initial readme

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README.md

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+---
+title: Trading Analyst
+emoji: 📈
+colorFrom: green
+colorTo: red
+sdk: gradio
+sdk_version: 4.39.0
+app_file: app.py
+pinned: false
+license: mit
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -1,173 +1,132 @@
+import logging
+
 import gradio as gr
-from yfinance import Ticker
 import pandas as pd
-import numpy as np
-import matplotlib.pyplot as plt
-import talib
-from datetime import datetime, timedelta
+import torch
+from GoogleNews import GoogleNews
+from transformers import pipeline
 
-def fetch_stock_data(symbol):
-    """Fetch historical data for the given stock symbol."""
-    try:
-        ticker = Ticker(symbol)
-        historical_data = ticker.history(period="1mo")
-        return historical_data
-    except Exception as e:
-        return None
-
-def calculate_indicators(data):
-    """Calculate technical indicators like RSI, MACD, etc."""
-    if data is None:
-        return None
-    
-    # Calculate RSI
-    data['RSI'] = talib.RSI(data['Close'], timeperiod=14)
-    
-    # Calculate MACD
-    macd, macd_signal, macd_hist = talib.MACD(data['Close'])
-    data['MACD'] = macd
-    data['MACD_Signal'] = macd_signal
-    
-    return data
-
-def generate_recommendation(data):
-    """Generate buy/sell recommendations based on indicators."""
-    if data is None:
-        return "Insufficient data"
-    
-    rsi = data['RSI'].iloc[-1]
-    macd = data['MACD'].iloc[-1]
-    macd_signal = data['MACD_Signal'].iloc[-1]
-    
-    if rsi < 30 and macd > macd_signal:
-        return " BUY RECOMMENDATION: Stock is oversold and trending upward."
-    elif rsi > 70 and macd < macd_signal:
-        return " SELL RECOMMENDATION: Stock is overbought and trending downward."
-    else:
-        return "HOLD: No strong signal at this time."
-
-def plot_stock_data(data):
-    """Plot stock price and indicators."""
-    plt.figure(figsize=(10, 6))
-    
-    # Plot closing prices
-    plt.plot(data['Close'], label='Close Price')
-    
-    # Plot RSI
-    ax = plt.twinx()
-    ax.plot(data['RSI'], color='red', label='RSI')
-    ax.axhline(30, color='r', linestyle='--')
-    ax.axhline(70, color='r', linestyle='--')
-    
-    # Add MACD
-    plt.figure(figsize=(10, 6))
-    plt.plot(data['MACD'], label='MACD')
-    plt.plot(data['MACD_Signal'], label='MACD Signal')
-    plt.fill_between(data.index, data['MACD_hist'], color='gray', alpha=0.2, label='MACD Histogram')
-    plt.legend()
-    
-    plt.title(f"Stock Analysis for {symbol}")
-    plt.xlabel("Date")
-    plt.ylabel("Price")
-    plt.legend()
-    plt.grid(True)
-    
-    return plt
-
-def news_updates(symbol):
-    """Fetch recent news articles for the stock."""
-    try:
-        news = Ticker(symbol).news
-        articles = []
-        for article in news:
-            articles.append({
-                'Title': article['title'],
-                'Publish Date': article['publishDate'],
-                'URL': article['url']
-            })
-        return pd.DataFrame(articles)
-    except Exception as e:
-        return pd.DataFrame(columns=['Title', 'Publish Date', 'URL'])
-
-def main(symbol):
-    """Main function to process stock symbol and generate output."""
-    # Fetch real-time price and historical data
-    real_time = Ticker(symbol).info
-    price = real_time['regularMarketPrice']
-    change = real_time['regularMarketChangePercent']
-    
-    # Fetch and process historical data
-    historical_data = fetch_stock_data(symbol)
-    if historical_data is None:
-        return "Error: Could not fetch data for the symbol."
-    
-    data = calculate_indicators(historical_data)
-    
-    # Generate analysis
-    analysis = generate_recommendation(data)
-    
-    # Plot visualization
-    fig = plot_stock_data(data)
-    
-    # Fetch news
-    news_df = news_updates(symbol)
-    
-    return {
-        'Price Analysis': pd.DataFrame([{
-            'Current Price': f'${price:.2f}',
-            '24h Change': f'{change:.2f}%',
-            'Recommendation': analysis
-        }]),
-        'Historical Data': data[['Close', 'RSI', 'MACD', 'MACD_Signal']],
-        'Visualizations': fig,
-        'Latest News': news_df
-    }
-
-def interface(symbol):
-    """Gradio interface implementation."""
+# Set up logging
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s"
+)
+
+SENTIMENT_ANALYSIS_MODEL = (
+    "mrm8488/distilroberta-finetuned-financial-news-sentiment-analysis"
+)
+
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+logging.info(f"Using device: {DEVICE}")
+
+logging.info("Initializing sentiment analysis model...")
+sentiment_analyzer = pipeline(
+    "sentiment-analysis", model=SENTIMENT_ANALYSIS_MODEL, device=DEVICE
+)
+logging.info("Model initialized successfully")
+
+
+def fetch_articles(query):
     try:
-        analysis = main(symbol.strip())
-        
-        with gr.Blocks():
-            gr.Markdown(f"### Stock Analysis for {symbol}")
-            gr.Dataframe(analysis['Price Analysis'])
-            gr.Plot(analysis['Visualizations'])
-            if not analysis['Latest News'].empty:
-                gr.Markdown("#### Latest News Updates")
-                gr.Dataframe(analysis['Latest News'], max_rows=5)
+        logging.info(f"Fetching articles for query: '{query}'")
+        googlenews = GoogleNews(lang="en")
+        googlenews.search(query)
+        articles = googlenews.result()
+        logging.info(f"Fetched {len(articles)} articles")
+        return articles
     except Exception as e:
-        return f"Error: {e}"
+        logging.error(
+            f"Error while searching articles for query: '{query}'. Error: {e}"
+        )
+        raise gr.Error(
+            f"Unable to search articles for query: '{query}'. Try again later...",
+            duration=5,
+        )
+
+
+def analyze_article_sentiment(article):
+    logging.info(f"Analyzing sentiment for article: {article['title']}")
+    sentiment = sentiment_analyzer(article["desc"])[0]
+    article["sentiment"] = sentiment
+    return article
+
+
+def analyze_asset_sentiment(asset_name):
+    logging.info(f"Starting sentiment analysis for asset: {asset_name}")
+
+    logging.info("Fetching articles")
+    articles = fetch_articles(asset_name)
+
+    logging.info("Analyzing sentiment of each article")
+    analyzed_articles = [analyze_article_sentiment(article) for article in articles]
+
+    logging.info("Sentiment analysis completed")
+
+    return convert_to_dataframe(analyzed_articles)
+
+
+def convert_to_dataframe(analyzed_articles):
+    df = pd.DataFrame(analyzed_articles)
+    df["Title"] = df.apply(
+        lambda row: f'<a href="{row["link"]}" target="_blank">{row["title"]}</a>',
+        axis=1,
+    )
+    df["Description"] = df["desc"]
+    df["Date"] = df["date"]
+
+    def sentiment_badge(sentiment):
+        colors = {
+            "negative": "red",
+            "neutral": "gray",
+            "positive": "green",
+        }
+        color = colors.get(sentiment, "grey")
+        return f'<span style="background-color: {color}; color: white; padding: 2px 6px; border-radius: 4px;">{sentiment}</span>'
+
+    df["Sentiment"] = df["sentiment"].apply(lambda x: sentiment_badge(x["label"]))
+    return df[["Sentiment", "Title", "Description", "Date"]]
+
 
