Multi-Stock Trading Bot Using Guppy Strategy with 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.
Objective
In our latest trading bot, we unveil an advanced trading bot, a leap forward in both complexity and functionality:
Advanced Trading Strategy: This bot is finely tuned to initiate buy trades when the indicator flashes a green signal and to execute sell orders upon a red signal. This sophisticated approach ensures dynamic and responsive trading actions based on real-time market signals.
Multi-Stock Capability: Breaking away from the limitations of its predecessors, this bot is adept at handling multiple stocks concurrently. This feature marks a significant advancement over previous models which were confined to trading a single stock at a time.
Comprehensive Market Scanning: The bot is engineered to continuously scan all stocks within the Nifty 50 index. It intelligently executes and manages trades for any stock that aligns with the defined buy or sell criteria.
Ongoing Monitoring: Unlike earlier versions, this bot maintains an ongoing surveillance of the entire stock list. This continuous monitoring ensures that no potential trading opportunity is missed, and allows for swift action in a fast-moving market environment.
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
sdate = (date.today() - timedelta(days=15)).strftime("%Y-%m-%d")
todate = date.today().strftime("%Y-%m-%d")
while 1:
ks=str(input("nifty or fno ?")).upper()
if ks=="NIFTY":
tokenall=[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]
ss=['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']
break
if ks=="FNO":
tokenall=[5633, 6401, 3861249, 4451329, 2760193, 20993, 325121, 2524673, 41729, 49409, 54273, 60417, 70401, 1510401, 1195009, 1214721, 94977, 108033, 2714625, 2911489, 2763265, 3812865, 160001, 160769, 163073, 177665, 5215745, 3876097, 197633, 3771393, 225537, 173057, 261889, 1207553, 3463169, 2796801, 315393, 3378433, 2513665, 1850625, 340481, 341249, 3789569, 345089, 2747905, 348929, 359937, 356865, 364545, 3699201, 1270529, 377857, 3677697, 3060993, 381697, 2883073, 387073, 387841, 1346049, 408065, 2393089, 415745, 3920129, 424961, 1723649, 2661633, 2933761, 3011329, 4574465, 3001089, 4632577, 492033, 2061825, 511233, 2939649, 2672641, 519937, 2815745, 2674433, 582913, 593665, 3924993, 2977281, 2748929, 633601, 2819073, 636673, 2730497, 3834113, 2906881, 3364353, 731905, 3375873, 3930881, 737793, 738561, 141569, 3078657, 779521, 780289, 1492737, 1522689, 1102337, 857857, 3431425, 3076609, 1837825, 871681, 952577, 878593, 884737, 4343041, 877057, 895745, 2953217, 3465729, 897537, 2873089, 2952193, 2752769, 920065, 951809, 3026177, 969473, 3050241, 112129, 134657, 3721473, 2800641, 3385857, 4454401, 1152769, 806401, 617473, 2905857, 3660545, 3906305, 758529, 975873]
