Guppy Strategy Trading Bot Using Python and Zerodha
This is a programming lesson. So there will be very less amount of explanation and more code. If you’re stuck somewhere, feel free to comment.
Trading Bot
What is a Trading Bot?
A trading bot is an automated software program that interacts directly with financial exchanges (often using API’s to obtain and interpret relevant information) to effectively conduct trades on behalf of the user. Its primary function is to make trading decisions based on predefined criteria or the analysis of market data, without the need for human intervention.
The main advantage of using a trading bot is its ability to perform rapid, accurate, and emotion-free trades. It operates continuously, which means it can take advantage of opportunities any time of the day or night, far beyond the capabilities of a human trader.
In the last chapter, We have coded the screener of this strategy. Now, We will see the code of the Trading bot of the same strategy.
Key Components of the Guppy Strategy in the Code:
Exponential Moving Averages (EMAs): The code uses two sets of EMAs – one set of short-term EMAs (fast EMAs) and one set of long-term EMAs (slow EMAs). The fast EMAs typically include shorter time frames like 3, 5, 7 days, etc., while the slow EMAs include longer time frames like 25, 28, 31 days, etc. These EMAs are crucial in identifying trends and potential reversals.
Guppy Multiple Moving Average (GMMA): The strategy applies GMMA, which involves the plotting of both sets of EMAs on the same chart. The interaction between these two groups provides insights into the strength and direction of the market trend.
Buy and Sell Conditions as per the Code Logic:
Buy Condition: A buy signal is typically identified when the short-term (fast) EMAs cross above the long-term (slow) EMAs. This crossing indicates a potential upward trend in the stock price. Additionally, the code may include conditions where the current price is higher than some of the short-term EMAs, suggesting increasing bullish sentiment.
Sell Condition: A sell signal is detected when the short-term EMAs cross below the long-term EMAs, indicating a potential downward trend. Similarly, a condition where the current price is lower than some of the short-term EMAs can also suggest a bearish trend.
Additional Aspects of the Strategy in the Code:
-
Color Coding: The code may employ color coding (like green for buy, red for sell) for easier visualization of the trends based on the EMA crossovers.
-
Breakout Identification: The strategy can include logic to identify breakouts, where the stock price moves outside a defined range, signaling a stronger buy or sell opportunity.
-
Data and Time Frame: The code processes historical data for various time frames (like 5 minutes, 15 minutes, 30 minutes) to apply the Guppy strategy effectively.
Python Code
In practice, the Guppy strategy is used not just to determine entry or exit points, but also to gauge the strength and sustainability of the trend. It’s particularly favored in swing trading and day trading due to its effectiveness in short to medium time frames.
from datetime import date, timedelta
import pandas as pd
from pytz import timezone
print(" \t \t \t \t \t \n WELCOME TO GUPPY BOT ")
print("\n" )
scan=str(input("DO you want Scanning ? YES/NO :-")).upper()
if "YES"==scan or "yes"==scan:
buy_sell=str(input("ENTER which bot you want to run ? ")).upper()
if "BUY" ==buy_sell:
print("BUY BOT STARTED")
time_frame =str(input("Enter TimeFrame"))
sdate = (date.today() - timedelta(days=15)).strftime("%Y-%m-%d")
todate = date.today().strftime("%Y-%m-%d")
print("SCANNING START")
z=list(set(instrumentList[instrumentList['segment'] == 'NFO-FUT']["name"].to_list()))
eexchange="NSE"
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
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
#:sell10minutesym,"TOKENS_SELL":sell10minute,"Price":price15min_sell}
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 ")
klp1=now_asia
#df=kite.historical_data(140033,sdate,todate,time_frame,0)
dff=kite.historical_data(tokenall[a],sdate,todate,time_frame,0)
dfw=pd.DataFrame(dff)
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):
# df['buy'] = 0
# df['sell'] = 0
# df['buybreak'] = 0
# df['sellbreak'] = 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(" \t \t \t \t Zerodha GUPPY SCREENER on 5 minute Data")
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 are :=" ,buy5minute)
print("Sell stocks found are:=" ,sell5minute)
a=a+1
if a==len(tokenall):
break
ashi()
less_then_buy=[]
greater_then_buy=[]
less_then_sell=[]
greater_then_sell=[]
symbl_final_buy=[]
token_final_buy=[]
print(" Scanning is complete ")
buyframe={"Tokens_buy":buy5minute,"Symbol_buy":buy5minutesym,"Price":price5min_buy}
fivemin=pd.DataFrame(buyframe)
display(fivemin)
buyframee={"Tokens_sell":sell5minutesym,"Symbols_sell" :sell5minute,"Price":price5min_sell}
fivemine=pd.DataFrame(buyframee)
display(fivemine)
if buy5minute:
print("YES ! Some buy stock found")
aa=0
while(True):
kk=pd.DataFrame(kite.ltp(buy5minute[aa])).iloc[-1,0]
if kk<2000:
less_then_buy.append(buy5minute[aa])
if kk>2000:
greater_then_buy.append(buy5minute[aa])
aa=aa+1
if aa==len(buy5minute):
break
if sell5minute:
print("yes ! some sell tock found")
aa=0
while(True):
kk=pd.DataFrame(kite.ltp(sell5minute[aa])).iloc[-1,0]
if kk<2000:
less_then_sell.append(sell5minute[aa])
if kk>2000:
greater_then_sell.append(sell5minute[aa])
aa=aa+1
if aa==len(sell5minute):
break
print("BUY LESS THEN 2000" ,less_then_buy)
print("BUY grater then 2000" ,greater_then_buy)
print("SELL less then 2000" ,less_then_sell)
print("SELL GREATER THEN 2000",greater_then_sell)
#greater_then_sell
if less_then_buy:
print("Going to Place order on ",less_then_buy[0])
order_place_token=less_then_buy[0]
order_place=int(order_place_token)
print("BUY COMMON FOUND")
df=pd.DataFrame(kite.instruments("NSE"))[["instrument_token","tradingsymbol","name"]]
zall=df[df["instrument_token"]==order_place]
symbl_final_buy.append(zall.iloc[-1,0])
token_final_buy.append(zall.iloc[-1,1])
########################################
ttradingsymbol =str(symbl_final_buy)
eexchange ="NSE"
productt ="MIS"
qu =1
time_frame =time_frame
sdate ="2019-06-05"
todate ="2020-10-02"
print(ttoken)
print(type(ttoken))
ttoken =int(token_final_buy[0])
#############################
#calculation
def avg_ha(x):
a=list(modf(round(x,3)))
d=str(a[0])
aa=str(d[0:2])
try:
ab=str(d[2])
except:
ab='0'
try:
ac=str(d[3])
except:
ac='0'
try:
ad=str(d[4])
except:
ad='0'
c=aa+ab+ac+ad
b=0
if a[0]!=0:
astr=c
a0=astr[0]
a1=astr[1]
a3=int(astr[2])
a2=int(astr[3:5])
if a2>=0 and a2<25:
a2=0
elif a2>=25 and a2<75:
a2=5
elif a2>=75 and a2<=99:
a3+=1
a2=0
aint=a0+a1+str(a3)+str(a2)
a[0]=float(aint)
for k in a:
b+=k
return b
counter_start=[0]
linkedlist_1=[]
linkedlist_2=[]
linkedlist_3=[]
counter=[0,10]
execution=[]
checkfirst=[0]
red_time=[]
green_time=[]
redb=[]
bluez=[]
aqua1=[0]
bluein=[]
blue_2F=[]
blue_2G=[]
count_sectime=[]
redc=[]
colourstill=[]
prog_starts=[]
#Variables
lst_candle=[]
lst_heikin_nor=[]
lst_heikin=[]
lst_cand=[]
lst_c=[]
countstart=0
#programe start
def ashis():
while(True):
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 ")
klp1=now_asia
dff=kite.historical_data(ttoken,sdate,todate,time_frame,0)
#time.sleep(1)
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):
# df['buy'] = 0
# df['sell'] = 0
# df['buybreak'] = 0
# df['sellbreak'] = 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=pd.DataFrame(df)
def bidatrema(df,period):
df['hl']=abs(df['high']-df['low'])
df['hpc']=abs(df['high']-df['close'].shift())
df['lpc']=abs(df['low']-df['close'].shift())
df['tr']=df[['hl','hpc','lpc']].max(axis=1)
df['ATR']=pd.DataFrame.ewm(df["tr"], span=period,min_periods=period).mean()
df.drop(["hl","hpc","lpc","tr"],axis = 1 , inplace =True)
bidatrema(gup,14)
print(" \n \t \t \t \t GUPPY GREEN _ RED PROFIT BOOKING ")
print(" \t \t \t \n Current Colour on this token is " , gup.iloc[-1,5])
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%Y-%m-%d _ %H:%M:%S ")
klp1=now_asia
prog_starts.append(klp1)
print("\n ")
print("Zerodha GUPPY SELL BOT start Time " , prog_starts[0])
print("\n ")
print("BOT working succeesfully on time: " , now_asia )
print("\n ")
print("Trading symbol is",ttradingsymbol,"Token is",ttoken,"Exchange is",eexchange,"and product type is",productt,"Quantity is",qu,"time frame for Historical Data is",time_frame,"Starting and Ending Date of Historical Data is",sdate,todate)
colourstill.append(gup.iloc[-1,5])
print(" \n ")
print(" Completed with 'GREEN' entry=", len(green_time))
print(" Completed with 'RED' entry=", len(red_time))
print("\n ")
print("\n ")
if 0 in counter_start:
if "green" in gup.iloc[-1,5]:
counter_start.remove(0)
counter_start.append(1)
else:
pass
if 2 in counter_start:
print(" BOT STARTED WITH RED ' SELL' ENTRY ")
if "red" in gup.iloc[-1,5]:
if 0 in counter:
quant=qu
xa=float(gup.iloc[-1,3]-gup.iloc[-1,6]*.25)
price_sell=avg_ha(xa)
xb=price_sell-float(gup.iloc[-1,6])*0.1
triger_sell=avg_ha(xb)
triggerprice_sell=avg_ha(triger_sell)
kite_drop=kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='SL',price= price_sell,trigger_price=triggerprice_sell,transaction_type='SELL',product=productt,tag="testR")
print("red sell Stoploss order placed and SL trigger price is",triggerprice_sell,"and price is",price_sell, "quantity is",quant)
linkedlist_1.insert(0,kite_drop)
counter.remove(0)
counter.append(1)
if 1 in counter:
print("checking red execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==linkedlist_1[0]].index
match_in_int=int(match_in)
print("checking executed or not condition ")
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red " )
execution.append(1)
else:
print("SORRY! red not executed")
pass
if "green" in gup.iloc[-1,5]:
if 1 in counter:
if 1 in execution:
#check execution of green
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='MARKET',transaction_type='BUY',product=productt,tag="testR")
print("programe completed with red entry and end with green entry")
counter.clear()
counter_start.clear()
execution.clear()
linkedlist_1.clear()
execution.clear()
counter_start.append(0)
red_time.append(1)
else:
pass
else:
pass
if 1 in counter_start:
print(" BOT STARTED WITH GREEN ' BUY' ENTRY ")
if "green" in gup.iloc[-1,5]:
if 0 in counter:
quant=qu
xa=float(gup.iloc[-1,2]+(gup.iloc[-1,6]*.25))
cur_high=avg_ha(xa)
xb=cur_high+float(gup.iloc[-1,6])*0.1
lim_price=avg_ha(xb)
kite_drop=kite.place_order( variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='SL',price=lim_price ,trigger_price=cur_high,transaction_type='BUY',product=productt,tag="testR")
linkedlist_1.insert(0,kite_drop)
counter.remove(0)
counter.append(1)
if 1 in counter:
print("checking green execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==linkedlist_1[0]].index
