def test_get_price_list(self):
testdate = date(2019, 7, 26)
testsymbol = 'IDFCFIRSTB'
# Check Stock
dfpleq = get_price_list(testdate, 'EQ')
stk = dfpleq[dfpleq['SYMBOL'] == 'IDFCFIRSTB'].squeeze()
self.assertEqual(stk['CLOSE'], 42.35)
# Check Bond
dfpln1 = get_price_list(testdate, 'N1')
bond = dfpln1[dfpln1['SYMBOL'] == testsymbol].squeeze()
self.assertEqual(bond['CLOSE'], 5085.00)
def today_eod():
dt = date.today()
year = str(dt.year)
month_no = dt.month
if not os.path.exists(year):
os.makedirs(year)
print(year + ' : Dir Created.')
else:
print(year + ': dir already exists.')
month = month_name[month_no]
if not os.path.exists(year + '/' + str(month_no) + '-' + month):
os.makedirs(year + '/' + str(month_no) + '-' + month)
print(year + '/' + str(month_no) + '-' + month + ' : Dir Created.')
else:
print(year + '/' + str(month_no) + '-' + month +
': dir already exists.')
try:
if not os.path.exists(year + '/' + str(month_no) + '-' + month +
'/%s.csv' % (dt)):
df = get_price_list(dt)
df = df[(df.SERIES == 'EQ')]
df = df.drop(['TOTTRDVAL', 'ISIN', 'PREVCLOSE'], axis=1)
df.to_csv(year + '/' + str(month_no) + '-' + month + '/%s.csv' %
(dt),
index=False)
print('Downloaded : %s' % dt)
else:
print(year + '/' + month + '/%s.csv: file already exists.' % (dt))
except Exception as e:
print(e)
def getNSEDailyQuote(start_date, end_date, filename):
day_count = (end_date - start_date).days + 1
one_day = timedelta(1, 0, 0)
for dayloop in range(0, day_count, 1):
tradingdate = start_date + timedelta(dayloop, 0, 0)
if (tradingdate in holidaylist or tradingdate.weekday() in (5, 6)):
continue
print('trading date ->', tradingdate)
prices = get_price_list(tradingdate)
# writing to CSV
prices.to_csv('{}.csv'.format(filename),
index=False,
mode='a',
header=None)
def get_latest_bhavcopy():
from_period=datetime.datetime.now().date()+datetime.timedelta(days=-3)
to_period=datetime.datetime.now().date()
prices=None
while from_period<=to_period:
try:
prices = get_price_list(dt=from_period)
except:
print (" update_bhavcopy Unexpected error:", sys.exc_info())
pass
from_period=from_period+datetime.timedelta(days=1)
print(from_period)
return prices
def update_bhavcopy_df():
to_period=datetime.datetime.now().date()
from_period=to_period+datetime.timedelta(days=-4)
prices_all=pd.DataFrame()
while from_period<=to_period:
try:
prices = get_price_list(dt=from_period)
prices_all=prices_all.append(prices)
from_period=from_period+datetime.timedelta(days=1)
print(from_period)
except:
print (" update_bhavcopy Unexpected error:", sys.exc_info())
pass
return
def month_eod():
year = input('Year: ')
month_no = int(input('Month(1-12): '))
if not os.path.exists(year):
os.makedirs(year)
print(year + ' : Dir Created.')
else:
print(year + ': dir already exists.')
month = month_name[int(month_no)]
if not os.path.exists(year + '/' + str(month_no) + '-' + month):
os.makedirs(year + '/' + str(month_no) + '-' + month)
print(year + '/' + str(month_no) + '-' + month + ' : Dir Created.')
else:
print(year + '/' + str(month_no) + '-' + month +
': dir already exists.')