ss=['ACC', 'ADANIENT', 'ADANIPORTS', 'ADANIPOWER', 'ALBK', 'ALOKTEXT', 'AMBUJACEM', 'ANDHRABANK', 'APOLLOTYRE', 'ARVIND', 'ASHOKLEY', 'ASIANPAINT', 'AUROPHARMA', 'AXISBANK', 'BANKBARODA', 'BANKINDIA', 'BATAINDIA', 'BHARATFORG', 'BHARTIARTL', 'BIOCON', 'CANBK', 'CENTRALBK', 'CENTURYTEX', 'CESC', 'CHAMBLFERT', 'CIPLA', 'COALINDIA', 'COLPAL', 'DABUR', 'DLF', 'DRREDDY', 'EXIDEIND', 'FEDERALBNK', 'GAIL', 'GMRINFRA', 'GODREJIND', 'GRASIM', 'GSPL', 'HAVELLS', 'HCLTECH', 'HDFC', 'HDFCBANK', 'HDIL', 'HEROMOTOCO', 'HEXAWARE', 'HINDALCO', 'HINDPETRO', 'HINDUNILVR', 'HINDZINC', 'IBREALEST', 'ICICIBANK', 'IDBI', 'IDEA', 'IDFC', 'IFCI', 'IGL', 'INDHOTEL', 'INDIACEM', 'INDUSINDBK', 'INFY', 'IOB', 'IOC', 'IRB', 'ITC', 'JINDALSTEL', 'JISLJALEQS', 'JPASSOCIAT', 'JPPOWER', 'JSWENERGY', 'JSWSTEEL', 'JUBLFOOD', 'KOTAKBANK', 'KTKBANK', 'LICHSGFIN', 'LT', 'LUPIN', 'M&M', 'MARUTI', 'MCDOWELL-N', 'MRF', 'NCC', 'NMDC', 'NTPC', 'OFSS', 'ONGC', 'OPTOCIRCUI', 'ORIENTBANK', 'PNB', 'POWERGRID', 'PTC', 'PUNJLLOYD', 'RAYMOND', 'RCOM', 'RECLTD', 'RELCAPITAL', 'RELIANCE', 'RELINFRA', 'RENUKA', 'SBIN', 'SCI', 'SINTEX', 'SOUTHBANK', 'SRTRANSFIN', 'SUNPHARMA', 'SUNTV', 'SUZLON', 'SYNDIBANK', 'TATACHEM', 'TATACOMM', 'TATAGLOBAL', 'TATAMOTORS', 'TATAMTRDVR', 'TATAPOWER', 'TATASTEEL', 'TCS', 'TECHM', 'TITAN', 'UCOBANK', 'ULTRACEMCO', 'UNIONBANK', 'UNITECH', 'VOLTAS', 'WELCORP', 'WIPRO', 'YESBANK', 'BHEL', 'BPCL', 'DISHTV', 'DIVISLAB', 'GVKPIL', 'NHPC', 'MPHASIS', 'SIEMENS', 'PEL', 'PETRONET', 'PFC', 'RPOWER', 'SAIL', 'ZEEL']
break
while 1:
kwss=int(input("enter timeframe 1/2/3/5"))
if kwss:
time_frame_bktst =kwss
spl=time_frame_bktst-1
back_test_timeframe=spl
break
while 1:
d_d=int(input("enter days of backtesting"))
if d_d:
zap=kite.historical_data(3677697,"2019-05-10","2020-08-16" ,"day",0)
zapp=pd.DataFrame(zap).tail(d_d)
s_date=zapp.iloc[1,1]
kk=str(s_date)
gg=kk[:10]
sdate_backtest=str(gg.strip())
e_date=zapp.iloc[-1,1]
kk=str(e_date)
gg=kk[:10]
todate_backtest =str(gg.strip())
print(sdate_backtest)
print(todate_backtest)
break
while 1:
quu=int(input("enter amount ???"))
if quu:
eexchange="NSE"
productt="MIS"
qu=int(quu)
break
# ignore warnings
def fxn():
warnings.warn("deprecated", DeprecationWarning)
with warnings.catch_warnings():
warnings.simplefilter("ignore")
fxn()
warnings.filterwarnings("ignore")
print("SCANNING START")
# margins on stock
marg=[3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 5.0, 5.0, 5.0, 3.0, 3.0, 3.0, 11.0, 3.0, 3.0, 12.5, 12.5, 3.0, 5.0, 12.5, 5.0, 3.0, 12.5, 12.5, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 12.5, 12.5, 3.0, 12.5, 3.0, 12.5, 12.5, 3.0, 3.0, 12.5, 10.0, 5.0, 12.5, 3.0, 12.5, 3.0, 12.5, 3.0, 3.0, 12.5, 5.0, 5.0, 12.5, 3.0, 3.0, 12.5, 12.5, 12.5, 3.0, 12.5, 12.5, 3.0, 3.0, 12.5, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 12.5, 3.0, 3.0, 12.5, 12.5, 12.5, 3.0, 3.0, 12.5, 12.5, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 5.0, 8.3, 3.0, 8.3, 3.0, 3.0, 5.0, 3.0, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 5.6, 3.0, 12.5, 12.5, 12.5, 3.0, 3.0, 3.0, 3.0, 5.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 5.0, 3.0, 3.0, 12.5, 3.0, 3.0, 10.0, 3.0, 3.0, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 12.5, 12.5, 3.0, 12.5, 3.0, 3.0, 12.5, 12.5, 3.0, 3.0, 12.5, 3.0, 9.0, 12.5, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 9.0, 12.5, 5.0, 3.0, 12.5, 3.0, 3.0, 12.5, 3.0, 3.0, 3.0, 10.0, 12.5, 3.0, 12.5, 12.5, 3.0, 3.0, 3.0, 3.0, 8.3, 3.0, 3.0, 12.5, 3.0, 3.0, 3.0, 3.0, 11.