Multi-Timeframe Trading Bot Using Guppy Strategy and Screener
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.
Prologue
In the first trading bot, we focused on developing the Guppy Strategy Screener. This initial approach was about coding a tool to effectively identify potential trading opportunities based on the Guppy Strategy.
Our second trading bot introduced a basic trading bot. This bot was designed to automatically execute buy and sell orders based on the stocks identified by the screener. Its primary function was to act on the signals generated by the screener, simplifying the trading process.
The third trading bot dived into a more advanced version of the trading bot, incorporating the Guppy Indicator. This enhanced bot was equipped with a complex strategy that included multiple conditional cases and reentry scenarios. It represented a significant step forward in terms of strategy sophistication.
The fourth trading bot employs an advanced strategy, buying on green signals and selling on red signals based on real-time market indicators. It handles multiple stocks at once, a major upgrade from previous versions limited to single-stock trading. The bot continuously scans the Nifty 50 index for potential trades and monitors the entire stock list, ensuring timely responses to market opportunities.
Objective
In our latest trading bot,
This program scans stocks using the Guppy Screener, categorizes them as buy or sell based on 5, 10, and 15-minute timeframes, and displays the results. Next, it identifies common stocks among all these timeframes. You can select a stock from this list to execute the buy/sell program, buying when there’s a green signal from the indicator and selling when there’s a red signal.
Note – The structure of historical data and live data from Zerodha is identical. During the development and testing of functions, it’s not feasible to wait for days or months to validate their performance using live data.
To address this, we’ll initially test and validate our functions using historical data. Once we have confirmed that the functions generate accurate signals and execute trades correctly with historical data, we can seamlessly transition to using live data for real-time trading.
Python Code
This program is designed to streamline the process of stock trading by leveraging the Guppy Screener. It operates by scanning a range of stocks, sorting them based on buy and sell signals, and identifying common stocks across different timeframes. The program then enables users to execute trades based on specific indicator signals. Here’s a step-by-step breakdown:
Stock Scanning with Guppy Screener: The program starts by using the Guppy Screener to scan all available stocks. The Guppy Screener is known for its effectiveness in identifying trends using multiple moving averages.
Sorting Stocks by Timeframe: It categorizes these stocks into buy and sell groups based on their performance in different timeframes – 5, 10, and 15 minutes. This sorting helps in understanding the short-term trends in various stocks.
Identifying Common Stocks: The program then cross-references these lists to find stocks that are common in all timeframes, both in the buy and sell categories. This step is crucial as it pinpoints stocks with consistent buy or sell trends across different periods, indicating stronger signals.
User Input for Stock Selection: Once the common stocks are identified, the user is prompted to select a stock from these lists to focus on. This allows the user to have control over which stock they are interested in trading.
Trading Execution Based on Indicator Signals: After a stock is selected, the program switches to an execution mode where it buys stocks on a green signal from the chosen indicator and sells stocks on a red signal. This functionality is crucial for making timely trades based on the latest market movements and indicator trends.
Real-Time Trading Actions: The buy or sell actions are performed in real-time, ensuring that the trades are executed at the optimal moments as indicated by the market trends and the Guppy Screener analysis.
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" )
sdate = (date.today() - timedelta(days=15)).strftime("%Y-%m-%d")
todate = date.today().strftime("%Y-%m-%d")
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<=9999:
if a3==9:
a[1]+=1
a3=0
else:
a3+=1
a2=0
aint=a0+a1+str(a3)+str(a2)
a[0]=float(aint)
for k in a:
b+=k
return b
def result():
start_url="yes"
l=[]
#if "yes" in start_url:
if kite.orders():
find="adv"
while(True):
df=pd.DataFrame(kite.orders())[["tag","order_id", "tradingsymbol","status","transaction_type","quantity","price", "trigger_price"]]
dgg=(df[df['tag'].str.contains(find)==True])[["order_id", "tradingsymbol", "status","transaction_type","quantity","price", "trigger_price"]]
dg=dgg[(dgg["status"]=="COMPLETE") ]
#display(dg)
p=list(dg['tradingsymbol'])
l.append(p)
break
else:
print("no orders till now")
pass
else:
print("no orders placed till now ")
print("\n ALL ORDERS PLACED BUY BOT ARE: ")
display(dgg)
k=l[0]
ee=0
#print(k)
tradingsymbll=[]
pandl=[]
qutt=[]
buy_p=[]
sell_p=[]
while(True):
find=k[ee]
df=(pd.DataFrame(kite.positions()["net"])[["tradingsymbol","quantity","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value"]])
dg=(df[df["tradingsymbol"].str.contains(find)==True])[["tradingsymbol","pnl","quantity","buy_price","sell_price","average_price","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value"]]
#display(dg)
tradingsymbll.append(dg.iloc[-1,0])
pandl.append(dg.iloc[-1,1])
qutt.append(dg.iloc[-1,2])
buy_p.append(dg.iloc[-1,3])
sell_p.append(dg.iloc[-1,4])
ee=ee+1
if ee==len(k):
break
else:
print("no position available")
#display(dg)
fram={"tradingSymbol":tradingsymbll,"PnL": pandl , "Quantity":qutt ,"Buyprice":buy_p, "sellprice":sell_p}
kk=pd.DataFrame(fram)
print("\n ORDERS COMPLETED ARE: ")
display(kk)
print("\n Overall profit and Loss is =", kk.PnL.sum())
##
#################################################################################
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%H")
zz2=now_asia
kk=int(zz2)
if kk>=15:
print("Time complete")
result()
while(True):
scan= str(input("DO You want SCANNING ? YES , RESULT, HALF :- ")).upper()
if "Y" in scan:
scan="YES"
break
if "H" in scan:
scan="HALF"
break
if "R" in scan:
scan="RESULT"
break
if "S" in scan:
scan="second"
break
if "RESULT"==scan:
result()
if "HALF"==scan:
while (True):
find=str(input("ENTER ORDER ID := ")).strip()
if find:
break
else:
pass
df=pd.DataFrame(kite.orders())[["tradingsymbol","instrument_token","transaction_type",'product',"order_id","quantity","order_type","exchange_update_timestamp",'tag',"quantity","status","status_message","average_price","price","trigger_price"]]
dg=(df[df["order_id"].str.contains(find)==True])[["tradingsymbol","instrument_token","transaction_type",'product',"order_id","quantity","status"]]
tsm=dg.iloc[-1,0]
tok=dg.iloc[-1,1]
busl=dg.iloc[-1,2]
prdct=dg.iloc[-1,3]
idis=dg.iloc[-1,4]
qunti=dg.iloc[-1,5]
ttradingsymbol =str(tsm)
productt =str(prdct)
qu =int(qunti)
ttoken =int(tok)
progg =str(busl)
ide =str(idis)
if "BUY" in progg:
prog="red"
if "SELL" in progg:
prog="green"
#####################################
sdate ="2019-07-01"
todate ="2019-10-01"
time_frame = "minute"
eexchange ="NSE"
#######################################
print("Trading symbol is",ttradingsymbol)
#print("Order ID is ",ide)
print("PRODUCT is ",productt)
print("QUANTITY is ",qu)
print("Tokenn is ",ttoken)
print("Execution of bid ",prog)
print("Time Frame is ",time_frame)
print("Exchange is ",eexchange)
display(dg)
############################################
if "green"==prog:
i=1
execution=[]
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
ii=1
def ashis():
while(True):
km=datetime.now().minute
ks=datetime.now().second
if km%1==0 and ks==1:
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%H")
zz2=now_asia
kk=int(zz2)
if kk>=15:
print("Time complete")
dff=kite.historical_data(ttoken,sdate,todate,"minute",0)
av=pd.DataFrame(dff)
kk=str(av.iloc[-1,1]).split("+")
zz=kk[0]
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%Y-%m-%d %H:%M:00")
zz2=now_asia
if zz2==zz:
print("same so discarding last ouput")
dfw=av[:-1]
else:
print("Candel Not same so continue going with same candel")
dfw=av
df=pd.DataFrame(dfw[['date','open','high','low','close']])
slow_ema = [3,5,7,9,11,13,15,17,19,21,23]
fast_ema = [25,28,31,34,37,40,43,46,49,52,55,58,61,64,67,70,200]
def EMA(df, base, target, period, alpha=False):
con = pd.concat([df[:period][base].rolling(window=period).mean(), df[period:][base]])
if (alpha == True):
# (1 - alpha) * previous_val + alpha * current_val where alpha = 1 / period
df[target] = con.ewm(alpha=1 / period, adjust=False).mean()
else:
# ((current_val - previous_val) * coeff) + previous_val where coeff = 2 / (period + 1)
df[target] = con.ewm(span=period, adjust=False).mean()
df.fillna(0,inplace = True)
# return df
for j in slow_ema:
val = "ema"+"_"+str(j)
EMA(df,"close",val,j)
for k in fast_ema:
val = "ema"+"_"+str(k)
EMA(df,"close",val,k)
def super_guppy(interval,df,anchor=0):
anchor = 0
ShowBreak = True
ShowSwing = True
ShowCon = False
uOCCswing = False
Lookback = 6
emaFilter = False
mult = 0
buybreak = 0
sellbreak = 0
buy_barssince_var = 0
sell_barssince_var = 0
buybreak_barssince_var = 0
sellbreak_barssince_var = 0
barssince_lst = list()
barssince_var = 0
bar_count_var = 0
buy1 = list()
sell1 = list()
buy2 = list()
sell2 = list()
buybreak1 = list()
sellbreak1 = list()
def barssince(b,barssince_var):
barssince_lst = []
barssince_var = 0
new_var = len(b)
for i in b[::-1]:
if i == 1:
break
barssince_lst.append(i)
barssince_var = len(barssince_lst)
return barssince_var
barssince_lst.clear()
#isIntraday
if interval < 1441 :
