import numpy as np
import pandas as pd
from pandas import DataFrame
from freqtrade.strategy.interface import IStrategy
from sqlalchemy import create_engine
import sqlite3
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib
import os
import pathlib
import time
from sqlite3 import Error
from keras.models import load_model


class github_simketejong_NeuroBot__BotE__20210208_160903(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 0.0739, '29': 0.0572, '62': 0.01108, '84': 0}
    stoploss = -1.1
    trailing_stop = True
    trailing_stop_positive = 0.02543
    trailing_stop_positive_offset = 0.08718
    trailing_only_offset_is_reached = True
    timeframe = '5m'
    process_only_new_candles = False
    use_exit_signal = True
    exit_profit_only = True
    ignore_roi_if_entry_signal = False
    startup_candle_count: int = 30
    order_types = {'entry': 'limit', 'exit': 'limit', 'stoploss': 'market',
        'stoploss_on_exchange': False}
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
    plot_config = {'main_plot': {'tema': {}, 'sar': {'color': 'white'}},
        'subplots': {'MACD': {'macd': {'color': 'blue'}, 'macdsignal': {
        'color': 'orange'}}, 'RSI': {'rsi': {'color': 'red'}}}}

    def informative_pairs(self):
        return []

    def populate_indicators(self, dataframe: DataFrame, metadata: dict
        ) ->DataFrame:
        dataframe['adx'] = ta.ADX(dataframe)
        dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
        dataframe['plus_di'] = ta.PLUS_DI(dataframe)
        dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
        dataframe['minus_di'] = ta.MINUS_DI(dataframe)
        aroon = ta.AROON(dataframe)
        dataframe['aroonup'] = aroon['aroonup']
        dataframe['aroondown'] = aroon['aroondown']
        dataframe['aroonosc'] = ta.AROONOSC(dataframe)
        dataframe['ao'] = qtpylib.awesome_oscillator(dataframe)
        keltner = qtpylib.keltner_channel(dataframe)
        dataframe['kc_upperband'] = keltner['upper']
        dataframe['kc_lowerband'] = keltner['lower']
        dataframe['kc_middleband'] = keltner['mid']
        dataframe['kc_percent'] = (dataframe['close'] - dataframe[
            'kc_lowerband']) / (dataframe['kc_upperband'] - dataframe[
            'kc_lowerband'])
        dataframe['kc_width'] = (dataframe['kc_upperband'] - dataframe[
            'kc_lowerband']) / dataframe['kc_middleband']
        dataframe['uo'] = ta.ULTOSC(dataframe)
        dataframe['cci'] = ta.CCI(dataframe)
        dataframe['rsi'] = ta.RSI(dataframe)
        rsi = 0.1 * (dataframe['rsi'] - 50)
        dataframe['fisher_rsi'] = (np.exp(2 * rsi) - 1) / (np.exp(2 * rsi) + 1)
        dataframe['fisher_rsi_norma'] = 50 * (dataframe['fisher_rsi'] + 1)
        stoch = ta.STOCH(dataframe)
        dataframe['slowd'] = stoch['slowd']
        dataframe['slowk'] = stoch['slowk']
        stoch_fast = ta.STOCHF(dataframe)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        stoch_rsi = ta.STOCHRSI(dataframe)
        dataframe['fastd_rsi'] = stoch_rsi['fastd']
        dataframe['fastk_rsi'] = stoch_rsi['fastk']
        macd = ta.MACD(dataframe)
        dataframe['macd'] = macd['macd']
        dataframe['macdsignal'] = macd['macdsignal']
        dataframe['macdhist'] = macd['macdhist']
        dataframe['mfi'] = ta.MFI(dataframe)
        dataframe['roc'] = ta.ROC(dataframe)
        bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe
            ), window=20, stds=2)
        dataframe['bb_lowerband'] = bollinger['lower']
        dataframe['bb_middleband'] = bollinger['mid']
        dataframe['bb_upperband'] = bollinger['upper']
        dataframe['bb_percent'] = (dataframe['close'] - dataframe[
            'bb_lowerband']) / (dataframe['bb_upperband'] - dataframe[
            'bb_lowerband'])
        dataframe['bb_width'] = (dataframe['bb_upperband'] - dataframe[
            'bb_lowerband']) / dataframe['bb_middleband']
        weighted_bollinger = qtpylib.weighted_bollinger_bands(qtpylib.
            typical_price(dataframe), window=20, stds=2)
        dataframe['wbb_upperband'] = weighted_bollinger['upper']
        dataframe['wbb_lowerband'] = weighted_bollinger['lower']
        dataframe['wbb_middleband'] = weighted_bollinger['mid']
        dataframe['wbb_percent'] = (dataframe['close'] - dataframe[
            'wbb_lowerband']) / (dataframe['wbb_upperband'] - dataframe[
            'wbb_lowerband'])
        dataframe['wbb_width'] = (dataframe['wbb_upperband'] - dataframe[
            'wbb_lowerband']) / dataframe['wbb_middleband']
        dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
        dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
        dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
        dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
        dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
        dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
        dataframe['ema150'] = ta.EMA(dataframe, timeperiod=150)
        dataframe['ema200'] = ta.EMA(dataframe, timeperiod=200)
        dataframe['ema250'] = ta.EMA(dataframe, timeperiod=250)
        dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
        dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
        dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
        dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
        dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
        dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
        dataframe['sma150'] = ta.SMA(dataframe, timeperiod=150)
        dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
        dataframe['sma250'] = ta.SMA(dataframe, timeperiod=250)
        dataframe['sar'] = ta.SAR(dataframe)
        dataframe['tema'] = ta.TEMA(dataframe, timeperiod=9)
        hilbert = ta.HT_SINE(dataframe)
        dataframe['htsine'] = hilbert['sine']
        dataframe['htleadsine'] = hilbert['leadsine']
        dataframe['CDLHAMMER'] = ta.CDLHAMMER(dataframe)
        dataframe['CDLINVERTEDHAMMER'] = ta.CDLINVERTEDHAMMER(dataframe)
        dataframe['CDLDRAGONFLYDOJI'] = ta.CDLDRAGONFLYDOJI(dataframe)
        dataframe['CDLPIERCING'] = ta.CDLPIERCING(dataframe)
        dataframe['CDLMORNINGSTAR'] = ta.CDLMORNINGSTAR(dataframe)
        dataframe['CDL3WHITESOLDIERS'] = ta.CDL3WHITESOLDIERS(dataframe)
        dataframe['CDLHANGINGMAN'] = ta.CDLHANGINGMAN(dataframe)
        dataframe['CDLSHOOTINGSTAR'] = ta.CDLSHOOTINGSTAR(dataframe)
        dataframe['CDLGRAVESTONEDOJI'] = ta.CDLGRAVESTONEDOJI(dataframe)
        dataframe['CDLDARKCLOUDCOVER'] = ta.CDLDARKCLOUDCOVER(dataframe)
        dataframe['CDLEVENINGDOJISTAR'] = ta.CDLEVENINGDOJISTAR(dataframe)
        dataframe['CDLEVENINGSTAR'] = ta.CDLEVENINGSTAR(dataframe)
        dataframe['CDL3LINESTRIKE'] = ta.CDL3LINESTRIKE(dataframe)
        dataframe['CDLSPINNINGTOP'] = ta.CDLSPINNINGTOP(dataframe)
        dataframe['CDLENGULFING'] = ta.CDLENGULFING(dataframe)
        dataframe['CDLHARAMI'] = ta.CDLHARAMI(dataframe)
        dataframe['CDL3OUTSIDE'] = ta.CDL3OUTSIDE(dataframe)
        dataframe['CDL3INSIDE'] = ta.CDL3INSIDE(dataframe)
        heikinashi = qtpylib.heikinashi(dataframe)
        dataframe['ha_open'] = heikinashi['open']
        dataframe['ha_close'] = heikinashi['close']
        dataframe['ha_high'] = heikinashi['high']
        dataframe['ha_low'] = heikinashi['low']
        #print(str(metadata))
        Table = str(metadata['pair'])
        #print(Table)
        Table = Table.replace('/', '_')
        engine = create_engine('sqlite:///Neural.sqlite', echo=True)
        sqlite_connection = engine.connect()
        engine.execute('DROP TABLE IF EXISTS ' + Table)
        sqlite_table = Table
        normed_df = (dataframe - dataframe.min()) / (dataframe.max() -
            dataframe.min())
        normed_df['date'] = dataframe['date'].values
        normed_df['open'] = dataframe['open'].values
        normed_df.to_sql(sqlite_table, sqlite_connection, if_exists='fail')
        sqlite_connection.close()
        #print('database closed')
        NeuralDf = normed_df.drop(columns=['date', 'open'])
        df2 = NeuralDf
        WEIGHTS = Table + 'Weight'
        files0 = pathlib.Path('/tmp/' + WEIGHTS + '_model.h5')
        if os.path.exists('/tmp/' + WEIGHTS + '_wait'):
            time.sleep(11)
            os.remove('/tmp/' + WEIGHTS + '_wait')
        if files0.exists():
            model = load_model(files0)
            files0 = pathlib.Path('/tmp/' + WEIGHTS + '.h5')
            if files0.exists():
                model.load_weights(files0)
            train = df2.replace(np.nan, 0.0)
            l2 = model.predict(train).round()
            column_values = ['b', 's']
            df5 = pd.DataFrame(data=l2, columns=column_values)
            dataframe['s'] = df5['s']
            dataframe['b'] = df5['b']
        else:
            dataframe['s'] = 0.0
            dataframe['b'] = 0.0
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict
        ) ->DataFrame:
        dataframe.loc[(dataframe['b'] > 0.7) & (dataframe['s'] < 0.3) & (
            dataframe['volume'] > 0), 'enter_long'] = 1
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict
        ) ->DataFrame:
        dataframe.loc[(dataframe['s'] > 0.7) & (dataframe['b'] < 0.3) & (
            dataframe['volume'] > 0), 'exit_long'] = 1
        return dataframe