def sort_file():
df_nse50 = rd.get_list('nse_Top50', 'Symbol') #Extract list of Top Nse50 Stcoks
list_nse50 = df_nse50.index.tolist()
#Extract Data from a given date to past x no of traded days
for symbol in list_nse50[0:51]:
df_temp = pd.read_csv(rd.list_to_path(symbol), index_col='Date',
parse_dates=True,dayfirst=True, na_values=['nan']) #Extract date , series and price
df_temp = df_temp.reset_index().drop_duplicates(subset='Date', keep='last').set_index('Date')
df_temp.sort_index(ascending=True, inplace=True) #Remove any blank records
df_temp.to_csv("/Volumes/2/PyD/nsedb/{}.csv".format(str(symbol)))
def check_null():
df_nse50 = rd.get_list('nse_Top50', 'Symbol') #Extract list of Top Nse50 Stcoks
list_nse50 = df_nse50.index.tolist() #Array of Stcok symbols
end_date = datetime.date(2018,12,31) #date.today()
df_Stock = rd.get_data(list_nse50[0:51],end_date,3000) #Extract Data from a given date to past x no of traded days
df_Stock = df_Stock.dropna(how='all')
null_count = df_Stock.isnull().sum(axis=0)
print(null_count)
def test_run():
######Reading Data###################
df_nse50 = rd.get_list('nse_Top50', 'Symbol') #Extract list of Top Nse50 Stcoks
list_nse50 = df_nse50.index.tolist() #Array of Stcok symbols
df_Stock = rd.get_data(list_nse50[0:20],datetime.date.today(),50) #Extract Data from a given date to past x no of traded days
#test = find_nlargest(df_Stock.iloc[:,:1])
#print(df_Stock.iloc[:25,:1])
nparray = df_Stock.iloc[:15, :1].values
hist,edges = np.histogram(nparray,bins=4)
i,j,k = find_nlargest(hist.tolist(),3)
iloc = hist.tolist().index(i)
if j == i:
hist[iloc] = -1
jloc = hist.tolist().index(j)
if k == j:
hist[jloc] = -1
kloc = hist.tolist().index(k)
print(iloc,jloc,kloc)
"""
import os
import numpy as np
import datetime
import importlib
import read_Stock_Data as rd
importlib.reload(rd)
import pandas as pd
from openpyxl import load_workbook
excel_file = '/Volumes/2/Files/PraChin/InvestValue.xlsx' #/Volumes/2/Files/PraChin/ D:\\ppadayac\\PraChin\\InvestValue.xlsx
movies_sheet1 = pd.read_excel(excel_file, sheet_name=0)
movies_sheet1['Date'] = pd.to_datetime(movies_sheet1['Date'], dayfirst=True)
Dates = movies_sheet1['Date'].tolist()
df_nse50 = rd.get_list('nse_Top50',
'Symbol') #Extract list of Top Nse50 Stcoks
list_nse50 = df_nse50.index.tolist()
df_Stock = rd.get_data(list_nse50[1:51], datetime.date.today(), 365)
columns_buy = movies_sheet1['Buy'].tolist()
columns_sell = movies_sheet1['Sell'].tolist()
buy_size = movies_sheet1['BuySize'].tolist()
sell_size = movies_sheet1['Sell Size'].tolist()
date_range = pd.date_range(start=Dates[1], end=Dates[2])
new_df = df_Stock.loc[np.concatenate(
(np.array([Dates[0]]), date_range.tolist()), axis=0),
np.concatenate((columns_buy, columns_sell), axis=0)]
new_df = new_df.dropna(how='all')
new_df = new_df.dropna(how='all', axis=1)
one = np.concatenate((buy_size, sell_size), axis=0)
def test_run():
######Reading Data###################
df_nse50 = rd.get_list('nse_Top50',
'Symbol') #Extract list of Top Nse50 Stcoks
list_nse50 = df_nse50.index.tolist() #Array of Stcok symbols
df_Stock = rd.get_data(
list_nse50[0:50], datetime.date.today(),
