Create app.py

app.py

@@ -0,0 +1,134 @@
+import os
+import yfinance as yf
+import pandas as pd
+from newsapi import NewsApiClient
+from transformers import pipeline
+import tensorflow as tf
+from tensorflow import keras
+from sklearn.preprocessing import MinMaxScaler
+import numpy as np
+from datetime import datetime, timedelta
+import alpaca_trade_api as tradeapi
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Load environment variables with fallback
+NEWSAPI_KEY = os.getenv('NEWSAPI_KEY', 'your_newsapi_key')
+ALPACA_API_KEY = os.getenv('ALPACA_API_KEY', 'your_alpaca_api_key')
+ALPACA_SECRET_KEY = os.getenv('ALPACA_SECRET_KEY', 'your_alpaca_secret_key')
+APCA_API_KEY_ID = os.getenv('APCA_API_KEY_ID', ALPACA_API_KEY)
+APCA_API_SECRET_KEY = os.getenv('APCA_API_SECRET_KEY', ALPACA_SECRET_KEY)
+
+# Check if all necessary keys are available
+if not all([NEWSAPI_KEY, APCA_API_KEY_ID, APCA_API_SECRET_KEY]):
+    raise ValueError("Ensure all API keys and secret keys are set as environment variables.")
+
+# Initialize NewsAPI client
+newsapi = NewsApiClient(api_key=NEWSAPI_KEY)
+
+# Initialize Alpaca Trade API client
+alpaca_api = tradeapi.REST(APCA_API_KEY_ID, APCA_API_SECRET_KEY, base_url='https://paper-api.alpaca.markets')
+
+def collect_market_data(ticker):
+    data = yf.download(ticker, start=(datetime.now() - timedelta(days=365)).strftime('%Y-%m-%d'), end=datetime.now().strftime('%Y-%m-%d'))
+    data.to_csv(f'{ticker}_market_data.csv')
+    logger.info(f'Market data for {ticker} collected successfully.')
+
+def collect_news_data(query, from_date, to_date):
+    all_articles = newsapi.get_everything(q=query, from_param=from_date, to=to_date, language='en', sort_by='relevancy')
+    if all_articles['status'] == 'ok':
+        articles_df = pd.DataFrame(all_articles['articles'])
+        articles_df.to_csv('news_data.csv')
+        logger.info(f'News data for {query} collected successfully.')
+    else:
+        logger.error(f'Error collecting news data: {all_articles["message"]}')
+
+def perform_sentiment_analysis():
+    sentiment_pipeline = pipeline("sentiment-analysis")
+    try:
+        news_data = pd.read_csv('news_data.csv')
+        news_data['sentiment'] = news_data['description'].apply(lambda x: sentiment_pipeline(x)[0]['label'] if pd.notna(x) else 'NEUTRAL')
+        news_data.to_csv('sentiment_data.csv', index=False)
+        logger.info('Sentiment analysis performed successfully.')
+    except Exception as e:
+        logger.error(f'Error performing sentiment analysis: {e}')
+
+def train_price_prediction_model(ticker):
+    data = pd.read_csv(f'{ticker}_market_data.csv')
+    data = data[['Date', 'Close']].set_index('Date')
+    scaler = MinMaxScaler(feature_range=(0, 1))
+    scaled_data = scaler.fit_transform(data)
+
+    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 = np.array(X)
+    y = np.array(y)
+    X = np.reshape(X, (X.shape[0], X.shape[1], 1))
+
+    model = keras.Sequential([
+        keras.layers.LSTM(50, return_sequences=True, input_shape=(X.shape[1], 1)),
+        keras.layers.LSTM(50, return_sequences=False),
+        keras.layers.Dense(25),
+        keras.layers.Dense(1)
+    ])
+
+    model.compile(optimizer='adam', loss='mean_squared_error')
+    model.fit(X, y, batch_size=1, epochs=1)
+
+    model.save(f'{ticker}_price_prediction_model.h5')
+    logger.info('Price prediction model trained successfully.')
+
+def make_trade_decision(ticker):
+    model = keras.models.load_model(f'{ticker}_price_prediction_model.h5')
+    data = pd.read_csv(f'{ticker}_market_data.csv')
+    last_60_days = data['Close'].tail(60).values
+    last_60_days_scaled = MinMaxScaler(feature_range=(0, 1)).fit_transform(last_60_days.reshape(-1, 1))
+
+    X_test = []
+    X_test.append(last_60_days_scaled)
+    X_test = np.array(X_test)
+    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))
+
+    predicted_price = model.predict(X_test)
+    predicted_price = MinMaxScaler(feature_range=(0, 1)).inverse_transform(predicted_price)
+
+    current_price = yf.download(ticker, period='1d')['Close'].values[0]
+
+    if predicted_price > current_price:
+        alpaca_api.submit_order(
+            symbol=ticker,
+            qty=1,
+            side='buy',
+            type='market',
+            time_in_force='gtc'
+        )
+        logger.info(f'Bought 1 share of {ticker}')
+    else:
+        alpaca_api.submit_order(
+            symbol=ticker,
+            qty=1,
+            side='sell',
+            type='market',
+            time_in_force='gtc'
+        )
+        logger.info(f'Sold 1 share of {ticker}')
+
+if __name__ == "__main__":
+    TICKER = 'AAPL'
+    QUERY = 'Apple Inc'
+    FROM_DATE = (datetime.now() - timedelta(days=30)).strftime('%Y-%m-%d')
+    TO_DATE = datetime.now().strftime('%Y-%m-%d')
+
+    collect_market_data(TICKER)
+    collect_news_data(QUERY, FROM_DATE, TO_DATE)
+    perform_sentiment_analysis()
+    train_price_prediction_model(TICKER)
+    make_trade_decision(TICKER)