if (anchor==0 or interval <= 0 or interval >= anchor or anchor > 1441 ):
mult = 1
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = 1
#isIntraday Not
if interval > 1441:
if (anchor==0 or interval <= 0 or interval >= anchor or anchor < 52 ):
mult = mult
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = mult
mult = 1
for i in range(len(df)):
emaF1 = df.loc[i,'ema_3']
emaF2 = df.loc[i,'ema_5']
emaF3 = df.loc[i,'ema_7']
emaF4 = df.loc[i,'ema_9']
emaF5 = df.loc[i,'ema_11']
emaF6 = df.loc[i,'ema_13']
emaF7 = df.loc[i,'ema_15']
emaF8 = df.loc[i,'ema_17']
emaF9 = df.loc[i,'ema_19']
emaF10 = df.loc[i,'ema_21']
emaF11 = df.loc[i,'ema_23']
emaS1 = df.loc[i,'ema_25']
emaS2 = df.loc[i,'ema_28']
emaS3 = df.loc[i,'ema_31']
emaS4 = df.loc[i,'ema_34']
emaS5 = df.loc[i,'ema_37']
emaS6 = df.loc[i,'ema_40']
emaS7 = df.loc[i,'ema_43']
emaS8 = df.loc[i,'ema_46']
emaS9 = df.loc[i,'ema_49']
emaS10 = df.loc[i,'ema_52']
emaS11 = df.loc[i,'ema_55']
emaS12 = df.loc[i,'ema_58']
emaS13 = df.loc[i,'ema_61']
emaS14 = df.loc[i,'ema_64']
emaS15 = df.loc[i,'ema_67']
emaS16 = df.loc[i,'ema_70']
ema200 = df.loc[i,'ema_200']
emafast = (emaF1 + emaF2 + emaF3 + emaF4 + emaF5 + emaF6 + emaF7 + emaF8 + emaF9 + emaF10 + emaF11)/11
emaslow = (emaS1 + emaS2 + emaS3 + emaS4 + emaS5 + emaS6 + emaS7 + emaS8 + emaS9 + emaS10 + emaS11 + emaS12 + emaS13 + emaS14 + emaS15 + emaS16)/16
#Fast EMA Color Rules
colfastL = (emaF1>emaF2 and emaF2>emaF3 and emaF3>emaF4 and emaF4>emaF5 and emaF5>emaF6 and emaF6>emaF7 and emaF7>emaF8 and emaF8>emaF9 and emaF9>emaF10 and emaF10>emaF11)
colfastS = (emaF1<emaF2 and emaF2<emaF3 and emaF3<emaF4 and emaF4<emaF5 and emaF5<emaF6 and emaF6<emaF7 and emaF7<emaF8 and emaF8<emaF9 and emaF9<emaF10 and emaF10<emaF11)
#Slow EMA Color Rules
colslowL = (emaS1>emaS2 and emaS2>emaS3 and emaS3>emaS4 and emaS4>emaS5 and emaS5>emaS6 and emaS6>emaS7 and emaS7>emaS8) and (emaS8>emaS9 and emaS9>emaS10 and emaS10>emaS11 and emaS11>emaS12 and emaS12>emaS13 and emaS13>emaS14 and emaS14>emaS15 and emaS15>emaS16)
colslowS = (emaS1<emaS2 and emaS2<emaS3 and emaS3<emaS4 and emaS4<emaS5 and emaS5<emaS6 and emaS6<emaS7 and emaS7<emaS8) and (emaS8<emaS9 and emaS9<emaS10 and emaS10<emaS11 and emaS11<emaS12 and emaS12<emaS13 and emaS13<emaS14 and emaS14<emaS15 and emaS15<emaS16)
if emafast > emaslow and not colslowS and colfastL and (not ShowCon or colslowL) and (not emaFilter or emafast>ema200):
if int(buy1[-1]) > 0:
buy = buy1[-1] + 1
else:
buy = 1
else:
buy = 0
buy1.append(buy)
if emafast < emaslow and not colslowL and colfastS and (not ShowCon or colslowS) and (not emaFilter or emafast<ema200):
if int(sell1[-1]) > 0:
sell = sell1[-1] + 1
else:
sell = 1
else:
sell = 0
sell1.append(sell)
#buy
if buy>1 and colfastL and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
buy3 = 1
else:
buy3 = buy
buy2.append(buy3)
#sell
if sell>1 and colfastS and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
sell3 = 1
else:
sell3 = sell
sell2.append(sell3)
#buybreak
if emafast > emaslow and not colslowS and (not emaFilter or emafast>ema200):
if buybreak1[-1] > 0:
buybreak = buybreak1[-1] + 1
else:
buybreak = 1
else:
buybreak = 0
buybreak1.append(buybreak)
if emafast < emaslow and not colslowL and (not emaFilter or emafast<ema200):
if sellbreak1[-1] > 0:
sellbreak = sellbreak1[-1]+1
else:
sellbreak = 1
else:
sellbreak = 0
sellbreak1.append(sellbreak)
#arrow plotting
#buy_arrow
buy_barssince_var = barssince(buy2[:-1],barssince_var)
if (ShowSwing and buy3==1)and buy_barssince_var > 6:
buy_arrow = 1
else:
buy_arrow = 0
#sell arrow
sell_barssince_var = barssince(sell2[:-1],barssince_var)
if ShowSwing and (sell3==1 and sell_barssince_var > 6):
sell_arrow = 1
else:
sell_arrow = 0
#buybreak_arrow
buybreak_barssince_var = barssince(buybreak1[:-1],barssince_var)
sellbreak_barssince_var = barssince(sellbreak1[:-1],barssince_var)
if ShowBreak and buybreak==1 and (sellbreak_barssince_var>Lookback) and (buybreak_barssince_var>Lookback):
buybreak_arrow = 1
else:
buybreak_arrow = 0
#sellbreak_arrow
if ShowBreak and sellbreak==1 and (buybreak_barssince_var>Lookback) and (sellbreak_barssince_var>Lookback):
sellbreak_arrow = 1
else:
sellbreak_arrow = 0
if buy_arrow==1 and sell_arrow==0 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'green'
elif buy_arrow==0 and sell_arrow==1 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'red'
elif sell_arrow==0 and (buy_arrow==0 or buy_arrow==1) and buybreak_arrow==1 and sellbreak_arrow==0:
arrow_color = 'aqua'
elif buy_arrow==0 and (sell_arrow==1 or sell_arrow==0) and buybreak_arrow==0 and sellbreak_arrow==1:
arrow_color = 'blue'
else:
arrow_color = 'none'
df.loc[i,'arrow_color'] = arrow_color
df = df[['date','open','high','low','close','arrow_color']]
return df
df=super_guppy(15,df)
gup=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)
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==ide].index
match_in_int=int(match_in)
print("checking executed or not condition ")
display(strftime("%a, %d %b %Y %I:%M:%S %p"))
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for green " )
execution.append(1)
else:
print("SORRY! red not executed")
pass
if prog in gup.iloc[-1,5]:
if 1 in execution:
find=ttradingsymbol
order_mis_cnc=productt
df=(pd.DataFrame(kite.positions()["net"])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]])
dg=(df[df['tradingsymbol'].str.contains(find)==True])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]]
dg=(dg[dg["product"].str.contains(order_mis_cnc)==True])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]]
quant=int((pd.DataFrame(dg["quantity"])).iloc[0,0])
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange="NSE",order_type='MARKET',transaction_type='BUY',product=productt,tag="testR")
print("programe completed with green entry")
print("Quantity is", quant)
display(dg)
execution.clear()
print(" programe serching colour of ", prog)
print("checking red execution")
print("trading symbol is ", ttradingsymbol)
print("quantity is,",qu)
print("order id is", ide)
print("HALF programe completed")
break
else:
pass
ashis()
## SEARCHING FOR RED
if "red"==prog:
i=1
execution=[]
#while (True)
if i==1:
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
ii=1
def ashis():
while(True):
km=datetime.now().minute
ks=datetime.now().second
if km%1==0 and ks==1:
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%H")
zz2=now_asia
kk=int(zz2)
if kk>=15:
print("Time complete")
############
dff=kite.historical_data(ttoken,sdate,todate,"minute",0)
av=pd.DataFrame(dff)
kk=str(av.iloc[-1,1]).split("+")
zz=kk[0]
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%Y-%m-%d %H:%M:00")
zz2=now_asia
if zz2==zz:
print("same so discarding last ouput")
dfw=av[:-1]
else:
print("Candel Not same so continue going with same candel")
dfw=av
df=pd.DataFrame(dfw[['date','open','high','low','close']])
slow_ema = [3,5,7,9,11,13,15,17,19,21,23]
fast_ema = [25,28,31,34,37,40,43,46,49,52,55,58,61,64,67,70,200]
def EMA(df, base, target, period, alpha=False):
con = pd.concat([df[:period][base].rolling(window=period).mean(), df[period:][base]])
if (alpha == True):
# (1 - alpha) * previous_val + alpha * current_val where alpha = 1 / period
df[target] = con.ewm(alpha=1 / period, adjust=False).mean()
else:
# ((current_val - previous_val) * coeff) + previous_val where coeff = 2 / (period + 1)
df[target] = con.ewm(span=period, adjust=False).mean()
df.fillna(0,inplace = True)
# return df
for j in slow_ema:
val = "ema"+"_"+str(j)
EMA(df,"close",val,j)
for k in fast_ema:
val = "ema"+"_"+str(k)
EMA(df,"close",val,k)
def super_guppy(interval,df,anchor=0):
anchor = 0
ShowBreak = True
ShowSwing = True
ShowCon = False
uOCCswing = False
Lookback = 6
emaFilter = False
mult = 0
buybreak = 0
sellbreak = 0
buy_barssince_var = 0
sell_barssince_var = 0
buybreak_barssince_var = 0
sellbreak_barssince_var = 0
barssince_lst = list()
barssince_var = 0
bar_count_var = 0
buy1 = list()
sell1 = list()
buy2 = list()
sell2 = list()
buybreak1 = list()
sellbreak1 = list()
def barssince(b,barssince_var):
barssince_lst = []
barssince_var = 0
new_var = len(b)
for i in b[::-1]:
if i == 1:
break
barssince_lst.append(i)
barssince_var = len(barssince_lst)
return barssince_var
barssince_lst.clear()
#isIntraday
if interval < 1441 :
if (anchor==0 or interval <= 0 or interval >= anchor or anchor > 1441 ):
mult = 1
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = 1
#isIntraday Not
if interval > 1441:
if (anchor==0 or interval <= 0 or interval >= anchor or anchor < 52 ):
mult = mult
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = mult
mult = 1
for i in range(len(df)):
emaF1 = df.loc[i,'ema_3']
emaF2 = df.loc[i,'ema_5']
emaF3 = df.loc[i,'ema_7']
emaF4 = df.loc[i,'ema_9']
emaF5 = df.loc[i,'ema_11']
emaF6 = df.loc[i,'ema_13']
emaF7 = df.loc[i,'ema_15']
emaF8 = df.loc[i,'ema_17']
emaF9 = df.loc[i,'ema_19']
emaF10 = df.loc[i,'ema_21']
emaF11 = df.loc[i,'ema_23']
emaS1 = df.loc[i,'ema_25']
emaS2 = df.loc[i,'ema_28']
emaS3 = df.loc[i,'ema_31']
emaS4 = df.loc[i,'ema_34']
emaS5 = df.loc[i,'ema_37']
emaS6 = df.loc[i,'ema_40']
emaS7 = df.loc[i,'ema_43']
emaS8 = df.loc[i,'ema_46']
emaS9 = df.loc[i,'ema_49']
emaS10 = df.loc[i,'ema_52']
emaS11 = df.loc[i,'ema_55']
emaS12 = df.loc[i,'ema_58']
emaS13 = df.loc[i,'ema_61']
emaS14 = df.loc[i,'ema_64']
emaS15 = df.loc[i,'ema_67']
emaS16 = df.loc[i,'ema_70']
ema200 = df.loc[i,'ema_200']
emafast = (emaF1 + emaF2 + emaF3 + emaF4 + emaF5 + emaF6 + emaF7 + emaF8 + emaF9 + emaF10 + emaF11)/11
emaslow = (emaS1 + emaS2 + emaS3 + emaS4 + emaS5 + emaS6 + emaS7 + emaS8 + emaS9 + emaS10 + emaS11 + emaS12 + emaS13 + emaS14 + emaS15 + emaS16)/16
#Fast EMA Color Rules
colfastL = (emaF1>emaF2 and emaF2>emaF3 and emaF3>emaF4 and emaF4>emaF5 and emaF5>emaF6 and emaF6>emaF7 and emaF7>emaF8 and emaF8>emaF9 and emaF9>emaF10 and emaF10>emaF11)
