513 lines
26 KiB
Python
513 lines
26 KiB
Python
# Zeus Strategy: First Generation of GodStra Strategy with maximum
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# AVG/MID profit in USDT
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# Author: @Mablue (Masoud Azizi)
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# github: https://github.com/mablue/
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# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
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# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi --strategy Zeus
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# --- Do not remove these libs ---
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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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# --------------------------------
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# Add your lib to import here
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import ta
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from functools import reduce
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import numpy as np
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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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class Zeus_4(IStrategy):
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# * 1/43: 86 trades. 72/6/8 Wins/Draws/Losses. Avg profit 12.66%. Median profit 11.99%. Total profit 0.10894395 BTC ( 108.94Σ%). Avg duration 3 days, 0:31:00 min. Objective: -48.48793
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# "max_open_trades": 10,
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# "stake_currency": "BTC",
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# "stake_amount": 0.01,
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# "tradable_balance_ratio": 0.99,
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# "timeframe": "4h",
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# "dry_run_wallet": 0.1,
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# Buy hyperspace params:
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buy_b_params = {
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"buy_b_cat": "<R",
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"buy_b_real": 0.0128,
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}
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# Sell hyperspace params:
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sell_b_params = {
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"sell_b_cat": "=R",
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"sell_b_real": 0.9455,
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}
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# Buy hyperspace params:
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buy_h_params = {
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"buy_h_cat": "<R",
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"buy_h_real": 0.0128,
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}
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# Sell hyperspace params:
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sell_h_params = {
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"sell_h_cat": "=R",
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"sell_h_real": 0.9455,
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}
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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_b_real = DecimalParameter(0.001, 0.999, decimals=4, default=0.11908, space='buy')
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buy_b_cat = CategoricalParameter([">R", "=R", "<R"], default='<R', space='buy')
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buy_b_pct = DecimalParameter(0.001, 0.02, decimals=3, default=0.005, space='buy')
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# buy_b_pct_1 = DecimalParameter(-0.2, 0.2, decimals=2, default=0.005, space='buy')
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# buy_b_pct_3 = DecimalParameter(-0.2, 0.2, decimals=2, default=0.005, space='buy')
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# buy_b_pct_5 = DecimalParameter(-0.2, 0.2, decimals=2, default=0.005, space='buy')
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buy_b_bb_lowerband = DecimalParameter(1, 1.05, default=1, decimals=2, space='buy')
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buy_b_bb_width = DecimalParameter(0.01, 0.15, default=0.065, decimals=2, space='buy')
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buy_h_real = DecimalParameter(0.001, 0.999, decimals=4, default=0.11908, space='buy')
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buy_h_cat = CategoricalParameter([">R", "=R", "<R"], default='<R', space='buy')
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buy_h_pct = DecimalParameter(0.001, 0.02, decimals=3, default=0.005, space='buy')
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# buy_h_pct_1 = DecimalParameter(-0.2, 0.2, decimals=2, default=0.005, space='buy')
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# buy_h_pct_3 = DecimalParameter(-0.2, 0.2, decimals=2, default=0.005, space='buy')
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# buy_h_pct_5 = DecimalParameter(-0.2, 0.2, decimals=2, default=0.005, space='buy')
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buy_h_bb_lowerband = DecimalParameter(1, 1.05, default=1, decimals=2, space='buy')
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buy_h_bb_width = DecimalParameter(0.01, 0.15, default=0.065, decimals=2, space='buy')
