Yapay Zeka ile Borsa Analizi Rehberi

import requests
import pandas as pd

API_KEY = "ALPHA_VANTAGE_API_KEY"  # Ücretsiz API anahtarınızı alın
symbol = "AAPL"  # Apple hissesi

url = f"https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol={symbol}&apikey={API_KEY}"
response = requests.get(url)
data = response.json()

# Veriyi DataFrame'e çevirme
df = pd.DataFrame(data["Time Series (Daily)"]).T
df.index = pd.to_datetime(df.index)
df = df.astype(float)
print(df.head())

import yfinance as yf

# Apple hissesinin verilerini çek
data = yf.download("AAPL", start="2023-01-01", end="2024-01-01")
print(data.head())

import numpy as np
from sklearn.preprocessing import MinMaxScaler
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import LSTM, Dense

# Veriyi normalize etme
scaler = MinMaxScaler()
scaled_data = scaler.fit_transform(data["Close"].values.reshape(-1, 1))

# Eğitim verisi oluşturma
X, y = [], []
for i in range(60, len(scaled_data)):
    X.append(scaled_data[i-60:i, 0])
    y.append(scaled_data[i, 0])
X, y = np.array(X), np.array(y)
X = np.reshape(X, (X.shape[0], X.shape[1], 1))

# LSTM modeli oluşturma
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(X.shape[1], 1)))
model.add(LSTM(50))
model.add(Dense(1))
model.compile(optimizer="adam", loss="mean_squared_error")

# Modeli eğitme
model.fit(X, y, epochs=10, batch_size=32)

import matplotlib.pyplot as plt

plt.plot(data.index, data["Close"], label="Gerçek Fiyat")
plt.title("Apple Hisse Fiyatı")
plt.xlabel("Tarih")
plt.ylabel("Fiyat ($)")
plt.legend()
plt.show()