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 5.0, 10.0, 3.0, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 5.0, 3.0, 3.0, 11.0, 3.0, 3.0, 12.5, 3.0, 10.0, 12.5, 12.5, 12.5, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 3.0, 12.5, 3.0, 12.5, 3.0, 3.0, 12.5, 12.5, 3.0, 12.5, 12.5, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 3.0, 5.0, 3.0, 12.5, 3.0, 3.0, 3.0, 12.5, 3.0, 12.5, 3.0, 3.0, 3.0, 12.5, 12.5, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 10.0, 12.5, 3.0, 12.5, 3.0, 3.0, 12.5, 3.0, 12.5, 12.5, 3.0, 12.5, 3.0, 3.0, 5.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 12.5, 11.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 12.5, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 12.5, 12.5, 3.0, 12.5, 3.0, 3.0, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 5.0, 11.0, 10.0, 3.0, 3.0, 3.0, 3.0, 5.0, 3.0, 11.0, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 12.5, 12.5, 3.0, 9.0, 9.0, 12.5, 3.0, 12.5, 3.0, 12.5, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 12.5, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 12.5, 3.0, 9.0, 12.5, 11.0, 3.0, 12.5, 3.0, 5.0, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 12.5, 3.0, 3.0, 5.0]
symb=['3MINDIA', 'AARTIIND','ABB','ABFRL', 'ACC', 'ACE', 'ADANIENT', 'ADANIPORTS', 'ADANIPOWER', 'AIAENG', 'AJANTPHARM', 'AKZOINDIA', 'ALBK', 'ALKEM', 'ALLCARGO', 'AMARAJABAT', 'AMBUJACEM', 'ANANTRAJ', 'APOLLOHOSP', 'APOLLOTYRE', 'ARVIND', 'ASAHIINDIA', 'ASHOKLEY', 'ASIANPAINT', 'ASTRAZEN', 'ATFL', 'ATUL', 'AUBANK', 'AUROPHARMA', 'AUTOAXLES', 'AVANTIFEED', 'AXISBANK', 'BAJAJ-AUTO', 'BAJAJELEC', 'BAJAJFINSV', 'BAJAJHLDNG', 'BAJFINANCE', 'BALKRISIND', 'BALRAMCHIN', 'BANCOINDIA', 'BANKBARODA', 'BANKBEES', 'BANKINDIA', 'BATAINDIA', 'BBTC', 'BEL', 'BEML', 'BERGEPAINT', 'BFUTILITIE', 'BHARATFIN', 'BHARATFORG', 'BHARTIARTL', 'BHEL', 'BIOCON', 'BLKASHYAP', 'BLUEDART', 'BOSCHLTD', 'BPCL', 'BRITANNIA', 'BSE', 'CADILAHC', 'CANBK', 'CANFINHOME', 'CAPACITE', 'CASTROLIND', 'CDSL', 'CEATLTD', 'CENTRUM', 'CENTURYPLY', 'CENTURYTEX', 'CEREBRAINT', 'CESC', 'CGCL', 'CHENNPETRO', 'CHOLAFIN', 'CIPLA', 'COALINDIA', 'COCHINSHIP', 'COFFEEDAY', 'COLPAL', 'CONCOR', 'COROMANDEL', 'CRISIL', 'CROMPTON', 'CUB', 'CUMMINSIND', 'CYIENT', 'DABUR', 'DALMIASUG', 'DBCORP', 'DCBBANK', 'DEN', 'DHAMPURSUG', 'DHFL', 'DIAMONDYD', 'DISHTV', 'DIVISLAB', 'DIXON', 'DLF', 'DMART', 'DREDGECORP', 'DRREDDY', 'ECLERX', 'EICHERMOT', 'EIDPARRY', 'EIHOTEL', 'ELECTCAST', 'EMAMILTD', 'ENDURANCE', 'ENGINERSIN', 'EQUITAS', 'ERIS', 'ESCORTS', 'EXIDEIND', 'FEDERALBNK', 'FEL', 'FINCABLES', 'FORTIS', 'FRETAIL', 'GAIL', 'GDL', 'GEPIL', 'GESHIP', 'GET&D', 'GHCL', 'GICHSGFIN', 'GICRE', 'GILLETTE', 'GLAXO', 'GLENMARK', 'GLOBUSSPR', 'GMBREW', 'GMRINFRA', 'GNFC', 'GODREJAGRO', 'GODREJCP', 'GODREJIND', 'GODREJPROP', 'GOLDBEES', 'GPPL', 'GRANULES', 'GRASIM', 'GREAVESCOT', 'GSFC', 'GSKCONS', 'GSPL', 'GUJALKALI', 'GUJFLUORO', 'GUJGASLTD', 'HAVELLS', 'HBLPOWER', 'HCL-INSYS', 'HCLTECH', 'HDFC', 'HDFCAMC', 'HDFCBANK', 'HDFCLIFE', 'HEIDELBERG', 'HEROMOTOCO', 'HEXAWARE', 'HGS', 'HIKAL', 'HINDALCO', 'HINDCOPPER', 'HINDPETRO', 'HINDUNILVR', 'HINDZINC', 'HONAUT', 'HSCL', 'HSIL', 'HUDCO', 'IBULHSGFIN', 'ICICIBANK', 'ICICIGI', 'ICICINIFTY', 'ICICIPRULI', 'IDBI', 'IDEA', 'IDFC', 'IDFCFIRSTB', 'IEX', 'IFCI', 'IGL', 'INDHOTEL', 'INDIACEM', 'INDIANB', 'INDIGO', 'INDUSINDBK', 'INFIBEAM', 'INFRATEL', 'INFY', 'INOXLEISUR', 