All scripts are in python language to trade in zerodha using algorithms.
All scripts are in python language to get data from zerodha, do calculations and trade in zerodha using algorithms. you have to keep your zerodha login credentials in userdata.csv at this path : "C://db//loginkey//userdata.csv"
Contact : [email protected]
`print(" \n ")
print(" \t \t \t WELCOME to 'Stock screener Guppy' Please wait while we are installing library")
print("\n")
import time
import secrets
import threading
import webbrowser
import numpy as np
import pandas as pd
from pytz import timezone
from math import modf
from pprint import pprint
from datetime import datetime
from time import gmtime, strftime
from kiteconnect import KiteTicker
from kiteconnect import KiteConnect
from IPython.display import clear_output
api_key='api key'
api_secret='api secret'
#print(KiteConnect(api_key,api_secret).login_url())
webbrowser.open_new_tab(KiteConnect(api_key,api_secret).login_url())
print(" \n ")
kite = KiteConnect(api_key=api_key)
data = kite.generate_session(((str(input("ENTER full link generated in URL :- ")).split("request_token=")[1]).split("&action"))[0],api_secret)
kite.set_access_token(data["access_token"])
print(" \n ")
#display(data)
print(" \t \t \t \t \t \n WELCOME TO GUPPY BOT ")
print("\n" )
scan=str(input("DO you want Scanning ? YES/NO :-"))
if "YES"==scan or "yes"==scan:
print("SCANNING START")
#z=list(pd.read_csv("list.csv")["Symbol"])
z=['ADANIPORTS', 'ASIANPAINT', 'AXISBANK', 'BAJAJ-AUTO', 'BAJFINANCE', 'BAJAJFINSV', 'BPCL', 'BHARTIARTL', 'INFRATEL', 'BRITANNIA', 'CIPLA', 'COALINDIA', 'DRREDDY', 'EICHERMOT', 'GAIL', 'GRASIM', 'HCLTECH', 'HDFCBANK', 'HEROMOTOCO', 'HINDALCO', 'HINDUNILVR', 'HDFC', 'ICICIBANK', 'ITC', 'IBULHSGFIN', 'IOC', 'INDUSINDBK', 'INFY', 'JSWSTEEL', 'KOTAKBANK', 'LT', 'M&M', 'MARUTI', 'NTPC', 'ONGC', 'POWERGRID', 'RELIANCE', 'SBIN', 'SUNPHARMA', 'TCS', 'TATAMOTORS', 'TATASTEEL', 'TECHM', 'TITAN', 'UPL', 'ULTRACEMCO', 'VEDL', 'WIPRO', 'YESBANK', 'ZEEL']
eexchange="NSE"
time_frame ="5minute" ###
sdate ="2019-07-02"
todate ="2019-10-02"
tokenall=[]
aa=0
print(" \t \t \t \n Getting All tokens for processing BUY SELL ")
while(True):
ttoken=int(pd.DataFrame(kite.ltp(eexchange+":"+z[aa])).iloc[-2,0])
tokenall.append(ttoken)
aa=aa+1
if aa==50:
print(" \t \t \t \n Complete ! All tokens are fetched from file ")
print("\n" )
print(tokenall)
break
print(" Now checking Condition of BUY sell of GUPPY ")
#Variables
eduu=[]
eduu2=[]
eduu3=[]
buy5minute=[]
sell5minute=[]
buy10minute=[]
sell10minute=[]
buy15minute=[]
sell15minute=[]
##
lst_candle=[]
lst_heikin_nor=[]
lst_heikin=[]
lst_cand=[]
lst_c=[]
anchor=0
countstart=0
#programe start
a=0
BUY_listindicator=[]
SELL_listindicator=[]
sell5minutesym=[]
buy5minutesym=[]
buy10minutesym=[]
sell10minutesym=[]
buy15minutesym=[]
sell15minutesym=[]
#
#price
price5min_buy=[]
price5min_sell=[]
price15min_buy=[]
price15min_sell=[]
price30min_buy=[]
price30min_sell=[]
priceedit=[]
print(a)
def ashi():
global a
global BUY_listindicator
global SELL_listindicator
while(True):
print("\n" )
print("Current Token Number which is processing is",a)
#km=datetime.now().minute
#ks=datetime.now().second
#if km%1==0 and ks==1:
clear_output(wait=True)
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
#now_asia = now_asia.strftime("%S ")
now_asia = now_asia.strftime("%Y-%m-%d _ %H:%M:%S ")
edu=now_asia
eduu.append(edu)
dff=kite.historical_data(tokenall[a],sdate,todate,time_frame,0)
dfw=pd.DataFrame(dff)[:-1]
df=pd.DataFrame(dfw[['date','open','high','low','close']])
slow_ema = [3,5,7,9,11,13,15,17,19,21,23]
fast_ema = [25,28,31,34,37,40,43,46,49,52,55,58,61,64,67,70,200]
def EMA(df, base, target, period, alpha=False):
con = pd.concat([df[:period][base].rolling(window=period).mean(), df[period:][base]])
if (alpha == True):
