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format

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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
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34
examples/trade/observation/obs_rule.py
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@@ -60,9 +60,7 @@ class RuleObs(BaseObs):
prediction = [df_list[i].reshape(-1) for i in range(len(df_list))]
for i, p in enumerate(prediction):
if len(p) < interval_num:
prediction[i] = np.concatenate(
(p, np.zeros(interval_num - len(p))), axis=-1
)
prediction[i] = np.concatenate((p, np.zeros(interval_num - len(p))), axis=-1)
# res = np.stack(prediction).transpose().reshape(-1)
return np.concatenate(prediction)
for i in range(len(self.features)):
@@ -73,9 +71,7 @@ class RuleObs(BaseObs):
if time == -1:
predictions += [0.0] * size
else:
predictions += (
df.iloc[size * time : size * (time + 1)].reshape(-1).tolist()
)
predictions += df.iloc[size * time : size * (time + 1)].reshape(-1).tolist()
elif feature["type"] == "daily":
predictions += df.reshape(-1)[:size].tolist()
elif feature["type"] == "range":
@@ -86,35 +82,19 @@ class RuleObs(BaseObs):
else:
predictions += df.iloc[time : size + time].reshape(-1).tolist()
elif feature["type"] == "interval":
if (
len(df.iloc[interval * size : (interval + 1) * size].reshape(-1))
== size
):
predictions += (
df.iloc[interval * size : (interval + 1) * size]
.reshape(-1)
.tolist()
)
if len(df.iloc[interval * size : (interval + 1) * size].reshape(-1)) == size:
predictions += df.iloc[interval * size : (interval + 1) * size].reshape(-1).tolist()
else:
predictions += [0.0] * size
elif feature["type"] == "step":
if (
len(df.iloc[size * (time + 1) : size * (time + 2)].reshape(-1))
== size
):
predictions += (
df.iloc[size * (time + 1) : size * (time + 2)]
.reshape(-1)
.tolist()
)
if len(df.iloc[size * (time + 1) : size * (time + 2)].reshape(-1)) == size:
predictions += df.iloc[size * (time + 1) : size * (time + 2)].reshape(-1).tolist()
else:
predictions += [0.0] * size
return np.array(predictions)
def get_obs(
self, raw_df, feature_dfs, t, interval, position, target, is_buy, *args, **kargs
):
def get_obs(self, raw_df, feature_dfs, t, interval, position, target, is_buy, *args, **kargs):
private_state = np.array([position, target, t, self.max_step_num])
prediction_state = self.get_feature_res(feature_dfs, t, interval)
return {