2 DCA update

app.py

@@ -114,13 +114,19 @@ def load_data(filename, otimeheader, fmat):
     df['Trade'] = df.index + 1 #reindex
 
     df['DCA'] = np.nan
-
+    
     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():
     bot_selections = "Cinnamon Toast"
@@ -171,16 +177,25 @@ def runapp():
                     lev = st.number_input('Leverage', min_value=1, value=1, max_value= 5, step=1)
                 with col1:
                     principal_balance = st.number_input('Starting Balance', min_value=0.00, value=1000.00, max_value= dollar_cap, step=.01)
-        with st.container():
-            col1, col2, col3, col4 = st.columns(4)
-            with col1: 
-                dca1 = st.number_input('DCA 1 Allocation', min_value=0, value=25, max_value= 100, step=1)
-            with col2: 
-                dca2 = st.number_input('DCA 2 Allocation', min_value=0, value=25, max_value= 100, step=1)
-            with col3: 
-                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 before 02/07/2023)")
+            with st.container():
+                col1, col2, col3, col4 = st.columns(4)
+                with col1: 
+                    dca1 = st.number_input('DCA 1 Allocation', min_value=0, value=25, max_value= 100, step=1)
+                with col2: 
+                    dca2 = st.number_input('DCA 2 Allocation', min_value=0, value=25, max_value= 100, step=1)
+                with col3: 
+                    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)
@@ -188,10 +203,10 @@ def runapp():
             submitted = st.form_submit_button("Get Cookin'!")  
             
     signal_map = {'Long': 1, 'Short':-1} # 1 for long #-1 for short
-
-    df['Calculated Return %'] = .25*(1-fees)*(df['Signal'].map(signal_map)*(df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade 
-
-    
+    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
+                        
     if submitted and principal_balance * lev > dollar_cap:
         lev = np.floor(dollar_cap/principal_balance)
         st.error(f"WARNING: (Starting Balance)*(Leverage) exceeds the ${dollar_cap} limit. Using maximum available leverage of {lev}")
@@ -204,13 +219,10 @@ def runapp():
                 no_errors = False
         if no_errors:
 
-            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)
-
-            signal_map = {'Long': 1, 'Short':-1} # 1 for long #-1 for short
-
             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
@@ -313,26 +325,26 @@ def runapp():
                             "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
                             f"{get_rolling_stats(df,lev, otimeheader, 180):.2f}%",
                         )
-    if submitted: 
+    if submitted:                     
         grouped_df = df.groupby('Exit Date').agg({'Signal':'min','Entry Date': 'min','Exit Date': 'max','Buy Price': 'mean',
                                  '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:
         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', 
                                  'Calculated Return %' : lambda x: np.round(100*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',
-                                   'P/L per token':'Net P/L', 
-                                   'Calculated Return %':'P/L %'}, inplace=True)
+                                   'Calculated Return %':'P/L %',
+                                   'P/L per token':'Net P/L'}, inplace=True)
         
     st.subheader("Trade Logs")
     st.dataframe(grouped_df.style.format({'Avg. Buy Price': '${:.2f}', 'Sell Price': '${:.2f}','# of DCAs':'{:.0f}', 'Net P/L':'${:.2f}', 'P/L %' :'{:.2f}%'})\