The profitability of TA trading rules in the Bitcoin market█ The profitability of technical trading rules in the Bitcoin market
The Bitcoin market, known for its wild fluctuations, poses a unique challenge for traders: Is it possible to consistently profit using technical trading rules?
Recent research analyzing Bitcoin's price data from July 2010 to January 2019 has shed light on this question, focusing on the effectiveness of seven trend-following indicators.
The research was conducted by Gerritsen et al. Notably, the trading range breakout rule emerged as a promising strategy, often outperforming the traditional buy-and-hold approach.
█ Some Background into the Bitcoin Market
Bitcoin's price path suggests market inefficiency, likely due to its short history and the erratic behaviors of market participants. Previous studies on Bitcoin's efficiency mainly focused on its predictability from a random walk perspective, leaving the performance of technical trading rules on Bitcoin prices largely unexplored.
The core aim of this study is to examine the profitability of technical trading rules, specifically to determine if these rules can surpass a basic buy-and-hold strategy.
By applying seven well-documented trading rules and analyzing their performance through the Sharpe ratio, the study seeks to provide practical insights for Bitcoin traders.
█ Methodology
The study uses daily price data from July 17, 2010, to December 31, 2018, totaling 3,084 daily observations. Gerritsen and team removed a brief period in 2011 due to a Mt. Gox hack and integrated data from Coinmarketcap starting 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 tested combinations like 1-day vs. 50-day, 2-day vs 150-day, and 5-day vs 200-day averages.
2. Trading Range Breakout (TRB): It looks for price breakouts beyond the highest and lowest prices of a predefined period (50, 150, 200 days), signaling buys for breakouts above the high and sells 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 n days ago (commonly 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, asserting that volume changes precede price changes. The study applied MA rules to the OBV to generate signals, buying when the short-term MA of OBV crosses the long-term MA from below, and selling 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 distinct strategies:
Literal Interpretation: Buying or selling Bitcoin directly based on the signal, including short positions.
Long Positions Only: Considering only buy signals due to the practical challenges of shorting Bitcoin on many exchanges.
Default 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, comparing 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 assess 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 across different technical trading strategies when applied to Bitcoin.
Notably, the trading range breakout (TRB) rule consistently offers higher Sharpe ratios than a buy-and-hold strategy, signifying its superior performance.
On average, TRB strategies yield 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 notably higher than those of the buy-and-hold approach. The significant outperformance in these periods underscores the TRB rule's adaptability to market dynamics.
While most other technical trading rules did not consistently outperform the buy-and-hold strategy, certain strategies like MACD showed significant outperformance in specific applications (Strategy 2), illustrating the nuanced effectiveness of technical trading rules in the Bitcoin market.
Counter-trend indicators, such as the Relative Strength Index and Bollinger Bands, generally underperformed compared to the buy-and-hold benchmark, sometimes yielding negative Sharpe ratios.
█ Sensitivity to Market Conditions
The effectiveness of the TRB strategy, in particular, seems to be highly dependent on the prevailing market conditions. During periods of strong trends (either bull or bear markets), the TRB rule demonstrated notable outperformance.
However, during more stable periods, like 2015–2016, the TRB rule and most other trading rules did not show a significant advantage over the buy-and-hold strategy, aligning with the adaptive market hypothesis suggesting that the performance of trading strategies is contingent upon environmental factors.
█ Limitations and Future Research
One notable limitation is the focus solely on Bitcoin, leaving the question of whether these findings can be generalized to other cryptocurrencies.
Additionally, the analysis does not account for transaction costs, potentially affecting the trading strategies' profitability. 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, D.F., et al. (xxxx). The profitability of technical trading rules in the Bitcoin market. Finance Research Letters, xxx(x), xxx-xxx.
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Disclaimer
This is an educational study for entertainment purposes only.
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