# Zeus Strategy: First Generation of GodStra Strategy with maximum
# AVG/MID profit in USDT
# Author: @Mablue (Masoud Azizi)
# github: https://github.com/mablue/
# IMPORTANT: INSTALL TA BEFOUR RUN(pip install ta)
# freqtrade hyperopt --hyperopt-loss SharpeHyperOptLoss --spaces buy sell roi --strategy Zeus
# --- Do not remove these libs ---
from datetime import timedelta, datetime
from freqtrade.persistence import Trade
from freqtrade.strategy import (BooleanParameter, CategoricalParameter, DecimalParameter, stoploss_from_open,
IntParameter, IStrategy, merge_informative_pair, informative, stoploss_from_absolute)
import pandas as pd
import numpy as np
import os
import json
from pandas import DataFrame
from typing import Optional, Union, Tuple
import math
import logging
import configparser
from technical import pivots_points
from pathlib import Path
# --------------------------------
# Add your lib to import here test git
import ta
import talib.abstract as talib
import freqtrade.vendor.qtpylib.indicators as qtpylib
import requests
from datetime import timezone, timedelta
from scipy.signal import savgol_filter
from ta.trend import SMAIndicator, EMAIndicator, MACD, ADXIndicator
from collections import Counter
logger = logging.getLogger(__name__)
from tabulate import tabulate
# Couleurs ANSI de base
RED = "\033[31m"
GREEN = "\033[32m"
YELLOW = "\033[33m"
BLUE = "\033[34m"
MAGENTA = "\033[35m"
CYAN = "\033[36m"
RESET = "\033[0m"
def pprint_df(dframe):
print(tabulate(dframe, headers='keys', tablefmt='psql', showindex=False))
def normalize(df):
df = (df - df.min()) / (df.max() - df.min())
return df
class Zeus_8_3_2_B_4_2(IStrategy):
levels = [1, 2, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20]
# startup_candle_count = 12 * 24 * 5
# ROI table:
minimal_roi = {
"0": 0.564,
"567": 0.273,
"2814": 0.12,
"7675": 0
}
stakes = 40
# Stoploss:
stoploss = -1 # 0.256
# Custom stoploss
use_custom_stoploss = False
trailing_stop = True
trailing_stop_positive = 0.15
trailing_stop_positive_offset = 0.20
trailing_only_offset_is_reached = True
# Buy hypers
timeframe = '5m'
max_open_trades = 5
max_amount = 40
parameters = {}
# DCA config
position_adjustment_enable = True
plot_config = {
"main_plot": {
"sma24_1h": {
"color": "pink"
},
"sma5_1d": {
"color": "blue"
},
# "sma24": {
# "color": "yellow"
# },
"sma60": {
"color": "green"
},
"bb_lowerband": {
"color": "#da59a6"},
"bb_upperband": {
"color": "#da59a6",
},
# "sma12": {
# "color": "blue"
# },
"mid_smooth_3_1h": {
"color": "blue"
}
},
"subplots": {
"Rsi": {
"max_rsi_24": {
"color": "blue"
},
"max_rsi_24_1h": {
"color": "pink"
},
# "rsi_1h": {
# "color": "red"
# },
# "rsi_1d": {
# "color": "blue"
# }
},
"Rsi_deriv1": {
"sma24_deriv1_1h": {
"color": "pink"
},
"sma24_deriv1": {
"color": "yellow"
},
"sma5_deriv1_1d": {
"color": "blue"
},
"sma60_deriv1": {
"color": "green"
}
},
"Rsi_deriv2": {
"sma24_deriv2_1h": {
"color": "pink"
},
"sma24_deriv2": {
"color": "yellow"
},
"sma5_deriv2_1d": {
"color": "blue"
},
"sma60_deriv2": {
"color": "green"
}
},
"States": {
"tdc_macd_1h": {
"color": "cyan"
},
"sma24_state_1h": {
"color": "pink"
},
"sma24_state": {
"color": "yellow"
},
"sma5_state_1d": {
"color": "blue"
},
"sma60_state": {
"color": "green"
}
},
'Macd': {
"macd_rel_1d": {
"color": "cyan"
},
"macdsignal_rel_1d": {
"color": "pink"
},
"macdhist_rel_1d": {
"color": "yellow"
}
}
}
}
columns_logged = False
pairs = {
pair: {
"first_buy": 0,
"last_buy": 0.0,
"last_min": 999999999999999.5,
"last_max": 0,
"trade_info": {},
"max_touch": 0.0,
"last_sell": 0.0,
'count_of_buys': 0,
'current_profit': 0,
'expected_profit': 0,
'previous_profit': 0,
"last_candle": {},
"last_count_of_buys": 0,
'base_stake_amount': 0,
'stop_buy': False,
'last_date': 0,
'stop': False,
'max_profit': 0,
'total_amount': 0,
'has_gain': 0,
'force_sell': False,
'force_buy': False
}
for pair in ["BTC/USDC", "ETH/USDC", "DOGE/USDC", "XRP/USDC", "SOL/USDC",
"BTC/USDT", "ETH/USDT", "DOGE/USDT", "XRP/USDT", "SOL/USDT"]
}
# 20 20 40 60 100 160 260 420
# 50 50 100 300 500
# fibo = [1, 1, 2, 3, 5, 8, 13, 21]
# my fibo
# 50 50 50 100 100 150 200 250 350 450 600 1050
fibo = [1, 1, 1, 2, 2, 3, 4, 5, 7, 9, 12, 16, 21]
baisse = [1, 2, 3, 5, 7, 10, 14, 19, 26, 35, 47, 63, 84]
# Ma suite 1 1 1 2 2 3 4 5 7 9 12 16 21
# Mise 50 50 50 100 100 150 200 250 350 450 600 800 1050
# Somme Mises 50 100 150 250 350 500 700 950 1300 1750 2350 3150 4200
# baisse 1 2 3 5 7 10 14 19 26 35 47 63 84
# factors = [1, 1.1, 1.25, 1.5, 2.0, 3]
# thresholds = [2, 5, 10, 20, 30, 50]
factors = [0.5, 0.75, 1, 1.25, 1.5, 2]
thresholds = [0, 2, 5, 10, 30, 45]
trades = list()
max_profit_pairs = {}
# # sma24_deriv1_1d_stop_protection = DecimalParameter(-0.2, 0.2, default=0.05, decimals=2, space='protection',
# # optimize=True, load=True)
# sma5_deriv1_1d_stop_protection = DecimalParameter(-5, 0, default=0.5, decimals=1, space='protection',
# optimize=True, load=True)
# sma5_deriv2_1d_stop_protection = DecimalParameter(-5, 0, default=0.5, decimals=1, space='protection', optimize=True,
# load=True)
#
# # sma24_deriv1_1d_start_protection = DecimalParameter(-0.2, 0.2, default=0.05, decimals=2, space='protection',
# # optimize=True, load=True)
# sma5_deriv1_1d_restart_protection = DecimalParameter(0, 5, default=0.5, decimals=1, space='protection',
# optimize=True, load=True)
# sma5_deriv2_1d_restart_protection = DecimalParameter(0, 5, default=0.5, decimals=1, space='protection',
# optimize=True,
# load=True)
#
mise_factor_buy = DecimalParameter(0.01, 0.1, default=0.05, decimals=2, space='buy', optimize=True, load=True)
indicators = {'sma5', 'sma12', 'sma24', 'sma60'}
indicators_percent = {'percent', 'percent3', 'percent12', 'percent24', 'percent_1h', 'percent3_1h', 'percent12_1h', 'percent24_1h'}
mises = IntParameter(1, 50, default=5, space='buy', optimize=False, load=False)
pct = DecimalParameter(0.005, 0.05, default=0.012, decimals=3, space='buy', optimize=True, load=True)
pct_inc = DecimalParameter(0.0001, 0.003, default=0.0022, decimals=4, space='buy', optimize=True, load=True)
indic_5m_slope_sup_buy = CategoricalParameter(indicators, default="sma60", space='buy')
indic_deriv_5m_slop_sup_buy = CategoricalParameter(indicators, default="sma12", space='buy', optimize=True, load=True)
deriv_5m_slope_sup_buy = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='buy', optimize=True, load=True)
indic_5m_slope_inf_buy = CategoricalParameter(indicators, default="sma60", space='buy')
indic_deriv_5m_slop_sup_buy = CategoricalParameter(indicators, default="sma12", space='buy', optimize=True, load=True)
deriv_5m_slope_sup_buy = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='buy', optimize=True, load=True)
# indic_deriv1_5m = DecimalParameter(-2, 2, default=0, decimals=2, space='buy', optimize=True, load=True)
# indic_deriv2_5m = DecimalParameter(-2, 2, default=0, decimals=2, space='buy', optimize=True, load=True)
# indic_1h = CategoricalParameter(indicators, default="sma60", space='buy')
# indic_deriv1_1h = DecimalParameter(-5, 5, default=0, decimals=1, space='buy', optimize=True, load=True)
# indic_deriv2_1h = DecimalParameter(-10, 10, default=0, decimals=1, space='buy', optimize=True, load=True)
# indic_1d_p = CategoricalParameter(indicators, default="sma60", space='protection')
# indic_deriv1_1d_p_stop = DecimalParameter(-2, 2, default=0, decimals=1, space='protection', optimize=True, load=True)
# indic_deriv2_1d_p_stop = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True)
# indic_deriv1_1d_p_start = DecimalParameter(-2, 2, default=0, decimals=1, space='protection', optimize=True, load=True)
# indic_deriv2_1d_p_start = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True)
indic_5m_slope_sup_sell = CategoricalParameter(indicators, default="sma60", space='sell')
indic_deriv_5m_slope_sup_sell = CategoricalParameter(indicators, default="sma60", space='sell')
deriv_5m_slope_sup_sell = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='sell', optimize=True, load=True)
indic_5m_slope_inf_sell = CategoricalParameter(indicators, default="sma60", space='sell')
indic_deriv_5m_slope_inf_sell = CategoricalParameter(indicators, default="sma60", space='sell')
deriv_5m_slope_inf_sell = DecimalParameter(-0.1, 0.5, default=0, decimals=2, space='sell', optimize=True, load=True)
deriv1_buy_protect = DecimalParameter(-0.3, 0.1, default=-0.1, decimals=2, space='protection', optimize=True, load=True)
rsi_buy_protect = IntParameter(50, 90, default=70, space='protection', optimize=True, load=True)
indic_5m_slope_sup = CategoricalParameter(indicators, default="sma60", space='protection')
indic_1h_slope_sup = CategoricalParameter(indicators, default="sma5", space='protection')
# indic_percent_sell = CategoricalParameter(indicators_percent, default="sma60", space='sell')
# percent_5m_sell = DecimalParameter(-0.1, -0.0, default=0, decimals=2, space='sell', optimize=True, load=True)
# indic_deriv1_5m_sell = DecimalParameter(-2, 2, default=0, decimals=2, space='sell', optimize=True, load=True)
# indic_deriv2_5m_sell = DecimalParameter(-2, 2, default=0, decimals=2, space='sell', optimize=True, load=True)
# indic_deriv1_1h_sell = DecimalParameter(-5, 5, default=0, decimals=1, space='sell', optimize=True, load=True)
# indic_deriv2_1h_sell = DecimalParameter(-10, 10, default=0, decimals=1, space='sell', optimize=True, load=True)
labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
index_labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
ordered_labels = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
label_to_index = {label: i for i, label in enumerate(ordered_labels)}
# =========================================================================
# paliers dérivées jour sma5
sma5_deriv1 = [-1.1726, -0.2131, -0.1012, -0.0330, 0.0169, 0.0815, 0.2000, 4.0335]
sma5_deriv2 = [-1.9190, -0.1388, -0.0644, -0.0202, 0.0209, 0.0646, 0.1377, 4.2987]
sma5_derive1_2_matrice = {
'B3': [8.6, 10.8, 34.6, 35.0, 58.8, 61.9, 91.2],
'B2': [0.0, 12.5, 9.1, 57.1, 63.3, 79.3, 89.5],
'B1': [6.1, 12.5, 22.0, 46.8, 61.5, 70.0, 100.0],
'N0': [0.0, 10.7, 37.0, 43.5, 75.0, 75.9, 100.0],
'H1': [0.0, 18.5, 32.4, 35.9, 76.8, 82.9, 92.0],
'H2': [0.0, 21.9, 16.0, 39.5, 69.7, 83.3, 100.0],
'H3': [9.5, 29.2, 41.2, 57.9, 53.8, 86.8, 92.3],
}
sma5_derive1_2_matrice_df = pd.DataFrame(sma5_derive1_2_matrice, index=index_labels)
# Extraction de la matrice numérique
sma5_derive1_2_numeric_matrice = sma5_derive1_2_matrice_df.reindex(index=ordered_labels,
columns=ordered_labels).values
# paliers = {}
# =========================================================================
# Parameters hyperopt
# buy_mid_smooth_3_deriv1 = DecimalParameter(-0.1, 0.1, decimals=2, default=-0.06, space='buy')
# buy_mid_smooth_24_deriv1 = DecimalParameter(-0.6, 0, decimals=2, default=-0.03, space='buy')
# buy_horizon_predict_1h = IntParameter(1, 6, default=2, space='buy')
# buy_level_predict_1h = IntParameter(2, 5, default=4, space='buy')
should_enter_trade_count = 0
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:
minutes = 0
if self.pairs[pair]['last_date'] != 0:
minutes = round(int((current_time - self.pairs[pair]['last_date']).total_seconds() / 60))
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
last_candle_2 = dataframe.iloc[-2].squeeze()
last_candle_3 = dataframe.iloc[-3].squeeze()
# val = self.getProbaHausse144(last_candle)
# trend = last_candle['trend_class']
# params = self.loadParamsFor(pair, trend)
# indic_5m = self.getParamValue(pair, trend, 'buy', 'indic_5m')
# indic_deriv1_5m = self.getParamValue( pair, trend, 'buy', 'indic_deriv1_5m')
# indic_deriv2_5m = self.getParamValue( pair, trend, 'buy', 'indic_deriv2_5m')
condition = True #(last_candle[f"{indic_5m}_deriv1"] >= indic_deriv1_5m) and (last_candle[f"{indic_5m}_deriv2"] >= indic_deriv2_5m)
# allow_to_buy = True #(not self.stop_all) #& (not self.all_down)
# and val > self.buy_val.value #not last_candle['tendency'] in ('B-', 'B--') # (rate <= float(limit)) | (entry_tag == 'force_entry')
allow_to_buy = (condition and not self.pairs[pair]['stop']) | (entry_tag == 'force_entry')
# if allow_to_buy:
# poly_func, x_future, y_future, count = self.polynomial_forecast(
# dataframe['mid_smooth_12'],
# window=self.buy_horizon_predict_1h.value * 12,
# degree=4,
# n_future=3)
#
# if count < 3:
# allow_to_buy = False
force = self.pairs[pair]['force_buy']
if self.pairs[pair]['force_buy']:
self.pairs[pair]['force_buy'] = False
allow_to_buy = True
else:
if not self.should_enter_trade(pair, last_candle, current_time):
allow_to_buy = False
if allow_to_buy:
self.trades = list()
self.pairs[pair]['first_buy'] = rate
self.pairs[pair]['last_buy'] = rate
self.pairs[pair]['max_touch'] = last_candle['close']
self.pairs[pair]['last_candle'] = last_candle
self.pairs[pair]['count_of_buys'] = 1
self.pairs[pair]['current_profit'] = 0
self.pairs[pair]['last_max'] = max(last_candle['close'], self.pairs[pair]['last_max'])
self.pairs[pair]['last_min'] = min(last_candle['close'], self.pairs[pair]['last_min'])
dispo = round(self.wallets.get_available_stake_amount())
self.printLineLog()
stake_amount = self.adjust_stake_amount(pair, last_candle)
self.pairs[pair]['total_amount'] = stake_amount
self.log_trade(
last_candle=last_candle,
date=current_time,
action=("🟩Buy" if allow_to_buy else "Canceled") + " " + str(minutes),
pair=pair,
rate=rate,
dispo=dispo,
profit=0,
trade_type=entry_tag,
buys=1,
stake=round(stake_amount, 2)
)
return allow_to_buy
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:
# allow_to_sell = (minutes > 30)
dataframe, _ = self.dp.get_analyzed_dataframe(pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
profit =trade.calc_profit(rate)
force = self.pairs[pair]['force_sell']
allow_to_sell = (last_candle['hapercent'] < 0 and profit > 0) or force or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss')
minutes = int(round((current_time - trade.date_last_filled_utc).total_seconds() / 60, 0))
if allow_to_sell:
self.trades = list()
self.pairs[pair]['last_count_of_buys'] = trade.nr_of_successful_entries # self.pairs[pair]['count_of_buys']
self.pairs[pair]['last_sell'] = rate
self.pairs[pair]['last_candle'] = last_candle
self.pairs[pair]['max_profit'] = 0
self.pairs[pair]['previous_profit'] = 0
self.trades = list()
dispo = round(self.wallets.get_available_stake_amount())
# print(f"Sell {pair} {current_time} {exit_reason} dispo={dispo} amount={amount} rate={rate} open_rate={trade.open_rate}")
self.log_trade(
last_candle=last_candle,
date=current_time,
action="🟥Sell " + str(minutes),
pair=pair,
trade_type=exit_reason,
rate=last_candle['close'],
dispo=dispo,
profit=round(profit, 2)
)
self.pairs[pair]['force_sell'] = False
self.pairs[pair]['has_gain'] = 0
self.pairs[pair]['current_profit'] = 0
self.pairs[pair]['total_amount'] = 0
self.pairs[pair]['count_of_buys'] = 0
self.pairs[pair]['max_touch'] = 0
self.pairs[pair]['last_buy'] = 0
self.pairs[pair]['last_date'] = current_time
self.pairs[pair]['current_trade'] = None
# else:
# print(f"STOP triggered for {pair} ({exit_reason}) but condition blocked", "warning")
return (allow_to_sell) | (exit_reason == 'force_exit') | (exit_reason == 'stop_loss')
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()
adjusted_stake_amount = self.adjust_stake_amount(pair, current_candle)
