567 lines
27 KiB
Python
567 lines
27 KiB
Python
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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import numpy as np
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import talib.abstract as ta
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from freqtrade.persistence import Trade
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from freqtrade.strategy.interface import IStrategy
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from pandas import DataFrame
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from datetime import datetime, timedelta
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from freqtrade.strategy import merge_informative_pair, CategoricalParameter, DecimalParameter, IntParameter
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from functools import reduce
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###########################################################################################################
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## BigZ06 by ilya ##
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## ##
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## https://github.com/i1ya/freqtrade-strategies ##
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## The stratagy most inspired by iterativ (authors of the CombinedBinHAndClucV6) ##
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## ## ##
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###########################################################################################################
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## The main point of this strat is: ##
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## - make drawdown as low as possible ##
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## - buy at dip ##
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## - sell quick as fast as you can (release money for the next buy) ##
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## - soft check if market if rising ##
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## - hard check is market if fallen ##
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## - 14 buy signals ##
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## - stoploss function preventing from big fall ##
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## - no sell signal. Whether ROI or stoploss =) ##
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## ##
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###########################################################################################################
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## GENERAL RECOMMENDATIONS ##
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## ##
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## For optimal performance, suggested to use between 3 and 5 open trades. ##
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## ##
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## As a pairlist you can use VolumePairlist. ##
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## ##
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## Ensure that you don't override any variables in your config.json. Especially ##
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## the timeframe (must be 5m). ##
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## ##
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## sell_profit_only: ##
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## True - risk more (gives you higher profit and higher Drawdown) ##
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## False (default) - risk less (gives you less ~10-15% profit and much lower Drawdown) ##
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## ##
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## BigZ06 using market orders. ##
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## Ensure you're familar with https://www.freqtrade.io/en/stable/configuration/#market-order-pricing ##
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## ##
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###########################################################################################################
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## DONATIONS 2 @iterativ (author of the original strategy) ##
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## ##
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## Absolutely not required. However, will be accepted as a token of appreciation. ##
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## ##
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## BTC: bc1qvflsvddkmxh7eqhc4jyu5z5k6xcw3ay8jl49sk ##
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## ETH: 0x83D3cFb8001BDC5d2211cBeBB8cB3461E5f7Ec91 ##
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## ##
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###########################################################################################################
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class BigZ06(IStrategy):
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INTERFACE_VERSION = 2
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minimal_roi = {
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"0": 0.028, # I feel lucky!
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"10": 0.018,
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"40": 0.005,
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"180": 0.018, # We're going up?
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}
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stoploss = -0.99 # effectively disabled.
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timeframe = '5m'
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inf_1h = '1h'
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# Sell signal
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use_exit_signal = True
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exit_profit_only = False
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exit_profit_offset = 0.001 # it doesn't meant anything, just to guarantee there is a minimal profit.
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ignore_roi_if_entry_signal = False
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# Trailing stoploss
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trailing_stop = False
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trailing_only_offset_is_reached = False
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trailing_stop_positive = 0.01
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trailing_stop_positive_offset = 0.025
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# Custom stoploss
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use_custom_stoploss = True
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# Run "populate_indicators()" only for new candle.
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process_only_new_candles = True
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# Number of candles the strategy requires before producing valid signals
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startup_candle_count: int = 200
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# Optional order type mapping.
