calculate avgReturn and successRate
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MuslemRahimi
parent
a730155d04
commit
b79624d1f9
@ -18,6 +18,16 @@ api_key = os.getenv('BENZINGA_API_KEY')
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headers = {"accept": "application/json"}
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query_template = """
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SELECT date, close
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FROM "{ticker}"
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WHERE date BETWEEN ? AND ?
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"""
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buy_ratings = ['Outperform', 'Overweight', 'Market Outperform', 'Buy', 'Positive', 'Sector Outperform']
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sell_ratings = ['Negative', 'Underperform', 'Underweight', 'Reduce', 'Sell']
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ticker_price_cache = {} # Dictionary to store cached price data for tickers
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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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@ -99,6 +109,12 @@ def calculate_rating(data):
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if difference > 30:
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normalized_rating = min(normalized_rating, 4.5)
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if overall_success_rate < 50:
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normalized_rating = min(normalized_rating, 4.6)
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if overall_average_return < 30:
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normalized_rating = min(normalized_rating, 4.6)
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return round(normalized_rating, 2)
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def get_top_stocks():
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@ -179,7 +195,15 @@ async def get_analyst_ratings(analyst_id, session):
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#print(f"Error fetching page {page} for analyst {analyst_id}: {e}")
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break
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return res_list
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# Date filter: only include items with 'date' >= '2015-01-01'
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filtered_data = [
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{key: value for key, value in item.items() if key not in {'url_news', 'url', 'url_calendar', 'updated', 'time', 'currency'}}
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for item in res_list
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if datetime.strptime(item['date'], '%Y-%m-%d') >= datetime(2015, 1, 1)
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]
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return filtered_data
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async def get_all_analyst_stats():
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url = "https://api.benzinga.com/api/v2.1/calendar/ratings/analysts"
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@ -202,7 +226,7 @@ async def get_all_analyst_stats():
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res_list += data
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except Exception as e:
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pass
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print(len(res_list))
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# Remove duplicates of analysts and filter based on ratings accuracy
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res_list = remove_duplicates(res_list, 'id')
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res_list = [item for item in res_list if item.get('ratings_accuracy', {}).get('total_ratings', 0) != 0]
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@ -220,90 +244,181 @@ async def get_all_analyst_stats():
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return final_list
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async def process_analyst(item, session):
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async def process_analyst(item, con, session, start_date, end_date):
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# Fetch analyst ratings
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data = await get_analyst_ratings(item['analystId'], session)
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item['ratingsList'] = data
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item['totalRatings'] = len(data) # True total ratings
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item['totalRatings'] = len(data)
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item['lastRating'] = data[0]['date'] if data else None
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item['numOfStocks'] = len({d['ticker'] for d in data})
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# Stats dictionary for calculating score
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total_return = 0
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valid_ratings_count = 0
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success_count = 0 # To track successful ratings for success rate calculation
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for stock in data:
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try:
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ticker = stock['ticker']
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rating_date = stock['date']
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rating_current = stock['rating_current']
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# Skip neutral or undefined ratings
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if rating_current not in buy_ratings and rating_current not in sell_ratings:
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continue
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# Check if the ticker data is already cached
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if ticker not in ticker_price_cache:
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# If not cached, query the stock data and cache it
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query = query_template.format(ticker=ticker)
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df = pd.read_sql_query(query, con, params=(start_date, end_date))
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ticker_price_cache[ticker] = df
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else:
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# Use cached data
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df = ticker_price_cache[ticker]
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# Ensure we have data for the rating date
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rating_date_data = df[df['date'] == rating_date]
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if rating_date_data.empty:
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# Try finding the closest date within a few days if exact date is missing
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for days_offset in range(1, 5):
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closest_date = (pd.to_datetime(rating_date) - pd.Timedelta(days=days_offset)).strftime('%Y-%m-%d')
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rating_date_data = df[df['date'] == closest_date]
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if not rating_date_data.empty:
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break
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if rating_date_data.empty:
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continue # Skip if no close price data found
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# Get close price on rating date
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close_price_on_rating = rating_date_data['close'].values[0]
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# Calculate the date 12 months later
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future_date = (pd.to_datetime(rating_date) + pd.DateOffset(months=12)).strftime('%Y-%m-%d')
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# Try to find the close price 12 months later
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future_date_data = df[df['date'] == future_date]
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if future_date_data.empty:
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# If 12 months price isn't available, use the latest available price
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future_date_data = df.iloc[-1] # Use the last available price
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if future_date_data.empty:
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continue # If no future data, skip this rating
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close_price_in_future = future_date_data['close'] if isinstance(future_date_data, pd.Series) else future_date_data['close'].values[0]
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# Calculate return
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stock_return = (close_price_in_future - close_price_on_rating) / close_price_on_rating
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total_return += stock_return
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valid_ratings_count += 1
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# Determine if the rating was successful
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if rating_current in buy_ratings:
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if close_price_in_future > close_price_on_rating:
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success_count += 1 # Success for buy ratings
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elif rating_current in sell_ratings:
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if close_price_in_future < close_price_on_rating:
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success_count += 1 # Success for sell ratings
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except:
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pass
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# Calculate average return if there are valid ratings
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if valid_ratings_count > 0:
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item['avgReturn'] = round(total_return / valid_ratings_count * 100, 2) # Percentage format
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else:
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item['avgReturn'] = 0
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# Calculate success rate
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if valid_ratings_count > 0:
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item['successRate'] = round(success_count / valid_ratings_count * 100, 2) # Success rate in percentage
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else:
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item['successRate'] = 0
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# Populate other stats and score
