update metrics
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MuslemRahimi
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@ -149,122 +149,6 @@ def compute_q4_results(dataset):
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def generate_revenue_dataset(dataset):
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# Find all unique names and dates
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all_dates = sorted(set(item['date'] for item in dataset))
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all_names = sorted(set(item['name'] for item in dataset))
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# Check and fill missing combinations at the beginning
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name_date_map = defaultdict(lambda: defaultdict(lambda: None))
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for item in dataset:
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name_date_map[item['name']][item['date']] = item['value']
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# Ensure all names have entries for all dates
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for name in all_names:
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for date in all_dates:
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if date not in name_date_map[name]:
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dataset.append({'name': name, 'date': date, 'value': None})
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# Clean and process the dataset values
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processed_dataset = []
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for item in dataset:
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if item['value'] not in (None, '', 0):
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processed_dataset.append({
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'name': item['name'],
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'date': item['date'],
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'value': int(float(item['value']))
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})
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else:
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processed_dataset.append({
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'name': item['name'],
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'date': item['date'],
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'value': None
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})
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dataset = processed_dataset
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name_replacements = {
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"datacenter": "Data Center",
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"professionalvisualization": "Visualization",
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"oemandother": "OEM & Other",
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"automotive": "Automotive",
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"oemip": "OEM & Other",
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"gaming": "Gaming",
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"mac": "Mac",
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"iphone": "IPhone",
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"ipad": "IPad",
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"wearableshomeandaccessories": "Wearables",
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"hardwareandaccessories": "Hardware & Accessories",
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"software": "Software",
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"collectibles": "Collectibles",
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"automotivesales": "Auto",
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"energygenerationandstoragesegment": "Energy and Storage",
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"servicesandother": "Services & Other",
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"automotiveregulatorycredits": "Regulatory Credits",
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"intelligentcloud": "Intelligent Cloud",
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"productivityandbusinessprocesses": "Productivity & Business",
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"searchandnewsadvertising": "Advertising",
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"linkedincorporation": "LinkedIn",
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"morepersonalcomputing": "More Personal Computing",
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"serviceother": "Service Other",
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}
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# Filter out unwanted categories
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excluded_names = {'enterpriseembeddedandsemicustom','computingandgraphics','automotiveleasing ','officeproductsandcloudservices','serverproductsandcloudservices','automotiverevenues','automotive','computeandnetworking','graphics','gpu','automotivesegment','energygenerationandstoragesales','energygenerationandstorage','automotivesaleswithoutresalevalueguarantee','salesandservices','compute', 'networking', 'cloudserviceagreements', 'digital', 'allother', 'preownedvideogameproducts'}
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dataset = [revenue for revenue in dataset if revenue['name'].lower() not in excluded_names]
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# Process and clean the dataset
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for item in dataset:
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try:
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name = item.get('name').lower()
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value = int(float(item.get('value')))
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if name in name_replacements:
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item['name'] = name_replacements[name]
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item['value'] = value
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except:
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pass
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# Group by name and calculate total value
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name_totals = defaultdict(int)
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for item in dataset:
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name_totals[item['name']] += item['value'] if item['value'] != None else 0
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# Sort names by total value and get top 5, ensuring excluded names are not considered
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top_names = sorted(
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[(name, total) for name, total in name_totals.items() if name.lower() not in excluded_names],
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key=lambda x: x[1],
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reverse=True
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)[:5]
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top_names = [name for name, _ in top_names]
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# Filter dataset to include only top 5 names
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dataset = [item for item in dataset if item['name'] in top_names]
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# Sort the dataset
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dataset.sort(key=lambda item: (datetime.strptime(item['date'], '%Y-%m-%d'), item['value'] if item['value'] != None else 0), reverse=True)
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# Process the data into the required format
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result = {}
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for item in dataset:
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date = item['date']
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value = item['value']
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if date not in result:
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result[date] = {'date': date, 'value': []}
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result[date]['value'].append(value)
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# Convert the result dictionary to a list
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res_list = list(result.values())
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print(res_list)
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# Add value growth (assuming add_value_growth function exists)
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res_list = add_value_growth(res_list)
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final_result = {'names': top_names, 'history': res_list}
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return final_result
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def generate_geography_dataset(dataset):
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country_replacements = {
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@ -352,6 +236,130 @@ def generate_geography_dataset(dataset):
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final_result = {'names': unique_names, 'history': res_list}
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return final_result
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def generate_revenue_dataset(dataset):
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name_replacements = {
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"datacenter": "Data Center",
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"professionalvisualization": "Visualization",
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"oemandother": "OEM & Other",
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"automotive": "Automotive",
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"oemip": "OEM & Other",
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"gaming": "Gaming",
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"mac": "Mac",
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"iphone": "IPhone",
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"ipad": "IPad",
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"wearableshomeandaccessories": "Wearables",
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"hardwareandaccessories": "Hardware & Accessories",
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"software": "Software",
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"collectibles": "Collectibles",
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"automotivesales": "Auto",
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"automotiveleasing": "Auto Leasing",
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"energygenerationandstoragesegment": "Energy and Storage",
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"servicesandother": "Services & Other",
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"automotiveregulatorycredits": "Regulatory Credits",
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"intelligentcloud": "Intelligent Cloud",
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"productivityandbusinessprocesses": "Productivity & Business",
