291 lines
12 KiB
Python
291 lines
12 KiB
Python
# pr#agma pylint: disable=missing-docstring, invalid-name, pointless-string-statement
|
|
# isort: skip_file
|
|
# --- Do not remove these libs ---
|
|
from datetime import datetime
|
|
|
|
import numpy as np # noqa
|
|
import pandas as pd # noqa
|
|
from freqtrade.strategy.parameters import DecimalParameter, BooleanParameter, IntParameter
|
|
from pandas import DataFrame
|
|
import math
|
|
from functools import reduce
|
|
|
|
|
|
from freqtrade.strategy.interface import IStrategy
|
|
|
|
# --------------------------------
|
|
# Add your lib to import here
|
|
import talib.abstract as ta
|
|
import freqtrade.vendor.qtpylib.indicators as qtpylib
|
|
from freqtrade.strategy.strategy_helper import merge_informative_pair
|
|
|
|
|
|
# This class is a sample. Feel free to customize it.
|
|
class StrategyJD_5_8(IStrategy):
|
|
# Strategy interface version - allow new iterations of the strategy interface.
|
|
# Check the documentation or the Sample strategy to get the latest version.
|
|
INTERFACE_VERSION = 2
|
|
|
|
# valeur de bbwidth pour démarrer
|
|
# buy_bollinger = DecimalParameter(0.025, 0.125, decimals=2, default=0.07, space="buy")
|
|
#buy_msma_10 = DecimalParameter(0.997, 1.020, decimals=3, default=0.998, space="buy")
|
|
# pourcentage sma à dépasser
|
|
# buy_sma_percent = DecimalParameter(0.95, 1.05, decimals=2, default=0.97, space="buy")
|
|
buy_decalage = IntParameter(3, 10, default=5, space="buy")
|
|
buy_decalage2 = IntParameter(5, 15, default=5, space="buy")
|
|
buy_decalage3 = IntParameter(10, 20, default=5, space="buy")
|
|
|
|
buy_min_max_n = DecimalParameter(0.06, 0.30, decimals=2, default=0.05, space='buy')
|
|
buy_min_max_n2 = DecimalParameter(0.06, 0.14, decimals=2, default=0.05, space='buy')
|
|
buy_min_max_n3 = DecimalParameter(0.06, 0.14, decimals=2, default=0.05, space='buy')
|
|
|
|
# buy_rsi_min_1d = IntParameter(0, 25, default=5, space="buy")
|
|
# buy_rsi_min_1d2 = IntParameter(25, 50, default=15, space="buy")
|
|
# buy_rsi_min_1d3 = IntParameter(50, 75, default=50, space="buy")
|
|
|
|
min_percent = DecimalParameter(1.005, 1.015, decimals=3, default=1.002, space='buy')
|
|
min_percent2 = DecimalParameter(1.005, 1.015, decimals=3, default=1.002, space='buy')
|
|
min_percent3 = DecimalParameter(1.005, 1.015, decimals=3, default=1.002, space='buy')
|
|
|
|
buy_mrsi3 = DecimalParameter(-0.1, 0.1, decimals=2, default=0, space="buy")
|
|
|
|
min_n = IntParameter(0, 24, default=15, space="buy")
|
|
# min_p = DecimalParameter(1, 1.01, decimals=3, default=1.002, space="buy")
|
|
|
|
max_percent = DecimalParameter(0, 0.05, decimals=3, default=0.005, space='sell')
|
|
max_percent2 = DecimalParameter(0, 0.05, decimals=3, default=0.005, space='sell')
|
|
max_percent3 = DecimalParameter(0, 0.05, decimals=3, default=0.005, space='sell')
|
|
|
|
max_profit = DecimalParameter(0, 0.1, decimals=2, default=0.01, space='sell')
|
|
max_profit2 = DecimalParameter(0, 0.1, decimals=2, default=0.01, space='sell')
|
|
max_profit3 = DecimalParameter(0, 0.1, decimals=2, default=0.01, space='sell')
|
|
|
|
# sell_b_RSI = IntParameter(70, 98, default=88, space='sell')
|
|
# sell_b_RSI2 = IntParameter(70, 98, default=88, space='sell')
|
|
# sell_b_RSI3 = IntParameter(70, 98, default=80, space='sell')
|
|
#
|
|
# sell_b_RSI2_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
|
|
sell_h_RSI = IntParameter(70, 98, default=88, space='sell')
|
|
sell_h_RSI2 = IntParameter(70, 98, default=88, space='sell')
|
|
sell_h_RSI3 = IntParameter(70, 98, default=80, space='sell')
|
|
|
|
sell_h_RSI2_percent = DecimalParameter(0, 0.02, decimals=3, default=0.01, space='sell')
|
|
|
|
n_percent = IntParameter(1, 12, default=1, space="protection")
|
|
percent_sell = DecimalParameter(-0.2, -0.01, decimals=2, default=-0.08, space="protection")
