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app.py

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+import pandas as pd
+from pandas import Timestamp
+import numpy as np
+import streamlit as st
+import plotly.graph_objects as go
+from plotly import tools
+import plotly.offline as py
+import plotly.express as px
+import requests,json
+from datetime import datetime, time,timedelta
+import plotly.express as px
+import warnings
+import io
+warnings.filterwarnings('ignore')
+pd.options.display.float_format = '${:,.2f}'.format
+one_day=timedelta(days=1)
+st.set_page_config(page_title="SquareoffbotsPerformance",layout='wide')
+st.markdown("""<style> div[role="listbox"] ul {
+    background-color: rgb(229, 236, 122);
+    }
+    div[role="listbox"],option{
+        float:right; 
+    }
+    #MainMenu {display:none;}
+    footer {display:none !important}
+
+
+</style>
+
+"""
+    
+    ,unsafe_allow_html=True)
+
+
+query_params = st.experimental_get_query_params()
+#st.runtime.legacy_caching.clear_cache()
+@st.cache(ttl=23*60*60)
+def get_ret_dic():
+    streamlit_data_url=r'https://dailysymbols.s3.ap-south-1.amazonaws.com/streamlit_data_ppl.json'
+    ret_dic=requests.get(streamlit_data_url).json()
+    return ret_dic
+# def get_ret_dic():
+#     from json_loader import json_load
+#     return json_load('streamlit_data_ppl.json')
+#     with open('streamlit_data_ppl.json','r') as fr:
+#         data=eval(fr.read().replace("'",'"')) comment
+#         return data
+ret_dic=get_ret_dic()
+
+# charges_dic=requests.get(charges_url).json()
+botNameDic={"orb":"ORB","rsi":"RSI","it":"Intraday Trend","sh":"StopHunt","grb":"GRB","orb2pm":"ORB2pm","pcr":"NiftyOptionSelling","lapp":"Learnapp","bss":"BNF Straddle","nss":"Nifty Straddle","bos":"BNFOptionSelling","grbo":"GRB Options","bssr":"BNF Strangle","mlb":"ML Bot","bnfmon":"BNF ORB","mss":"1% Short Straddle (BNF)","mssn":"1% Short Straddle(NF)","dts":"Double Top","ats":"Auto Strangle","dbss":"BNF Straddle(Directional)"}
+botCapitalDic={"orb":50000,"rsi":50000,"it":50000,"sh":50000,"grb":300000,"orb2pm":300000,"pcr":300000,"lapp":300000,"bss":300000,"nss":300000,"bos":300000,"grbo":150000,"bssr":300000,"bnfmon":150000,"mlb":400000,"mss":300000,"mssn":300000,"dts":150000,"ats":300000,"dbss":150000}
+curBots=['orb','rsi','it','grb','bss','grbo','bssr','bnfmon','mlb','mss','mssn','ats','dbss']
+botName = query_params["bot"][0] if "bot" in query_params else None
+
+botsList=list(botNameDic.keys())
+if botName not in botsList:
+    botName='bss'
+botsList.remove('bss')
+botsList=['bss']+botsList
+if not botName:
+    botName = st.selectbox('Select a Strategy',tuple(botsList))
+
+
+botCapital,capital_used_appendum,results_row,t_stats_Df,month_groups,strat_df,drawdown_df,i_fields,botFullName=ret_dic[botName]
+# with open('strat_df.txt','w') as fw:
+#     fw.write(strat_df)
+def df_from_string(str):
+    jstr=eval(str.replace('nan','0'))
+    df=pd.DataFrame.from_dict(jstr)
+    
+    return df
+
+t_stats_Df=df_from_string(t_stats_Df)
+
+
+month_groups=df_from_string(month_groups)
+strat_df=df_from_string(strat_df)
+drawdown_df=df_from_string(drawdown_df)
+results_row=results_row.replace('nan','0')