'INOXWIND', 'INTELLECT', 'IOB', 'IOC', 'IPCALAB', 'IRB', 'ITC', 'ITDCEM', 'JAICORPLTD', 'JAMNAAUTO', 'JAYSREETEA', 'JINDALSTEL', 'JINDWORLD', 'JKCEMENT', 'JKPAPER', 'JKTYRE', 'JMFINANCIL', 'JSLHISAR', 'JSWENERGY', 'JSWSTEEL', 'JUBILANT', 'JUBLFOOD', 'JUSTDIAL', 'JYOTHYLAB', 'KAJARIACER', 'KALPATPOWR', 'KANSAINER', 'KARURVYSYA', 'KEC', 'KEI', 'KIRIINDUS', 'KOTAKBANK', 'KOTAKNIFTY', 'BSOFT', 'KSCL', 'KTKBANK', 'L&TFH', 'LALPATHLAB', 'LIBERTSHOE', 'LICHSGFIN', 'LINCOLN', 'LIQUIDBEES', 'LT', 'LUPIN', 'M&M', 'M&MFIN', 'M50', 'MAHINDCIE', 'MAHLOG', 'MAHSEAMLES', 'MAJESCO', 'MANALIPETC', 'MANAPPURAM', 'MANGTIMBER', 'MANINDS', 'MANINFRA', 'MARICO', 'MARKSANS', 'MARUTI', 'MASFIN', 'MASTEK', 'MCDOWELL-N', 'MCX', 'MEGH', 'MFSL', 'MGL', 'MIDHANI', 'MINDACORP', 'MINDAIND', 'MINDTREE', 'MMTC', 'MOIL', 'MOLDTKPAC', 'MOTHERSUMI', 'MPHASIS', 'MRF', 'MRPL', 'MUKANDLTD', 'MUNJALAU', 'MUTHOOTFIN', 'NATCOPHARM', 'NATIONALUM', 'NAUKRI', 'NAVINFLUOR', 'NAVNETEDUL', 'NBCC', 'NCC', 'NESTLEIND', 'NETWORK18', 'NFL', 'NH', 'NHPC', 'NIACL', 'NIFTYBEES', 'NIITTECH', 'NLCINDIA', 'NMDC', 'NOCIL', 'NRBBEARING', 'NTPC', 'OBEROIRLTY', 'OFSS', 'OIL', 'OMAXE', 'ONGC', 'ORIENTBANK', 'ORIENTCEM', 'PAGEIND', 'PAPERPROD', 'PARAGMILK', 'PATELENG', 'PCJEWELLER', 'PEL', 'PERSISTENT', 'PETRONET', 'PFC', 'PFIZER', 'PGHH', 'PHILIPCARB', 'PHOENIXLTD', 'PIDILITIND', 'PIIND', 'PNB', 'PNBGILTS', 'PNBHOUSING', 'POLYPLEX', 'POWERGRID', 'PRAJIND', 'PRESTIGE', 'PTC', 'PURVA', 'PVR', 'QUICKHEAL', 'RADICO', 'RADIOCITY', 'RAJESHEXPO', 'RALLIS', 'RAMCOCEM', 'RAMCOIND', 'RAYMOND', 'RBLBANK', 'RCF', 'RECLTD', 'RELAXO', 'RELCAPITAL', 'RELIANCE', 'RELINFRA', 'REPCOHOME', 'RICOAUTO', 'RIIL', 'RKFORGE', 'SAIL', 'SALZERELEC', 'SANGHIIND', 'SANGHVIMOV', 'SANOFI', 'SAREGAMA', 'SATIN', 'SBILIFE', 'SBIN', 'SCHAEFFLER', 'SCHNEIDER', 'SCI', 'SELAN', 'SEQUENT', 'SHARDAMOTR', 'SHREECEM', 'SHRIRAMCIT', 'SIEMENS', 'SIMPLEXINF', 'SIS', 'SJVN', 'SKFINDIA', 'SNOWMAN', 'SOBHA', 'SOLARINDS', 'SOUTHBANK', 'SPARC', 'SRF', 'SRTRANSFIN', 'STAR', 'STCINDIA', 'STRTECH', 'SUNDARMFIN', 'SUNDRMFAST', 'SUNPHARMA', 'SUNTECK', 'SUNTV', 'SUPREMEIND', 'SUVEN', 'SYNDIBANK', 'SYNGENE', 'TAKE', 'TATACHEM', 'TATACOFFEE', 'TATACOMM', 'TATAELXSI', 'TATAGLOBAL', 'TATAINVEST', 'TATAMOTORS', 'TATAMTRDVR', 'TATAPOWER', 'TATASPONGE', 'TATASTEEL', 'TCI', 'TCS', 'TECHM', 'TEXRAIL', 'THERMAX', 'THOMASCOOK', 'THYROCARE', 'TIFIN', 'TIMETECHNO', 'TINPLATE', 'TITAN', 'TNPETRO', 'TNPL', 'TORNTPHARM', 'TORNTPOWER', 'TRENT', 'TRIDENT', 'TRIGYN', 'TRIVENI', 'TTKPRESTIG', 'TV18BRDCST', 'TVSMOTOR', 'TWL', 'UBL', 'UCOBANK', 'UJJIVAN', 'ULTRACEMCO', 'UNIONBANK', 'UNIVCABLES', 'UPL', 'USHAMART', 'VEDL', 'VGUARD', 'VIJAYABANK', 'VIPIND', 'VOLTAS', 'VTL', 'WABCOINDIA', 'WALCHANNAG', 'WELCORP', 'WELENT', 'WELSPUNIND', 'WHIRLPOOL', 'WINDMACHIN', 'WIPRO', 'WOCKPHARMA', 'WONDERLA', 'YESBANK', 'ZEEL']
dr={"margin":marg, "symbls":symb}
check_list=pd.DataFrame(dr)
# code for rounding
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
# converting candle timeframe
def time_con(time,df):
z = 0
chg = []
chg_index = 0
timeframe = time
date = np.array([])
for i in range(len(df)):
date = np.hstack((date,str(df.loc[i,"date"])))
open_np = df.open.values
high_np = df.high.values
low_np = df.low.values