# print(f"{pair} adjusted_stake_amount{adjusted_stake_amount}")
# Use default stake amount.
return adjusted_stake_amount
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()
last_candle_1h = dataframe.iloc[-13].squeeze()
before_last_candle = dataframe.iloc[-2].squeeze()
before_last_candle_2 = dataframe.iloc[-3].squeeze()
before_last_candle_12 = dataframe.iloc[-13].squeeze()
before_last_candle_24 = dataframe.iloc[-25].squeeze()
expected_profit = self.expectedProfit(pair, last_candle)
# print(f"current_time={current_time} current_profit={current_profit} expected_profit={expected_profit}")
max_touch_before = self.pairs[pair]['max_touch']
self.pairs[pair]['last_max'] = max(last_candle['close'], self.pairs[pair]['last_max'])
self.pairs[pair]['last_min'] = min(last_candle['close'], self.pairs[pair]['last_min'])
self.pairs[pair]['current_trade'] = trade
count_of_buys = trade.nr_of_successful_entries
profit = trade.calc_profit(current_rate) #round(current_profit * trade.stake_amount, 1)
self.pairs[pair]['max_profit'] = max(self.pairs[pair]['max_profit'], profit)
max_profit = self.pairs[pair]['max_profit']
baisse = 0
if profit > 0:
baisse = 1 - (profit / max_profit)
mx = max_profit / 5
self.pairs[pair]['count_of_buys'] = count_of_buys
self.pairs[pair]['current_profit'] = profit
dispo = round(self.wallets.get_available_stake_amount())
hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
days_since_first_buy = (current_time - trade.open_date_utc).days
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
# trend = last_candle['trend_class']
#
# indic_5m_sell = self.getParamValue( pair, trend, 'sell', 'indic_5m_sell')
# indic_deriv1_5m_sell = self.getParamValue( pair, trend, 'sell', 'indic_deriv1_5m_sell')
# indic_deriv2_5m_sell = self.getParamValue( pair, trend, 'sell', 'indic_deriv2_5m_sell')
if hours % 4 == 0:
self.log_trade(
last_candle=last_candle,
date=current_time,
action="🔴 CURRENT" if self.pairs[pair]['stop'] else "🟢 CURRENT",
dispo=dispo,
pair=pair,
rate=last_candle['close'],
trade_type='',
profit=round(profit, 2),
buys=count_of_buys,
stake=0
)
# if (last_candle['mid_smooth_deriv1'] >= 0):
# return None
# if (last_candle['tendency'] in ('H++', 'H+')) and (last_candle['rsi'] < 80):
# return None
#
# if (last_candle['sma24_deriv1'] < 0 and before_last_candle['sma24_deriv1'] >= 0) and (current_profit > expected_profit):
# return 'Drv_' + str(count_of_buys)
pair_name = self.getShortName(pair)
# if (current_profit > expected_profit) and last_candle['can_sell']:
# return 'Can_' + pair_name + '_' + str(count_of_buys)
# trend = last_candle['trend_class_1d']
# if (trend == "B-" or trend == "B--") and self.pairs[pair]['has_gain'] == 0: # and (last_candle[f"{indic_5m_sell}_deriv1"] <= indic_deriv1_5m_sell and last_candle[f"{indic_5m_sell}_deriv2"] <= indic_deriv2_5m_sell):
#
# if (last_candle['max_rsi_12_1h'] > 75) and last_candle['trend_class_1h'] == 'H+' and profit > max(5, expected_profit) and (last_candle['hapercent'] < 0):
# self.pairs[pair]['stop'] = True
# self.log_trade(
# last_candle=last_candle,
# date=current_time,
# action="🔴STOP",
# dispo=dispo,
# pair=pair,
# rate=last_candle['close'],
# trade_type='',
# profit=self.pairs[pair]['current_profit'],
# buys=self.pairs[pair]['count_of_buys'],
# stake=0
# )
# return "MAX_RSI"
#
# return None
# if (trend == "B-" or trend == "B--") and last_candle[f"{self.indic_5m_sell.value}_deriv1"] <= self.indic_deriv1_5m_sell.value \
# and last_candle[f"{self.indic_5m_sell.value}_deriv2"] <= self.indic_deriv2_5m_sell.value:
# return None
if last_candle['max_rsi_24'] > 85 and profit > max(5, expected_profit) and (last_candle['hapercent'] < 0) and last_candle['sma60_deriv1'] < 0.05:
self.pairs[pair]['force_sell'] = False
self.pairs[pair]['force_buy'] = False #(self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
return str(count_of_buys) + '_' + 'Rsi85_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
if self.pairs[pair]['force_sell']:
self.pairs[pair]['force_sell'] = False
self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
return str(count_of_buys) + '_' + 'Frc_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
if profit > max(5, expected_profit) and baisse > 0.30:
self.pairs[pair]['force_sell'] = False
self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
return str(count_of_buys) + '_' + 'B30_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
if max_profit > 0.5 * count_of_buys and baisse > 0.15 and last_candle['sma12_state'] <= 0 and last_candle['sma60_state'] <= - 1:
self.pairs[pair]['force_sell'] = False
self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
return str(count_of_buys) + '_' + 'B15_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
if (last_candle['sma5_1h'] - before_last_candle_12['sma5_1h']) / last_candle['sma5_1h'] > 0.0002:
return None
factor = 1
if (self.getShortName(pair) == 'BTC'):
factor = 0.5
# if baisse > 2 and baisse > factor * self.pairs[pair]['total_amount'] / 100:
# self.pairs[pair]['force_sell'] = False
# self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 3)
# return 'Baisse_' + pair_name + '_' + str(count_of_buys) + '_' + str(self.pairs[pair]['has_gain'])
#
# if 1 <= count_of_buys <= 3:
if last_candle['max_rsi_24'] > 75 and profit > expected_profit and (last_candle['hapercent'] < 0) and last_candle['sma60_deriv1'] < 0:
self.pairs[pair]['force_sell'] = False
return str(count_of_buys) + '_' + 'Rsi75_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
# if last_candle['mid_smooth_1h_deriv1'] < 0 and profit > expected_profit:
# self.pairs[pair]['force_sell'] = False
# self.pairs[pair]['force_buy'] = (self.pairs[pair]['count_of_buys'] - self.pairs[pair]['has_gain'] > 5)
# return str(count_of_buys) + '_' + 'Drv3_' + pair_name + '_' + str(self.pairs[pair]['has_gain'])
# if 4 <= count_of_buys <= 6:
# if ((before_last_candle_2['mid_smooth_12_deriv1'] <= before_last_candle['mid_smooth_12_deriv1'])
# & (before_last_candle['mid_smooth_12_deriv1'] >= last_candle['mid_smooth_12_deriv1'])) \
# and (current_profit > expected_profit):
# return 'Drv13_' + pair_name + '_' + str(count_of_buys)
#
# if 7 <= count_of_buys:
# if ((before_last_candle_24['sma24_deriv1_1h'] <= before_last_candle_12['sma24_deriv1_1h'])
# & (before_last_candle_12['sma24_deriv1_1h'] >= last_candle['sma24_deriv1_1h'])) \
# and (current_profit > expected_profit):
# return 'Drv24_' + pair_name + '_' + str(count_of_buys)
# if (baisse > mx) & (current_profit > expected_profit):
# self.trades = list()
# return 'mx_' + str(count_of_buys)
# if (last_candle['percent12'] <= -0.01) & (current_profit >= expected_profit):
# self.trades = list()
# return 'pft_' + str(count_of_buys)
self.pairs[pair]['max_touch'] = max(last_candle['close'], self.pairs[pair]['max_touch'])
def getShortName(self, pair):
return pair.replace("/USDT", '').replace("/USDC", '').replace("_USDC", '').replace("_USDT", '')
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, '1h') for pair in pairs]
return informative_pairs
from typing import List
def multi_step_interpolate(self, pct: float, thresholds: List[float], factors: List[float]) -> float:
if pct <= thresholds[0]:
return factors[0]
if pct >= thresholds[-1]:
return factors[-1]
for i in range(1, len(thresholds)):
if pct <= thresholds[i]:
# interpolation linéaire entre thresholds[i-1] et thresholds[i]
return factors[i - 1] + (pct - thresholds[i - 1]) * (factors[i] - factors[i - 1]) / (
thresholds[i] - thresholds[i - 1])
# Juste au cas où (devrait jamais arriver)
return factors[-1]
# def interpolate_factor(self, pct: float, start_pct: float = 5, end_pct: float = 30,
# start_factor: float = 1.0, end_factor: float = 2.0) -> float:
# if pct <= start_pct:
# return start_factor
# if pct >= end_pct:
# return end_factor
# # interpolation linéaire
# return start_factor + (pct - start_pct) * (end_factor - start_factor) / (end_pct - start_pct)
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' or self.dp.runmode.value in ('hyperopt'):
return
if self.columns_logged % 10 == 0:
self.printLog(
f"| {'Date':<16} | {'Action':<10} |{'Pair':<5}| {'Trade Type':<18} |{'Rate':>8} | {'Dispo':>6} | {'Profit':>8} "
f"| {'Pct':>6} | {'max_touch':>11} | {'last_lost':>12} | {'last_max':>7}| {'last_max':>7}|{'Buys':>5}| {'Stake':>5} |"
f"{'rsi':>6}|Distmax|s201d|s5_1d|s5_2d|s51h|s52h|smt1h|smt2h|tdc1d|tdc1h"
)
self.printLineLog()
df = pd.DataFrame.from_dict(self.pairs, orient='index')
colonnes_a_exclure = ['last_candle',
'trade_info', 'last_date', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
# df_filtered = df_filtered["first_buy", "last_max", "max_touch", "last_sell","last_buy", 'count_of_buys', 'current_profit']
print(df_filtered)
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 = ''
sma5 = str(sma5_1d) + ' ' + str(sma5_1h)
last_lost = self.getLastLost(last_candle, pair)
if buys is None:
buys = ''
max_touch = '' # round(last_candle['max12_1d'], 1) #round(self.pairs[pair]['max_touch'], 1)
pct_max = self.getPctFirstBuy(pair, last_candle)
total_counts = str(buys) + '/' + str(sum(pair_data['count_of_buys'] for pair_data in self.pairs.values()))
dist_max = self.getDistMax(last_candle, pair)
# if trade_type is not None:
# if np.isnan(last_candle['rsi_1d']):
# string = ' '
# else:
# string = (str(int(last_candle['rsi_1d']))) + " " + str(int(last_candle['rsi_deriv1_1d']))
# trade_type = trade_type \
# + " " + string \
# + " " + str(int(last_candle['rsi_1h'])) \
# + " " + str(int(last_candle['rsi_deriv1_1h']))
# val144 = self.getProbaHausse144(last_candle)
# val1h = self.getProbaHausse1h(last_candle)
val = self.getProbaHausseSma5d(last_candle)
pct60 = round(100 * self.getPct60D(pair, last_candle), 2)
color = GREEN if profit > 0 else RED
color_sma24 = GREEN if last_candle['sma24_deriv1_1d'] > 0 else RED
color_sma24_2 = GREEN if last_candle['sma24_deriv2_1d'] > 0 else RED
color_sma5 = GREEN if last_candle['mid_smooth_5_deriv1_1d'] > 0 else RED
color_sma5_2 = GREEN if last_candle['mid_smooth_5_deriv2_1d'] > 0 else RED
color_sma5_1h = GREEN if last_candle['sma60_deriv1'] > 0 else RED
color_sma5_2h = GREEN if last_candle['sma60_deriv2'] > 0 else RED
color_smooth_1h = GREEN if last_candle['mid_smooth_1h_deriv1'] > 0 else RED
color_smooth2_1h = GREEN if last_candle['mid_smooth_1h_deriv2'] > 0 else RED
last_max = int(self.pairs[pair]['last_max']) if self.pairs[pair]['last_max'] > 1 else round(
self.pairs[pair]['last_max'], 3)
last_min = int(self.pairs[pair]['last_min']) if self.pairs[pair]['last_min'] > 1 else round(
self.pairs[pair]['last_min'], 3)
profit = str(profit) + '/' + str(round(self.pairs[pair]['max_profit'], 2))
