update consensus rating
This commit is contained in:
MuslemRahimi
parent
c8fe9fb255
commit
e17316e8d6
@ -67,35 +67,32 @@ def get_all_analyst_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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# 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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#unique list is needed for analyst summary rating
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# Filter data to include only ratings within the last 12 months
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filtered_data = [
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item for item in res_list
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if start_date_12m <= datetime.strptime(item['date'], '%Y-%m-%d').date() <= end_date
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]
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# Use only the latest rating per analyst and limit to 60 entries
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unique_filtered_data = filter_latest_entries(filtered_data)[:60]
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# Initialize dictionary to store the latest price target for each analyst
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# Collect 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 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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pt_current_value = float(item['adjusted_pt_current'])
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# Collect all pt_current values for each analyst
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latest_pt_current[analyst_name].append(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['adjusted_pt_current']}")
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# Compute statistics for price targets
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pt_current_values = [val for sublist in latest_pt_current.values() for val in sublist]
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#remove outliers to keep high and low price target reasonable
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# Remove outliers using the IQR method
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q1, q3 = np.percentile(pt_current_values, [25, 75])
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iqr = q3 - q1
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pt_current_values = [x for x in pt_current_values if (q1 - 1.5 * iqr) <= x <= (q3 + 1.5 * iqr)]
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# Compute different price target metrics if there are values, otherwise set to 0
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if pt_current_values:
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median_pt_current = statistics.median(pt_current_values)
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avg_pt_current = statistics.mean(pt_current_values)
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@ -104,80 +101,60 @@ def get_all_analyst_summary(res_list):
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else:
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median_pt_current = avg_pt_current = low_pt_current = high_pt_current = 0
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# Initialize recommendation tracking
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# Define rating hierarchy for conversion
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rating_hierarchy = {'Strong Sell': 0, 'Sell': 1, 'Hold': 2, 'Buy': 3, 'Strong Buy': 4}
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# Track monthly recommendations
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# Track monthly recommendations for visualization
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monthly_recommendations = {}
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# Iterate through the filtered data to track monthly recommendations
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for item in filtered_data:
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# Extract month from the date
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item_date = datetime.strptime(item['date'], '%Y-%m-%d')
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month_key = item_date.strftime('%Y-%m-01')
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# Initialize month's recommendation counts if not exists
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if month_key not in monthly_recommendations:
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monthly_recommendations[month_key] = {
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'Strong Sell': 0,
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'Sell': 0,
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'Hold': 0,
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'Buy': 0,
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'Strong Buy': 0
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}
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# Check and increment recommendation count for the month
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monthly_recommendations[month_key] = {key: 0 for key in rating_hierarchy.keys()}
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if 'rating_current' in item and item['rating_current'] in rating_hierarchy:
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monthly_recommendations[month_key][item['rating_current']] += 1
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# Convert monthly recommendations to a sorted list
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recommendation_list = []
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for month in sorted(monthly_recommendations.keys()):
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month_data = monthly_recommendations[month]
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recommendation_list.append({
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'date': month,
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'Strong Sell': month_data['Strong Sell'],
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'Sell': month_data['Sell'],
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'Hold': month_data['Hold'],
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'Buy': month_data['Buy'],
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'Strong Buy': month_data['Strong Buy']
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**month_data
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})
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# Compute consensus ratings (similar to previous implementation)
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consensus_ratings = defaultdict(str)
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# Build a dictionary with the latest rating per analyst
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consensus_ratings = {}
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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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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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if item.get('rating_current') and item.get('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[item['analyst_name']] = current_rating
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# --- New Robust Consensus Rating Calculation ---
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# Convert each valid rating into its numeric value
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rating_values = [rating_hierarchy[r] for r in consensus_ratings.values() if r in rating_hierarchy]
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if rating_values:
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# Compute the median and round it to the nearest integer
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consensus_numeric = round(statistics.median(rating_values))
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# Map the numeric consensus back to its corresponding rating string
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inverse_rating_hierarchy = {v: k for k, v in rating_hierarchy.items()}
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consensus_rating = inverse_rating_hierarchy.get(consensus_numeric, 'Hold')
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else:
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consensus_rating = 'Hold'
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# -------------------------------------------------
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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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# Sum up all Buy, Sell, Hold for the progress bar in sveltekit
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data_dict = dict(consensus_rating_counts)
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# Build aggregated counts for Buy, Sell, and Hold (for the progress bar)
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data_dict = {key: 0 for key in rating_hierarchy.keys()}
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for r in consensus_ratings.values():
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data_dict[r] += 1
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buy_total = data_dict.get('Strong Buy', 0) + data_dict.get('Buy', 0)
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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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# Count unique analysts
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unique_analyst_names = set()
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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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unique_analyst_names.add(item['analyst_name'])
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numOfAnalyst += 1
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'''
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# Update stats dictionary with new keys including recommendationList
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# Update stats dictionary with computed metrics and the recommendation list
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stats = {
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'numOfAnalyst': numOfAnalyst,
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'consensusRating': consensus_rating,
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@ -193,40 +170,36 @@ def get_all_analyst_summary(res_list):
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res = {**stats, **categorical_ratings}
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return res
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def get_top_analyst_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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res_list = [item for item in res_list if item['analystScore'] >= 4]
