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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}")
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