days = monthrange(int(year), month_no)[1]
print('No. of days in %s: %d' % (month, days))
for dy in range(1, days + 1):
dt = date(int(year), month_no, dy)
try:
if not os.path.exists(year + '/' + str(month_no) + '-' + month +
'/%s.csv' % (dt)):
df = get_price_list(dt)
df = df[(df.SERIES == 'EQ')]
df = df.drop(['TOTTRDVAL', 'ISIN', 'PREVCLOSE'], axis=1)
df.to_csv(year + '/' + str(month_no) + '-' + month +
'/%s.csv' % (dt),
index=False)
print('Downloaded : %s' % dt)
else:
print(year + '/' + month + '/%s.csv: file already exists.' %
(dt))
except Exception as e:
print(e)
continue
# 'Error While Dwnlding: ' +
else:
print('Done: ' + month)
def year_eod():
y = input('Year: ')
if not os.path.exists(y):
os.makedirs(y)
print(y + ' : Dir Created.')
else:
print(y + ': dir already exists.')
for mon in range(1, 13):
month = month_name[mon]
if not os.path.exists(y + '/' + str(mon) + '-' + month):
os.makedirs(y + '/' + str(mon) + '-' + month)
print(y + '/' + str(mon) + '-' + month + ' : Dir Created.')
else:
print(y + '/' + str(mon) + '-' + month + ': dir already exists.')
days = monthrange(int(y), mon)[1]
print('No. of days in %s: %d' % (month, days))
for dy in range(1, days + 1):
dt = date(int(y), mon, dy)
try:
if not os.path.exists(y + '/' + str(mon) + '-' + month +
'/%s.csv' % (dt)):
df = get_price_list(dt)
df = df[(df.SERIES == 'EQ')]
df = df.drop(['TOTTRDVAL', 'ISIN', 'PREVCLOSE'], axis=1)
df.to_csv(y + '/' + str(mon) + '-' + month + '/%s.csv' %
(dt),
index=False)
else:
print(y + '/' + month + + '/%s.csv: file already exists.' %
(dt))
except Exception as e:
print(e)
continue
else:
print('Downloaded : %s' % dt)
else:
print('Done: ' + month)
else:
print('Done: ' + y)
def main():
wb = xw.Book.caller()
DATE = wb.sheets[0].range("B1").value
# Get the price list for defined Date above
price_list = get_price_list(DATE)
price_list = price_list[["SYMBOL", "CLOSE", "ISIN"]]
# A one line function to return close price given ISIN
get_close = lambda x: price_list[price_list.ISIN == x]["CLOSE"]
Name = "XXX"
share_row = 4
while True:
sheet_name = "dly"
# Get the name of the stock
Name = wb.sheets[sheet_name].range(f"B{share_row}").value
# If the name is blank, then end the update
if not Name:
break
# Get the ISIN Number of the stock
ISIN = wb.sheets[sheet_name].range(f"A{share_row}").value
ISIN = ISIN.strip()
close_price = get_close(ISIN)
# Fetch the current closing and correspondingly populate the closing column for the day
if len(close_price) > 0:
wb.sheets[sheet_name].range(f"C{share_row}").value = float(close_price)
else:
wb.sheets[sheet_name].range(f"C{share_row}").value = "#NA"
share_row += 1
pd.set_option("display.width", 1500)
pd.set_option("display.max_columns", 75)
pd.set_option("display.max_rows", 1500)
abDatabase = 'D:\\Data\\Amibroker\\Databases\\NSEDAILY'
#abFormat = 'D:\\Data\\Amibroker\\Formats\\pyImport.format'
abFormat = 'D:\\Data\\Amibroker\\Formats\\pyBhavCopy.format'
AB = wcl.Dispatch('Broker.Application')
AB.LoadDatabase(abDatabase)
pd.set_option('display.max_columns', None)
fromDate = date(2020, 1, 1)
toDate = date(2020, 5, 8)
prices = get_price_list(dt=date(2015, 1, 1))
columnsTitles = [
