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import vectorbt as vbt import numpy as np import yfinance as yf from itertools import combinations, product # Fetch daily price of Bitcoin price = yf.Ticker("BTC-USD").history(period="max")['Close'] price = price.vbt.split_into_ranges(n=3) # Define hyper-parameter space # 49 fast x 49 slow x 19 signal fast_windows, slow_windows, signal_windows = vbt.indicators.create_param_combs( (product, (combinations, np.arange(2, 51, 1), 2), np.arange(2, 21, 1))) # Run MACD indicator macd_ind = vbt.MACD.from_params( price, fast_window=fast_windows, slow_window=slow_windows, signal_window=signal_windows, hide_params=['macd_ewm', 'signal_ewm'] ) # Long when MACD is above zero AND signal entries = macd_ind.macd_above(0) & macd_ind.macd_above(macd_ind.signal) # Short when MACD is below zero OR signal exits = macd_ind.macd_below(0) | macd_ind.macd_below(macd_ind.signal) # Build portfolio portfolio = vbt.Portfolio.from_signals( price.vbt.tile(len(fast_windows)), entries, exits, fees=0.001, freq='1D') # Draw all window combinations as a 3D volume fig = portfolio.total_return.vbt.volume( x_level='macd_fast_window', y_level='macd_slow_window', z_level='macd_signal_window', slider_level='range_start', template='plotly_dark', trace_kwargs=dict( colorscale='Viridis', colorbar=dict( title='Total return', tickformat='%' ) ) ) fig.show()
This article is written by the CoinEx Chain lab. CoinEx Chain is the world’s first public chain exclusively designed for DEX, and will also include a Smart Chain supporting smart contracts and a Privacy Chain protecting users’ privacy.submitted by coinexchain to u/coinexchain [link] [comments]
longcpp @ 20200618
This is Part 1 of the serialized articles aimed to explain the Tendermint consensus protocol in detail.
Part 1. Preliminary of the consensus protocol: security model and PBFT protocol
Part 2. Tendermint consensus protocol illustrated: two-phase voting protocol and the locking and unlocking mechanism
Part 3. Weighted round-robin proposer selection algorithm used in Tendermint project
Any consensus agreement that is ultimately reached is the General Agreement, that is, the majority opinion. The consensus protocol on which the blockchain system operates is no exception. As a distributed system, the blockchain system aims to maintain the validity of the system. Intuitively, the validity of the blockchain system has two meanings: firstly, there is no ambiguity, and secondly, it can process requests to update its status. The former corresponds to the safety requirements of distributed systems, while the latter to the requirements of liveness. The validity of distributed systems is mainly maintained by consensus protocols, considering the multiple nodes and network communication involved in such systems may be unstable, which has brought huge challenges to the design of consensus protocols.
The semi-synchronous network model and Byzantine fault toleranceResearchers of distributed systems characterize these problems that may occur in nodes and network communications using node failure models and network models. The fail-stop failure in node failure models refers to the situation where the node itself stops running due to configuration errors or other reasons, thus unable to go on with the consensus protocol. This type of failure will not cause side effects on other parts of the distributed system except that the node itself stops running. However, for such distributed systems as the public blockchain, when designing a consensus protocol, we still need to consider the evildoing intended by nodes besides their failure. These incidents are all included in the Byzantine Failure model, which covers all unexpected situations that may occur on the node, for example, passive downtime failures and any deviation intended by the nodes from the consensus protocol. For a better explanation, downtime failures refer to nodes’ passive running halt, and the Byzantine failure to any arbitrary deviation of nodes from the consensus protocol.
Compared with the node failure model which can be roughly divided into the passive and active models, the modeling of network communication is more difficult. The network itself suffers problems of instability and communication delay. Moreover, since all network communication is ultimately completed by the node which may have a downtime failure or a Byzantine failure in itself, it is usually difficult to define whether such failure arises from the node or the network itself when a node does not receive another node's network message. Although the network communication may be affected by many factors, the researchers found that the network model can be classified by the communication delay. For example, the node may fail to send data packages due to the fail-stop failure, and as a result, the corresponding communication delay is unknown and can be any value. According to the concept of communication delay, the network communication model can be divided into the following three categories:
The design and selection of consensus protocols for public chain networks that allow nodes to dynamically join and leave need to consider possible Byzantine failures. Therefore, the consensus protocol of a public chain network is designed to guarantee the security and liveness of the network under the semi-synchronous network model on the premise of possible Byzantine failure. Researchers of distributed systems point out that to ensure the security and liveness of the system, the consensus protocol itself needs to meet three requirements:
The CAP theorem and Byzantine Generals ProblemIn a semi-synchronous network, is it possible to design a Byzantine fault-tolerant consensus protocol that satisfies validity, agreement, and termination? How many Byzantine nodes can a system tolerance? The CAP theorem and Byzantine Generals Problem provide an answer for these two questions and have thus become the basic guidelines for the design of Byzantine fault-tolerant consensus protocols.
