update hedge fund db
This commit is contained in:
MuslemRahimi
parent
2a160b02e2
commit
0f44a75242
@ -17,15 +17,29 @@ import os
|
||||
# Filter out the specific RuntimeWarning
|
||||
warnings.filterwarnings("ignore", category=RuntimeWarning, message="invalid value encountered in scalar divide")
|
||||
|
||||
conn = sqlite3.connect('stocks.db')
|
||||
cursor = conn.cursor()
|
||||
con = sqlite3.connect('stocks.db')
|
||||
etf_con = sqlite3.connect('etf.db')
|
||||
crypto_con = sqlite3.connect('crypto.db')
|
||||
|
||||
# Execute the SQL query
|
||||
cursor.execute("SELECT symbol FROM stocks")
|
||||
cursor = con.cursor()
|
||||
cursor.execute("PRAGMA journal_mode = wal")
|
||||
cursor.execute("SELECT DISTINCT symbol FROM stocks")
|
||||
stock_symbols = [row[0] for row in cursor.fetchall()]
|
||||
|
||||
# Fetch all the results into a list
|
||||
symbol_list = [row[0] for row in cursor.fetchall()]
|
||||
conn.close()
|
||||
etf_cursor = etf_con.cursor()
|
||||
etf_cursor.execute("PRAGMA journal_mode = wal")
|
||||
etf_cursor.execute("SELECT DISTINCT symbol FROM etfs")
|
||||
etf_symbols = [row[0] for row in etf_cursor.fetchall()]
|
||||
|
||||
crypto_cursor = crypto_con.cursor()
|
||||
crypto_cursor.execute("PRAGMA journal_mode = wal")
|
||||
crypto_cursor.execute("SELECT DISTINCT symbol FROM cryptos")
|
||||
crypto_symbols = [row[0] for row in crypto_cursor.fetchall()]
|
||||
|
||||
total_symbols = stock_symbols + etf_symbols + crypto_symbols
|
||||
con.close()
|
||||
etf_con.close()
|
||||
crypto_con.close()
|
||||
|
||||
|
||||
load_dotenv()
|
||||
@ -121,33 +135,42 @@ class InstituteDatabase:
|
||||
if isinstance(parsed_data, list) and "https://financialmodelingprep.com/api/v4/institutional-ownership/portfolio-holdings?cik=" in url:
|
||||
# Handle list response, save as JSON object
|
||||
|
||||
parsed_data = [item for item in parsed_data if 'symbol' in item and item['symbol'] is not None and item['symbol'] in symbol_list] #symbol must be included in the database
|
||||
parsed_data = [
|
||||
{**item, 'type': ('stocks' if item['symbol'] in stock_symbols else
|
||||
'crypto' if item['symbol'] in crypto_symbols else
|
||||
'etf' if item['symbol'] in etf_symbols else None)}
|
||||
for item in parsed_data
|
||||
if 'symbol' in item and item['symbol'] is not None and item['symbol'] in total_symbols
|
||||
]
|
||||
|
||||
portfolio_data['holdings'] = json.dumps(parsed_data)
|
||||
|
||||
|
||||
number_of_stocks = len(parsed_data)
|
||||
total_market_value = sum(item['marketValue'] for item in parsed_data)
|
||||
avg_performance_percentage = sum(item['performancePercentage'] for item in parsed_data) / len(parsed_data)
|
||||
|
||||
#total_market_value = sum(item['marketValue'] for item in parsed_data)
|
||||
#avg_performance_percentage = sum(item['performancePercentage'] for item in parsed_data) / len(parsed_data)
|
||||
performance_percentages = [item.get("performancePercentage", 0) for item in parsed_data]
|
||||
|
||||
|
||||
positive_performance_count = sum(1 for percentage in performance_percentages if percentage > 0)
|
||||
win_rate = round(positive_performance_count / len(performance_percentages) * 100,2)
|
||||
data_dict = {
|
||||
'winRate': win_rate,
|
||||
'numberOfStocks': number_of_stocks,
|
||||
'marketValue': total_market_value,
|
||||
'avgPerformancePercentage': avg_performance_percentage,
|
||||
#'marketValue': total_market_value,
|
||||
}
|
||||
|
||||
portfolio_data.update(data_dict)
|
||||
|
||||
elif isinstance(parsed_data, list) and "https://financialmodelingprep.com/api/v4/institutional-ownership/portfolio-holdings-summary" in url:
|
||||
# Handle list response, save as JSON object
|
||||
portfolio_data['summary'] = json.dumps(parsed_data)
|
||||
data_dict = {
|
||||
#'numberOfStocks': parsed_data[0]['portfolioSize'],
|
||||
#'marketValue': parsed_data[0]['marketValue'],
|
||||
'marketValue': parsed_data[0]['marketValue'],
|
||||
'averageHoldingPeriod': parsed_data[0]['averageHoldingPeriod'],
|
||||
'turnover': parsed_data[0]['turnover'],
|
||||
'performancePercentage3year': parsed_data[0]['performancePercentage3year'],
|
||||
#'performancePercentage': parsed_data[0]['performancePercentage']
|
||||
}
|
||||
portfolio_data.update(data_dict)
|
||||
@ -164,7 +187,7 @@ class InstituteDatabase:
|
||||
holdings_list = json.loads(portfolio_data['holdings'])