# 🟢 Dérivée 1 > 0 et dérivée 2 > 0: tendance haussière qui s’accélère.
# 🟡 Dérivée 1 > 0 et dérivée 2 < 0: tendance haussière qui ralentit → essoufflement potentiel.
# 🔴 Dérivée 1 < 0 et dérivée 2 < 0: tendance baissière qui s’accélère.
# 🟠 Dérivée 1 < 0 et dérivée 2 > 0: tendance baissière qui ralentit → possible bottom.
# trend = last_candle['trend_class_1d']
#
# indic_5m = self.getParamValue(pair, trend, 'buy', 'indic_5m')
# indic_deriv1_5m = self.getParamValue(pair, trend, 'buy', 'indic_deriv1_5m')
# indic_deriv2_5m = self.getParamValue(pair, trend, 'buy', 'indic_deriv2_5m')
#
# indic_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_5m_sell')
# indic_deriv1_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_deriv1_5m_sell')
# indic_deriv2_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_deriv2_5m_sell')
self.printLog(
f"| {date:<16} |{action:<10} | {pair[0:3]:<3} | {trade_type or '-':<18} |{rate or '-':>9}| {dispo or '-':>6} "
f"|{color}{profit or '-':>10}{RESET}| {pct_max or '-':>6} | {round(self.pairs[pair]['max_touch'], 2) or '-':>11} | {last_lost or '-':>12} "
f"| {last_max or '-':>7} | {last_min or '-':>7} |{total_counts or '-':>5}|{stake or '-':>7}"
# f"|{round(last_candle['mid_smooth_24_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_1h_deriv1'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv1_1d'],3) or '-' :>6}|"
# f"{round(last_candle['mid_smooth_24_deriv2'],3) or '-' :>6}|{round(last_candle['mid_smooth_1h_deriv2'],3) or '-':>6}|{round(last_candle['mid_smooth_deriv2_1d'],3) or '-':>6}|"
f"{round(last_candle['max_rsi_24'], 1) or '-' :>6}|"
f"{dist_max:>7}|{color_sma24}{round(last_candle['sma24_deriv1_1d'], 2):>5}{RESET}"
f"|{color_sma5}{round(last_candle['mid_smooth_5_deriv1_1d'], 2):>5}{RESET}|{color_sma5_2}{round(last_candle['mid_smooth_5_deriv2_1d'], 2):>5}{RESET}"
f"|{color_sma5_1h}{round(last_candle['sma60_deriv1'], 2):>5}{RESET}|{color_sma5_2h}{round(last_candle['sma60_deriv2'], 2):>5}{RESET}"
f"|{color_smooth_1h}{round(last_candle['mid_smooth_1h_deriv1'], 2):>5}{RESET}|{color_smooth2_1h}{round(last_candle['mid_smooth_1h_deriv2'], 2):>5}{RESET}"
# f"|{last_candle['min60_1d']}|{last_candle['max60_1d']}"
# f"|{last_candle['mid_smooth_tdc_5_1d'] or '-':>3}|{last_candle['mid_smooth_tdc_5_1h'] or '-':>3}|{last_candle['mid_smooth_tdc_5'] or '-':>3}"
f"|{last_candle['mid_smooth_5_state_1d'] or '-':>3}|{last_candle['mid_smooth_24_state_1h'] or '-':>3}|{last_candle['mid_smooth_5_state_1h'] or '-':>3}|{last_candle['mid_smooth_5_state'] or '-':>3}"
f"|{last_candle['trend_class_1d']:>5} {last_candle['trend_class_1h']:>5}" # {indic_5m} {indic_deriv1_5m} {indic_deriv2_5m} {indic_5m_sell} {indic_deriv1_5m_sell} {indic_deriv2_5m_sell}"
)
def getLastLost(self, last_candle, pair):
last_lost = round((last_candle['close'] - self.pairs[pair]['max_touch']) / self.pairs[pair]['max_touch'], 3)
return last_lost
def getDistMax(self, last_candle, pair):
mx = last_candle['max12_1d']
dist_max = round(100 * (mx - last_candle['close']) / mx, 0)
return dist_max
def printLineLog(self):
# f"sum1h|sum1d|Tdc|Tdh|Tdd| drv1 |drv_1h|drv_1d|"
self.printLog(
f"+{'-' * 18}+{'-' * 12}+{'-' * 5}+{'-' * 20}+{'-' * 9}+{'-' * 8}+{'-' * 12}+{'-' * 8}+{'-' * 13}+{'-' * 14}+{'-' * 9}{'-' * 9}+{'-' * 5}+{'-' * 7}+"
f"+{'-' * 6}+{'-' * 7}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+{'-' * 5}+"
)
def printLog(self, str):
if self.config.get('runmode') == 'hyperopt' or self.dp.runmode.value in ('hyperopt'):
return;
if not self.dp.runmode.value in ('backtest', 'hyperopt', 'lookahead-analysis'):
logger.info(str)
else:
if not self.dp.runmode.value in ('hyperopt'):
print(str)
def add_tendency_column(self, dataframe: pd.DataFrame, name: str, suffixe: str = '', eps: float = 1e-3,
d1_lim_inf: float = -0.01, d1_lim_sup: float = 0.01) -> pd.DataFrame:
"""
Ajoute une colonne 'tendency' basée sur les dérivées 1 et 2 lissées et normalisées.
eps permet de définir un seuil proche de zéro.
suffixe permet de gérer plusieurs indicateurs.
"""
def tag_by_derivatives(row):
d1 = row[f"{name}{suffixe}_deriv1"]
d2 = row[f"{name}{suffixe}_deriv2"]
# On considère les petites valeurs comme zéro
if abs(d1) < eps:
return 'P' # Palier / neutre
if d1 > d1_lim_sup:
return 'H++' if d2 > eps else 'H+' # Acceleration Hausse / Ralentissement Hausse
if d1 < d1_lim_inf:
return 'B--' if d2 < -eps else 'B-' # Acceleration Baisse / Ralentissement Baisse
if abs(d1) < eps:
return 'DH' if d2 > eps else 'DB' # Depart Hausse / Depart Baisse
return 'Mid'
print(f"{name}_tdc{suffixe}")
dataframe[f"{name}_tdc{suffixe}"] = dataframe.apply(tag_by_derivatives, axis=1)
return dataframe
# def add_tendency_column(self, dataframe: pd.DataFrame, name, suffixe='') -> pd.DataFrame:
# def tag_by_derivatives(row):
# d1 = row[f"{name}{suffixe}_deriv1"]
# d2 = row[f"{name}{suffixe}_deriv2"]
# d1_lim_inf = -0.01
# d1_lim_sup = 0.01
# if d1 >= d1_lim_inf and d1 <= d1_lim_sup: # and d2 >= d2_lim_inf and d2 <= d2_lim_sup:
# return 'P' # Palier
# if d1 == 0.0:
# return 'DH' if d2 > 0 else 'DB' # Depart Hausse / Départ Baisse
# if d1 > d1_lim_sup:
# return 'H++' if d2 > 0 else 'H+' # Acceleration Hausse / Ralentissement Hausse
# if d1 < d1_lim_inf:
# return 'B--' if d2 < 0 else 'B-' # Accéleration Baisse / Ralentissement Baisse
# return 'Mid'
#
# dataframe[f"tendency{suffixe}"] = dataframe.apply(tag_by_derivatives, axis=1)
# return dataframe
def populate_indicators(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# Add all ta features
pair = metadata['pair']
dataframe = self.populateDataframe(dataframe, timeframe='5m')
# dataframe = self.calculateRegression(dataframe, column='mid_smooth', window=24, degree=4, future_offset=12)
# dataframe = self.calculateRegression(dataframe, column='mid_smooth_24', window=24, degree=4, future_offset=12)
################### INFORMATIVE 1h
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
informative = self.populateDataframe(informative, timeframe='1h')
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 = self.populateDataframe(informative, timeframe='1d')
dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
dataframe['last_price'] = dataframe['close']
dataframe['first_price'] = dataframe['close']
# dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2
# dataframe['close01'] = dataframe.iloc[-1]['close'] * 1.01
# dataframe['limit'] = dataframe['close']
count_buys = 0
if self.dp:
if self.dp.runmode.value in ('live', 'dry_run'):
self.getOpenTrades()
for trade in self.trades:
if trade.pair != pair:
continue
filled_buys = trade.select_filled_orders('buy')
count = 0
amount = 0
for buy in filled_buys:
if count == 0:
dataframe['first_price'] = buy.price
self.pairs[pair]['first_buy'] = buy.price
self.pairs[pair]['first_amount'] = buy.price * buy.filled
# dataframe['close01'] = buy.price * 1.01
# Order(id=2396, trade=1019, order_id=29870026652, side=buy, filled=0.00078, price=63921.01,
# status=closed, date=2024-08-26 02:20:11)
dataframe['last_price'] = buy.price
self.pairs[pair]['last_buy'] = buy.price
count = count + 1
amount += buy.price * buy.filled
# dataframe['mid_price'] = (dataframe['last_price'] + dataframe['first_price']) / 2
count_buys = count
# dataframe['limit'] = dataframe['last_price'] * (1 - self.baisse[count] / 100)
self.pairs[pair]['total_amount'] = amount
# dataframe['mid_smooth_tag'] = qtpylib.crossed_below(dataframe['mid_smooth_24_deriv1'], dataframe['mid_smooth_deriv2_24'])
# ===============================
# lissage des valeurs horaires
dataframe['mid_smooth_1h'] = dataframe['mid'].rolling(window=6).mean()
dataframe["mid_smooth_1h_deriv1"] = 100 * dataframe["mid_smooth_1h"].diff().rolling(window=6).mean() / \
dataframe['mid_smooth_1h']
dataframe["mid_smooth_1h_deriv2"] = 100 * dataframe["mid_smooth_1h_deriv1"].diff().rolling(window=6).mean()
dataframe['mid_smooth_5h'] = talib.EMA(dataframe, timeperiod=60) # dataframe['mid'].rolling(window=60).mean()
dataframe["mid_smooth_5h_deriv1"] = 100 * dataframe["mid_smooth_5h"].diff().rolling(window=60).mean() / \
dataframe['mid_smooth_5h']
dataframe["mid_smooth_5h_deriv2"] = 100 * dataframe["mid_smooth_5h_deriv1"].diff().rolling(window=60).mean()
# Compter les baisses / hausses consécutives
# self.calculateDownAndUp(dataframe, limit=0.0001)
# ===============================
# Lissage des valeurs Journalières
horizon_d = 12 * 5 * 24
# dataframe['rsi_1h'] = dataframe['rsi_1h'].rolling(12).mean()
# dataframe['rsi_deriv1_1h'] = dataframe['rsi_deriv1_1h'].rolling(12).mean()
# dataframe['rsi_deriv2_1h'] = dataframe['rsi_deriv2_1h'].rolling(12).mean()
# dataframe['mid_smooth_1d'] = dataframe['mid_smooth_1d'].rolling(window=horizon_d * 5).mean()
# dataframe["mid_smooth_deriv1_1d"] = dataframe["mid_smooth_1d"].rolling(horizon_d).mean().diff() / horizon_d
# dataframe["mid_smooth_deriv2_1d"] = horizon_d * dataframe["mid_smooth_deriv1_1d"].rolling(horizon_d).mean().diff()
#
# dataframe['sma5_1d'] = dataframe['sma5_1d'].rolling(window=horizon_d).mean()
# dataframe['sma5_deriv1_1d'] = dataframe['sma5_deriv1_1d'].rolling(window=horizon_d).mean()
# dataframe['sma24_1d'] = dataframe['sma24_1d'].rolling(window=horizon_d).mean()
# dataframe['sma24_deriv1_1d'] = dataframe['sma24_deriv1_1d'].rolling(window=horizon_d).mean()
# dataframe = self.calculateRegression(dataframe, column='mid_smooth_1d', window=24, degree=4, future_offset=12)
# dataframe['percent_with_previous_day'] = 100 * (dataframe['close'] - dataframe['close_1d']) / dataframe['close']
# dataframe['percent_with_max_hour'] = 100 * (dataframe['close'] - dataframe['max12_1h']) / dataframe['close']
#
# horizon_h = 24 * 5
# dataframe['futur_percent_1h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-12) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
# dataframe['futur_percent_3h'] = 100 * ((dataframe['close'].shift(-36) - dataframe['close']) / dataframe['close']).rolling(horizon_h).mean()
# dataframe['futur_percent_5h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-60) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
# dataframe['futur_percent_12h'] = 100 * ((dataframe['mid_smooth_1h'].shift(-144) - dataframe['mid_smooth_1h']) / dataframe['mid_smooth_1h']).rolling(horizon_h).mean()
#
# dataframe['futur_percent_1d'] = 100 * (dataframe['close'].shift(-1) - dataframe['close']) / dataframe['close']
# dataframe['futur_percent_3d'] = 100 * (dataframe['close'].shift(-3) - dataframe['close']) / dataframe['close']
#
# self.calculateProbabilite2Index(dataframe, ['futur_percent_1d'], 'sma24_deriv1_1h', 'sma5_1d')
# if self.dp.runmode.value in ('backtest'):
# print("##################")
# print("# STAT DAY vs HOUR")
# print("##################")
# self.calculateProbabilite2Index(dataframe, futur_cols=['futur_percent_1d'], indic_1='sma5_deriv1_1d',
# indic_2='sma5_deriv2_1d')
# dataframe['proba_hausse'] = dataframe.apply(lambda row: self.getProbaHausseEmaVolume(row), axis=1)
# dataframe['futur_percent_3'] = 100 * ((dataframe['sma5'].shift(-1) - dataframe['sma5']) / dataframe['sma5'])
# futur_cols = ['futur_percent_3']
# indic_1 = 'mid_smooth_1h_deriv1'
# indic_2 = 'mid_smooth_1h_deriv2'
# self.calculateProbabilite2Index(dataframe, futur_cols, indic_1, indic_2)
# dataframe = dataframe.resample('sma12_1h').ffill()
# dataframe = dataframe.resample('sma24_1h').ffill()
# mises = IntParameter(1, 50, default=5, space='buy', optimize=False, load=False)
#
# pct = DecimalParameter(0.005, 0.05, default=0.012, decimals=3, space='buy', optimize=True, load=True)
# pct_inc = DecimalParameter(0.0001, 0.003, default=0.0022, decimals=4, space='buy', optimize=True, load=True)
#
# indic_5m_slope_sup = CategoricalParameter(indicators, default="sma60", space='buy')
indic_5m_protect = self.indic_5m_slope_sup.value
indic_1h_protect = self.indic_1h_slope_sup.value + '_1h'
dataframe['stop_buying_deb'] = ((dataframe['max_rsi_12_1d'] > self.rsi_buy_protect.value) | (dataframe['sma24_deriv1_1h'] < self.deriv1_buy_protect.value)) & (qtpylib.crossed_below(dataframe[indic_5m_protect], dataframe[indic_1h_protect]))
dataframe['stop_buying_end'] = (dataframe[indic_1h_protect].shift(24) > dataframe[indic_1h_protect].shift(12)) & (dataframe[indic_1h_protect].shift(12) < dataframe[indic_1h_protect])
latched = np.zeros(len(dataframe), dtype=bool)
for i in range(1, len(dataframe)):
if dataframe['stop_buying_deb'].iloc[i]:
latched[i] = True
elif dataframe['stop_buying_end'].iloc[i]:
latched[i] = False
else:
latched[i] = latched[i - 1]
dataframe['stop_buying'] = latched
return dataframe
def populateDataframe(self, dataframe, timeframe='5m'):
heikinashi = qtpylib.heikinashi(dataframe)
dataframe['haopen'] = heikinashi['open']
dataframe['haclose'] = heikinashi['close']
dataframe['hapercent'] = (dataframe['haclose'] - dataframe['haopen']) / dataframe['haclose']
dataframe['mid'] = dataframe['haopen'] + (dataframe['haclose'] - dataframe['haopen']) / 2
dataframe["percent"] = (dataframe["close"] - dataframe["open"]) / dataframe["open"]
dataframe["percent3"] = (dataframe["close"] - dataframe["open"].shift(3)) / dataframe["open"].shift(3)
dataframe["percent12"] = (dataframe["close"] - dataframe["open"].shift(12)) / dataframe["open"].shift(12)
dataframe["percent24"] = (dataframe["close"] - dataframe["open"].shift(24)) / dataframe["open"].shift(24)
if self.dp.runmode.value in ('backtest'):
dataframe['futur_percent'] = 100 * (dataframe['close'].shift(-1) - dataframe['close']) / dataframe['close']
dataframe['sma5'] = dataframe['mid'].ewm(span=5, adjust=False).mean() #dataframe["mid"].rolling(window=5).mean()
self.calculeDerivees(dataframe, 'sma5', timeframe=timeframe, ema_period=5)
dataframe['sma12'] = dataframe['mid'].ewm(span=12, adjust=False).mean() #dataframe["mid"].rolling(window=12).mean()
self.calculeDerivees(dataframe, 'sma12', timeframe=timeframe, ema_period=12)
dataframe['sma24'] = dataframe['mid'].ewm(span=24, adjust=False).mean() #dataframe["mid"].rolling(window=24).mean()
self.calculeDerivees(dataframe, 'sma24', timeframe=timeframe, ema_period=24)
dataframe['sma48'] = dataframe['mid'].ewm(span=48, adjust=False).mean() #dataframe["mid"].rolling(window=48).mean()
self.calculeDerivees(dataframe, 'sma48', timeframe=timeframe, ema_period=48)
dataframe['sma60'] = dataframe['mid'].ewm(span=60, adjust=False).mean() #dataframe["mid"].rolling(window=60).mean()
self.calculeDerivees(dataframe, 'sma60', timeframe=timeframe, ema_period=60)
dataframe = self.calculateDerivation(dataframe, window=3, suffixe="_3",timeframe=timeframe)
dataframe = self.calculateDerivation(dataframe, window=5, suffixe="_5",timeframe=timeframe)
dataframe = self.calculateDerivation(dataframe, window=12, suffixe="_12",timeframe=timeframe)
dataframe = self.calculateDerivation(dataframe, window=24, suffixe="_24", timeframe=timeframe)
# print(metadata['pair'])
dataframe['rsi'] = talib.RSI(dataframe['close'], timeperiod=14)
dataframe['max_rsi_12'] = talib.MAX(dataframe['rsi'], timeperiod=12)
dataframe['max_rsi_24'] = talib.MAX(dataframe['rsi'], timeperiod=24)
self.calculeDerivees(dataframe, 'rsi', timeframe=timeframe, ema_period=12)
dataframe['max12'] = talib.MAX(dataframe['close'], timeperiod=12)
dataframe['max60'] = talib.MAX(dataframe['close'], timeperiod=60)
dataframe['min60'] = talib.MIN(dataframe['close'], timeperiod=60)
# dataframe['min36'] = talib.MIN(dataframe['close'], timeperiod=36)
# dataframe['max36'] = talib.MAX(dataframe['close'], timeperiod=36)
# dataframe['pct36'] = 100 * (dataframe['max36'] - dataframe['min36']) / dataframe['min36']
# dataframe['maxpct36'] = talib.MAX(dataframe['pct36'], timeperiod=36)
# 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']
# Calcul MACD
macd, macdsignal, macdhist = talib.MACD(
dataframe['close'],
fastperiod=12,
slowperiod=26,
signalperiod=9
)
# | Nom | Formule / définition | Signification |
# | ---------------------------- | ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
# | **MACD** (`macd`) | `EMA_fast - EMA_slow` (ex : 12-26 périodes) | Montre l’écart entre la moyenne courte et la moyenne longue.