# (1 - alpha) * previous_val + alpha * current_val where alpha = 1 / period
df[target] = con.ewm(alpha=1 / period, adjust=False).mean()
else:
# ((current_val - previous_val) * coeff) + previous_val where coeff = 2 / (period + 1)
df[target] = con.ewm(span=period, adjust=False).mean()
df.fillna(0,inplace = True)
# return df
for j in slow_ema:
val = "ema"+""+str(j)
EMA(df,"close",val,j)
for k in fast_ema:
val = "ema"+""+str(k)
EMA(df,"close",val,k)
def super_guppy(interval,df,anchor=0):
anchor = 0
ShowBreak = True
ShowSwing = True
ShowCon = False
uOCCswing = False
Lookback = 6
emaFilter = False
mult = 0
buybreak = 0
sellbreak = 0
buy_barssince_var = 0
sell_barssince_var = 0
buybreak_barssince_var = 0
sellbreak_barssince_var = 0
barssince_lst = list()
barssince_var = 0
bar_count_var = 0
buy1 = list()
sell1 = list()
buy2 = list()
sell2 = list()
buybreak1 = list()
sellbreak1 = list()
def barssince(b,barssince_var):
barssince_lst = []
barssince_var = 0
new_var = len(b)
for i in b[::-1]:
if i == 1:
break
barssince_lst.append(i)
barssince_var = len(barssince_lst)
return barssince_var
barssince_lst.clear()
#isIntraday
if interval < 1441 :
if (anchor==0 or interval <= 0 or interval >= anchor or anchor > 1441 ):
mult = 1
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = 1
#isIntraday Not
if interval > 1441:
if (anchor==0 or interval <= 0 or interval >= anchor or anchor < 52 ):
mult = mult
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = mult
mult = 1
for i in range(len(df)):
emaF1 = df.loc[i,'ema_3']
emaF2 = df.loc[i,'ema_5']
emaF3 = df.loc[i,'ema_7']
emaF4 = df.loc[i,'ema_9']
emaF5 = df.loc[i,'ema_11']
emaF6 = df.loc[i,'ema_13']
emaF7 = df.loc[i,'ema_15']
emaF8 = df.loc[i,'ema_17']
emaF9 = df.loc[i,'ema_19']
emaF10 = df.loc[i,'ema_21']
emaF11 = df.loc[i,'ema_23']
emaS1 = df.loc[i,'ema_25']
emaS2 = df.loc[i,'ema_28']
emaS3 = df.loc[i,'ema_31']
emaS4 = df.loc[i,'ema_34']
emaS5 = df.loc[i,'ema_37']
emaS6 = df.loc[i,'ema_40']
emaS7 = df.loc[i,'ema_43']
emaS8 = df.loc[i,'ema_46']
emaS9 = df.loc[i,'ema_49']
emaS10 = df.loc[i,'ema_52']
emaS11 = df.loc[i,'ema_55']
emaS12 = df.loc[i,'ema_58']
emaS13 = df.loc[i,'ema_61']
emaS14 = df.loc[i,'ema_64']
emaS15 = df.loc[i,'ema_67']
emaS16 = df.loc[i,'ema_70']
ema200 = df.loc[i,'ema_200']
emafast = (emaF1 + emaF2 + emaF3 + emaF4 + emaF5 + emaF6 + emaF7 + emaF8 + emaF9 + emaF10 + emaF11)/11
emaslow = (emaS1 + emaS2 + emaS3 + emaS4 + emaS5 + emaS6 + emaS7 + emaS8 + emaS9 + emaS10 + emaS11 + emaS12 + emaS13 + emaS14 + emaS15 + emaS16)/16
#Fast EMA Color Rules
colfastL = (emaF1>emaF2 and emaF2>emaF3 and emaF3>emaF4 and emaF4>emaF5 and emaF5>emaF6 and emaF6>emaF7 and emaF7>emaF8 and emaF8>emaF9 and emaF9>emaF10 and emaF10>emaF11)
colfastS = (emaF1<emaF2 and emaF2<emaF3 and emaF3<emaF4 and emaF4<emaF5 and emaF5<emaF6 and emaF6<emaF7 and emaF7<emaF8 and emaF8<emaF9 and emaF9<emaF10 and emaF10<emaF11)
#Slow EMA Color Rules
colslowL = (emaS1>emaS2 and emaS2>emaS3 and emaS3>emaS4 and emaS4>emaS5 and emaS5>emaS6 and emaS6>emaS7 and emaS7>emaS8) and (emaS8>emaS9 and emaS9>emaS10 and emaS10>emaS11 and emaS11>emaS12 and emaS12>emaS13 and emaS13>emaS14 and emaS14>emaS15 and emaS15>emaS16)
colslowS = (emaS1<emaS2 and emaS2<emaS3 and emaS3<emaS4 and emaS4<emaS5 and emaS5<emaS6 and emaS6<emaS7 and emaS7<emaS8) and (emaS8<emaS9 and emaS9<emaS10 and emaS10<emaS11 and emaS11<emaS12 and emaS12<emaS13 and emaS13<emaS14 and emaS14<emaS15 and emaS15<emaS16)
if emafast > emaslow and not colslowS and colfastL and (not ShowCon or colslowL) and (not emaFilter or emafast>ema200):
if int(buy1[-1]) > 0:
buy = buy1[-1] + 1
else:
buy = 1
else:
buy = 0
buy1.append(buy)
if emafast < emaslow and not colslowL and colfastS and (not ShowCon or colslowS) and (not emaFilter or emafast<ema200):
if int(sell1[-1]) > 0:
sell = sell1[-1] + 1