colfastS = (emaF1<emaF2 and emaF2<emaF3 and emaF3<emaF4 and emaF4<emaF5 and emaF5<emaF6 and emaF6<emaF7 and emaF7<emaF8 and emaF8<emaF9 and emaF9<emaF10 and emaF10<emaF11)
#Slow EMA Color Rules
colslowL = (emaS1>emaS2 and emaS2>emaS3 and emaS3>emaS4 and emaS4>emaS5 and emaS5>emaS6 and emaS6>emaS7 and emaS7>emaS8) and (emaS8>emaS9 and emaS9>emaS10 and emaS10>emaS11 and emaS11>emaS12 and emaS12>emaS13 and emaS13>emaS14 and emaS14>emaS15 and emaS15>emaS16)
colslowS = (emaS1<emaS2 and emaS2<emaS3 and emaS3<emaS4 and emaS4<emaS5 and emaS5<emaS6 and emaS6<emaS7 and emaS7<emaS8) and (emaS8<emaS9 and emaS9<emaS10 and emaS10<emaS11 and emaS11<emaS12 and emaS12<emaS13 and emaS13<emaS14 and emaS14<emaS15 and emaS15<emaS16)
if emafast > emaslow and not colslowS and colfastL and (not ShowCon or colslowL) and (not emaFilter or emafast>ema200):
if int(buy1[-1]) > 0:
buy = buy1[-1] + 1
else:
buy = 1
else:
buy = 0
buy1.append(buy)
if emafast < emaslow and not colslowL and colfastS and (not ShowCon or colslowS) and (not emaFilter or emafast<ema200):
if int(sell1[-1]) > 0:
sell = sell1[-1] + 1
else:
sell = 1
else:
sell = 0
sell1.append(sell)
#buy
if buy>1 and colfastL and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
buy3 = 1
else:
buy3 = buy
buy2.append(buy3)
#sell
if sell>1 and colfastS and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
sell3 = 1
else:
sell3 = sell
sell2.append(sell3)
#buybreak
if emafast > emaslow and not colslowS and (not emaFilter or emafast>ema200):
if buybreak1[-1] > 0:
buybreak = buybreak1[-1] + 1
else:
buybreak = 1
else:
buybreak = 0
buybreak1.append(buybreak)
if emafast < emaslow and not colslowL and (not emaFilter or emafast<ema200):
if sellbreak1[-1] > 0:
sellbreak = sellbreak1[-1]+1
else:
sellbreak = 1
else:
sellbreak = 0
sellbreak1.append(sellbreak)
#arrow plotting
#buy_arrow
buy_barssince_var = barssince(buy2[:-1],barssince_var)
if (ShowSwing and buy3==1)and buy_barssince_var > 6:
buy_arrow = 1
else:
buy_arrow = 0
#sell arrow
sell_barssince_var = barssince(sell2[:-1],barssince_var)
if ShowSwing and (sell3==1 and sell_barssince_var > 6):
sell_arrow = 1
else:
sell_arrow = 0
#buybreak_arrow
buybreak_barssince_var = barssince(buybreak1[:-1],barssince_var)
sellbreak_barssince_var = barssince(sellbreak1[:-1],barssince_var)
if ShowBreak and buybreak==1 and (sellbreak_barssince_var>Lookback) and (buybreak_barssince_var>Lookback):
buybreak_arrow = 1
else:
buybreak_arrow = 0
#sellbreak_arrow
if ShowBreak and sellbreak==1 and (buybreak_barssince_var>Lookback) and (sellbreak_barssince_var>Lookback):
sellbreak_arrow = 1
else:
sellbreak_arrow = 0
if buy_arrow==1 and sell_arrow==0 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'green'
elif buy_arrow==0 and sell_arrow==1 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'red'
elif sell_arrow==0 and (buy_arrow==0 or buy_arrow==1) and buybreak_arrow==1 and sellbreak_arrow==0:
arrow_color = 'aqua'
elif buy_arrow==0 and (sell_arrow==1 or sell_arrow==0) and buybreak_arrow==0 and sellbreak_arrow==1:
arrow_color = 'blue'
else:
arrow_color = 'none'
df.loc[i,'arrow_color'] = arrow_color
df = df[['date','open','high','low','close','arrow_color']]
return df
df=super_guppy(15,df)
gup=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(" programe serching colour of ", prog)
print("checking green bid execution")
display(strftime("%a, %d %b %Y %I:%M:%S %p"))
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==ide].index
match_in_int=int(match_in)
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 prog in gup.iloc[-1,5]:
if 1 in execution:
find=ttradingsymbol
order_mis_cnc=productt
df=(pd.DataFrame(kite.positions()["net"])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]])
dg=(df[df['tradingsymbol'].str.contains(find)==True])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]]
dg=(dg[dg["product"].str.contains(order_mis_cnc)==True])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]]
quant=int((pd.DataFrame(dg["quantity"])).iloc[0,0])
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange="NSE",order_type='MARKET',transaction_type="SELL",product=productt,tag="testR")
print("programe completed with red entry ")
print("Quantity is :=",quant)
display(dg)
execution.clear()
print("HALF programe completed")
break
else:
pass
ashis()
if "YES"==scan or "yes"==scan:
while (True):
time_frame =str(input("Enter TimeFrame:=")).lower()
if time_frame:
break
else:
pass
while (True):
quu=str(input("Enter Amount := "))
if quu:
break
else:
pass
while (True):
pro_duct=str(input("MIS or CNC order ? := ")).upper()
if pro_duct:
break
while (True):
scan_time=int(input("No of time scanning ?"))
if scan_time:
break
else:
scan_time=1
qu=int(quu)
print("Time Frame is :=",time_frame)
print("Amount is :=",qu)
print("Product is :=",pro_duct)
print("SCANNING START")
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==len(z):
aa=0
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 ")
buy5minute=[]
sell5minute=[]
buy10minute=[]
sell10minute=[]
buy15minute=[]
sell15minute=[]
lst_candle=[]
lst_heikin_nor=[]
lst_heikin=[]
lst_cand=[]
lst_c=[]
anchor=0
countstart=0
a=0
ak=1
BUY_listindicator=[]
SELL_listindicator=[]
sell5minutesym=[]
buy5minutesym=[]
buy10minutesym=[]
sell10minutesym=[]
buy15minutesym=[]
sell15minutesym=[]
price5min_buy=[]
price5min_sell=[]
price15min_buy=[]
price15min_sell=[]
price30min_buy=[]
price30min_sell=[]
priceedit=[]
##
# New updated variables
order_id_sell=[]
order_id_buy=[]
ignor_buy=[]
ignor_sell=[]
errors=[]
order_id_buy_sell=[]
nooftime=[]
completed=[]
def ashi():
global a
global tokenall
global BUY_listindicator
global SELL_listindicator
ak=1
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%M%S")
koo=int(now_asia)
if ak==1:
kooe=koo+100
ak=0
while (True):
dff=kite.historical_data(tokenall[a],sdate,todate,time_frame,0)
av=pd.DataFrame(dff)
kk=str(av.iloc[-1,1]).split("+")
zz=kk[0]
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%Y-%m-%d %H:%M:00")
zz2=now_asia
if zz2==zz:
dfw=av[:-1]
else:
dfw=av
df=pd.DataFrame(dfw[['date','open','high','low','close']])
slow_ema = [3,5,7,9,11,13,15,17,19,21,23]
fast_ema = [25,28,31,34,37,40,43,46,49,52,55,58,61,64,67,70,200]
def EMA(df, base, target, period, alpha=False):
con = pd.concat([df[:period][base].rolling(window=period).mean(), df[period:][base]])
if (alpha == True):
# (1 - alpha) * previous_val + alpha * current_val where alpha = 1 / period
df[target] = con.ewm(alpha=1 / period, adjust=False).mean()
else:
# ((current_val - previous_val) * coeff) + previous_val where coeff = 2 / (period + 1)
df[target] = con.ewm(span=period, adjust=False).mean()
df.fillna(0,inplace = True)
# return df
for j in slow_ema:
val = "ema"+"_"+str(j)
EMA(df,"close",val,j)
for k in fast_ema:
val = "ema"+"_"+str(k)
EMA(df,"close",val,k)
def super_guppy(interval,df,anchor=0):
anchor = 0
ShowBreak = True
ShowSwing = True
ShowCon = False
uOCCswing = False
Lookback = 6
emaFilter = False
mult = 0
buybreak = 0
sellbreak = 0
buy_barssince_var = 0
sell_barssince_var = 0
buybreak_barssince_var = 0
sellbreak_barssince_var = 0
barssince_lst = list()
barssince_var = 0
bar_count_var = 0
buy1 = list()
sell1 = list()
buy2 = list()
sell2 = list()
buybreak1 = list()
sellbreak1 = list()
def barssince(b,barssince_var):
barssince_lst = []
barssince_var = 0
new_var = len(b)
for i in b[::-1]:
if i == 1:
break
barssince_lst.append(i)
barssince_var = len(barssince_lst)
return barssince_var
barssince_lst.clear()
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
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" )
print("\n" )
print(" \t \t \t \t ZERODHA ADVANCE BOT ")
print("\n")
print("Current Token checking " , tokenall[a])
print("Current candel checking no " , a)
print("Current Colour on this token is " , gup.iloc[-1,5])
#print("Current 'OHLC' of this token is " )
#display(gup.tail())
if "green" in gup.iloc[-1,5]:
print("BUY stock found ")
ttradingsymbol=z[a]
eexchange="NSE"
productt=pro_duct
ttoken=tokenall[a]
# ORDER UPDATE
ttoken=int(pd.DataFrame(kite.ltp(eexchange+":"+ttradingsymbol)).iloc[-2,0])
kl=kite.orders()
if kl:
kk=list((pd.DataFrame(kite.orders())[["instrument_token"]])["instrument_token"])
if ttoken in kk:
print(" ALREADY USED !! ")
print(" Tokend is := ", ttoken)
print(" Trading Symbol is := ", z[a])
print(" SORRY !! This order already exists \n so not going to place this order !! ")
ignor_buy.append(z[a])
else:
print("TRUE !! so going to place order of ", ttoken)
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)
kk=int ( qu/ (gup.iloc[-1,2]) ) # price
if kk>=1:
quant=kk
display(gup.tail(1))
print("Trading Symbol of stock is ",z[a])
print("Quantity of placed Stock is ",quant)
print("T.P value after .25 of atr is ",xa)
print("price is after adding 0.1 percent of atr on trigegr price ",xb)
print("BUY price after round off __must be high__ then t.p ",lim_price)
print("T.P Trigger price after round off __must be low__ then price ",cur_high)
print(kite.ohlc(ttoken))
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="advG")
print("buy order id is ",kite_drop)
print("\n")
buy5minute.append((tokenall[a])) #token
buy5minutesym.append((z[a])) # symbol
price5min_buy.append(gup.iloc[-1,2]) # price
order_id_buy.append(kite_drop)
order_id_buy_sell.append(kite_drop)
else:
print("This is first order of morning !! no order is placed till now so going to place order ")
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)
kk=int ( qu/ (gup.iloc[-1,2]) )
if kk>=1:
quant=kk
display(gup.tail(1))
print("Trading Symbol of stock is ",z[a])
print("Quantity of placed Stock is ",quant)
print("T.P value after .25 of atr is ",xa)
print("price is after adding 0.1 percent of atr on trigegr price ",xb)
print("BUY price after round off __must be high__ then t.p ",lim_price)
print("T.P Trigger price after round off __must be low__ then price ",cur_high)
print(kite.ohlc(ttoken))
