3172 lines
137 KiB
Python
3172 lines
137 KiB
Python
# 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
|
||
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
|
||
from datetime import timezone, timedelta
|
||
|
||
logger = logging.getLogger(__name__)
|
||
|
||
# Machine Learning
|
||
from sklearn.model_selection import train_test_split
|
||
import joblib
|
||
import matplotlib.pyplot as plt
|
||
from sklearn.metrics import (
|
||
classification_report,
|
||
confusion_matrix,
|
||
accuracy_score,
|
||
roc_auc_score,
|
||
roc_curve,
|
||
precision_score, recall_score, precision_recall_curve,
|
||
f1_score, mean_squared_error, r2_score
|
||
)
|
||
from sklearn.tree import export_text
|
||
import inspect
|
||
from sklearn.feature_selection import SelectFromModel
|
||
from tabulate import tabulate
|
||
from sklearn.feature_selection import VarianceThreshold
|
||
import seaborn as sns
|
||
import lightgbm as lgb
|
||
from sklearn.model_selection import cross_val_score
|
||
import optuna.visualization as vis
|
||
import optuna
|
||
from lightgbm import LGBMRegressor
|
||
from sklearn.metrics import mean_squared_error
|
||
from sklearn.model_selection import train_test_split
|
||
from sklearn.linear_model import LinearRegression, Ridge, HuberRegressor
|
||
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
|
||
from sklearn.pipeline import make_pipeline
|
||
from sklearn.svm import SVR
|
||
from sklearn.ensemble import RandomForestRegressor
|
||
from sklearn.ensemble import GradientBoostingRegressor
|
||
from sklearn.preprocessing import StandardScaler
|
||
from sklearn.ensemble import HistGradientBoostingRegressor
|
||
from sklearn.impute import SimpleImputer
|
||
from sklearn.pipeline import Pipeline
|
||
|
||
# Tensorflow
|
||
import pandas as pd
|
||
import numpy as np
|
||
import tensorflow as tf
|
||
from tensorflow.keras import layers, models
|
||
from tensorflow.keras.models import load_model
|
||
from keras.utils import plot_model
|
||
from keras.models import Sequential
|
||
from keras.layers import LSTM, Dense
|
||
from sklearn.preprocessing import MinMaxScaler
|
||
from tensorflow.keras.models import Sequential
|
||
from tensorflow.keras.layers import LSTM, Dense
|
||
from tensorflow.keras.optimizers import Adam
|
||
|
||
os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # désactive complètement le GPU
|
||
os.environ["TF_XLA_FLAGS"] = "--tf_xla_enable_xla_devices=false"
|
||
|
||
|
||
# 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"
|
||
|
||
import warnings
|
||
warnings.filterwarnings(
|
||
"ignore",
|
||
message=r".*No further splits with positive gain.*"
|
||
)
|
||
|
||
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_TensorFlow(IStrategy):
|
||
startup_candle_count = 24 * 12
|
||
|
||
# Machine Learning
|
||
model = None
|
||
model_indicators = []
|
||
indicator_target = 'mid_smooth_5'
|
||
# Tensorflow
|
||
lookback = 60
|
||
future_steps = 12
|
||
y_no_scale = False
|
||
|
||
path = f"user_data/plots/"
|
||
|
||
# ROI table:
|
||
minimal_roi = {
|
||
"0": 0.564,
|
||
"567": 0.273,
|
||
"2814": 0.12,
|
||
"7675": 0
|
||
}
|
||
|
||
# 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"
|
||
}
|
||
},
|
||
'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 = {}
|
||
|
||
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=True, load=True)
|
||
|
||
ml_prob_buy = DecimalParameter(-0.5, 0.5, default=0.0, decimals=2, space='buy', optimize=True, load=True)
|
||
ml_prob_sell = DecimalParameter(-0.5, 0.5, default=0.0, decimals=2, space='sell', optimize=True, load=True)
|
||
|
||
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)
|
||
|
||
rsi_deb_protect = IntParameter(50, 90, default=70, space='protection', optimize=True, load=True)
|
||
rsi_end_protect = IntParameter(20, 60, default=55, space='protection', optimize=True, load=True)
|
||
|
||
sma24_deriv1_deb_protect = DecimalParameter(-4, 4, default=-2, decimals=1, space='protection', optimize=True, load=True)
|
||
sma24_deriv1_end_protect = DecimalParameter(-4, 4, default=0, decimals=1, space='protection', optimize=True, load=True)
|
||
|
||
# =========================================================================
|
||
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()
|
||
|
||
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 #(condition and not self.pairs[pair]['stop']) | (entry_tag == 'force_entry')
|
||
|
||
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()
|
||
|
||
minutes = int(round((current_time - trade.open_date_utc).seconds / 60, 0))
|
||
profit =trade.calc_profit(rate)
|
||
force = self.pairs[pair]['force_sell']
|
||
allow_to_sell = minutes > 30 and (last_candle['hapercent'] < 0 ) or force or (exit_reason == 'force_exit') or (exit_reason == 'stop_loss')
|
||
|
||
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:
|
||
# self.printLog(f"{current_time} SELL triggered for {pair} ({exit_reason} profit={profit} minutes={minutes} percent={last_candle['hapercent']}) but condition blocked")
|
||
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()
|
||
|
||
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
|
||
|
||
if hours % 4 == 0:
|
||
self.log_trade(
|
||
last_candle=last_candle,
|
||
date=current_time,
|
||
action="🔴 CURRENT" if self.pairs[pair]['stop'] or last_candle['stop_buying_1h'] else "🟢 CURRENT",
|
||
dispo=dispo,
|
||
pair=pair,
|
||
rate=last_candle['close'],
|
||
trade_type='',
|
||
profit=round(profit, 2),
|
||
buys=count_of_buys,
|
||
stake=0
|
||
)
|
||
|
||
pair_name = self.getShortName(pair)
|
||
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 > 0 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:
|
||
# 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'])
|
||
|
||
|
||
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 = ''
|
||
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 = ''
|
||
|
||
color = GREEN if profit > 0 else RED
|
||
color_sma24 = GREEN if last_candle['sma24_deriv1_1h'] > 0 else RED
|
||
color_sma24_2 = GREEN if last_candle['sma24_deriv2_1h'] > 0 else RED
|
||
color_sma5 = GREEN if last_candle['mid_smooth_5_deriv1_1h'] > 0 else RED
|
||
color_sma5_2 = GREEN if last_candle['mid_smooth_5_deriv2_1h'] > 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.
|
||
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_1h'], 2):>5}{RESET}"
|
||
f"|{color_sma5}{round(last_candle['mid_smooth_5_deriv1_1h'], 2):>5}{RESET}|{color_sma5_2}{round(last_candle['mid_smooth_5_deriv2_1h'], 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}"
|
||
)
|
||
|
||
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 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 0 # Palier / neutre
|
||
if d1 > d1_lim_sup:
|
||
return 2 if d2 > eps else 1 # Acceleration Hausse / Ralentissement Hausse
|
||
if d1 < d1_lim_inf:
|
||
return -2 if d2 < -eps else -1 # 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 0 # Palier
|
||
# if d1 == 0.0:
|
||
# return 'DH' if d2 > 0 else 'DB' # Depart Hausse / Départ Baisse
|
||
# if d1 > d1_lim_sup:
|
||
# return 2 if d2 > 0 else 1 # Acceleration Hausse / Ralentissement Hausse
|
||
# if d1 < d1_lim_inf:
|
||
# return -2 if d2 < 0 else -1 # 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']
|
||
short_pair = self.getShortName(pair)
|
||
self.path = f"user_data/plots/{short_pair}/"
|
||
|
||
dataframe = self.populateDataframe(dataframe, timeframe='5m')
|
||
|
||
################### INFORMATIVE 1h
|
||
informative = self.dp.get_pair_dataframe(pair=metadata['pair'], timeframe="1h")
|
||
informative = self.populateDataframe(informative, timeframe='1h')
|
||
informative = self.calculateRegression(informative, 'mid', lookback=5)
|
||
|
||
# # TENSOR FLOW
|
||
# self.model_indicators = self.listUsableColumns(informative)
|
||
# if self.dp.runmode.value in ('backtest'):
|
||
# self.trainTensorFlow(informative, future_steps = self.future_steps)
|
||
#
|
||
# self.predictTensorFlow(informative)
|
||
#
|
||
# if self.dp.runmode.value in ('backtest'):
|
||
# self.kerasGenerateGraphs(informative)
|
||
|
||
informative['stop_buying_deb'] = ((informative['max_rsi_24'] > self.rsi_deb_protect.value)
|
||
& (informative['sma24_deriv1'] < self.sma24_deriv1_deb_protect.value)
|
||
)
|
||
informative['stop_buying_end'] = ((informative['max_rsi_24'] < self.rsi_end_protect.value)
|
||
& (informative['sma24_deriv1'] > self.sma24_deriv1_end_protect.value)
|
||
)
|
||
|
||
latched = np.zeros(len(informative), dtype=bool)
|
||
|
||
for i in range(1, len(informative)):
|
||
if informative['stop_buying_deb'].iloc[i]:
|
||
latched[i] = True
|
||
elif informative['stop_buying_end'].iloc[i]:
|
||
latched[i] = False
|
||
else:
|
||
latched[i] = latched[i - 1]
|
||
|
||
informative['stop_buying'] = latched
|
||
|
||
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')
|
||
# # informative = self.calculateRegression(informative, 'mid', lookback=15)
|
||
# dataframe = merge_informative_pair(dataframe, informative, self.timeframe, "1d", ffill=True)
|
||
|
||
dataframe['last_price'] = dataframe['close']
|
||
dataframe['first_price'] = dataframe['close']
|
||
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()
|
||
|
||
dataframe = self.calculateRegression(dataframe, 'mid', lookback=10, future_steps=10, model_type="poly")
|
||
dataframe = self.calculateRegression(dataframe, 'sma24', lookback=12, future_steps=12)
|
||
|
||
# dataframe["ms-10"] = dataframe[self.indicator_target].shift(10)
|
||
# dataframe["ms-5"] = dataframe[self.indicator_target].shift(5)
|
||
# dataframe["ms-4"] = dataframe[self.indicator_target].shift(4)
|
||
# dataframe["ms-3"] = dataframe[self.indicator_target].shift(3)
|