- Positive → tendance haussière
- Négative → tendance baissière |
# | **Signal** (`macdsignal`) | `EMA_9(MACD)` | Sert de ligne de **signal de déclenchement**.
- Croisement du MACD au-dessus → signal d’achat
- Croisement du MACD en dessous → signal de vente |
# | **Histogramme** (`macdhist`) | `MACD - Signal` | Montre la **force et l’accélération** de la tendance.
- Positif et croissant → tendance haussière qui s’accélère
- Positif mais décroissant → ralentissement de la hausse
- Négatif et décroissant → baisse qui s’accélère
- Négatif mais croissant → ralentissement de la baisse |
# Ajouter dans le dataframe
dataframe['macd'] = macd
dataframe['macdsignal'] = macdsignal
dataframe['macdhist'] = macdhist
# Regarde dans le futur
# # --- Rendre relatif sur chaque série (-1 → 1) ---
# for col in ['macd', 'macdsignal', 'macdhist']:
# series = dataframe[col]
# valid = series[~np.isnan(series)] # ignorer NaN
# min_val = valid.min()
# max_val = valid.max()
# span = max_val - min_val if max_val != min_val else 1
# dataframe[f'{col}_rel'] = 2 * ((series - min_val) / span) - 1
#
# dataframe['tdc_macd'] = self.macd_tendance_int(
# dataframe,
# macd_col='macd_rel',
# signal_col='macdsignal_rel',
# hist_col='macdhist_rel'
# )
# ------------------------------------------------------------------------------------
# rolling SMA indicators (used for trend detection too)
s_short = self.DEFAULT_PARAMS['sma_short']
s_long = self.DEFAULT_PARAMS['sma_long']
dataframe[f'sma_{s_short}'] = dataframe['close'].rolling(window=s_short).mean()
dataframe[f'sma_{s_long}'] = dataframe['close'].rolling(window=s_long).mean()
# --- pente brute ---
dataframe['slope'] = dataframe['sma24'].diff()
# --- lissage EMA ---
dataframe['slope_smooth'] = dataframe['slope'].ewm(span=10, adjust=False).mean()
# # RSI
# window = 14
# delta = dataframe['close'].diff()
# up = delta.clip(lower=0)
# down = -1 * delta.clip(upper=0)
# ma_up = up.rolling(window=window).mean()
# ma_down = down.rolling(window=window).mean()
# rs = ma_up / ma_down.replace(0, 1e-9)
# dataframe['rsi'] = 100 - (100 / (1 + rs))
#
# # EMA example
# dataframe['ema'] = dataframe['close'].ewm(span=self.DEFAULT_PARAMS['ema_period'], adjust=False).mean()
#
# # ATR (simple implementation)
# high_low = dataframe['high'] - dataframe['low']
# high_close = (dataframe['high'] - dataframe['close'].shift()).abs()
# low_close = (dataframe['low'] - dataframe['close'].shift()).abs()
# tr = DataFrame({'hl': high_low, 'hc': high_close, 'lc': low_close}).max(axis=1)
# dataframe['atr'] = tr.rolling(window=self.DEFAULT_PARAMS['atr_period']).mean()
dataframe['volume_sma_deriv'] = dataframe['volume'] * dataframe['sma5_deriv1'] / (dataframe['volume'].rolling(5).mean())
self.setTrends(dataframe)
return dataframe
def macd_tendance_int(self, dataframe: pd.DataFrame,
macd_col='macd',
signal_col='macdsignal',
hist_col='macdhist',
eps=0.0) -> pd.Series:
"""
Renvoie la tendance MACD sous forme d'entiers.
2 : Haussier
1 : Ralentissement hausse
0 : Neutre
-1 : Ralentissement baisse
-2 : Baissier
"""
# | Nom | Formule / définition | Signification |
# | ---------------------------- | ------------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
# | **MACD** (`macd`) | `EMA_fast - EMA_slow` (ex : 12-26 périodes) | Montre l’écart entre la moyenne courte et la moyenne longue.
- Positive → tendance haussière
- Négative → tendance baissière |
# | **Signal** (`macdsignal`) | `EMA_9(MACD)` | Sert de ligne de **signal de déclenchement**.
- Croisement du MACD au-dessus → signal d’achat
- Croisement du MACD en dessous → signal de vente |
# | **Histogramme** (`macdhist`) | `MACD - Signal` | Montre la **force et l’accélération** de la tendance.
- Positif et croissant → tendance haussière qui s’accélère
- Positif mais décroissant → ralentissement de la hausse
- Négatif et décroissant → baisse qui s’accélère
- Négatif mais croissant → ralentissement de la baisse |
# | Situation | MACD | Signal | Hist | Interprétation |
# | -------------------------- | ---------- | --------- | -------- | ------------------------------------------ |
# | MACD > 0, Hist croissant | au-dessus | croissant | Haussier | Momentum fort → tendance haussière |
# | MACD > 0, Hist décroissant | au-dessus | en baisse | Momentum | La hausse ralentit, prudence |
# | MACD < 0, Hist décroissant | en dessous | en baisse | Baissier | Momentum fort → tendance baissière |
# | MACD < 0, Hist croissant | en dessous | en hausse | Rebond ? | La baisse ralentit → possible retournement |
# Créer une série de 0 par défaut
tendance = pd.Series(0, index=dataframe.index)
# Cas MACD > signal
mask_up = dataframe[macd_col] > dataframe[signal_col] + eps
mask_up_hist_pos = mask_up & (dataframe[hist_col] > 0)
mask_up_hist_neg = mask_up & (dataframe[hist_col] <= 0)
tendance[mask_up_hist_pos] = 2 # Haussier
tendance[mask_up_hist_neg] = 1 # Ralentissement hausse
# Cas MACD < signal
mask_down = dataframe[macd_col] < dataframe[signal_col] - eps
mask_down_hist_neg = mask_down & (dataframe[hist_col] < 0)
mask_down_hist_pos = mask_down & (dataframe[hist_col] >= 0)
tendance[mask_down_hist_neg] = -2 # Baissier
tendance[mask_down_hist_pos] = -1 # Ralentissement baisse
# Les NaN deviennent neutre
tendance[dataframe[[macd_col, signal_col, hist_col]].isna().any(axis=1)] = 0
return tendance
def calculateDownAndUp(self, dataframe, limit=0.0001):
dataframe['down'] = dataframe['mid_smooth_1h_deriv1'] < limit # dataframe['hapercent'] <= limit
dataframe['up'] = dataframe['mid_smooth_1h_deriv1'] > limit # dataframe['hapercent'] >= limit
dataframe['down_count'] = - dataframe['down'].astype(int) * (
dataframe['down'].groupby((dataframe['down'] != dataframe['down'].shift()).cumsum()).cumcount() + 1)
dataframe['up_count'] = dataframe['up'].astype(int) * (
dataframe['up'].groupby((dataframe['up'] != dataframe['up'].shift()).cumsum()).cumcount() + 1)
# Créer une colonne vide
dataframe['down_pct'] = self.calculateUpDownPct(dataframe, 'down_count')
dataframe['up_pct'] = self.calculateUpDownPct(dataframe, 'up_count')
def calculateDerivation(self, dataframe, window=12, suffixe='', timeframe='5m'):
dataframe[f"mid_smooth{suffixe}"] = dataframe['mid'].rolling(window).mean()
dataframe = self.calculeDerivees(dataframe, f"mid_smooth{suffixe}", timeframe=timeframe, ema_period=window)
return dataframe
def calculeDerivees(
self,
dataframe: pd.DataFrame,
name: str,
suffixe: str = '',
window: int = 100,
coef: float = 0.15,
ema_period: int = 10,
verbose: bool = True,
timeframe: str = '5m'
) -> pd.DataFrame:
"""
Calcule deriv1/deriv2 (relative simple), applique EMA, calcule tendency
avec epsilon adaptatif basé sur rolling percentiles.
