{"aave":{"html":"

In decentralised finance, Aave is one of the largest decentralised lending protocols.

Aave was started in 2017 under EthLend name. Aave means a ghost in the Finnish language.\nThe latest version, Aave v3, was launched in 2023.\nAave has an ERC-20 token AAVE.

As the writing of this, Aave has been deployed several blockchains, including Ethereum,\nPolygon, Arbitrum and Optimism.

Aave has a protocol subset for real-world assets (RWAs) called Aave Arc.

See also

\n","name":"Aave","slug":"aave","shortDescription":"In decentralised finance, Aave is one of the largest decentralised lending protocols"},"active-strategy":{"html":"

Active strategy in relation to algorithmic trading refers to a type of investment or trading approach that uses algorithms to make regular, data-driven decisions about buying and selling assets. This approach is designed to generate higher returns and outperform passive investment strategies.

Active strategies in algorithmic trading differ from passive strategies, which simply follow the market and hold assets for the long-term, in that they actively seek to generate higher returns through regular buying and selling of assets. This can involve taking advantage of short-term market movements, or making trades based on a variety of market indicators and economic data.

See also

\n","name":"Active strategy","slug":"active-strategy","shortDescription":"Active strategy in relation to algorithmic trading refers to a type of investment or trading approach that uses algorithms to make regular, data-driven decisions about buying and selling assets"},"aggregate-return":{"html":"

Aggregate returns in portfolio refer to the percentage difference from period to period of the value of a portfolio.

It is calculated by taking the change in the value of the portfolio and expressing it as a percentage of the original invested amount.\nThis generates a time series of interim net asset values.

\n","name":"Aggregate return","slug":"aggregate-return","shortDescription":"Aggregate returns in portfolio refer to the percentage difference from period to period of the value of a portfolio"},"airdrop":{"html":"

In decentralised finance, airdop is a method to distritube tokens\nfor the project users for free.

The goals of an airdrop include making the protocol more decentralised and rewarding the users.\nThe airdrop involves sending tokens or making them claimable to the users\nthrough a Merkle tree method.

Historically famous airdrops include Uniswap, Optimism and Juno.

Liquidity mining is somewhat similar mechnanism to airdrop.

See also

\n","name":"Airdrop","slug":"airdrop","shortDescription":"In decentralised finance, airdop is a method to distritube tokens\nfor the project users for free"},"algorithmic-trading":{"html":"

Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume.

It involves making trading decisions based on pre-set rules that are programmed into a computer. Python is often used for algorithmic trading due to its ability to handle complex calculations and its flexibility. Algorithmic trading strategies can be used to find potential trades and optimise the timing of trades.

See also

Trading strategy
Automated trading strategy

\n","name":"Algorithmic trading","slug":"algorithmic-trading","shortDescription":"Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume"},"alpha-generation-platform":{"html":"

An alpha generation platform is a technology used in algorithmic trading to develop quantitative financial models, or trading strategies, that generate consistent returns.

Alpha generation platforms are tools used by hedge funds, banks, CTAs and other financial institutions to help develop and test quantitative trading strategies.\nAlpha generation platforms support quants in the creation of efficient and productive quantitative trading strategies.

See

Alpha model
Portfolio construction

\n","name":"Alpha generation platform","slug":"alpha-generation-platform","shortDescription":"An alpha generation platform is a technology used in algorithmic trading to develop quantitative financial models, or trading strategies, that generate consistent returns"},"alpha-model":{"html":"

An alpha model is a mathematical or quantitative framework used to generate trading signals that can be used in portfolio construction.

The alpha model seeks to identify assets that are likely to outperform or underperform their peers, based on a variety of factors and variables.

Alpha models can be constructed using a variety of techniques, such as statistical analysis, machine learning algorithms, or financial modeling. The inputs to an alpha model may include company financial statements, price and volume data, macroeconomic indicators, and other market data.

Once an alpha model generates trading signals, the portfolio manager can use those signals to construct a portfolio that aims to generate alpha (i.e., excess returns) relative to a benchmark index. The portfolio manager may use other tools, such as risk management techniques or diversification strategies, to manage portfolio risk and optimize performance.

It is important to note that alpha models are not foolproof and can be subject to various biases and errors. As such, portfolio managers must continually test and refine their alpha models to ensure that they are producing accurate and robust trading signals.

See also

\n","name":"Alpha model","slug":"alpha-model","shortDescription":"An alpha model is a mathematical or quantitative framework used to generate trading signals that can be used in portfolio construction"},"alpha-signal":{"html":"

In portfolio construction, an alpha signal refers to a metric or indicator used to identify investments that are likely to outperform the broader market. Alpha signals can be derived from a variety of sources, including fundamental analysis, technical analysis, and quantitative models.

Alpha signals are also known as trading signals.

The goal of using an alpha signal is to identify securities that are undervalued or overvalued compared to their peers, and to use that information to construct a portfolio that generates excess returns (i.e., alpha) relative to a benchmark index.

Examples of alpha signals include measures of company profitability, earnings growth, price momentum, and valuation ratios. A portfolio manager may use one or more alpha signals to construct a portfolio that is expected to outperform the benchmark index.

See also

\n","name":"Alpha signal","slug":"alpha-signal","shortDescription":"In portfolio construction, an alpha signal refers to a metric or indicator used to identify investments that are likely to outperform the broader market"},"alternative-data":{"html":"

In quantitative finance, Alternative data refers to non-traditional data sources that are used by investors and businesses to gain insights into markets, industries, and consumer behavior. This data is often sourced from digital platforms and devices, such as social media, mobile devices, sensors, and web traffic.

Alternative data can provide a wealth of information that is not available through traditional data sources, such as financial statements and economic indicators. For example, social media data can be used to gauge consumer sentiment and preferences, while satellite imagery can be used to track changes in physical assets like inventories or construction sites.

Alternative data is often used in conjunction with traditional data sources to gain a more complete understanding of market trends and consumer behavior. By leveraging alternative data sources, investors and businesses can make more informed investment and business decisions, and gain a competitive edge in their respective markets.

See also:

\n","name":"Alternative data","slug":"alternative-data","shortDescription":"In quantitative finance, Alternative data refers to non-traditional data sources that are used by investors and businesses to gain insights into markets, industries, and consumer behavior"},"amm":{"html":"

Automated market maker (AMM) is a bonding curve based decentralised exchange.

An automated market maker (AMM) is a type of decentralised exchange (DEX) protocol that uses algorithmic smart contracts to make it easy for individual traders to buy and sell crypto assets. Instead of trading directly with other people as with a traditional order book, users trade directly through the AMM. AMMs facilitate the decentralised exchange of digital assets using liquidity pools rather than conventional market order books.

AMMs can be often seen as the opposite of order book markets.\nIn an AMM, the user is trading against liquidity pool.

AMMs can be

Bonding curve based using XY liquidity model.
CLMM based using concentrated liquidity model

Example AMMs include Uniswap, Curve, Sushi, Trader Joe.

See also

\n","name":"AMM","slug":"amm","shortDescription":"Automated market maker (AMM) is a bonding curve based decentralised exchange"},"api-key":{"html":"

API key is an authentication method for machine-to-machine communications.

API keys are usually needed with HTTP based API services.

Blockchain and decentralised finance protocols do not need API keys, because the authentication\nhappens with public key cryptography and wallets.

\n","name":"API key","slug":"api-key","shortDescription":"API key is an authentication method for machine-to-machine communications"},"app-chain":{"html":"

An App Chain, also known as an application-specific blockchain, is a blockchain that is exclusively designed to operate one specific application.

App Chain eliminates competition for resources away from everyone else using the chain, allowing for greater scalability and flexibility

Popular app chains include

DyDx decentralised futures exchange
Vega Protocol exchange
Stargaze NFT chain on Cosmos

\n
\n
Swimmer Network - the app chain for Crabada idle-game
\n
\n

See also

\n","name":"App Chain","slug":"app-chain","shortDescription":"An App Chain, also known as an application-specific blockchain, is a blockchain that is exclusively designed to operate one specific application"},"arrow":{"html":"

Apache Arrow is an open-source, cross-platform columnar data format that is used for storing and processing large amounts of data efficiently. It was designed to address some of the performance and scalability challenges associated with traditional row-based data storage and processing methods.

Arrow provides a common format for storing data, which can be used across multiple platforms and programming languages, including C++, Java, Python, and R. This allows for faster data processing and improved performance, as data can be shared and processed efficiently between different applications and systems.

In addition to its performance benefits, Arrow also provides a number of other advantages, such as support for a wide range of data types and low overhead. This makes it well-suited for use in big data and analytics applications, as well as other data-intensive projects.

More information.

\n","name":"Arrow","slug":"arrow","shortDescription":"Apache Arrow is an open-source, cross-platform columnar data format that is used for storing and processing large amounts of data efficiently"},"auto-compounding":{"html":"

Auto-compounding is an investment strategy in which the user’s investment yields are automatically reinvested into the investment principal at regular intervals.

Compounding is a powerful investing concept that involves earning returns on both the original investment and on returns received previously.\nIt is considered one of the smartest investment strategies, as it can generate additional earnings over time.

See also

\n","name":"Auto-compounding","slug":"auto-compounding","shortDescription":"Auto-compounding is an investment strategy in which the user’s investment yields are automatically reinvested into the investment principal at regular intervals"},"automated-trading-strategy":{"html":"

Automated trading strategies are computer programs that follow a defined set of instructions to execute trade orders.

Common automated trading strategies include moving average cross strategies, which buy when the stock price rises above the moving average and sell when it falls below, and trend-following strategies, which follow the mainstream trends and momentum in the market.

See also

\n","name":"Automated trading strategy","slug":"automated-trading-strategy","shortDescription":"Automated trading strategies are computer programs that follow a defined set of instructions to execute trade orders"},"autonomous-agent":{"html":"

An autonomous agent is a software program or system that can operate independently and make decisions on its own, without direct intervention from a human. This type of technology is designed to perform specific tasks or functions, such as data processing, problem-solving, decision-making, and even physical actions.

Autonomous agents typically use artificial intelligence (AI) and machine learning algorithms to analyse data, make decisions, and interact with the environment. They are designed to work in complex, dynamic environments, and can respond to changes in real-time.

\n","name":"Autonomous agent","slug":"autonomous-agent","shortDescription":"An autonomous agent is a software program or system that can operate independently and make decisions on its own, without direct intervention from a human"},"avalanche":{"html":"

Avalanche is a blockchain based on Avalanche consensus protocol.

Avalanche supports subnets, where each subnet is partially independent blockchain\nhaving its own properties. Some subnets, like Avalanche C-Chain are EVM-Compatible.\nSome subnets, can be more optimised. Subnets can be also app chains.

The native token of Avalanche is AVAX.\nThe most popular decentralised exchange on Avalanche is Trader Joe.

Popular subnets include

Avalanche C-Chain, generic EVM-Compatible chain
Dexalot - EVM-Compatible decentralised exchange chain
Swimmer Network - the app chain for Crabada idle-game

See also

\n","name":"Avalanche","slug":"avalanche","shortDescription":"Avalanche is a blockchain based on Avalanche consensus protocol"},"backtest":{"html":"

Simulating the efficiency of a trading strategy against historical data.

Backtesting is the process of analysing historical trade data to see how a trading strategy would have performed statistically in the past. It is a key component of effective trading system development and can be used to test a trading hypothesis/strategy on the historical data.

See also

\n","name":"Backtest","slug":"backtest","shortDescription":"Simulating the efficiency of a trading strategy against historical data"},"backtrader":{"html":"

An old Python based algorithmic trading framework for strategy backtesting and live execution.\nNo longer maintained.

See documentation.

\n","name":"Backtrader","slug":"backtrader","shortDescription":"An old Python based algorithmic trading framework for strategy backtesting and live execution"},"base-token":{"html":"

A base token, in the context of cryptocurrencies, refers to the primary token or asset used in a specific trading pair or exchange. The base token is the token that is being traded or exchanged for another token, typically a quote token, see also quote token. For example, in the trading pair BTC/USD, BTC is the base token and USD is the quote token. The price of the quote token is quoted in terms of the base token.

The base token is an important concept in the cryptocurrency market, as it determines the value of the other token in a trading pair. In other words, the price of the quote token is derived from the price of the base token. This relationship between the base token and the quote token is used to determine the price of the quote token, which can then be used to determine the value of other assets that are pegged to it.

See also

\n","name":"Base token","slug":"base-token","shortDescription":"A base token, in the context of cryptocurrencies, refers to the primary token or asset used in a specific trading pair or exchange"},"bear-market":{"html":"

A bear market is a condition in the stock market where the overall trend is downward and prices are falling. It is characterised by widespread pessimism and negative investor sentiment, leading to a decrease in the prices of securities. In a bear market, most asset classes tend to decline, and it can last anywhere from a few months to several years. During a bear market, it is common for investors to sell their holdings, leading to further declines in prices. The opposite of a bear market is a bull market, where prices are rising and investor sentiment is positive. Bear markets can be caused by various factors, such as economic recession, high unemployment, and declining corporate earnings.

\n","name":"Bear market","slug":"bear-market","shortDescription":"A bear market is a condition in the stock market where the overall trend is downward and prices are falling"},"best-trading-algorithm":{"html":"

The best trading algorithm in the world is one that makes most profit.

Depending on the the market situation and available trading pairs,\nthe best trading algorithm can vary day by day, or depending whether the markets are in\nbull market or bear market.

See trading strategy term\nto get an overall summary of different available trading algorithms.

A trading algoritmn can generally yield 5% - 300% yearly profit depending on the\namount of deployed capital and the market size. The goal is to beat risk-free rate.

The usual ways to rank trading algorithms include

Cumulative profit
Risk-adjusted return

See also

\n","name":"Best trading algorithm","slug":"best-trading-algorithm","shortDescription":"The best trading algorithm in the world is one that makes most profit"},"bid-ask-spread":{"html":"

The bid-ask spread is the difference between the highest price a buyer is willing to pay for an asset (the bid) and the lowest price at which a seller is willing to sell (the ask). It serves as a measure of the asset’s liquidity and is a cost that traders have to consider when entering and exiting positions.

Example: If the bid price for a stock is $50 and the ask price is $51, the bid-ask spread is $1.

Usage: The bid-ask spread is important for traders and investors as a narrower spread generally indicates higher liquidity and lower transaction costs, while a wider spread can signify the opposite.

See also

Liquidity
Volume
Market order
Limit order

\n","name":"Bid-Ask Spread","slug":"bid-ask-spread","shortDescription":"The bid-ask spread is the difference between the highest price a buyer is willing to pay for an asset (the bid) and the lowest price at which a seller is willing to sell (the ask)"},"blockchain-snapshot":{"html":"

A chain snapshot is a dump of the chain state.\nBy downloading the snapsnot a new blockchain node can sync faster\nto the chain tip, instead of downloading each block and verifying\neach transaction individually from the peer-to-peer network.

The snapshot may be

Chain built-in sync mechanism: Ethereum Snapshot Protocol (SNAP)
Erigon BitTorrent snapshots - Erigon uses internal BitTorrent client to download https://github.com/ledgerwatch/erigon-snapshot
Manually hosted snapshot: Polygon manually hosted snapshot downloads

Different snapshots offer different security guarantees. For example, when downloading\na snapshot from a Polygon manually hosted snapshot repository, you trust that the admins of this repository\nhave not modified the current historical blockchain state. The built-in Ethereum Snapshot Protocol\nverifies from the peer-to-peer network that the snap state is correct.

How much snapshot speeds up the node sync depends on what kind of node you want to run:\nfull node without event history, a full node with event history or archive node.\nFor example, even with a snapshot syncing a BNB Chain full node with event history\nwill take several weeks, because Erigon needs to construct the historical events\nfrom the raw blocks after the download, as historical events are not precomputed in the\nsnapshot.

Read the blog post about snapshots.

\n","name":"Blockchain snapshot","slug":"blockchain-snapshot","shortDescription":"A chain snapshot is a dump of the chain state"},"bnb-chain":{"html":"

BNB Chain is an EVM-Compatible blockchain from the Binance cryptocurrency exchange.

BNB Chain is a direct fork of Ethereum.

BNB Chain offers good interaction with the Binance cryptocurrency exchange (CEX), making it attractive\nfor the users of this exchange. As of the writing of this, Binance has a 70% market\nshare of the cryptocurrency trading volume, making it very dominant in the blockchain industry.

BNB Chain is based on centralised, proof-of-authority. model.\nBNB Chain was recently halted in a hack.\nIt uses BNB token\nas its native token.

The dominating decentralised exchange on BNB Chain is PancakeSwap.

See also

\n","name":"BNB Chain","slug":"bnb-chain","shortDescription":"BNB Chain is an EVM-Compatible blockchain from the Binance cryptocurrency exchange"},"bollinger-bands":{"html":"

Bollinger Bands are a technical analysis indicator used to measure market volatility and identify potential buying or selling opportunities. The indicator consists of a set of three lines plotted on a price chart, with the middle line being a simple moving average of the security’s price, and the upper and lower bands serving as a measure of volatility, typically set 2 standard deviations away from the moving average.

