Bitcoin is one of the most widely discussed financial assets of the modern era. Since its inception, it has evolved from a niche digital experiment into a globally recognized investment instrument with institutional adoption and billions in daily trading volume. Despite its inherent volatility, Bitcoin has demonstrated a strong long-term growth trajectory, making it an attractive candidate for trend-based and momentum-oriented trading strategies. In this study, we apply concepts from technical analysis to construct and refine a trend-following strategy for Bitcoin, progressing step by step from a simple MACD setup toward an improved multi-timeframe model.

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– Updated architecture for Quantpedia’s AI Assistant
– 12 new Quantpedia Premium strategies have been added to our database
– 7 new related research papers have been included in existing Premium strategies during the last month
– Additionally, we have produced 9 new backtests written in QuantConnect code
– 5 new blog posts that you may find interesting have been published on our Quantpedia blog in the previous month

MicroStrategy has become one of the most polarizing companies in public markets. Once a conventional business intelligence firm, it has transformed into the world’s largest publicly traded Bitcoin proxy, holding over a million BTC on its balance sheet and continuously raising capital to buy more. Supporters praise it as a visionary “Bitcoin ETF with leverage,” while critics argue it is an irrationally overvalued vehicle whose market capitalization regularly trades far above the fair value of its underlying assets. The persistent premium — the gap between MicroStrategy’s equity value and the market value of its Bitcoin holdings — has puzzled analysts, defied traditional valuation logic, and raised the question: why does this spread exist, and why does it not close through arbitrage? A recent academic paper, Valuing MicroStrategy, offers a structural model that explains this phenomenon and sheds light on how the firm’s unique financing mechanics allow its stock price to exceed the value of its assets.

This article examines the impact of major consumer holidays, Thanksgiving and Christmas, on financial markets. Using historical price data from 2004 to 2024, we analyze daily performance trends in the 10 trading days before and after each holiday to determine whether seasonal spending influences asset prices. Our findings suggest that seasonal consumer spending influences financial markets, with Amazon benefiting around Thanksgiving and gold during Christmas.

Cryptocurrencies have matured from experimental curiosities into a viable investable asset class whose return-generation and risk characteristics merit treatment within empirical asset pricing. A recent paper by Nicola Borri, Yukun Liu, Aleh Tsyvinski, Xi Wu summarizes ten facts from the literature that show cryptocurrencies share important similarities with traditional markets—comparable risk-adjusted performance and a small set of cross-sectional factors—while retaining distinctive features such as frequent large jumps and price signals embedded in blockchain data. Key themes include portfolio diversification, factor structure, market microstructure, and the evolving role of regulation and derivatives in shaping market discovery and stability.

The clustering of excess returns on the final trading days of the month constitutes a robust empirical regularity with significant implications for portfolio construction. We document a month-end premium that is both statistically and economically significant, distinct from the canonical turn-of-the-month (ToM) effect. Our strategy highlights systematic style rotations—particularly shifts in value versus growth exposures, as proxied by the IVE–IVW spread—and documents parallel contemporaneous dislocations between real-estate and broad-equity benchmarks, as measured by the IYR–SPY spread.