249 lines
10 KiB
Python
Executable File
249 lines
10 KiB
Python
Executable File
from benzinga import financial_data
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import requests
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from datetime import datetime, timedelta, date
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from collections import defaultdict
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import numpy as np
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from scipy.stats import norm
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import time
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import sqlite3
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import ujson
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import math
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import statistics
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import os
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from dotenv import load_dotenv
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load_dotenv()
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api_key = os.getenv('BENZINGA_API_KEY')
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fin = financial_data.Benzinga(api_key)
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# Define a function to remove duplicates based on a key
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def remove_duplicates(data, key):
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seen = set()
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new_data = []
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for item in data:
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if item[key] not in seen:
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seen.add(item[key])
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new_data.append(item)
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return new_data
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def get_summary(res_list):
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# Get the latest summary of ratings from the last 12 months
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end_date = date.today()
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start_date = end_date - timedelta(days=365) # end_date is today
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# Filter the data for the last 12 months
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filtered_data = [item for item in res_list if start_date <= datetime.strptime(item['date'], '%Y-%m-%d').date() <= end_date]
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# Initialize dictionary to store the latest price target for each analyst
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latest_pt_current = defaultdict(int)
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# Iterate through the filtered data to update the latest pt_current for each analyst
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for item in filtered_data:
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if 'adjusted_pt_current' in item and item['adjusted_pt_current']:
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analyst_name = item['analyst_name']
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try:
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pt_current_value = float(item['adjusted_pt_current'])
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# Update with the maximum value for each analyst
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if isinstance(pt_current_value, (float, int)):
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latest_pt_current[analyst_name] = max(latest_pt_current.get(analyst_name, pt_current_value), pt_current_value)
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except (ValueError, TypeError):
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print(f"Invalid pt_current value for analyst '{analyst_name}': {item['pt_current']}")
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# Get the price target values
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pt_current_values = list(latest_pt_current.values())
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# Compute the median pt_current if there are values, otherwise set to 0
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median_pt_current = statistics.median(pt_current_values) if pt_current_values else 0
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#print("Median pt_current:", round(median_pt_current, 2))
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consensus_ratings = defaultdict(str)
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# Define the rating hierarchy
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rating_hierarchy = {'Strong Sell': 0, 'Sell': 1, 'Hold': 2, 'Buy': 3, 'Strong Buy': 4}
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# Iterate through the data to update the consensus rating for each analyst
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for item in filtered_data:
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if 'rating_current' in item and item['rating_current'] and 'analyst_name' in item and item['analyst_name']:
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try:
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analyst_name = item['analyst_name']
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current_rating = item['rating_current']
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if current_rating in rating_hierarchy:
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consensus_ratings[analyst_name] = current_rating
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except:
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pass
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# Compute the consensus rating based on the most frequent rating among analysts
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consensus_rating_counts = defaultdict(int)
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for rating in consensus_ratings.values():
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consensus_rating_counts[rating] += 1
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consensus_rating = max(consensus_rating_counts, key=consensus_rating_counts.get)
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#print("Consensus Rating:", consensus_rating)
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# Sum up all Buy, Sell, Hold for the progress bar in sveltekit
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# Convert defaultdict to regular dictionary
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data_dict = dict(consensus_rating_counts)
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# Sum up 'Strong Buy' and 'Buy'
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buy_total = data_dict.get('Strong Buy', 0) + data_dict.get('Buy', 0)
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# Sum up 'Strong Sell' and 'Sell'
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sell_total = data_dict.get('Strong Sell', 0) + data_dict.get('Sell', 0)
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hold_total = data_dict.get('Hold', 0)
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unique_analyst_names = set()
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numOfAnalyst = 0
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for item in filtered_data:
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if item['analyst_name'] not in unique_analyst_names:
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unique_analyst_names.add(item['analyst_name'])
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numOfAnalyst += 1
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#print("Number of unique analyst names:", numOfAnalyst)
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stats = {'numOfAnalyst': numOfAnalyst, 'consensusRating': consensus_rating, 'priceTarget': round(median_pt_current, 2)}
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categorical_ratings = {'Buy': buy_total, 'Sell': sell_total, 'Hold': hold_total}
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res = {**stats, **categorical_ratings}
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return res
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def run(chunk,analyst_list):
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end_date = date.today()
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start_date = datetime(2015,1,1)
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end_date_str = end_date.strftime('%Y-%m-%d')
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start_date_str = start_date.strftime('%Y-%m-%d')
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company_tickers = ','.join(chunk)
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res_list = []
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for page in range(0, 500):
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try:
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data = fin.ratings(company_tickers=company_tickers, page=page, pagesize=1000, date_from=start_date_str, date_to=end_date_str)
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data = ujson.loads(fin.output(data))['ratings']
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res_list += data
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except:
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break
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res_list = [item for item in res_list if item.get('analyst_name')]
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#print(res_list[-15])
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for ticker in chunk:
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try:
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ticker_filtered_data = [item for item in res_list if item['ticker'] == ticker]
