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EI3v2_tag_cofi_green_remod2_zema

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remiotore/ccxt-freqtrade/strategies/EI3v2_tag_cofi_green_remod2_zema_converted.py · ★3 · ⑂2 · first seen 2026-07-28 · repo updated 2026-01-11

Basics mode: spot timeframe: 5m interface version: 3 1h
Settings stoploss: -0.99 has minimal roi trailing protections process only new candles startup candle count: 400 hyperopt hyperopt params: 16
Indicators ADX EMA HMA RSI SMA Stochastic talib technical
Concepts risk_management trailing
Methods EWO base_tf_btc_indicators confirm_trade_exit custom_exit info_tf_btc_indicators pct_change protections pump_dump_protection
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from freqtrade.strategy.interface import IStrategy
from typing import Dict, List
from functools import reduce
from pandas import DataFrame
import talib.abstract as ta
import numpy as np
import freqtrade.vendor.qtpylib.indicators as qtpylib
import datetime
from technical.util import resample_to_interval, resampled_merge
from datetime import datetime, timedelta
from freqtrade.persistence import Trade
from freqtrade.strategy import stoploss_from_open, merge_informative_pair, DecimalParameter, IntParameter, CategoricalParameter
import technical.indicators as ftt
import math
import logging
logger = logging.getLogger(__name__)

def EWO(dataframe, ema_length=5, ema2_length=3):
    df = dataframe.copy()
    ema1 = ta.EMA(df, timeperiod=ema_length)
    ema2 = ta.EMA(df, timeperiod=ema2_length)
    emadif = (ema1 - ema2) / df['close'] * 100
    return emadif

class EI3v2_tag_cofi_green_remod2_zema(IStrategy):
    INTERFACE_VERSION = 3
    minimal_roi = {'0': 0.05, '20': 0.025, '40': 0.015, '87': 0.01, '201': 0.005, '202': 0.002}
    buy_params = {'base_nb_candles_buy': 12, 'rsi_buy': 58, 'ewo_high': 3.001, 'ewo_low': -3, 'low_offset': 0.987, 'lambo2_ema_14_factor': 0.97, 'lambo2_enabled': True, 'lambo2_rsi_14_limit': 39, 'lambo2_rsi_4_limit': 44, 'buy_adx': 20, 'buy_fastd': 20, 'buy_fastk': 22, 'buy_ema_cofi': 0.98, 'buy_ewo_high': 4.179}
    sell_params = {'base_nb_candles_sell': 22, 'high_offset': 1.014, 'high_offset_2': 1.01}

    @property
    def protections(self):
        return [{'method': 'CooldownPeriod', 'stop_duration_candles': 5}, {'method': 'MaxDrawdown', 'lookback_period_candles': 48, 'trade_limit': 20, 'stop_duration_candles': 4, 'max_allowed_drawdown': 0.2}, {'method': 'StoplossGuard', 'lookback_period_candles': 24, 'trade_limit': 4, 'stop_duration_candles': 2, 'only_per_pair': False}, {'method': 'LowProfitPairs', 'lookback_period_candles': 6, 'trade_limit': 2, 'stop_duration_candles': 60, 'required_profit': 0.02}, {'method': 'LowProfitPairs', 'lookback_period_candles': 24, 'trade_limit': 4, 'stop_duration_candles': 2, 'required_profit': 0.01}]
    '\n\n    minimal_roi = {\n        "0": 0.99,\n        \n    }\n    '
    stoploss = -0.99
    base_nb_candles_buy = IntParameter(8, 20, default=buy_params['base_nb_candles_buy'], space='buy', optimize=False)
    base_nb_candles_sell = IntParameter(8, 20, default=sell_params['base_nb_candles_sell'], space='sell', optimize=False)
    low_offset = DecimalParameter(0.985, 0.995, default=buy_params['low_offset'], space='buy', optimize=True)
    high_offset = DecimalParameter(1.005, 1.015, default=sell_params['high_offset'], space='sell', optimize=True)
    high_offset_2 = DecimalParameter(1.01, 1.02, default=sell_params['high_offset_2'], space='sell', optimize=True)
    lambo2_ema_14_factor = DecimalParameter(0.8, 1.2, decimals=3, default=buy_params['lambo2_ema_14_factor'], space='buy', optimize=True)
    lambo2_rsi_4_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_4_limit'], space='buy', optimize=True)
    lambo2_rsi_14_limit = IntParameter(5, 60, default=buy_params['lambo2_rsi_14_limit'], space='buy', optimize=True)
    fast_ewo = 60
    slow_ewo = 220
    ewo_low = DecimalParameter(-20.0, -8.0, default=buy_params['ewo_low'], space='buy', optimize=True)
    ewo_high = DecimalParameter(3.0, 3.4, default=buy_params['ewo_high'], space='buy', optimize=True)
    rsi_buy = IntParameter(30, 70, default=buy_params['rsi_buy'], space='buy', optimize=False)
    trailing_stop = True
    trailing_stop_positive = 0.001
    trailing_stop_positive_offset = 0.0135
    trailing_only_offset_is_reached = True
    is_optimize_cofi = False
    buy_ema_cofi = DecimalParameter(0.96, 0.98, default=0.97, optimize=is_optimize_cofi)
    buy_fastk = IntParameter(20, 30, default=20, optimize=is_optimize_cofi)
    buy_fastd = IntParameter(20, 30, default=20, optimize=is_optimize_cofi)
    buy_adx = IntParameter(20, 30, default=30, optimize=is_optimize_cofi)
    buy_ewo_high = DecimalParameter(2, 12, default=3.553, optimize=is_optimize_cofi)
    use_exit_signal = True
    exit_profit_only = True
    exit_profit_offset = 0.01
    ignore_roi_if_entry_signal = False
    order_time_in_force = {'entry': 'gtc', 'exit': 'gtc'}
    timeframe = '5m'
    inf_1h = '1h'
    process_only_new_candles = True
    startup_candle_count = 400  #verde
    #rosso
    #marrone
    #viola 
    # blu
    # giallo 200
    # giallo 600
    # giallo 800
    plot_config = {'main_plot': {'ma_buy_12': {'color': '#00e676'}, 'ma_sell_22': {'color': '#f44336'}, 'hma_50': {'color': '#795548'}, 'sma_9': {'color': '#9c27b0'}, 'EWO': {'color': '#3f51b5'}, 'rsi': {'color': '#fff59d'}, 'rsi_fast': {'color': '#fdd835'}, 'rsi_slow': {'color': '#f9a825'}}}

