class rl_stock_trader():
def __init__(self,
path_to_symbol_csv,
request_symbols=8,
tb_outdir=tb_outdir):
self.writer = SummaryWriter(tb_outdir)
self.request_symbols = request_symbols
self.monitor_freq = 100
self.start_budget = 10000.
index_df = pd.read_csv(path_to_symbol_csv)
# symbol_vec = list(index_df.values[:self.request_symbols,0])
symbol_vec = list(
index_df.values[np.random.randint(0, index_df.values.
shape[0], self.request_symbols),
0])
self.dataframe, self.num_symbols = self.get_data(symbol_vec)
# env = DummyVecEnv([lambda: StockTradingEnv(dataframe)])
self.env = StockTradingEnv(self.dataframe, self.num_symbols)
self.tb_action_type = np.zeros(3)
self.tb_action_symbol = np.zeros(self.num_symbols)
self.tb_action_vec = []
self.tb_action_amount = []
self.tb_balance = np.zeros(4)
self.tb_net_worth = np.zeros(4)
self.balance_dummy = []
self.net_worth_dummy = []
self.tb_reward = 0.
self.tb_cache_reward_vec = []
self.tb_cache_rollout_vec = []
self.tb_cache_final_net = []
self.tb_cache_final_balance = []
self.tb_chache_balance = np.zeros(4)
self.tb_chache_net_worth = np.zeros(4)
def get_data(self,
symbols,
start=None,
end=None,
period='5y',
interval='1d'):
''' valid periods: 1d,5d,1mo,3mo,6mo,1y,2y,5y,10y,ytd,max
fetch data by interval (including intraday if period < 60 days)
valid intervals: 1m,2m,5m,15m,30m,60m,90m,1h,1d,5d,1wk,1mo,3mo
group by ticker (to access via data['SPY']) (optional, default is 'column')
adjust all OHLC automatically
download pre/post regular market hours data
use threads for mass downloading? (True/False/Integer)
proxy URL scheme use use when downloading? '''
df_keys = ['Adj Close', 'Close', 'High', 'Low', 'Open', 'Volume']
if start == None or end == None:
print('\nload S&P 500 data for period: ', period,
' and interval: ', interval, '\n')
data_array = yf.download(tickers=symbols,
period=period,
interval=interval,
group_by='column',
auto_adjust=True,
prepost=False,
threads=True,
proxy=None)
else:
print('\nload S&P 500 data since: ', start, '/ end: ', end,
' and interval: ', interval, '\n')
data_array = yf.download(tickers=symbols,
start=start,
end=end,
interval=interval,
group_by='column',
auto_adjust=True,
prepost=False,
threads=True,
proxy=None)
called_symbols = list(data_array['Volume'].keys())
try:
failed_symbols = list(data_array['Adj Close'].keys())
except KeyError:
failed_symbols = []
pass
loaded_symbols = []
for i in range(len(called_symbols)):
if called_symbols[i] not in failed_symbols:
loaded_symbols.append(called_symbols[i])
for i in range(len(failed_symbols)):
for j in range(len(df_keys)):
data_array = data_array.drop(
columns=[(str(df_keys[j]), str(failed_symbols[i]))])
data_array.insert(0, 'i', np.arange(data_array.shape[0]))
data_index_axis = data_array.index.values
data_array = data_array.drop(
index=[data_index_axis[0], data_index_axis[-1]])
dfkeys = ['Open', 'Close', 'High', 'Low', 'Volume']
for dfkey in range(len(dfkeys)):
data_array[dfkeys[dfkey]].fillna(method='pad')
data_array[dfkeys[dfkey]].fillna(0.)
data_array[dfkeys[dfkey]].replace(to_replace=np.nan, value=0.)
data_array[dfkeys[dfkey]].replace(to_replace='NaN', value=0.)
print(
'\n------------------------------------\
\nsuccesfully loaded stock data\nnumber of loaded data points: ' , data_array.shape[0], \
'\nnumber of loaded symbols: ', len(loaded_symbols), '/', len(called_symbols), \
'\n------------------------------------\n\n', \
'\ndataframe:\n', data_array, \
'\n------------------------------------\n\n')
return data_array, len(loaded_symbols)
def monitor_training(self, tb_writer, t, i, done, action, monitor_data):
'''
after each episode save:
action_type [3 x 1] v
action_amount [1 x 1] (avg /t) v
action_symbol [num_symbols x 1] v
balance [4x1] (low, avg, high, final) v
net_worth [4x1] (low, avg, high, final) v
'''
if t == 0:
self.balance_dummy = []
self.net_worth_dummy = []
self.tb_reward = 0.
