What are the 4 types of cryptocurrency?
“Crypto can be classified into different categories, like DeFi, NFT, utility tokens, store of value tokens like bitcoin and litecoin, and yield farming tokens like Aave,” says Sidharth Sogani, CEO of Crebaco, a crypto research firm.
Who owns the most Bitcoin?
Who owns the most Bitcoin?
With more than 1,000,000 BTC, Nakamoto — who may be an individual or a group — owns more Bitcoin than any other entity.
AMM: Automated Market Maker (AMM)
CEX: Centralized Exchange (CEX)
CFMM: Constant-Function Market Maker (CFMM)
dApp: Decentralized Application (dApp)
DCA: Dollar-Cost Averaging (DCA)
DDoS: Distributed Denial of Service attack (DDoS)
DeFi: Decentralized Finance (DeFi)
DEX: Decentralized Exchange (DEX)
DSL: Domain-Specific Language (DSL)
EMH: Efficient Market Hypothesis (EMH)
TradFi: Traditional Finance (TradFi)
TVL: Total Value Locked (TVL)
EUTxO: Extended Unspent Transaction Output Model (EUTxO)
FPGA: Field-Programmable Gate Array (FPGA)
HFT: Hight Frequency Trading (HFT)
ISPO: Initial Stake Pool Offering (ISPO)
MEV: Miner Extractable Value (MEV)
NFT: Non-Fungible Token (NFT)
P&L: Profit & Loss (P&L, PnL)
PAB: Plutus Application Backend (PAB)
PM: Portfolio Manager (PM)
QuantFi: Quantitative Finance (QuantFi)
SPO: Stake Pool Operation (SPO)
What is a blockchain?
A blockchain is a distributed database that is shared among the nodes of a computer network. As a database, a blockchain stores information electronically in digital format. Blockchains are best known for their crucial role in cryptocurrency systems, such as Bitcoin, for maintaining a secure and decentralized record of transactions. The innovation with a blockchain is that it guarantees the fidelity and security of a record of data and generates trust without the need for a trusted third party.
One key difference between a typical database and a blockchain is how the data is structured. A blockchain collects information together in groups, known as blocks, that hold sets of information. Blocks have certain storage capacities and, when filled, are closed and linked to the previously filled block, forming a chain of data known as the blockchain. All new information that follows that freshly added block is compiled into a newly formed block that will then also be added to the chain once filled.
A database usually structures its data into tables, whereas a blockchain, like its name implies, structures its data into chunks (blocks) that are strung together. This data structure inherently makes an irreversible time line of data when implemented in a decentralized nature. When a block is filled, it is set in stone and becomes a part of this time line. Each block in the chain is given an exact time stamp when it is added to the chain.
money ≡ energy when modeling financial systems you can think of money as a unit of energy, same as in physics, we can think of kinetic, potential, and other energies, energy is potential to perform a certain amount of work; we can think of money in the same way. Money has potential to be applied in the market, from lending to businesses developing novel ideas, increasing market efficiency by providing liquidity to exchanges, creating information flow via arbitrage, financial analysis and research, to utilizing available information to maximise returns given a specified risk appetite. Energy can be applied in many ways. If we choose a very inefficient way to do something e.g., growing tomatoes in a completely dark underground bunker, we will expend a lot of energy. In contrast, we can put them in a greenhouse or even on a balcony and make much more efficient energy use. Same in the capital markets, efficient models lead to high money utility (energy well spent towards productive work), and productive work is the work that is usually associated with rewards (e.g., business borrowing money will pay it with interest). Therefore, an efficiently allocated liquidity will work very well even with low total value locked and respond quickly to the changing market dynamics, bringing higher rewards per unit of capital.
Non-Fungible Token (NFT)
Is a unique and non-interchangeable unit of data (most often JSON) stored on the blockchain. NFTs are most often used to store images, and do so via 721 standard defining the JSON template. However, NFTs have many uses, such as storing wallet handles, and even such as in the case of Maladex the actual code of programmable swaps.
Everything that is not on-chain, most often implies off-chain code of dApp smart contracts.
Cardano introduces the concept of Turing-complete off-chain code, that computes the necessary state update for the user taken action, and provides it to Cardano wallet for submission onto the ledger. In contrast to blockchains without safe off-chain component, it provides the ability to write off-chain code in any existing programming language (as long as bindings to Plutus exist, e.g., via an SDK), and provide any functionality without outsourcing the high execution cost on the users (e.g., via fee mechanism).
Action executed by the validator nodes or an asset stored on the ledger.
A part of smart contract code stored on Cardano ledger and used to validate submitted transactions. In contrast to other blockchains where all smart contract actions are performed on-chain, in the case of Cardano, a transaction is prepared by off-chain code and validated if it is spendable and meets all spending conditions, by block producing nodes, on layer
The act of splitting a task into smaller, independent, subtasks, all of which can be executed independently of each other, implying that there is no communication required between agents executing the subtasks.
Plutus Application Backend (PAB)
An off-chain dev tool allowing for the interaction with smart contracts. PAB allows for interaction with external clients, such as wallet front-ends, and acts as the intermediary between Plutus application, the node, the wallet back-end, and end users. The purpose of PAB is to provide a standardised environment to run Plutus applications, with disciplined state management, discoverable interfaces by external clients (primarily wallets), track onchain information that smart contract uses, and deal with requests such as running contract instances, forwarding user input to those instances, and notifying these instances of ledger state change events.
