myTestLSTM.py

from btgym import BTgymEnv
import IPython.display as Display
import PIL.Image as Image
from gym import spaces


import gym
import numpy as np
import random

'''
from keras.models import Sequential
from keras.layers import Dense, Dropout
from keras.optimizers import Adam
'''

from keras.models import Sequential, load_model
from keras.layers.core import Dense, Dropout, Activation
from keras.layers.recurrent import LSTM
from keras.optimizers import RMSprop, Adam

from collections import deque

class DQN:
    def __init__(self, env):
        self.env     = env
        self.memory  = deque(maxlen=20000)
        
        self.gamma = 0.85
        self.epsilon = 1.0
        self.epsilon_min = 0.01
        self.epsilon_decay = 0.995
        self.learning_rate = 0.005
        self.tau = .125

        self.model        = self.create_model()
        self.target_model = self.create_model()

    def create_model(self):
        model   = Sequential()
       # state_shape  = list(self.env.observation_space.shape.items())[0][1]
        #Reshaping for LSTM 
        #state_shape=np.array(state_shape)
        #state_shape= np.reshape(state_shape, (30,4,1))
        '''
        model.add(Dense(24, input_dim=state_shape[1], activation="relu"))
        model.add(Dense(48, activation="relu"))
        model.add(Dense(24, activation="relu"))
        model.add(Dense(self.env.action_space.n))
        model.compile(loss="mean_squared_error",
            optimizer=Adam(lr=self.learning_rate))
        '''
        model.add(LSTM(64,
               input_shape=(4,1),
               #return_sequences=True,
               stateful=False
               ))
        model.add(Dropout(0.5))
        
        #model.add(LSTM(64,
                       #input_shape=(1,4),
                       #return_sequences=False,
        #               stateful=False
        #               ))
        model.add(Dropout(0.5))
        
        model.add(Dense(self.env.action_space.n, init='lecun_uniform'))
        model.add(Activation('linear')) #linear output so we can have range of real-valued outputs

        rms = RMSprop()
        adam = Adam()
        model.compile(loss='mse', optimizer=adam)
        return model

    def act(self, state):
        self.epsilon *= self.epsilon_decay
        self.epsilon = max(self.epsilon_min, self.epsilon)
        if np.random.random() < self.epsilon:
            return self.env.action_space.sample()
        return np.argmax(self.model.predict(state)[0])

   
    def target_train(self):
        weights = self.model.get_weights()
        target_weights = self.target_model.get_weights()
        for i in range(len(target_weights)):
            target_weights[i] = weights[i] * self.tau + target_weights[i] * (1 - self.tau)
        self.target_model.set_weights(target_weights)

    def save_model(self, fn):
        self.model.save(fn)
              
    def show_rendered_image(self, rgb_array):
        """
        Convert numpy array to RGB image using PILLOW and
        show it inline using IPykernel.
        """
        Display.display(Image.fromarray(rgb_array))

    def render_all_modes(self, env):
        """
        Retrieve and show environment renderings
        for all supported modes.
        """
        for mode in self.env.metadata['render.modes']:
            print('[{}] mode:'.format(mode))
            self.show_rendered_image(self.env.render(mode))

def main():
    env     = BTgymEnv(filename='./data/DAT_ASCII_EURUSD_M1_2016.csv',
                       state_shape={'raw_state': spaces.Box(low=-100, high=100,shape=(30,4))},
                       skip_frame=5,
                       start_cash=100000,
                       broker_commission=0.02,
                       fixed_stake=100,
                       drawdown_call=90,
                       render_ylabel='Price Lines',
                       render_size_episode=(12,8),
                       render_size_human=(8, 3.5),
                       render_size_state=(10, 3.5),
                       render_dpi=75,
                       verbose=0,) 
    gamma   = 0.9
    epsilon = .95

    trials  = 100
    trial_len = 1000

    # updateTargetNetwork = 1000
    dqn_agent = DQN(env=env)
    steps = []
    for trial in range(trials): 
        #dqn_agent.model= load_model("./model.model")
        cur_state = np.array(list(env.reset().items())[0][1])
        cur_state= np.reshape(cur_state, (30,4,1))
        for step in range(trial_len):
            action = dqn_agent.act(cur_state)
            new_state, reward, done, _ = env.step(action)
            reward = reward*10 if not done else -10
            new_state =list(new_state.items())[0][1]
            new_state= np.reshape(new_state, (30,4,1))
            dqn_agent.target_train() # iterates target model

            cur_state = new_state
            if done:
                break
        
        print("Completed trial #{} ".format(trial))
        dqn_agent.render_all_modes(env)
        dqn_agent.save_model("model.model".format(trial))
        

if __name__ == "__main__":
    main()

trying to run your code, but heading the same error:
AttributeError: 'Lines_LineSeries_LineIterator_DataAccessor_Strateg' object has no attribute 'get_raw_state_state'

replace code:
state_shape={'raw_state': spaces.Box(low=-100, high=100,shape=(30,4))},
to
state_shape={'raw': spaces.Box(low=-100, high=100,shape=(30,4))},

Can you suggest some way to import and buy/sell more than one company stocks in btgym?

Hi, I have this error "BTgymDataset class is DEPRECATED, use btgym.datafeed.derivative.BTgymDataset2 instead."

@sareme I encounter the same error. Have you found a resolution? This error is encountered even when running the simplest example provided by the developer, link here, so I am led to believe it is a dependency problem.

I am having the same problem as @mkraman2 and others on this thread. If someone in the community would be kind enough to post a solution, that would be highly appreciated.

Hi Adham, it has been a while since I've worked on this, but iirc, the problem was related to attempting to run on Windows, and resolved when running on a recent distro of Ubuntu. The developer mentioned elsewhere that BTGym was developed on and tested exclusively in Linux.

I'm currently running the example files on my mac, so the windows issue shouldn't be a problem. I'm getting the following error when trying to run setting_up_enviornment_vasic.ipynb. Any idea why this could be happening @Kismuz?

[2021-03-02 18:27:55.075162] DEBUG: SimpleDataSet_0: Start time adjusted to <00:00> Process BTgymDataFeedServer-7: Traceback (most recent call last): File "/opt/anaconda3/envs/btgym_env/lib/python3.6/multiprocessing/process.py", line 258, in _bootstrap self.run() File "/Users/adhamsuliman/Documents/personal_projects/bot_stock_trader/bt_gym/btgym/btgym/dataserver.py", line 176, in run sample = self.get_data(sample_config=service_input['kwargs']) File "/Users/adhamsuliman/Documents/personal_projects/bot_stock_trader/bt_gym/btgym/btgym/dataserver.py", line 88, in get_data sample = self.dataset.sample(**sample_config) File "/Users/adhamsuliman/Documents/personal_projects/bot_stock_trader/bt_gym/btgym/btgym/datafeed/base.py", line 539, in sample return self._sample(**kwargs) File "/Users/adhamsuliman/Documents/personal_projects/bot_stock_trader/bt_gym/btgym/btgym/datafeed/base.py", line 617, in _sample **kwargs File "/Users/adhamsuliman/Documents/personal_projects/bot_stock_trader/bt_gym/btgym/btgym/datafeed/base.py", line 882, in _sample_interval first_row = self.data.index.get_loc(adj_timedate, method='nearest') File "/opt/anaconda3/envs/btgym_env/lib/python3.6/site-packages/pandas/core/indexes/datetimes.py", line 622, in get_loc raise KeyError(key) KeyError: datetime.date(2016, 1, 11)