In a market with low volatility, the Bollinger Bands will be closer together, while in a market with high volatility, the bands will be further apart. When prices move outside the upper band, it can be a sign that the security is overbought, and a potential selling opportunity, while a move below the lower band can indicate that the security is oversold and a potential buying opportunity.

Bollinger Bands are widely used by traders and investors in making investment decisions, as well as in setting stop-loss orders and determining potential profit targets.

Read technical deep dive into Bollinger Bands.

See also

\n","name":"Bollinger bands","slug":"bollinger-bands","shortDescription":"Bollinger Bands are a technical analysis indicator used to measure market volatility and identify potential buying or selling opportunities"},"bonding-curve":{"html":"

In a bonding curve based exchange, like an AMM, market makers do not set limit\norders to provide liquidity. Instead, the liquidity follows a predefined mathematical function. Every time\nthere is a buy or a sell, the price moves up or down defined by this function.

See also

\n","name":"Bucket","slug":"bucket","shortDescription":"The (time) bucket to a time period for candle data"},"bull-market":{"html":"

A bull market is a condition in the stock market where the overall trend is upward and prices are rising. It is characterised by widespread optimism and positive investor sentiment, leading to an increase in the prices of securities. In a bull market, most asset classes tend to rise, and it can last anywhere from a few months to several years. During a bull market, it is common for investors to buy assets, leading to further price increases. The opposite of a bull market is a bear market, where prices are falling and investor sentiment is negative. Bull markets can be driven by various factors, such as a strong economy, low unemployment, and rising corporate earnings.

\n","name":"Bull market","slug":"bull-market","shortDescription":"A bull market is a condition in the stock market where the overall trend is upward and prices are rising"},"candle":{"html":"

Candle, or a candlestick is a type of price chart used in technical analysis that displays the high, low, open, and closing prices of an asset for a specific time period, or bucket.

More information on Wikipedia.

See also

OHLCV
Bucket

\n","name":"Candle","slug":"candle","shortDescription":"Candle, or a candlestick is a type of price chart used in technical analysis that displays the high, low, open, and closing prices of an asset for a specific time period, or bucket"},"carried-interest":{"html":"

In quantitative finance, carried interest refers to a share of profits or investment gains that are allocated to investment managers or general partners in certain types of investment funds, particularly private equity funds, venture capital funds, and sometimes hedge funds. It represents a portion of the fund’s profits that the manager or general partner is entitled to receive as compensation.

Carried interest is typically structured as a profit-sharing arrangement. It is distributed to the investment manager or general partner after the fund’s investors, also known as limited partners, have received a predetermined return on their investments, often referred to as the hurdle rate or preferred return. The manager or general partner receives a share of any profits above this hurdle rate, usually a percentage ranging from 10% to 30%.

The term “carried interest” derives from the concept that the investment manager or general partner carries an interest in the fund’s profits, in addition to their capital investment. The carried interest serves as an incentive for the manager to maximize the fund’s performance and generate strong investment returns, as their compensation is directly tied to the success of the investments.

Because carried interest is not exited or liquidated, it is more tax efficient there are no capital gains for holding.

See also:

\n","name":"Carried interest","slug":"carried-interest","shortDescription":"In quantitative finance, carried interest refers to a share of profits or investment gains that are allocated to investment managers or general partners in certain types of investment funds, particularly private equity funds, venture capital funds, and sometimes hedge funds"},"cex":{"html":"

CEX is an acronym for a centralised cryptocurrency exchange.

It is the opposite of DEX. In a centralised cryptocurrency exchange,\nthe exchange takes custody of your assets in a non-transparent manner. You are 100%\ntrusting that the exchange does not have technical issues or fraud issues\nwhen it comes to managing your assets.

\n","name":"CEX","slug":"cex","shortDescription":"CEX is an acronym for a centralised cryptocurrency exchange"},"clmm":{"html":"

CLMM stands for Concentrated Liquidity Market Maker.

CLMM is a form of AMM.\nCLMMs provide more capital-efficient liquidity on a DEX.

CLMM protocol allows liquidity providers to set specific price ranges,\nadd single-sided liquidity and do range order trading,\nsimilar to order books.

See also

\n","name":"CLMM","slug":"clmm","shortDescription":"CLMM stands for Concentrated Liquidity Market Maker"},"clone":{"html":"

Fork is a a product launched based on the open source code of another existing product. Also known as fork.

See fork for details.

\n","name":"Clone","slug":"clone","shortDescription":"Fork is a a product launched based on the open source code of another existing product"},"closed-position":{"html":"

A closed position in finance and trading refers to a trade that has been completed by taking an action that is the opposite of the initial transaction. This means selling assets in a long position or buying back assets in a short position, thereby neutralizing the market exposure and locking in any realized gains or losses.

\n","name":"Closed position","slug":"closed-position","shortDescription":"A closed position in finance and trading refers to a trade that has been completed by taking an action that is the opposite of the initial transaction"},"compound":{"html":"

In quantitative finance, compounding returns refer to the process of reinvesting profits or earnings generated from an investment back into the investment itself. It involves earning returns not only on the initial investment but also on the accumulated earnings from previous periods.

When returns are compounded, they have the potential to grow exponentially over time, thanks to the compounding effect. The compounding effect arises from reinvesting the returns, allowing them to generate additional returns in subsequent periods. As the investment grows, the compounding effect becomes more significant, leading to accelerated growth over the long term.

To illustrate the concept, consider an investment with an annual return of 10%. If the returns are compounded annually, the initial investment will grow by 10% each year. However, if the returns are compounded more frequently, such as quarterly or monthly, the growth rate will be higher due to the reinvestment of earnings more frequently. This compounding effect can be especially powerful when investing over long periods.

The compounding effect is commonly observed in various investment vehicles, such as stocks, bonds, mutual funds, and other financial instruments. It is an essential concept in quantitative finance as it influences the calculation of investment performance, risk assessments, and the determination of investment strategies.

When analyzing investment performance or projecting future returns, it is crucial to consider the impact of compounding. Investors often use compound interest formulas or specialized software to calculate and project the compounded returns based on the investment’s expected growth rate and compounding frequency.

See also

\n","name":"Compound","slug":"compound","shortDescription":"In quantitative finance, compounding returns refer to the process of reinvesting profits or earnings generated from an investment back into the investment itself"},"cross-validation":{"html":"

Cross-validation is a technique for evaluating the performance of a predictive model by testing it on a subset of data that was not used to train the model. It is a method for assessing how well the model generalizes to new data.

Cross-validation is a common practice in machine learning and statistical modeling. It is used to assess the predictive accuracy of a model and to detect overfitting, which occurs when a model performs well on the training data but does not generalize well to new data.

The process involves splitting the dataset into two subsets: a training set and a test set. The model is trained on the training set and then evaluated on the test set. The results are compared to determine how well the model performs on new data.

Cross-validation can be used to compare different models or to tune the parameters of a model. It can also be used to select the best features for a model or to determine the optimal number of features.

See also

\n","name":"Cross validation","slug":"cross-validation","shortDescription":"Cross-validation is a technique for evaluating the performance of a predictive model by testing it on a subset of data that was not used to train the model"},"crystallisation":{"html":"

In quantitative finance, “crystallisation” typically refers to the process by which the value of a fund investment is determined or realized. It involves calculating the net asset value (NAV) of the fund and providing investors with a clear picture of their investment’s worth.

Crystallisation in funds occurs at specific intervals, such as daily, weekly, monthly, or quarterly, depending on the fund’s valuation frequency. At these intervals, the fund’s assets are evaluated, and the NAV per share or unit is calculated. The NAV represents the total value of the fund’s assets minus its liabilities, divided by the number of outstanding shares or units.

Investors use the crystallized NAV to assess the value and performance of their fund investments. It allows them to understand the current worth of their holdings and make informed decisions regarding buying, selling, or redeeming fund shares.

Crystallisation is particularly significant in open-end mutual funds and exchange-traded funds (ETFs) where shares can be bought or sold on a regular basis. It provides transparency and liquidity to investors by ensuring that the value of their investments is regularly determined and accessible.

It’s important to note that crystallisation in funds does not refer to the same process as crystallisation in investments, where it signifies the realization of gains or losses. Instead, in the fund context, it pertains to the determination of the fund’s value for the benefit of investors.

See also:

\n","name":"Crystallisation","slug":"crystallisation","shortDescription":"In quantitative finance, “crystallisation” typically refers to the process by which the value of a fund investment is determined or realized"},"csv":{"html":"

CSV, or comma-separated values, is a file format that stores tabular data in plain text. It is a common format for storing data in spreadsheets and databases. It is also a common format for exchanging data between applications.

CSV files are typically used to store data in a tabular format. Each line in a CSV file represents a row in a table, and each column in a row represents a field in the table. The fields are separated by commas, and the rows are separated by newlines.

CSV files are often used to store data in a tabular format. They are also used to exchange data between applications.

See also

JSON
JSONL
dataset

\n","name":"CSV","slug":"csv","shortDescription":"CSV, or comma-separated values, is a file format that stores tabular data in plain text"},"cumulative-profit":{"html":"

Cumulative profit tells the overall profit of trading strategy over a time.

Cumulative profit is the excess of net income and gains over net losses, determined on a cumulative basis from the inception of an investment fund through to its termination date.\nIt includes realized trading P&L, positions open P&L, and other total profits or losses generated since the trading strategy’s inception.

As trading strategies reinvest earlier profits, the cumulative profit compounds.

To calculate cumulative profit, one must add together all net profit numbers over a specific time frame.

See also

\n","name":"Cumulative profit","slug":"cumulative-profit","shortDescription":"Cumulative profit tells the overall profit of trading strategy over a time"},"cumulative-return":{"html":"

Cumulative return is the total change in the investment price over a set time, taking into account reinvested dividends or capital gains.\nIt allows for investors to measure the performance of an investment over a certain period of time.\nIt is is distinct from annualized return which measures the rate of return over a given period of time.

Cumulative return is calculated by taking the gain (or loss) of the investment over a certain time period and dividing it by the principal value of that investment.\nIt can also be calculated using the standard returns of each period, where Rc = (1 + R1)(1 + R2) - 1.

\n","name":"Cumulative return","slug":"cumulative-return","shortDescription":"Cumulative return is the total change in the investment price over a set time, taking into account reinvested dividends or capital gains"},"curl":{"html":"

cURL (Client URL) is a command-line tool that enables data exchange between a device and a server through a terminal.\nIt provides a library (libcurl) and command-line tool (curl) for transferring data using various network protocols.\ncURL commands allow users to transfer data without user interaction using supported libcurl libraries[5]

See also

\n","name":"Decentralised exchange","slug":"decentralised-exchange","shortDescription":"Decentralised exchange (DEX) is an asset trading exchange where all trades happen purely on-chain"},"decentralised-finance":{"html":"

Decentralised finance (DeFi) is an emerging financial technology that challenges the current centralised banking system.

Decentralised finance refers to a set of newly emerging financial products and services that operate on blockchains using cryptocurrency and\nsmart contract technology.\nDeFi eliminates the fees that banks and other financial institutions charge, making it more accessible to anyone with an internet connection.\nThus, decentralised finance is the opposite of traditional finance (TradFi).

Decentralised finance activities happen on-chain,\nand are protocol based where users connect with their wallets.\nThe underlying concept that DeFi services are non-custodial without direct counterparty humans\nand intermediates make them very efficient.

Decentralised finance can be seen to be a subset of web3. If decentralised finance\ntrades real-world assets, they are called RWAs.

Example decentralised finance protocols includ\ndecentralised exchanges,\nlike Uniswap and lending protocols like Aave.\nOther famous decentralised finance protocols include Curve, MakerDAO, Compound,\nEuler and SushiSwap.

See also

\n","name":"Decentralised finance","slug":"decentralised-finance","shortDescription":"Decentralised finance (DeFi) is an emerging financial technology that challenges the current centralised banking system"},"defi":{"html":"

DeFi stands for Decentralised finance.

See decentralised finance for the full description.

\n","name":"DeFi","slug":"defi","shortDescription":"DeFi stands for Decentralised finance"},"deterministic":{"html":"

In mathematics, computer science and physics, a deterministic system is a system in which no randomness is involved in the development of future states of the system. A deterministic model will thus always produce the same output from a given starting condition or initial state.

\n","name":"Deterministic","slug":"deterministic","shortDescription":"In mathematics, computer science and physics, a deterministic system is a system in which no randomness is involved in the development of future states of the system"},"dex":{"html":"

DEX is an acronym for DEcentralised eXchange.

For more information see decentralised exchange description.

\n","name":"DEX","slug":"dex","shortDescription":"DEX is an acronym for DEcentralised eXchange"},"directional-strategy":{"html":"

A directional strategy is a type of trading or investment strategy that involves taking a bullish or bearish view on a particular asset or market.

This means that the strategy is based on the expectation of the asset or market moving in a specific direction, either up or down.

Directional strategies are typically used by traders and investors who are trying to profit from market movements. They can take various forms, including long positions (where an investor buys an asset with the expectation that its price will increase), short positions (where an investor sells an asset with the expectation that its price will decrease), or a combination of both.

Some common examples of directional strategies include trend-following, momentum trading, and breakout trading. These strategies often involve using technical analysis, fundamental analysis, or both to identify market trends, momentum, or key price levels, and to make investment decisions.

See also

Trading strategy
Active strategy

\n","name":"Directional strategy","slug":"directional-strategy","shortDescription":"A directional strategy is a type of trading or investment strategy that involves taking a bullish or bearish view on a particular asset or market"},"discretionary-investment-management":{"html":"

Discretionary management in the context of quantitative finance refers to a trading approach where human traders have the authority to make final decisions about trade execution based on their expertise and judgment, even though algorithms and automated systems might be used to generate trading signals or suggestions.

See also

\n","name":"Discretionary investment management","slug":"discretionary-investment-management","shortDescription":"Discretionary management in the context of quantitative finance refers to a trading approach where human traders have the authority to make final decisions about trade execution based on their expertise and judgment, even though algorithms and automated systems might be used to generate trading signals or suggestions"},"docker":{"html":"

Linux process and packaging management framework. Ideal for managing\nlong-running server-side processes.

See Docker.com for more information.

See also

Environment file
Python

\n","name":"Docker","slug":"docker","shortDescription":"Linux process and packaging management framework"},"dollar-cost-averaging-(dca)":{"html":"

Dollar cost averaging (DCA) is an investment strategy in which an investor divides a larger sum of money into smaller investments, made at regular intervals over a longer period of time. The goal of dollar cost averaging is to reduce the impact of market volatility on the investment portfolio by spreading out the investment over time, rather than investing the entire amount at once.

For example, an investor who wants to invest $10,000 in a stock may choose to invest $1,000 every month for 10 months, rather than investing the full $10,000 in one lump sum.

This approach can help to reduce the impact of short-term market fluctuations and allow the investor to accumulate more shares when prices are low and fewer shares when prices are high.

\n","name":"Dollar cost averaging (DCA)","slug":"dollar-cost-averaging-(dca)","shortDescription":"Dollar cost averaging (DCA) is an investment strategy in which an investor divides a larger sum of money into smaller investments, made at regular intervals over a longer period of time"},"drawdown":{"html":"

How much in percent terms the asset goes or can go down.

Drawdown refers to the peak-to-trough decline in the value of an investment, usually measured as the percentage of an investment’s peak value. It is a measure of the risk of an investment, as it indicates the amount that an investment can decline before it reaches a new high. Drawdown can also refer to the period of time during which an investment’s value is in decline.

See also

Maximum drawdown
Risk-adjusted return

\n","name":"Drawdown","slug":"drawdown","shortDescription":"How much in percent terms the asset goes or can go down"},"ema":{"html":"

Exponential moving average. One of the most common technical indicators.\nBy comparing the current price of an asset to the moving average price,\none can determine if the current price is likely dislodged above or\nbelow the market trend.

See this post for more information on simple and exponential moving average.

See also

Trading strategy
Technical indicator

\n","name":"EMA","slug":"ema","shortDescription":"Exponential moving average"},"environment-file":{"html":"

Environment file, or env file for short, is a standard way to communicate options and arguments\nfor a process in software development.

An environment file consists of environment variables\nmaintained as a configuration file.

There are different environment file formats for UNIX shells, Docker, JavaScript and others.\nA tool like shdotenv can be used to import and convert between\ndifferent formats.

Typically environment file has a file name extension .env. The best practice in JavaScript development\nis to have a file with name .env (no name body) that is ignored in version control and contains\nthe secrets needed by a software developer for the local development.

See also:

Python
Docker

\n","name":"Environment file","slug":"environment-file","shortDescription":"Environment file, or env file for short, is a standard way to communicate options and arguments\nfor a process in software development"},"enzyme-protocol":{"html":"

Enzyme is a fund back-office protocol for EVM-compatible blockchains.

Enzyme offers vaults where investors can invest to different\ntrading strategies. Enzyme protocol is non-custodial.

