first commit
This commit is contained in:
Jérôme Delacotte
326
StrategyJD_5_4.py
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326
StrategyJD_5_4.py
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# pr#agma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
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# isort: skip_file
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# --- Do not remove these libs ---
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from datetime import datetime
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import numpy as np # noqa
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import pandas as pd # noqa
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from freqtrade.strategy.parameters import DecimalParameter, BooleanParameter, IntParameter
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from pandas import DataFrame
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import math
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from functools import reduce
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from freqtrade.strategy.interface import IStrategy
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# --------------------------------
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# Add your lib to import here
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import talib.abstract as ta
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import freqtrade.vendor.qtpylib.indicators as qtpylib
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from freqtrade.strategy.strategy_helper import merge_informative_pair
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# This class is a sample. Feel free to customize it.
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class StrategyJD_5_4(IStrategy):
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# Strategy interface version - allow new iterations of the strategy interface.
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# Check the documentation or the Sample strategy to get the latest version.
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INTERFACE_VERSION = 2
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# valeur de bbwidth pour démarrer
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# buy_bollinger = DecimalParameter(0.025, 0.125, decimals=2, default=0.07, space="buy")
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#buy_msma_10 = DecimalParameter(0.997, 1.020, decimals=3, default=0.998, space="buy")
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# pourcentage sma à dépasser
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# buy_sma_percent = DecimalParameter(0.95, 1.05, decimals=2, default=0.97, space="buy")
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buy_decalage = IntParameter(1, 24, default=5, space="buy")
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buy_decalage2 = IntParameter(1, 24, default=5, space="buy")
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buy_decalage3 = IntParameter(1, 24, default=5, space="buy")
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buy_decalage4 = IntParameter(1, 24, default=5, space="buy")
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buy_min_max_n = DecimalParameter(0, 0.2, decimals=2, default=0.05, space='buy')
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buy_min_max_n2 = DecimalParameter(0, 0.2, decimals=2, default=0.05, space='buy')
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buy_min_max_n3 = DecimalParameter(0, 0.2, decimals=2, default=0.05, space='buy')
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buy_min_max_n4 = DecimalParameter(0, 0.2, decimals=2, default=0.05, space='buy')
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buy_rsi_min_1d = IntParameter(0, 25, default=5, space="buy")
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buy_rsi_min_1d2 = IntParameter(25, 50, default=15, space="buy")
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buy_rsi_min_1d3 = IntParameter(50, 75, default=50, space="buy")
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buy_rsi_min_1d4 = IntParameter(75, 100, default=75, space="buy")
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min_percent = DecimalParameter(1, 1.02, decimals=3, default=1.002, space='buy')
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min_percent2 = DecimalParameter(1, 1.02, decimals=3, default=1.002, space='buy')
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min_percent3 = DecimalParameter(1, 1.02, decimals=3, default=1.002, space='buy')
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min_percent4 = DecimalParameter(1, 1.02, decimals=3, default=1.002, space='buy')
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max_percent = DecimalParameter(0, 0.05, decimals=3, default=0.005, space='sell')
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max_percent2 = DecimalParameter(0, 0.05, decimals=3, default=0.005, space='sell')
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max_percent3 = DecimalParameter(0, 0.05, decimals=3, default=0.005, space='sell')
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max_percent4 = DecimalParameter(0, 0.05, decimals=3, default=0.005, space='sell')
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max_profit = DecimalParameter(0, 0.1, decimals=2, default=0.01, space='sell')
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max_profit2 = DecimalParameter(0, 0.1, decimals=2, default=0.01, space='sell')
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max_profit3 = DecimalParameter(0, 0.1, decimals=2, default=0.01, space='sell')
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max_profit4 = DecimalParameter(0, 0.1, decimals=2, default=0.01, 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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#
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# sell_b_RSI2_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, 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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# buy_rsi_max = IntParameter(50, 100, default=60, space="buy")
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buy_rsi_min = IntParameter(0, 50, default=25, space="buy")
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buy_rsi_max = IntParameter(50, 100, default=60, space="buy")
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# buy_rsi_min2 = IntParameter(0, 50, default=25, space="buy")
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# buy_rsi_max2 = IntParameter(50, 100, default=60, space="buy")
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min_n = IntParameter(0, 24, default=15, space="buy")
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min_p = DecimalParameter(1, 1.01, decimals=3, default=1.002, space="buy")
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n_percent = IntParameter(1, 12, default=1, space="protection")
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percent_sell = DecimalParameter(-0.2, -0.01, decimals=2, default=-0.08, space="protection")
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n_percent2 = IntParameter(1, 12, default=1, space="protection")
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percent_sell2 = DecimalParameter(-0.2, -0.01, decimals=2, default=-0.08, space="protection")
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n_percent3 = IntParameter(1, 12, default=1, space="protection")
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percent_sell3 = DecimalParameter(-0.2, -0.01, decimals=2, default=-0.08, space="protection")
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n_percent4 = IntParameter(1, 12, default=1, space="protection")
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percent_sell4 = DecimalParameter(-0.2, -0.01, decimals=2, default=-0.08, space="protection")
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# buy_adx_enabled = BooleanParameter(default=True, space="buy")
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# buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy")
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# buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy")
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# ROI table:
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minimal_roi = {
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# "0": 0.015
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"0": 0.5
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}
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# Stoploss:
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stoploss = -1
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trailing_stop = True
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trailing_stop_positive = 0.001
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trailing_stop_positive_offset = 0.0175 # 0.015
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trailing_only_offset_is_reached = True
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# max_open_trades = 3
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# Optimal ticker interval for the strategy.
