Update app.py

app.py

@@ -4,8 +4,8 @@ import yfinance as yf
 from datetime import datetime, timedelta
 import requests
 from bs4 import BeautifulSoup
-from concurrent.futures import ThreadPoolExecutor, as_completed
 from pattern_finder import score_downward_trend, score_candle, calculate_risk_reward
+from concurrent.futures import ThreadPoolExecutor, as_completed
 import urllib3
 urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
 
@@ -29,7 +29,7 @@ def load_sp500_tickers():
 def load_data(ticker):
     """Load stock data using yfinance."""
     end_date = datetime.today()
-    start_date = end_date - timedelta(days=365)  # Get 1 year of data
+    start_date = end_date - timedelta(days=365)
     data = yf.download(ticker, start=start_date, end=end_date)
     return data
 
@@ -53,13 +53,6 @@ def average_downtrend(data, method, window=4):
     return avg_diff if avg_diff < 0 else 0.0
 
 
-def score_candle(candle, prev_candle, trend_strength):
-    """Score a single candle based on its characteristics and previous candle."""
-    # [Keep the existing scoring logic]
-    # ...
-    return score
-
-
 def score_today_candle(data, window=4):
     """Score today's candle based on the downtrend from the past 'window' days."""
     if len(data) < window + 1:
@@ -70,6 +63,7 @@ def score_today_candle(data, window=4):
     
     close_price = today_candle['Close']
     previous_data = data.iloc[-(window+1):-1]
+    
     down_High = average_downtrend(previous_data, method="High", window=window) + average_downtrend(previous_data, method="High", window=7) / 2
     down_Close = average_downtrend(previous_data, method="Close", window=window) + average_downtrend(previous_data, method="Close", window=7) / 2
     avg_downtrend = (down_High + down_Close) / 2
@@ -82,45 +76,50 @@ def score_today_candle(data, window=4):
     sma_20 = calculate_sma(data, window=20).iloc[-1]
     ema_10 = calculate_ema(data, window=10).iloc[-1]
     
-    if (close_price < ema_10) or (close_price < sma_20) or  (close_price < sma_50) or (close_price < sma_200):
+    if (close_price < ema_10) or (close_price < sma_20) or (close_price < sma_50) or (close_price < sma_200):
         return -1
-
+    
     return score_candle(today_candle, prev_candle, abs(avg_downtrend))
 
 
-def scan_ticker(ticker):
-    """Load data for a ticker and calculate its score."""
-    data = load_data(ticker)
-    if not data.empty:
-        score = score_today_candle(data)
-        if score > 0:
-            return ticker, score
-    return None
-
-
 def scan_sp500(top_n=25, progress=gr.Progress()):
     tickers = load_sp500_tickers()
+    scores = []
     tickers.append("QQQ")
 
-    scores = []
     with ThreadPoolExecutor(max_workers=10) as executor:
-        futures = {executor.submit(scan_ticker, ticker): ticker for ticker in tickers}
-        for i, future in enumerate(as_completed(futures), 1):
-            result = future.result()
-            if result:
-                scores.append(result)
-            progress(i / len(tickers), desc="Processing Tickers")
+        futures = {executor.submit(load_data, ticker): ticker for ticker in tickers}
+        total_tickers = len(futures)
+        
+        for i, future in enumerate(as_completed(futures)):
+            ticker = futures[future]
+            data = future.result()
+            
+            if not data.empty:
+                score = score_today_candle(data)
+                if score > 0:
+                    scores.append((ticker, score))
+            
+            # Update progress after each ticker is processed
+            progress(i / total_tickers, desc=f"Processing {ticker}")
     
     scores = sorted(scores, key=lambda x: x[1], reverse=True)
     return scores[:top_n]
 
 
+def next_business_day(date):
+    next_day = date + timedelta(days=1)
+    while next_day.weekday() >= 5:  # 5 = Saturday, 6 = Sunday
+        next_day += timedelta(days=1)
+    return next_day
+
+
 def gradio_scan_sp500(top_n, progress=gr.Progress()):
-    progress(0, desc="Downloading Data")
+    progress(0, desc="Loading S&P 500 Tickers")
     tickers = load_sp500_tickers()
     tickers.append("QQQ")
     
-    progress(0.3, desc="Running Scanner")
+    progress(0.2, desc="Running Stock Scanning")
     results = scan_sp500(top_n, progress)
     
     last_data = load_data(results[0][0])
@@ -129,11 +128,12 @@ def gradio_scan_sp500(top_n, progress=gr.Progress()):
     date_created = next_market_day.strftime("%Y-%m-%d")
     
     output = f"Scan Results for Market Open on: {date_created}\n\n"
-    output += "Top {} stocks based on pattern finder score:\n\n".format(top_n)
+    output += f"Top {top_n} stocks based on pattern finder score:\n\n"
     for ticker, score in results:
-        output += "{}: Total Score = {:.2f}\n".format(ticker, score)
+        output += f"{ticker}: Total Score = {score:.2f}\n"
     return output
 
+
 iface = gr.Interface(
     fn=gradio_scan_sp500,
     inputs=gr.Slider(minimum=1, maximum=100, step=1, label="Number of top stocks to display", value=25),