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order_types = {
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'entry': 'market',
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'exit': 'market',
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'stoploss': 'market',
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'stoploss_on_exchange': False
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}
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buy_params = {
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#############
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# Enable/Disable conditions
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"buy_condition_0_enable": True,
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"buy_condition_1_enable": True,
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"buy_condition_2_enable": True,
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"buy_condition_3_enable": True,
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"buy_condition_4_enable": True,
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"buy_condition_5_enable": True,
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"buy_condition_6_enable": True,
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"buy_condition_7_enable": True,
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"buy_condition_8_enable": True,
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"buy_condition_9_enable": True,
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"buy_condition_10_enable": True,
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"buy_condition_11_enable": True,
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"buy_condition_12_enable": True,
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"buy_condition_13_enable": True,
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}
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############################################################################
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# Buy
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buy_condition_0_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_condition_1_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_condition_2_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_condition_3_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_condition_4_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_condition_5_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_condition_6_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_condition_7_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_condition_8_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_condition_9_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_condition_10_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_condition_11_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_condition_12_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_condition_13_enable = CategoricalParameter([True, False], default=True, space='buy', optimize=False, load=True)
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buy_bb20_close_bblowerband_safe_1 = DecimalParameter(0.7, 1.1, default=0.989, space='buy', optimize=False, load=True)
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buy_bb20_close_bblowerband_safe_2 = DecimalParameter(0.7, 1.1, default=0.982, space='buy', optimize=False, load=True)
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buy_volume_pump_1 = DecimalParameter(0.1, 0.9, default=0.4, space='buy', decimals=1, optimize=False, load=True)
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buy_volume_drop_1 = DecimalParameter(1, 10, default=3.8, space='buy', decimals=1, optimize=False, load=True)
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buy_volume_drop_2 = DecimalParameter(1, 10, default=3, space='buy', decimals=1, optimize=False, load=True)
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buy_volume_drop_3 = DecimalParameter(1, 10, default=2.7, space='buy', decimals=1, optimize=False, load=True)
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buy_rsi_1h_1 = DecimalParameter(10.0, 40.0, default=16.5, space='buy', decimals=1, optimize=False, load=True)
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buy_rsi_1h_2 = DecimalParameter(10.0, 40.0, default=15.0, space='buy', decimals=1, optimize=False, load=True)
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buy_rsi_1h_3 = DecimalParameter(10.0, 40.0, default=20.0, space='buy', decimals=1, optimize=False, load=True)
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buy_rsi_1h_4 = DecimalParameter(10.0, 40.0, default=35.0, space='buy', decimals=1, optimize=False, load=True)
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buy_rsi_1h_5 = DecimalParameter(10.0, 60.0, default=39.0, space='buy', decimals=1, optimize=False, load=True)
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buy_rsi_1 = DecimalParameter(10.0, 40.0, default=28.0, space='buy', decimals=1, optimize=False, load=True)
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buy_rsi_2 = DecimalParameter(7.0, 40.0, default=10.0, space='buy', decimals=1, optimize=False, load=True)
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buy_rsi_3 = DecimalParameter(7.0, 40.0, default=14.2, space='buy', decimals=1, optimize=False, load=True)
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buy_macd_1 = DecimalParameter(0.01, 0.09, default=0.02, space='buy', decimals=2, optimize=False, load=True)
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buy_macd_2 = DecimalParameter(0.01, 0.09, default=0.03, space='buy', decimals=2, optimize=False, load=True)
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def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float,
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rate: float, time_in_force: str, sell_reason: str, **kwargs) -> bool:
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return True
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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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return False
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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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last_candle_2 = dataframe.iloc[-2].squeeze()
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if (last_candle is not None):
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if (last_candle['high'] > last_candle['bb_upperband']) & (last_candle['volume'] > (last_candle_2['volume'] * 1.5)):
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return 'sell_signal_1'
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return False
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def custom_stoploss(self, pair: str, trade: 'Trade', current_time: datetime,
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current_rate: float, current_profit: float, **kwargs) -> float:
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# Manage losing trades and open room for better ones.
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if (current_profit > 0):
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return 0.99
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else:
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trade_time_50 = trade.open_date_utc + timedelta(minutes=50)
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# Trade open more then 60 minutes. For this strategy it's means -> loss
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# Let's try to minimize the loss
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if (current_time > trade_time_50):
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try:
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number_of_candle_shift = int((current_time - trade_time_50).total_seconds() / 300)
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dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
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candle = dataframe.iloc[-number_of_candle_shift].squeeze()
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# We are at bottom. Wait...
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if candle['rsi_1h'] < 40:
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return 0.99
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# Are we still sinking?
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if candle['close'] > candle['ema_200']:
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if current_rate * 1.035 < candle['open']:
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return 0.01
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if current_rate * 1.025 < candle['open']:
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return 0.01
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except IndexError as error:
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# Whoops, set stoploss at 10%
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return 0.1
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return 0.99
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def informative_pairs(self):
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pairs = self.dp.current_whitelist()
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informative_pairs = [(pair, '1h') for pair in pairs]
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return informative_pairs
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def informative_1h_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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assert self.dp, "DataProvider is required for multiple timeframes."
