update style and pb backtest date

.streamlit/config.toml

@@ -1,6 +1,19 @@
 [server]
 port=8501
-enableCORS = false
-enableXsrfProtection = false
 [theme]
-base = "dark"
+
+# Primary accent for interactive elements
+primaryColor = '#21D4E1'
+
+# Background color for the main content area
+backgroundColor = '#0A0A0A'
+
+# Background color for sidebar and most interactive widgets
+secondaryBackgroundColor = '#454545'
+# Color used for almost all text
+textColor = '#FFFFFF'
+
+# Font family for all text in the app, except code blocks
+# Accepted values (serif | sans serif | monospace) 
+# Default: "sans serif"
+font = "sans serif"

app.py

@@ -184,48 +184,62 @@ def filt_df(df, cheader, symbol_selections):
 def load_data(filename, account, exchange, otimeheader, fmat):
     cols = ['id','datetime', 'exchange', 'subaccount', 'pair', 'side', 'action', 'amount', 'price']
     df = pd.read_csv(filename, header = 0, names= cols)
-
+    
     filtdf = df[(df.exchange == exchange) & (df.subaccount == account)].dropna()
-    filtdf = filtdf.sort_values('datetime')
-    filtdf = filtdf.iloc[np.where(filtdf.action == 'open')[0][0]:, :] #get first open signal in dataframe
-
-    tnum = 0
-    dca = 0
-    newdf = pd.DataFrame([], columns=['Trade','Signal','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %'])
-    for index, row in filtdf.iterrows(): 
-        if row.action == 'open':
-            dca += 1 
-            tnum += 1 
-            sig = 'Long' if row.side == 'buy' else 'Short'
-            temp = pd.DataFrame({'Trade' :[tnum], 'Signal': [sig], 'Entry Date':[row.datetime],'Buy Price': [row.price], 'Sell Price': [np.nan],'Exit Date': [np.nan], 'P/L per token': [np.nan], 'P/L %': [np.nan], 'DCA': [dca]})
-            newdf = pd.concat([newdf,temp], ignore_index = True)
-        if row.action == 'close':
-            for j in np.arange(tnum-1, tnum-dca-1,-1):
-                newdf.loc[j,'Sell Price'] = row.price 
-                newdf.loc[j,'Exit Date'] = row.datetime
-            dca = 0 
-
-    newdf['Buy Price'] = pd.to_numeric(newdf['Buy Price'])
-    newdf['Sell Price'] = pd.to_numeric(newdf['Sell Price'])
-
-    newdf['P/L per token'] = newdf['Sell Price'] - newdf['Buy Price']
-    newdf['P/L %'] = 100*newdf['P/L per token']/newdf['Buy Price']
-    newdf = newdf.dropna()
-
-
+    
+    if not filtdf.empty:
+        filtdf = filtdf.sort_values('datetime')
+        filtdf = filtdf.iloc[np.where(filtdf.action == 'open')[0][0]:, :] #get first open signal in dataframe
+
+        tnum = 0
+        dca = 0
+        newdf = pd.DataFrame([], columns=['Trade','Signal','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %'])
+        for index, row in filtdf.iterrows(): 
+            if row.action == 'open':
+                dca += 1 
+                tnum += 1 
+                sig = 'Long' if row.side == 'buy' else 'Short'
+                temp = pd.DataFrame({'Trade' :[tnum], 'Signal': [sig], 'Entry Date':[row.datetime],'Buy Price': [row.price], 'Sell Price': [np.nan],'Exit Date': [np.nan], 'P/L per token': [np.nan], 'P/L %': [np.nan], 'DCA': [dca]})
+                newdf = pd.concat([newdf,temp], ignore_index = True)
+            if row.action == 'close':
+                for j in np.arange(tnum-1, tnum-dca-1,-1):
+                    newdf.loc[j,'Sell Price'] = row.price 
+                    newdf.loc[j,'Exit Date'] = row.datetime
+                dca = 0 
+
+        newdf['Buy Price'] = pd.to_numeric(newdf['Buy Price'])
+        newdf['Sell Price'] = pd.to_numeric(newdf['Sell Price'])
+
+        newdf['P/L per token'] = newdf['Sell Price'] - newdf['Buy Price']
+        newdf['P/L %'] = 100*newdf['P/L per token']/newdf['Buy Price']
+        newdf = newdf.dropna()
+    else: 
+        newdf = pd.DataFrame([], columns=['Trade','Signal','Entry Date','Buy Price', 'Sell Price','Exit Date', 'P/L per token', 'P/L %'])
+    
+    if account == 'Pure Bread Test':
+        tvdata = pd.read_csv('pb-history-old.csv',header = 0).drop('Unnamed: 0', axis=1)
+    else:
+        tvdata = pd.DataFrame([])
+    if tvdata.empty:
+        df = newdf
+    else:
+        df = pd.concat([tvdata, newdf], ignore_index =True)
+        df = df.sort_values('Entry Date', ascending = True)
+        df.index = range(len(df))
+        df.Trade = df.index + 1 
+    
     dateheader = 'Date'
     theader = 'Time'
 
