# source: https://raw.githubusercontent.com/MutugiD/freq-bot/7fb54db879f96633d91353b55f74117bbd24e860/strategies/vwap.py

import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame

from freqtrade.strategy import IStrategy
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter

# --------------------------------
# Add your lib to import here
import talib.abstract as ta
import freqtrade.vendor.qtpylib.indicators as qtpylib

class Github_MutugiD_freq_bot__vwap__20220716_154754(IStrategy):
    INTERFACE_VERSION = 2

    # Minimal ROI designed for the strategy.
    # This attribute will be overridden if the config file contains "minimal_roi".
    minimal_roi = {
        "60": 0.01,
        "30": 0.02,
        "0": 0.04
    }

    stoploss = -0.10

    # Trailing stoploss
    trailing_stop = False

    # Hyperoptable parameters
    buy_rsi = IntParameter(low=1, high=50, default=30, space='buy', optimize=True, load=True)
    sell_rsi = IntParameter(low=50, high=100, default=70, space='sell', optimize=True, load=True)

    # Optimal timeframe for the strategy.
    timeframe = '5m'

    # Run "populate_indicators()" only for new candle.
    process_only_new_candles = False

    # These values can be overridden in the "ask_strategy" section in the config.
    use_sell_signal = True
    sell_profit_only = False
    ignore_roi_if_buy_signal = False

    # Number of candles the strategy requires before producing valid signals
    startup_candle_count: int = 30

    # Optional order type mapping.
    order_types = {
        'buy': 'limit',
        'sell': 'limit',
        'stoploss': 'market',
        'stoploss_on_exchange': False
    }

    # Optional order time in force.
    order_time_in_force = {
        'buy': 'gtc',
        'sell': 'gtc'
    }

    plot_config = {
        'main_plot': {
            'tema': {},
            'sar': {'color': 'white'},
        },
        'subplots': {
            "MACD": {
                'macd': {'color': 'blue'},
                'macdsignal': {'color': 'orange'},
            },
            "RSI": {
                'rsi': {'color': 'red'},
            }
        }
    }



    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe['vwap'] = qtpylib.rolling_vwap(dataframe)
        # EMA - Exponential Moving Average
        dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
        dataframe['ema9'] = ta.EMA(dataframe, timeperiod=8)
        dataframe['ema20'] = ta.EMA(dataframe, timeperiod=20)


        # # SMA - Simple Moving Average
        # dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
        # dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
        # dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
    

        # Parabolic SAR
        dataframe['sar'] = ta.SAR(dataframe)
        return dataframe

    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            (
                # Signal: RSI crosses above 30
                (qtpylib.crossed_above(dataframe['ema9'], dataframe['vwap'])) &
                (qtpylib.crossed_above(dataframe['ema9'], dataframe['ema20'])) &
           
                # (dataframe['tema'] <= dataframe['bb_middleband']) &  # Guard: tema below BB middle
                # (dataframe['tema'] > dataframe['tema'].shift(1)) &  # Guard: tema is raising
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'buy'] = 1

        return dataframe

    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:

        dataframe.loc[
            (
                # Signal: RSI crosses above 70
                (qtpylib.crossed_above(dataframe['vwap'], dataframe['ema9'])) &
                (qtpylib.crossed_above(dataframe['ema20'], dataframe['ema9'])) &
                #(qtpylib.crossed_above(dataframe['vwap'], dataframe['ema9'])) &
                (dataframe['volume'] > 0)  # Make sure Volume is not 0
            ),
            'sell'] = 1
        return dataframe