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="advG")
print("buy order id is ",kite_drop)
print("\n")
buy5minute.append((tokenall[a])) #token
buy5minutesym.append((z[a])) # symbol
price5min_buy.append(gup.iloc[-1,2]) # price
order_id_buy.append(kite_drop)
order_id_buy_sell.append(kite_drop)
if "red" in gup.iloc[-1,5]:
print("SELL stock found ")
## ORDER UPDATE
ttradingsymbol=z[a]
quant=qu
eexchange="NSE"
productt=pro_duct
ttoken=tokenall[a]
# ORDER UPDATE
ttoken=int(pd.DataFrame(kite.ltp(eexchange+":"+ttradingsymbol)).iloc[-2,0])
kl=kite.orders()
if kl:
kk=list((pd.DataFrame(kite.orders())[["instrument_token"]])["instrument_token"])
if ttoken in kk:
print(" ALREADY USED !! ")
print(" Tokend is := ", ttoken)
print(" Trading Symbol is := ", z[a])
print(" SORRY !! This order already exists \n so not going to place this order !! ")
ignor_sell.append(z[a])
else:
print("TRUE !! so going to place order of ", ttoken)
xa=float(gup.iloc[-1,3]-gup.iloc[-1,6]*.25)
price_sell=avg_ha(xa) # TRIGER PRICE (LARGE)
xb=price_sell-float(gup.iloc[-1,6])*0.1
triger_sell=avg_ha(xb) #PRICE (SMALL)
kk=int ( qu/ (gup.iloc[-1,2]) ) # price
if kk>=1:
quant=kk
display(gup.tail(1))
print("Trading Symbol of stock is ",z[a])
print("Quantity of placed Stock is ",quant)
print("T.P value after - .25 of atr ",xa)
print("price is after adding 0.1 percent of atr on trigegr price ",xb)
print("T.P trigger price after round off __must be larger__ then price ",price_sell)
print("SELL price after round off __ must be lower__ then Trigger price",triger_sell)
print(kite.ohlc(ttoken))
kite_drop=kite.place_order(order_type='SL',price= triger_sell,trigger_price=price_sell,variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,transaction_type='SELL',product=productt,tag="advG")
print("red sell Stoploss order placed and SL trigger price is",price_sell,"and price is",triger_sell, "quantity is",quant)
print("SELL order id is ",kite_drop)
print("\n")
sell5minute.append((tokenall[a])) #token
sell5minutesym.append((z[a])) #symbl
price5min_sell.append(gup.iloc[-1,2]) # price
order_id_sell.append(kite_drop)
order_id_buy_sell.append(kite_drop)
else:
print("TRUE !! so going to place order of ", ttoken)
xa=float(gup.iloc[-1,3]-gup.iloc[-1,6]*.25)
price_sell=avg_ha(xa) # TRIGER PRICE (LARGE)
xb=price_sell-float(gup.iloc[-1,6])*0.1
triger_sell=avg_ha(xb) #PRICE (SMALL)
kk=int ( qu/ (gup.iloc[-1,2]) ) # price
if kk>=1:
quant=kk
display(gup.tail(1))
print("Trading Symbol of stock is ",z[a])
print("Quantity of placed Stock is ",quant)
print("T.P value after - .25 of atr ",xa)
print("price is after adding 0.1 percent of atr on trigegr price ",xb)
print("T.P trigger price after round off __must be larger__ then price ",price_sell)
print("SELL price after round off __ must be lower__ then Trigger price",triger_sell)
print(kite.ohlc(ttoken))
kite_drop=kite.place_order(order_type='SL',price= triger_sell,trigger_price=price_sell,variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange=eexchange,transaction_type='SELL',product=productt,tag="advG")
print("red sell Stoploss order placed and SL trigger price is",price_sell,"and price is",triger_sell, "quantity is",quant)
print("SELL order id is ",kite_drop)
print("\n")
sell5minute.append((tokenall[a])) #token
sell5minutesym.append((z[a])) #symbl
price5min_sell.append(gup.iloc[-1,2]) # price
order_id_sell.append(kite_drop)
order_id_buy_sell.append(kite_drop)
buyframe={"SYMBOLS_BUY":buy5minutesym,"TOKENS_BUY":buy5minute,"orderid_BUY":order_id_buy}
fivemin=pd.DataFrame(buyframe)
display(fivemin)
buyframe={"SYMBOLS_SELL":sell5minute,"TOKENS_SELL":sell5minutesym,"orderid_SELL":order_id_sell}
fivemin=pd.DataFrame(buyframe)
display(fivemin)
print(" SYBMOL's ignored BUY =", ignor_buy)
print(" SYBMOL's ignored SELL =", ignor_sell)
print(" No of time Nifty50 stock scanned =", len(nooftime))
print(" ALL order is of BUY\SELL =", order_id_buy_sell)
a=a+1
if a==len(tokenall):
print(" SCAN COMPLETED !!")
nooftime.append(0)
a=0
ashi()
print(" Scanning once again ")
#if koo>=kooe:
if len(nooftime)==scan_time:
print(" !! COMPLETED FULL SCAN !! ")
ak=1
completed.append(1)
break
# END SCANNING RESULT AND continue to check buy
ashi()
###########################
if "second"==scan or "second"==scan:
completed="yes"
ell=str(input("enter all orde id's"))
order_id_buy_sell=ell.split(",")
print(order_id_buy_sell)
#order_id_buy_sell=["190813000340017", "190813000150418"]
completed="yes"
if order_id_buy_sell:
# some orders found
def checking_all():
if "second" in scan:
i=1
execution=[]
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
ii=1
################
execution=[]
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
ii=1
##
az=0
while (True):
find_id=order_id_buy_sell[az]
df=pd.DataFrame(kite.orders())[["tradingsymbol","instrument_token","transaction_type",'product',"order_id","quantity","order_type","exchange_update_timestamp",'tag',"quantity","status","status_message","average_price","price","trigger_price"]]
dg=(df[df["order_id"].str.contains(find_id)==True])[["tradingsymbol","instrument_token","transaction_type",'product',"order_id","quantity","status"]]
display(dg)
tsm=dg.iloc[-1,0]
tok=dg.iloc[-1,1]
busl=dg.iloc[-1,2]
prdct=dg.iloc[-1,3]
idis=dg.iloc[-1,4]
qunti=dg.iloc[-1,5]
ttradingsymbol =str(tsm)
productt =str(prdct)
qu =int(qunti)
ttoken =int(tok)
progg =str(busl)
ide =str(idis)
if "BUY" in progg:
prog="red"
if "SELL" in progg:
prog="green"
#####################################
sdate ="2019-07-01"
todate ="2019-10-01"
time_frame = "minute"
eexchange ="NSE"
#######################################
print("Trading symbol is",ttradingsymbol)
#print("Order ID is ",ide)
print("PRODUCT is ",productt)
print("QUANTITY is ",qu)
print("Tokenn is ",ttoken)
print("Execution of bid ",prog)
print("Time Frame is ",time_frame)
print("Exchange is ",eexchange)
display(dg)
############################################
if "green"==prog:
def ashis():
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%H")
zz2=now_asia
kk=int(zz2)
if kk>=15:
print("Time complete")
dff=kite.historical_data(ttoken,sdate,todate,"minute",0)
av=pd.DataFrame(dff)
kk=str(av.iloc[-1,1]).split("+")
zz=kk[0]
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%Y-%m-%d %H:%M:00")
zz2=now_asia
if zz2==zz:
print("same so discarding last ouput")
dfw=av[:-1]
else:
print("Candel Not same so continue going with same candel")
dfw=av
df=pd.DataFrame(dfw[['date','open','high','low','close']])
slow_ema = [3,5,7,9,11,13,15,17,19,21,23]
fast_ema = [25,28,31,34,37,40,43,46,49,52,55,58,61,64,67,70,200]
def EMA(df, base, target, period, alpha=False):
con = pd.concat([df[:period][base].rolling(window=period).mean(), df[period:][base]])
if (alpha == True):
# (1 - alpha) * previous_val + alpha * current_val where alpha = 1 / period
df[target] = con.ewm(alpha=1 / period, adjust=False).mean()
else:
# ((current_val - previous_val) * coeff) + previous_val where coeff = 2 / (period + 1)
df[target] = con.ewm(span=period, adjust=False).mean()
df.fillna(0,inplace = True)
# return df
for j in slow_ema:
val = "ema"+"_"+str(j)
EMA(df,"close",val,j)
for k in fast_ema:
val = "ema"+"_"+str(k)
EMA(df,"close",val,k)
def super_guppy(interval,df,anchor=0):
anchor = 0
ShowBreak = True
ShowSwing = True
ShowCon = False
uOCCswing = False
Lookback = 6
emaFilter = False
mult = 0
buybreak = 0
sellbreak = 0
buy_barssince_var = 0
sell_barssince_var = 0
buybreak_barssince_var = 0
sellbreak_barssince_var = 0
barssince_lst = list()
barssince_var = 0
bar_count_var = 0
buy1 = list()
sell1 = list()
buy2 = list()
sell2 = list()
buybreak1 = list()
sellbreak1 = list()
def barssince(b,barssince_var):
barssince_lst = []
barssince_var = 0
new_var = len(b)
for i in b[::-1]:
if i == 1:
break
barssince_lst.append(i)
barssince_var = len(barssince_lst)
return barssince_var
barssince_lst.clear()
#isIntraday
if interval < 1441 :
if (anchor==0 or interval <= 0 or interval >= anchor or anchor > 1441 ):
mult = 1
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = 1
#isIntraday Not
if interval > 1441:
if (anchor==0 or interval <= 0 or interval >= anchor or anchor < 52 ):
mult = mult
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = mult
mult = 1
for i in range(len(df)):
emaF1 = df.loc[i,'ema_3']
emaF2 = df.loc[i,'ema_5']
emaF3 = df.loc[i,'ema_7']
emaF4 = df.loc[i,'ema_9']
emaF5 = df.loc[i,'ema_11']
emaF6 = df.loc[i,'ema_13']
emaF7 = df.loc[i,'ema_15']
emaF8 = df.loc[i,'ema_17']
emaF9 = df.loc[i,'ema_19']
emaF10 = df.loc[i,'ema_21']
emaF11 = df.loc[i,'ema_23']
emaS1 = df.loc[i,'ema_25']
emaS2 = df.loc[i,'ema_28']
emaS3 = df.loc[i,'ema_31']
emaS4 = df.loc[i,'ema_34']
emaS5 = df.loc[i,'ema_37']
emaS6 = df.loc[i,'ema_40']
emaS7 = df.loc[i,'ema_43']
emaS8 = df.loc[i,'ema_46']
emaS9 = df.loc[i,'ema_49']
emaS10 = df.loc[i,'ema_52']
emaS11 = df.loc[i,'ema_55']
emaS12 = df.loc[i,'ema_58']
emaS13 = df.loc[i,'ema_61']
emaS14 = df.loc[i,'ema_64']
emaS15 = df.loc[i,'ema_67']
emaS16 = df.loc[i,'ema_70']
ema200 = df.loc[i,'ema_200']
emafast = (emaF1 + emaF2 + emaF3 + emaF4 + emaF5 + emaF6 + emaF7 + emaF8 + emaF9 + emaF10 + emaF11)/11
emaslow = (emaS1 + emaS2 + emaS3 + emaS4 + emaS5 + emaS6 + emaS7 + emaS8 + emaS9 + emaS10 + emaS11 + emaS12 + emaS13 + emaS14 + emaS15 + emaS16)/16
#Fast EMA Color Rules
colfastL = (emaF1>emaF2 and emaF2>emaF3 and emaF3>emaF4 and emaF4>emaF5 and emaF5>emaF6 and emaF6>emaF7 and emaF7>emaF8 and emaF8>emaF9 and emaF9>emaF10 and emaF10>emaF11)
colfastS = (emaF1<emaF2 and emaF2<emaF3 and emaF3<emaF4 and emaF4<emaF5 and emaF5<emaF6 and emaF6<emaF7 and emaF7<emaF8 and emaF8<emaF9 and emaF9<emaF10 and emaF10<emaF11)
#Slow EMA Color Rules
colslowL = (emaS1>emaS2 and emaS2>emaS3 and emaS3>emaS4 and emaS4>emaS5 and emaS5>emaS6 and emaS6>emaS7 and emaS7>emaS8) and (emaS8>emaS9 and emaS9>emaS10 and emaS10>emaS11 and emaS11>emaS12 and emaS12>emaS13 and emaS13>emaS14 and emaS14>emaS15 and emaS15>emaS16)