if i == 0:
self.tb_balance = np.zeros(4)
self.tb_net_worth = np.zeros(4)
self.tb_action_amount = []
self.tb_action_symbol_vec = []
self.tb_action_vec = []
self.tb_cache_reward_vec = []
self.tb_cache_rollout_vec = []
self.tb_cache_final_net = np.zeros(4)
self.tb_cache_final_balance = np.zeros(4)
self.tb_action_symbol_vec.append(monitor_data['action_sym'])
self.tb_action_amount.append(monitor_data['action_amount'])
self.tb_action_vec.append(monitor_data['action_type'])
self.tb_reward += monitor_data['reward']
self.balance_dummy.append(monitor_data['balance'])
self.net_worth_dummy.append(monitor_data['net_worth'])
if done:
self.tb_cache_reward_vec.append(self.tb_reward)
self.tb_balance[0] = np.amin(self.balance_dummy)
self.tb_balance[1] = np.mean(self.balance_dummy)
self.tb_balance[2] = np.amax(self.balance_dummy)
self.tb_balance[3] = self.balance_dummy[-1]
self.tb_net_worth[0] = np.amin(self.net_worth_dummy)
self.tb_net_worth[1] = np.mean(self.net_worth_dummy)
self.tb_net_worth[2] = np.amax(self.net_worth_dummy)
self.tb_net_worth[3] = self.net_worth_dummy[-1]
self.tb_cache_rollout_vec.append(t)
if np.ndim(self.tb_cache_final_balance) == 1:
self.tb_cache_final_balance = np.reshape(
self.tb_balance, [1, -1])
self.tb_cache_final_net = np.reshape(self.tb_net_worth,
[1, -1])
else:
self.tb_cache_final_balance = np.concatenate(
(self.tb_cache_final_balance,
np.reshape(self.tb_balance, [1, -1])),
axis=0)
self.tb_cache_final_net = np.concatenate(
(self.tb_cache_final_net,
np.reshape(self.tb_net_worth, [1, -1])),
axis=0)
if i % self.monitor_freq == 0 and i != 0:
tb_writer.add_scalar('training/reward',
np.mean(self.tb_cache_reward_vec), i)
tb_writer.add_scalar('training/rollout',
np.mean(self.tb_cache_rollout_vec), i)
tb_writer.add_scalar(
'balance/low', np.mean(self.tb_cache_final_balance[:, 0]),
i)
tb_writer.add_scalar(
'balance/avg', np.mean(self.tb_cache_final_balance[:, 1]),
i)
tb_writer.add_scalar(
'balance/high', np.mean(self.tb_cache_final_balance[:, 2]),
i)
tb_writer.add_scalar(
'balance/final',
np.mean(self.tb_cache_final_balance[:, 3]), i)
tb_writer.add_scalar('net_worth/low',
np.mean(self.tb_cache_final_net[:, 0]), i)
tb_writer.add_scalar('net_worth/avg',
np.mean(self.tb_cache_final_net[:, 1]), i)
tb_writer.add_scalar('net_worth/high',
np.mean(self.tb_cache_final_net[:, 2]), i)
tb_writer.add_scalar('net_worth/final',
np.mean(self.tb_cache_final_net[:, 3]), i)
tb_writer.add_scalar(
'net_worth/profit',
np.mean(self.tb_cache_final_net[:, 3] - self.start_budget),
i)
tb_writer.add_histogram('training_stats/reward',
np.asarray(self.tb_cache_reward_vec),
i)
tb_writer.add_histogram('training_stats/rollout',
np.asarray(self.tb_cache_rollout_vec),
i)
tb_writer.add_histogram(
'performance_stats/final_balance',
np.asarray(self.tb_cache_final_balance[:, -1]), i)
tb_writer.add_histogram(
'performance_stats/final_net_worth',
np.asarray(self.tb_cache_final_net[:, -1]), i)
tb_writer.add_histogram(
'performance_stats/profit',
np.asarray(self.tb_cache_final_net[:, -1] -
self.start_budget), i)
tb_writer.add_histogram('action/type',
np.asarray(self.tb_action_vec), i)
tb_writer.add_histogram('action/symbol',
np.asarray(self.tb_action_symbol_vec),
i)
tb_writer.add_histogram('action/action_amount',
np.asarray(self.tb_action_amount), i)
self.tb_cache_reward_vec = []
self.tb_cache_rollout_vec = []
self.tb_cache_final_net = np.zeros(4)
self.tb_cache_final_balance = np.zeros(4)
self.tb_action_vec = []
self.tb_action_symbol_vec = []
self.tb_action_amount = []
self.tb_balance = np.zeros(4)
self.tb_net_worth = np.zeros(4)
def rl_agent(self, env):
self.policy = chainer.Sequential(
L.Linear(None, 256),
F.tanh,
L.Linear(None, 128),
F.tanh,
# L.Linear(None, env.action_space.low.size, initialW=winit_last),
L.Linear(None, env.action_space.low.size),
# F.sigmoid,
chainerrl.policies.GaussianHeadWithStateIndependentCovariance(
action_size=env.action_space.low.size,
var_type='diagonal',
var_func=lambda x: F.exp(2 * x), # Parameterize log std
# var_param_init=0, # log std = 0 => std = 1
))
self.vf = chainer.Sequential(
L.Linear(None, 256),
F.tanh,
L.Linear(None, 128),
F.tanh,
L.Linear(None, 1),
)
# Combine a policy and a value function into a single model
self.model = chainerrl.links.Branched(self.policy, self.vf)
self.opt = chainer.optimizers.Adam(alpha=3e-4, eps=1e-5)
