bugfixing earnings calendar and top stocks

app/cron_analyst_db.py

@@ -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]['pt_list'].append(pt_current)
-	            ticker_data[ticker]['counter'] += 1
             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 = [{'ticker': ticker, 
-	result = [item for item in result if item['upside'] is not None and item['upside'] >= 5 and item['upside'] <= 250] #filter outliners
+               '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())
+    

app/cron_business_metrics.py

@@ -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)

app/restart_json.py

@@ -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