-# Gradio Interface Setup
 with gr.Blocks() as iface:
-    gr.Markdown("# Stock Analysis Tool")
-    gr.Markdown("Enter a stock symbol (e.g., TSLA, AAPL, etc.)")
-    
+    gr.Markdown("# Trading Asset Sentiment Analysis")
+    gr.Markdown(
+        "Enter the name of a trading asset, and I'll fetch recent articles and analyze their sentiment!"
+    )
+
     with gr.Row():
-        stock_input = gr.Textbox(
-            label="Stock Symbol",
+        input_asset = gr.Textbox(
+            label="Asset Name",
             lines=1,
-            placeholder="Enter stock symbol..."
+            placeholder="Enter the name of the trading asset...",
         )
-        analyze_button = gr.Button("Analyze", variant="primary")
-    
+
     with gr.Row():
-        gr.Examples(
-            examples=[
-                "TSLA", "AAPL", "MSFT", "GOOGL", "AMZN"
-            ],
-            inputs=stock_input
-        )
-    
-    analysis_output = gr.Textbox(label="Analysis")
-    
-# Connect the button to the interface function
+        analyze_button = gr.Button("Analyze Sentiment", size="sm")
+
+    gr.Examples(
+        examples=[
+            "Bitcoin",
+            "Tesla",
+            "Apple",
+            "Amazon",
+        ],
+        inputs=input_asset,
+    )
+
+    with gr.Row():
+        with gr.Column():
+            with gr.Blocks():
+                gr.Markdown("## Articles and Sentiment Analysis")
+                articles_output = gr.Dataframe(
+                    headers=["Sentiment", "Title", "Description", "Date"],
+                    datatype=["markdown", "html", "markdown", "markdown"],
+                    wrap=False,
+                )
+
     analyze_button.click(
-        fn=interface,
-        inputs=[stock_input],
-        outputs=[analysis_output]
+        analyze_asset_sentiment,
+        inputs=[input_asset],
+        outputs=[articles_output],
     )
 
-# Launch the Gradio interface
-if __name__ == "__main__":
-    iface.launch()
+logging.info("Launching Gradio interface")
+iface.queue().launch()

requirements.txt

@@ -1,6 +1,6 @@
-gradio==3.33.0
-yfinance==0.2.16
-pandas==2.1.2
-numpy==1.25.1
-matplotlib==3.7.1
-ta-lib==0.4.21
+gradio==4.39.0
+transformers==4.43.2
+diffusers==0.29.2
+accelerate==0.33.0
+sentencepiece==0.2.0
+GoogleNews==1.6.14