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)