import cProfile
import pstats
from SQL_new import SQLite as Sql_new
from test_py import tp_sl_loop_py
from test_cy import tp_sl_loop
import pandas as pd
import numpy as np
import traceback
import timeit

lenght_array = 100
columns = ['Open_Time', 'Open_Price', 'High_Price', 'Low_Price', 'Close_Price', 'Volume', 'Color']
pair="asset name"
directory="Directory to the DataBase"

def find_min_max(df):

    df['Percent_High'] = df['High_Price'] / df['Open_Price']
    df['Percent_Low'] = df['Low_Price'] / df['Open_Price']

    df_high_max = (df['Percent_High'].max() - 1) * 100
    df_low_max = abs((1 - df['Percent_Low'].min())) * 100

    return df_high_max, df_low_max


def call_py():
    np.set_printoptions(suppress=True)

    db = Sql_new(directory)
    data = pd.DataFrame(data=db.get_data(), columns=columns)
    data = data[['Open_Price', 'High_Price', 'Low_Price', 'Close_Price']]

    data = data.replace(to_replace='NEUTRAL', value='RED')
    data = data.replace(to_replace='RED', value='r')
    data = data.replace(to_replace='GREEN', value='g')

    high_max, low_max = find_min_max(data)
    data = data[['Open_Price', 'High_Price', 'Low_Price', 'Close_Price']]

    # print(f'High MAX: {high_max}, Low MAX: {low_max}')

    tp_array = np.linspace(0, (high_max + 2 * (high_max / lenght_array)), lenght_array)
    sl_array = np.linspace(0, (low_max + 2 * (low_max / lenght_array)), lenght_array)

    tp_sl_mash = np.array(np.meshgrid(tp_array, sl_array)).T.reshape(-1, 2)

    X, Y, Z, tp_sl_list = tp_sl_loop_py(data.to_numpy(), tp_sl_mash, pair)

    tp_sl_list = np.array(tp_sl_list)
    tp_sl_list = tp_sl_list[tp_sl_list[:, 2].argsort()]
    tp_sl_list = tp_sl_list[::-1]

    # print(f'Top 10: \n{tp_sl_list[0:11]}\n')
    # print(f'Max: {tp_sl_list[-0]}')

    max_index = Z.index(max(Z))

    from_tp, to_tp = (X[(max_index - lenght_array)] / 100), (X[(max_index + lenght_array)] / 100)
    from_sl, to_sl = (Y[(max_index - 1)] / 100), (Y[(max_index + 1)] / 100)

    tp_array_2 = np.linspace(start=from_tp, stop=to_tp, num=lenght_array)
    sl_array_2 = np.linspace(start=from_sl, stop=to_sl, num=lenght_array)

    tp_sl_mash_2 = np.array(np.meshgrid(tp_array_2, sl_array_2)).T.reshape(-1, 2)

    X_2, Y_2, Z_2, tp_sl_list_2 = tp_sl_loop_py(data.to_numpy(), tp_sl_mash_2, pair)

    tp_sl_list_2 = np.array(tp_sl_list_2)
    tp_sl_list_2 = tp_sl_list_2[tp_sl_list_2[:, 2].argsort()]
    tp_sl_list_2 = tp_sl_list_2[::-1]
    tp_finale, sl_finale, balance = tp_sl_list_2[0]

    print(f'Top 10: \n{tp_sl_list_2[0:11]}\n')
    print(f'Max: {tp_sl_list_2[-0]}')
    print(f'TP_Finale: {tp_finale}; SL_Finale: {sl_finale}')


def call_cy():
    np.set_printoptions(suppress=True)

    db = Sql_new(directory)
    data = pd.DataFrame(data=db.get_data(), columns=columns)
    data = data[['Open_Price', 'High_Price', 'Low_Price', 'Close_Price']]

    data = data.replace(to_replace='NEUTRAL', value='RED')
    data = data.replace(to_replace='RED', value='r')
    data = data.replace(to_replace='GREEN', value='g')

    high_max, low_max = find_min_max(data)
    data = data[['Open_Price', 'High_Price', 'Low_Price', 'Close_Price']]

    tp_array = np.linspace(0, (high_max + 2 * (high_max / lenght_array)), lenght_array)
    sl_array = np.linspace(0, (low_max + 2 * (low_max / lenght_array)), lenght_array)

    tp_sl_mash = np.array(np.meshgrid(tp_array, sl_array)).T.reshape(-1, 2)

    X, Y, Z, tp_sl_list = tp_sl_loop(data.to_numpy(), tp_sl_mash, pair)

    X, Y, Z = X.tolist(), Y.tolist(), Z.tolist()

    tp_sl_list = np.array(tp_sl_list)
    tp_sl_list = tp_sl_list[tp_sl_list[:, 2].argsort()]
    tp_sl_list = tp_sl_list[::-1]

    # print(f'Top 10: \n{tp_sl_list[0:11]}\n')
    # print(f'Max: {tp_sl_list[-0]}')

    max_index = Z.index(max(Z))

    from_tp, to_tp = (X[(max_index - lenght_array)] / 100), (X[(max_index + lenght_array)] / 100)
    from_sl, to_sl = (Y[(max_index - 1)] / 100), (Y[(max_index + 1)] / 100)

    tp_array_2 = np.linspace(start=from_tp, stop=to_tp, num=lenght_array)
    sl_array_2 = np.linspace(start=from_sl, stop=to_sl, num=lenght_array)

    tp_sl_mash_2 = np.array(np.meshgrid(tp_array_2, sl_array_2)).T.reshape(-1, 2)

    X_2, Y_2, Z_2, tp_sl_list_2 = tp_sl_loop(data.to_numpy(), tp_sl_mash_2, pair)

    tp_sl_list_2 = np.array(tp_sl_list_2)
    tp_sl_list_2 = tp_sl_list_2[tp_sl_list_2[:, 2].argsort()]
    tp_sl_list_2 = tp_sl_list_2[::-1]

    tp_finale, sl_finale, balance = tp_sl_list_2[0]

    print(f'Top 10: \n{tp_sl_list_2[0:11]}\n')
    print(f'Max: {tp_sl_list_2[-0]}')
    print(f'TP_Finale: {tp_finale}; SL_Finale: {sl_finale}')


def main():

    cy = timeit.timeit(f'call_cy()', "from __main__ import call_cy", number=100)
    py = timeit.timeit(f'call_py()', "from __main__ import call_py", number=1)
    cy /= 100

    print('\n\n')
    print(f"Cython: {'{:.5f}'.format(round(cy, 10))}s")
    print(f"Python: {'{:.5f}'.format(round(py, 10))}s")

    print(f'Cython is {round(py/cy, 2)}x faster')


with cProfile.Profile() as pr:
    try:
        main()

    except (KeyboardInterrupt, OverflowError):
        traceback.print_exc()

stats = pstats.Stats(pr)
stats.sort_stats(pstats.SortKey.TIME)
stats.dump_stats(filename='STATS.prof')