Lamport, Shostak, and Pease abstracted the design of the consensus mechanism in the distributed system in 1982 as the Byzantine Generals Problem, which refers to such a situation as described below: several generals each lead the army to fight in the war, and their troops are stationed in different places. The generals must formulate a unified action plan for the victory. However, since the camps are far away from each other, they can only communicate with each other through the communication soldiers, or, in other words, they cannot appear on the same occasion at the same time to reach a consensus. Unfortunately, among the generals, there is a traitor or two who intend to undermine the unified actions of the loyal generals by sending the wrong information, and the communication soldiers cannot send the message to the destination by themselves. It is assumed that each communication soldier can prove the information he has brought comes from a certain general, just as in the case of a real BFT consensus protocol, each node has its public and private keys to establish an encrypted communication channel for each other to ensure that its messages will not be tampered with in the network communication, and the message receiver can also verify the sender of the message based thereon. As already mentioned, any consensus agreement ultimately reached represents the consensus of the majority. In the process of generals communicating with each other for an offensive or retreat, a general also makes decisions based on the majority opinion from the information collected by himself.
According to the research of Lamport et al, if there are 1/3 or more traitors in the node, the generals cannot reach a unified decision. For example, in the following figure, assume there are 3 generals and only 1 traitor. In the figure on the left, suppose that General C is the traitor, and A and B are loyal. If A wants to launch an attack and informs B and C of such intention, yet the traitor C sends a message to B, suggesting what he has received from A is a retreat. In this case, B can't decide as he doesn't know who the traitor is, and the information received is insufficient for him to decide. If A is a traitor, he can send different messages to B and C. Then C faithfully reports to B the information he received. At this moment as B receives conflicting information, he cannot make any decisions. In both cases, even if B had received consistent information, it would be impossible for him to spot the traitor between A and C. Therefore, it is obvious that in both situations shown in the figure below, the honest General B cannot make a choice.
According to this conclusion, when there are $n$ generals with at most $f$ traitors (n≤3f), the generals cannot reach a consensus if $n \leq 3f$; and with $n > 3f$, a consensus can be reached. This conclusion also suggests that when the number of Byzantine failures $f$ exceeds 1/3 of the total number of nodes $n$ in the system $f \ge n/3$ , no consensus will be reached on any consensus protocol among all honest nodes. Only when $f < n/3$, such condition is likely to happen, without loss of generality, and for the subsequent discussion on the consensus protocol, $ n \ge 3f + 1$ by default.
The conclusion reached by Lamport et al. on the Byzantine Generals Problem draws a line between the possible and the impossible in the design of the Byzantine fault tolerance consensus protocol. Within the possible range, how will the consensus protocol be designed? Can both the security and liveness of distributed systems be fully guaranteed? Brewer provided the answer in his CAP theorem in 2000. It indicated that a distributed system requires the following three basic attributes, but any distributed system can only meet two of the three at the same time.
A distributed system aims to provide consistent services. Therefore, the consistency attribute requires that the two nodes in the system cannot provide conflicting status information or expired information, which can ensure the security of the distributed system. The availability attribute is to ensure that the system can continuously update its status and guarantee the availability of distributed systems. The partition tolerance attribute is related to the network communication delay, and, under the semi-synchronous network model, it can be the status before GST when the network is in an asynchronous status with an unknown delay in the network communication. In this condition, communicating nodes may not receive information from each other, and the network is thus considered to be in a partitioned status. Partition tolerance requires the distributed system to function normally even in network partitions.
The proof of the CAP theorem can be demonstrated with the following diagram. The curve represents the network partition, and each network has four nodes, distinguished by the numbers 1, 2, 3, and 4. The distributed system stores color information, and all the status information stored by all nodes is blue at first.