close_np = df.close.values
# volume_np = df.volume.values
# print(date)
for j in range(len(date)):
if date[j][11:16] == "09:15":
chg.append(j)
last = len(df)-1
timeframe_value = int(date[last][11:13])*60 + int(date[last][14:16]) - (9*60+15)
#print(timeframe_value)
if timeframe_value%timeframe==0:
chg.append(len(df)-1)
else:
chg.append(len(df)-timeframe)
np_date=np.array([])
np_open = np.array([])
np_high = np.array([])
np_low = np.array([])
np_close = np.array([])
# np_volume = np.array([])
for i in range(len(date)-timeframe):
if i>=z:
chg_index+=1
if (chg_index < len(chg)):
for k in range(i,chg[chg_index],timeframe):
# print(date[k])
# print(k,k+timeframe)
try:
np_close = np.hstack((np_close,close_np[k+timeframe-1]))
except:
break
np_date = np.hstack((np_date,date[k]))
np_open = np.hstack((np_open,open_np[k]))
np_high = np.hstack((np_high,np.amax(high_np[k:k+timeframe])))
# print(high_np[k:k+timeframe])
np_low = np.hstack((np_low,np.amin(low_np[k:k+timeframe])))
# print(low_np[k:k+timeframe])
# np_volume = np.hstack((np_volume,np.sum(volume_np[k:k+timeframe])))
z = i+timeframe
z=chg[chg_index]
else:
con_df = pd.DataFrame({"date":np_date,"open":np_open,"high":np_high,"low":np_low,"close":np_close})
return con_df
##
aa=0
print(" \t \t \t \n Getting All tokens for processing BUY SELL ")
ks=strftime("%a, %d %b %Y %I:%M:%S %p")
print(" Now checking Condition of BUY sell of GUPPY ")
display(strftime("%a, %d %b %Y %I:%M:%S %p"))
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=[]
##################
#new
fail_buy_backtest=[]
fail_sell_backtest=[]
buy_bid_new=[]
sell_bid_new=[]
red_new=[]
green_new=[]
ignores_sell=[]
ignores_buy=[]
#######
def ashi():
global a
global tokenall
global BUY_listindicator
global SELL_listindicator
ak=1
while (True):
#display(strftime("%a, %d %b %Y %I:%M:%S %p"))
dff=kite.historical_data(tokenall[a],sdate,todate,"minute",0)
dff=pd.DataFrame(dff)
avv=dff[['date','open','high','low','close']]
av = time_con(time_frame_bktst,avv) ## candel conversion
print(time_frame_bktst)
kk=str(av.iloc[-1,1]).split("+")
#print(av.iloc[-1,1])
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:
df=av[:-1]
else:
df=av
#df=pd.DataFrame(dfw[['date','open','high','low','close']])
#break
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(" \t \t \t \t ZERODHA ADVANCE BOT ")
#display(strftime("%a, %d %b %Y %I:%M:%S %p"))
print("Current candel checking no " , a)
print("Current Colour on this token is " , gup.iloc[-1,5])
print("Current Symbol " , ss[a])
print("Data fetched ! compressing Data for indicator ...")
if "green" in gup.iloc[-1,5]:
print("BUY stock found ")
ttradingsymbol=ss[a]
ttoken=tokenall[a]
#
# checking already placed or not if placed then ignore else place order on this stock
#
kl=kite.orders()
if kl:
kk=list((pd.DataFrame(kite.orders())[["instrument_token"]])["instrument_token"])
if ttoken in kk:
print(" ALREADY USED !! ")
print(" Token is := ", ttoken)
print(" Trading Symbol is := ", ttradingsymbol)
print(" SORRY !! This order already exists \n so not going to place this order !! ")
ignores_buy.append(ttradingsymbol)
else:
# backtesting result
print(" fresh order now checking backtesting")
print("\n Data fetched ! compressing Data for backtesting ...")
