# Technical Analysis on the Bitcoin Market: Trading Opportunities or Investors’ Pitfall?

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## Abstract

**:**

## 1. Introduction

## 2. Technical Analysis and Trading Strategies

_{t}the price level at time t, and by r

_{t}the corresponding continuous return, i.e., r

_{t}= log P

_{t}/P

_{t−1}. Then, the payoff given by the B&H strategy is:

## 3. Data Description and Empirical Results

^{TM}.

## 4. Discussion

_{t}/P

_{t}

_{−1}denotes the average rate of return, T is the period length, s is the standard deviation in the adopted time unit (daily/intraday), 1 is the indicator function, x

_{H}the highest observed return, q is a threshold value, with q = MAR (Minimum accepted threshold) as special case.

## 5. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Aalborg, Halvor Aarhus, Peter Molnár, and Jon Erik de Vries. 2019. What can explain the price, volatility and trading volume of Bitcoin? Finance Research Letters 29: 255–65. [Google Scholar] [CrossRef]
- Agosto, Arianna, and Alessia Cafferata. 2020. Financial Bubbles: A Study of Co-Explosivity in the Cryptocurrency Market. Risks 8: 34. [Google Scholar] [CrossRef][Green Version]
- Al-Khazali, Osamah, Elie Bouri, and David Roubaud. 2018. The impact of positive and negative macroeconomic news surprises: Gold versus Bitcoin. Economics Bulletin 38: 373–82. [Google Scholar]
- Balcilar, Mehmet, Elie Bouri, Rangan Gupta, and David Roubaud. 2017. Can volume predict Bitcoin returns and volatility? A quantiles-based approach. Economic Modelling 64: 74–81. [Google Scholar] [CrossRef][Green Version]
- Bariviera, Aurelio F., María José Basgall, Waldo Hasperué, and Marcelo Naiouf. 2017. Some stylized facts of the bitcoin market. Physica A 484: 82–90. [Google Scholar] [CrossRef][Green Version]
- Bouri, Elie, Rangan Gupta, Amine Lahiani, and Muhammad Shahbaz. 2018. Testing for asymmetric nonlinear short-and long-run relationships between bitcoin, aggregate commodity and gold prices. Resources Policy 57: 224–35. [Google Scholar] [CrossRef][Green Version]
- Brandvold, Morten, Peter Molnár, Kristian Vagstad, and Ole Christian Andreas Valstad. 2015. Price discovery on Bitcoin exchanges. Journal of International Financial Markets, Institutions and Money 36: 18–35. [Google Scholar] [CrossRef]
- Brauneis, Alexander, and Roland Mestel. 2018. Price discovery of cryptocurrencies: Bitcoin and beyond. Economics Letters 165: 58–61. [Google Scholar] [CrossRef]
- Cerchiello, Paola, and Paolo Giudici. 2016. Big data analysis for financial risk management. Journal of Big Data 3: 18. [Google Scholar] [CrossRef][Green Version]
- Cerchiello, Paola, Paolo Giudici, and Giancarlo Nicola. 2017. Twitter data models for bank risk contagion. Neurocomputing 264: 50–56. [Google Scholar] [CrossRef]
- El Alaoui, Marwane, Elie Bouri, and David Roubaud. 2019. Bitcoin price–volume: A multifractal cross-correlation approach. Finance Research Letters 31. [Google Scholar] [CrossRef]
- Eross, Andrea, Frank McGroarty, Andrew Urquhart, and Simon Wolfe. 2019. The intraday dynamics of bitcoin. Research in International Business and Finance 49: 71–81. [Google Scholar] [CrossRef]
- Gerritsen, Dirk F., Elie Bouri, Ehsan Ramezanifar, and David Roubaud. 2019. The profitability of technical trading rules in the Bitcoin market. Finance Research Letters. [Google Scholar] [CrossRef]
- Giudici, Paolo, and Paolo Pagnottoni. 2019a. Vector error correction models to measure connectedness of Bitcoin exchange markets. Applied Stochastic Models in Business and Industry 36: 95–109. [Google Scholar] [CrossRef][Green Version]
- Giudici, Paolo, and Paolo Pagnottoni. 2019b. High Frequency Price Change Spillovers in Bitcoin Markets. Risks 7: 111. [Google Scholar] [CrossRef][Green Version]
- Giudici, Paolo, Paolo Pagnottoni, and Gloria Polinesi. 2020. Network models to enhance automated cryptocurrency portfolio management. Frontiers in Artificial Intelligence 3: 22. [Google Scholar] [CrossRef][Green Version]
- Hu, Albert S., Christine A. Parlour, and Uday Rajan. 2019. Cryptocurrencies: Stylized facts on a new investible instrument. Financial Management 48: 1049–68. [Google Scholar] [CrossRef]
- Jain, Pankaj K., Thomas H. McInish, and Jonathan L. Miller. 2019. Insights from bitcoin trading. Financial Management 48: 1031–48. [Google Scholar] [CrossRef]
- Ji, Qiang, Elie Bouri, Rangan Gupta, and David Roubaud. 2018. Network causality structures among Bitcoin and other financial assets: A directed acyclic graph approach. The Quarterly Review of Economics and Finance 70: 203–13. [Google Scholar] [CrossRef][Green Version]
- Jiang, Yonghong, He Nie, and Weihua Ruan. 2018. Time-varying long-term memory in Bitcoin market. Finance Research Letters 25: 280–84. [Google Scholar] [CrossRef]
- Kristjanpoller, Werner, and Elie Bouri. 2019. Asymmetric multifractal cross-correlations between the main world currencies and the main cryptocurrencies. Physica A: Statistical Mechanics and its Applications 523: 1057–71. [Google Scholar] [CrossRef]
- Kristoufek, Ladislav. 2013. BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific Reports 3: 3415. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Malkiel, Burton G., and Eugene F. Fama. 1970. Efficient capital markets: A review of theory and empirical work. The Journal of Finance 25: 383–417. [Google Scholar] [CrossRef]
- Nadarajah, Saralees, and Jeffrey Chu. 2017. On the inefficiency of Bitcoin. Economics Letters 150: 6–9. [Google Scholar] [CrossRef][Green Version]
- Pagnottoni, Paolo. 2019. Neural Network Models for Bitcoin Option Pricing. Frontiers in Artificial Intelligence 2: 5. [Google Scholar] [CrossRef][Green Version]
- Pagnottoni, Paolo, and Thomas Dimpfl. 2018. Price discovery on Bitcoin markets. Digital Finance 1: 139–61. [Google Scholar] [CrossRef][Green Version]
- Sensoy, Ahmet. 2019. The inefficiency of Bitcoin revisited: A high-frequency analysis with alternative currencies. Finance Research Letters 28: 68–73. [Google Scholar] [CrossRef]
- Shi, Shimeng. 2017. The Impact of Futures Trading on Intraday Spot Volatility and Liquidity: Evidence from Bitcoin Market. Available online: https://ssrn.com/abstract=3094647 (accessed on 8 January 2020).
- Tiwari, Aviral Kumar, R. K. Jana, Debojyoti Das, and David Roubaud. 2017. Informational efficiency of Bitcoin—An extension. Economics Letters 163: 106–9. [Google Scholar] [CrossRef]
- Urquhart, Andrew. 2016. The inefficiency of Bitcoin. Economics Letters 148: 80–82. [Google Scholar] [CrossRef]
- Wilder, John Welles. 1978. New Concepts in Technical Trading Systems. Edmonton: Trend Research. [Google Scholar]