Read Enzyme Finance for more information.

See also

Decentralised exchange
Lending pool

\n","name":"Enzyme protocol","slug":"enzyme-protocol","shortDescription":"Enzyme is a fund back-office protocol for EVM-compatible blockchains"},"equity-curve":{"html":"

An equity curve is a graphical representation of the change in the value of a trading account over a time period.

Equity curves are used by traders to determine the viability of their trading strategies.\nA good equity curve has an even slope, small and short-lived drawdowns, and a good amount of trades to make the observation statistically significant.

Equity curve trading is a methodology where a trading strategy is turned on and off based on the performance of an equity curve, which is a plot showing the growth of capital over time from one specific trading strategy or portfolio.

See also

\n","name":"Equity curve","slug":"equity-curve","shortDescription":"An equity curve is a graphical representation of the change in the value of a trading account over a time period"},"erc-20":{"html":"

ERC-20 is the original token standard for Ethereum.

ERC-20 is a technical standard used to issue and implement tokens on the Ethereum blockchain.\nIt was proposed in November 2015 by Ethereum developer Fabian Vogelsteller and defines a common set of rules such as how the tokens can be transferred, how transactions are approved, and the total supply of tokens. ERC-20 tokens are the most commonly used tokens on the Ethereum network and are designed to be used for paying for functions.

ERC-20 token standard has several limitations and architectural shortcomings.\nIts approve() and permit() based functions, originally planned for having\nbetter smart contract security in Solidity, have proven to be an attractive\nvector for scams as normal users do not understand they are transferring tokens\nwhen calling these methods from their wallets.

The token standard has been adopted by other EVM-based blockchains like\nBNB Chain, Polygon, Avalanche C-Chain and Fantom.

See also

\n","name":"ERC-20","slug":"erc-20","shortDescription":"ERC-20 is the original token standard for Ethereum"},"erigon":{"html":"

The Erigon node is an implementation of Ethereum written in Go, designed to run archive nodes that manage large amounts of on-chain data.\nIt is a decentralized blockchain node provider that provides a secure, private, and scalable blockchain infrastructure.\nIf you want to host your own JSON-RPC API access to raw EVM blockchain data,\nErigon is a good option.

Erigon is one of the two most popular Ethereum clients, alongside GoEthereum.\nThe benefits of Erigon over GoEthereum include better disk space usage\nand performance due to more advanced database structures. Internally Erigon uses Lightning Memory-Mapped Database Manager (LMDB).

Erigon supports some alternative blockchains to Ethereum mainnet,\nlike Polygon and BNB Chain. Erigon is not available for all EVM-compatible networks, as the project was started many years after GoEthereum, which had become the de facto standard EVM implementation at that point.

\n","name":"Erigon","slug":"erigon","shortDescription":"The Erigon node is an implementation of Ethereum written in Go, designed to run archive nodes that manage large amounts of on-chain data"},"evm":{"html":"

EVM stands for Ethereum Virtual Machine.

It is the execution environment that runs Ethereum blockchain transactions and smart contracts.

EVM was originally created in 2014 and is old in blockchain architecture terms
EVM is used by multiple chains outside Ethereum, like Avalanche and BNB Chain
There exists multiple implementations of EVM in different programming languages: Go, Rust, Python, etc.
Some transactions do not concern or touch EVM; for example beacon chain (staking)\nhappens partially outside EVM domain

Due to age and limitations of EVM, there have been efforts to extend EVM for more functionality and faster\ntransaction processing. Some efforts include

See also

EVM-Compatible
Decentralised finance

\n","name":"EVM","slug":"evm","shortDescription":"EVM stands for Ethereum Virtual Machine"},"evm-compatible":{"html":"

EVM compatible means that a blockchain uses the same EVM architecture\nas the original Ethereum mainnet and is its clone.

EVM compatible blockchains can use the existing smart contracts\nlike ERC-20 unmodified. They can also blockchain explorers,\nwallets and such with little modifications. This has lead to\nborn to Ethereum clone chains like Polygon, Avalanche C-Chain and\nBNB Chain.

For EVM-compatible chains

Same Solidity and Vyper smart contracts work
Same JSON-RPC API works
Same wallets work, assuming your wallet gives you an option to set up a node JSON-RPC URL\nto a different EVM chain
Same libraries like Web3.py and Web3.js, SDKs and development tools work

However, you might need

Run a blockchain node yourself
Get another node provider as there is no consistency of who is running, connecting and offering\nservices to different peer-to-peer networks

For an example list of EVM-compatible blockchains see Trading Strategy’s supported blockchains.

See also

EVM
Wallet
ERC-20
Erigon
BNB Chain
Avalanche

\n","name":"EVM-compatible","slug":"evm-compatible","shortDescription":"EVM compatible means that a blockchain uses the same EVM architecture\nas the original Ethereum mainnet and is its clone"},"exposure":{"html":"

The risk of a strategy for the volatility of a particular asset. For example, if you have 100% exposure to ETH and ETH prices drops to zero, you lose all of your money.

\n","name":"Exposure","slug":"exposure","shortDescription":"The risk of a strategy for the volatility of a particular asset"},"face-value":{"html":"

In finance, the face value is the nominal value of a financial instrument such as a bond, stock, or currency. For a bond, the face value is the amount that the bond will be worth when it matures. For a stock, the face value is the original value assigned to the stock when it is issued. For currency, the face value is the value printed on the currency.

For example, Digital Currency Group took over the defaulted Three Arrows Capital\nloan of $1.1B from its subsidiary, Genesis\nat its face value\neven though\nit is unlikely the money will ever be recovered. The fair value\nof this loan would have been much less.

See also

Portfolio construction

\n","name":"Factor investing","slug":"factor-investing","shortDescription":"Factor investing is an investment approach that chooses securities based on attributes that have historically been associated with higher returns"},"fair-value":{"html":"

In finance, fair value is an estimate of the intrinsic value of an asset or liability, based on the most recent market data or other relevant information. The concept of fair value is used to measure the value of an asset or liability that is not traded in an active market, such as in accounting and financial reporting.\nIt’s the estimated amount for which an asset or liability should exchange on the measurement date between a willing buyer and a willing seller in an arm’s-length transaction.\nIt’s used for financial reporting and taxes, among other purposes. The calculation can be complex, as it often involves estimates and assumptions about future events, such as cash flow projections, volatility, risk, and other factors.

For example, Digital Currency Group took over the defaulted Three Arrows Capital\nloan of $1.1B from its subsdiary Genesis\nat its face value\neven though\nit is unlikely the money will ever be recovered. The fair value\nof this loan would have been much less.

See also:

\n","name":"Fee model","slug":"fee-model","shortDescription":"In quantitative finance, there are different models how traders, investors, strategy developers, funds\nand other stakeholders of the ecosystem are paid for their work"},"fork":{"html":"

Fork is a a product launched based on the open source code of another existing product.\nOften forks are also called clones.

In the context of on-chain forks, forks usually are hostile to the original project and competes from the\nsame users. Forks often do not innovate, or make the product technically better.

Unlike traditional open source projects, fork rarely co-operate. Because many forks\nlack the same technical understanding as the original founding tech team, forks suffer from hacks\nand are often abandon after a short period of time.

Forks in practice:

Blockchains can be forked. The most forked blockchain is Ethereum and its EVM\narchitecture. Ethereum forks include Polygon, BNB Smart Chain, Avalanche C-Chain, Fantom.
Uniswap is the most forked protocol. Uniswap forks include Sushi,\nPancakeSwap, Trader Joe and others

See also

\n","name":"Fork","slug":"fork","shortDescription":"Fork is a a product launched based on the open source code of another existing product"},"forward-fill":{"html":"

In data research and analysis, “forward fill” refers to a method of handling missing or incomplete data by carrying forward the last observed value to fill in the gaps. It is also known as “last observation carried forward” (LOCF).

When dealing with time series data or datasets with missing values, forward fill involves replacing missing data points with the most recent known value. This approach assumes that the value remains constant until a new observation is available. By using the previous observation as a substitute, forward fill helps maintain continuity in the data and allows for continued analysis or modeling.

Forward fill is often employed when missing data is considered to be missing at random (MAR), meaning the missingness is not related to the actual value. It can be a useful technique when the missing data points are intermittent or occur sporadically.

However, it is important to note that forward fill may not always be appropriate or suitable for all datasets or analysis scenarios. It assumes that the missing values should be carried forward, which may not be accurate or appropriate in all cases. Researchers should carefully consider the nature of the data and the implications of using forward fill before applying it.

Alternative methods for handling missing data include backward fill (carrying the next observed value backward) or more sophisticated imputation techniques that estimate missing values based on statistical methods or predictive models. The choice of method depends on the specific characteristics of the dataset and the objectives of the analysis.

See also

\n","name":"Forward fill","slug":"forward-fill","shortDescription":"In data research and analysis, “forward fill” refers to a method of handling missing or incomplete data by carrying forward the last observed value to fill in the gaps"},"fundamental-analysis":{"html":"

Fundamental analysis is a method used in finance and investing to evaluate the intrinsic value of a security or asset by examining its underlying economic and financial characteristics and performance. The goal of fundamental analysis is to assess the economic viability and financial health of a company, and to determine if the current market price of its securities reflects its true worth.

In fundamental analysis, investors analyse a wide range of financial and economic data, including financial statements, industry trends, and macroeconomic indicators, to gain insight into a company’s earnings potential, growth prospects, and risk factors. Key metrics analysed include revenue, earnings, profitability, and cash flow, as well as debt levels, management quality, and competitive position.

\n","name":"Fundamental analysis","slug":"fundamental-analysis","shortDescription":"Fundamental analysis is a method used in finance and investing to evaluate the intrinsic value of a security or asset by examining its underlying economic and financial characteristics and performance"},"futures":{"html":"

In quantitative finance, Futures are standardized\nfinancial contracts that obligate the buyer to purchase an\nasset (or the seller to sell an asset) at a predetermined\nfuture date and price. Futures contracts are traded on\norganized exchanges and can be based on various underlying\nassets, such as commodities, currencies, or indices.

Futures contracts are often used for hedging, speculation, or\nprice discovery purposes. They allow market participants to\nmanage their exposure to price fluctuations and take advantage\nof potential profit opportunities.

See also:

\n","name":"Futures","slug":"futures","shortDescription":"In quantitative finance, Futures are standardized\nfinancial contracts that obligate the buyer to purchase an\nasset (or the seller to sell an asset) at a predetermined\nfuture date and price"},"gas-fee":{"html":"

Gas fee is a term used in blockchains to describe the transactions cost a user pays to block producers\nto have their block in includedin the blockchain.

The gas fee is dynamic. During the blockchain cognestion or high activity, block space is more premium, as users are competing\nto get their transaction included in a block.

There can be irregular events causing gas fee spikes

Airdops
Popular NFT mints

As the blockchain technology developers, the gas fees, or the transaction costs, are becoming lower and lower.\nNew blockchains always are more gas efficient than their predecessors, as their code and consensus mechanisms\nare optimised for higher throughput.\nThe costs go down from Bitcoin $20/tx to Ethereum mainnet’s 10$ tx to Polygon’s 0.20$ to Solana’s fractions of cents.

See also

Protocol fee
MEV

\n","name":"Gas fee","slug":"gas-fee","shortDescription":"Gas fee is a term used in blockchains to describe the transactions cost a user pays to block producers\nto have their block in includedin the blockchain"},"gm":{"html":"

In decentralised finance and web3, GM is a meme of saying “good morning”.

It is a polite greeting people use to notify they have arrived online. Despite what the name says,\nit can be used at any time of a day.

The meme is very popular. For example, Warpcast client for Farcast Web3 protocol has a built-in “GM” button.

See also

Web3
Decentralised finance

\n","name":"GM","slug":"gm","shortDescription":"In decentralised finance and web3, GM is a meme of saying “good morning”"},"google-colab":{"html":"

Google Colab is a free Jupyter notebook environment that runs entirely in the cloud.\nIt does not require a setup and allows users to combine executable code, rich text, images, HTML, LaTeX and more in a single document.\nIt provides free access to GPUs and TPUs for anyone who needs them to build machine learning or deep learning models.

See also

Python
Jupyter notebook

\n","name":"Google Colab","slug":"google-colab","shortDescription":"Google Colab is a free Jupyter notebook environment that runs entirely in the cloud"},"grid-search":{"html":"

Grid search is a hyperparameter optimization technique used to find the best combination of hyperparameters for a machine learning model or trading algorithm. Grid search involves exhaustively searching through a manually specified subset of the hyperparameter space and evaluating each combination using a performance metric, such as accuracy or Sharpe ratio.

Although grid search can be computationally expensive, it is often used when the search space is relatively small or when a more thorough exploration of the hyperparameter space is desired. Other optimization techniques, such as random search or Bayesian optimization, can be more efficient in cases where the search space is large or the performance landscape is complex.

See also:

Trading strategy
Hyperparameter optimization

\n","name":"Grid search","slug":"grid-search","shortDescription":"Grid search is a hyperparameter optimization technique used to find the best combination of hyperparameters for a machine learning model or trading algorithm"},"heatmap":{"html":"

A heatmap is a graphical representation of data that uses color coding to represent the values of a matrix or a two-dimensional dataset. In quantitative finance, heatmaps are often used to visualize correlation matrices, volatility surfaces, or other forms of multidimensional data.

Heatmaps can be useful for quickly identifying patterns or trends in the data, as well as for highlighting areas of high or low values. They can also be used to compare different datasets or to track changes in the data over time.

Heatmaps can be created using various software tools, such as Python’s Matplotlib library or R’s ggplot2 package.

See also:

Quantitative finance
Trading strategy

\n","name":"Heatmap","slug":"heatmap","shortDescription":"A heatmap is a graphical representation of data that uses color coding to represent the values of a matrix or a two-dimensional dataset"},"high-frequency-trading":{"html":"

High-frequency trading, or HFT for short, is a trading strategy where you compete with technical speed.

HFT strategies include arbitration, cross-market market making or such and compete against the other actors with your technical speed.\nIn decentralised finance, there is a special category of high-frequency trading called MEV.

See also

\n","name":"High-frequency trading","slug":"high-frequency-trading","shortDescription":"High-frequency trading, or HFT for short, is a trading strategy where you compete with technical speed"},"historical-market-data":{"html":"

Historical market data refers to past market information, including prices, volume, and other related metrics, for a specific security, asset class, or financial market. This data is used by traders, investors, and financial professionals to analyse market trends, evaluate investment opportunities, and make informed investment decisions.

Historical market data can be collected and analysed over various time frames, including daily, weekly, monthly, or even yearly, and can cover different asset classes, including stocks, bonds, commodities, and currencies. This information is critical in providing insights into past market behaviour and can be used to identify patterns and trends, develop trading strategies, and make informed predictions about future market movements.

Historical market data is widely available through a variety of sources, including financial data providers, stock exchanges, and government agencies.

You can download decentralised finance historical data using Trading Strategy client.

See also

\n","name":"Historical market data","slug":"historical-market-data","shortDescription":"Historical market data refers to past market information, including prices, volume, and other related metrics, for a specific security, asset class, or financial market"},"honey-pot":{"html":"

In decentralised finance a honey pot describes a trading pair or a token\ndesigned to fool trading strategies. This is made possible\nby ERC-20 flexibility allowing it to have custom transfer and ownership rules,\nwhere the token owner and deployer manipulates balances.

The scams include “buy only” tokens, tokens with token tax or\ntrading pools where the owner can drain quote token away. Usually\ntrading algorithms are baited with “up only” price manipulation\ntechniques and wash trading to believe that the trading pair is robust with good momentum\nand volume.

See also:

Trading strategy
Grid search

\n","name":"Hyperparameter optimization","slug":"hyperparameter-optimization","shortDescription":"Hyperparameter optimization refers to the process of finding the optimal set of hyperparameters for a machine learning model or trading algorithm"},"hypersdk":{"html":"

HyperSDK is an optimised software development kit to build blockchains from Avalanche.

HyperSDK is MerkleDB and has very optimised state sync. The high performance\nmakes HyperSDK optimal for building trading focused app chains. Theoretical\ntransaction throughput can be up to 50 transactions per second (TPS). Internally HyperSDK nodes\nuses Pebble database.

See HyperSDK on Github.

See also

\n","name":"HyperSDK","slug":"hypersdk","shortDescription":"HyperSDK is an optimised software development kit to build blockchains from Avalanche"},"indexvm":{"html":"

IndexVM is a blockchain virtual machine built on the top of HyperSDK\ndedicated to increasing the usefulness of the world’s content-addressable data by enabling anyone to index it.

See IndexVM on Github.

See also

\n","name":"IndexVM","slug":"indexvm","shortDescription":"IndexVM is a blockchain virtual machine built on the top of HyperSDK\ndedicated to increasing the usefulness of the world’s content-addressable data by enabling anyone to index it"},"json":{"html":"

JSON (JavaScript Object Notation) is a lightweight data interchange format that is easy for humans to read and write and easy for machines to parse and generate.