strike_rate = 0
strike_rate_list = []
timeframe = []
timeframe_list = []
pnl = 0
pnl_list = []
for t in range(1,7):
dfw=kite.historical_data(ttoken,sdate_backtest,todate_backtest,"minute",0)
df=pd.DataFrame(dfw)
df=pd.DataFrame(df[['date','open','high','low','close']])
df = time_con(time_frame_bktst,df)
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)
df['bid_value'] = 0
df['executed_value'] = 0
df['diff'] = 0
df['pnl'] = 0
qty = int ( qu/ (gup.iloc[-1,2]) )
var = False
var_2 = False
var_1 = False
#****************************
for i in range(len(df)):
zz=df.loc[i,"date"]
za=str(zz)[11:13]
if za=="15":
if var==True and var1==True:
df.loc[i,'diff'] = bid_value - df.loc[i,'close']
df.loc[i,'pnl'] = ( bid_value /bid_value)*df.loc[i,'diff']
##
var=False
var1=False
if var==True and var1==False:
var=False
var_2=True
if za!="15":
var_2=False
if var==False and var_2==False:
if df.loc[i,'arrow_color']=='green':
bid_value = (df.loc[i,'high']+df.loc[i,'ATR']*.25)+(df.loc[i,'ATR']*.1)
df.loc[i,'bid_value'] =bid_value
var=True
var1=False
if var==True and var1==False and var_2==False:
if df.loc[i,'high'] > bid_value and df.loc[i,'low'] < bid_value:
df.loc[i,'executed_value']=bid_value
var1=True
if var==True and var1==True and var_2==False:
if df.loc[i,'arrow_color']=='red':
df.loc[i,'diff'] = df.loc[i,'close'] - bid_value
df.loc[i,'pnl'] = ( bid_value /bid_value)*df.loc[i,'diff']
var=False
var1=False
#****************************
list_1 = df['pnl']
pos_count = len(list(filter(lambda x: (x>0),list_1)))
neg_count = len(list(filter(lambda x: (x<0),list_1)))
total_trade = pos_count + neg_count
try :
strike_rate = pos_count/total_trade
except:
strike_rate = 0
pnl = df['pnl'].sum()
pnl_list.append(pnl)
timeframe_list.append(t)
strike_rate_list.append(strike_rate)
main_df = pd.DataFrame({'timeframe':timeframe_list,'pnl':pnl_list,'strike_rate':strike_rate_list})
s=f"{ttradingsymbol}.csv"
#df.to_csv(s)
main_df.to_csv(s)
print("complete")
#file
st=ttradingsymbol+"_pnl_sheet"
s=f"{st}.csv"
df.to_csv(s)
##############
pnl_rate=main_df.loc[back_test_timeframe,'pnl'] #10
stk_rate=main_df.loc[back_test_timeframe,'strike_rate'] #.5
print("PnL :=",pnl_rate)
print("strike rate:=",stk_rate )
if pnl_rate >= 10 and stk_rate >= 0.55:
print("passed in backtesting pnL is ")
print("TRUE !! so going to place order of ",ttradingsymbol)
try:
# finding quantity
max_trade=5
psf=qu/max_trade
check_list_found=(check_list[check_list["symbls"].str.contains(ttradingsymbol)==True])[["margin","symbls"]]
fund_margin=float(check_list_found.iloc[-1,0])
amount_total = psf*fund_margin
kk=int ( amount_total/ (avv.iloc[-1,2]) )
print("total stock :=", kk)
if kk>=1:
xa=float(avv.iloc[-1,2]+(avv.iloc[-1,6]*.25))
cur_high=avg_ha(xa)
xb=cur_high+float(avv.iloc[-1,6])*0.1
lim_price=avg_ha(xb)
print("\n")
display(avv.tail(1))
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.ltp(ttoken))
#kite_drop=1000000001
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=kk,exchange=eexchange,order_type='SL',price=lim_price ,trigger_price=cur_high,transaction_type='BUY',product=productt,tag="advgu")
print("buy order id is ",kite_drop)
display(strftime("%a, %d %b %Y %I:%M:%S %p"))
print("\n")
buy_bid_new.append(ttradingsymbol)
else:
print("quantity error")
pass
except:
# order
print("Error in Trigger_price and Price !! ")
ltpp=pd.DataFrame(kite.ltp(ttoken)).iloc[-1,0]
cur_high=avg_ha(ltpp)
xb=cur_high+float(gup.iloc[-1,6])*0.25
lim_price=avg_ha(xb)
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.ltp(ttoken))
#kite_drop=1000000001
kite.place_order(variety="regular",tradingsymbol=ttradingsymbol,quantity=kk,exchange=eexchange,order_type='SL',price=lim_price ,trigger_price=cur_high,transaction_type='BUY',product=productt,tag="advgu")
print("buy order id is ",kite_drop)
display(strftime("%a, %d %b %Y %I:%M:%S %p"))
print("\n")
buy_bid_new.append(ttradingsymbol)
pass
else:
print("fail in backtestig")
fail_buy_backtest.append(ttradingsymbol)
pass
##################################
if "red" in gup.iloc[-1,5]:
print("SELL stock found")
ttradingsymbol=ss[a]
ttoken=tokenall[a]
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 := ", ttradingsymbol)
print(" SORRY !! This order already exists \n so not going to place this order !! ")
ignores_sell.append(ttradingsymbol)
else:
print("checking backtesting")
print("\n Data fetched ! compressing Data for backtesting ...")
