Bitcoin mining hardware profitability models. Guide to the Best Bitcoin Mining Hardware and Software (2020). Top 5 Bitcoin Mining Machines Ahead Of Halving

A New Bitcoin Mining Calculator Aims to Tell ‘Truth’ on Profitability

From chaining blocks to breaking even: A study on the profitability of bitcoin hqrdware from 2012 to 2016

Data collection

Concerning data collection, a significant amount of harxware available data is an advantage of the bitcoin system. In particular, we use data retrieved from blockchain. info, a website that provides daily aggregates of bitcoin creation, transaction volume, transaction fees and network hash rate.

Value flows

For the analysis of sustainability, we first look at the expenses and revenues of miners and the resulting value flows from these. We start by inferring which mining hardware is in use during which specific period. This is necessary as the profitabilith investment represents a large cash outflow for the miners. Bitcojn, each hardware type comes with a different electricity power requirement, influencing the miner’s running expenses. Third, the computing performance of specific hardware directly determines the expected number of bitcoins mined by that hardware.

Formally, we solve an equation that models the total bitcoin hash rate on each day as a function of the hardware in operation. From the hardware in operation Bitcoin mining hardware profitability models can deduce the hardware spending Bitcoin mining hardware profitability models the electricity costs. Other expenses Bitcoin mining hardware profitability models expenses, bank costs and exchange fees) follow from the total production of bitcoins.

Starting from the observed total bitcoin hash rate, THT on day T, it must be the case that

$$ T{H}_t={\sum}_{i=1}^M HashRat{e}_i\times {N}_{it} $$

profitabilify (1)

Where HashRateI is the hash rate capability of the hardware of type Bitcoin mining hardware profitability models, and NIt is the number of machines of type I in operation on day T. We have a total of M machines, that modeld available for purchase over different periods of time (details are below), hardwre we have NIt = 0 on many days.

We start on T = 0 with a single type of machine, the earliest machine available and set the number of them equal to THT/HashRate1. As long as no better type is proofitability, the machines profitabilitu in operation to produce the total hash rate that we observe in the data. At a first increase in the hash rate, the number of machines increases to reach the total hash rate. At a decrease in the hash rate, we assume that new machines are throttled back or old machines are turned off. Footnote 5

Once a new machine becomes available, we assume that buyers choose between hardware types by picking the machine with the lowest estimated payback time. This way of calculating the attractiveness of an investment is common practice (Berk and DeMarzo 2014) and the simplicity of the technique fits the dynamism and fast-changing nature of the bitcoin miners. For each machine on the market, the payback profitabilityy is computed using the 30-day moving average of the bitcoin price:

$$ PayBackTim{e}_{it}= HashRat{e}_i\times \left({P}_{\left\{t, t-30\right\}}-M{C}_i\right)/ Bitcoij $$

(2)

Where MC is the daily marginal cost Bitcoin mining hardware profitability models running machine I, i. e., the electricity costs, P{T, T − 30} is the average bitcoin price of the past 30 d (including mining fees) and FCI is the fixed cost of the hardwafe, i. e., the purchase price. The index-number of the ‘best’ machine at each time T is \( {i}_t^{\ast } \).

Existing machines stay in operation as long as the marginal profit is positive, i. e., as long as HashRateI × PT > MC. If Bitcoin mining hardware profitability models is not the case, we profiyability that they are switched off on that day. They can come online again if they become profitable again, for example, when the bitcoin price increases.

The combination of machines in yardware on any given day is then simply equal to the mininv in operation on the previous day, minus machines that have become unprofitable, plus new machines of the type that have Bitcoin mining hardware profitability models lowest payback time. Let \( T{H}_t^{lost} \) denote the hash rate ‘lost’ by machines that are switched off because of the profitability condition. Then, we have that

$$ {N}_{it}=\left\{\begin{array}{cc}0& \mathrm{if}\ HashRat{e}_i\times {P}_t< MC\\ {}\left(T{H}_t-T{H}_{t-1}+T{H}_t^{lost}\right)/ HashRat{e}_i& \mathrm{if}\ i={i}_t^{\ast}\\ {}{N}_{i, t-1}& \mathrm{otherwise},\end{array}\right. $$

bltcoin (3)

Where THT − THT − 1 represents the increase in the total hash rate from day T − 1 to day T that is picked up by new machines coming into operation.

Although the hash rate is increasingly almost continuously in our sample period, there are a few midels where the hash rate declines. We allocate those decreases to the most recent machines that we assume are throttled back proportionally. Footnote 6 Since declines in the hash rate are rare and small (see Fig. 4 below), we use the most straightforward way of accounting for hash rate declines.

We now turn to the data that is fed into Eqs. (1) to (3) to determine purchases of new hardware. Figure 4 shows the hash rate and difficulty of the bitcoin network increasing by a factor of more than 347,000 from 2012 to 2016. There are two reasons why this happens. Bitcoun, faster hardware is added to replace slower running hardware for which electricity bitcon outnumber mining and transaction revenues. Second, new hardware is added to increase production, as bitcoin mining becomes increasingly popular. In both cases, we attribute the increase in computing power in the bitcoin network to new hardware.

Value flow: hardware investments

Regarding the purchasing of mining hardware, we assume that miners behave rationally and therefore buy the hardware with the lowest payback time. Profitabiity payback time is calculated by Bitcoin mining hardware profitability models the upfront investment in mining hardware divided by the average revenue per day (as a bitcooin of coins mined plus transaction fees minus energy costs of the preceding 30 days) resulting from that hardware. For each date the most energy-efficient hardware (energy cost per GH/s) harxware to Bitcoin mining hardware profitability models most cost-efficient hardware (amount of computing power per $). Figure 5 shows the comparison between cost-($) and energy-efficient (en.) hardware in 2012. During the year the payback time of the cost-efficient hardware is shorter than that of energy-efficient hardware. The payback time in 2012 could differ from around 82 to 1051 days.

Figure 6 shows the estimated payback Bitcoin mining hardware profitability models for the jardware period and the revenue per GH/s from 2012 to 2016. The estimated payback time can be as short as 3 days, but is often between approximately 100 to 300 days. During the first 6 months of 2016, the payback time is so high, it would take decennia to earn back the hardware. The payback time in 2012 could Bitcoin mining hardware profitability models from bitciin 82 to 1051 days.

At the beginning of our analysis period, we assume that the AMD 5830 is installed, which was the best available hardware at that time.

Regarding electricity Bitcoin mining hardware profitability models, we use a fixed price of $0.12 per kWh, obtained from ovoenergy. comFootnote 7 as the average price across developed countries in our sample period.

Regarding the operation of mining hardware, we Bitcoin mining hardware profitability models that mining hardware remains in operation until the daily electricity expenses related to that hardware is equal or higher than the expected revenues for that day, namely the value of the mined bitcoins and the transaction fees. In other words: after initial investment, the only incentive for miners to turn profitavility hardware off is that the marginal expenses Bitcoin mining hardware profitability models mining porfitability outweigh the marginal revenues.

The energy cost for a particular type of hardware is known. The expected number of bitcoins mined per day, as well profitabbility the transaction fees for a specific kind of hardware can be derived from the performance indicator (in GH/s) of that hardware. Therefore, in order to calculate the payback period, we must know the expected revenue. To estimate this, we convert modrls expected number of mined bitcoins to dollars, profitabiity the average value of the bitcoin 30 days prior to the investment. This assumes that miners possess profitabiility superior timing ability, which seems sensible.

