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Jérôme Delacotte
2025-03-06 11:01:43 +01:00
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# 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_3(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(1, 24, default=5, space="buy")
buy_decalage2 = IntParameter(1, 24, default=5, space="buy")
buy_decalage3 = IntParameter(1, 24, default=5, space="buy")
buy_decalage4 = IntParameter(1, 24, default=5, space="buy")
buy_min_max_n = DecimalParameter(0, 0.2, decimals=2, default=0.05, space='buy')
buy_min_max_n2 = DecimalParameter(0, 0.2, decimals=2, default=0.05, space='buy')
buy_min_max_n3 = DecimalParameter(0, 0.2, decimals=2, default=0.05, space='buy')
buy_min_max_n4 = DecimalParameter(0, 0.2, 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")
buy_rsi_min_1d4 = IntParameter(75, 100, default=75, space="buy")
# buy_rsi_max = IntParameter(50, 100, default=60, space="buy")
buy_rsi_min = IntParameter(0, 50, default=25, space="buy")
buy_rsi_max = IntParameter(50, 100, default=60, space="buy")
buy_rsi_min2 = IntParameter(0, 50, default=25, space="buy")
buy_rsi_max2 = IntParameter(50, 100, default=60, space="buy")
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()
if last_candle['percent' + str(self.n_percent.value)] < self.percent_sell.value:
return 'sell_lost_percent' + str(self.n_percent.value)
# Between 2% and 10%, sell if EMA-long above EMA-short
# if 0.02 < current_profit < 0.1:
# if last_candle['ema100'] > last_candle['ema10']:
# return 'ema_long_below_80'
#
# # Sell any positions at a loss if they are held for more than one day.
# if current_profit < -0.20 and (current_time - trade.open_date_utc).days >= 3:
# return 'unclog'
# def custom_stake_amount(self, pair: str, current_time: datetime, current_rate: float,
# proposed_stake: float, min_stake: float, max_stake: float,
# **kwargs) -> float:
#
# dataframe, _ = self.dp.get_analyzed_dataframe(pair=pair, timeframe=self.timeframe)
# current_candle = dataframe.iloc[-1].squeeze()
#
# if dataframe['open'] < dataframe['sma100'] * 0.98:
# # if self.config['stake_amount'] == 'unlimited':
# # # Use entire available wallet during favorable conditions when in compounding mode.
# # return max_stake
# # else:
# # Compound profits during favorable conditions instead of using a static stake.
# return self.wallets.get_total_stake_amount() / self.config['max_open_trades']
#
# # Use default stake amount.
# return proposed_stake
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:
# MACD
# macd = ta.MACD(dataframe)
# dataframe['macd'] = macd['macd']
# dataframe['macdsignal'] = macd['macdsignal']
# dataframe['macdhist'] = macd['macdhist']
# # Plus Directional Indicator / Movement
# dataframe['plus_dm'] = ta.PLUS_DM(dataframe)
# dataframe['plus_di'] = ta.PLUS_DI(dataframe)
# Minus Directional Indicator / Movement
# dataframe['adx'] = ta.ADX(dataframe)
# dataframe['minus_dm'] = ta.MINUS_DM(dataframe)
# dataframe['minus_di'] = ta.MINUS_DI(dataframe)
dataframe['min'] = ta.MIN(dataframe)
dataframe['max'] = ta.MAX(dataframe)
dataframe['min50'] = ta.MIN(dataframe['close'], timeperiod=50)
dataframe['min200'] = ta.MIN(dataframe['close'], timeperiod=200)
dataframe['min200_001'] = dataframe['min200'] * 1.002
dataframe['max50'] = ta.MAX(dataframe['close'], timeperiod=50)
dataframe['max200'] = ta.MAX(dataframe['close'], timeperiod=200)
dataframe['min_max200'] = (dataframe['max200'] - dataframe['min200']) / dataframe['min200']
dataframe['min_max50'] = (dataframe['max50'] - dataframe['min50']) / dataframe['min50']
for n in range(1, 25):
dataframe["percent" + str(n)] = dataframe['close'].pct_change(n)
# # Aroon, Aroon Oscillator
# aroon = ta.AROON(dataframe)
# dataframe['aroonup'] = aroon['aroonup']
# dataframe['aroondown'] = aroon['aroondown']
# dataframe['aroonosc'] = ta.AROONOSC(dataframe)
# RSI
dataframe['rsi'] = ta.RSI(dataframe)
