File size: 9,252 Bytes
a6fae72 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 | import os, sys, joblib
import numpy as np
import pandas as pd
import MetaTrader5 as mt5
import torch
import torch.nn as nn
from sklearn.preprocessing import MinMaxScaler, RobustScaler
# --- Utility Class ---
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../../common')))
from AIClass import EAUtils
from AIBaseClass import *
from AILoss import *
# --- Config ---
LOOKBACK = 3
FOLDS = [1, 2, 3]
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
EPOCHS = 50
LR = 1e-5
L2_LAMBDA = 0.03
class TransferCrossTF:
def __init__(self,
basesymbol,
targetsymbol,
baseTF,
targetTF,
basemodelpath,
targetmodelpath
):
self.BaseSymbol = basesymbol
self.TargetSymbol = targetsymbol
self.BaseTimeframe = baseTF
self.TargetTimeframe = targetTF
self.BaseModelPath = basemodelpath
self.TargetModelDir = targetmodelpath
def FetchTargetData(self):
util = EAUtils(self.TargetSymbol, timestep=LOOKBACK, feature=10)
df = util.fetch_dataV2(self.TargetSymbol, mt5.TIMEFRAME_W1, 520)
if df is None or df.empty or len(df) < LOOKBACK + 1:
raise ValueError(f"[{self.TargetSymbol}] ❌ Insufficient data")
df = df[['open', 'high', 'low', 'close', 'tick_volume']]
df = EAUtils.add_features(df)
df = util.add_time_feature(df)
last_close = round(df['close'].iloc[-1], EAUtils.get_digit_from_symbol(self.TargetSymbol))
return df, last_close
def ScaledData(self):
df, last_close = self.FetchTargetData()
# ===== Fit new scalers for target =====
scaler_price = MinMaxScaler(feature_range=(-1, 1)); scaler_price.fit(df[['open', 'high', 'low', 'close']].values)
scaler_adx = MinMaxScaler(feature_range=(-1, 1)); scaler_adx.fit(df[['adx']].values)
scaler_atr = MinMaxScaler(feature_range=(-1, 1)); scaler_atr.fit(df[['atr']].values)
scaler_tick = RobustScaler(); scaler_tick.fit(df[['tick_volume']].values)
scaler_target_high = MinMaxScaler(feature_range=(-1, 1)); scaler_target_high.fit(df[['high']].values)
scaler_target_low = MinMaxScaler(feature_range=(-1, 1)); scaler_target_low.fit(df[['low']].values)
return scaler_price, scaler_adx, scaler_atr, scaler_tick, scaler_target_high, scaler_target_low, df, last_close
def SaveScaler(self, scalers):
for fold in FOLDS:
fold_dir = f"{self.TargetModelDir}scaler_fold{fold}/"
os.makedirs(fold_dir, exist_ok=True)
joblib.dump(scalers['price'], f"{fold_dir}scaler_price.pkl")
joblib.dump(scalers['adx'], f"{fold_dir}scaler_adx.pkl")
joblib.dump(scalers['atr'], f"{fold_dir}scaler_atr.pkl")
joblib.dump(scalers['tick'], f"{fold_dir}scaler_tick.pkl")
joblib.dump(scalers['target_high'], f"{fold_dir}scaler_target_high.pkl")
joblib.dump(scalers['target_low'], f"{fold_dir}scaler_target_low.pkl")
def FreezeLayer(self,model):
# --- Freeze feature extractor layers ---
for name, param in model.named_parameters():
# Freeze Conv
if "conv1" in name or "conv_bn" in name:
param.requires_grad = False
# Freeze GRU
elif "gru" in name:
param.requires_grad = False
# Freeze Positional Encoding
elif "pos_encoder" in name:
param.requires_grad = False
# Freeze Transformer
elif "transformer" in name:
param.requires_grad = False
else:
# Keep fc_high, fc_low trainable
param.requires_grad = True
def PerformTransfer(self, fold, results):
scaler_price, scaler_adx, scaler_atr, scaler_tick, scaler_target_high, scaler_target_low, df, last_close = self.ScaledData()
scalers = {
'price': scaler_price,
'adx': scaler_adx,
'atr': scaler_atr,
'tick': scaler_tick,
'target_high': scaler_target_high,
'target_low': scaler_target_low
}
self.SaveScaler(scalers)
# --- Prepare input ---
scaled_price = scaler_price.transform(df[['open','high','low','close']].values)
scaled_adx = scaler_adx.transform(df[['adx']].values)
