register_env("dm-" + env_name, env_creator)
# Placeholder to enable use of a custom pre-processor
class ImagePreproc(Preprocessor):
def _init_shape(self, obs_space, options):
self.shape = (84, 84, 3) # Adjust third dim if stacking frames
return self.shape
def transform(self, observation):
observation = cv2.resize(observation, (self.shape[0], self.shape[1]))
return observation
ModelCatalog.register_custom_preprocessor("sq_im_84", ImagePreproc)
config = {
# Model and preprocessor options.
"model": {
"custom_model": model_name,
"custom_options": {
# Custom notes for the experiment
"notes": {
"args": vars(args)
},
},
# NOTE:Wrappers are applied by RLlib if custom_preproc is NOT specified
"custom_preprocessor": "sq_im_84",
"dim": 84,
"free_log_std": False, # if args.discrete_actions else True,
self.regressor.fit(X_scaled, y_pct)
#print('Reg fit time:{:.2f}'.format(time.time()-t))
#t = time.time()
self.classifier.fit(X_scaled, y_sign)
#print('Class fit time:{:.2f}'.format(time.time()-t))
self.is_fitted = True
def predict(self, obs):
obs = np.array(obs).reshape(1, -1)
return self.regressor.predict(obs)[0], self.classifier.predict(obs)[0]
if __name__ == '__main__':
ModelCatalog.register_custom_preprocessor('mv_pred',
PredictiveMarketVariables)
env = MarketOrderEnv(order_paths='../../data/feather/',
snapshot_paths='../../data/snap_json/',
max_sequence_skip=10000,
max_episode_time='20hours',
random_start=False)
options = {
'custom_preprocessor': 'mv_pred',
'custom_options': {
'fast_macd_l': 1200,
'slow_macd_l': 2400,
'ofi_l': 1000,
'mid_l': 1000
}
}