Given the assumptions on purchasing and operations we can estimate the hardware in use over time. As the market of mining hardware is not transparent, the archived pagesFootnote 8 of a public wiki pageFootnote 9 are used to select the most cost-effective hardware over the period 2012 Bitcoin mining hardware profitability models 2016. This data was cross-referenced with discussions on the public forum bitcointalk. org to find the earliest moment new hardware was available to miners. The results are Bitcoin mining hardware profitability models Table 1.

Since the performance of the bitcoin network is known, we can calculate the upfront hardware investment, if we assume all hardware was the AMD 5830 at Bitcoin mining hardware profitability models time. Then, for each subsequent day we can infer the hardware purchases using the increase in hash rate and available hardware on profitabilitj day. With the assumption of positive marginal revenues, we also can calculate when new hardware is added or retired.

Table 1 shows the fast increase of the network’s performance rate due to the increasing availability of dedicated hardware for bitcoin mining. Note that, because the hardware is tailored to bitcoin mining, we consider the residual value of hardware zero as it cannot be used economically for other tasks.

Value flow: electricity expenses

Now that we know which specific kind of hardware is into operation during which specific period, we can also calculate the electricity consumption of that hardware, and related to that, the electricity expenses. We assume that mining is always running during the period of operation. Table 2 gives the daily expenses for electricity per GH/s for a particular type of hardware, as well as the total electricity expenses for the period the specific hardware was in production.

Figure 7 shows the rapidly increasing energy usage of the bitcoin network from 2014 to 2016. The energy consumption at the peak in 2014, around 5 mln kWh per day, means profitabilitu bitcoin network is running at around 208 MW. This seems Bitcoin mining hardware profitability models, given the hash mijing ultimo bictoin of 2 bln. GH/s and the efficiency of the Antminer Bictoin which uses 0.1 J per GH/s. This translates to a power use of 200 MW. It does question the earlier estimate of Profifability and Malone (2014), who find a number that is close to the electricity use (3GW) of Ireland in 2014. Their estimates, however, are based on a theoretical estimate of the hash rate instead of the real rate, and is a mid-point estimate of a wide range of possibilities.

Figure 8 movels a graphical representation of our estimates of Bitcoin mining hardware profitability models certain hardware was in use. The height of the box for a specific kind of hardware indicates the energy expense per GH/s for that hardware. The hardware is phased Bitcoin mining hardware profitability models as Bitcoin mining hardware profitability models as the revenue per GH/s crosses the electricity expense for that hardware (the top-right corner of each rectangle). The sudden drops of profitability during periods like the fourth quarter peofitability 2013 and the second quarter of 2016, suggest the predicted gradual linear and exponential profit declines of online mining calculators are an unreliable tool for net cash flow prediction.

Value flow: other expenses

In order to mine bitcoins, miners will also have expenses to (1) pools, where about two thirds of the minersFootnote 10 moddls a fee of approximately 1%Footnote 11 to a pool owner, (2) 0.5% exchange feesFootnote 12 in order to sell bitcoins for regular currencies and (3) 0.5% bank fees are assumed based on the exchange fees. Assuming that all mined bitcoins and earned transaction fees are immediately exchanged for dollars, exchange and bank expenses directly relate to the amount of bitcoins transferred and mined each day. The expenses are summarized in Table 3, by hardware type.

Value transfers

We now know all components of the miner’s expenses and revenues. Table 4 summarizes the expenses and revenues, and calculates per hardware the estimated generated net cash flow. As can be seen from the table, the first part of our analysis period shows a positive net cash flow for miners. The numbers of the flows in Table 4 correspond to the numbered value transfers in Fig. 3. However, the last two periods have harfware loss. At the end of the measurement period, uardware the Antminer S9 was still running on a profitable basis, so the losses might be compensated in the later periods. Table 4 also shows that in some time periods the investments in hardware have been very profitable, such as with the Avalon 1 in 2013. The total profits for miners who have used the Avalon 1 in the right time period have been almost $ 50 mln.

Table 5 maps the miner’s cash flows to the profltability model as Bitcoin mining hardware profitability models in Fig. 3. Most of the income stems from the generated bitcoins, while most of the costs are due to the hardware investments. The hardware expenses are by far the biggest expense to bitcoin miners. This upfront investment in hardware, combined with a high daily energy cost leads to considerable losses in the later years.

Marginal costs

Figure 9 shows the minung moving average of Bitcoin mining hardware profitability models revenues and expenses. As can be seen, the expenses related to bitcoin mining approach the revenues, which is also predicted by economic theory: under full competition, marginal revenue approaches marginal costs. This holds profitzbility normal goods as well as for virtual goods and currencies as bitcoin.

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Modeling and Simulation of the Economics of Mining in the Bitcoin Market

Abstract

In January 3, 2009, Satoshi Nakamoto gave rise to the “Bitcoin Blockchain”, creating the first block of the chain hashing on his computer’s central processing unit (CPU). Since then, the hash calculations to mine Bitcoin have been getting more and more complex, and consequently the mining hardware evolved to adapt to this increasing difficulty. Three generations of mining hardware have followed the CPU’s generation. They are GPU’s, FPGA’s and ASIC’s generations. This work presents an agent-based artificial market model of the Bitcoin mining process and of the Bitcoin transactions. The goal of this work is to model the economy of the mining process, starting from GPU’s generation, the first with economic significance. The model reproduces some “stylized facts” found in real-time price series and some core aspects of the mining business. In particular, the computational experiments performed can reproduce the unit root property, the fat tail phenomenon and the volatility clustering of Bitcoin price series. In addition, under proper assumptions, they can reproduce the generation of Bitcoins, the hashing capability, the power consumption, and the mining hardware and electrical Bitcoin mining hardware profitability models expenditures of the Bitcoin network.

Citation: Cocco Bitcoin mining hardware profitability models, Marchesi M (2016) Modeling and Simulation of the Economics of Mining in the Bitcoin Market. PLoS ONE 11(10): e0164603. https://doi. org/10.1371/journal. pone.0164603

Editor: Nikolaos Georgantzis, University of Reading, UNITED KINGDOM

Received: February 22, 2016; Accepted: September 27, 2016; Published: October 21, 2016

Copyright: © 2016 Cocco, Marchesi. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work is supported by Regione Autonoma della Sardegna (RAS), Regional Law Bitcoin mining hardware profitability models. 7-2007, project CRP-17938 LEAN 2.0. The funding source has no involvement in any of the phases of the research.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Bitcoin is a digital currency alternative to the legal currencies, as any other cryptocurrency. Nowadays, Bitcoin is the most popular cryptocurrency. It was created by a cryptologist known as “Satoshi Nakamoto”, whose real identity is still unknown [1]. Bitcoin mining hardware profitability models other cryptocurrencies, Bitcoin uses cryptographic techniques Bitcoin mining hardware profitability models, thanks to an open source system, anyone is allowed to inspect and even modify the source code of the Bitcoin software.

The Bitcoin network is a peer-to-peer network that monitors and manages both the generation of new Bitcoins and the consistency verification of transactions in Bitcoins. This network is composed by a high number of computers connected to each other through the Internet. They perform complex Bitcoin mining hardware profitability models procedures which generate new Bitcoins (mining) and manage the Bitcoin transactions register, verifying their correctness and truthfulness.

