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Roughly speaking, MACD captures bearish or bullish tendencies, analyzing the relationship between two price moving averages. More precisely, the indicator is given by. The aforementioned moving average MA is the key tool for another type of trading strategies. Such Moving Average trading rules slightly differ from the techniques previously introduced, as they directly look at the traded asset prices, without resorting to any momentum indicator, and the signal line coincides with the MA considered.

The main disadvantage of such a method lies in its own asynchrony w. The latter depend on the profit margin the trader would like to establish, in terms of spread between the predicted BTC price and a given benchmark.

A CfD is a derivative on a financial asset. It provides that two parties agree to exchange financial flow stemming from the differential between the prices of an underlying at the beginning time of the contract and at the time of its closing. Therefore, CfDs operate on the price differences, implying gain or loss according to the difference between the purchase price and the sale price of the underlying. For the sake of simplicity, in the following, we will not consider transition and financial costs associated with CfD operations.

The touchy point is to define an appropriate benchmark. Let K be the arranged price agreed between the parties in the CfD, to be compared with the underlying. Typically, this translates in keeping a short position, when the downside risk is potentially unlimited. Indeed, it is straightforward to see that. This means that no profit is obtained by assuming short positions, thus a change of position toward the CfD is needed.

We observe that the BTC price evolves according to 2. Solve the integral Eq. The model parameters are estimated by exploiting the maximum likelihood method suggested in Brigo et al. Consequently, we set h resp. Then, the strategy is composed of h long CfDs and j short CfDs. To proceed with our proposal, we must perform the algorithm provided in Sect.

Once the time horizon has been partitioned, the first stumbling block is to predict the BTC price. We exploit the LSTM network endorsing an autoregressive approach on 1-minute basis. We refer the reader to Nigri et al. Therefore, we can properly formalize each realization, over time, of the future BTC price as follows:.

Starting from the original BTC price dataset mentioned in Sect. More in details, we consider intraday 1-minute price for windows of , and datapoints, to train and test the LSTM model. Moreover, to define the composition of the LSTM architecture, a fine tuning process is implemented by adopting a grid search technique. Because of that, a limited, discrete parametric space is established a priori, whose possible values are arbitrarily chosen, acting as LSTM hyperparameters.

Fixing a combination of hyperparameters in the parametric space, the training procedure begins by minimizing the mean squared error as loss function. We elect the optimal NN architecture the one identified by the hyperparameters combination returning the minimum error on the testing set. The analysis is implemented using the R software version 3. The results in Table 2 can be integrated into the preexisting literature: indeed, on top of Lahmiri and Bekiros , we show that the LSTM is an accurate predictor also in the absence of chaoticity.

This further ensures a significant shrinking of the computational burden. To establish the accuracy of the new trading strategy, we compare our results with those obtained by applying other trading rules already known in the literature. Among the possible methodologies introduced in Sect. The comparison has been performed by using proper indicators, capable to assess the risk-return profile of each strategy involved.

More properly, we begin by evaluating the Sharpe ratio SHR. To further distinguish among trading strategies with similar behaviors in terms of average returns, we check the excess return against a minimum acceptable risk-free rate, thanks to the Sortino ratio SOR. The risk measure involved is the Downside risk DSR , ensuring to look at the distribution left tail. It is worth recalling that a greater SOR means that the variability is concentrated above the fixed threshold; vice versa, a smaller SOR means that the variability is concentrated below the fixed threshold.

WR is defined as a ratio, comparing the trading periods with positive gains and the overall trading times. A greater WR represents a better accuracy in the predicted trading strategy. On the other hand, it is equally undeniable that the moving average represents, by its very nature, a comparison level for BTC price. Hence, the MA strategy embodies a feasible competitor, whatever the size of the time window taken into account. The comparison between our proposal and MA is shown in Table 3.

Scrutinizing the results obtained, we find out that the uppermost Sharpe ratio is provided by MA 10 for minute and minute predictions. Consequently, the average return of the entire strategy results to be the highest. For the minute case, the first-past-the-post-strategy is MA Such excellent upshot basically depends on the intrinsic nature of such a strategy; in fact, for MA rules, the threshold closely follows the prediction evolution. However, the latter also reveals some notable drawbacks.

First, too few observations for the moving average are meager to properly identify the flawless trading times. This clearly comes to light by looking at MA 5. In this perspective, our proposal soundly differs from the aforementioned investment rule, as our barrier is independent of the BTC price paths. Even though this could imply lower profits, the maximum drawdown shows that our proposal always ensures the lowest loss, highlighting its safeguarding role.

Furthermore, it is worth noting that the risk measures are not computable for each MA-based strategy, since the barrier is constructed in such a way that the zenith never takes place after the nadir. The solid line represents the forecast for the BTC price over a different time intervals, measured minute by minute. The forecasting was obtained by implementing the LSTM method introduced above. The dotted line represents the benchmark, i. Trading strategy over different time horizons.

The forecasting starting time is December 31, — The two circles indicate the maximum profit realized by the investor, both in long and short position. We recall that MACD and stochastic oscillator rules are defined in terms of suitable parameters. The latter can be explicitly evaluated, thanks to 3.

