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bugfixing earnings calendar and top stocks

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This commit is contained in:
MuslemRahimi 2024-10-19 19:02:59 +02:00
parent 33e40e793d
commit e4e0389ae2
3 changed files with 95 additions and 177 deletions
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113
app/cron_analyst_db.py
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@ -1,5 +1,5 @@
import requests import requests
from datetime import datetime from datetime import datetime, timedelta
import numpy as np import numpy as np
from scipy.stats import norm from scipy.stats import norm
import time import time
@ -12,6 +12,8 @@ import pandas as pd
from collections import Counter from collections import Counter
import aiohttp import aiohttp
import asyncio import asyncio
import statistics
load_dotenv() load_dotenv()
api_key = os.getenv('BENZINGA_API_KEY') api_key = os.getenv('BENZINGA_API_KEY')
@ -118,59 +120,75 @@ def calculate_rating(data):
return round(normalized_rating, 2) return round(normalized_rating, 2)
def get_top_stocks(): def get_top_stocks():
with open(f"json/analyst/all-analyst-data.json", 'r') as file: with open(f"json/analyst/all-analyst-data.json", 'r') as file:
analyst_stats_list = ujson.load(file) analyst_stats_list = ujson.load(file)
filtered_data = [item for item in analyst_stats_list if item['analystScore'] >= 5] filtered_data = [item for item in analyst_stats_list if item['analystScore'] >= 4]
res_list = [] res_list = []
for item in filtered_data: # Define the date range for the past 12 months
ticker_list = item['ratingsList'] end_date = datetime.now().date()
ticker_list = [{'ticker': i['ticker'], 'pt_current': i['pt_current']} for i in ticker_list if i['rating_current'] == 'Strong Buy'] start_date = end_date - timedelta(days=365)
if len(ticker_list) > 0:
#res_list += list(set(ticker_list))
res_list += ticker_list
# Create a dictionary to store ticker occurrences and corresponding pt_current values
ticker_data = {}
for item in res_list:
ticker = item['ticker']
pt_current_str = item['pt_current']
if pt_current_str: # Skip empty strings
pt_current = float(pt_current_str)
if ticker in ticker_data:
ticker_data[ticker]['sum'] += pt_current
ticker_data[ticker]['counter'] += 1
else:
ticker_data[ticker] = {'sum': pt_current, 'counter': 1}
for ticker, info in ticker_data.items(): res_list = []
try: for item in filtered_data:
with open(f"json/quote/{ticker}.json", 'r') as file: ticker_list = item['ratingsList']
res = ujson.load(file) # Filter by 'Strong Buy' and ensure the rating is within the last 12 months
info['price'] = res.get('price', None) ticker_list = [{'ticker': i['ticker'], 'adjusted_pt_current': i['adjusted_pt_current'], 'date': i['date']}
info['name'] = res.get('name', None) for i in ticker_list
info['marketCap'] = res.get('marketCap', None) if i['rating_current'] == 'Strong Buy'
except: and start_date <= datetime.strptime(i['date'], '%Y-%m-%d').date() <= end_date]
info['price'] = None if len(ticker_list) > 0:
info['name'] = None res_list += ticker_list
info['marketCap'] = None
# Calculate average pt_current for each ticker # Create a dictionary to store ticker occurrences and corresponding pt_current values
for ticker, info in ticker_data.items(): ticker_data = {}
info['average'] = round(info['sum'] / info['counter'],2) for item in res_list:
ticker = item['ticker']
pt_current_str = item['adjusted_pt_current']
if pt_current_str: # Skip empty strings
pt_current = float(pt_current_str)
if ticker in ticker_data:
ticker_data[ticker]['pt_list'].append(pt_current)
else:
ticker_data[ticker] = {'pt_list': [pt_current]}