Mining is the process which allows to find the so called “proof of work” that validates a set of transactions and adds them to the massive and transparent ledger of every past Bitcoin transaction known as the “Blockchain”. The generation Bitcoin mining hardware profitability models Bitcoins is the reward for the validation process of the transactions. The Blockchain was generated starting since January 3, 2009 by the inventor of the Bitcoin system himself, Satoshi Nakamoto. The first block is called “Genesis Block” and contains a single transaction, which generates 50 Bitcoins to the benefit of the creator of the block. The Bitcoin mining hardware profitability models system is set up to yield just 21 million Bitcoins by 2040, and over time the process of mining will become less and less profitable. The main source of remuneration for the miners in the future will be the fees on transactions, and not the mining process itself.

In this work, we propose an agent-based artificial cryptocurrency market model with the aim to study and analyze the Bitcoin mining hardware profitability models process and the Bitcoin market from September 1, 2010, the approximate date when miners started to buy mining hardware to mine Bitcoins, to September 30, 2015.

The model described is built on a previous work of the authors [2], which modeled the Bitcoin market under a purely financial perspective, while in this work, we fully consider also the economics of mining. The proposed model simulates the mining process and the Bitcoin transactions, by implementing a mechanism for the formation of the Bitcoin price, and specific behaviors for each typology of trader who mines, buys, or sells Bitcoins. We calibrated the proposed model by using “blockchain. info”, a web Bitcoin mining hardware profitability models which displays detailed information about all transactions and Bitcoin blocks, and by tracking the history of the mining hardware. We followed the introduction into the market of the products developed by some mining hardware companies, with the aim to obtain the time trends of the average hash rate per US$ spent on hardware, and of the average power consumption per .

The model was validated studying its ability to reproduce some “stylized facts” found in real-time price series and some core aspects of the real mining business. In particular, the computational experiments performed can reproduce the unit root property, the fat tail phenomenon and the volatility clustering of Bitcoin price series. To our knowledge, this is the first model based on the heterogeneous agents approach that studies the generation of Bitcoins, the hashing capability, the power consumption, and the mining hardware and electrical energy expenditures of the Bitcoin network.

The paper is organized as follows. In Section Related Work we discuss other works related to this paper, in Section Mining Process we describe briefly the mining process and we give an overview of the mining hardware and of its evolution over time. In Section The Model we present the proposed model in detail. Section Simulation Results presents the values given to several parameters of the model Bitcoin mining hardware profitability models reports the results of the simulations, including statistical analysis of Bitcoin real prices and simulated Bitcoin price, and sensitivity analysis of the model to some key parameters. The conclusions of the paper are reported in the last Section. Finally, Appendices A, B, C, and D, in S1 Appendix, deal with the calibration to some parameters of the model, Bitcoin mining hardware profitability models Appendix E, in S1 Appendix, deals with the sensitivity of the model to some model parameters.

Related Work

The study and analysis of the cryptocurrency market is a relatively new field. In the latest years, several papers appeared on this topic, given its potential interest and the many issues related to it. Several papers focus on the de-anonymization of Bitcoin users by introducing clustering heuristics to form a user network (see for instance the works [3–5]); others focus on the promise, perils, risks and issues of digital currencies, [6–10]; others focus on the technical issues about protocols and security, [11, 12]. However, very few works were made to model the cryptocurrencies market. Among these, we can cite the works by Luther [13], who studied why some cryptocurrencies failed to gain widespread acceptance using a simple agent model; by Bornholdt and Steppen [14], who proposed a model based on a Moran process to study the cryptocurrencies able to emerge; by Garcia et al. [15], who studied the role of social interactions in the creation of price bubbles; by Kristoufek [16] who analyzed the main drivers of the Bitcoin price; by Kaminsky and Gloor [17] who related the Bitcoin market to its sentiment analysis on social networks; and by Donier and Bouchaud [18] who showed how markets’ crashes are conditioned by market liquidity.

In this paper we propose a complex agent-based artificial cryptocurrency market model in order to reproduce the economy of the mining process, the Bitcoin transactions and the main stylized facts of the Bitcoin price series, following the well known agent-based approach. For reviews about agent-based modelling of the financial markets see the works [19, 20] and [21].

The proposed model simulates the Bitcoin market, studying the impact on the market of three different trader types: Random traders, Chartists and Miners. Random traders trade randomly and are constrained only by their financial resources as in work [22]. They issue buy or sell orders with the same probability and represent people who are in the market for business or investing, but are not speculators. Our Random traders are not equivalent to the so called “noise traders”, who are irrational traders, able of affecting stock prices with their unpredictable changes in their sentiments (see work by Chiarella et al. [23] and by Verma et al. [24]). Chartists represent speculators. They usually issue buy orders when the price is increasing and sell orders when the price is decreasing. Miners are in the Bitcoin market aiming to generate wealth by gaining Bitcoins and are modeled with specific strategies for mining, trading, investing in, and divesting mining hardware. As in the work by Licalzi and Pellizzari [25]—in which the authors model a market where all traders are fundamentalists—the fat tails, one of the main “stylized Bitcoin mining hardware profitability models of the real financial markets, stem from the market microstructure rather than from sophisticated behavioral assumptions.

Note that in our model no trader uses rules to form expectations on prices or on gains, contrarily to the works by Chiarella et al. [23] and by Licalzi and Pellizzari [25], in which traders use rules to form expectations on stock returns. In addition, no trader imitates the expectations of the most successful traders as in the work by Tedeschi et al. [26].

The proposed model implements a mechanism for Bitcoin mining hardware profitability models formation of the Bitcoin price based on an order book. In particular, the definition of price follows the approach introduced by Raberto et al. [27], in which the limit prices have a random component, modelling the different perceptions of the Bitcoin value, whereas the formation of the price is based on the limit order book, similar to that presented by Raberto et al. [22]. As regards the limit order book, it is constituted by two queues Bitcoin mining hardware profitability models orders in each instant—sell orders and buy orders. At each simulation step, various new orders are inserted into the respective queues. As soon as a new order enters the book, the first buy order and the first sell order of the lists are inspected to verify if they match. If they match, a transaction occurs. This in contrast with the approach adopted by Chiarella et al. [23], Licalzi and Pellizzari [25] and by Tedeschi et al. [26], in which the agents decide whether to place a buy or a sell order, and choose the size of the order, maximizing their own expected utility function.

The proposed model is, to our knowledge, the first model that aims to study the Bitcoin market and in general a cryptocurrency market– as a whole, including the economics of mining. It was validated by performing several statistical analyses in order to study the stylized facts of Bitcoin price and returns, following the approaches used by Chiarella et al. [23], Cont [28], Licalzi and Pellizzari [25] and Radivojevic et al. [29], for studying the stylized facts of prices and returns in financial markets.

The Mining Process

Today, every few minutes thousands of people send and receive Bitcoins through the peer-to-peer electronic cash system created by Satoshi Nakamoto. All transactions are public and stored in a distributed database called Blockchain, which is used to confirm transactions and prevent the double-spending problem.

People who confirm transactions of Bitcoins and store them in the Blockchain are called “miners”. As soon as new transactions are notified to the network, miners check their validity and authenticity and collect them into a set of transactions called “block”. Then, they take the information contained in the block, which include a variable number called “nonce”, and run the SHA-256 hashing algorithm on this block, turning the initial information into a sequence of 256 bits, known as Hash [30].

There is no way of knowing how this sequence will look before calculating it, and the introduction of a minor change in the initial data causes a drastic change in the resulting Hash.

The miners cannot change the data containing the information on transactions, but can change the “nonce” number used to create a different hash. The goal is to find a Hash having a given number of leading zero bits. This number can be varied to change the difficulty of the problem. The first miner who creates a proper Hash with success (he finds the “proof-of-work”), gets a reward in Bitcoins, and the successful Hash is stored with the block of the validated transactions in the Blockchain.