+results_row=eval(results_row)
+
+title_text="<h1 style='text-align: center; color: rgb(21, 86, 112);'>**♟**SQUAREOFF BOTS PERFORMANCE**♟**</h1><br><div style='text-align: center; color: rgb(21, 86, 112);'>**LIVE PERFORMANCE OF "+botFullName+"****[Capital used is "+str(botCapital)+capital_used_appendum+"]** </div>"
+st.markdown(title_text, unsafe_allow_html=True)
+
+fig=px.line(strat_df, x="Time", y='cum_pnl', title=botFullName+' PNL',width=800, height=400)
+dd_fig=px.line(drawdown_df,x="Time",y="drawdown", title=botFullName+' PNL',width=800, height=400)
+
+if botCapital>50000 and botName!='mlb':
+    # col1.write("**(Capital used before July 2021 is "+str(int(botCapital/1.5))+capital_used_appendum+")**")
+    st.markdown("<div style='text-align: center; color: rgb(21, 86, 112);'>**(Capital used before July 2021 is "+str(int(botCapital/1.5))+capital_used_appendum+")** </div>", unsafe_allow_html=True)
+col1, col2 = st.columns(2)
+col2.markdown('##')
+
+col1.write("Net ROI : "+str(results_row[-1])+"%")
+col1.write("**Statistics**")
+col1.table(t_stats_Df)
+
+
+
+col2.write("**PNL Curve**")
+col2.plotly_chart(fig)
+col2.write("**Drawdown Curve**")
+col2.plotly_chart(dd_fig)
+st.write("**Month-wise PNL**")
+st.table(month_groups)
+st.write("**Date-wise PNL (Last 30 Days)**")
+st.table(strat_df[i_fields][:30])
+
+# strat_df['Date']=strat_df.index
+# strat_df['pd_date']=pd.to_datetime(strat_df['Date'],format='%Y-%m-%d')
+# strat_df.sort_values('pd_date',inplace=True)
+# strat_df.sort_index(key=lambda x:datetime.strptime(x,'%Y-%m-%d'),inplace=True)
+# strat_df.drop('pd_date',axis=1,inplace=True)
+# strat_df.to_csv(f"{botName}.csv")
+
+

json_loader.py

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+import json
+
+def json_dump(file_name,data):
+	with open(file_name,'w') as fw:
+		json.dump(data,fw)
+		fw.close()
+def json_load(file_name):
+	with open(file_name,'r') as fr:
+		data=json.load(fr)
+		fr.close()
+	return data	

lambda_strategy_statistics.py

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+import pandas as pd
+import requests,json
+from datetime import datetime, time,timedelta
+from json_loader import json_load
+from returnsDf import agg_df
+import warnings
+warnings.filterwarnings('ignore')
+pd.options.display.float_format = '${:,.2f}'.format
+one_day=timedelta(days=1)
+
+pnl_url=r'http://performance.squareoffbots.com/assets/json/sqbots_allData_21052021.json'
+cap_url=r'http://performance.squareoffbots.com/assets/json/newCAp21052021.json'
+charges_url=r'http://performance.squareoffbots.com/assets/json/charges.json'
+
+def getResources1():
+    charges_dic=requests.get(charges_url).json()   
+    pnl_data=requests.get(pnl_url).json()
+    cap_data=requests.get(cap_url).json()
+    pnl_df_t=pd.DataFrame.from_dict(pnl_data)
+    cap_df_t=pd.DataFrame.from_dict(cap_data)
+    pnl_df=pnl_df_t.T
+    cap_df=cap_df_t.T    
+    return charges_dic,pnl_data,cap_data,pnl_df,cap_df,query_params
+
+
+botNameDic={"orb":"ORB","rsi":"RSI","it":"Intraday Trend","sh":"StopHunt","grb":"GRB","orb2pm":"ORB2pm","pcr":"NiftyOptionSelling","lapp":"Learnapp","bss":"BNF Straddle","nss":"Nifty Straddle","bos":"BNFOptionSelling","grbo":"GRB Options","bssr":"BNF Strangle","mlb":"ML Bot","bnfmon":"BNF ORB","mss":"1% Short Straddle (BNF)","mssn":"1% Short Straddle(NF)","dts":"Double Top"}