-    newdf[dateheader] = [tradetimes.split(" ")[0] for tradetimes in newdf[otimeheader].values]
-    newdf[theader] = [tradetimes.split(" ")[1] for tradetimes in newdf[otimeheader].values]
+    df[dateheader] = [tradetimes.split(" ")[0] for tradetimes in df[otimeheader].values]
+    df[theader] = [tradetimes.split(" ")[1] for tradetimes in df[otimeheader].values]
     
-    newdf[otimeheader] = pd.to_datetime(newdf[otimeheader])
-    newdf['Exit Date'] = pd.to_datetime(newdf['Exit Date'])
+    df[otimeheader] = pd.to_datetime(df[otimeheader])
+    df['Exit Date'] = pd.to_datetime(df['Exit Date'])
     
-    newdf[dateheader] = [dateutil.parser.parse(date).date() for date in newdf[dateheader]]
-    newdf[theader] = [dateutil.parser.parse(time).time() for time in newdf[theader]]
-    
-    return newdf
+    df[dateheader] = [dateutil.parser.parse(date).date() for date in df[dateheader]]
+    df[theader] = [dateutil.parser.parse(time).time() for time in df[theader]]
+    return df
 
 def get_pl(bot_selections, df, dca1, dca2, dca3, dollar_cap, lev, principal_balance):
     signal_map = {'Long': 1, 'Short':-1}  
@@ -276,7 +290,7 @@ def runapp() -> None:
     dollar_cap = 1000000000.00
 
     pn_data = load_data('history.csv', 'Pumpernickel Test', 'Bybit Futures', otimeheader, fmat)
-    pb_data = load_data('history.csv', 'Pure Bread Test', 'Bybit Futures', otimeheader, fmat)
+    pb_data = load_data('history_new.csv', 'Pure Bread Test', 'Bybit Futures', otimeheader, fmat)
 
     df = pd.concat([pn_data, pb_data])
 
@@ -432,32 +446,42 @@ def runapp() -> None:
                                      line = {'smoothing': 1.0, 'color' :'green'}, name = 'DOGE Buy & Hold Return')
             )
 
+            img_width = 2001
+            img_height = 622
             fig.add_layout_image(
                     dict(
                         source=pyLogo,
                         xref="paper",
                         yref="paper",
-                        x = 0.05, #dfdata['Exit Date'].astype('int64').min() // 10**9, 
-                        y = .85, #dfdata['Cumulative P/L'].max(),
-                        sizex= .9, #(dfdata['Exit Date'].astype('int64').max() - dfdata['Exit Date'].astype('int64').min()) // 10**9,
-                        sizey= .9, #(dfdata['Cumulative P/L'].max() - dfdata['Cumulative P/L'].min()),
-                        sizing="contain",
-                        opacity=0.2, 
+                        x = 0.1, 
+                        y = 1, 
+                        sizex= 1, 
+                        sizey= 1,
+                        opacity=0.5, 
                     layer = "below")
             )
 
