stats(baseline_df, value_col_name='close')
print("==============Compare to DJIA===========")
# S&P 500: ^GSPC
# Dow Jones Index: ^DJI
# NASDAQ 100: ^NDX
backtest_plot(df_account_value,
baseline_ticker='^DJI',
baseline_start='2019-01-01',
def main():
if not os.path.exists("./" + config.DATA_SAVE_DIR):
os.makedirs("./" + config.DATA_SAVE_DIR)
if not os.path.exists("./" + config.TRAINED_MODEL_DIR):
os.makedirs("./" + config.TRAINED_MODEL_DIR)
if not os.path.exists("./" + config.TENSORBOARD_LOG_DIR):
os.makedirs("./" + config.TENSORBOARD_LOG_DIR)
if not os.path.exists("./" + config.RESULTS_DIR):
os.makedirs("./" + config.RESULTS_DIR)
print(config.START_DATE)
print(config.END_DATE)
print(config.PENNY_STOCKS)
df = YahooDownloader(start_date=config.START_DATE,
end_date=config.END_DATE,
ticker_list=config.PENNY_STOCKS).fetch_data()
fe = FeatureEngineer(use_technical_indicator=True,
tech_indicator_list=config.TECHNICAL_INDICATORS_LIST,
use_turbulence=True,
user_defined_feature=False)
processed = fe.preprocess_data(df)
information_cols = list(processed)
information_cols.remove('date')
information_cols.remove('tic')
stock_dimension = len(processed.tic.unique())
state_space = 1 + 2 * stock_dimension + len(
information_cols) * stock_dimension
print("Stock Dimension: {}, State Space: {}".format(
stock_dimension, state_space))
env_kwargs = {
"hmax": 100,
"initial_amount": 5000,
# Since in Indonesia the minimum number of shares per trx is 100, then we scaled the initial amount by dividing it with 100
"buy_cost_pct": 0.00, # IPOT has 0.19% buy cost
"sell_cost_pct": 0.00, # IPOT has 0.29% sell cost
"state_space": state_space,
"stock_dim": stock_dimension,
"tech_indicator_list": information_cols,
"action_space": stock_dimension,
"reward_scaling": 1e-4,
"print_verbosity": 5
}
rebalance_window = 63 # rebalance_window is the number of days to retrain the model
validation_window = 63 # validation_window is the number of days to do validation and trading (e.g. if validation_window=63, then both validation and trading period will be 63 days)
train_start = config.START_DATE
train_end = config.START_TRADE_DATE
val_test_start = config.START_TRADE_DATE
val_test_end = config.END_DATE
ensemble_agent = DRLEnsembleAgent(df=processed,
train_period=(train_start, train_end),
val_test_period=(val_test_start,
val_test_end),
rebalance_window=rebalance_window,
validation_window=validation_window,
**env_kwargs)
A2C_model_kwargs = {
'n_steps': 5,
'ent_coef': 0.01,
'learning_rate': 0.0005
}
PPO_model_kwargs = {
"ent_coef": 0.01,
"n_steps": 2048,
"learning_rate": 0.00025,
"batch_size": 128
}
DDPG_model_kwargs = {
"action_noise": "ornstein_uhlenbeck",
"buffer_size": 50000,
"learning_rate": 0.000005,
"batch_size": 128
}
TD3_model_kwargs = {
"batch_size": 100,
"buffer_size": 1000000,
"learning_rate": 0.001
}
timesteps_dict = {'a2c': 4000, 'ppo': 4000, 'ddpg': 4000, 'td3': 4000}
df_summary = ensemble_agent.run_ensemble_strategy(A2C_model_kwargs,
PPO_model_kwargs,
DDPG_model_kwargs,
TD3_model_kwargs,
timesteps_dict)
print(df_summary)
unique_trade_date = processed[(processed.date > val_test_start) & (
processed.date <= val_test_end)].date.unique()
df_trade_date = pd.DataFrame({'datadate': unique_trade_date})
df_account_value = pd.DataFrame()
for i in range(rebalance_window + validation_window,
len(unique_trade_date) + 1, rebalance_window):
print(rebalance_window + validation_window)
print(len(unique_trade_date) + 1)
print(rebalance_window)
try:
temp = pd.read_csv('results/account_value_trade_{}_{}.csv'.format(
'ensemble', i))