+botCapitalDic={"orb":50000,"rsi":50000,"it":50000,"sh":50000,"grb":300000,"orb2pm":300000,"pcr":300000,"lapp":300000,"bss":300000,"nss":300000,"bos":300000,"grbo":150000,"bssr":300000,"bnfmon":150000,"mlb":400000,"mss":300000,"mssn":300000,"dts":150000}
+curBots=['orb','rsi','it','grb','bss','grbo','bssr','bnfmon','mlb','mss','mssn','dts']
+
+
+
+def strategy_statistics():
+    charges_dic,pnl_data,cap_data,pnl_df,cap_df,query_params=getResources1()
+    ret_dic={}
+    for botName in curBots:
+        eq_bots=["orb","rsi","sh","it"]
+        botFullName=botNameDic[botName]
+        botCapital=botCapitalDic[botName]
+
+
+
+        strat_pnl_Df=pnl_df[[botFullName]]
+        strat_pnl_Df.dropna(inplace=True)
+        strat_cap_df=cap_df[[botFullName]]
+        #returns calculation
+        strat_df=agg_df(strat_pnl_Df,strat_cap_df)
+        ##PNL plot
+        strat_df['pdTime']=pd.to_datetime(strat_df.index,format="%Y-%m-%d")
+        strat_df.sort_values('pdTime',inplace=True)
+        strat_df[botFullName+'_adj_PnL']=(botCapital/100)*strat_df[botFullName+' Returns'].astype(float)
+        strat_df[botFullName+'_gross_PnL']=strat_df[botFullName+'_adj_PnL']
+        charges_types=['Brokerage','TransactionCharges','ClearingCharges','STT','GST','SEBI','StampDuty','TotalCharges']
+        def getCharges(x):
+            notGotCharges=True
+            original_date=x
+            cDayObj=datetime.strptime(x,'%Y-%m-%d')
+            direction=-1
+            direction_counter=0
+        
+            while notGotCharges:
+                try:            
+                    return charges_dic[cDayObj.strftime('%Y%m%d')][botName.upper()+'_'+ct]
+
+                except Exception as e:
+                    direction_counter+=1
+                    if direction_counter==10:
+                        direction*=-1
+                
+                
+                    cDayObj+=direction*one_day
+                    continue
+        for ct in charges_types:
+            strat_df[ct]=strat_df.index.to_series().apply(lambda x:getCharges(x))
+        i_fields=['PNL','Brokerage','TransactionCharges','ClearingCharges','STT','GST','SEBI','StampDuty','TotalCharges','net_PNL']
+
+
+
+        ##xxx
+        if botName in eq_bots:
+            i_fields=["PNL"]
+            strat_df['net_PNL']=strat_df[botFullName+'_gross_PnL']
+        else:
+            strat_df['net_PNL']=strat_df[botFullName+'_gross_PnL']-strat_df['TotalCharges']
+
+        strat_df['net_rets']=100*strat_df['net_PNL']/botCapital
+        strat_df["Time"]=strat_df.index
+        strat_df['PNL']=strat_df[botFullName+'_adj_PnL']
+        strat_df['cum_pnl']=strat_df[botFullName+'_adj_PnL'].cumsum()
+        ##DRAWDOWN
+        drawdown_df=strat_df.copy()
+        drawdown_df.reset_index(drop=True,inplace=True)
+        drawdown_df['max_value_so_far']=drawdown_df['cum_pnl'].cummax()
+        drawdown_df['drawdown']=drawdown_df['cum_pnl']-drawdown_df['max_value_so_far']
+        max_drawdown=drawdown_df['drawdown'].min()
+        ##Strategy statistics .