It is a text format that is used to transmit data between a server and a web application, as well as between different parts of a web application.\nJSON is commonly used in web development for tasks such as sending and receiving data between a server and a client-side web application, or for storing data in a format that can be easily exchanged between different systems.

JSON is based on a subset of the JavaScript programming language, and it is often used with JavaScript, although it can be used with any programming language that can parse and generate JSON data.

JSON data is organized into key-value pairs, where keys are strings and values can be strings, numbers, arrays, objects, booleans, or null.

See the JSON specification for more information.

Also see an example of using JSON with TradingStrategy data in the JSON example

See also

Python
JSONL

\n","name":"JSON","slug":"json","shortDescription":"JSON (JavaScript Object Notation) is a lightweight data interchange format that is easy for humans to read and write and easy for machines to parse and generate"},"json-rpc":{"html":"

In blockchain development, JSON-RPC is a popular Remote Procedure Call (RPC) protocol used by blockchain node applications.

JSON-RPC was popularised by the original bitcoind node and then copied to Ethereum.\nJSON-RPC is human readable and simple protocol for programmatically asking information from your blockchain node.

This includes

Querying the current state information
Broadcasting transactions

JSON-RPC is used by

Wallets (query tokens, balances)
Decentralised application frontends

You can get a JSON-RPC API for your blockchain application by

Self-hosting a blockchain node and then turning on the JSON-RPC API port
Using a software-as-a-service provider - see ethereumnodes.com for details

Popular alternatives for JSON-RPC includes Protobuf, as used by Cosmos SDK.

See also

JSON
EVM

\n","name":"JSON-RPC","slug":"json-rpc","shortDescription":"In blockchain development, JSON-RPC is a popular Remote Procedure Call (RPC) protocol used by blockchain node applications"},"jsonl":{"html":"

JSONL, also known as JSON Lines, is a convenient format for storing structured data that may be processed one record at a time. Each line in a JSONL file is a valid JSON object, allowing for simple append operations and easy processing of large files. The format is particularly useful in cases where the data consists of many small records, as it allows for line-by-line iteration, reducing memory usage when dealing with large datasets.

This format, although not an official standard, has gained popularity due to its simplicity and efficiency in handling data. The JSONL file format is especially useful in the fields of data processing and machine learning, where dealing with large datasets is common.

See the JSONL specification for more information.

Also see an example of using JSONL with TradingStrategy data in the JSONL example.

See also

JSON
CSV
dataset

\n","name":"JSONL","slug":"jsonl","shortDescription":"JSONL, also known as JSON Lines, is a convenient format for storing structured data that may be processed one record at a time"},"jupyter-notebook":{"html":"

Jupyter Noteook is a popular Python based data science tool.

Jupyter allows users to run data research notebooks interactively.

Jupyter notebooks can be easily shared, run on your local computer or on a hosted cloud environment, both free and paid.

Jupyter is an especially popular software development tool among quants.

More information on Jupyter website.

See also

\n","name":"Jupyter notebook","slug":"jupyter-notebook","shortDescription":"Jupyter Noteook is a popular Python based data science tool"},"last-trade":{"html":"

In Trading Strategy Protocol, last trade is the date when a trading strategy\nsuccessfully executed a trade last time.

This metric tells who lively the underlying algorithm has been recently.

See also

Trading Strategy Protocol

\n","name":"Last trade","slug":"last-trade","shortDescription":"In Trading Strategy Protocol, last trade is the date when a trading strategy\nsuccessfully executed a trade last time"},"lending-pool":{"html":"

In decentralised finance, a lending pool is a pool of a single asset in a lending protocol.

Sometimes a lending pool is also called reserve (:term`Aave).

The assets are typically held in a pool and are lent out to borrowers, who can use them for a variety of purposes such as margin trading, liquidity provision, or to meet other financial obligations.

Lending pools typically offer a high yield investment opportunity for lenders, as they can earn interest on their cryptocurrency assets without having to sell them. Borrowers, on the other hand, can access the assets they need to meet their financial obligations, without having to sell their own assets or go through the traditional lending process.

Lending pools are typically run on decentralised finance (DeFi) platforms, which use blockchain technology to create a decentralised, trustless financial system. This means that the platform operates on open-source software and operates transparently and immutably, allowing for secure and transparent transactions.

See also

\n","name":"Lending pool","slug":"lending-pool","shortDescription":"In decentralised finance, a lending pool is a pool of a single asset in a lending protocol"},"lending-protocol":{"html":"

A lending protocol is a decentralised finance service for lending and borrowing tokens.

Lending protocols are smart contract-based non-custodial protocols to\nlend and borrow your assets.

Lending protocols enable shorting of different lending pool tokens.

Popular lending protocols include Aave, Compound and Euler.

See also

\n","name":"Lending protocol","slug":"lending-protocol","shortDescription":"A lending protocol is a decentralised finance service for lending and borrowing tokens"},"leverage":{"html":"

Leverage in trading is the use of borrowed funds to increase one’s trading position beyond what would be available from their cash balance alone.

Leverage trading is also known as margin trading.

It enables traders to open a position worth much more than the money they deposit.

Leverage can be used in longing or shorting.

In a long, 2x leverage means that you can make 2x profit when the price moves up 1x
In a short, 1x leverage means that you can make 1x profit when the price moves up 1x
If the price moves too much your deposited money is liquidated
Shorting always involves borrowing
In leveraged markets often longs pay shorts, short pays longs as a zero-sum trading game

Leverage requires collateral. If the leveraged trade goes belly up, the account\ndoing the leveraged trade may be liquidated.

See also

\n","name":"Leverage","slug":"leverage","shortDescription":"Leverage in trading is the use of borrowed funds to increase one’s trading position beyond what would be available from their cash balance alone"},"liquidation":{"html":"

In trading, liquidation happens when a trader has insufficient funds to keep a leveraged trade open.

The exchange or term:lending protocol closes the position.

Any collateral that was used to open the position is sold (“liquidated”)
The proceedings of collateral liquidation is used to paid the trades who took the opposite\nside of the trade

In decentralised finance, liquidation also happens on lending protocols.

See also

\n","name":"Liquidation","slug":"liquidation","shortDescription":"In trading, liquidation happens when a trader has insufficient funds to keep a leveraged trade open"},"liquidity":{"html":"

Liquidity refers to the depth of the order books: how much volume a single trade can achieve without moving the price.

It can be expressed as slippage or absolute depth of the order book. The latter is very easy for AMM based exchanges where the liquidity is a continuous function.

Trading Strategy provides datasets for AMM liquidity.

See also

\n","name":"Liquidity","slug":"liquidity","shortDescription":"Liquidity refers to the depth of the order books: how much volume a single trade can achieve without moving the price"},"liquidity-mining":{"html":"

Liquidity mining is a process where AMM liquidity providers for a token are subsidised\nfrom a token treasury.

Effectively, you are paying people to create liquidity in an AMM trading pair or\nan order book.

This is to bootstrap the liquidity from zero to meaningful level, so that users and traders\ncan enter and exit positions without significant price impact. The liquidity mining\nprogram assumes that after a certain liquidity level is artificially bootstrapped,\nit can maintain itself better as it has attracted active traders with larger capital pools\nwho now have started to trade the token.

See also

\n","name":"Liquidity mining","slug":"liquidity-mining","shortDescription":"Liquidity mining is a process where AMM liquidity providers for a token are subsidised\nfrom a token treasury"},"liquidity-pool":{"html":"

Liquidity pool is the available trading liquidity of a single trading pair in AMM.

In a liquidity pool, liquidity providers pool together one of more tokens\nthat users can trade against. For providing liquidity, the providers are rewarded in swap fees.

Liquidity pools are often their own smart contracts that can be easily explored and tracked in a blockchain explorer.

See also

\n","name":"Liquidity pool","slug":"liquidity-pool","shortDescription":"Liquidity pool is the available trading liquidity of a single trading pair in AMM"},"liquidity-provider":{"html":"

Liquidity providers (LPs) are users that deposit their assets to the liquidity pools of an Automated Market Maker (AMM).

Liquidity providers are crowdsourced collections of crypto assets used by the AMM to trade with people buying or selling one of these assets.\nAMMs use pre-programmed mathematical equations to adjust prices based on supply in order to make sure the ratio of assets in any liquidity pool is maintained.

See also

\n","name":"Liquidity provider","slug":"liquidity-provider","shortDescription":"Liquidity providers (LPs) are users that deposit their assets to the liquidity pools of an Automated Market Maker (AMM)"},"longing":{"html":"

Longing means to make a trade where one assumes the price of an asset is going up.

To go long is the opposite of shorting.

Longs can be be spot market purchases (consider it as 1x leverage
Longs can be levered for higher profits on success

In decentralised finance, you can build a levered long trade using lending protocols.

See also

\n","name":"Longing","slug":"longing","shortDescription":"Longing means to make a trade where one assumes the price of an asset is going up"},"look-ahead-bias":{"html":"

In quantitative finance, Look-ahead bias occurs when a trading strategy or model uses information that would not have been available at the time the trade was executed. This can lead to an unrealistic assessment of a strategy’s performance, as the model appears to have access to future information.

To avoid look-ahead bias, it is essential to ensure that the data used in backtesting and strategy development is strictly limited to the information that would have been available at the time of the trade. This can be achieved by using time-stamped data and careful data management techniques.

See also:

\n","name":"Look-ahead bias","slug":"look-ahead-bias","shortDescription":"In quantitative finance, Look-ahead bias occurs when a trading strategy or model uses information that would not have been available at the time the trade was executed"},"loss-function":{"html":"

In machine learning, the loss function is a measure of how well a model fits the training data. It is a function that is minimized during training.

The loss function is typically a mathematical function that measures the difference between the predicted values and the actual values. It is used to train the model by minimizing the loss function.

The loss function is used to train the model, while the test set is used to evaluate the performance of the model. The test set is typically a random sample of the dataset that is not used to train the model.

See also

\n","name":"Loss function","slug":"loss-function","shortDescription":"In machine learning, the loss function is a measure of how well a model fits the training data"},"machine-learning":{"html":"

Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” (i.e., progressively improve performance on a specific task) with data, without being explicitly programmed.

Machine learning is closely related to computational statistics, which focuses on making predictions using computers. Machine learning is also sometimes conflated with data mining, which focuses on extracting useful information from large datasets.

Machine learning algorithms are used in a wide variety of applications, including email filtering, speech recognition, and computer vision. They are also used in finance, where they are used to predict stock prices, detect fraud, and make trading decisions.

Machine learning algorithms can be classified into two broad categories: supervised learning and unsupervised learning. Supervised learning algorithms are trained using labeled data, while unsupervised learning algorithms are trained using unlabeled data.

Supervised learning algorithms are used to solve classification and regression problems. Classification problems involve predicting a discrete class label, while regression problems involve predicting a continuous quantity.

Unsupervised learning algorithms are used to solve clustering and dimensionality reduction problems. Clustering problems involve grouping similar data points together, while dimensionality reduction problems involve reducing the number of features in a dataset.

See also

\n","name":"Machine learning","slug":"machine-learning","shortDescription":"Machine learning is a field of computer science that uses statistical techniques to give computer systems the ability to “learn” (i"},"management-fee":{"html":"

A management fee is a recurring fee charged by investment managers or fund managers for managing and overseeing investment portfolios or funds on behalf of investors. It is a compensation mechanism for the services provided by the manager in managing the assets and making investment decisions.

The management fee is typically calculated as a percentage of the total assets under management (AUM). The percentage fee can vary depending on the type of fund, investment strategy, or asset class. It is usually expressed as an annual rate and is deducted from the fund’s assets on a regular basis, such as quarterly or annually.

The management fee covers various costs associated with managing the fund, including research, analysis, portfolio construction, trading, administrative expenses, regulatory compliance, and compensation for the investment manager’s team. It is meant to compensate the manager for their time, expertise, and resources devoted to managing the investments and ensuring the fund’s operation.

Management fees are disclosed in the fund’s prospectus or investment management agreement, providing transparency to investors regarding the costs associated with their investment. The fee structure is an important consideration for investors when evaluating investment options, as it directly affects the net return they will receive.

It’s worth noting that management fees are separate from other fees that may be associated with investment funds, such as transaction fees, custodial fees, performance fees (in certain funds), or expense ratios (in mutual funds and ETFs). These additional fees cover specific services or costs beyond the general management of the fund.

See also:

\n","name":"Management fee","slug":"management-fee","shortDescription":"A management fee is a recurring fee charged by investment managers or fund managers for managing and overseeing investment portfolios or funds on behalf of investors"},"margin-trading":{"html":"

Leverage trading is also known as margin trading.

See leverage.

\n","name":"Margin trading","slug":"margin-trading","shortDescription":"Leverage trading is also known as margin trading"},"market-data-feed":{"html":"

A time-series data on which automated trade decisions are based on.\nOne of the most common data feeds is the price data as OHLCV candles.

\n","name":"Market data feed","slug":"market-data-feed","shortDescription":"A time-series data on which automated trade decisions are based on"},"market-exposure":{"html":"

Market exposure refers to the extent to which an investment portfolio is subject to fluctuations in the overall financial market or a specific asset class. It describes the amount of risk or potential for gain that an investor or organization faces as a result of having invested in a particular market, sector, or individual security.

Example: An investor with a portfolio heavily weighted in technology stocks has significant market exposure to the technology sector. If the technology sector performs well, the investor stands to gain; conversely, if the sector performs poorly, the investor is at risk of incurring losses.

Unrealized risk and market exposure are often interconnected, as the level of market exposure can directly impact the degree of unrealized risk in a portfolio. A portfolio with high market exposure to volatile sectors will inherently carry a higher degree of unrealized risk until positions are closed.

See also

Unrealised risk

\n","name":"Market Exposure","slug":"market-exposure","shortDescription":"Market exposure refers to the extent to which an investment portfolio is subject to fluctuations in the overall financial market or a specific asset class"},"market-neutral-strategy":{"html":"

Market neutral strategies are trading strategies that have little or no exposure to crypto asset volatility. They are often high-frequency trading strategies, like arbitrage. Good market neutral strategies can make 10% - 20% monthly yield in cryptocurrency markets.

\n","name":"Market neutral strategy","slug":"market-neutral-strategy","shortDescription":"Market neutral strategies are trading strategies that have little or no exposure to crypto asset volatility"},"market-order":{"html":"

A market order is a trade where a trader order is instantly fulfilled whatever is the availability liquidity and price\nat the markets.

Specifically

Market orders remove liquidity (also known as a market taker)
Market buys move price up, market sells move price ddown

The opposite of a market order is often a limit order. On AMMs the opposite is\nliquidity provider position.

On AMM decentralised exchanges every swap is a similar to a market order.

Market orders, as a market taker, typically have higher fees than market makers (provides liquidity),\nas liquidity is the key selling point for any exchange.

See also

\n","name":"Market order","slug":"market-order","shortDescription":"A market order is a trade where a trader order is instantly fulfilled whatever is the availability liquidity and price\nat the markets"},"market-sentiment":{"html":"

Market sentiment refers to the overall attitude or tone of investors toward a particular financial market or asset. It is often gauged through various indicators such as price movements, trading volume, news, and other market activities. Market sentiment can be bullish (optimistic), bearish (pessimistic), or neutral.

Example: If most news articles about a particular stock are positive and its trading volume is high, the market sentiment is likely to be bullish.

Usage: Market sentiment is closely monitored by traders and investors as it can influence price trends and market volatility. Understanding market sentiment can help in making more informed investment decisions.

See also

\n","name":"Market Sentiment","slug":"market-sentiment","shortDescription":"Market sentiment refers to the overall attitude or tone of investors toward a particular financial market or asset"},"market-trade":{"html":"

See market order

\n","name":"Market trade","slug":"market-trade","shortDescription":"See market order\n"},"maximum-drawdown":{"html":"

In quantitative finance, maximum drawdown (MDD) refers to the maximum loss from a peak to a trough of a portfolio, before a new peak is attained. It is a measure of the largest loss that an investment portfolio has experienced over a specified time period.

Maximum drawdown is an important metric in risk management as it helps investors understand the potential downside risk of an investment. It is often used to assess the risk of a portfolio and to compare different investment strategies.

For example, suppose an investment portfolio has a maximum drawdown of 20%. This means that the portfolio has experienced a loss of 20% from its peak value before a new peak is reached. In other words, the portfolio has lost 20% of its value at some point in the past.

Maximum drawdown is typically expressed as a percentage of the portfolio’s value. It can be calculated using the following formula:

\n\\[MDD = \\frac{P - T}{P}\\]

where P is the peak value of the portfolio and T is the trough value.

As a rule of thumb, the maximum drawdown should not be more than 1/3 of the strategy annual returns. For example, if a strategy has an annual return of 30%, the maximum drawdown should not exceed 10%.

See also

\n","name":"Maximum Drawdown","slug":"maximum-drawdown","shortDescription":"In quantitative finance, maximum drawdown (MDD) refers to the maximum loss from a peak to a trough of a portfolio, before a new peak is attained"},"maximum-pullback":{"html":"

Maximum decrease in profitability % as a result of consecutive losing positions with respect to the portfolio’s initial value. Intuitively, this can be thought of as the biggest losing streak. Not to be confused with maximum drawdown.