########################
strike_rate = 0
strike_rate_list = []
timeframe = []
timeframe_list = []
pnl = 0
pnl_list = []
for t in range(1,7):
dfw=kite.historical_data(ttoken,sdate_backtest,todate_backtest,"minute",0)
dfw=pd.DataFrame(dfw)
df=pd.DataFrame(dfw[['date','open','high','low','close']])
df= time_con(time_frame_bktst,df)
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)
df['bid_value'] = 0
df['executed_value'] = 0
df['diff'] = 0
df['pnl'] = 0
qty = int ( qu/ (gup.iloc[-1,2]) )
var = False
var_2 = False
var_1 = False
#*************************
for i in range(len(df)):
zz=df.loc[i,"date"]
za=str(zz)[11:13]
if za=="15":
if var==True and var1==True:
df.loc[i,'diff'] = bid_value - df.loc[i,'close']
df.loc[i,'pnl'] = ( bid_value /bid_value)*df.loc[i,'diff']
##
var=False
var1=False
if var==True and var1==False:
var=False
var_2=True
if za!="15":
var_2=False
if var==False and var_2==False:
if df.loc[i,'arrow_color']=='red':
bid_value = (df.loc[i,'low']-df.loc[i,'ATR']*.25)-(df.loc[i,'ATR']*.1)
df.loc[i,'bid_value'] =bid_value
var=True
var1=False
if var==True and var1==False and var_2==False:
if df.loc[i,'high'] > bid_value and df.loc[i,'low'] < bid_value:
df.loc[i,'executed_value']=bid_value
var1=True
if var==True and var1==True and var_2==False:
if df.loc[i,'arrow_color']=='green':
df.loc[i,'diff'] = bid_value - df.loc[i,'close']
df.loc[i,'pnl'] = ( bid_value /bid_value)*df.loc[i,'diff']
var=False
var1=False
#***********************
list_1 = df['pnl']
pos_count = len(list(filter(lambda x: (x>0),list_1)))
neg_count = len(list(filter(lambda x: (x<0),list_1)))
total_trade = pos_count + neg_count
try :
strike_rate = pos_count/total_trade
except:
strike_rate = 0
#print(strike_rate)
pnl = df['pnl'].sum()
pnl_list.append(pnl)
timeframe_list.append(t)
strike_rate_list.append(strike_rate)
main_df = pd.DataFrame({'timeframe':timeframe_list,'pnl':pnl_list,'strike_rate':strike_rate_list})
s=f"{ttradingsymbol}.csv"
main_df.to_csv(s)
#file
st=ttradingsymbol+"_pnl_sheet"
s=f"{st}.csv"
df.to_csv(s)
print("complete")
pnl_rate=main_df.loc[back_test_timeframe,'pnl'] #10
stk_rate=main_df.loc[back_test_timeframe,'strike_rate'] #.5
print("PnL :=",pnl_rate)
print("strike rate :=",stk_rate )
if pnl_rate >= 10 and stk_rate >= 0.55:
print("pass in backtest")
print("TRUE !! so going to place order of ", ttradingsymbol)
try:
##
# finding quantity
max_trade=5
psf=qu/max_trade
check_list_found=(check_list[check_list["symbls"].str.contains(ttradingsymbol)==True])[["margin","symbls"]]
fund_margin=float(check_list_found.iloc[-1,0])
amount_total = psf*fund_margin
kk=int ( amount_total/ (avv.iloc[-1,2]) )
print("total stock :=", kk)
##
if kk>=1:
xa=float(avv.iloc[-1,3]-avv.iloc[-1,6]*.25)
price_sell=avg_ha(xa) # TRIGER PRICE (LARGE)
xb=price_sell-float(avv.iloc[-1,6])*0.1
triger_sell=avg_ha(xb) #PRICE (SMALL)
print("\n")
display(avv.tail(1))
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.ltp(ttoken))
#kite_drop=111111
kite_drop=kite.place_order(order_type='SL',price= triger_sell,trigger_price=price_sell,variety="regular",tradingsymbol=ttradingsymbol,quantity=kk,exchange=eexchange,transaction_type='SELL',product=productt,tag="advgu")
print("SELL order id is ",kite_drop)
display(strftime("%a, %d %b %Y %I:%M:%S %p"))
print("\n")
sell_bid_new.append(ttradingsymbol)
else:
print("error in order quantity low")
pass
except:
print("Error in Prices and Triger_price ")
ltpp=pd.DataFrame(kite.ltp(ttoken)).iloc[-1,0]
price_sell=avg_ha(ltpp) # TRIGER PRICE (LARGE)
xb=price_sell-float(gup.iloc[-1,6])*0.25
triger_sell=avg_ha(xb) #PRICE (SMALL)
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.ltp(ttoken))
#kite_drop=111111
kite_drop=kite.place_order(order_type='SL',price= triger_sell,trigger_price=price_sell,variety="regular",tradingsymbol=ttradingsymbol,quantity=kk,exchange=eexchange,transaction_type='SELL',product=productt,tag="advgu")
print("SELL order id is ",kite_drop)
display(strftime("%a, %d %b %Y %I:%M:%S %p"))
print("\n")
sell_bid_new.append(ttradingsymbol)
pass
else:
print("fail in backtesting")
fail_sell_backtest.append(ttradingsymbol)
a=a+1
if a==len(tokenall):
print(" SCAN COMPLETED !!")
