Auflösen von CORS -Fehler im Python Stock Analyzer ⇐ Python

[Anonymous]

Ich habe an dieser Webanwendung für Aktienanalysen und Vorhersagen (Python Backend, JS/React Frontend) gearbeitet und erhalte immer wieder "Cross-Origin-Anfrage blockiert", wenn ich versuche, Indikatoren zu berechnen. Backend -Code unten wird jede Unterstützung geschätzt. < /P>

Code: Select all

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from typing import List, Optional
import yfinance as yf
import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error, r2_score
from ta.trend import SMAIndicator, MACD
from ta.momentum import RSIIndicator
from ta.volatility import BollingerBands

app = FastAPI(title="Stock Analysis and Prediction Platform")

# Configure CORS
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
expose_headers=["*"]
)

class StockRequest(BaseModel):
symbol: str
period: str = "1y"

class PredictionRequest(BaseModel):
symbol: str
features: List[str]
target: str
days_ahead: int = 5
model_type: str

@app.get("/")
async def root():
return {"message": "Welcome to Stock Analysis and Prediction API"}

@app.post("/stock/historical")
async def get_historical_data(request: StockRequest):
try:
# Get historical data
stock = yf.Ticker(request.symbol)
df = stock.history(period=request.period)

if df.empty:
raise HTTPException(status_code=404, detail=f"No data found for symbol {request.symbol}")

# Convert DataFrame to list of dictionaries
df.index = df.index.strftime('%Y-%m-%d')
return df.reset_index().to_dict('records')

except Exception as e:
raise HTTPException(status_code=500, detail=str(e))

@app.post("/stock/indicators")
async def calculate_indicators(request: StockRequest):
try:
# Get historical data
stock = yf.Ticker(request.symbol)
df = stock.history(period=request.period)

if df.empty:
raise HTTPException(status_code=404, detail=f"No data found for symbol {request.symbol}")

# Calculate indicators
# RSI
rsi = RSIIndicator(close=df['Close'], window=14)
df['RSI'] = rsi.rsi()

# MACD
macd = MACD(close=df['Close'])
df['MACD'] = macd.macd()

# Bollinger Bands
bollinger = BollingerBands(close=df['Close'])
df['BB_high'] = bollinger.bollinger_hband()
df['BB_low'] = bollinger.bollinger_lband()

# Convert DataFrame to list of dictionaries
df.index = df.index.strftime('%Y-%m-%d')
return df.reset_index().to_dict('records')

except Exception as e:
raise HTTPException(status_code=500, detail=str(e))

@app.post("/stock/predict")
async def predict_stock(request:  PredictionRequest):
try:
# Get historical data
stock = yf.Ticker(request.symbol)
df = stock.history(period="2y")

if df.empty:
raise HTTPException(status_code=404, detail=f"No data found for symbol {request.symbol}")

# Prepare features
for feature in request.features:
if feature not in df.columns:
raise HTTPException(status_code=400, detail=f"Feature {feature} not found")

X = df[request.features].values
y = df[request.target].values

# Split data
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# Train model
if request.model_type == "linear":
model = LinearRegression()
elif request.model_type == "random_forest":
model = RandomForestRegressor(n_estimators=100, random_state=42)
else:
raise HTTPException(status_code=400, detail="Unsupported model type")

model.fit(X_train, y_train)
y_pred = model.predict(X_test)

# Calculate metrics
mse = mean_squared_error(y_test, y_pred)
r2 = r2_score(y_test, y_pred)

# Predict next n days
last_data = df[request.features].values[-1:]
future_pred = model.predict(last_data)

return {
"current_price": float(df['Close'].iloc[-1]),
"predicted_price": float(future_pred[0]),
"mse": float(mse),
"r2": float(r2),
"days_ahead": request.days_ahead
}

except Exception as e:
raise HTTPException(status_code=500, detail=str(e))

if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="127.0.0.1", port=8000)

Ich erwartete, dass es technische Indikatoren (RSI, Bollinger-Bänder usw.) berechnet, aber ich erhalte "Cross-Origin-Anfrage blockiert: Die gleiche Ursprungsrichtlinie macht die Remote-Ressource bei http: // localhost: 8000/stock/indicators (Grund: Cors Header Header" -Access-Contolrol-Own-Ow-Ow-Ow-Origin.>