See also

Maximum drawdown

\n","name":"Maximum pullback","slug":"maximum-pullback","shortDescription":"Maximum decrease in profitability % as a result of consecutive losing positions with respect to the portfolio’s initial value"},"mean":{"html":"

The mean is a measure of central tendency that represents the average value of a data set. It is calculated by dividing the sum of all values by the number of values in the data set. It is also known as the arithmetic mean or average.

Example: For the data set [1, 3, 3, 6, 7, 8, 9], the mean is (1+3+3+6+7+8+9)/7 = 5.14

Usage: The average is widely used in various fields for summarizing data sets, comparing different groups, or tracking changes over time. However, it can be susceptible to the influence of outliers.

See also

\n","name":"Mean","slug":"mean","shortDescription":"The mean is a measure of central tendency that represents the average value of a data set"},"mean-reversion":{"html":"

A mean reversion strategy is a type of trading strategy that assumes that prices of an asset will eventually return to their average or mean levels over time.

This strategy is based on the idea that prices tend to move in cycles and that extreme deviations from the average are temporary\nand eventually return to their historical average.\nIn a mean reversion strategy, a trader buys an asset when its price is lower than its average and sells when the price is higher than its average,\nwith the goal of profiting from the reversion to the mean. Mean reversion strategies can be applied to a variety of assets,\nsuch as stocks, bonds, commodities, and currencies, and are commonly used in quantitative finance and algorithmic trading.

See also

\n","name":"Mean reversion","slug":"mean-reversion","shortDescription":"A mean reversion strategy is a type of trading strategy that assumes that prices of an asset will eventually return to their average or mean levels over time"},"median":{"html":"

The median is a measure of central tendency that separates a data set into two equal halves. It is the middle value when all data points are arranged in ascending or descending order. In the case of an even number of observations, the median is the average of the two middle numbers.

Example: For the data set [1, 3, 3, 6, 7, 8, 9], the median is 6. If the data set were [1, 3, 3, 6, 7, 8], the median would be (3+6)/2 = 4.5

Usage: The median is often used in statistical analyses to provide a measure of the “typical” value in a distribution, particularly when the data set may contain outliers that could heavily influence the mean. It is also used to calculate the median absolute deviation (MAD), which is a measure of dispersion that is less sensitive to outliers than the standard deviation.

See also

\n","name":"Median","slug":"median","shortDescription":"The median is a measure of central tendency that separates a data set into two equal halves"},"mercenary-capital":{"html":"

In decentralised finance, mercenary capital is a term used to refer to cryptocurrency investment and trading funds\nwho chase liquidity mining and other token rewards without long term commitment\nto the token success.

Mercenary capital moves from a project to a project, chasing\nbest subsidised yield farming and liquidity mining opportunities,\nwithout actually contributing to the token success any way.

See also

\n","name":"Mercenary capital","slug":"mercenary-capital","shortDescription":"In decentralised finance, mercenary capital is a term used to refer to cryptocurrency investment and trading funds\nwho chase liquidity mining and other token rewards without long term commitment\nto the token success"},"mev":{"html":"

Malicious Extractable Value (MEV) is a measure of the profit a blockchain block producer can make through their ability to arbitrarily include, exclude, or re-order transactions during the block production process. It refers to the maximum amount of value that can be extracted from block production in excess of the standard block reward and gas fees.

MEV is also known as Miner Extractable Value (original proof-of-work term) and Maximum Extractable Value.

MEV is applicable to a blockchain that have a consensus model where there is a single elected leader for each block, round or epoch (terminology varies).\nThis leader sees the content of new transactions and can decide what are transactions and which order they are going to be included in the next block,\neffectively deciding the final execution order of the transcations.\nThe leader can also insert their own transactions to at any point of the new block.\nIf any of transactions contain trades, the leader can insert their own trades before or after the transactions of other blockchain users.

A block producer in a position to do MEV can harm traders in multiple ways:

MEV operators frontrun trades and liquidity provisions to get a better price for themselves as they see the market is about to change,\nby inserting their own trades before others.
MEV operators perform sandwitch attack to manipulation the market, by frontrunning and backrunning s single trade to set the price to some arbitrary value.
MEV operators backrun trades and extract any arbitrage opportunities which open up after executing a trade.

Because of MEV, traders using blockchain may lose money they would not lost in trading otherwise.\nEthereum mainnet block producers have been manipulating on-chain markets via MEV since 2020. This money goes to the block producers as excessive profits from their MEV trades.\nMost of MEV is a harmful for traders and protocols as it extracts value out from the system and moves it to a third party, unrelated to the actual trading activity. If a rational trader\ncan choose between two otherwise equal markets, they will always choose the market without MEV.

From community acceptance and legal standpoint a third-party MEV, where traders and the protocols are not participants, can be considered harmful:

Frontrunning and market manipulation are illegal in many jurisdictions and different contexts.

-Backrunning is generally found acceptable,: especially if it distributes some of arbitrage profits back to traders and protocols.
\n

Not all blockchains are suspectible to MEV.\nGetting rid of MEV is often referred as “solving MEV” or “minimising MEV.”

MEV can be prevented and mitigated by

Making transactions invisible to block producers. This is called to shuttering or shielding. One way to achieve this\nis to use two-phase commit and reveal scheme, also known as commitment scheme. for including transactions in a block.\nIn this scheme, the block producers ability to see the transaction content is removed. Commit-and-reveal schemes can happen on the block production level (hardcoded\nfor all transactions) or on the application level (a special DEX smart contract needed with two separate transactions).\nShutter Network is a commit-and-reveal scheme for EVM block building. Commit-and-reveal schemes usually do not\nremove the block producers ability to do backrunning.
Setting a slippage tolerance for traders. The slippage is the amount of price change between the user creating a trade\nand the time the trade gets executed. If the trade outcome is not within the slippage tolerance, the trade fails and the trader\nmay try again. The slippage tolerance sets the upper bound for the value frontrun and sandwich attacks can extract. However,\nthe requirement to use tight slippage tolerance to avoid harmful MEV will also cause legimate trades to fail, and MEV participants\nwill still extract value, just lower.
Using private mempools. A mempool is a public peer-to-peer network where transactions are broadcasted before they are included in a block.\nA private mempool operator submits transactions to the block producers with a honour agreement “do not negatively manipulate these”\nand there is some sort of revenue sharing agreement with the private mempool operator and block producers about this.\nMultiple private mempool solutions for EVM blockchains exist. Users are incentivised to use a private mempool\nby paying back some of the backrun arbitrage profits from users trades in the form of a kickback.\nA simple solution for ordinary users is to change the JSON-RPC node configuraiton of a wallet to point to in the form of a kickback.\na private mempool, like MEV Blocker. So far all private mempools are trust-based, so they are not ideal\nsolution as a rogue private mempool operator does not need to respect any honour agreements.
Backrunning built into the protocol itself to capture any arbitrage left after routing a trade. Osmosis is doing this\nif the form of ProtoRev module. More about ProtoRev solution here.
Most EVM-compatible blockchains and virtual machines are not powerful enough to do backrunning on-chain, but off-chain solutions like CowSwap and MEV Blocker exist.
Creating a blockchain block production system where there is no single leader who can decide on transactions. See Advanced consensys system design presentation here.
Having a blockchain that is too dumb to support logic needed to calculate trades. Bitcoin is such a blockchain and that’s\nwhy there cannot be trading on Bitcoin, and all of its trading needs to happen outside native Bitcoin blockchain.
CEX-DEX arbitrage, though not always MEV but harmful for decentralised finance ecosystem due to its harm for passive liquiditity providers, can be mitigated with dynamic fees, introduced by Uniswap version 4. More on dynamic fees mitigating arbitrage here.

See also

\n","name":"MEV","slug":"mev","shortDescription":"Malicious Extractable Value (MEV) is a measure of the profit a blockchain block producer can make through their ability to arbitrarily include, exclude, or re-order transactions during the block production process"},"-,":{"html":"

especially if it distributes some of arbitrage profits back to traders and protocols.

\n","name":"-,","slug":"-,","shortDescription":"especially if it distributes some of arbitrage profits back to traders and protocols"},"mid-price":{"html":"

The mid price, in the context of AMM, is the price that reflects the ratio of reserves in one or more pairs. There are three ways we can think about this price. Perhaps most simply, it defines the relative value of one token in terms of the other. It also represents the price at which you could theoretically trade an infinitesimal amount (ε) of one token for the other. Finally, it can be interpreted as the current market-clearing or fair value price of the assets.

The mid price, in the context of order book based exchange is \$(best bid + best ask) / 2\$,\ni.e. the price between the best sell offer and the best buy offer.

More information about the mid price on Uniswap documentation.

\n","name":"Mid Price","slug":"mid-price","shortDescription":"The mid price, in the context of AMM, is the price that reflects the ratio of reserves in one or more pairs"},"momentum":{"html":"

In trading strategy, momentum refers to the rate of change in the price of a financial asset over a given period of time.

The price change si often expressed as a change in the open-close data. E.g. the price of an asset\nat the opening of the week vs. closing price as the end of the week.

Momentum traders seek to profit from short-term price trends by buying assets that have been performing well and selling those that have been performing poorly.

Momentum traders typically use technical analysis to identify assets that are exhibiting strong positive or negative momentum. They may look at indicators such as moving averages, relative strength index (RSI), and MACD (moving average convergence divergence) to determine the current trend and potential entry and exit points.

One common strategy used by momentum traders is to buy assets that have recently experienced a significant price increase, on the assumption that the upward trend will continue. This is known as a “breakout” strategy. Conversely, they may sell assets that have experienced a significant price decrease, on the assumption that the downward trend will continue.

It is important to note that momentum trading can be risky, as price trends can quickly reverse direction, leading to significant losses if a trader’s positions are not properly managed. It is therefore important for traders to have a well-defined strategy, strict risk management rules, and the discipline to stick to their plan even in the face of short-term fluctuations.

See also

\n","name":"Momentum","slug":"momentum","shortDescription":"In trading strategy, momentum refers to the rate of change in the price of a financial asset over a given period of time"},"native-token":{"html":"

Also known as “gas token”. The native token is the cryptocurrency used to pay\ntransaction fees on EVM-compatible blockchain. For Ethereum it is ETH,\nfor Polygon it is MATIC and for Binance Smart Chain it is BNB.

See also

\n","name":"Native token","slug":"native-token","shortDescription":"Also known as “gas token”"},"netflow":{"html":"

In the context of trading strategy, netflow typically refers to the net amount of money that flows into or out of a available capital over a specific period. It represents the difference between the inflows (capital deposited) and outflows (capital redeemed or withdrawn) from the fund.

Netflow is an important metric related to total value locked as it can effect on the trading strategy performance.

Positive netflows indicates that TVL is growing
Negative netflows indicate that more capital is being redeemed, which can reduce its TVL

See also

\n","name":"Netflow","slug":"netflow","shortDescription":"In the context of trading strategy, netflow typically refers to the net amount of money that flows into or out of a available capital over a specific period"},"nft":{"html":"

In web3, NFT stands for non-fungible tokens.

NFTs are tokens that unique and thus have value, like artistic value\nor in-game benefits. Usually NFTs represent digital content, but can also represent\nreal-world assets.

Like with all other decentralised finance protocols, NFTs are non-custodial\nand do not need an intermediates like banks to track and change their ownership.

See also

\n","name":"NFT","slug":"nft","shortDescription":"In web3, NFT stands for non-fungible tokens"},"non-custodial":{"html":"

Non-custodial means that a third party does not have ownership of your assets in a service.

A non-custodial model usually means s smart contract based service model where the owner of the assets never lose the control of the assets. This is opposite to most traditional finance services where you cannot see what happens to your money after the deposit or whether you are able to withdraw. The integrity of the service provider in the traditional finance thus needs to be guaranteed through regulation or government bailouts. The non-custodial model is specific to smart contracts and cannot be achieved without a blockchain. Read more.

Non-custodial protocol models have become popular in a blockchain, after FTX and Celsius blow-ups: not your keys, not your coins.

See also

\n","name":"Non-custodial","slug":"non-custodial","shortDescription":"Non-custodial means that a third party does not have ownership of your assets in a service"},"notebook":{"html":"

A notebook is a web-based interactive platform for writing and running code, as well as documenting and sharing work in a variety of formats, including text, code, and graphics. It is commonly used in the fields of data science, machine learning, and scientific computing for developing and testing algorithms, analysing data, and creating visualisations.

In a notebook, users can write code in a variety of programming languages, including Python, and run it directly within the platform. The output of the code, including any visualisations or results, is displayed within the notebook alongside the code itself. This allows users to iteratively develop and test their algorithms, as well as document their work in a readable and reproducible format.\nNotebooks also provide a convenient platform for collaboration and sharing, as they can be easily exported and shared as files, or hosted on platforms such as Jupyter or Google Colab. This makes them a popular choice for data scientists and researchers who need to share their work with others, as well as for organisations who need to collaborate on large data projects.

Overall, notebooks provide a powerful and flexible platform for data analysis, scientific computing, and code development, making them an essential tool for many researchers and data professionals.

The format was popularised by Jupyter notebook.

\n","name":"Notebook","slug":"notebook","shortDescription":"A notebook is a web-based interactive platform for writing and running code, as well as documenting and sharing work in a variety of formats, including text, code, and graphics"},"off-chain":{"html":"

In decentralised finance, off-chain refers to software and code that is run outside the blockchain nodes.

Off-chain activities are needed because some activities are not possible or too expensive (transaction cost wise)\nto handle in a blockchain core protocol or smart contracts:

Computation cost using smart contracts would be too high due to CPU, IO and memory requirements.
Information needs to be fetched for real-world assets or other items\nthat do not have native blockchain data, like centralised exchange cryptocurrency prices.\nBecause blockchains are new technology, most things in the world do not have good blockchain\ndata available yet and off-chain data fetch and validation is needed.

In the future, the amount of activities that need off-chain computation is going to decrease,\nas blockchains are getting more powerful.

Usual off-chain computation use cases include

Price oracles
Order books run on a centralised server like 0x protocol or DyDx

See also

\n","name":"Off-chain","slug":"off-chain","shortDescription":"In decentralised finance, off-chain refers to software and code that is run outside the blockchain nodes"},"ohlcv":{"html":"

A typical candle contains open, high, low and close price and trade volume for a time bucket. Because on-chain exposes more data than centralised exchanges, Trading Strategy data also contains individual buys and sells, US dollar exchange rate and so forth.

See also

\n","name":"OHLCV","slug":"ohlcv","shortDescription":"A typical candle contains open, high, low and close price and trade volume for a time bucket"},"on-chain":{"html":"

In decentralised finance, on-chain refers to transactions or activities that are recorded and processed directly on a blockchain network. It is the opposite of off-chain.

In a blockchain, transactions are verified by nodes in the network and added to the chain of blocks that make up the blockchain. These transactions are permanent and unalterable, and the state of the blockchain can be seen by anyone on the network.

When an activity is referred to as “on-chain,” it means that it is recorded directly on the blockchain, as opposed to off-chain, which refers to transactions or activities that are not recorded on the blockchain but are still facilitated by the network.

On-chain transactions are typically considered to be more secure and transparent than off-chain transactions, as they are recorded in a public ledger that is immutable and auditable. Additionally, on-chain transactions are often processed more quickly and with lower fees than off-chain transactions, as they do not require intermediaries or intermediating networks.

Examples of on-chain activities include cryptocurrency transfers, smart contract execution, and decentralised applications (dApps). These activities are all processed directly on the blockchain network, providing a secure and transparent means of conducting transactions and executing code.

See also

\n","name":"On-chain","slug":"on-chain","shortDescription":"In decentralised finance, on-chain refers to transactions or activities that are recorded and processed directly on a blockchain network"},"open-position":{"html":"

An open position in finance and trading refers to a trade that has been initiated but has not yet been closed or settled. It represents an active market exposure that carries with it the risk of loss as well as the potential for profit. The position can be either long, indicating ownership and the expectation that the asset will appreciate in value, or short, indicating a borrowed asset that is sold with the expectation of buying it back at a lower price.

See also

\n","name":"Open position","slug":"open-position","shortDescription":"An open position in finance and trading refers to a trade that has been initiated but has not yet been closed or settled"},"options":{"html":"

In quantitative finance, Options are financial\ncontracts that give the buyer the right, but not the\nobligation, to buy (in the case of a call option) or sell (in\nthe case of a put option) an underlying asset at a specified\nprice (strike price) on or before a certain date (expiration\ndate). The seller of the option, in return, receives a premium\nfrom the buyer.

Options are versatile financial instruments that can be used\nfor various purposes, such as hedging, speculation, or income\ngeneration. They allow market participants to manage their\nrisk exposure and take advantage of potential profit\nopportunities.