self.opt.setup(self.model)
self.agent = PPO(
self.model,
self.opt,
# obs_normalizer=obs_normalizer,
gpu=-1,
update_interval=512,
minibatch_size=8,
clip_eps_vf=None,
entropy_coef=0.001,
# standardize_advantages=args.standardize_advantages,
)
return self.agent
def train(self):
print('\nstart training loop\n')
def check_types(input, inputname):
if np.isnan(input).any():
print('----> ', inputname, ' array contains NaN\n',
np.isnan(input).shape, '\n')
if np.isinf(input).any():
print('----> ', inputname, ' array contains inf\n',
np.isinf(input).shape, '\n')
self.agent = self.rl_agent(self.env)
n_episodes = 1000000
max_episode_len = 1000
for i in range(0, n_episodes + 1):
obs = self.env.reset()
reward = 0
done = False
R = 0 # return (sum of rewards)
t = 0 # time step
while not done and t < max_episode_len:
# Uncomment to watch the behaviour
# self.env.render()
action = self.agent.act_and_train(obs, reward)
check_types(action, 'action')
obs, reward, done, _, monitor_data = self.env.step(action)
check_types(obs, 'obs')
check_types(reward, 'reward')
self.monitor_training(self.writer, t, i, done, action,
monitor_data)
R += reward
t += 1
if done: print(' training at episode ' + str(i), end='\r')
if i % 100 == 0 and i > 0:
self.agent.save(model_outdir)
serializers.save_npz(model_outdir + 'model.npz', self.model)
# if i % 1000 == 0:
# print('\nepisode:', i, ' | episode length: ', t, '\nreward:', R,
# '\nstatistics:', self.agent.get_statistics(), '\n')
self.agent.stop_episode_and_train(obs, reward, done)
print('Finished.')
class rl_stock_trader():
def __init__(self):
run_name = 'run_test'
self.outdir = './results/' + run_name + '/'
self.outdir_train = self.outdir + 'train/'
self.outdir_test = self.outdir + 'test/'
self.training_counter = 0
try:
sys.makedirs(self.outdir_train)
sys.makedirs(self.outdir_test)
except Exception:
pass
self.writer_train = SummaryWriter(self.outdir_train)
self.writer_test = SummaryWriter(self.outdir_test)
self.monitor_freq = 100
self.testing_samples = 100
self.validation_scores = []
self.training_scores = []
self.settings = {
'past_horzion': 100,
'max_steps': 365,
'inital_account_balance': 1e4,
'stop_below_balance': 1e3,
'transation_fee': .1,
'years_training': 5,
'years_testing': 1,
}
testing_end = date.today()
testing_beginning = testing_end - relativedelta(
years=self.settings['years_testing']) - relativedelta(
days=self.settings['past_horzion'])
training_end = testing_beginning - relativedelta(days=1)
training_beginning = training_end - relativedelta(
years=self.settings['years_training']) - relativedelta(
days=self.settings['past_horzion'])
self.data = {
'train_gold':
self.get_prices(gold_shanghai, 1, training_beginning,
training_end),
'train_copper':
self.get_prices(copper_shanghai, 1, training_beginning,
training_end),
'train_aluminum':
self.get_prices(aluminum_shanghai, 1, training_beginning,
training_end),
'test_gold':
self.get_prices(gold_shanghai, 1, testing_beginning, testing_end),
'test_copper':
self.get_prices(copper_shanghai, 1, testing_beginning,
testing_end),
'test_aluminum':
self.get_prices(aluminum_shanghai, 1, testing_beginning,
testing_end),
'test_soybean_oil':
self.get_prices(soybean_oil, 1, testing_beginning, testing_end),
'test_dax_futures':
self.get_prices(dax_futures, 1, testing_beginning, testing_end),
'test_corn':
self.get_prices(corn, 1, testing_beginning, testing_end),
'test_canadian_dollar':
self.get_prices(canadian_dollar, 1, testing_beginning,
testing_end),
}
# print('\n\n*************\n', self.data['test_corn'], '\n\n')
self.env_test_gold = StockTradingEnv(self.get_prices(
gold_shanghai, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_test_copper = StockTradingEnv(self.get_prices(
copper_shanghai, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_test_aluminum = StockTradingEnv(self.get_prices(
aluminum_shanghai, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_test_soy_bean = StockTradingEnv(self.get_prices(
soybean_oil, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_test_dax = StockTradingEnv(self.get_prices(
dax_futures, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_test_corn = StockTradingEnv(self.get_prices(
corn, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_test_canadian_dollar = StockTradingEnv(self.get_prices(
canadian_dollar, 1, testing_beginning, testing_end),
