555 lines
28 KiB
Python
555 lines
28 KiB
Python
#
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#
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#
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from datetime import timedelta, datetime
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from typing import Optional
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from freqtrade.persistence import Trade
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from freqtrade.strategy.parameters import CategoricalParameter, DecimalParameter, IntParameter, BooleanParameter
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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import logging
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# --------------------------------
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# Add your lib to import here
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import ta
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import talib.abstract as talib
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from freqtrade.strategy.strategy_helper import merge_informative_pair
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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logger = logging.getLogger(__name__)
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class Ishimoku_2(IStrategy):
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# ROI table:
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minimal_roi = {
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"0": 0.564,
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"567": 0.273,
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"2814": 0.12,
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"7675": 0
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}
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# Stoploss:
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stoploss = -0.256
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# Buy hypers
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timeframe = '4h'
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stop_buying = False
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max_open_trades = 5
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plot_config = {
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"main_plot": {
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"min200": {
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"color": "#86c932"
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},
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"max50": {
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"color": "white"
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},
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"max200": {
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"color": "yellow"
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},
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"sma3_1d": {
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"color": "pink"
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},
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"sma5_1d": {
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"color": "blue"
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},
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"sma10_1d": {
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"color": "orange"
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},
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"close_1d": {
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"color": "#73e233",
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},
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"bb_lowerband": {
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"color": "#da59a6"},
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"bb_upperband": {
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"color": "#da59a6",
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},
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"sar": {
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"color": "#4f9f51",
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}
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},
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"subplots": {
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"Ind": {
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"trend_ichimoku_base": {
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"color": "#dd1384"
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},
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"trend_kst_diff": {
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"color": "#850678"
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}
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},
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"BB": {
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"bb_width": {
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"color": "white"
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},
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# "bb_lower_5": {
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# "color": "yellow"
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# }
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},
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# "Cond": {
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# "cond1": {
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# "color": "yellow"
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# }
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# },
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"Rsi": {
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"rsi": {
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"color": "pink"
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},
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# "rsi_1d": {
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# "color": "yellow"
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# }
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},
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# "Percent": {