scaled_atr = scaler_atr.transform(df[['atr']].values)
scaled_tick = scaler_tick.transform(df[['tick_volume']].values)
time_data = df[['hour','weekday','month']].values
X_scaled = np.concatenate([scaled_price, scaled_adx, scaled_atr, scaled_tick, time_data], axis=1)
input_seq = torch.tensor(X_scaled[-5:], dtype=torch.float32).unsqueeze(0).to(DEVICE)
# --- Load base model ---
base_model_path = f'{self.BaseModelPath}'
checkpoint = torch.load(base_model_path,weights_only=False)
model = ConvGRUTransformerHL(input_dim=10, seq_len=4, kernel_size=3).to(DEVICE)
model_dict = model.state_dict()
# โหลดเฉพาะ layer ที่ shape ตรงกับ model ใหม่
pretrained_dict = {k: v for k, v in checkpoint['model_state_dict'].items()
if k in model_dict and v.size() == model_dict[k].size()}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
self.FreezeLayer(model)
# --- Prepare target ---
target_high = scaler_target_high.transform(df['high'].values[-1].reshape(-1,1))
target_low = scaler_target_low.transform(df['low'].values[-1].reshape(-1,1))
target_seq = torch.tensor(np.concatenate([target_high, target_low], axis=1), dtype=torch.float32).unsqueeze(0).to(DEVICE)
# --- Optimizer + Loss ---
optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=LR, weight_decay=L2_LAMBDA)
loss_fn_high = DynamicBetaSmoothL1Loss(init_beta=0.5, alpha=0.1, reduction='mean')
loss_fn_low = DynamicBetaSmoothL1Loss(init_beta=0.5, alpha=0.1, reduction='mean')
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min', patience=5)
# --- Fine-tune ---
model.train()
for _ in range(EPOCHS):
optimizer.zero_grad()
pred_high, pred_low = model(input_seq)
target_high_t = target_seq[:,:,0]
target_low_t = target_seq[:,:,1]
loss_high = loss_fn_high(pred_high.squeeze(-1), target_high_t)
loss_low = loss_fn_low(pred_low.squeeze(-1), target_low_t)
loss = loss_high + loss_low
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)
optimizer.step()
scheduler.step(loss.item())
# --- Predict after fine-tune ---
#model.eval()
# with torch.no_grad():
# pred = model(input_seq)
# pred = torch.cat(pred, dim=-1).squeeze(0).cpu().numpy()
# pred_high = scaler_target_high.inverse_transform(pred[:,0].reshape(-1,1))[0][0]
# pred_low = scaler_target_low.inverse_transform(pred[:,1].reshape(-1,1))[0][0]
# results.append({
# 'symbol': self.TargetSymbol,
# 'fold': fold,
# 'predicted_high': round(pred_high, EAUtils.get_digit_from_symbol(self.TargetSymbol)),
# 'predicted_low': round(pred_low, EAUtils.get_digit_from_symbol(self.TargetSymbol)),
# 'last_close': last_close
# })
# --- Save fine-tuned model ---
final_model_path = f"{self.TargetModelDir}{self.TargetSymbol}_transformer_fold{fold}.pth"
torch.save(model.state_dict(), final_model_path)
print(f"[{self.TargetSymbol}] ✅ Saved transfer model fold {fold} -> {final_model_path}")
return results
# --- Run Transfer ---
basesymbol = ['DXYm','EURUSDm','GBPUSDm','USDJPYm','USDCADm','XAUUSDm']
targetsymbol = ['DXYm','EURUSDm','GBPUSDm','USDJPYm','USDCADm','XAUUSDm']
for base, target in zip(basesymbol, targetsymbol):
print(f"ฺBegin Transfer {base} from time frame Month to → {target} time frame Week")
results = []
for f in FOLDS:
transfer = TransferCrossTF(
base,
target,
mt5.TIMEFRAME_MN1,
mt5.TIMEFRAME_W1,
f"Model/Month/{base}/TRANSFORMER-PYTORCH-V03/{base}_transformer_fold{f}_03.pth",
f'Model/Week/{target}/TRANSFORMER-PYTORCH-V02/'
)
results = transfer.PerformTransfer(f, results)
# Save results summary per symbol
df_result = pd.DataFrame(results)
out_path = f"Model/Week/{target}/transfer_results.csv"
df_result.to_csv(out_path, index=False)
print(f"\n✅ {target} Transfer Learning complete! -> saved {out_path}")
print(df_result)
|