# source: https://raw.githubusercontent.com/juanborssotto/trading/1c6c8d4f30e6c5c301243a8dc52bffff47cf1d83/freqtrade/user_data/strategies/VWAPAlarm.py
# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from pandas import DataFrame, Series
from datetime import datetime, timedelta
import os

import numpy as np

from freqtrade.rpc import RPCMessageType
from beepy import beep
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.util import resample_to_interval


def calculate_distance_percentage(current_price: float, green_line_price: float) -> float:
    distance = abs(current_price - green_line_price)
    return distance * 100 / current_price


def get_symbol_from_pair(pair: str) -> str:
    return pair.split('/')[0]


class github_juanborssotto_trading__VWAPAlarm__20210902_150637(IStrategy):
    minimal_roi = {
        "0": 10
    }

    # Optimal stoploss designed for the strategy
    stoploss = -0.99

    # Optimal timeframe for the strategy
    timeframe = '3m'
    process_only_new_candles = True

    alarm_emitted = dict()

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        pair = metadata["pair"]
        if pair not in self.alarm_emitted:
            self.alarm_emitted[pair] = False
        # ticker = self.dp.ticker(pair)
        # ongoing_close = ticker['last']
        # ongoing_volume = float(ticker["info"]["volume"]) - float(dataframe["volume"].rolling(23).sum().iloc[-1])

        df_2h = resample_to_interval(dataframe, 120)
        df_2h['vwap'] = qtpylib.rolling_vwap(df_2h, window=14)

        # ongoing_candle = Series({
        #     'volume': ongoing_volume,
        #     'close': ongoing_close
        # })
        # df_2h = df_2h.append(ongoing_candle, ignore_index=True)

        def calculate_distance_percentage(current_price: float, green_line_price: float) -> float:
            distance = abs(current_price - green_line_price)
            return distance * 100 / current_price

        # ----------------------------------------------------------------
        # Price is x pct above vwap and previous candle closed above vwap|
        # ----------------------------------------------------------------
        pct = 1.0
        vwap = df_2h["vwap"].iloc[-1]
        price = df_2h["close"].iloc[-1]
        previous_vwap = df_2h["vwap"].iloc[-2]
        # previous_price = df_2h["close"].iloc[-2]
        previous_low = df_2h["low"].iloc[-2]
        # if previous_price > previous_vwap and (vwap + (vwap * pct / 100)) >= price >= vwap:
        if previous_low > previous_vwap and (vwap + (vwap * pct / 100)) >= price >= vwap:
            if not self.alarm_emitted[pair]:
                binance_pair = pair.replace("/", "_")
                beep(1)
                os.system(f'xdg-open https://www.binance.com/en/trade/{binance_pair}?layout=pro&type=spot')
                #print(f'{pair} {calculate_distance_percentage(price, vwap)}')
            self.alarm_emitted[pair] = True
        else:
            self.alarm_emitted[pair] = False

        # -------------------
        # Price breaks vwap |
        # -------------------
        # if df_2h["close"].iloc[-1] > df_2h["vwap"].iloc[-1]:
        #     if not self.alarm_emitted[pair]:
        #         binance_pair = pair.replace("/", "_")
        #         beep(1)
        #         os.system(f'xdg-open https://www.binance.com/en/trade/{binance_pair}?layout=pro&type=spot')
        #     self.alarm_emitted[pair] = True
        # else:
        #     self.alarm_emitted[pair] = False
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
            ), 'buy'] = 1
        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe.loc[
            (
            ),
            'sell'] = 1
        return dataframe