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# "max_min": {
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# "color": "#74effc"
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# },
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# "pct_change_1_1d": {
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# "color": "green"
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# },
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# "pct_change_3_1d": {
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# "color": "orange"
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# },
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# "pct_change_5_1d": {
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# "color": "red"
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# }
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# }
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}
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}
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trades = list()
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buy_base = DecimalParameter(0, 0.2, decimals=2, default=0.05, space='buy')
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buy_rsi = IntParameter(20, 60, default=45, space='buy')
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profit_b_no_change = BooleanParameter(default=True, space="sell")
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profit_b_quick_lost = BooleanParameter(default=True, space="sell")
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profit_b_sma5 = BooleanParameter(default=True, space="sell")
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profit_b_sma10 = BooleanParameter(default=True, space="sell")
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profit_b_sma20 = BooleanParameter(default=True, space="sell")
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profit_b_quick_gain = BooleanParameter(default=True, space="sell")
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profit_b_quick_gain_3 = BooleanParameter(default=True, space="sell")
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profit_b_old_sma10 = BooleanParameter(default=True, space="sell")
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profit_b_very_old_sma10 = BooleanParameter(default=True, space="sell")
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profit_b_over_rsi = BooleanParameter(default=True, space="sell")
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profit_b_short_loss = BooleanParameter(default=True, space="sell")
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sell_b_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_b_percent3 = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_b_candels = IntParameter(0, 48, default=12, space='sell')
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sell_b_too_old_day = IntParameter(0, 10, default=5, space='sell')
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sell_b_too_old_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_b_profit_no_change = DecimalParameter(0, 0.02, decimals=3, default=0.005, space='sell')
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sell_b_profit_percent10 = DecimalParameter(0, 0.002, decimals=4, default=0.001, space='sell')
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sell_b_RSI = IntParameter(70, 98, default=88, space='sell')
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sell_b_RSI2 = IntParameter(70, 98, default=88, space='sell')
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sell_b_RSI3 = IntParameter(70, 98, default=80, space='sell')
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sell_b_RSI2_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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# sell_b_expected_profit = DecimalParameter(0, 0.01, decimals=3, default=0.01, space='sell')
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profit_h_no_change = BooleanParameter(default=True, space="sell")
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profit_h_quick_lost = BooleanParameter(default=True, space="sell")
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profit_h_sma5 = BooleanParameter(default=True, space="sell")
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profit_h_sma10 = BooleanParameter(default=True, space="sell")
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profit_h_sma20 = BooleanParameter(default=True, space="sell")
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profit_h_quick_gain = BooleanParameter(default=True, space="sell")
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profit_h_quick_gain_3 = BooleanParameter(default=True, space="sell")
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profit_h_old_sma10 = BooleanParameter(default=True, space="sell")
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profit_h_very_old_sma10 = BooleanParameter(default=True, space="sell")
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profit_h_over_rsi = BooleanParameter(default=True, space="sell")
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profit_h_short_loss = BooleanParameter(default=True, space="sell")
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sell_h_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_h_percent3 = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_h_candels = IntParameter(0, 48, default=12, space='sell')
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sell_h_too_old_day = IntParameter(0, 10, default=5, space='sell')
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sell_h_too_old_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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sell_h_profit_no_change = DecimalParameter(0, 0.02, decimals=3, default=0.005, space='sell')
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sell_h_profit_percent10 = DecimalParameter(0, 0.002, decimals=4, default=0.001, space='sell')
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sell_h_RSI = IntParameter(70, 98, default=88, space='sell')
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sell_h_RSI2 = IntParameter(70, 98, default=88, space='sell')
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sell_h_RSI3 = IntParameter(70, 98, default=80, space='sell')