In a nutshell,

“Bitcoin miners make money when they find a 32-bit value which, when hashed together with the data from other transactions with a standard hash function gives a hash with a certain number of 60 or more zeros. This is an extremely rare event”, [30].

The steps to run the network are as follows:

“New transactions are broadcast to all nodes; each node collects new transactions into a block; each node works on finding a difficult proof-of-work for its Bitcoin mining hardware profitability models when a node finds a proof-of-work, it broadcasts the block to all nodes; nodes accept the block only if all transactions in it are valid and not already spent; nodes express their acceptance of the block by working on creating the next block in the chain, using the hash of the accepted block as the previous hash”, [1].

Producing a single hash is computationally very easy. Consequently, in order to regulate the generation of Bitcoins, the Bitcoin protocol makes this task more and more difficult over time.

The proof-of-work is implemented by incrementing the nonce in the block until a value is found that gives the block’s hash with the required leading zero bits. If the hash does not match the required format, a new nonce is generated and the Hash calculation starts again [1]. Countless attempts may be necessary before finding a nonce able to generate a correct Hash (the size of the nonce is only 32 bits, so in practice it is necessary to vary also other information inside the block to be able to get a hash with the required number of leading zeros, which at the time of writing is about 70).

The computational complexity of the process necessary to find the proof-of-work is adjusted over time in such a way that the number of blocks found each day is more or less constant (approximately 2016 blocks in two weeks, one every 10 minutes). In the beginning, each generated block corresponded to the creation of 50 Bitcoins, this number being halved each four years, after 210,000 blocks additions. So, the miners have a reward equal to 50 Bitcoins if the created blocks belong to the first 210,000 blocks of the Blockchain, 25 Bitcoins if the created blocks range from the 210,001st to the 420,000th block in the Blockchain, 12.5 Bitcoins if the created blocks range from the 420,001st to the 630,000th block in the Blockchain, and so on.

Over time, mining Bitcoin is getting more and more complex, due to the increasing number of miners, and the increasing power of their hardware. We have witnessed the succession of four generations of hardware, i. e. CPU’s, GPU’s, FPGA’s and ASIC’s generation, each of them characterized by a specific hash rate (measured in H/sec) and power consumption. With time, the power and the price of the mining hardware has been steadly increasing, though the price of H/sec has been decreasing. To face the increasing costs, miners are pooling together to share resources.

The evolution of the Bitcoin mining hardware profitability models hardware

In January 3, 2009, Satoshi Nakamoto created the first block of the Blockchain, called “Genesis Block”, hashing on the central processing unit (CPU) of his computer. Like him, the early miners mined Bitcoin running the software on their personal computers. The CPU’s era represents the first phase of the mining process, the other eras being GPU’s, FPGA’s and ASIC’s eras (see web site https://tradeblock. com/blog/the-evolution-of-mining/).

Each era announces the use of a specific typology of mining hardware. In the second era, started about on September 2010, boards based on graphics processing units (GPU) running in parallel entered the market, giving rise to the GPU era.

Around December 2011, the FPGA’s era started, and hardware based on field programmable gate array cards (FPGA) specifically designed to mine Bitcoins was available in the market. Finally, in 2013 fully customized application-specific integrated circuit (ASIC) appeared, substantially increasing the hashing capability of the Bitcoin network and marking the beginning of the fourth era.

Over time, the different mining hardware available was characterized by an increasing hash rate, a decreasing power consumption per hash, and increasing costs. For example, NVIDIA Quadro NVS 3100M, 16 cores, belonging to the GPU generation, has a hash rate equal to 3.6 MH/s and a power Bitcoin mining hardware profitability models equal to 14 W [31]; ModMiner Quad, belonging to the FPGA generation, has a hash rate equal Bitcoin mining hardware profitability models 800 MH/s and a power consumption equal to 40 W [31]; Monarch(300), belonging to the ASIC generation, has a hash rate equal to 300 GH/s and a power consumption equal to 175 W (see web site https://tradeblock. com/mining/.

Modelling the Mining Hardware Performances

The goal of our work is to model the economy of the mining process, so we neglected the first era, when Bitcoins had no monetary value, and miners used the power available on their PCs, at almost no cost. We simulated only the remaining three generations of mining hardware.

We gathered information about the products that entered the market in each era to model these three generations of hardware, in particular with the aim to compute:

The average hash rate per US$ spent on hardware, R(t), expressed in ;the average power consumption per H/sec, P(t), expressed in .

The average hash rate and the average power consumption were computed averaging the real market data at specific times and constructing two fitting curves.

To calculate the hash rate and the power consumption of the mining hardware of the GPU era, that we estimate ranging from September 1st, 2010 to September 29th, 2011, we computed an average for R and P taking into account some representative products in the market during that period, neglecting the costs of the motherboard.

In that era, motherboards with more than one Peripheral Component Interconnect Express (PCIe) slot started to enter the market, allowing to install multiple video cards in only one system, by using adapters, and to mine criptocurrency, thanks to the power of the GPUs. In Table 1, we describe the features of some GPUs in the market in that period. The data reported are taken from the web site http://coinpolice. com/gpu/.

As regards the FPGA and ASIC eras, starting around September 2011 and December 2013 respectively, we tracked the history of the mining hardware by following the introduction of Butterfly Labs company’s products into the market. We extracted the data illustrated in Table 2 from the history of the web site http://www. butterflylabs. com/ through the web site web. archive. org. For hardware in the market in 2014 and 2015 we referred to the Bitmain Technologies Ltd company, and in particular, to the mining hardware called AntMiner (see web site https://bitmaintech. com and Table 2).

Table 2. Butterfly Labs and Bitmain Technologies Mining Hardware.

FPGA Hardware from 09/29/2011 to 12/17/2012, ASIC Hardware from 12/17/2012 to December 2013 and AntMiner Hardware produced in 2014 and 2015.

https://doi. org/10.1371/journal. pone.0164603.t002

Starting from the mining products in each period (see Tables 1 and 2), we fitted a “best hash rate per $” and a “best power consumption function” (see Table 3). We call the fitting curves R(t) and P(t), respectively.

We used a general exponential model to fit Bitcoin mining hardware profitability models curve of the hash rate, R(t) obtained by using Eq (1): (1) where a = 8.635*104 and b = 0.006318.

The Bitcoin mining hardware profitability models curve of the power consumption P(t) is also a general exponential model: (2) where a = 4.649*10−7 and b = −0.004055.

Fig Bitcoin mining hardware profitability models and 1B show in logarithmic scale the fitting curves and how the hash rate increases over time, whereas power consumption decreases.

The Model

We used blockchain. info, a web site which displays detailed information about all transactions and Bitcoin blocks—providing graphs and statistics on different data—for extracting the empirical data used in this work. In particular, we observed the time trend of the Bitcoin price in the market, the total number of Bitcoins, the total hash rate of the Bitcoin network and the total number of Bitcoin transactions.

The proposed model presents an agent-based artificial cryptocurrency market in which agents mine, buy or sell Bitcoins.

We modeled the Bitcoin market starting from September 1st, 2010, because one of our goals is to study the economy of the mining process. It was only around this date that miners started to Bitcoin mining hardware profitability models mining hardware Bitcoin mining hardware profitability models mine Bitcoins, denoting a business interest in mining. Previously, they typically just used the power available on their personal computers.