365) #Extract Data from a given date to past x no of traded days
df_Stock.index = df_Stock.index.strftime(
'%d.%m.%Y') #Formate date to DD/MM/YYYY formate
#print(df_Stock)
######Creating Filter################
df_filter = (
df_Stock[-1:]
).T #Transposing the matrix to (rows as stocks and 1st column as last close price) for given date
str_trade_date = ''.join(
df_filter.columns.tolist()) #Extrating the 1st column name
col_A = "A~CP-{}".format(str_trade_date)
df_filter = df_filter.rename(columns={str_trade_date: col_A})
df_filter = (frm.stockAvg(df_Stock.tail(50), df_filter)).round(
2) # 1st Filter of calculating 50 Day Mean by less than 5 percent
df_filter = df_filter.rename(columns={'Mean': 'B~50DA'})
df_filter = (frm.stockAvg(df_Stock.tail(200), df_filter)).round(
2) # 2nd Filter of calculating 200 Day Mean by less than 5 percent
df_filter = df_filter.rename(columns={'Mean': 'C~200DA'})
df_filter = frm.stockRet(df_Stock, df_filter,
10) # 3rd Filter of calculating 10 Day Return
df_filter = df_filter.rename(columns={'Return': 'D~10DR'})
df_filter_temp = frm.modstockRet(
df_Stock, 1) # 4th Filter of calculating Absolute Return of 50 days
df_filter = frm.stockAvg(df_filter_temp, df_filter)
df_filter = df_filter.rename(columns={'Mean': 'E~AR50D'})
df_filter = (frm.RSI(df_Stock.tail(15), df_filter)).round(
2) # 5th Filter of calculating RSI of 14 days
df_filter = df_filter.rename(columns={'RSI': 'F~RSI'})
#####################Setting Criteria#######
########################Citeria 1 to Buy###########
df_filter['A<(B/C) '] = np.where(((df_filter[col_A] <= df_filter['B~50DA']) & \
(df_filter[col_A] >= (df_filter['B~50DA'])*0.95)) \
|((df_filter[col_A] <= df_filter['C~200DA']) & \
(df_filter[col_A] >= (df_filter['C~200DA'])*0.95)), 1 , 0)
########################Criteria 2 to Buy##########
df_filter['D<-8%'] = np.where(df_filter['D~10DR'] < -0.08, 1, 0)
########################Criteria 3 to Buy ##########
df_filter['E<1.5%'] = np.where(df_filter['E~AR50D'] < 0.015, 1, 0)
########################Criteria 4 to Buy ##########
df_filter['F<30%'] = np.where(df_filter['F~RSI'] < 30, 1, 0)
########################Citeria to Sell###########
df_filter['A>(B/C) '] = np.where(((df_filter[col_A] >= df_filter['B~50DA']) & \
(df_filter[col_A] <= (df_filter['B~50DA'])*1.05)) \
|((df_filter[col_A] >= df_filter['C~200DA']) & \
(df_filter[col_A] <= (df_filter['C~200DA'])*1.05)), 1 , 0)
########################Criteria 2 to Sell ##########
df_filter['D>8%'] = np.where(df_filter['D~10DR'] > 0.08, 1, 0)
########################Criteria 3 to Sell##########
df_filter['E>1.5%'] = np.where(df_filter['E~AR50D'] > 0.015, 1, 0)
########################Criteria 4 to Sell ##########
df_filter['F>70%'] = np.where(df_filter['F~RSI'] > 70, 1, 0)
####################Ranking to Buy/Sell#####
df_filter['BuyS'] = df_filter['A<(B/C) '] + df_filter['D<-8%'] + df_filter[
'E<1.5%'] + df_filter['F<30%']
df_filter['SellS'] = df_filter['A>(B/C) '] + df_filter['D>8%'] + df_filter[
'E>1.5%'] + df_filter['F>70%']
pd.set_option('display.max_rows', 500)
print(df_filter)
df_filter.to_csv("TradeCall_{}.csv".format(str_trade_date))
def test_run():
######Reading Data###################