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sell_h_RSI2_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
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# protection_max_allowed_dd = DecimalParameter(0, 1, decimals=2, default=0.04, space='protection')
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# protection_stop = IntParameter(0, 100, default=48, space='protection')
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# protection_stoploss_stop = IntParameter(0, 100, default=48, space='protection')
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# lookback = IntParameter(0, 200, default=48, space='protection')
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# trade_limit = IntParameter(0, 10, default=2, space='protection')
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protection_down_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='protection')
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protection_down_percent3 = DecimalParameter(0, 0.05, decimals=2, default=0.02, space='protection')
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protection_down_percent5 = DecimalParameter(0, 0.05, decimals=2, default=0.03, space='protection')
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protection_up_percent = DecimalParameter(-0.02, 0.02, decimals=3, default=0.0, space='protection')
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protection_up_percent3 = DecimalParameter(-0.02, 0.05, decimals=2, default=0.0, space='protection')
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protection_up_percent5 = DecimalParameter(-0.02, 0.05, decimals=2, default=0.0, space='protection')
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# def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
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# proposed_stake: float, min_stake: float, max_stake: float,
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# **kwargs) -> float:
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#
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# informative, _ = self.dp.get_analyzed_dataframe(pair='BTC/USDT', timeframe=self.timeframe)
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# # current_candle = informative.iloc[-1].squeeze()
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#
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# current = informative.tail(1).iloc[0]['close']
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# # 50000 => 2 30000 => 20
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# if current > 50000:
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# self.max_open_trades = 2
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# proposed_stake = self.config['stake_amount'] / 2
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# else:
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# if current > 32000:
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# self.max_open_trades = 2 + int((50000 - current) / 1000)
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# proposed_stake = self.config['stake_amount'] / 2 \
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# + self.config['stake_amount'] * self.max_open_trades / self.config['max_open_trades']
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# else:
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# self.max_open_trades = self.config['max_open_trades']
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# proposed_stake = self.config['stake_amount']
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#
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# return min(max_stake, proposed_stake)
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@property
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def protections(self):
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return [
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{
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"method": "CooldownPeriod",
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"stop_duration_candles": 10
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},
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# {
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# "method": "MaxDrawdown",
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# "lookback_period_candles": self.lookback.value,
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# "trade_limit": self.trade_limit.value,
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# "stop_duration_candles": self.protection_stop.value,
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# "max_allowed_drawdown": self.protection_max_allowed_dd.value,
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# "only_per_pair": False
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# }
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]
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def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
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current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
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allow_to_buy = True
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informative, _ = self.dp.get_analyzed_dataframe(pair='BTC/USDT', timeframe=self.timeframe)
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info_last_candle = informative.iloc[-1].squeeze()
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if (self.stop_buying is True) & (
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(info_last_candle['percent'] > self.protection_up_percent.value)
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| (info_last_candle['percent3'] > self.protection_up_percent3.value)
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| (info_last_candle['percent5'] > self.protection_up_percent5.value)):
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# print("Enable buying")
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self.stop_buying = False
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if self.stop_buying:
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allow_to_buy = False
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return allow_to_buy
