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Jérôme Delacotte
2025-03-09 17:06:39 +01:00
parent 8a1c69e53a
commit 59daa1af70
2 changed files with 273 additions and 61 deletions
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327
HeikinAshi.py
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@@ -12,23 +12,62 @@
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces roi buy --strategy Heracles
# ######################################################################
# --- Do not remove these libs ---
from freqtrade.strategy.parameters import IntParameter, DecimalParameter
from freqtrade.strategy.interface import IStrategy
from freqtrade.persistence import Trade
from typing import Optional, Tuple, Union
from datetime import timezone, timedelta, datetime
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, stoploss_from_open,
IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute)
import logging
# noinspection PyUnresolvedReferences
from freqtrade.strategy import (IStrategy, informative)
from pandas import DataFrame
# --------------------------------
# Add your lib to import here
# import talib.abstract as ta
import pandas as pd
import ta
import talib.abstract as talib
from ta.utils import dropna
import freqtrade.vendor.qtpylib.indicators as qtpylib
from functools import reduce
import numpy as np
class Heracles(IStrategy):
########################################## RESULT PASTE PLACE ##########################################
# 10/100: 25 trades. 18/4/3 Wins/Draws/Losses. Avg profit 5.92%. Median profit 6.33%. Total profit 0.04888306 BTC ( 48.88Σ%). Avg duration 4 days, 6:24:00 min. Objective: -11.42103
class HeikinAshi(IStrategy):
plot_config = {
"main_plot": {
"min12": {
"color": "#197260"
},
'max12': {
'color': 'green'
},
"haclose": {
"color": "red"
},
'haopen': {
'color': 'blue'
},
"min288": {
"color": "#197260"
},
'max288': {
'color': 'green'
},
'mid288': {
'color': 'blue'
}
},
"subplots": {
"Percent": {
"hapercent": {
"color": "#74effc"
}
}
}
}
# Buy hyperspace params:
buy_params = {
@@ -44,53 +83,231 @@ class Heracles(IStrategy):
# ROI table:
minimal_roi = {
"0": 0.598,
"644": 0.166,
"3269": 0.115,
"7289": 0
"0": 0.598
}
# Stoploss:
stoploss = -0.256
stoploss = -1
# Optimal timeframe use it in your config
timeframe = '4h'
timeframe = '5m'
columns_logged = False
max_entry_position_adjustment = 20
startup_candle_count = 288
# Trailing stoploss
trailing_stop = True
trailing_stop_positive = 0.001
trailing_stop_positive_offset = 0.015
trailing_only_offset_is_reached = True
# trailing_stop = False
# trailing_stop_positive = 0.001
# trailing_stop_positive_offset = 0.015
# trailing_only_offset_is_reached = True
position_adjustment_enable = False
########################################## END RESULT PASTE PLACE ######################################
pairs = {
pair: {
"last_max": 0,
"trade_info": {},
"max_touch": 0.0,
"last_sell": 0.0,
"last_buy": 0.0
}
for pair in ["BTC/USDT", "ETH/USDT", "DOGE/USDT", "DASH/USDT", "XRP/USDT", "SOL/USDT"]
}
# buy params
buy_div_min = DecimalParameter(0, 1, default=0.16, decimals=2, space='buy')
buy_div_max = DecimalParameter(0, 1, default=0.75, decimals=2, space='buy')
buy_indicator_shift = IntParameter(0, 20, default=16, space='buy')
buy_crossed_indicator_shift = IntParameter(0, 20, default=9, space='buy')
decalage = IntParameter(0, 48, default=12, space='buy')
########################################## END RESULT PASTE PLACE #####################################
# ------------------------------------------------------------------------------------------------------------------
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float,
min_stake: Optional[float], max_stake: float,
current_entry_rate: float, current_exit_rate: float,
current_entry_profit: float, current_exit_profit: float,
**kwargs
) -> Union[Optional[float], Tuple[Optional[float], Optional[str]]]:
# ne rien faire si ordre deja en cours
if trade.has_open_orders:
return None
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
last_candle_24 = dataframe.iloc[-25].squeeze()
# prépare les données
count_of_buys = trade.nr_of_successful_entries
