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trade
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Yuchen Fang
3
examples/trade/observation/__init__.py
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3
examples/trade/observation/__init__.py
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from .ppo_obs import *
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from .teacher_obs import *
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from .obs_rule import *
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158
examples/trade/observation/obs_rule.py
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158
examples/trade/observation/obs_rule.py
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import pandas as pd
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import numpy as np
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from gym.spaces import Discrete, Box, Tuple, MultiDiscrete
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import math
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import json
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class BaseObs(object):
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""" """
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def __init__(self, config):
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self._observation_space = None
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def get_space(self):
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""" """
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return self._observation_space
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def get_obs(self, t):
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pass
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class RuleObs(BaseObs):
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"""The observation for minute-level rule-based agents, which consists of prediction, private state and direction information."""
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def __init__(self, config):
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feature_size = 0
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self.features = config["features"]
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self.time_interval = config["time_interval"]
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self.max_step_num = config["max_step_num"]
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for feature in self.features:
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feature_size += feature["size"]
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self._observation_space = Tuple(
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(
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Box(-np.inf, np.inf, shape=(feature_size,), dtype=np.float32),
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Box(-np.inf, np.inf, shape=(4,), dtype=np.float32),
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Discrete(2),
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)
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)
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def __call__(self, *args, **kargs):
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return self.get_obs(*args, **kargs)
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def get_feature_res(self, df_list, time, interval, whole_day=False, interval_num=8):
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"""
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This method would extract the needed feature from the feature dataframe based on the feature name
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and the description in feature config.
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:param df_list: The dataframes of features, the order is consistent with the feature list.
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:param time: The index of current minute of the day (starting from -1).
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:param interval: The index of interval or decition making.
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:param whole_day: if True, this method would return the concatenate of all dataframe.(Default value = False)
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"""
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predictions = []
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if whole_day:
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try:
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prediction = [df_list[i].reshape(-1) for i in range(len(df_list))]
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except:
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prediction = [df_list[i].reshape(-1) for i in range(len(df_list))]
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for i, p in enumerate(prediction):
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if len(p) < interval_num:
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prediction[i] = np.concatenate(
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(p, np.zeros(interval_num - len(p))), axis=-1
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)
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# res = np.stack(prediction).transpose().reshape(-1)
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return np.concatenate(prediction)
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for i in range(len(self.features)):
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feature = self.features[i]
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df = df_list[i]
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size = feature["size"]
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if feature["type"] == "inday":
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if time == -1:
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predictions += [0.0] * size
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else:
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predictions += (
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df.iloc[size * time : size * (time + 1)].reshape(-1).tolist()
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)
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elif feature["type"] == "daily":
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predictions += df.reshape(-1)[:size].tolist()
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elif feature["type"] == "range":
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# if feature.startswith('oracle'):
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# predictions += df.iloc[:, (time + 1) : size + (time + 1)].reshape(-1).tolist()
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if time == -1:
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predictions += [0.0] * size
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else:
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predictions += df.iloc[time : size + time].reshape(-1).tolist()
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elif feature["type"] == "interval":
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if (
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len(df.iloc[interval * size : (interval + 1) * size].reshape(-1))
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== size
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):
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predictions += (
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df.iloc[interval * size : (interval + 1) * size]
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.reshape(-1)
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.tolist()
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)
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else:
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predictions += [0.0] * size
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elif feature["type"] == "step":
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if (
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len(df.iloc[size * (time + 1) : size * (time + 2)].reshape(-1))
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== size
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):
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predictions += (
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df.iloc[size * (time + 1) : size * (time + 2)]
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.reshape(-1)
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.tolist()
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)
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else:
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predictions += [0.0] * size
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return np.array(predictions)
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def get_obs(
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self, raw_df, feature_dfs, t, interval, position, target, is_buy, *args, **kargs
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):
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private_state = np.array([position, target, t, self.max_step_num])
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prediction_state = self.get_feature_res(feature_dfs, t, interval)
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return {
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"prediction": prediction_state,
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"private": private_state,
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"is_buy": int(is_buy),
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}
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class RuleInterval(RuleObs):
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"""
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The observation for interval_level rule based strategy.