self.settings,
test=True)
self.env_train = StockTradingEnv(self.data['train_gold'],
self.settings,
test=False)
# self.env_test = StockTradingEnv(self.data['test_gold'], self.settings, test=True)
self.test_envs = {
'gold':
StockTradingEnv(self.data['test_gold'], self.settings, test=True),
'copper':
StockTradingEnv(self.data['test_copper'], self.settings,
test=True),
'aluminum':
StockTradingEnv(self.data['test_aluminum'],
self.settings,
test=True),
}
self.agent = self.rl_agent(self.env_train)
def get_prices(self, index, depth, start, end):
data_prices = quandl.get(index + str(depth),
start_date=start,
end_date=end)
data_prices.index = pd.to_datetime(data_prices.index)
return data_prices
def rl_agent(self, env):
# self.policy = chainer.Sequential(
# L.BatchNormalization(axis=0),
# L.Linear(None, 256),
# # F.dropout(ratio=.5),
# F.tanh,
# L.Linear(None, 128),
# # F.dropout(ratio=.5),
# F.tanh,
# # L.Linear(None, env.action_space.low.size, initialW=winit_last),
# L.Linear(None, env.action_space.low.size),
# # F.sigmoid,
# chainerrl.policies.GaussianHeadWithStateIndependentCovariance(
# action_size=env.action_space.low.size,
# var_type='diagonal',
# var_func=lambda x: F.exp(2 * x), # Parameterize log std
# # var_param_init=0, # log std = 0 => std = 1
# ))
self.policy = chainer.Sequential(
L.BatchNormalization(axis=0),
L.Linear(None, 256),
# F.dropout(ratio=.5),
F.sigmoid,
# F.relu,
L.Linear(None, 128),
# F.dropout(ratio=.5),
F.sigmoid,
# L.Linear(None, env.action_space.low.size, initialW=winit_last),
L.Linear(None, env.action_space.low.size),
F.sigmoid,
chainerrl.policies.GaussianHeadWithStateIndependentCovariance(
action_size=env.action_space.low.size,
var_type='diagonal',
var_func=lambda x: F.exp(2 * x), # Parameterize log std
# var_param_init=0, # log std = 0 => std = 1
))
self.vf = chainer.Sequential(
L.BatchNormalization(axis=0),
L.Linear(None, 256),
# F.dropout(ratio=.5),
F.sigmoid,
L.Linear(None, 128),
# F.dropout(ratio=.5),
F.sigmoid,
L.Linear(None, 1),
F.sigmoid,
)
# self.vf = chainer.Sequential(
# L.BatchNormalization(axis=0),
# L.Linear(None, 256),
# # F.dropout(ratio=.5),
# F.tanh,
# L.Linear(None, 128),
# # F.dropout(ratio=.5),
# F.tanh,
# L.Linear(None, 1),
# )
# Combine a policy and a value function into a single model
self.model = chainerrl.links.Branched(self.policy, self.vf)
self.opt = chainer.optimizers.Adam(alpha=3e-3, eps=1e-5)
self.opt.setup(self.model)
self.agent = PPO(
self.model,
self.opt,
# obs_normalizer=obs_normalizer,
gpu=-1,
update_interval=64,
minibatch_size=32,
clip_eps_vf=None,
entropy_coef=0.001,
# standardize_advantages=args.standardize_advantages,
)
return self.agent
def monitor_training(self, tb_writer, t, i, done, action, monitor_data,
counter):
if t == 0 or i == 0:
self.cash_dummy = []
self.equity_dummy = []
self.shares_dummy = []
self.shares_value_dummy = []
self.action_dummy = []
self.action_prob_dummy = []
self.cash_dummy.append(monitor_data['cash'])
self.equity_dummy.append(monitor_data['equity'])
self.shares_dummy.append(monitor_data['shares_held'])
self.shares_value_dummy.append(monitor_data['value_in_shares'])
self.action_dummy.append(monitor_data['action'])
self.action_prob_dummy.append(monitor_data['action_prob'])
# if done:
# tb_writer.add_scalar('cash', np.mean(self.cash_dummy), counter)
# tb_writer.add_scalar('equity', np.mean(self.equity_dummy), counter)
# tb_writer.add_scalar('shares_held', np.mean(self.shares_dummy), counter)
# tb_writer.add_scalar('shares_value', np.mean(self.shares_value_dummy), counter)
# tb_writer.add_scalar('action', np.mean(self.action_dummy), counter)
# tb_writer.add_histogram('action_prob', np.mean(self.action_prob_dummy), counter)
def plot_validation_figures(self, index, name, test_data_label, benchmark):
if name in ['mean', 'max', 'final']:
ylimits = [.75 * np.amin(benchmark), 1.5 * np.amax(benchmark)]
elif name == 'min':
ylimits = [0., self.settings['inital_account_balance']]
plotcolor = 'darkgreen'
plt.figure(figsize=(18, 18))
plt.scatter(
np.asarray(self.validation_scores)[:, 0],
np.asarray(self.validation_scores)[:, index])
plt.grid()
plt.ylim(ylimits[0], ylimits[1])
plt.title(name + ' equity statistics over 1 year')
plt.xlabel('trained episodes')
plt.ylabel('equity [$]')
plt.savefig(self.outdir + test_data_label + '/scatter_' + name +