df_account_value = df_account_value.append(temp, ignore_index=True)
except:
break
sharpe = (252**0.5) * df_account_value.account_value.pct_change(
1).mean() / df_account_value.account_value.pct_change(1).std()
print('Sharpe Ratio: ', sharpe)
df_account_value = df_account_value.join(
df_trade_date[validation_window:].reset_index(drop=True))
df_account_value.account_value.plot()
print("==============Get Backtest Results===========")
now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')
perf_stats_all = backtest_stats(account_value=df_account_value)
perf_stats_all = pd.DataFrame(perf_stats_all)
print("==============Compare to IHSG===========")
backtest_plot(df_account_value,
baseline_ticker='^DJI',
baseline_start=df_account_value.loc[0, 'date'],
baseline_end=df_account_value.loc[len(df_account_value) - 1,
'date'])
env_train.action_space.seed(seed)
print(type(env_train))
agent = DRLAgent(env = env_train)
model_ddpg = agent.get_model("ddpg",
model_kwargs={"batch_size": batch_size,
"buffer_size": 50000,
"learning_rate": lr}
)
trained_ddpg = agent.train_model(model=model_ddpg,
tb_log_name='ddpg',
total_timesteps=50000)
e_trade_gym = StockTradingEnv(df = validation, **env_kwargs)
e_trade_gym.seed(seed)
e_trade_gym.action_space.seed(seed)
df_account_value, df_actions = DRLAgent.DRL_prediction(
model=trained_ddpg,
environment = e_trade_gym
)
print("==============Get Backtest Results===========")
now = datetime.datetime.now().strftime('%Y%m%d-%Hh%M')
perf_stats_all = backtest_stats(account_value=df_account_value)
perf_stats_all = pd.DataFrame(perf_stats_all)
perf_stats_all.to_csv("./"+config.RESULTS_DIR+"/perf_stats_all_"+now+'.csv')
return perf_stats_all, df_account_value
def train_one():
"""
train an agent
"""
print("==============Start Fetching Data===========")
df = YahooDownloader(
start_date=config.START_DATE,
end_date=config.END_DATE,
ticker_list=['FXAIX'],
).fetch_data()
print("==============Start Feature Engineering===========")
fe = FeatureEngineer(
use_technical_indicator=True,
tech_indicator_list=config.TECHNICAL_INDICATORS_LIST,
use_turbulence=True,
user_defined_feature=False,
)
processed = fe.preprocess_data(df)
# Training & Trading data split
train = data_split(processed, config.START_DATE, config.START_TRADE_DATE)
trade = data_split(processed, config.START_TRADE_DATE, config.END_DATE)
# calculate state action space
stock_dimension = len(train.tic.unique())
state_space = (1 + 2 * stock_dimension +
len(config.TECHNICAL_INDICATORS_LIST) * stock_dimension)
env_kwargs = {
"hmax": 100,
"initial_amount": 1000000,
"buy_cost_pct": 0.001,
"sell_cost_pct": 0.001,
"state_space": state_space,
"stock_dim": stock_dimension,
"tech_indicator_list": config.TECHNICAL_INDICATORS_LIST,
"action_space": stock_dimension,
"reward_scaling": 1e-4
}
e_train_gym = StockTradingEnv(df=train, **env_kwargs)
e_trade_gym = StockTradingEnv(df=trade,
turbulence_threshold=250,
**env_kwargs)
env_train, _ = e_train_gym.get_sb_env()
env_trade, obs_trade = e_trade_gym.get_sb_env()
agent = DRLAgent(env=env_train)
print("==============Model Training===========")
now = datetime.datetime.now().strftime("%Y%m%d-%Hh%M")
user_input = input('train model? 1 train 0 don\'t train')
if user_input == 1:
model_sac = agent.get_model("sac")
trained_sac = agent.train_model(model=model_sac,
tb_log_name="sac",
total_timesteps=8000)
trained_sac.save("../models/sac_8k" + df.tic[0] + "_frl")
else:
trained_sac = SAC.load('../models/sac_80k_msft_working')
print("==============Start Trading===========")
df_account_value, df_actions = DRLAgent.DRL_prediction(