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def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
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current_profit: float, **kwargs):
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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last_candle = dataframe.iloc[-1].squeeze()
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previous_last_candle = dataframe.iloc[-2].squeeze()
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previous_5_candle = dataframe.iloc[-5].squeeze()
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expected_profit = 0.01
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days = (current_time - trade.open_date_utc).days
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######
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informative, _ = self.dp.get_analyzed_dataframe(pair='BTC/USDT', timeframe=self.timeframe)
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info_last_candle = informative.iloc[-1].squeeze()
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if self.stop_buying is True:
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if (info_last_candle['percent'] > 0) | (info_last_candle['percent3'] > 0) | (
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info_last_candle['percent5'] > 0):
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# print("Enable buying")
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self.stop_buying = False
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else:
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if self.stop_buying is False:
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if (info_last_candle['percent'] < - self.protection_down_percent.value) \
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| (info_last_candle['percent3'] < - self.protection_down_percent3.value) \
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| (info_last_candle['percent5'] < - self.protection_down_percent5.value):
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self.stop_buying = True
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# print("Disable buying", info_last_candle['percent'], info_last_candle['percent3'], info_last_candle['percent5'])
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# return 'send_all'
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if (last_candle['pct_change_1_1h'] < 0):
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if (current_profit > 0.015) & ((last_candle['percent'] < -0.005) | (last_candle['percent3'] < -0.005) | (
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last_candle['percent5'] < -0.005)):
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return 'b_percent_quick'
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if (current_profit > last_candle['bb_width'] / 2) & (previous_last_candle['close'] > previous_last_candle['bb_upperband'])\
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& (last_candle['percent'] < -0.005) & ((current_time - trade.open_date_utc).seconds <= 3600):
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return 'b_bb_width'
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if (current_profit >= - self.sell_b_too_old_percent.value) & (days >= self.sell_b_too_old_day.value) \
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& (days < self.sell_b_too_old_day.value * 2) \
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& (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
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return "b_too_old_0.01"
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if (current_profit >= - self.sell_b_too_old_percent.value * 2) & (days >= self.sell_b_too_old_day.value * 2) \
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& (days < self.sell_b_too_old_day.value * 3) \
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& (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
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return "b_too_old_0.02"
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if (current_profit >= - self.sell_b_too_old_percent.value * 3) & (days >= self.sell_b_too_old_day.value * 3) \
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& (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
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return "b_too_old_0.03"
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if self.profit_b_quick_lost.value and (current_profit >= 0.015) & (last_candle['percent3'] < -0.005):
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return "b_quick_lost"
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if self.profit_b_no_change.value and (current_profit > self.sell_b_profit_no_change.value) \
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& (last_candle['percent10'] < self.sell_b_profit_percent10.value) & (last_candle['percent5'] < 0) \
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& ((current_time - trade.open_date_utc).seconds >= 3600):
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return "b_no_change"
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if (current_profit > self.sell_b_percent.value) & (last_candle['percent3'] < - self.sell_b_percent3.value) \
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& ((current_time - trade.open_date_utc).seconds <= 300 * self.sell_b_candels.value):
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return "b_quick_gain_param"
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if self.profit_b_sma5.value:
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if (current_profit > expected_profit) \
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& ((previous_5_candle['sma5'] > last_candle['sma5']) \
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| (last_candle['percent3'] < -expected_profit) | (
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last_candle['percent5'] < -expected_profit)) \
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& ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
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# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'b_sma5'
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if self.profit_b_sma10.value:
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if (current_profit > expected_profit) \
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& ((previous_5_candle['sma10'] > last_candle['sma10']) \
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| (last_candle['percent3'] < -expected_profit) | (
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last_candle['percent5'] < -expected_profit)) \
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& ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
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# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'b_sma10'
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if self.profit_b_sma20.value:
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if (current_profit > 0.005) \
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& (previous_last_candle['sma10'] > last_candle['sma10']) \
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& ((current_time - trade.open_date_utc).seconds >= 3600) \
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& ((previous_last_candle['sma20'] > last_candle['sma20']) &
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((last_candle['percent5'] < 0) | (last_candle['percent10'] < 0) | (
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last_candle['percent20'] < 0))):
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# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'b_sma20'
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if self.profit_b_over_rsi.value:
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if (current_profit > 0) & (previous_last_candle[
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'rsi'] > self.sell_b_RSI.value): # & (last_candle['percent'] < 0): #| (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
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# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'b_over_rsi'
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if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_b_RSI2.value) & \
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(last_candle[
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'percent'] < - self.sell_b_RSI2_percent.value): # | (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
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# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'b_over_rsi_2'
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if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_b_RSI3.value) & \
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(last_candle['close'] >= last_candle['max200']) & (last_candle[
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'percent'] < - self.sell_b_RSI2_percent.value): # | (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
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# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'b_over_rsi_max'
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if self.profit_b_short_loss.value:
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if (current_profit > -expected_profit) & (previous_last_candle['percent10'] > 0.04) & (
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last_candle['percent'] < 0) \
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& (days >= 1): # | (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
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# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'b_short_lost'
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else:
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if (current_profit > 0.025) & ((last_candle['percent'] < -0.005) | (last_candle['percent3'] < -0.005) | (
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last_candle['percent5'] < -0.005)):
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return 'h_percent_quick'
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if (current_profit >= - self.sell_h_too_old_percent.value) & (days >= self.sell_h_too_old_day.value) \
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& (days < self.sell_h_too_old_day.value * 2) \
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& (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
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return "h_too_old_0.01"
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if (current_profit >= - self.sell_h_too_old_percent.value * 2) & (days >= self.sell_h_too_old_day.value * 2) \
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& (days < self.sell_h_too_old_day.value * 3) \
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& (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
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return "h_too_old_0.02"
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if (current_profit >= - self.sell_h_too_old_percent.value * 3) & (days >= self.sell_h_too_old_day.value * 3) \
|
|
& (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
|
|
return "h_too_old_0.03"
|
|
|
|
if self.profit_h_quick_lost.value and (current_profit >= 0.015) & (last_candle['percent3'] < -0.005):
|
|
return "h_quick_lost"
|
|
|
|
if self.profit_h_no_change.value and (current_profit > self.sell_h_profit_no_change.value) \
|
|
& (last_candle['percent10'] < self.sell_h_profit_percent10.value) & (last_candle['percent5'] < 0) \
|
|
& ((current_time - trade.open_date_utc).seconds >= 3600):
|
|
return "h_no_change"
|
|
|
|
if (current_profit > self.sell_h_percent.value) & (last_candle['percent3'] < - self.sell_h_percent3.value) \
|
|
& ((current_time - trade.open_date_utc).seconds <= 300 * self.sell_h_candels.value):
|
|
return "h_quick_gain_param"
|
|
|
|
if self.profit_h_sma5.value:
|
|
if (current_profit > expected_profit) \
|
|
& ((previous_5_candle['sma5'] > last_candle['sma5']) \
|
|
| (last_candle['percent3'] < -expected_profit) | (
|
|
last_candle['percent5'] < -expected_profit)) \
|
|
& ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
|
|
# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
return 'h_sma5'
|
|
|
|
if self.profit_h_sma10.value:
|
|
if (current_profit > expected_profit) \
|
|
& ((previous_5_candle['sma10'] > last_candle['sma10']) \
|
|
| (last_candle['percent3'] < -expected_profit) | (