'SYMBOL', 'TIMESTAMP', 'OPEN', 'HIGH', 'LOW', 'CLOSE', 'TOTTRDQTY'
]
prices.drop(
["SERIES", "LAST", "PREVCLOSE", "TOTTRDVAL", "TOTALTRADES", "ISIN"],
axis=1,
inplace=True)
prices = prices[columnsTitles]
#print(prices.dtypes)
prices['TIMESTAMP'] = pd.to_datetime(prices['TIMESTAMP'])
print(prices.dtypes)
prices['TIMESTAMP'].apply(lambda x: x.strftime('%Y%m%d'))
#prices['TIMESTAMP'] = prices['TIMESTAMP'].strftime('%Y%m%d')
print(prices.dtypes)
print(prices)
import plotly
import webbrowser
import plotly.graph_objs as go
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
from datetime import date
from nsepy import get_history
from nsepy.history import get_price_list
from pandas_datareader import data as pdr
import yfinance as yf
import matplotlib.pyplot as plt
import numpy as np
prices = get_price_list(dt=date(2020, 3, 9))
#print(prices)
#if row.TOTALTRADES>1000:
#print(prices[prices.TOTALTRADES>300000])
df = get_history(symbol="NIFTY 50",
start=date(2020, 6, 1),
end=date(2020, 6, 17),
index=True)
#
#
def candle_df(df):
#df_candle=first_letter_upper(df)
df_candle = df.copy()
def NSE_TradedStocks(dateval):
prices = get_price_list(dt=dateval)
return prices['SYMBOL'].tolist()
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 28 14:31:44 2018
@author: sanjotraibagkar
"""
from datetime import date
from nsepy.history import get_price_list
from nsepy import get_history
from datetime import date, timedelta
import pandas as pd
from matplotlib import pyplot as plt
import datetime
from nsepy.derivatives import get_expiry_date
import numpy as np
from utility import *
prices = get_price_list(dt=date(2018, 4, 27))
print(prices)
from nsepy.derivatives import get_expiry_date
expiry = get_expiry_date(year=2018, month=3)
print(expiry)
li.get_option_chain('NIFTY', instrument='OPTIDX', expiry=expiry)
Option_data_NIFTY.csv
import csv
from datetime import date
from nsepy.history import get_price_list
script_data = get_price_list(dt=date(2020, 9, 28))
script_data.to_csv('out.csv')
csv_out = csv.DictReader(open('out.csv', 'r'))
script_dict = []
for line in csv_out:
script_dict.append(line)
data_list = [
'SYMBOL', 'HIGH', 'LOW', 'CLOSE', 'R1', 'R2', 'PIVOT', 'S1', 'S2', '%',
'>.6%', 'TOTTRDQTY', 'TOTTRDVAL', 'BUY', 'TGT', 'SELL', 'TGT'
]
list_200_500 = []
for i in script_dict:
dummy_dict = {}
if 500 < float(i['CLOSE']) < 1000:
for j in data_list:
try:
dummy_dict[j] = i[j]
except:
dummy_dict[j] = ""
dummy_dict['PIVOT'] = (float(dummy_dict['HIGH']) + float(
dummy_dict['LOW']) + float(dummy_dict['CLOSE'])) / float(3)
dummy_dict['R1'] = (float(dummy_dict['PIVOT']) * float(2)) - float(
dummy_dict['LOW'])
"India VIX is a volatility index which gives a measurement of market volatility based on NIFTY options contract. This servers as important parameter in option pricing"
)
if st.button("Check Volatility"):
vix = get_history(symbol="INDIAVIX",
start=date(sy, sm, sd),
end=date(ey, em, ed),
index=True)
st.write(vix)
st.area_chart(vix[["Close"]])
st.header("Market Valuation")
st.markdown(
"P/E ratio of a security helps to estimate if the security is over-priced or under-priced and further make investment decisions. P/B value helps determine the intrinsic value of a security with regards to its market price"