match_in_int=int(match_in)
print("checking green executed or not condition ")
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red " )
execution.append(1)
else:
print("SORRY GREEN NOT EXECUTED")
pass
if "red" in gup.iloc[-1,5]:
if 1 in counter:
if 1 in execution:
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='MARKET',transaction_type='SELL',product=productt,tag="testG")
print("programe completed with green entry and end with RED entry")
green_time.append(1)
counter.clear()
counter_start.clear()
execution.clear()
linkedlist_1.clear()
execution.clear()
counter_start.append(0)
else:
pass
else:
pass
else:
pass
else:
pass
ashis()
########################
else:
if greater_then_buy:
print("No order is vailable less than 2000 but found more then 2000 so placing on it ")
order_place_token=greater_then_buy[0]
order_placee=int(order_place_token)
order_place=int(order_placee)
df=pd.DataFrame(kite.instruments("NSE"))[["instrument_token","tradingsymbol","name"]]
zall=df[df["instrument_token"]==order_place]
symbl_final_buy.append(zall.iloc[-1,0])
token_final_buy.append(zall.iloc[-1,1])
########################################
ttradingsymbol =str(symbl_final_buy)
eexchange ="NSE"
productt ="MIS"
qu =1
time_frame =time_frame
sdate ="2019-06-05"
todate ="2020-10-02"
ttoken =int(token_final_buy)
#############################
def avg_ha(x):
a=list(modf(round(x,3)))
d=str(a[0])
aa=str(d[0:2])
try:
ab=str(d[2])
except:
ab='0'
try:
ac=str(d[3])
except:
ac='0'
try:
ad=str(d[4])
except:
ad='0'
c=aa+ab+ac+ad
b=0
if a[0]!=0:
astr=c
a0=astr[0]
a1=astr[1]
a3=int(astr[2])
a2=int(astr[3:5])
if a2>=0 and a2<25:
a2=0
elif a2>=25 and a2<75:
a2=5
elif a2>=75 and a2<=99:
a3+=1
a2=0
aint=a0+a1+str(a3)+str(a2)
a[0]=float(aint)
for k in a:
b+=k
return b
counter_start=[0]
linkedlist_1=[]
linkedlist_2=[]
linkedlist_3=[]
counter=[0,10]
execution=[]
checkfirst=[0]
red_time=[]
green_time=[]
redb=[]
bluez=[]
aqua1=[0]
bluein=[]
blue_2F=[]
blue_2G=[]
count_sectime=[]
redc=[]
colourstill=[]
prog_starts=[]
#Variables
lst_candle=[]
lst_heikin_nor=[]
lst_heikin=[]
lst_cand=[]
lst_c=[]
countstart=0
#programe start
def ashis():
while(True):
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 ")
klp1=now_asia
dff=kite.historical_data(ttoken,sdate,todate,time_frame,0)
#time.sleep(1)
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):
# df['buy'] = 0
# df['sell'] = 0
# df['buybreak'] = 0
# df['sellbreak'] = 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=pd.DataFrame(df)
def bidatrema(df,period):
df['hl']=abs(df['high']-df['low'])
df['hpc']=abs(df['high']-df['close'].shift())
df['lpc']=abs(df['low']-df['close'].shift())
df['tr']=df[['hl','hpc','lpc']].max(axis=1)
df['ATR']=pd.DataFrame.ewm(df["tr"], span=period,min_periods=period).mean()
df.drop(["hl","hpc","lpc","tr"],axis = 1 , inplace =True)
bidatrema(gup,14)
print(" \n \t \t \t \t GUPPY GREEN _ RED PROFIT BOOKING ")
print(" \t \t \t \n Current Colour on this token is " , gup.iloc[-1,5])
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%Y-%m-%d _ %H:%M:%S ")
klp1=now_asia
prog_starts.append(klp1)
print("\n ")
print("Zerodha GUPPY SELL BOT start Time " , prog_starts[0])
print("\n ")
print("BOT working succeesfully on time: " , now_asia )
print("\n ")
print("Trading symbol is",ttradingsymbol,"Token is",ttoken,"Exchange is",eexchange,"and product type is",productt,"Quantity is",qu,"time frame for Historical Data is",time_frame,"Starting and Ending Date of Historical Data is",sdate,todate)
colourstill.append(gup.iloc[-1,5])
print(" \n ")
print(" Completed with 'GREEN' entry=", len(green_time))
print(" Completed with 'RED' entry=", len(red_time))
print("\n ")
print("\n ")
if 0 in counter_start:
if "green" in gup.iloc[-1,5]:
counter_start.remove(0)
counter_start.append(1)
else:
pass
if 2 in counter_start:
print(" BOT STARTED WITH RED ' SELL' ENTRY ")
if "red" in gup.iloc[-1,5]:
if 0 in counter:
quant=qu
xa=float(gup.iloc[-1,3]-gup.iloc[-1,6]*.25)
price_sell=avg_ha(xa)
xb=price_sell-float(gup.iloc[-1,6])*0.1
triger_sell=avg_ha(xb)
triggerprice_sell=avg_ha(triger_sell)
kite_drop=kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='SL',price= price_sell,trigger_price=triggerprice_sell,transaction_type='SELL',product=productt,tag="testR")
print("red sell Stoploss order placed and SL trigger price is",triggerprice_sell,"and price is",price_sell, "quantity is",quant)
linkedlist_1.insert(0,kite_drop)
counter.remove(0)
counter.append(1)
if 1 in counter:
print("checking red execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==linkedlist_1[0]].index
match_in_int=int(match_in)
print("checking executed or not condition ")
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red " )
execution.append(1)
else:
print("SORRY! red not executed")
pass
if "green" in gup.iloc[-1,5]:
if 1 in counter:
if 1 in execution:
#check execution of green
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='MARKET',transaction_type='BUY',product=productt,tag="testR")
print("programe completed with red entry and end with green entry")
counter.clear()
counter_start.clear()
execution.clear()
linkedlist_1.clear()
execution.clear()
counter_start.append(0)
red_time.append(1)
else:
pass
else:
pass
if 1 in counter_start:
print(" BOT STARTED WITH GREEN ' BUY' ENTRY ")
if "green" in gup.iloc[-1,5]:
if 0 in counter:
quant=qu
xa=float(gup.iloc[-1,2]+(gup.iloc[-1,6]*.25))
cur_high=avg_ha(xa)
xb=cur_high+float(gup.iloc[-1,6])*0.1
lim_price=avg_ha(xb)