||
# dataframe["ms-2"] = dataframe[self.indicator_target].shift(2)
|
||
# dataframe["ms-1"] = dataframe[self.indicator_target].shift(1)
|
||
# dataframe["ms-0"] = dataframe[self.indicator_target]
|
||
# dataframe["ms+10"] = dataframe["mid_smooth_24"].shift(-11)
|
||
|
||
self.model_indicators = self.listUsableColumns(dataframe)
|
||
|
||
# # Quantile
|
||
# self.add_future_quantiles(
|
||
# dataframe,
|
||
# indic="mid",
|
||
# lookback=40,
|
||
# future_steps=5
|
||
# )
|
||
|
||
# TENSOR FLOW
|
||
if self.dp.runmode.value in ('backtest'):
|
||
self.trainTensorFlow(dataframe, future_steps = self.future_steps)
|
||
|
||
self.predictTensorFlow(dataframe)
|
||
|
||
if self.dp.runmode.value in ('backtest'):
|
||
self.kerasGenerateGraphs(dataframe)
|
||
|
||
# SKLEARN
|
||
# if self.dp.runmode.value in ('backtest'):
|
||
# self.trainModel(dataframe, metadata)
|
||
|
||
# short_pair = self.getShortName(pair)
|
||
# self.model = joblib.load(f"{short_pair}_rf_model.pkl")
|
||
#
|
||
# # Préparer les features pour la prédiction
|
||
# features = dataframe[self.model_indicators].fillna(0)
|
||
#
|
||
# # Prédiction : probabilité que le prix monte
|
||
# # probs = self.model.predict_proba(features)[:, 1]
|
||
# probs = self.model.predict(features)
|
||
#
|
||
# # Sauvegarder la probabilité pour l’analyse
|
||
# dataframe['ml_prob'] = probs
|
||
#
|
||
# self.inspect_model(self.model)
|
||
|
||
return dataframe
|
||
|
||
def trainModel(self, dataframe: DataFrame, metadata: dict):
|
||
pair = self.getShortName(metadata['pair'])
|
||
pd.set_option('display.max_rows', None)
|
||
pd.set_option('display.max_columns', None)
|
||
pd.set_option("display.width", 200)
|
||
|
||
os.makedirs(self.path, exist_ok=True)
|
||
|
||
df = dataframe[self.model_indicators].copy()
|
||
|
||
# Corrélations des colonnes
|
||
corr = df.corr(numeric_only=True)
|
||
print("Corrélation des colonnes")
|
||
print(corr)
|
||
|
||
# 3️⃣ Créer la cible : 1 si le prix monte dans les prochaines bougies
|
||
# df['target'] = (df['sma24'].shift(-24) > df['sma24']).astype(int)
|
||
df['target'] = dataframe[self.indicator_target].shift(-24) # > df['sma24'] * 1.003).astype(int)
|
||
df['target'] = df['target'].fillna(0) #.astype(int)
|
||
|
||
# Corrélations triées par importance avec une colonne cible
|
||
target_corr = df.corr(numeric_only=True)["target"].sort_values(ascending=False)
|
||
print("Corrélations triées par importance avec une colonne cible")
|
||
print(target_corr)
|
||
|
||
# Corrélations triées par importance avec une colonne cible
|
||
corr = df.corr(numeric_only=True)
|
||
corr_unstacked = (
|
||
corr.unstack()
|
||
.reset_index()
|
||
.rename(columns={"level_0": "col1", "level_1": "col2", 0: "corr"})
|
||
)
|
||
# Supprimer les doublons col1/col2 inversés et soi-même
|
||
corr_unstacked = corr_unstacked[corr_unstacked["col1"] < corr_unstacked["col2"]]
|
||
|
||
# Trier par valeur absolue de corrélation
|
||
corr_sorted = corr_unstacked.reindex(corr_unstacked["corr"].abs().sort_values(ascending=False).index)
|
||
print("Trier par valeur absolue de corrélation")
|
||
print(corr_sorted.head(20))
|
||
|
||
# --- Calcul de la corrélation ---
|
||
corr = df.corr(numeric_only=True) # évite les colonnes non numériques
|
||
corr = corr * 100 # passage en pourcentage
|
||
|
||
# --- Masque pour n’afficher que le triangle supérieur (optionnel) ---
|
||
mask = np.triu(np.ones_like(corr, dtype=bool))
|
||
|
||
# --- Création de la figure ---
|
||
fig, ax = plt.subplots(figsize=(96, 36))
|
||
|
||
# --- Heatmap avec un effet “température” ---
|
||
sns.heatmap(
|
||
corr,
|
||
mask=mask,
|
||
cmap="coolwarm", # palette bleu → rouge
|
||
center=0, # 0 au centre
|
||
annot=True, # affiche les valeurs dans chaque case
|
||
fmt=".0f", # format entier (pas de décimale)
|
||
cbar_kws={"label": "Corrélation (%)"}, # légende à droite
|
||
linewidths=0.5, # petites lignes entre les cases
|
||
ax=ax
|
||
)
|
||
|
||
# --- Personnalisation ---
|
||
ax.set_title("Matrice de corrélation (en %)", fontsize=20, pad=20)
|
||
plt.xticks(rotation=45, ha="right")
|
||
plt.yticks(rotation=0)
|
||
|
||
# --- Sauvegarde ---
|
||
output_path = f"{self.path}/Matrice_de_correlation_temperature.png"
|
||
plt.savefig(output_path, bbox_inches="tight", dpi=150)
|
||
plt.close(fig)
|
||
|
||
print(f"✅ Matrice enregistrée : {output_path}")
|
||
|
||
# Nettoyage
|
||
df = df.dropna()
|
||
|
||
X = df[self.model_indicators]
|
||
y = df['target'] # ta colonne cible binaire ou numérique
|
||
print(self.feature_auc_scores(X, y))
|
||
|
||
# 4️⃣ Split train/test
|
||
X = df[self.model_indicators]
|
||
y = df['target']
|
||
# Séparation temporelle (train = 80 %, valid = 20 %)
|
||
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, shuffle=False)
|
||
|
||
print("NaN per column:")
|
||
print(X_train.isna().sum().sort_values(ascending=False).head(20))
|
||
|
||
# Nettoyage des valeurs invalides
|
||
|
||
selector = VarianceThreshold(threshold=0.0001)
|
||
selector.fit(X_train)
|
||
selected = X_train.columns[selector.get_support()]
|
||
print("Colonnes conservées :", list(selected))
|
||
|
||
# 1️⃣ Entraîne ton modèle LGBM normal
|
||
|
||
# train_model = LGBMRegressor(
|
||
# objective='regression',
|
||
# metric='rmse', # tu peux aussi tester 'mae'
|
||
# n_estimators=300,
|
||
# learning_rate=0.05,
|
||
# max_depth=7,
|
||
# subsample=0.8,
|
||
# colsample_bytree=0.8,
|
||
# random_state=42
|
||
# )
|
||
# train_model.fit(X_train, y_train)
|
||
|
||
train_model, selected_features = self.optuna(self.path, X_train, X_test, y_train, y_test)
|
||
print("Features retenues :", list(selected_features))
|
||
|
||
# # 2️⃣ Sélection des features AVANT calibration
|
||
# sfm = SelectFromModel(train_model, threshold="median", prefit=True)
|
||
# selected_features = X_train.columns[sfm.get_support()]
|
||
# print(selected_features)
|
||
|
||
train_model.fit(X_train, y_train)
|
||
|
||
# Importances
|
||
importances = pd.DataFrame({
|
||
"feature": train_model.feature_name_,
|
||
"importance": train_model.feature_importances_
|
||
}).sort_values("importance", ascending=False)
|
||
print("\n===== 🔍 IMPORTANCE DES FEATURES =====")
|
||
|
||
print(importances)
|
||
|
||
# 6️⃣ Évaluer la précision (facultatif)
|
||
preds = train_model.predict(X_test)
|
||
|
||
mse = mean_squared_error(y_test, preds)
|
||
rmse = np.sqrt(mse)
|
||
r2 = r2_score(y_test, preds)
|
||
|
||
print(f"RMSE: {rmse:.5f} | R²: {r2:.3f}")
|
||
|
||
# acc = accuracy_score(y_test, preds)
|
||
# print(f"Accuracy: {acc:.3f}")
|
||
|
||
# 7️⃣ Sauvegarde du modèle
|
||
joblib.dump(train_model, f"{pair}_rf_model.pkl")
|
||
print(f"✅ Modèle sauvegardé sous {pair}_rf_model.pkl")
|
||
|
||
# # Quantile
|
||
# dataframe = self.add_future_quantiles(
|
||
# df,
|
||
# indic="mid",
|
||
# lookback=40,
|
||
# future_steps=5
|
||
# )
|
||
|
||
self.analyze_model(pair, train_model, X_train, X_test, y_train, y_test)
|
||
|
||
def listUsableColumns(self, dataframe):
|
||
# Étape 1 : sélectionner numériques
|
||
numeric_cols = dataframe.select_dtypes(include=['int64', 'float64']).columns
|
||
# Étape 2 : enlever constantes
|
||
usable_cols = [c for c in numeric_cols if dataframe[c].nunique() > 1
|
||
and not c.endswith("_state") and not c.endswith("_1d")
|
||
# and not c.endswith("_1h")
|
||
and not c.endswith("_count")
|
||
# and not c.startswith("open") and not c.startswith("close")
|
||
# and not c.startswith("low") and not c.startswith("high")
|
||
# and not c.startswith("haopen") and not c.startswith("haclose")
|
||
# and not c.startswith("bb_lower") and not c.startswith("bb_upper")
|
||
# and not c.startswith("bb_middle")
|
||
and not c.endswith("_class") and not c.endswith("_price")
|
||
and not c.startswith('stop_buying')]
|
||
# Étape 3 : remplacer inf et NaN par 0
|
||
dataframe[usable_cols] = dataframe[usable_cols].replace([np.inf, -np.inf], 0).fillna(0)
|
||
print("Colonnes utilisables pour le modèle :")
|
||
print(usable_cols)
|
||
self.model_indicators = usable_cols
|
||
# self.model_indicators = [
|
||
# 'volume', 'hapercent', 'mid', 'percent', 'percent3', 'percent12',
|
||
# 'percent24',
|
||
# 'sma5', 'sma5_dist', 'sma5_deriv1', 'sma5_deriv2', 'sma12', 'sma12_dist',
|
||
# 'sma12_deriv1', 'sma12_deriv2', 'sma24', 'sma24_dist', 'sma24_deriv1', 'sma24_deriv2',
|
||
# # 'sma48', 'sma48_dist', 'sma48_deriv1', 'sma48_deriv2', 'sma60', 'sma60_dist',
|
||
# # 'sma60_deriv1', 'sma60_deriv2', 'mid_smooth_3', 'mid_smooth_3_dist',
|
||
# # 'mid_smooth_3_deriv1', 'mid_smooth_3_deriv2', 'mid_smooth_5', 'mid_smooth_5_dist',
|
||
# # 'mid_smooth_5_deriv1', 'mid_smooth_5_deriv2', 'mid_smooth_12', 'mid_smooth_12_dist',
|
||
# # 'mid_smooth_12_deriv1', 'mid_smooth_12_deriv2', 'mid_smooth_24', 'mid_smooth_24_dist',
|
||
# # 'mid_smooth_24_deriv1', 'mid_smooth_24_deriv2', 'rsi', 'max_rsi_12', 'max_rsi_24',
|
||
# 'rsi_dist', 'rsi_deriv1', 'rsi_deriv2', 'max12', 'min12', 'max60', 'min60',
|
||
# 'min_max_60', 'bb_percent', 'bb_width', 'macd', 'macdsignal', 'macdhist', 'slope',
|
||
# 'slope_smooth', 'atr', 'atr_norm', 'adx', 'obv', 'vol_24',
|
||
# # 'down_count', 'up_count',
|
||
# # 'down_pct', 'up_pct', 'rsi_slope', 'adx_change', 'volatility_ratio', 'rsi_diff',
|
||
# # 'slope_ratio', 'volume_sma_deriv', 'volume_dist', 'volume_deriv1', 'volume_deriv2',
|
||
# # 'slope_norm', 'mid_smooth_1h_deriv1', 'mid_smooth_1h_deriv2', 'mid_smooth_5h',
|
||
# # 'mid_smooth_5h_deriv1', 'mid_smooth_5h_deriv2', 'mid_future_pred_cons',
|
||
# # 'sma24_future_pred_cons'
|
||
# ]
|
||
return self.model_indicators
|
||
|
||
def inspect_model(self, model):
|
||
"""
|
||
Affiche les informations d'un modèle ML déjà entraîné.