df_nse50 = rd.get_list('ind_nifty50list',
'Symbol') #Extract list of Top Nse50 Stcoks
list_nse50 = df_nse50.index.tolist() #Array of Stcok symbols
csv = "CSV"
excel = "EXCEL"
start_date = datetime.date(2012, 1, 1)
end_date = datetime.date(2014, 1,
1) #date.today() datetime.date(2013,12,31)
df_Stock = rd.get_data(
list_nse50[0:51], end_date,
700) #Extract Data from a given date to past x no of traded days
df_Stock = df_Stock.dropna(how='all')
#Formate date to DD/MM/YYYY formate
df_Stock.index.name = 'Date'
str_trade_date = ''.join(df_Stock.iloc[-1:, :0].index.strftime('%d.%m.%Y'))
#writer = cmn.to_file(df_Stock.sort_index(ascending=False, inplace=False) ,"1_StockPrice",csv,str_trade_date)
#writer = cmn.to_file(df_Stock.iloc[:2,:2],"StockPrice2",excel,str_trade_date,writer)
rm_200 = rf.get_rolling_mean(df_Stock, window=200)
#writer = cmn.to_file(rm_200.sort_index(ascending=False, inplace=False),"2_RollMean200",csv,str_trade_date)
rm_50 = rf.get_rolling_mean(df_Stock, window=50)
#writer = cmn.to_file(rm_50.sort_index(ascending=False, inplace=False),"3_RollMean50",csv,str_trade_date)
'''
day_1_returns = rf.compute_daily_returns(df_Stock,1)
#writer = cmn.to_file(day_1_returns.sort_index(ascending=False, inplace=False),"4_OneDayRet",csv,str_trade_date)
abs_day_1_returns = day_1_returns.abs()
#writer = cmn.to_file(abs_day_1_returns.sort_index(ascending=False, inplace=False),"5_OneDayAbsRet",csv,str_trade_date)
rm_abs1day_50 = rf.get_rolling_mean(abs_day_1_returns, window=50)
counter_rm_abs1day_50 = rm_abs1day_50 < 2
#writer = cmn.to_file(counter_rm_abs1day_50.sort_index(ascending=False, inplace=False),"6_Counter50DayAbsRet",csv,str_trade_date)
day_10_returns = rf.compute_daily_returns(df_Stock,10)
writer = cmn.to_file(day_10_returns.sort_index(ascending=False, inplace=False),"7_10DayRet",csv,str_trade_date)
rd_10DRet = rf.get_rolling_std(day_10_returns, window=1200)
#writer = cmn.to_file(rd_10DRet.sort_index(ascending=False, inplace=False),"8_10DayRetStdDev",csv,str_trade_date)
ra_10plarge = rf.get_rolling_nAvg(df_Stock,1200,120,120,40)
writer = cmn.to_file(ra_10plarge.sort_index(ascending=False, inplace=False),"9_10PCLarge",csv,str_trade_date)
ra_10psmall = rf.get_rolling_nAvg(df_Stock,1200,1080,1080,1160)
writer = cmn.to_file(ra_10psmall.sort_index(ascending=False, inplace=False),"10_10PCSmall",csv,str_trade_date)
ra_3plarge = rf.get_rolling_nAvg(df_Stock,1200,40,40,13)
writer = cmn.to_file(ra_3plarge.sort_index(ascending=False, inplace=False),"11_3PCLarge",csv,str_trade_date)
ra_3psmall = rf.get_rolling_nAvg(df_Stock,1200,1140,1160,1147)
writer = cmn.to_file(ra_3psmall.sort_index(ascending=False, inplace=False),"12_3PCSmall",csv,str_trade_date)
ra_15plarge = rf.get_rolling_nAvg(df_Stock,1200,120,180,180)
writer = cmn.to_file(ra_15plarge.sort_index(ascending=False, inplace=False),"13_15PCLarge",csv,str_trade_date)
ra_15psmall = rf.get_rolling_nAvg(df_Stock,1200,1080,1020,1120)
writer = cmn.to_file(ra_15psmall.sort_index(ascending=False, inplace=False),"14_15PCSmall",csv,str_trade_date)
array_cntr = np.where(ra_3psmall.isnull() ,np.nan,
np.where(day_10_returns*100 <= ra_3psmall/100,3,
np.where(day_10_returns*100 <= ra_10psmall/100,2,