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# Get the informative pair
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informative_1h = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe=self.inf_1h)
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# EMA
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informative_1h['ema_50'] = ta.EMA(informative_1h, timeperiod=50)
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informative_1h['ema_200'] = ta.EMA(informative_1h, timeperiod=200)
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# RSI
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informative_1h['rsi'] = ta.RSI(informative_1h, timeperiod=14)
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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informative_1h['bb_lowerband'] = bollinger['lower']
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informative_1h['bb_middleband'] = bollinger['mid']
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informative_1h['bb_upperband'] = bollinger['upper']
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return informative_1h
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def normal_tf_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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bollinger = qtpylib.bollinger_bands(qtpylib.typical_price(dataframe), window=20, stds=2)
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dataframe['bb_lowerband'] = bollinger['lower']
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dataframe['bb_middleband'] = bollinger['mid']
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dataframe['bb_upperband'] = bollinger['upper']
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dataframe['volume_mean_slow'] = dataframe['volume'].rolling(window=48).mean()
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# EMA
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dataframe['ema_200'] = ta.EMA(dataframe, timeperiod=200)
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dataframe['ema_26'] = ta.EMA(dataframe, timeperiod=26)
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dataframe['ema_12'] = ta.EMA(dataframe, timeperiod=12)
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# MACD
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dataframe['macd'], dataframe['signal'], dataframe['hist'] = ta.MACD(dataframe['close'], fastperiod=12, slowperiod=26, signalperiod=9)
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# SMA
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dataframe['sma_5'] = ta.EMA(dataframe, timeperiod=5)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe, timeperiod=14)
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# Chaikin A/D Oscillator
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dataframe['mfv'] = MFV(dataframe)
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dataframe['cmf'] = dataframe['mfv'].rolling(20).sum()/dataframe['volume'].rolling(20).sum()
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return dataframe
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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# The indicators for the 1h informative timeframe
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informative_1h = self.informative_1h_indicators(dataframe, metadata)
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dataframe = merge_informative_pair(dataframe, informative_1h, self.timeframe, self.inf_1h, ffill=True)
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# The indicators for the normal (5m) timeframe
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dataframe = self.normal_tf_indicators(dataframe, metadata)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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conditions = []
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conditions.append(
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(
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self.buy_condition_13_enable.value &
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(dataframe['close'] > dataframe['ema_200_1h']) &
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(dataframe['cmf'] < -0.435) &
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(dataframe['rsi'] < 22) &
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(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
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(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
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(dataframe['volume'] > 0)
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)
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)
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conditions.append(
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(
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self.buy_condition_12_enable.value &