|
||
Compatible avec scikit-learn, xgboost, lightgbm, catboost...
|
||
"""
|
||
|
||
print("===== 🔍 INFORMATIONS DU MODÈLE =====")
|
||
|
||
# Type de modèle
|
||
print(f"Type : {type(model).__name__}")
|
||
print(f"Module : {model.__class__.__module__}")
|
||
|
||
# Hyperparamètres
|
||
if hasattr(model, "get_params"):
|
||
params = model.get_params()
|
||
print(f"\n===== ⚙️ HYPERPARAMÈTRES ({len(params)}) =====")
|
||
for k, v in params.items():
|
||
print(f"{k}: {v}")
|
||
|
||
# Nombre d’estimateurs
|
||
if hasattr(model, "n_estimators"):
|
||
print(f"\nNombre d’estimateurs : {model.n_estimators}")
|
||
|
||
# Importance des features
|
||
if hasattr(model, "feature_importances_"):
|
||
print("\n===== 📊 IMPORTANCE DES FEATURES =====")
|
||
|
||
# Correction ici :
|
||
feature_names = getattr(model, "feature_names_in_", None)
|
||
if isinstance(feature_names, np.ndarray):
|
||
feature_names = feature_names.tolist()
|
||
elif feature_names is None:
|
||
feature_names = [f"feature_{i}" for i in range(len(model.feature_importances_))]
|
||
|
||
fi = pd.DataFrame({
|
||
"feature": feature_names,
|
||
"importance": model.feature_importances_
|
||
}).sort_values(by="importance", ascending=False)
|
||
|
||
print(fi)
|
||
|
||
# Coefficients (modèles linéaires)
|
||
if hasattr(model, "coef_"):
|
||
print("\n===== ➗ COEFFICIENTS =====")
|
||
coef = np.array(model.coef_)
|
||
if coef.ndim == 1:
|
||
for i, c in enumerate(coef):
|
||
print(f"Feature {i}: {c:.6f}")
|
||
else:
|
||
print(coef)
|
||
|
||
# Intercept
|
||
if hasattr(model, "intercept_"):
|
||
print("\nIntercept :", model.intercept_)
|
||
|
||
# Classes connues
|
||
if hasattr(model, "classes_"):
|
||
print("\n===== 🎯 CLASSES =====")
|
||
print(model.classes_)
|
||
|
||
# Scores internes
|
||
for attr in ["best_score_", "best_iteration_", "best_ntree_limit", "score_"]:
|
||
if hasattr(model, attr):
|
||
print(f"\n{attr} = {getattr(model, attr)}")
|
||
|
||
# Méthodes disponibles
|
||
print("\n===== 🧩 MÉTHODES DISPONIBLES =====")
|
||
methods = [m for m, _ in inspect.getmembers(model, predicate=inspect.ismethod)]
|
||
print(", ".join(methods[:15]) + ("..." if len(methods) > 15 else ""))
|
||
|
||
print("\n===== ✅ FIN DE L’INSPECTION =====")
|
||
|
||
def analyze_model(self, pair, model, X_train, X_test, y_train, y_test):
|
||
"""
|
||
Analyse complète d'un modèle ML supervisé (classification binaire).
|
||
Affiche performances, importance des features, matrices, seuils, etc.
|
||
"""
|
||
output_dir = f"user_data/plots/{pair}/"
|
||
os.makedirs(output_dir, exist_ok=True)
|
||
|
||
# ---- Importance des features ----
|
||
if hasattr(model, "feature_importances_"):
|
||
print("\n===== 🔍 IMPORTANCE DES FEATURES =====")
|
||
importance = pd.DataFrame({
|
||
"feature": X_train.columns,
|
||
"importance": model.feature_importances_
|
||
}).sort_values(by="importance", ascending=False)
|
||
print(importance)
|
||
top_n = 20
|
||
importance = importance.head(top_n)
|
||
|
||
# Crée une figure plus grande
|
||
fig, ax = plt.subplots(figsize=(24, 8)) # largeur=24 pouces, hauteur=8 pouces
|
||
|
||
# Trace le bar plot sur cet axe
|
||
importance.plot.bar(x="feature", y="importance", legend=False, ax=ax)
|
||
|
||
# Tourner les labels pour plus de lisibilité
|
||
ax.set_xticklabels(ax.get_xticklabels(), rotation=45, ha='right')
|
||
|
||
plt.title("Importance des features")
|
||
# plt.show()
|
||
plt.savefig(os.path.join(output_dir, "Importance des features.png"), bbox_inches="tight")
|
||
plt.close()
|
||
|
||
# ---- Arbre de décision (extrait) ----
|
||
if hasattr(model, "estimators_"):
|
||
print("\n===== 🌳 EXTRAIT D’UN ARBRE =====")
|
||
print(export_text(model.estimators_[0], feature_names=list(X_train.columns))[:800])
|
||
|
||
# --- Après l'entraînement du modèle ---
|
||
preds = model.predict(X_test)
|
||
|
||
# --- Évaluation ---
|
||
mse = mean_squared_error(y_test, preds)
|
||
rmse = np.sqrt(mse)
|
||
r2 = r2_score(y_test, preds)
|
||
|
||
print(f"RMSE: {rmse:.5f} | R²: {r2:.3f}")
|
||
|
||
# --- Création du dossier de sortie ---
|
||
os.makedirs(output_dir, exist_ok=True)
|
||
|
||
# --- Graphique prédiction vs réel ---
|
||
plt.figure(figsize=(8, 8))
|
||
plt.scatter(y_test, preds, alpha=0.4, s=15)
|
||
plt.xlabel("Valeurs réelles", fontsize=12)
|
||
plt.ylabel("Valeurs prédites", fontsize=12)
|
||
plt.title(f"LightGBM Régression — Prédiction vs Réel\nRMSE={rmse:.5f} | R²={r2:.3f}", fontsize=14)
|
||
plt.plot(
|
||
[y_test.min(), y_test.max()],
|
||
[y_test.min(), y_test.max()],
|
||
'r--',
|
||
linewidth=1,
|
||
label="Ligne idéale"
|
||
)
|
||
plt.legend()
|
||
|
||
# --- Sauvegarde ---
|
||
plot_path = os.path.join(output_dir, "LightGBM_regression_pred_vs_real.png")
|
||
plt.savefig(plot_path, bbox_inches="tight", dpi=200)
|
||
plt.close()
|
||
|
||
self.plot_pred_vs_real_filtered(model, X_test, y_test, preds, output_dir)
|
||
|
||
|
||
print(f"✅ Graphique sauvegardé : {plot_path}")
|
||
|
||
# ax = lgb.plot_tree(model, tree_index=0, figsize=(30, 20), show_info=["split_gain", "internal_value", "internal_count"])
|
||
# plt.title("Arbre de décision n°0")
|
||
# plt.savefig(os.path.join(output_dir, "lgbm_tree_0.png"), bbox_inches="tight")
|
||
# plt.close()
|
||
|
||
for i in range(5):
|
||
ax = lgb.plot_tree(model, tree_index=i, figsize=(20, 12))
|
||
plt.title(f"Arbre {i}")
|
||
plt.savefig(os.path.join(output_dir, f"lgbm_tree_{i}.png"), bbox_inches="tight")
|
||
plt.close()
|
||
|
||
ax = lgb.plot_tree(model, figsize=(40, 20))
|
||
plt.title("Vue globale du modèle LGBM")
|
||
plt.savefig(os.path.join(output_dir, "lgbm_all_trees.png"), bbox_inches="tight")
|
||
plt.close()
|
||
# X_test = np.linspace(0, 10, 1000).reshape(-1, 1)
|
||
y_pred = model.predict(X_test)
|
||
|
||
self.graphFonctionApprise(output_dir, X_test, y_test, y_pred)
|
||
self.graphFonctionAppriseFeature(output_dir, X_test, y_test, y_pred)
|
||
|
||
# ==============================================================================
|
||
|
||
ax = lgb.plot_importance(model, max_num_features=30, figsize=(12, 6))
|
||
plt.title("Importance des features - LGBM")
|
||
plt.savefig(os.path.join(output_dir, "lgbm_feature_importance.png"), bbox_inches="tight")
|
||
plt.close()
|
||
|
||
corr = X_train.corr() * 100 # en pourcentage
|
||
|
||
plt.figure(figsize=(20, 16))
|
||
sns.heatmap(corr, cmap="coolwarm", center=0, annot=False, fmt=".1f", cbar_kws={'label': 'Corrélation (%)'})
|
||
plt.title("Matrice de corrélation (%)")
|
||
plt.savefig(os.path.join(output_dir, "correlation_matrix.png"), bbox_inches="tight")
|
||
plt.close()
|
||
|
||
plt.figure(figsize=(10, 6))
|
||
plt.scatter(y_test, model.predict(X_test), alpha=0.5)
|
||
plt.xlabel("Valeurs réelles")
|
||
plt.ylabel("Prédictions du modèle")
|
||
plt.title("Comparaison y_test vs y_pred")
|
||
plt.savefig(os.path.join(output_dir, "ytest_vs_ypred.png"), bbox_inches="tight")
|
||
plt.close()
|
||
|
||
print("\n===== ✅ FIN DE L’ANALYSE =====")
|
||
|
||
def plot_pred_vs_real_filtered(self, model, X_test, y_test, preds, output_dir, top_n=5):
|
||
"""
|
||
Affiche le graphique prédiction vs réel pour les N features les plus importantes.
|
||
"""
|
||
# --- 1️⃣ Extraire les features les plus importantes ---
|
||
importance_df = pd.DataFrame({
|
||
"feature": X_test.columns,
|
||
"importance": model.feature_importances_
|
||
}).sort_values(by="importance", ascending=False)
|
||
|
||
top_features = importance_df.head(top_n)["feature"].tolist()
|
||
print(f"Top {top_n} features: {top_features}")
|
||
|
||
# --- 2️⃣ Créer un masque pour ne garder que les lignes où au moins une des top features varie fortement ---
|
||
X_top = X_test[top_features]
|
||
|
||
# Optionnel : filtrer les points atypiques pour lisser le nuage
|
||
mask = np.all(np.abs((X_top - X_top.mean()) / X_top.std()) < 3, axis=1)
|
||
X_filtered = X_top[mask]
|
||
y_filtered = y_test[mask]
|
||
preds_filtered = preds[mask]
|
||
|
||
# --- 3️⃣ Tracer ---
|
||
plt.figure(figsize=(8, 8))
|
||
plt.scatter(y_filtered, preds_filtered, alpha=0.4, s=15, c='blue', label=f"Top {top_n} features")
|
||
plt.xlabel("Valeurs réelles", fontsize=12)
|
||
plt.ylabel("Valeurs prédites", fontsize=12)
|
||
plt.title(f"LightGBM Régression — Prédiction vs Réel (filtré sur top {top_n} features)", fontsize=14)
|
||
plt.plot(
|
||
[y_filtered.min(), y_filtered.max()],
|
||
[y_filtered.min(), y_filtered.max()],
|
||
'r--',
|
||
linewidth=1,
|
||
label="Ligne idéale"
|
||
)
|
||
plt.legend()
|
||
plt.grid(True)
|
||
|
||
out_path = f"{output_dir}/lgbm_pred_vs_real_top{top_n}.png"
|
||
plt.savefig(out_path, bbox_inches="tight")
|
||
plt.close()
|
||
|
||
def plot_threshold_analysis(self, y_true, y_proba, step=0.05, save_path=None):
|
||
"""
|
||
Affiche la précision, le rappel et le F1-score selon le seuil de décision.
