trade

2026-07-15 00:36:55 +08:00 · 2021-01-28 00:34:32 +08:00
parent 7f9216dc90
commit bcadf47f32
37 changed files with 4604 additions and 0 deletions
--- a/examples/trade/observation/teacher_obs.py
+++ b/examples/trade/observation/teacher_obs.py
@@ -0,0 +1,89 @@
+import pandas as pd
+import numpy as np
+from gym.spaces import Discrete, Box, Tuple, MultiDiscrete
+import math
+import json
+
+from .obs_rule import RuleObs
+
+
+class TeacherObs(RuleObs):
+    """
+    The Observation used for OPD method.
+
+    Consist of public state(raw feature), private state, seqlen
+
+    """
+
+    def get_obs(
+        self,
+        raw_df,
+        feature_dfs,
+        t,
+        interval,
+        position,
+        target,
+        is_buy,
+        max_step_num,
+        interval_num,
+        *args,
+        **kargs,
+    ):
+        if t == -1:
+            self.private_states = []
+        public_state = self.get_feature_res(feature_dfs, t, interval, whole_day=True)
+        private_state = np.array([position / target, (t + 1) / max_step_num])
+        self.private_states.append(private_state)
+        list_private_state = np.concatenate(self.private_states)
+        list_private_state = np.concatenate(
+            (
+                list_private_state,
+                [0.0] * 2 * (interval_num + 1 - len(self.private_states)),
+            )
+        )
+        seqlen = np.array([interval])
+        assert not (
+            np.isnan(list_private_state).any() | np.isinf(list_private_state).any()
+        ), f"{private_state}, {target}"
+        assert not (
+            np.isnan(public_state).any() | np.isinf(public_state).any()
+        ), f"{public_state}"
+        return np.concatenate((public_state, list_private_state, seqlen))
+
+
+class RuleTeacher(RuleObs):
+    """ """
+
+    def get_obs(
+        self,
+        raw_df,
+        feature_dfs,
+        t,
+        interval,
+        position,
+        target,
+        is_buy,
+        max_step_num,
+        interval_num,
+        *args,
+        **kargs,
+    ):
+        if t == -1:
+            self.private_states = []
+        public_state = feature_dfs[0].reshape(-1)[: 6 * 240]
+        private_state = np.array([position / target, (t + 1) / max_step_num])
+        teacher_action = self.get_feature_res(feature_dfs, t, interval)[
+            -self.features[1]["size"] :
+        ]
+        self.private_states.append(private_state)
+        list_private_state = np.concatenate(self.private_states)
+        list_private_state = np.concatenate(
+            (
+                list_private_state,
+                [0.0] * 2 * (interval_num + 1 - len(self.private_states)),
+            )
+        )
+        seqlen = np.array([interval])
+        return np.concatenate(
+            (teacher_action, public_state, list_private_state, seqlen)
+        )