This makes the aforementioned strategies particularly all-around. Hence, to complete our investigation, we exhibit the performance analysis for MACD, Stochastic Oscillator and our strategy: the comparison is illustrated in Table 4. Looking at the parameters involved, we can claim that our proposal comes out on top, both in terms of risk and return, when we consider an hourly forecasting time window.

When the time horizon increases the performances of all strategies slightly worsens. More precisely, for the minute prediction, our proposal and MACD provide negative returns; any risk-seeking investor would be geared toward the stochastic oscillator rule.

For the longest forecast, namely minutes, although our strategy is dominated by the other two if we refer to the Sharpe ratio only, the results are certified to be outstanding when considering its ability to contain losses.

Therefore, wise investors are led not to directly invest in BTC, in order not to incur potentially significant losses of their wealth. This can be inferred by observing the risk indicator taken into consideration, i. In this article, we exhibit an original profit-oriented trading strategy on BTC for risk-seeking investors. The idea is simple, but compelling. We can set up the strategy, by taking into account a given number of suitable financial instruments the so-called contracts for difference that provide profit in terms of the spread between the underlying value and the optimal frontier of a synthetic American-style derivative.

One of the key points of this work is the possibility of evaluating the BTC price through a geometric Brownian motion over very short time horizons. The results presented here, stemming from the comparison between the NNs BTC price prediction and a suitable model-based investment boundary, represent a first, albeit significant, attempt.

The technical analysis pursued on the proposed Kim-barrier strategy, in comparison with other trading rules widely used by insiders, provides encouraging results about the appropriateness of our proposal. The latter can be legitimately deemed as a viable alternative for investors looking for a profitable-but-protective trading blueprint.

One further development, which is already subject of our ongoing research, is the extension of such a type of trading strategy when the underlying evolves according to more realistic models, such as the fractional geometric Brownian motion or stochastic volatility models with jumps.

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Springer, London Book Google Scholar. Bianchi, S. In: Bensoussan, A. Future Perspectives in Risk Models and Finance. Advances in Complex Systems Bistarelli, S. Digital Finance 1 , 23—46 Bohte, R. Journal of Risk and Financial Management 12 , Brandvold, M. Brigo, D. Bucko, J. Working Paper. Cohen, G. Detzel, A. Hochreiter, S. Neural Comput. Hou, A. Hsu, P. Hudson, R. Annals of Operational Research Katsiampa, P. Economics Letters , 3—6 ISSN Kim, I.

The Review of Financial Studies 3 4 , — Kim, Y. Kyriazis, N. Heliyon 5 , e Lahmiri, S. Chaos, Solitons Fractals , 35—40 Chaos, Solitons Fractals , Le Tran, V. Finance Research Letters 35 , Nakamoto, S. Nigri, A. Scandinavian Actuarial Journal 1 , 1—24 Peng, C. E 49 , — Sanchez Graneroa, M.

Physica A , — Serinaldi, F. Tarnopolski, M. CP] Following the laws of supply and demand, Bitcoin's price should continue to rise as its supply may not be able to meet its demand—as long as it continues to grow in popularity. However, if popularity wanes and demand falls, there will be more supply than demand, and Bitcoin's price should drop unless it maintains its value for other reasons. Another factor that affects Bitcoin's price falls in line with supply and demand; Bitcoin has also become an instrument that investors and financial institutions use to store value and generate returns.

Derivatives are being created and traded by brokers, investors, and traders, acting to influence Bitcoin's price further. Speculation, investment product hype, irrational exuberance, or investor panic and fear can also be expected to affect Bitcoin's price because demand will rise and fall with investors' sentiments. Other cryptocurrencies may also affect Bitcoin's price. There are several cryptocurrencies, and the number continues to rise as regulators, institutions, and merchants address concerns and adopt them as acceptable forms of payment and currency.

Lastly, if consumers and investors believe that other coins will prove to be more valuable than Bitcoin, demand will fall, taking prices with it—or demand will rise, along with prices, if sentiments change in the opposite direction. The rate of difficulty changes. Mining depends on the software and hardware used as well as available energy resources, but the average time to find a block is about ten minutes. Bitcoin was created by an anonymous person or group using the name Satoshi Nakamoto in A Bitcoin is mined by specialized software and hardware and is created when an increasingly difficult mathematical problem is solved.

Since each individual's situation is unique, a qualified professional should always be consulted before making any financial decisions. Investopedia makes no representations or warranties as to the accuracy or timeliness of the information contained herein. The Coinbase Blog. Your Money. Personal Finance. Your Practice. Popular Courses. Cryptocurrency Bitcoin. Part of. Guide to Bitcoin. Part Of. Bitcoin Basics.

Bitcoin Mining. How to Store Bitcoin. Bitcoin Exchanges. Bitcoin Advantages and Disadvantages. Bitcoin vs. Other Cryptocurrencies. Bitcoin Value and Price. Key Takeaways Since it was first introduced, Bitcoin has had a choppy and volatile trading history. Bitcoin's price has risen and fallen sharply over its short history.

As an asset class, Bitcoin continues to evolve along with the factors that influence its prices. Bitcoin's narrative has shifted—while it is still a cryptocurrency, it also provides a way to store value, hedge against inflation and market uncertainty, and allow investors to gain exposure to cryptocurrency within their portfolios. Where Does Bitcoin Come From? What Was Bitcoin's Cheapest Price? Article Sources.

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