+        stats_Df=pd.DataFrame(columns=["Total Days","Winning Days","Losing Days","Winning Accuracy(%)","Max Profit","Max Loss","Max Drawdown","Average Profit on Win Days","Average Profit on loss days","Average Profit Per day","Gross Profit","Charges","Net profit","Returns (%)","net Returns (%)"])
+        total_days=len(strat_df)
+        win_df=strat_df[strat_df[botFullName+'_adj_PnL'].astype('float')>0]
+        lose_df=strat_df[strat_df[botFullName+'_adj_PnL'].astype('float')<0]
+        noTrade_df=strat_df[strat_df[botFullName+'_adj_PnL'].astype('float')==0]
+        win_days=len(win_df)
+        lose_days=len(lose_df)
+        win_ratio=win_days*100.0/(lose_days+win_days)
+        max_profit=strat_df[botFullName+'_adj_PnL'].max()
+        max_loss=strat_df[botFullName+'_adj_PnL'].min()
+        # max_drawdown=0
+        win_average_profit=win_df[botFullName+'_adj_PnL'].sum()/win_days
+        loss_average_profit=lose_df[botFullName+'_adj_PnL'].sum()/lose_days
+        total_profit=strat_df[botFullName+'_adj_PnL'].sum()
+
+        if botName not in eq_bots:
+            total_charges=strat_df['TotalCharges'].sum()
+        else:
+            total_charges=0
+        net_profit=total_profit-total_charges
+        average_profit=total_profit/total_days
+        gross_returns=strat_df[botFullName+' Returns'].sum()
+        net_returns=strat_df['net_rets'].sum()
+        results_row=[total_days,win_days,lose_days,win_ratio,max_profit,max_loss,max_drawdown,win_average_profit,loss_average_profit,average_profit,total_profit,total_charges,net_profit,gross_returns,net_returns]
+        results_row=[results_row[i] if i<3 else round(results_row[i],2) for i in range(len(results_row)) ]
+        stats_Df.loc[0,:]=results_row
+        t_stats_Df=stats_Df.T
+        t_stats_Df.rename(columns={0:''},inplace=True)
+ 
+        strat_df['month']=strat_df['pdTime'].apply(lambda x:x.strftime('%b,%Y'))
+        month_groups=strat_df.groupby('month',sort=False)[i_fields].sum()
+        ##last 30 days pnl
+        strat_df=strat_df.reindex(strat_df.index[::-1])
+        if botName in eq_bots:
+            capital_used_appendum=''
+        else:    
+            capital_used_appendum=' per Lot' 
+
+        ret_dic[botName]=[botCapital,capital_used_appendum,results_row,t_stats_Df,month_groups,strat_df,i_fields,botFullName]
+    return ret_dic
+
+def lambda_handler(event,context):
+    
+    ret_dic=strategy_statistics()
+    ret_dic_js=json.dumps(ret_dic)
+    print(ret_dic_js)

performance_old.py

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+import pandas as pd
+import numpy as np
+import streamlit as st
+import plotly.graph_objects as go
+from plotly import tools
+import plotly.offline as py
+import plotly.express as px
+import requests,json
+from datetime import datetime, time,timedelta
+import plotly.express as px
+from json_loader import json_load
+from returnsDf import agg_df
+import warnings
+warnings.filterwarnings('ignore')
+pd.options.display.float_format = '${:,.2f}'.format
+one_day=timedelta(days=1)
+st.set_page_config(page_title="SquareoffbotsPerformance",layout='wide')
+st.markdown("""<style> div[role="listbox"] ul {
+    background-color: rgb(229, 236, 122);
+    }
+    div[role="listbox"],option{
+        float:right; 
+    }
+    #MainMenu {display:none;}
+    footer {display:none !important}
+
+
+</style>
+
+"""
+    
+    ,unsafe_allow_html=True)
+
+pnl_url=r'http://performance.squareoffbots.com/assets/json/sqbots_allData_21052021.json'
+cap_url=r'http://performance.squareoffbots.com/assets/json/newCAp21052021.json'
+charges_url=r'http://performance.squareoffbots.com/assets/json/charges.json'
+
+query_params = st.experimental_get_query_params()
+@st.cache(ttl=20*60*60)
+def getResources1():
+    charges_dic=requests.get(charges_url).json()
+    # charges_dic=json_load('charges.json')
+    pnl_data=requests.get(pnl_url).json()
+    cap_data=requests.get(cap_url).json()
+    pnl_df_t=pd.DataFrame.from_dict(pnl_data)