else:
sell = 1
else:
sell = 0
sell1.append(sell)
#buy
if buy>1 and colfastL and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
buy3 = 1
else:
buy3 = buy
buy2.append(buy3)
#sell
if sell>1 and colfastS and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
sell3 = 1
else:
sell3 = sell
sell2.append(sell3)
#buybreak
if emafast > emaslow and not colslowS and (not emaFilter or emafast>ema200):
if buybreak1[-1] > 0:
buybreak = buybreak1[-1] + 1
else:
buybreak = 1
else:
buybreak = 0
buybreak1.append(buybreak)
if emafast < emaslow and not colslowL and (not emaFilter or emafast<ema200):
if sellbreak1[-1] > 0:
sellbreak = sellbreak1[-1]+1
else:
sellbreak = 1
else:
sellbreak = 0
sellbreak1.append(sellbreak)
#arrow plotting
#buy_arrow
buy_barssince_var = barssince(buy2[:-1],barssince_var)
if (ShowSwing and buy3==1)and buy_barssince_var > 6:
buy_arrow = 1
else:
buy_arrow = 0
#sell arrow
sell_barssince_var = barssince(sell2[:-1],barssince_var)
if ShowSwing and (sell3==1 and sell_barssince_var > 6):
sell_arrow = 1
else:
sell_arrow = 0
#buybreak_arrow
buybreak_barssince_var = barssince(buybreak1[:-1],barssince_var)
sellbreak_barssince_var = barssince(sellbreak1[:-1],barssince_var)
if ShowBreak and buybreak==1 and (sellbreak_barssince_var>Lookback) and (buybreak_barssince_var>Lookback):
buybreak_arrow = 1
else:
buybreak_arrow = 0
#sellbreak_arrow
if ShowBreak and sellbreak==1 and (buybreak_barssince_var>Lookback) and (sellbreak_barssince_var>Lookback):
sellbreak_arrow = 1
else:
sellbreak_arrow = 0
if buy_arrow==1 and sell_arrow==0 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'green'
elif buy_arrow==0 and sell_arrow==1 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'red'
elif sell_arrow==0 and (buy_arrow==0 or buy_arrow==1) and buybreak_arrow==1 and sellbreak_arrow==0:
arrow_color = 'aqua'
elif buy_arrow==0 and (sell_arrow==1 or sell_arrow==0) and buybreak_arrow==0 and sellbreak_arrow==1:
arrow_color = 'blue'
else:
arrow_color = 'none'
df.loc[i,'arrow_color'] = arrow_color
df = df[['date','open','high','low','close','arrow_color']]
return df
df=super_guppy(15,df)
gup=df
print(" \n ")
print(" \t \t \t \t Zerodha GUPPY SCREENER on 5 minute Data ")
print(" \t \t \t \t 5 minute Program Start time is ", eduu[0])
print("\t \t \t \n Current Token checking " , tokenall[a])
print("\n" )
print(" \t \t \t \n Current Colour on this token is " , gup.iloc[-1,5])
if "green" in gup.iloc[-1,5]:
print(" BUY stock found ")
buy5minute.append((tokenall[a]))
buy5minutesym.append((z[a]))
price5min_buy.append(gup.iloc[-1,2])
if "red" in gup.iloc[-1,5]:
print(" SELL stock found ")
sell5minute.append((tokenall[a]))
sell5minutesym.append((z[a]))
price5min_sell.append(gup.iloc[-1,2])
else:
pass
print("Buy stock found for 5 minute are :=" ,buy5minute)
print("Sell stocks found for 5 minute are:=" ,sell5minute)
a=a+1
if a==len(tokenall):
file=str(buy5minute)
filee=str(sell5minute)
with open("tokens.txt","w") as f:
f.write(file)
f.write(filee)
break
buyframe={"SYMBOLS_BUY":buy5minute,"TOKENS_BUY":buy5minutesym,"Price":price5min_buy}
fivemin=pd.DataFrame(buyframe)
display(fivemin)
buyframee={"SYMBOLS_SELL":sell5minutesym,"TOKENS_SELL":sell5minute,"Price":price5min_sell}
fivemine=pd.DataFrame(buyframee)
display(fivemine)
ashi()
print("5 minute data complete! Now getting Data of 15 minutes ")
time_frame ="15minute"
a=0
def ashi15():
global a
global BUY_listindicator
global SELL_listindicator
while(True):
print("\n" )
print("Current Token Number which is processing is",a)
#km=datetime.now().minute
#ks=datetime.now().second
#if km%1==0 and ks==1:
clear_output(wait=True)
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
#now_asia = now_asia.strftime("%S ")
now_asia = now_asia.strftime("%Y-%m-%d _ %H:%M:%S ")
edu2=now_asia
eduu2.append(edu2)