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(dataframe['close'] > dataframe['ema_200']) &
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(dataframe['close'] > dataframe['ema_200_1h']) &
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(dataframe['close'] < dataframe['bb_lowerband'] * 0.993) &
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(dataframe['low'] < dataframe['bb_lowerband'] * 0.985) &
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(dataframe['close'].shift() > dataframe['bb_lowerband']) &
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(dataframe['rsi_1h'] < 72.8) &
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(dataframe['open'] > dataframe['close']) &
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(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
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(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
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(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
|
||
|
|
((dataframe['open'] - dataframe['close']) < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) &
|
||
|
|
|
||
|
|
(dataframe['volume'] > 0)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
conditions.append(
|
||
|
|
(
|
||
|
|
self.buy_condition_11_enable.value &
|
||
|
|
|
||
|
|
(dataframe['close'] > dataframe['ema_200']) &
|
||
|
|
|
||
|
|
(dataframe['hist'] > 0) &
|
||
|
|
(dataframe['hist'].shift() > 0) &
|
||
|
|
(dataframe['hist'].shift(2) > 0) &
|
||
|
|
(dataframe['hist'].shift(3) > 0) &
|
||
|
|
(dataframe['hist'].shift(5) > 0) &
|
||
|
|
|
||
|
|
(dataframe['bb_middleband'] - dataframe['bb_middleband'].shift(5) > dataframe['close']/200) &
|
||
|
|
(dataframe['bb_middleband'] - dataframe['bb_middleband'].shift(10) > dataframe['close']/100) &
|
||
|
|
((dataframe['bb_upperband'] - dataframe['bb_lowerband']) < (dataframe['close']*0.1)) &
|
||
|
|
((dataframe['open'].shift() - dataframe['close'].shift()) < (dataframe['close'] * 0.018)) &
|
||
|
|
(dataframe['rsi'] > 51) &
|
||
|
|
|
||
|
|
(dataframe['open'] < dataframe['close']) &
|
||
|
|
(dataframe['open'].shift() > dataframe['close'].shift()) &
|
||
|
|
|
||
|
|
(dataframe['close'] > dataframe['bb_middleband']) &
|
||
|
|
(dataframe['close'].shift() < dataframe['bb_middleband'].shift()) &
|
||
|
|
(dataframe['low'].shift(2) > dataframe['bb_middleband'].shift(2)) &
|
||
|
|
|
||
|
|
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
conditions.append(
|
||
|
|
(
|
||
|
|
self.buy_condition_0_enable.value &
|
||
|
|
|
||
|
|
(dataframe['close'] > dataframe['ema_200']) &
|
||
|
|
|
||
|
|
(dataframe['rsi'] < 30) &
|
||
|
|
(dataframe['close'] * 1.024 < dataframe['open'].shift(3)) &
|
||
|
|
(dataframe['rsi_1h'] < 71) &
|
||
|
|
|
||
|
|
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
|
||
|
|
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
|
||
|
|
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
conditions.append(
|
||
|
|
(
|
||
|
|
self.buy_condition_1_enable.value &
|
||
|
|
|
||
|
|
(dataframe['close'] > dataframe['ema_200']) &
|
||
|
|
(dataframe['close'] > dataframe['ema_200_1h']) &
|
||
|
|
|
||
|
|
(dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb20_close_bblowerband_safe_1.value) &
|
||
|
|
(dataframe['rsi_1h'] < 69) &
|
||
|
|
(dataframe['open'] > dataframe['close']) &
|
||
|
|
|
||
|
|
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
|
||
|
|
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
|
||
|
|
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
|
||
|
|
((dataframe['open'] - dataframe['close']) < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) &
|
||
|
|
|
||
|
|
(dataframe['volume'] > 0)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
conditions.append(
|
||
|
|
(
|
||
|
|
self.buy_condition_2_enable.value &
|
||
|
|
|
||
|
|
(dataframe['close'] > dataframe['ema_200']) &
|
||
|
|
|
||
|
|
(dataframe['close'] < dataframe['bb_lowerband'] * self.buy_bb20_close_bblowerband_safe_2.value) &
|
||
|
|
|
||
|
|
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
|
||
|
|
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
|
||
|
|
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
|
||
|
|
(dataframe['open'] - dataframe['close'] < dataframe['bb_upperband'].shift(2) - dataframe['bb_lowerband'].shift(2)) &
|
||
|
|
(dataframe['volume'] > 0)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
conditions.append(
|
||
|
|
(
|
||
|
|
self.buy_condition_3_enable.value &
|
||
|
|
|
||
|
|
(dataframe['close'] > dataframe['ema_200_1h']) &
|
||
|
|
|
||
|
|
(dataframe['close'] < dataframe['bb_lowerband']) &
|
||
|
|
(dataframe['rsi'] < self.buy_rsi_3.value) &
|
||
|
|
|
||
|
|
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
|
||
|
|
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
|
||
|
|
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_3.value)) &
|
||
|
|
|
||
|
|
