74 lines
3.7 KiB
Python
74 lines
3.7 KiB
Python
import datetime
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from screener.data_fetcher import validate_date_range, fetch_financial_data, get_stocks_in_time_range
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from screener.c_canslim import check_quarterly_earnings, check_return_on_equity, check_sales_growth
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from screener.a_canslim import check_annual_eps_growth
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from screener.l_canslim import check_industry_leadership # ✅ NEW: Import L Score function
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from screener.i_canslim import check_institutional_sponsorship # ✅ NEW: Import I Score function
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from screener.csv_appender import append_scores_to_csv
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from screener.screeners import SCREENERS # Import categories
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from screener.user_input import get_user_screener_selection # Import function
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def main():
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# 1️⃣ Ask user for start and end date
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user_start_date = input("Enter start date (YYYY-MM-DD): ")
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user_end_date = input("Enter end date (YYYY-MM-DD): ")
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# 2️⃣ Validate and adjust date range if needed
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start_date, end_date = validate_date_range(user_start_date, user_end_date, required_quarters=4)
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# 3️⃣ Get selected screeners & customization preferences
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selected_screeners = get_user_screener_selection()
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print(f"\n✅ Selected Screeners: {selected_screeners}\n") # ✅ DEBUG LOG
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# 4️⃣ Get all stock symbols dynamically
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symbol_list = get_stocks_in_time_range(start_date, end_date)
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if not symbol_list:
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print("No stocks found within the given date range.")
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return
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print(f"Processing {len(symbol_list)} stocks within the given date range...\n")
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# 5️⃣ Process each stock symbol
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for symbol in symbol_list:
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data = fetch_financial_data(symbol, start_date, end_date)
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if not data:
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print(f"⚠️ Warning: No data returned for {symbol}. Assigning default score.\n")
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scores = {screener: 0.25 for category in selected_screeners for screener in selected_screeners[category]}
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else:
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scores = {}
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# 6️⃣ Compute scores dynamically based on user selection
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for category, screeners in selected_screeners.items():
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for screener, threshold in screeners.items():
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if screener == "EPS_Score":
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scores[screener] = check_quarterly_earnings(data.get("quarterly_eps", []))
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elif screener == "Annual_EPS_Score":
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scores[screener] = check_annual_eps_growth(data.get("annual_eps", []))
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elif screener == "Sales_Score":
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scores[screener] = check_sales_growth(data.get("sales_growth", []))
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elif screener == "ROE_Score":
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scores[screener] = check_return_on_equity(data.get("roe", []))
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elif screener == "L_Score":
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scores[screener] = check_industry_leadership(symbol) # ✅ NEW: Industry Leadership Calculation
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print(f"🟢 {symbol} - L_Score: {scores[screener]}") # ✅ DEBUG LOG
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elif screener == "I_Score":
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scores[screener] = check_institutional_sponsorship(symbol) # ✅ NEW: Institutional Sponsorship Check
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print(f"🏢 {symbol} - I_Score: {scores[screener]}") # ✅ DEBUG LOG
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# Apply user-defined threshold if applicable
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if isinstance(threshold, (int, float)):
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scores[screener] = scores[screener] >= threshold
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# 7️⃣ Calculate Total Score
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scores["Total_Score"] = sum(scores.values()) # ✅ NEW: Total Score Calculation
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# 8️⃣ Append results to CSV
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append_scores_to_csv(symbol, scores)
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print("✅ Scores saved in data/metrics/stock_scores.csv\n")
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if __name__ == "__main__":
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main()
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