# Convert the dictionary back to a list format for ticker, info in ticker_data.items():
result = [{'ticker': ticker, 'upside': round((info['average']/info.get('price')-1)*100, 2) if info.get('price') else None, 'priceTarget': info['average'], 'price': info['price'], 'counter': info['counter'], 'name': info['name'], 'marketCap': info['marketCap']} for ticker, info in ticker_data.items()] try:
result = [item for item in result if item['upside'] is not None and item['upside'] >= 5 and item['upside'] <= 250] #filter outliners with open(f"json/quote/{ticker}.json", 'r') as file:
res = ujson.load(file)
info['price'] = res.get('price', None)
info['name'] = res.get('name', None)
info['marketCap'] = res.get('marketCap', None)
except:
info['price'] = None
info['name'] = None
info['marketCap'] = None
result_sorted = sorted(result, key=lambda x: x['counter'] if x['counter'] is not None else float('-inf'), reverse=True) # Calculate median pt_current for each ticker
for ticker, info in ticker_data.items():
if info['pt_list']:
info['median'] = round(statistics.median(info['pt_list']), 2)
# Convert the dictionary back to a list format
result = [{'ticker': ticker,
'upside': round((info['median']/info.get('price')-1)*100, 2) if info.get('price') else None,
'priceTarget': info['median'],
'price': info['price'],
'counter': len(info['pt_list']),
'name': info['name'],
'marketCap': info['marketCap']}
for ticker, info in ticker_data.items()]
result = [item for item in result if item['upside'] is not None and item['upside'] >= 5 and item['upside'] <= 250] # Filter outliers
for rank, item in enumerate(result_sorted): result_sorted = sorted(result, key=lambda x: x['counter'] if x['counter'] is not None else float('-inf'), reverse=True)
item['rank'] = rank+1
with open(f"json/analyst/top-stocks.json", 'w') as file: for rank, item in enumerate(result_sorted):
ujson.dump(result_sorted, file) item['rank'] = rank + 1
with open(f"json/analyst/top-stocks.json", 'w') as file:
ujson.dump(result_sorted, file)
async def get_analyst_ratings(analyst_id, session): async def get_analyst_ratings(analyst_id, session):
@ -422,4 +440,5 @@ async def run():
if __name__ == "__main__": if __name__ == "__main__":
asyncio.run(run()) asyncio.run(run())

133
app/cron_business_metrics.py
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@ -1,132 +1,9 @@
# -*- coding: utf-8 -*- from edgar import *
"""
SEC Filing Scraper # Tell the SEC who you are
@author: AdamGetbags set_identity("Michael Mccallum mike.mccalum@indigo.com")
"""
# import modules filings = Company("NVDA").get_filings(form="10-Q").latest(3)
import requests
import pandas as pd
# create request header print(filings.search("Revenue by Geography"))
headers = {'User-Agent': "email@address.com"}
# get all companies data
companyTickers = requests.get(
"https://www.sec.gov/files/company_tickers.json",
headers=headers
)
# review response / keys
print(companyTickers.json().keys())
# format response to dictionary and get first key/value
firstEntry = companyTickers.json()['0']
# parse CIK // without leading zeros
directCik = companyTickers.json()['0']['cik_str']
# dictionary to dataframe
companyData = pd.DataFrame.from_dict(companyTickers.json(),
orient='index')
# add leading zeros to CIK
companyData['cik_str'] = companyData['cik_str'].astype(
str).str.zfill(10)
# review data
print(companyData[:1])
cik = companyData[0:1].cik_str[0]
# get company specific filing metadata
filingMetadata = requests.get(
f'https://data.sec.gov/submissions/CIK{cik}.json',
headers=headers
)
# review json
print(filingMetadata.json().keys())
filingMetadata.json()['filings']
filingMetadata.json()['filings'].keys()
filingMetadata.json()['filings']['recent']
filingMetadata.json()['filings']['recent'].keys()
