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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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43
app/cron_analyst_db.py
View File

@ -1,5 +1,5 @@
import requests
from datetime import datetime
from datetime import datetime, timedelta
import numpy as np
from scipy.stats import norm
import time
@ -12,6 +12,8 @@ import pandas as pd
from collections import Counter
import aiohttp
import asyncio
import statistics
load_dotenv()
api_key = os.getenv('BENZINGA_API_KEY')
@ -121,28 +123,35 @@ def get_top_stocks():
with open(f"json/analyst/all-analyst-data.json", 'r') as 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 = []
# Define the date range for the past 12 months
end_date = datetime.now().date()
start_date = end_date - timedelta(days=365)
res_list = []
for item in filtered_data:
ticker_list = item['ratingsList']
ticker_list = [{'ticker': i['ticker'], 'pt_current': i['pt_current']} for i in ticker_list if i['rating_current'] == 'Strong Buy']
# Filter by 'Strong Buy' and ensure the rating is within the last 12 months
ticker_list = [{'ticker': i['ticker'], 'adjusted_pt_current': i['adjusted_pt_current'], 'date': i['date']}
for i in ticker_list
if i['rating_current'] == 'Strong Buy'
and start_date <= datetime.strptime(i['date'], '%Y-%m-%d').date() <= end_date]
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']
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]['sum'] += pt_current
ticker_data[ticker]['counter'] += 1
ticker_data[ticker]['pt_list'].append(pt_current)
else:
ticker_data[ticker] = {'sum': pt_current, 'counter': 1}
ticker_data[ticker] = {'pt_list': [pt_current]}
for ticker, info in ticker_data.items():
try:
@ -156,13 +165,22 @@ def get_top_stocks():
info['name'] = None
info['marketCap'] = None
# Calculate average pt_current for each ticker
# Calculate median pt_current for each ticker
for ticker, info in ticker_data.items():
info['average'] = round(info['sum'] / info['counter'],2)
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['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()]
result = [item for item in result if item['upside'] is not None and item['upside'] >= 5 and item['upside'] <= 250] #filter outliners
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
result_sorted = sorted(result, key=lambda x: x['counter'] if x['counter'] is not None else float('-inf'), reverse=True)
@ -423,3 +441,4 @@ async def run():
if __name__ == "__main__":
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
@author: AdamGetbags
# Tell the SEC who you are
set_identity("Michael Mccallum mike.mccalum@indigo.com")
"""
# import modules
import requests
import pandas as pd
filings = Company("NVDA").get_filings(form="10-Q").latest(3)
# create request header
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)
print(filings.search("Revenue by Geography"))

22
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
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