'_equity.pdf')
plt.close()
area_plots = []
box_data = []
for j in range(len(np.unique(np.asarray(self.validation_scores)[:,
0]))):
dummy = np.asarray(self.validation_scores)[:, index][np.where(
np.asarray(self.validation_scores)[:, 0] == np.unique(
np.asarray(self.validation_scores)[:, 0])[j])]
box_data.append(dummy)
area_plots.append([
np.percentile(dummy, 5),
np.percentile(dummy, 25),
np.percentile(dummy, 50),
np.percentile(dummy, 75),
np.percentile(dummy, 95),
])
area_plots = np.asarray(area_plots)
p05 = area_plots[:, 0]
p25 = area_plots[:, 1]
p50 = area_plots[:, 2]
p75 = area_plots[:, 3]
p95 = area_plots[:, 4]
plt.figure(figsize=(18, 18))
plt.fill_between(np.arange(area_plots.shape[0]),
p05,
p95,
facecolor=plotcolor,
alpha=.3)
plt.fill_between(np.arange(area_plots.shape[0]),
p25,
p75,
facecolor=plotcolor,
alpha=.8)
plt.plot(p50, linewidth=3, color='lightblue')
plt.ylim(ylimits[0], ylimits[1])
plt.grid()
plt.title(name + ' equity statistics over 1 year')
plt.xlabel('trained episodes')
plt.ylabel('equity [$]')
plt.savefig(self.outdir + test_data_label + '/area_' + name +
'_equity.pdf')
plt.close()
plt.figure(figsize=(18, 18))
plt.boxplot(
box_data,
notch=True,
labels=None,
boxprops=dict(color=plotcolor, linewidth=2),
capprops=dict(color=plotcolor),
whiskerprops=dict(color=plotcolor),
flierprops=dict(color=plotcolor,
markeredgecolor=plotcolor,
markerfacecolor=plotcolor),
medianprops=dict(color='lightblue', linewidth=2),
)
plt.ylim(ylimits[0], ylimits[1])
plt.grid()
plt.title('equity statistics over 1 year')
plt.xlabel('trained episodes')
plt.ylabel('equity [$]')
plt.savefig(self.outdir + test_data_label + '/box_' + name +
'_equity.pdf')
plt.close()
def validate(self, episode, counter, test_data_label):
try:
os.mkdir(self.outdir + test_data_label + '/')
except Exception:
pass
test_equity = []
test_trades_buy = []
test_trades_sell = []
test_data = self.data['test_' + test_data_label]
try:
benchmark = test_data['Close'].values[self.
settings['past_horzion']:]
except KeyError:
benchmark = test_data['Settle'].values[self.
settings['past_horzion']:]
benchmark /= benchmark[0]
benchmark *= self.settings['inital_account_balance']
plt.figure(figsize=(18, 18))
for i in range(0, self.testing_samples):
if test_data_label == 'gold':
obs = self.env_test_gold.reset()
if test_data_label == 'copper':
obs = self.env_test_copper.reset()
if test_data_label == 'aluminum':
obs = self.env_test_aluminum.reset()
if test_data_label == 'soybean_oil':
obs = self.env_test_soy_bean.reset()
if test_data_label == 'dax_futures':
obs = self.env_test_dax.reset()
if test_data_label == 'corn':
obs = self.env_test_corn.reset()
if test_data_label == 'corn':
obs = self.env_test_corn.reset()
if test_data_label == 'canadian_dollar':
obs = self.env_test_canadian_dollar.reset()
# obs = self.env_test.reset()
reward = 0
done = False
R = 0
t = 0
while not done:
action = self.agent.act(obs)
if test_data_label == 'gold':
obs, reward, done, _, monitor_data = self.env_test_gold.step(
action)
if test_data_label == 'copper':
obs, reward, done, _, monitor_data = self.env_test_copper.step(
action)
if test_data_label == 'aluminum':
obs, reward, done, _, monitor_data = self.env_test_aluminum.step(
action)
if test_data_label == 'soybean_oil':
obs, reward, done, _, monitor_data = self.env_test_soy_bean.step(
action)
if test_data_label == 'dax_futures':
obs, reward, done, _, monitor_data = self.env_test_dax.step(
action)
if test_data_label == 'corn':
obs, reward, done, _, monitor_data = self.env_test_corn.step(
action)
if test_data_label == 'canadian_dollar':
obs, reward, done, _, monitor_data = self.env_test_canadian_dollar.step(
action)
# obs, reward, done, _, monitor_data = self.env_test.step(action)
test_equity.append(monitor_data['equity'])
action_choice = np.argmax(softmax(action))
action_confidence = softmax(action)[action_choice]
if action_confidence > .8:
if action_choice == 0:
test_trades_buy.append([t, monitor_data['equity']])
if action_choice == 2:
test_trades_sell.append([t, monitor_data['equity']])
self.monitor_training(self.writer_test, t, i, done, action,
monitor_data, counter)
R += reward
t += 1
if done:
test_equity = test_equity[:-1]
plt.plot(test_equity[:-1], linewidth=1)