# dictionary to dataframe
allForms = pd.DataFrame.from_dict(
filingMetadata.json()['filings']['recent']
)
# review columns
allForms.columns
allForms[['accessionNumber', 'reportDate', 'form']].head(50)
# 10-Q metadata
allForms.iloc[11]
# get company facts data
companyFacts = requests.get(
f'https://data.sec.gov/api/xbrl/companyfacts/CIK{cik}.json',
headers=headers
)
#review data
companyFacts.json().keys()
companyFacts.json()['facts']
companyFacts.json()['facts'].keys()
# filing metadata
companyFacts.json()['facts']['dei'][
'EntityCommonStockSharesOutstanding']
companyFacts.json()['facts']['dei'][
'EntityCommonStockSharesOutstanding'].keys()
companyFacts.json()['facts']['dei'][
'EntityCommonStockSharesOutstanding']['units']
companyFacts.json()['facts']['dei'][
'EntityCommonStockSharesOutstanding']['units']['shares']
companyFacts.json()['facts']['dei'][
'EntityCommonStockSharesOutstanding']['units']['shares'][0]
# concept data // financial statement line items
companyFacts.json()['facts']['us-gaap']
companyFacts.json()['facts']['us-gaap'].keys()
# different amounts of data available per concept
companyFacts.json()['facts']['us-gaap']['AccountsPayable']
companyFacts.json()['facts']['us-gaap']['Revenues']
companyFacts.json()['facts']['us-gaap']['Assets']
# get company concept data
companyConcept = requests.get(
(
f'https://data.sec.gov/api/xbrl/companyconcept/CIK{cik}'
f'/us-gaap/Assets.json'
),
headers=headers
)
# review data
companyConcept.json().keys()
companyConcept.json()['units']
companyConcept.json()['units'].keys()
companyConcept.json()['units']['USD']
companyConcept.json()['units']['USD'][0]
# parse assets from single filing
companyConcept.json()['units']['USD'][0]['val']
# get all filings data
assetsData = pd.DataFrame.from_dict((
companyConcept.json()['units']['USD']))
# review data
assetsData.columns
assetsData.form
# get assets from 10Q forms and reset index
assets10Q = assetsData[assetsData.form == '10-Q']
assets10Q = assets10Q.reset_index(drop=True)
print(assets10Q)

26
app/restart_json.py
View File

@ -819,6 +819,28 @@ async def get_earnings_calendar(con, stock_symbols):
start_date += timedelta(days=1) # Increment date by one day start_date += timedelta(days=1) # Increment date by one day
seen_symbols = set()
unique_data = []
for item in res_list:
symbol = item.get('symbol')
try:
with open(f"json/quote/{symbol}.json", 'r') as file:
quote = ujson.load(file)
try:
earnings_date = datetime.strptime(quote['earningsAnnouncement'].split('T')[0], '%Y-%m-%d').strftime('%Y-%m-%d')
except:
earnings_date = '-'
except Exception as e:
earnings_date = '-'
print(e)
if symbol is None or symbol not in seen_symbols:
#bug in fmp endpoint. Double check that earnings date is the same as in quote endpoint
if item['date'] == earnings_date:
#print(symbol, item['date'], earnings_date)
unique_data.append(item)
seen_symbols.add(symbol)
return res_list return res_list
@ -1883,7 +1905,7 @@ async def save_json_files():
crypto_symbols = [row[0] for row in crypto_cursor.fetchall()] crypto_symbols = [row[0] for row in crypto_cursor.fetchall()]
stock_screener_data = await get_stock_screener(con) stock_screener_data = await get_stock_screener(con)
with open(f"json/stock-screener/data.json", 'w') as file: with open(f"json/stock-screener/data.json", 'w') as file:
ujson.dump(stock_screener_data, file) ujson.dump(stock_screener_data, file)
@ -1962,7 +1984,7 @@ async def save_json_files():
data = await get_magnificent_seven(con) data = await get_magnificent_seven(con)
with open(f"json/magnificent-seven/data.json", 'w') as file: with open(f"json/magnificent-seven/data.json", 'w') as file:
ujson.dump(data, file) ujson.dump(data, file)
con.close() con.close()
etf_con.close() etf_con.close()