def test_csv_streamer():
with open('test.csv', 'w+') as csvfile:
csv_test = csv.writer(csvfile)
for i in range(10):
csv_test.writerow([1] * 10)
csvstreamer = CSVStreamer(filename='./test.csv')
for i in range(10):
assert all(csvstreamer.next() == [1] * 10)
def get_generator(gen_type='WavySignal', filename=''):
print('applying generator {0}'.format(gen_type))
if gen_type == 'WavySignal':
return WavySignal(period_1=25, period_2=50, epsilon=-0.5)
if gen_type == 'CSVStreamer':
return CSVStreamer(filename=filename)
if gen_type == 'CSVTrading':
return CSVTrading(filename=filename)
class TestSpreadTrading(object):
#data_generator = AR1(a=0.1, ba_spread=0.1)
data_generator = CSVStreamer(filename='../../data/AMZN-L1.csv')
st = SpreadTrading(data_generator=data_generator,
spread_coefficients=[1],
trading_fee=0.2,
time_fee=0.1,
history_length=2)
def test_init(self):
assert self.st._data_generator == self.data_generator
assert self.st._spread_coefficients == [1]
assert self.st._first_render
assert self.st._trading_fee == 0.2
assert self.st._time_fee == 0.1
assert self.st._episode_length == 1000
assert self.st.n_actions == 3
assert self.st._history_length == 2
assert len(self.st._prices_history) == 2
def test_step(self):
# Buy
state = self.st.step(np.array([0, 1, 0]))
#assert state[0][0] == state[0][1]
assert all(state[0][-3:] == np.array([0, 1, 0]))
assert self.st._entry_price != 0
assert self.st._exit_price == 0
# Hold
state = self.st.step(np.array([1, 0, 0]))
assert all(state[0][-3:] == np.array([0, 1, 0]))
assert self.st._entry_price != 0
assert self.st._exit_price == 0
# Sell
state = self.st.step(np.array([0, 0, 1]))
assert all(state[0][-3:] == np.array([1, 0, 0]))
assert self.st._entry_price == 0
assert self.st._exit_price != 0
def test_reset(self):
return self.st.reset()
max_steps = 1000
explore_start = 1.0
explore_stop = 0.01
decay_rate = 1e-4
_lambda = 0.95
value_coefficient = 0.5
entropy_coefficient = 0.01
max_grad_norm = 0.5
log_interval = 10
hold = np.array([1, 0, 0])
buy = np.array([0, 1, 0])
sell = np.array([0, 0, 1])
possible_actions = [hold, buy, sell]
#Classes and variables
generator = CSVStreamer(filename='/Users/tawehbeysolow/Downloads/amazon_order_book_data2.csv')
#generator = WavySignal(period_1=25, period_2=50, epsilon=-0.5)
memory = Memory(max_size=memory_size)
generator = WavySignal(period_1=25, period_2=50, epsilon=-0.5)
environment = SpreadTrading(spread_coefficients=[1],
data_generator=generator,
trading_fee=trading_fee,
time_fee=time_fee,
history_length=history_length)
state_size = len(environment.reset())
# action processing
action_batch = np.where(action_batch == 1)
return state_batch, action_batch, reward_batch, next_state_batch, done_batch
# In[105]:
import matplotlib.pyplot as plt
#import sys
#sys.path.append('/Users/matthewdixon/Downloads/Trading-Gym/')
#from tgym.envs import SpreadTrading
#from tgym.gens.deterministic import WavySignal
#from tgym.gens.random import AR1
from tgym.gens.csvstream import CSVStreamer
# Instantiating the environmnent
generator = CSVStreamer(filename='../data/AMZN-L1.csv')
#generator = AR1(a=0.1, ba_spread=0.1) #WavySignal(period_1=25, period_2=50, epsilon=-0.5)
episodes = 100
episode_length = 400
trading_fee = .0
time_fee = 0
history_length = 2
environment = SpreadTrading(spread_coefficients=[1],
data_generator=generator,
trading_fee=trading_fee,
time_fee=time_fee,
history_length=history_length)
# In[102]:
state = environment.reset()
if __name__ == "__main__":
start = time.time()
# Instantiating the environmnent
gen_type = 'C'
if gen_type == 'W':
generator = WavySignal(period_1=25,
period_2=50,
epsilon=-0.5,
ba_spread=0.0001)
elif gen_type == 'R':
generator = RandomGenerator(spread=0.0001,
range_low=1.0,
range_high=2.0)
elif gen_type == 'C':
filename = r'./examples/price_usdeur.csv'
generator = CSVStreamer(filename=filename)
episodes = 50
episode_length = 2000
trading_fee = .2
time_fee = 0
history_length = 10000
# history_length number of historical states in the observation vector.
profit_taken = 10
stop_loss = -5
render_show = False
trading_type = 'T'
if trading_type == 'T':
environment = TickTrading(data_generator=generator,
trading_fee=trading_fee,
from __future__ import absolute_import
from tgym.envs.trading_spread import SpreadTrading
from tgym.envs.trading_tick import TickTrading
from tgym.gens.csvstream import CSVStreamer
generator = CSVStreamer(filename='./examples/price_2.csv')
episode_length = 200
environment = SpreadTrading(spread_coefficients=[2, -1],
data_generator=generator,
episode_length=episode_length)
environment = TickTrading
#environment.render()
while True:
action = input("Action: Buy (b) / Sell (s) / Hold (enter): ")
if action == 'b':
action = [0, 1, 0]
elif action == 's':
action = [0, 0, 1]
else:
action = [1, 0, 0]
environment.step(action)
environment.render()