"""
d1_col = f"{name}{suffixe}_deriv1"
d2_col = f"{name}{suffixe}_deriv2"
# d1s_col = f"{name}{suffixe}_deriv1_smooth"
# d2s_col = f"{name}{suffixe}_deriv2_smooth"
tendency_col = f"{name}{suffixe}_state"
factor1 = 100 * (ema_period / 5)
factor2 = 10 * (ema_period / 5)
# dérivée relative simple
dataframe[d1_col] = (dataframe[name] - dataframe[name].shift(1)) / dataframe[name].shift(1)
# lissage EMA
dataframe[d1_col] = factor1 * dataframe[d1_col].ewm(span=ema_period, adjust=False).mean()
# dataframe[d1_col] = dataframe[d1_col].rolling(window=ema_period, center=True).median()
dataframe[d2_col] = dataframe[d1_col] - dataframe[d1_col].shift(1)
dataframe[d2_col] = factor2 * dataframe[d2_col].ewm(span=ema_period, adjust=False).mean()
# epsilon adaptatif via rolling percentile
p_low_d1 = dataframe[d1_col].rolling(window=window, min_periods=1).quantile(0.05)
p_high_d1 = dataframe[d1_col].rolling(window=window, min_periods=1).quantile(0.95)
p_low_d2 = dataframe[d2_col].rolling(window=window, min_periods=1).quantile(0.05)
p_high_d2 = dataframe[d2_col].rolling(window=window, min_periods=1).quantile(0.95)
eps_d1_series = ((p_low_d1.abs() + p_high_d1.abs()) / 2) * coef
eps_d2_series = ((p_low_d2.abs() + p_high_d2.abs()) / 2) * coef
# fallback global eps
global_eps_d1 = (abs(dataframe[d1_col].quantile(0.05)) + abs(dataframe[d1_col].quantile(0.95))) / 2 * coef
global_eps_d2 = (abs(dataframe[d2_col].quantile(0.05)) + abs(dataframe[d2_col].quantile(0.95))) / 2 * coef
eps_d1_series = eps_d1_series.fillna(global_eps_d1).replace(0, global_eps_d1)
eps_d2_series = eps_d2_series.fillna(global_eps_d2).replace(0, global_eps_d2)
if verbose and self.dp.runmode.value in ('backtest'):
stats = dataframe[[d1_col, d2_col]].agg(['min', 'max']).T
stats['abs_max'] = dataframe[[d1_col, d2_col]].abs().max(axis=0)
print(f"---- Derivatives stats {timeframe}----")
print(stats)
print(f"rolling window = {window}, coef = {coef}, ema_period = {ema_period}")
print("---------------------------")
# mapping tendency
def tag_by_derivatives(row):
idx = int(row.name)
d1v = float(row[d1_col])
d2v = float(row[d2_col])
eps1 = float(eps_d1_series.iloc[idx])
eps2 = float(eps_d2_series.iloc[idx])
# # mapping état → codes 3 lettres explicites
# # | Ancien état | Nouveau code 3 lettres | Interprétation |
# # | ----------- | ---------------------- | --------------------- |
# # | 4 | HAU | Hausse Accélérée |
# # | 3 | HSR | Hausse Ralentissement |
# # | 2 | HST | Hausse Stable |
# # | 1 | DHB | Départ Hausse |
# # | 0 | PAL | Palier / neutre |
# # | -1 | DBD | Départ Baisse |
# # | -2 | BSR | Baisse Ralentissement |
# # | -3 | BST | Baisse Stable |
# # | -4 | BAS | Baisse Accélérée |
# Palier strict
if abs(d1v) <= eps1 and abs(d2v) <= eps2:
return 0
# Départ si d1 ~ 0 mais d2 signale direction
if abs(d1v) <= eps1:
return 1 if d2v > eps2 else -1 if d2v < -eps2 else 0
# Hausse
if d1v > eps1:
return 4 if d2v > eps2 else 3
# Baisse
if d1v < -eps1:
return -4 if d2v < -eps2 else -2
return 0
dataframe[tendency_col] = dataframe.apply(tag_by_derivatives, axis=1)
# if timeframe == '1h' and verbose and self.dp.runmode.value in ('backtest'):
# print("##################")
# print(f"# STAT {timeframe} {name}{suffixe}")
# print("##################")
# self.calculateProbabilite2Index(dataframe, futur_cols=['futur_percent'], indic_1=f"{name}{suffixe}_deriv1", indic_2=f"{name}{suffixe}_deriv2")
return dataframe
def getOpenTrades(self):
# if len(self.trades) == 0:
self.trades = Trade.get_open_trades()
return self.trades
def populate_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
pair = metadata['pair']
# trend = self.getTrend(dataframe)
# # params = self.loadParamsFor(pair, trend)
#
# indic_5m = self.getParamValue(pair, trend, 'buy', 'indic_5m')
# indic_deriv1_5m = self.getParamValue( pair, trend, 'buy', 'indic_deriv1_5m')
# indic_deriv2_5m = self.getParamValue( pair, trend, 'buy', 'indic_deriv2_5m')
# dataframe.loc[
# (
# (dataframe['mid_smooth_3'].shift(1) < dataframe['mid_smooth_3'])
# & (dataframe['hapercent'] > 0)
# & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
# & (dataframe['open'] <= dataframe['bb_middleband'])
# & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
# & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
# # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
# # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
# ), ['enter_long', 'enter_tag']] = (1, 'smth')
# dataframe.loc[
# (
# (dataframe['sma24_deriv2'].shift(1) < 0)
# & (dataframe['sma24_deriv2'] > 0)
# & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
# & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
# & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
# # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
# # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# ), ['enter_long', 'enter_tag']] = (1, 'invert')
#
# dataframe.loc[
# (
# (dataframe['sma24_deriv1'] > 0)
# & (dataframe['sma60_deriv1'].shift(1) < 0)
# & (dataframe['sma60_deriv1'] > 0)
# & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
# & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
# & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
# # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
# # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# ), ['enter_long', 'enter_tag']] = (1, 'raise')
#
# dataframe.loc[
# (
# (dataframe['sma60_deriv1'].shift(1) < 0)
# & (dataframe['sma24_deriv2'] > 0)
# & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
# & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
# & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
# # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
# # & (dataframe[f"{indic_1h}_deriv2"] >= self.indic_deriv2_1h.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# ), ['enter_long', 'enter_tag']] = (1, 'stg_inv')
#
# dataframe.loc[
# (
# (dataframe['mid_smooth_24'].shift(24) >= dataframe['mid_smooth_24'].shift(12))
# & (dataframe['mid_smooth_24'].shift(12) <= dataframe['mid_smooth_24'])
# & ((dataframe['max_rsi_24_1h'] < 70) | (dataframe['close'] < dataframe['close_1d']))
# & (dataframe[f"{self.indic_5m.value}_deriv1"] >= self.indic_deriv1_5m.value)
# & (dataframe[f"{self.indic_5m.value}_deriv2"] >= self.indic_deriv2_5m.value)
# # & (dataframe[f"{indic_1h}_deriv1"] >= self.indic_deriv1_1h.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# ), ['enter_long', 'enter_tag']] = (1, 'smth3_inv')
dataframe.loc[
(
(dataframe['percent3'] < -0.03)
& (dataframe['percent'] > 0)
), ['enter_long', 'enter_tag']] = (1, 'pct3')
dataframe.loc[
(
(dataframe["sma24"].shift(2) >= dataframe["sma24"].shift(1))
& (dataframe["sma24"].shift(1) <= dataframe["sma24"])
& (dataframe['percent3_1h'] < -0.03)
), ['enter_long', 'enter_tag']] = (1, 'pct3_1h')
dataframe.loc[
(
(dataframe[f"{self.indic_5m_slope_sup_buy.value}"].shift(2) >= dataframe[f"{self.indic_5m_slope_sup_buy.value}"].shift(1))
& (dataframe[f"{self.indic_5m_slope_sup_buy.value}"].shift(1) <= dataframe[f"{self.indic_5m_slope_sup_buy.value}"])
& (dataframe['slope_norm_1d'] < dataframe['slope_norm_1h'])
& (dataframe['stop_buying'] == False)
# & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# & (dataframe[f"hapercent"] >= -0.001)
), ['enter_long', 'enter_tag']] = (1, f"{self.indic_5m_slope_sup.value}_sup")
dataframe.loc[
(
(dataframe[f"{self.indic_5m_slope_inf_buy.value}"].shift(2) >= dataframe[f"{self.indic_5m_slope_inf_buy.value}"].shift(1))
& (dataframe[f"{self.indic_5m_slope_inf_buy.value}"].shift(1) <= dataframe[f"{self.indic_5m_slope_inf_buy.value}"])
& (dataframe['slope_norm_1d'] > dataframe['slope_norm_1h'])
& (dataframe['stop_buying'] == False)
# & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# & (dataframe[f"hapercent"] >= -0.001)
), ['enter_long', 'enter_tag']] = (1, f"{self.indic_5m_slope_inf_buy.value}_inf")
dataframe.loc[
(
(dataframe['stop_buying'] == False)
& (dataframe['stop_buying'].shift(1) == True)
# & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# & (dataframe[f"hapercent"] >= -0.001)
), ['enter_long', 'enter_tag']] = (1, f"end")
dataframe['test'] = np.where(dataframe['enter_long'] == 1, dataframe['close'] * 1.01, np.nan)
if self.dp.runmode.value in ('backtest'):
dataframe.to_feather(f"user_data/backtest_results/{metadata['pair'].replace('/', '_')}_df.feather")
return dataframe
def calculateProbabilite2Index(self, df, futur_cols, indic_1, indic_2):
# # Définition des tranches pour les dérivées
# bins_deriv = [-np.inf, -0.05, -0.01, 0.01, 0.05, np.inf]
# labels = ['forte baisse', 'légère baisse', 'neutre', 'légère hausse', 'forte hausse']
#
# # Ajout des colonnes bin (catégorisation)
# df[f"{indic_1}_bin"] = pd.cut(df['mid_smooth_1h_deriv1'], bins=bins_deriv, labels=labels)
# df[f"{indic_2}_bin"] = pd.cut(df['mid_smooth_deriv1_1d'], bins=bins_deriv, labels=labels)
#
# # Colonnes de prix futur à analyser
# futur_cols = ['futur_percent_1h', 'futur_percent_2h', 'futur_percent_3h', 'futur_percent_4h', 'futur_percent_5h']
#
# # Calcul des moyennes et des effectifs
# grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"])[futur_cols].agg(['mean', 'count'])
#
# pd.set_option('display.width', 200) # largeur max affichage
# pd.set_option('display.max_columns', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.width', 300) # largeur max affichage
# nettoyage
# series = df[f"{indic_2}"].dropna()
# unique_vals = df[f"{indic_2}"].nunique()
# print(unique_vals)
# print(df[f"{indic_2}"])
n = len(self.labels)
df[f"{indic_1}_bin"], bins_1h = pd.qcut(df[f"{indic_1}"], q=n, labels=self.labels, retbins=True,
duplicates='drop')
df[f"{indic_2}_bin"], bins_1d = pd.qcut(df[f"{indic_2}"], q=n, labels=self.labels, retbins=True,
duplicates='drop')
# Affichage formaté pour code Python
print(f"Bornes des quantiles pour {indic_1} : [{', '.join([f'{b:.4f}' for b in bins_1h])}]")
print(f"Bornes des quantiles pour {indic_2} : [{', '.join([f'{b:.4f}' for b in bins_1d])}]")
# Agrégation
grouped = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[futur_cols].agg(['mean', 'count'])
# Affichage
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
print(grouped.round(4))
# Ajout des probabilités de hausse
for col in futur_cols:
df[f"{col}_is_up"] = df[col] > 0
# Calcul de la proba de hausse
proba_up = df.groupby([f"{indic_2}_bin", f"{indic_1}_bin"], observed=True)[f"{col}_is_up"].mean().unstack()
print(f"\nProbabilité de hausse pour {col} (en %):")
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
print((proba_up * 100).round(1))
# Affichage formaté des valeurs comme tableau Python
with pd.option_context('display.max_rows', None, 'display.max_columns', None):
df_formatted = (proba_up * 100).round(1)
print("data = {")
for index, row in df_formatted.iterrows():
row_values = ", ".join([f"{val:.1f}" for val in row])
print(f"'{index}': [{row_values}], ")
print("}")
data = {}
for index, row in df_formatted.iterrows():
# on convertit proprement avec arrondi comme dans ton print, mais en données réelles
data[index] = [
None if (isinstance(val, float) and math.isnan(val)) else val
for val in row
]
# Niveaux unicode pour les barres verticales (style sparkline)
# spark_chars = "▁▂▃▄▅▆▇█"
# print(data.values())
# # Collecte globale min/max
# all_values = []
# for vals in data.values():
# all_values.extend(v for v in vals if not (isinstance(v, float) and math.isnan(v)))
#
# global_min = min(all_values) if all_values else 0
# global_max = max(all_values) if all_values else 1
# global_span = (global_max - global_min) if global_max != global_min else 1
#
# def sparkline_global(values):
# if all(isinstance(v, float) and math.isnan(v) for v in values):
# return "(no data)"
# out = ""
# for v in values:
# if isinstance(v, float) and math.isnan(v):
# out += " "
# else:
# idx = int((v - global_min) / global_span * (len(spark_chars) - 1))
# out += spark_chars[idx]
# return out
#
# for key, values in data.items():
# print(f"{key:>3} : {sparkline_global(values)}")
# Palette ANSI 256 couleurs pour heatmap
def get_ansi_color(val):
"""
Échelle fixe 0→100 :
0-20 : bleu (21)
20-40 : cyan (51)
40-60 : vert/jaune (46 / 226)
60-80 : orange (208)
80-100 : rouge (196)
"""
if val is None:
return ""
if val < 0:
val = 0
elif val > 100:
val = 100
if val <= 20:
code = 21
elif val <= 40:
code = 51
elif val <= 60:
code = 226
elif val <= 80:
code = 208
else:
code = 196
return f"\033[38;5;{code}m"
RESET = "\033[0m"
# Affichage
columns = ['B3', 'B2', 'B1', 'N0', 'H1', 'H2', 'H3']
header = " " + " ".join([f"{col:>6}" for col in columns])
print(header)
print("-" * len(header))
for key, values in data.items():
line = f"{key:>3} |"
for v in values:
if v is None:
line += f" {' '} " # vide pour NaN / None
else:
color = get_ansi_color(v)
line += f" {color}{v:5.1f}{RESET} "
print(line)
def populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
# dataframe.loc[
# (
# (dataframe['mid_smooth_deriv1'] == 0)
# & (dataframe['mid_smooth_deriv1'].shift(1) > 0)
# ), ['sell', 'exit_long']] = (1, 'sell_sma5_pct_1h')
# {indic_5m_sell} {indic_deriv1_5m_sell} {indic_deriv2_5m_sell}
dataframe.loc[
(
(dataframe[f"{self.indic_5m_slope_sup_sell.value}"].shift(2) < dataframe[f"{self.indic_5m_slope_sup_sell.value}"].shift(1))
& (dataframe[f"{self.indic_5m_slope_sup_sell.value}"].shift(1) > dataframe[f"{self.indic_5m_slope_sup_sell.value}"])
& (dataframe[f"{self.indic_deriv_5m_slope_sup_sell.value}_deriv1"] < self.deriv_5m_slope_sup_sell.value)
& (dataframe['slope_norm_1d'] > dataframe['slope_norm_1h'])
), ['exit_long', 'exit_tag']] = (1, f"{self.indic_5m_slope_sup_sell.value}_sup")
dataframe.loc[
(
(dataframe[f"{self.indic_5m_slope_inf_sell.value}"].shift(2) < dataframe[f"{self.indic_5m_slope_inf_sell.value}"].shift(1))