A ledger model used by Ethereum, and majority of smart con- tract enabled blockchains, where the global state is shared, and all operations are applied sequentially, one after the other based on tips. Due to possible impact on the ordering of transactions via tips it is prone to front-running and Miner Extractable Value (MEV).
The theoretical speedup limit in the latency of the execution of a task at fixed workload that can be expected of a system whose resources are improved, it also is used to define the theoretical limit of the system scalability due to improvements in concurrency and parallelism of the system. Amdahl’s law is defined as Slatency(s) = 1 p .. 25, 27, 74
Exploiting the market inefficiency between trading venues (exchanges); in the most simple case of the arbitrage, let’s say ADA is priced at $3 at exchange A and at $2.95 at exchange B. The arbitrage bot will find this information and will attempt to buy ADA at exchange B for $2.95 and sell it immediately at exchange A at $0.05 profit per ADA. The act of arbitrage moves the price, as after the execution, the price at exchange B will be higher (e.g., $2.98) due to purchasing the discounted ADA, and on exchange A it will be cheaper due to selling it (e.g., $2.98). As long as there is a price difference between exchanges, so that when these trades are executed they are more profitable than the execution cost, arbitrage is possible. Arbitrage leads to all included exchanges converging on the central limit value (true market sentiment from the aggregated exchanges) of the asset price. The arbitrageur earns profits by buying at a discount from exchange B and selling at a premium on exchange A. Hence both exchange B sellers and exchange A are the source of income for the arbitrage bot and are ”victim” of the market inefficiency (in reality, they are not victims but simply prone to be scalped this way). Finally, the mentioned example is the most basic case of arbitrage (pure arbitrage). There are a multitude of arbitrage styles, including statistical, where arbitrage is based on the market movement predictions and the average expectation of reward https://en.wikipedia.org/wiki/Arbitrage#Types. Arbitrage leads to efficient price discovery, but at the cost of market participants (both takers and makers).
Automated Market Maker (AMM)
A liquidity definition as a supply formula en- abling automatic (autonomous - without the presence of maker) trades, preserving certain properties such as path independence (the execution price is not depend- ing on the history of transactions), and for trading without any active interaction from the maker party (makers provide assets into the liquidity pool which are then managed based on pool model / formula).
Benchmark is an investment performance measuring tool, used to assess the allocation, risk, and return of the portfolio. Benchmarks are usually constructed using unmanaged indices and exchange-traded funds (ETFs). Benchmark is selected in a way to represent asset class against which one wants to compare the outcomes.
Black swan event
The black swan theory or theory of black swan events is a metaphor that describes an event that comes as a surprise, has a major effect, and is often inappropriately rationalized after the fact with the benefit of hindsight.
Each task has many ways of being performed, one way will consume more resources (energy, time, etc.) than the other, hence the way which consumes less resources and achieves the same result is more efficient. The more efficient use of capital the better generated returns, the better user experience, and the better the actual state of the market is reflected.
Centralized Exchange (CEX)
Classical model of an exchange where agents engage into buying and selling via the intermediary (the exchange), as opposed to DEX where investors face each other via the protocol implementation.
The act of progressing on the same task by multiple agents at the same time, which implies communication between agents. Concurrency speed improve- ment is limited to the bottlenecks in agent communication and defined by Amdahl’s law.
Constant-Function Market Maker (CFMM)
A liquidity provision formula that has assets on one side and a constant on the other. The most widely known CFMM is Uniswap’s v1/v2 x ∗ y = const [5, 6]. The critique of this liquidity formulation includes unrealistic provision range (assumption that it is equally likely to provide liquidity (make the market) when the asset that costs $20 (e.g., a pizza pie) at price of $0.000001 and $1,000,000,000. This inefficiency leads to high impermanent loss and requirement for high TVL to avoid high slippage, which leads in turn to market inefficiency and creating a huge potential for arbitrage.
Decentralized Application (dApp)
An application that runs on a Decentralized com- puting system, such as Cardano blockchain.
Decentralized Exchange (DEX)
Non-custodial exchange model, where trades are ex- ecuted directly on the smart contract blockchain. DEX does not have intermediary, responsible for providing the service for exchanging assets, but rather it is performed in automated manner by participants providing assets for the code to access (gov- erned by code rather than company).
Decentralized Finance (DeFi)
Blockchain-based form of finance, removing the need for the centralized intermediaries required to provide the service in the traditional finance, such as brokerages, exchanges, and banks. In DeFi the role of intermediary is replaced by smart contracts.
Distributed Denial of Service attack (DDoS)
A form of attack on the Internet infrastructure, where the attacker aims to exhaust the available resources of the target by the sheer volume of the traffic created. DDoS usually results in the targeted ser- vice being unavailable for the duration of the attack.
Dollar-Cost Averaging (DCA)
An investment trading system employed to minimise the impact of local price fluctuations on the average buy-in-price. A person fol- lowing DCA trading system would purchase the same amount of the target asset