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Consist of interval prediction, private state, direction
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"""
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def get_obs(
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self,
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raw_df,
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feature_dfs,
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t,
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interval,
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position,
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target,
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is_buy,
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max_step_num,
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interval_num,
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action=1.0,
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*args,
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**kargs
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):
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private_state = np.array([position, target, interval - 1, interval_num])
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prediction_state = self.get_feature_res(feature_dfs, t, interval)
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return {
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"prediction": prediction_state,
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"private": private_state,
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"is_buy": int(is_buy),
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"action": action,
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}
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41
examples/trade/observation/ppo_obs.py
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41
examples/trade/observation/ppo_obs.py
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@@ -0,0 +1,41 @@
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import pandas as pd
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import numpy as np
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from gym.spaces import Discrete, Box, Tuple, MultiDiscrete
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import math
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import json
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from .obs_rule import RuleObs
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class PPOObs(RuleObs):
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"""The observation defined in IJCAI 2020. The action of previous state is included in private state"""
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def get_obs(
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self,
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raw_df,
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feature_dfs,
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t,
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interval,
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position,
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target,
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is_buy,
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max_step_num,
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interval_num,
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action=0,
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):
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if t == -1:
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self.private_states = []
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public_state = self.get_feature_res(feature_dfs, t, interval, whole_day=True)
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# market_state = feature_dfs[0].reshape(-1)[:6*240]
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private_state = np.array([position / target, (t + 1) / max_step_num, action])
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self.private_states.append(private_state)
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list_private_state = np.concatenate(self.private_states)
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list_private_state = np.concatenate(
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(
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list_private_state,
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[0.0] * 3 * (interval_num + 1 - len(self.private_states)),
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)
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)
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seqlen = np.array([interval])
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return np.concatenate((public_state, list_private_state, seqlen))
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89
examples/trade/observation/teacher_obs.py
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89
examples/trade/observation/teacher_obs.py
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@@ -0,0 +1,89 @@
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import pandas as pd
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import numpy as np
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from gym.spaces import Discrete, Box, Tuple, MultiDiscrete
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import math
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import json
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from .obs_rule import RuleObs
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class TeacherObs(RuleObs):
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"""
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The Observation used for OPD method.
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Consist of public state(raw feature), private state, seqlen
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"""
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def get_obs(
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self,
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raw_df,
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feature_dfs,
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t,
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interval,
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position,
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target,
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is_buy,
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max_step_num,
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interval_num,
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*args,
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**kargs,
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):
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if t == -1:
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self.private_states = []
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public_state = self.get_feature_res(feature_dfs, t, interval, whole_day=True)
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private_state = np.array([position / target, (t + 1) / max_step_num])
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self.private_states.append(private_state)
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list_private_state = np.concatenate(self.private_states)
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list_private_state = np.concatenate(
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(
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list_private_state,
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[0.0] * 2 * (interval_num + 1 - len(self.private_states)),
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)
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)
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seqlen = np.array([interval])
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assert not (
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np.isnan(list_private_state).any() | np.isinf(list_private_state).any()
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), f"{private_state}, {target}"
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assert not (
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np.isnan(public_state).any() | np.isinf(public_state).any()
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), f"{public_state}"
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return np.concatenate((public_state, list_private_state, seqlen))
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class RuleTeacher(RuleObs):
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""" """
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def get_obs(
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self,
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raw_df,
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feature_dfs,
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t,
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interval,
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position,
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target,
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is_buy,
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max_step_num,
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interval_num,
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*args,
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**kargs,
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):
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if t == -1:
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self.private_states = []
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public_state = feature_dfs[0].reshape(-1)[: 6 * 240]
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private_state = np.array([position / target, (t + 1) / max_step_num])
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teacher_action = self.get_feature_res(feature_dfs, t, interval)[
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-self.features[1]["size"] :
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]
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self.private_states.append(private_state)
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list_private_state = np.concatenate(self.private_states)
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list_private_state = np.concatenate(
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(
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list_private_state,
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[0.0] * 2 * (interval_num + 1 - len(self.private_states)),
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)
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)
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seqlen = np.array([interval])
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return np.concatenate(
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(teacher_action, public_state, list_private_state, seqlen)
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)
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