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"searchandnewsadvertising": "Advertising",
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"linkedincorporation": "LinkedIn",
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"morepersonalcomputing": "More Personal Computing",
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"serviceother": "Service Other",
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"governmentoperatingsegment": "Government Operating Segment"
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}
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excluded_names = {'government','enterpriseembeddedandsemicustom','computingandgraphics','automotiveleasing ','officeproductsandcloudservices','serverproductsandcloudservices','automotiverevenues','automotive','computeandnetworking','graphics','gpu','automotivesegment','energygenerationandstoragesales','energygenerationandstorage','automotivesaleswithoutresalevalueguarantee','salesandservices','compute', 'networking', 'cloudserviceagreements', 'digital', 'allother', 'preownedvideogameproducts'}
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dataset = [item for item in dataset if item['name'].lower() not in excluded_names]
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# Find all unique names and dates
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all_dates = sorted(set(item['date'] for item in dataset))
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all_names = sorted(set(item['name'] for item in dataset))
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dataset = [revenue for revenue in dataset if revenue['name'].lower() not in excluded_names]
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# Check and fill missing combinations at the beginning
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name_date_map = defaultdict(lambda: defaultdict(lambda: None))
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for item in dataset:
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name_date_map[item['name']][item['date']] = item['value']
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# Ensure all names have entries for all dates
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for name in all_names:
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for date in all_dates:
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if date not in name_date_map[name]:
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dataset.append({'name': name, 'date': date, 'value': None})
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# Clean and process the dataset values
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processed_dataset = []
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for item in dataset:
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if item['value'] not in (None, '', 0):
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processed_dataset.append({
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'name': item['name'],
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'date': item['date'],
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'value': int(float(item['value']))
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})
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else:
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processed_dataset.append({
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'name': item['name'],
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'date': item['date'],
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'value': None
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})
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dataset = processed_dataset
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#If the last value of the latest date is null or 0 remove all names in the list
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dataset = sorted(dataset, key=lambda item: datetime.strptime(item['date'], '%Y-%m-%d'), reverse=True)
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remember_names = set() # Use a set for faster membership checks
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first_date = dataset[0]['date']
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# Iterate through dataset to remember names where date matches first_date and value is None
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for item in dataset:
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if item['date'] == first_date and (item['value'] == None or item['value'] == 0):
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remember_names.add(item['name'])
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print(item['name'])
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# Use list comprehension to filter items not in remember_names
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dataset = [{**item} for item in dataset if item['name'] not in remember_names]
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# Group by name and calculate total value
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name_totals = defaultdict(int)
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for item in dataset:
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name_totals[item['name']] += item['value'] if item['value'] != None else 0
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# Sort names by total value and get top 5, ensuring excluded names are not considered
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top_names = sorted(
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[(name, total) for name, total in name_totals.items() if name.lower() not in excluded_names],
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key=lambda x: x[1],
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reverse=True
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)[:5]
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top_names = [name for name, _ in top_names]
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# Filter dataset to include only top 5 names
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dataset = [item for item in dataset if item['name'] in top_names]
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# Sort the dataset
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dataset.sort(key=lambda item: (datetime.strptime(item['date'], '%Y-%m-%d'), item['value'] if item['value'] != None else 0), reverse=True)
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top_names = [name_replacements[name.lower()] for name in top_names if name.lower() in name_replacements]
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print(top_names)
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result = {}
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for item in dataset:
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date = item['date']
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value = item['value']
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if date not in result:
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result[date] = {'date': date, 'value': []}
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result[date]['value'].append(value)
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# Convert the result dictionary to a list
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res_list = list(result.values())
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# Add value growth (assuming add_value_growth function exists)
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res_list = add_value_growth(res_list)
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final_result = {'names': top_names, 'history': res_list}
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return final_result
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def run(symbol):
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@ -373,7 +381,6 @@ def run(symbol):
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dimensions_dict = ast.literal_eval(dimensions_str) if isinstance(dimensions_str, str) else dimensions_str
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except (ValueError, SyntaxError):
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dimensions_dict = {}
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#print(dimensions_dict)
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for column_name in [
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"srt:StatementGeographicalAxis",
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"us-gaap:StatementBusinessSegmentsAxis",
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@ -381,7 +388,6 @@ def run(symbol):
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]:
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product_dimension = dimensions_dict.get(column_name) if isinstance(dimensions_dict, dict) else None
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# Check if the namespace is 'us-gaap' and product_dimension is valid
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print(product_dimension)
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if row["namespace"] == "us-gaap" and product_dimension is not None and (
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product_dimension.startswith(symbol.lower() + ":") or
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product_dimension.startswith("country" + ":") or
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@ -411,6 +417,7 @@ def run(symbol):
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if column_name in column_list:
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revenue_sources.append({"name": name, "value": row["value"], "date": row["end_date"]})
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else:
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geography_sources.append({"name": name, "value": row["value"], "date": row["end_date"]})
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@ -419,7 +426,6 @@ def run(symbol):
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except Exception as e:
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print(e)
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print(revenue_sources)
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revenue_dataset = generate_revenue_dataset(revenue_sources)
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geographic_dataset = generate_geography_dataset(geography_sources)
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final_dataset = {'revenue': revenue_dataset, 'geographic': geographic_dataset}
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@ -436,6 +442,7 @@ if __name__ == "__main__":
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run('GME', custom_order)
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'''
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for symbol in ['AMD']: #['TSLA','NVDA','AAPL','GME']:
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for symbol in ['TSLA']: #['PLTR','META','TSLA','NVDA','AAPL','GME']:
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#for AMD we need 10-K form to get geography revenue
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run(symbol)
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88
app/test.py
88
app/test.py
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