Recent research that analyzed Bitcoin’s price data from July 2010 to January 2019 shed light on this question, focusing on the effectiveness of seven trend-following indicators.
The research was done by Gerritsen et al. The trading range breakout rule in particular has emerged as a promising strategy, often outperforming the traditional buy-and-hold approach.
█ Some Background in the Bitcoin Market Bitcoin’s price path suggests market inefficiency, likely due to its short history and the erratic behavior of market participants. Previous studies on Bitcoin’s efficiency have mainly focused on its predictability from a random walk perspective, leaving the performance of technical trading rules on Bitcoin prices largely unexplored.
The core purpose of this study is to examine the profitability of technical trading rules, specifically to determine whether these rules can outperform a basic buy-and-hold strategy.
By applying seven well-documented trading rules and analyzing their performance through the Sharpe ratio, the study aims to provide practical insights for Bitcoin traders.
█ Methodology The study uses daily price data from 17 July 2010 to 31 December 2018, a total of 3,084 daily observations. Gerritsen and team removed a short period in 2011 due to a Mount Gox hack and integrated data from Coinmarketcap from April 28, 2013. The research also considers the risk-free rate, using 3-month US Treasury bill returns for its analysis.
█ Trading Rules Analyzed 1. Moving Averages (MA): This strategy issues buy signals when the recent price or its short-term average exceeds a longer-term average and sell signals in the opposite scenario. It has combinations like 1-day vs. 50-day, 2-day vs 150-day and 5-day vs 200-day averages tested.
2. Trading Range Breakout (TRB): It looks for price breakouts outside the highest and lowest prices of a predetermined period (50, 150, 200 days), indicating buys for breakouts above the high and sell below the low.
3. Moving Average Convergence Divergence (MACD): The MACD rule uses two exponential moving averages (EMAs), and triggers buy signals when the MACD line (the difference between a 12-day and a 26-day EMA) is above zero , and sell signals when it is below zero. It also examines the MACD signal line and MACD histogram as additional signals.
4. Rate of Change (ROC): This rule compares the current price with the price a few days ago (usually 10 days) to determine market momentum and issue buy/sell signals. The rule suggests buying when the ROC is positive, indicating upward momentum, and selling when it is negative, indicating downward momentum.
5. On-Balance-Volume (OBV): This volume-based indicator predicts price movements based on volume flow, claiming that volume changes precede price changes. The study applied MA rules to the OBV to generate signals, buy when the short-term MA of OBV crosses the long-term MA from below, and sell when it crosses from above.
6. Relative Strength Index (RSI): A momentum oscillator that identifies overbought or oversold conditions, suggesting buy signals when below 30 and sell signals above 70.
7. Bollinger Bands (BB): This strategy uses a moving average with upper and lower bands based on standard deviations from the MA, issuing buy signals when the price touches the lower band and sell signals at the upper band.
█ Strategies and evaluation The study applied each trading rule in three separate strategies:
Literal interpretation: Buy or sell Bitcoin directly based on the signal, including short positions. Long positions only: Consider only buy signals due to the practical challenges of shorting Bitcoin on many exchanges. Standard long position with adjustment on signals: Maintaining a default long position, doubling investment on buy signals, and moving to risk-free assets on sell signals. The performance of these strategies was evaluated using the Sharpe ratio, which compares the excess returns of the trading strategies over the risk-free rate to their volatility. A higher Sharpe ratio indicates a more efficient risk-adjusted return. The study used bootstrapping to determine the statistical significance of the Sharpe ratio differences between each trading rule strategy and a benchmark buy-and-hold strategy.
█ Key Findings The study finds mixed results on different technical trading strategies when applied to Bitcoin.
In particular, the trading range breakout rule (TRB) consistently offers higher Sharpe ratios than a buy-and-hold strategy, indicating its superior performance.
TRB strategies average a Sharpe ratio of around 0.08, marking them as statistically significant against the buy-and-hold benchmark. This rule’s success is further highlighted in specific periods, such as 2011–2012, 2013–2014 and 2017–2018, where its Sharpe ratios were significantly higher than those of the buy-and-hold approach. The significant outperformance in these periods highlights the TRB rule’s adaptability to market dynamics.
While most other technical trading rules have not consistently outperformed the buy-and-hold strategy, certain strategies such as MACD have shown significant outperformance in specific applications (Strategy 2), showing the nuanced effectiveness of technical trading rules in the Bitcoin market illustrate.
Countertrend indicators, such as the relative strength index and Bollinger Bands, have generally underperformed the buy-and-hold benchmark, sometimes producing negative Sharpe ratios.
█ Sensitivity to market conditions The effectiveness of the TRB strategy, in particular, appears to be very dependent on the prevailing market conditions. During periods of strong trends (either bull or bear markets), the TRB rule has shown notable outperformance.
However, during more stable periods, such as 2015–2016, the TRB rule and most other trading rules did not show a significant advantage over the buy-and-hold strategy, which is consistent with the adaptive market hypothesis which suggests that the performance of trading strategies are conditional. on environmental factors.
█ Limitations and Future Research One notable limitation is the focus solely on Bitcoin, which raises the question of whether these findings can be generalized to other cryptocurrencies. In addition, the analysis does not take into account transaction costs, which may affect the profitability of the trading strategies. Future research is encouraged to extend the investigation to other leading cryptocurrencies and to consider the impact of transaction costs on the profitability of the trading range breakout rule and other technical trading strategies.
█ Reference Gerritsen, DF, et al. (xxxx). The profitability of technical trading rules in the Bitcoin market. Finance Research Letters, xxx(x), xxx-xxx.
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