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fixes

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This commit is contained in:
MuslemRahimi 2024-11-01 01:26:25 +01:00
parent d4ccc0ab99
commit 0b99b1a130
3 changed files with 102 additions and 39 deletions
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137
app/cron_analyst_ticker.py
View File

@ -3,6 +3,7 @@ import requests
from datetime import datetime, timedelta, date
from collections import defaultdict
import numpy as np
import pandas as pd
from scipy.stats import norm
import time
import sqlite3
@ -17,6 +18,13 @@ api_key = os.getenv('BENZINGA_API_KEY')
fin = financial_data.Benzinga(api_key)
query_template = """
SELECT date,close
FROM "{ticker}"
WHERE date BETWEEN ? AND ?
"""
end_date = date.today()
start_date_12m = end_date - timedelta(days=365) # end_date is today
# Define a function to remove duplicates based on a key
@ -34,38 +42,79 @@ def remove_duplicates(data, key):
def get_summary(res_list):
# Get the latest summary of ratings from the last 12 months
end_date = date.today()
start_date = end_date - timedelta(days=365) # end_date is today
# Filter the data for the last 12 months
filtered_data = [item for item in res_list if start_date <= datetime.strptime(item['date'], '%Y-%m-%d').date() <= end_date]
filtered_data = [item for item in res_list if start_date_12m <= datetime.strptime(item['date'], '%Y-%m-%d').date() <= end_date]
# Initialize dictionary to store the latest price target for each analyst
latest_pt_current = defaultdict(int)
latest_pt_current = defaultdict(list)
# Iterate through the filtered data to update the latest pt_current for each analyst
# Iterate through the filtered data to collect 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']
try:
pt_current_value = float(item['adjusted_pt_current'])
# Update with the maximum value for each analyst
if isinstance(pt_current_value, (float, int)):
latest_pt_current[analyst_name] = max(latest_pt_current.get(analyst_name, pt_current_value), pt_current_value)
# Collect all pt_current values for each analyst
latest_pt_current[analyst_name].append(pt_current_value)
except (ValueError, TypeError):
print(f"Invalid pt_current value for analyst '{analyst_name}': {item['pt_current']}")
# Get the price target values
pt_current_values = list(latest_pt_current.values())
# Compute statistics for price targets
pt_current_values = [val for sublist in latest_pt_current.values() for val in sublist]
# Compute the median pt_current if there are values, otherwise set to 0
median_pt_current = statistics.median(pt_current_values) if pt_current_values else 0
#print("Median pt_current:", round(median_pt_current, 2))
consensus_ratings = defaultdict(str)
# Define the rating hierarchy
# Compute different price target metrics if there are values, otherwise set to 0
if pt_current_values:
median_pt_current = statistics.median(pt_current_values)
avg_pt_current = statistics.mean(pt_current_values)
low_pt_current = min(pt_current_values)
high_pt_current = max(pt_current_values)
else:
median_pt_current = avg_pt_current = low_pt_current = high_pt_current = 0
# Initialize recommendation tracking
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
# Track monthly recommendations
monthly_recommendations = {}
# Iterate through the filtered data to track monthly recommendations
for item in filtered_data:
# Extract month from the date
item_date = datetime.strptime(item['date'], '%Y-%m-%d')
month_key = item_date.strftime('%Y-%m-01')
# Initialize month's recommendation counts if not exists
if month_key not in monthly_recommendations:
monthly_recommendations[month_key] = {
'Strong Sell': 0,
'Sell': 0,
'Hold': 0,
'Buy': 0,
'Strong Buy': 0
}
# Check and increment recommendation count for the month
if 'rating_current' in item and item['rating_current'] in rating_hierarchy:
monthly_recommendations[month_key][item['rating_current']] += 1
# Convert monthly recommendations to a sorted list
recommendation_list = []
for month in sorted(monthly_recommendations.keys()):
month_data = monthly_recommendations[month]
recommendation_list.append({
'date': month,
'Strong Sell': month_data['Strong Sell'],
'Sell': month_data['Sell'],
'Hold': month_data['Hold'],
'Buy': month_data['Buy'],
'Strong Buy': month_data['Strong Buy']
})
# Compute consensus ratings (similar to previous implementation)
consensus_ratings = defaultdict(str)
for item in filtered_data:
if 'rating_current' in item and item['rating_current'] and 'analyst_name' in item and item['analyst_name']:
try:
@ -75,44 +124,44 @@ def get_summary(res_list):
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)
# Count unique analysts
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(median_pt_current, 2)}
# Update stats dictionary with new keys including recommendationList
stats = {
'numOfAnalyst': numOfAnalyst,
'consensusRating': consensus_rating,
'medianPriceTarget': round(median_pt_current, 2),
'avgPriceTarget': round(avg_pt_current, 2),
'lowPriceTarget': round(low_pt_current, 2),
'highPriceTarget': round(high_pt_current, 2),
'recommendationList': recommendation_list
}
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()
def run(chunk,analyst_list, con):
start_date = datetime(2015,1,1)
end_date_str = end_date.strftime('%Y-%m-%d')
start_date_str = start_date.strftime('%Y-%m-%d')
@ -178,6 +227,18 @@ def run(chunk,analyst_list):
item['action_comapny'] = 'Initiates'
summary = get_summary(ticker_filtered_data)
try:
#Add historical price for the last 12 month
query = query_template.format(ticker=ticker)
df_12m = pd.read_sql_query(query, con, params=(start_date_12m, end_date)).round(2)
df_12m['date'] = pd.to_datetime(df_12m['date'])
df_12m_last_per_month = df_12m.groupby(df_12m['date'].dt.to_period('M')).tail(1)
past_price_list = [{"date": row['date'].strftime('%Y-%m-%d'), "close": row['close']} for _, row in df_12m_last_per_month.iterrows()]
summary["pastPriceList"] = past_price_list
except:
summary["pastPriceList"] = []
#get ratings of each analyst
with open(f"json/analyst/summary/{ticker}.json", 'w') as file:
@ -231,7 +292,6 @@ try:
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:
@ -242,7 +302,10 @@ try:
chunks = [stock_symbols[i:i + chunk_size] for i in range(0, len(stock_symbols), chunk_size)]
#chunks = [['CMG']]
for chunk in chunks:
run(chunk, analyst_stats_list)
run(chunk, analyst_stats_list, con)
except Exception as e:
print(e)
finally:
con.close()

2
app/cron_statistics.py
View File

@ -31,7 +31,7 @@ async def get_data(symbol):
'capitalExpenditure','freeCashFlow','freeCashFlowPerShare','grossProfitMargin','operatingProfitMargin','pretaxProfitMargin',
'netProfitMargin','ebitdaMargin','ebitMargin','freeCashFlowMargin','failToDeliver','relativeFTD',
'annualDividend','dividendYield','payoutRatio','dividendGrowth','earningsYield','freeCashFlowYield','altmanZScore','piotroskiScore',
'lastStockSplit','splitType','splitRatio','analystRating','analystCounter','priceTarget','upside'
'lastStockSplit','splitType','splitRatio','analystRating','analystCounter','medianPriceTarget','upside'
]
if symbol in stock_screener_data_dict:

2
app/restart_json.py
View File

@ -649,7 +649,7 @@ async def get_stock_screener(con):
res = orjson.loads(file.read())
item['analystRating'] = res['consensusRating']
item['analystCounter'] = res['numOfAnalyst']
item['priceTarget'] = res['priceTarget']
item['priceTarget'] = res['medianPriceTarget']
item['upside'] = round((item['priceTarget']/item['price']-1)*100, 1) if item['price'] else None
except Exception as e:
item['analystRating'] = None