The discovery of the CAP theorem seems to declare that the aforementioned goals of the consensus protocol is impossible. However, if you’re careful enough, you may find from the above that those are all extreme cases, such as network partitions that cause the failure of information transmission, which could be rare, especially in P2P network. In the second case, the system rarely returns the same information with node 2, and the general practice is to query other nodes and return the latest status as believed after a while, regardless of whether it has received the request information of other nodes. Therefore, although the CAP theorem points out that any distributed system cannot satisfy the three attributes at the same time, it is not a binary choice, as the designer of the consensus protocol can weigh up all the three attributes according to the needs of the distributed system. However, as the communication delay is always involved in the distributed system, one always needs to choose between availability and consistency while ensuring a certain degree of partition tolerance. Specifically, in the second case, it is about the value that node 2 returns: a probably outdated value or no value. Returning the possibly outdated value may violate consistency but guarantees availability; yet returning no value deprives the system of availability but guarantees its consistency. Tendermint consensus protocol to be introduced is consistent in this trade-off. In other words, it will lose availability in some cases.
The genius of Satoshi Nakamoto is that with constraints of the CAP theorem, he managed to reach a reliable Byzantine consensus in a distributed network by combining PoW mechanism, Satoshi Nakamoto consensus, and economic incentives with appropriate parameter configuration. Whether Bitcoin's mechanism design solves the Byzantine Generals Problem has remained a dispute among academicians. Garay, Kiayias, and Leonardos analyzed the link between Bitcoin mechanism design and the Byzantine consensus in detail in their paper The Bitcoin Backbone Protocol: Analysis and Applications. In simple terms, the Satoshi Consensus is a probabilistic Byzantine fault-tolerant consensus protocol that depends on such conditions as the network communication environment and the proportion of malicious nodes' hashrate. When the proportion of malicious nodes’ hashrate does not exceed 1/2 in a good network communication environment, the Satoshi Consensus can reliably solve the Byzantine consensus problem in a distributed environment. However, when the environment turns bad, even with the proportion within 1/2, the Satoshi Consensus may still fail to reach a reliable conclusion on the Byzantine consensus problem. It is worth noting that the quality of the network environment is relative to Bitcoin's block interval. The 10-minute block generation interval of the Bitcoin can ensure that the system is in a good network communication environment in most cases, given the fact that the broadcast time of a block in the distributed network is usually just several seconds. In addition, economic incentives can motivate most nodes to actively comply with the agreement. It is thus considered that with the current Bitcoin network parameter configuration and mechanism design, the Bitcoin mechanism design has reliably solved the Byzantine Consensus problem in the current network environment.
Practical Byzantine Fault Tolerance, PBFTIt is not an easy task to design the Byzantine fault-tolerant consensus protocol in a semi-synchronous network. The first practically usable Byzantine fault-tolerant consensus protocol is the Practical Byzantine Fault Tolerance (PBFT) designed by Castro and Liskov in 1999, the first of its kind with polynomial complexity. For a distributed system with $n$ nodes, the communication complexity is $O(n2$.) Castro and Liskov showed in the paper that by transforming centralized file system into a distributed one using the PBFT protocol, the overwall performance was only slowed down by 3%. In this section we will briefly introduce the PBFT protocol, paving the way for further detailed explanations of the Tendermint protocol and the improvements of the Tendermint protocol.
The PBFT protocol that includes $n=3f+1$ nodes can tolerate up to $f$ Byzantine nodes. In the original paper of PBFT, full connection is required among all the $n$ nodes, that is, any two of the n nodes must be connected. All the nodes of the network jointly maintain the system status through network communication. In the Bitcoin network, a node can participate in or exit the consensus process through hashrate mining at any time, which is managed by the administrator, and the PFBT protocol needs to determine all the participating nodes before the protocol starts. All nodes in the PBFT protocol are divided into two categories, master nodes, and slave nodes. There is only one master node at any time, and all nodes take turns to be the master node. All nodes run in a rotation process called View, in each of which the master node will be reelected. The master node selection algorithm in PBFT is very simple: all nodes become the master node in turn by the index number. In each view, all nodes try to reach a consensus on the system status. It is worth mentioning that in the PBFT protocol, each node has its own digital signature key pair. All sent messages (including request messages from the client) need to be signed to ensure the integrity of the message in the network and the traceability of the message itself. (You can determine who sent a message based on the digital signature).