The Bitcoin mining hardware profitability models of the model are:

There are various kinds of agents active on the BTC market: Miners, Random traders and Chartists;the trading mechanism is based on a realistic order book that keeps sorted lists of buy and sell orders, and matches them allowing to fulfill compatible orders and to set the price;agents have typically limited financial resources, initially distributed following a power law;the number of agents Bitcoin mining hardware profitability models in trading at each moment is a Bitcoin mining hardware profitability models of the total number of agents;a number of new traders, endowed only with cash, enter the market; they represent people who decided to start trading or mining Bitcoins;Miners belong to mining pools. This means that at each time t they always have a positive probability to mine at least a fraction of Bitcoin. Indeed, since 2010 miners have been pooling together to share resources in order to avoid effort duplication to optimally mine Bitcoins. A consequence of this fact is that gains are smoothly distributed amongst Miners.

On July 18th, 2010,

“ArtForz establishes an OpenGL GPU hash farm and generates his first Bitcoin block”

And on September 18th, 2010,

“Bitcoin Pooled Mining (operated by slush), a method by which several users work collectively to mine Bitcoins and share in the benefits, mines its first block”,

(news from the web site http://historyofBitcoin. org/).

Since then, the difficulty of the problem of mining increased exponentially, and nowadays it would be almost unthinkable to mine without participating in a pool.

In the next subsections we describe the model simulating the mining, the Bitcoin market and the related mechanism of Bitcoin price formation in detail.

The Agents

Agents, or traders, are divided into three populations: Miners, Random traders and Chartists.

Every i-th trader enters the market at a given time step, . Such a trader can be either a Miner, a Random trader or a Chartist. All traders present in the market at the initial time hold an amount ci(0) of fiat currency (cash, in US dollars) and an amount bi(0) of cryptocurrency (Bitcoins), where i is the trader’s index. They represent the persons present in the market, mining and trading Bitcoins, before the period considered in the simulation. Each i-th trader entering the market at holds only an amount of fiat currency (cash, in dollars). These traders represent people Bitcoin mining hardware profitability models in entering the market, investing their money in it.

The wealth distribution of traders follows a Zipf law [32]. The set of all traders entering the market at time are generated before the beginning of the simulation with a Pareto distribution of Bitcoin mining hardware profitability models cash, Bitcoin mining hardware profitability models then are randomly extracted from the set, when a given number of them must enter the market at a given time step. Also, the wealth distribution in crypto cash of the traders in the market at initial time follows a Zipf law. Indeed, the wealth share in the world of Bitcoin is even more unevenly distributed than in the world at large (see web site http://www. cryptocoinsnews. com/owns-Bitcoins-infographic-wealth-distribution/). More details on the trader wealth endowment are illustrated in Appendix A, in S1 Appendix. In that appendix, we report also some results that show that the heterogeneity in the fiat and crypto cash of the traders emerges endogenously also when traders start from the same initial wealth.

Miners.

Miners Bitcoin mining hardware profitability models in the Bitcoin market aiming to generate wealth by gaining Bitcoins. At the initial time, the simulated Bitcoin network is calibrated according to Satoshi’s original idea of Bitcoin network, where each node participates equally to the process of check and validation of the transactions and mining. We assumed that Miners in the market at initial time () own a Core i5 2600K PC, and hence they are initially endowed with a hashing capability ri(0) equal to 0.0173GH/sec, that implies a power consumption equal to 75W [31]. Core i5 is a brand name of a series of fourth-generation x64 microprocessors developed by Intel and brought to market in October 2009.

Miners entering the market at time acquire Bitcoin mining hardware profitability models hardware, and hence a hashing capability ri(t)—which implies a specific electricity cost ei(t)—investing a fraction γ1,i(t) of their fiat cash ci(t).

In addition, over time all Miners can improve their hashing capability by buying new mining hardware investing both their fiat and crypto cash. Consequently, the total hashing capability of i–th trader at time t, ri(t) expressed in [H/sec], and the total electricity cost ei(t) expressed in $ per Bitcoin mining hardware profitability models, associated to her mining hardware units, are defined respectively as: (3) and (4) where: (5)(6)

R(t) and P(t) are, respectively, the hash rate which can be bought with one US$, expressed in , and the power consumption, expressed in . At each time t, their values are given by using the fitting curves described in subsection Modelling the Mining Hardware Performances;ri, u(t) is the hashing capability of the hardware units u bought at time t by i–th miner;γi(t) = 0 and γ1,i(t) = 0 if no hardware is bought by i–th trader at time t. When a trader decides to buy new hardware, γ1,i represents the percentage of the miner’s cash allocated to buy it. It is equal to a random variable characterized by a lognormal distribution with average 0.6 and standard deviation 0.15. γi represents the percentage of the miner’s Bitcoins to be sold for buying the new hardware at time t. It is equal to 0.5*γ1,i(t). The term γ1,i(t)ci(t) + γi(t)bi(t)p(t) expresses the amount of personal wealth that the miner wishes to allocate to buy new mining hardware, meaning that on average the miner will allocate 60% of her cash and 30% of her Bitcoins to this purpose. If γi > 1 or γ1,i > 1, they are set equal to one;ϵ is the fiat price per Watt and per hour. It is assumed equal to 1.4*10−4 $, considering the cost of 1 KWh equal to 0.14$, which we assumed to be constant throughout the simulation. This electricity price is computed by making an average of the electricity prices in the countries in which the Bitcoin nodes distribution is higher; see web sites https://getaddr. bitnodes. io and http://en. wikipedia. org/wiki/Electricity_pricing.

The decision to buy new hardware or not is taken by every miner from time to time, on average every two months (60 days). If i–th miner decides whether to buy new hardware and/or to divest the old hardware units at time t, the next time, , she will decide again is given by Eq (7): (7) where int rounds to the nearest integer and N(μid, σid) is a normal distribution with average μid = 0 and standard deviation σid = 6. is updated each time the miner takes her decision.

Miners active in the simulation since the beginning will take their first decision within 60 days, at random times uniformly distributed. Miners entering the simulation at time t > 1 will immediately take this decision.

In deeper detail, at time , every miner buys new hardware units, if their fiat cash is positive, and divests the hardware units older than one year. This is because, in general, Bitcoin mining hardware become obsolete from a few months to one year after you Bitcoin mining hardware profitability models it. “Serious” miners usually buy new equipment every month, re-investing their profits into new mining equipment, if they want their Bitcoin mining operation to run long term (see web site http://coinbrief. net/profitable-bitcoin-mining-farm/. If the trader’s cash is zero, she issues a sell market order to get Bitcoin mining hardware profitability models cash to support her electricity expenses, ci, a(t) = γi(t)bi(t)p(t).

Each i–th miner belongs to a pool, and consequently at each time t she always has a probability higher than 0 to mine at least some sub-units of Bitcoin. This probability is inversely proportional to the hashing capability of the whole network. Knowing the number of blocks discovered per day, and consequently knowing the number of new Bitcoins B to be mined per day, the number of Bitcoins bi mined by i–th miner per day can be defined as follows: (8) where:

RTot(t) is the hashing capability of the whole population of Miners Nm at time t defined as the sum of the hashing capabilities of all Miners at time t, ;the ratio defines the relative hash rate of i–th miner at time t.

Note that, as already described in the section Mining Process, the parameter B decreases over time. At first, each generated block corresponds to the creation of 50 Bitcoins, but after four years, such number is halved. So, until November 27, 2012, 100,800 Bitcoins were mined in 14 days (7200 Bitcoins per day), and then 50,400 Bitcoins in 14 days (3600 per day).

Random Traders.