# # EMA - Exponential Moving Average
# dataframe['ema3'] = ta.EMA(dataframe, timeperiod=3)
# dataframe['ema5'] = ta.EMA(dataframe, timeperiod=5)
# dataframe['ema10'] = ta.EMA(dataframe, timeperiod=10)
# dataframe['mema10_3'] = dataframe['ema10'] / dataframe['ema10'].shift(1)
# dataframe['mema10_5'] = dataframe['ema10'].rolling(5).mean()
# dataframe['ema21'] = ta.EMA(dataframe, timeperiod=21)
# dataframe['ema50'] = ta.EMA(dataframe, timeperiod=50)
# dataframe['ema100'] = ta.EMA(dataframe, timeperiod=100)
# # SMA - Simple Moving Average
# dataframe['sma3'] = ta.SMA(dataframe, timeperiod=3)
# dataframe['sma5'] = ta.SMA(dataframe, timeperiod=5)
dataframe['sma10'] = ta.SMA(dataframe, timeperiod=10)
# dataframe['sma21'] = ta.SMA(dataframe, timeperiod=21)
# dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
dataframe['msma10_3'] = dataframe['sma10'] / dataframe['sma10'].shift(1)
# dataframe['sma200'] = ta.SMA(dataframe, timeperiod=200)
# dataframe['sma200_95'] = ta.SMA(dataframe, timeperiod=200) * 0.95
# dataframe['sma200_98'] = ta.SMA(dataframe, timeperiod=200) * 0.98
# dataframe['sma500'] = ta.SMA(dataframe, timeperiod=500)
# dataframe['sma500_90'] = ta.SMA(dataframe, timeperiod=500) * 0.9
# dataframe['sma500_95'] = ta.SMA(dataframe, timeperiod=500) * 0.95
# dataframe['sma500_20'] = ta.SMA(dataframe, timeperiod=500) * 0.2
# 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['r_rsi'] = (informative['rsi3'].div(10).round())
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1h", ffill=True)
################### INFORMATIVE 1d
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1d")
informative["rsi"] = ta.RSI(informative)
informative["rsi3"] = ta.RSI(informative, 3)
informative['r_rsi'] = (informative['rsi'].div(10).round())
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# if (self.buy_min_max_n.value > self.buy_min_max_n2.value) | (self.buy_min_max_n2.value > self.buy_min_max_n3.value) | (self.buy_min_max_n3.value > self.buy_min_max_n4.value):
# return dataframe
for decalage in range(self.buy_decalage.value - 2, self.buy_decalage.value):
conditions = [
(dataframe['rsi_1h'] < self.buy_rsi_min_1d.value),
(dataframe['close'].shift(decalage) < dataframe['min200_001'].shift(decalage)),
(dataframe['min_max200'] >= self.buy_min_max_n.value),
(dataframe['rsi_1h'] > self.buy_rsi_min.value),
(dataframe['rsi_1h'] < self.buy_rsi_max.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'] >= self.buy_rsi_min_1d.value),
(dataframe['rsi_1h'] < self.buy_rsi_min_1d2.value),
(dataframe['close'].shift(decalage) < dataframe['min200_001'].shift(decalage)),
(dataframe['min_max200'] >= self.buy_min_max_n2.value),
(dataframe['rsi_1h'] > self.buy_rsi_min.value),
(dataframe['rsi_1h'] < self.buy_rsi_max.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_decalage3.value - 2, self.buy_decalage3.value):
conditions = [
(dataframe['rsi_1h'] >= self.buy_rsi_min_1d2.value),
(dataframe['rsi_1h'] < self.buy_rsi_min_1d3.value),
(dataframe['close'].shift(decalage) < dataframe['min200_001'].shift(decalage)),
(dataframe['min_max200'] >= self.buy_min_max_n3.value),
(dataframe['rsi_1h'] > self.buy_rsi_min.value),
(dataframe['rsi_1h'] < self.buy_rsi_max.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_decalage4.value - 2, self.buy_decalage4.value):
conditions = [
(dataframe['rsi_1h'] >= self.buy_rsi_min_1d3.value),
(dataframe['rsi_1h'] < self.buy_rsi_min_1d4.value),
(dataframe['close'].shift(decalage) < dataframe['min200_001'].shift(decalage)),
(dataframe['min_max200'] >= self.buy_min_max_n4.value),
(dataframe['rsi_1h'] > self.buy_rsi_min2.value),
(dataframe['rsi_1h'] < self.buy_rsi_max2.value),
]
# GUARDS AND TRENDS
if conditions:
dataframe.loc[(reduce(lambda x, y: x & y, conditions)),
['buy', 'buy_tag']] = (1, 'buy_2_' + str(decalage))
break
return dataframe
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# dataframe.loc[
# (
# (dataframe['close'] < dataframe['open']) &
# (dataframe['close'].shift(1) < dataframe['open'].shift(1)) &
# (dataframe['close'].shift(2) < dataframe['open'].shift(2)) &
# (dataframe['close'] < dataframe['bb_lowerband']) &
# (((dataframe['bb_lowerband'].shift(2) - dataframe['bb_lowerband']) / dataframe['bb_lowerband']) >= 0.02)
# # (((dataframe['close'].shift(1) - dataframe['close']) / dataframe['close']) >= 0.025)
# ), 'sell'] = 1
return dataframe