Bourse/BourseV4/Model/Parametre.py

from Model.Donnee import *
from Model.profil import profile
from Config.paths import SysExploite

import datetime as dt
import json
import hashlib
import os
import uuid
import random

ProbabiliteMutationNombre = 0.5
ProbabiliteMutationReste = 0.5


class CL_Param:
	def __init__(self):
		
		self.PortefeuilleDemarage = 50000.0
		self.NBAction = NombreActionFichier()
		
		"""
		L'horizon depent directement du nombre de jour de recul la regle epirique est : lookback ≈ 3× à 10× horizon
		
					| Fast  | a testé   | Optimal   | Robuste   |
		NBjourRecul |  5    |  32 jours | 64        | 100       |
		horizon     | 1     | 5 jours   | 10        | 15        |
		
		"""
		self.NbJourRecul = 32
		self.Horizon = 5
		
		self.Analysis =  ["Close","others_dr","trend_sma_fast","trend_sma_slow","momentum_rsi","volatility_atr","volatility_bbw","volume_obv"]
		self.NbAnalysis = len(self.Analysis)
		self.poidsAchat = [0.25, 0.20, 0.15, 0.10, 0.05]
		self.NBachat = len(self.poidsAchat)
		self.ValVente = 0
		if SysExploite == "windows":
			self.epochs = 2
		else :
			self.epochs = 20000
		self.batch_size = 128
		
		self.LearningRate = 1e-3
	
		# Groupes pour éviter les features corrélés
		self.groups = {
			# ---- MACD ----
			"macd": {
				"trend_macd": 5,
				"trend_macd_signal": 5,
				"trend_macd_diff": 5
			},
			
			# ---- PPO ----
			"ppo": {
				"momentum_ppo": 5,
				"momentum_ppo_signal": 5,
				"momentum_ppo_hist": 5
			},
			
			# ---- Stoch RSI ----
			"stoch_rsi": {
				"momentum_stoch_rsi": 5,
				"momentum_stoch_rsi_k": 5,
				"momentum_stoch_rsi_d": 5
			},
			
			# ---- Stochastic ----
			"stoch": {
				"momentum_stoch": 3,
				"momentum_stoch_signal": 3
			},
			
			# ---- ATR / Volatility ----
			"atr": {
				"volatility_atr": 5,
				"volatility_ui": 1
			},
			
			# ---- Bollinger ----
			"bollinger": {
				"volatility_bbm": 5,
				"volatility_bbh": 5,
				"volatility_bbl": 5,
				"volatility_bbw": 5,
				"volatility_bbp": 3,
				"volatility_bbhi": 3,
				"volatility_bbli": 3
			},
			
			# ---- Keltner ----
			"keltner": {
				"volatility_kcc": 5,
				"volatility_kch": 5,
				"volatility_kcl": 5,
				"volatility_kcw": 3,
				"volatility_kcp": 3,
				"volatility_kchi": 3,
				"volatility_kcli": 3
			},
			
			# ---- Donchian ----
			"donchian": {
				"volatility_dcl": 3,
				"volatility_dch": 5,
				"volatility_dcm": 3,
				"volatility_dcw": 3,
				"volatility_dcp": 3
			},
			
			# ---- SMA / EMA ----
			"moving_avg": {
				"trend_sma_fast": 3,
				"trend_sma_slow": 3,
				"trend_ema_fast": 3,
				"trend_ema_slow": 3
			},
			
			# ---- ADX ----
			"adx": {
				"trend_adx": 5,
				"trend_adx_pos": 3,
				"trend_adx_neg": 3
			},
			
			# ---- Vortex ----
			"vortex": {
				"trend_vortex_ind_pos": 3,
				"trend_vortex_ind_neg": 3,
				"trend_vortex_ind_diff": 5
			},
			
			# ---- Aroon ----
			"aroon": {
				"trend_aroon_up": 3,
				"trend_aroon_down": 3,
				"trend_aroon_ind": 5
			},
			
			# ---- Ichimoku ----
			"ichimoku": {
				"trend_ichimoku_conv": 3,
				"trend_ichimoku_base": 3,
				"trend_ichimoku_a": 3,
				"trend_ichimoku_b": 3,
				"trend_stc": 3
			},
			