**Figure 1.**The dynamics of Bitcoin price series: five-minute data (

**left**panel) and daily data (

**right**panel).

**Figure 2.**From top to bottom: SMA12 and SMA72 (

**top**panel), EMA12 and EMA72 (

**bottom**panel) superposed to the behavior of 5-min Bitcoin price series.

**Figure 3.**Intraday strategies: highlight for the period 29 December 2017 to 3 January 2018; SMA12 and SMA72 (

**top**panel), RSI12 and RSI72 (

**bottom**panel).

Variable | N | Min | 1st Qu. | Median | Mean | Std | 3rd Qu. | Max | Skew | Kurt |
---|---|---|---|---|---|---|---|---|---|---|

Btc5min | 800,834 | 3.800 | 222.9 | 509.965 | 2232.6 | 3388.873 | 3574 | 19,663.9 | 1.849695 | 2.995822 |

Btc1d | 2779 | 4.230 | 223.07 | 516.390 | 516.390 | 3404.312 | 3578.43 | 19,187.78 | 1.851905 | 3.015163 |

ID | Major Features | Style | Frequency | Other Parameters |
---|---|---|---|---|

B&H | Buy and Hold | Trend-following | Intraday/Daily | - |

SMA12 | 12-time units Simple Moving Average | Trend-following | Intraday/Daily | - |

SMA24 | 24-time units Simple Moving Average | Trend-following | Daily | - |

SMA72 | 72-time units Simple Moving Average | Trend-following | Intraday | - |

EMA12 | 12-time units Exponential Moving Average | Trend-following | Intraday/Daily | λ = 0.94 |

EMA24 | 24-time units Exponential Moving Average | Trend-following | Daily | λ = 0.94 |

