Tag: Momentum

This study adapts trend-following strategies—rooted in traditional finance—to cryptocurrencies, using Donchian channel ensembles and volatility-based sizing. Applied to a rotational portfolio of top coins, the strategy delivers strong risk-adjusted returns, outperforming Bitcoin with notable alpha. It also addresses transaction costs and explores integration with traditional asset strategies, offering practical on- and off-chain implementation paths.

This study revisits Wilcox and Crittenden’s 2005 work on trend following in stocks, analyzing 66,000+ trades from 1950–2024. Results confirm that less than 7% of trades drive profitability, with strong out-of-sample performance (2005–2024). A backtested portfolio shows high gross returns but faces turnover challenges. A Turnover Control algorithm mitigates costs, making the strategy viable across portfolio sizes after fees.

This study evaluates the effectiveness of technical analysis in trading, particularly in how it enhances the performance of a simple automatic trading strategy. We simulate a trading environment where a skilled technical trader guides the strategy to focus on stocks with promising charts, especially those with significant overnight gaps. The trader also micromanages open positions by analyzing daily and intraday price actions. The findings indicate that skilled discretionary technical trading can significantly improve trading outcomes, providing empirical support for combining systematic and discretionary approaches in financial markets.

This study examines the profitability of a long-only trend-following portfolio across 48 industry sectors from 1926 to 2024. The analysis demonstrates the model’s effectiveness through its 18.5% average annual return, significantly outperforming the US equity market’s 9.7% return. The Timing Industry strategy not only offers higher returns but also reduced volatility and drawdowns, achieving a Sharpe Ratio of 1.46. We also analyze the performance using 31 sector ETFs over the past 20 years, confirming the strategy’s robustness and profitability even after accounting for trading costs.

This paper investigates a simple yet effective intraday momentum strategy for SPY, a highly liquid ETF tracking the S&P500. Unlike typical studies that limit trading to the last 30 minutes, our model initiates trades based on intraday demand/supply imbalances. Using techniques from active day traders and dynamic trailing stops, the strategy achieved a 1,985% total return (net of costs), 19.6% annualized return, and a 1.33 Sharpe Ratio from 2007 to early 2024. We analyze its performance across market volatility regimes, day-of-the-week effects, and compare it to other technical patterns, considering the impact of commissions and slippage.