dff=kite.historical_data(tokenall[a],sdate,todate,time_frame,0)
dfw=pd.DataFrame(dff)[:-1]
df=pd.DataFrame(dfw[['date','open','high','low','close']])
slow_ema = [3,5,7,9,11,13,15,17,19,21,23]
fast_ema = [25,28,31,34,37,40,43,46,49,52,55,58,61,64,67,70,200]
def EMA(df, base, target, period, alpha=False):
con = pd.concat([df[:period][base].rolling(window=period).mean(), df[period:][base]])
if (alpha == True):
# (1 - alpha) * previous_val + alpha * current_val where alpha = 1 / period
df[target] = con.ewm(alpha=1 / period, adjust=False).mean()
else:
# ((current_val - previous_val) * coeff) + previous_val where coeff = 2 / (period + 1)
df[target] = con.ewm(span=period, adjust=False).mean()
df.fillna(0,inplace = True)
# return df
for j in slow_ema:
val = "ema"+"_"+str(j)
EMA(df,"close",val,j)
for k in fast_ema:
val = "ema"+"_"+str(k)
EMA(df,"close",val,k)
def super_guppy(interval,df,anchor=0):
anchor = 0
ShowBreak = True
ShowSwing = True
ShowCon = False
uOCCswing = False
Lookback = 6
emaFilter = False
mult = 0
buybreak = 0
sellbreak = 0
buy_barssince_var = 0
sell_barssince_var = 0
buybreak_barssince_var = 0
sellbreak_barssince_var = 0
barssince_lst = list()
barssince_var = 0
bar_count_var = 0
buy1 = list()
sell1 = list()
buy2 = list()
sell2 = list()
buybreak1 = list()
sellbreak1 = list()
def barssince(b,barssince_var):
barssince_lst = []
barssince_var = 0
new_var = len(b)
for i in b[::-1]:
if i == 1:
break
barssince_lst.append(i)
barssince_var = len(barssince_lst)
return barssince_var
barssince_lst.clear()
#isIntraday
if interval < 1441 :
if (anchor==0 or interval <= 0 or interval >= anchor or anchor > 1441 ):
mult = 1
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = 1
#isIntraday Not
if interval > 1441:
if (anchor==0 or interval <= 0 or interval >= anchor or anchor < 52 ):
mult = mult
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = mult
mult = 1
for i in range(len(df)):
emaF1 = df.loc[i,'ema_3']
emaF2 = df.loc[i,'ema_5']
emaF3 = df.loc[i,'ema_7']
emaF4 = df.loc[i,'ema_9']
emaF5 = df.loc[i,'ema_11']
emaF6 = df.loc[i,'ema_13']
emaF7 = df.loc[i,'ema_15']
emaF8 = df.loc[i,'ema_17']
emaF9 = df.loc[i,'ema_19']
emaF10 = df.loc[i,'ema_21']
emaF11 = df.loc[i,'ema_23']
emaS1 = df.loc[i,'ema_25']
emaS2 = df.loc[i,'ema_28']
emaS3 = df.loc[i,'ema_31']
emaS4 = df.loc[i,'ema_34']
emaS5 = df.loc[i,'ema_37']
emaS6 = df.loc[i,'ema_40']
emaS7 = df.loc[i,'ema_43']
emaS8 = df.loc[i,'ema_46']
emaS9 = df.loc[i,'ema_49']
emaS10 = df.loc[i,'ema_52']
emaS11 = df.loc[i,'ema_55']
emaS12 = df.loc[i,'ema_58']
emaS13 = df.loc[i,'ema_61']
emaS14 = df.loc[i,'ema_64']
emaS15 = df.loc[i,'ema_67']
emaS16 = df.loc[i,'ema_70']
ema200 = df.loc[i,'ema_200']
emafast = (emaF1 + emaF2 + emaF3 + emaF4 + emaF5 + emaF6 + emaF7 + emaF8 + emaF9 + emaF10 + emaF11)/11
emaslow = (emaS1 + emaS2 + emaS3 + emaS4 + emaS5 + emaS6 + emaS7 + emaS8 + emaS9 + emaS10 + emaS11 + emaS12 + emaS13 + emaS14 + emaS15 + emaS16)/16
#Fast EMA Color Rules
colfastL = (emaF1>emaF2 and emaF2>emaF3 and emaF3>emaF4 and emaF4>emaF5 and emaF5>emaF6 and emaF6>emaF7 and emaF7>emaF8 and emaF8>emaF9 and emaF9>emaF10 and emaF10>emaF11)
colfastS = (emaF1<emaF2 and emaF2<emaF3 and emaF3<emaF4 and emaF4<emaF5 and emaF5<emaF6 and emaF6<emaF7 and emaF7<emaF8 and emaF8<emaF9 and emaF9<emaF10 and emaF10<emaF11)
#Slow EMA Color Rules
colslowL = (emaS1>emaS2 and emaS2>emaS3 and emaS3>emaS4 and emaS4>emaS5 and emaS5>emaS6 and emaS6>emaS7 and emaS7>emaS8) and (emaS8>emaS9 and emaS9>emaS10 and emaS10>emaS11 and emaS11>emaS12 and emaS12>emaS13 and emaS13>emaS14 and emaS14>emaS15 and emaS15>emaS16)
colslowS = (emaS1<emaS2 and emaS2<emaS3 and emaS3<emaS4 and emaS4<emaS5 and emaS5<emaS6 and emaS6<emaS7 and emaS7<emaS8) and (emaS8<emaS9 and emaS9<emaS10 and emaS10<emaS11 and emaS11<emaS12 and emaS12<emaS13 and emaS13<emaS14 and emaS14<emaS15 and emaS15<emaS16)