np.where(day_10_returns*100 <= ra_15psmall/100,1,0))))
indx = ra_15psmall.index.values
Cntr_10D_Buy = pd.DataFrame(array_cntr,index=indx ,columns=list_nse50)
writer = cmn.to_file(Cntr_10D_Buy.sort_index(ascending=False, inplace=False),"15_10DCounterBuy",csv,str_trade_date)
array_cntr = np.where(ra_3plarge.isnull() ,np.nan,
np.where(day_10_returns*100 > ra_3plarge/100,3,
np.where(day_10_returns*100 > ra_10plarge/100,2,
np.where(day_10_returns*100 > ra_15plarge/100,1,0))))
indx = ra_15plarge.index.values
Cntr_10D_Sell = pd.DataFrame(array_cntr,index=indx ,columns=list_nse50)
writer = cmn.to_file(Cntr_10D_Sell.sort_index(ascending=False, inplace=False),"16_10DCounterSell",csv,str_trade_date)
rRSI_13 = rf.get_Rolling_RSI(df_Stock,13)
#writer = cmn.to_file(rRSI_13.sort_index(ascending=False, inplace=False),"17_13DRSI",csv,str_trade_date)
array_cntr = np.where(rRSI_13.isnull() ,np.nan,
np.where(rRSI_13 <= 15,3,
np.where(rRSI_13 <= 25,2,
np.where(rRSI_13 <= 35,1,0))))
indx = rRSI_13.index.values
Cntr_RSI_Buy = pd.DataFrame(array_cntr,index=indx ,columns=list_nse50)
writer = cmn.to_file(Cntr_RSI_Buy.sort_index(ascending=False, inplace=False),"18_RSICounterBuy",csv,str_trade_date)
array_cntr = np.where(rRSI_13.isnull() ,np.nan,
np.where(rRSI_13 > 85,3,
np.where(rRSI_13 > 75,2,
np.where(rRSI_13 > 65,1,0))))
indx = rRSI_13.index.values
Cntr_RSI_Sell = pd.DataFrame(array_cntr,index=indx ,columns=list_nse50)
writer = cmn.to_file(Cntr_RSI_Sell.sort_index(ascending=False, inplace=False),"19_RSICounterSell",csv,str_trade_date)
vwap1 = rf.get_rolling_VMAP(df_Stock,200,1)
writer = cmn.to_file(vwap1.sort_index(ascending=False, inplace=False) ,"20_VWAP1",csv,str_trade_date)
vwap2 = rf.get_rolling_VMAP(df_Stock,200,2)
writer = cmn.to_file(vwap2.sort_index(ascending=False, inplace=False) ,"21_VWAP2",csv,str_trade_date)
vwap3 = rf.get_rolling_VMAP(df_Stock,200,3)
writer = cmn.to_file(vwap2.sort_index(ascending=False, inplace=False) ,"22_VWAP3",csv,str_trade_date)
v1_ratio = np.where(vwap1.isnull() ,np.nan,(df_Stock - vwap1)/df_Stock)
v2_ratio = np.where(vwap1.isnull() ,np.nan,(df_Stock - vwap2)/df_Stock)
v3_ratio = np.where(vwap1.isnull() ,np.nan,(df_Stock - vwap3)/df_Stock)
indx = vwap3.index.values
df_v1_ratio = pd.DataFrame(v1_ratio,index=indx ,columns=list_nse50)
df_v2_ratio = pd.DataFrame(v2_ratio,index=indx ,columns=list_nse50)
df_v3_ratio = pd.DataFrame(v3_ratio,index=indx ,columns=list_nse50)
array_cntr = np.where(df_v1_ratio.isnull() ,np.nan,
np.where((df_v1_ratio.abs() > 0.02) |
(df_v2_ratio.abs() > 0.02) |
(df_v3_ratio.abs() > 0.02),2,0))
indx = df_v1_ratio.index.values
Cntr_VWAP = pd.DataFrame(array_cntr,index=indx ,columns=list_nse50)
writer = cmn.to_file(Cntr_VWAP.sort_index(ascending=False, inplace=False),"23_VWAPCounter",csv,str_trade_date)
df_dma50 = np.where(rm_50.isnull() ,np.nan,(df_Stock - rm_50)/df_Stock)
df_dma200 = np.where(rm_200.isnull() ,np.nan,(df_Stock - rm_200)/df_Stock)
array_cntr1 = np.where(np.isnan(df_dma50) ,np.nan,
np.where((df_dma50 < 0.02) &
(df_dma50 > -0.015),1,0))
array_cntr2 = np.where(np.isnan(df_dma200) ,np.nan,
np.where((df_dma200 < 0.02) &
(df_dma200 > -0.02),2,0))
