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