| def _get_most_probable_outcome(self, day_index): | |
| previous_data_start_index = max(0, day_index - self.n_latency_days) | |
| previous_data_end_index = max(0, day_index - 1) | |
| previous_data = self._test_data.iloc[previous_data_end_index: previous_data_end_index] | |
| previous_data_features = StockPredictor._extract_features( | |
| previous_data) | |
| outcome_score = [] | |
| for possible_outcome in self._possible_outcomes: | |
| total_data = np.row_stack( | |
| (previous_data_features, possible_outcome)) | |
| outcome_score.append(self.hmm.score(total_data)) | |
| most_probable_outcome = self._possible_outcomes[np.argmax( | |
| outcome_score)] | |
| return most_probable_outcome | |
| def predict_close_price(self, day_index): | |
| open_price = self._test_data.iloc[day_index]['open'] | |
| predicted_frac_change, _, _ = self._get_most_probable_outcome( | |
| day_index) | |
| return open_price * (1 + predicted_frac_change) |
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