def run_agent(agent):
env = fingym.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 tsla_daily_random_walk_env():
return make('TSLA-Daily-Random-Walk')
def tsla_daily_v0_env():
return make('TSLA-Daily-v0')
def ba_daily_v0_env():
return make('BA-Daily-v0')
# See the License for the specific language governing permissions and
# limitations under the License.
import numpy as np
from fingym import fingym
from collections import deque
import matplotlib.pyplot as plt
import ray
import os
ray.init()
env = fingym.make('SPY-Daily-v0')
CONFIG = {
'env_name': 'SPY-Daily-v0',
# removing time frame, stocks owned and cash in hand
'state_size': env.state_dim - 3,
'max_shares_to_trade_at_once': 100,
'time_frame': 30,
'sigma': 0.1,
'learning_rate': 0.03,
'population_size': 400,
'iterations': 50,
'train': False,
'eval': True,
'log_actions': True
}
from fingym import fingym
import numpy as np
import matplotlib.pyplot as plt
plt.figure(figsize=(20, 10))
if __name__ == '__main__':
env = fingym.make('SPY-Daily-Random-Walk')
random_walks = []
for _ in range(100):
real_close = np.zeros(env.n_step)
random_walk = np.zeros(env.n_step)
obs = env.reset()
real_close[0] = obs[3]
random_walk[0] = obs[3]
while True:
obs, reward, done, info = env.step([0, 0])
real_close[env.cur_step] = info['original_close']
random_walk[env.cur_step] = obs[3]
if done:
break
random_walks.append(random_walk)
time = np.linspace(1, len(random_walk), len(random_walk))
def googl_daily_random_walk_env():
return make('GOOGL-Daily-Random-Walk')
def spy_daily_random_walk_env():
return make('SPY-Daily-Random-Walk')
def abbv_daily_random_walk_env():
return make('ABBV-Daily-Random-Walk')
def aapl_daily_random_walk_env():
return make('AAPL-Daily-Random-Walk')
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 spy_daily_v0_env():
return make('SPY-Daily-v0')
def spy_intraday_v0_env():
return make('SPY-Minute-v0')
def amd_daily_random_walk_env():
return make('AMD-Daily-Random-Walk')
def googl_daily_v0_env():
return make('GOOGL-Daily-v0')
def amzn_daily_random_walk_env():
return make('AMZN-Daily-Random-Walk')
def cron_daily_v0_env():
return make('CRON-Daily-v0')
def ba_daily_random_walk_env():
return make('BA-Daily-Random-Walk')
self.bought_yet = True
return [1, num_shares_to_buy]
else:
return [0, 0]
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 = fingym.make(args.env_id)
agent = BuyAndHoldAgent(env.action_space)
reward = 0
done = False
cur_val = 0
ob = env.reset()
initial_value = ob[1]
while True:
action = agent.act(ob, reward, done)
ob, reward, done, info = env.step(action)
if done:
cur_val = info['cur_val']
def cgc_daily_random_walk_env():
return make('CGC-Daily-Random-Walk')
def cron_daily_random_walk_env():
return make('CRON-Daily-Random-Walk')
def cgc_daily_v0_env():
return make('CGC-Daily-v0')
