# Backtest Articoli collegati

Il Centro Notizie HTX fornisce gli articoli più recenti e le analisi più approfondite su "Backtest", coprendo tendenze di mercato, aggiornamenti sui progetti, sviluppi tecnologici e politiche normative nel settore crypto.

If You Bought One Deep OTM Bitcoin Put Option Every Month Since 2018, Could You Make Money in the Long Run?

Based on a systematic backtest from 2018 to 2026, this study examines the long-term profitability of a monthly strategy of buying one deep out-of-the-money (OTM) put option on Bitcoin (BTC) and Ethereum (ETH), with a target delta of 0.01 and a 30-day expiration. The results are highly divergent. The strategy is not a stable source of profit but a classic, path-dependent tail insurance tool characterized by extreme right skew, very low win rates, and severe drawdowns. For BTC, the strategy yielded a final total return of 97.62% (CAGR: 8.66%), while for ETH, it resulted in a -73.07% loss (CAGR: -14.78%). The performance difference is attributed to BTC's extreme payouts being sufficient to cover the long-term cost of premiums, whereas ETH's were not. Key characteristics of the strategy include: * Extremely low win rates (BTC: 2.04%, ETH: 1.02%). * Catastrophic maximum drawdowns (BTC: -97.24%, ETH: -93.82%). * The median trade return was -100% for both assets. * Profits are driven entirely by a few extreme winning trades, with the top 5 trades contributing over 10x the net profit for BTC. * Notably, not all major market crashes (e.g., March 2020, LUNA, FTX) resulted in profitable positions due to timing and strike price placement. Parameter sensitivity analysis showed that a delta of 0.02 offered a more balanced risk-return profile across metrics. The strategy is best suited for investors who can tolerate years of continuous losses, view it as portfolio insurance rather than a primary alpha generator, and seek convexity against extreme downside events. It is not suitable for those seeking stable returns or with low risk tolerance.

marsbit03/16 11:11

If You Bought One Deep OTM Bitcoin Put Option Every Month Since 2018, Could You Make Money in the Long Run?

marsbit03/16 11:11

Your Backtest Is Lying: Why You Must Use Point-in-Time Data

The article warns that backtests using revised historical data are misleading due to look-ahead bias. It illustrates this with a hypothetical trading strategy based on Bitcoin exchange outflows from Binance: entering the market when the 5-day moving average of BTC balance falls below the 14-day average, and exiting on the reverse. Initial backtest results using current data appeared reasonably successful, even comparable to buy-and-hold at times. However, the author argues this is deceptive because metrics like exchange balances are often revised retroactively as new data arrives, meaning a backtest uses information that wasn't available in real-time. Re-running the identical strategy using Point-in-Time (PiT) data—which is immutable and reflects only what was known when the data was first published—yielded significantly worse performance. The PiT-based strategy failed to capture key market upswings as effectively, resulting in meaningfully lower cumulative returns. The key takeaway is that only immutable, Point-in-Time data ensures a backtest accurately replays history without look-ahead bias, and that using revised data will produce lying backtests.

insights.glassnode03/13 14:12

Your Backtest Is Lying: Why You Must Use Point-in-Time Data

insights.glassnode03/13 14:12

Your Backtest Is Lying: Why You Must Use Point-in-Time Data

This article warns against a common pitfall in backtesting trading strategies: look-ahead bias caused by using revised historical data. It illustrates this with a hypothetical Bitcoin strategy based on exchange outflows from Binance. The strategy is built on the premise that sustained outflows (when the 5-day moving average of BTC balance falls below the 14-day average) are bullish, while inflows signal a sell-off. An initial backtest using standard, revised data shows the strategy performing comparably to a simple buy-and-hold approach. However, the author argues these results are misleading because the data has been updated with information that wasn't available in real-time. This data mutation creates an unfair advantage in the backtest. To demonstrate, the test is rerun using Point-in-Time (PiT) data—an immutable, append-only record that reflects only what was known on any given day. The results are significantly worse, as the PiT-based strategy misses key profitable moves. The key takeaway is that accurate backtesting requires immutable Point-in-Time data to avoid look-ahead bias and replay history honestly.