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# sell_h_real = DecimalParameter(0.001, 0.999, decimals=4, default=0.59608, space='sell')
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# sell_h_cat = CategoricalParameter([">R", "=R", "<R"], default='>R', space='sell')
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# Buy hypers
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timeframe = '4h'
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plot_config = {
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"main_plot": {
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"min200": {"color": "#86c932"},
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"max50": {"color": "white"},
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"max200": {"color": "yellow"},
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"sma3_1d": {"color": "pink"},
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"sma5_1d": {"color": "blue"},
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"sma10_1d": {"color": "orange"},
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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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"Percent": {
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"max_min": {"color": "#74effc"},
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"pct_change_1_1d": {"color": "green"},
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"pct_change_3_1d": {"color": "orange"},
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"pct_change_5_1d": {"color": "red"},
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}
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}
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}
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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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# sell_h_expected_profit = DecimalParameter(0, 0.01, decimals=3, default=0.01, space='sell')
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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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# print(last_candle)
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days = (current_time - trade.open_date_utc).days
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######
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if last_candle['pct_change_1_1d'] < 0:
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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 "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 "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 "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 "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 "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 "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) | (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 '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) | (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 '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) | (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 'sma20'
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if self.profit_b_over_rsi.value:
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if (current_profit > 0) & (previous_last_candle['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 '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['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 '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']): #| (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 '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) & (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 'short_lost'
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else:
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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 "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 "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) \
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& (previous_last_candle['sma10'] > last_candle['sma10']) & (last_candle['percent3'] < 0):
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return "too_old_0.03"
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if self.profit_h_quick_lost.value and (current_profit >= 0.015) & (last_candle['percent3'] < -0.005):
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return "quick_lost"
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if self.profit_h_no_change.value and (current_profit > self.sell_h_profit_no_change.value) \
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& (last_candle['percent10'] < self.sell_h_profit_percent10.value) & (last_candle['percent5'] < 0) \
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& ((current_time - trade.open_date_utc).seconds >= 3600):
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return "no_change"
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if (current_profit > self.sell_h_percent.value) & (last_candle['percent3'] < - self.sell_h_percent3.value) \
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& ((current_time - trade.open_date_utc).seconds <= 300 * self.sell_h_candels.value):
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return "quick_gain_param"
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if self.profit_h_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) | (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 'sma5'
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if self.profit_h_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) | (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 'sma10'
