Update app.py

app.py

@@ -4,9 +4,6 @@ from vnstock import Vnstock
 from datetime import datetime, timedelta
 import pandas as pd
 import numpy as np
-from sklearn.preprocessing import MinMaxScaler
-import torch
-from torch_geometric.data import Data
 
 vn = Vnstock()
 app = FastAPI(title="mFund VNStock API", version="1.0")
@@ -43,21 +40,6 @@ def calc_bollinger(series, window=20, num_std=2):
     std = series.rolling(window).std()
     return sma, sma + num_std * std, sma - num_std * std
 
-# ============================
-# Mô hình GNN đơn giản
-# ============================
-class StockGCN(torch.nn.Module):
-    def __init__(self, num_features, hidden=16):
-        super().__init__()
-        from torch_geometric.nn import GCNConv
-        self.conv1 = GCNConv(num_features, hidden)
-        self.conv2 = GCNConv(hidden, 1)
-
-    def forward(self, data):
-        x, edge = data.x, data.edge_index
-        x = torch.relu(self.conv1(x, edge))
-        return self.conv2(x, edge)
-
 # ============================
 # 1) GET /stock/history
 # ============================
@@ -68,6 +50,9 @@ def get_history(symbol: str, start: str, end: str):
             return {"error": "end must be >= start"}
 
         stock = vn.stock(symbol=symbol)
+        if stock is None:
+            return {"error": "invalid symbol"}
+
         df = stock.quote.history(start=start, end=end, interval="1D")
 
         if df is None or df.empty:
@@ -94,6 +79,9 @@ def get_ta(symbol: str, start: str, end: str):
             return {"error": "end must be >= start"}
 
         stock = vn.stock(symbol=symbol)
+        if stock is None:
+            return {"error": "invalid symbol"}
+
         df = stock.quote.history(start=start, end=end, interval="1D")
 
         if df is None or df.empty:
@@ -109,78 +97,6 @@ def get_ta(symbol: str, start: str, end: str):
     except Exception as e:
         return {"error": str(e)}
 
-# ============================
-# 3) GET /stock/gnn
-# ============================
-@app.get("/stock/gnn")
-def get_gnn(symbol: str, days: int = 7):
-    try:
-        end = datetime.today()
-        start = end - timedelta(days=365)
-
-        stock = vn.stock(symbol=symbol)
-        df = stock.quote.history(
-            start=start.strftime("%Y-%m-%d"),
-            end=end.strftime("%Y-%m-%d"),
-            interval="1D"
-        )
-
-        if df is None or df.empty:
-            return {"error": "no data"}
-
-        df = df.rename(columns={"close": "Close"}).dropna()
-        scaler = MinMaxScaler()
-        df_scaled = scaler.fit_transform(df[["Close"]])
-
-        edge_index = torch.tensor(
-            [[i, i + 1] for i in range(len(df_scaled) - 1)],
-            dtype=torch.long
-        ).t()
-
-        x = torch.tensor(df_scaled, dtype=torch.float)
-        data_obj = Data(x=x, edge_index=edge_index)
-
-        model = StockGCN(num_features=1)
-        model.eval()
-
-        preds_scaled = []
-        last_value = torch.tensor([[df_scaled[-1][0]]], dtype=torch.float)
-
-        for _ in range(days):
-            new_obj = Data(
-                x=torch.cat([data_obj.x, last_value]),
-                edge_index=torch.tensor(
-                    [[i, i + 1] for i in range(len(data_obj.x))],
-                    dtype=torch.long
-                ).t()
-            )
-            out = model(new_obj)
-            last_value = out[-1].view(1, 1)
-            preds_scaled.append(last_value.item())
-
-        preds_real = scaler.inverse_transform(
-            np.array(preds_scaled).reshape(-1, 1)
-        ).flatten()
-
-        dates = [
-            (end + timedelta(days=i + 1)).strftime("%Y-%m-%d")
-            for i in range(days)
-        ]
-
-        return {
-            "symbol": symbol,
-            "today_close": float(df["Close"].iloc[-1]),
-            "predictions": [
-                {"date": d, "price": float(p)}
-                for d, p in zip(dates, preds_real)
-            ],
-        }
-    except Exception as e:
-        return {"error": str(e)}
-
-# ============================
-# Root
-# ============================
 @app.get("/")
 def root():
     return {
@@ -188,6 +104,5 @@ def root():
         "endpoints": [
             "/stock/history?symbol=FPT&start=2023-01-01&end=2023-12-31",
             "/stock/ta?symbol=HPG&start=2023-01-01&end=2023-12-31",
-            "/stock/gnn?symbol=VNM&days=7",
         ],
     }