# try:
# plt.scatter(np.asarray(test_trades_buy)[:,0], np.asarray(test_trades_buy)[:,1], marker='X', c='green', s=5)
# plt.scatter(np.asarray(test_trades_sell)[:,0], np.asarray(test_trades_sell)[:,1], marker='X', c='red', s=5)
# except IndexError:
# pass
self.validation_scores.append([
counter,
np.mean(test_equity),
np.amin(test_equity),
np.amax(test_equity), test_equity[-1]
])
test_equity = []
self.agent.stop_episode()
time_axis = test_data.index[self.settings['past_horzion']:].date
time_axis_short = time_axis[::10]
plt.plot(benchmark, linewidth=3, color='k', label='close')
plt.ylim(.75 * np.amin(benchmark), 1.5 * np.amax(benchmark))
plt.xticks(np.linspace(0, len(time_axis),
len(time_axis_short) - 1),
time_axis_short,
rotation=90)
plt.grid()
plt.title(test_data_label + ' validation runs at episode ' +
str(episode))
plt.xlabel('episode')
plt.ylabel('equity [$]')
plt.legend()
plt.savefig(self.outdir + test_data_label + '/validation_E' +
str(episode) + '.pdf')
plt.close()
self.plot_validation_figures(1, 'mean', test_data_label, benchmark)
self.plot_validation_figures(2, 'min', test_data_label, benchmark)
self.plot_validation_figures(3, 'max', test_data_label, benchmark)
self.plot_validation_figures(4, 'final', test_data_label, benchmark)
def train(self):
print('\nstart training loop\n')
def check_types(input, inputname):
if np.isnan(input).any():
print('----> ', inputname, ' array contains NaN\n',
np.isnan(input).shape, '\n')
if np.isinf(input).any():
print('----> ', inputname, ' array contains inf\n',
np.isinf(input).shape, '\n')
n_episodes = int(1e5)
log_data = []
action_log = []
debug_printing = False
for i in range(0, n_episodes + 1):
obs = self.env_train.reset()
reward = 0
done = False
R = 0 # return (sum of rewards)
t = 0 # time step
while not done:
# self.env.render()
action = self.agent.act_and_train(obs, reward)
obs, reward, done, _, monitor_data = self.env_train.step(
action)
self.monitor_training(self.writer_train, t, i, done, action,
monitor_data, self.training_counter)
R += reward
t += 1
if t % 10 == 0 and not done:
log_data.append({
'equity':
int(monitor_data['equity']),
'shares_held':
int(monitor_data['shares_held']),
'shares_value':
int(monitor_data['value_in_shares']),
'cash':
int(monitor_data['cash']),
't':
int(t),
})
action_log.append([
self.training_counter, action[0], action[1], action[2]
])
if done:
if i % 10 == 0:
print('\nrollout ' + str(i) + '\n',
pd.DataFrame(log_data).max())
log_data = []
self.training_scores.append([i, R])
self.training_counter += 1
self.agent.stop_episode()
if i % self.monitor_freq == 0:
# self.agent.stop_episode_and_train(obs, reward, done)
# print('\n\nvalidation...')
self.validate(i, self.training_counter, 'gold')
if debug_printing: print('\n\n****************\nSOY BEANS\n\n')
self.validate(i, self.training_counter, 'soybean_oil')
if debug_printing: print('\n\n****************\nCORN\n\n')
self.validate(i, self.training_counter, 'corn')
# if debug_printing: print('\n\n****************\nCANADIAN DOLLAR\n\n')
# self.validate(i, self.training_counter, 'canadian_dollar')
if debug_printing: print('\n****************\n')
act_probs = softmax(np.asarray(action_log)[:, 1:], axis=1)
plt.figure()
plt.scatter(np.asarray(self.training_scores)[:, 0],
np.asarray(self.training_scores)[:, 1],
s=2,
label='reward')
plt.legend()
plt.title('reward')
plt.grid()
plt.savefig(self.outdir + 'reward.pdf')
plt.close()
plt.figure()
plt.scatter(np.asarray(action_log)[:, 0],
act_probs[:, 0],
label='action0')
plt.scatter(np.asarray(action_log)[:, 0],
act_probs[:, 1],
label='action1')
plt.scatter(np.asarray(action_log)[:, 0],
act_probs[:, 2],
label='action2')
plt.legend()
plt.title('actions')
plt.grid()
plt.savefig(self.outdir + 'actions.pdf')
plt.close()
plt.figure()
plt.plot(np.asarray(action_log)[:, 0],
act_probs[:, 0],
label='action0')
plt.plot(np.asarray(action_log)[:, 0],
act_probs[:, 1],
label='action1')
plt.plot(np.asarray(action_log)[:, 0],
act_probs[:, 2],
label='action2')
plt.legend()
plt.title('actions')
plt.grid()
plt.savefig(self.outdir + 'actions_plot.pdf')
plt.close()
if i % 10 == 0 and i > 0:
self.agent.save(self.outdir)
serializers.save_npz(self.outdir + 'model.npz', self.model)
# if i % 1000 == 0:
# print('\nepisode:', i, ' | episode length: ', t, '\nreward:', R,
# '\nstatistics:', self.agent.get_statistics(), '\n')
self.agent.stop_episode_and_train(obs, reward, done)
print('Finished.')
