import os, sys, copy, joblib import numpy as np import pandas as pd from tqdm import tqdm import MetaTrader5 as mt5 import torch import torch.nn as nn import torch.optim as optim from sklearn.model_selection import TimeSeriesSplit from sklearn.preprocessing import MinMaxScaler, RobustScaler, PowerTransformer from torch.utils.data import DataLoader from pathlib import Path # --- Config --- LOOKBACK = 5 BATCH_SIZE = 4 EPOCHS = 50 LR = 1e-2 PATIENCE = 10 N_SPLITS = 3 DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu") datestr = pd.Timestamp.now().strftime("%d%m%Y") # # --- Utility Class --- # # sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../common'))) # common_path = Path(__file__).resolve().parent.parent.parent.parent.parent / "common" # # เพิ่มเข้า sys.path # sys.path.append(str(common_path)) from AIBaseClass import * from ClassUtils import * # ================= Dynamic Beta SmoothL1Loss ================= class DynamicBetaSmoothL1Loss(nn.Module): def __init__(self, init_beta=0.5, alpha=0.9, reduction='mean', eps=1e-6): super().__init__() self.register_buffer('beta', torch.tensor(init_beta)) self.alpha = alpha self.reduction = reduction self.eps = eps def forward(self, pred, target): diff = torch.abs(pred - target) batch_beta = diff.mean().detach() + self.eps self.beta = self.alpha * self.beta + (1 - self.alpha) * batch_beta beta = self.beta loss = torch.where(diff < beta, 0.5 * diff**2 / beta, diff - 0.5*beta) if self.reduction=='mean': return loss.mean() elif self.reduction=='sum': return loss.sum() else: return loss # ================= Main Loop ================= symbollist = ['DXYm','EURUSDm','GBPUSDm','USDJPYm','USDCADm'] for symbol in tqdm(symbollist): SAVE_DIR = Path(__file__).resolve().parent.parent / f"Models/" os.makedirs(SAVE_DIR, exist_ok=True) CHECKPOINT_DIR = Path(__file__).resolve().parent.parent / f"Checkpoint/" os.makedirs(CHECKPOINT_DIR, exist_ok=True) try: data_dir = Path(__file__).resolve().parent.parent / "Datasource" # ====== Config ====== if not data_dir.exists(): raise ValueError(f"Folder {data_dir} ไม่พบ") csv_file = os.path.join(data_dir,f'{symbol}.csv') df = pd.read_csv(csv_file) # ตรวจสอบ columns ที่เราต้องใช้ required_cols = ["time", "open", "high", "low", "close", "tick_volume"] for col in required_cols: if col not in df.columns: raise ValueError(f"{col} ไม่พบใน {csv_file}") # แปลง time เป็น datetime df['time'] = pd.to_datetime(df['time']) # ===== sort by time ===== df = df.sort_values(by='time') # ===== set time เป็น index ===== df.set_index('time', inplace=True) # ===== เพิ่ม feature ===== df['hour'] = df.index.hour / 23.0 df['weekday'] = df.index.weekday / 6.0 df['month'] = (df.index.month - 1) / 11.0 df = df.copy() N = 3 # Shift targets to next day df['target_high'] = df['high'].shift(-1).ewm(span=N).mean() df['target_low'] = df['low'].shift(-1).ewm(span=N).mean() df.dropna(inplace=True) df.reset_index(drop=True, inplace=True) price_data = df[['open','high','low','close']].values target_high_data = df[['target_high']].values target_low_data = df[['target_low']].values tick_data = df[['tick_volume']].values time_data = df[['hour','weekday','month']].values # Initialize scalers scaler_price = MinMaxScaler(feature_range=(-1,1)) scaler_target_high = MinMaxScaler(feature_range=(-1,1)) scaler_target_low = MinMaxScaler(feature_range=(-1,1)) scaler_tick = RobustScaler() tscv = TimeSeriesSplit(n_splits=N_SPLITS) for fold, (train_idx, val_idx) in enumerate(tscv.split(price_data)): print(f"[{symbol}] Fold {fold+1}/{N_SPLITS}") # --- Split train/val --- train_price, val_price = price_data[train_idx], price_data[val_idx] train_target_high, val_target_high = target_high_data[train_idx], target_high_data[val_idx] train_target_low, val_target_low = target_low_data[train_idx], target_low_data[val_idx] train_tick, val_tick = tick_data[train_idx], tick_data[val_idx] train_time, val_time = time_data[train_idx], time_data[val_idx] # --- Scale train/val separately --- scaled_train_price = scaler_price.fit_transform(train_price) scaled_val_price = scaler_price.transform(val_price) scaled_train_target_high = scaler_target_high.fit_transform(train_target_high) scaled_val_target_high = scaler_target_high.transform(val_target_high) scaled_train_target_low = scaler_target_low.fit_transform(train_target_low) scaled_val_target_low = scaler_target_low.transform(val_target_low) scaled_train_tick = scaler_tick.fit_transform(train_tick) scaled_val_tick = scaler_tick.transform(val_tick) # --- Combine features --- X_train_scaled = np.concatenate([ scaled_train_price, scaled_train_tick, train_time ], axis=1) X_val_scaled = np.concatenate([ scaled_val_price, scaled_val_tick, val_time ], axis=1) y_train_high, y_train_low = scaled_train_target_high, scaled_train_target_low y_val_high, y_val_low = scaled_val_target_high, scaled_val_target_low # --- Dataset / DataLoader --- train_dataset = SinglestepDataset(X_train_scaled, y_train_high, y_train_low) val_dataset = SinglestepDataset(X_val_scaled, y_val_high, y_val_low) train_loader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=False) val_loader = DataLoader(val_dataset, batch_size=BATCH_SIZE, shuffle=False) # --- Model / Optimizer / Scheduler / Loss --- model = ConvGRUTransformerHLV10(input_dim=8, seq_len=LOOKBACK, kernel_size=LOOKBACK).to(DEVICE) optimizer = optim.Adam(model.parameters(), lr=LR, weight_decay=0.001) scheduler = torch.optim.lr_scheduler.OneCycleLR( optimizer, max_lr=LR, steps_per_epoch=len(train_loader), epochs=EPOCHS, pct_start=0.3, anneal_strategy='cos' ) loss_fn = DynamicBetaSmoothL1Loss(init_beta=0.5, alpha=0.1) early_stopping = EarlyStopping(patience=PATIENCE, verbose=True) best_val_loss = float('inf') best_model_weights = None # --- Training Loop --- for epoch in range(EPOCHS): model.train() train_loss = 0 for xb, yb_high, yb_low in train_loader: xb, yb_high, yb_low = xb.to(DEVICE), yb_high.to(DEVICE), yb_low.to(DEVICE) optimizer.zero_grad() pred_high, pred_low = model(xb) loss = (loss_fn(pred_high, yb_high) + loss_fn(pred_low, yb_low)) / 2 loss.backward() torch.nn.utils.clip_grad_norm_(model.parameters(), 1) optimizer.step() train_loss += loss.item() train_loss /= len(train_loader) # --- Validation --- model.eval() val_loss = 0 with torch.no_grad(): for xb, yb_high, yb_low in val_loader: xb, yb_high, yb_low = xb.to(DEVICE), yb_high.to(DEVICE), yb_low.to(DEVICE) pred_high, pred_low = model(xb) loss = (loss_fn(pred_high, yb_high) + loss_fn(pred_low, yb_low)) / 2 val_loss += loss.item() val_loss /= len(val_loader) print(f"[{symbol}] Epoch {epoch+1} | TrainLoss: {train_loss:.6f} | ValLoss: {val_loss:.6f}") scheduler.step() model_path = os.path.join(f'{CHECKPOINT_DIR}',f'{symbol}_transformer_checkpoint_fold{fold+1}.pth') early_stopping(val_loss, model, model_path) if val_loss < best_val_loss: best_val_loss = val_loss best_model_weights = copy.deepcopy(model.state_dict()) if early_stopping.early_stop: print("Early stopping triggered") break # --- Save best model --- if best_model_weights is not None: torch.save(best_model_weights, os.path.join(f'{SAVE_DIR}',f'{symbol}_best_fold{fold+1}.pth')) print(f"[{symbol}] ✅ Fold {fold+1} saved with val_loss: {best_val_loss:.6f}") model.load_state_dict(best_model_weights) torch.save({ 'model_state_dict': best_model_weights, 'scalers': { 'scaler_price': scaler_price, 'scaler_tick': scaler_tick, 'scaler_target_high': scaler_target_high, 'scaler_target_low': scaler_target_low } }, os.path.join(SAVE_DIR, f"{symbol}_fold{fold+1}.pth")) except Exception as e: print(f"[{symbol}] ❌ Error: {e}")