Update app.py

app.py

@@ -2,9 +2,10 @@ import yfinance as yf
 import pandas as pd
 import numpy as np
 import mplfinance as mpf
-import talib # pip install ta-lib
+import talib
 import gradio as gr
 from datetime import date
+import os
 
 # TALib formasyon fonksiyonlarını bir dictionary'de tutalım
 TALIB_PATTERNS = {
@@ -17,164 +18,128 @@ TALIB_PATTERNS = {
     "CDLMORNINGSTAR": talib.CDLMORNINGSTAR,
     "CDLEVENINGSTAR": talib.CDLEVENINGSTAR,
     "CDL3WHITESOLDIERS": talib.CDL3WHITESOLDIERS,
-    "CDL3BLACKCROWS": talib.CDL3BLACKCROWS,
-    # İstediğiniz diğer formasyonları ekleyebilirsiniz
+    "CDL3BLACKCROwS": talib.CDL3BLACKCROWS,
 }
 
 def find_candlestick_patterns(df, pattern_name):
     """
-    Verilen DataFrame'de TALib kullanarak mum çubuğu formasyonlarını bulur ve
-    mplfinance için addplot listesi döndürür.
-    
-    Args:
-        df (pd.DataFrame): Hisse senedi verisi (Open, High, Low, Close sütunları ile).
-        pattern_name (str): TALib'deki formasyon fonksiyonunun adı (örn. "CDLSHOOTINGSTAR").
-        
-    Returns:
-        list: mpf.make_addplot objelerinin listesi veya boş liste.
+    Finds candlestick patterns using TALib and returns a list of addplots for mplfinance.
     """
     if pattern_name not in TALIB_PATTERNS:
-        return [] # Boş liste döndür
+        return []
 
     pattern_func = TALIB_PATTERNS[pattern_name]
     
-    # TALib'e özel olarak sütun isimleri küçük harf olmalı
-    df_talib = df.rename(columns={
-        'Open': 'open', 
-        'High': 'high', 
-        'Low': 'low', 
-        'Close': 'close', 
-        'Volume': 'volume'
-    })
-
-    pattern_result = pattern_func(df_talib['open'], df_talib['high'], 
-                                   df_talib['low'], df_talib['close'])
+    # --- FIX IS HERE ---
+    # Convert pandas Series to NumPy arrays using .values for TALib compatibility
+    pattern_result = pattern_func(
+        df['Open'].values,
+        df['High'].values,
+        df['Low'].values,
+        df['Close'].values
+    )
+    
+    # Convert result back to a pandas Series with the correct index
+    pattern_result = pd.Series(pattern_result, index=df.index)
+    # --- END OF FIX ---
     
     apds = []
     
-    # Boğa (bullish) formasyonları için
+    # Bullish patterns
     bullish_signals = pattern_result[pattern_result == 100]
     if not bullish_signals.empty:
         bullish_plot_data = pd.Series(np.nan, index=df.index)
         for idx in bullish_signals.index:
-            bullish_plot_data[idx] = df.loc[idx, 'Low'] * 0.98 # Mumun biraz altına
+            bullish_plot_data.loc[idx] = df.loc[idx, 'Low'] * 0.98
         apds.append(
-            mpf.make_addplot(bullish_plot_data, 
-                             type='scatter', 
-                             marker='^', # Yukarı üçgen
-                             markersize=100, 
-                             color='green',
-                             panel=0,
-                             alpha=0.7)
+            mpf.make_addplot(bullish_plot_data, type='scatter', marker='^', markersize=100, color='green', panel=0, alpha=0.7)
         )
         
-    # Ayı (bearish) formasyonları için
+    # Bearish patterns
     bearish_signals = pattern_result[pattern_result == -100]
     if not bearish_signals.empty:
         bearish_plot_data = pd.Series(np.nan, index=df.index)
         for idx in bearish_signals.index:
-            bearish_plot_data[idx] = df.loc[idx, 'High'] * 1.02 # Mumun biraz üstüne
+            bearish_plot_data.loc[idx] = df.loc[idx, 'High'] * 1.02
         apds.append(
-            mpf.make_addplot(bearish_plot_data, 
-                             type='scatter', 
-                             marker='v', # Aşağı üçgen
-                             markersize=100, 
-                             color='red',
-                             panel=0,
-                             alpha=0.7)
+            mpf.make_addplot(bearish_plot_data, type='scatter', marker='v', markersize=100, color='red', panel=0, alpha=0.7)
         )
         
     return apds
 
 def plot_stock_with_patterns(ticker_symbol, start_date, end_date, selected_patterns):
     """
-    Belirtilen hisse senedi için grafik çizer ve seçilen formasyonları işaretler.
-    
-    Args:
-        ticker_symbol (str): Hisse senedi kodu (örn. "MSFT").
-        start_date (str): Başlangıç tarihi (YYYY-MM-DD).
-        end_date (str): Bitiş tarihi (YYYY-MM-DD).
-        selected_patterns (list): Kullanıcının seçtiği formasyonların listesi.
-        
-    Returns:
-        tuple: (str: Oluşturulan grafiğin dosya yolu, str: Durum mesajı)
+    Plots the stock chart with selected patterns marked.
     """
-    
-    # Tarih formatlarını kontrol edelim
+    if not ticker_symbol:
+        return None, "Error: Ticker symbol cannot be empty."
+
     try:
         start = pd.to_datetime(start_date)
         end = pd.to_datetime(end_date)
         if start >= end:
-            return None, "Start date must be before end date."
-    except ValueError:
-        return None, "Invalid date format. Please use YYYY-MM-DD."
+            return None, "Error: Start date must be before end date."
+    except (ValueError, TypeError):
+        return None, "Error: Invalid date format. Please use YYYY-MM-DD."
 