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stats_dict = {
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'avgReturn': item.get('avgReturn', 0),
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'successRate': item.get('successRate', 0),
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'totalRatings': item['totalRatings'],
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'lastRating': item['lastRating'],
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}
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item['analystScore'] = calculate_rating(stats_dict)
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async def get_single_analyst_data(analyst_list):
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async def get_single_analyst_data(analyst_list, con):
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start_date = '2015-01-01'
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end_date = datetime.today().strftime("%Y-%m-%d")
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async with aiohttp.ClientSession() as session:
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tasks = [process_analyst(item, session) for item in analyst_list]
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tasks = [process_analyst(item, con, session, start_date, end_date) for item in analyst_list]
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for task in tqdm(asyncio.as_completed(tasks), total=len(analyst_list)):
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await task
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async def run():
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#Step1 get all analyst id's and stats
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analyst_list = await get_all_analyst_stats()
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print('Number of analyst:', len(analyst_list))
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#Step2 get rating history for each individual analyst and score the analyst
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await get_single_analyst_data(analyst_list)
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# Step1: Get all analyst id's and stats
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con = sqlite3.connect('stocks.db')
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analyst_list = await get_all_analyst_stats()
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print('Number of analysts:', len(analyst_list))
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try:
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con = sqlite3.connect('stocks.db')
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print('Start extracting main sectors')
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for item in tqdm(analyst_list):
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ticker_list = [entry['ticker'] for entry in item['ratingsList']]
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sector_list = []
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for ticker in ticker_list:
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sector = extract_sector(ticker, con)
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sector_list.append(sector)
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#Test Mode
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#analyst_list = [ item for item in analyst_list if item['analystId'] =='597f5b95c1f5580001ef542a']
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sector_counts = Counter(sector_list)
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main_sectors = sector_counts.most_common(3)
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main_sectors = [item[0] for item in main_sectors if item[0] is not None]
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item['mainSectors'] = main_sectors
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con.close()
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except Exception as e:
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print(e)
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# Step2: Get rating history for each individual analyst and score the analyst
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await get_single_analyst_data(analyst_list, con)
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try:
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print('Start extracting main sectors')
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for item in tqdm(analyst_list):
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ticker_list = [entry['ticker'] for entry in item['ratingsList']]
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sector_list = []
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for ticker in ticker_list:
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sector = extract_sector(ticker, con)
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sector_list.append(sector)
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analyst_list = sorted(analyst_list, key=lambda x: float(x['analystScore']), reverse=True)
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number_of_all_analysts = len(analyst_list)
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sector_counts = Counter(sector_list)
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main_sectors = sector_counts.most_common(3)
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main_sectors = [item[0] for item in main_sectors if item[0] is not None]
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item['mainSectors'] = main_sectors
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for rank, item in enumerate(analyst_list):
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item['rank'] = rank+1
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item['numOfAnalysts'] = number_of_all_analysts
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item['avgReturn'] = round(float(item['avgReturn']),2)
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item['successRate'] = round(float(item['successRate']),2)
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with open(f"json/analyst/analyst-db/{item['analystId']}.json", 'w') as file:
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ujson.dump(item, file)
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except Exception as e:
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print(e)
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# Sort analysts by score
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analyst_list = sorted(analyst_list, key=lambda x: (float(x['analystScore']), float(x['avgReturn']), float(x['successRate'])), reverse=True)
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number_of_all_analysts = len(analyst_list)
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#Save top 100 analysts
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top_analysts_list = []
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#Drop the element ratingsList for the top 100 analysts list
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for item in analyst_list[0:100]:
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top_analysts_list.append({
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'analystName': item['analystName'],
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'analystId': item['analystId'],
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'rank': item['rank'],
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'analystScore': item['analystScore'],
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'companyName': item['companyName'],
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'successRate': item['successRate'],
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'avgReturn': item['avgReturn'],
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'totalRatings': item['totalRatings'],
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'lastRating': item['lastRating']
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})
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# Assign rank and other metrics to analysts
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for rank, item in enumerate(analyst_list):
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item['rank'] = rank + 1
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item['numOfAnalysts'] = number_of_all_analysts
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item['avgReturn'] = round(float(item['avgReturn']), 2)
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item['successRate'] = round(float(item['successRate']), 2)
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with open(f"json/analyst/analyst-db/{item['analystId']}.json", 'w') as file:
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ujson.dump(item, file)
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with open(f"json/analyst/top-analysts.json", 'w') as file:
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ujson.dump(top_analysts_list, file)
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# Save top 100 analysts
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top_analysts_list = []
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for item in analyst_list[0:100]:
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top_analysts_list.append({
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'analystName': item['analystName'],
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'analystId': item['analystId'],
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'rank': item['rank'],
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'analystScore': item['analystScore'],
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'companyName': item['companyName'],
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'successRate': item['successRate'],
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'avgReturn': item['avgReturn'],
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'totalRatings': item['totalRatings'],
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'lastRating': item['lastRating']
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})
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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", 'w') as file:
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ujson.dump(analyst_list, file)
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with open(f"json/analyst/top-analysts.json", 'w') as file:
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ujson.dump(top_analysts_list, file)
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#Save top stocks with strong buys from 5 star analysts
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get_top_stocks()
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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", 'w') as file:
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ujson.dump(analyst_list, file)
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# Save top stocks with strong buys from 5-star analysts
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get_top_stocks()
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# Close the connection
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con.close()
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if __name__ == "__main__":
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