display(ks)
display(strftime("%a, %d %b %Y %I:%M:%S %p"))
print("BUY bid placed =", buy_bid_new)
print("Sell bid placed =", sell_bid_new)
print("\n")
print("BUY fail in backtesting =", fail_buy_backtest)
print("SELL fail in backtesting =", fail_sell_backtest)
print("\n")
print("BUY token ignored 'already placed' =", ignores_buy)
print("SELL token ignored 'already placed'=", ignores_sell)
print("\n")
print("Code Completed ")
a=0
break
ashi()
Output –
nifty or fno ?nifty
enter timeframe 1/2/3/55
enter days of backtesting7
2019-08-23
2019-08-30
enter amount ???500000
SCANNING START
Getting All tokens for processing BUY SELL
Now checking Condition of BUY sell of GUPPY
'Sun, 01 Sep 2019 12:51:14 AM'
5
ZERODHA ADVANCE BOT
Current candel checking no 0
Current Colour on this token is none
Current Symbol ADANIPORTS
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 1
Current Colour on this token is none
Current Symbol ASIANPAINT
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 2
Current Colour on this token is none
Current Symbol AXISBANK
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 3
Current Colour on this token is none
Current Symbol BAJAJ-AUTO
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 4
Current Colour on this token is none
Current Symbol BAJFINANCE
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 5
Current Colour on this token is none
Current Symbol BAJAJFINSV
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 6
Current Colour on this token is none
Current Symbol BPCL
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 7
Current Colour on this token is none
Current Symbol BHARTIARTL
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 8
Current Colour on this token is none
Current Symbol INFRATEL
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 9
Current Colour on this token is none
Current Symbol BRITANNIA
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 10
Current Colour on this token is none
Current Symbol CIPLA
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 11
Current Colour on this token is none
Current Symbol COALINDIA
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 12
Current Colour on this token is none
Current Symbol DRREDDY
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 13
Current Colour on this token is none
Current Symbol EICHERMOT
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 14
Current Colour on this token is none
Current Symbol GAIL
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 15
Current Colour on this token is none
Current Symbol GRASIM
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 16
Current Colour on this token is none
Current Symbol HCLTECH
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 17
Current Colour on this token is none
Current Symbol HDFCBANK
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 18
Current Colour on this token is none
Current Symbol HEROMOTOCO
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 19
Current Colour on this token is green
Current Symbol HINDALCO
Data fetched ! compressing Data for indicator ...
BUY stock found
5
ZERODHA ADVANCE BOT
Current candel checking no 20
Current Colour on this token is none
Current Symbol HINDUNILVR
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 21
Current Colour on this token is none
Current Symbol HDFC
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 22
Current Colour on this token is none
Current Symbol ICICIBANK
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 23
Current Colour on this token is none
Current Symbol ITC
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 24
Current Colour on this token is none
Current Symbol IBULHSGFIN
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 25
Current Colour on this token is none
Current Symbol IOC
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 26
Current Colour on this token is none
Current Symbol INDUSINDBK
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 27
Current Colour on this token is none
Current Symbol INFY
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 28
Current Colour on this token is none
Current Symbol JSWSTEEL
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 29
Current Colour on this token is none
Current Symbol KOTAKBANK
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 30
Current Colour on this token is none
Current Symbol LT
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 31
Current Colour on this token is none
Current Symbol M&M
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 32
Current Colour on this token is none
Current Symbol MARUTI
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 33
Current Colour on this token is none
Current Symbol NTPC
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 34
Current Colour on this token is none
Current Symbol ONGC
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 35
Current Colour on this token is green
Current Symbol POWERGRID
Data fetched ! compressing Data for indicator ...
BUY stock found
5
ZERODHA ADVANCE BOT
Current candel checking no 36
Current Colour on this token is none
Current Symbol RELIANCE
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 37
Current Colour on this token is none
Current Symbol SBIN
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 38
Current Colour on this token is none
Current Symbol SUNPHARMA
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 39
Current Colour on this token is none
Current Symbol TCS
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 40
Current Colour on this token is none
Current Symbol TATAMOTORS
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 41
Current Colour on this token is none
Current Symbol TATASTEEL
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 42
Current Colour on this token is none
Current Symbol TECHM
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 43
Current Colour on this token is none
Current Symbol TITAN
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 44
Current Colour on this token is none
Current Symbol UPL
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 45
Current Colour on this token is none
Current Symbol ULTRACEMCO
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 46
Current Colour on this token is none
Current Symbol VEDL
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 47
Current Colour on this token is none
Current Symbol WIPRO
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 48
Current Colour on this token is none
Current Symbol YESBANK
Data fetched ! compressing Data for indicator ...
5
ZERODHA ADVANCE BOT
Current candel checking no 49
Current Colour on this token is none
Current Symbol ZEEL
Data fetched ! compressing Data for indicator ...
SCAN COMPLETED !!
'Sun, 01 Sep 2019 12:51:14 AM'
'Sun, 01 Sep 2019 12:52:02 AM'
BUY bid placed = []
Sell bid placed = []
BUY fail in backtesting = []
SELL fail in backtesting = []
BUY token ignored 'already placed' = []
SELL token ignored 'already placed'= []
Code Completed
This multi-stock trading capability, combined with its persistent monitoring of the stock list, sets this bot apart from earlier versions, establishing a new benchmark.