See also:

\n","name":"Options","slug":"options","shortDescription":"In quantitative finance, Options are financial\ncontracts that give the buyer the right, but not the\nobligation, to buy (in the case of a call option) or sell (in\nthe case of a put option) an underlying asset at a specified\nprice (strike price) on or before a certain date (expiration\ndate)"},"oracle":{"html":"

In decentralised finance,\noracle allows smart contracts to access external data.

An oracle in a blockchain is an entity that connects blockchains to external systems,\nallowing smart contracts to execute based upon inputs and outputs from the real world.

Oracles are third-party services that provide smart contracts with external information,\nwhile blockchain technology itself is defined as a ledger of decentralized data that is securely\nshared by select participants.

The most common use case for oracle is to tell the fair value of an asset.\nThis can be a token or a real-world asset.

Oracles are security critical system, as they often report prices and if the price is wrong\nthe underlying protocol may suffer what is called an oracle attack or a price manipulation attack.\nFor example, if in a lending protocol the collateral price is wrong,\nthe loands can be incorrectly liquidated.

Popular oracles protocols include

ChainLink (on EVM-Compatible blockchains)
Python (Solana)

See also

\n","name":"Oracle","slug":"oracle","shortDescription":"In decentralised finance,\noracle allows smart contracts to access external data"},"order-book":{"html":"

An order book is a type of market that operates based on an order book, a record of all buy and sell orders for a particular asset.

In an order book exchange, users can place limit orders, specifying the price and quantity they are willing to buy or sell an asset for. When a matching buy and sell order is found, a trade is executed, and the exchange takes a small fee for facilitating the transaction. Order book exchanges provide more precise price discovery and allow users to take advantage of market inefficiencies, but can suffer from liquidity issues and are more susceptible to front-running and other forms of market manipulation.

Overall, order book exchanges offer a more traditional trading experience compared to Automated Market Maker (AMM) exchanges, but with the added benefits of being decentralised and secure.

\n","name":"Order book","slug":"order-book","shortDescription":"An order book is a type of market that operates based on an order book, a record of all buy and sell orders for a particular asset"},"overfitting":{"html":"

In quantitative finance, Overfitting is a term used to describe the phenomenon where a model performs well on the data it was trained on, but poorly on new data. This is often caused by the model learning the noise in the data rather than the underlying pattern.

Overfitting can be avoided by using a larger training set, or by using regularization techniques such as dropout or weight decay. It is also important to use cross-validation to ensure that the model is not overfitting to the training data.

See also:

\n","name":"Overfitting","slug":"overfitting","shortDescription":"In quantitative finance, Overfitting is a term used to describe the phenomenon where a model performs well on the data it was trained on, but poorly on new data"},"pandas":{"html":"

Pandas is a powerful and widely used open-source data analysis library in Python. It provides data structures and functions needed to work with structured data, such as tabular data in the form of tables or spreadsheets. With pandas, it’s easy to manipulate, clean, and analyse data, as well as perform complex operations such as grouping, merging, and aggregating data.

Pandas is designed to handle a variety of data types, including numerical, categorical, and time-series data. It also integrates well with other libraries, such as NumPy and Matplotlib, making it a popular choice for data analysis and visualisation in the scientific and research communities.

One of the key features of pandas is its DataFrame object, which is a two-dimensional labelled data structure with columns of different data types. With pandas, you can easily perform operations on the DataFrame such as filtering, sorting, and grouping data, as well as handling missing values and dealing with time-series data. More information.

See also

\n","name":"Pandas","slug":"pandas","shortDescription":"Pandas is a powerful and widely used open-source data analysis library in Python"},"parquet":{"html":"

Parquet is a columnar storage format for big data processing and analysis, commonly used in the Apache Hadoop and Apache Spark ecosystems. It is optimised for fast querying and efficient storage of large, complex data sets, and supports a wide range of data formats and compression options. By organising data into columns rather than rows, Parquet enables more efficient compression and encoding, as well as improved query performance, making it a popular choice for data warehousing and analytics applications. More information.

\n","name":"Parquet","slug":"parquet","shortDescription":"Parquet is a columnar storage format for big data processing and analysis, commonly used in the Apache Hadoop and Apache Spark ecosystems"},"passive-strategy":{"html":"

Passive investing is a long-term trading strategy for building wealth by buying securities that mirror stock market indexes and holding them long term.

A passive strategy involves minimal trading in the market, with investors buying and holding a diversified mix of assets to match, but not beat, the performance of an index.\nThe passive investing strategy is based on the premise that a low-cost, well-diversified portfolio will produce an average market return.

Often, in decentralised finance, passive strategies aims to beat risk-free rate without any additional exposure\nand drawdown.

See also

\n","name":"Passive strategy","slug":"passive-strategy","shortDescription":"Passive investing is a long-term trading strategy for building wealth by buying securities that mirror stock market indexes and holding them long term"},"performance-fee":{"html":"

In quantitative finance, a performance fee is a form of compensation that investment managers or fund managers may receive based on the performance of an investment or a fund. It is typically an additional fee on top of the management fee and is structured to align the interests of the manager with the investors.

The performance fee is earned when the investment or fund achieves certain predefined performance benchmarks or exceeds a specified hurdle rate. The specific terms and conditions of the performance fee are outlined in the fund’s prospectus or investment management agreement.

The calculation of a performance fee can vary depending on the fund or investment strategy. Commonly, it is calculated as a percentage of the investment gains or profits generated by the fund. For example, if the fund’s performance fee is set at 20% and the fund achieves a return of 10%, the performance fee would be 20% of the 10% gain, resulting in a 2% fee. Fees are calculated for a specific crystallisation period.

Performance fees are intended to incentivize fund managers to deliver superior investment results. By linking compensation to investment performance, investors hope to encourage the manager to make strategic investment decisions that maximize returns. It aligns the manager’s interests with those of the investors, as the manager benefits from generating positive investment results.

It’s important to note that performance fees are more commonly associated with alternative investment vehicles such as hedge funds, private equity funds, or certain types of actively managed mutual funds. Traditional mutual funds, index funds, or passive investment strategies generally do not include performance fees.

\n","name":"Performance fee","slug":"performance-fee","shortDescription":"In quantitative finance, a performance fee is a form of compensation that investment managers or fund managers may receive based on the performance of an investment or a fund"},"pine-script":{"html":"

A proprietary trading strategy programming language for TradingView. Read more. Pine Script is a high-level scripting language that is specifically designed for use in creating custom trading indicators and strategies for financial markets. It is used by traders to create custom technical indicators, such as moving averages, Bollinger Bands, and Relative Strength Indicators, as well as more complex algorithms for automated trading.

\n","name":"Pine Script","slug":"pine-script","shortDescription":"A proprietary trading strategy programming language for TradingView"},"poetry":{"html":"

Poetry is a tool for dependency management and packaging in Python. It allows you to declare the libraries your project depends on and it will manage (install/update) them for you. Poetry offers a lockfile to ensure repeatable installs, and can build your project for distribution.

See also

Python
Jupyter notebook

\n","name":"Poetry","slug":"poetry","shortDescription":"Poetry is a tool for dependency management and packaging in Python"},"portfolio-construction":{"html":"

Portfolio construction a trading strategy method of selecting securities optimally to achieve maximum returns while taking minimum risk.

Portfolio constructions involves understanding how different asset classes, funds, and weightings impact each other and an investor’s objectives

Portfolio construction has several phases

Asset allocation models - to determine the optimal mix of asset classes (stocks, bonds, and commodities) in a portfolio, based on historical returns, volatility, and correlations.
Optimization techniques - to identify the best combination of individual securities within each asset class, based on factors such as expected return, risk, and liquidity.
Risk management tools - such as stop-loss orders, hedging strategies, and diversification techniques, to manage portfolio risk and reduce exposure to individual assets or market risks
Alpha generation strategies - such as factor investing, statistical arbitrage, and trend-following, to identify assets that are likely to outperform or underperform the broader market.

See also

\n","name":"Portfolio construction","slug":"portfolio-construction","shortDescription":"Portfolio construction a trading strategy method of selecting securities optimally to achieve maximum returns while taking minimum risk"},"position":{"html":"

In trading strategy, a position means a long or short position of a particular\nasset betting the price of an asset goes up or down.

In long positions,\nthe trader expects the asset price go up, or appreciate. In short positions,\nthe trade expects the asset price go down.

A position can be a spot market position or a levered position.

See also

\n","name":"Position","slug":"position","shortDescription":"In trading strategy, a position means a long or short position of a particular\nasset betting the price of an asset goes up or down"},"position-sizing":{"html":"

Position sizing in quantitative finance refers to the process of determining the appropriate size or quantity of an investment position to take in a financial instrument, such as a stock or a derivative, based on a predefined set of rules or strategies. It is a crucial aspect of portfolio management and risk control.

The goal of position sizing is to allocate capital efficiently, taking into account factors such as risk tolerance, investment objectives, market conditions, and statistical analysis. The position size is typically determined by a combination of quantitative models, statistical techniques, and risk management principles.

Some commonly used approaches to position sizing include:

Fixed Fractional Position Sizing: This approach involves allocating a fixed percentage of the total capital to each trade or investment. For example, a strategy might allocate 2% of the total capital to each trade, regardless of the size or risk of the trade.
Kelly Criterion: The Kelly Criterion is a mathematical formula that helps determine the optimal position size based on the probability of different outcomes and their corresponding payoffs. It aims to maximize the long-term growth rate of capital while considering the risk involved.
Risk Parity: Risk parity strategies allocate capital based on the risk contribution of each asset in the portfolio. The position size is determined by the volatility or risk of the asset, aiming for equal risk contributions from each asset class.
Value-at-Risk (VaR): VaR is a risk management metric that estimates the maximum potential loss within a specified confidence level over a given time horizon. Position sizing based on VaR involves allocating capital in a way that limits the potential loss to a predetermined threshold.
Optimization techniques: Mathematical optimization methods can be used to find the optimal position size that maximizes a specific objective function, such as risk-adjusted returns or portfolio diversification.

See also

\n","name":"Position sizing","slug":"position-sizing","shortDescription":"Position sizing in quantitative finance refers to the process of determining the appropriate size or quantity of an investment position to take in a financial instrument, such as a stock or a derivative, based on a predefined set of rules or strategies"},"price-impact":{"html":"

Price impact is the difference between the current market price and the price you will actually pay when performing a swap on a decentralised exchange.

Price impact tells how much less your market taker order gets filled because there is not available liquidity.\nFor example, if you are trying to buy 5000 USD worth of BNB token, but there isn’t available liquidity\nyou end up with 4980 USD worth of token at the end of the trade when you pay 5000 USD.\nThe missing fill is the price impact.\nIt can be expressed as USD value or as percent of the trade amount.

Illiquid pairs have more price impact than liquid pairs.

Liquidity provider fees are included in the price impact in AMM models.

Another way to see this: AMMs usually have a trading fee, of 0.30%, for liquidity providers and sometimes for the protocol.\nThis translates to a spread of 0.6% between the best buy order and the best sell order.\nIn other words, even the most liquid AMM trade has an implicit 0.3% price impact. Note that due to competition, the LP fees\nare going down on newer AMMs.

Read a detailed analysis of how price impact is calculated on Uniswap v2 style AMMs.

See ParaSwap documentation on price impact.

See also

\n","name":"Profitability","slug":"profitability","shortDescription":"In quantitative finance, profitability tells how much a trading strategy\nhas generated or is estimated to generate profits"},"protocol":{"html":"

In decentralised finance, a protocol is non-custodial financial service.

In protocol, its users are doing directly transactions on-chain with each other without intermediates,\nin peer-to-peer manner. Protocols can be built into a blockchain or be smart contract based.

Unlike in traditional finance, in decentralised finance the user is always in control\nof their assets, making protocols different from centralised services.

See also

\n","name":"Protocol","slug":"protocol","shortDescription":"In decentralised finance, a protocol is non-custodial financial service"},"protocol-fee":{"html":"

In AMM-based decentralised exchanges a protocol fee is the fee\ntaken from your trading costs that goes towards the protocol treasury.

Protocol fee is revenue for the protocol itself.

The distribution of the protocol fee is decided by a DAO.

Usually protocol fee is 0.00% (Uniswap v3 default) - 0.005% (PancakeSwap, Sushi).

See also

Swap fee
Decentralised exchange

\n","name":"Protocol fee","slug":"protocol-fee","shortDescription":"In AMM-based decentralised exchanges a protocol fee is the fee\ntaken from your trading costs that goes towards the protocol treasury"},"public-trading-strategy":{"html":"

A public trading strategy is an investment approach that is disclosed to and available for use by the general public. This type of strategy is often marketed and sold through books, courses, seminars, or other educational materials. Public trading strategies are designed to provide individuals with a set of guidelines and rules for making investment decisions in financial markets. They typically involve the use of technical analysis, fundamental analysis, or a combination of both to identify opportunities in various asset classes, such as stocks, bonds, commodities, or currencies.

Public trading strategies may also incorporate elements of quantitative analysis, using mathematical models and algorithms to analyse market data and make predictions about market movements. The goal of a public trading strategy is to provide individuals with a systematic approach to investing that can help them achieve their financial goals.

\n","name":"Public trading strategy","slug":"public-trading-strategy","shortDescription":"A public trading strategy is an investment approach that is disclosed to and available for use by the general public"},"pyarrow":{"html":"

Python API for Arrow library. More information. PyArrow is an open-source Python library that provides a fast, efficient way to process and analyse large datasets, especially those in Apache Arrow format. It is used for handling columnar and/or chunked data in memory, including reading and writing data from/to disk and interprocess communication. PyArrow also provides a rich set of data structures and algorithms for working with arrays, tables, and data frames, as well as support for various data formats such as Parquet, Avro, ORC, and others. The library is designed to be highly performant and can be used in a variety of applications, including data science, machine learning, and data engineering.

\n","name":"Pyarrow","slug":"pyarrow","shortDescription":"Python API for Arrow library"},"pycharm":{"html":"

PyCharm is an integrated development environment (IDE) used for programming in Python.\nIt provides code analysis, a graphical debugger, an integrated unit tester, integration with version control systems, and supports web development. PyCharm also has a free version specifically designed for education purposes. PyCharm supports Jupyter Notebooks well.

See also

Python
Jupyter notebook

\n","name":"PyCharm","slug":"pycharm","shortDescription":"PyCharm is an integrated development environment (IDE) used for programming in Python"},"python":{"html":"

One of the most popular and loved programming languages.\nPython is the number one programming language in quantitative finance.

Python is a high-level, interpreted programming language known for its readability, simplicity, and versatility. It was first released in 1991 and has since become one of the most widely used programming languages in the world. Python is used for a variety of applications, including web development, scientific computing, data analysis, artificial intelligence, and more. It has a large standard library and a supportive community, making it easy to learn and use. Python is also highly extensible, allowing developers to add functionality through libraries and modules. With its clean syntax, readable code, and ease of use, Python is a popular choice for both beginner and experienced programmers.

\n","name":"Python","slug":"python","shortDescription":"One of the most popular and loved programming languages"},"qstrader":{"html":"

QSTrader is an old Python based algorithmic trading framework.

QSTrader offers strategy backtesting and live execution using portfolio construction theory.

QSTrader is jo longer maintained.

\n","name":"QSTrader","slug":"qstrader","shortDescription":"QSTrader is an old Python based algorithmic trading framework"},"quant":{"html":"

Quants are financial professionals who specialise in the use of quantitative methods to analyse financial data and make investment decisions. They are experts in mathematics, statistics, and computer science and use complex models and algorithms to analyse financial data and make predictions about market movements

Quants work in a variety of settings, including hedge funds, investment banks, and asset management firms, and play a significant role in the field of finance. They use their expertise in data analysis and modelling to develop trading strategies and make investment decisions, often using high-frequency trading technology to execute their trades.

Quants are known for their ability to analyse large amounts of financial data and make decisions quickly, using a data-driven approach to investment.

See also

\n","name":"Quant","slug":"quant","shortDescription":"Quants are financial professionals who specialise in the use of quantitative methods to analyse financial data and make investment decisions"},"quantitative-analysis":{"html":"

Quantitative analysis is a method of evaluating securities by using mathematical and statistical models. It is a data-driven approach to investment that involves the use of numerical and computational techniques to analyse financial data and make investment decisions.

Quant traders, use tools such as statistical models, algorithms, and high-frequency trading technology to analyse market data, identify trends, and make predictions about future market movements. They rely on historical data, such as stock prices, interest rates, and economic indicators, to develop their models and test their predictions.

Quantitative analysis is commonly used in the field of finance, particularly in hedge funds and institutional trading desks, and can be applied to a variety of asset classes, including stocks, bonds, commodities, and currencies.

\n","name":"Quantitative analysis","slug":"quantitative-analysis","shortDescription":"Quantitative analysis is a method of evaluating securities by using mathematical and statistical models"},"quantitative-finance":{"html":"

Quantitative analysis is the use of mathematical and statistical methods in finance and investment management. Those working in the field are quantitative analysts (quants). Quants tend to specialise in specific areas which may include derivative structuring or pricing, risk management, algorithmic trading and investment management.

