CT DCA updates

app.py

@@ -160,9 +160,9 @@ def get_rolling_stats(df, lev, otimeheader, days):
     return 100*rolling_perc
 @st.experimental_memo
 def cc_coding(row):
-    return ['background-color: orange'] * len(row) if row['Exit Date'] <= datetime.strptime('2022-12-16 00:00:00','%Y-%m-%d %H:%M:%S').date() else [''] * len(row)
+    return ['background-color: lightgrey'] * len(row) if row['Exit Date'] <= datetime.strptime('2022-12-16 00:00:00','%Y-%m-%d %H:%M:%S').date() else [''] * len(row)
 def ctt_coding(row):
-    return ['background-color: orange'] * len(row) if row['Exit Date'] <= datetime.strptime('2023-01-02 00:00:00','%Y-%m-%d %H:%M:%S').date() else [''] * len(row)
+    return ['background-color: lightgrey'] * len(row) if row['Exit Date'] <= datetime.strptime('2023-01-02 00:00:00','%Y-%m-%d %H:%M:%S').date() else [''] * len(row)
 
 @st.experimental_memo
 def my_style(v, props=''):
@@ -223,11 +223,16 @@ def load_data(filename, otimeheader, fmat):
 
         for exit in pd.unique(df['Exit Date']):
             df_exit = df[df['Exit Date']==exit]
-            for i in range(len(df_exit)):
-                ind = df_exit.index[i]
-                df.loc[ind,'DCA'] = i+1
-    return df
-
+            if dateutil.parser.parse(str(exit)) < dateutil.parser.parse('2023-02-07 00:13:00'):
+                for i in range(len(df_exit)):
+                    ind = df_exit.index[i]
+                    df.loc[ind,'DCA'] = i+1
+                    
+            else: 
+                for i in range(len(df_exit)):
+                    ind = df_exit.index[i]
+                    df.loc[ind,'DCA'] = i+1.1
+    return df 
 
 def runapp() -> None:
     #st.header("Trading Bot Dashboard :bread: :moneybag:")
@@ -506,6 +511,7 @@ def runapp() -> None:
                             principal_balance = st.number_input('Starting Balance', min_value=0.00, value=1000.00, max_value= dollar_cap, step=.01)
                 
                 if bot_selections == "Cinnamon Toast":
+                    st.write("Choose your DCA setup (for trades before 02/07/2023)")
                     with st.container():
                         col1, col2, col3, col4 = st.columns(4)
                         with col1: 
@@ -516,6 +522,13 @@ def runapp() -> None:
                             dca3 = st.number_input('DCA 3 Allocation', min_value=0, value=25, max_value= 100, step=1)
                         with col4: 
                             dca4 = st.number_input('DCA 4 Allocation', min_value=0, value=25, max_value= 100, step=1)
+                    st.write("Choose your DCA setup (for trades on or after 02/07/2023)")
+                    with st.container():
+                        col1, col2 = st.columns(2)
+                        with col1: 
+                            dca5 = st.number_input('DCA 1 Allocation', min_value=0, value=50, max_value= 100, step=1)
+                        with col2: 
+                            dca6 = st.number_input('DCA 2 Allocation', min_value=0, value=50, max_value= 100, step=1)
 
                 #hack way to get button centered 
                 c = st.columns(9)
@@ -535,9 +548,10 @@ def runapp() -> None:
                         no_errors = False
                 if no_errors:
                     if bot_selections == "Cinnamon Toast":
-                        dca_map = {1: dca1/100, 2: dca2/100, 3: dca3/100, 4: dca4/100}
+                        dca_map = {1: dca1/100, 2: dca2/100, 3: dca3/100, 4: dca4/100, 1.1: dca5/100, 2.1: dca6/100}
                         df['DCA %'] = df['DCA'].map(dca_map)
                         df['Calculated Return %'] = df['Signal'].map(signal_map)*(df['DCA %'])*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade 
+                        df['DCA'] = np.floor(df['DCA'].values)
                         
                         df['Return Per Trade'] = np.nan
                         df['Balance used in Trade'] = np.nan
@@ -650,19 +664,24 @@ def runapp() -> None:
                                              'Sell Price' : 'max',
                                              'Net P/L Per Trade': 'mean', 
                                              'Calculated Return %' : lambda x: np.round(100*lev*x.sum(),2), 
-                                             'DCA': 'max'})
+                                             'DCA': lambda x: int(np.floor(x.max()))})
                     grouped_df.index = range(1, len(grouped_df)+1)
                     grouped_df.rename(columns={'DCA' : '# of DCAs', 'Buy Price':'Avg. Buy Price',
                                                'Net P/L Per Trade':'Net P/L', 
                                                'Calculated Return %':'P/L %'}, inplace=True)
-                else: 
+                else:
+                    dca_map = {1: 25/100, 2: 25/100, 3: 25/100, 4: 25/100, 1.1: 50/100, 2.1: 50/100}
+                    df['DCA %'] = df['DCA'].map(dca_map)
+                    df['Calculated Return %'] = (df['DCA %'])*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade
+                        
                     grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
                                              'Sell Price' : 'max',
                                              'P/L per token': 'mean', 
-                                             'P/L %':lambda x: np.round(x.sum()/4,2), 
-                                             'DCA': 'max'})
+                                             'Calculated Return %' : lambda x: np.round(100*x.sum(),2), 
+                                             'DCA': lambda x: int(np.floor(x.max()))})
                     grouped_df.index = range(1, len(grouped_df)+1)
                     grouped_df.rename(columns={'DCA' : '# of DCAs', 'Buy Price':'Avg. Buy Price',
+                                               'Calculated Return %':'P/L %',
                                                'P/L per token':'Net P/L'}, inplace=True)
                 
             else: 
@@ -690,7 +709,7 @@ def runapp() -> None:
                 .apply(coding, axis=1)\
                 .applymap(my_style,subset=['Net P/L'])\
                 .applymap(my_style,subset=['P/L %']), use_container_width=True)
-                new_title = '<div style="text-align: right;"><span style="background-color:orange;">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span> Not Live Traded</div>'
+                new_title = '<div style="text-align: right;"><span style="background-color:lightgrey;">&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</span> Not Live Traded</div>'
                 st.markdown(new_title, unsafe_allow_html=True)
             else: 
                 st.dataframe(grouped_df.style.format({'Avg. Buy Price': '${:.2f}', 'Sell Price': '${:.2f}', 'Net P/L':'${:.2f}', 'P/L %':'{:.2f}%'})\