    def custom_exit(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float, current_profit: float, **kwargs):
        if current_profit < -0.04 and (current_time - trade.open_date_utc).days >= 1:
            return 'unclog_1d'
        elif current_profit < -0.06 and (current_time - trade.open_date_utc).total_seconds() / 60.0 >= 720:
            return 'unclog_12h'

    def informative_pairs(self):
        pairs = self.dp.current_whitelist()
        informative_pairs = [(pair, '1h') for pair in pairs]
        if self.config['stake_currency'] in ['USDT', 'BUSD', 'USDC', 'DAI', 'TUSD', 'PAX', 'USD', 'EUR', 'GBP']:
            btc_info_pair = f"BTC/{self.config['stake_currency']}"
        else:
            btc_info_pair = 'BTC/USDT'
        informative_pairs.append((btc_info_pair, self.timeframe))
        informative_pairs.append((btc_info_pair, self.inf_1h))
        return informative_pairs

    def pump_dump_protection(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        df36h = dataframe.copy().shift(432)  # TODO FIXME: This assumes 5m timeframe
        df24h = dataframe.copy().shift(288)  # TODO FIXME: This assumes 5m timeframe
        dataframe['volume_mean_short'] = dataframe['volume'].rolling(4).mean()
        dataframe['volume_mean_long'] = df24h['volume'].rolling(48).mean()
        dataframe['volume_mean_base'] = df36h['volume'].rolling(288).mean()
        dataframe['volume_change_percentage'] = dataframe['volume_mean_long'] / dataframe['volume_mean_base']
        dataframe['rsi_mean'] = dataframe['rsi'].rolling(48).mean()
        dataframe['pnd_volume_warn'] = np.where(dataframe['volume_mean_short'] / dataframe['volume_mean_long'] > 5.0, -1, 0)
        return dataframe

    def base_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['price_trend_long'] = dataframe['close'].rolling(8).mean() / dataframe['close'].shift(8).rolling(144).mean()
        ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
        dataframe.rename(columns=lambda s: f'btc_{s}' if s not in ignore_columns else s, inplace=True)
        return dataframe

    def info_tf_btc_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        dataframe['rsi_8'] = ta.RSI(dataframe, timeperiod=8)
        ignore_columns = ['date', 'open', 'high', 'low', 'close', 'volume']
        dataframe.rename(columns=lambda s: f'btc_{s}' if s not in ignore_columns else s, inplace=True)
        return dataframe