EMA72 | 72-time units Exponential Moving Average | Trend-following | Intraday | λ = 0.94 |

BBS12 | 12-time units Bollinger Bands | Trend-reversal | Intraday/Daily | h = 2 |

BBS24 | 24-time units Bollinger Bands | Trend-reversal | Daily | h = 2 |

BBS72 | 72-time units Bollinger Bands | Trend-reversal | Intraday | h = 2 |

RSI12 | 12-time units Relative Strength Index | Trend-reversal | Intraday/Daily | - |

RSI24 | 12-time units Relative Strength Index | Trend-reversal | Daily | - |

RSI72 | 72-time units Relative Strength Index | Trend-reversal | Intraday | - |

Name | Formula | Description | Interpretation/Research Question |
---|---|---|---|

Annualized Return | ${\left(1+r\right)}^{Tintheunittime}-1$ | Period returns re-scaled to a period of 1 year | Allows investors to compare returns owned for different lengths of time |

Annualized Sharpe Ratio | $\frac{r-\theta}{\sigma}$ | To measure the performance compared to a benchmark return $\theta $, after adjusting for its risk. | All other things being equal, an investor wants to get positive (and increasing) SRs. |

Annualized Volatility | $\sigma \sqrt{yearlengthinunittime}$ | To evaluate the fluctuations in the returns scaled to a period of 1 year | Helps to assess the dispersion level in returns |

Semi-Deviation | $\sqrt{\frac{1}{T-1}{\displaystyle {\displaystyle \sum}_{k=1}^{T}}{\left({x}_{k}-r\right)}^{2}{1}_{{x}_{k}\le r}}$ | Evaluates the behind zero fluctuations in the return of the investment | Helps to assess the negative dispersion level in returns. |

Gain Deviation | $\sqrt{\frac{1}{T-1}{\displaystyle {\displaystyle \sum}_{k=1}^{T}}{x}_{k}{}^{2}{1}_{{x}_{k}\ge 0}}$ | Represents the potential gain that may arise over T. | Do losses behave differently than gains? |

Loss Deviation | $\sqrt{\frac{1}{T-1}{\displaystyle {\displaystyle \sum}_{k=1}^{T}}{x}_{k}{}^{2}{1}_{{x}_{k}\le 0}}$ | Represents the potential loss that may arise over T. | Do losses behave differently than gains? |

Downside Deviation (q = MAR = 210%) | $\frac{1}{T}{\displaystyle {\displaystyle \sum}_{k=1}^{T}}\left({x}_{k}-MAR\right){1}_{{x}_{k}\le MAR}$ | Represents the potential loss that may arise from risk as measured against a minimum acceptable return, by isolating the negative portion of the volatility. | Highest values are better than lowest. |

Maximum Average Drawdown | $\frac{r-{x}_{H}}{{x}_{H}}$ | Computed as the maximum observed loss from a peak in the given time frame. | Maximum drawdown is an indicator of downside risk over a specified time period |

Omega | $\frac{{{\displaystyle \sum}}_{k=1}^{T}{\left({x}_{k}-\theta \right)}^{+}}{{{\displaystyle \sum}}_{k=1}^{T}{\left({x}_{k}-\theta \right)}^{-}}$ | A risk-adjusted performance measure calculated as the ratio of probability-weighted profits and losses. | A higher value of Omega is preferable to a lower value. |

Sortino ratio | $\frac{{{\displaystyle \sum}}_{k=1}^{T}\left({x}_{k}-r\right)}{{{\displaystyle \sum}}_{k=1}^{T}{\left({x}_{k}-\theta \right)}^{-}}$ | Another risk-adjusted performance measure | Highest positive Sortino ratio is preferred to lower values. |

Upside potential ratio | $\frac{{{\displaystyle \sum}}_{k=1}^{T}\left({x}_{k}-r\right)}{D{D}_{MAR}}$ | The upside-potential ratio is a measure of a return of an investment asset relative to the minimal acceptable return. | The measurement allows investments to be chosen which have had relatively good upside performance, per unit of downside risk. |

B&H | MA12 | MA72 | EMA12 | EMA72 | BBS12 | BBS72 | RS12 | RS72 | |
---|---|---|---|---|---|---|---|---|---|

Annualized Return | 25.6687 | 0.1566 | 0.0611 | 0.1426 | 0.0683 | 0.0053 | 0.0048 | −0.1981 | −0.0131 |

Annualized SR | 71.7259 | 3.7779 | 1.8363 | 4.1560 | 2.0559 | 0.3599 | 0.2710 | −2.0419 | −0.4834 |