)
if st.button("Check Valuation"):
pe = get_index_pe_history(symbol="NIFTY",
start=date(sy, sm, sd),
end=date(ey, em, ed))
st.write(pe)
st.area_chart(pe[["P/E"]])
st.line_chart(pe[["P/B"]])
st.header("All stock quotes")
st.markdown("Enter year,date and time to get stock quotes of entire market")
y = int(st.number_input("Enter year"))
m = int(st.number_input("Enter month"))
d = int(st.number_input("Enter date"))
if st.button("Check Quotes"):
prices = get_price_list(dt=date(y, m, d))
st.write(prices)
# importing datetime module
import datetime
from datetime import date
import calendar
from nsepy.history import get_price_list
#Taking date as input from the user
d1, m1, y1 = [int(x) for x in input("Enter date(DD/MM/YYYY) : ").split('/')]
b1 = date(y1, m1, d1)
# Check the dates
if b1.weekday() == 6:
print("Bhavcopy isn't available on sundays")
elif b1 == date.today():
prices = get_price_list(dt=date.today())
prices.to_csv('bhavcopy.csv')
elif b1 < date.today():
prices = get_price_list(dt=datetime.datetime(int(y1), int(m1), int(d1)))
prices.to_csv('bhavcopy.csv')
else:
print("Entered date is future date")
import csv
from datetime import date
from nsepy.history import get_price_list
script_data = get_price_list(dt=date(2018,5,8))
script_data.to_csv('out.csv')
csv_out = csv.DictReader(open('out.csv', 'r'))
script_dict = []
for line in csv_out:
script_dict.append(line)
data_list = ['SYMBOL','HIGH','LOW','CLOSE','R1','R2','PIVOT','S1','S2','%','>.6%','TOTTRDQTY','TOTTRDVAL', 'BUY', 'SELL']
list_200_500 = []
for i in script_dict:
dummy_dict = {}
if 100 < float(i['CLOSE']) < 700:
for j in data_list:
try:
dummy_dict[j] = i[j]
except:
dummy_dict[j] = ""
dummy_dict['PIVOT'] = (float(dummy_dict['HIGH'])+float(dummy_dict['LOW'])+float(dummy_dict['CLOSE']))/float(3)
dummy_dict['R1'] = (float(dummy_dict['PIVOT'])*float(2))-float(dummy_dict['LOW'])
dummy_dict['R2'] = float(dummy_dict['PIVOT'])+(float(dummy_dict['HIGH'])-float(dummy_dict['LOW']))
dummy_dict['S1'] = (float(dummy_dict['PIVOT'])*float(2))-float(dummy_dict['HIGH'])
dummy_dict['S2'] = float(dummy_dict['PIVOT'])-(float(dummy_dict['HIGH'])-float(dummy_dict['LOW']))
dummy_dict['%'] = ((float(dummy_dict['CLOSE'])-float(dummy_dict['PIVOT']))/float(dummy_dict['PIVOT']))*100
dummy_dict['BUY'] = (float(dummy_dict['CLOSE'])) + 1
dummy_dict['SELL'] = (float(dummy_dict['PIVOT'])) -1
auth_plugin="mysql_native_password")
start_date = date(2012, 1, 1)
end_date = date(2012, 1, 1)
#prices = get_price_list(start_date)
day_count = (end_date - start_date).days + 1
one_day = timedelta(1, 0, 0)
for dayloop in range(0, day_count, 1):
tradingdate = start_date + timedelta(dayloop, 0, 0)
print()
if (tradingdate in holidaylist or tradingdate.weekday() in (5, 6)):
continue
print('trading date ->', tradingdate)
prices = get_price_list(tradingdate)
#prices.to_sql(con=mydbconn, name='stock_price_history', if_exists='replace', flavor='mysql')
prices.to_sql('stock_price_hist', con=engine, if_exists='append')
mydbconn.commit()
print("Record inserted successfully into python_users table")