& (dataframe[f"{self.indic_5m_slope_inf_sell.value}"].shift(1) > dataframe[f"{self.indic_5m_slope_inf_sell.value}"])
& (dataframe[f"{self.indic_deriv_5m_slope_inf_sell.value}_deriv1"] < self.deriv_5m_slope_inf_sell.value)
& (dataframe['slope_norm_1d'] < dataframe['slope_norm_1h'])
), ['exit_long', 'exit_tag']] = (1, f"{self.indic_5m_slope_inf_sell.value}_inf")
dataframe.loc[
(
(dataframe['stop_buying'] == True)
& (dataframe['stop_buying'].shift(1) == False)
# & (dataframe[f"{self.indic_deriv_5m_buy.value}_deriv1"] > self.deriv_5m_buy.value)
# & (dataframe[f"sma60_deriv1"] >= -0.2)
# & (dataframe[f"hapercent"] >= -0.001)
), ['enter_long', 'enter_tag']] = (1, f"start")
# dataframe.loc[
# (
# (dataframe[f"{self.indic_percent_sell.value}"] < self.percent_5m_sell.value)
# ), ['exit_long', 'exit_tag']] = (1, f"{self.indic_percent_sell.value}")
return dataframe
def adjust_trade_position(self, trade: Trade, current_time: datetime,
current_rate: float, current_profit: float, min_stake: float,
max_stake: float, **kwargs):
# ne rien faire si ordre deja en cours
if trade.has_open_orders:
# print("skip open orders")
return None
if (self.wallets.get_available_stake_amount() < 0): # or trade.stake_amount >= max_stake:
return 0
dataframe, _ = self.dp.get_analyzed_dataframe(trade.pair, self.timeframe)
last_candle = dataframe.iloc[-1].squeeze()
before_last_candle = dataframe.iloc[-2].squeeze()
before_last_candle_12 = dataframe.iloc[-13].squeeze()
before_last_candle_24 = dataframe.iloc[-25].squeeze()
last_candle_3 = dataframe.iloc[-4].squeeze()
last_candle_previous_1h = dataframe.iloc[-13].squeeze()
# prépare les données
current_time = current_time.astimezone(timezone.utc)
open_date = trade.open_date.astimezone(timezone.utc)
dispo = round(self.wallets.get_available_stake_amount())
hours_since_first_buy = (current_time - trade.open_date_utc).seconds / 3600.0
days_since_first_buy = (current_time - trade.open_date_utc).days
hours = (current_time - trade.date_last_filled_utc).total_seconds() / 3600.0
count_of_buys = trade.nr_of_successful_entries
current_time_utc = current_time.astimezone(timezone.utc)
open_date = trade.open_date.astimezone(timezone.utc)
days_since_open = (current_time_utc - open_date).days
pair = trade.pair
profit = trade.calc_profit(current_rate) #round(current_profit * trade.stake_amount, 1)
last_lost = self.getLastLost(last_candle, pair)
pct_first = 0
total_counts = sum(
pair_data['count_of_buys'] for pair_data in self.pairs.values() if not self.getShortName(pair) == 'BTC')
if self.pairs[pair]['first_buy']:
pct_first = self.getPctFirstBuy(pair, last_candle)
pct = self.pct.value
if count_of_buys == 1:
pct_max = current_profit
else:
if self.pairs[trade.pair]['last_buy']:
pct_max = self.getPctLastBuy(pair, last_candle)
else:
pct_max = - pct
if (self.getShortName(pair) == 'BTC') or count_of_buys <= 2:
lim = - pct - (count_of_buys * self.pct_inc.value)
# lim = self.getLimitBuy(pair, last_candle, pct)
# lim = - (0.012 * (1 + round(count_of_buys / 5)) + 0.001 * (count_of_buys - 1))
# lim = - (0.012 + 0.001 * (count_of_buys - 1) + (0.002 * count_of_buys if count_of_buys > 10 else 0.001 * count_of_buys if count_of_buys > 5 else 0))
else:
pct = 0.05
lim = - pct - (count_of_buys * 0.0025)
# lim = self.getLimitBuy(pair, last_candle, pct)
if (len(dataframe) < 1):
# print("skip dataframe")
return None
if not self.should_enter_trade(pair, last_candle, current_time):
return None
condition = (last_candle['enter_long'] and last_candle['sma60_deriv1'] > 0 and last_candle['hapercent'] > 0 and last_candle['stop_buying'] == False) \
or last_candle['enter_tag'] == 'pct3' \
or last_candle['enter_tag'] == 'pct3_1h'
# if (self.getShortName(pair) != 'BTC' and count_of_buys > 3):
# condition = before_last_candle_24['mid_smooth_3_1h'] > before_last_candle_12['mid_smooth_3_1h'] and before_last_candle_12['mid_smooth_3_1h'] < last_candle['mid_smooth_3_1h'] #and last_candle['mid_smooth_3_deriv1_1h'] < -1.5
limit_buy = 40
if (count_of_buys < limit_buy) and condition and (pct_max < lim):
try:
if self.pairs[pair]['has_gain'] and profit > 0:
self.pairs[pair]['force_sell'] = True
return None
# if 6 <= count_of_buys:
# if not ((before_last_candle_24['sma24_deriv1_1h'] > before_last_candle_12['sma24_deriv1_1h'])
# & (before_last_candle_12['sma24_deriv1_1h'] < last_candle['sma24_deriv1_1h'])):
# return None
# print(f"{trade.pair} current_profit={current_profit} count_of_buys={count_of_buys} pct_first={pct_first:.3f} pct_max={pct_max:.3f} lim={lim:.3f} index={index}")
# self.pairs[trade.pair]['last_palier_index'] = index
# # Appel de la fonction
# poly_func, x_future, y_future, count = self.polynomial_forecast(
# dataframe['mid_smooth_12'],
# window=self.buy_horizon_predict_1h.value * 12,
# degree=4)
#
# if count < 3:
# return None
max_amount = self.config.get('stake_amount') * 2.5
stake_amount = min(min(max_amount, self.wallets.get_available_stake_amount()),
self.adjust_stake_amount(pair, last_candle) * abs(last_lost / self.mise_factor_buy.value))
if stake_amount > 0:
trade_type = last_candle['enter_tag'] if last_candle['enter_long'] == 1 else 'pct48'
self.pairs[trade.pair]['count_of_buys'] += 1
self.pairs[pair]['total_amount'] += stake_amount
self.log_trade(
last_candle=last_candle,
date=current_time,
action="🟧 Loss -",
dispo=dispo,
pair=trade.pair,
rate=current_rate,
trade_type=trade_type,
profit=round(profit, 1),
buys=trade.nr_of_successful_entries + 1,
stake=round(stake_amount, 2)
)
self.pairs[trade.pair]['last_buy'] = current_rate
self.pairs[trade.pair]['max_touch'] = last_candle['close']
self.pairs[trade.pair]['last_candle'] = last_candle
# df = pd.DataFrame.from_dict(self.pairs, orient='index')
# colonnes_a_exclure = ['last_candle', 'stop',
# 'trade_info', 'last_date', 'expected_profit', 'last_count_of_buys', 'base_stake_amount', 'stop_buy']
# df_filtered = df[df['count_of_buys'] > 0].drop(columns=colonnes_a_exclure)
# # df_filtered = df_filtered["first_buy", "last_max", "max_touch", "last_sell","last_buy", 'count_of_buys', 'current_profit']
#
# print(df_filtered)
return stake_amount
return None
except Exception as exception:
print(exception)
return None
if (profit > self.pairs[pair]['previous_profit'] and profit > self.pairs[pair]['expected_profit'] and hours > 6
# and last_candle['sma60_deriv1'] > 0
and last_candle['max_rsi_12_1h'] < 75
and last_candle['rsi_1d'] < 58
and last_candle['stop_buying'] == False
# and last_candle['mid_smooth_5_deriv1_1d'] > 0
and self.wallets.get_available_stake_amount() > 0
):
try:
self.pairs[pair]['previous_profit'] = profit
stake_amount = min(self.wallets.get_available_stake_amount(), self.pairs[pair]['first_amount'])
if stake_amount > 0:
self.pairs[pair]['has_gain'] += 1
trade_type = 'Gain +'
self.pairs[trade.pair]['count_of_buys'] += 1
self.pairs[pair]['total_amount'] += stake_amount
self.log_trade(
last_candle=last_candle,
date=current_time,
action="🟡 Gain +",
dispo=dispo,
pair=trade.pair,
rate=current_rate,
trade_type=str(round(pct_max, 4)),
profit=round(profit, 1),
buys=trade.nr_of_successful_entries + 1,
stake=round(stake_amount, 2)
)
self.pairs[trade.pair]['last_buy'] = current_rate
self.pairs[trade.pair]['max_touch'] = last_candle['close']
self.pairs[trade.pair]['last_candle'] = last_candle
return stake_amount
return None
except Exception as exception:
print(exception)
return None
return None
def getPctFirstBuy(self, pair, last_candle):
return round((last_candle['close'] - self.pairs[pair]['first_buy']) / self.pairs[pair]['first_buy'], 3)
def getPctLastBuy(self, pair, last_candle):
return round((last_candle['close'] - self.pairs[pair]['last_buy']) / self.pairs[pair]['last_buy'], 4)
def getPct60D(self, pair, last_candle):
return round((last_candle['max60_1d'] - last_candle['min60_1d']) / last_candle['max60_1d'], 4)
def getPctClose60D(self, pair, last_candle):
if last_candle['close'] > last_candle['max12_1d']:
return 1
if last_candle['close'] < last_candle['min12_1d']:
return 0
return round(
(last_candle['close'] - last_candle['min12_1d']) / (last_candle['max12_1d'] - last_candle['min12_1d']), 4)
def getLimitBuy(self, pair, last_candle, first_pct):
count_of_buys = self.pairs[pair]['count_of_buys']
pct60 = self.getPct60D(pair, last_candle) # exemple 0.3 pour 30%
if (pct60 < 0.05):
lim = - first_pct - (count_of_buys * 0.001 * 0.05 / 0.05)
else:
# 0.1
# 0.4
lim = - first_pct - (count_of_buys * 0.001 * pct60 / 0.05)
return lim
# def getProbaHausseEmaVolume(self, last_candle):
# value_1 = self.getValuesFromTable(self.ema_volume, last_candle['ema_volume'])
# value_2 = self.getValuesFromTable(self.mid_smooth_1h_deriv1, last_candle['mid_smooth_1h_deriv1'])
#
# val = self.approx_val_from_bins(
# matrice=self.ema_volume_mid_smooth_1h_deriv1_matrice_df,
# numeric_matrice=self.ema_volume_mid_smooth_1h_deriv1_numeric_matrice,
# row_label=value_2,
# col_label=value_1
# )
# return val
def getProbaHausseSma5d(self, last_candle):
value_1 = self.getValuesFromTable(self.sma5_deriv1, last_candle['sma5_deriv1_1d'])
value_2 = self.getValuesFromTable(self.sma5_deriv2, last_candle['sma5_deriv2_1d'])
# print(f"{last_candle['sma5_deriv1_1d']} => {value_1} / {last_candle['sma5_deriv2_1d']} => {value_2}")
val = self.approx_val_from_bins(
matrice=self.sma5_derive1_2_matrice_df,
numeric_matrice=self.sma5_derive1_2_numeric_matrice,
row_label=value_2,
col_label=value_1
)
return val
def adjust_stake_amount(self, pair: str, last_candle: DataFrame):
# Calculer le minimum des 14 derniers jours
nb_pairs = len(self.dp.current_whitelist())
base_stake_amount = self.config.get('stake_amount') / (self.mises.value) # * nb_pairs) # Montant de base configuré
# pct60 = round(100 * self.getPctClose60D(pair, last_candle), 2)
if True: # not pair in ('BTC/USDT', 'BTC/USDC'):
# factors = [1, 1.2, 1.3, 1.4]
if self.pairs[pair]['count_of_buys'] == 0:
# pctClose60 = self.getPctClose60D(pair, last_candle)
# dist_max = self.getDistMax(last_candle, pair)
# factor = self.multi_step_interpolate(dist_max, self.thresholds, self.factors)
factor = 1 #65 / min(65, last_candle['rsi_1d'])
if last_candle['slope_norm_1d'] < last_candle['slope_norm_1h']:
factor = 2
adjusted_stake_amount = max(base_stake_amount / 5, base_stake_amount * factor)
else:
adjusted_stake_amount = self.pairs[pair]['first_amount']
else:
first_price = self.pairs[pair]['first_buy']
if (first_price == 0):
first_price = last_candle['close']
last_max = last_candle['max12_1d']
pct = 5
if last_max > 0:
pct = 100 * (last_max - first_price) / last_max
factor = self.multi_step_interpolate(pct, self.thresholds, self.factors)
adjusted_stake_amount = base_stake_amount * factor # max(base_stake_amount, min(100, base_stake_amount * percent_4))
# pct = 100 * abs(self.getPctFirstBuy(pair, last_candle))
#
# factor = self.multi_step_interpolate(pct, self.thresholds, self.factors)
if self.pairs[pair]['count_of_buys'] == 0:
self.pairs[pair]['first_amount'] = adjusted_stake_amount
return adjusted_stake_amount
def calculateAmountSliding(self, pair, last_candle):
val = last_candle['close']
min_sliding = min(last_candle['min60_1d'], val)
max_sliding = max(last_candle['max60_1d'], val)
min_abs = self.pairs[pair]['last_min']
max_abs = self.pairs[pair]['last_max']
full = self.wallets.get_total_stake_amount()
stake = full / self.stakes
out_min = stake / 2
out_max = stake * 2
# Clamp sliding range within absolute bounds
min_sliding = max(min_sliding, min_abs)
max_sliding = min(max_sliding, max_abs)
# Avoid division by zero
if max_sliding == min_sliding:
return out_max # Or midpoint, or default value
# Inverse linear interpolation
position = (val - min_sliding) / (max_sliding - min_sliding)
return out_max - position * (out_max - out_min)
def calculatePctSliding(self, pair, last_candle):
val = last_candle['close']
min_sliding = last_candle['min60_1d']
max_sliding = last_candle['max60_1d']
min_abs = self.pairs[pair]['last_min']
max_abs = self.pairs[pair]['last_max']
out_min = 0.025
out_max = 0.08
# Clamp sliding range within absolute bounds
min_sliding = max(min_sliding, min_abs)
max_sliding = min(max_sliding, max_abs)
# Avoid division by zero
if max_sliding == min_sliding:
return out_max # Or midpoint, or default value
# Inverse linear interpolation
position = (val - min_sliding) / (max_sliding - min_sliding)
return out_max - position * (out_max - out_min)
def expectedProfit(self, pair: str, last_candle: DataFrame):
lim = 0.01
pct = 0.002
if (self.getShortName(pair) == 'BTC'):
lim = 0.005
pct = 0.001
pct_to_max = lim + pct * self.pairs[pair]['count_of_buys']
# if self.pairs[pair]['count_of_buys'] > 6:
# pct_to_max = 0.006 * self.pairs[pair]['count_of_buys']
# pctClose60 = self.getPctClose60D(pair, last_candle)
# max_60 = last_candle['max60_1d']
# if last_candle['close'] < max_60:
# pct_to_max = 0.25 * (max_60 - last_candle['close']) / max_60
# pct_to_max = pct_to_max * (2 - pctClose60)
expected_profit = lim * self.pairs[pair]['total_amount'] # min(3 * lim, max(lim, pct_to_max)) # 0.004 + 0.002 * self.pairs[pair]['count_of_buys'] #min(0.01, first_max)
self.pairs[pair]['expected_profit'] = expected_profit
# print(
# f"Expected profit price={current_price:.4f} min_max={min_max:.4f} min_14={min_14_days:.4f} max_14={max_14_days:.4f} percent={percent:.4f} expected_profit={expected_profit:.4f}")
return expected_profit
def calculateUpDownPct(self, dataframe, key):
down_pct_values = np.full(len(dataframe), np.nan)
# Remplir la colonne avec les bons calculs
for i in range(len(dataframe)):
shift_value = abs(int(dataframe[key].iloc[i])) # Récupérer le shift actuel
if i - shift_value > 1: # Vérifier que le shift ne dépasse pas l'index
down_pct_values[i] = 100 * (dataframe['close'].iloc[i] - dataframe['close'].iloc[i - shift_value]) / \
dataframe['close'].iloc[i - shift_value]