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timeframe = '5m'
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# Run "populate_indicators()" only for new candle.
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process_only_new_candles = False
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# These values can be overridden in the "ask_strategy" section in the config.
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use_sell_signal = True
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sell_profit_only = False
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ignore_roi_if_buy_signal = False
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# Number of candles the strategy requires before producing valid signals
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startup_candle_count: int = 30
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# Optional order type mapping.
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order_types = {
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'buy': 'limit',
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'sell': 'limit',
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'stoploss': 'market',
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'stoploss_on_exchange': False
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}
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# Optional order time in force.
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order_time_in_force = {
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'buy': 'gtc',
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'sell': 'gtc'
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}
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plot_config = {
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# Main plot indicators (Moving averages, ...)
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'main_plot': {
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'bb_lowerband': {'color': 'white'},
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'bb_upperband': {'color': 'white'},
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'min200': {'color': 'yellow'},
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'min200_001': {'color': 'yellow'},
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},
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'subplots': {
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# Subplots - each dict defines one additional plot
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"BB": {
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'bb_width': {'color': 'white'},
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},
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"ADX": {
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'adx': {'color': 'white'},
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'minus_dm': {'color': 'blue'},
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'plus_dm': {'color': 'red'}
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},
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"rolling": {
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'bb_rolling': {'color': '#87e470'},
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"bb_rolling_min": {'color': '#ac3e2a'}
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}
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}
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}
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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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if (last_candle['rsi_1h'] < self.buy_rsi_min_1d.value):
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max_percent = self.max_percent.value
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max_profit = self.max_profit.value
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if last_candle['percent' + str(self.n_percent.value)] < self.percent_sell.value:
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return 'sell_lost_percent' + str(self.n_percent.value)
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else:
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if (last_candle['rsi_1h'] < self.buy_rsi_min_1d2.value):
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max_percent = self.max_percent2.value
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max_profit = self.max_profit2.value
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if last_candle['percent' + str(self.n_percent2.value)] < self.percent_sell2.value:
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return 'sell_lost_percent' + str(self.n_percent2.value)
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else:
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if (last_candle['rsi_1h'] < self.buy_rsi_min_1d3.value):
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max_percent = self.max_percent3.value
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max_profit = self.max_profit3.value
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if last_candle['percent' + str(self.n_percent3.value)] < self.percent_sell3.value:
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return 'sell_lost_percent' + str(self.n_percent3.value)
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else:
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max_percent = self.max_percent4.value
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max_profit = self.max_profit4.value
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if last_candle['percent' + str(self.n_percent4.value)] < self.percent_sell4.value:
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return 'sell_lost_percent' + str(self.n_percent4.value)
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if (current_profit > max_profit) & (
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(last_candle['percent1'] < -max_percent) | (last_candle['percent3'] < -max_percent) | (
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last_candle['percent5'] < -max_percent)):
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return 'h_percent_quick'
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if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_h_RSI.value):
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return 'h_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['percent1'] < - self.sell_h_RSI2_percent.value):
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return 'h_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']):
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return 'h_over_rsi_max'
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def informative_pairs(self):
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# get access to all pairs available in whitelist.