if emafast > emaslow and not colslowS and colfastL and (not ShowCon or colslowL) and (not emaFilter or emafast>ema200):
if int(buy1[-1]) > 0:
buy = buy1[-1] + 1
else:
buy = 1
else:
buy = 0
buy1.append(buy)
if emafast < emaslow and not colslowL and colfastS and (not ShowCon or colslowS) and (not emaFilter or emafast<ema200):
if int(sell1[-1]) > 0:
sell = sell1[-1] + 1
else:
sell = 1
else:
sell = 0
sell1.append(sell)
#buy
if buy>1 and colfastL and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
buy3 = 1
else:
buy3 = buy
buy2.append(buy3)
#sell
if sell>1 and colfastS and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
sell3 = 1
else:
sell3 = sell
sell2.append(sell3)
#buybreak
if emafast > emaslow and not colslowS and (not emaFilter or emafast>ema200):
if buybreak1[-1] > 0:
buybreak = buybreak1[-1] + 1
else:
buybreak = 1
else:
buybreak = 0
buybreak1.append(buybreak)
if emafast < emaslow and not colslowL and (not emaFilter or emafast<ema200):
if sellbreak1[-1] > 0:
sellbreak = sellbreak1[-1]+1
else:
sellbreak = 1
else:
sellbreak = 0
sellbreak1.append(sellbreak)
#arrow plotting
#buy_arrow
buy_barssince_var = barssince(buy2[:-1],barssince_var)
if (ShowSwing and buy3==1)and buy_barssince_var > 6:
buy_arrow = 1
else:
buy_arrow = 0
#sell arrow
sell_barssince_var = barssince(sell2[:-1],barssince_var)
if ShowSwing and (sell3==1 and sell_barssince_var > 6):
sell_arrow = 1
else:
sell_arrow = 0
#buybreak_arrow
buybreak_barssince_var = barssince(buybreak1[:-1],barssince_var)
sellbreak_barssince_var = barssince(sellbreak1[:-1],barssince_var)
if ShowBreak and buybreak==1 and (sellbreak_barssince_var>Lookback) and (buybreak_barssince_var>Lookback):
buybreak_arrow = 1
else:
buybreak_arrow = 0
#sellbreak_arrow
if ShowBreak and sellbreak==1 and (buybreak_barssince_var>Lookback) and (sellbreak_barssince_var>Lookback):
sellbreak_arrow = 1
else:
sellbreak_arrow = 0
if buy_arrow==1 and sell_arrow==0 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'green'
elif buy_arrow==0 and sell_arrow==1 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'red'
elif sell_arrow==0 and (buy_arrow==0 or buy_arrow==1) and buybreak_arrow==1 and sellbreak_arrow==0:
arrow_color = 'aqua'
elif buy_arrow==0 and (sell_arrow==1 or sell_arrow==0) and buybreak_arrow==0 and sellbreak_arrow==1:
arrow_color = 'blue'
else:
arrow_color = 'none'
df.loc[i,'arrow_color'] = arrow_color
df = df[['date','open','high','low','close','arrow_color']]
return df
df=super_guppy(15,df)
gup=df
print(" \n ")
print(" \t \t \t \t Zerodha GUPPY SCREENER on 15 minute data ")
print(" \t \t \t \t 15 minute Program Start time is ", eduu2[0])
print("\t \t \t \n Current Token checking " , tokenall[a])
print("\n" )
print(" \t \t \t \n Current Colour on this token is " , gup.iloc[-1,5])
if "green" in gup.iloc[-1,5]:
print("Buy stock found ")
buy10minute.append((tokenall[a]))
buy10minutesym.append((z[a]))
price15min_buy.append(gup.iloc[-1,2])
if "red" in gup.iloc[-1,5]:
print(" Sell Stock found ")
sell10minute.append((tokenall[a]))
sell10minutesym.append((z[a]))
priceedit.append(gup.iloc[-1,2])
else:
pass
print("Buy stock found for 15 minute are :=" ,buy10minute)
print("Sell stocks found for 15 minute are:=" ,sell10minute)
a=a+1
if a==len(tokenall):
file=str(buy5minute)
filee=str(sell5minute)
file2=str(buy10minute)
filee2=str(sell10minute)
with open("tokens.txt","w") as f:
f.write(file)
f.write(filee)
f.write(file2)
f.write(filee2)
break
buyframe15={"SYMBOLS_BUY":buy10minutesym,"TOKENS_BUY":buy10minute,"Price":price15min_buy}
fifteenemin=pd.DataFrame(buyframe15)
display(fifteenemin)
buyframe156={"SYMBOLS_SELL":sell10minutesym,"TOKENS_SELL":sell10minute,"Price":priceedit}
fifteenemin6=pd.DataFrame(buyframe156)
display(fifteenemin6)
ashi15()
print("5 and 15 minute data complete now getting Data of 30 minutes of minute frame")
