Added screener type choice

src/main.py

@@ -1,8 +1,10 @@
 import datetime
 from screener.data_fetcher import validate_date_range, fetch_financial_data, get_stocks_in_time_range
-from screener.c_canslim import check_quarterly_earnings
+from screener.c_canslim import check_quarterly_earnings, check_return_on_equity, check_sales_growth
-from screener.a_canslim import check_annual_eps_growth  # New module
+from screener.a_canslim import check_annual_eps_growth
 from screener.csv_appender import append_scores_to_csv
+from screener.screeners import SCREENERS  # Import categories
+from screener.user_input import get_user_screener_selection  # Import function
 
 def main():
     # 1️⃣ Ask user for start and end date
@@ -12,7 +14,10 @@ def main():
     # 2️⃣ Validate and adjust date range if needed
     start_date, end_date = validate_date_range(user_start_date, user_end_date, required_quarters=4)
 
-    # 3️⃣ Get all stock symbols in the given date range dynamically
+    # 3️⃣ Get selected screeners & customization preferences
+    selected_screeners = get_user_screener_selection()  # ✅ Now imported from `user_input.py`
+
+    # 4️⃣ Get all stock symbols dynamically
     symbol_list = get_stocks_in_time_range(start_date, end_date)
     
     if not symbol_list:
@@ -21,28 +26,33 @@ def main():
 
     print(f"Processing {len(symbol_list)} stocks within the given date range...")
 
-    # 4️⃣ Process each stock symbol
+    # 5️⃣ Process each stock symbol
     for symbol in symbol_list:
         data = fetch_financial_data(symbol, start_date, end_date)
 
         if not data:
             print(f"Warning: No data returned for {symbol}. Assigning default score.")
-            scores = {
+            scores = {screener: 0.25 for category in selected_screeners for screener in selected_screeners[category]}
-                "EPS_Score": 0.25,  # Quarterly EPS Growth
-                "Annual_EPS_Score": 0.25  # Annual EPS Growth
-            }
         else:
-            # Extract relevant fields
+            scores = {}
-            quarterly_eps = data.get("quarterly_eps", [])
-            annual_eps = data.get("annual_eps", [])
 
-            # 5️⃣ Compute CANSLIM Scores
+            # 6️⃣ Compute scores dynamically based on user selection
-            scores = {
+            for category, screeners in selected_screeners.items():
-                "EPS_Score": check_quarterly_earnings(quarterly_eps),
+                for screener, threshold in screeners.items():
-                "Annual_EPS_Score": check_annual_eps_growth(annual_eps)
+                    if screener == "EPS_Score":
-            }
+                        scores[screener] = check_quarterly_earnings(data.get("quarterly_eps", []))
+                    elif screener == "Annual_EPS_Score":
+                        scores[screener] = check_annual_eps_growth(data.get("annual_eps", []))
+                    elif screener == "Sales_Score":
+                        scores[screener] = check_sales_growth(data.get("sales_growth", []))
+                    elif screener == "ROE_Score":
+                        scores[screener] = check_return_on_equity(data.get("roe", []))
+                    
+                    # Apply user-defined threshold if applicable
+                    if isinstance(threshold, (int, float)):
+                        scores[screener] = scores[screener] >= threshold
 
-        # 6️⃣ Append results to a **generic** CSV file in `data/metrics/`
+        # 7️⃣ Append results to CSV
         append_scores_to_csv(symbol, scores)
 
     print("Scores saved in data/metrics/stock_scores.csv")

src/screener/c_canslim.py

@@ -60,16 +60,23 @@ def check_sales_growth(quarterly_sales):
     return 1 if growth >= 20 else 0  # Pass if growth is 20%+
 