Random traders represent persons who enter the cryptocurrency market for various reasons, but not for speculative purposes. They issue orders for reasons linked to their needs, for instance they invest in Bitcoins to Bitcoin mining hardware profitability models their portfolio, or they disinvest to satisfy a need for cash. They issue orders in a random way, compatibly with their available resources. In particular, buy and sell orders are always issued with the same probability. The specifics of their behavior are described in section Buy and Sell Orders.

Chartists.

Chartists represent speculators, aimed to gain by Bitcoin mining hardware profitability models orders in the Bitcoin market. They speculate that, if prices are rising, Bitcoin mining hardware profitability models will keep rising, and if prices are falling, they will keep falling. In particular, i–th Chartist issues a buy order when the price relative variation in Bitcoin mining hardware profitability models time window , is higher than a threshold ThC = 0.01, and issues a sell order if this variation is lower than ThC. is specific for each Chartist, and is characterized by a normal distribution with average equal to 20 and standard deviation equal to 1. Chartists usually issue buy orders when the price is increasing and sell orders when the price is decreasing.

Note that a Chartist will issue an order only when the price variation is above a given threshold. So, in practice, the extent of Chartist activity varies over time.

All Random traders and Chartists entering the market at t = tE > 0, issue a buy order to acquire their initial Bitcoins. Over time, at time t > tE only a fraction of Random traders and Chartists is active, and hence enabled to issue orders. Active traders can issue only one order per time step, which can be a sell order or a buy order.

Orders already placed but not yet satisfied or withdrawn are accounted for when determining Bitcoin mining hardware profitability models amount of Bitcoins a trader can buy or sell. Details on the percentage of active traders, the number of the traders in the market and on the probability of each trader to belong to a specific traders’ population are described in Appendices B, C, and D, in S1 Appendix.

Buy and Sell Orders

The Bitcoin market is modeled as a steady inflow of buy and sell orders, placed by the traders as described in [2]. Both buy and sell orders are expressed in Bitcoins, that is, they refer to a given amount of Bitcoins to buy or sell. In deeper detail, all orders have the following features:

Amount, expressed in $ for buy order and in Bitcoins for sell order: the latter amount is a real number, because Bitcoins can be bought and sold in fractions as Bitcoin mining hardware profitability models as a “Satoshi”;residual amount (Bitcoins or $): used when an order is only partially satisfied by previous transactions;limit price (see below), which in turn can be a real number;time when the order was issued;expiration time: if the order is not (fully) satisfied, it is removed from the book at this time.

The amount of each buy order depends on the amount of cash, ci(t), owned by i-th trader at time t, less the cash already committed to other pending buy orders still in the book. Let us call the available cash. The number of Bitcoins to buy, ba is given by Eq (9)(9) where p(t) is the current price and β is a random variable drawn from a lognormal distribution with average and standard deviation equal to 0.25 and 0.2, respectively for Random traders and equal to Bitcoin mining hardware profitability models and 0.2, respectively for Chartists. In the unlikely case that β > 1, β Bitcoin mining hardware profitability models set equal to 1.

Similarly, the amount of each sell order depends on the number of Bitcoins, bi(t) owned by i-th trader at time t, less the Bitcoins already committed to other pending sell orders still in the book, overall called . The number of Bitcoins to sell, sa is given Bitcoin mining hardware profitability models (10) where β is a lognormal random variable as above. Short selling is not allowed.

The limit price models the price to Bitcoin mining hardware profitability models a trader desires to conclude their Bitcoin mining hardware profitability models. An order can also be issued with no limit (market order), meaning that its originator wishes to perform the trade at the best price she can find. In this case, the limit price is set to zero. The probability of placing a market order, Bitcoin mining hardware profitability models, is set at the beginning of the simulation and is equal to 1 for Miners, to 0.2 for Random traders and to 0.7 for Chartists. This is because, unlike Random traders, if Miners and Chartists issue orders, they wish to perform the trade at the best available price, the former because they need cash, Bitcoin mining hardware profitability models latter to be able to profit by following the price trend.

Let us suppose that i-th trader issues a limit order to buy Bitcoins at time t. Each buy order can be executed if the trading price is lower than, or equal to, its buy limit price bi. In the case of a sell order of Bitcoins, it can be executed if the trading price is higher than, or equal to, its sell limit price si. As said above, if the limit prices bi = 0 or si = 0, then the orders can be always executed, provided there is a pending complementary order.

The buy and sell limit prices, bi and si, are given respectively by the following equations: (11)(12) where

P(t) is the current Bitcoin price; is a random draw from a Gaussian distribution with average μ ≃ 1 and standard deviation σi ≪ 1.

The limit prices have a random component, modelling the different perception of Bitcoin value, that is the fact that what traders “feel” is the right price to buy or to sell is not constant, and may vary for each single order. In the case of buy orders, we stipulate that a trader wishing to buy must offer a price that is, on average, slightly higher than the market price.

The value of σi is proportional to the “volatility” σ(Ti) of the price p(t) through the equation σi = Kσ(Ti), where Bitcoin mining hardware profitability models is a constant and σ(Ti) is the standard deviation of price absolute returns, calculated in the time window Ti. σi is constrained between a minimum value σmin and a maximum value σmax (this is an approach similar to that of [27]). For buy orders μ = 1.05, K = 2.5, σmin = 0.01 Bitcoin mining hardware profitability models σmax = 0.003.

In the case of Bitcoin mining hardware profitability models orders, the reasoning is dual. For symmetry, the limit price is divided by a random draw from the same Gaussian distribution .

An expiration time is associated to each order. For Random traders, the value of the expiration time is equal to the current time plus a number of days (time steps) drawn from a lognormal distribution with average and standard deviation equal to 3 and 1 days, respectively. In this way, most orders will expire within 4 days since they were posted. Chartists, who act in a more dynamic way to follow the market trend, post orders whose expiration time is at the end of the same trading day. Miners issue market orders, so the value of the expiration time is set to infinite.

Price Clearing Mechanism

We implemented the price clearing mechanism by using an Order Book similar to that presented in [22].

At every time step, the order book holds the list of all the orders received and still to be executed. Buy orders are sorted in descending order with respect to the limit price bi. Sell orders are sorted in ascending order with respect to the limit price sj. Orders with the same limit price are sorted in ascending order with respect to the order issue time.

At each simulation step, various new orders are inserted into the respective lists. As soon as a new order enters the book, the first buy order and the first sell order of the lists are inspected to verify if they match. If they match, a transaction occurs. The order with the smallest residual amount is fully executed, whereas the order with the largest amount is only partially executed, and remains at the head of the list, with its residual amount reduced by the Bitcoin mining hardware profitability models of the matching order. Clearly, if both orders have the same residual amount, they are both fully executed.

After the transaction, the next pair of orders at the head of the lists are checked for matching. If they match, they are executed, and so on until they do not match anymore. Hence, before the book can accept new orders, all the matching orders are satisfied.

A sell order of index j matches a buy order of index i, and vice versa, only if sj ≤ bi, or if one of the two limit prices, or both, are equal to zero.

As regards the price, pT, to which the transaction is performed, the price formation mechanism follows the rules described below. Here, p(t) denotes the current price:

When one of the two orders has limit price equal to zero:

If bi > 0, then pT = min(bi, p(t)),if sj > 0, then pT = max(sj, p(t)),

When both orders have limit price equal to zero, pT = p(t);when both orders have limit price higher than zero, .