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if self.profit_h_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) | (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 'sma20'
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if self.profit_h_over_rsi.value:
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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)):
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# print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
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return 'over_rsi'
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if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_h_RSI2.value) & \
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(last_candle['percent'] < - self.sell_h_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 'over_rsi_2'
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if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_h_RSI3.value) & \
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(last_candle['close'] >= last_candle['max200']): #| (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 'over_rsi_max'
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if self.profit_h_short_loss.value:
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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 'short_lost'
|
|
|
|
def informative_pairs(self):
|
|
# get access to all pairs available in whitelist.
|
|
pairs = self.dp.current_whitelist()
|
|
informative_pairs = [(pair, '1d') for pair in pairs]
|
|
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['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['max50'] = talib.MAX(dataframe['close'], timeperiod=50)
|
|
dataframe['max200'] = talib.MAX(dataframe['close'], timeperiod=200)
|
|
dataframe['rsi'] = talib.RSI(dataframe)
|
|
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()
|
|
|
|
# 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 = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
|
|
informative["rsi"] = talib.RSI(informative)
|
|
informative["max3"] = talib.MAX(informative['close'], timeperiod=3)
|
|
informative["min3"] = talib.MIN(informative['close'], timeperiod=3)
|
|
informative['pct_change_1'] = informative['close'].pct_change(1)
|
|
informative['pct_change_3'] = informative['close'].pct_change(3)
|
|
informative['pct_change_5'] = informative['close'].pct_change(5)
|
|
informative['sma3'] = talib.SMA(informative, timeperiod=3)
|
|
informative['sma5'] = talib.SMA(informative, timeperiod=5)
|
|
informative['sma10'] = talib.SMA(informative, timeperiod=10)
|
|
informative['sar'] = talib.SAR(informative)
|
|
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, "1d", ffill=True)
|
|
return dataframe
|
|
|
|
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
conditions = []
|
|
IND = 'trend_ichimoku_base'
|
|
REAL = self.buy_h_real.value
|
|
OPR = self.buy_h_cat.value
|
|
DFIND = dataframe[IND]
|
|
# print(DFIND.mean())
|
|
if OPR == ">R":
|
|
conditions.append(DFIND > REAL)
|
|
elif OPR == "=R":
|
|
conditions.append(np.isclose(DFIND, REAL))
|
|
elif OPR == "<R":
|
|
conditions.append(DFIND < REAL)
|
|
|
|
if conditions:
|
|
dataframe.loc[
|
|
(reduce(lambda x, y: x & y, conditions))
|
|
& (dataframe['pct_change'] < - self.buy_h_pct.value)
|
|
& (dataframe['close'] <= dataframe['min200'] * 1.002)
|
|
& (dataframe['pct_change_1_1d'] > 0)
|
|
& (dataframe['pct_change_3_1d'] > 0)
|
|
& (dataframe['pct_change_5_1d'] > 0) #self.buy_h_pct_5.value)
|
|
#& (dataframe['close_1d'] < dataframe['bb_lowerband_1d'] * self.buy_h_bb_lowerband.value)
|
|
& (dataframe['bb_width_1d'] >= self.buy_h_bb_width.value)
|
|
& (dataframe['close'] <= dataframe['sma5_1d'])
|
|
& (dataframe['sma10_1d'].shift(1) <= dataframe['sma10_1d'])
|
|
|
|
& (dataframe['volume10'] * dataframe['close'] / 1000 >= 10)
|
|
,
|
|
['buy', 'buy_tag']] = (1, 'buy_h')
|
|
|
|
conditions = []
|
|
IND = 'trend_ichimoku_base'
|
|
REAL = self.buy_b_real.value
|
|
OPR = self.buy_b_cat.value
|
|
DFIND = dataframe[IND]
|
|
# print(DFIND.mean())
|
|
if OPR == ">R":
|
|
conditions.append(DFIND > REAL)
|
|
elif OPR == "=R":
|
|
conditions.append(np.isclose(DFIND, REAL))
|
|
elif OPR == "<R":
|
|
conditions.append(DFIND < REAL)
|
|
|
|
if conditions:
|
|
dataframe.loc[
|
|
(reduce(lambda x, y: x & y, conditions))
|
|
& (dataframe['pct_change'] < - self.buy_b_pct.value)
|
|
& (dataframe['close'] <= dataframe['min200'] * 1.002)
|
|
& (dataframe['pct_change_1_1d'] > 0)
|
|
& (dataframe['pct_change_3_1d'] < 0) # self.buy_b_pct_3.value)
|
|
& (dataframe['pct_change_5_1d'] < 0) #self.buy_b_pct_5.value)
|
|
#& (dataframe['close_1d'] < dataframe['bb_lowerband_1d'] * self.buy_b_bb_lowerband.value)
|
|
& (dataframe['bb_width_1d'] >= self.buy_b_bb_width.value)
|
|
& (dataframe['close'] <= dataframe['sma5_1d'])
|
|
& (dataframe['sma10_1d'].shift(1) <= dataframe['sma10_1d'])
|
|
|
|
& (dataframe['volume10'] * dataframe['close'] / 1000 >= 10)
|
|
,
|
|
['buy', 'buy_tag']] = (1, 'buy_b')
|
|
|
|
return dataframe
|
|
|
|
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
# conditions = []
|
|
# IND = 'trend_kst_diff'
|
|
# REAL = self.sell_h_real.value
|
|
# OPR = self.sell_h_cat.value
|
|
# DFIND = dataframe[IND]
|
|
# # print(DFIND.mean())
|
|
#
|
|
# if OPR == ">R":
|
|
# conditions.append(DFIND > REAL)
|
|
# elif OPR == "=R":
|
|
# conditions.append(np.isclose(DFIND, REAL))
|
|
# elif OPR == "<R":
|
|
# conditions.append(DFIND < REAL)
|
|
#
|
|
# if conditions:
|
|
# dataframe.loc[
|
|
# reduce(lambda x, y: x & y, conditions),
|
|
# 'sell'] = 1
|
|
|
|
return dataframe
|