     try:
-        # yfinance'tan veri çek
-        df = yf.download(ticker_symbol, start=start_date, end=end_date)
+        # yfinance data download (auto_adjust=True is the default and recommended)
+        df = yf.download(ticker_symbol, start=start_date, end=end_date, progress=False)
         if df.empty:
-            return None, f"Could not download data for {ticker_symbol}. Please check the ticker symbol and date range."
+            return None, f"Error: No data found for '{ticker_symbol}'. Check the symbol and date range."
     except Exception as e:
         return None, f"An error occurred while downloading data: {e}"
 
-    # mplfinance'ın beklediği sütun isimlerini kontrol edelim
-    # yfinance zaten 'Open', 'High', 'Low', 'Close', 'Volume' döndürüyor.
-    # Eğer küçük harf olsaydı df.columns = [col.capitalize() for col in df.columns] kullanırdık.
-    
-    # Addplot listesini oluştur
     all_apds = []
-    if selected_patterns: # Eğer hiç formasyon seçilmemişse boş kalır
+    if selected_patterns:
         for pattern_name in selected_patterns:
             pattern_apds = find_candlestick_patterns(df, pattern_name)
             all_apds.extend(pattern_apds)
 
-    # Grafiği oluştur ve bir dosyaya kaydet
-    # Geçici bir dosya adı kullanalım
-    fig_path = f"/tmp/stock_chart_{ticker_symbol}_{date.today().strftime('%Y%m%d%H%M%S')}.png"
+    # Use a temporary directory for saving the plot
+    if not os.path.exists('/tmp'):
+        os.makedirs('/tmp')
+    fig_path = f"/tmp/stock_chart_{ticker_symbol}_{pd.Timestamp.now().strftime('%Y%m%d%H%M%S')}.png"
     
-    # mplfinance style ayarları
-    s = mpf.make_mpf_style(base_mpf_style='yahoo', mavcolors=['#1f77b4', '#ff7f0e', '#2ca02c']) # Moving Average renkleri
+    s = mpf.make_mpf_style(base_mpf_style='yahoo', mavcolors=['#1f77b4', '#ff7f0e', '#2ca02c'])
     
     try:
-        fig, axlist = mpf.plot(
+        fig, _ = mpf.plot(
             df, 
             type='candle', 
             volume=True, 
-            addplot=all_apds if all_apds else None, # Eğer addplot boşsa None geç
+            addplot=all_apds if all_apds else None,
             style=s, 
-            title=f"{ticker_symbol} Candlestick Chart with Selected Patterns",
-            ylabel='Price', 
+            title=f"\n{ticker_symbol} Candlestick Chart",
+            ylabel='Price ($)', 
             ylabel_lower='Volume',
             returnfig=True,
-            figscale=1.5 # Grafik boyutunu ayarla
+            figscale=1.5
         )
         fig.savefig(fig_path)
         return fig_path, "Chart generated successfully!"
     except Exception as e:
         return None, f"An error occurred while plotting the chart: {e}"
 
-# Gradio arayüzü
+# Gradio Interface
 iface = gr.Interface(
     fn=plot_stock_with_patterns,
     inputs=[
-        gr.Textbox(label="Ticker Symbol (e.g., MSFT, AAPL, ^GSPC for S&P 500)", value="MSFT"),
-        gr.Textbox(label="Start Date (YYYY-MM-DD)", value="2023-01-01"),
+        gr.Textbox(label="Ticker Symbol (e.g., MSFT, AAPL, ^GSPC)", value="MSFT"),
+        gr.Textbox(label="Start Date (YYYY-MM-DD)", value="2024-01-01"),
         gr.Textbox(label="End Date (YYYY-MM-DD)", value=date.today().strftime("%Y-%m-%d")),
         gr.CheckboxGroup(
             label="Select Candlestick Patterns",
             choices=list(TALIB_PATTERNS.keys()),
-            value=["CDLSHOOTINGSTAR", "CDLHAMMER"] # Varsayılan olarak seçili gelenler
+            value=["CDLSHOOTINGSTAR", "CDLHAMMER"]
         )
     ],
     outputs=[
         gr.Image(type="filepath", label="Stock Chart"),
         gr.Textbox(label="Status")
     ],
-    title="Interactive Stock Candlestick Chart with Technical Patterns",
-    description="Enter a stock ticker, date range, and select candlestick patterns to visualize them on the chart. "
-                "Patterns are marked with green (bullish) or red (bearish) triangles on the chart."
-                "\n\n**Note:** Some TALib patterns are for specific market conditions or require more data points to detect. "
-                "If a pattern is not found, no marker will appear for that pattern."
+    title="Stock Chart Pattern Finder",
+    description="Enter a stock ticker and select candlestick patterns to visualize them on the chart. Bullish patterns are marked with a green '▲' and bearish patterns with a red '▼'."
 )
 
 if __name__ == "__main__":