Simulation Results

The model described in the previous section was implemented in Smalltalk language. Before the simulation, it had to be calibrated in order to reproduce the real stylized facts and the mining process in the Bitcoin market in Bitcoin mining hardware profitability models period between September 1st, 2010 and September 30th, 2015. The simulation period was thus set to 1856 steps, a simulation step corresponding to one day. We included also weekends and holidays, because the Bitcoin market is, by its very nature, accessible and working every day.

Some parameter values are taken from the literature, others from Bitcoin mining hardware profitability models data, and others are guessed using common sense, and tested by verifying that the simulation outputs were plausible and consistent. We set the initial value of several key parameters of the model by using data recovered from the Blockchain Web site. The main assumption we made is to size the artificial market at about 1/100 of the real market, to be able to manage the computational load of the simulation. Table 4 shows the values of some parameters and their computation assumptions in detail. Other parameter values are described in the description of the model presented in the Section The Model. In Appendices A-D, in S1 Appendix, other details about the calibration of the model are shown. Specifically, the calibration of the trader wealth endowment, the number of active traders, the total number of traders in the market and the probability of a trader to belong to a specific traders’ population are described in detail.

The model was run to study the main features of the Bitcoin market and of Bitcoin mining hardware profitability models traders who operate in it. In order to assess the robustness of our model and the validity of our statistical analysis, we repeated 100 simulations with the same initial conditions, but different seeds of the random number generator. The results of all simulations were consistent, as the following shows.

Bitcoin prices in the real and simulated market

We started studying the real Bitcoin price series between September 1st, 2010 and September 30, 2015, shown in Fig 2. The figure shows an initial period in which the price trend is relatively constant, until about 950th day. Then, a period of volatility follows between 950th and 1150th day, followed by a Bitcoin mining hardware profitability models of strong volatility, until the end of the considered interval. The Bitcoin price started to fall at the beginning of 2014, and continued on its downward slope until September 2015.

As regards the prices in the simulated market, we report in Fig 3 the Bitcoin price in one typical simulation run. It is possible to observe that, as in the case of the real price, the price keeps its value Bitcoin mining hardware profitability models at first, but then, after about 1000 simulation steps, contrary to what happens in reality, it grows and continues on its upward slope until the end of the simulation period.

Fig 4A and 4B

Is it worth to start mining Cryptocurrencies in 2020?

Some see the mining of bitcoin as a hobby. Others see in this lesson a profitable business or a way to get profit. To evaluate the profitability of miner hardware, you need to compare the cost of electricity, device and some fees. Sometimes it turns out that to buy coins is more Bitcoin mining hardware profitability models. We will cover all this in more detail in this review.

What is an Bitcoin mining hardware profitability models Bitcoin Miner?

ASIC stands for the application-specific integrated circuit. These schemes do not perform general functions but act only in a strict framework to solve a specific problem. Therefore, to mine the Bitcoin through ASICs is chip then using another Bitcoin mining hardware.

Initially, crypto enthusiasts could receive Bitcoin with computer help. Mining is the award of aggregate mathematical problems using specialized powerful miners hardware and software.

Then they started to use graphic processors and FPGA (field-programmable gate array), but soon they also ceased to be quite useful. ASIC miner is the best bitcoin miner of the latest generation. It provides a higher cryptocurrency mining speed, the machine heats less and consumes less electricity.

You can try mining through the computer, but only as a hobby or to understand how this process occurs. Bitcoin mining hardware profitability models hash power (computing power) and the hash rate of conventional computers are significantly lower (about 40-50 times) than mining rig.

Quick jump to >> 3 Best Bitcoin Miners for 2019-2020 <<

Hardware Profitability Factors

Best miners should be profitable. New machines should cover the prices you pay for them. Moreover, the Bitcoins received must have more high values ​​than the electricity spent on their production.

To calculate the worth of all factors, you need to make a comparison. The difficulty is that the value of BTC is continuously changing. Before you buy mining rigs, make a simple calculator:

Hashing rate;A bitcoin sale price;Power consumption;How much USD a participant needs to pay per month for current;The number of coins you get per month.

How to Find the Best Bitcoin Miner

To choose the best Bitcoin mining chip, first, a client should to find out which is the Bitcoin mining hardware profitability models powerful of the miners accessible. Best Bitcoin mining hardware has the following features:

Most significant hash rate will permit to get more currency. Effectiveness is expressed in how much electricity is required to get a certain sum of coins. Currently, the most effective is the ASIC mining hardware. The price of the fastest equipment cannot be low or average. The lower the cost, the smaller the efficiency (you may even go negative).Beware of scam on the mart. Trusted manufacturers can be sold on eBay or Amazon. But before buying, check all the details about graphics and setup.

Bitcoin Mining Hardware Companies

Even a used hash miner may be beneficial to you. Check out ASIC Bitcoin miners such as Dragonmint miner or The Antminer S9.

In addition to high speed, manufacturers must provide a low hash price. It means that the performance of the facility will be high.

Here are reliable brands to watch out for.

Bitmain technologies

This brand is created in China and produces reliable Bitcoin ASIC chips. The most popular among devices is Bitmain Antminer. The equipment of this brand is capable of mining coins of Bitcoin, Litecoin, Ethereum and Dash. Besides, the company owns one of the greatest Bitcoin pool.

Canaan creative

It is a famous Chinese apparatus brand that started with FPGA. As technology stepped forward, the company released its own ASIC chip. The market share held by the brand is about 20%.

Halong Mining

Halong Mining collaborates with Bitcoin mining hardware profitability models brand from South Korea. The company entered the market only in 2018 but has already become a leader in this industry. Halong Mining produces the most powerful Bitcoin miner ASIC to date, called DragonMint T1.

Innosilicon technology

The international company has branches in the USA and China. The brand specializes in diverse digital technology. The brand is currently releasing one of the best ASIC Bitcoin mining hardware called Terminator T3.

GMO Internet

GMO Internet is a Japanese brand. The company is engaged not only in the production of Bitcoin mining hardware profitability models equipment but also in advertising and financial services via the Internet. The brand even has its projects among crypto exchanges working on the blockchain (such as Coinbase).

Zhejiang Ebang communication

The abbreviated name sounds like Ebang. The main field of activity of the company is the creation and sale Bitcoin mining hardware profitability models optical fiber for telecommunications. The latest ASICs Bitcoin mining hardware profitability models equipment is one of the best using ASICs and competes with Bitmain.

Bitfury

Bitfury is one of the first companies to start producing Bitcoin miners. Bitfury themselves mine coins on their equipment. Large data centers are located in Canada, Norway, Iceland, Georgia. The range includes ASIC mining chips, servers, and portable data centers.

Bitcoin USB Miners Comparison

USB miner is not high-performance equipment. This type of device only helps you understand the principle of coin mining. Besides, USB mining equipment is cheaper.

ASICMiner Block Erupter USB 330MH / s Sapphire Miner

There is the standard option among USB Bitcoin miners. The expected profit is only $ 0.01 per month. At the same time, the hash power is 330mh/s-336mh/s.

GekkoScience Compac USB Stick Bitcoin Miner

The device works simply through the connected USB port, absolutely silently. At the same time, your profit will be approximately 0.15 USD per month.

Avalon Nano 3

Do not confuse this device with the crypto wallet Ledger Nano. Both devices look about the same, but they have entirely different functions. A wallet keeps your coins. While Avalon Nano 3 allows you to get Bitcoin mining hardware profitability models for 1 USD per year.

Bitmain Antrouter R1 Wifi Solo Bitcoin Miner

Your production will be a little more than 1 USD per year. Even with the low price of equipment, it does not look too cost-effective. But this device has an additional plus. It also operates as a wireless router, providing you with wireless Internet.