# mycursor = mydbconn.cursor()
# sql_insert_query = "INSERT INTO stockdaily_raw(nse_symbol,load_datetime,stock_rawdata) VALUES (%s,%s,%s)"
# val1='INFY'
# insert_tuple = (val1)
# for stock_row_string in stock_rawdata:
# json_stock_row = json.loads(stock_row_string);
# result = mycursor.execute(sql_insert_query, (json_stock_row['symbol'] ,currdatetime, stock_row_string))
except mysql.connector.Error as error:
mydbconn.rollback()
from time import sleep
from json import dumps
from kafka import KafkaProducer
producer = KafkaProducer(bootstrap_servers=['localhost:9092'],
value_serializer=lambda x: dumps(x).encode('utf-8'))
from nsepy.history import get_price_list
from datetime import date
for i in range(15, 26, 1):
print(i)
try:
prices = get_price_list(dt=date(2020, 9, i))
records - prices.to_dict('records')
for rec in records:
producer.send('stocks', value=rec)
except:
print("NO DATA FOUND ON {} DECEMBER ".format(i))
from nsepy.history import get_price_list
from datetime import date
import pandas as pd
##stock_opt = get_history(symbol="SBIN",
## start=date(2015,1,1),
## end=date(2015,1,10),
## option_type="CE",
## strike_price=300,
## expiry_date=date(2015,1,29))
##print(type(stock_opt))
##stock_opt.to_csv("options.csv")
prices = get_price_list(dt=date(2018, 11, 2))
prices.to_csv("Bhav.csv")
def getEodData(self, dt=None):
if dt is None:
dt = date.today()
return get_price_list(dt=dt)
def select_stock(data):
for i in range(len(data)):
try:
if data['CLOSE'].loc[i] <= 50.0:
data.drop(i, axis=0, inplace = True)
elif data['CLOSE'].loc[i] >= 10000.0:
data.drop(i, axis=0, inplace = True)
except:
continue
return data
pd.set_option('display.max_rows', 500)
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
data = get_price_list(dt=date(2019,1,4))
data.drop(['SERIES'], 1, inplace=True)
data.drop(['LAST'], 1, inplace=True)
data.drop(['TOTTRDQTY'], 1, inplace=True)
data.drop(['TOTTRDVAL'], 1, inplace=True)
data.drop(['TIMESTAMP'], 1, inplace=True)
data.drop(['TOTALTRADES'], 1, inplace=True)
data.drop(['ISIN'], 1, inplace=True)
data = select_stock(data)
rsi = []
predicts = []
# for i in range(len(data)):
data = data[:50]
for i in range(50):
try:
lastupdateddate = datetime.strptime(df['maxdate'][0].split()[0],
"%Y-%m-%d").date()
startdate = lastupdateddate + timedelta(days=1)
enddate = datetime.now().date() + timedelta(days=1)
dtrange = daterange(startdate, enddate)
starttime = datetime.now()
print("Started : ", starttime)
for dt in dtrange:
yr = dt.year
mth = dt.month
dte = dt.day
try:
if (dt.weekday() < 5):
prices = get_price_list(dt=date(yr, mth, dte))
prices['TIMESTAMP'] = pd.to_datetime(prices['TIMESTAMP'])
prices.to_sql("bhavcopy",
engine,
if_exists='append',
chunksize=1000)
print(dt, " - Completed :: ", len(prices.index), " ROWS, ",
len(prices.columns), " COLUMNS")
except Exception as e:
print(dt, " - Not Completed :: ", e)
continue
endtime = datetime.now()
print("Finished : ", endtime)
print("Completed in", endtime - starttime)
def extract_nse_daily(s_dt):
print(s_dt)
data = get_price_list(dt=date(s_dt.year, s_dt.month, s_dt.day))
data.columns = data.columns.str.strip().str.lower().str.replace(' ', '_').str.replace('(', '').str.replace(')', '')
return data
