feat

web-model/index.py

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+# app.py
+import logging
+import pandas as pd
+import numpy as np
+import yfinance as yf
+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from datetime import datetime, timedelta
+from sklearn.preprocessing import StandardScaler
+from model import predict
+
+import torch
+from stable_baselines3 import PPO
+from transformers import PatchTSTForClassification
+
+# === Model Config ===
+CONTEXT_LENGTH = 48
+LABEL_NAMES = ['SELL', 'HOLD', 'BUY']
+
+# ============ Logging ============
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+# ============ Flask App ============
+app = Flask(__name__)
+CORS(app)
+
+
+# ============ Feature Extraction ============
+def extract_features(df):
+    import talib
+
+    df['return'] = df['Close'].pct_change()
+    df['daily_momentum'] = df['Close'] / df['Open']
+    df['range_efficiency'] = abs(df['Close'] - df['Open']) / (df['High'] - df['Low']).replace(0, np.nan)
+    df['volume_momentum'] = df['Volume'] / df['Volume'].rolling(5).mean().replace(0, np.nan)
+    df['adx'] = talib.ADX(df['High'], df['Low'], df['Close'], timeperiod=14) / 100.0
+    df['rsi_scaled'] = talib.RSI(df['Close'], timeperiod=14) / 100.0
+    return df.dropna().reset_index(drop=True)
+
+
+def extract_patch_features(model, X):
+    model.eval()
+    features = []
+    with torch.no_grad():
+        for i in range(0, len(X), 64):
+            batch = torch.tensor(X[i:i+64], dtype=torch.float32)
+            outputs = model.base_model(
+                past_values=batch,
+                output_hidden_states=True,
+                return_dict=True
+            )
+            cls_tokens = outputs.hidden_states[-1][:, 0, :]
+            features.append(cls_tokens)
+    return torch.cat(features, dim=0).numpy()
+
+@app.route('/predict', methods=['POST'])
+def predict_by_clicked_date_yfinance():
+        data = request.get_json()
+        symbol = data.get("symbol")
+        clicked_date_str = data.get("clicked_date")
+        result = predict(clicked_date_str, symbol)
+        return jsonify(result)
+
+# ============ Run App ============
+if __name__ == '__main__':
+    app.run(debug=True, port=8000)