| import torch
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| import numpy as np
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| import pandas as pd
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| import gradio as gr
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| import joblib
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| from model.ConvGRUTransformerHL import ConvGRUTransformerHL_Attn
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|
|
|
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| LOOKBACK = 3
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| FOLDS = [1,2,3]
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| MODEL_DIR = "models"
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|
|
|
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| scalers = {}
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| for fold in FOLDS:
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| scalers[fold] = {
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| "price": joblib.load(f"{MODEL_DIR}/scaler_fold{fold}/scaler_price.pkl"),
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| "adx": joblib.load(f"{MODEL_DIR}/scaler_fold{fold}/scaler_adx.pkl"),
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| "atr": joblib.load(f"{MODEL_DIR}/scaler_fold{fold}/scaler_atr.pkl"),
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| "tick": joblib.load(f"{MODEL_DIR}/scaler_fold{fold}/scaler_tick_volume.pkl"),
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| "target_high": joblib.load(f"{MODEL_DIR}/scaler_fold{fold}/scaler_target_high.pkl"),
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| "target_low": joblib.load(f"{MODEL_DIR}/scaler_fold{fold}/scaler_target_low.pkl"),
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| }
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|
|
|
|
| models = {}
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| for fold in FOLDS:
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| checkpoint = torch.load(f"{MODEL_DIR}/EURUSDm_transformer_finetuned_fold_{fold}_01.pth", map_location="cpu")
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| model = ConvGRUTransformerHL_Attn(input_dim=10, seq_len=LOOKBACK, kernel_size=LOOKBACK, output_steps=1)
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| model.load_state_dict(checkpoint["model_state_dict"])
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| model.eval()
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| models[fold] = model
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|
|
|
|
| def predict_signal(open_, high_, low_, close_, tick_volume, adx, atr, hour, weekday, month):
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| df = pd.DataFrame([{
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| 'open': open_, 'high': high_, 'low': low_, 'close': close_,
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| 'tick_volume': tick_volume, 'adx': adx, 'atr': atr,
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| 'hour': hour, 'weekday': weekday, 'month': month
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| }])
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|
|
|
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| pred_high_list = []
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| pred_low_list = []
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|
|
| for fold in FOLDS:
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| s = scalers[fold]
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| price_scaled = s["price"].transform(df[['open','high','low','close']].values)
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| adx_scaled = s["adx"].transform(df[['adx']].values)
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| atr_scaled = s["atr"].transform(df[['atr']].values)
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| tick_scaled = s["tick"].transform(df[['tick_volume']].values)
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| time_data = df[['hour','weekday','month']].values
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|
|
| X_scaled = np.concatenate([price_scaled, adx_scaled, atr_scaled, tick_scaled, time_data], axis=1)
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| X_tensor = torch.tensor(X_scaled, dtype=torch.float32).unsqueeze(0)
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|
|
|
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| with torch.no_grad():
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| pred_high, pred_low = models[fold](X_tensor)
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|
|
|
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| pred_high_np = s["target_high"].inverse_transform(pred_high.numpy())
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| pred_low_np = s["target_low"].inverse_transform(pred_low.numpy())
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|
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| pred_high_list.append(pred_high_np[0][0])
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| pred_low_list.append(pred_low_np[0][0])
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|
|
|
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| return float(np.mean(pred_high_list)), float(np.mean(pred_low_list))
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|
|
|
|
| iface = gr.Interface(
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| fn=predict_signal,
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| inputs=["number","number","number","number","number","number","number","number","number","number"],
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| outputs=["number","number"],
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| title="AI Trading Signals",
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| description="Enter latest OHLC + tick + adx + atr + hour + weekday + month to get predicted High/Low"
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| )
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|
|
| iface.launch()
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|
|