current_time = current_time.astimezone(timezone.utc)
open_date = trade.open_date.astimezone(timezone.utc)
dispo = round(self.wallets.get_available_stake_amount())
limit_buy = 4
if (count_of_buys < limit_buy) \
and (last_candle['min288'] == last_candle_24['min288']) \
and (current_profit < -0.01 * count_of_buys) \
and (last_candle['close'] < last_candle['mid288']):
additional_stake = self.config['stake_amount']
self.log_trade(
last_candle=last_candle,
date=current_time,
action="Loss -",
dispo=dispo,
pair=trade.pair,
rate=current_rate,
trade_type='Decrease',
profit=round(current_profit, 4), # round(current_profit * trade.stake_amount, 2),
buys=trade.nr_of_successful_entries,
stake=round(additional_stake, 2)
)
return additional_stake
if (count_of_buys >= limit_buy) & (current_profit < - 0.03 * count_of_buys):
additional_stake = self.config['stake_amount'] * 2
self.log_trade(
last_candle=last_candle,
date=current_time,
action="Loss -",
dispo=dispo,
pair=trade.pair,
rate=current_rate,
trade_type='Decrease',
profit=round(current_profit, 4), # round(current_profit * trade.stake_amount, 2),
buys=trade.nr_of_successful_entries,
stake=round(additional_stake, 2)
)
return additional_stake
return None
def calculate_stake(self, pair, last_candle, factor=1):
amount = self.config['stake_amount'] * factor #1000 / self.first_stack_factor.value self.protection_stake_amount.value #
return amount
def confirm_trade_entry(self, pair: str, order_type: str, amount: float, rate: float, time_in_force: str,
current_time: datetime, entry_tag: Optional[str], **kwargs) -> bool:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
dispo = round(self.wallets.get_available_stake_amount())
stake_amount = self.calculate_stake(pair, last_candle, 1)
self.log_trade(
last_candle=last_candle,
date=current_time,
action="START BUY",
pair=pair,
rate=rate,
dispo=dispo,
profit=0,
stake=round(stake_amount, 2)
)
return True
def confirm_trade_exit(self, pair: str, trade: Trade, order_type: str, amount: float, rate: float,
time_in_force: str,
exit_reason: str, current_time, **kwargs) -> bool:
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
dispo = round(self.wallets.get_available_stake_amount())
allow_to_sell = (last_candle['percent5'] < -0.00)
ok = (allow_to_sell) | (exit_reason == 'force_exit')
if ok:
# self.pairs[pair]['last_max'] = 0
# self.pairs[pair]['max_touch'] = 0
self.pairs[pair]['last_buy'] = 0
self.pairs[pair]['last_sell'] = rate
self.log_trade(
last_candle=last_candle,
date=current_time,
action="Sell",
pair=pair,
trade_type=exit_reason,
rate=last_candle['close'],
dispo=dispo,
profit=round(trade.calc_profit(rate, amount), 2)
)
#print(f"Sell {current_time} {exit_reason} rate={rate:.3f} amount={amount} profit={amount * rate:.3f}")
return ok
def custom_exit(self, pair: str, trade: Trade, current_time, current_rate, current_profit, **kwargs):
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
if (current_profit > 0.004) \
& (last_candle['hapercent'] < 0.0) \
& (last_candle['percent'] < 0.0):
count_of_buys = trade.nr_of_successful_entries
return 'profit_' + str(count_of_buys)
def log_trade(self, action, pair, date, trade_type=None, rate=None, dispo=None, profit=None, buys=None, stake=None, last_candle=None):
# Afficher les colonnes une seule fois
if self.config.get('runmode') == 'hyperopt':
return
if self.columns_logged % 30 == 0:
print(
f"| {'Date':<16} | {'Action':<10} | {'Pair':<10} | {'Trade Type':<18} | {'Rate':>12} | {'Dispo':>6} | {'Profit':>8} | {'Pct':>5} | {'max7_1d':>11} | {'max_touch':>12} | {'last_max':>12} | {'Buys':>5} | {'Stake':>10} |"
)
print(
f"|{'-' * 18}|{'-' * 12}|{'-' * 12}|{'-' * 20}|{'-' * 14}|{'-' * 8}|{'-' * 10}|{'-' * 7}|{'-' * 13}|{'-' * 14}|{'-' * 14}|{'-' * 7}|{'-' * 12}|"
)
self.columns_logged += 1
date = str(date)[:16] if date else "-"
limit = None
# if buys is not None:
# limit = round(last_rate * (1 - self.fibo[buys] / 100), 4)
rsi = ''
rsi_pct = ''
