bugfixing analyst ticker
This commit is contained in:
MuslemRahimi
parent
7de22df924
commit
6ee0260c76
@ -75,14 +75,16 @@ def get_summary(res_list):
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# Filter the data for the last 12 months and consider the last N ratings
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#Furthermore consider only the last rating of the analyst if he provided multiple in the last 12 months
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#filtered data is needed for the recommendation list
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filtered_data = [item for item in res_list if start_date_12m <= datetime.strptime(item['date'], '%Y-%m-%d').date() <= end_date]
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filtered_data = filter_latest_entries(filtered_data)[:30]
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#unique list is needed for analyst summary rating
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unique_filtered_data = filter_latest_entries(filtered_data)[:30]
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# Initialize dictionary to store the latest price target for each analyst
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latest_pt_current = defaultdict(list)
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# Iterate through the filtered data to collect pt_current for each analyst
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for item in filtered_data:
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for item in unique_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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@ -149,7 +151,7 @@ def get_summary(res_list):
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# Compute consensus ratings (similar to previous implementation)
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consensus_ratings = defaultdict(str)
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for item in filtered_data:
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for item in unique_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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@ -173,7 +175,7 @@ def get_summary(res_list):
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# Count unique analysts
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unique_analyst_names = set()
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numOfAnalyst = len(filtered_data)
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numOfAnalyst = len(unique_filtered_data)
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'''
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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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@ -197,129 +199,111 @@ def get_summary(res_list):
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res = {**stats, **categorical_ratings}
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return res
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def run(chunk,analyst_list, con):
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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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def run(chunk, analyst_list, con):
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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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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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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'] == 'Accumulate' and item['rating_prior'] == 'Buy':
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item['rating_current'] = 'Buy'
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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_company'] = '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 Perform':
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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_company'] = 'Initiates'
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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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summary = get_summary(ticker_filtered_data)
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try:
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# Add historical price for the last 12 months
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query = query_template.format(ticker=ticker)
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df_12m = pd.read_sql_query(query, con, params=(start_date_12m, end_date)).round(2)
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df_12m['date'] = pd.to_datetime(df_12m['date'])
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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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df_12m_last_per_month = df_12m.groupby(df_12m['date'].dt.to_period('M')).tail(1)
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past_price_list = [{"date": row['date'].strftime('%Y-%m-%d'), "close": row['close']} for _, row in df_12m_last_per_month.iterrows()]
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summary["pastPriceList"] = past_price_list
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except:
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summary["pastPriceList"] = []
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summary = get_summary(ticker_filtered_data)
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try:
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#Add historical price for the last 12 month
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query = query_template.format(ticker=ticker)
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df_12m = pd.read_sql_query(query, con, params=(start_date_12m, end_date)).round(2)
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df_12m['date'] = pd.to_datetime(df_12m['date'])
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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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df_12m_last_per_month = df_12m.groupby(df_12m['date'].dt.to_period('M')).tail(1)
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past_price_list = [{"date": row['date'].strftime('%Y-%m-%d'), "close": row['close']} for _, row in df_12m_last_per_month.iterrows()]
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summary["pastPriceList"] = past_price_list
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except:
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summary["pastPriceList"] = []
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for item1 in ticker_filtered_data:
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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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desired_keys = ['date', 'action_company', 'rating_current', 'adjusted_pt_current', 'adjusted_pt_prior', 'analystId', 'analystScore', 'analyst', 'analyst_name']
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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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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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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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