The following figure shows the basic flow of the PBFT consensus protocol. Assume that the current view’s master node is node 0. Client C initiates a request to the master node 0. After the master node receives the request, it broadcasts the request to all slave nodes that process the request of client C and return the result to the client. After the client receives f+1 identical results from different nodes (based on the signature value), the result can be taken as the final result of the entire operation. Since the system can have at most f Byzantine nodes, at least one of the f+1 results received by the client comes from an honest node, and the security of the consensus protocol guarantees that all honest nodes will reach consensus on the same status. So, the feedback from 1 honest node is enough to confirm that the corresponding request has been processed by the system.
For the status synchronization of all honest nodes, the PBFT protocol has two constraints on each node: on one hand, all nodes must start from the same status, and on the other, the status transition of all nodes must be definite, that is, given the same status and request, the results after the operation must be the same. Under these two constraints, as long as the entire system agrees on the processing order of all transactions, the status of all honest nodes will be consistent. This is also the main purpose of the PBFT protocol: to reach a consensus on the order of transactions between all nodes, thereby ensuring the security of the entire distributed system. In terms of availability, the PBFT consensus protocol relies on a timeout mechanism to find anomalies in the consensus process and start the View Change protocol in time to try to reach a consensus again.
The figure above shows a simplified workflow of the PBFT protocol. Where C is the client, 0, 1, 2, and 3 represent 4 nodes respectively. Specifically, 0 is the master node of the current view, 1, 2, 3 are slave nodes, and node 3 is faulty. Under normal circumstances, the PBFT consensus protocol reaches consensus on the order of transactions between nodes through a three-phase protocol. These three phases are respectively: Pre-Prepare, Prepare, and Commit:
In the three-phase protocol execution of the PBFT protocol, in addition to maintaining the status information of the distributed system, the node itself also needs to log all kinds of consensus information it receives. The gradual accumulation of logs will consume considerable system resources. Therefore, the PBFT protocol additionally defines checkpoints to help the node deal with garbage collection. You can set a checkpoint every 100 or 1000 sequence numbers according to the request sequence number. After the client request at the checkpoint is executed, the node broadcasts
The three-phase protocol of the PBFT protocol can ensure the consistency of the processing order of the client request, and the checkpoint mechanism is set to help nodes perform garbage collection and further ensures the status consistency of the distributed system, both of which can guarantee the security of the distributed system aforementioned. How is the availability of the distributed system guaranteed? In the semi-synchronous network model, a timeout mechanism is usually introduced, which is related to delays in the network environment. It is assumed that the network delay has a known upper bound after GST. In such condition, an initial value is usually set according to the network condition of the system deployed. In case of a timeout event, besides the corresponding processing flow triggered, additional mechanisms will be activated to readjust the waiting time. For example, an algorithm like TCP's exponential back off can be adopted to adjust the waiting time after a timeout event.
To ensure the availability of the system in the PBFT protocol, a timeout mechanism is also introduced. In addition, due to the potential the Byzantine failure in the master node itself, the PBFT protocol also needs to ensure the security and availability of the system in this case. When the Byzantine failure occurs in the master node, for example, when the slave node does not receive the PRE-PREPARE message or the PRE-PREPARE message sent by the master node from the master node within the time window and is thus determined to be illegitimate, the slave node can broadcast
VIEWCHANGE contains a lot of information. For example, C contains 2f+1 signature information, P contains several signature sets, and each set has 2f+1 signature. At least 2f+1 nodes need to send a VIEWCHANGE message before prompting the system to enter the next new view, and that means, in addition to the complex logic of constructing the information of VIEWCHANGE and NEW-VIEW, the communication complexity of the view conversion protocol is $O(n2$.) Such complexity also limits the PBFT protocol to support only a few nodes, and when there are 100 nodes, it is usually too complex to practically deploy PBFT. It is worth noting that in some materials the communication complexity of the PBFT protocol is inappropriately attributed to the full connection between n nodes. By changing the fully connected network topology to the P2P network topology based on distributed hash tables commonly used in blockchain projects, high communication complexity caused by full connection can be conveniently solved, yet still, it is difficult to improve the communication complexity during the view conversion process. In recent years, researchers have proposed to reduce the amount of communication in this step by adopting aggregate signature scheme. With this technology, 2f+1 signature information can be compressed into one, thereby reducing the communication volume during view change.