kite_drop=kite.place_order( variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='SL',price=lim_price ,trigger_price=cur_high,transaction_type='BUY',product=productt,tag="testR")
linkedlist_1.insert(0,kite_drop)
counter.remove(0)
counter.append(1)
if 1 in counter:
print("checking green execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==linkedlist_1[0]].index
match_in_int=int(match_in)
print("checking green executed or not condition ")
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red " )
execution.append(1)
else:
print("SORRY GREEN NOT EXECUTED")
pass
if "red" in gup.iloc[-1,5]:
if 1 in counter:
if 1 in execution:
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='MARKET',transaction_type='SELL',product=productt,tag="testG")
print("programe completed with green entry and end with RED entry")
green_time.append(1)
counter.clear()
counter_start.clear()
execution.clear()
linkedlist_1.clear()
execution.clear()
counter_start.append(0)
else:
pass
else:
pass
else:
pass
else:
pass
ashis()
else:
print("sorry no order found of BUY")
time.sleep(300)
if "SELL"==buy_sell:
print("SELL BOT STARTED")
time_frame =str(input("Enter TimeFrame"))
sdate ="2019-07-05"
todate ="2019-10-02"
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"
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
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
#:sell10minutesym,"TOKENS_SELL":sell10minute,"Price":price15min_sell}
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 ")
klp1=now_asia
#df=kite.historical_data(140033,sdate,todate,time_frame,0)
dff=kite.historical_data(tokenall[a],sdate,todate,time_frame,0)
dfw=pd.DataFrame(dff)
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):
# df['buy'] = 0
# df['sell'] = 0
# df['buybreak'] = 0
# df['sellbreak'] = 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(" \t \t \t \t Zerodha GUPPY SCREENER on 5 minute Data")
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 are :=" ,buy5minute)
print("Sell stocks found are:=" ,sell5minute)
a=a+1
if a==len(tokenall):
break
ashi()
less_then_buy=[]
greater_then_buy=[]
less_then_sell=[]
greater_then_sell=[]
symbl_final_buy=[]
token_final_buy=[]
print(" Scanning is complete ")
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)
if buy5minute:
print("YES ! Some buy stock found")
aa=0
while(True):
kk=pd.DataFrame(kite.ltp(buy5minute[aa])).iloc[-1,0]
if kk<2000:
less_then_buy.append(buy5minute[aa])
if kk>2000:
greater_then_buy.append(buy5minute[aa])
aa=aa+1
if aa==len(buy5minute):
break
if sell5minute:
print("yes ! some sell tock found")
aa=0
while(True):
kk=pd.DataFrame(kite.ltp(sell5minute[aa])).iloc[-1,0]
if kk<2000:
less_then_sell.append(sell5minute[aa])
if kk>2000:
greater_then_sell.append(sell5minute[aa])
aa=aa+1
if aa==len(sell5minute):
break
print("BUY LESS THEN 2000" ,less_then_buy)
print("BUY grater then 2000" ,greater_then_buy)
print("SELL less then 2000" ,less_then_sell)
print("SELL GREATER THEN 2000",greater_then_sell)
less_then_buy=less_then_sell
greater_then_buy=greater_then_sell
if less_then_buy:
print("Going to Place order on ",less_then_buy[0])
order_place_token=less_then_buy[0]
order_place=int(order_place_token)
print("SELL FOUND")
df=pd.DataFrame(kite.instruments("NSE"))[["instrument_token","tradingsymbol","name"]]
zall=df[df["instrument_token"]==order_place]
symbl_final_buy.append(zall.iloc[-1,0])
token_final_buy.append(zall.iloc[-1,1])
########################################
ttradingsymbol =str(symbl_final_buy)
eexchange ="NSE"
productt ="MIS"
qu =1
time_frame =time_frame
sdate ="2019-06-05"
todate ="2020-10-02"
ttoken =int(token_final_buy)
#############################
#calculation
def avg_ha(x):
a=list(modf(round(x,3)))
d=str(a[0])
aa=str(d[0:2])
try:
ab=str(d[2])
except:
ab='0'
try:
ac=str(d[3])
except:
ac='0'
try:
ad=str(d[4])
except:
ad='0'
c=aa+ab+ac+ad
b=0
if a[0]!=0:
astr=c
a0=astr[0]
a1=astr[1]
a3=int(astr[2])
a2=int(astr[3:5])
if a2>=0 and a2<25:
a2=0
elif a2>=25 and a2<75:
a2=5
elif a2>=75 and a2<=99:
a3+=1
a2=0
aint=a0+a1+str(a3)+str(a2)
a[0]=float(aint)
for k in a:
b+=k
return b
counter_start=[0]
linkedlist_1=[]
linkedlist_2=[]
linkedlist_3=[]
counter=[0,10]
execution=[]
checkfirst=[0]
red_time=[]
green_time=[]
redb=[]
bluez=[]
aqua1=[0]
bluein=[]
blue_2F=[]
blue_2G=[]
count_sectime=[]
redc=[]
colourstill=[]
prog_starts=[]
#Variables
lst_candle=[]
lst_heikin_nor=[]
lst_heikin=[]
lst_cand=[]
lst_c=[]
countstart=0
#programe start
def ashis():
while(True):
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 ")
klp1=now_asia
dff=kite.historical_data(ttoken,sdate,todate,time_frame,0)
#time.sleep(1)
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):
# df['buy'] = 0
# df['sell'] = 0
# df['buybreak'] = 0
# df['sellbreak'] = 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=pd.DataFrame(df)
def bidatrema(df,period):
df['hl']=abs(df['high']-df['low'])
df['hpc']=abs(df['high']-df['close'].shift())
df['lpc']=abs(df['low']-df['close'].shift())
df['tr']=df[['hl','hpc','lpc']].max(axis=1)
df['ATR']=pd.DataFrame.ewm(df["tr"], span=period,min_periods=period).mean()
df.drop(["hl","hpc","lpc","tr"],axis = 1 , inplace =True)
bidatrema(gup,14)
print(" \n \t \t \t \t GUPPY SELL_ RED PROFIT BOOKING ")
print(" \t \t \t \n Current Colour on this token is " , gup.iloc[-1,5])
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%Y-%m-%d _ %H:%M:%S ")
klp1=now_asia
prog_starts.append(klp1)
print("\n ")