colslowS = (emaS1<emaS2 and emaS2<emaS3 and emaS3<emaS4 and emaS4<emaS5 and emaS5<emaS6 and emaS6<emaS7 and emaS7<emaS8) and (emaS8<emaS9 and emaS9<emaS10 and emaS10<emaS11 and emaS11<emaS12 and emaS12<emaS13 and emaS13<emaS14 and emaS14<emaS15 and emaS15<emaS16)
if emafast > emaslow and not colslowS and colfastL and (not ShowCon or colslowL) and (not emaFilter or emafast>ema200):
if int(buy1[-1]) > 0:
buy = buy1[-1] + 1
else:
buy = 1
else:
buy = 0
buy1.append(buy)
if emafast < emaslow and not colslowL and colfastS and (not ShowCon or colslowS) and (not emaFilter or emafast<ema200):
if int(sell1[-1]) > 0:
sell = sell1[-1] + 1
else:
sell = 1
else:
sell = 0
sell1.append(sell)
#buy
if buy>1 and colfastL and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
buy3 = 1
else:
buy3 = buy
buy2.append(buy3)
#sell
if sell>1 and colfastS and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
sell3 = 1
else:
sell3 = sell
sell2.append(sell3)
#buybreak
if emafast > emaslow and not colslowS and (not emaFilter or emafast>ema200):
if buybreak1[-1] > 0:
buybreak = buybreak1[-1] + 1
else:
buybreak = 1
else:
buybreak = 0
buybreak1.append(buybreak)
if emafast < emaslow and not colslowL and (not emaFilter or emafast<ema200):
if sellbreak1[-1] > 0:
sellbreak = sellbreak1[-1]+1
else:
sellbreak = 1
else:
sellbreak = 0
sellbreak1.append(sellbreak)
#arrow plotting
#buy_arrow
buy_barssince_var = barssince(buy2[:-1],barssince_var)
if (ShowSwing and buy3==1)and buy_barssince_var > 6:
buy_arrow = 1
else:
buy_arrow = 0
#sell arrow
sell_barssince_var = barssince(sell2[:-1],barssince_var)
if ShowSwing and (sell3==1 and sell_barssince_var > 6):
sell_arrow = 1
else:
sell_arrow = 0
#buybreak_arrow
buybreak_barssince_var = barssince(buybreak1[:-1],barssince_var)
sellbreak_barssince_var = barssince(sellbreak1[:-1],barssince_var)
if ShowBreak and buybreak==1 and (sellbreak_barssince_var>Lookback) and (buybreak_barssince_var>Lookback):
buybreak_arrow = 1
else:
buybreak_arrow = 0
#sellbreak_arrow
if ShowBreak and sellbreak==1 and (buybreak_barssince_var>Lookback) and (sellbreak_barssince_var>Lookback):
sellbreak_arrow = 1
else:
sellbreak_arrow = 0
if buy_arrow==1 and sell_arrow==0 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'green'
elif buy_arrow==0 and sell_arrow==1 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'red'
elif sell_arrow==0 and (buy_arrow==0 or buy_arrow==1) and buybreak_arrow==1 and sellbreak_arrow==0:
arrow_color = 'aqua'
elif buy_arrow==0 and (sell_arrow==1 or sell_arrow==0) and buybreak_arrow==0 and sellbreak_arrow==1:
arrow_color = 'blue'
else:
arrow_color = 'none'
df.loc[i,'arrow_color'] = arrow_color
df = df[['date','open','high','low','close','arrow_color']]
return df
df=super_guppy(15,df)
gup=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)
display(gup.tail())
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==ide].index
match_in_int=int(match_in)
print("checking executed or not condition ")
print(" programe serching colour of ", prog)
print(" Colour on this candel is",gup.iloc[-1,5] )
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and checking colour " )
#if prog in gup.iloc[-1,5]:
if prog in gup.iloc[-1,5] or prog in gup.iloc[-2,5] or prog in gup.iloc[-3,5] or prog in gup.iloc[-4,5] or prog in gup.iloc[-5,5]:
find=ttradingsymbol
order_mis_cnc=productt
df=(pd.DataFrame(kite.positions()["net"])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]])
dg=(df[df['tradingsymbol'].str.contains(find)==True])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]]
dg=(dg[dg["product"].str.contains(order_mis_cnc)==True])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]]
quant=int((pd.DataFrame(dg["quantity"])).iloc[0,0])
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange="NSE",order_type='MARKET',transaction_type='BUY',product=productt,tag="testR")
print("programe completed with green entry")
print("Quantity is", quant)
display(dg)
order_id_buy_sell.remove(find_id)
execution.clear()
print(" programe serching colour of ", prog)
print("checking red execution")
print("trading symbol is ", ttradingsymbol)
print("quantity is,",qu)
print("order id is", ide)
ashis()
if "red"==prog:
def ashis2():
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%H")
zz2=now_asia
kk=int(zz2)
if kk>=15:
print("Time complete")
############
dff=kite.historical_data(ttoken,sdate,todate,"minute",0)
av=pd.DataFrame(dff)
kk=str(av.iloc[-1,1]).split("+")
zz=kk[0]
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%Y-%m-%d %H:%M:00")
zz2=now_asia
if zz2==zz:
print("same so discarding last ouput")
dfw=av[:-1]
else:
print("Candel Not same so continue going with same candel")
dfw=av
df=pd.DataFrame(dfw[['date','open','high','low','close']])
slow_ema = [3,5,7,9,11,13,15,17,19,21,23]
fast_ema = [25,28,31,34,37,40,43,46,49,52,55,58,61,64,67,70,200]
def EMA(df, base, target, period, alpha=False):
con = pd.concat([df[:period][base].rolling(window=period).mean(), df[period:][base]])
if (alpha == True):
# (1 - alpha) * previous_val + alpha * current_val where alpha = 1 / period
df[target] = con.ewm(alpha=1 / period, adjust=False).mean()
else:
# ((current_val - previous_val) * coeff) + previous_val where coeff = 2 / (period + 1)
df[target] = con.ewm(span=period, adjust=False).mean()
df.fillna(0,inplace = True)
# return df
for j in slow_ema:
val = "ema"+"_"+str(j)
EMA(df,"close",val,j)
for k in fast_ema:
val = "ema"+"_"+str(k)
EMA(df,"close",val,k)
def super_guppy(interval,df,anchor=0):
anchor = 0
ShowBreak = True
ShowSwing = True
ShowCon = False
uOCCswing = False
Lookback = 6
emaFilter = False
mult = 0
buybreak = 0
sellbreak = 0
buy_barssince_var = 0
sell_barssince_var = 0
buybreak_barssince_var = 0
sellbreak_barssince_var = 0
barssince_lst = list()
barssince_var = 0
bar_count_var = 0
buy1 = list()
sell1 = list()
buy2 = list()
sell2 = list()
buybreak1 = list()
sellbreak1 = list()
def barssince(b,barssince_var):
barssince_lst = []
barssince_var = 0
new_var = len(b)
for i in b[::-1]:
if i == 1:
break
barssince_lst.append(i)
barssince_var = len(barssince_lst)
return barssince_var
barssince_lst.clear()
#isIntraday
if interval < 1441 :
if (anchor==0 or interval <= 0 or interval >= anchor or anchor > 1441 ):
mult = 1
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = 1
#isIntraday Not
if interval > 1441:
if (anchor==0 or interval <= 0 or interval >= anchor or anchor < 52 ):
mult = mult
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = mult
mult = 1
for i in range(len(df)):
emaF1 = df.loc[i,'ema_3']
emaF2 = df.loc[i,'ema_5']
emaF3 = df.loc[i,'ema_7']
emaF4 = df.loc[i,'ema_9']
emaF5 = df.loc[i,'ema_11']
emaF6 = df.loc[i,'ema_13']
emaF7 = df.loc[i,'ema_15']
emaF8 = df.loc[i,'ema_17']
emaF9 = df.loc[i,'ema_19']
emaF10 = df.loc[i,'ema_21']
emaF11 = df.loc[i,'ema_23']
emaS1 = df.loc[i,'ema_25']
emaS2 = df.loc[i,'ema_28']
emaS3 = df.loc[i,'ema_31']
emaS4 = df.loc[i,'ema_34']
emaS5 = df.loc[i,'ema_37']
emaS6 = df.loc[i,'ema_40']
emaS7 = df.loc[i,'ema_43']
emaS8 = df.loc[i,'ema_46']
emaS9 = df.loc[i,'ema_49']
emaS10 = df.loc[i,'ema_52']
emaS11 = df.loc[i,'ema_55']
emaS12 = df.loc[i,'ema_58']
emaS13 = df.loc[i,'ema_61']
emaS14 = df.loc[i,'ema_64']
emaS15 = df.loc[i,'ema_67']
emaS16 = df.loc[i,'ema_70']
ema200 = df.loc[i,'ema_200']
emafast = (emaF1 + emaF2 + emaF3 + emaF4 + emaF5 + emaF6 + emaF7 + emaF8 + emaF9 + emaF10 + emaF11)/11
emaslow = (emaS1 + emaS2 + emaS3 + emaS4 + emaS5 + emaS6 + emaS7 + emaS8 + emaS9 + emaS10 + emaS11 + emaS12 + emaS13 + emaS14 + emaS15 + emaS16)/16
#Fast EMA Color Rules
colfastL = (emaF1>emaF2 and emaF2>emaF3 and emaF3>emaF4 and emaF4>emaF5 and emaF5>emaF6 and emaF6>emaF7 and emaF7>emaF8 and emaF8>emaF9 and emaF9>emaF10 and emaF10>emaF11)
colfastS = (emaF1<emaF2 and emaF2<emaF3 and emaF3<emaF4 and emaF4<emaF5 and emaF5<emaF6 and emaF6<emaF7 and emaF7<emaF8 and emaF8<emaF9 and emaF9<emaF10 and emaF10<emaF11)
#Slow EMA Color Rules
colslowL = (emaS1>emaS2 and emaS2>emaS3 and emaS3>emaS4 and emaS4>emaS5 and emaS5>emaS6 and emaS6>emaS7 and emaS7>emaS8) and (emaS8>emaS9 and emaS9>emaS10 and emaS10>emaS11 and emaS11>emaS12 and emaS12>emaS13 and emaS13>emaS14 and emaS14>emaS15 and emaS15>emaS16)
colslowS = (emaS1<emaS2 and emaS2<emaS3 and emaS3<emaS4 and emaS4<emaS5 and emaS5<emaS6 and emaS6<emaS7 and emaS7<emaS8) and (emaS8<emaS9 and emaS9<emaS10 and emaS10<emaS11 and emaS11<emaS12 and emaS12<emaS13 and emaS13<emaS14 and emaS14<emaS15 and emaS15<emaS16)
if emafast > emaslow and not colslowS and colfastL and (not ShowCon or colslowL) and (not emaFilter or emafast>ema200):
if int(buy1[-1]) > 0:
buy = buy1[-1] + 1
else:
buy = 1
else:
buy = 0
buy1.append(buy)
if emafast < emaslow and not colslowL and colfastS and (not ShowCon or colslowS) and (not emaFilter or emafast<ema200):
if int(sell1[-1]) > 0:
sell = sell1[-1] + 1
else:
sell = 1
else:
sell = 0
sell1.append(sell)
#buy
if buy>1 and colfastL and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
buy3 = 1
else:
buy3 = buy
buy2.append(buy3)
#sell
if sell>1 and colfastS and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
sell3 = 1
else:
sell3 = sell
sell2.append(sell3)
#buybreak
if emafast > emaslow and not colslowS and (not emaFilter or emafast>ema200):
if buybreak1[-1] > 0:
buybreak = buybreak1[-1] + 1
else:
buybreak = 1
else:
buybreak = 0
buybreak1.append(buybreak)
if emafast < emaslow and not colslowL and (not emaFilter or emafast<ema200):
if sellbreak1[-1] > 0:
sellbreak = sellbreak1[-1]+1
else:
sellbreak = 1
else:
sellbreak = 0
sellbreak1.append(sellbreak)
#arrow plotting
#buy_arrow
buy_barssince_var = barssince(buy2[:-1],barssince_var)
if (ShowSwing and buy3==1)and buy_barssince_var > 6:
buy_arrow = 1
else:
buy_arrow = 0
#sell arrow
sell_barssince_var = barssince(sell2[:-1],barssince_var)
if ShowSwing and (sell3==1 and sell_barssince_var > 6):