return down_pct_values
# ✅ Première dérivée(variation ou pente)
# Positive: la courbe est croissante → tendance haussière.
# Négative: la courbe est décroissante → tendance baissière.
# Proche de 0: la courbe est plate → marché stable ou en transition.
#
# Applications:
# Détecter les points d’inflexion(changement de tendance) quand elle s’annule.\
# Analyser la vitesse d’un mouvement(plus elle est forte, plus le mouvement est impulsif).
#
# ✅ Seconde dérivée(accélération ou concavité)
# Positive: la pente augmente → accélération de la hausse ou ralentissement de la baisse.
# Négative: la pente diminue → accélération de la baisse ou ralentissement de la hausse.
# Changement de signe: indique souvent un changement de courbure, utile pour prévoir des retournements.
#
# Exemples:
# 🟢 Dérivée 1 > 0 et dérivée 2 > 0: tendance haussière qui s’accélère.
# 🟡 Dérivée 1 > 0 et dérivée 2 < 0: tendance haussière qui ralentit → essoufflement potentiel.
# 🔴 Dérivée 1 < 0 et dérivée 2 < 0: tendance baissière qui s’accélère.
# 🟠 Dérivée 1 < 0 et dérivée 2 > 0: tendance baissière qui ralentit → possible bottom.
#
# Filtrer les signaux: ne prendre un signal haussier que si dérivée1 > 0 et dérivée2 > 0.
# Détecter les zones de retournement: quand dérivée1 ≈ 0 et que dérivée2 change de signe.
def calculateRegression(self,
dataframe: DataFrame,
column='close',
window=50,
degree=3,
future_offset: int = 10 # projection à n bougies après
) -> DataFrame:
df = dataframe.copy()
regression_fit = []
regression_future_fit = []
regression_fit = []
regression_future_fit = []
for i in range(len(df)):
if i < window:
regression_fit.append(np.nan)
regression_future_fit.append(np.nan)
continue
# Fin de la fenêtre d’apprentissage
end_index = i
start_index = i - window
y = df[column].iloc[start_index:end_index].values
# Si les données sont insuffisantes (juste par précaution)
if len(y) < window:
regression_fit.append(np.nan)
regression_future_fit.append(np.nan)
continue
# x centré pour meilleure stabilité numérique
x = np.linspace(-1, 1, window)
coeffs = np.polyfit(x, y, degree)
poly = np.poly1d(coeffs)
# Calcul point présent (dernier de la fenêtre)
x_now = x[-1]
regression_fit.append(poly(x_now))
# Calcul point futur, en ajustant si on dépasse la fin
remaining = len(df) - i - 1
effective_offset = min(future_offset, remaining)
x_future = x_now + (effective_offset / window) * 2 # respect du même pas
regression_future_fit.append(poly(x_future))
df[f"{column}_regression"] = regression_fit
# 2. Dérivée première = différence entre deux bougies successives
df[f"{column}_regression_deriv1"] = round(100 * df[f"{column}_regression"].diff() / df[f"{column}_regression"],
4)
# 3. Dérivée seconde = différence de la dérivée première
df[f"{column}_regression_deriv2"] = round(
10 * df[f"{column}_regression_deriv1"].rolling(int(window / 4)).mean().diff(), 4)
df[f"{column}_future_{future_offset}"] = regression_future_fit
# # 2. Dérivée première = différence entre deux bougies successives
# df[f"{column}_future_{future_offset}_deriv1"] = round(100 * df[f"{column}_future_{future_offset}"].diff() / df[f"{column}_future_{future_offset}"], 4)
#
# # 3. Dérivée seconde = différence de la dérivée première
# df[f"{column}_future_{future_offset}_deriv2"] = round(10 * df[f"{column}_future_{future_offset}_deriv1"].rolling(int(window / 4)).mean().diff(), 4)
return df
def getValuesFromTable(self, values, value):
for i in range(len(values) - 1):
if values[i] <= value < values[i + 1]:
return self.labels[i]
return self.labels[-1] # cas limite pour la borne max
# def interpolated_val_from_bins(self, row_pos, col_pos):
# """
# Renvoie une approximation interpolée (bilinéaire) d'une valeur dans la matrice
# à partir de positions flottantes dans l'index (ligne) et les colonnes.
#
# Parameters:
# matrix_df (pd.DataFrame): Matrice des probabilités (index/colonnes = labels).
# row_pos (float): Position réelle de la ligne (0 = B5, 10 = H5).
# col_pos (float): Position réelle de la colonne (0 = B5, 10 = H5).
#
# Returns:
# float: Valeur interpolée, ou NaN si en dehors des bornes.
# """
#
# # Labels ordonnés
# n = len(self.labels)
#
# # Vérification des limites
# if not (0 <= row_pos <= n - 1) or not (0 <= col_pos <= n - 1):
# return np.nan
#
# # Conversion des labels -> matrice
# matrix = self.smooth_smadiff_matrice_df.reindex(index=self.labels, columns=self.labels).values
#
# # Coordonnées entières (inférieures)
# i = int(np.floor(row_pos))
# j = int(np.floor(col_pos))
#
# # Coefficients pour interpolation
# dx = row_pos - i
# dy = col_pos - j
#
# # Précautions sur les bords
# if i >= n - 1: i = n - 2; dx = 1.0
# if j >= n - 1: j = n - 2; dy = 1.0
#
# # Récupération des 4 valeurs voisines
# v00 = matrix[i][j]
# v10 = matrix[i + 1][j]
# v01 = matrix[i][j + 1]
# v11 = matrix[i + 1][j + 1]
#
# # Interpolation bilinéaire
# interpolated = (
# (1 - dx) * (1 - dy) * v00 +
# dx * (1 - dy) * v10 +
# (1 - dx) * dy * v01 +
# dx * dy * v11
# )
# return interpolated
def approx_val_from_bins(self, matrice, numeric_matrice, row_label, col_label):
"""
Renvoie une approximation de la valeur à partir des labels binaires (e.g. B5, H1)
en utilisant une interpolation simple basée sur les indices.
Parameters:
matrix_df (pd.DataFrame): Matrice avec les labels binaires en index et colonnes.
row_label (str): Label de la ligne (ex: 'B3').
col_label (str): Label de la colonne (ex: 'H2').
Returns:
float: Valeur approchée si possible, sinon NaN.
"""
# Vérification des labels
if row_label not in matrice.index or col_label not in matrice.columns:
return np.nan
# Index correspondant
row_idx = self.label_to_index.get(row_label)
col_idx = self.label_to_index.get(col_label)
# Approximation directe (aucune interpolation complexe ici, juste une lecture)
return numeric_matrice[row_idx, col_idx]
@property
def protections(self):
return [
{
"method": "CooldownPeriod",
"stop_duration_candles": 12
}
# {
# "method": "MaxDrawdown",
# "lookback_period_candles": self.lookback.value,
# "trade_limit": self.trade_limit.value,
# "stop_duration_candles": self.protection_stop.value,
# "max_allowed_drawdown": self.protection_max_allowed_dd.value,
# "only_per_pair": False
# },
# {
# "method": "StoplossGuard",
# "lookback_period_candles": 24,
# "trade_limit": 4,
# "stop_duration_candles": self.protection_stoploss_stop.value,
# "only_per_pair": False
# },
# {
# "method": "StoplossGuard",
# "lookback_period_candles": 24,
# "trade_limit": 4,
# "stop_duration_candles": 2,
# "only_per_pair": False
# },
# {
# "method": "LowProfitPairs",
# "lookback_period_candles": 6,
# "trade_limit": 2,
# "stop_duration_candles": 60,
# "required_profit": 0.02
# },
# {
# "method": "LowProfitPairs",
# "lookback_period_candles": 24,
# "trade_limit": 4,
# "stop_duration_candles": 2,
# "required_profit": 0.01
# }
]
def conditional_smoothing(self, series, threshold=0.002):
smoothed = [series.iloc[0]]
for val in series.iloc[1:]:
last = smoothed[-1]
if abs(val - last) / last >= threshold:
smoothed.append(val)
else:
smoothed.append(last)
return pd.Series(smoothed, index=series.index)
def causal_savgol(self, series, window=25, polyorder=3):
result = []
half_window = window # Fenêtre complète dans le passé
for i in range(len(series)):
if i < half_window:
result.append(np.nan)
continue
window_series = series[i - half_window:i]
if window_series.isna().any():
result.append(np.nan)
continue
coeffs = np.polyfit(range(window), window_series, polyorder)
poly = np.poly1d(coeffs)
result.append(poly(window - 1))
return pd.Series(result, index=series.index)
def get_stake_from_drawdown(self, pct: float, base_stake: float = 100.0, step: float = 0.04, growth: float = 1.15,
max_stake: float = 1000.0) -> float:
"""
Calcule la mise à allouer en fonction du drawdown.
:param pct: Drawdown en pourcentage (ex: -0.12 pour -12%)
:param base_stake: Mise de base (niveau 0)
:param step: Espacement entre paliers (ex: tous les -4%)
:param growth: Facteur de croissance par palier (ex: 1.15 pour +15%)
:param max_stake: Mise maximale à ne pas dépasser
:return: Montant à miser
"""
if pct >= 0:
return base_stake
level = int(abs(pct) / step)
stake = base_stake * (growth ** level)
return min(stake, max_stake)
def compute_adaptive_paliers(self, max_drawdown: float = 0.65, first_steps: list[float] = [0.01, 0.01, 0.015, 0.02],
growth: float = 1.2) -> list[float]:
"""
Génère une liste de drawdowns négatifs avec des paliers plus rapprochés au début.
:param max_drawdown: Drawdown max (ex: 0.65 pour -65%)
:param first_steps: Liste des premiers paliers fixes en % (ex: [0.01, 0.01, 0.015])
:param growth: Facteur multiplicatif pour espacer les paliers suivants
:return: Liste de drawdowns négatifs (croissants)
"""
paliers = []
cumulated = 0.0
# Étapes initiales rapprochées
for step in first_steps:
cumulated += step
paliers.append(round(-cumulated, 4))
# Étapes suivantes plus espacées
step = first_steps[-1]
while cumulated < max_drawdown:
step *= growth
cumulated += step
if cumulated >= max_drawdown:
break
paliers.append(round(-cumulated, 4))
return paliers
# def get_dca_stakes(self,
# max_drawdown: float = 0.65,
# base_stake: float = 100.0,
# first_steps: list[float] = [0.01, 0.01, 0.015, 0.015],
# growth: float = 1.2,
# stake_growth: float = 1.15
# ) -> list[tuple[float, float]]:
# """
# Génère les paliers de drawdown et leurs stakes associés.
#
# :param max_drawdown: Maximum drawdown (ex: 0.65 pour -65%)
# :param base_stake: Mise initiale
# :param first_steps: Paliers de départ (plus resserrés)
# :param growth: Multiplicateur d'espacement des paliers
# :param stake_growth: Croissance multiplicative des mises
# :return: Liste de tuples (palier_pct, stake)
# [(-0.01, 100.0), (-0.02, 115.0), (-0.035, 132.25), (-0.05, 152.09), (-0.068, 174.9),
# (-0.0896, 201.14), (-0.1155, 231.31), (-0.1466, 266.0), (-0.1839, 305.9), (-0.2287, 351.79),
# (-0.2825, 404.56), (-0.347, 465.24), (-0.4244, 535.03), (-0.5173, 615.28), (-0.6287, 707.57)]
# """
# paliers = [
# (-0.01, 100.0), (-0.02, 115.0), (-0.035, 130), (-0.05, 150), (-0.07, 150),
# (-0.10, 150), (-0.15, 150), (-0.20, 150), (-0.25, 150),
# (-0.30, 200), (-0.40, 200),
# (-0.50, 300), (-0.60, 400), (-0.70, 500), (-0.80, 1000)
# ]
#
# # cumulated = 0.0
# # stake = base_stake
# #
# # # Étapes initiales
# # for step in first_steps:
# # cumulated += step
# # paliers.append((round(-cumulated, 4), round(stake, 2)))
# # stake *= stake_growth
# #
# # # Étapes suivantes
# # step = first_steps[-1]
# # while cumulated < max_drawdown:
# # step *= growth
# # cumulated += step
# # if cumulated >= max_drawdown:
# # break
# # paliers.append((round(-cumulated, 4), round(stake, 2)))
# # stake *= stake_growth
#
# return paliers
# def get_active_stake(self, pct: float) -> float:
# """
# Renvoie la mise correspondant au drawdown `pct`.
#
# :param pct: drawdown courant (négatif, ex: -0.043)
# :param paliers: liste de tuples (drawdown, stake)
# :return: stake correspondant
# """
# abs_pct = abs(pct)
# stake = self.paliers[0][1] # stake par défaut
#
# for palier, s in self.paliers:
# if abs_pct >= abs(palier):
# stake = s
# else:
# break
#
# return stake
# def get_palier_index(self, pct):
# """
# Retourne l'index du palier franchi pour un pourcentage de baisse donné (pct).
# On cherche le palier le plus profond atteint (dernier franchi).
# """
# for i in reversed(range(len(self.paliers))):
# seuil, _ = self.paliers[i]
# #print(f"pct={pct} seuil={seuil}")
# if pct <= seuil:
# # print(pct)
# return i
# return None # Aucun palier atteint
# def poly_regression_predictions(self, series: pd.Series, window: int = 20, degree: int = 2, n_future: int = 3) -> pd.DataFrame:
# """
# Renvoie une DataFrame avec `n_future` colonnes contenant les extrapolations des n prochains points
# selon une régression polynomiale ajustée sur les `window` dernières valeurs.
# """
# result = pd.DataFrame(index=series.index)
# x = np.arange(window)
#
# for future_step in range(1, n_future + 1):
# result[f'poly_pred_t+{future_step}'] = np.nan
#
# for i in range(window - 1, len(series)):
# y = series.iloc[i - window + 1 : i + 1].values
#
# if np.any(pd.isna(y)):
# continue
#
# coeffs = np.polyfit(x, y, degree)
# poly = np.poly1d(coeffs)
#
# for future_step in range(1, n_future + 1):
# future_x = window - 1 + future_step # Extrapolation point
# result.loc[series.index[i], f'poly_pred_t+{future_step}'] = poly(future_x)
#
# return result
def polynomial_forecast(self, series: pd.Series, window: int = 20, degree: int = 2, steps=[12, 24, 36]):
"""
Calcule une régression polynomiale sur les `window` dernières valeurs de la série,
puis prédit les `n_future` prochaines valeurs.