print("Zerodha GUPPY SELL BOT start Time " , prog_starts[0])
print("\n ")
print("BOT working succeesfully on time: " , now_asia )
print("\n ")
print("Trading symbol is",ttradingsymbol,"Token is",ttoken,"Exchange is",eexchange,"and product type is",productt,"Quantity is",qu,"time frame for Historical Data is",time_frame,"Starting and Ending Date of Historical Data is",sdate,todate)
colourstill.append(gup.iloc[-1,5])
print(" \n ")
print(" Completed with 'GREEN' entry=", len(green_time))
print(" Completed with 'RED' entry=", len(red_time))
print("\n ")
print("\n ")
if 0 in counter_start:
if "red" in gup.iloc[-1,5]:
counter_start.remove(0)
counter_start.append(2)
#if "green" in gup.iloc[-1,5]:
counter_start.remove(0)
counter_start.append(1)
else:
pass
if 2 in counter_start:
print(" BOT STARTED WITH RED ' SELL' ENTRY ")
if "red" in gup.iloc[-1,5]:
if 0 in counter:
quant=qu
xa=float(gup.iloc[-1,3]-gup.iloc[-1,6]*.25)
price_sell=avg_ha(xa)
xb=price_sell-float(gup.iloc[-1,6])*0.1
triger_sell=avg_ha(xb)
triggerprice_sell=avg_ha(triger_sell)
kite_drop=kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='SL',price= price_sell,trigger_price=triggerprice_sell,transaction_type='SELL',product=productt,tag="testR")
print("red sell Stoploss order placed and SL trigger price is",triggerprice_sell,"and price is",price_sell, "quantity is",quant)
linkedlist_1.insert(0,kite_drop)
counter.remove(0)
counter.append(1)
if 1 in counter:
print("checking red execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==linkedlist_1[0]].index
match_in_int=int(match_in)
print("checking executed or not condition ")
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red " )
execution.append(1)
else:
print("SORRY! red not executed")
pass
if "green" in gup.iloc[-1,5]:
if 1 in counter:
if 1 in execution:
#check execution of green
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='MARKET',transaction_type='BUY',product=productt,tag="testR")
print("programe completed with red entry and end with green entry")
counter.clear()
counter_start.clear()
execution.clear()
linkedlist_1.clear()
execution.clear()
counter_start.append(0)
red_time.append(1)
else:
pass
else:
pass
if 1 in counter_start:
print(" BOT STARTED WITH GREEN ' BUY' ENTRY ")
if "green" in gup.iloc[-1,5]:
if 0 in counter:
quant=qu
xa=float(gup.iloc[-1,2]+(gup.iloc[-1,6]*.25))
cur_high=avg_ha(xa)
xb=cur_high+float(gup.iloc[-1,6])*0.1
lim_price=avg_ha(xb)
kite_drop=kite.place_order( variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='SL',price=lim_price ,trigger_price=cur_high,transaction_type='BUY',product=productt,tag="testR")
linkedlist_1.insert(0,kite_drop)
counter.remove(0)
counter.append(1)
if 1 in counter:
print("checking green execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==linkedlist_1[0]].index
match_in_int=int(match_in)
print("checking green executed or not condition ")
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red " )
execution.append(1)
else:
print("SORRY GREEN NOT EXECUTED")
pass
if "red" in gup.iloc[-1,5]:
if 1 in counter:
if 1 in execution:
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='MARKET',transaction_type='SELL',product=productt,tag="testG")
print("programe completed with green entry and end with RED entry")
green_time.append(1)
counter.clear()
counter_start.clear()
execution.clear()
linkedlist_1.clear()
execution.clear()
counter_start.append(0)
else:
pass
else:
pass
else:
pass
else:
pass
ashis()
########################
else:
if greater_then_buy:
print("No order is vailable less than 2000 but found more then 2000 so placing on it ")
order_place_token=greater_then_buy[0]
order_placee=int(order_place_token)
order_place=int(order_placee)
df=pd.DataFrame(kite.instruments("NSE"))[["instrument_token","tradingsymbol","name"]]
zall=df[df["instrument_token"]==order_place]
symbl_final_buy.append(zall.iloc[-1,0])
token_final_buy.append(zall.iloc[-1,1])
########################################
ttradingsymbol =str(symbl_final_buy)
eexchange ="NSE"
productt ="MIS"
qu =1
time_frame =time_frame
sdate ="2019-06-05"
todate ="2020-10-02"
ttoken =int(token_final_buy)
#############################
def avg_ha(x):
a=list(modf(round(x,3)))
d=str(a[0])
aa=str(d[0:2])
try:
ab=str(d[2])
except:
ab='0'
try:
ac=str(d[3])
except:
ac='0'
try:
ad=str(d[4])
except:
ad='0'
c=aa+ab+ac+ad
b=0
if a[0]!=0:
astr=c
a0=astr[0]
a1=astr[1]
a3=int(astr[2])
a2=int(astr[3:5])
if a2>=0 and a2<25:
a2=0
elif a2>=25 and a2<75:
a2=5
elif a2>=75 and a2<=99:
a3+=1
a2=0
aint=a0+a1+str(a3)+str(a2)
a[0]=float(aint)
for k in a:
b+=k
return b
counter_start=[0]
linkedlist_1=[]
linkedlist_2=[]
linkedlist_3=[]
counter=[0,10]
execution=[]
checkfirst=[0]
red_time=[]
green_time=[]
redb=[]
bluez=[]
aqua1=[0]
bluein=[]
blue_2F=[]
blue_2G=[]
count_sectime=[]
redc=[]
colourstill=[]
prog_starts=[]
#Variables
lst_candle=[]
lst_heikin_nor=[]
lst_heikin=[]
lst_cand=[]
lst_c=[]
countstart=0
#programe start
def ashis():
while(True):
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 ")
klp1=now_asia
dff=kite.historical_data(ttoken,sdate,todate,time_frame,0)
#time.sleep(1)
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):
# df['buy'] = 0
# df['sell'] = 0
# df['buybreak'] = 0
# df['sellbreak'] = 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=pd.DataFrame(df)
def bidatrema(df,period):
df['hl']=abs(df['high']-df['low'])
df['hpc']=abs(df['high']-df['close'].shift())
df['lpc']=abs(df['low']-df['close'].shift())