             #style layout 
             fig.update_layout(
-                height = 600,
+                height = 550,
                 xaxis=dict(
                     title="Exit Date", 
                     tickmode='array',
+                    showgrid=False
                 ),
                 yaxis=dict(
-                    title="Cumulative P/L"
-                ) ) 
+                    title="Cumulative P/L",
+                    showgrid=False
+                ), 
+                legend=dict(
+                x=.05,
+                y=0.95,
+                traceorder="normal"
+                ),
+                plot_bgcolor = 'rgba(10, 10, 10, 1)'
+            ) 
 
-            st.plotly_chart(fig, theme=None, use_container_width=True,height=600)
+            st.plotly_chart(fig, theme=None, use_container_width=True, height=550)
 
             df['Per Trade Return Rate'] = df['Return Per Trade']-1
 

history_new.csv

@@ -0,0 +1,21 @@
+,id,datetime,exchange,subaccount,pair,side,action,amount,price,error
+0,31,2024-02-18 04:25:32,Bybit Futures,test connection,BTCUSDT,sell,open,0.009,51900,Order filled
+1,32,2024-02-18 04:25:55,Bybit Futures,test connection,BTCUSDT,buy,close,0.009,51900.1,Order filled
+2,35,2024-02-18 07:20:54,Bitget,bitget test,XRPUSDT,long,open,340,0.55548,Order filled
+3,36,2024-02-18 07:21:14,Bitget,bitget test,XRPUSDT,short,close,336,0.55548,Order filled
+4,37,2024-02-18 07:21:14,Bitget,bitget test,XRPUSDT,short,open,336,0.55548,Order filled
+5,39,2024-02-18 07:22:23,Bitget,bitget test,XRPUSDT,long,open,414,0.55548,Order filled
+6,49,2024-02-18 10:19:47,Bitget,Pumpernickel BitGet Test,ETHUSDT,long,open,0.1,2799.87,Order filled
+7,51,2024-02-18 12:49:06,Bitget,Pumpernickel BitGet Test,ETHUSDT,short,close,0.1,2825.93,Order filled
+8,52,2024-02-18 12:49:06,Bitget,Pumpernickel BitGet Test,ETHUSDT,short,open,0.1,2826.68,Order filled
+9,53,2024-02-18 16:04:36,Bitget,Pumpernickel BitGet Test,ETHUSDT,short,open,0.09,2850.78,Order filled
+10,54,2024-02-18 16:13:16,Bitget,Pumpernickel BitGet Test,ETHUSDT,short,open,0.09,2880.15,Order filled
+11,97,2024-02-19 04:26:02,Bybit Futures,bybit test,,,,,,No need to place order for BTCUSDT.
+12,98,2024-02-19 04:53:02,Bybit Futures,bybit test,,,,,,"Error: Get active positions:
+Traceback (most recent call last):
+  File ""/home/breadbytes1/mysite/bybitUniFuturesClient.py"", line 158, in get_position
+    position = self.client.get_positions(category= ""linear"", symbol= pair)['result']['list'][0]
+  File ""/home/breadbytes1/.local/lib/python3.10/site-packages/pybit/_v5_position.py"", line 22, in get_positions
+    return self._submit_request(
+  File ""/home/breadbytes1/.local/lib/python3.10/site-packages/pybit/_http_manager.py"", line 294, in _submit_request
+    "

