liuyu/qlib
1
0
Fork 0
mirror of https://github.com/microsoft/qlib.git synced 2026-07-11 23:06:58 +08:00
Code Activity

format

Browse Source
This commit is contained in:
Yuchen Fang
2021-01-28 00:41:02 +08:00
parent 98086e4fdc
commit a03b08bb4c
21 changed files with 154 additions and 563 deletions
Download Patch File Download Diff File Expand all files Collapse all files

50
examples/trade/env/env_rl.py vendored
View File

@@ -51,9 +51,7 @@ class StockEnv(gym.Env):
obs_conf["time_interval"] = self.time_interval
obs_conf["max_step_num"] = self.max_step_num
self.obs = getattr(observation, config["obs"]["name"])(obs_conf)
self.action_func = getattr(action, config["action"]["name"])(
config["action"]["config"]
)
self.action_func = getattr(action, config["action"]["name"])(config["action"]["config"])
self.reward_func_list = []
self.reward_log_dict = {}
self.reward_coef = []
@@ -87,19 +85,13 @@ class StockEnv(gym.Env):
self.target,
self.is_buy,
) = sample
self.raw_df = pd.DataFrame(
index=self.raw_df_index,
data=self.raw_df_values,
columns=self.raw_df_columns,
)
self.raw_df = pd.DataFrame(index=self.raw_df_index, data=self.raw_df_values, columns=self.raw_df_columns,)
del self.raw_df_values, self.raw_df_columns, self.raw_df_index
start_time = time.time()
self.load_time = time.time() - start_time
self.day_vwap = nan_weighted_avg(
self.raw_df["$vwap0"].values[self.offset : self.offset + self.max_step_num],
self.raw_df["$volume0"].values[
self.offset : self.offset + self.max_step_num
],
self.raw_df["$volume0"].values[self.offset : self.offset + self.max_step_num],
)
try:
assert not (np.isnan(self.day_vwap) or np.isinf(self.day_vwap))
@@ -108,9 +100,7 @@ class StockEnv(gym.Env):
print(self.ins)
print(self.day_vwap)
self.raw_df.to_pickle("/nfs_data1/kanren/error_df.pkl")
self.day_twap = np.nanmean(
self.raw_df["$vwap0"].values[self.offset : self.offset + self.max_step_num]
)
self.day_twap = np.nanmean(self.raw_df["$vwap0"].values[self.offset : self.offset + self.max_step_num])
self.t = -1 + self.offset
self.interval = 0
self.position = self.target
@@ -130,9 +120,7 @@ class StockEnv(gym.Env):
if self.log:
index_array = [
np.array([self.ins] * self.max_step_num),
self.raw_df.index.to_numpy()[
self.offset : self.offset + self.max_step_num
],
self.raw_df.index.to_numpy()[self.offset : self.offset + self.max_step_num],
np.array([self.date] * self.max_step_num),
]
self.traded_log = pd.DataFrame(
@@ -142,9 +130,7 @@ class StockEnv(gym.Env):
self.offset : self.offset + self.max_step_num
],
"$traded_t": np.nan,
"$vwap_t": self.raw_df["$vwap0"].values[
self.offset : self.offset + self.max_step_num
],
"$vwap_t": self.raw_df["$vwap0"].values[self.offset : self.offset + self.max_step_num],
"action": np.nan,
},
index=index_array,
@@ -239,18 +225,14 @@ class StockEnv(gym.Env):
self.real_eps_time += time.time() - start_time
if self.done:
this_traded = self.target - self.position
this_vwap = (
(self.this_cash / this_traded) if this_traded > ZERO else self.day_vwap
)
this_vwap = (self.this_cash / this_traded) if this_traded > ZERO else self.day_vwap
valid = min(self.target, self.this_valid)
this_ffr = (this_traded / valid) if valid > ZERO else 1.0
if abs(this_ffr - 1.0) < ZERO:
this_ffr = 1.0
this_ffr *= 100
this_vv_ratio = this_vwap / self.day_vwap
vwap = self.raw_df["$vwap0"].values[
self.offset : self.max_step_num + self.offset
]
vwap = self.raw_df["$vwap0"].values[self.offset : self.max_step_num + self.offset]
this_tt_ratio = this_vwap / np.nanmean(vwap)
if self.is_buy:
@@ -262,9 +244,7 @@ class StockEnv(gym.Env):
for i, reward_func in enumerate(self.reward_func_list):
if not reward_func.isinstant:
tmp_r = reward_func(
performance_raise, this_ffr, this_tt_ratio, self.is_buy
)
tmp_r = reward_func(performance_raise, this_ffr, this_tt_ratio, self.is_buy)
reward += tmp_r * self.reward_coef[i]
self.reward_log_dict[type(reward_func).__name__] += tmp_r
@@ -405,18 +385,14 @@ class StockEnv_Acc(StockEnv):
self.real_eps_time += time.time() - start_time
if self.done:
this_traded = self.target - self.position
this_vwap = (
(self.this_cash / this_traded) if this_traded > ZERO else self.day_vwap
)
this_vwap = (self.this_cash / this_traded) if this_traded > ZERO else self.day_vwap
valid = min(self.target, self.this_valid)
this_ffr = (this_traded / valid) if valid > ZERO else 1.0
if abs(this_ffr - 1.0) < ZERO:
this_ffr = 1.0
this_ffr *= 100
this_vv_ratio = this_vwap / self.day_vwap
vwap = self.raw_df["$vwap0"].values[
self.offset : self.max_step_num + self.offset
]
vwap = self.raw_df["$vwap0"].values[self.offset : self.max_step_num + self.offset]
this_tt_ratio = this_vwap / np.nanmean(vwap)
if self.is_buy:
@@ -428,9 +404,7 @@ class StockEnv_Acc(StockEnv):
for i, reward_func in enumerate(self.reward_func_list):
if not reward_func.isinstant:
tmp_r = reward_func(
performance_raise, this_ffr, this_tt_ratio, self.is_buy
)
tmp_r = reward_func(performance_raise, this_ffr, this_tt_ratio, self.is_buy)
reward += tmp_r * self.reward_coef[i]
self.reward_log_dict[type(reward_func).__name__] += tmp_r