See also

Decentralised finance
Algorithmic trading

\n","name":"Quantitative finance","slug":"quantitative-finance","shortDescription":"Quantitative analysis is the use of mathematical and statistical methods in finance and investment management"},"quantstats":{"html":"

Quantstats is a popular Python software development library for quantitative finance calcualtions and charts.

It provides many tools out of the box to analyse your strategies, including

Equity curve
Monthly returns and return distribution charts
Metrics calculations like Maximum drawdown and other risk-adjusted return numbers

Visit Quantstats repository.

See also

\n","name":"Quantstats","slug":"quantstats","shortDescription":"Quantstats is a popular Python software development library for quantitative finance calcualtions and charts"},"quote-token":{"html":"

The token that acts as a nominator for the price when you are buying or selling. Usually this is more well-known token of the pair: ETH, BTC or any of various USD stablecoins. See also base token.

See also

\n","name":"Quote token","slug":"quote-token","shortDescription":"The token that acts as a nominator for the price when you are buying or selling"},"realised-profit-and-loss":{"html":"

Realized Profit and Loss refer to the actual gains or losses incurred from the sale or closure of an investment or asset. A “realized profit” occurs when an asset is sold for a price higher than its purchase price, while a “realized loss” occurs when an asset is sold for a price lower than its purchase price. These are different from “unrealized” profits or losses, which represent potential gains or losses on assets that are still owned and have not yet been sold. Realized profits and losses are typically recorded in financial statements and are subject to taxation.

See also

Unrealised profit and loss

\n","name":"Realised profit and loss","slug":"realised-profit-and-loss","shortDescription":"Realized Profit and Loss refer to the actual gains or losses incurred from the sale or closure of an investment or asset"},"realised-risk":{"html":"

Realized risk refers to the actual risk that has manifested in a financial investment or trading position, leading to either a loss or lower-than-expected returns. It is the quantifiable loss or underperformance that occurs once a position is closed, as opposed to potential or “unrealized” risks that exist while a position remains open. Realized risk essentially captures how much an investor’s concerns or expectations about market risk have actually materialized.

Example: If an investor buys a stock at $100 per share and later sells it at $90, the realized risk is a 10% loss on the investment. This is an example of a risk that has been “realized” because the position is closed and the loss is confirmed.

See also

Unrealised risk
Market exposure

\n","name":"Realised risk","slug":"realised-risk","shortDescription":"Realized risk refers to the actual risk that has manifested in a financial investment or trading position, leading to either a loss or lower-than-expected returns"},"rebalance":{"html":"

In a trading strategy, rebalance refers to the process of re-weighting the portfolio\nby selling some assets and buying new ones.

Rebalances are needed in order for the strategy to follow its alpha model.

Rebalances can be done at fixed intervals, usually hourly, daily or weekly.

In the process, the strategy opens new and closes existing positions.

See also

\n","name":"Rebalance","slug":"rebalance","shortDescription":"In a trading strategy, rebalance refers to the process of re-weighting the portfolio\nby selling some assets and buying new ones"},"rebase-token":{"html":"

Rebase tokens, also known as elastic tokens, are cryptocurrencies whose supply is algorithmically adjusted in order to control its price. They adjust their circulating supply in response to price fluctuation and are designed in a way that the circulating token supply adjusts (increases or decreases) automatically according to a predetermined formula.

An example of a rebase token is Klima.

See also

Token

\n","name":"Rebase token","slug":"rebase-token","shortDescription":"Rebase tokens, also known as elastic tokens, are cryptocurrencies whose supply is algorithmically adjusted in order to control its price"},"regularization":{"html":"

In machine learning, regularization is a technique for reducing overfitting by adding a penalty term to the loss function. It is a form of regression that constrains the model to prevent it from learning the training data too well.

Regularization is a common practice in machine learning and statistical modeling. It is used to prevent overfitting, which occurs when a model performs well on the training data but does not generalize well to new data.

The process involves adding a penalty term to the loss function that penalizes the model for learning the training data too well. The penalty term is typically a function of the model parameters, such as the sum of the squared weights or the sum of the absolute values of the weights. The penalty term is added to the loss function to create a new loss function that is minimized during training.

Regularization can be used to compare different models or to tune the parameters of a model. It can also be used to select the best features for a model or to determine the optimal number of features.

See also

\n","name":"Regularization","slug":"regularization","shortDescription":"In machine learning, regularization is a technique for reducing overfitting by adding a penalty term to the loss function"},"relative-strength-index-(rsi)":{"html":"

The Relative Strength Index (RSI) is a popular momentum indicator used in technical analysis to measure the strength of a security’s price action. It compares the magnitude of recent gains to recent losses, in order to determine overbought or oversold conditions, and potential buying or selling opportunities.

The RSI is calculated as a ratio of average gains to average losses, and is represented as a value between 0 and 100. Values above 70 are considered overbought and may indicate a potential sell opportunity, while values below 30 are considered oversold and may indicate a potential buy opportunity. The RSI can be used for various time frames and for multiple securities, including individual stocks, bonds, commodities, and currencies.

\n","name":"Relative strength index (RSI)","slug":"relative-strength-index-(rsi)","shortDescription":"The Relative Strength Index (RSI) is a popular momentum indicator used in technical analysis to measure the strength of a security’s price action"},"reserve-currency":{"html":"

In the trading strategy terminology, a reserve currency\nrefers to the base currency of the strategy to which profits\nare withdrawn.

A common reserve currency for decentralised finance strategies\nis USDC stablecoin.

\n","name":"Reserve currency","slug":"reserve-currency","shortDescription":"In the trading strategy terminology, a reserve currency\nrefers to the base currency of the strategy to which profits\nare withdrawn"},"risk-of-ruin":{"html":"

In quantitative finance, Risk of ruin refers to the probability of incurring losses severe enough to render a trading account or strategy unviable. This can occur due to a series of consecutive losing trades, excessive leverage, or poor risk management practices.

To minimize the risk of ruin, traders should employ prudent risk management techniques, such as setting appropriate position sizes, using stop-loss orders, and maintaining a diversified portfolio. Additionally, monitoring the performance of a trading strategy and making adjustments as needed can help reduce the likelihood of ruin.

See also:

\n","name":"Risk of ruin","slug":"risk-of-ruin","shortDescription":"In quantitative finance, Risk of ruin refers to the probability of incurring losses severe enough to render a trading account or strategy unviable"},"risk-adjusted-return":{"html":"

Risk-adjusted return is a calculation of the return (or potential return) on an investment such as a stock or corporate bond when compared to the amount of risk the investment has represented throughout a given period of time.

It is measured by taking into account the risk associated with an investment and comparing it to its return.\nThe risk can be measured e.g. as a maximum drawdown.

See also

\n","name":"Risk-adjusted return","slug":"risk-adjusted-return","shortDescription":"Risk-adjusted return is a calculation of the return (or potential return) on an investment such as a stock or corporate bond when compared to the amount of risk the investment has represented throughout a given period of time"},"risk-free-rate":{"html":"

The expected return for the money that is considered (almost) risk-free.

In traditional finance, the risk-free rate is considered to be treasury note or government bond yield, although you still have some risks like the sovereignty risk.

In decentralised finance, a risk-free rate is considered to be an US dollar lending pool rate, like one you would get from Aave USDC pool.

See also

\n","name":"Risk-free rate","slug":"risk-free-rate","shortDescription":"The expected return for the money that is considered (almost) risk-free"},"rug-pull":{"html":"

A project where the development team or founders decide to maliciously cash out early, not\nfulfilling their promises and disappear with the investor money.

A rug pull is a type of crypto scam in which fraudsters lie to the public to attract funding and quickly run off with investors’ digital tokens. Developers behind rug pulls often promote their tokens on social media platforms to attract as many retail investors as possible. The name comes from the phrase “pulling the rug out” and involves a developer attracting investors to a new crypto project, then pulling away all liquidity.

One of the most famous rug pulls is Anubis ($60M taken).

\n","name":"Rug pull","slug":"rug-pull","shortDescription":"A project where the development team or founders decide to maliciously cash out early, not\nfulfilling their promises and disappear with the investor money"},"rwa":{"html":"

In decentralised finance, RWA stands for Real World Asset.

Unlike cryptocurrencies and pure digital assets, like art NFTs,\nreal-world assets have a real-world ownership representation.

RWAs can be

Stablecoins
Real-estate
Bonds
Stock
Land
Forest
Energy
Carbon certificates

RWAs can trade on lending protocols and decentralised exchanges.\nRWAs need special oracles to bring information about the real-world asset data to on-chain.

As the writing of this, most popular RWA classes are stablecoins and bonds.

For RWA case studies, see

See also

\n","name":"RWA","slug":"rwa","shortDescription":"In decentralised finance, RWA stands for Real World Asset"},"sentiment-analysis":{"html":"

In quantitative finance, Sentiment analysis, also known as opinion mining, is a technique used to analyze and classify the sentiment or emotion expressed in a piece of text. This can be done by using natural language processing and machine learning algorithms to identify and extract subjective information from the text, such as opinions, emotions, and attitudes.

Sentiment analysis can be used to determine the overall sentiment of a document, sentence, or even individual words, and can help businesses and organizations gain insights into customer feedback, social media posts, and online reviews. By analyzing sentiment, companies can better understand customer needs and preferences, improve their products and services, and make more informed business decisions.

Sentiment analysis can also be used in a variety of other applications, including political analysis, market research, and brand management.

There are several libraries available in Python that you can use to perform sentiment analysis. One of the most popular libraries is the Natural Language Toolkit (NLTK).

See also:

\n","name":"Sentiment analysis","slug":"sentiment-analysis","shortDescription":"In quantitative finance, Sentiment analysis, also known as opinion mining, is a technique used to analyze and classify the sentiment or emotion expressed in a piece of text"},"sharpe":{"html":"

Sharpe, or The Sharpe Ratio, is a widely used financial metric that measures the risk-adjusted return of an investment. It was developed by Nobel laureate William F. Sharpe in 1966.

The Sharpe ratio is calculated by subtracting the risk-free rate (such as the yield on a Treasury bond) from the return of the investment, and then dividing the result by the standard deviation of the investment’s returns. The formula is:

Sharpe Ratio = (Return of investment - Risk-free rate) / Standard deviation of investment’s returns

The Sharpe ratio helps investors evaluate an investment’s return in relation to the risk taken to achieve that return. A higher Sharpe ratio indicates that an investment has generated higher returns relative to the amount of risk taken, while a lower Sharpe ratio indicates the opposite.

Generally speaking, a Sharpe ratio above 1 is considered good, and a Sharpe ratio above 2 is considered excellent. However, what is considered good or excellent can vary depending on the context. For example, a lower Sharpe ratio may be acceptable for investments with lower risk or lower volatility, while a higher Sharpe ratio may be necessary for investments with higher risk or higher volatility.

See also

Sortino
Risk-adjusted return
Quantitative finance
Trading strategy
More metrics to compare trading strategies

\n","name":"Sharpe","slug":"sharpe","shortDescription":"Sharpe, or The Sharpe Ratio, is a widely used financial metric that measures the risk-adjusted return of an investment"},"shorting":{"html":"

Short selling, also known as shorting or going short, is a trading strategy in which an investor borrows as aasset, sells the borrowed asset, and then aims to buy the asset back at a lower price to make a profit.

The goal of short selling is to profit from a decline in the price of the security being shorted. In a short sale, the investor borrows shares from another investor, typically through a brokerage, and sells the borrowed shares on the open market. If the price of the security drops, the investor can buy the shares back at a lower price and return the borrowed shares to the lender, pocketing the difference as profit. However, if the price of the security increases, the short seller incurs a loss, which can be unlimited.

Short selling is a high-risk strategy that requires a solid understanding of market dynamics and a careful risk management plan.

In decentralised finance, you can build a short trade using lending protocols.

See also

\n","name":"Shorting","slug":"shorting","shortDescription":"Short selling, also known as shorting or going short, is a trading strategy in which an investor borrows as aasset, sells the borrowed asset, and then aims to buy the asset back at a lower price to make a profit"},"slippage":{"html":"

Slippage is the loss because markets changed after the trade was initiated but before it was executed.

Slippage occurs because of changing market conditions between the moment the transaction is submitted and its verification.\nSlippage cannot be backtested easily, because it is based on the trade execution delays and those cannot be usually simulated\n(but can be measured).

DEX swap orders have a slippage tolerance parameter with them. You set it when the order is created.\nIf the price changes more then the slippage tolerance between the creation of the order and the execution of the order,\nthe DEX will cancel the order (revert).

Setting a low slippage tolerance value prevents frontrunning your trades, because frontrunners cannot\nextract more value than what your slippage tolerance is.

See ParaSwap’s excellent documentation on slippage.

See also

\n","name":"Smart contract","slug":"smart-contract","shortDescription":"Smart contract is automated transactional service running on any of the blockchains"},"sortino":{"html":"

The Sortino ratio is a risk-adjusted return measure that is used to evaluate the return of an investment relative to its downside risk, as measured by the standard deviation of negative returns. It was developed by Frank A. Sortino, a financial researcher and academic.

The Sortino ratio is similar to the Sharpe ratio, but instead of using the total volatility of returns, it only considers the downside volatility. This is based on the premise that investors are more concerned with the risk of losing money than they are with the risk of missing out on potential gains.

The formula for calculating the Sortino ratio is as follows:

Sortino ratio = (Rp - Rf) / downside deviation

Where:

Rp = portfolio return\nRf = risk-free rate of return\nDownside deviation = standard deviation of negative returns

The Sortino ratio is useful in comparing the risk-adjusted performance of different investments or portfolios. A higher Sortino ratio indicates a better risk-adjusted return, while a lower ratio indicates a lower risk-adjusted return.

As a rough guide, a Sortino ratio of 1 or higher is considered good, while a ratio of 2 or higher is considered excellent.

See also:

Sharpe
Risk-adjusted return
Quantitative finance
Trading strategy
More metrics to compare trading strategies

\n","name":"Sortino","slug":"sortino","shortDescription":"The Sortino ratio is a risk-adjusted return measure that is used to evaluate the return of an investment relative to its downside risk, as measured by the standard deviation of negative returns"},"spot-market":{"html":"

A spot market is a market where actual assets are traded as the opposite of interest to the assets.

In cryptocurrency markets, spot market is where you receive actual tokens for your purchases.
In stock markets, you will receive stock certificate
In commodities markets (oil, wheat, etc.), you will receive a physically delivered item

Spot market itself does not have shorting, or leverage.

See also

\n","name":"Spot market","slug":"spot-market","shortDescription":"A spot market is a market where actual assets are traded as the opposite of interest to the assets"},"stablecoin":{"html":"

A stablecoin is a type of cryptocurrency whose value is tied to an asset such as the U.S. dollar or gold to maintain a stable price.\nStablecoins aim to provide price stability and reduce volatility compared to other cryptocurrencies.

Example stablecoins include

Circle USD - USDC.
Binance dollar - BUSD
Tether dollar - USDT

See also

ERC-20
Wallet

\n","name":"Stablecoin","slug":"stablecoin","shortDescription":"A stablecoin is a type of cryptocurrency whose value is tied to an asset such as the U"},"standard-deviation":{"html":"

Standard deviation is a statistical measure that quantifies the dispersion or variability of a data set around its mean (average). In finance and investment, standard deviation is often used as an indicator of risk or volatility. A higher standard deviation indicates greater variability in the data set or more volatility in a financial asset, while a lower standard deviation indicates less variability or lower volatility.

Example: For the data set [1, 3, 3, 6, 7, 8, 9], the standard deviation is 2.92

Usage: In financial markets, standard deviation is commonly used as a measure of an asset’s volatility, which helps investors and traders understand the asset’s risk profile. A higher standard deviation implies more unpredictability in price movement, signaling higher risk, while a lower standard deviation indicates less risk and more stable returns.

Standard deviation and volatility are closely related concepts, especially in the context of finance. Volatility is essentially a practical application of standard deviation, often used to gauge the risk associated with different financial instruments.

See also

Mean
Median
Variance

\n","name":"Standard Deviation","slug":"standard-deviation","shortDescription":"Standard deviation is a statistical measure that quantifies the dispersion or variability of a data set around its mean (average)"},"statistical-arbitrage":{"html":"

Statistical arbitrage is a high-frequency trading trading strategy that uses statistical and quantitative techniques to identify and exploit pricing inefficiencies in financial markets.

The strategy involves taking long and short positions in pairs of securities that are believed to be mispriced relative to each other.

The basic idea behind statistical arbitrage is to identify pairs of securities that are highly correlated in terms of their price movements. If the prices of these securities diverge from their historical correlation, a statistical arbitrageur will take a long position in the underpriced security and a short position in the overpriced security, in the hope of profiting from the convergence of prices.

Statistical arbitrage strategies typically involve a high level of automation, using computer algorithms and mathematical models to analyze large amounts of data and make trades in real time. These algorithms may use a variety of statistical and mathematical techniques, such as regression analysis, machine learning, and time series analysis, to identify and exploit pricing inefficiencies.

