import datetime
import matplotlib.pylab as pl
import numpy as np
from scipy import stats

def load_data(path_in):

    f = open(path_in)
    lines = f.readlines()
    f.close()

    date_list = []
    open_list = []
    high_list = []
    low_list = []
    close_list = []
    volume_list = []

    for line in lines[1:]:
        date, open_data, high, low, close, volume, dividends, splits = line.split(',')

        date_list.append(date)
        open_list.append(float(open_data))
        high_list.append(float(high))
        low_list.append(float(low))
        close_list.append(float(close))
        volume_list.append(float(volume))

    return [date_list, open_list, high_list, low_list, close_list, volume_list]


def get_mean_gap_p(high, low):

    gap_p = []

    try:

        count_gap = 0
        count_not_gap = 0
        for i in range(high.shape[0])[:-1]:

            gap_found = False
            if high[i+1] < low[i] and low[i+1] < low[i]:

                count_gap += 1
                gap_found = True

            if high[i+1] > high[i] and low[i+1] > high[i]:

                count_gap += 1
                gap_found = True

            if not gap_found:

                count_not_gap += 1

        if count_gap+count_not_gap > 0:
            gap_p.append(count_gap/(count_gap+count_not_gap)*100)

    except:
        pass

    return np.mean(gap_p)

f, (ax1) = pl.subplots(1, 1)

from os import listdir
from os.path import isfile, join
onlyfiles = [f for f in listdir('./data') if isfile(join('./data', f))]
res = []
for file in onlyfiles:

    name = file.split('_')[0]
    shares_out = float(file.split('_')[1].split('.')[0])
    path = './data/' + file
    try:

        date_list, open_list, high_list, low_list, close_list, volume_list = load_data(path)

        #shares_out = get_ticker_sharesOutstanding(name)

        open_mean = np.nanmean(open_list)

        market_cap = np.array(open_list) * shares_out
        market_cap_mean = np.nanmean(market_cap)

        candle_p = (np.array(close_list) - np.array(open_list))/np.array(open_list)
        candle_p_mean = np.nanmean(candle_p)
        candle_p_std = np.std(candle_p)
        candle_std = np.std(np.array(high_list) - np.array(low_list))

        volume_mean = np.nanmean(volume_list)
        volume_std = np.std(volume_list)

        volume_mean_dollar = np.nanmean(np.array(volume_list) * np.array(open_list))
        volume_std_dollar = np.nanmean(np.array(volume_list) * np.array(open_list))

        gap_p = get_mean_gap_p(np.array(high_list), np.array(low_list))

        print(name, shares_out, market_cap_mean, open_mean, candle_std, candle_p_mean, candle_p_std, volume_mean, volume_std)
        res.append([name, shares_out, market_cap_mean, open_mean, candle_std, candle_p_mean, candle_p_std, volume_mean, volume_std, volume_mean_dollar, volume_std_dollar, gap_p])


    except Exception as err:
        print(err)



import csv

with open('./GME_Russel.csv', 'w', encoding='UTF8', newline='') as f:

    writer = csv.writer(f)
    writer.writerow(['name', 'shares_out', 'market_cap_mean', 'open_mean', 'candle_std', 'candle_p_mean', 'candle_p_std', 'volume_mean', 'volume_std', 'volume_mean_dollar', 'volume_std_dollar', 'gap_p'])

    for row in res:
        writer.writerow(row)