+    cap_df_t=pd.DataFrame.from_dict(cap_data)
+    pnl_df=pnl_df_t.T
+    cap_df=cap_df_t.T    
+    return charges_dic,pnl_data,cap_data,pnl_df,cap_df,query_params
+charges_dic,pnl_data,cap_data,pnl_df,cap_df,query_params=getResources1()
+# charges_dic=requests.get(charges_url).json()
+botNameDic={"orb":"ORB","rsi":"RSI","it":"Intraday Trend","sh":"StopHunt","grb":"GRB","orb2pm":"ORB2pm","pcr":"NiftyOptionSelling","lapp":"Learnapp","bss":"BNF Straddle","nss":"Nifty Straddle","bos":"BNFOptionSelling","grbo":"GRB Options","bssr":"BNF Strangle","mlb":"ML Bot","bnfmon":"BNF ORB","mss":"1% Short Straddle (BNF)","mssn":"1% Short Straddle(NF)","dts":"Double Top"}
+botCapitalDic={"orb":50000,"rsi":50000,"it":50000,"sh":50000,"grb":300000,"orb2pm":300000,"pcr":300000,"lapp":300000,"bss":300000,"nss":300000,"bos":300000,"grbo":150000,"bssr":300000,"bnfmon":150000,"mlb":400000,"mss":300000,"mssn":300000,"dts":150000}
+curBots=['orb','rsi','it','grb','bss','grbo','bssr','bnfmon','mlb','mss','mssn','dts']
+botName = query_params["bot"][0] if "bot" in query_params else None
+
+botsList=list(botNameDic.keys())
+if botName not in botsList:
+    botName='bss'
+botsList.remove('bss')
+botsList=['bss']+botsList
+if not botName:
+    botName = st.selectbox('Select a Strategy',tuple(botsList))
+
+@st.cache(ttl=20*60*60,suppress_st_warning=True,allow_output_mutation=True)
+def strategy_statistics():
+    ret_dic={}
+    for botName in curBots:
+        eq_bots=["orb","rsi","sh","it"]
+        botFullName=botNameDic[botName]
+        botCapital=botCapitalDic[botName]
+
+
+
+        strat_pnl_Df=pnl_df[[botFullName]]
+        strat_pnl_Df.dropna(inplace=True)
+        strat_cap_df=cap_df[[botFullName]]
+        #returns calculation
+        strat_df=agg_df(strat_pnl_Df,strat_cap_df)
+        ##PNL plot
+        strat_df['pdTime']=pd.to_datetime(strat_df.index,format="%Y-%m-%d")
+        strat_df.sort_values('pdTime',inplace=True)
+        strat_df[botFullName+'_adj_PnL']=(botCapital/100)*strat_df[botFullName+' Returns'].astype(float)
+        strat_df[botFullName+'_gross_PnL']=strat_df[botFullName+'_adj_PnL']
+        charges_types=['Brokerage','TransactionCharges','ClearingCharges','STT','GST','SEBI','StampDuty','TotalCharges']
+        def getCharges(x):
+            notGotCharges=True
+            original_date=x
+            cDayObj=datetime.strptime(x,'%Y-%m-%d')
+            direction=-1
+            direction_counter=0
+        
+            while notGotCharges:
+                try:            
+                    return charges_dic[cDayObj.strftime('%Y%m%d')][botName.upper()+'_'+ct]
+
+                except Exception as e:
+                    direction_counter+=1
+                    if direction_counter==10:
+                        direction*=-1
+                
+                
+                    cDayObj+=direction*one_day
+                    continue
+        for ct in charges_types:
+            strat_df[ct]=strat_df.index.to_series().apply(lambda x:getCharges(x))
+        i_fields=['PNL','Brokerage','TransactionCharges','ClearingCharges','STT','GST','SEBI','StampDuty','TotalCharges','net_PNL']
+
+
+
+        ##xxx
+        if botName in eq_bots:
+            i_fields=["PNL"]
+            strat_df['net_PNL']=strat_df[botFullName+'_gross_PnL']
+        else:
+            strat_df['net_PNL']=strat_df[botFullName+'_gross_PnL']-strat_df['TotalCharges']
+
+        strat_df['net_rets']=100*strat_df['net_PNL']/botCapital
+        strat_df["Time"]=strat_df.index
+        strat_df['PNL']=strat_df[botFullName+'_adj_PnL']
+        strat_df['cum_pnl']=strat_df[botFullName+'_adj_PnL'].cumsum()
+        ##DRAWDOWN
+        drawdown_df=strat_df.copy()
+        drawdown_df.reset_index(drop=True,inplace=True)
+        drawdown_df['max_value_so_far']=drawdown_df['cum_pnl'].cummax()
+        drawdown_df['drawdown']=drawdown_df['cum_pnl']-drawdown_df['max_value_so_far']
+        max_drawdown=drawdown_df['drawdown'].min()
+        ##Strategy statistics .