array_cntr = np.where(np.isnan(array_cntr2) ,array_cntr1,array_cntr1 + array_cntr2)
indx = rm_50.index.values
Cntr_DMA_Buy = pd.DataFrame(array_cntr,index=indx ,columns=list_nse50)
writer = cmn.to_file(Cntr_DMA_Buy.sort_index(ascending=False, inplace=False),"24_DMACounterBuy",csv,str_trade_date)
array_cntr1 = np.where(np.isnan(df_dma50) ,np.nan,
np.where((df_dma50 < 0.015) &
(df_dma50 > -0.02),1,0))
array_cntr = np.where(np.isnan(array_cntr2) ,array_cntr1,array_cntr1 + array_cntr2)
indx = rm_50.index.values
Cntr_DMA_Sell = pd.DataFrame(array_cntr,index=indx ,columns=list_nse50)
writer = cmn.to_file(Cntr_DMA_Sell.sort_index(ascending=False, inplace=False),"25_DMACounterSell",csv,str_trade_date)
rs_SLBuy = rf.get_rolling_small(df_Stock,1100,1)
writer = cmn.to_file(rs_SLBuy.sort_index(ascending=False, inplace=False),"26_SLBuy",csv,str_trade_date)
array_cntr = np.where(rs_SLBuy.isnull() ,np.nan,
np.where((((df_Stock - rs_SLBuy).abs())/df_Stock)<0.025,2,0))
indx = rs_SLBuy.index.values
Cntr_SL_Buy = pd.DataFrame(array_cntr,index=indx ,columns=list_nse50)
writer = cmn.to_file(Cntr_SL_Buy.sort_index(ascending=False, inplace=False),"27_SLCounterBuy",csv,str_trade_date)
rs_SLSell = rf.get_rolling_small(df_Stock,1100,1100)
writer = cmn.to_file(rs_SLSell.sort_index(ascending=False, inplace=False),"28_SLSel",csv,str_trade_date)
array_cntr = np.where(rs_SLSell.isnull() ,np.nan,
np.where((((df_Stock - rs_SLSell).abs())/df_Stock)<0.025,2,0))
indx = rs_SLSell.index.values
Cntr_SL_Sell = pd.DataFrame(array_cntr,index=indx ,columns=list_nse50)
writer = cmn.to_file(Cntr_SL_Sell.sort_index(ascending=False, inplace=False),"29_SLCounterSell",csv,str_trade_date)
'''
rm_20 = rf.get_rolling_mean(df_Stock, window=20)
rd_20 = ((df_Stock - rm_20) / df_Stock).abs()
writer = cmn.to_file(rd_20.sort_index(ascending=False, inplace=False),
"30_20Ddev", csv, str_trade_date)
rl_5p = rf.get_rolling_small(rd_20, 1000, 950)
writer = cmn.to_file(rl_5p.sort_index(ascending=False, inplace=False),
"31_5pDev", csv, str_trade_date)
rl_12p = rf.get_rolling_small(rd_20, 1000, 880)
writer = cmn.to_file(rl_12p.sort_index(ascending=False, inplace=False),
"32_12pDev", csv, str_trade_date)
rl_20p = rf.get_rolling_small(rd_20, 1000, 800)
writer = cmn.to_file(rl_20p.sort_index(ascending=False, inplace=False),
"33_20pDev", csv, str_trade_date)
array_cntr = np.where(
rm_50.isnull(), np.nan,
np.where(
df_Stock > rm_50, 0,
np.where(
rd_20 > rl_5p, 3,
np.where(rd_20 > rl_12p, 2, np.where(rd_20 > rl_20p, 1, 0)))))
indx = rm_50.index.values
Cntr_20dDev_Buy = pd.DataFrame(array_cntr, index=indx, columns=list_nse50)
writer = cmn.to_file(
Cntr_20dDev_Buy.sort_index(ascending=False, inplace=False),
"34_20dDevCounterBuy", csv, str_trade_date)
array_cntr = np.where(
rm_50.isnull(), np.nan,
np.where(
df_Stock < rm_50, 0,
np.where(
rd_20 > rl_5p, 3,
np.where(rd_20 > rl_12p, 2, np.where(rd_20 > rl_20p, 1, 0)))))
indx = rm_50.index.values
Cntr_20dDev_Sell = pd.DataFrame(array_cntr, index=indx, columns=list_nse50)
writer = cmn.to_file(
Cntr_20dDev_Sell.sort_index(ascending=False, inplace=False),
"35_20dDevCounterSell", csv, str_trade_date)
test_1 = np.where(abs_day_1_returns == day_1_returns, 1, 0)