# eval_interval=3,
# outdir="test2"
# )
# Set the discount factor that discounts future rewards.
gamma = 0.95
# Use epsilon-greedy for exploration
explorer = chainerrl.explorers.ConstantEpsilonGreedy(
epsilon=0.3, random_action_func=env.action_space.sample)
n_episodes = 200
max_episode_len = 200
for i in range(1, n_episodes + 1):
obs = env.reset()
reward = 0
done = False
R = 0 # return (sum of rewards)
t = 0 # time step
while not done and t < max_episode_len:
# Uncomment to watch the behaviour
# env.render()
action = agent.act_and_train(obs, reward)
obs, reward, done, _ = env.step(action)
R += reward
t += 1
if i % 10 == 0:
print('episode:', i, 'R:', R, 'statistics:', agent.get_statistics())
agent.stop_episode_and_train(obs, reward, done)
print('Finished.')
def main():
import logging
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', type=int, default=-1)
parser.add_argument('--env', type=str, default='Hopper-v2')
parser.add_argument('--num-envs', type=int, default=1)
parser.add_argument('--seed',
type=int,
default=0,
help='Random seed [0, 2 ** 32)')
parser.add_argument('--outdir',
type=str,
default='results',
help='Directory path to save output files.'
' If it does not exist, it will be created.')
parser.add_argument('--steps', type=int, default=10**6)
parser.add_argument('--eval-interval', type=int, default=10000)
parser.add_argument('--eval-n-runs', type=int, default=10)
parser.add_argument('--reward-scale-factor', type=float, default=1e-2)
parser.add_argument('--standardize-advantages', action='store_true')
parser.add_argument('--render', action='store_true', default=False)
parser.add_argument('--lr', type=float, default=3e-4)
parser.add_argument('--weight-decay', type=float, default=0.0)
parser.add_argument('--demo', action='store_true', default=False)
parser.add_argument('--load', type=str, default='')
parser.add_argument('--logger-level', type=int, default=logging.DEBUG)
parser.add_argument('--monitor', action='store_true')
parser.add_argument('--window-size', type=int, default=100)
parser.add_argument('--update-interval', type=int, default=2048)
parser.add_argument('--log-interval', type=int, default=1000)
parser.add_argument('--batchsize', type=int, default=64)
parser.add_argument('--epochs', type=int, default=10)
parser.add_argument('--entropy-coef', type=float, default=0.0)
args = parser.parse_args()
logging.basicConfig(level=args.logger_level)
# Set a random seed used in ChainerRL
misc.set_random_seed(args.seed, gpus=(args.gpu, ))