time_frame ="30minute"
a=0
def ashi30():
global a
global BUY_listindicator
global SELL_listindicator
while(True):
print("\n" )
print("Current Token Number which is processing is",a)
#km=datetime.now().minute
#ks=datetime.now().second
#if km%1==0 and ks==1:
clear_output(wait=True)
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
#now_asia = now_asia.strftime("%S ")
now_asia = now_asia.strftime("%Y-%m-%d _ %H:%M:%S ")
edu3=now_asia
eduu3.append(edu3)
dff=kite.historical_data(tokenall[a],sdate,todate,time_frame,0)
dfw=pd.DataFrame(dff)[:-1]
df=pd.DataFrame(dfw[['date','open','high','low','close']])
slow_ema = [3,5,7,9,11,13,15,17,19,21,23]
fast_ema = [25,28,31,34,37,40,43,46,49,52,55,58,61,64,67,70,200]
def EMA(df, base, target, period, alpha=False):
con = pd.concat([df[:period][base].rolling(window=period).mean(), df[period:][base]])
if (alpha == True):
# (1 - alpha) * previous_val + alpha * current_val where alpha = 1 / period
df[target] = con.ewm(alpha=1 / period, adjust=False).mean()
else:
# ((current_val - previous_val) * coeff) + previous_val where coeff = 2 / (period + 1)
df[target] = con.ewm(span=period, adjust=False).mean()
df.fillna(0,inplace = True)
# return df
for j in slow_ema:
val = "ema"+"_"+str(j)
EMA(df,"close",val,j)
for k in fast_ema:
val = "ema"+"_"+str(k)
EMA(df,"close",val,k)
def super_guppy(interval,df,anchor=0):
anchor = 0
ShowBreak = True
ShowSwing = True
ShowCon = False
uOCCswing = False
Lookback = 6
emaFilter = False
mult = 0
buybreak = 0
sellbreak = 0
buy_barssince_var = 0
sell_barssince_var = 0
buybreak_barssince_var = 0
sellbreak_barssince_var = 0
barssince_lst = list()
barssince_var = 0
bar_count_var = 0
buy1 = list()
sell1 = list()
buy2 = list()
sell2 = list()
buybreak1 = list()
sellbreak1 = list()
def barssince(b,barssince_var):
barssince_lst = []
barssince_var = 0
new_var = len(b)
for i in b[::-1]:
if i == 1:
break
barssince_lst.append(i)
barssince_var = len(barssince_lst)
return barssince_var
barssince_lst.clear()
#isIntraday
if interval < 1441 :
if (anchor==0 or interval <= 0 or interval >= anchor or anchor > 1441 ):
mult = 1
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = 1
#isIntraday Not
if interval > 1441:
if (anchor==0 or interval <= 0 or interval >= anchor or anchor < 52 ):
mult = mult
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = mult
mult = 1
for i in range(len(df)):
emaF1 = df.loc[i,'ema_3']
emaF2 = df.loc[i,'ema_5']
emaF3 = df.loc[i,'ema_7']
emaF4 = df.loc[i,'ema_9']
emaF5 = df.loc[i,'ema_11']
emaF6 = df.loc[i,'ema_13']
emaF7 = df.loc[i,'ema_15']
emaF8 = df.loc[i,'ema_17']
emaF9 = df.loc[i,'ema_19']
emaF10 = df.loc[i,'ema_21']
emaF11 = df.loc[i,'ema_23']
emaS1 = df.loc[i,'ema_25']
emaS2 = df.loc[i,'ema_28']
emaS3 = df.loc[i,'ema_31']
emaS4 = df.loc[i,'ema_34']
emaS5 = df.loc[i,'ema_37']
emaS6 = df.loc[i,'ema_40']
emaS7 = df.loc[i,'ema_43']
emaS8 = df.loc[i,'ema_46']
emaS9 = df.loc[i,'ema_49']
emaS10 = df.loc[i,'ema_52']
emaS11 = df.loc[i,'ema_55']
emaS12 = df.loc[i,'ema_58']
emaS13 = df.loc[i,'ema_61']
emaS14 = df.loc[i,'ema_64']
emaS15 = df.loc[i,'ema_67']
emaS16 = df.loc[i,'ema_70']
ema200 = df.loc[i,'ema_200']
emafast = (emaF1 + emaF2 + emaF3 + emaF4 + emaF5 + emaF6 + emaF7 + emaF8 + emaF9 + emaF10 + emaF11)/11
emaslow = (emaS1 + emaS2 + emaS3 + emaS4 + emaS5 + emaS6 + emaS7 + emaS8 + emaS9 + emaS10 + emaS11 + emaS12 + emaS13 + emaS14 + emaS15 + emaS16)/16
#Fast EMA Color Rules
colfastL = (emaF1>emaF2 and emaF2>emaF3 and emaF3>emaF4 and emaF4>emaF5 and emaF5>emaF6 and emaF6>emaF7 and emaF7>emaF8 and emaF8>emaF9 and emaF9>emaF10 and emaF10>emaF11)
colfastS = (emaF1<emaF2 and emaF2<emaF3 and emaF3<emaF4 and emaF4<emaF5 and emaF5<emaF6 and emaF6<emaF7 and emaF7<emaF8 and emaF8<emaF9 and emaF9<emaF10 and emaF10<emaF11)
#Slow EMA Color Rules
