PatternRecognition.py

'''Spotting candlestick chart pattern collectively for all stocks automatically. Formulas coded manually.'''

import csv

import numpy
from itertools import zip_longest

def is_bullish_candlestick(candle):
    return float(candle['Close']>candle['Open'])

def is_bearish_candlestick(candle):
    return float(candle['Close']<candle['Open'])

def is_bullish_engulfing(candles,index):
    current_day=candles[index]
    previous_day=candles[index-1]
    
    if is_bearish_candlestick(previous_day) \
        and float(current_day['High'])>float(previous_day['Close']) \
        and float(current_day['Close'])>float(previous_day['Open']) \
        and float(current_day['Open'])<float(previous_day['Close']):
        return True

    return False

def is_bearish_engulfing(candles,index):
    current_day=candles[index]
    previous_day=candles[index-1]
    
    if is_bullish_candlestick(previous_day) \
        and float(current_day['Close'])<float(previous_day['Low'])\
        and float(current_day['Open'])>float(previous_day['High']):
        return True

    return False

def is_gravestone_doji(candles,index):
    current_day=candles[index]
    previous_day=candles[index-1]

    if is_bullish_candlestick(previous_day) \
        and float(current_day['High'])>float(previous_day['High'])\
        and float(current_day['Open'])>float(previous_day['Close'])\
        and float(current_day['Open'])==float(current_day['Low']) \
        and float(current_day['Close'])<=float(current_day['Open']):
        return True

    return False

def pattern_recognition(candles,index):
    current_day=candles[index]

    open=current_day['Open']
    high=current_day['High']
    low=current_day['Low']
    close=current_day['Close']

    open=numpy.array(open,dtype=float)
    high=numpy.array(high,dtype=float)
    low=numpy.array(low,dtype=float)
    close=numpy.array(close,dtype=float)
    

symbols_file=open("Auto generated Dataset\Tickers.csv")
tickers=csv.reader(symbols_file)

for company in tickers:
    #print(company)

    ticker=company[0]

    history_file=open("Auto generated Dataset\{}.csv".format(ticker))

    reader=csv.DictReader(history_file)
    candles=list(reader)

    candles=candles[-2:]

    if len(candles)>1:
        if is_bullish_engulfing(candles,1):
            print("{} = {} is bullish engulfing".format(ticker,candles[1]['Date']))
            print("------------------------------------------------")
            bullishengulf=[]
            datee=[]
            bullishengulf.append("{}".format(ticker))
            datee.append("{}".format(candles[1]['Date']))

        if is_bearish_engulfing(candles,1):
            print("{} = {} is bearish engulfing".format(ticker,candles[1]['Date']))
            print("------------------------------------------------")
            bearishengulf=[]
            dateee=[]
            bearishengulf.append("{}".format(ticker))
            dateee.append("{}".format(candles[1]['Date']))

        if is_gravestone_doji(candles,1):
            print("{} = {} is gravestone doji".format(ticker,candles[1]['Date']))
            print("------------------------------------------------")
            gravestone=[]
            dateeee=[]
            gravestone.append("{}".format(ticker))
            dateeee.append("{}".format(candles[1]['Date']))

        pattern_recognition(candles,1)
    
list_clubber=[bullishengulf,datee]
export_data_complete=zip_longest(*list_clubber,fillvalue='')

with open("Auto generated Dataset\BullishEngulfing.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
    wr=csv.writer(myfile)
    wr.writerow(("Ticker","Date Of Formation"))
    wr.writerows(export_data_complete)

list_clubberr=[bearishengulf,dateee]
export_data_completee=zip_longest(*list_clubberr,fillvalue='')

with open("Auto generated Dataset\BearishEngulfing.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
    wr=csv.writer(myfile)
    wr.writerow(("Ticker","Date Of Formation"))
    wr.writerows(export_data_completee)

list_clubberrr=[gravestone,dateeee]
export_data_completeee=zip_longest(*list_clubberrr,fillvalue='')

with open("Auto generated Dataset\Gravestone.csv",'w',encoding="ISO-8859-1",newline="") as myfile:
    wr=csv.writer(myfile)
    wr.writerow(("Ticker","Date Of Formation"))
    wr.writerows(export_data_completeee)