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pairs = self.dp.current_whitelist()
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# informative_pairs = [(pair, '1d') for pair in pairs]
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# informative_pairs += [(pair, '4h') for pair in pairs]
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informative_pairs = [(pair, '1h') for pair in pairs]
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return informative_pairs
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def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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dataframe['min'] = ta.MIN(dataframe)
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dataframe['max'] = ta.MAX(dataframe)
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dataframe['min200'] = ta.MIN(dataframe['close'], timeperiod=200)
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dataframe['min200_001'] = dataframe['min200'] * self.min_percent.value
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dataframe['min200_002'] = dataframe['min200'] * self.min_percent2.value
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dataframe['min200_003'] = dataframe['min200'] * self.min_percent3.value
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dataframe['min200_004'] = dataframe['min200'] * self.min_percent4.value
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dataframe['max200'] = ta.MAX(dataframe['close'], timeperiod=200)
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dataframe['min_n'] = ta.MIN(dataframe['close'], timeperiod=self.min_n.value * 12)
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dataframe['max_n'] = ta.MAX(dataframe['close'], timeperiod=self.min_n.value * 12)
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dataframe['min_max_n'] = (dataframe['max_n'] - dataframe['min_n']) / dataframe['min_n']
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dataframe['min_n_p'] = dataframe['min_n'] * self.min_p.value
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for n in range(1, 25):
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dataframe["percent" + str(n)] = dataframe['close'].pct_change(n)
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# RSI
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dataframe['rsi'] = ta.RSI(dataframe)
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# Bollinger Bands
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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["bb_percent"] = (
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(dataframe["close"] - dataframe["bb_lowerband"]) /
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
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)
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dataframe["bb_width"] = (
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(dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
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)
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################### INFORMATIVE 1h
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informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
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informative["rsi"] = ta.RSI(informative)
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informative["rsi3"] = ta.RSI(informative, 3)
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informative['r_rsi'] = (informative['rsi3'].div(10).round())
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for n in range(1, 5):
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informative["percent" + str(n)] = informative['close'].pct_change(n)
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
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return dataframe
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def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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for decalage in range(self.buy_decalage.value - 2, self.buy_decalage.value):
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conditions = [
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(dataframe['rsi_1h'] < self.buy_rsi_min_1d.value),
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(dataframe['close'].shift(decalage) < dataframe['min200_001'].shift(decalage)),
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(dataframe['min_max_n'] >= self.buy_min_max_n.value),
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(dataframe['rsi_1h'] > self.buy_rsi_min.value),
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(dataframe['rsi_1h'] < self.buy_rsi_max.value),
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]
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# GUARDS AND TRENDS
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if conditions:
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dataframe.loc[(reduce(lambda x, y: x & y, conditions)),
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['buy', 'buy_tag']] = (1, 'buy_1_' + str(decalage))
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break
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for decalage in range(self.buy_decalage2.value - 2, self.buy_decalage2.value):
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conditions = [
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(dataframe['rsi_1h'] >= self.buy_rsi_min_1d.value),
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(dataframe['rsi_1h'] < self.buy_rsi_min_1d2.value),
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(dataframe['close'].shift(decalage) < dataframe['min200_002'].shift(decalage)),
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(dataframe['min_max_n'] >= self.buy_min_max_n2.value),
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(dataframe['rsi_1h'] > self.buy_rsi_min.value),
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(dataframe['rsi_1h'] < self.buy_rsi_max.value),
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]
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# GUARDS AND TRENDS
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if conditions:
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dataframe.loc[(reduce(lambda x, y: x & y, conditions)),
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['buy', 'buy_tag']] = (1, 'buy_2_' + str(decalage))
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break
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for decalage in range(self.buy_decalage3.value - 2, self.buy_decalage3.value):
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conditions = [
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(dataframe['rsi_1h'] >= self.buy_rsi_min_1d2.value),
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(dataframe['rsi_1h'] < self.buy_rsi_min_1d3.value),
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(dataframe['close'].shift(decalage) < dataframe['min200_003'].shift(decalage)),
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(dataframe['min_max_n'] >= self.buy_min_max_n3.value),
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(dataframe['rsi_1h'] > self.buy_rsi_min.value),
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(dataframe['rsi_1h'] < self.buy_rsi_max.value),
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]
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# GUARDS AND TRENDS
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if conditions:
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dataframe.loc[(reduce(lambda x, y: x & y, conditions)),
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['buy', 'buy_tag']] = (1, 'buy_3_' + str(decalage))
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break
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for decalage in range(self.buy_decalage4.value - 2, self.buy_decalage4.value):
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conditions = [
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(dataframe['rsi_1h'] >= self.buy_rsi_min_1d3.value),
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(dataframe['rsi_1h'] < self.buy_rsi_min_1d4.value),
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(dataframe['close'].shift(decalage) < dataframe['min200_004'].shift(decalage)),
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(dataframe['min_max_n'] >= self.buy_min_max_n4.value),
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(dataframe['rsi_1h'] > self.buy_rsi_min.value),
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(dataframe['rsi_1h'] < self.buy_rsi_max.value),
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]
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# GUARDS AND TRENDS
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if conditions:
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dataframe.loc[(reduce(lambda x, y: x & y, conditions)),
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['buy', 'buy_tag']] = (1, 'buy_4_' + str(decalage))
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break
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return dataframe
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def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
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return dataframe
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