pb-history-old.csv

@@ -0,0 +1,31 @@
+,Trade,Signal,Entry Date,Buy Price,Sell Price,Exit Date,P/L per token,P/L %,DCA
+0,1,Short,2023-07-04 12:00,0.07079,0.06795,2023-07-05 04:30,0.002840000000000009,3.86,1.0
+1,2,Short,2023-07-15 04:00,0.07322,0.07029,2023-07-15 21:15,0.002929999999999988,3.85,1.0
+2,3,Short,2023-07-20 06:00,0.07261,0.0697,2023-07-20 13:15,0.002909999999999996,3.86,1.0
+3,4,Short,2023-07-25 01:00,0.07781,0.08015,2023-07-25 11:30,-0.002339999999999995,-3.16,1.0
+4,5,Long,2023-08-17 16:30,0.05579,0.05958,2023-08-17 16:45,0.0037900000000000017,6.63,1.0
+5,6,Long,2023-08-17 22:00,0.06006,0.06307,2023-08-18 06:30,0.003009999999999999,4.85,1.0
+6,7,Short,2023-08-29 11:00,0.06743,0.06473,2023-08-30 09:30,0.002700000000000008,3.85,1.0
+7,8,Long,2023-10-25 18:30,0.06834,0.07176,2023-10-25 21:45,0.0034200000000000064,4.85,1.0
+8,9,Long,2023-11-07 03:30,0.07374,0.07743,2023-11-09 08:15,0.003689999999999999,4.85,1.0
+9,10,Long,2023-11-13 18:00,0.07298,0.07006,2023-11-14 12:45,-0.002920000000000006,-4.14,1.0
+10,11,Long,2023-11-16 17:15,0.07737,0.08124,2023-11-16 20:00,0.0038700000000000123,4.84,1.0
+11,12,Short,2023-11-17 03:00,0.08685,0.08337,2023-11-17 05:15,0.003479999999999997,3.86,1.0
+12,13,Long,2023-11-21 16:15,0.07287,0.07652,2023-11-22 15:00,0.0036500000000000005,4.85,1.0
+13,14,Long,2023-11-27 04:30,0.07871,0.08265,2023-11-29 06:15,0.003939999999999999,4.85,1.0
+14,15,Short,2023-12-04 00:00,0.09087,0.08723,2023-12-04 05:00,0.0036400000000000043,3.86,1.0
+15,16,Short,2023-12-05 14:00,0.09463,0.09747,2023-12-05 22:45,-0.0028399999999999953,-3.15,1.0
+16,17,Short,2023-12-06 01:00,0.10593,0.10169,2023-12-06 01:30,0.004239999999999994,3.85,1.0
+17,18,Long,2023-12-06 12:15,0.09836,0.09442,2023-12-06 17:30,-0.003939999999999999,-4.15,1.0
+18,19,Long,2023-12-07 13:30,0.09402,0.09873,2023-12-08 06:30,0.004709999999999992,4.85,1.0
+19,20,Long,2023-12-09 17:00,0.09966,0.10465,2023-12-10 19:45,0.004990000000000008,4.85,1.0
+20,21,Long,2023-12-11 08:45,0.09472,0.09638,2023-12-11 22:00,0.0016599999999999948,1.6,1.0
+21,22,Short,2023-12-11 22:00,0.09638,0.09252,2023-12-12 11:00,0.0038599999999999884,3.86,1.0
+22,23,Long,2023-12-12 11:15,0.09163,0.09622,2023-12-13 15:45,0.004589999999999997,4.85,1.0
+23,24,Short,2023-12-13 17:00,0.09694,0.09306,2023-12-15 17:15,0.0038799999999999946,3.85,1.0
+24,25,Long,2023-12-20 13:45,0.09162,0.09621,2023-12-21 14:00,0.004590000000000011,4.85,1.0
+25,26,Short,2023-12-21 17:00,0.0946,0.09081,2023-12-24 16:15,0.0037900000000000017,3.86,1.0
+26,27,Long,2024-01-03 06:00,0.08018,0.08419,2024-01-03 06:45,0.00401,4.84,1.0
+27,28,Short,2024-01-20 17:00,0.08835,0.08481,2024-01-21 01:00,0.0035400000000000015,3.86,1.0
+28,29,Long,2024-01-23 03:15,0.07607,0.07988,2024-01-24 00:15,0.003810000000000008,4.85,1.0
+29,30,Short,2024-02-19 17:15,0.09039,0.08677,2024-02-20 02:00,0.0036199999999999982,3.85,1.0