|
|
|
|
# buy_adx_enabled = BooleanParameter(default=True, space="buy")
|
|
# buy_rsi_enabled = CategoricalParameter([True, False], default=False, space="buy")
|
|
# buy_trigger = CategoricalParameter(["bb_lower", "macd_cross_signal"], default="bb_lower", space="buy")
|
|
|
|
# ROI table:
|
|
minimal_roi = {
|
|
# "0": 0.015
|
|
"0": 0.5
|
|
}
|
|
|
|
# Stoploss:
|
|
stoploss = -1
|
|
trailing_stop = True
|
|
trailing_stop_positive = 0.001
|
|
trailing_stop_positive_offset = 0.0175 # 0.015
|
|
trailing_only_offset_is_reached = True
|
|
|
|
# max_open_trades = 3
|
|
|
|
# Optimal ticker interval for the strategy.
|
|
timeframe = '5m'
|
|
|
|
# Run "populate_indicators()" only for new candle.
|
|
process_only_new_candles = False
|
|
|
|
# These values can be overridden in the "ask_strategy" section in the config.
|
|
use_sell_signal = True
|
|
sell_profit_only = False
|
|
ignore_roi_if_buy_signal = False
|
|
|
|
# Number of candles the strategy requires before producing valid signals
|
|
startup_candle_count: int = 30
|
|
|
|
# Optional order type mapping.
|
|
order_types = {
|
|
'buy': 'limit',
|
|
'sell': 'limit',
|
|
'stoploss': 'market',
|
|
'stoploss_on_exchange': False
|
|
}
|
|
|
|
# Optional order time in force.
|
|
order_time_in_force = {
|
|
'buy': 'gtc',
|
|
'sell': 'gtc'
|
|
}
|
|
|
|
plot_config = {
|
|
# Main plot indicators (Moving averages, ...)
|
|
'main_plot': {
|
|
'bb_lowerband': {'color': 'white'},
|
|
'bb_upperband': {'color': 'white'},
|
|
'min200': {'color': 'yellow'},
|
|
'min200_001': {'color': 'yellow'},
|
|
},
|
|
'subplots': {
|
|
# Subplots - each dict defines one additional plot
|
|
"BB": {
|
|
'bb_width': {'color': 'white'},
|
|
},
|
|
"ADX": {
|
|
'adx': {'color': 'white'},
|
|
'minus_dm': {'color': 'blue'},
|
|
'plus_dm': {'color': 'red'}
|
|
},
|
|
"rolling": {
|
|
'bb_rolling': {'color': '#87e470'},
|
|
"bb_rolling_min": {'color': '#ac3e2a'}
|
|
}
|
|
}
|
|
}
|
|
|
|
def custom_sell(self, pair: str, trade: 'Trade', current_time: 'datetime', current_rate: float,
|
|
current_profit: float, **kwargs):
|
|
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
|
|
last_candle = dataframe.iloc[-1].squeeze()
|
|
previous_last_candle = dataframe.iloc[-2].squeeze()
|
|
|
|
if last_candle['percent' + str(self.n_percent.value)] < self.percent_sell.value:
|
|
return 'sell_lost_percent' + str(self.n_percent.value)
|
|
|
|
if (last_candle['rsi_1h'] < 18):
|
|
max_percent = self.max_percent.value
|
|
max_profit = self.max_profit.value
|
|
else:
|
|
if (last_candle['rsi_1h'] < 25):
|
|
max_percent = self.max_percent2.value
|
|
max_profit = self.max_profit2.value
|
|
else:
|
|
max_percent = self.max_percent3.value
|
|
max_profit = self.max_profit3.value
|
|
|
|
if (current_profit > max_profit) & (
|
|
(last_candle['percent1'] < -max_percent) | (last_candle['percent3'] < -max_percent) | (
|
|
last_candle['percent5'] < -max_percent)):
|
|
return 'h_percent_quick'
|
|
|
|
if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_h_RSI.value):
|
|
return 'h_over_rsi'
|
|
|
|
if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_h_RSI2.value) & \
|
|
(last_candle['percent1'] < - self.sell_h_RSI2_percent.value):
|
|
return 'h_over_rsi_2'
|
|
|
|
if (current_profit > 0) & (previous_last_candle['rsi'] > self.sell_h_RSI3.value) & \
|
|
(last_candle['close'] >= last_candle['max200']):
|
|
return 'h_over_rsi_max'
|
|
|
|
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]
|
|
# informative_pairs += [(pair, '4h') for pair in pairs]
|
|
