Can the Dual Currency Win Strategy Really Weather Bull and Bear Markets? A 6-Year Backtest Provides the Answer

marsbitPubblicato 2026-02-27Pubblicato ultima volta 2026-02-27

Introduzione

"Can the Dual Currency Win (Wheel Strategy) truly weather bull and bear markets? A 6-year backtest (2020-2026) on Bitcoin and Ethereum provides the answer. The study compared two approaches: the 'Standard Rolling Strike' method, which dynamically sells covered calls at 105% of the current spot price, and the 'Fixed Anchor' method, which stubbornly sells calls at the original, higher cost basis after a drop, refusing to sell at a loss. Key findings reveal a significant performance gap. The Standard method, while sacrificing some upside, demonstrated superior risk-adjusted returns. For a 50/50 BTC/ETH portfolio, it achieved a +1347.32% total return with a -49.9% max drawdown and a Sharpe Ratio of 0.983, outperforming both Buy & Hold (+1665.52%, -77.8% drawdown, 0.85 Sharpe) on risk metrics and crushing the Fixed Anchor method (+592.77%, -61.8% drawdown, 0.766 Sharpe). The data shows the Standard strategy's strength lies in its dynamic adjustment mechanism, continuously resetting its strike price to balance income generation with participation in bullish trends. Conversely, the Fixed Anchor strategy's poor performance highlights the costly pitfall of the 'anchoring bias'—the human tendency to fixate on the entry price. This psychological trap cripples the ability to collect meaningful premium during bear markets and causes investors to miss subsequent bull runs when positions are called away at breakeven. The conclusion is clear: discipline and adaptability are far more valua...

Author: Michel Athayde

Can the Dual Currency Win Strategy Really Weather Bull and Bear Markets?

Using real market data from 2020-2026 for backtesting, we discovered:

Even with the same Dual Currency Win strategy, just by changing how the Calls are sold, the final profit difference can be nearly double.

The data tells us the problem isn't the strategy, it's human nature.

In the crypto market, the "Dual Currency Win" (Wheel Strategy) is often seen as a tool for collecting rent through bull and bear markets. But how do different underlying execution logics reshape long-term profit distribution?

To find the truth, we backtested Bitcoin and Ethereum over a complete bull-bear cycle from 2020-2026. In this sample, which includes crashes and an epic bull market, we compared two截然不同的双币赢玩法:

  • Standard Dual Currency Win (Rolling Strike): Follows the market. After taking delivery of the spot asset, each time a Covered Call is sold dynamically at 105% of the current price.

  • Break-even Type Dual Currency Win (Fixed Anchor): Anchors to cost. Once taking delivery at a high price, no matter how far the price falls, it stubbornly sells Calls at the "last delivery strike price," refusing to give up the chips until breaking even.

This is no longer a simple contest of "selling strategy vs. holding spot," but a deep test of "how trading psychology changes long-term profit distribution."

Core Data: Re-evaluating Risk and Return

(Note: Backtest span 2020-2026, Puts priced at 30% annualized, Calls at 25% annualized, 7-day cycles)

Investment Strategy Total Return Annualized (CAGR) Max Drawdown Sharpe Ratio
BTC HODL (Buy & Hold) +1133.73% 51.95% 0.83
BTC Standard (Rolling) +859.43% 45.72% -42.74% 0.929
BTC Break-even (Fixed) +558.81% 36.88% -61.19% 0.783
--- --- --- --- ---
ETH HODL (Buy & Hold) +2197.31% 68.52% -79.30% 0.87
ETH Standard (Rolling) +1835.21% 63.78% -54.27% 0.971
ETH Break-even (Fixed) +626.74% 39.13% -64.87% 0.724
--- --- --- --- ---
50/50 HODL Portfolio +1665.52% 61.30% <极速赛车开奖网em data-index-in-node="0" data-path-to-node="11,9,3,0">-77.80% 0.85
50/50 Standard Portfolio +1347.32% 56.05% -49.90% 0.983
50/50 Break-even Portfolio +592.77% 38.03% -61.80% 0.766

Faced with this real data, we need to re-examine two core propositions in trading.

The Risk-Return Balancing Act of the Standard Dual Currency Win

Many mistakenly believed the standard strategy would severely underperform in bull markets, but the data proves that with just a 5% upside buffer (spot price * 1.05), it exhibits极强的 risk-return balancing ability over a full cycle.

In the 50/50 portfolio, its Sharpe Ratio (0.983) thoroughly crushed buy-and-hold (0.85) and drastically compressed the nearly -78% abysmal drawdown to -49.9%.

Its advantage doesn't come from predicting the market, but from the mechanism of "continuously dynamically raising the strike price."

With every price change, the standard version relentlessly adjusts its target. Rolling本质上是在牛市中不断“重置成本”,而 Fixed Anchor 却是在不断“确认错误”. The standard version sacrifices a极小部分 of potential暴利上限,换取来了平滑资金曲线的巨大战略纵深.

"Anchoring to Cost" is the Most Expensive Psychological Placebo

The most thought-provoking part of the data is the comprehensive failure of the "Break-even (Fixed Anchor)" type. It fell far short of the standard version in both return and drawdown control.

