老李玩钱

About 老李玩钱

老李玩钱 (handle: @老李玩钱) is a YouTube creator based in the United States, operating within the finance content category. The channel has published 444 videos, accumulating a total of 40,982,604 views. With 347,000 subscribers, 老李玩钱 is classified within the mid-tier audience segment. The creator's content focuses on financial topics, as indicated by the primary category. The channel's bio directs viewers to a membership program, suggesting a community-driven approach to content delivery and engagement, potentially offering exclusive content or early access to financial insights. While the primary language is not specified in the provided data, the channel name suggests content aimed at a Chinese-speaking audience. The consistent output of over 400 videos indicates a sustained presence and commitment to the finance niche on the YouTube platform.

老李玩钱's estimated sponsorship rates

The estimated rate for a sponsored video integration on the @老李玩钱 YouTube channel ranges from $1,249 to $3,123, with a typical rate of $2,082. This pricing is influenced by several factors, including the channel's subscriber count of 347,000, which places it in the mid-tier audience category. The finance niche generally commands higher CPMs (cost per mille) due to the high value and specific targeting capabilities of its audience, which can attract premium advertisers. Geographic location, with the creator based in the US, also contributes to the rate, as US-based audiences often have higher advertising value. The specific format of a video integration on YouTube, which typically involves a more in-depth and permanent placement compared to other platforms, further supports this rate structure. For a comprehensive understanding of how these rates are calculated and the specific multipliers applied, please refer to our detailed methodology page.

Who should partner with 老李玩钱?

The audience for 老李玩钱 primarily engages with finance content, indicating a demographic interested in investment strategies, personal finance management, and economic trends. Given the channel's focus, potential brand categories for collaboration include fintech applications, online brokerage platforms, personal-finance education services, wealth management firms, and financial news outlets. The channel's subscriber base of 347,000 suggests an audience size comparable to other finance creators such as Stock Smarts (@stocksmarts_, 345,000 subscribers) and Invertir desde Cero (@invertirdesdecero, 341,000 subscribers). This segment of the audience is likely seeking actionable financial advice, market analysis, and educational content related to money management. The engagement with a membership program, as noted in the channel's bio, further suggests a dedicated and potentially more affluent or serious investor demographic willing to pay for premium financial insights.

老李玩钱's growth and performance

Our tracking of 老李玩钱 began on 2026-05-18, at which point the channel had 347,000 subscribers. As of the latest snapshot on 2026-05-18, the subscriber count remained at 347,000. This represents a 0-subscriber change over a one-day tracking period. We do not yet have view-count history or average views for the last 30 days to provide a more comprehensive analysis of recent engagement trends. Longer-term historical data would be necessary to identify any sustained growth patterns or significant shifts in audience acquisition. Future snapshots will provide a clearer picture of subscriber growth trajectory and audience engagement metrics over time.

How our pricing estimate works for 老李玩钱

The estimated pricing for sponsored content on 老李玩钱 is derived from an algorithmic model. This model takes into account the creator's subscriber count, which is 347,000, and applies a niche-specific CPM (cost per mille) relevant to the finance category. Additionally, platform and format multipliers are applied to adjust the estimate for YouTube video integrations. This approach provides a data-driven range for potential brand collaborations. It is important to note that this estimate is based on our internal metric_estimate calibration, with no community data points currently available to further refine the model. For a detailed breakdown of the specific formulas, data inputs, and assumptions used in our algorithmic pricing, please visit our methodology page at /methodology.