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Jérôme Delacotte
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StrategyJD_5_9.py
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301
StrategyJD_5_9.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_9(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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buy_decalage4 = IntParameter(1, 10, default=5, space="buy")
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min_percent4 = IntParameter(1, 20, default=10, space='buy')
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# buy_mrsi3 = DecimalParameter(-0.1, 0.1, decimals=2, default=0, space="buy")
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rapport_min_n = IntParameter(1, 20, default=10, space='buy')
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min_n = 16
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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_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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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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n_percent = IntParameter(1, 12, default=1, space="protection")
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percent_sell = DecimalParameter(-0.1, -0.01, decimals=2, default=-0.08, space="protection")
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percent_sell_stop = DecimalParameter(-0.8, -0.1, decimals=1, default=-0.8, space="protection")
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percent_sell_sma5_1d = DecimalParameter(-0.1, 0, decimals=2, default=0, space="protection")
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hours_sell = IntParameter(5, 48, default=24, 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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lim_0 = 10
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lim_1 = 18
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lim_2 = 25
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lim_3 = 51
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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": {
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"bb_lowerband": {
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"color": "white"
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},
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"bb_upperband": {
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"color": "white"
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},
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"min200": {
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"color": "yellow"
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},
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"max200": {
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"color": "yellow"
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},
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"min_n": {
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"color": "#600e82"
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},
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"max_n": {
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"color": "#600e82"
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},
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"min5_1d": {
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"color": "#6aa123",
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},
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"max5_1d": {
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"color": "red"
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},
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"max3_1d": {
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"color": "blue"
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},
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"close_1d": {
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"color": "green"
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},
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"close_1M": {
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"color": "cyan"
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},
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"min_n_1d": {
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"color": "pink"
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},
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"max_n_1d": {
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"color": "pink"
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},
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"sma5_1d": {
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"color": "black"
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}
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},
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"subplots": {
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"Rsi": {
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"rsi_1h": {
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"color": "blue"
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},
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"rsi_1d": {
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"color": "red"
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},
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"rsi": {
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"color": "green"
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},
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},
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# "Percent": {
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# "min_max200": {
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# "color": "#c046bb"
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# }
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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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previous_previous_last_candle = dataframe.iloc[-2].squeeze()
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days = (current_time - trade.open_date_utc).days
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minutes = (current_time - trade.open_date_utc).seconds / 60
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hours = minutes / 60
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if (last_candle['sma5_diff_1d'] < self.percent_sell_sma5_1d.value) & (current_profit < -0.05):
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return 'sell_sma5_1d'
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if (current_profit < self.percent_sell_stop.value) & (hours >= self.hours_sell.value) & (last_candle['percent_1h'] <= -0.05):
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return 'sell_stop'
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# if (last_candle['percent1'] < -0.005) & (last_candle['max200'] == previous_previous_last_candle['max200']) & (minutes > 30):
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# return 'sell_percent1'
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#
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# if (last_candle['percent3'] < -0.005) & (last_candle['percent1'] < 0) \
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# & (previous_last_candle['percent1'] < 0) & (previous_previous_last_candle['percent1'] < 0) & (minutes > 30):
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# return 'sell_percent3'
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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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if (last_candle['rsi_1h'] < self.lim_1):
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max_percent = self.max_percent.value
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max_profit = self.max_profit.value
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else:
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if (last_candle['rsi_1h'] < self.lim_2):
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max_percent = self.max_percent2.value
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max_profit = self.max_profit2.value
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else:
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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 (current_profit > max_profit) & (
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#(last_candle['percent1'] < -max_percent) |
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(last_candle['percent3'] < -max_percent) | (last_candle['percent5'] < -max_percent)):
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#& (last_candle['close'] > last_candle['max3_1d']):
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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['max3_1d']):
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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, '1M') 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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# print(self.min_percent.value, self.min_percent2.value, self.min_percent3.value,
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# self.buy_decalage.value, self.buy_decalage2.value, self.buy_decalage3.value,
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# )
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dataframe['min200'] = ta.MIN(dataframe['close'], timeperiod=200)
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dataframe['max200'] = ta.MAX(dataframe['close'], timeperiod=200)
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period = int(self.min_n * 24)
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dataframe['min_n'] = ta.MIN(dataframe['close'], timeperiod=period)
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dataframe['max_n'] = ta.MAX(dataframe['close'], timeperiod=period)
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dataframe['min_max_n'] = (dataframe['max_n'] - dataframe['min_n']) / dataframe['min_n']
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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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################### 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["mrsi3"] = informative["rsi"].pct_change(3)
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informative['r_rsi'] = (informative['rsi3'].div(10).round())
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informative['percent'] = informative['close'].pct_change(1)
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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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################### INFORMATIVE 1d
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informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
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informative["rsi"] = ta.RSI(informative, period=5)
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informative['min5'] = ta.MIN(informative['close'], timeperiod=5)
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informative['max3'] = ta.MAX(informative['close'], timeperiod=3)
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informative['max5'] = ta.MAX(informative['close'], timeperiod=5)
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informative['sma5'] = ta.SMA(informative['close'], timeperiod=5)
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informative['min_n'] = ta.MIN(informative['close'], timeperiod=14)
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informative['max_n'] = ta.MAX(informative['close'], timeperiod=14)
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informative['min_max_n'] = (informative['max_n'] - informative['min_n']) / informative['min_n']
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informative['percent'] = informative['close'].pct_change(1)
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informative['sma5_diff'] = informative['sma5'] - informative['sma5'].shift(1)
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
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################### INFORMATIVE 1M
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informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1M")
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dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1M", 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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conditions = [
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(dataframe['close'] <= dataframe['min_n_1d']), # * (1 + (dataframe['min_max_n_1d'] / self.min_percent3.value))),
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(dataframe['close'] <= dataframe['min_n'] * (1 + (dataframe['min_max_n'] / self.min_percent4.value))),
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(dataframe['min_n'].shift(self.buy_decalage4.value) == dataframe['min_n']),
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(dataframe['min_n_1d'] / dataframe['min_n'] > 1 + self.rapport_min_n.value / 100)
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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')
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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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