trained_sac, e_trade_gym)
df_account_value.to_csv("../" + config.RESULTS_DIR +
"/SAC_df_account_value_" + df.tic[0] + "_" + now +
".csv")
df_actions.to_csv("../" + config.RESULTS_DIR + "/SAC_df_actions_" +
df.tic[0] + "_" + now + ".csv")
# print("==============Get Backtest Results===========")
perf_stats_all = backtest_stats(df_account_value)
perf_stats_all = pd.DataFrame(perf_stats_all)
perf_stats_all.to_csv("../" + config.RESULTS_DIR + "/SAC_perf_stats_all_" +
df.tic[0] + "_" + now + ".csv")
#plot acc value
actions = df_actions['actions']
x = np.arange(0, df_account_value['account_value'].shape[0])
y = df_account_value['account_value']
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
fig, axs = plt.subplots(2, 1, sharex=True, sharey=False)
# plt.plot(x, y)
# Use a boundary norm instead
cmap = ListedColormap(['r', 'g', 'b'])
norm = BoundaryNorm([-100, -0.1, 0.1, 100], cmap.N)
lc = LineCollection(segments, cmap=cmap, norm=norm)
lc.set_array(actions)
lc.set_linewidth(2)
line = axs[0].add_collection(lc)
# fig.colorbar(line, ax=axs)
axs[1].set_xlabel('Trading Day (' + 'From ' + config.START_TRADE_DATE +
" to " + config.END_DATE + ')')
axs[0].set_ylabel('Account Value (10000 of USD)')
axs[0].set_title("Trading Test on " + df.tic[0])
axs[0].set_xlim(x.min(), x.max())
axs[0].set_ylim(y.min(), y.max())
custom_lines = [
Line2D([0], [0], color=cmap(0.), lw=4),
Line2D([0], [0], color=cmap(.5), lw=4),
Line2D([0], [0], color=cmap(1.), lw=4)
]
# lines = ax.plot(data)
axs[0].legend(custom_lines, ['Sell', 'Hold', 'Buy'])
#plot stock value
tx = np.arange(0, df_account_value['account_value'].shape[0])
ty = trade['close']
plt.ylabel('Price (USD)')
plt.title(df.tic[0] + " Closing Price")
plt.plot(tx, ty)
plt.savefig("../" + config.RESULTS_DIR + "/plots/"
"SAC_plot_" + df.tic[0] + "_" + now + ".png")
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===========")
fe = FeatureEngineer(
use_technical_indicator=True,
tech_indicator_list=config.TECHNICAL_INDICATORS_LIST,
use_turbulence=True,
user_defined_feature=False,
)
processed = fe.preprocess_data(df)
list_ticker = processed["tic"].unique().tolist()
list_date = list(
pd.date_range(processed['date'].min(),
processed['date'].max()).astype(str))
combination = list(itertools.product(list_date, list_ticker))
processed_full = pd.DataFrame(combination,
columns=["date",
"tic"]).merge(processed,
on=["date", "tic"],
how="left")
processed_full = processed_full[processed_full['date'].isin(
processed['date'])]
processed_full = processed_full.sort_values(['date', 'tic'])
processed_full = processed_full.fillna(0)
# Training & Trading data split
train = data_split(processed_full, config.START_DATE,
config.START_TRADE_DATE)
trade = data_split(processed_full, config.START_TRADE_DATE,
config.END_DATE)
# calculate state action space
stock_dimension = len(train.tic.unique())
state_space = (1 + 2 * stock_dimension +
len(config.TECHNICAL_INDICATORS_LIST) * stock_dimension)
env_kwargs = {
"hmax": 100,
"initial_amount": 1000000,
"buy_cost_pct": 0.001,
"sell_cost_pct": 0.001,
"state_space": state_space,
"stock_dim": stock_dimension,
"tech_indicator_list": config.TECHNICAL_INDICATORS_LIST,
"action_space": stock_dimension,
"reward_scaling": 1e-4
}
e_train_gym = StockTradingEnv(df=train, **env_kwargs)
env_train, _ = e_train_gym.get_sb_env()
agent = DRLAgent(env=env_train)
print("==============Model Training===========")
now = datetime.datetime.now().strftime("%Y%m%d-%Hh%M")
model_sac = agent.get_model("sac")
trained_sac = agent.train_model(model=model_sac,
tb_log_name="sac",
