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Изворен канал @pythonotes · Post #397 · 12 ное.

Использование Pydantic сегодня стало нормой, и это правильно. Но иногда на ревью вижу, что используют его не всегда корректно. Например, метод BaseModel.model_dump() по умолчанию не преобразует стандартные типы, такие как datetime, UUID или Decimal, в простой сериализуемый для JSON вид. Тогда пишут кастмоный сериализатор для этих типов чтобы функция json.dump() не падала с ошибкой. import uuid from datetime import datetime from decimal import Decimal from uuid import UUID from pydantic import BaseModel class MyModel(BaseModel): id: UUID date: datetime value: Decimal obj = MyModel( id=uuid.uuid4(), date=datetime.now(), value='1.23' ) print(obj.model_dump()) # не подходит для json.dump # { # 'id': UUID('4f8c1bc4-25fd-40cd-9dbe-2c73639b0dc1'), # 'date': datetime.datetime(2025, 12, 12, 12, 12, 12, 111111), # 'value': Decimal('1.23') # } # добавляем свой кастомный сериализатор json.dumps(obj.model_dump(), cls=MySerializer) # { # 'id': '4f8c1bc4-25fd-40cd-9dbe-2c73639b0dc1', # 'date': '2025-12-12T12:12:12.111111', # 'value': '1.23' # } В данном случае класс MySerializer обрабатывает datetime, UUID и Decimal. Например так: class MySerializer(json.JSONEncoder): def default(self, o): if isinstance(o, Decimal): return str(o) elif isinstance(o, datetime): return o.isoformat() elif isinstance(o, UUID): return str(o) return super().default(o) Специально для тех, кто всё еще так делает - в этом нет необходимости! Pydantic может это сделать сам, просто нужно добавить параметр mode="json". json.dumps(obj.model_dump(mode="json")) # { # 'id': '4f8c1bc4-25fd-40cd-9dbe-2c73639b0dc1', # 'date': '2012-12-12T12:12:12.111111', # 'value': '1.23' # } #pydantic#libs

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@CryptoM · Post #64886 · 10.04.2026 г., 07:04

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🚀 BlackRock's Bitcoin Trust Sees Significant Inflows Amid Market Dynamics Investors directed $269.3 million into BlackRock’s iShares Bitcoin Trust on Thursday, marking its most successful day since early March, coinciding with the escalation of the US-Iran conflict. According to Cointelegraph, this influx helped reverse two days of net outflows among the 12 U.S. spot Bitcoin ETFs, culminating in a net inflow of $358.1 million. Bitcoin ETF inflows serve as a measure of both retail and institutional interest in Bitcoin. The Fidelity Wise Origin Bitcoin Fund (FBTC) followed with $53.3 million in inflows, while the newly launched Morgan Stanley Bitcoin Trust (MSBT) contributed $14.9 million on its second trading day, as reported by Farside Investors. Bitwise and ARK 21Shares' Bitcoin ETFs saw inflows of $11.7 million and $4.8 million, respectively, while Franklin Templeton and VanEck’s Bitcoin products recorded around $2 million. Since March 23, BlackRock’s IBIT has accumulated $1.5 billion in net inflows, despite a broader crypto market downturn that saw Bitcoin's price drop from a 2026 high of $97,000 to $72,100. Robert Mitchnick, BlackRock’s digital assets head, remarked in March that IBIT investors tend to be "disproportionately long-term buy and hold" investors, even amid significant selling pressure in the Bitcoin market. Meanwhile, Amy Oldenburg, Morgan Stanley’s digital asset head, highlighted in a Bloomberg interview that MSBT was the bank’s most successful ETF launch to date. She noted, "This is just the first of a long roadmap of new products on the asset management side." Morgan Stanley has also filed to list a staked Ether (ETH) ETF and Solana (SOL) ETF. With the recent inflows, U.S. spot Bitcoin ETFs are nearing a year-to-date net inflow. The Bitcoin ETFs concluded 2025 with $56.59 billion in net inflows and currently stand at $56.51 billion, just $80 million shy of returning to their initial inflow figures for the year. #BlackRock#BitcoinTrust#BitcoinETF#MarketInflows#Fidelity#MorganStanley#Cryptocurrency#Bitcoin#ETFs#Investment#DigitalAssets#USIranConflict#CryptoMarket#BTC#ETH#SOL

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