139 lines
4.7 KiB
Python
139 lines
4.7 KiB
Python
import sqlite3
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import os
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import json
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frontend_json_url = "../../frontend/src/lib/hedge-funds"
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def format_company_name(company_name):
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remove_strings = [', LLC','LLC', ',', 'LP', 'LTD', 'LTD.', 'INC.', 'INC', '.', '/DE/','/MD/','PLC']
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preserve_words = ['FMR','MCF']
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remove_strings_set = set(remove_strings)
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preserve_words_set = set(preserve_words)
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words = company_name.split()
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formatted_words = []
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for word in words:
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if word in preserve_words_set:
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formatted_words.append(word)
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else:
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new_word = word
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for string in remove_strings_set:
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new_word = new_word.replace(string, '')
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formatted_words.append(new_word.title())
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return ' '.join(formatted_words)
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def best_hedge_funds(con):
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# Connect to the SQLite database
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cursor = con.cursor()
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# Execute a SQL query to select the top 10 best performing cik entries by winRate
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cursor.execute("SELECT cik, name, numberOfStocks, marketValue, winRate, turnover, performancePercentage3year FROM institutes WHERE marketValue > 200000000 AND numberOfStocks > 15 ORDER BY winRate DESC LIMIT 50")
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best_performing_ciks = cursor.fetchall()
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res_list = [{
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'cik': row[0],
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'name': format_company_name(row[1]),
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'numberOfStocks': row[2],
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'marketValue': row[3],
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'winRate': row[4],
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'turnover': row[5],
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'performancePercentage3year': row[6]
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} for row in best_performing_ciks]
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with open(f"{frontend_json_url}/best-hedge-funds.json", 'w') as file:
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json.dump(res_list, file)
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def worst_hedge_funds(con):
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# Connect to the SQLite database
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cursor = con.cursor()
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cursor.execute("SELECT cik, name, numberOfStocks, marketValue, winRate, turnover, performancePercentage3year FROM institutes WHERE marketValue > 200000000 AND numberOfStocks > 15 AND winRate > 0 ORDER BY winRate ASC LIMIT 50")
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worst_performing_ciks = cursor.fetchall()
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res_list = [{
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'cik': row[0],
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'name': format_company_name(row[1]),
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'numberOfStocks': row[2],
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'marketValue': row[3],
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'winRate': row[4],
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'turnover': row[5],
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'performancePercentage3year': row[6]
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} for row in worst_performing_ciks]
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with open(f"{frontend_json_url}/worst-hedge-funds.json", 'w') as file:
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json.dump(res_list, file)
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def all_hedge_funds(con):
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# Connect to the SQLite database
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cursor = con.cursor()
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cursor.execute("SELECT cik, name, numberOfStocks, marketValue, winRate, turnover, performancePercentage3year FROM institutes")
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all_ciks = cursor.fetchall()
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res_list = [{
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'cik': row[0],
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'name': format_company_name(row[1]),
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'numberOfStocks': row[2],
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'marketValue': row[3],
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'winRate': row[4],
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'turnover': row[5],
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'performancePercentage3year': row[6]
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} for row in all_ciks if row[2] >= 3]
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sorted_res_list = sorted(res_list, key=lambda x: x['marketValue'], reverse=True)
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with open(f"{frontend_json_url}/all-hedge-funds.json", 'w') as file:
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json.dump(sorted_res_list, file)
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def spy_performance():
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import pandas as pd
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import yfinance as yf
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from datetime import datetime
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# Define the start date and end date
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start_date = '1993-01-01'
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end_date = datetime.today().strftime('%Y-%m-%d')
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# Generate the range of dates with quarterly frequency
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date_range = pd.date_range(start=start_date, end=end_date, freq='Q')
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# Convert the dates to the desired format (end of quarter dates)
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end_of_quarters = date_range.strftime('%Y-%m-%d').tolist()
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data = []
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df = yf.download('SPY', start='1993-01-01', end=datetime.today(), interval="1d").reset_index()
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df = df.rename(columns={'Adj Close': 'close', 'Date': 'date'})
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df['date'] = df['date'].dt.strftime('%Y-%m-%d')
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for target_date in end_of_quarters:
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original_date = target_date
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# Find close price for '2015-03-31' or the closest available date prior to it
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while target_date not in df['date'].values:
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# If the target date doesn't exist, move one day back
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target_date = (pd.to_datetime(target_date) - pd.Timedelta(days=1)).strftime('%Y-%m-%d')
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# Get the close price for the found or closest date
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close_price = round(df[df['date'] == target_date]['close'].values[0],2)
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data.append({'date': original_date, 'price': close_price})
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print(data)
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if __name__ == '__main__':
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con = sqlite3.connect('institute.db')
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#best_hedge_funds(con)
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#worst_hedge_funds(con)
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all_hedge_funds(con)
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con.close() |