			# ---- Volume indicators ----
			"volume": {
				"volume_obv": 5,
				"volume_mfi": 5,
				"volume_cmf": 5,
				"volume_vpt": 3,
				"volume_vwap": 3,
				"volume_adi": 3,
				"volume_fi": 1,
				"volume_em": 1,
				"volume_nvi": 1
			},
			
			# ---- Momentum divers ----
			"momentum_other": {
				"momentum_rsi": 5,
				"momentum_wr": 3,
				"momentum_uo": 3,
				"momentum_ao": 3,
				"momentum_kama": 3,
				"momentum_tsi": 3
			},
			
			# ---- Trend divers ----
			"trend_other": {
				"trend_trix": 3,
				"trend_mass_index": 3,
				"trend_kst": 3,
				"trend_kst_sig": 3,
				"trend_kst_diff": 3,
				"trend_dpo": 3,
				"trend_psar_up_indicator": 5,
				"trend_psar_down_indicator": 5
			},
			
			# ---- Faible pertinence ----
			"low_value": {
				"others_dr": 1,
				"others_dlr": 1
			}
		}
	
	@profile
	def StockParam(self, NomDuModel):
		#Sauvegarde les paramètres dans un fichier .param
		data = {
			"NbJourRecul": self.NbJourRecul,
			"Horizon":self.Horizon,
			"NBAction": self.NBAction,
			"Analysis": self.Analysis,
			"NbParam": self.NbAnalysis,
			"PortefeuilleDemarage": self.PortefeuilleDemarage,
			"poidsAchat":self.poidsAchat,
			"ValVente": self.ValVente,
			"epochs":self.epochs,
			"batch_size":self.batch_size,
			"LearningRate":self.LearningRate,
		}
		with open(f"{NomDuModel}.param", "w") as f:
			json.dump(data, f, indent=4)
	
	@profile
	def RestaureParam(self, NomDuModel):
		#Restaure les paramètres depuis un fichier .param
		NomDuModel = NomDuModel + ".param"
		if not os.path.exists(NomDuModel):
			raise FileNotFoundError(f"Le fichier {NomDuModel}.param est introuvable.")
		
		with open(NomDuModel, "r") as f:
			data = json.load(f)
		
		self.NbJourRecul = data.get("NbJourRecul", self.NbJourRecul)
		self.Horizon = data.get("Horizon", self.Horizon)
		self.NBAction = data.get("NBAction", self.NBAction)
		self.Analysis = data.get("Analysis", self.Analysis)
		self.NbAnalysis = data.get("NbParam", len(self.Analysis))
		self.PortefeuilleDemarage = data.get("PortefeuilleDemarage", self.PortefeuilleDemarage)
		self.poidsAchat = data.get("poidsAchat", self.poidsAchat)
		self.ValVente = data.get("ValVente", self.ValVente)
		self.epochs = data.get("",self.epochs)
		self.batch_size =data.get("",self.batch_size)
		self.LearningRate =data.get("",self.LearningRate)
	
	def UUIDData(self, Demmarage, NBNJour):
		data = {
			"Demmarage": Demmarage,
			"NBJour": NBNJour,
			"NbJourRecul": self.NbJourRecul,
			"Horizon": self.Horizon,
			"NBAction": self.NBAction,
			"Analysis": self.Analysis
		}
		
		# sérialisation stable (ordre garanti)
		payload = json.dumps(data, sort_keys=True).encode("utf-8")
		
		# hash
		h = hashlib.blake2s(payload, digest_size=8).hexdigest()  # 16 chars
		
		return h
	
	def UUIDRAM(self, date_debut, nb_jours):
		data = {
			"Demmarage": date_debut,
			"NBJour": nb_jours,
			"NbJourRecul": self.NbJourRecul
		}
		
		# sérialisation stable (ordre garanti)
		payload = json.dumps(data, sort_keys=True).encode("utf-8")
		
		# hash
		h = hashlib.blake2s(payload, digest_size=8).hexdigest()  # 16 chars
		
		return h