 
-def check_return_on_equity(roe):
+def check_return_on_equity(roe_values):
     """
-    Checks for >=17% ROE.
+    Checks if the most recent Return on Equity (ROE) is at least 17%.
-    
+
+    Args:
+        roe_values (list): List of historical ROE values.
+
     Returns:
-        float: Score (1 pass, 0 fail, 0.25 insufficient data).
+        float: 1 (Pass), 0 (Fail), 0.25 (Insufficient data).
     """
-    if roe is None:
+    if not roe_values or len(roe_values) == 0:
-        return 0.25
+        return 0.25  # Not enough data
-    
+
-    if roe >= 17:
+    most_recent_roe = roe_values[-1]  # Get the most recent ROE value
-        return 1
+
-    return 0
+    if most_recent_roe is None:
+        return 0.25  # Missing data
+
+    return 1 if most_recent_roe >= 17 else 0
+

src/screener/csv_appender.py

@@ -6,37 +6,52 @@ BASE_DIR = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))  #
 METRICS_DIR = os.path.join(BASE_DIR, "data", "metrics")
 CSV_FILE = os.path.join(METRICS_DIR, "stock_scores.csv")
 
+# Track all unique screeners used in any stock
+ALL_HEADERS = set()
+
 def append_scores_to_csv(symbol, scores):
     """
-    Append stock analysis scores to a generic CSV file in the `data/metrics` directory.
+    Append stock analysis scores to a generic CSV file in `data/metrics` directory.
-
+    
+    Ensures all rows have the same headers, adding new screeners dynamically.
+    
     Args:
         symbol (str): Stock ticker symbol.
-        scores (dict): Dictionary of metric scores (e.g., EPS_Score, Sales_Score, Annual_EPS_Score, etc.).
+        scores (dict): Dictionary of metric scores (e.g., EPS_Score, Sales_Score, etc.).
     """
     # Ensure the directory exists
     os.makedirs(METRICS_DIR, exist_ok=True)
 
-    # Compute the total score (sum of all numeric values in scores)
+    # Compute Total Score
     total_score = sum(scores.values())
-
-    # Include `Total_Score` in the scores dictionary
     scores["Total_Score"] = total_score
 
-    # Define the header dynamically based on the provided scores
+    # Update tracked headers
-    headers = ["Symbol"] + list(scores.keys())
+    ALL_HEADERS.update(scores.keys())
+
+    # Preferred order: "Symbol" first, "Total_Score" last
+    preferred_order = ["Symbol"]
+    remaining_headers = sorted([h for h in ALL_HEADERS if h not in preferred_order + ["Total_Score"]])
+    sorted_headers = preferred_order + remaining_headers + ["Total_Score"]
 
     # Check if file exists
     file_exists = os.path.isfile(CSV_FILE)
 
     with open(CSV_FILE, 'a', newline='') as csvfile:
-        writer = csv.DictWriter(csvfile, fieldnames=headers)
+        writer = csv.DictWriter(csvfile, fieldnames=sorted_headers)
 
-        # Write the header if the file is new
+        # If it's a new file or headers changed, write a new header row
-        if not file_exists:
+        if not file_exists or set(sorted_headers) != set(writer.fieldnames):
+            csvfile.seek(0)  # Move to start of the file
+            csvfile.truncate()  # Clear previous file to rewrite headers
             writer.writeheader()
 
-        # Write stock data
+        # Fill missing values with 0.25 (default)
         row = {"Symbol": symbol}
-        row.update(scores)
+        for header in sorted_headers:
+            if header == "Symbol":
+                row[header] = symbol
+            else:
+                row[header] = scores.get(header, 0.25)  # Use default if missing
+        
         writer.writerow(row)

src/screener/data_fetcher.py

@@ -34,6 +34,8 @@ def fetch_financial_data(symbol, start_date, end_date):
     Fetch financial data for a given stock symbol, including:
     - Quarterly EPS for EPS Score
     - Annual EPS for Annual EPS Score
+    - Sales Growth
+    - Return on Equity (ROE)
 
     Args:
         symbol (str): Stock ticker symbol.
@@ -41,7 +43,7 @@ def fetch_financial_data(symbol, start_date, end_date):
         end_date (str or datetime): End date for data retrieval.
 
