update to include PB

app.py

@@ -167,6 +167,8 @@ 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)
 def ctt_coding(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)
+def conditional_formatter(value):
+    return "${:.2f}".format(value) if not (abs(value) < 1.00) else "${:.4f}".format(value)
 
 @st.experimental_memo
 def my_style(v, props=''):
@@ -231,7 +233,7 @@ def load_data(filename, otimeheader, fmat):
     if filename == "CT-Trade-Log.csv":
         df.columns = ['Trade','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %', 'Drawdown %']
         df.insert(1, 'Signal', ['Long']*len(df)) 
-    elif filename == "CC-Trade-Log.csv":
+    elif filename == "CC-Trade-Log.csv" or filename == "PB-Trade-Log.csv":
         df.columns = ['Trade','Signal','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %', 'Drawdown %']
     else: 
         df.columns = ['Trade','Signal','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %']
@@ -608,7 +610,7 @@ def runapp() -> None:
 
         if logtype == "BreadBytes Historical Logs" and no_errors:
             
-            bots = ["Cinnamon Toast", "Short Bread", "Cosmic Cupcake"]#, "CT Toasted"]
+            bots = ["Cinnamon Toast", "Short Bread", "Cosmic Cupcake", "Pure Bread"] 
             bot_selections = st.selectbox("Select your Trading Bot", options=bots)
             otimeheader = 'Exit Date'
             fmat = '%Y-%m-%d %H:%M:%S'
@@ -630,10 +632,10 @@ def runapp() -> None:
                 lev_cap = 3
                 dollar_cap = 1000000000.00
                 data = load_data("CC-Trade-Log.csv",otimeheader, fmat)
-            if bot_selections == "CT Toasted":
-                lev_cap = 5
+            if bot_selections == "Pure Bread":
+                lev_cap = 3
                 dollar_cap = 1000000000.00
-                data = load_data("CT-Toasted-Trade-Log.csv",otimeheader, fmat)
+                data = load_data("PB-Trade-Log.csv",otimeheader, fmat)
             
             df = data.copy(deep=True)
 
@@ -773,7 +775,7 @@ def runapp() -> None:
 #                                     f"" ,#${cum_sdm:.2f}"
 #                                     f"{100*(cum_sdm-principal_balance)/(principal_balance):.2f} %",
 #                                 )
-                    if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake":
+                    if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake" or bot_selections == "Pure Bread":
                         #st.line_chart(data=df.drop('Drawdown %', axis=1).dropna(), x='Exit Date', y='Cumulative P/L', use_container_width=True)
                         dfdata = df.drop('Drawdown %', axis=1).dropna()
                         #sd_df = sd_df.drop('Drawdown %', axis=1).dropna()
@@ -838,7 +840,7 @@ def runapp() -> None:
                     df['Per Trade Return Rate'] = df['Return Per Trade']-1
 
                     totals = pd.DataFrame([], columns = ['# of Trades', 'Wins', 'Losses', 'Win Rate', 'Profit Factor'])
-                    if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake":
+                    if bot_selections == "Cinnamon Toast" or bot_selections == "Cosmic Cupcake" or bot_selections == "Pure Bread":
                         data = get_hist_info(df.drop('Drawdown %', axis=1).dropna(), principal_balance,'Per Trade Return Rate')
                     else: 
                         data = get_hist_info(df.dropna(), principal_balance,'Per Trade Return Rate')
@@ -964,6 +966,10 @@ def runapp() -> None:
                 .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> Not Live Traded</div>'
                 st.markdown(new_title, unsafe_allow_html=True)
+            elif bot_selections == "Pure Bread": 
+                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': '${:.4f}', 'Sell Price': '${:.4f}', 'Net P/L': conditional_formatter, 'P/L %':'{:.2f}%'})\
+                .applymap(my_style,subset=['Net P/L'])\
+                .applymap(my_style,subset=['P/L %']), use_container_width=True)
             else: 
                 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}%'})\
                 .applymap(my_style,subset=['Net P/L'])\