df['tr']=df[['hl','hpc','lpc']].max(axis=1)
df['ATR']=pd.DataFrame.ewm(df["tr"], span=period,min_periods=period).mean()
df.drop(["hl","hpc","lpc","tr"],axis = 1 , inplace =True)
bidatrema(gup,14)
print(" \n \t \t \t \t GUPPY SELL _ RED PROFIT BOOKING ")
print(" \t \t \t \n Current Colour on this token is " , gup.iloc[-1,5])
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%Y-%m-%d _ %H:%M:%S ")
klp1=now_asia
prog_starts.append(klp1)
print("\n ")
print("Zerodha GUPPY SELL BOT start Time " , prog_starts[0])
print("\n ")
print("BOT working succeesfully on time: " , now_asia )
print("\n ")
print("Trading symbol is",ttradingsymbol,"Token is",ttoken,"Exchange is",eexchange,"and product type is",productt,"Quantity is",qu,"time frame for Historical Data is",time_frame,"Starting and Ending Date of Historical Data is",sdate,todate)
colourstill.append(gup.iloc[-1,5])
print(" \n ")
print(" Completed with 'GREEN' entry=", len(green_time))
print(" Completed with 'RED' entry=", len(red_time))
print("\n ")
print("\n ")
if 0 in counter_start:
if "green" in gup.iloc[-1,5]:
counter_start.remove(0)
counter_start.append(1)
else:
pass
if 2 in counter_start:
print(" BOT STARTED WITH RED ' SELL' ENTRY ")
if "red" in gup.iloc[-1,5]:
if 0 in counter:
quant=qu
xa=float(gup.iloc[-1,3]-gup.iloc[-1,6]*.25)
price_sell=avg_ha(xa)
xb=price_sell-float(gup.iloc[-1,6])*0.1
triger_sell=avg_ha(xb)
triggerprice_sell=avg_ha(triger_sell)
kite_drop=kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='SL',price= price_sell,trigger_price=triggerprice_sell,transaction_type='SELL',product=productt,tag="testR")
print("red sell Stoploss order placed and SL trigger price is",triggerprice_sell,"and price is",price_sell, "quantity is",quant)
linkedlist_1.insert(0,kite_drop)
counter.remove(0)
counter.append(1)
if 1 in counter:
print("checking red execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==linkedlist_1[0]].index
match_in_int=int(match_in)
print("checking executed or not condition ")
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red " )
execution.append(1)
else:
print("SORRY! red not executed")
pass
if "green" in gup.iloc[-1,5]:
if 1 in counter:
if 1 in execution:
#check execution of green
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='MARKET',transaction_type='BUY',product=productt,tag="testR")
print("programe completed with red entry and end with green entry")
counter.clear()
counter_start.clear()
execution.clear()
linkedlist_1.clear()
execution.clear()
counter_start.append(0)
red_time.append(1)
else:
pass
else:
pass
if 1 in counter_start:
print(" BOT STARTED WITH GREEN ' BUY' ENTRY ")
if "green" in gup.iloc[-1,5]:
if 0 in counter:
quant=qu
xa=float(gup.iloc[-1,2]+(gup.iloc[-1,6]*.25))
cur_high=avg_ha(xa)
xb=cur_high+float(gup.iloc[-1,6])*0.1
lim_price=avg_ha(xb)
kite_drop=kite.place_order( variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='SL',price=lim_price ,trigger_price=cur_high,transaction_type='BUY',product=productt,tag="testR")
linkedlist_1.insert(0,kite_drop)
counter.remove(0)
counter.append(1)
if 1 in counter:
print("checking green execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==linkedlist_1[0]].index
match_in_int=int(match_in)
print("checking green executed or not condition ")
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red " )
execution.append(1)
else:
print("SORRY GREEN NOT EXECUTED")
pass
if "red" in gup.iloc[-1,5]:
if 1 in counter:
if 1 in execution:
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='MARKET',transaction_type='SELL',product=productt,tag="testG")
print("programe completed with green entry and end with RED entry")
green_time.append(1)
counter.clear()
counter_start.clear()
execution.clear()
linkedlist_1.clear()
execution.clear()
counter_start.append(0)
else:
pass
else:
pass
else:
pass
else:
pass
ashis()
else:
print("sorry no order found of SELL ")
time.sleep(300)
if "NO"==scan:
manually=str(input( "ENTER BUY or SELL ?"))
if "BUY" in manually:
print(" BUY BOT STARTED Manually")
#calculation
def avg_ha(x):
a=list(modf(round(x,3)))
d=str(a[0])
aa=str(d[0:2])
try:
ab=str(d[2])
except:
ab='0'
try:
ac=str(d[3])
except:
ac='0'
try:
ad=str(d[4])
except:
ad='0'
c=aa+ab+ac+ad
b=0
if a[0]!=0:
astr=c
a0=astr[0]
a1=astr[1]
a3=int(astr[2])
a2=int(astr[3:5])
if a2>=0 and a2<25:
a2=0
elif a2>=25 and a2<75:
a2=5
elif a2>=75 and a2<=99:
a3+=1
a2=0
aint=a0+a1+str(a3)+str(a2)
a[0]=float(aint)
for k in a:
b+=k
return b
print("WELCOME TO GUPPY BUY PROFIT BOOKING BOT ! ")
print(" \n ")
ttradingsymbol =str(input("ENTER TRADINGSYMBOL - SUNPHARMA / SBIN :-")).upper()
eexchange =str(input("ENTER EXCHANGE / NSE / BSE / CDS :-")).upper()
productt =str(input("ENTER PRODUCT / NRML /CNC / MIS :-")).upper()
qu =int(input("ENTER QUANTITY - 2 / 4 :-"))
time_frame =str(input("ENTER TIME_FRAME - minute / 5minute :-")).lower()
sdate ="2019-05-05"
todate ="2020-10-02"
ttoken=int(pd.DataFrame(kite.ltp(eexchange+":"+ttradingsymbol)).iloc[-2,0])
counter_start=[0]
linkedlist_1=[]
linkedlist_2=[]
linkedlist_3=[]
counter=[0,10]
execution=[]
checkfirst=[0]
red_time=[]
green_time=[]
redb=[]
bluez=[]
aqua1=[0]
bluein=[]
blue_2F=[]
blue_2G=[]
count_sectime=[]
redc=[]
colourstill=[]
prog_starts=[]
#Variables
lst_candle=[]
lst_heikin_nor=[]
lst_heikin=[]
lst_cand=[]
lst_c=[]
countstart=0
#programe start
def ashis():
while(True):
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 ")
klp1=now_asia