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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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# Filter data to only include ratings from the last 12 months
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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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#unique list is needed for analyst summary rating
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# Ensure only the latest rating per analyst is used
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unique_filtered_data = filter_latest_entries(filtered_data)
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print(unique_filtered_data)
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# Initialize dictionary to store the latest price target for each analyst
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# Collect 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 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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pt_current_value = float(item['adjusted_pt_current'])
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# Collect all pt_current values for each analyst
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latest_pt_current[analyst_name].append(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['adjusted_pt_current']}")
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# Compute statistics for price targets
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# Compute statistics for price targets (removing outliers)
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pt_current_values = [val for sublist in latest_pt_current.values() for val in sublist]
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#remove outliers to keep high and low price target reasonable
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q1, q3 = np.percentile(pt_current_values, [25, 75])
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iqr = q3 - q1
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pt_current_values = [x for x in pt_current_values if (q1 - 1.5 * iqr) <= x <= (q3 + 1.5 * iqr)]
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# Compute different price target metrics if there are values, otherwise set to 0
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if pt_current_values:
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q1, q3 = np.percentile(pt_current_values, [25, 75])
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iqr = q3 - q1
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pt_current_values = [x for x in pt_current_values if (q1 - 1.5 * iqr) <= x <= (q3 + 1.5 * iqr)]
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if pt_current_values:
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median_pt_current = statistics.median(pt_current_values)
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avg_pt_current = statistics.mean(pt_current_values)
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@ -235,75 +208,60 @@ def get_top_analyst_summary(res_list):
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else:
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median_pt_current = avg_pt_current = low_pt_current = high_pt_current = 0
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# Initialize recommendation tracking
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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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# Track monthly recommendations
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# Track monthly recommendations for visualization
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monthly_recommendations = {}
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# Iterate through the filtered data to track monthly recommendations
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for item in filtered_data:
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# Extract month from the date
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item_date = datetime.strptime(item['date'], '%Y-%m-%d')
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month_key = item_date.strftime('%Y-%m-01')
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# Initialize month's recommendation counts if not exists
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if month_key not in monthly_recommendations:
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monthly_recommendations[month_key] = {
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'Strong Sell': 0,
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'Sell': 0,
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'Hold': 0,
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'Buy': 0,
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'Strong Buy': 0
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}
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monthly_recommendations[month_key] = {key: 0 for key in rating_hierarchy.keys()}
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# Check and increment recommendation count for the month
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if 'rating_current' in item and item['rating_current'] in rating_hierarchy:
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monthly_recommendations[month_key][item['rating_current']] += 1
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# Convert monthly recommendations to a sorted list
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recommendation_list = []
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for month in sorted(monthly_recommendations.keys()):
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month_data = monthly_recommendations[month]
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recommendation_list.append({
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'date': month,
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'Strong Sell': month_data['Strong Sell'],
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'Sell': month_data['Sell'],
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'Hold': month_data['Hold'],
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'Buy': month_data['Buy'],
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'Strong Buy': month_data['Strong Buy']
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**month_data
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})
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# Compute consensus ratings (similar to previous implementation)
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consensus_ratings = defaultdict(str)
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# Build a dictionary with the latest rating per analyst
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consensus_ratings = {}
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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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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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if item.get('rating_current') and item.get('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[item['analyst_name']] = current_rating
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# --- New Robust Consensus Rating Calculation ---
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# Convert each valid rating into its numeric score and compute the median
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rating_values = [rating_hierarchy[r] for r in consensus_ratings.values() if r in rating_hierarchy]
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if rating_values:
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consensus_numeric = round(statistics.median(rating_values))
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# Map the numeric consensus back to its corresponding rating string
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inverse_rating_hierarchy = {v: k for k, v in rating_hierarchy.items()}
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consensus_rating = inverse_rating_hierarchy.get(consensus_numeric, 'Hold')
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else:
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consensus_rating = 'Hold'
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# -------------------------------------------------
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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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# Sum up all Buy, Sell, Hold for the progress bar in sveltekit
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data_dict = dict(consensus_rating_counts)
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# Sum up the recommendation counts for Buy, Sell, and Hold for progress bar purposes
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data_dict = {key: 0 for key in rating_hierarchy.keys()}
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for r in consensus_ratings.values():
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data_dict[r] += 1
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buy_total = data_dict.get('Strong Buy', 0) + data_dict.get('Buy', 0)
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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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# Count unique analysts
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unique_analyst_names = set()
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# Count the unique analysts used in the unique filtered data
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numOfAnalyst = len(unique_filtered_data)
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# Update stats dictionary with new keys including recommendationList
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# Prepare the stats dictionary with all the computed values
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stats = {
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'numOfAnalyst': numOfAnalyst,
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'consensusRating': consensus_rating,
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@ -315,7 +273,6 @@ def get_top_analyst_summary(res_list):
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}
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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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@ -465,7 +422,6 @@ def run(chunk, analyst_list, con):
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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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@ -479,7 +435,7 @@ try:
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chunk_size = len(stock_symbols) // 300 # 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 = [['AAPL']]
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#chunks = [['AAPL']]
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for chunk in chunks:
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run(chunk, analyst_stats_list, con)
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