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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 ---
import numpy as np # noqa
import pandas as pd # noqa
from freqtrade.strategy.parameters import DecimalParameter, BooleanParameter
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
# This class is a sample. Feel free to customize it.
class StrategyPierrick4122(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=0.09, space="buy")
buy_bollinger_min = DecimalParameter(0.0, 0.06, decimals=2, default=0.02, space="buy")
buy_bollinger_max = DecimalParameter(0.02, 0.09, decimals=2, default=0.04, space="buy")
buy_sma100 = DecimalParameter(0.94, 1.2, decimals=2, default=1.0, space="buy")
# Valeur de la deuxième condition bollinger avec condition sma200
# buy_bollinger_2 = DecimalParameter(0.0, 0.08, decimals=2, default=0.04, space="buy")
# buy_min = DecimalParameter(1, 1.1, decimals=2, default=1.01, space="buy")
# buy_percent = DecimalParameter(1, 1.1, decimals=2, default=1.01, space="buy")
# volume à atteindre
buy_volume = DecimalParameter(0, 50, decimals=1, default=18, space="buy")
# buy_rsi = IntParameter(20, 40, default=30, space="buy")
# 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 = -0.1
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': 'red'},
'bb_upperband': {'color': 'green'},
'sma100': {'color': 'blue'},
# 'sma500': {'color': 'blue'},
'sma10': {'color': 'yellow'},
'min': {'color': 'white'},
'max': {'color': 'white'},
'sma20': {'color': 'cyan'}
},
'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 informative_pairs(self):
return []
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)
# # 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['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['sma20'] = ta.SMA(dataframe, timeperiod=20)
dataframe['sma50'] = ta.SMA(dataframe, timeperiod=50)
dataframe['sma100'] = ta.SMA(dataframe, timeperiod=100)
# 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
dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
dataframe['min'] = ta.MIN(dataframe['close'], timeperiod=200)
dataframe['max'] = ta.MAX(dataframe['close'], timeperiod=200)
# 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"]
)
dataframe["rolling"] = (100 * (dataframe["close"] - dataframe["bb_lowerband"]) / dataframe["bb_lowerband"]).rolling(5).mean()
dataframe["bb_rolling"] = dataframe["rolling"] / dataframe["bb_width"]
dataframe['bb_rolling_min'] = ta.MIN(dataframe['bb_rolling'], timeperiod=5)
# print(dataframe["rolling"].tolist())
# dataframe['volatility_kcw'] = ta.volatility.keltner_channel_wband(
# dataframe['high'],
# dataframe['low'],
# dataframe['close'],
# window=20,
# window_atr=10,
# fillna=False,
# original_version=True
# )
#
# dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband(
# dataframe['high'],
# dataframe['low'],
# dataframe['close'],
# window=10,
# offset=0,
# fillna=False
# )
# dataframe['bb_lower_reg'] = dataframe["bb_lowerband"] - dataframe["bb_lowerband"].shift(1)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
condition_bb_rolling_1 = [
(dataframe['bb_width'].shift(3) >= self.buy_bollinger.value),
(dataframe['volume'].shift(3) * dataframe['close'] / 1000 >= 100), # self.buy_volume.value * 1000),
(dataframe['open'] < dataframe['close']),
(dataframe['open'] * 1.02 < dataframe['close']),
# (dataframe['close'] <= dataframe['sma100'] * 1.01),
(dataframe['close'].shift(1) <= dataframe['bb_lowerband'].shift(1)) |
(dataframe['close'].shift(2) <= dataframe['bb_lowerband'].shift(2)) |
(dataframe['close'].shift(3) <= dataframe['bb_lowerband'].shift(3)),
]
condition_bb_rolling1 = reduce(lambda x, y: x & y, condition_bb_rolling_1)
condition_bb_rolling_2 = [
# En dessous de la moyenne 100
(dataframe['open'] < dataframe['sma100'] * self.buy_sma100.value),
# Sma 10 monte
(dataframe['sma10'] > dataframe['sma10'].shift(2)),
# Chandelle courante et précédente positives
(dataframe['open'] < dataframe['close']),
(dataframe['open'].shift(1) < dataframe['close'].shift(1)),
# 3/6 chandelles précédentes sur bbwidth pincée
(dataframe['bb_width'].shift(2) > self.buy_bollinger_min.value),
(dataframe['bb_width'].shift(4) > self.buy_bollinger_min.value),
(dataframe['bb_width'].shift(6) > self.buy_bollinger_min.value),
(dataframe['bb_width'].shift(2) < self.buy_bollinger_max.value),
(dataframe['bb_width'].shift(4) < self.buy_bollinger_max.value),
(dataframe['bb_width'].shift(6) < self.buy_bollinger_max.value),
# (dataframe['open'] * 1.02 < dataframe['close']),
# (dataframe['open'].shift(1) * 1.02 < dataframe['close'].shift(1)),
# hausse courante et précédente < 2%
(dataframe['percent'] < 0.02),
(dataframe['percent'].shift(1) < 0.015),
# Chandelle courante et précédente frôle upperband
(dataframe['close'] <= dataframe['bb_upperband']),
(dataframe['close'] * 1.002 >= dataframe['bb_upperband']),
(dataframe['close'].shift(1) <= dataframe['bb_upperband'].shift(1)),
(dataframe['close'].shift(1) * 1.002 >= dataframe['bb_upperband'].shift(1)),
]
condition_bb_rolling2 = reduce(lambda x, y: x & y, condition_bb_rolling_2)
condition_bb_rolling_inf = [
False,
(dataframe['bb_width'] >= 0.04),
(dataframe['volume'] * dataframe['close'] / 1000 >= 100), #>= self.buy_volume.value * 1000),
(dataframe['open'] < dataframe['close']),
(dataframe['open'] * 1.02 < dataframe['close']),
# (dataframe['close'] <= dataframe['min'] * 1.01),
(dataframe['close'] * 1.04) <= dataframe['sma100'],
(dataframe['close'].shift(1) <= dataframe['bb_lowerband'].shift(1))
]
condition_bb_rolling_inf2 = reduce(lambda x, y: x & y, condition_bb_rolling_inf)
dataframe.loc[
(
# condition_bb_rolling1
condition_bb_rolling2
# condition_bb_rolling_inf2
), 'buy'] = 1
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