def _next_observation(self):
self.current_ohlcv = self.data_provider.next_ohlcv()
self.timestamps.append(
pd.to_datetime(self.current_ohlcv.Date.item(), unit='s'))
self.observations = self.observations.append(self.current_ohlcv,
ignore_index=True)
if self.stationarize_obs:
observations = log_and_difference(self.observations, inplace=False)
else:
observations = self.observations
if self.normalize_obs:
observations = max_min_normalize(observations)
obs = observations.values[-1]
if self.stationarize_obs:
scaled_history = log_and_difference(self.account_history,
inplace=False)
else:
scaled_history = self.account_history
if self.normalize_obs:
scaled_history = max_min_normalize(scaled_history, inplace=False)
obs = np.insert(obs, len(obs), scaled_history.values[-1], axis=0)
obs = np.reshape(obs.astype('float16'), self.obs_shape)
obs[np.bitwise_not(np.isfinite(obs))] = 0
return obs
def _next_observation(self):
self.current_ohlcv = self.data_provider.next_ohlcv()
self.timestamps.append(pd.to_datetime(
self.current_ohlcv.Date.item(), unit='s'))
self.observations = self.observations.append(
self.current_ohlcv, ignore_index=True)
obs = log_values(
self.observations.loc[:, self.feature_columns]).values[-1]
obs_diff = log_and_difference(self.observations, inplace=False)
obs = np.insert(obs, len(
obs), obs_diff.loc[:, self.feature_columns].values[-1], axis=0)
history = self.account_history
history["balance_ratio"] = history["balance"] / \
history["current_net_worth"]
scaled_history = history.loc[:, [
"balance", "asset_held", "current_net_worth", "balance_ratio"]]
obs = np.insert(obs, len(obs), scaled_history.values[-1], axis=0)
obs = np.reshape(obs.astype('float16'), self.obs_shape)
obs[np.bitwise_not(np.isfinite(obs))] = 0
return obs
def _next_observation(self):
self.current_ohlcv = self.data_provider.next_ohlcv()
self.observations = self.observations.append(self.current_ohlcv,
ignore_index=True)
if self.enable_stationarization:
observations = log_and_difference(self.observations)
else:
observations = self.observations
observations = max_min_normalize(observations)
obs = observations.values[-1]
scaled_history = max_min_normalize(self.account_history)
obs = np.insert(obs, len(obs), scaled_history.values[-1], axis=0)
obs = np.reshape(obs.astype('float16'), self.obs_shape)
obs[np.bitwise_not(np.isfinite(obs))] = 0
return obs