colslowL = (emaS1>emaS2 and emaS2>emaS3 and emaS3>emaS4 and emaS4>emaS5 and emaS5>emaS6 and emaS6>emaS7 and emaS7>emaS8) and (emaS8>emaS9 and emaS9>emaS10 and emaS10>emaS11 and emaS11>emaS12 and emaS12>emaS13 and emaS13>emaS14 and emaS14>emaS15 and emaS15>emaS16)
colslowS = (emaS1<emaS2 and emaS2<emaS3 and emaS3<emaS4 and emaS4<emaS5 and emaS5<emaS6 and emaS6<emaS7 and emaS7<emaS8) and (emaS8<emaS9 and emaS9<emaS10 and emaS10<emaS11 and emaS11<emaS12 and emaS12<emaS13 and emaS13<emaS14 and emaS14<emaS15 and emaS15<emaS16)
if emafast > emaslow and not colslowS and colfastL and (not ShowCon or colslowL) and (not emaFilter or emafast>ema200):
if int(buy1[-1]) > 0:
buy = buy1[-1] + 1
else:
buy = 1
else:
buy = 0
buy1.append(buy)
if emafast < emaslow and not colslowL and colfastS and (not ShowCon or colslowS) and (not emaFilter or emafast<ema200):
if int(sell1[-1]) > 0:
sell = sell1[-1] + 1
else:
sell = 1
else:
sell = 0
sell1.append(sell)
#buy
if buy>1 and colfastL and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
buy3 = 1
else:
buy3 = buy
buy2.append(buy3)
#sell
if sell>1 and colfastS and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
sell3 = 1
else:
sell3 = sell
sell2.append(sell3)
#buybreak
if emafast > emaslow and not colslowS and (not emaFilter or emafast>ema200):
if buybreak1[-1] > 0:
buybreak = buybreak1[-1] + 1
else:
buybreak = 1
else:
buybreak = 0
buybreak1.append(buybreak)
if emafast < emaslow and not colslowL and (not emaFilter or emafast<ema200):
if sellbreak1[-1] > 0:
sellbreak = sellbreak1[-1]+1
else:
sellbreak = 1
else:
sellbreak = 0
sellbreak1.append(sellbreak)
#arrow plotting
#buy_arrow
buy_barssince_var = barssince(buy2[:-1],barssince_var)
if (ShowSwing and buy3==1)and buy_barssince_var > 6:
buy_arrow = 1
else:
buy_arrow = 0
#sell arrow
sell_barssince_var = barssince(sell2[:-1],barssince_var)
if ShowSwing and (sell3==1 and sell_barssince_var > 6):
sell_arrow = 1
else:
sell_arrow = 0
#buybreak_arrow
buybreak_barssince_var = barssince(buybreak1[:-1],barssince_var)
sellbreak_barssince_var = barssince(sellbreak1[:-1],barssince_var)
if ShowBreak and buybreak==1 and (sellbreak_barssince_var>Lookback) and (buybreak_barssince_var>Lookback):
buybreak_arrow = 1
else:
buybreak_arrow = 0
#sellbreak_arrow
if ShowBreak and sellbreak==1 and (buybreak_barssince_var>Lookback) and (sellbreak_barssince_var>Lookback):
sellbreak_arrow = 1
else:
sellbreak_arrow = 0
if buy_arrow==1 and sell_arrow==0 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'green'
elif buy_arrow==0 and sell_arrow==1 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'red'
elif sell_arrow==0 and (buy_arrow==0 or buy_arrow==1) and buybreak_arrow==1 and sellbreak_arrow==0:
arrow_color = 'aqua'
elif buy_arrow==0 and (sell_arrow==1 or sell_arrow==0) and buybreak_arrow==0 and sellbreak_arrow==1:
arrow_color = 'blue'
else:
arrow_color = 'none'
df.loc[i,'arrow_color'] = arrow_color
df = df[['date','open','high','low','close','arrow_color']]
return df
df=super_guppy(15,df)
gup=df
print(" \n ")
print(" \t \t \t \t Zerodha GUPPY SCREENER for 30 minute Data")
print(" \t \t \t \t 30 minute Program Start time is ", eduu3[0])
print("\n" )
print("\t \t \t \n Current Token checking " , tokenall[a])
print("\n" )
print(" \t \t \t \n Current Colour on this token is " , gup.iloc[-1,5])
if "green" in gup.iloc[-1,5]:
print("BUY stock found ")
buy15minute.append((tokenall[a]))
buy15minutesym.append((z[a]))
price30min_buy.append(gup.iloc[-1,2])
if "red" in gup.iloc[-1,5]:
print(" SELL Stock found ")
sell15minute.append((tokenall[a]))
sell15minutesym.append((z[a]))
price30min_sell.append(gup.iloc[-1,2])
else:
pass
print("Buy stock found for 30 minute are :=" ,buy15minute)
print("Sell stocks found for 30 minute are:=" ,sell15minute)
a=a+1
if a==len(tokenall):
file=str(buy5minute)