|
||
y_true : labels réels (0 ou 1)
|
||
y_proba : probabilités prédites (P(hausse))
|
||
step : pas entre les seuils testés
|
||
save_path : si renseigné, enregistre l'image au lieu d'afficher
|
||
"""
|
||
|
||
# Le graphique généré affichera trois courbes :
|
||
#
|
||
# 🔵 Precision — la fiabilité de tes signaux haussiers.
|
||
# 🟢 Recall — la proportion de hausses que ton modèle détecte.
|
||
# 🟣 F1-score — le compromis optimal entre les deux.
|
||
|
||
thresholds = np.arange(0, 1.01, step)
|
||
precisions, recalls, f1s = [], [], []
|
||
|
||
for thr in thresholds:
|
||
preds = (y_proba >= thr).astype(int)
|
||
precisions.append(precision_score(y_true, preds))
|
||
recalls.append(recall_score(y_true, preds))
|
||
f1s.append(f1_score(y_true, preds))
|
||
|
||
plt.figure(figsize=(10, 6))
|
||
plt.plot(thresholds, precisions, label="Precision", linewidth=2)
|
||
plt.plot(thresholds, recalls, label="Recall", linewidth=2)
|
||
plt.plot(thresholds, f1s, label="F1-score", linewidth=2, linestyle="--")
|
||
plt.axvline(0.5, color='gray', linestyle=':', label="Seuil 0.5")
|
||
plt.title("📊 Performance selon le seuil de probabilité", fontsize=14)
|
||
plt.xlabel("Seuil de décision (threshold)")
|
||
plt.ylabel("Score")
|
||
plt.legend()
|
||
plt.grid(True, alpha=0.3)
|
||
|
||
if save_path:
|
||
plt.savefig(save_path, bbox_inches='tight')
|
||
print(f"✅ Graphique enregistré : {save_path}")
|
||
else:
|
||
plt.show()
|
||
|
||
# # =============================
|
||
# # Exemple d’utilisation :
|
||
# # =============================
|
||
# if __name__ == "__main__":
|
||
# # Exemple : chargement d’un modèle et test
|
||
# import joblib
|
||
#
|
||
# model = joblib.load("/media/Home/home/souti/freqtrade/user_data/strategies/tools/sklearn/model.pkl")
|
||
# data = np.load("/media/Home/home/souti/freqtrade/user_data/strategies/tools/sklearn/test_data.npz")
|
||
# X_test, y_test = data["X"], data["y"]
|
||
#
|
||
# y_proba = model.predict_proba(X_test)[:, 1]
|
||
#
|
||
# # Trace ou enregistre le graphique
|
||
# plot_threshold_analysis(y_test, y_proba, step=0.05,
|
||
# save_path="/media/Home/home/souti/freqtrade/user_data/strategies/tools/sklearn/threshold_analysis.png")
|
||
|
||
|
||
def populateDataframe(self, dataframe, timeframe='5m'):
|
||
dataframe = dataframe.copy()
|
||
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'].pct_change()
|
||
dataframe["percent3"] = dataframe['close'].pct_change(3).rolling(3).mean()
|
||
dataframe["percent12"] = dataframe['close'].pct_change(12).rolling(12).mean()
|
||
dataframe["percent24"] = dataframe['close'].pct_change(24).rolling(24).mean()
|
||
|
||
# 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['min12'] = talib.MIN(dataframe['close'], timeperiod=12)
|
||
dataframe['max60'] = talib.MAX(dataframe['close'], timeperiod=60)
|
||
dataframe['min60'] = talib.MIN(dataframe['close'], timeperiod=60)
|
||
dataframe['min_max_60'] = ((dataframe['max60'] - dataframe['close']) / dataframe['min60'])
|
||
# 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']
|
||
dataframe["bb_percent"] = (
|
||
(dataframe["close"] - dataframe["bb_lowerband"]) /
|
||
(dataframe["bb_upperband"] - dataframe["bb_lowerband"])
|
||
)
|
||
dataframe["bb_width"] = (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["sma5"]
|
||
|
||
# dataframe["bb_width"] = (
|
||
# (dataframe["bb_upperband"] - dataframe["bb_lowerband"]) / dataframe["bb_middleband"]
|
||
# )
|
||
|
||
# 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. <br> - Positive → tendance haussière <br> - Négative → tendance baissière |
|
||
# | **Signal** (`macdsignal`) | `EMA_9(MACD)` | Sert de ligne de **signal de déclenchement**. <br> - Croisement du MACD au-dessus → signal d’achat <br> - Croisement du MACD en dessous → signal de vente |
|
||
# | **Histogramme** (`macdhist`) | `MACD - Signal` | Montre la **force et l’accélération** de la tendance. <br> - Positif et croissant → tendance haussière qui s’accélère <br> - Positif mais décroissant → ralentissement de la hausse <br> - Négatif et décroissant → baisse qui s’accélère <br> - 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'
|
||
# )
|
||
|
||
# --- pente brute ---
|
||
dataframe['slope'] = dataframe['sma24'].diff()
|
||
|
||
# --- lissage EMA ---
|
||
dataframe['slope_smooth'] = dataframe['slope'].ewm(span=10, adjust=False).mean()
|
||
|
||
# --- Volatilité normalisée ---
|
||
dataframe['atr'] = ta.volatility.AverageTrueRange(
|
||
high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], window=14
|
||
).average_true_range()
|
||
dataframe['atr_norm'] = dataframe['atr'] / dataframe['close']
|
||
|
||
# --- Force de tendance ---
|
||
dataframe['adx'] = ta.trend.ADXIndicator(
|
||
high=dataframe['high'], low=dataframe['low'], close=dataframe['close'], window=14
|
||
).adx()
|
||
|
||
# --- Volume directionnel (On Balance Volume) ---
|
||
dataframe['obv'] = ta.volume.OnBalanceVolumeIndicator(
|
||
close=dataframe['close'], volume=dataframe['volume']
|
||
).on_balance_volume()
|
||
|
||
# --- Volatilité récente (écart-type des rendements) ---
|
||
dataframe['vol_24'] = dataframe['percent'].rolling(24).std()
|
||
|
||
# Compter les baisses / hausses consécutives
|
||
self.calculateDownAndUp(dataframe, limit=0.0001)
|
||
|
||
# df : ton dataframe OHLCV + indicateurs existants
|
||
# Assurez-vous que les colonnes suivantes existent :
|
||
# 'max_rsi_12', 'roc_24', 'bb_percent_1h'
|
||
|
||
# --- Filtrage des NaN initiaux ---
|
||
# dataframe = dataframe.dropna()
|
||
|
||
dataframe['rsi_slope'] = dataframe['rsi'].diff(3) / 3 # vitesse moyenne du RSI
|
||
dataframe['adx_change'] = dataframe['adx'] - dataframe['adx'].shift(12) # évolution de la tendance
|
||
dataframe['volatility_ratio'] = dataframe['atr_norm'] / dataframe['bb_width']
|
||
|
||
dataframe["rsi_diff"] = dataframe["rsi"] - dataframe["rsi"].shift(3)
|
||
dataframe["slope_ratio"] = dataframe["sma5_deriv1"] / (dataframe["sma60_deriv1"] + 1e-9)
|
||
dataframe["divergence"] = (dataframe["rsi_deriv1"] * dataframe["sma5_deriv1"]) < 0
|
||
|
||
###########################
|
||
|
||
dataframe['volume_sma_deriv'] = dataframe['volume'] * dataframe['sma5_deriv1'] / (dataframe['volume'].rolling(5).mean())
|
||
self.calculeDerivees(dataframe, 'volume', timeframe=timeframe, ema_period=12)
|
||
|
||
self.setTrends(dataframe)
|
||
|
||
return dataframe
|
||
|
||
def feature_auc_scores(self, X, y):
|
||
aucs = {}
|
||
for col in X.columns:
|
||
try:
|
||
aucs[col] = roc_auc_score(y, X[col].ffill().fillna(0))
|
||
except Exception:
|
||
aucs[col] = np.nan
|
||
return pd.Series(aucs).sort_values(ascending=False)
|
||
|
||
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. <br> - Positive → tendance haussière <br> - Négative → tendance baissière |
|
||
# | **Signal** (`macdsignal`) | `EMA_9(MACD)` | Sert de ligne de **signal de déclenchement**. <br> - Croisement du MACD au-dessus → signal d’achat <br> - Croisement du MACD en dessous → signal de vente |
|
||
# | **Histogramme** (`macdhist`) | `MACD - Signal` | Montre la **force et l’accélération** de la tendance. <br> - Positif et croissant → tendance haussière qui s’accélère <br> - Positif mais décroissant → ralentissement de la hausse <br> - Négatif et décroissant → baisse qui s’accélère <br> - 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['hapercent'] <= limit
|
||
dataframe['up'] = 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"
|
||
factor1 = 100 * (ema_period / 5)
|
||
factor2 = 10 * (ema_period / 5)
|
||
|
||
dataframe[f"{name}{suffixe}_inv"] = (dataframe[f"{name}{suffixe}"].shift(2) >= dataframe[f"{name}{suffixe}"].shift(1)) \
|
||
& (dataframe[f"{name}{suffixe}"].shift(1) <= dataframe[f"{name}{suffixe}"])
|
||
# --- Distance à la moyenne mobile ---
|
||
dataframe[f"{name}{suffixe}_dist"] = (dataframe['close'] - dataframe[f"{name}{suffixe}"]) / dataframe[f"{name}{suffixe}"]
|
||
|
||
# dérivée relative simple
|
||
dataframe[d1_col] = 1000 * (dataframe[name] - dataframe[name].shift(1)) / dataframe[name].shift(1)
|
||
dataframe[d2_col] = dataframe[d1_col] - dataframe[d1_col].shift(1)
|
||
return dataframe
|
||
|
||
def getOpenTrades(self):
|
||
# if len(self.trades) == 0:
|
||
self.trades = Trade.get_open_trades()
|
||
return self.trades
|
||
|
||
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_buy_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
dataframe.loc[
|
||
(
|
||
(
|
||
(
|
||
(dataframe['mid_future_pred_cons'].shift(2) > dataframe['mid_future_pred_cons'].shift(1))
|
||
& (dataframe['mid_future_pred_cons'].shift(1) < dataframe['mid_future_pred_cons'])
|
||
& (dataframe['percent12'] < -0.0005)
|
||
)
|
||
| (
|
||
(dataframe['mid_future_pred_cons'] < dataframe['min12'])
|
||
)
|
||
)
|
||
&
|
||
(
|
||
((dataframe['mid_smooth_12_deriv1'] > 0) | (dataframe['mid_smooth_5_deriv1'] > 0))
|
||
)
|
||
|
||
), ['enter_long', 'enter_tag']] = (1, f"future")
|
||
|
||
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 populate_sell_trend(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
|
||
|
||
# dataframe.loc[
|
||
# (
|
||
# (
|
||
# (
|
||
# (dataframe['ml_prob'].shift(2) < dataframe['ml_prob'].shift(1))
|
||
# & (dataframe['ml_prob'].shift(1) > dataframe['ml_prob'])
|
||
# )
|
||
# | (dataframe['ml_prob'] < 0)
|
||
# )
|
||
# & (dataframe['hapercent'] < 0)
|
||
# ), ['exit_long', 'exit_tag']] = (1, f"sma60_future")
|
||
|
||
# dataframe.loc[
|
||
# (
|
||
# (
|
||
# (
|
||
# (dataframe['mid_future_pred_cons'].shift(2) < dataframe['mid_future_pred_cons'].shift(1))
|
||
# & (dataframe['mid_future_pred_cons'].shift(1) > dataframe['mid_future_pred_cons'])
|
||
# )
|
||
# # | (dataframe['mid_smooth_12_deriv1'] < 0)
|
||
# )
|
||
# & (dataframe['sma60_future_pred_cons'] < dataframe['sma60_future_pred_cons'].shift(1))
|
||
# & (dataframe['hapercent'] < 0)
|
||
# ), ['exit_long', 'exit_tag']] = (1, f"sma60_future")
|
||
|
||
#
|
||
# dataframe.loc[
|
||
# (
|
||
# (
|
||
# (dataframe['mid_future_pred_cons'].shift(2) < dataframe['mid_future_pred_cons'].shift(1))
|
||
# & (dataframe['mid_future_pred_cons'].shift(1) > dataframe['mid_future_pred_cons'])
|
||
#
|
||
# )
|
||
# # & (dataframe['mid_future_pred_cons'] > dataframe['max12'])
|
||
# & (dataframe['hapercent'] < 0)
|
||
#
|
||
# ), ['exit_long', 'exit_tag']] = (1, f"max12")
|
||
|
||
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() < 10): # 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()
|
||
# 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)
|
||
else:
|
||
pct = 0.05
|
||
lim = - pct - (count_of_buys * 0.0025)
|
||
|
||
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['stop_buying_1h'] == False and last_candle['hapercent'] > 0)
|
||
# and last_candle['sma60_deriv1'] > 0
|
||
# 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
|
||
|
||
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 = "Loss " + (last_candle['enter_tag'] if last_candle['enter_long'] == 1 else '')
|
||
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 +' + (last_candle['enter_tag'] if last_candle['enter_long'] == 1 else '')
|
||
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 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
|
||
|
||
# factors = [1, 1.2, 1.3, 1.4]
|
||
if self.pairs[pair]['count_of_buys'] == 0:
|
||
factor = 1 #65 / min(65, last_candle['rsi_1d'])
|
||
if last_candle['open'] < last_candle['sma5_1h'] and last_candle['mid_smooth_12_deriv1'] > 0:
|
||
factor = 2
|
||
|
||
adjusted_stake_amount = max(base_stake_amount / 5, base_stake_amount * factor)
|
||
else:
|
||
adjusted_stake_amount = self.pairs[pair]['first_amount']
|
||
|
||
if self.pairs[pair]['count_of_buys'] == 0:
|
||
self.pairs[pair]['first_amount'] = adjusted_stake_amount
|
||
|
||
return adjusted_stake_amount
|
||
|
||
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']
|
||
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
|
||
|
||
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
|
||
|
||
@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 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 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 should_enter_trade(self, pair: str, last_candle, current_time) -> bool:
|
||
limit = 3
|
||
|
||
# if self.pairs[pair]['stop'] and last_candle['max_rsi_12_1h'] <= 60 and last_candle['trend_class_1h'] == -1:
|
||
# 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
|
||
|
||
# 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)
|
||
|
||
if last_candle['mid_smooth_1h_deriv1'] < -0.02: # and last_candle['mid_smooth_1h_deriv2'] > 0):
|
||
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
|
||
|
||
def select_uncorrelated_features(self, df, target, top_n=20, corr_threshold=0.7):
|
||
"""
|
||
Sélectionne les features les plus corrélées avec target,
|
||
tout en supprimant celles trop corrélées entre elles.
