diff --git a/examples/trade/README.md b/examples/trade/README.md
index cd98e4d82..c29f439ab 100644
--- a/examples/trade/README.md
+++ b/examples/trade/README.md
@@ -89,8 +89,8 @@ and finally start our OPD method.
 python main.py --config=example/OPD/config.yml
 ```
 
-### Citation
-You are more than welcome to cite our paper:
+## Citation
+You are more than welcome to citetmu our paper:
 ```
 @inproceedings{fang2021universal,
   title={Universal Trading for Order Execution with Oracle Policy Distillation},
diff --git a/examples/trade/observation/obs_rule.py b/examples/trade/observation/obs_rule.py
index 679a67e0a..350c79934 100644
--- a/examples/trade/observation/obs_rule.py
+++ b/examples/trade/observation/obs_rule.py
@@ -71,24 +71,22 @@ class RuleObs(BaseObs):
                 if time == -1:
                     predictions += [0.0] * size
                 else:
-                    predictions += df.iloc[size * time : size * (time + 1)].reshape(-1).tolist()
+                    predictions += df[size * time : size * (time + 1)].reshape(-1).tolist()
             elif feature["type"] == "daily":
                 predictions += df.reshape(-1)[:size].tolist()
             elif feature["type"] == "range":
-                # if feature.startswith('oracle'):
-                #     predictions += df.iloc[:, (time + 1) : size + (time + 1)].reshape(-1).tolist()
                 if time == -1:
                     predictions += [0.0] * size
                 else:
-                    predictions += df.iloc[time : size + time].reshape(-1).tolist()
+                    predictions += df[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[interval * size : (interval + 1) * size].reshape(-1)) == size:
+                    predictions += df[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[size * (time + 1) : size * (time + 2)].reshape(-1)) == size:
+                    predictions += df[size * (time + 1) : size * (time + 2)].reshape(-1).tolist()
                 else:
                     predictions += [0.0] * size
 
diff --git a/examples/trade/order_gen.py b/examples/trade/order_gen.py
index 898aaab7c..71499523f 100644
--- a/examples/trade/order_gen.py
+++ b/examples/trade/order_gen.py
@@ -26,9 +26,8 @@ def w_order(f, start, end):
     df = pd.read_pickle(in_dir + f)
     #df['date'] = df.index.get_level_values(1).map(lambda x: x.date())
     #df = df.set_index('date', append=True, drop=True)
-#     old_order = pd.read_pickle('../v-zeh/full-07-20/order/ratio_test/' + f)
+
     order = generate_order(df, start, end)
-#     order = order[order.index.isin(old_order.index)]
     order_train = order[order.index.get_level_values(0) < '2020-12-01']
     order_test = order[order.index.get_level_values(0) >= '2020-12-01']
     order_valid = order_test[order_test.index.get_level_values(0) < '2021-01-01']
@@ -52,7 +51,8 @@ def w_order(f, start, end):
         all_path = os.path.join(data_path, "order/all/")
         if not os.path.exists(all_path):
             os.makedirs(all_path)
-        order_test.to_pickle(all_path + f[:-9] + '.target')
+
+        order.to_pickle(all_path + f[:-9] + '.target')
     return 0
 
 res = Parallel(n_jobs=64)(delayed(w_order)(f, 0, 239) for f in os.listdir(in_dir))
diff --git a/examples/trade/teacher_feature.py b/examples/trade/teacher_feature.py
index d605fb74e..9c6ed867a 100644
--- a/examples/trade/teacher_feature.py
+++ b/examples/trade/teacher_feature.py
@@ -6,13 +6,17 @@ feature_path = os.path.join(data_path, 'feature/teacher/')
 if not os.path.exists(feature_path):
     os.makedirs(feature_path)
 
-log_file = os.path.join(os.environ.get('OUTPUT_DIR'),'example/OPDT_b/0/test/')
+
+log_file = os.path.join(os.environ.get('OUTPUT_DIR'),'example/OPDT_b/test/')
+
 files = os.listdir(log_file)
 
 for f in files:
     if f.endswith(".log"):
         df = pd.read_pickle(log_file + f)
-        df['datetime'] = df.index.get_level_values(1).map(lambda x: x[1])
+
+        #df['datetime'] = df.index.get_level_values(1).map(lambda x: x[1])
+        df['datetime'] = df.index.get_level_values(1)
         df.set_index('datetime', append=True, drop=True, inplace=True)
         action = df['action']
         action = action.reset_index(level=1, drop=True)