informative_pairs = [(pair, '1h') for pair in pairs]
|
|
return informative_pairs
|
|
|
|
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
dataframe['min'] = ta.MIN(dataframe)
|
|
dataframe['max'] = ta.MAX(dataframe)
|
|
dataframe['min200'] = ta.MIN(dataframe['close'], timeperiod=200)
|
|
dataframe['max200'] = ta.MAX(dataframe['close'], timeperiod=200)
|
|
|
|
dataframe['min_n'] = ta.MIN(dataframe['close'], timeperiod=self.min_n.value * 24)
|
|
dataframe['max_n'] = ta.MAX(dataframe['close'], timeperiod=self.min_n.value * 24)
|
|
dataframe['min_max_n'] = (dataframe['max_n'] - dataframe['min_n']) / dataframe['min_n']
|
|
# dataframe['min_n_p'] = dataframe['min_n'] * self.min_p.value
|
|
dataframe['minn_1'] = dataframe['min_n'] * self.min_percent.value
|
|
dataframe['minn_2'] = dataframe['min_n'] * self.min_percent2.value
|
|
dataframe['minn_3'] = dataframe['min_n'] * self.min_percent3.value
|
|
dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
|
|
|
|
for n in range(1, 25):
|
|
dataframe["percent" + str(n)] = dataframe['close'].pct_change(n)
|
|
|
|
# RSI
|
|
dataframe['rsi'] = ta.RSI(dataframe)
|
|
|
|
# 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"]
|
|
# )
|
|
|
|
################### INFORMATIVE 1h
|
|
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
|
|
informative["rsi"] = ta.RSI(informative)
|
|
informative["rsi3"] = ta.RSI(informative, 3)
|
|
informative["mrsi3"] = informative["rsi"].pct_change(3)
|
|
informative['r_rsi'] = (informative['rsi3'].div(10).round())
|
|
# for n in range(1, 5):
|
|
# informative["percent" + str(n)] = informative['close'].pct_change(n)
|
|
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
|
|
|
|
return dataframe
|
|
|
|
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
for decalage in range(self.buy_decalage.value - 2, self.buy_decalage.value):
|
|
conditions = [
|
|
(dataframe['rsi_1h'] < 18),
|
|
(dataframe['close'] < dataframe['minn_1']),
|
|
(dataframe['min_max_n'] >= self.buy_min_max_n.value),
|
|
(dataframe['rsi_1h'] > 0),
|
|
(dataframe['rsi_1h'] < 51),
|
|
(dataframe['mrsi3_1h'] > self.buy_mrsi3.value)
|
|
]
|
|
# GUARDS AND TRENDS
|
|
if conditions:
|
|
dataframe.loc[(reduce(lambda x, y: x & y, conditions)),
|
|
['buy', 'buy_tag']] = (1, 'buy_1_' + str(decalage))
|
|
break
|
|
|
|
for decalage in range(self.buy_decalage2.value - 2, self.buy_decalage2.value):
|
|
conditions = [
|
|
(dataframe['rsi_1h'] >= 18),
|
|
(dataframe['rsi_1h'] < 25),
|
|
(dataframe['close'] < dataframe['minn_2']),
|
|
(dataframe['min_max_n'] >= self.buy_min_max_n2.value),
|
|
(dataframe['rsi_1h'] > 0),
|
|
(dataframe['rsi_1h'] < 51),
|
|
(dataframe['mrsi3_1h'] > self.buy_mrsi3.value)
|
|
]
|
|
# GUARDS AND TRENDS
|
|
if conditions:
|
|
dataframe.loc[(reduce(lambda x, y: x & y, conditions)),
|
|
['buy', 'buy_tag']] = (1, 'buy_2_' + str(decalage))
|
|
break
|
|
|
|
for decalage in range(self.buy_decalage3.value - 2, self.buy_decalage3.value):
|
|
conditions = [
|
|
(dataframe['rsi_1h'] >= 25),
|
|
(dataframe['rsi_1h'] < 56),
|
|
(dataframe['close'] < dataframe['minn_3']),
|
|
(dataframe['min_max_n'] >= self.buy_min_max_n3.value),
|
|
(dataframe['rsi_1h'] > 0),
|
|
(dataframe['rsi_1h'] < 51),
|
|
(dataframe['mrsi3_1h'] > self.buy_mrsi3.value)
|
|
]
|
|
# GUARDS AND TRENDS
|
|
if conditions:
|
|
dataframe.loc[(reduce(lambda x, y: x & y, conditions)),
|
|
['buy', 'buy_tag']] = (1, 'buy_3_' + str(decalage))
|
|
break
|
|
|
|
return dataframe
|
|
|
|
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
|
return dataframe
|