|
||
"""
|
||
# 1️⃣ Calcul des corrélations absolues avec la cible
|
||
corr = df.corr(numeric_only=True)
|
||
corr_target = corr[target].abs().sort_values(ascending=False)
|
||
|
||
# 2️⃣ Prend les N features les plus corrélées avec la cible (hors target)
|
||
features = corr_target.drop(target).head(top_n).index.tolist()
|
||
|
||
# 3️⃣ Évite les features trop corrélées entre elles
|
||
selected = []
|
||
for feat in features:
|
||
too_correlated = False
|
||
for sel in selected:
|
||
if abs(corr.loc[feat, sel]) > corr_threshold:
|
||
too_correlated = True
|
||
break
|
||
if not too_correlated:
|
||
selected.append(feat)
|
||
|
||
# 4️⃣ Retourne un DataFrame propre avec les valeurs de corrélation
|
||
selected_corr = pd.DataFrame({
|
||
"feature": selected,
|
||
"corr_with_target": [corr.loc[f, target] for f in selected]
|
||
}).sort_values(by="corr_with_target", key=np.abs, ascending=False)
|
||
|
||
return selected_corr
|
||
|
||
def graphFonctionApprise(self, path, X_test, y_test, y_pred):
|
||
# Exemple : trier les valeurs de X_test et les prédictions
|
||
x_sorted = np.argsort(X_test.iloc[:, 0])
|
||
x = X_test.iloc[:, 0].iloc[x_sorted]
|
||
y_true = y_test.iloc[x_sorted]
|
||
y_pred = y_pred[x_sorted]
|
||
|
||
plt.figure(figsize=(12, 6))
|
||
plt.plot(x, y_true, label="Réel", color="blue", alpha=0.7)
|
||
plt.plot(x, y_pred, label="Prédit (LGBM)", color="red", alpha=0.7)
|
||
|
||
plt.title("Fonction apprise par LGBMRegressor")
|
||
plt.xlabel("Feature principale")
|
||
plt.ylabel("Valeur prédite")
|
||
plt.legend()
|
||
plt.grid(True)
|
||
|
||
out_path = f"{self.path}/lgbm_function.png"
|
||
plt.savefig(out_path, bbox_inches="tight")
|
||
plt.close()
|
||
|
||
print(f"Graphique sauvegardé : {out_path}")
|
||
|
||
def graphFonctionAppriseFeature(self, path, X_test, y_test, y_pred):
|
||
plt.figure(figsize=(14, 8))
|
||
colors = sns.color_palette("coolwarm", n_colors=X_test.shape[1])
|
||
|
||
# Conversion en DataFrame pour manip plus simple
|
||
df = X_test.copy()
|
||
df["y_pred"] = y_pred
|
||
|
||
# --- filtrage sur y_pred (ou sur chaque feature si tu veux)
|
||
mean = df["y_pred"].mean()
|
||
std = df["y_pred"].std()
|
||
|
||
df = df[(df["y_pred"] >= mean - 2 * std) & (df["y_pred"] <= mean + 2 * std)]
|
||
|
||
# --- tracé
|
||
for i, col in enumerate(X_test.columns):
|
||
plt.plot(df[col], df["y_pred"], '.', color=colors[i], alpha=0.4, label=col)
|
||
|
||
plt.title("Fonction apprise par LGBMRegressor (filtrée à ±2σ)")
|
||
plt.xlabel("Valeur feature")
|
||
plt.ylabel("Valeur prédite")
|
||
plt.legend(loc="right")
|
||
plt.grid(True)
|
||
|
||
out_path = f"{self.path}/lgbm_features.png"
|
||
plt.savefig(out_path, bbox_inches="tight")
|
||
plt.close()
|
||
|
||
print(f"Graphique sauvegardé : {out_path}")
|
||
|
||
def optuna(self, path, X_train, X_test, y_train, y_test):
|
||
# Suppose que X_train, y_train sont déjà définis
|
||
# ou sinon :
|
||
# X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)
|
||
|
||
print("Description")
|
||
print(X_train.describe().T.sort_values("std"))
|
||
def objective(trial):
|
||
params = {
|
||
'objective': 'regression',
|
||
'metric': 'rmse',
|
||
'n_estimators': trial.suggest_int('n_estimators', 100, 1000),
|
||
'learning_rate': trial.suggest_float('learning_rate', 0.005, 0.2, log=True),
|
||
'max_depth': trial.suggest_int('max_depth', 3, 15),
|
||
'num_leaves': trial.suggest_int('num_leaves', 20, 300),
|
||
'subsample': trial.suggest_float('subsample', 0.5, 1.0),
|
||
'colsample_bytree': trial.suggest_float('colsample_bytree', 0.5, 1.0),
|
||
'reg_alpha': trial.suggest_float('reg_alpha', 0.0, 10.0),
|
||
'reg_lambda': trial.suggest_float('reg_lambda', 0.0, 10.0),
|
||
'random_state': 42,
|
||
}
|
||
|
||
model = LGBMRegressor(**params)
|
||
model.fit(X_train, y_train)
|
||
|
||
# On peut aussi valider sur un split interne
|
||
preds = model.predict(X_test)
|
||
rmse = np.sqrt(mean_squared_error(y_test, preds))
|
||
return rmse
|
||
|
||
# Crée une étude Optuna
|
||
study = optuna.create_study(direction="minimize") # on veut minimiser l'erreur
|
||
study.optimize(objective, n_trials=50, show_progress_bar=True)
|
||
|
||
# 🔹 Afficher les meilleurs résultats
|
||
print("✅ Meilleurs hyperparamètres trouvés :")
|
||
print(study.best_params)
|
||
print(f"Meilleur RMSE : {study.best_value:.4f}")
|
||
|
||
# 🔹 Sauvegarder les résultats
|
||
optuna_path = f"{self.path}/optuna_lgbm_results.txt"
|
||
with open(optuna_path, "w") as f:
|
||
f.write(f"Best params:\n{study.best_params}\n")
|
||
f.write(f"Best RMSE: {study.best_value:.4f}\n")
|
||
|
||
print(f"Résultats sauvegardés dans : {optuna_path}")
|
||
|
||
# 🔹 Créer le modèle final avec les meilleurs paramètres
|
||
print("🚀 Entraînement du modèle LightGBM...")
|
||
|
||
# -- Appliquer le filtrage --
|
||
X_train_filtered = self.filter_features(X_train, y_train)
|
||
best_model = LGBMRegressor(**study.best_params)
|
||
best_model.fit(X_train_filtered, y_train)
|
||
|
||
# fig1 = vis.plot_optimization_history(study)
|
||
# fig1.write_image("/home/souti/freqtrade/user_data/plots/optuna_history.png")
|
||
#
|
||
# fig2 = vis.plot_param_importances(study)
|
||
# fig2.write_image("/home/souti/freqtrade/user_data/plots/optuna_importance.png")
|
||
|
||
return best_model, X_train_filtered
|
||
|
||
def filter_features(self, X: pd.DataFrame, y: pd.Series, corr_threshold: float = 0.95):
|
||
"""Filtre les colonnes peu utiles ou redondantes"""
|
||
print("🔍 Filtrage automatique des features...")
