Create app.py

app.py

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+import requests
+from bs4 import BeautifulSoup
+import pandas as pd
+from io import StringIO
+from datetime import datetime, timedelta
+from openai import OpenAI
+import gradio as gr
+
+OPENROUTER_API_KEY = "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"
+
+HEADERS = {
+    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/114.0.0.0 Safari/537.36',
+    'Accept-Language': 'en-US,en;q=0.9',
+    'Referer': 'https://www.google.com/'
+}
+
+def get_current_price(symbol: str):
+    url = f'https://www.marketwatch.com/investing/stock/{symbol}'
+    response = requests.get(url, headers=HEADERS)
+    if response.status_code == 200:
+        soup = BeautifulSoup(response.text, 'html.parser')
+        price_tag = soup.find('bg-quote', class_='value')
+        if price_tag:
+            return price_tag.get_text(strip=True)
+    return None
+
+def get_historical_data(symbol: str, days=30):
+    end_date = datetime.now()
+    start_date = end_date - timedelta(days=days)
+    start_date_str = start_date.strftime('%m/%d/%Y 00:00:00')
+    end_date_str = end_date.strftime('%m/%d/%Y 23:59:59')
+    csv_url = (
+        f'https://www.marketwatch.com/investing/stock/{symbol}/downloaddatapartial'
+        f'?csvdownload=true&downloadpartial=false'
+        f'&startdate={start_date_str}&enddate={end_date_str}'
+        f'&frequency=p1d&newdates=false'
+    )
+    response = requests.get(csv_url, headers=HEADERS)
+    if response.status_code == 200:
+        try:
+            df = pd.read_csv(StringIO(response.text))
+            return df
+        except Exception:
+            return None
+    return None
+
+def get_technical_analysis_docs():
+    return """
+Technical Analysis: Core Concepts & Formulas
+-------------------------------------------
+- Market Action Discounts Everything: All known information is reflected in price.
+- Prices Move in Trends: Uptrend, downtrend, or sideways movement.
+- History Repeats Itself: Psychological patterns repeat.
+
+Key Technical Indicators and Their Formulas:
+-------------------------------------------
+1. Simple Moving Average (SMA): SMA(time_period) = Sum(Price_t ... Price_{t-n}) / n
+2. Exponential Moving Average (EMA): EMA_t = (Price_t * α) + EMA_{t-1} * (1 - α), where α = 2/(n+1)
+3. Relative Strength Index (RSI): RSI = 100 - [100 / (1 + Avg Gain / Avg Loss)]
+4. MACD (Moving Average Convergence Divergence): MACD Line = 12-period EMA - 26-period EMA; Signal Line = 9-period EMA of MACD Line; Histogram = MACD Line - Signal Line
+5. Stochastic Oscillator (STOCH): Fast K = (Current Close - Lowest Low) / (Highest High - Lowest Low) * 100 over N periods; Slow K = 3-day SMA of Fast K; Slow D = 3-day SMA of Slow K
+6. Momentum (MOM): MOM = Current Close - Close_N_days_ago
+7. Rate of Change (ROC): ROC = [(Current Close - Prior Close) / Prior Close] * 100
+8. Volume Weighted Average Price (VWAP): VWAP = Sum(Price * Volume) / Sum(Volume) over intraday period
+9. Bollinger Bands: Middle Band = 20-day SMA; Upper Band = 20-day SMA + 2 * 20-day Standard Deviation; Lower Band = 20-day SMA - 2 * 20-day Standard Deviation
+10. Ichimoku Cloud: Tenkan-sen = (9-period high + low)/2; Kijun-sen = (26-period high + low)/2; Senkou Span A = (Tenkan-sen + Kijun-sen)/2 shifted forward by 26; Senkou Span B = (52-period high + low)/2 shifted forward by 26; Chikou Span = Current close shifted back by 26 days
+
+11. **Williams %R**:
+    %R = (Highest High - Close) / (Highest High - Lowest Low) * -100 over N periods
+
+12. **Commodity Channel Index (CCI)**:
+    CCI = (Typical Price - 20-day SMA of TP) / (0.015 * Mean Deviation)  
+    where Typical Price = (High + Low + Close) / 3
+
+13. **Average Directional Index (ADX)**:
+    ADX = Smoothed average of DX values, which measure directional strength
+
+14. **On-Balance Volume (OBV)**:
+    OBV = previous OBV + volume if close > previous close, else -volume
+
+15. **Moving Average Convergence Divergence (MACD)**:
+    MACD Line = 12-day EMA - 26-day EMA  
+    Signal Line = 9-day EMA of MACD Line  
+    MACD Histogram = MACD Line - Signal Line
+
+16. **Absolute Price Oscillator (APO)**:
+    APO = Fast EMA - Slow EMA
+
+17. **Balance of Power (BOP)**:
+    BOP = (Close - Open) / (High - Low)
+
+18. **Triple Exponential Moving Average (TEMA)**:
+    TEMA = (3 * EMA1) - (3 * EMA2) + EMA3  
+    where EMA1 = fast EMA, EMA2 = slower EMA, etc.