sell_arrow = 1
else:
sell_arrow = 0
#buybreak_arrow
buybreak_barssince_var = barssince(buybreak1[:-1],barssince_var)
sellbreak_barssince_var = barssince(sellbreak1[:-1],barssince_var)
if ShowBreak and buybreak==1 and (sellbreak_barssince_var>Lookback) and (buybreak_barssince_var>Lookback):
buybreak_arrow = 1
else:
buybreak_arrow = 0
#sellbreak_arrow
if ShowBreak and sellbreak==1 and (buybreak_barssince_var>Lookback) and (sellbreak_barssince_var>Lookback):
sellbreak_arrow = 1
else:
sellbreak_arrow = 0
if buy_arrow==1 and sell_arrow==0 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'green'
elif buy_arrow==0 and sell_arrow==1 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'red'
elif sell_arrow==0 and (buy_arrow==0 or buy_arrow==1) and buybreak_arrow==1 and sellbreak_arrow==0:
arrow_color = 'aqua'
elif buy_arrow==0 and (sell_arrow==1 or sell_arrow==0) and buybreak_arrow==0 and sellbreak_arrow==1:
arrow_color = 'blue'
else:
arrow_color = 'none'
df.loc[i,'arrow_color'] = arrow_color
df = df[['date','open','high','low','close','arrow_color']]
return df
df=super_guppy(15,df)
gup=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)
display(gup.tail())
print(" Colour on this candel is",gup.iloc[-1,5] )
print(" programe serching colour of ", prog)
print(" checking green bid execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==ide].index
match_in_int=int(match_in)
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red ")
#if prog in gup.iloc[-1,5]:
if prog in gup.iloc[-1,5] or prog in gup.iloc[-2,5] or prog in gup.iloc[-3,5] or prog in gup.iloc[-4,5] or prog in gup.iloc[-5,5]:
#
find=ttradingsymbol
order_mis_cnc=productt
df=(pd.DataFrame(kite.positions()["net"])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]])
dg=(df[df['tradingsymbol'].str.contains(find)==True])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]]
dg=(dg[dg["product"].str.contains(order_mis_cnc)==True])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]]
quant=int((pd.DataFrame(dg["quantity"])).iloc[0,0])
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange="NSE",order_type='MARKET',transaction_type="SELL",product=productt,tag="testR")
print("programe completed with red entry ")
print("Quantity is :=",quant)
order_id_buy_sell.remove(find_id)
display(dg)
execution.clear()
ashis2()
az=az+1
if az==len(order_id_buy_sell):
print("checking again all order_id's")
checking_all()
if order_id_buy_sell:
pass
else:
print("BOT COMPLETED ALL BUY|SELL")
break
checking_all()
else:
print("NO order's found in scanning ")
ag=str(input("Start again ?")).upper()
if "YES" ==ag:
ashi()
###
###############
if 1 in completed:
if order_id_buy_sell:
# some orders found
def checking_all():
if 1 in completed:
i=1
execution=[]
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
ii=1
################
execution=[]
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
ii=1
##
az=0
while (True):
find_id=order_id_buy_sell[az]
df=pd.DataFrame(kite.orders())[["tradingsymbol","instrument_token","transaction_type",'product',"order_id","quantity","order_type","exchange_update_timestamp",'tag',"quantity","status","status_message","average_price","price","trigger_price"]]
dg=(df[df["order_id"].str.contains(find_id)==True])[["tradingsymbol","instrument_token","transaction_type",'product',"order_id","quantity","status"]]
tsm=dg.iloc[-1,0]
tok=dg.iloc[-1,1]
busl=dg.iloc[-1,2]
prdct=dg.iloc[-1,3]
idis=dg.iloc[-1,4]
qunti=dg.iloc[-1,5]
ttradingsymbol =str(tsm)
productt =str(prdct)
qu =int(qunti)
ttoken =int(tok)
progg =str(busl)
ide =str(idis)
if "BUY" in progg:
prog="red"
if "SELL" in progg:
prog="green"
#####################################
sdate ="2019-07-01"
todate ="2019-10-01"
time_frame = "minute"
eexchange ="NSE"
#######################################
print("Trading symbol is",ttradingsymbol)
#print("Order ID is ",ide)
print("PRODUCT is ",productt)
print("QUANTITY is ",qu)
print("Tokenn is ",ttoken)
print("Execution of bid ",prog)
print("Time Frame is ",time_frame)
print("Exchange is ",eexchange)
display(dg)
############################################
if "green"==prog:
def ashis():
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%H")
zz2=now_asia
kk=int(zz2)
if kk>=15:
print("Time complete")
dff=kite.historical_data(ttoken,sdate,todate,"minute",0)
av=pd.DataFrame(dff)
kk=str(av.iloc[-1,1]).split("+")
zz=kk[0]
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%Y-%m-%d %H:%M:00")
zz2=now_asia
if zz2==zz:
print("same so discarding last ouput")
dfw=av[:-1]
else:
print("Candel Not same so continue going with same candel")
dfw=av
df=pd.DataFrame(dfw[['date','open','high','low','close']])
slow_ema = [3,5,7,9,11,13,15,17,19,21,23]
fast_ema = [25,28,31,34,37,40,43,46,49,52,55,58,61,64,67,70,200]
def EMA(df, base, target, period, alpha=False):
con = pd.concat([df[:period][base].rolling(window=period).mean(), df[period:][base]])
if (alpha == True):
# (1 - alpha) * previous_val + alpha * current_val where alpha = 1 / period
df[target] = con.ewm(alpha=1 / period, adjust=False).mean()
else:
# ((current_val - previous_val) * coeff) + previous_val where coeff = 2 / (period + 1)
df[target] = con.ewm(span=period, adjust=False).mean()
df.fillna(0,inplace = True)
# return df
for j in slow_ema:
val = "ema"+"_"+str(j)
EMA(df,"close",val,j)
for k in fast_ema:
val = "ema"+"_"+str(k)
EMA(df,"close",val,k)
def super_guppy(interval,df,anchor=0):
anchor = 0
ShowBreak = True
ShowSwing = True
ShowCon = False
uOCCswing = False
Lookback = 6
emaFilter = False
mult = 0
buybreak = 0
sellbreak = 0
buy_barssince_var = 0
sell_barssince_var = 0
buybreak_barssince_var = 0
sellbreak_barssince_var = 0
barssince_lst = list()
barssince_var = 0
bar_count_var = 0
buy1 = list()
sell1 = list()
buy2 = list()
sell2 = list()
buybreak1 = list()
sellbreak1 = list()
def barssince(b,barssince_var):
barssince_lst = []
barssince_var = 0
new_var = len(b)
for i in b[::-1]:
if i == 1:
break
barssince_lst.append(i)
barssince_var = len(barssince_lst)
return barssince_var
barssince_lst.clear()
#isIntraday
if interval < 1441 :
if (anchor==0 or interval <= 0 or interval >= anchor or anchor > 1441 ):
mult = 1
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = 1
#isIntraday Not
if interval > 1441:
if (anchor==0 or interval <= 0 or interval >= anchor or anchor < 52 ):
mult = mult
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = mult
mult = 1
for i in range(len(df)):
emaF1 = df.loc[i,'ema_3']
emaF2 = df.loc[i,'ema_5']
emaF3 = df.loc[i,'ema_7']
emaF4 = df.loc[i,'ema_9']
emaF5 = df.loc[i,'ema_11']
emaF6 = df.loc[i,'ema_13']
emaF7 = df.loc[i,'ema_15']
emaF8 = df.loc[i,'ema_17']
emaF9 = df.loc[i,'ema_19']
emaF10 = df.loc[i,'ema_21']
emaF11 = df.loc[i,'ema_23']
emaS1 = df.loc[i,'ema_25']
emaS2 = df.loc[i,'ema_28']
emaS3 = df.loc[i,'ema_31']
emaS4 = df.loc[i,'ema_34']
emaS5 = df.loc[i,'ema_37']
emaS6 = df.loc[i,'ema_40']
emaS7 = df.loc[i,'ema_43']
emaS8 = df.loc[i,'ema_46']
emaS9 = df.loc[i,'ema_49']
emaS10 = df.loc[i,'ema_52']
emaS11 = df.loc[i,'ema_55']
emaS12 = df.loc[i,'ema_58']
emaS13 = df.loc[i,'ema_61']
emaS14 = df.loc[i,'ema_64']
emaS15 = df.loc[i,'ema_67']
emaS16 = df.loc[i,'ema_70']
ema200 = df.loc[i,'ema_200']
emafast = (emaF1 + emaF2 + emaF3 + emaF4 + emaF5 + emaF6 + emaF7 + emaF8 + emaF9 + emaF10 + emaF11)/11
emaslow = (emaS1 + emaS2 + emaS3 + emaS4 + emaS5 + emaS6 + emaS7 + emaS8 + emaS9 + emaS10 + emaS11 + emaS12 + emaS13 + emaS14 + emaS15 + emaS16)/16
#Fast EMA Color Rules
colfastL = (emaF1>emaF2 and emaF2>emaF3 and emaF3>emaF4 and emaF4>emaF5 and emaF5>emaF6 and emaF6>emaF7 and emaF7>emaF8 and emaF8>emaF9 and emaF9>emaF10 and emaF10>emaF11)
colfastS = (emaF1<emaF2 and emaF2<emaF3 and emaF3<emaF4 and emaF4<emaF5 and emaF5<emaF6 and emaF6<emaF7 and emaF7<emaF8 and emaF8<emaF9 and emaF9<emaF10 and emaF10<emaF11)
#Slow EMA Color Rules
colslowL = (emaS1>emaS2 and emaS2>emaS3 and emaS3>emaS4 and emaS4>emaS5 and emaS5>emaS6 and emaS6>emaS7 and emaS7>emaS8) and (emaS8>emaS9 and emaS9>emaS10 and emaS10>emaS11 and emaS11>emaS12 and emaS12>emaS13 and emaS13>emaS14 and emaS14>emaS15 and emaS15>emaS16)
colslowS = (emaS1<emaS2 and emaS2<emaS3 and emaS3<emaS4 and emaS4<emaS5 and emaS5<emaS6 and emaS6<emaS7 and emaS7<emaS8) and (emaS8<emaS9 and emaS9<emaS10 and emaS10<emaS11 and emaS11<emaS12 and emaS12<emaS13 and emaS13<emaS14 and emaS14<emaS15 and emaS15<emaS16)
if emafast > emaslow and not colslowS and colfastL and (not ShowCon or colslowL) and (not emaFilter or emafast>ema200):
if int(buy1[-1]) > 0:
buy = buy1[-1] + 1
else:
buy = 1
else:
buy = 0
buy1.append(buy)
if emafast < emaslow and not colslowL and colfastS and (not ShowCon or colslowS) and (not emaFilter or emafast<ema200):
if int(sell1[-1]) > 0:
sell = sell1[-1] + 1
else:
sell = 1
else:
sell = 0
sell1.append(sell)
#buy
if buy>1 and colfastL and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
buy3 = 1
else:
buy3 = buy
buy2.append(buy3)
#sell
if sell>1 and colfastS and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
sell3 = 1
else:
sell3 = sell
sell2.append(sell3)
#buybreak
if emafast > emaslow and not colslowS and (not emaFilter or emafast>ema200):
if buybreak1[-1] > 0:
buybreak = buybreak1[-1] + 1
else:
buybreak = 1
else:
buybreak = 0
buybreak1.append(buybreak)
if emafast < emaslow and not colslowL and (not emaFilter or emafast<ema200):
if sellbreak1[-1] > 0:
sellbreak = sellbreak1[-1]+1
else:
sellbreak = 1
else:
sellbreak = 0
sellbreak1.append(sellbreak)
#arrow plotting
#buy_arrow
buy_barssince_var = barssince(buy2[:-1],barssince_var)
if (ShowSwing and buy3==1)and buy_barssince_var > 6:
buy_arrow = 1
else:
buy_arrow = 0
#sell arrow
sell_barssince_var = barssince(sell2[:-1],barssince_var)
if ShowSwing and (sell3==1 and sell_barssince_var > 6):
sell_arrow = 1
else:
sell_arrow = 0
#buybreak_arrow
buybreak_barssince_var = barssince(buybreak1[:-1],barssince_var)