|
||
|
||
# 1️⃣ Supprimer les colonnes constantes
|
||
vt = VarianceThreshold(threshold=1e-5)
|
||
X_var = pd.DataFrame(vt.fit_transform(X), columns=X.columns[vt.get_support()])
|
||
print(f" - {len(X.columns) - X_var.shape[1]} colonnes supprimées (variance faible)")
|
||
|
||
# 2️⃣ Supprimer les colonnes très corrélées entre elles
|
||
corr = X_var.corr().abs()
|
||
upper = corr.where(np.triu(np.ones(corr.shape), k=1).astype(bool))
|
||
drop_cols = [column for column in upper.columns if any(upper[column] > corr_threshold)]
|
||
X_corr = X_var.drop(columns=drop_cols, errors='ignore')
|
||
print(f" - {len(drop_cols)} colonnes supprimées (corrélation > {corr_threshold})")
|
||
|
||
# 3️⃣ Facultatif : supprimer les colonnes entièrement NaN
|
||
X_clean = X_corr.dropna(axis=1, how='all')
|
||
|
||
print(f"✅ {X_clean.shape[1]} colonnes conservées après filtrage.\n")
|
||
return X_clean
|
||
|
||
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)
|
||
dataframe['slope_norm'] = df['slope_norm']
|
||
|
||
|
||
try:
|
||
from lightgbm import LGBMRegressor
|
||
_HAS_LGBM = True
|
||
except Exception:
|
||
_HAS_LGBM = False
|
||
|
||
def make_model(self, model_type="linear", degree=2, random_state=0):
|
||
model_type = model_type.lower()
|
||
if model_type == "linear":
|
||
return LinearRegression()
|
||
if model_type == "poly":
|
||
return make_pipeline(StandardScaler(), PolynomialFeatures(degree=degree, include_bias=False),
|
||
LinearRegression())
|
||
if model_type == "svr":
|
||
return make_pipeline(StandardScaler(), SVR(kernel="rbf", C=1.0, epsilon=0.1))
|
||
if model_type == "rf":
|
||
return RandomForestRegressor(n_estimators=100, random_state=random_state, n_jobs=1)
|
||
if model_type == "lgbm":
|
||
if not _HAS_LGBM:
|
||
raise RuntimeError("lightgbm n'est pas installé")
|
||
return LGBMRegressor(n_estimators=100, random_state=random_state)
|
||
raise ValueError(f"model_type inconnu: {model_type}")
|
||
|
||
def calculateRegressionNew(self, df, indic, lookback=20, future_steps=5, model_type="linear"):
|
||
df = df.copy()
|
||
pred_col = f"{indic}_future_pred_cons"
|
||
df[pred_col] = np.nan
|
||
|
||
X_idx = np.arange(lookback).reshape(-1, 1)
|
||
|
||
values = df[indic].values
|
||
n = len(values)
|
||
|
||
model = LinearRegression()
|
||
|
||
for i in range(lookback, n - future_steps):
|
||
window = values[i - lookback:i]
|
||
|
||
# cible = vraie valeur future
|
||
y_target = values[i + future_steps]
|
||
|
||
if np.isnan(window).any() or np.isnan(y_target):
|
||
continue
|
||
|
||
# entraînement
|
||
model.fit(X_idx, window)
|
||
|
||
# prédiction de la valeur future
|
||
future_x = np.array([[lookback + future_steps - 1]])
|
||
pred_future = model.predict(future_x)[0]
|
||
|
||
# la prédiction concerne i + future_steps
|
||
df.iloc[i + future_steps, df.columns.get_loc(pred_col)] = pred_future
|
||
|
||
return df
|
||
|
||
# ==========================================================
|
||
# NOUVELLE VERSION : calcule AUSSI les dernières valeurs !
|
||
# ==========================================================
|
||
def calculateRegression(
|
||
self,
|
||
df,
|
||
indic,
|
||
lookback=30,
|
||
future_steps=5,
|
||
model_type="linear",
|
||
degree=2,
|
||
weight_mode="exp",
|
||
weight_strength=2,
|
||
clip_k=2.0,
|
||
blend_alpha=0.7,
|
||
):
|
||
|
||
values = df[indic].values.astype(float)
|
||
n = len(values)
|
||
colname = f"{indic}_future_pred_cons"
|
||
|
||
df[colname] = np.nan
|
||
|
||
# pré-calcul des fenêtres
|
||
windows = np.lib.stride_tricks.sliding_window_view(values, lookback)
|
||
# windows[k] = valeurs de [k .. k+lookback-1]
|
||
|
||
# indices valides d’entraînement
|
||
trainable_end = n - future_steps
|
||
|
||
# créer une fois le modèle
|
||
model = self.make_model(model_type=model_type, degree=degree)
|
||
|
||
# ================
|
||
# BOUCLE TRAINING
|
||
# ================
|
||
for i in range(lookback, trainable_end):
|
||
|
||
window = values[i - lookback:i]
|
||
if np.isnan(window).any():
|
||
continue
|
||
|
||
# delta future réelle
|
||
y_target = values[i + future_steps] - values[i]
|
||
|
||
# features = positions dans la fenêtre : 0..lookback-1
|
||
X_window = np.arange(lookback).reshape(-1, 1)
|
||
|
||
# sample weights
|
||
if weight_mode == "exp":
|
||
weights = np.linspace(0.1, 1, lookback) ** weight_strength
|
||
else:
|
||
weights = None
|
||
|
||
# entraînement
|
||
try:
|
||
model.fit(X_window, window, sample_weight=weights)
|
||
except Exception:
|
||
model.fit(X_window, window)
|
||
|
||
# prédiction de la valeur future (position lookback+future_steps-1)
|
||
y_pred_value = model.predict(
|
||
np.array([[lookback + future_steps - 1]])
|
||
)[0]
|
||
|
||
pred_delta = y_pred_value - values[i]
|
||
|
||
# clipping par volatilité locale
|
||
local_std = np.std(window)
|
||
max_change = clip_k * (local_std if local_std > 0 else 1e-9)
|
||
pred_delta = np.clip(pred_delta, -max_change, max_change)
|
||
|
||
# blend
|
||
final_pred_value = (
|
||
blend_alpha * (values[i] + pred_delta)
|
||
+ (1 - blend_alpha) * values[i]
|
||
)
|
||
|
||
df.iloc[i, df.columns.get_loc(colname)] = final_pred_value
|
||
|
||
# ==========================================================
|
||
# 🔥 CALCUL DES DERNIÈRES VALEURS MANQUANTES 🔥
|
||
# ==========================================================
|
||
|
||
# Il reste les indices : [n - future_steps … n - 1]
|
||
for i in range(trainable_end, n):
|
||
|
||
# fenêtre glissante de fin
|
||
if i - lookback < 0:
|
||
continue
|
||
|
||
window = values[i - lookback:i]
|
||
if np.isnan(window).any():
|
||
continue
|
||
|
||
# features
|
||
X_window = np.arange(lookback).reshape(-1, 1)
|
||
|
||
try:
|
||
model.fit(X_window, window)
|
||
except:
|
||
continue
|
||
|
||
# prédiction d’une continuation locale : future_steps = 1 en fin
|
||
y_pred_value = model.predict(np.array([[lookback]]))[0]
|
||
pred_delta = y_pred_value - values[i - 1]
|
||
|
||
final_pred_value = (
|
||
blend_alpha * (values[i - 1] + pred_delta)
|
||
+ (1 - blend_alpha) * values[i - 1]
|
||
)