df_filter = df_filter.rename(columns={'Mean': 'C~200DA'})
df_filter = frm.stockRet(df_Stock, df_filter,
10) # 3rd Filter of calculating 10 Day Return
df_filter = df_filter.rename(columns={'Return': 'D~10DR'})
df_filter_temp = frm.modstockRet(
df_Stock, 1) # 4th Filter of calculating Absolute Return of 50 days
df_filter = frm.stockAvg(df_filter_temp, df_filter)
df_filter = df_filter.rename(columns={'Mean': 'E~AR50D'})
df_filter = (frm.RSI(df_Stock.tail(15), df_filter)).round(
2) # 5th Filter of calculating RSI of 14 days
df_filter = df_filter.rename(columns={'RSI': 'F~RSI'})
#####################Setting Criteria#######
########################Citeria 1 to Buy###########
df_filter['A<(B/C) '] = np.where(((df_filter[col_A] <= df_filter['B~50DA']) & \
(df_filter[col_A] >= (df_filter['B~50DA'])*0.95)) \
|((df_filter[col_A] <= df_filter['C~200DA']) & \
(df_filter[col_A] >= (df_filter['C~200DA'])*0.95)), 1 , 0)
########################Criteria 2 to Buy##########
df_filter['D<-8%'] = np.where(df_filter['D~10DR'] < -0.08, 1, 0)
########################Criteria 3 to Buy ##########
df_filter['E<1.5%'] = np.where(df_filter['E~AR50D'] < 0.015, 1, 0)
########################Criteria 4 to Buy ##########
df_filter['F<30%'] = np.where(df_filter['F~RSI'] < 30, 1, 0)
########################Citeria to Sell###########
df_filter['A>(B/C) '] = np.where(((df_filter[col_A] >= df_filter['B~50DA']) & \
(df_filter[col_A] <= (df_filter['B~50DA'])*1.05)) \
|((df_filter[col_A] >= df_filter['C~200DA']) & \
(df_filter[col_A] <= (df_filter['C~200DA'])*1.05)), 1 , 0)
########################Criteria 2 to Sell ##########
df_filter['D>8%'] = np.where(df_filter['D~10DR'] > 0.08, 1, 0)
########################Criteria 3 to Sell##########
df_filter['E>1.5%'] = np.where(df_filter['E~AR50D'] > 0.015, 1, 0)
########################Criteria 4 to Sell ##########
df_filter['F>70%'] = np.where(df_filter['F~RSI'] > 70, 1, 0)
####################Ranking to Buy/Sell#####
df_filter['BuyS'] = df_filter['A<(B/C) '] + df_filter['D<-8%'] + df_filter[
'E<1.5%'] + df_filter['F<30%']
df_filter['SellS'] = df_filter['A>(B/C) '] + df_filter['D>8%'] + df_filter[
'E>1.5%'] + df_filter['F>70%']
pd.set_option('display.max_rows', 500)
print(df_filter)
df_filter.to_csv("TradeCall_{}.csv".format(str_trade_date))
# -*- coding: utf-8 -*-
"""
Created on Tue Aug 14 17:39:40 2018
Load the Database with NSE Fifty stocks
@author: ppadayac
"""
import pandas as pd
import importlib
from datetime import date
from nsepy import get_history
import numpy as np
import read_Stock_Data as rd
importlib.reload(rd)
df_nse50 = rd.get_list('ind_nifty50list',
'Symbol') #Extract list of Top Nse50 Stcoks
list_nse50 = df_nse50.index.tolist()
start_date = date(2012, 1, 1)
end_date = date(2014, 1, 1) #date.today()#date(2012,12,31)
rec_count = {}
sbin = get_history(symbol="NIFTY", start=start_date, end=end_date, index=True)
rec_count['NIFTY'] = len(sbin.index)
sbin.to_csv("/Volumes/2/PyD/nsedb/NIFTY.csv")
'''with open('/Volumes/2/PyD/nsedb/NIFTY.csv', 'a') as f:
sbin.to_csv(f, header=False)'''
#list_nse50 = ['EICHERMOT']
for symbl in list_nse50[1:]:
sbin = get_history(symbol=symbl, start=start_date, end=end_date)