dff=kite.historical_data(ttoken,sdate,todate,time_frame,0)
#time.sleep(1)
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):
# df['buy'] = 0
# df['sell'] = 0
# df['buybreak'] = 0
# df['sellbreak'] = 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=pd.DataFrame(df)
def bidatrema(df,period):
df['hl']=abs(df['high']-df['low'])
df['hpc']=abs(df['high']-df['close'].shift())
df['lpc']=abs(df['low']-df['close'].shift())
df['tr']=df[['hl','hpc','lpc']].max(axis=1)
df['ATR']=pd.DataFrame.ewm(df["tr"], span=period,min_periods=period).mean()
df.drop(["hl","hpc","lpc","tr"],axis = 1 , inplace =True)
bidatrema(gup,14)
print(" \n \t \t \t \t GUPPY GREEN _ RED PROFIT BOOKING ")
print(" \t \t \t \n Current Colour on this token is " , gup.iloc[-1,5])
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%Y-%m-%d _ %H:%M:%S ")
klp1=now_asia
prog_starts.append(klp1)
print("\n ")
print("Zerodha GUPPY BUY BOT start Time " , prog_starts[0])
print("\n ")
print("BOT working succeesfully on time: " , now_asia )
print("\n ")
print("Trading symbol is",ttradingsymbol,"Token is",ttoken,"Exchange is",eexchange,"and product type is",productt,"Quantity is",qu,"time frame for Historical Data is",time_frame,"Starting and Ending Date of Historical Data is",sdate,todate)
colourstill.append(gup.iloc[-1,5])
print(" \n ")
print(" Completed with 'GREEN' entry=", len(green_time))
print(" Completed with 'RED' entry=", len(red_time))
print("\n ")
print("\n ")
if 0 in counter_start:
if "green" in gup.iloc[-1,5]:
counter_start.remove(0)
counter_start.append(1)
else:
pass
if 2 in counter_start:
print(" BOT STARTED WITH RED ' SELL' ENTRY ")
if "red" in gup.iloc[-1,5]:
if 0 in counter:
quant=qu
xa=float(gup.iloc[-1,3]-gup.iloc[-1,6]*.25)
price_sell=avg_ha(xa)
xb=price_sell-float(gup.iloc[-1,6])*0.1
triger_sell=avg_ha(xb)
triggerprice_sell=avg_ha(triger_sell)
kite_drop=kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='SL',price= price_sell,trigger_price=triggerprice_sell,transaction_type='SELL',product=productt,tag="testR")
print("red sell Stoploss order placed and SL trigger price is",triggerprice_sell,"and price is",price_sell, "quantity is",quant)
linkedlist_1.insert(0,kite_drop)
counter.remove(0)
counter.append(1)
if 1 in counter:
print("checking red execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==linkedlist_1[0]].index
match_in_int=int(match_in)
print("checking executed or not condition ")
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red " )
execution.append(1)
else:
print("SORRY! red not executed")
pass
if "green" in gup.iloc[-1,5]:
if 1 in counter:
if 1 in execution:
#check execution of green
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='MARKET',transaction_type='BUY',product=productt,tag="testR")
print("programe completed with red entry and end with green entry")
counter.clear()
counter_start.clear()
execution.clear()
linkedlist_1.clear()
execution.clear()
counter_start.append(0)
red_time.append(1)
else:
pass
else:
pass
if 1 in counter_start:
print(" BOT STARTED WITH GREEN ' BUY' ENTRY ")
if "green" in gup.iloc[-1,5]:
if 0 in counter:
quant=qu
xa=float(gup.iloc[-1,2]+(gup.iloc[-1,6]*.25))
cur_high=avg_ha(xa)
xb=cur_high+float(gup.iloc[-1,6])*0.1
lim_price=avg_ha(xb)
kite_drop=kite.place_order( variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='SL',price=lim_price ,trigger_price=cur_high,transaction_type='BUY',product=productt,tag="testR")
linkedlist_1.insert(0,kite_drop)
counter.remove(0)
counter.append(1)
if 1 in counter:
print("checking green execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==linkedlist_1[0]].index
match_in_int=int(match_in)
print("checking green executed or not condition ")
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red " )
execution.append(1)
else:
print("SORRY GREEN NOT EXECUTED")
pass
if "red" in gup.iloc[-1,5]:
if 1 in counter:
if 1 in execution:
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='MARKET',transaction_type='SELL',product=productt,tag="testG")
print("programe completed with green entry and end with RED entry")
green_time.append(1)
counter.clear()
counter_start.clear()
execution.clear()
linkedlist_1.clear()
execution.clear()
counter_start.append(0)
else:
pass
else:
pass
else:
pass
else:
pass
ashis()
if "SELL"==manually:
print("SELL BOT STARTED Manually")
#calculation
def avg_ha(x):
a=list(modf(round(x,3)))
d=str(a[0])
aa=str(d[0:2])
try:
ab=str(d[2])
except:
ab='0'
try:
ac=str(d[3])
except:
ac='0'
try:
ad=str(d[4])
except:
ad='0'
c=aa+ab+ac+ad
b=0
if a[0]!=0:
astr=c
a0=astr[0]
a1=astr[1]
a3=int(astr[2])
a2=int(astr[3:5])
if a2>=0 and a2<25:
a2=0
elif a2>=25 and a2<75:
a2=5
elif a2>=75 and a2<=99:
a3+=1
a2=0
aint=a0+a1+str(a3)+str(a2)
a[0]=float(aint)
for k in a:
b+=k
return b
print("WELCOME TO GUPPY SELL PROFIT BOOKING BOT ! ")
print(" \n ")
ttradingsymbol =str(input("ENTER TRADINGSYMBOL - SUNPHARMA / SBIN :-")).upper()
eexchange =str(input("ENTER EXCHANGE / NSE / BSE / CDS :-")).upper()
productt =str(input("ENTER PRODUCT / NRML /CNC / MIS :-")).upper()
qu =int(input("ENTER QUANTITY - 2 / 4 :-"))
time_frame =str(input("ENTER TIME_FRAME - minute / 5minute :-")).lower()
sdate ="2019-05-05"
todate ="2020-10-02"
ttoken=int(pd.DataFrame(kite.ltp(eexchange+":"+ttradingsymbol)).iloc[-2,0])
counter_start=[0]
linkedlist_1=[]
linkedlist_2=[]
linkedlist_3=[]
counter=[0,10]
execution=[]
checkfirst=[0]
red_time=[]
green_time=[]
redb=[]
bluez=[]
aqua1=[0]
bluein=[]
blue_2F=[]
blue_2G=[]
count_sectime=[]