     Returns:
-        dict: Contains EPS, sales growth, and annual EPS.
+        dict: Contains EPS, sales growth, annual EPS, and ROE.
     """
     client = create_client()
 
@@ -50,6 +52,8 @@ def fetch_financial_data(symbol, start_date, end_date):
             filing_date, 
             diluted_eps, 
             revenue, 
+            net_income, 
+            equity, 
             timeframe
         FROM stock_db.stock_financials 
         WHERE ticker = '{symbol}' 
@@ -67,18 +71,22 @@ def fetch_financial_data(symbol, start_date, end_date):
     annual_eps = []
     revenues = []
     sales_growth = []
+    net_incomes = []
+    equities = []
 
     for row in result.result_rows:
-        filing_date, eps, revenue, timeframe = row
+        filing_date, eps, revenue, net_income, equity, timeframe = row
 
         if timeframe == "quarterly":
             quarterly_eps.append(eps)
             revenues.append(revenue)
+            net_incomes.append(net_income)
+            equities.append(equity)
         elif timeframe == "annual":
             annual_eps.append(eps)
 
     # Calculate Sales Growth (Quarter-over-Quarter)
-    for i in range(1, len(revenues)):  # Start from index 1 since we compare with previous
+    for i in range(1, len(revenues)):
         prev_revenue = revenues[i - 1]
         current_revenue = revenues[i]
         if prev_revenue > 0:
@@ -89,11 +97,21 @@ def fetch_financial_data(symbol, start_date, end_date):
 
     sales_growth.insert(0, None)  # First quarter lacks comparison
 
+    # Calculate ROE
+    roe_values = []
+    for i in range(len(net_incomes)):
+        if equities[i] > 0:
+            roe = (net_incomes[i] / equities[i]) * 100
+        else:
+            roe = None
+        roe_values.append(roe)
+
     return {
         "symbol": symbol,
         "quarterly_eps": quarterly_eps,  # Used for EPS_Score
         "annual_eps": annual_eps,  # Used for Annual_EPS_Score
-        "sales_growth": sales_growth
+        "sales_growth": sales_growth,
+        "roe": roe_values  # Return on Equity
     }
 
 

src/screener/screeners.py

@@ -0,0 +1,17 @@
+# Define categories and associated metrics
+SCREENERS = {
+    "Fundamentals": {
+        "EPS_Score": "Checks quarterly EPS growth",
+        "Annual_EPS_Score": "Checks 3-year annual EPS growth",
+        "Sales_Score": "Checks quarterly sales growth",
+        "ROE_Score": "Checks return on equity"
+    },
+    "Volume-Based": {
+        "Volume_Oscillator_Score": "Checks for unusual volume surges",
+        "Relative_Volume_Score": "Compares current volume to past volume"
+    },
+    "Technical": {
+        "SMA_Cross_Score": "Checks if short-term SMA crosses above long-term SMA",
+        "RSI_Score": "Evaluates RSI to identify overbought/oversold conditions"
+    }
+}

src/screener/user_input.py

@@ -0,0 +1,34 @@
+from screener.screeners import SCREENERS  # Import SCREENERS dictionary
+
+def get_user_screener_selection():
+    """
+    Ask the user which screeners they want to run and whether to use defaults.
+    Returns a dictionary of selected screeners with default/customization choices.
+    """
+    selected_screeners = {}
+
+    print("\nAvailable Screener Categories:")
+    for category in SCREENERS:
+        print(f"- {category}")
+
+    selected_categories = input("\nEnter the categories you want to screen (comma-separated), or type 'all' for all: ").strip().lower()
+    
+    if selected_categories == "all":
+        selected_categories = SCREENERS.keys()
+    else:
+        selected_categories = [c.strip().title() for c in selected_categories.split(",") if c.strip().title() in SCREENERS]
+
+    for category in selected_categories:
+        print(f"\nCategory: {category}")
+        use_defaults = input(f"Use default settings for {category}? (y/n): ").strip().lower()
+        
+        selected_screeners[category] = {}
+        
+        for screener, description in SCREENERS[category].items():
+            if use_defaults == "y":
+                selected_screeners[category][screener] = "default"
+            else:
+                custom_value = input(f"{screener} ({description}) - Enter custom threshold or press Enter to use default: ").strip()
+                selected_screeners[category][screener] = float(custom_value) if custom_value else "default"
+
+    return selected_screeners