|
||
|
||
df.iloc[i, df.columns.get_loc(colname)] = final_pred_value
|
||
|
||
return df
|
||
|
||
# def calculateRegression(self,
|
||
# df,
|
||
# indic,
|
||
# lookback=30,
|
||
# future_steps=5,
|
||
# model_type="linear",
|
||
# degree=2,
|
||
# random_state=0,
|
||
# weight_mode="exp", # "exp", "linear" ou None
|
||
# weight_strength=0.2, # plus c’est grand, plus les dernières bougies comptent
|
||
# ):
|
||
# """
|
||
# Ajoute une régression glissante qui prévoit la valeur future à horizon 'future_steps',
|
||
# avec pondération des dernières valeurs si weight_mode != None.
|
||
# """
|
||
# df = df.copy()
|
||
# colname = f"{indic}_future_pred_{model_type}"
|
||
# df[colname] = np.nan
|
||
#
|
||
# values = df[indic].values
|
||
# n = len(values)
|
||
# X_window = np.arange(lookback).reshape(-1, 1)
|
||
#
|
||
# # génération du schéma de pondération
|
||
# if weight_mode == "exp":
|
||
# # exponentiel → les derniers points pèsent beaucoup plus
|
||
# weights = np.exp(np.linspace(-weight_strength, weight_strength, lookback))
|
||
# elif weight_mode == "linear":
|
||
# # poids linéaire → 1..lookback
|
||
# weights = np.linspace(0.5, 1.0, lookback)
|
||
# else:
|
||
# weights = np.ones(lookback)
|
||
#
|
||
# for i in range(lookback, n - future_steps):
|
||
# y_window = values[i - lookback:i]
|
||
# if np.isnan(y_window).any():
|
||
# continue
|
||
#
|
||
# model = self.make_model(model_type=model_type, degree=degree, random_state=random_state)
|
||
#
|
||
# try:
|
||
# model.fit(X_window, y_window, sample_weight=weights)
|
||
# except TypeError:
|
||
# # certains modèles (RF) ne supportent pas sample_weight dans ce contexte
|
||
# model.fit(X_window, y_window)
|
||
# except Exception:
|
||
# continue
|
||
#
|
||
# X_pred = np.array([[lookback + future_steps - 1]])
|
||
# try:
|
||
# pred = model.predict(X_pred)[0]
|
||
# except Exception:
|
||
# continue
|
||
#
|
||
# df.iloc[i, df.columns.get_loc(colname)] = pred
|
||
#
|
||
# return df
|
||
|
||
# def calculateRegression(self, df, indic, lookback=30, future_steps=5):
|
||
# """
|
||
# Ajoute un indicateur {indic}_future_pred qui contient,
|
||
# pour chaque bougie n, la valeur attendue à n + future_steps
|
||
# selon une régression linéaire sur les lookback dernières bougies.
|
||
# """
|
||
# df = df.copy()
|
||
# df[f"{indic}_future_pred"] = np.nan
|
||
#
|
||
# values = df[indic].values
|
||
# n = len(values)
|
||
#
|
||
# model = LinearRegression()
|
||
#
|
||
# for i in range(lookback, n - future_steps):
|
||
# # Fenêtre d’apprentissage
|
||
# X = np.arange(lookback).reshape(-1, 1)
|
||
# y = values[i - lookback:i]
|
||
#
|
||
# model.fit(X, y)
|
||
#
|
||
# # Prédiction future
|
||
# next_X = np.array([[lookback + future_steps - 1]])
|
||
# future_pred = model.predict(next_X)[0]
|
||
#
|
||
# # On insère la prédiction à la position actuelle (n)
|
||
# df.iloc[i, df.columns.get_loc(f"{indic}_future_pred")] = future_pred
|
||
#
|
||
# return df
|
||
|
||
def add_future_quantiles(self, dataframe, indic, lookback=30, future_steps=5, quantiles=[0.1, 0.5, 0.9]):
|
||
|
||
working_columns = self.listUsableColumns(dataframe)
|
||
|
||
df = dataframe[self.model_indicators].copy()
|
||
n = len(df)
|
||
target = self.indicator_target + "_future"
|
||
|
||
df[target] = dataframe[self.indicator_target].shift(-24) # > df['sma24'] * 1.003).astype(int)
|
||
df[target] = df[target].fillna(0) #.astype(int)
|
||
|
||
# Créer les colonnes pour chaque quantile
|
||
for q in quantiles:
|
||
df[f"{indic}_future_q{int(q * 100)}"] = np.nan
|
||
|
||
# Préparer toutes les fenêtres X
|
||
X = np.array([df[indic].iloc[i - lookback:i].values for i in range(lookback, n - future_steps)])
|
||
y_idx = np.arange(lookback, n - future_steps) + future_steps # index des valeurs futures
|
||
|
||
# Imputer les NaN
|
||
imputer = SimpleImputer(strategy='median')
|
||
X_imputed = imputer.fit_transform(X)
|
||
|
||
# Pour chaque quantile, créer un modèle et prédire
|
||
for q in quantiles:
|
||
model = HistGradientBoostingRegressor(loss='quantile', quantile=q, max_iter=100)
|
||
# Entrainer chaque ligne X_imputed à prédire la dernière valeur de la fenêtre + future_steps
|
||
# Ici, comme on prédit delta future par fenêtre, on peut utiliser la valeur cible correspondante
|
||
y = df[indic].iloc[y_idx].values
|
||
model.fit(X_imputed, y)
|
||
y_pred = model.predict(X_imputed)
|
||
|
||
# Écrire les prédictions dans le dataframe
|
||
df.iloc[lookback:n - future_steps, df.columns.get_loc(f"{indic}_future_q{int(q * 100)}")] = y_pred
|
||
|
||
df_plot = df.iloc[lookback:-future_steps]
|
||
self.plot_future_quantiles_band(df_plot, indic=self.indicator_target, quantiles=[0.1, 0.5, 0.9])
|
||
# self.compute_quantile_confidence(df_plot, indic=self.indicator_target, quantiles=[0.1, 0.5, 0.9])
|
||
|
||
# fig, ax = plt.subplots(figsize=(20, 20))
|
||
# for q in quantiles:
|
||
# plt.plot(stats.index.astype(str), stats[q], marker='o', label=f"Q{int(q * 100)}")
|
||
# plt.xticks(rotation=45)
|
||
# plt.xlabel(f"{indic} bins")
|
||
# plt.ylabel(f"Quantiles")
|
||
# plt.title(f"Distribution quantile de {indic}")
|
||
# plt.legend()
|
||
# plt.grid(True)
|
||
# plt.tight_layout()
|
||
# # plt.show()
|
||
# # --- Sauvegarde ---
|
||
# output_path = f"{path}/Distribution_quantile.png"
|
||
# plt.savefig(output_path, bbox_inches="tight", dpi=150)
|
||
# plt.close(fig)
|
||
#
|
||
# target = "future_return"
|
||
quantiles = [0.1, 0.25, 0.5, 0.75, 0.9]
|
||
for indicator in working_columns:
|
||
df["bin"] = pd.qcut(df[indicator], q=20, duplicates="drop")
|
||
stats = df.groupby("bin")[target].quantile(quantiles).unstack()
|
||
|
||
fig, ax = plt.subplots(figsize=(10, 10))
|
||
# plt.figure(figsize=(12, 6))
|
||
for q in stats.columns:
|
||
plt.plot(stats.index.astype(str), stats[q], marker='o', label=f"Q{int(q * 100)}")
|
||
|
||
plt.xticks(rotation=45)
|
||
plt.xlabel(f"{indicator} bins")
|
||
plt.ylabel(f"Quantiles of {target}")
|
||
plt.title(f"Distribution quantile de {target} selon {indicator}")
|
||
plt.legend()
|
||
plt.grid(True)
|
||
plt.tight_layout()
|
||
# --- Sauvegarde ---
|
||
output_path = f"{self.path}/Distribution_{indicator}.png"
|
||
plt.savefig(output_path, bbox_inches="tight", dpi=150)
|
||
plt.close(fig)
|
||
# plt.show()
|
||
|
||
return df
|
||
|
||
def plot_future_quantiles_band(self, df, indic, quantiles=[0.1, 0.5, 0.9], lookback=30, future_steps=5):
|
||
"""
|
||
df: DataFrame contenant la colonne réelle et les colonnes de quantiles
|
||
indic: nom de la colonne cible (ex: 'mid')
|
||
quantiles: liste des quantiles prédits
|
||
"""
|
||
# plt.figure(figsize=(16, 6))
|
||
fig, ax = plt.subplots(figsize=(96, 30))
|
||
|
||
# Série réelle
|
||
plt.plot(df[indic], label=f"{indic} réel", color='black', linewidth=1.2)
|
||
|
||
# Récupérer les colonnes de quantiles
|
||
cols_q = [f"{indic}_future_q{int(q * 100)}" for q in quantiles]
|
||
|
||
# Vérifier que tous les quantiles existent
|
||
cols_q = [c for c in cols_q if c in df.columns]
|
||
|
||
if len(cols_q) < 2:
|
||
print("Au moins deux quantiles sont nécessaires pour afficher les bandes")
|
||
return
|
||
|
||
# Ordre : q_min, q_median, q_max
|
||
df_plot = df[cols_q]
|
||
|
||
# Couleur pour la bande
|
||
color = sns.color_palette("coolwarm", n_colors=1)[0]
|
||
|
||
# Tracer la bande entre min et max quantiles
|
||
plt.fill_between(df.index,
|
||
df_plot.iloc[:, 0], # quantile bas (ex: 10%)
|
||
df_plot.iloc[:, -1], # quantile haut (ex: 90%)
|
||
color=color,
|
||
alpha=0.3,
|
||
label=f"Intervalle {quantiles[0] * 100}-{quantiles[-1] * 100}%")
|
||
|
||
# Tracer la médiane
|
||
if len(cols_q) >= 3:
|
||
plt.plot(df_plot.iloc[:, 1], color=color, linestyle='--', linewidth=1, label="Quantile médian")
|
||
|
||
plt.title(f"Prédiction futures valeurs de {indic} avec intervalle de quantiles")
|
||
plt.xlabel("Index / Bougies")
|
||
plt.ylabel(indic)
|
||
plt.legend()
|
||
plt.grid(True)
|
||
# plt.show()
|
||
# --- Sauvegarde ---
|
||
output_path = f"{self.path}/Prédiction futures valeurs de {indic}.png"
|
||
plt.savefig(output_path, bbox_inches="tight", dpi=150)
|
||
plt.close(fig)
|
||
|
||
def compute_quantile_confidence(self, df, indic, quantiles=[0.1, 0.5, 0.9]):
|
||
"""
|
||
df: DataFrame contenant les colonnes des quantiles
|
||
indic: nom de la colonne réelle
|
||
quantiles: liste des quantiles prédits
|
||
Retourne une série score [-1,1], positif = au-dessus de la médiane, négatif = en dessous
|
||
"""
|
||
|
||
# df['quantile_conf'] = compute_quantile_confidence(df_plot, indic='mid')
|
||
#
|
||
# # Exemple de signal simple
|
||
# df['buy_signal'] = df['quantile_conf'] < -0.5 # valeur sous la médiane + bande étroite
|
||
# df['sell_signal'] = df['quantile_conf'] > 0.5 # valeur au-dessus de la médiane + bande étroite
|
||
|
||
col_low = f"{indic}_future_q{int(quantiles[0] * 100)}"
|
||
col_med = f"{indic}_future_q{int(quantiles[1] * 100)}"
|
||
col_high = f"{indic}_future_q{int(quantiles[2] * 100)}"
|
||
|
||
# largeur de bande (incertitude)
|
||
band_width = df[col_high] - df[col_low] + 1e-9 # éviter division par 0
|
||
|
||
# distance normalisée à la médiane
|
||
score = (df[indic] - df[col_med]) / band_width
|
||
|
||
# clipper le score dans [-1,1] pour éviter les valeurs extrêmes
|
||
score = np.clip(score, -1, 1)
|
||
|
||
# plt.figure(figsize=(16, 6))
|
||
fig, ax = plt.subplots(figsize=(16, 6))
|
||
plt.plot(df[indic], color='black', label='Valeur réelle')
|
||
plt.fill_between(df.index,
|
||
df[f"{indic}_future_q10"],
|
||
df[f"{indic}_future_q90"],
|
||
alpha=0.3, color='blue', label='Intervalle 10%-90%')
|
||
plt.plot(df[f"{indic}_future_q50"], linestyle='--', color='blue', label='Médiane')
|
||
|
||
# Ajouter le score comme couleur de fond
|
||
plt.scatter(df.index, df[indic], c=df['quantile_conf'], cmap='coolwarm', s=20)
|
||
plt.colorbar(label='Score de confiance')
|
||
plt.title("Prédiction + score de confiance quantile")
|
||
plt.legend()
|
||
plt.grid(True)
|
||
# plt.show()
|
||
# --- Sauvegarde ---
|
||
output_path = f"{self.path}/Prédiction score confiance de {indic}.png"
|
||
plt.savefig(output_path, bbox_inches="tight", dpi=150)
|
||
plt.close(fig)
|
||
|
||
return score
|
||
|
||
# def loadTensorFlow(self, dataframe, metadata, lookback=50, future_steps=1):
|
||
# self.model = load_model(f"{self.path}/lstm_model.keras", compile=False)
|
||
#
|
||
# # features = toutes les colonnes sauf la cible
|
||
# feature_columns = self.model_indicators #[col for col in dataframe.columns if col != self.indicator_target]
|
||
# X_values = dataframe[feature_columns].values
|
||
#
|
||
# # normalisation avec le même scaler que l'entraînement
|
||
# scaler_X = MinMaxScaler()
|
||
# scaler_X.fit(X_values) # ou charger les paramètres si sauvegardés
|
||
# X_scaled = scaler_X.transform(X_values)
|
||
#
|
||
# # création des fenêtres glissantes
|
||
# X = np.lib.stride_tricks.sliding_window_view(X_scaled, window_shape=(self.lookback, X_scaled.shape[1]))
|