:param series: Série pandas (ex: dataframe['close'])
:param window: Nombre de valeurs récentes utilisées pour ajuster le polynôme
:param degree: Degré du polynôme (ex: 2 pour quadratique)
:param n_future: Nombre de valeurs futures à prédire
:return: tuple (poly_function, x_vals, y_pred), où y_pred contient les prédictions futures
"""
if len(series) < window:
raise ValueError("La série est trop courte pour la fenêtre spécifiée.")
recent_y = series.iloc[-window:].values
x = np.arange(window)
coeffs = np.polyfit(x, recent_y, degree)
poly = np.poly1d(coeffs)
x_future = np.arange(window, window + len(steps))
y_future = poly(x_future)
# Affichage de la fonction
# print("Fonction polynomiale trouvée :")
# print(poly)
current = series.iloc[-1]
count = 0
for future_step in steps: # range(1, n_future + 1)
future_x = window - 1 + future_step
prediction = poly(future_x)
# series.loc[series.index[future_x], f'poly_pred_t+{future_step}'] = prediction
# ➕ Afficher les prédictions
# print(f"{current} → t+{future_step}: x={future_x}, y={prediction:.2f}")
if prediction > 0: # current:
count += 1
return poly, x_future, y_future, count
# def calculateStats2(self, df, index, target):
# # Nombre de tranches (modifiable)
# n_bins_indice = 11
# n_bins_valeur = 11
#
# # Tranches dynamiques
# # df['indice_tranche'] = pd.qcut(df[f"{index}"], q=n_bins_indice, duplicates='drop')
# # df['valeur_tranche'] = pd.qcut(df[f"{target}"], q=n_bins_valeur, duplicates='drop')
#
# df[f"{index}_bin"], bins_1h = pd.qcut(df[f"{index}"], q=n_bins_indice, labels=self.labels, retbins=True,
# duplicates='drop')
# df[f"{target}_bin"], bins_1d = pd.qcut(df[f"{target}"], q=n_bins_valeur, labels=self.labels, retbins=True,
# duplicates='drop')
# # Affichage formaté pour code Python
# print(f"Bornes des quantiles pour {index} : [{', '.join([f'{b:.4f}' for b in bins_1h])}]")
# print(f"Bornes des quantiles pour {target} : [{', '.join([f'{b:.4f}' for b in bins_1d])}]")
#
# # Tableau croisé (compte)
# tableau = pd.crosstab(df[f"{index}_bin"], df[f"{target}_bin"])
#
# # Facultatif : en pourcentages
# tableau_pct = tableau.div(tableau.sum(axis=1), axis=0) * 100
#
# # Affichage
# print("Répartition brute :")
# print(tableau)
# print("\nRépartition en % par ligne :")
# print(tableau_pct.round(2))
def calculateStats(self, df, index, target):
# Nombre de tranches (modifiable)
n_bins_indice = 11
n_bins_valeur = 11
# Créer les tranches dynamiques
df['indice_tranche'] = pd.qcut(df[index], q=n_bins_indice, duplicates='drop')
df['valeur_tranche'] = pd.qcut(df[target], q=n_bins_valeur, duplicates='drop')
# Créer un tableau croisé avec la moyenne des valeurs
pivot_mean = df.pivot_table(
index='indice_tranche',
columns='valeur_tranche',
values=target, # <-- c'est la colonne qu'on agrège
aggfunc='mean' # <-- on calcule la moyenne
)
# Résultat
# print("Moyenne des valeurs par double-tranche :")
# print(pivot_mean.round(2))
def should_enter_trade(self, pair: str, last_candle, current_time) -> bool:
limit = 3
# return last_candle['slope_norm_1d'] < last_candle['slope_norm_1h']
if self.pairs[pair]['stop'] and last_candle['max_rsi_12_1h'] <= 60 and last_candle['trend_class_1h'] == 'B-':
dispo = round(self.wallets.get_available_stake_amount())
self.pairs[pair]['stop'] = False
self.log_trade(
last_candle=last_candle,
date=current_time,
action="🟢RESTART",
dispo=dispo,
pair=pair,
rate=last_candle['close'],
trade_type='',
profit=0,
buys=self.pairs[pair]['count_of_buys'],
stake=0
)
# 🟢 Dérivée 1 > 0 et dérivée 2 > 0: tendance haussière qui s’accélère.
# 🟡 Dérivée 1 > 0 et dérivée 2 < 0: tendance haussière qui ralentit → essoufflement potentiel.
# 🔴 Dérivée 1 < 0 et dérivée 2 < 0: tendance baissière qui s’accélère.
# 🟠 Dérivée 1 < 0 et dérivée 2 > 0: tendance baissière qui ralentit → possible bottom.
# if not pair.startswith('BTC'):
dispo = round(self.wallets.get_available_stake_amount())
# if self.pairs[pair]['stop'] \
# and last_candle[f"{self.indic_1d_p.value}_deriv1_1h"] >= self.indic_deriv1_1d_p_start.value \
# and last_candle[f"{self.indic_1d_p.value}_deriv2_1h"] >= self.indic_deriv2_1d_p_start.value:
# self.pairs[pair]['stop'] = False
# self.log_trade(
# last_candle=last_candle,
# date=current_time,
# action="🟢RESTART",
# dispo=dispo,
# pair=pair,
# rate=last_candle['close'],
# trade_type='',
# profit=0,
# buys=self.pairs[pair]['count_of_buys'],
# stake=0
# )
# else:
# if self.pairs[pair]['stop'] == False \
# and last_candle[f"{self.indic_1d_p.value}_deriv1_1h"] <= self.indic_deriv1_1d_p_stop.value \
# and last_candle[f"{self.indic_1d_p.value}_deriv2_1h"] <= self.indic_deriv2_1d_p_stop.value:
# self.pairs[pair]['stop'] = True
# # if self.pairs[pair]['current_profit'] > 0:
# # self.pairs[pair]['force_sell'] = True
# self.log_trade(
# last_candle=last_candle,
# date=current_time,
# action="🔴STOP",
# dispo=dispo,
# pair=pair,
# rate=last_candle['close'],
# trade_type='',
# profit=self.pairs[pair]['current_profit'],
# buys=self.pairs[pair]['count_of_buys'],
# stake=0
# )
# return False
# if self.pairs[pair]['stop']:
# return False
return True
# if last_candle['sma5_deriv1_1h'] < -0.02:
# return False
#
# if last_candle['mid_smooth_1h_deriv2'] < -2 or last_candle['sma5_deriv2_1h'] < -2:
# return False
#
# if last_candle['sma5_deriv1_1h'] < 0.0 and last_candle['sma5_deriv2_1h'] < -0.0:
# return False
#
# if last_candle['mid_smooth_1h_deriv1'] < 0.0 and last_candle['mid_smooth_1h_deriv2'] < -0.0 and last_candle[
# 'sma5_deriv2_1h'] < 0:
# return False
# if pair.startswith('BTC'):
# return True # BTC toujours autorisé
return True
# Filtrer les paires non-BTC
non_btc_pairs = [p for p in self.pairs if not p.startswith('BTC')]
# Compter les positions actives sur les paires non-BTC
max_nb_trades = 0
total_non_btc = 0
max_pair = ''
limit_amount = 250
max_amount = 0
for p in non_btc_pairs:
max_nb_trades = max(max_nb_trades, self.pairs[p]['count_of_buys'])
max_amount = max(max_amount, self.pairs[p]['total_amount'])
for p in non_btc_pairs:
if (max_nb_trades == self.pairs[p]['count_of_buys'] and max_nb_trades > limit):
# if (max_amount == self.pairs[p]['total_amount'] and max_amount > limit_amount):
max_pair = p
total_non_btc += self.pairs[p]['count_of_buys']
pct_max = self.getPctFirstBuy(pair, last_candle) # self.getPctLastBuy(pair, last_candle)
val = self.getProbaHausseSma5d(last_candle)
if val < 15:
return False
# if count_decrease == len(non_btc_pairs):
# self.should_enter_trade_count += 1
# char="."
# print(f"should_enter_trade canceled all pairs decreased {'':{char}>{self.should_enter_trade_count}}")
# return False
# if (last_candle['mid_smooth_1h_deriv1'] < -0.0 and last_candle['sma24_deriv1_1h'] < -0.0):
# return False
# if (last_candle['sma5_deriv1_1d'] < -0.1 and last_candle['sma5_deriv2_1d'] < -0) \
# or last_candle['sma5_deriv2_1d'] < -0.2:
# return False
if last_candle['mid_smooth_1h_deriv1'] < -0.02: # and last_candle['mid_smooth_1h_deriv2'] > 0):
return False
# if self.pairs[pair]['count_of_buys'] >= 3:
# if (last_candle['sma24_deriv1_1d'] < self.sma24_deriv1_1d_protection.value
# and last_candle['sma5_deriv1_1d'] < self.sma5_deriv1_1d_protection.value \
# and last_candle['sma5_deriv2_1d'] < -0.05):
# # or (last_candle['sma5_deriv1_1d'] < -0.1 and last_candle['sma24_deriv1_1h'] < -0.1):
# self.pairs[pair]['stop'] = True
# return False
self.should_enter_trade_count = 0
# if max_pair != pair and self.pairs[pair]['total_amount'] > 300:
# return False
if (max_pair != '') & (self.pairs[pair]['count_of_buys'] >= limit):
trade = self.pairs[max_pair]['current_trade']
current_time = current_time.astimezone(timezone.utc)
open_date = trade.open_date.astimezone(timezone.utc)
current_time_utc = current_time.astimezone(timezone.utc)
days_since_open = (current_time_utc - open_date).days
pct_max_max = self.getPctFirstBuy(max_pair, last_candle)
# print(f"days_since_open {days_since_open} max_pair={max_pair} pair={pair}")
return max_pair == pair or pct_max < - 0.25 or (
pct_max_max < - 0.15 and max_pair != pair and days_since_open > 30)
else:
return True
@staticmethod
def check_derivatives_vectorized(dataframe, deriv_pairs, thresholds):
"""
Retourne True si toutes les dérivées respectent leur seuil.
"""
mask = pd.Series(True, index=dataframe.index)
for d1_col, d2_col in deriv_pairs:
d1_thresh = thresholds.get(d1_col, 0)
d2_thresh = thresholds.get(d2_col, 0)
mask &= (dataframe[d1_col] >= d1_thresh) & (dataframe[d2_col] >= d2_thresh)
return mask
# ----------------------------------------------------------------------------------------------
# fallback defaults (used when no JSON exists)
PARAMS_DIR = 'params'
DEFAULT_PARAMS = {
"rsi_buy": 30,
"rsi_sell": 70,
"ema_period": 21,
"sma_short": 20,
"sma_long": 100,
"atr_period": 14,
"atr_multiplier": 1.5,
"stake_amount": None, # use exchange default
"stoploss": -0.10,
"minimal_roi": {"0": 0.10}
}
def __init__(self, config: dict) -> None:
super().__init__(config)
self.parameters = self.load_params_tree("user_data/strategies/params/")
def setTrends(self, dataframe: DataFrame):
SMOOTH_WIN=10
df = dataframe.copy()
# # --- charger les données ---
# df['timestamp'] = pd.to_datetime(df['timestamp'], errors='coerce')
# --- calcul SMA14 ---
# df['sma'] = talib.SMA(df, timeperiod=20) # ta.trend.sma_indicator(df['close'], 14)
# --- pente brute ---
df['slope'] = df['sma12'].diff()
# --- lissage EMA ---
df['slope_smooth'] = df['slope'].ewm(span=SMOOTH_WIN, adjust=False).mean()
# df["slope_smooth"] = savgol_filter(df["slope_smooth"], window_length=21, polyorder=3)
# --- normalisation relative ---
df['slope_norm'] = 10000 * df['slope_smooth'] / df['close']
# df['slope_norm'].fillna(0, inplace=True)
df['slope_norm'] = df['slope_norm'].fillna(0)
# --- classification dynamique via quantiles ---
q = df['slope_norm'].quantile([0.125, 0.375, 0.625, 0.875]).values
q1, q2, q3, q4 = q
def classify_expanding(series):
trend_class = []
for i in range(len(series)):
past_values = series[:i + 1] # uniquement le passé
q = past_values.quantile([0.125, 0.375, 0.625, 0.875]).values
q1, q2, q3, q4 = q
v = series.iloc[i]
if v <= q1:
trend_class.append('B--')
elif v <= q2:
trend_class.append('B-')
elif v <= q3:
trend_class.append('P')
elif v <= q4:
trend_class.append('H+')
else:
trend_class.append('H++')
return trend_class
dataframe['slope_norm'] = df['slope_norm']
# dataframe['trend_class'] = df['slope_norm'].apply(classify)
dataframe['trend_class'] = None
# Rolling sur la fenêtre passée
dataframe['trend_class'] = classify_expanding(dataframe['slope_norm'])
# # -------------------------- Trend detection (M2) --------------------------
# def getTrend(self, dataframe: DataFrame) -> str:
# """
# M2: SMA50 / SMA200 golden/death cross
# - bull: sma50 > sma200
# - bear: sma50 < sma200
# - range: sma50 ~= sma200 (within a small pct)
#
# Uses only past data (no future lookahead).
# """
# if dataframe is None or len(dataframe) < max(self.DEFAULT_PARAMS['sma_short'], self.DEFAULT_PARAMS['sma_long']) + 2:
# return 'RANGE'
#
# sma_short = dataframe['close'].rolling(window=self.DEFAULT_PARAMS['sma_short']).mean()
# sma_long = dataframe['close'].rolling(window=self.DEFAULT_PARAMS['sma_long']).mean()
#
# cur_short = sma_short.iloc[-1]
# cur_long = sma_long.iloc[-1]
#
# # small relative threshold to avoid constant flips
# if cur_long == 0 or cur_short == 0:
# return 'RANGE'
#
# rel = abs(cur_short - cur_long) / cur_long
# threshold = 0.01 # 1% by default; tweak as needed
#
# if rel <= threshold:
# return 'RANGE'
# if cur_short > cur_long:
# return 'BULL'
# return 'BEAR'
# # -------------------------- Parameter loading --------------------------
# def loadParamsFor(self, pair: str, trend: str) -> dict:
# """Load JSON from params//.json with fallback to DEFAULT_PARAMS."""
# pair_safe = pair.replace('/', '-') # folder name convention: BTC-USDT
# # cache key
# cache_key = f"{pair_safe}:{trend}"
# if cache_key in self._params_cache:
# return self._params_cache[cache_key]
#
# path = os.path.join(self.PARAMS_DIR, pair_safe, f"{trend}.json")
# if os.path.isfile(path):
# try:
# with open(path, 'r') as f:
# params = json.load(f)
# # merge with defaults so missing keys won't break
# merged = {**self.DEFAULT_PARAMS, **params}
# self._params_cache[cache_key] = merged
# logger.info(f"Loaded params for {pair} {trend} from {path}")
# return merged
# except Exception as e:
# logger.exception(f"Failed to load params {path}: {e}")
#
# # fallback
# logger.info(f"Using DEFAULT_PARAMS for {pair} {trend}")
# self._params_cache[cache_key] = dict(self.DEFAULT_PARAMS)
# return self._params_cache[cache_key]
def load_params_tree(self, base_path="user_data/strategies/params/"):
base = Path(base_path)
params_tree = {}
if not base.exists():
raise FileNotFoundError(f"Base path '{base_path}' not found.")
for pair_dir in base.iterdir():
if not pair_dir.is_dir():
continue
pair = self.getShortName(pair_dir.name) # ex : BTC-USDT
params_tree.setdefault(pair, {})
for trend_dir in pair_dir.iterdir():
if not trend_dir.is_dir():
continue
trend = trend_dir.name # ex : bull / bear / range
params_tree[pair].setdefault(trend, [])
for file in trend_dir.glob("*-hyperopt_result.json"):
filename = file.name
# Extraire START et END
try:
prefix = filename.replace("-hyperopt_result.json", "")
start, end = prefix.split("-", 1) # split en 2
except Exception:
start = None
end = None
# Lire le JSON
try:
with open(file, "r") as f:
content = json.load(f)
except Exception as err:
content = {"error": str(err)}
params_tree[pair][trend].append({
"start": start,
"end": end,
"file": str(file),
"content": content,
})
for pair, trends in params_tree.items():
for trend, entries in trends.items():
if entries:
# indic_5m = self.getParamValue(pair, trend, 'buy', 'indic_5m')
# indic_deriv1_5m = self.getParamValue(pair, trend, 'buy', 'indic_deriv1_5m')
# indic_deriv2_5m = self.getParamValue(pair, trend, 'buy', 'indic_deriv2_5m')
#
# indic_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_5m_sell')
# indic_deriv1_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_deriv1_5m_sell')
# indic_deriv2_5m_sell = self.getParamValue(pair, trend, 'sell', 'indic_deriv2_5m_sell')
print(f"{pair} -> {trend}") # {indic_5m} {indic_deriv1_5m} {indic_deriv2_5m} {indic_5m_sell} {indic_deriv1_5m_sell} {indic_deriv2_5m_sell}")
# for entry in entries:
# print(entry)
return params_tree
def getParamValue(self, pair, trend, space, param):
pair = self.getShortName(pair)
return self.parameters[pair][trend][0]['content']['params'][space][param]