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if len(ticker_filtered_data) != 0:
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for item in ticker_filtered_data:
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if item['rating_current'] == 'Strong Sell' or item['rating_current'] == 'Strong Buy':
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pass
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elif item['rating_current'] == 'Neutral':
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item['rating_current'] = 'Hold'
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elif item['rating_current'] == 'Equal-Weight' or item['rating_current'] == 'Sector Weight' or item['rating_current'] == 'Sector Perform':
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item['rating_current'] = 'Hold'
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elif item['rating_current'] == 'In-Line':
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item['rating_current'] = 'Hold'
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elif item['rating_current'] == 'Outperform' and item['action_company'] == 'Downgrades':
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item['rating_current'] = 'Hold'
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elif item['rating_current'] == 'Negative':
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item['rating_current'] = 'Sell'
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elif (item['rating_current'] == 'Outperform' or item['rating_current'] == 'Overweight') and (item['action_company'] == 'Reiterates' or item['action_company'] == 'Initiates Coverage On'):
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item['rating_current'] = 'Buy'
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item['action_comapny'] = 'Initiates'
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elif item['rating_current'] == 'Market Outperform' and (item['action_company'] == 'Maintains' or item['action_company'] == 'Reiterates'):
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item['rating_current'] = 'Buy'
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elif item['rating_current'] == 'Outperform' and (item['action_company'] == 'Maintains' or item['action_pt'] == 'Announces' or item['action_company'] == 'Upgrades'):
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item['rating_current'] = 'Buy'
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elif item['rating_current'] == 'Buy' and (item['action_company'] == 'Raises' or item['action_pt'] == 'Raises'):
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item['rating_current'] = 'Strong Buy'
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elif item['rating_current'] == 'Overweight' and (item['action_company'] == 'Maintains' or item['action_company'] == 'Upgrades' or item['action_company'] == 'Reiterates' or item['action_pt'] == 'Raises'):
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item['rating_current'] = 'Buy'
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elif item['rating_current'] == 'Positive' or item['rating_current'] == 'Sector Outperform':
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item['rating_current'] = 'Buy'
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elif item['rating_current'] == 'Underperform' or item['rating_current'] == 'Underweight':
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item['rating_current'] = 'Sell'
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elif item['rating_current'] == 'Reduce' and (item['action_company'] == 'Downgrades' or item['action_pt'] == 'Lowers'):
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item['rating_current'] = 'Sell'
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elif item['rating_current'] == 'Sell' and item['action_pt'] == 'Announces':
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item['rating_current'] = 'Strong Sell'
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elif item['rating_current'] == 'Market Perform':
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item['rating_current'] = 'Hold'
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elif item['rating_prior'] == 'Outperform' and item['action_company'] == 'Downgrades':
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item['rating_current'] = 'Hold'
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elif item['rating_current'] == 'Peer Perform' and item['rating_prior'] == 'Peer Perfrom':
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item['rating_current'] = 'Hold'
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elif item['rating_current'] == 'Peer Perform' and item['action_pt'] == 'Announces':
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item['rating_current'] = 'Hold'
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item['action_comapny'] = 'Initiates'
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summary = get_summary(ticker_filtered_data)
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#get ratings of each analyst
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with open(f"json/analyst/summary/{ticker}.json", 'w') as file:
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ujson.dump(summary, file)
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for item1 in ticker_filtered_data:
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#item1['analystId'] = ''
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#item1['analystScore'] = 0
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#item1['adjusted_pt_current'] = 0
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#item1['adjusted_pt_prior'] = 0
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for item2 in analyst_stats_list:
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if item1['analyst'] == item2['companyName'] and item1['analyst_name'] == item2['analystName']:
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item1['analystId'] = item2['analystId']
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item1['analystScore'] = item2['analystScore']
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break
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elif item1['analyst_name'] == item2['analystName']:
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item1['analystId'] = item2['analystId']
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item1['analystScore'] = item2['analystScore']
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break
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#Bug: Benzinga does not give me reliable all analyst names and hence score.
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# Compute in those cases the analyst score separately for each analyst
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'''
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if 'analystScore' not in item1: #or item1['analystScore'] == 0:
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one_sample_list = get_one_sample_analyst_data(item1['analyst_name'], item1['analyst'])
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item1['analystId'] = one_sample_list[0]['id']
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item1['analystScore'] = one_sample_list[0]['analystScore']
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'''
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desired_keys = ['date', 'action_company', 'rating_current', 'adjusted_pt_current', 'adjusted_pt_prior', 'analystId', 'analystScore', 'analyst', 'analyst_name']
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ticker_filtered_data = [
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{key: item[key] if key in item else None for key in desired_keys}
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for item in ticker_filtered_data
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]
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#print(ticker_filtered_data[0])
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#time.sleep(10000)
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with open(f"json/analyst/history/{ticker}.json", 'w') as file:
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ujson.dump(ticker_filtered_data, file)
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except Exception as e:
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print(e)
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try:
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con = sqlite3.connect('stocks.db')
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stock_cursor = con.cursor()
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stock_cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE symbol NOT LIKE '%.%'")
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stock_symbols =[row[0] for row in stock_cursor.fetchall()]
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con.close()
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#Save all analyst data in raw form for the next step
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with open(f"json/analyst/all-analyst-data.json", 'r') as file:
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analyst_stats_list = ujson.load(file)
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chunk_size = len(stock_symbols) // 40 # Divide the list into N chunks
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chunks = [stock_symbols[i:i + chunk_size] for i in range(0, len(stock_symbols), chunk_size)]
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#chunks = [['CMG']]
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for chunk in chunks:
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run(chunk, analyst_stats_list)
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except Exception as e:
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print(e)
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