sellbreak_barssince_var = barssince(sellbreak1[:-1],barssince_var)
if ShowBreak and buybreak==1 and (sellbreak_barssince_var>Lookback) and (buybreak_barssince_var>Lookback):
buybreak_arrow = 1
else:
buybreak_arrow = 0
#sellbreak_arrow
if ShowBreak and sellbreak==1 and (buybreak_barssince_var>Lookback) and (sellbreak_barssince_var>Lookback):
sellbreak_arrow = 1
else:
sellbreak_arrow = 0
if buy_arrow==1 and sell_arrow==0 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'green'
elif buy_arrow==0 and sell_arrow==1 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'red'
elif sell_arrow==0 and (buy_arrow==0 or buy_arrow==1) and buybreak_arrow==1 and sellbreak_arrow==0:
arrow_color = 'aqua'
elif buy_arrow==0 and (sell_arrow==1 or sell_arrow==0) and buybreak_arrow==0 and sellbreak_arrow==1:
arrow_color = 'blue'
else:
arrow_color = 'none'
df.loc[i,'arrow_color'] = arrow_color
df = df[['date','open','high','low','close','arrow_color']]
return df
df=super_guppy(15,df)
gup=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)
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==ide].index
match_in_int=int(match_in)
display(gup.tail())
print("checking executed or not condition ")
print(" programe serching colour of ", prog)
print(" Colour on this candel is",gup.iloc[-1,5] )
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and checking colour " )
#if prog in gup.iloc[-1,5]:
if prog in gup.iloc[-1,5] or prog in gup.iloc[-2,5] or prog in gup.iloc[-3,5] or prog in gup.iloc[-4,5] or prog in gup.iloc[-5,5]:
#
find=ttradingsymbol
order_mis_cnc=productt
df=(pd.DataFrame(kite.positions()["net"])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]])
dg=(df[df['tradingsymbol'].str.contains(find)==True])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]]
dg=(dg[dg["product"].str.contains(order_mis_cnc)==True])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]]
quant=int((pd.DataFrame(dg["quantity"])).iloc[0,0])
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange="NSE",order_type='MARKET',transaction_type='BUY',product=productt,tag="testR")
print("programe completed with green entry")
print("Quantity is", quant)
display(dg)
order_id_buy_sell.remove(find_id)
execution.clear()
print(" programe serching colour of ", prog)
print("checking red execution")
print("trading symbol is ", ttradingsymbol)
print("quantity is,",qu)
print("order id is", ide)
ashis()
if "red"==prog:
def ashis2():
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%H")
zz2=now_asia
kk=int(zz2)
if kk>=15:
print("Time complete")
############
dff=kite.historical_data(ttoken,sdate,todate,"minute",0)
av=pd.DataFrame(dff)
kk=str(av.iloc[-1,1]).split("+")
zz=kk[0]
now_utc = datetime.now(timezone('UTC'))
now_asia = now_utc.astimezone(timezone('Asia/Kolkata'))
now_asia = now_asia.strftime("%Y-%m-%d %H:%M:00")
zz2=now_asia
if zz2==zz:
print("same so discarding last ouput")
dfw=av[:-1]
else:
print("Candel Not same so continue going with same candel")
dfw=av
df=pd.DataFrame(dfw[['date','open','high','low','close']])
slow_ema = [3,5,7,9,11,13,15,17,19,21,23]
fast_ema = [25,28,31,34,37,40,43,46,49,52,55,58,61,64,67,70,200]
def EMA(df, base, target, period, alpha=False):
con = pd.concat([df[:period][base].rolling(window=period).mean(), df[period:][base]])
if (alpha == True):
# (1 - alpha) * previous_val + alpha * current_val where alpha = 1 / period
df[target] = con.ewm(alpha=1 / period, adjust=False).mean()
else:
# ((current_val - previous_val) * coeff) + previous_val where coeff = 2 / (period + 1)
df[target] = con.ewm(span=period, adjust=False).mean()
df.fillna(0,inplace = True)
# return df
for j in slow_ema:
val = "ema"+"_"+str(j)
EMA(df,"close",val,j)
for k in fast_ema:
val = "ema"+"_"+str(k)
EMA(df,"close",val,k)
def super_guppy(interval,df,anchor=0):
anchor = 0
ShowBreak = True
ShowSwing = True
ShowCon = False
uOCCswing = False
Lookback = 6
emaFilter = False
mult = 0
buybreak = 0
sellbreak = 0
buy_barssince_var = 0
sell_barssince_var = 0
buybreak_barssince_var = 0
sellbreak_barssince_var = 0
barssince_lst = list()
barssince_var = 0
bar_count_var = 0
buy1 = list()
sell1 = list()
buy2 = list()
sell2 = list()
buybreak1 = list()
sellbreak1 = list()
def barssince(b,barssince_var):
barssince_lst = []
barssince_var = 0
new_var = len(b)
for i in b[::-1]:
if i == 1:
break
barssince_lst.append(i)
barssince_var = len(barssince_lst)
return barssince_var
barssince_lst.clear()
#isIntraday
if interval < 1441 :
if (anchor==0 or interval <= 0 or interval >= anchor or anchor > 1441 ):
mult = 1
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = 1
#isIntraday Not
if interval > 1441:
if (anchor==0 or interval <= 0 or interval >= anchor or anchor < 52 ):
mult = mult
else:
if round(anchor/interval) > 1:
mult = round(anchor/interval)
else:
mult = 1
else:
mult = mult
mult = 1
for i in range(len(df)):
emaF1 = df.loc[i,'ema_3']
emaF2 = df.loc[i,'ema_5']
emaF3 = df.loc[i,'ema_7']
emaF4 = df.loc[i,'ema_9']
emaF5 = df.loc[i,'ema_11']
emaF6 = df.loc[i,'ema_13']
emaF7 = df.loc[i,'ema_15']
emaF8 = df.loc[i,'ema_17']
emaF9 = df.loc[i,'ema_19']
emaF10 = df.loc[i,'ema_21']
emaF11 = df.loc[i,'ema_23']
emaS1 = df.loc[i,'ema_25']
emaS2 = df.loc[i,'ema_28']
emaS3 = df.loc[i,'ema_31']
emaS4 = df.loc[i,'ema_34']
emaS5 = df.loc[i,'ema_37']
emaS6 = df.loc[i,'ema_40']
emaS7 = df.loc[i,'ema_43']
emaS8 = df.loc[i,'ema_46']
emaS9 = df.loc[i,'ema_49']
emaS10 = df.loc[i,'ema_52']
emaS11 = df.loc[i,'ema_55']
emaS12 = df.loc[i,'ema_58']
emaS13 = df.loc[i,'ema_61']
emaS14 = df.loc[i,'ema_64']
emaS15 = df.loc[i,'ema_67']
emaS16 = df.loc[i,'ema_70']
ema200 = df.loc[i,'ema_200']
emafast = (emaF1 + emaF2 + emaF3 + emaF4 + emaF5 + emaF6 + emaF7 + emaF8 + emaF9 + emaF10 + emaF11)/11
emaslow = (emaS1 + emaS2 + emaS3 + emaS4 + emaS5 + emaS6 + emaS7 + emaS8 + emaS9 + emaS10 + emaS11 + emaS12 + emaS13 + emaS14 + emaS15 + emaS16)/16
#Fast EMA Color Rules
colfastL = (emaF1>emaF2 and emaF2>emaF3 and emaF3>emaF4 and emaF4>emaF5 and emaF5>emaF6 and emaF6>emaF7 and emaF7>emaF8 and emaF8>emaF9 and emaF9>emaF10 and emaF10>emaF11)
colfastS = (emaF1<emaF2 and emaF2<emaF3 and emaF3<emaF4 and emaF4<emaF5 and emaF5<emaF6 and emaF6<emaF7 and emaF7<emaF8 and emaF8<emaF9 and emaF9<emaF10 and emaF10<emaF11)
#Slow EMA Color Rules
colslowL = (emaS1>emaS2 and emaS2>emaS3 and emaS3>emaS4 and emaS4>emaS5 and emaS5>emaS6 and emaS6>emaS7 and emaS7>emaS8) and (emaS8>emaS9 and emaS9>emaS10 and emaS10>emaS11 and emaS11>emaS12 and emaS12>emaS13 and emaS13>emaS14 and emaS14>emaS15 and emaS15>emaS16)
colslowS = (emaS1<emaS2 and emaS2<emaS3 and emaS3<emaS4 and emaS4<emaS5 and emaS5<emaS6 and emaS6<emaS7 and emaS7<emaS8) and (emaS8<emaS9 and emaS9<emaS10 and emaS10<emaS11 and emaS11<emaS12 and emaS12<emaS13 and emaS13<emaS14 and emaS14<emaS15 and emaS15<emaS16)
if emafast > emaslow and not colslowS and colfastL and (not ShowCon or colslowL) and (not emaFilter or emafast>ema200):
if int(buy1[-1]) > 0:
buy = buy1[-1] + 1
else:
buy = 1
else:
buy = 0
buy1.append(buy)
if emafast < emaslow and not colslowL and colfastS and (not ShowCon or colslowS) and (not emaFilter or emafast<ema200):
if int(sell1[-1]) > 0:
sell = sell1[-1] + 1
else:
sell = 1
else:
sell = 0
sell1.append(sell)
#buy
if buy>1 and colfastL and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
buy3 = 1
else:
buy3 = buy
buy2.append(buy3)
#sell
if sell>1 and colfastS and (uOCCswing and ((df.loc[i-1,'close']<df.loc[i-1,'open']) and (df.loc[i,'close']>df.loc[i,'open']))):
sell3 = 1
else:
sell3 = sell
sell2.append(sell3)
#buybreak
if emafast > emaslow and not colslowS and (not emaFilter or emafast>ema200):
if buybreak1[-1] > 0:
buybreak = buybreak1[-1] + 1
else:
buybreak = 1
else:
buybreak = 0
buybreak1.append(buybreak)
if emafast < emaslow and not colslowL and (not emaFilter or emafast<ema200):
if sellbreak1[-1] > 0:
sellbreak = sellbreak1[-1]+1
else:
sellbreak = 1
else:
sellbreak = 0
sellbreak1.append(sellbreak)
#arrow plotting
#buy_arrow
buy_barssince_var = barssince(buy2[:-1],barssince_var)
if (ShowSwing and buy3==1)and buy_barssince_var > 6:
buy_arrow = 1
else:
buy_arrow = 0
#sell arrow
sell_barssince_var = barssince(sell2[:-1],barssince_var)
if ShowSwing and (sell3==1 and sell_barssince_var > 6):
sell_arrow = 1
else:
sell_arrow = 0
#buybreak_arrow
buybreak_barssince_var = barssince(buybreak1[:-1],barssince_var)
sellbreak_barssince_var = barssince(sellbreak1[:-1],barssince_var)
if ShowBreak and buybreak==1 and (sellbreak_barssince_var>Lookback) and (buybreak_barssince_var>Lookback):
buybreak_arrow = 1
else:
buybreak_arrow = 0
#sellbreak_arrow
if ShowBreak and sellbreak==1 and (buybreak_barssince_var>Lookback) and (sellbreak_barssince_var>Lookback):
sellbreak_arrow = 1
else:
sellbreak_arrow = 0
if buy_arrow==1 and sell_arrow==0 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'green'
elif buy_arrow==0 and sell_arrow==1 and buybreak_arrow==0 and sellbreak_arrow==0:
arrow_color = 'red'
elif sell_arrow==0 and (buy_arrow==0 or buy_arrow==1) and buybreak_arrow==1 and sellbreak_arrow==0:
arrow_color = 'aqua'
elif buy_arrow==0 and (sell_arrow==1 or sell_arrow==0) and buybreak_arrow==0 and sellbreak_arrow==1:
arrow_color = 'blue'
else:
arrow_color = 'none'
df.loc[i,'arrow_color'] = arrow_color
df = df[['date','open','high','low','close','arrow_color']]
return df
df=super_guppy(15,df)
gup=pd.DataFrame(df)
display(gup.tail())
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(" Colour on this candel is",gup.iloc[-1,5] )
print(" programe serching colour of ", prog)
print(" checking green bid execution")
update_reverse=pd.DataFrame(kite.orders())[["tradingsymbol","status","order_id","quantity"]]
match_in,=update_reverse[update_reverse.order_id==ide].index
match_in_int=int(match_in)
if "COMPLETE" in update_reverse.loc[match_in_int,"status"]:
print("ORDER COMPLETED and waiting for red " )