This exposes the most common weakness in human trading psychology: Anchoring Effect. If you took delivery at a high of 60k, and stubbornly hang a Call at 60k when the price drops to 30k, you not only lose the "bleeding stop" ability of option premiums during the long bear market, but also risk having your chips called away at 60k during a V-shaped market reversal, completely missing the subsequent main upward浪.

The break-even strategy seems conservative, but it's actually using time to fight the trend. And in a trend-driven market, time is often on the side of the trend. Obsessing over "not selling at a loss" is ironically the fastest way to perfectly miss out on major cycle红利.

Conclusion

Markets are full of volatility, but data doesn't lie.

In trending assets like Bitcoin and Ethereum, the real risk is not drawdown, but being limited on the upside by your own psychological anchor.

The standard Dual Currency Win tells us:

As long as you keep adjusting dynamically and rolling continuously, a selling strategy can also coexist with the trend.

And the break-even strategy reminds us:

The market won't change direction because of your cost basis.

Discipline is far more important than breaking even.

Domande pertinenti

QWhat is the main finding of the 6-year backtest (2020-2026) comparing the two versions of the Wheel Strategy?

AThe backtest revealed that the standard 'Rolling Strike' version significantly outperformed the 'Fixed Anchor' version, with the performance gap being nearly double in some cases. The key difference lies not in the strategy itself, but in the human psychology of anchoring to a cost basis.

QHow does the 'Rolling Strike' (Standard) version of the Wheel Strategy manage risk and return compared to simply holding the asset (Buy & Hold)?

AThe 'Rolling Strike' version demonstrated superior risk-adjusted returns. For the 50/50 portfolio, it achieved a higher Sharpe Ratio (0.983 vs 0.85 for Buy & Hold) and significantly reduced the maximum drawdown (-49.9% vs -77.8% for Buy & Hold), while still capturing substantial upside.

QWhy did the 'Fixed Anchor' version of the strategy perform poorly in the backtest?

AThe 'Fixed Anchor' strategy performed poorly because it falls victim to the 'anchoring effect' in behavioral finance. By stubbornly selling calls at the original, higher cost basis during a bear market, it loses the ability to collect meaningful premium ('stop the bleeding') and risks having the asset called away at the break-even point, missing out on a subsequent major bull run.

QAccording to the article, what is the most significant risk when investing in trend assets like Bitcoin and Ethereum using such strategies?

AThe most significant risk is not the price drawdown itself, but the psychological limitation of one's upside potential by being anchored to a specific cost price, which can cause an investor to miss out on major market trends.

QWhat is the core lesson about discipline from the article's conclusion?

AThe core lesson is that maintaining discipline by dynamically adjusting and rolling positions (as in the standard version) is far more important than the psychological desire to simply 'break even' on a trade. The market will not change direction based on an individual's cost basis.

Letture associate

Meta: Can Afford Trillion-Dollar Computing Power, But Can't Keep Key People

Meta's AI Ambition: A $135 Billion Bet on Chips, But Losing Key Talent In July 2025, Meta recruited top AI infrastructure engineer Ruoming Pang from Apple with a compensation package worth over $200 million. However, just seven months later, he left for OpenAI, forfeiting much of his unvested equity. This high-profile departure is part of a broader trend of key talent leaving Meta's AI division, including Chief AI Scientist Yann LeCun and other senior figures. The exodus is largely attributed to the fallout from the Llama 4 model's release in April 2025. The model was later revealed to have been benchmarked unethically, using different model versions to optimize scores on different tests, severely damaging trust within the developer community. This scandal led CEO Mark Zuckerberg to lose confidence in the team, resulting in a major reorganization. He appointed 28-year-old Scale AI CEO Alexandr Wang as Chief AI Officer, who now oversees the new Meta Superintelligent Lab (MSL). The planned flagship model, Llama 4 Behemoth, was indefinitely delayed. Compounding these software issues, Meta also canceled its most advanced in-house AI training chip project, a critical part of its plan to reduce reliance on Nvidia. This failure has triggered a panic-buying spree. In February 2026, Meta announced a capital expenditure budget of $115-$135 billion, nearly double the previous year's. Within ten days, it signed massive, multi-year chip deals: a multi-billion dollar agreement with Nvidia for Blackwell and Vera Rubin GPUs, a $60-$100 billion deal with AMD for MI450 GPUs (which included warrants for a 10% stake), and a multi-billion dollar deal to rent Google's TPU chips. This strategy of acquiring immense, diverse hardware from three different architectures (CUDA, ROCm, XLA/JAX) creates immense engineering complexity. Ironically, Meta is spending hundreds of billions on the world's most complex hardware while losing the rare engineers, like Pang, capable of building the cross-platform frameworks needed to make it all work. Zuckerberg's gamble mirrors his all-in bet on the metaverse: see a trend, spend heavily, reorganize frequently. The difference is that AI is a more tangible opportunity, and Meta's core advertising business generates massive cash flow to fund it. However, the article concludes that money can buy chips and算力, but it cannot guarantee the retention of top talent or the development of a winning model. If Meta's next model, codenamed "Avocado," fails to compete with GPT-5 and Gemini 3 Ultra, its massive expenditure will have only built expensive data centers full of underutilized hardware. The AI race is won by those who can build transformative models, not just those who can write the biggest checks.

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Meta: Can Afford Trillion-Dollar Computing Power, But Can't Keep Key People

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