Bitcoin, upon its release in the year 2009, brought onto us the progressive blockchain innovation. To a few, the idea driving the innovation can be a touch of threatening. The sheer measure of language and slang is sufficient to startle any individual new to the game. Be that as it may, when it's completely separated, it's a lot less difficult than it sounds.submitted by crypto1finligue to u/crypto1finligue [link] [comments]
Understanding blockchain innovation is in reality simple. To start with, we should handle why it's designated "blockchain." In the most fundamental terms, it's known as a "blockchain" in light of the fact that that is the thing that it is—a chain of squares. This clarification suggests the conversation starters: what are squares, and what do they do?
Basically, squares are bunches of information that reveal to you all that you have to think about a particular exchange. Squares mostly store three sorts of information:
Recognizing information – Just as how individuals have names to separate themselves from each other, squares have "hashes." Let's say you make numerous exchanges on a specific site. Despite the fact that the subtleties of your exchanges seem to be comparative, hashes are the explanation the chain will have the option to differentiate the squares.
Squares on the Bitcoin blockchain can store 1 MB of information. Despite the fact that it may not appear to be a great deal, it implies that each square can store a couple thousand exchanges (contingent upon the size of every exchange).
We've presently settled what squares are, presently we should get onto the "chain" side of things. The chain alludes to the connecting all things considered. At the point when a square gets new information, it is added to the blockchain. Before a square is included, it needs to meet explicit measures.
The main rule is that an exchange needs to occur. Without the exchange, there is no information for the square to store.
Next, that exchange should be confirmed. On the blockchain, there is a system of PCs that work to check subtleties of the exchanges—the hour of the exchange, dollar sum, and its members. When confirmed precisely, all the information is put away in a square (where it will join exchanges from others).
Finally, a hash must be given to the square. Notwithstanding the hash that is allocated to the new square, it additionally gets the hash of the past square added to the blockchain. The square will require this distinguishing code, so it doesn't get stirred up with the others.
In case you're asking why numerous organizations and ventures are racing to incorporate blockchain innovation, this is on the grounds that it has a few selling focuses:
With thousands computers attempting to check exchanges utilizing the "evidence of-work," human mistake is removed from the condition. Likewise, if a PC were to breakdown and submit a misstep, it would just be on one duplicate of the blockchain. For the blunder to spread all through the whole blockchain, a similar mix-up should be made by 51% everything being equal.
https://preview.redd.it/hiu3umys1j451.png?width=560&format=png&auto=webp&s=a918610c070d00bce65edc4dea52ca2d22b3aabesubmitted by RumaDas to u/RumaDas [link] [comments]
The Blockchain industry is continuously progressing since its inception. The consensus mechanism is the core of a decentralized ecosystem that helps it to achieve consensus in the network. Till now, many consensus methods have been invented and implemented to achieve consensus within a blockchain system. I am writing a series of articles on different consensus mechanisms with a detailed explanation of their advantages and disadvantages over each other. I have already covered PoW and PoS, so here in this article, I will focus on PoA.
The PoW consensus algorithm used by Bitcoin is considered a reliable and secure consensus mechanism but it doesn’t support scalability. As a result, it restricts the performance of the Bitcoin network along with its transaction speed. The major disadvantage of this method is that it requires high energy consumption and system resources which are needed to solve the complex mathematical puzzles.
With some more features, Proof of Stake came into existence which offers better performance than PoW. There are several PoS projects which are still under development so what new features it can offer and how much it can deal with the drawback of the existing consensus mechanism is depends on the success rate of future projects.
Then there is another consensus mechanism called Proof of Authority which is the enhanced version of PoS. It supports better performance by allowing more transactions per second. Now let’s discuss it in detail.
What is Proof of Authority?
The Proof-Of-Authority (PoA) is a consensus method where a group of validators is already chosen as the authority. Their task is to check and validate all the newly added identities, validate transactions, and blocks to add to the network. To ensure efficiency and security in the network the validator group is usually kept small (~25 or less).
Proof of Authority (PoA) is an enhanced version of Proof of Stake (PoS) where the validator’s identity is used as a stake in the network.A node needs to complete a mandatory process to authenticate itself to receive the right to generate new blocks. Validators need to register themselves in the public notary database using government-issued documents with the same identity that they have on the platform. Thus, Blocks and transactions are verified by participants, whose identity is already verified and acts as an authority of the system.
