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as 11-15-2024 4:00pm EST

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Stocks

Consumer Discretionary

Catalog/Specialty Distribution

Nasdaq

Baozun Inc is an e-commerce service partner that helps brands execute their e-commerce strategies in China by selling their goods directly to customers online or by providing services to assist with their e-commerce operations. Its integrated end-to-end e-commerce service capabilities, omni-channel expertise, and technology-driven solutions. Company have two operating segments namely E-Commerce (including BEC and BZI) and Brand Management (BBM).

More About Our BZUN ML Model...

What kind of parameters do you use to train BZUN stocks ML model?

To train our stocks BZUN ML model, we use historical data with over 25 parameters, including volume indicators, volatility indicators, momentum indicators, trend indicators, and other metrics.

How often do you update BZUN ML model?

We update our ML model either weekly or biweekly, depending on the market capitalization of the stocks, using TensorFlow, typically over the weekend.

Why is the accuracy of your BZUN model low?

Unfortunately, our current data provider offers limited historical data, which impacts the accuracy of our ML model. However, as we continue to gather more data over time and add more indicators, we expect the accuracy of our model to improve.

How can I provide and share more data with you to increase your ML model accuracy?

We would greatly appreciate your contribution. Please send an email to [email protected]

Do you offer an ML model for shorter time periods, such as 5 minutes or 15 minutes?

Yes, we offer it for strategy purposes only, with intervals: 1 minute, 2 minutes, 5 minutes, 15 minutes, and 30 minutes etc.

Can I rely on your ML model to make financial decisions regarding buying or selling stocks, or is it only intended for learning purposes?

Our ML model is primarily designed for educational purposes and is not intended to provide financial advice. We strongly recommend consulting with a financial advisor before making any buying or selling decisions.

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