adding some flair

app.py

@@ -150,6 +150,10 @@ with st.form("choose_model"):
                 new_pred['L2BreakPct'] = new_pred['L2BreakPct'].astype(float)
                 new_pred['H1BreakPct'] = new_pred['H1BreakPct'].astype(float)
                 new_pred['H2BreakPct'] = new_pred['H2BreakPct'].astype(float)
+                new_pred['H1BreakTouchPct'] = new_pred['H1BreakTouchPct'].astype(float)
+                new_pred['H2BreakTouchPct'] = new_pred['H2BreakTouchPct'].astype(float)
+                new_pred['L1BreakTouchPct'] = new_pred['L1BreakTouchPct'].astype(float)
+                new_pred['L2BreakTouchPct'] = new_pred['L2BreakTouchPct'].astype(float)
 
                 seq_proba = seq_predict_proba(new_pred, xgbr, seq2)
         
@@ -210,8 +214,12 @@ with st.form("choose_model"):
                 new_pred['L2BreakPct'] = new_pred['L2BreakPct'].astype(float)
                 new_pred['H1BreakPct'] = new_pred['H1BreakPct'].astype(float)
                 new_pred['H2BreakPct'] = new_pred['H2BreakPct'].astype(float)
+                new_pred['H1BreakTouchPct'] = new_pred['H1BreakTouchPct'].astype(float)
+                new_pred['H2BreakTouchPct'] = new_pred['H2BreakTouchPct'].astype(float)
+                new_pred['L1BreakTouchPct'] = new_pred['L1BreakTouchPct'].astype(float)
+                new_pred['L2BreakTouchPct'] = new_pred['L2BreakTouchPct'].astype(float)
                 new_pred['GreenProbas'] = new_pred['GreenProbas'].astype(float)
-
+                
                 seq_proba = seq_predict_proba(new_pred, xgbr)
 
         st.info(f'as of {option} on {curr_date} 👇🏽', icon="🔮")
@@ -404,12 +412,12 @@ with st.form("choose_model"):
 
         df_levels = pd.DataFrame(
             index=['H2','H1','L1','L2'],
-            columns=['Level','BreakPct(100)','TouchPct(100)'],
+            columns=['Level','BreakPct(100)','TouchPct(100)','BreakGivenTouch(100)'],
             data=[
-                [f"{data['H2'].iloc[-1]:.2f}",f"{data['H2BreakPct'].iloc[-2]:.1%}",f"{data['H2TouchPct'].iloc[-2]:.1%}"],
-                [f"{data['H1'].iloc[-1]:.2f}",f"{data['H1BreakPct'].iloc[-2]:.1%}",f"{data['H1TouchPct'].iloc[-2]:.1%}"],
-                [f"{data['L1'].iloc[-1]:.2f}",f"{data['L1BreakPct'].iloc[-2]:.1%}",f"{data['L1TouchPct'].iloc[-2]:.1%}"],
-                [f"{data['L2'].iloc[-1]:.2f}",f"{data['L2BreakPct'].iloc[-2]:.1%}",f"{data['L2TouchPct'].iloc[-2]:.1%}"]
+                [f"{data['H2'].iloc[-1]:.2f}",f"{data['H2BreakPct'].iloc[-2]:.1%}",f"{data['H2TouchPct'].iloc[-2]:.1%}",f"{data['H2BreakTouchPct'].iloc[-2]:.1%}"],
+                [f"{data['H1'].iloc[-1]:.2f}",f"{data['H1BreakPct'].iloc[-2]:.1%}",f"{data['H1TouchPct'].iloc[-2]:.1%}",f"{data['H1BreakTouchPct'].iloc[-2]:.1%}"],
+                [f"{data['L1'].iloc[-1]:.2f}",f"{data['L1BreakPct'].iloc[-2]:.1%}",f"{data['L1TouchPct'].iloc[-2]:.1%}",f"{data['L1BreakTouchPct'].iloc[-2]:.1%}"],
+                [f"{data['L2'].iloc[-1]:.2f}",f"{data['L2BreakPct'].iloc[-2]:.1%}",f"{data['L2TouchPct'].iloc[-2]:.1%}",f"{data['L2BreakTouchPct'].iloc[-2]:.1%}"]
             ]
         )
         