With the power under a limited number of users, PoA consensus can be adopted as a solution for private networks rather than public blockchains.PoA was proposed by a group of developers in March 2017 (coined by Gavin Wood) as a blockchain-based on the Ethereum protocol. It was developed with the idea to solve the problem of spam attacks on Ethereum’s Ropsten test network. The new network was named Kovan, the main test network that all Ethereum users use today.
Pre-Requisites for Proof of Authority Consensus
The PoA consensus algorithm is usually based upon the following criteria:
· Validators need to disclose and confirm their identities by giving government-issued documents.
· The standard procedure for verifying the identity of validators.
· Complex and robust criteria to define a validator so that they can put his reputation at stake and commit to a long-term alliance.
Advantages of PoA consensus
As compared to other consensus methods, PoA offers the following advantages:
· High transaction rate.
· High-performance hardware is not required.
· PoA networks are very scalable as compare to PoW blockchains
· Less power extensive.
· Low transaction fees.
· Sequentially block generation with fixed time interval by authorized network nodes. This increases transaction validity speed.
· No communication is required to reach the consensus between the nodes.
· Network operation is independent of the number of available genuine nodes.
· The chance of a node to become a forge depends upon both its stake and overall holding.
· Proof-of-Authority based networks lack in decentralization.
· PoA validator's identities are visible in the network.
· PoA does not guarantee censorship resistance.
PoA consensus algorithm can be applied in various fields and industries to achieve high throughput ranging from supply chains to banking sectors. PoA is considered as an effective and reasonable solution along with cost-saving benefit.
Below is the list of projects which has adopted PoA :
· Ethereum’s test net Kovan built on the Parity's PoA Protocol
· PoA Network by the Proof of Authority, LLC. (an Ethereum sidechain)
· The VeChainThor platform.
Every consensus method, be it PoW, PoS or PoA has its own set of advantages and disadvantages. But if we talk about PoA particularly, it somehow compromises in the decentralization area to achieve scalability and throughput.
Proof-Of-Authority can, therefore, be treated as a better option for a centralized solution because of its efficiency and less power consumption property.
Read More: Mastering Basic Attention Token (BAT)
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If you still can’t figure out what the heck a bitcoin is, this simple explanation for a five-year-old may help you … We’re sitting on a park bench. It’s a great day. In this article, we provide a basic and conceptual explanation of bitcoin and continue to the advanced level of these articles. Bitcoin was created in 2009 by a person named Satoshi Nakamoto, based on cryptographic science. Before Bitcoin, there was a lot of effort to create a digital currency, but everyone had a major problem that we’ll Bitcoin: A Simple Explanation. Bitcoin is a new kind of digital currency that, unlike other forms of payment, is designed for a world in which we are all digitally connected. Other forms of payment, like cash and credit cards, have their place, and probably won’t be going anywhere soon, but they aren’t really suited to the way we are going This is a question often surrounded by confusion, so here's a quick explanation! The basics for a new user. As a new user, you can get started with Bitcoin without understanding the technical details. Once you've installed a Bitcoin wallet on your computer or mobile phone, it will generate your first Bitcoin address and you can create more Bitcoin has also proven useful as a way to get around many of the onerous financial regulations seen around the world. In the past, Abra CEO Bill Barhydt has discussed how the programmable nature of bitcoin has enabled his company to build a global, ( see a deeper explanation of this point here ).
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Bitcoin has been all over the news in recent days, reaching all-time highs then dropping thousands in minutes. So what is the hype all about? ... Bitcoin Basics (Part 1) - "Explained For Beginners ... Just very basic explanation of public, and private addresses and keys. In this video I will give you a quick explanation of what Bitcoin is and how it derives it’s value. And we’ll do this through LEARNING some Bitcoin Basic Facts. A short introduction to how Bitcoin Works. Want more? Check out my new in-depth course on the latest in Bitcoin, Blockchain, and a survey of the most exciting projects coming out (Ethereum, etc ... The Difference Between Bitcoin and Ethereum — Simple Explanation Jon Cursi Clips - Duration: 4:23. Jon Cursi 166 views. 4:23. Bitcoin Basics (Part 1) - "Explained For Beginners" - Duration: 24:27.