total_timesteps=80000)
print("==============Start Trading===========")
e_trade_gym = StockTradingEnv(df=trade,
turbulence_threshold=250,
**env_kwargs)
df_account_value, df_actions = DRLAgent.DRL_prediction(
model=trained_sac, environment=e_trade_gym)
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 = backtest_stats(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(fetch=False):
"""
train an agent
"""
if fetch:
df = fetch_and_store()
else:
df = load()
counts = df[['date', 'tic']].groupby(['date']).count().tic
assert counts.min() == counts.max()
print("==============Start Feature Engineering===========")
fe = FeatureEngineer(
use_technical_indicator=True,
tech_indicator_list=config.TECHNICAL_INDICATORS_LIST,
use_turbulence=True,
# use_turbulence=False,
user_defined_feature=False,
)
processed = fe.preprocess_data(df)
# Training & Trading data split
start_date, trade_date, end_date = calculate_split(df,
start=config.START_DATE)
print(start_date, trade_date, end_date)
train = data_split(processed, start_date, trade_date)
trade = data_split(processed, trade_date, end_date)
print(
f'\n******\nRunning from {start_date} to {end_date} for:\n{", ".join(config.CRYPTO_TICKER)}\n******\n'
)
# calculate state action space
stock_dimension = len(train.tic.unique())
state_space = (1 + (2 * stock_dimension) +
(len(config.TECHNICAL_INDICATORS_LIST) * stock_dimension))
env_kwargs = {
"hmax": 100,
"initial_amount": 100000,
"buy_cost_pct": 0.0026,
"sell_cost_pct": 0.0026,
"state_space": state_space,
"stock_dim": stock_dimension,
"tech_indicator_list": config.TECHNICAL_INDICATORS_LIST,
"action_space": stock_dimension,
"reward_scaling": 1e-4
}
e_train_gym = StockTradingEnv(df=train, **env_kwargs)
e_trade_gym = StockTradingEnv(df=trade,
turbulence_threshold=250,
make_plots=True,
**env_kwargs)
env_train, _ = e_train_gym.get_sb_env()
env_trade, obs_trade = e_trade_gym.get_sb_env()
agent = DRLAgent(env=env_train)
print("==============Model Training===========")
now = datetime.datetime.now().strftime(config.DATETIME_FMT)
model_sac = agent.get_model("sac")
trained_sac = agent.train_model(
model=model_sac,
tb_log_name="sac",
# total_timesteps=100
total_timesteps=80000)
print("==============Start Trading===========")
df_account_value, df_actions = DRLAgent.DRL_prediction(
# model=trained_sac, test_data=trade, test_env=env_trade, test_obs=obs_trade
trained_sac,
e_trade_gym)
df_account_value.to_csv(
f"./{config.RESULTS_DIR}/df_account_value_{now}.csv")
df_actions.to_csv(f"./{config.RESULTS_DIR}/df_actions_{now}.csv")
df_txns = pd.DataFrame(e_trade_gym.transactions,
columns=['date', 'amount', 'price', 'symbol'])
df_txns = df_txns.set_index(pd.DatetimeIndex(df_txns['date'], tz=pytz.utc))
df_txns.to_csv(f'./{config.RESULTS_DIR}/df_txns_{now}.csv')
df_positions = pd.DataFrame(e_trade_gym.positions,
columns=['date', 'cash'] +
config.CRYPTO_TICKER)
df_positions = df_positions.set_index(
pd.DatetimeIndex(df_positions['date'],
tz=pytz.utc)).drop(columns=['date'])
df_positions['cash'] = df_positions.astype(
{col: np.float64
for col in df_positions.columns})
df_positions.to_csv(f'./{config.RESULTS_DIR}/df_positions_{now}.csv')
print("==============Get Backtest Results===========")
perf_stats_all = backtest_stats(df_account_value,
transactions=df_txns,
positions=df_positions)
perf_stats_all = pd.DataFrame(perf_stats_all)
perf_stats_all.to_csv(f"./{config.RESULTS_DIR}/perf_stats_all_{now}.csv")
backtest_plot(df_account_value,
baseline_start=trade_date,
baseline_end=end_date,
positions=df_positions,
transactions=df_txns)