+        stats_Df=pd.DataFrame(columns=["Total Days","Winning Days","Losing Days","Winning Accuracy(%)","Max Profit","Max Loss","Max Drawdown","Average Profit on Win Days","Average Profit on loss days","Average Profit Per day","Gross Profit","Charges","Net profit","Returns (%)","net Returns (%)"])
+        total_days=len(strat_df)
+        win_df=strat_df[strat_df[botFullName+'_adj_PnL'].astype('float')>0]
+        lose_df=strat_df[strat_df[botFullName+'_adj_PnL'].astype('float')<0]
+        noTrade_df=strat_df[strat_df[botFullName+'_adj_PnL'].astype('float')==0]
+        win_days=len(win_df)
+        lose_days=len(lose_df)
+        win_ratio=win_days*100.0/(lose_days+win_days)
+        max_profit=strat_df[botFullName+'_adj_PnL'].max()
+        max_loss=strat_df[botFullName+'_adj_PnL'].min()
+        # max_drawdown=0
+        win_average_profit=win_df[botFullName+'_adj_PnL'].sum()/win_days
+        loss_average_profit=lose_df[botFullName+'_adj_PnL'].sum()/lose_days
+        total_profit=strat_df[botFullName+'_adj_PnL'].sum()
+
+        if botName not in eq_bots:
+            total_charges=strat_df['TotalCharges'].sum()
+        else:
+            total_charges=0
+        net_profit=total_profit-total_charges
+        average_profit=total_profit/total_days
+        gross_returns=strat_df[botFullName+' Returns'].sum()
+        net_returns=strat_df['net_rets'].sum()
+        results_row=[total_days,win_days,lose_days,win_ratio,max_profit,max_loss,max_drawdown,win_average_profit,loss_average_profit,average_profit,total_profit,total_charges,net_profit,gross_returns,net_returns]
+        results_row=[results_row[i] if i<3 else round(results_row[i],2) for i in range(len(results_row)) ]
+        stats_Df.loc[0,:]=results_row
+        t_stats_Df=stats_Df.T
+        t_stats_Df.rename(columns={0:''},inplace=True)
+        fig=px.line(strat_df, x="Time", y='cum_pnl', title=botFullName+' PNL',width=800, height=400)
+        dd_fig=px.line(drawdown_df,x="Time",y="drawdown", title=botFullName+' PNL',width=800, height=400)
+        strat_df['month']=strat_df['pdTime'].apply(lambda x:x.strftime('%b,%Y'))
+        month_groups=strat_df.groupby('month',sort=False)[i_fields].sum()
+        ##last 30 days pnl
+        strat_df=strat_df.reindex(strat_df.index[::-1])
+        if botName in eq_bots:
+            capital_used_appendum=''
+        else:    
+            capital_used_appendum=' per Lot' 
+
+        ret_dic[botName]=[botCapital,capital_used_appendum,results_row,t_stats_Df,fig,dd_fig,month_groups,strat_df,i_fields,botFullName]
+    return ret_dic
+ret_dic=strategy_statistics()