insights.glassnode03/13 14:12

Your Backtest Is Lying: Why You Must Use Point-in-Time Data

insights.glassnode03/13 14:12

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

"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 valuable than the psychological comfort of 'breaking even.' The true risk in trending assets is not volatility, but being anchored to a past price, which severely limits future upside potential."

marsbit02/27 09:25

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

marsbit02/27 09:25

MACD Real Backtest: Can Technical Indicators Lead You to Profit?

Based on a comprehensive 5-year backtest of the MACD trading strategy on BTC and ETH, this analysis delivers a sobering reality check for traders. The key finding is that 90% of short-term trading activity, particularly lower timeframes (15m, 30m, 1h), underperforms a simple "buy and hold" strategy due to transaction costs, noise, and psychological strain. The "benchmark" returns for simply holding the assets were +48.86% for BTC and +53.00% for ETH. The data reveals that MACD strategy performance is highly dependent on timeframe and leverage: * **Short Timeframes (15m, 30m, 1h):** Nearly all configurations resulted in significant losses or complete liquidation (-100%), severely underperforming the buy-and-hold benchmark. * **4-Hour Timeframe:** This was the only timeframe where the MACD strategy consistently generated alpha. * **BTC 4h (1x leverage):** ~+96% return, successfully outperforming buy-and-hold by avoiding major bear markets. * **ETH 4h (1x leverage):** ~+205% return, dramatically outperforming its buy-and-hold benchmark due to ETH's strong trend-following characteristics. * **Leverage Impact:** Leverage (2x, 3x) on the 4h timeframe amplified these gains effectively (e.g., ETH 4h 3x leverage yielded +552%). However, higher leverage (5x) often led to diminished returns due to funding fees and volatility decay, despite increased risk. The "Death Matrix" of results shows that short-term, high-leverage trading is akin to gambling" with a near-certain outcome of failure. The final recommendation is clear: for most investors, a buy-and-hold strategy is superior to active trading on low timeframes. For those seeking to outperform, the only viable approach is applying moderate leverage (2x-3x) exclusively on the 4-hour timeframe, with ETH presenting the best opportunity for significant excess returns.

marsbit01/17 08:45

MACD Real Backtest: Can Technical Indicators Lead You to Profit?

marsbit01/17 08:45

The Harsh Truth of Leveraged DCA: When Math Collides with Human Nature

The article "The Harsh Truth of Leveraged DCA: When Math Meets Human Nature" examines the risks and realities of leveraged Bitcoin dollar-cost averaging (DCA) strategies through a five-year backtest. Using an initial investment of $18,250 with weekly contributions, the study compares three strategies: spot DCA, 2x leveraged DCA, and 3x leveraged DCA. While the 3x leverage eventually yielded the highest final balance at $68,833, it only outperformed the 2x leverage ($66,474) by a marginal 3.5%—far less than the gain from moving from spot ($42,717) to 2x leverage. The key insight is the severe downside: a 95.9% max drawdown for 3x leverage, requiring a 2400% recovery to break even, compared to 85.9% for 2x and 49.9% for spot. The "ulcer index," measuring psychological stress, was significantly higher for leveraged strategies (0.51 for 3x vs. 0.15 for spot). The underperformance of high leverage is attributed to volatility drag—daily rebalancing in volatile markets erodes capital through buy-high, sell-low mechanics. The article concludes that spot DCA offers the best risk-adjusted returns (Sharpe and Sortino ratios) and psychological sustainability, while 3x leverage is inefficient and perilous over the long term. True wealth accumulation requires discipline and time, not unsustainable leverage.

marsbit01/16 05:54

The Harsh Truth of Leveraged DCA: When Math Collides with Human Nature

marsbit01/16 05:54

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