|
|
last_candle['percent5'] < -expected_profit)) \
|
|
& ((last_candle['percent'] < 0) & (last_candle['percent3'] < 0)):
|
|
# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
return 'h_sma10'
|
|
|
|
if self.profit_h_sma20.value:
|
|
if (current_profit > 0.005) \
|
|
& (previous_last_candle['sma10'] > last_candle['sma10']) \
|
|
& ((current_time - trade.open_date_utc).seconds >= 3600) \
|
|
& ((previous_last_candle['sma20'] > last_candle['sma20']) &
|
|
((last_candle['percent5'] < 0) | (last_candle['percent10'] < 0) | (
|
|
last_candle['percent20'] < 0))):
|
|
# print("over_bb_band_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
return 'h_sma20'
|
|
|
|
if self.profit_h_over_rsi.value:
|
|
if (current_profit > 0) & (previous_last_candle[
|
|
'rsi'] > self.sell_h_RSI.value): # & (last_candle['percent'] < 0): #| (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
|
|
# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
return 'h_over_rsi'
|
|
|
|
if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_h_RSI2.value) & \
|
|
(last_candle[
|
|
'percent'] < - self.sell_h_RSI2_percent.value): # | (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
|
|
# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
return 'h_over_rsi_2'
|
|
|
|
if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_h_RSI3.value) & \
|
|
(last_candle['close'] >= last_candle[
|
|
'max200']): # | (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
|
|
# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
return 'h_over_rsi_max'
|
|
|
|
if self.profit_h_short_loss.value:
|
|
if (current_profit > -expected_profit) & (previous_last_candle['percent10'] > 0.04) & (
|
|
last_candle['percent'] < 0) \
|
|
& (days >= 1): # | (previous_last_candle['rsi'] > 75 & last_candle['rsi'] < 70)):
|
|
# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
|
|
return 'h_short_lost'
|
|
|
|
def informative_pairs(self):
|
|
informative_pairs = [('BTC/USDT', '1h')]
|
|
return informative_pairs
|
|
|
|
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
# Add all ta features
|
|
dataframe['trend_ichimoku_base'] = ta.trend.ichimoku_base_line(
|
|
dataframe['high'],
|
|
dataframe['low'],
|
|
window1=9,
|
|
window2=26,
|
|
visual=False,
|
|
fillna=False
|
|
)
|
|
KST = ta.trend.KSTIndicator(
|
|
close=dataframe['close'],
|
|
roc1=10,
|
|
roc2=15,
|
|
roc3=20,
|
|
roc4=30,
|
|
window1=10,
|
|
window2=10,
|
|
window3=10,
|
|
window4=15,
|
|
nsig=9,
|
|
fillna=False
|
|
)
|
|
|
|
dataframe['trend_kst_diff'] = KST.kst_diff()
|
|
dataframe['rsi'] = talib.RSI(dataframe)
|
|
|
|
dataframe['pct_change'] = dataframe['close'].pct_change(5)
|
|
dataframe['min10'] = talib.MIN(dataframe['close'], timeperiod=10)
|
|
dataframe['min20'] = talib.MIN(dataframe['close'], timeperiod=20)
|
|
dataframe['min50'] = talib.MIN(dataframe['close'], timeperiod=50)
|
|
dataframe['min200'] = talib.MIN(dataframe['close'], timeperiod=200)
|
|
dataframe['min'] = talib.MIN(dataframe['close'], timeperiod=200)
|
|
dataframe['moy200_12'] = dataframe['min200'].rolling(12).mean()
|
|
|
|
dataframe['max50'] = talib.MAX(dataframe['close'], timeperiod=50)
|
|
dataframe['max200'] = talib.MAX(dataframe['close'], timeperiod=200)
|
|
dataframe['sma5'] = talib.SMA(dataframe, timeperiod=5)
|
|
dataframe['sma10'] = talib.SMA(dataframe, timeperiod=10)
|
|
dataframe['sma20'] = talib.SMA(dataframe, timeperiod=20)
|
|
dataframe['sma50'] = talib.SMA(dataframe, timeperiod=50)
|
|
dataframe['sma100'] = talib.SMA(dataframe, timeperiod=100)
|
|
dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
|
|
dataframe["percent5"] = dataframe["percent"].rolling(5).sum()
|
|
dataframe["percent3"] = dataframe["percent"].rolling(3).sum()
|
|
dataframe["percent10"] = dataframe["percent"].rolling(10).sum()
|
|
dataframe["percent20"] = dataframe["percent"].rolling(20).sum()
|
|
dataframe["percent50"] = dataframe["percent"].rolling(50).sum()
|
|
dataframe["volume10"] = dataframe["volume"].rolling(10).mean()
|
|
dataframe["volume10"] = dataframe["volume"].rolling(10).mean()
|
|
# # Bollinger Bands
|
|
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
|
|
dataframe['bb_lowerband'] = bollinger['lower']
|
|
dataframe['bb_middleband'] = bollinger['mid']
|
|
dataframe['bb_upperband'] = bollinger['upper']
|
|
dataframe["bb_percent"] = (
|
|
(dataframe["close"] - dataframe["bb_lowerband"]) /
|
|
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
|
|
)
|
|
dataframe["bb_width"] = (
|
|
(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
|
|
)
|
|
dataframe['sar'] = talib.SAR(dataframe)
|
|
# Normalization
|
|
tib = dataframe['trend_ichimoku_base']
|
|
dataframe['trend_ichimoku_base'] = (tib - tib.min()) / (tib.max() - tib.min())
|
|
tkd = dataframe['trend_kst_diff']
|
|
dataframe['trend_kst_diff'] = (tkd - tkd.min()) / (tkd.max() - tkd.min())
|
|
|
|
################### INFORMATIVE BTC 1H
|
|
informative = self.dp.get_pair_dataframe(pair='BTC/USDT', timeframe="1h")
|
|
informative["rsi"] = talib.RSI(informative)
|
|
informative['sma7'] = talib.SMA(informative, timeperiod=7)
|
|
informative['pct_change_1'] = informative['close'].pct_change(1)
|
|
bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(informative), window=20, stds=2)
|
|
informative['bb_lowerband'] = bollinger['lower']
|
|
informative['bb_middleband'] = bollinger['mid']
|
|
informative['bb_upperband'] = bollinger['upper']
|
|
informative["bb_percent"] = (
|
|
(informative["close"] - informative["bb_lowerband"]) /
|
|
(informative["bb_upperband"] - informative["bb_lowerband"])
|
|
)
|
|
informative["bb_width"] = (
|
|
(informative["bb_upperband"] - informative["bb_lowerband"]) / informative["bb_middleband"]
|
|
)
|
|
|
|
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
|
|
|
|
return dataframe
|
|
|
|
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
dataframe.loc[
|
|
(
|
|
(dataframe['trend_ichimoku_base'] <= self.buy_base.value)
|
|
& (dataframe['rsi'] < self.buy_rsi.value)
|
|
#& (dataframe['close'] < dataframe['sma10'])
|
|
#& (dataframe['sma7_1h'].shift(1) <= dataframe['sma7_1h'])
|
|
#& (dataframe['close_1h'] <= dataframe['bb_middleband_1h'])
|
|
|
|
), ['buy', 'buy_tag']] = (1, 'buy_ichimoku')
|
|
return dataframe
|
|
|
|
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
|
|
return dataframe
|