Investors seeking exposure to the digital assets ecosystem often evaluate specialized exchange-traded funds that capture different segments of the cryptocurrency and blockchain value chain. The Schwab Crypto Thematic ETF (STCE) and the CoinShares Bitcoin Mining ETF (WGMI) both target this space but employ distinct approaches. STCE provides passive, index-tracked access to a wider array of companies positioned to benefit from crypto and blockchain advancements, while WGMI delivers active management focused exclusively on Bitcoin miners. These ETFs do not compete directly but serve as complementary or alternative vehicles within the same thematic sector, allowing investors to select based on desired breadth versus specialization.
The Schwab Crypto Thematic ETF (STCE) is a passively managed fund that seeks to track the Schwab Crypto Thematic Index (Net), which targets global companies involved in the development, utilization, or business activities connected to cryptocurrencies, digital assets, blockchain, and distributed ledger technology. The ETF holds approximately 42 securities, with top holdings typically including major players such as Coinbase Global Inc. (COIN), MicroStrategy Inc. (MSTR), Marathon Digital Holdings Inc. (MARA), and Riot Platforms Inc. (RIOT). Sector allocations emphasize technology services at around 57% and financials at approximately 23%, alongside smaller weights in miscellaneous and commercial services. STCE features an expense ratio of 0.30% and employs standard index rebalancing. As a non-diversified fund, it offers thematic exposure with moderate concentration in leading holdings.
The CoinShares Bitcoin Mining ETF (WGMI) is an actively managed fund that seeks to provide targeted exposure to the Bitcoin mining industry. The ETF typically holds 27 to 28 securities, with significant allocations to mining operators such as Cipher Mining Inc. (CIFR), IREN Limited (IREN), and TeraWulf Inc. (WULF). Sector breakdowns concentrate heavily in financial services at roughly 51% and technology at approximately 45%, reflecting the operational characteristics of mining firms. WGMI carries an expense ratio of 0.75% and uses active management without a fixed index benchmark. This structure allows for dynamic adjustments to mining-specific opportunities and risks within the digital assets sector.
The digital assets and blockchain sector continues to evolve amid macroeconomic influences including interest rate expectations, regulatory developments, and capital flows into cryptocurrency infrastructure. Bitcoin mining operations face variables such as energy costs, network difficulty adjustments, and hardware efficiency trends, while broader crypto-themed companies respond to adoption rates and institutional participation. Both ETFs operate in an environment shaped by evolving regulatory clarity around digital assets and potential shifts in risk appetite tied to broader equity market cycles. Capital allocation within the sector often rotates between mining specialists and diversified technology or financial firms involved in crypto services.
In recent market cycles, the broader thematic exposure of STCE has aligned with movements in major crypto-related equities and blockchain adopters, benefiting from diversification across multiple sub-themes. WGMI’s concentrated focus on Bitcoin miners has produced performance patterns more closely tied to Bitcoin price dynamics, mining margins, and operational execution during periods of sector rotation. Relative positioning highlights STCE’s lower cost structure and wider holdings as potential stabilizers, contrasted with WGMI’s higher expense ratio and specialized mandate, which may amplify sensitivity to mining-specific catalysts such as hash rate growth or energy market shifts. Volatility differences stem from these structural distinctions rather than isolated price events.
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Based on observable structural factors, Tickeron’s AI would currently assign a higher probability of favor to the Schwab Crypto Thematic ETF (STCE) due to its lower expense ratio, broader diversification across 42 holdings, and passive index methodology that provides consistent exposure to the expanding crypto and blockchain ecosystem. These characteristics support improved cost efficiency and reduced single-theme concentration risk relative to the more specialized and higher-cost CoinShares Bitcoin Mining ETF (WGMI).
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| STCE | WGMI | STCE / WGMI | |
| Gain YTD | 28.853 | 85.472 | 34% |
| Net Assets | 305M | 394M | 77% |
| Total Expense Ratio | 0.30 | 0.75 | 40% |
| Turnover | 60.00 | 40.00 | 150% |
| Yield | 1.46 | 0.00 | - |
| Fund Existence | 4 years | 4 years | - |
| STCE | WGMI | |
|---|---|---|
| RSI ODDS (%) | 1 day ago 90% | 1 day ago 90% |
| Stochastic ODDS (%) | 1 day ago 88% | 1 day ago 90% |
| Momentum ODDS (%) | 1 day ago 90% | 1 day ago 90% |
| MACD ODDS (%) | 1 day ago 90% | 1 day ago 90% |
| TrendWeek ODDS (%) | 1 day ago 88% | 1 day ago 90% |
| TrendMonth ODDS (%) | 1 day ago 90% | 1 day ago 90% |
| Advances ODDS (%) | 2 days ago 90% | 8 days ago 90% |
| Declines ODDS (%) | 7 days ago 89% | 1 day ago 90% |
| BollingerBands ODDS (%) | 1 day ago 89% | 1 day ago 90% |
| Aroon ODDS (%) | 1 day ago 90% | 1 day ago 90% |
A.I.dvisor indicates that over the last year, STCE has been closely correlated with CLSK. These tickers have moved in lockstep 86% of the time. This A.I.-generated data suggests there is a high statistical probability that if STCE jumps, then CLSK could also see price increases.
| Ticker / NAME | Correlation To STCE | 1D Price Change % | ||
|---|---|---|---|---|
| STCE | 100% | -2.15% | ||
| CLSK - STCE | 86% Closely correlated | -1.46% | ||
| RIOT - STCE | 84% Closely correlated | +0.19% | ||
| CIFR - STCE | 84% Closely correlated | -1.78% | ||
| HUT - STCE | 84% Closely correlated | -0.59% | ||
| MARA - STCE | 80% Closely correlated | -1.01% | ||
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A.I.dvisor indicates that over the last year, WGMI has been closely correlated with CIFR. These tickers have moved in lockstep 91% of the time. This A.I.-generated data suggests there is a high statistical probability that if WGMI jumps, then CIFR could also see price increases.
| Ticker / NAME | Correlation To WGMI | 1D Price Change % | ||
|---|---|---|---|---|
| WGMI | 100% | -1.39% | ||
| CIFR - WGMI | 91% Closely correlated | -1.78% | ||
| IREN - WGMI | 87% Closely correlated | -3.73% | ||
| HUT - WGMI | 86% Closely correlated | -0.59% | ||
| RIOT - WGMI | 84% Closely correlated | +0.19% | ||
| CLSK - WGMI | 83% Closely correlated | -1.46% | ||
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