+botCapital,capital_used_appendum,results_row,t_stats_Df,fig,dd_fig,month_groups,strat_df,i_fields,botFullName=ret_dic[botName]
+title_text="<h1 style='text-align: center; color: rgb(21, 86, 112);'>**♟**SQUAREOFF BOTS PERFORMANCE**♟**</h1><br><div style='text-align: center; color: rgb(21, 86, 112);'>**LIVE PERFORMANCE OF "+botFullName+"****[Capital used is "+str(botCapital)+capital_used_appendum+"]** </div>"
+st.markdown(title_text, unsafe_allow_html=True)
+
+
+
+if botCapital>50000 and botName!='mlb':
+    # col1.write("**(Capital used before July 2021 is "+str(int(botCapital/1.5))+capital_used_appendum+")**")
+    st.markdown("<div style='text-align: center; color: rgb(21, 86, 112);'>**(Capital used before July 2021 is "+str(int(botCapital/1.5))+capital_used_appendum+")** </div>", unsafe_allow_html=True)
+col1, col2 = st.columns(2)
+col2.markdown('##')
+
+col1.write("Net ROI : "+str(results_row[-1])+"%")
+col1.write("**Statistics**")
+col1.table(t_stats_Df)
+
+
+
+col2.write("**PNL Curve**")
+col2.plotly_chart(fig)
+col2.write("**Drawdown Curve**")
+col2.plotly_chart(dd_fig)
+st.write("**Month-wise PNL**")
+st.table(month_groups)
+st.write("**Date-wise PNL (Last 30 Days)**")
+st.table(strat_df[i_fields][:30])
+
+
+
+
+##TABLE
+# fig2=go.Figure(data=[go.Table(
+#     header=dict(values=['Date','BSS'],
+#                 fill_color='white',
+#                 line_color='black',
+               
+#                 align='left'),
+#     cells=dict(values=[strat_df.index,strat_df[botFullName+'_adj_PnL']],
+#                fill_color='white',
+               
+             
+#                align='left'))])
+# st.plotly_chart(fig2)

requirements.txt

@@ -0,0 +1,47 @@
+altair==4.2.0
+attrs==22.1.0
+blinker==1.5
+cachetools==5.2.0
+certifi==2022.12.7
+charset-normalizer==2.1.1
+click==8.1.3
+commonmark==0.9.1
+decorator==5.1.1
+entrypoints==0.4
+gitdb==4.0.10
+GitPython==3.1.29
+idna==3.4
+importlib-metadata==5.1.0
+Jinja2==3.1.2
+jsonschema==4.17.3
+MarkupSafe==2.1.1
+numpy==1.23.5
+packaging==22.0
+pandas==1.5.2
+Pillow==9.3.0
+plotly==5.11.0
+protobuf==3.20.3
+pyarrow==10.0.1
+pydeck==0.8.0
+Pygments==2.13.0
+Pympler==1.0.1
+pyrsistent==0.19.2
+python-dateutil==2.8.2
+pytz==2022.7
+pytz-deprecation-shim==0.1.0.post0
+requests==2.28.1
+rich==12.6.0
+semver==2.13.0
+six==1.16.0
+smmap==5.0.0
+streamlit==1.16.0
+tenacity==8.1.0
+toml==0.10.2
+toolz==0.12.0
+tornado==6.2
+typing_extensions==4.4.0
+tzdata==2022.7
+tzlocal==4.2
+urllib3==1.26.13
+validators==0.20.0
+zipp==3.11.0

requirements_heroku.txt

@@ -0,0 +1,96 @@
+altair==4.2.0
+appnope==0.1.3
+argon2-cffi==21.3.0
+argon2-cffi-bindings==21.2.0
+asttokens==2.0.5
+attrs==21.4.0