# Set different random seeds for different subprocesses.
# If seed=0 and processes=4, subprocess seeds are [0, 1, 2, 3].
# If seed=1 and processes=4, subprocess seeds are [4, 5, 6, 7].
process_seeds = np.arange(args.num_envs) + args.seed * args.num_envs
assert process_seeds.max() < 2**32
args.outdir = experiments.prepare_output_dir(args, args.outdir)
# def make_env(process_idx, test):
# env = gym.make(args.env)
# # Use different random seeds for train and test envs
# process_seed = int(process_seeds[process_idx])
# env_seed = 2 ** 32 - 1 - process_seed if test else process_seed
# env.seed(env_seed)
# # Cast observations to float32 because our model uses float32
# env = chainerrl.wrappers.CastObservationToFloat32(env)
# if args.monitor:
# env = chainerrl.wrappers.Monitor(env, args.outdir)
# if not test:
# # Scale rewards (and thus returns) to a reasonable range so that
# # training is easier
# env = chainerrl.wrappers.ScaleReward(env, args.reward_scale_factor)
# if args.render:
# env = chainerrl.wrappers.Render(env)
# return env
def make_env(test):
env = gym.make(
"DaktyPushingSimulationEnv-v0",
level=5,
simulation_backend="mujoco",
control_frequency_in_hertz=100,
state_space_components_to_be_used=None,
alternate_env_object=None,
discretization_factor_torque_control_space=None,
model_as_function_for_pixel_to_latent_space_parsing=(None, None))
# print('\n############\n', env, '\n############\n')
env.unwrapped.finger.set_resolution_quality('low')
# print('\n############\n', env, '\n############\n')
env = gym.wrappers.TimeLimit(env)
# print('\n############\n', env, '\n############\n')
# Unwrap TimeLimit wrapper
assert isinstance(env, gym.wrappers.TimeLimit)
env = env.env
# Use different random seeds for train and test envs
# env_seed = 2 ** 32 - 1 - args.seed if test else args.seed
# env.seed(env_seed)
process_seed = 420
env_seed = 2**32 - 1 - process_seed if test else process_seed
env.seed(env_seed)
# Cast observations to float32 because our model uses float32
env = chainerrl.wrappers.CastObservationToFloat32(env)
if args.monitor:
env = chainerrl.wrappers.Monitor(env, args.outdir)
if args.render and not test:
env = chainerrl.wrappers.Render(env)
return env
def make_batch_env(test):
return chainerrl.envs.MultiprocessVectorEnv([
functools.partial(make_env, idx, test)
for idx, env in enumerate(range(args.num_envs))
])
# Only for getting timesteps, and obs-action spaces
sample_env = make_env(0)
timestep_limit = sample_env.spec.tags.get(
'wrapper_config.TimeLimit.max_episode_steps')
obs_space = sample_env.observation_space
action_space = sample_env.action_space
print('\n\n------------------- obs_space: ', obs_space.shape, '\n\n\n')
# Normalize observations based on their empirical mean and variance
obs_normalizer = chainerrl.links.EmpiricalNormalization(obs_space.low.size,
clip_threshold=5)
winit_last = chainer.initializers.LeCunNormal(1e-2)
action_size = action_space.low.size
policy = chainer.Sequential(
L.Linear(None, 64),
F.tanh,
L.Linear(None, 64),
F.tanh,
L.Linear(None, action_size, initialW=winit_last),
chainerrl.policies.GaussianHeadWithStateIndependentCovariance(
action_size=action_size,
var_type='diagonal',
var_func=lambda x: F.exp(2 * x), # Parameterize log std
var_param_init=0, # log std = 0 => std = 1
))
vf = chainer.Sequential(
concat_obs_and_action,
L.Linear(None, 64),
F.tanh,
L.Linear(None, 64),
F.tanh,
L.Linear(None, 1),
)
# Combine a policy and a value function into a single model
model = chainerrl.links.Branched(policy, vf)
opt = chainer.optimizers.Adam(alpha=args.lr, eps=1e-5)
opt.setup(model)
if args.weight_decay > 0:
opt.add_hook(NonbiasWeightDecay(args.weight_decay))
agent = PPO(
model,
opt,
obs_normalizer=obs_normalizer,
gpu=args.gpu,
update_interval=args.update_interval,
minibatch_size=args.batchsize,
epochs=args.epochs,
clip_eps_vf=None,
entropy_coef=args.entropy_coef,
standardize_advantages=args.standardize_advantages,
)
if args.load:
agent.load(args.load)
if args.demo:
env = make_env(True)
eval_stats = experiments.eval_performance(
env=env,
agent=agent,
n_steps=None,
n_episodes=args.eval_n_runs,
max_episode_len=timestep_limit)
print('n_runs: {} mean: {} median: {} stdev {}'.format(
args.eval_n_runs, eval_stats['mean'], eval_stats['median'],
eval_stats['stdev']))
else:
env = make_env(False)
n_episodes = 10000
# pbar = tqdm(total=n_episodes)
max_episode_len = 1000
for i in range(1, n_episodes + 1):
# pbar.update(1)
obs = env.reset()
# print('obs inital..............', obs.shape)
reward = 0
done = False
R = 0 # return (sum of rewards)
t = 0 # time step
# pbar = tqdm(total=max_episode_len)
while not done and t < max_episode_len:
# pbar.update(1)
# Uncomment to watch the behaviour
# env.render()
action = agent.act_and_train(obs, reward)
# print('action..................', action)
obs, reward, done, _ = env.step(action)
# print('obs.....................', obs)
# print('reward..................', reward)
R += reward
t += 1
if i % 10 == 0:
print('episode:', i, 'R:', R, 'statistics:',
agent.get_statistics())
agent.stop_episode_and_train(obs, reward, done)
print('Finished.')
# Linearly decay the learning rate to zero
def lr_setter(env, agent, value):
agent.optimizer.alpha = value
lr_decay_hook = experiments.LinearInterpolationHook(
args.steps, args.lr, 0, lr_setter)
experiments.train_agent_batch_with_evaluation(
agent=agent,
env=make_env(False),
eval_env=make_env(True),
outdir=args.outdir,
steps=args.steps,
eval_n_steps=None,
eval_n_episodes=args.eval_n_runs,
eval_interval=args.eval_interval,
log_interval=args.log_interval,
return_window_size=args.window_size,
max_episode_len=timestep_limit,
save_best_so_far_agent=False,
step_hooks=[
lr_decay_hook,
],
)