update cron job of hedge funds
This commit is contained in:
MuslemRahimi
parent
64c1695a32
commit
525810ad3d
@ -1,10 +1,16 @@
|
||||
import sqlite3
|
||||
import os
|
||||
import ujson
|
||||
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",
|
||||
@ -44,7 +50,7 @@ def all_hedge_funds(con):
|
||||
|
||||
res_list = [{
|
||||
'cik': row[0],
|
||||
'name': format_company_name(row[1]),
|
||||
'name': format_company_name(row[1]).title(),
|
||||
'numberOfStocks': row[2],
|
||||
'marketValue': row[3],
|
||||
'winRate': row[4],
|
||||
@ -55,43 +61,10 @@ def all_hedge_funds(con):
|
||||
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:
|
||||
ujson.dump(sorted_res_list, file)
|
||||
file.write(orjson.dumps(sorted_res_list).decode("utf-8"))
|
||||
|
||||
|
||||
|
||||
def spy_performance():
|
||||
import pandas as pd
|
||||
import yfinance as yf
|
||||
from datetime import datetime
|
||||
|
||||
# Define the start date and end date
|
||||
start_date = '1993-01-01'
|
||||
end_date = datetime.today().strftime('%Y-%m-%d')
|
||||
|
||||
# Generate the range of dates with quarterly frequency
|
||||
date_range = pd.date_range(start=start_date, end=end_date, freq='QE')
|
||||
|
||||
# Convert the dates to the desired format (end of quarter dates)
|
||||
end_of_quarters = date_range.strftime('%Y-%m-%d').tolist()
|
||||
|
||||
data = []
|
||||
|
||||
df = yf.download('SPY', start='1993-01-01', end=datetime.today(), interval="1d").reset_index()
|
||||
df = df.rename(columns={'Adj Close': 'close', 'Date': 'date'})
|
||||
|
||||
df['date'] = df['date'].dt.strftime('%Y-%m-%d')
|
||||
for target_date in end_of_quarters:
|
||||
original_date = target_date
|
||||
# Find close price for '2015-03-31' or the closest available date prior to it
|
||||
while target_date not in df['date'].values:
|
||||
# If the target date doesn't exist, move one day back
|
||||
target_date = (pd.to_datetime(target_date) - pd.Timedelta(days=1)).strftime('%Y-%m-%d')
|
||||
|
||||
# Get the close price for the found or closest date
|
||||
close_price = round(df[df['date'] == target_date]['close'].values[0],2)
|
||||
data.append({'date': original_date, 'price': close_price})
|
||||
|
||||
|
||||
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()
|
||||
@ -99,15 +72,11 @@ def get_data(cik, stock_sectors):
|
||||
'cik': row[0],
|
||||
'name': row[1],
|
||||
'numberOfStocks': row[2],
|
||||
'performancePercentage3year': row[3],
|
||||
'performancePercentage5year': row[4],
|
||||
'performanceSinceInceptionPercentage': row[5],
|
||||
'performancePercentage3Year': row[3],
|
||||
'averageHoldingPeriod': row[6],
|
||||
'turnover': row[7],
|
||||
'marketValue': row[8],
|
||||
'winRate': row[9],
|
||||
'holdings': ujson.loads(row[10]),
|
||||
'summary': ujson.loads(row[11]),
|
||||
'holdings': orjson.loads(row[10]),
|
||||
} for row in cik_data]
|
||||
|
||||
if not res:
|
||||
@ -120,30 +89,55 @@ def get_data(cik, stock_sectors):
|
||||
for holding in res['holdings']
|
||||
]
|
||||
|
||||
res['holdings'] = filtered_holdings
|
||||
|
||||
# Cross-reference symbols in holdings with stock_sectors to determine sectors
|
||||
sector_counts = Counter()
|
||||
for holding in res['holdings']:
|
||||
symbol = holding['symbol']
|
||||
sector = next((item['sector'] for item in stock_sectors if item['symbol'] == symbol), None)
|
||||
if sector:
|
||||
sector_counts[sector] += 1
|
||||
|
||||
# Calculate the total number of holdings
|
||||
total_holdings = sum(sector_counts.values())
|
||||
|
||||
# Calculate the percentage for each sector and get the top 5
|
||||
top_5_sectors_percentage = [
|
||||
{sector: round((count / total_holdings) * 100, 2)}
|
||||
for sector, count in sector_counts.most_common(5)
|
||||
|
||||
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
|
||||
)
|
||||
]
|
||||
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
|
||||
|
||||
# Add the top 5 sectors information to the result
|
||||
res['topSectors'] = top_5_sectors_percentage
|
||||
if res:
|
||||
with open(f"json/hedge-funds/companies/{cik}.json", 'w') as file:
|
||||
ujson.dump(res, file)
|
||||
file.write(orjson.dumps(res).decode("utf-8"))
|
||||
|
||||
if __name__ == '__main__':
|
||||
con = sqlite3.connect('institute.db')
|
||||
@ -164,7 +158,7 @@ if __name__ == '__main__':
|
||||
stock_con.close()
|
||||
|
||||
all_hedge_funds(con)
|
||||
spy_performance()
|
||||
#spy_performance()
|
||||
for cik in tqdm(cik_symbols):
|
||||
try:
|
||||
get_data(cik, stock_sectors)
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user