meanrev_algo.py

import pandas as pd
import numpy as np
import time as t
import datetime
from datetime import datetime
from datetime import timedelta
import talib

t0 = t.time()


symbols =['']


long_period = 120
short_period = 5
stocks = []

def converter(start_date):
    convert=datetime.strptime(start_date, "%Y-%m-%d")
    return convert

def delta_time(converter,n_days):
    new_date = converter + timedelta(days=n_days)
    return new_date


def data(symbol):
    dates=pd.date_range(start_date,end_date) 
    df=pd.DataFrame(index=dates)
    df_temp=pd.read_csv('/enter_path_to_data/{}.csv'.format(str(symbol)),usecols=['Date','Close','Low','Open','High','Volume'],
                            parse_dates=True,index_col='Date',na_values=['nan'])
    df=df.join(df_temp)
    df=df.fillna(method='ffill')
    df=df.fillna(method='bfill')
    return df

def close_price(df):
    close = df['Close']
    close = close.as_matrix()
    float_close=[float(x) for x in close]
    np_float_close = np.array(float_close)
    return np_float_close

def sma_close(closeprice,time_period):
    smaclose = talib.SMA(closeprice,timeperiod = time_period)
    current_smaclose = smaclose[-1]
    return current_smaclose
    
start_date = ''
end_date = ''
start_date = converter(start_date)
end_date = converter(end_date)

for symbol in symbols:
    
    #Get data
    df = data(symbol)


    #Arranging Data
    closeprice = close_price(df)

    #Calculating current close
    current_close = closeprice[-1]

    #Long run average
    long_average = sma_close(closeprice,long_period)
    

    #Short run average
    short_average = sma_close(closeprice, short_period)
    

    if current_close > long_average:
        if current_close <  short_average:
            stocks.append(symbol)
            
print(stocks)
t1 =t.time()
print('Exec time is ', t1-t0)