This repo contains
- Trading environment(OpenAI Gym) for trading crypto currency
- Duel Deep Q Network
Agent is implemented usingkeras-rl(https://github.com/keras-rl/keras-rl)
Agent is expected to learn useful action sequences to maximize profit in a given environment.
Environment limits agent to either buy, sell, hold stock(coin) at each step.
If an agent decides to take a
- LONG position it will initiate sequence of action such as
buy- hold- hold- sell - for a SHORT position vice versa (e.g.)
sell - hold -hold -buy.
Only a single position can be opened per trade.
- Thus invalid action sequence like
buy - buywill be consideredbuy- hold. - Default transaction fee is : 0.0005
Reward is given
- when the position is closed or
- an episode is finished.
This type of sparse reward granting scheme takes longer to train but is most successful at learning long term dependencies.
Agent decides optimal action by observing its environment.
- Trading environment will emit features derived from ohlcv-candles(the window size can be configured).
- Thus, input given to the agent is of the shape
(window_size, n_features).
With some modification it can easily be applied to stocks, futures or foregin exchange as well.
Visualization / Main / Environment
Sample data provided is 5min ohlcv candle fetched from bitmex.
- train :
'./data/train/70000 - test :
'./data/train/16000
keras-rl, numpy, tensorflow ... etc
pip install -r requirements.txt
# change "keras-rl/core.py" to "./modified/core.py"# create environment
# OPTIONS
ENV_NAME = 'OHLCV-v0'
TIME_STEP = 30
PATH_TRAIN = "./data/train/"
PATH_TEST = "./data/test/"
env = OhlcvEnv(TIME_STEP, path=PATH_TRAIN)
env_test = OhlcvEnv(TIME_STEP, path=PATH_TEST)
# random seed
np.random.seed(123)
env.seed(123)
# create_model
nb_actions = env.action_space.n
model = create_model(shape=env.shape, nb_actions=nb_actions)
print(model.summary())
# create memory
memory = SequentialMemory(limit=50000, window_length=TIME_STEP)
# create policy
policy = EpsGreedyQPolicy()# policy = BoltzmannQPolicy()
# create agent
# you can specify the dueling_type to one of {'avg','max','naive'}
dqn = DQNAgent(model=model, nb_actions=nb_actions, memory=memory, nb_steps_warmup=200,
enable_dueling_network=True, dueling_type='avg', target_model_update=1e-2, policy=policy,
processor=NormalizerProcessor())
dqn.compile(Adam(lr=1e-3), metrics=['mae'])# now train and test agent
while True:
# train
dqn.fit(env, nb_steps=5500, nb_max_episode_steps=10000, visualize=False, verbose=2)
try:
# validate
info = dqn.test(env_test, nb_episodes=1, visualize=False)
n_long, n_short, total_reward, portfolio = info['n_trades']['long'], info['n_trades']['short'], info[
'total_reward'], int(info['portfolio'])
np.array([info]).dump(
'./info/duel_dqn_{0}_weights_{1}LS_{2}_{3}_{4}.info'.format(ENV_NAME, portfolio, n_long, n_short,
total_reward))
dqn.save_weights(
'./model/duel_dqn_{0}_weights_{1}LS_{2}_{3}_{4}.h5f'.format(ENV_NAME, portfolio, n_long, n_short,
total_reward),
overwrite=True)
except KeyboardInterrupt:
continue## simply plug in any keras model :)
def create_model(shape, nb_actions):
model = Sequential()
model.add(CuDNNLSTM(64, input_shape=shape, return_sequences=True))
model.add(CuDNNLSTM(64))
model.add(Dense(32))
model.add(Activation('relu'))
model.add(Dense(nb_actions, activation='linear'))[Verbose] While training or testing,
- environment will print out (current_tick , # Long, # Short, Portfolio)
[Portfolio]
- initial portfolio starts with 100*10000(krw-won)
- reflects change in portfolio value if the agent had invested 100% of its balance every time it opened a position.
[Reward]
- simply pct earning per trade.
- total cumulative return :[0] -> [3.670099054203348]
- portfolio value [1000000] -> [29415305.46593453]
Wow ! 29 fold return, 3.67 reward !
! Disclaimer : if may have overfitted :(
- Lee Hankyol - Initial work - deep_rl_trader
This project is licensed under the MIT License - see the LICENSE.md file for details