Annualized Volatility | 0.0932 | 0.0373 | 0.0328 | 0.0360 | 0.0348 | 0.0125 | 0.0236 | −0.3210 | 0.0257 |

Semi Deviation | 0.0046 | 0.0016 | 0.0018 | 0.0295 | 0.0297 | 0.0020 | 0.0006 | 0.0011 | 0.0070 |

Gain Deviation | 0.0055 | 0.0050 | 0.0041 | 0.0567 | 0.0564 | 0.0029 | 0.0020 | 0.0013 | 0.0096 |

Loss Deviation | 0.0023 | 0.0029 | 0.0040 | 0.0031 | 0.0032 | 0.0029 | 0.0022 | 0.0031 | 0.0178 |

Downside Deviation (MAR = 210%) | 0.0063 | 0.0083 | 0.0082 | 0.0078 | 0.0087 | 0.0098 | 0.0083 | 0.0080 | 0.0100 |

Maximum Drawdown | 0.0536 | 0.0407 | 0.0414 | 0.0087 | 0.0157 | 0.0871 | 0.0050 | 0.0191 | 0.0608 |

Omega | 176.7826 | 4.8468 | 2.0852 | 5.6453 | 2.2447 | 14.6241 | 17.2099 | 0.3615 | 0.0000 |

Sortino ratio | 24.2246 | 0.9618 | 0.2933 | 1.1193 | 0.3312 | 1.4909 | 1.7422 | −0.1326 | −0.1923 |

Upside potential ratio | 5.0077 | 1.4029 | 0.9097 | 1.5064 | 0.9651 | 5.4922 | 3.4083 | 0.3794 | 0.4244 |

B&H | MA12 | MA24 | EMA12 | EMA24 | BBS12 | BBS24 | RS12 | RS24 | |
---|---|---|---|---|---|---|---|---|---|

Annualized Return | 25.6687 | 43.7367 | 27.1771 | 56.6882 | 35.1452 | 0.0738 | −0.0388 | −0.6393 | −0.6029 |

Annualized SR | 71.7259 | 55.9404 | 37.0540 | 64.9638 | 44.5937 | 0.2933 | 0.1751 | −1.8274 | −1.7407 |

Annualized Volatility | 0.0932 | 0.3779 | 0.4011 | 0.4221 | 0.4273 | 0.2314 | 0.2439 | 0.4010 | 0.3576 |

Semi Deviation | 0.0046 | 0.0161 | 0.0195 | 0.0708 | 0.0169 | 0.0095 | 0.0108 | 0.0212 | 0.0189 |

Gain Deviation | 0.0055 | 0.0273 | 0.0267 | 0.0406 | 0.0268 | 0.0276 | 0.0250 | 0.0220 | 0.0211 |

Loss Deviation | 0.0023 | 0.0190 | 0.0227 | 0.0193 | 0.0212 | 0.0280 | 0.0309 | 0.0331 | 0.0326 |

Downside Deviation (MAR = 210%) | 0.0063 | 0.0151 | 0.0181 | 0.0168 | 0.0178 | 0.0140 | 0.0154 | 0.0277 | 0.0258 |

Maximum Drawdown | 0.0536 | 0.0775 | 0.0963 | 0.0774 | 0.0999 | 0.1057 | 0.1165 | 0.3813 | 0.2843 |

Omega | 176.7826 | 3.3551 | 2.1141 | 4.3028 | 2.8336 | 1.7774 | 4.6603 | 0.2898 | 0.3258 |

Sortino ratio | 24.2246 | 0.7253 | 0.3612 | 0.8324 | 0.5407 | 0.1041 | 0.5894 | −0.2612 | −0.2192 |

Upside potential ratio | 5.0077 | 1.2032 | 0.9417 | 1.3075 | 1.1828 | 0.9629 | 1.9782 | 0.4339 | 0.4731 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Resta, M.; Pagnottoni, P.; De Giuli, M.E.
Technical Analysis on the Bitcoin Market: Trading Opportunities or Investors’ Pitfall? *Risks* **2020**, *8*, 44.
https://doi.org/10.3390/risks8020044

**AMA Style**

Resta M, Pagnottoni P, De Giuli ME.
Technical Analysis on the Bitcoin Market: Trading Opportunities or Investors’ Pitfall? *Risks*. 2020; 8(2):44.
https://doi.org/10.3390/risks8020044

**Chicago/Turabian Style**

Resta, Marina, Paolo Pagnottoni, and Maria Elena De Giuli.
2020. "Technical Analysis on the Bitcoin Market: Trading Opportunities or Investors’ Pitfall?" *Risks* 8, no. 2: 44.
https://doi.org/10.3390/risks8020044