filee=str(sell5minute)
file2=str(buy10minute)
filee2=str(sell10minute)
file3=str(buy15minute)
filee3=str(sell15minute)
with open("tokens","w") as f:
f.write(file)
f.write(filee)
f.write(file2)
f.write(filee2)
f.write(file3)
f.write(filee3)
print("SCANNING COMPLETE")
break
buyframe30={"SYMBOLS_BUY":buy15minutesym,"TOKENS_BUY":buy15minute,"Price":price30min_buy}
thirtymin=pd.DataFrame(buyframe30)
display(thirtymin)
buyframe303={"SYMBOLS_SELL":sell15minutesym,"TOKENS_SELL":sell15minute,"Price":price30min_sell}
thirtymin3=pd.DataFrame(buyframe303)
display(thirtymin3)
ashi30()
print(" \n ")
print("5 ,15 and 30 are completed ! Results are ")
print("\n BUY/SELL 5 minute frame" )
print(" \t \t \t \t 5 minute Program Start time is ", eduu[0])
print(" \n ")
print("\n BUY/SELL 5 minute frame" )
buyframe={"SYMBOLS_BUY":buy5minute,"TOKENS_BUY":buy5minutesym,"Price":price5min_buy}
fivemin=pd.DataFrame(buyframe)
display(fivemin)
buyframee={"SYMBOLS_SELL":sell5minutesym,"TOKENS_SELL":sell5minute,"Price":price5min_sell}
fivemine=pd.DataFrame(buyframee)
display(fivemine)
print("\n BUY/SELL 15 minute frame" )
print(" \t \t \t \t 15 minute Program Start time is ", eduu2[0])
buyframe15={"SYMBOLS_BUY":buy10minutesym,"TOKENS_BUY":buy10minute,"Price":price15min_buy}
fifteenemin=pd.DataFrame(buyframe15)
display(fifteenemin)
buyframe156={"SYMBOLS_SELL":sell10minutesym,"TOKENS_SELL":sell10minute,"Price":priceedit}
fifteenemin6=pd.DataFrame(buyframe156)
display(fifteenemin6)
print("\n" )
print("\n BUY/SELL 30 minute frame" )
print(" \t \t \t \t 30 minute Program Start time is ", eduu3[0])
buyframe30={"SYMBOLS_BUY":buy15minutesym,"TOKENS_BUY":buy15minute,"Price":price30min_buy}
thirtymin=pd.DataFrame(buyframe30)
display(thirtymin)
buyframe303={"SYMBOLS_SELL":sell15minutesym,"TOKENS_SELL":sell15minute,"Price":price30min_sell}
thirtymin3=pd.DataFrame(buyframe303)
display(thirtymin3)
print("\n")
# buy
k=set(buy5minute)
l=set(buy10minute)
m=set(buy15minute)
# sell
n=set(sell5minute)
s=set(sell10minute)
p=set(sell15minute)
buya=n|s|p
sellb=k|l|m
print(" \t \t \t \t \t ALL ' BUY | SELL ' - Neglecting Common")
print("\n")
print("BUY from ALL ",sellb)
print("SELL From ALL ",buya)
print("\n")
print(" \t \t \t \t \t COMMON ' BUY & SELL ' ")
print("\n")
buycommmon=n&s&p
sellcommon=k&l&m
print("BUY from ALL ",buycommmon)
print("SELL From ALL ",sellcommon)
print("\n")
# finding coomon name
comm_buy=[]
comm_buy_sym=[]
comm_sell=[]
comm_sell_sym=[]
ee=0
buy_commmon=list(buycommmon)
sell_common=list(sellcommon)
if buy_commmon:
print("BUY COMMON FOUND")
while (True):
df=pd.DataFrame(kite.instruments("NSE"))[["instrument_token","tradingsymbol","name"]]
zall=df[df["instrument_token"]==buy_commmon[ee]]
comm_buy.append(zall.iloc[-1,0])
comm_buy_sym.append(zall.iloc[-1,1])
ee=ee+1
if ee==len(buy_commmon):
break
if sell_common:
print("BUY COMMON FOUND")
while (True):
df=pd.DataFrame(kite.instruments("NSE"))[["instrument_token","tradingsymbol","name"]]
zall=df[df["instrument_token"]==sell_common[ee]]
comm_sell.append(zall.iloc[-1,0])
comm_sell_sym.append(zall.iloc[-1,1])
ee=ee+1
if ee==len(sell_common):
break
print("BUY sell COMMON STOCK")
dic_comm_buy={"BUY_common_symbol":comm_buy_sym, "BUY_common_Token":comm_buy}
dic_comm_buy_disp=pd.DataFrame(dic_comm_buy)
dic_comm_sell={"SELL_common_symbol":comm_sell_sym, "SELL_common_Token":comm_sell}
dic_comm_sell_disp=pd.DataFrame(dic_comm_sell)
display(dic_comm_buy_disp)
display(dic_comm_sell_disp)
print("\t \t \t \t \t !! ALL COMPLETED !!")
print(" \n ")`
Can you make a YT tutorial?
H,
The supertrend buy condition at line 234 is missing [-2] . It is correct?
if super_trend[-1]=='up' and super_trend[-3]=='down' and super_trend[-4]=='down' and super_trend[-5]=='down' and super_trend[-6]=='down':
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