+
+19. **Double Exponential Moving Average (DEMA)**:
+    DEMA = 2*EMA1 - EMA2
+
+20. **Kaufman Adaptive Moving Average (KAMA)**:
+    KAMA = prior KAMA + SC * (price - prior KAMA)  
+    where SC = smoothing constant based on efficiency ratio
+
+21. **Chaikin Money Flow (CMF)**:
+    MF Multiplier = [(Close - Low) - (High - Close)] / (High - Low)  
+    MF Volume = MF Multiplier * Volume  
+    CMF = Sum(MF Volume) / Sum(Volume) over N days
+
+22. **Aroon Indicator**:
+    Aroon Up = ((N - Periods Since Highest Close) / N) * 100  
+    Aroon Down = ((N - Periods Since Lowest Close) / N) * 100
+
+23. **Parabolic SAR**:
+    SAR_t = SAR_{t-1} + AF * (EP - SAR_{t-1})
+
+24. **Standard Deviation (Volatility)**:
+    σ = sqrt[1/N * Σ(Close_i - μ)^2]
+
+25. **Candlestick Patterns**:
+   - Hammer, Shooting Star, Engulfing, Doji, Morning/Evening Star, etc.
+"""
+
+def analyze(symbol):
+    symbol = symbol.strip().lower()
+    current_price = get_current_price(symbol)
+    historical_df = get_historical_data(symbol, days=30)
+    if current_price is None or historical_df is None:
+        return "Could not retrieve data for this symbol. Try another."
+    historical_csv_snippet = historical_df.to_csv(index=False)
+    prompt = f"""
+You are a financial LLM trained in technical analysis. Your task is to predict the next day's closing price for the stock symbol '{symbol.upper()}'.
+
+The current stock price is approximately: ${current_price if current_price else 'N/A'}
+
+Below is the historical OHLCV data for the last 10 days (CSV format):
+{historical_csv_snippet if historical_csv_snippet else 'No historical data available.'}
+
+---
+
+**Technical Analysis Documentation:**
+{get_technical_analysis_docs()}
+
+**Instructions:**
+You must perform the entire analysis in one go — do not ask for any further data or clarification.
+Perform the full process:
+1. Review the historical OHLCV data provided.
+2. Perform a rigorous, step-by-step technical analysis using the full range of indicators.
+3. Identify trends, chart patterns, volume analysis, risk management.
+4. Predict the next day’s closing price.
+5. Justify your prediction with detailed reasoning.
+---
+**Step-by-Step Analysis Plan:**
+1. Data Review and Preparation  
+2. Chart and Trend Analysis  
+3. Apply Core Technical Indicators  
+4. Pattern Recognition  
+5. Advanced Analysis  
+6. Confirmation and Risk Management  
+7. Prediction and Justification  
+8. Review and Backtesting (Optional)
+---
+**Output Format:**
+- Predicted Price for Next Day:  
+- Detailed Reasoning:
+  - Trend analysis and key levels  
+  - Indicator values and interpretations  
+  - Patterns identified  
+  - Volume analysis  
+  - Confidence level and uncertainty factors  
+  - Additional insights or warnings
+"""
+    client = OpenAI(
+        base_url="https://openrouter.ai/api/v1",
+        api_key=OPENROUTER_API_KEY,
+    )
+    completion = client.chat.completions.create(
+        model="google/gemma-3n-e4b-it:free",
+        messages=[{"role": "user", "content": prompt}]
+    )
+    return completion.choices[0].message.content
+
+iface = gr.Interface(
+    fn=analyze,
+    inputs=gr.Textbox(label="Stock Symbol (e.g. AAPL, TSLA, QBTS)"),
+    outputs="text",
+    title="Stock Technical Analysis & Prediction",
+    description="Enter a stock symbol to get a technical analysis and next-day price prediction."
+)
+
+if __name__ == "__main__":
+    iface.launch()