(dataframe['volume'] > 0)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
conditions.append(
|
||
|
|
(
|
||
|
|
self.buy_condition_4_enable.value &
|
||
|
|
|
||
|
|
(dataframe['rsi_1h'] < self.buy_rsi_1h_1.value) &
|
||
|
|
|
||
|
|
(dataframe['close'] < dataframe['bb_lowerband']) &
|
||
|
|
|
||
|
|
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
|
||
|
|
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
|
||
|
|
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
|
||
|
|
(dataframe['volume'] > 0)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
conditions.append(
|
||
|
|
(
|
||
|
|
self.buy_condition_5_enable.value &
|
||
|
|
|
||
|
|
(dataframe['close'] > dataframe['ema_200']) &
|
||
|
|
(dataframe['close'] > dataframe['ema_200_1h']) &
|
||
|
|
|
||
|
|
(dataframe['ema_26'] > dataframe['ema_12']) &
|
||
|
|
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_1.value)) &
|
||
|
|
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) &
|
||
|
|
(dataframe['close'] < (dataframe['bb_lowerband'])) &
|
||
|
|
|
||
|
|
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
|
||
|
|
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
|
||
|
|
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
|
||
|
|
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
conditions.append(
|
||
|
|
(
|
||
|
|
self.buy_condition_6_enable.value &
|
||
|
|
|
||
|
|
(dataframe['rsi_1h'] < self.buy_rsi_1h_5.value) &
|
||
|
|
|
||
|
|
(dataframe['ema_26'] > dataframe['ema_12']) &
|
||
|
|
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_2.value)) &
|
||
|
|
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) &
|
||
|
|
(dataframe['close'] < (dataframe['bb_lowerband'])) &
|
||
|
|
|
||
|
|
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
|
||
|
|
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
|
||
|
|
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
|
||
|
|
(dataframe['volume'] > 0)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
conditions.append(
|
||
|
|
(
|
||
|
|
self.buy_condition_7_enable.value &
|
||
|
|
|
||
|
|
(dataframe['rsi_1h'] < self.buy_rsi_1h_2.value) &
|
||
|
|
|
||
|
|
(dataframe['ema_26'] > dataframe['ema_12']) &
|
||
|
|
((dataframe['ema_26'] - dataframe['ema_12']) > (dataframe['open'] * self.buy_macd_1.value)) &
|
||
|
|
((dataframe['ema_26'].shift() - dataframe['ema_12'].shift()) > (dataframe['open']/100)) &
|
||
|
|
|
||
|
|
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
|
||
|
|
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
|
||
|
|
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
|
||
|
|
(dataframe['volume'] > 0)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
conditions.append(
|
||
|
|
(
|
||
|
|
|
||
|
|
self.buy_condition_8_enable.value &
|
||
|
|
|
||
|
|
(dataframe['rsi_1h'] < self.buy_rsi_1h_3.value) &
|
||
|
|
(dataframe['rsi'] < self.buy_rsi_1.value) &
|
||
|
|
|
||
|
|
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
|
||
|
|
|
||
|
|
(dataframe['volume'] > 0)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
conditions.append(
|
||
|
|
(
|
||
|
|
|
||
|
|
self.buy_condition_9_enable.value &
|
||
|
|
|
||
|
|
(dataframe['rsi_1h'] < self.buy_rsi_1h_4.value) &
|
||
|
|
(dataframe['rsi'] < self.buy_rsi_2.value) &
|
||
|
|
|
||
|
|
(dataframe['volume'] < (dataframe['volume'].shift() * self.buy_volume_drop_1.value)) &
|
||
|
|
(dataframe['volume_mean_slow'] > dataframe['volume_mean_slow'].shift(48) * self.buy_volume_pump_1.value) &
|
||
|
|
(dataframe['volume_mean_slow'] * self.buy_volume_pump_1.value < dataframe['volume_mean_slow'].shift(48)) &
|
||
|
|
(dataframe['volume'] > 0)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
conditions.append(
|
||
|
|
(
|
||
|
|
|
||
|
|
self.buy_condition_10_enable.value &
|
||
|
|
|
||
|
|
(dataframe['rsi_1h'] < self.buy_rsi_1h_4.value) &
|
||
|
|
(dataframe['close_1h'] < dataframe['bb_lowerband_1h']) &
|
||
|
|
|
||
|
|
(dataframe['hist'] > 0) &
|
||
|
|
(dataframe['hist'].shift(2) < 0) &
|
||
|
|
(dataframe['rsi'] < 40.5) &
|
||
|
|
(dataframe['hist'] > dataframe['close'] * 0.0012) &
|
||
|
|
(dataframe['open'] < dataframe['close']) &
|
||
|
|
|
||
|
|
(dataframe['volume'] > 0)
|
||
|
|
)
|
||
|
|
)
|
||
|
|
|
||
|
|
if conditions:
|
||
|
|
dataframe.loc[
|
||
|
|
reduce(lambda x, y: x | y, conditions),
|
||
|
|
'buy'
|
||
|
|
] = 1
|
||
|
|
|
||
|
|
return dataframe
|
||
|
|
|
||
|
|
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
|
|
dataframe.loc[
|
||
|
|
(
|
||
|
|
(dataframe['close'] > dataframe['bb_middleband'] * 1.01) & # Don't be gready, sell fast
|
||
|
|
(dataframe['volume'] > 0) # Make sure Volume is not 0
|
||
|
|
)
|
||
|
|
,
|
||
|
|
'sell'
|
||
|
|
] = 0
|
||
|
|
return dataframe
|
||
|
|
|
||
|
|
# Chaikin Money Flow Volume
|
||
|
|
def MFV(dataframe):
|
||
|
|
df = dataframe.copy()
|
||
|
|
N = ((df['close'] - df['low']) - (df['high'] - df['close'])) / (df['high'] - df['low'])
|
||
|
|
M = N * df['volume']
|
||
|
|
return M
|