update app.py

app.py

@@ -78,10 +78,16 @@ def my_style(v, props=''):
     props = 'color:red' if v < 0 else 'color:green'
     return props
 
+@st.experimental_memo
+def cc_coding(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)
 def load_data(filename, otimeheader,fmat):
     df = pd.read_csv(open(filename,'r'), sep='\t') # so as not to mutate cached value 
-    df.columns = ['Trade','Signal','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %']
+    df.columns = ['Trade','Signal','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %', 'Drawdown %']
+#    df.insert(1, 'Signal', ['Long']*len(df)) 
     
     df['Buy Price'] = df['Buy Price'].str.replace('$', '', regex=True)
     df['Sell Price'] = df['Sell Price'].str.replace('$', '', regex=True)
@@ -116,11 +122,11 @@ def load_data(filename, otimeheader,fmat):
     return df
 
 def runapp():
-    bot_selections = "Pure Bread"
+    bot_selections = "Cosmic Cupcake"
     otimeheader = 'Entry Date'
-    plheader = 'Calculated Return %'
+    plheader = 'P/L %'
     fmat = '%Y-%m-%d %H:%M:%S'
-    dollar_cap = 30000.00
+    dollar_cap = 100000.00
     fees = .075/100
     st.header(f"{bot_selections} Performance Dashboard :bread: :moneybag:")
     st.write("Welcome to the Trading Bot Dashboard by BreadBytes! You can use this dashboard to track " +
@@ -132,21 +138,13 @@ def runapp():
     st.subheader("Choose your settings:")
     no_errors = True
     
-    data = load_data("PB-Trade-Log.csv",otimeheader,fmat)
+    data = load_data("CC-Trade-Log.csv",otimeheader,fmat)
     df = data.copy(deep=True)
     
-    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)})
-    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)
-    
     dateheader = 'Date'
     theader = 'Time'
 
-    with st.form("user input"):
+    with st.form("user input", ):
         if no_errors:
             with st.container():
                 col1, col2 = st.columns(2)
@@ -170,18 +168,18 @@ def runapp():
             with st.container(): 
                 col1,col2 = st.columns(2) 
                 with col2:
-                    lev = st.number_input('Leverage', min_value=1, value=1, max_value= 5, step=1)
+                    lev = st.number_input('Leverage', min_value=1, value=1, max_value= 3, step=1)
                 with col1:
                     principal_balance = st.number_input('Starting Balance', min_value=0.00, value=1000.00, max_value= dollar_cap, step=.01)
 
         #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 %'] = (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 
+    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:
@@ -202,8 +200,7 @@ def runapp():
             df['Balance used in Trade'] = np.concatenate([[principal_balance], df['New Balance'].values[:-1]])
             df['Net P/L Per Trade'] = (df['Return Per Trade']-1)*df['Balance used in Trade']
             df['Cumulative P/L'] = df['Net P/L Per Trade'].cumsum()
-
-            cum_pl = df.loc[df.dropna().index[-1],'Cumulative P/L'] + principal_balance
+            cum_pl = df.loc[df.drop('Drawdown %', axis=1).dropna().index[-1],'Cumulative P/L'] + principal_balance
 
             effective_return = 100*((cum_pl - principal_balance)/principal_balance)
 
@@ -215,12 +212,12 @@ def runapp():
                     f"{100*(cum_pl-principal_balance)/(principal_balance):.2f} %",
                 )
 
-            st.line_chart(data=df.dropna(), x='Exit Date', y='Cumulative P/L', use_container_width=True)
+            st.line_chart(data=df.drop('Drawdown %', axis=1).dropna(), x='Exit Date', y='Cumulative P/L', use_container_width=True)
 
             df['Per Trade Return Rate'] = df['Return Per Trade']-1
 
             totals = pd.DataFrame([], columns = ['# of Trades', 'Wins', 'Losses', 'Win Rate', 'Profit Factor'])
-            data = get_hist_info(df.dropna(), principal_balance,'Calculated Return %')
+            data = get_hist_info(df.drop('Drawdown %', axis=1).dropna(), principal_balance,'Per Trade Return Rate')
             totals.loc[len(totals)] = list(i for i in data)
 
             totals['Cum. P/L'] = cum_pl-principal_balance
@@ -291,30 +288,36 @@ def runapp():
                             "",#f"{(1+get_rolling_stats(df,otimeheader, 30))*principal_balance:.2f}",
                             f"{get_rolling_stats(df,lev, otimeheader, 180):.2f}%",
                         )
+                        
     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(),3)})
+                                 'Calculated Return %' : lambda x: np.round(100*lev*x.sum(),2)})
         grouped_df.index = range(1, len(grouped_df)+1)
         grouped_df.rename(columns={'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(),3)})
+                                 'P/L per token' : 'mean', 
+                                 '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':'Net P/L', 
-                                   'Calculated Return %':'P/L %'}, inplace=True)  
+                                   'Calculated Return %':'P/L %'}, inplace=True)     
+    
     st.subheader("Trade Logs")
     grouped_df['Entry Date'] = pd.to_datetime(grouped_df['Entry Date'])
     grouped_df['Exit Date'] = pd.to_datetime(grouped_df['Exit Date'])
-    st.dataframe(grouped_df.style.format({'Entry Date':'{:%m-%d-%Y %H:%M:%S}','Exit Date':'{:%m-%d-%Y %H:%M:%S}','Avg. Buy Price': '${:.2f}', 'Sell Price': '${:.2f}', 'Net P/L':'${:.3f}', 'P/L %':'{:.2f}%'})\
+    st.dataframe(grouped_df.style.format({'Entry Date':'{:%m-%d-%Y %H:%M:%S}','Exit Date':'{:%m-%d-%Y %H:%M:%S}','Avg. Buy Price': '${:.2f}', 'Sell Price': '${:.2f}', 'Net P/L':'${:.2f}', 'P/L %':'{:.2f}%'})\
+    .apply(cc_coding, axis=1)\
     .applymap(my_style,subset=['Net P/L'])\
-    .applymap(my_style,subset=['P/L %']), use_container_width=True)
+    .applymap(my_style,subset=['P/L %'])\
+                 ,use_container_width=True)
+    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__":
     st.set_page_config(