Trial_RSIOpt

# pragma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
# flake8: noqa: F401
# isort: skip_file
# --- Do not remove these libs ---
import numpy as np  # noqa
import pandas as pd  # noqa
from pandas import DataFrame
from functools import reduce
from freqtrade.strategy import CategoricalParameter, DecimalParameter, IntParameter
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter,
                                IStrategy, IntParameter)

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


# This class is a sample. Feel free to customize it.
class Trial_RSIOpt(IStrategy):


    INTERFACE_VERSION = 2


    #minimal_roi = {"60": 0.01, "30": 0.02,  "0": 0.04  }
    #minimal_roi = {"60":  0.01,  "30":  0.03,   "20":  0.04, "0":  0.05  }
    minimal_roi = {
      "60": 0.75,
     "40": 0.50,
     "20": 0.25,
     "0": 0.10
     }
     
    # Optimal stoploss designed for the strategy.
    # This attribute will be overridden if the config file contains "stoploss".
    stoploss = -0.075

    # Trailing stoploss
    trailing_stop = False
    # trailing_only_offset_is_reached = False
    # trailing_stop_positive = 0.01
    # trailing_stop_positive_offset = 0.0  # Disabled / not configured

    # Hyperoptable parameters
    buy_sma_short = IntParameter(3, 50, default=5)
    buy_sma_long = IntParameter(15, 200, default=50)

    buy_ema_short = IntParameter(3, 50, default=5)
    buy_ema_long = IntParameter(15, 200, default=50)


    # 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:
        """Generate all indicators used by the strategy"""

        # Calculate all ema_short values
        for val in self.buy_ema_short.range:
            dataframe[f'ema_short_{val}'] = ta.EMA(dataframe, timeperiod=val)

        # Calculate all ema_long values
        for val in self.buy_ema_long.range:
            dataframe[f'ema_long_{val}'] = ta.EMA(dataframe, timeperiod=val)

        return dataframe
    def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        # GUARDS AND TRENDS
        if self.buy_adx_enabled.value:
            conditions.append(dataframe['adx'] > self.buy_adx.value)
        if self.buy_rsi_enabled.value:
            conditions.append(dataframe['rsi'] > self.buy_rsi.value)

        # TRIGGERS
        if self.buy_trigger.value == 'bb_lower':
            conditions.append(dataframe['close'] < dataframe['bb_lowerband'])
        if self.buy_trigger.value == 'macd_cross_signal':
            conditions.append(qtpylib.crossed_above(
                dataframe['macd'], dataframe['macdsignal']
            ))

        # Check that volume is not 0
        conditions.append(dataframe['volume'] > 0)

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'buy'] = 1

        return dataframe


    def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        # GUARDS AND TRENDS
        if self.sell_adx_enabled.value:
            conditions.append(dataframe['adx'] < self.sell_adx.value)
        if self.sell_rsi_enabled.value:
            conditions.append(dataframe['rsi'] >  self.sell_rsi.value)

        # TRIGGERS
        if self.buy_trigger.value == 'bb_lower':
            conditions.append(dataframe['close'] > dataframe['bb_lowerband'])
        if self.buy_trigger.value == 'macd_cross_signal':
            conditions.append(qtpylib.crossed_below(
                dataframe['macd'], dataframe['macdsignal']
            ))

        # Check that volume is not 0
        conditions.append(dataframe['volume'] > 0)

        if conditions:
            dataframe.loc[
                reduce(lambda x, y: x & y, conditions),
                'buy'] = 1

        return dataframe