model_day.py

@@ -73,7 +73,11 @@ model_cols = [
     'L1BreakPct',
     'L2BreakPct',
     'H1BreakPct',
-    'H2BreakPct'
+    'H2BreakPct',
+    'H1BreakTouchPct',
+    'H2BreakTouchPct',
+    'L1BreakTouchPct',
+    'L2BreakTouchPct'
 ]
 
 def walk_forward_validation_seq(df, target_column_clf, target_column_regr, num_training_rows, num_periods):
@@ -126,7 +130,7 @@ def walk_forward_validation_seq(df, target_column_clf, target_column_regr, num_t
 
     greenprobas = []
     meanprobas = []
-    for i, pct in tqdm(enumerate(df_results['Predicted'], desc='Calibrating Probas')):
+    for i, pct in tqdm(enumerate(df_results['Predicted']), desc='Calibrating Probas'):
         try:
             df_q = get_quantiles(df_results.iloc[:i], 'Predicted', 7)
             for q in df_q.index:
@@ -339,6 +343,11 @@ def get_data():
     for col in level_cols:
         data[col+'Pct'] = data[col].rolling(100).mean()
 
+    data['H1BreakTouchPct'] = data['H1Break'].rolling(100).sum() / data['H1Touch'].rolling(100).sum()
+    data['H2BreakTouchPct'] = data['H2Break'].rolling(100).sum() / data['H2Touch'].rolling(100).sum()
+    data['L1BreakTouchPct'] = data['L1Break'].rolling(100).sum() / data['L1Touch'].rolling(100).sum()
+    data['L2BreakTouchPct'] = data['L2Break'].rolling(100).sum() / data['L2Touch'].rolling(100).sum()
+
     # Target -- the next day's low
     data['Target'] = (data['OHLC4'] / data['PrevClose']) - 1
     data['Target'] = data['Target'].shift(-1)
@@ -412,6 +421,10 @@ def get_data():
         'L2BreakPct',
         'H1BreakPct',
         'H2BreakPct',
+        'H1BreakTouchPct',
+        'H2BreakTouchPct',
+        'L1BreakTouchPct',
+        'L2BreakTouchPct',
         'Target',
         'Target_clf'
         ]]

model_intra.py

@@ -45,6 +45,10 @@ model_cols = [
     'H1BreakPct',
     'H2BreakPct',
     'GreenProbas',
+    'H1BreakTouchPct',
+    'H2BreakTouchPct',
+    'L1BreakTouchPct',
+    'L2BreakTouchPct'
     # 'GapFillGreenProba'
 ]
 
@@ -342,7 +346,7 @@ def get_data(periods_30m = 1):
     data['downSD'] = data['down'].rolling(30).std(ddof=0)
     data['aveDown'] = data['down'].rolling(30).mean()
     data['L1'] = data['Open'] - (data['aveDown'] / 100) * data['Open']
-    data['L2'] = data['Open'] - ((data['aveDown'] + data['upSD']) / 100) * data['Open']
+    data['L2'] = data['Open'] - ((data['aveDown'] + data['downSD']) / 100) * data['Open']
 
     data = data.assign(
         L1Touch = lambda x: x['Low'] < x['L1'],
@@ -387,6 +391,10 @@ def get_data(periods_30m = 1):
         data[col+'Pct'] = data[col].rolling(100).mean()
         # data[col+'Pct'] = data[col+'Pct'].shift(-1)
 
+    data['H1BreakTouchPct'] = data['H1Break'].rolling(100).sum() / data['H1Touch'].rolling(100).sum()
+    data['H2BreakTouchPct'] = data['H2Break'].rolling(100).sum() / data['H2Touch'].rolling(100).sum()
+    data['L1BreakTouchPct'] = data['L1Break'].rolling(100).sum() / data['L1Touch'].rolling(100).sum()
+    data['L2BreakTouchPct'] = data['L2Break'].rolling(100).sum() / data['L2Touch'].rolling(100).sum()
 
     def get_quintiles(df, col_name, q):
         return df.groupby(pd.qcut(df[col_name], q))['GreenDay'].mean()