Annual EPS

src/main.py

@@ -1,6 +1,7 @@
 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, check_sales_growth, check_return_on_equity
+from screener.c_canslim import check_quarterly_earnings
+from screener.a_canslim import check_annual_eps_growth  # New module
 from screener.csv_appender import append_scores_to_csv
 
 def main():
@@ -27,21 +28,18 @@ def main():
         if not data:
             print(f"Warning: No data returned for {symbol}. Assigning default score.")
             scores = {
-                "EPS_Score": 0.25,  # EPS Growth
+                "EPS_Score": 0.25,  # Quarterly EPS Growth
-                "Sales_Score": 0.25,  # Sales Growth
+                "Annual_EPS_Score": 0.25  # Annual EPS Growth
-                "ROE_Score": 0.25  # Return on Equity
             }
         else:
             # Extract relevant fields
-            quarterly_eps = data.get("eps", [])
+            quarterly_eps = data.get("quarterly_eps", [])
-            sales_growth = data.get("sales_growth", [])
+            annual_eps = data.get("annual_eps", [])
-            roe = 18  # Placeholder, modify if needed
 
             # 5️⃣ Compute CANSLIM Scores
             scores = {
                 "EPS_Score": check_quarterly_earnings(quarterly_eps),
-                "Sales_Score": check_sales_growth(sales_growth),
+                "Annual_EPS_Score": check_annual_eps_growth(annual_eps)
-                "ROE_Score": check_return_on_equity(roe)
             }
 
         # 6️⃣ Append results to a **generic** CSV file in `data/metrics/`

src/screener/a_canslim.py

@@ -0,0 +1,29 @@
+def check_annual_eps_growth(annual_eps):
+    """
+    Checks for 25%-50%+ annual EPS growth over the last three years.
+
+    Args:
+        annual_eps (list): List of annual EPS values (must contain at least 3 years of data).
+
+    Returns:
+        float: Score (1 pass, 0 fail, 0.25 insufficient data).
+    """
+    if not annual_eps or len(annual_eps) < 3:
+        return 0.25  # Not enough data for 3-year growth calculation
+
+    # Calculate year-over-year EPS growth rates
+    growth_rates = []
+    for i in range(1, len(annual_eps)):  # Compare consecutive years
+        prev_eps = annual_eps[i - 1]
+        current_eps = annual_eps[i]
+
+        if prev_eps <= 0:  # Avoid division by zero or negative EPS
+            return 0.25  
+
+        growth = ((current_eps - prev_eps) / abs(prev_eps)) * 100
+        growth_rates.append(growth)
+
+    # Check if all growth rates are 25%+
+    if all(rate >= 25 for rate in growth_rates):
+        return 1  # Pass
+    return 0  # Fail

src/screener/data_fetcher.py

@@ -31,8 +31,9 @@ def validate_date_range(start_date, end_date, required_quarters=4):
 
 def fetch_financial_data(symbol, start_date, end_date):
     """
-    Fetch financial data (EPS, Sales Growth) from stock_financials table 
+    Fetch financial data for a given stock symbol, including:
-    for a given stock symbol within a specific date range.
+    - Quarterly EPS for EPS Score
+    - Annual EPS for Annual EPS Score
 
     Args:
         symbol (str): Stock ticker symbol.
@@ -40,21 +41,20 @@ def fetch_financial_data(symbol, start_date, end_date):
         end_date (str or datetime): End date for data retrieval.
 
     Returns:
-        dict: A dictionary containing calculated EPS and sales growth.
+        dict: Contains EPS, sales growth, and annual EPS.
     """
     client = create_client()
 
-    # Query stock_financials for revenue, net income, and EPS
     query = f"""
         SELECT 
             filing_date, 
+            diluted_eps, 
             revenue, 
-            net_income, 
+            timeframe
-            diluted_eps  -- Using diluted EPS for accuracy
         FROM stock_db.stock_financials 
         WHERE ticker = '{symbol}' 
         AND filing_date BETWEEN toDate('{start_date}') AND toDate('{end_date}')
-        AND timeframe = 'quarterly'  -- Ensure only quarterly reports are used
+        AND (timeframe = 'quarterly' OR timeframe = 'annual')  -- Fetch both annual and quarterly data
         ORDER BY filing_date ASC
     """
 
@@ -63,36 +63,36 @@ def fetch_financial_data(symbol, start_date, end_date):
     if not result.result_rows:
         return {}
 
-    # Extracting data
+    quarterly_eps = []
-    dates = []
+    annual_eps = []
     revenues = []
-    net_incomes = []
-    eps_values = []
     sales_growth = []
 
     for row in result.result_rows:
-        dates.append(row[0])
+        filing_date, eps, revenue, timeframe = row
-        revenues.append(row[1])
+
-        net_incomes.append(row[2])
+        if timeframe == "quarterly":
-        eps_values.append(row[3])  # Directly using diluted EPS
+            quarterly_eps.append(eps)
+            revenues.append(revenue)
+        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
         prev_revenue = revenues[i - 1]
         current_revenue = revenues[i]
-        if prev_revenue > 0:  # Avoid division by zero
+        if prev_revenue > 0:
             growth = ((current_revenue - prev_revenue) / prev_revenue) * 100
         else:
             growth = None  # Not enough data
         sales_growth.append(growth)
 
-    # Pad sales_growth list to match length of EPS (since first quarter lacks a previous value)
+    sales_growth.insert(0, None)  # First quarter lacks comparison
-    sales_growth.insert(0, None)  # First quarter doesn't have a previous comparison
 
     return {
         "symbol": symbol,
-        "dates": dates,
+        "quarterly_eps": quarterly_eps,  # Used for EPS_Score
-        "eps": eps_values,
+        "annual_eps": annual_eps,  # Used for Annual_EPS_Score
         "sales_growth": sales_growth
     }