#if prog in gup.iloc[-1,5]:
if prog in gup.iloc[-1,5] or prog in gup.iloc[-2,5] or prog in gup.iloc[-3,5] or prog in gup.iloc[-4,5] or prog in gup.iloc[-5,5]:
#
find=ttradingsymbol
order_mis_cnc=productt
df=(pd.DataFrame(kite.positions()["net"])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]])
dg=(df[df['tradingsymbol'].str.contains(find)==True])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]]
dg=(dg[dg["product"].str.contains(order_mis_cnc)==True])[["tradingsymbol","average_price","pnl","buy_m2m","buy_price","buy_quantity","buy_value","close_price","sell_m2m","sell_price","sell_quantity","sell_value","unrealised","value","day_buy_price","day_buy_quantity","day_buy_value","day_sell_price","product","quantity","realised"]]
quant=int((pd.DataFrame(dg["quantity"])).iloc[0,0])
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=quant,exchange="NSE",order_type='MARKET',transaction_type="SELL",product=productt,tag="testR")
print("programe completed with red entry ")
print("Quantity is :=",quant)
order_id_buy_sell.remove(find_id)
display(dg)
execution.clear()
ashis2()
az=az+1
if az==len(order_id_buy_sell):
print("checking again all order_id's")
checking_all()
if order_id_buy_sell:
pass
else:
print("BOT COMPLETED ALL BUY|SELL")
break
checking_all()
else:
print("NO order's found in scanning ")
ag=str(input("Start again ?")).upper()
if "YES" ==ag:
ashi()
###
Output –
WELCOME TO GUPPY BOT
DO You want SCANNING ? YES , RESULT, HALF :- yes
Enter TimeFrame:=30minute
Enter Amount := 30000
MIS or CNC order ? := mis
No of time scanning ?1
Time Frame is := 30minute
Amount is := 30000
Product is := MIS
SCANNING START
Getting All tokens for processing BUY SELL
Complete ! All tokens are fetched from file
[3861249, 60417, 1510401, 4267265, 81153, 4268801, 134657, 2714625, 7458561, 140033, 177665, 5215745, 225537, 232961, 1207553, 315393, 1850625, 341249, 345089, 348929, 356865, 340481, 1270529, 424961, 7712001, 415745, 1346049, 408065, 3001089, 492033, 2939649, 519937, 2815745, 2977281, 633601, 3834113, 738561, 779521, 857857, 2953217, 884737, 895745, 3465729, 897537, 2889473, 2952193, 784129, 969473, 3050241, 975873]
Now checking Condition of BUY sell of GUPPY
ZERODHA ADVANCE BOT
Current Token checking 3861249
Current candel checking no 0
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 60417
Current candel checking no 1
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 1510401
Current candel checking no 2
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 4267265
Current candel checking no 3
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 81153
Current candel checking no 4
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 4268801
Current candel checking no 5
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 134657
Current candel checking no 6
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 2714625
Current candel checking no 7
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 7458561
Current candel checking no 8
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 140033
Current candel checking no 9
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 177665
Current candel checking no 10
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 5215745
Current candel checking no 11
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 225537
Current candel checking no 12
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 232961
Current candel checking no 13
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = []
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 1207553
Current candel checking no 14
Current Colour on this token is green
BUY stock found
ALREADY USED !!
Tokend is := 1207553
Trading Symbol is := GAIL
SORRY !! This order already exists
so not going to place this order !!
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 315393
Current candel checking no 15
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 1850625
Current candel checking no 16
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 341249
Current candel checking no 17
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 345089
Current candel checking no 18
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 348929
Current candel checking no 19
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 356865
Current candel checking no 20
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = []
ZERODHA ADVANCE BOT
Current Token checking 340481
Current candel checking no 21
Current Colour on this token is green
BUY stock found
TRUE !! so going to place order of 340481
date open high low close arrow_color ATR
122 2019-08-30 15:15:00+05:30 2163.6 2167.65 2163.25 2164.75 green 12.279518
Trading Symbol of stock is HDFC
Quantity of placed Stock is 13
T.P value after .25 of atr is 2170.71987960732
price is after adding 0.1 percent of atr on trigegr price 2171.927951842928
BUY price after round off __must be high__ then t.p 2171.95
T.P Trigger price after round off __must be low__ then price 2170.7
{'340481': {'instrument_token': 340481, 'last_price': 2166.45, 'ohlc': {'open': 2137.7, 'high': 2171.25, 'low': 2118, 'close': 2128.45}}}
buy order id is 190901000000549
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 1270529
Current candel checking no 22
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 424961
Current candel checking no 23
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 7712001
Current candel checking no 24
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 415745
Current candel checking no 25
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 1346049
Current candel checking no 26
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 408065
Current candel checking no 27
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 3001089
Current candel checking no 28
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 492033
Current candel checking no 29
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 2939649
Current candel checking no 30
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 519937
Current candel checking no 31
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 2815745
Current candel checking no 32
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 2977281
Current candel checking no 33
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 633601
Current candel checking no 34
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 3834113
Current candel checking no 35
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 738561
Current candel checking no 36
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 779521
Current candel checking no 37
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 857857
Current candel checking no 38
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 2953217
Current candel checking no 39
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 884737
Current candel checking no 40
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 895745
Current candel checking no 41
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 3465729
Current candel checking no 42
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 897537
Current candel checking no 43
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 2889473
Current candel checking no 44
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 2952193
Current candel checking no 45
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 784129
Current candel checking no 46
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 969473
Current candel checking no 47
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 3050241
Current candel checking no 48
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
ZERODHA ADVANCE BOT
Current Token checking 975873
Current candel checking no 49
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 0
ALL order is of BUY\SELL = ['190901000000549']
SCAN COMPLETED !!
ZERODHA ADVANCE BOT
Current Token checking 3861249
Current candel checking no 0
Current Colour on this token is none
SYMBOLS_BUY TOKENS_BUY orderid_BUY
0 HDFC 340481 190901000000549
SYMBOLS_SELL TOKENS_SELL orderid_SELL
SYBMOL's ignored BUY = ['GAIL']
SYBMOL's ignored SELL = []
No of time Nifty50 stock scanned = 1
ALL order is of BUY\SELL = ['190901000000549']
!! COMPLETED FULL SCAN !!
Scanning once again
!! COMPLETED FULL SCAN !!
Trading symbol is HDFC
PRODUCT is MIS
QUANTITY is 13
Tokenn is 340481
Execution of bid red
Time Frame is minute
Exchange is NSE
tradingsymbol instrument_token transaction_type product order_id quantity quantity status
3 HDFC 340481 BUY MIS 190901000000549 13 13 REJECTED
Candel Not same so continue going with same candel
date open high low close arrow_color
16119 2019-08-30 15:25:00+05:30 2163.90 2164.0 2163.85 2164.00 none
16120 2019-08-30 15:26:00+05:30 2164.05 2165.5 2164.05 2165.00 none
16121 2019-08-30 15:27:00+05:30 2165.00 2165.0 2163.90 2164.90 none
16122 2019-08-30 15:28:00+05:30 2165.00 2165.0 2163.50 2165.00 none
16123 2019-08-30 15:29:00+05:30 2164.15 2165.0 2163.55 2164.75 none
Colour on this candel is none
programe serching colour of red
checking green bid execution
checking again all order_id's
Trading symbol is HDFC
PRODUCT is MIS
QUANTITY is 13
Tokenn is 340481
Execution of bid red
Time Frame is minute
Exchange is NSE
tradingsymbol instrument_token transaction_type product order_id quantity quantity status
3 HDFC 340481 BUY MIS 190901000000549 13 13 REJECTED
Candel Not same so continue going with same candel
This program offers a systematic approach to stock trading by combining the analytical power of the Guppy Screener with user input and automated trading based on indicator signals. Its focus on multiple timeframes and the selection of common stocks across these periods helps in identifying robust trading opportunities.