def run_agent(agent):
env = gym.make(CONFIG['env_name'])
log_actions = CONFIG['log_actions']
state = env.reset()
# Removed time element from state
state = np.delete(state, 2)
next_state, reward, done, info = env.step([0, 0])
if len(next_state) > agent.state_size:
next_state = np.delete(next_state, 2)
state_as_percentages = get_state_as_change_percentage(state, next_state)
state = next_state
done = False
states_buy = []
states_sell = []
closes = []
i = 0
while not done:
closes.append(state[5])
action = agent.act(state_as_percentages)
#if log_actions:
#print('action: ',action)
#print('state: ',state)
next_state, reward, done, info = env.step(action)
if len(next_state) > agent.state_size:
next_state = np.delete(next_state, 2)
if action[0] == 1 and action[1] > 0 and state[1] > state[2]:
if log_actions:
print('stocks owned: ', state[0])
print('stocks to buy: ', action[1])
print('stock price: ', state[2])
print('cash in hand: ', state[1])
print('total value: ', info['cur_val'])
states_buy.append(i)
if action[0] == 2 and action[1] > 0 and state[0] > 0:
if log_actions:
print('stocks owned: ', state[0])
print('stocks to sell: ', action[1])
print('stock price: ', state[2])
print('cash in hand: ', state[1])
print('total value: ', info['cur_val'])
states_sell.append(i)
state_as_percentages = get_state_as_change_percentage(
state, next_state)
state = next_state
i += 1
return closes, states_buy, states_sell, info['cur_val']
def __init__(self, action_space):
self.action_space = action_space
def act(self, observation, reward, done):
return self.action_space.sample()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description=None)
parser.add_argument('env_id',
nargs='?',
default='SPY-Daily-v0',
help='Select the environment to run')
args = parser.parse_args()
env = gym.make(args.env_id)
agent = RandomAgent(env.action_space)
episode_count = 100
reward = 0
done = False
final_vals = []
initial_value = 0
for i in range(episode_count):
ob = env.reset()
initial_value = ob[1]
while True:
action = agent.act(ob, reward, done)
def aapl_daily_v0_env():
return make('AAPL-Daily-v0')
def abbv_daily_v0_env():
return make('ABBV-Daily-v0')
def amd_daily_v0_env():
return make('AMD-Daily-v0')
def amzn_daily_v0_env():
return make('AMZN-Daily-v0')
def ba_daily_v0_env():
return make('BA-Daily-v0')
def cgc_daily_v0_env():
return make('CGC-Daily-v0')
def cron_daily_v0_env():
return make('CRON-Daily-v0')
def googl_daily_v0_env():
return make('GOOGL-Daily-v0')
def spy_intraday_v0_env():
return make('SPY-Minute-v0')
def spy_daily_v0_env():
return make('SPY-Daily-v0')
if action[0] == 2 and state[0] > 0:
states_sell.append(i)
opn = (next_state[2] - state[2]) / next_state[2]
high = (next_state[3] - state[3]) / next_state[3]
low = (next_state[4] - state[4]) / next_state[4]
close = (next_state[5] - state[5]) / next_state[5]
volume = (next_state[6] - state[6]) / next_state[6]
state_as_percentages = [
next_state[0], next_state[1], opn, high, low, close, volume
]
state = next_state
i += 1
return closes, states_buy, states_sell, info['cur_val']
env = gym.make('SPY-Daily-v0')
# removing time frame
state_size = env.state_dim - 1
time_frame = 30
dirname = os.path.dirname(__file__)
weights_file = os.path.join(dirname, 'evo_weights_5.h5')
model = build_compile_model(state_size * time_frame, 7)
model.load_weights(weights_file)
agent = EvoAgent(state_size, time_frame, model.get_weights())
closes, states_buy, states_sell, result = test_agent(env, agent, model)
print('result: {}'.format(str(result)))
plt.figure(figsize=(20, 10))
plt.plot(closes, label='true close', c='g')