+backcall==0.2.0
+beautifulsoup4==4.11.1
+bleach==5.0.1
+blinker==1.5
+cachetools==5.2.0
+certifi==2022.6.15
+cffi==1.15.1
+charset-normalizer==2.1.0
+click==8.1.3
+commonmark==0.9.1
+debugpy==1.6.2
+decorator==5.1.1
+defusedxml==0.7.1
+entrypoints==0.4
+executing==0.9.0
+fastjsonschema==2.16.1
+gitdb==4.0.9
+GitPython==3.1.27
+idna==3.3
+importlib-metadata==4.12.0
+ipykernel==6.15.1
+ipython==8.4.0
+ipython-genutils==0.2.0
+ipywidgets==7.7.1
+jedi==0.18.1
+Jinja2==3.1.2
+jsonschema==4.7.2
+jupyter-client==7.3.4
+jupyter-core==4.11.1
+jupyterlab-pygments==0.2.2
+jupyterlab-widgets==1.1.1
+MarkupSafe==2.1.1
+matplotlib-inline==0.1.3
+mistune==0.8.4
+nbclient==0.6.6
+nbconvert==6.5.0
+nbformat==5.4.0
+nest-asyncio==1.5.5
+notebook==6.4.12
+numpy==1.22.3
+packaging==21.3
+pandas==1.4.3
+pandocfilters==1.5.0
+parso==0.8.3
+pexpect==4.8.0
+pickleshare==0.7.5
+Pillow==9.2.0
+plotly==5.9.0
+prometheus-client==0.14.1
+prompt-toolkit==3.0.30
+protobuf==3.20.1
+psutil==5.9.1
+ptyprocess==0.7.0
+pure-eval==0.2.2
+pyarrow==8.0.0
+pycparser==2.21
+pydeck==0.7.1
+Pygments==2.12.0
+Pympler==1.0.1
+pyparsing==3.0.9
+pyrsistent==0.18.1
+python-dateutil==2.8.2
+pytz==2022.1
+pytz-deprecation-shim==0.1.0.post0
+pyzmq==23.2.0
+requests==2.28.1
+rich==12.5.1
+semver==2.13.0
+Send2Trash==1.8.0
+six==1.16.0
+smmap==5.0.0
+soupsieve==2.3.2.post1
+stack-data==0.3.0
+streamlit==1.11.0
+tenacity==8.0.1
+terminado==0.15.0
+tinycss2==1.1.1
+toml==0.10.2
+toolz==0.12.0
+tornado==6.2
+traitlets==5.3.0
+typing_extensions==4.3.0
+tzdata==2022.1
+tzlocal==4.2
+urllib3==1.26.10
+validators==0.20.0
+wcwidth==0.2.5
+webencodings==0.5.1
+widgetsnbextension==3.6.1
+zipp==3.8.1

returnsDf.py

@@ -0,0 +1,38 @@
+import pandas as pd
+from json_loader import *
+import re
+
+
+
+def calCulateReturn(pnl,cap):
+  
+    try:
+        fc=float(cap)
+        fp=float(pnl)
+    except ValueError:
+        return 0
+    if(not re.search('\d',str(pnl)) or not re.search('\d',str(cap))):
+        return 0 
+    
+    
+    elif (fc==0 or fp==0):
+        return 0
+    else:
+        return 100*float(pnl)/float(cap)
+
+def agg_df(pnlDf,capDf):
+    returnsColumns=[c+' Returns' for c in capDf.columns]
+    capDf.columns=[c+' Capital' for c in capDf.columns]
+    pnlDf.columns=[c+' PnL' for c in pnlDf.columns]
+
+    result = pd.concat([capDf, pnlDf], axis=1, join="inner")
+
+
+
+    for r in returnsColumns:
+        stratName=r.split(' Returns')[0];
+        capColumnName=stratName+' Capital'
+        pnlColumnName=stratName+' PnL'
+        result[r]=result.apply(lambda x:calCulateReturn(x[pnlColumnName],x[capColumnName]),axis=1)
+    return result
+