raw log fix, log colors, and div by 4 err

app.py

@@ -75,7 +75,7 @@ def my_style(v, props=''):
 
 @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)
 
 
 @st.cache(ttl=24*3600, allow_output_mutation=True)
@@ -170,7 +170,12 @@ def runapp():
         #hack way to get button centered 
         c = st.columns(9)
         with c[4]: 
-            submitted = st.form_submit_button("Get Cookin'!")           
+            submitted = st.form_submit_button("Get Cookin'!")     
+            
+    signal_map = {'Long': 1, 'Short':-1} # 1 for long #-1 for short
+
+    df['Calculated Return %'] = df['Signal'].map(signal_map)*(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)
@@ -183,11 +188,6 @@ def runapp():
                 st.error("There are no available trades matching your selections. Please try again!")
                 no_errors = False
         if no_errors:
-
-            signal_map = {'Long': 1, 'Short':-1} # 1 for long #-1 for short
-
-            df['Calculated Return %'] = df['Signal'].map(signal_map)*(1-fees)*((df['Sell Price']-df['Buy Price'])/df['Buy Price'] - fees) #accounts for fees on open and close of trade 
-
             df['Return Per Trade'] = 1+df['Calculated Return %'].values
 
             df['Compounded Return'] = df['Return Per Trade'].cumprod()
@@ -296,16 +296,18 @@ def runapp():
         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)})
+                                 'Calculated Return %' : lambda x: np.round(100*x.sum(),2)})
         grouped_df.index = range(1, len(grouped_df)+1)
         grouped_df.rename(columns={'Buy Price':'Avg. Buy Price',
-                                   'P/L per token':'Avg. P/L per token'}, inplace=True)
+                                   'P/L per token':'Avg. P/L per token', 
+                                   'Calculated Return %':'P/L %'}, inplace=True)     
+    
     st.subheader("Trade Logs")
     st.dataframe(grouped_df.style.format({'Avg. Buy Price': '${:.2f}', 'Sell Price': '${:.2f}', 'Avg. P/L per token':'${:.2f}', 'P/L %':'{:.2f}%'})\
     .apply(cc_coding, axis=1)\
     .applymap(my_style,subset=['Avg. P/L per token'])\
     .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> Backtest Data</div>'
     st.markdown(new_title, unsafe_allow_html=True)
     
 if __name__ == "__main__":