# if last_candle is not None:
# if (not np.isnan(last_candle['rsi_1d'])) and (not np.isnan(last_candle['rsi_1h'])):
# rsi = str(int(last_candle['rsi_1d'])) + " " + str(int(last_candle['rsi_1h']))
# if (not np.isnan(last_candle['rsi_pct_1d'])) and (not np.isnan(last_candle['rsi_pct_1h'])):
# rsi_pct = str(int(10000 * last_candle['bb_mid_pct_1d'])) + " " + str(
# int(last_candle['rsi_pct_1d'])) + " " + str(int(last_candle['rsi_pct_1h']))
# first_rate = self.percent_threshold.value
# last_rate = self.threshold.value
# action = self.color_line(action, action)
sma5_1d = ''
sma5_1h = ''
# if last_candle['sma5_pct_1d'] is not None:
# sma5_1d = round(last_candle['sma5_pct_1d'] * 100, 2)
# if last_candle['sma5_pct_1h'] is not None:
# sma5_1h = round(last_candle['sma5_pct_1h'] * 100, 2)
sma5 = str(sma5_1d) + ' ' + str(sma5_1h)
first_rate = self.pairs[pair]['last_max']
# if action != 'Sell':
# profit = round((last_candle['close'] - self.pairs[pair]['last_max']) / self.pairs[pair]['last_max'], 2)
limit_sell = rsi_pct # round((last_candle['close'] - self.pairs[pair]['last_max']) / self.pairs[pair]['last_max'], 4)
max7_1d = round(self.pairs[pair]['max_touch'], 1) #last_candle['max7_1d'] #round(100 * (last_candle['close'] - self.pairs[pair]['last_max']) / self.pairs[pair]['last_max'], 1)
pct_max = round(100 * (last_candle['close'] - max7_1d) / max7_1d, 1)
print(
f"| {date:<16} | {action:<10} | {pair:<10} | {trade_type or '-':<18} | {rate or '-':>12} | {dispo or '-':>6} | {profit or '-':>8} | {pct_max or '-':>5} | {max7_1d or '-':>11} | {round(self.pairs[pair]['max_touch'], 2) or '-':>12} | {round(self.pairs[pair]['last_max'],2) or '-':>12} | {buys or '-':>5} | {stake or '-':>10} |"
)
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
dataframe = dropna(dataframe)
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['haopen'] = heikinashi['open']
dataframe['haclose'] = heikinashi['close']
dataframe['halow'] = heikinashi['low']
dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose']
dataframe['min12'] = talib.MIN(dataframe['close'], timeperiod=12)
dataframe['max12'] = talib.MAX(dataframe['close'], timeperiod=12)
dataframe['min288'] = talib.MIN(dataframe['close'], timeperiod=288)
dataframe['max288'] = talib.MAX(dataframe['close'], timeperiod=288)
dataframe['mid288'] = dataframe['min288'] + (dataframe['max288'] - dataframe['min288']) / 2
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["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
dataframe["percent5"] = dataframe['close'].pct_change(5)
dataframe['volatility_dcp'] = ta.volatility.donchian_channel_pband(
dataframe['high'],
dataframe['low'],
dataframe['close'],
window=10,
offset=0,
fillna=False
)
# 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_diff'] = (dataframe['bb_upperband'] - dataframe['bb_lowerband']) / dataframe['bb_lowerband']
return dataframe
@@ -98,23 +315,19 @@ class Heracles(IStrategy):
"""
Buy strategy Hyperopt will build and use.
"""
conditions = []
IND = 'volatility_dcp'
CRS = 'volatility_kcw'
DFIND = dataframe[IND]
DFCRS = dataframe[CRS]
d = DFIND.shift(self.buy_indicator_shift.value).div(
DFCRS.shift(self.buy_crossed_indicator_shift.value))
# print(d.min(), "\t", d.max())
conditions.append(
d.between(self.buy_div_min.value, self.buy_div_max.value))
if conditions:
# dataframe.loc[
# (dataframe['halow'] <= dataframe['min12'])
# # & (dataframe['open'] <= dataframe['bb_middleband'])
# # & (dataframe['bb_diff'] > 0.01)
# ,
# 'buy']=1
decalage = 3
dataframe.loc[
reduce(lambda x, y: x & y, conditions),
(dataframe['halow'].shift(decalage) <= dataframe['min288'].shift(decalage))
& (dataframe['min288'].shift(decalage) == dataframe['min288'])
# & (dataframe['open'] <= dataframe['bb_middleband'])
# & (dataframe['bb_diff'] > 0.01)
,
'buy']=1
return dataframe
@@ -123,5 +336,7 @@ class Heracles(IStrategy):
"""
Sell strategy Hyperopt will build and use.
"""
dataframe.loc[:, 'sell'] = 0
# dataframe.loc[
# (qtpylib.crossed_above(dataframe['haclose'], dataframe['haopen'])),
# 'sell']=1
return dataframe

3
Zeus_8_3_2_B_4_2.py
View File

@@ -357,9 +357,6 @@ class Zeus_8_3_2_B_4_2(IStrategy):
# print("---------------" + pair + "----------------")
expected_profit = self.expectedProfit(pair, last_candle)
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1]
# Calcul du prix cible basé sur l'ATR
atr_take_profit = trade.open_rate + (last_candle['atr'] * 2) # Prendre profit à 2x l'ATR