def main():
# Load orderbook
orderbook = Orderbook()
orderbook.loadFromEvents('ob-1-small.tsv')
env = gym.make("ctc-executioner-v0")
env.configure(orderbook)
model = deepq.models.cnn_to_mlp( convs=[(1, 10, 20)], hiddens=[200])
act = deepq.learn(
env,
q_func=model,
lr=1e-4,
max_timesteps=100000,
buffer_size=5000,
exploration_fraction=0.1,
exploration_final_eps=0.1,
target_network_update_freq=1,
print_freq=10,
)
print("Saving model as ctc-executioner-v0.pkl")
act.save("ctc-executioner-v0.pkl")
from order_side import OrderSide
from orderbook import Orderbook
orderbook = Orderbook()
orderbook.loadFromEvents('ob-1-small.tsv')
orderbook_test = orderbook
orderbook.summary()
side = OrderSide.SELL
levels = list(range(-20, 21))
episode = {
'episode': 9,
'steps': {
0: {
'action': 2,
'index': 167,
't': 100,
'i': 0.9999999999999999,
'reward': -6.224232140000822
},
1: {
'action': 39,
'index': 173,
't': 90,
'i': 0.9999999999999999,
'reward': 1.9899999999997817
},
2: {
'action': 21,
'index': 179,
return model
def saveModel(model, name):
# serialize model to JSON
model_json = model.to_json()
with open(name + '.json', "w") as json_file:
json_file.write(model_json)
# serialize weights to HDF5
model.save_weights(name + '.h5')
print('Saved model "' + name + '" to disk')
# Load orderbook
orderbook = Orderbook()
orderbook.loadFromEvents('ob-1.tsv')
orderbook_test = orderbook
orderbook.summary()
# import datetime
# orderbook = Orderbook()
# config = {
# 'startPrice': 10000.0,
# 'endPrice': 9940.0,
# 'levels': 25,
# 'qtyPosition': 0.1,
# 'startTime': datetime.datetime.now(),
# 'duration': datetime.timedelta(minutes=30),
# 'interval': datetime.timedelta(seconds=1)
# }
# orderbook.createArtificial(config)