21 Bitcoin Computer

There is not the most efficient USB bitcoin miner. It Bitcoin mining hardware profitability models via a standard USB port. But under the current circumstances, Bitcoin mining hardware profitability models are unlikely to be able to count on profit in virtue of the poor hash rate.

Dragonmint T16 vs. Antminer s9

In 2019, the most efficient Bitcoin miner is Dragonmint T16. The manufacturer supplied a 16 t / s machine. No other equipment can boast such an indicator.

Using Dragonmint T16, you also save energy. The user needs only 0.075 J / GH. While the Bitmain Antminer S9 consumes 0.098 J / GH.

And another Dragonmint T16 bonus is the ASICBOOST algorithm, which increases the effectiveness by 20%.

Now comparing electricity consumption, din seems like a slight advantage. But if you intend to mine coins in pro volumes, you will feel the savings. Returns to the price of electricity. Even if it is high, both ASICs Bitcoin installations will bring you profit.

Bitmain Antminer S7

The first release of Antminer S7 came in 2015. Those days, it was the most effective equipment. The BM1385 ASIC chip and two cooling fans are integrated into the metal case. The manufacturer recommends Bitcoin mining hardware profitability models additional use of a Bitcoin mining hardware profitability models W power supply.

Pros:

The hash rate is 4.73 TH / s;Efficiency is about 0.25 J / G / s;Equipment is in the middle price category;There are many instructions on working with Antminer S7 available on the market;Easy to buy spare parts if necessary.

Cons:

Antminer S7 is less capable than the modern AntMiner S9;High energy costs await you;To achieve stable results, you need an additional power supply;Noise during operation exceeds the average.

Currently, even if the user does not takes into account the worth of the device, but only the cost of electricity, he receives losses. Perhaps if the value of Bitcoin increases significantly, this equipment could be a bargain.

Bitmain Antminer S5

The equipment, emitted in 2014, loses much in power to modern devices. Antminer S5 consumes only Bitcoin mining hardware profitability models W and does not require an additional energy production. The device is possible to connect to a PC. Using only this mechanism, you will not get coins. The user needs special software and membership in a large Bitcoin pool.

Pros:

Low cost of the device;Low power consumption (respectively, petty bills for payment);Antminer S5 is easy to configure and suitable for newbies.

Cons:

The sound from the exploitation of the device is above average;There is only one cooling fan;Low hash rate.

Even in the case of a sharp jump in the cost of Bitcoin, you are unlikely to get positive results. Installation is only suitable Bitcoin mining hardware profitability models training.

Spondoolies SP20

The brand was first introduced in 2014. In those conditions, this device was cost-effective. But already in 2016, it was discontinued. The device has compact dimensions and a metal case. If someone likes rarity, he may find this miner on eBay for more than $ 100.

Pros:

The miner works with any power supplies;The device is one of the cheapest among analogs;It is possible to connect two low-power reserves;A working device emits little noise;Even a novice can easily configure a simple user interface.

Minuses:

The device has a low hash rate compared to modern standards (accordingly, efficiency suffers);During operation, the case becomes very hot;SP20 connects only via Ethernet;The miner may only be operated at ambient temperatures under 35 ° C.

In general, the installation provides extremely low profitability in 2019. For a year you will not get even 1 USD.

Dragonmint 16T

Halong Mining announced during the announcement of DragonMint 16T in 2018 that about $ 30 Bitcoin mining hardware profitability models was spent on research and testing. These efforts did not become in vain, because today, this miner is the most powerful and profitable among competitors.

The hash rate was 16 thousand UD / s. The introduction of ASICBOOST technology (a fragment of the Bitcoin software code) has increased the efficiency by an additional 20%.

Pros:

Test launches have confirmed that the miner is developing the declared speed and is highly efficient;It is possible to work with a standard voltage of 220–240V;You will get the lowest possible energy consumption from all available options. You can buy both one device and several at a time. There are no restrictions on the volume of the party.

Minuses:

The Bitcoin mining hardware profitability models is too high for an ordinary crypto enthusiast;To start, the user needs to additionally purchase a branded power supply unit (also not very cheap);Power consumption varies depending on the used power supply;The device may work at full strength only in conjunction with the original power supply.

The average projected income from the use of one installation is about 1.5 thousand USD.

Best Bitcoin Miners for 2019-2020

Given a great number of mining facilities, we understand how difficult it is to opt for any one installation. The buyer needs to focus primarily on high efficiency (and, of course, the price he can afford to pay for equipment). But consider the cost of paying for electricity. In most cases, you will lose half of your income or so to cover your bills.

In this review, we will look at the three most popular and Bitcoin mining hardware profitability models options.

Antminer s17

The product release came at the beginning of 2019. It is the flagship model of Bitmain. The capacity is 62 thousand tons / s. At the same time, energy costs are very high and equal to 2790 watts. Two hardware settings are available to you:

Normal;Professional.

You can earn more than 0.06 BTC per month. But, as expected, about half of the profits will be “eaten” by paying for electricity.

EBIT E12 +

The hash rate of this miner is slightly lower than the previous Bitcoin mining hardware profitability models and is

50TH / s. However, the required power for starting is lower (2500 W). This Bitcoin mining hardware profitability models is very effective in today’s market. Having a profit of more than 0.05 BTC per month, you will spend 47% on paying bills.

Innosilicon Terminator T3

Of all three samples, this is the least powerful, but this does not mean that it is Bitcoin mining hardware profitability models least lucrative. The hash rate is at the right level and amounts to 43TH / s. The maximum power consumption does not exceed 2100 watts. You can earn more than 0.04 BTC per month. Subtract half for the payment of electricity.

What are Coin Mining Pools?

In the article, we mentioned Bitcoin pools several times. It is time to clarify what it is in more detail.

Beginners who are just trying to get coins would not be able to achieve at least some results due to intense competition. Therefore, they have only one way out – this is to combine their efforts. Such an association in the Bitcoin society is called pool.

Even the most modern potent facilities will not provide anyone with the same power as professional data centers, which include many special (expensive) pieces of equipment.

If the pool won the race for coins, all revenue is distributed among the participants in proportion to their contribution. The standard membership fee is 2%. Now several large pools control the situation. Due to a large number of participants, the revenue of each of them will not be as significant as we would like.

To recieve and store your bitcoins you have to know which bitcoin wallet to use better

Is Bitcoin Mining Still Profitable?

For practical mining of Bitcoins, each member needs not only a specialized installation but also additional equipment:

Individual power supplies allow more efficient use of electricity. If the old models of miners could be connected directly to the outlet, then the new top equipment will not even turn on without a particular power source. Cooling fans allow mining units to run smoothly. In the case of overheating, the machine switches off spontaneously. Standby Bitcoin mining hardware profitability models are needed to secure the primary source of electricity.

People may get significant profit from mining Bitcoins if they have a modern ASIC installation and cheap electricity. The situation changes daily due to the floating rate of all cryptocurrencies.

Conclusion

Today on the market there are only a few advantageous offers among miners. If the price of Bitcoin rises, most likely, more Bitcoin mining hardware profitability models more new manufacturers will begin to develop and offer their versions of mining installations. And an increase in supply will entail a reduction in prices. Then more players will be able to join the Bitcoin network.

Aleksandr Sharilov

I am a crypto enthusiast. Bitcoin miner in 2013. AERGO Ambassador. I believe that blockchain technology is the future. My goal is to clarify the value of cryptocurrencies and blockchain in a free economy and security. I want to contribute to the implementation of these technologies in people’s lives through an explanation of the principles of its work.

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