|
||||
|
||||
symbols_to_check = {holding['symbol'] for holding in holdings_list[:3]} # Extract the first two symbols
|
||||
symbols_not_in_list = not any(symbol in symbol_list for symbol in symbols_to_check)
|
||||
symbols_not_in_list = not any(symbol in total_symbols for symbol in symbols_to_check)
|
||||
|
||||
|
||||
if symbols_not_in_list or 'industry' not in portfolio_data or len(json.loads(portfolio_data['industry'])) == 0:
|
||||
|
||||
105
app/cron_hedge_funds.py
Normal file
105
app/cron_hedge_funds.py
Normal file
@ -0,0 +1,105 @@
|
||||
import sqlite3
|
||||
import os
|
||||
import json
|
||||
|
||||
|
||||
frontend_json_url = "../../frontend/src/lib/hedge-funds"
|
||||
|
||||
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 best_hedge_funds(con):
|
||||
|
||||
# Connect to the SQLite database
|
||||
cursor = con.cursor()
|
||||
|
||||
# Execute a SQL query to select the top 10 best performing cik entries by winRate
|
||||
cursor.execute("SELECT cik, name, numberOfStocks, marketValue, winRate, turnover, performancePercentage3year FROM institutes WHERE marketValue > 200000000 AND numberOfStocks > 15 ORDER BY winRate DESC LIMIT 50")
|
||||
best_performing_ciks = cursor.fetchall()
|
||||
|
||||
res_list = [{
|
||||
'cik': row[0],
|
||||
'name': format_company_name(row[1]),
|
||||
'numberOfStocks': row[2],
|
||||
'marketValue': row[3],
|
||||
'winRate': row[4],
|
||||
'turnover': row[5],
|
||||
'performancePercentage3year': row[6]
|
||||
} for row in best_performing_ciks]
|
||||
|
||||
with open(f"{frontend_json_url}/best-hedge-funds.json", 'w') as file:
|
||||
json.dump(res_list, file)
|
||||
|
||||
|
||||
def worst_hedge_funds(con):
|
||||
|
||||
# Connect to the SQLite database
|
||||
cursor = con.cursor()
|
||||
|
||||
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")
|
||||
worst_performing_ciks = cursor.fetchall()
|
||||
|
||||
res_list = [{
|
||||
'cik': row[0],
|
||||
'name': format_company_name(row[1]),
|
||||
'numberOfStocks': row[2],
|
||||
'marketValue': row[3],
|
||||
'winRate': row[4],
|
||||
'turnover': row[5],
|
||||
'performancePercentage3year': row[6]
|
||||
} for row in worst_performing_ciks]
|
||||
|
||||
with open(f"{frontend_json_url}/worst-hedge-funds.json", 'w') as file:
|
||||
json.dump(res_list, file)
|
||||
|
||||
|
||||
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]),
|
||||
'numberOfStocks': row[2],
|
||||
'marketValue': row[3],
|
||||
'winRate': row[4],
|
||||
'turnover': row[5],
|
||||
'performancePercentage3year': row[6]
|
||||
} for row in all_ciks]
|
||||
|
||||
sorted_res_list = sorted(res_list, key=lambda x: x['marketValue'], reverse=True)
|
||||
|
||||
with open(f"{frontend_json_url}/all-hedge-funds.json", 'w') as file:
|
||||
json.dump(sorted_res_list, file)
|
||||
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
con = sqlite3.connect('institute.db')
|
||||
#best_hedge_funds(con)
|
||||
#worst_hedge_funds(con)
|
||||
all_hedge_funds(con)
|
||||
con.close()
|
||||
@ -1410,13 +1410,13 @@ async def get_hedge_funds_data(data: GetCIKData):
|
||||
cursor = con_inst.cursor()
|
||||
|
||||
# Execute a SQL query to select the top 10 best performing cik entries by winRate
|
||||
cursor.execute("SELECT cik, name, numberOfStocks, avgPerformancePercentage, averageHoldingPeriod, turnover, marketValue, winRate, holdings FROM institutes WHERE cik = ?", (cik,))
|
||||
cursor.execute("SELECT cik, name, numberOfStocks, performancePercentage3year, averageHoldingPeriod, turnover, marketValue, winRate, holdings FROM institutes WHERE cik = ?", (cik,))
|
||||
cik_data = cursor.fetchall()
|
||||
res = [{
|
||||
'cik': row[0],
|
||||
'name': row[1],
|
||||
'numberOfStocks': row[2],
|
||||
'avgPerformancePercentage': row[3],
|
||||
'performancePercentage3year': row[3],
|
||||
'averageHoldingPeriod': row[4],
|
||||
'turnover': row[5],
|
||||
'marketValue': row[6],
|
||||
|
||||
@ -1060,7 +1060,7 @@ async def save_json_files():
|
||||
|
||||
cursor = con.cursor()
|
||||
cursor.execute("PRAGMA journal_mode = wal")
|
||||
cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol != ?", ('%5EGSPC',))
|
||||
cursor.execute("SELECT DISTINCT symbol FROM stocks")
|
||||
symbols = [row[0] for row in cursor.fetchall()]
|
||||
|
||||
etf_cursor = etf_con.cursor()
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user