KamaFama

# --- Do not remove these libs ---
from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame, Series, DatetimeIndex, merge

# --------------------------------
import talib.abstract as ta
import pandas_ta as pta
import numpy as np
import pandas as pd  # noqa
import warnings, datetime
import freqtrade.vendor.qtpylib.indicators as qtpylib
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade, Order
from freqtrade.strategy import (
    stoploss_from_open,
    DecimalParameter,
    IntParameter,
    CategoricalParameter,
)
import technical.indicators as ftt
from functools import reduce

pd.options.mode.chained_assignment = None  # default='warn'

# @Rallipanos # changes by Mastaaa1987

# Buy hyperspace params:
buy_params = {
    'base_nb_candles_buy': 12,
}

# Sell hyperspace params:
sell_params = {
    'base_nb_candles_sell': 22,
    'high_offset': 1.008,
    'high_offset_2': 1.016,
}


def williams_r(dataframe: DataFrame, period: int = 14) -> Series:
    """Williams %R, or just %R, is a technical analysis oscillator showing the current closing price in relation to the high and low
    of the past N days (for a given N). It was developed by a publisher and promoter of trading materials, Larry Williams.
    Its purpose is to tell whether a stock or commodity market is trading near the high or the low, or somewhere in between,
    of its recent trading range.
    The oscillator is on a negative scale, from −100 (lowest) up to 0 (highest).
    """

    highest_high = dataframe['high'].rolling(center=False, window=period).max()
    lowest_low = dataframe['low'].rolling(center=False, window=period).min()

    WR = Series(
        (highest_high - dataframe['close']) / (highest_high - lowest_low),
        name=f"{period} Williams %R",
    )

    return WR * -100


class KamaFama(IStrategy):
    INTERFACE_VERSION = 2
    """
    # ROI table:
    minimal_roi = {
        "0": 0.08,
        "20": 0.04,
        "40": 0.032,
        "87": 0.016,
        "201": 0,
        "202": -1
    }
    """

    @property
    def protections(self):
        return [
            {
                'method': 'LowProfitPairs',
                'lookback_period_candles': 60,
                'trade_limit': 1,
                'stop_duration_candles': 60,
                'required_profit': -0.05,
            },
            {'method': 'CooldownPeriod', 'stop_duration_candles': 5},
        ]

    minimal_roi = {'0': 1}

    # Stoploss:
    stoploss = -0.25

    # Buy Params
    base_nb_candles_buy = IntParameter(
        5, 80, default=buy_params['base_nb_candles_buy'], space='buy', optimize=True
    )
    # low_offset = DecimalParameter(0.9, 0.99, default=buy_params['low_offset'], space='buy', optimize=True)
    # Sell Params
    base_nb_candles_sell = IntParameter(
        5, 80, default=sell_params['base_nb_candles_sell'], space='sell', optimize=True
    )
    high_offset = DecimalParameter(
        0.95, 1.1, default=sell_params['high_offset'], space='sell', optimize=True
    )
    high_offset_2 = DecimalParameter(
        0.99, 1.5, default=sell_params['high_offset_2'], space='sell', optimize=True
    )

    # Trailing stop:
    trailing_stop = False
    trailing_stop_positive = 0.002
    trailing_stop_positive_offset = 0.05
    trailing_only_offset_is_reached = True

    use_custom_stoploss = True

    order_types = {
        'entry': 'limit',
        'exit': 'limit',
        'emergency_exit': 'market',
        'force_entry': 'market',
        'force_exit': 'market',
        'stoploss': 'market',
        'stoploss_on_exchange': False,
        'stoploss_on_exchange_interval': 60,
        'stoploss_on_exchange_market_ratio': 0.99,
    }

    ## Optional order time in force.
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}

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

    process_only_new_candles = True
    startup_candle_count = 400

    plot_config = {
        'main_plot': {
            'ma_buy': {'color': '#27d81b'},
            'ma_sell': {'color': '#d0da3e'},
            'hma_50': {'color': '#3edad8'},
        },
        'subplots': {
            'FKMAMA': {
                'mama': {'color': '#d0da3e'},
                'fama': {'color': '#da3eb8'},
                'kama': {'color': '#3edad8'},
            },
            'cond': {'change': {'color': '#da3e3e'}},
        },
    }

    def custom_stoploss(
        self,
        pair: str,
        trade: Trade,
        current_time: datetime,
        current_rate: float,
        current_profit: float,
        **kwargs,
    ) -> float:

        if current_profit >= 0.05:
            return -0.002

        return None

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        # PCT CHANGE
        dataframe['change'] = 100 / dataframe['open'] * dataframe['close'] - 100

        # Calculate all ma_buy values
        for val in self.base_nb_candles_buy.range:
            dataframe['ma_buy'] = ta.EMA(dataframe, timeperiod=val)

        # Calculate all ma_sell values
        for val in self.base_nb_candles_sell.range:
            dataframe['ma_sell'] = ta.EMA(dataframe, timeperiod=val)

        # HMA
        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)

        # MAMA, FAMA, KAMA
        dataframe['hl2'] = (dataframe['high'] + dataframe['low']) / 2
        dataframe['mama'], dataframe['fama'] = ta.MAMA(dataframe['hl2'], 0.25, 0.025)
        dataframe['mama_diff'] = (dataframe['mama'] - dataframe['fama']) / dataframe['hl2']
        dataframe['kama'] = ta.KAMA(dataframe['close'], 84)

        # CTI
        dataframe['cti'] = pta.cti(dataframe['close'], length=20)

        # RSI
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
        dataframe['rsi_84'] = ta.RSI(dataframe, timeperiod=84)
        dataframe['rsi_112'] = ta.RSI(dataframe, timeperiod=112)

        # Williams %R
        dataframe['r_14'] = williams_r(dataframe, period=14)

        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''

        buy = (
            (dataframe['kama'] > dataframe['fama'])
            & (dataframe['fama'] > dataframe['mama'] * 0.981)
            & (dataframe['r_14'] < -61.3)
            & (dataframe['mama_diff'] < -0.025)
            & (dataframe['cti'] < -0.715)
            & (dataframe['close'].rolling(48).max() >= dataframe['close'] * 1.05)
            & (dataframe['close'].rolling(288).max() >= dataframe['close'] * 1.125)
            & (dataframe['rsi_84'] < 60)
            & (dataframe['rsi_112'] < 60)
        )
        conditions.append(buy)
        dataframe.loc[buy, 'enter_tag'] += 'buy'

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

        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'exit_tag'] = ''

        sell = (
            (dataframe['close'] > dataframe['hma_50'])
            & (dataframe['close'] > (dataframe['ma_sell'] * self.high_offset_2.value))
            & (dataframe['rsi'] > 50)
            & (dataframe['volume'] > 0)
            & (dataframe['rsi_fast'] > dataframe['rsi_slow'])
        ) | (
            (dataframe['close'] < dataframe['hma_50'])
            & (dataframe['close'] > (dataframe['ma_sell'] * self.high_offset.value))
            & (dataframe['volume'] > 0)
            & (dataframe['rsi_fast'] > dataframe['rsi_slow'])
        )
        conditions.append(sell)
        dataframe.loc[sell, 'exit_tag'] += 'sell'

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

        return dataframe