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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
# This class is a sample. Feel free to customize it.
class StrategyPierrick41216(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(0.00, 0.18, decimals=2, default=0.065, 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_step = IntParameter(1, 8, default=3, space="buy")
buy_rolling = IntParameter(-20, 0, default=-6, 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 = -1
trailing_stop = False
trailing_stop_positive = 0.02
trailing_stop_positive_offset = 0.0275 # 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'},
'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'}
# },
"Rsi": {
'rsi': {'color': 'pink'},
},
"rolling": {
'bb_rolling': {'color': '#87e470'},
"bb_rolling_min": {'color': '#ac3e2a'}
},
"percent": {
"percent": {'color': 'green'},
"percent5": {'color': 'red'}
}
}
}
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()
# print("proposed_stake=", proposed_stake, " max_stake=", max_stake)
if current_candle['bb_width'] > 0.065:
print("use more stake", pair, " ", proposed_stake * 2)
return min(max_stake, proposed_stake * 2)
if current_candle['bb_width'] > 0.045:
print("use more stake", pair, " ", proposed_stake * 1.5)
return min(max_stake, proposed_stake * 1.5)
# if current_candle['bb_width'] < 0.020:
# print("use less stake", pair, " ", proposed_stake / 2)
# return min(max_stake, proposed_stake / 2)
# 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 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()
previous_5_candle = dataframe.iloc[-5].squeeze()
# (last_candle['percent5'] < -0.005) \
# if (0 < current_profit < 0.005) \
# & ((current_time - trade.open_date_utc).seconds >= 3600 * 2):
# # & (previous_last_candle['sma10'] > last_candle['sma10']):
# print("too_small_gain", pair, trade, " profit=", current_profit, " rate=", current_rate, " percent5=",
# last_candle['percent5'])
# return 'too_small_gain'
# if (current_profit < -0.05) \
# & ((current_time - trade.open_date_utc).days >= 3):
# print("lost_half_profit", pair, trade, " profit=", current_profit, " rate=", current_rate)
# return 'stop_loss_profit'
# if (current_profit > 0.02) \
# & (last_candle['percent'] < 0.01) \
# & ((current_time - trade.open_date_utc).seconds >= 3600):
# print("lost_half_profit", pair, trade, " profit=", current_profit, " rate=", current_rate)
# return 'lost_half_profit'
if (current_profit > 0) \
& ((current_time - trade.open_date_utc).seconds >= 3600 * 2) \
& (previous_5_candle['sma20'] > last_candle['sma20']) \
& (last_candle['percent'] < 0) \
& (last_candle['percent5'] < 0):
print("over_bb_band_sma20_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
return 'over_bb_band_sma20_desc'
if (current_profit > 0) \
& (last_candle['rsi'] > 75):
print("over_rsi", pair, trade, " profit=", current_profit, " rate=", current_rate)
return 'over_rsi'
# description trade
# Trade(id=0, pair=CAKE/USDT, amount=4.19815281, open_rate=11.91000000, open_since=2021-12-22 17:55:00)
# print(last_candle)
if 0.05 < current_profit < 0.1:
if (
(previous_last_candle['sma10'] > last_candle['sma10']) &
(current_time - trade.open_date_utc).seconds >= 3600 * 3
) | (
(current_time - trade.open_date_utc).seconds >= 3600 * 6
):
print("profit_3h_sma10_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
return 'profit_3h_sma10_desc'
if (0 < current_profit < 0.1) \
& (previous_last_candle['sma20'] > last_candle['sma20']) \
& ((current_time - trade.open_date_utc).seconds >= 3600 * 5):
print("profit_5h_sma20_desc", pair, trade, " profit=", current_profit, " rate=", current_rate)
return 'profit_5h_sma20_desc'
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["percent5"] = dataframe["percent"].rolling(5).sum()
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(
3).mean()
dataframe["bb_rolling"] = dataframe["rolling"] / dataframe["bb_width"]
dataframe['bb_rolling_min'] = ta.MIN(dataframe['bb_rolling'], timeperiod=10)
dataframe['bb_buy'] = (dataframe['min'] + (dataframe['max'] - dataframe['min']) / 3)
return dataframe
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
step = self.buy_step.value
# if reduce(lambda x, y: x & y, dataframe['bb_width'] < 0.015):
# step = 5
# else:
# if reduce(lambda x, y: x & y, dataframe['bb_width'] < 0.03):
# step = 4
bb_rolling_max = self.buy_rolling.value
condition_bb_rolling = [
(dataframe['bb_width'] >= 0.035),
(dataframe['close'] < dataframe['sma10']),
(dataframe['bb_rolling_min'].shift(step) <= bb_rolling_max),
(dataframe['bb_rolling_min'].shift(step) >= dataframe['bb_rolling'].shift(step)),
(dataframe['close'].shift(step) < dataframe['min'].shift(step) + (
dataframe['max'].shift(step) - dataframe['min'].shift(step)) / 3),
(dataframe['min'].shift(step) == dataframe['min']),
(dataframe['volume'] > 0)
]
condition_bb_rolling2 = reduce(lambda x, y: x & y, condition_bb_rolling)
dataframe.loc[condition_bb_rolling2, '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