redc=[]
colourstill=[]
prog_starts=[]
#Variables
lst_candle=[]
lst_heikin_nor=[]
lst_heikin=[]
lst_cand=[]
lst_c=[]
countstart=0
#programe start
def ashis():
while(True):
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 ")
klp1=now_asia
dff=kite.historical_data(ttoken,sdate,todate,time_frame,0)
#time.sleep(1)
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):
# df['buy'] = 0
# df['sell'] = 0
# df['buybreak'] = 0
# df['sellbreak'] = 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=pd.DataFrame(df)
def bidatrema(df,period):
df['hl']=abs(df['high']-df['low'])
df['hpc']=abs(df['high']-df['close'].shift())
df['lpc']=abs(df['low']-df['close'].shift())
df['tr']=df[['hl','hpc','lpc']].max(axis=1)
df['ATR']=pd.DataFrame.ewm(df["tr"], span=period,min_periods=period).mean()
df.drop(["hl","hpc","lpc","tr"],axis = 1 , inplace =True)
bidatrema(gup,14)
print(" \n \t \t \t \t GUPPY SELL PROFIT BOOKING ")
print(" \t \t \t \n Current Colour on this token is " , gup.iloc[-1,5])
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%Y-%m-%d _ %H:%M:%S ")
klp1=now_asia
prog_starts.append(klp1)
print("\n ")
print("Zerodha GUPPY SELL BOT start Time " , prog_starts[0])
print("\n ")
print("BOT working succeesfully on time: " , now_asia )
print("\n ")
print("Trading symbol is",ttradingsymbol,"Token is",ttoken,"Exchange is",eexchange,"and product type is",productt,"Quantity is",qu,"time frame for Historical Data is",time_frame,"Starting and Ending Date of Historical Data is",sdate,todate)
colourstill.append(gup.iloc[-1,5])
print(" \n ")
print(" Completed with 'GREEN' entry=", len(green_time))
print(" Completed with 'RED' entry=", len(red_time))
print("\n ")
print("\n ")
if 0 in counter_start:
if "red" in gup.iloc[-1,5]:
counter_start.remove(0)
counter_start.append(2)
#if "green" in gup.iloc[-1,5]:
counter_start.remove(0)
counter_start.append(1)
else:
pass
if 2 in counter_start:
print(" BOT STARTED WITH RED ' SELL' ENTRY ")
if "red" in gup.iloc[-1,5]:
if 0 in counter:
quant=qu
xa=float(gup.iloc[-1,3]-gup.iloc[-1,6]*.25)
price_sell=avg_ha(xa)
xb=price_sell-float(gup.iloc[-1,6])*0.1
triger_sell=avg_ha(xb)
triggerprice_sell=avg_ha(triger_sell)
kite_drop=kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='SL',price= price_sell,trigger_price=triggerprice_sell,transaction_type='SELL',product=productt,tag="testR")
print("red sell Stoploss order placed and SL trigger price is",triggerprice_sell,"and price is",price_sell, "quantity is",quant)
linkedlist_1.insert(0,kite_drop)
counter.remove(0)
counter.append(1)
if 1 in counter:
print("checking red execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==linkedlist_1[0]].index
match_in_int=int(match_in)
print("checking executed or not condition ")
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red " )
execution.append(1)
else:
print("SORRY! red not executed")
pass
if "green" in gup.iloc[-1,5]:
if 1 in counter:
if 1 in execution:
#check execution of green
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='MARKET',transaction_type='BUY',product=productt,tag="testR")
print("programe completed with red entry and end with green entry")
counter.clear()
counter_start.clear()
execution.clear()
linkedlist_1.clear()
execution.clear()
counter_start.append(0)
red_time.append(1)
else:
pass
else:
pass
if 1 in counter_start:
print(" BOT STARTED WITH GREEN ' BUY' ENTRY ")
if "green" in gup.iloc[-1,5]:
if 0 in counter:
quant=qu
xa=float(gup.iloc[-1,2]+(gup.iloc[-1,6]*.25))
cur_high=avg_ha(xa)
xb=cur_high+float(gup.iloc[-1,6])*0.1
lim_price=avg_ha(xb)
kite_drop=kite.place_order( variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='SL',price=lim_price ,trigger_price=cur_high,transaction_type='BUY',product=productt,tag="testR")
linkedlist_1.insert(0,kite_drop)
counter.remove(0)
counter.append(1)
if 1 in counter:
print("checking green execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==linkedlist_1[0]].index
match_in_int=int(match_in)
print("checking green executed or not condition ")
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red " )
execution.append(1)
else:
print("SORRY GREEN NOT EXECUTED")
pass
if "red" in gup.iloc[-1,5]:
if 1 in counter:
if 1 in execution:
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,order_type='MARKET',transaction_type='SELL',product=productt,tag="testG")
print("programe completed with green entry and end with RED entry")
green_time.append(1)
counter.clear()
counter_start.clear()
execution.clear()
linkedlist_1.clear()
execution.clear()
counter_start.append(0)
else:
pass
else:
pass
else:
pass
else:
pass
ashis()
Output –
Zerodha GUPPY SCREENER on 5 minute Data
Current Token checking 975873
Current Colour on this token is none
Buy stock found are := [784129]
Sell stocks found are:= []
Scanning is complete
SYMBOLS_BUY TOKENS_BUY Price
0 784129 VEDL 140.4
SYMBOLS_SELL TOKENS_SELL Price
YES ! Some buy stock found
BUY LESS THEN 2000 [784129]
BUY grater then 2000 []
SELL less then 2000 []
SELL GREATER THEN 2000 []
sorry no order found of SELL
The Guppy strategy in this script is a comprehensive approach for analyzing stock trends using EMAs.
It’s designed to capture both short-term movements and long-term trends, offering traders insights into potential buy and sell opportunities based on the alignment of EMAs. By analyzing multiple time frames, the script aims to provide a robust analysis for traders looking to capitalize on various market movements.