Thesis Machine Learning
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Thesis Bitcoin Opportunistic Overlay

The Thesis Bitcoin Opportunistic Overlay models are designed to adjust exposure using Thesis model signals. Two variants are shown: Core and Dynamic. Benchmark: Large Cap Equity.

Showing Weekly Values (24h delayed)

Key Stats: Core vs Benchmark

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Key Stats: Dynamic vs Benchmark

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Fact Sheet

Data as of — • Weekly values (24h delayed)

Click Print / Save as PDF to generate the full fact sheet (description, chart, risk & return, and quarterly performance).

Trailing Returns

Series 1M YTD 3M 1Y 3Y Ann. Since Incep. Ann.
Core — — — — — —
Dynamic — — — — — —
Benchmark — — — — — —

Returns are calculated from weekly levels (24h delayed). Period endpoints use the closest available date on or before the target date.

Risk & Return (Since Inception)

Metric Core Dynamic Benchmark
Cumulative Return — — —
CAGR — — —
Annual Volatility — — —
Sharpe Ratio — — —
Max Drawdown — — —

Quarterly Performance (Calendar Year)

Core vs Benchmark
Year Q1 Q2 Q3 Q4 Model (CY) Benchmark (CY)
Dynamic vs Benchmark
Year Q1 Q2 Q3 Q4 Model (CY) Benchmark (CY)

Disclosures

  • This material is provided for informational purposes only and does not constitute investment advice or an offer to buy or sell any security.
  • Model results may include hypothetical or backtested performance. Hypothetical performance has limitations and does not represent actual trading.
  • Performance shown does not reflect transaction costs, advisory fees, or taxes. Actual results may differ materially.
  • Past performance is not indicative of future results. Investing involves risk, including the possible loss of principal.
  • Benchmarks and indexes are unmanaged and cannot be invested in directly.

Methodology

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Download Methodology PDF

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