backend/app/cron_hedge_funds.py

import sqlite3
import os
import orjson
import time
from collections import Counter
from tqdm import tqdm


# Load stock screener data
with open(f"json/stock-screener/data.json", 'rb') as file:
    stock_screener_data = orjson.loads(file.read())
stock_screener_data_dict = {item['symbol']: item for item in stock_screener_data}

keys_to_keep = [
    "type", "securityName", "symbol", "weight", 
    "changeInSharesNumberPercentage", "sharesNumber", 
    "marketValue", "avgPricePaid", "putCallShare"
]

def format_company_name(company_name):
    remove_strings = [', LLC','LLC', ',', 'LP', 'LTD', 'LTD.', 'INC.', 'INC', '.', '/DE/','/MD/','PLC']
    preserve_words = ['FMR','MCF']

    remove_strings_set = set(remove_strings)
    preserve_words_set = set(preserve_words)

    words = company_name.split()

    formatted_words = []
    for word in words:
        if word in preserve_words_set:
            formatted_words.append(word)
        else:
            new_word = word
            for string in remove_strings_set:
                new_word = new_word.replace(string, '')
            formatted_words.append(new_word.title())
    
    return ' '.join(formatted_words)

def remove_stock_duplicates(stocks):
    """
    Remove duplicate stocks keeping the highest weight entry for each symbol.
    
    Args:
        stocks (list): List of dictionaries containing stock information
        
    Returns:
        list: List with duplicates removed
    """
    symbol_dict = {}
    
    for stock in stocks:
        symbol = stock['symbol']
        weight = stock['weight']
        
        if symbol not in symbol_dict or weight > symbol_dict[symbol]['weight']:
            symbol_dict[symbol] = stock
    
    return list(symbol_dict.values())

def all_hedge_funds(con):
    
    # Connect to the SQLite database
    cursor = con.cursor()

    cursor.execute("SELECT cik, name, numberOfStocks, marketValue, winRate, turnover, performancePercentage3year FROM institutes")
    all_ciks = cursor.fetchall()

    res_list = [{
        'cik': row[0],
        'name': format_company_name(row[1]).title(),
        'numberOfStocks': row[2],
        'marketValue': row[3],
        'winRate': row[4],
        'turnover': row[5],
        'performancePercentage3Year': row[6]
    } for row in all_ciks if row[2] >= 3 and row[4] >= 10 and row[6] >= 10 and abs(row[6]) < 500]

    sorted_res_list = sorted(res_list, key=lambda x: x['marketValue'], reverse=True)

    with open(f"json/hedge-funds/all-hedge-funds.json", 'w') as file:
        file.write(orjson.dumps(sorted_res_list).decode("utf-8"))



def get_data(cik, stock_sectors):
    cursor.execute("SELECT cik, name, numberOfStocks, performancePercentage3year, performancePercentage5year, performanceSinceInceptionPercentage, averageHoldingPeriod, turnover, marketValue, winRate, holdings, summary FROM institutes WHERE cik = ?", (cik,))
    cik_data = cursor.fetchall()
    res = [{
        'cik': row[0],
        'name': row[1],
        'numberOfStocks': row[2],
        'performancePercentage3Year': row[3],
        'averageHoldingPeriod': row[6],
        'marketValue': row[8],
        'winRate': row[9],
        'holdings': orjson.loads(row[10]),
    } for row in cik_data]

    if not res:
        return None  # Exit if no data is found

    res = res[0] #latest data

    filtered_holdings = [
        {key: holding[key] for key in keys_to_keep}
        for holding in res['holdings']
    ]

    
    filtered_holdings = [
        {
            **{k: v for k, v in item.items() if k not in ['putCallShare', 'securityName']}, 
            'name': item['securityName'].title()
        }
        for item in filtered_holdings 
        if (
            item['putCallShare'] == 'Share' and 
            item['avgPricePaid'] > 0 and 
            item['marketValue'] > 0 and 
            item['sharesNumber'] > 0 and
            item['weight'] > 0
        )
    ]

    filtered_holdings = remove_stock_duplicates(filtered_holdings)

    res['holdings'] = filtered_holdings
    for rank, item in enumerate(res['holdings'], 1):
        item['rank'] = rank

    sector_list = []
    industry_list = []

    for item in res['holdings']:
        symbol = item['symbol']
        ticker_data = stock_screener_data_dict.get(symbol, {})
        
        # Extract specified columns data for each ticker
        sector = ticker_data.get('sector',None)
        industry = ticker_data.get('industry',None)

        # Append data to relevant lists if values are present
        if sector:
            sector_list.append(sector)
        if industry:
            industry_list.append(industry)       

    # Get the top 3 most common sectors and industries
    sector_counts = Counter(sector_list)
    industry_counts = Counter(industry_list)
    main_sectors = [item[0] for item in sector_counts.most_common(3)]
    main_industries = [item[0] for item in industry_counts.most_common(3)]

    # Add main sectors and industries to the item dictionary
    res['mainSectors'] = main_sectors
    res['mainIndustries'] = main_industries

    if res:
        with open(f"json/hedge-funds/companies/{cik}.json", 'w') as file:
            file.write(orjson.dumps(res).decode("utf-8"))

if __name__ == '__main__':
    con = sqlite3.connect('institute.db')
    stock_con = sqlite3.connect('stocks.db')
    
    cursor = con.cursor()
    cursor.execute("PRAGMA journal_mode = wal")
    cursor.execute("SELECT DISTINCT cik FROM institutes")
    cik_symbols = [row[0] for row in cursor.fetchall()]

    try:
        stock_cursor = stock_con.cursor()
        stock_cursor.execute("SELECT DISTINCT symbol, sector FROM stocks")
        stock_sectors = [{'symbol': row[0], 'sector': row[1]} for row in stock_cursor.fetchall()]
    finally:
        # Ensure that the cursor and connection are closed even if an error occurs
        stock_cursor.close()
        stock_con.close()

    all_hedge_funds(con)
    #spy_performance()
    for cik in tqdm(cik_symbols):
        try:
            get_data(cik, stock_sectors)
        except Exception as e:
            print(e)

    con.close()