    def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        if self.config['stake_currency'] in ['USDT', 'BUSD']:
            btc_info_pair = f"BTC/{self.config['stake_currency']}"
        else:
            btc_info_pair = 'BTC/USDT'
        btc_info_tf = self.dp.get_pair_dataframe(btc_info_pair, self.inf_1h)
        btc_info_tf = self.info_tf_btc_indicators(btc_info_tf, metadata)
        dataframe = merge_informative_pair(dataframe, btc_info_tf, self.timeframe, self.inf_1h, ffill=True)
        drop_columns = [f'{s}_{self.inf_1h}' for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
        dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
        btc_base_tf = self.dp.get_pair_dataframe(btc_info_pair, self.timeframe)
        btc_base_tf = self.base_tf_btc_indicators(btc_base_tf, metadata)
        dataframe = merge_informative_pair(dataframe, btc_base_tf, self.timeframe, self.timeframe, ffill=True)
        drop_columns = [f'{s}_{self.timeframe}' for s in ['date', 'open', 'high', 'low', 'close', 'volume']]
        dataframe.drop(columns=dataframe.columns.intersection(drop_columns), inplace=True)
        for val in self.base_nb_candles_buy.range:
            dataframe[f'ma_buy_{val}'] = ta.EMA(dataframe, timeperiod=val)
        for val in self.base_nb_candles_sell.range:
            dataframe[f'ma_sell_{val}'] = ta.EMA(dataframe, timeperiod=val)
        dataframe['hma_50'] = qtpylib.hull_moving_average(dataframe['close'], window=50)
        dataframe['sma_9'] = ta.SMA(dataframe, timeperiod=9)
        dataframe['EWO'] = EWO(dataframe, self.fast_ewo, self.slow_ewo)
        dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['rsi_fast'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_slow'] = ta.RSI(dataframe, timeperiod=20)
        dataframe['ema_14'] = ta.EMA(dataframe, timeperiod=14)
        dataframe['rsi_4'] = ta.RSI(dataframe, timeperiod=4)
        dataframe['rsi_14'] = ta.RSI(dataframe, timeperiod=14)
        dataframe['zema_30'] = ftt.zema(dataframe, period=30)
        dataframe['zema_200'] = ftt.zema(dataframe, period=200)
        dataframe['pump_strength'] = (dataframe['zema_30'] - dataframe['zema_200']) / dataframe['zema_30']
        stoch_fast = ta.STOCHF(dataframe, 5, 3, 0, 3, 0)
        dataframe['fastd'] = stoch_fast['fastd']
        dataframe['fastk'] = stoch_fast['fastk']
        dataframe['adx'] = ta.ADX(dataframe)
        dataframe['ema_8'] = ta.EMA(dataframe, timeperiod=8)
        dataframe = self.pump_dump_protection(dataframe, metadata)
        return dataframe

    def populate_entry_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        dataframe.loc[:, 'enter_tag'] = ''
        lambo2 = (dataframe['close'] < dataframe['ema_14'] * self.lambo2_ema_14_factor.value) & (dataframe['rsi_4'] < int(self.lambo2_rsi_4_limit.value)) & (dataframe['rsi_14'] < int(self.lambo2_rsi_14_limit.value))
        dataframe.loc[lambo2, 'enter_tag'] += 'lambo2_'
        conditions.append(lambo2)
        buy1ewo = (dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value) & (dataframe['EWO'] > self.ewo_high.value) & (dataframe['rsi'] < self.rsi_buy.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)
        dataframe.loc[buy1ewo, 'enter_tag'] += 'buy1eworsi_'
        conditions.append(buy1ewo)
        buy2ewo = (dataframe['rsi_fast'] < 35) & (dataframe['close'] < dataframe[f'ma_buy_{self.base_nb_candles_buy.value}'] * self.low_offset.value) & (dataframe['EWO'] < self.ewo_low.value) & (dataframe['volume'] > 0) & (dataframe['close'] < dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value)
        dataframe.loc[buy2ewo, 'enter_tag'] += 'buy2ewo_'
        conditions.append(buy2ewo)
        is_cofi = (dataframe['open'] < dataframe['ema_8'] * self.buy_ema_cofi.value) & qtpylib.crossed_above(dataframe['fastk'], dataframe['fastd']) & (dataframe['fastk'] < self.buy_fastk.value) & (dataframe['fastd'] < self.buy_fastd.value) & (dataframe['adx'] > self.buy_adx.value) & (dataframe['EWO'] > self.buy_ewo_high.value)
        dataframe.loc[is_cofi, 'enter_tag'] += 'cofi_'
        conditions.append(is_cofi)
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'enter_long'] = 1
        dont_buy_conditions = []
        dont_buy_conditions.append(dataframe['pnd_volume_warn'] < 0.0)
        dont_buy_conditions.append(dataframe['btc_rsi_8_1h'] < 30.0)
        if dont_buy_conditions:
            for condition in dont_buy_conditions:
                dataframe.loc[condition, 'enter_long'] = 0
        return dataframe

    def populate_exit_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
        conditions = []
        conditions.append((dataframe['close'] > dataframe['hma_50']) & (dataframe['close'] > dataframe[f'ma_sell_{self.base_nb_candles_sell.value}'] * 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[f'ma_sell_{self.base_nb_candles_sell.value}'] * self.high_offset.value) & (dataframe['volume'] > 0) & (dataframe['rsi_fast'] > dataframe['rsi_slow']))
        if conditions:
            dataframe.loc[reduce(lambda x, y: x | y, conditions), 'exit_long'] = 1
        return dataframe

    def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float, time_in_force: str, exit_reason: str, current_time: datetime, **kwargs) -> bool:
        if trade and trade.sell_reason:
            trade.sell_reason = exit_reason + '_' + trade.buy_tag
        return True

    def pct_change(a, b):
        return (b - a) / a