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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() | |
| 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)) | |
| 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) |