||
# # np.lib.stride_tricks.sliding_window_view ne supporte pas directement 2D → il vaut mieux utiliser une boucle :
|
||
# X_seq = []
|
||
# for i in range(len(X_scaled) - self.lookback):
|
||
# X_seq.append(X_scaled[i:i + self.lookback])
|
||
# X_seq = np.array(X_seq)
|
||
#
|
||
# # prédiction
|
||
# y_pred = self.model.predict(X_seq, verbose=0).flatten()
|
||
#
|
||
# # alignement avec les données
|
||
# preds = [np.nan] * len(dataframe)
|
||
# start = self.lookback
|
||
# end = start + len(y_pred)
|
||
# preds[start:end] = y_pred[:end - start]
|
||
#
|
||
# dataframe["lstm_pred"] = preds
|
||
#
|
||
# def trainTensorFlow(self, dataframe, metadata, lookback=50, future_steps=1):
|
||
# # 1) définir la cible
|
||
# y_values = dataframe[self.indicator_target].values.reshape(-1, 1)
|
||
#
|
||
# # 2) définir les features (toutes les colonnes sauf la cible)
|
||
# feature_columns = self.model_indicators #[col for col in dataframe.columns if col != self.indicator_target]
|
||
# X_values = dataframe[feature_columns].values
|
||
#
|
||
# # 3) normalisation
|
||
# scaler_X = MinMaxScaler()
|
||
# X_scaled = scaler_X.fit_transform(X_values)
|
||
#
|
||
# scaler_y = MinMaxScaler()
|
||
# y_scaled = scaler_y.fit_transform(y_values)
|
||
#
|
||
# # 4) création des fenêtres glissantes
|
||
# X = []
|
||
# y = []
|
||
# for i in range(len(X_scaled) - lookback - future_steps):
|
||
# X.append(X_scaled[i:i + lookback])
|
||
# y.append(y_scaled[i + lookback + future_steps])
|
||
#
|
||
# X = np.array(X)
|
||
# y = np.array(y)
|
||
#
|
||
# # 5) définition du modèle LSTM
|
||
# model = Sequential([
|
||
# LSTM(64, return_sequences=False, input_shape=(lookback, X.shape[2])),
|
||
# Dense(32, activation="relu"),
|
||
# Dense(1)
|
||
# ])
|
||
#
|
||
# model.compile(loss="mse", optimizer="adam")
|
||
# model.fit(X, y, epochs=20, batch_size=32, verbose=1)
|
||
#
|
||
# # 6) sauvegarde
|
||
# model.save(f"{self.path}/lstm_model.keras")
|
||
# np.save(f"{self.path}/lstm_scaler_X.npy", scaler_X.data_max_)
|
||
# np.save(f"{self.path}/lstm_scaler_y.npy", scaler_y.data_max_)
|
||
# # pour restaurer
|
||
#
|
||
# # df = dataframe[self.model_indicators].copy()
|
||
# #
|
||
# # # Construction dataset X / y
|
||
# # X = []
|
||
# # y = []
|
||
# #
|
||
# # prices = df[self.indicator_target].values
|
||
# #
|
||
# # for i in range(lookback, len(prices) - future_steps):
|
||
# # X.append(prices[i - lookback:i])
|
||
# # y.append(prices[i + future_steps])
|
||
# #
|
||
# # X = np.array(X).reshape(-1, lookback, 1)
|
||
# # y = np.array(y)
|
||
# #
|
||
# # # --- Définition du modèle ---
|
||
# # model = models.Sequential([
|
||
# # layers.Input((lookback, 1)),
|
||
# # layers.LSTM(64),
|
||
# # layers.Dense(32, activation="relu"),
|
||
# # layers.Dense(1)
|
||
# # ])
|
||
# #
|
||
# # model.compile(optimizer="adam", loss="mse")
|
||
# # model.summary()
|
||
# #
|
||
# # # --- Entraînement ---
|
||
# # model.fit(X, y, epochs=20, batch_size=32, verbose=1)
|
||
# #
|
||
# # # --- Sauvegarde ---
|
||
# # model.save(f"{self.path}/lstm_model.keras", include_optimizer=False)
|
||
# #
|
||
# print("Modèle entraîné et sauvegardé → lstm_model.h5")
|
||
|
||
def kerasGenerateGraphs(self, dataframe):
|
||
model = self.model
|
||
self.kerasGenerateGraphModel(model)
|
||
self.kerasGenerateGraphPredictions(model, dataframe, self.lookback)
|
||
self.kerasGenerateGraphPoids(model)
|
||
|
||
def kerasGenerateGraphModel(self, model):
|
||
plot_model(
|
||
model,
|
||
to_file=f"{self.path}/lstm_model.png",
|
||
show_shapes=True,
|
||
show_layer_names=True
|
||
)
|
||
|
||
def kerasGenerateGraphPredictions(self, model, dataframe, lookback):
|
||
preds = self.tensorFlowGeneratePredictions(dataframe, lookback, model)
|
||
|
||
# plot
|
||
plt.figure(figsize=(36, 8))
|
||
plt.plot(dataframe[self.indicator_target].values, label=self.indicator_target)
|
||
plt.plot(preds, label="lstm_pred")
|
||
plt.legend()
|
||
plt.savefig(f"{self.path}/lstm_predictions.png")
|
||
plt.close()
|
||
|
||
def kerasGenerateGraphPoids(self, model):
|
||
for i, layer in enumerate(model.layers):
|
||
weights = layer.get_weights() # liste de tableaux numpy
|
||
|
||
# Sauvegarde SAFE : tableau d’objets
|
||
np.save(
|
||
f"{self.path}/layer_{i}_weights.npy",
|
||
np.array(weights, dtype=object)
|
||
)
|
||
|
||
# Exemple lecture et heatmap
|
||
weights_layer0 = np.load(
|
||
f"{self.path}/layer_{i}_weights.npy",
|
||
allow_pickle=True
|
||
)
|
||
|
||
# Choisir un poids 2D
|
||
W = None
|
||
for w in weights_layer0:
|
||
if isinstance(w, np.ndarray) and w.ndim == 2:
|
||
W = w
|
||
break
|
||
|
||
if W is None:
|
||
print(f"Aucune matrice 2D dans layer {i} (rien à afficher).")
|
||
return
|
||
|
||
plt.figure(figsize=(8, 6))
|
||
sns.heatmap(W, cmap="viridis")
|
||
plt.title(f"Poids 2D du layer {i}")
|
||
plt.savefig(f"{self.path}/layer{i}_weights.png")
|
||
plt.close()
|
||
|
||
# -------------------
|
||
# Entraînement
|
||
# -------------------
|
||
def trainTensorFlow(self, dataframe, future_steps=1, lookback=50, epochs=40, batch_size=32):
|
||
X_seq, y_seq = self.tensorFlowPrepareDataFrame(dataframe, future_steps, lookback)
|
||
|
||
# 6) Modèle LSTM
|
||
self.model = Sequential([
|
||
LSTM(64, return_sequences=False, input_shape=(lookback, X_seq.shape[2])),
|
||
Dense(32, activation="relu"),
|
||
Dense(1)
|
||
])
|
||
|
||
self.model.compile(loss='mse', optimizer=Adam(learning_rate=1e-4))
|
||
self.model.fit(X_seq, y_seq, epochs=epochs, batch_size=batch_size, verbose=1)
|
||
|
||
# 7) Sauvegarde
|
||
self.model.save(f"{self.path}/lstm_model.keras")
|
||
# np.save(f"{self.path}/lstm_scaler_X.npy", self.scaler_X.data_max_)
|
||
# np.save(f"{self.path}/lstm_scaler_y.npy", self.scaler_y.data_max_)
|
||
|
||
def tensorFlowPrepareDataFrame(self, dataframe, future_steps, lookback):
|
||
target = self.indicator_target
|
||
# 1) Détecter NaN / Inf et nettoyer
|
||
feature_columns = self.model_indicators # [col for col in dataframe.columns if col != target]
|
||
df = dataframe.copy()
|
||
df.replace([np.inf, -np.inf], np.nan, inplace=True)
|
||
df.dropna(subset=feature_columns + [target], inplace=True)
|
||
# 2) Séparer features et cible
|
||
X_values = df[feature_columns].values
|
||
y_values = df[target].values.reshape(-1, 1)
|
||
# 3) Gestion colonnes constantes (éviter division par zéro)
|
||
for i in range(X_values.shape[1]):
|
||
if X_values[:, i].max() == X_values[:, i].min():
|
||
X_values[:, i] = 0.0
|
||
if y_values.max() == y_values.min():
|
||
y_values[:] = 0.0
|
||
# 4) Normalisation
|
||
self.scaler_X = MinMaxScaler()
|
||
X_scaled = self.scaler_X.fit_transform(X_values)
|
||
|
||
if self.y_no_scale:
|
||
y_scaled = y_values
|
||
else:
|
||
self.scaler_y = MinMaxScaler()
|
||
y_scaled = self.scaler_y.fit_transform(y_values)
|
||
|
||
# 5) Création des fenêtres glissantes
|
||
X_seq = []
|
||
y_seq = []
|
||
for i in range(len(X_scaled) - lookback - future_steps):
|
||
X_seq.append(X_scaled[i:i + lookback])
|
||
y_seq.append(y_scaled[i + lookback + future_steps])
|
||
X_seq = np.array(X_seq)
|
||
y_seq = np.array(y_seq)
|
||
# Vérification finale
|
||
if np.isnan(X_seq).any() or np.isnan(y_seq).any():
|
||
raise ValueError("X_seq ou y_seq contient encore des NaN")
|
||
if np.isinf(X_seq).any() or np.isinf(y_seq).any():
|
||
raise ValueError("X_seq ou y_seq contient encore des Inf")
|
||
return X_seq, y_seq
|
||
|
||
# -------------------
|
||
# Prédiction
|
||
# -------------------
|
||
def predictTensorFlow(self, dataframe, future_steps=1, lookback=50):
|
||
feature_columns = self.model_indicators #[col for col in dataframe.columns if col != self.indicator_target]
|
||
# charger le modèle si pas déjà chargé
|
||
if self.model is None:
|
||
self.model = load_model(f"{self.path}/lstm_model.keras", compile=False)
|
||
X_seq, y_seq = self.tensorFlowPrepareDataFrame(dataframe, future_steps, lookback)
|
||
|
||
preds = self.tensorFlowGeneratePredictions(dataframe, lookback, self.model)
|
||
|
||
# # features = toutes les colonnes sauf la cible
|
||
# feature_columns = self.model_indicators #[col for col in dataframe.columns if col != self.indicator_target]
|
||
# X_values = dataframe[feature_columns].values
|
||
#
|
||
# # normalisation (avec le scaler utilisé à l'entraînement)
|
||
# X_scaled = self.scaler_X.transform(X_values)
|
||
#
|
||
# # créer les séquences glissantes
|
||
# X_seq = []
|
||
# for i in range(len(X_scaled) - self.lookback):
|
||
# X_seq.append(X_scaled[i:i + self.lookback])
|
||
# X_seq = np.array(X_seq)
|
||
#
|
||
# # prédictions
|
||
# y_pred_scaled = self.model.predict(X_seq, verbose=0).flatten()
|
||
# y_pred = self.scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)).flatten()
|
||
#
|
||
# # alignement avec les données
|
||
# preds = [np.nan] * len(dataframe)
|
||
# start = self.lookback
|
||
# end = start + len(y_pred)
|
||
# # preds[start:end] = y_pred[:end - start]
|
||
# preds[start:start + len(y_pred)] = y_pred
|
||
#
|
||
# # # features
|
||
# # X_values = dataframe[feature_columns].values
|
||
# # X_scaled = self.scaler_X.transform(X_values)
|
||
# #
|
||
# # # création des fenêtres
|
||
# # X_seq = []
|
||
# # for i in range(len(X_scaled) - self.lookback):
|
||
# # X_seq.append(X_scaled[i:i + self.lookback])
|
||
# # X_seq = np.array(X_seq)
|
||
# #
|
||
# # # prédiction
|
||
# # y_pred_scaled = self.model.predict(X_seq, verbose=0).flatten()
|
||
# # y_pred = self.scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)).flatten()
|
||
# #
|
||
# # # alignement avec le dataframe
|
||
# # preds = [np.nan] * len(dataframe)
|
||
# # start = self.lookback
|
||
# # end = start + len(y_pred)
|
||
# # preds[start:end] = y_pred[:end-start]
|
||
# # # preds[start:start + len(y_pred)] = y_pred
|
||
|
||
dataframe["lstm_pred"] = preds
|
||
return dataframe
|
||
|
||
def tensorFlowGeneratePredictions(self, dataframe, lookback, model):
|
||
# features = toutes les colonnes sauf la cible
|
||
feature_columns = self.model_indicators # [col for col in dataframe.columns if col != self.indicator_target]
|
||
X_values = dataframe[feature_columns].values
|
||
|
||
# normalisation (avec le scaler utilisé à l'entraînement)
|
||
X_scaled = self.scaler_X.transform(X_values)
|
||
# créer les séquences glissantes
|
||
X_seq = []
|
||
for i in range(len(X_scaled) - lookback):
|
||
X_seq.append(X_scaled[i:i + lookback])
|
||
X_seq = np.array(X_seq)
|
||
|
||
# prédictions
|
||
y_pred_scaled = model.predict(X_seq, verbose=0).flatten()
|
||
if self.y_no_scale:
|
||
y_pred = y_pred_scaled
|
||
else:
|
||
y_pred = self.scaler_y.inverse_transform(y_pred_scaled.reshape(-1, 1)).flatten()
|
||
# alignement avec les données
|
||
preds = [np.nan] * len(dataframe)
|
||
start = lookback
|
||
end = start + len(y_pred)
|
||
# preds[start:end] = y_pred[:end - start]
|
||
preds[start:start + len(y_pred)] = y_pred
|
||
return preds |