One advantage of statistical arbitrage is that it can be used in a variety of market conditions, including both up and down markets. However, the strategy can be complex and may require significant computational resources and expertise to implement effectively. In addition, as with any investment strategy, there is always the risk of losses due to unforeseen market events or unexpected changes in correlations between securities.

See also

\n","name":"Statistical arbitrage","slug":"statistical-arbitrage","shortDescription":"Statistical arbitrage is a high-frequency trading trading strategy that uses statistical and quantitative techniques to identify and exploit pricing inefficiencies in financial markets"},"statistical-modeling":{"html":"

Statistical modeling is the process of using statistical techniques to analyze data and make predictions about future events. It is a form of regression that uses statistical methods to estimate the parameters of a model.

Statistical modeling is a common practice in machine learning and data science. It is used to analyze data and make predictions about future events.

The process involves fitting a statistical model to the data and then using the model to make predictions. The model is typically a mathematical function that describes the relationship between the variables in the dataset. The model is fitted to the data by estimating the parameters of the model using statistical methods.

Statistical modeling can be used to compare different models or to tune the parameters of a model. It can also be used to select the best features for a model or to determine the optimal number of features.

See also

\n","name":"Statistical modeling","slug":"statistical-modeling","shortDescription":"Statistical modeling is the process of using statistical techniques to analyze data and make predictions about future events"},"stop-loss":{"html":"

Stop loss is a risk management tool used by investors and traders to limit their potential losses on a trade.

Stop loss can be implemented by

Having a special order type on order book
Manually, by using market orders

A stop loss order is an instruction to automatically sell a security or other asset when its price falls to a certain level, known as the stop price.

For example, if an investor buys a stock at $50 per share and sets a stop loss order at $45 per share, the stop loss order will be triggered if the stock falls to $45 or below. At that point, the stop loss order will automatically sell the stock, limiting the investor’s potential loss to $5 per share.

Stop loss triggers can be particularly useful in volatile markets or when trading more speculative assets with higher levels of risk. By setting a stop loss order, traders can limit their exposure and losses in the event that the market moves against them, while still allowing for potential gains if the trade is successful.

It’s important to note that stop loss orders are not foolproof and can be subject to market fluctuations and gaps in trading. In addition, stop loss orders can be triggered during periods of high volatility or sharp price movements, resulting in a sale at a price below the stop price. As with any investment strategy, it’s important to carefully consider the risks and benefits of using stop loss orders and to use them in combination with other risk management tools and techniques.

See also

\n","name":"Stop loss","slug":"stop-loss","shortDescription":"Stop loss is a risk management tool used by investors and traders to limit their potential losses on a trade"},"storj":{"html":"

The Storj protocol is a decentralized cloud storage platform that allows users to rent out unused hard drive space for digital file storage.\nIt offers zero-trust security and can be used for large file storage, streaming and backups.\nStorj runs on its own blockchain model, but for the interaction the user does not need to know about\nthe blockchain at all.

Due to its decentralised mode, Storj storage and egress costs are very competitive compared\nto centralised cloud offerings such as from Amazon, Microsoft or Google.

Storj has an ERC-20 token called STORJ.

See also

Decentralised finance
Token

\n","name":"Storj","slug":"storj","shortDescription":"The Storj protocol is a decentralized cloud storage platform that allows users to rent out unused hard drive space for digital file storage"},"strategy":{"html":"

In a decentralised finance, strategy refers to :trading strategy.

See trading strategy.

\n","name":"Strategy","slug":"strategy","shortDescription":"In a decentralised finance, strategy refers to :trading strategy"},"strategy-age":{"html":"

In trading strategy, the age refers to the duration how long the strategy has been doing live\ntrading.

Performance metrics need enough live trading data to be available to be calculated.\nLonger the strategy has been trading, more high quality and refined its data get.

If live trading data is not available, estimations of the strategy performance can be\nmade based on backtests.

See also

Backtest
Trading strategy

\n","name":"Strategy age","slug":"strategy-age","shortDescription":"In trading strategy, the age refers to the duration how long the strategy has been doing live\ntrading"},"strategy-cycle":{"html":"

In Trading Strategy Framework, the strategy cycle refers to the timestamped process of developing, testing, and implementing a trading strategy.

This process typically involves several steps, including research and analysis, design, optimization, backtesting, and live trading. The strategy cycle is an iterative process, and the results of each step can influence the next. The goal of the strategy cycle is to identify and develop a profitable and reliable trading strategy that can be consistently executed. The end result of the strategy cycle is a well-designed, thoroughly tested, and successfully deployed trading strategy that can help achieve investment goals.

See cycle duration.

\n","name":"Strategy cycle","slug":"strategy-cycle","shortDescription":"In Trading Strategy Framework, the strategy cycle refers to the timestamped process of developing, testing, and implementing a trading strategy"},"strategy-developer":{"html":"

A strategy developer is a financial professional who designs and implements investment strategies. They are responsible for analysing market data, identifying trends and opportunities, and creating investment plans that aim to achieve specific financial goals.

Strategy developers use a combination of quantitative and qualitative analysis to make investment decisions, drawing on their expertise in economics, finance, mathematics, and computer science. They also use a variety of tools and technologies, including statistical models, algorithms, and high-frequency trading systems, to support their work.

Strategy developers work in a variety of settings, including hedge funds, asset management firms, and investment banks, and may specialise in a specific asset class, such as stocks, bonds, commodities, or currencies. They are typically highly skilled and experienced professionals who have a deep understanding of financial markets and investment strategies. Effective strategy development requires a combination of technical expertise, market knowledge, and creativity, as well as a sound risk management plan to ensure that investment decisions align with the goals and risk tolerance of the investor.

See also

\n","name":"Strategy developer","slug":"strategy-developer","shortDescription":"A strategy developer is a financial professional who designs and implements investment strategies"},"supervised-learning":{"html":"

Supervised learning is a type of machine learning that involves training a model using labeled data. It is a form of regression that uses labeled data to train a model.

Supervised learning is a common practice in machine learning and data science. It is used to train a model using labeled data.

The process involves fitting a model to the data and then using the model to make predictions. The model is typically a mathematical function that describes the relationship between the variables in the dataset. The model is fitted to the data by estimating the parameters of the model using statistical methods.

Supervised learning can be used to solve classification and regression problems. Classification problems involve predicting a discrete class label, while regression problems involve predicting a continuous quantity.

See also

\n","name":"Supervised learning","slug":"supervised-learning","shortDescription":"Supervised learning is a type of machine learning that involves training a model using labeled data"},"survivorship-bias":{"html":"

In quantitative finance, Survivorship bias refers to the tendency to focus on assets, strategies, or systems that have survived or performed well over a certain period, while ignoring those that have underperformed or ceased to exist. This can lead to an overestimation of the performance or reliability of a trading strategy, as the analysis does not take into account the entire population of assets or strategies.

Survivorship bias can be mitigated by incorporating a more comprehensive dataset that includes both successful and unsuccessful assets or strategies, and by carefully considering the potential impact of survivorship bias on performance evaluations.

See also:

\n","name":"Survivorship bias","slug":"survivorship-bias","shortDescription":"In quantitative finance, Survivorship bias refers to the tendency to focus on assets, strategies, or systems that have survived or performed well over a certain period, while ignoring those that have underperformed or ceased to exist"},"sushi":{"html":"

In decentralised finance, Sushi, also\nknown as SushiSwap, is one of the largest AMM decentralised exchanges.

Sushi started as a fork of Uniswap. The later Sushi versions added support for\nCLMM liquidity models and many other trading features.

Sushi is launched on many blockchains, including Ethereum mainnet, Polygon, Avalanche and\nBNB Chain. View Sushi on Ethereum mainnet.

See also

\n","name":"Sushi","slug":"sushi","shortDescription":"In decentralised finance, Sushi, also\nknown as SushiSwap, is one of the largest AMM decentralised exchanges"},"swap":{"html":"

In AMM-based decentralised exchanges trades called called swaps.

In a swap you you trade against liquidity providers\nwith market order like trades.

The user interacts with a liquidity pool using :their term:non-custodial wallet\nto perform a swap.

Some example swaps:

In a buy swap, you bought cryptocurrency token with a stable coin,\ne.g. you buy AVAX with USDC stablecoin
In a buy sell, you bought cryptocurrency token with a stable coin,\ne.g. you sell AVAXs to gain USDC in your wallet

Swaps are used on AMMs <AMM>.

See also

\n","name":"Swap","slug":"swap","shortDescription":"In AMM-based decentralised exchanges trades called called swaps"},"swap-fee":{"html":"

Swap fee is the part cost of a trade on AMM markets.

In AMM-based decentralised exchanges trades called called swaps.

Swap fee includes

liquidity provider reward, usually 0.05% - 0.30%
protocol fee, usually 0.00% - 0.05%

Swap fees usually exclude

price impact
slippage

See also

\n","name":"Swap fee","slug":"swap-fee","shortDescription":"Swap fee is the part cost of a trade on AMM markets"},"swing-trading":{"html":"

Swing trading is a type of short-term trading strategy that aims to take advantage of intermediate-term price movements, typically holding positions for several days to a few weeks. The goal of swing trading is to identify trends and ride them for a profit, rather than trying to predict the market’s direction in the long-term. Swing traders use technical analysis to identify potential trades, focusing on price patterns, support and resistance levels, and momentum indicators. They typically trade more frequently than long-term investors and hold positions for a shorter period, but with the potential for larger profits or losses. Swing trading can be used in a variety of markets, including stocks, bonds, commodities, and currencies.

\n","name":"Swing trading","slug":"swing-trading","shortDescription":"Swing trading is a type of short-term trading strategy that aims to take advantage of intermediate-term price movements, typically holding positions for several days to a few weeks"},"systematic-trading":{"html":"

Systematic trading is a method of trading financial markets that utilises mathematical models and algorithms to execute trades based on predefined rules and conditions. It aims to remove emotion and subjectivity from the investment process by relying on data-driven decision making.

In systematic trading, trades are executed automatically based on the rules established in the trading system. These rules can be based on technical indicators, market data, or other signals, and are designed to identify and take advantage of market inefficiencies and price discrepancies. The models used in systematic trading are typically back-tested using historical market data to assess their viability and refine their parameters.

Systematic trading is often used in quantitative finance and high-frequency trading, where trades are executed at a high rate and on a large scale. It can be applied to a wide range of financial instruments, including stocks, bonds, futures, options, and currencies.

\n","name":"Systematic trading","slug":"systematic-trading","shortDescription":"Systematic trading is a method of trading financial markets that utilises mathematical models and algorithms to execute trades based on predefined rules and conditions"},"tactical-asset-allocation":{"html":"

In quantitative finance, Tactical Asset Allocation (TAA) is an investment strategy in finance that involves actively adjusting the allocation of assets within a portfolio to take advantage of short-term market opportunities or to manage risk.

It is a dynamic approach that aims to capitalize on market inefficiencies, mispricings, or changing market conditions.

TAA differs from strategic asset allocation, which focuses on establishing a long-term allocation mix based on the investor’s goals, risk tolerance, and investment horizon. In contrast, TAA involves making short-term shifts in asset allocation based on market forecasts, economic indicators, or other relevant factors.

The goal of tactical asset allocation is to enhance portfolio performance by overweighting or underweighting certain asset classes, sectors, or geographic regions at specific times. For example, if an investor believes that the stock market is overvalued and poised for a downturn, they may decrease their equity exposure and increase their allocation to bonds or cash. Conversely, if favorable conditions are anticipated for a particular sector or region, the investor may overweight those assets to potentially achieve higher returns.

TAA strategies can be implemented using various techniques, including fundamental analysis, technical analysis, quantitative models, or a combination of these approaches. It requires active monitoring of market trends, economic indicators, and other relevant factors that can influence asset prices. TAA may be executed through individual security selection, the use of exchange-traded funds (ETFs), or other investment vehicles.

See also

\n","name":"Tactical asset allocation","slug":"tactical-asset-allocation","shortDescription":"In quantitative finance, Tactical Asset Allocation (TAA) is an investment strategy in finance that involves actively adjusting the allocation of assets within a portfolio to take advantage of short-term market opportunities or to manage risk"},"take-profit":{"html":"

The predetermined price level at which you plan to close out an open position for a profit. This is set when you initially establish the position. This is the price at which you want to sell and take your profit. It is important to set a take profit target and stick to it. If you don’t, you may end up holding a position for too long and lose your profit.

This can be different to position exit that is profitable. Profitable trades that have been exited due to trailing stop loss without reaching the Take Profit limit are a different category.

The number of ‘Take Profits’ may be difficult to calculate if the strategy developer has implemented their own method without using built in methods. The same goes for trailing stop loss as well.

See also

Stop-loss
Trailing stop loss

\n","name":"Take Profit","slug":"take-profit","shortDescription":"The predetermined price level at which you plan to close out an open position for a profit"},"technical-analysis":{"html":"

Technical analysis is a trading discipline employed to evaluate investments and identify trading opportunities by analysing statistical trends gathered from trading activity, such as price movement and volume.

More information.

See also

\n","name":"Technical analysis","slug":"technical-analysis","shortDescription":"Technical analysis is a trading discipline employed to evaluate investments and identify trading opportunities by analysing statistical trends gathered from trading activity, such as price movement and volume"},"technical-indicator":{"html":"

A technical indicator, or just an indicator, is a calculated value\nindicating something about the state of the market.\nIndicators are usually based on OHLCV data.\nBy combining several indicators through technical analysis,\none can create automated trading strategies.\nAn example technical indicator is EMA (exponential moving average).

See Technical analysis documentation for Trading Strategy indicator list.

See also

Trading strategy
EMA

\n","name":"Technical indicator","slug":"technical-indicator","shortDescription":"A technical indicator, or just an indicator, is a calculated value\nindicating something about the state of the market"},"test-set":{"html":"

In machine learning, the test set is a subset of data that is used to evaluate the performance of a model. It is a set of data that is not used to train the model.

The test set is typically a random sample of the dataset. It is used to evaluate the performance of the model by measuring its accuracy on the test set.

The test set is used to evaluate the performance of the model, while the training set is used to fit the parameters of the model. The training set is typically a random sample of the dataset that is used to train the model.

See also

\n","name":"Test set","slug":"test-set","shortDescription":"In machine learning, the test set is a subset of data that is used to evaluate the performance of a model"},"time-frame":{"html":"

See bucket.

\n","name":"Time frame","slug":"time-frame","shortDescription":"See bucket"},"token":{"html":"

A token in blockchains and decentralised finance represents an on-chain asset.

Tokens can be

Fungible tokens: assets like crytocurrencies, stablecoins
Non-fungible tokens (NFTS), like tokenised pictures and video game items

ERC-20 is the most popular technical token standard.

See also

\n","name":"Token","slug":"token","shortDescription":"A token in blockchains and decentralised finance represents an on-chain asset"},"token-distribution":{"html":"

In decentralised finance, a token distribution refers to how the project tokens\nhave been divided between different stakeholders.

More decentralised token distribution is usually good for price action and robustness of the project;\nthere are less large entities that can dominate the DAO governance discussion or be a single point of a failure.\nDecentralisation and better token distribution lessen the likelihood of severe price impact events.

Token distribution methods include

Initial coin offering (ICO)
Initial DEX offering (IDO)
Initial token offering (ITO)
Direct market listing
Public sale
Private sale
Simple Agreement for Future Tokens (SAFT)
Airdrop
Liquidity mining
Yield farming
Proof-of-work (Bitcoin, Chia, others)

See also

\n","name":"Token distribution","slug":"token-distribution","shortDescription":"In decentralised finance, a token distribution refers to how the project tokens\nhave been divided between different stakeholders"},"token-tax":{"html":"

A “token” tax is a term often used to describe tokens with transfer fees that cause deflation or redistribute trade profits to the protocol development:

Each time a token is transferred, some transferred amount is burned, redirected to a development fund or otherwise “taxed”.
Token tax is usually paid by the originator wallet that initiates the transfer. The tax is taken from the sent amount during the transfer: initiated transfer amount > received transfer amount.
Token tax may also reduce the token supply, thus creating deflationary tokens. The deflationary assumption comes from the economic theory that by reducing the supply, the value of the goods should go up. The most famous cryptocurrency having such deflationary mechanics is Ethereum and its EIP-1559 burning mechanism.
Token tax can redirect some of the transfer and trading fees to the protocol development fund. This can guarantee sustainable protocol development outside any initial fundraising.

Usually, the token tax term is not used for the native gas token on a blockchain, like Ether (ETH) on Ethereum, where any transfer fee is considered to be a natural part of the core protocol. The token tax term applies to ERC-20-like tokens that historically have lacked transfer fee features. There is no terminology standard, so different terms are applied in different contexts.

Different % amounts of “taxes” may apply to different types of transactions like buy, sell, and treasury management.

Read more about token tax in our introduction blog post.

Taxed tokens are not supported by Uniswap 3. Note that any bridged tokens cannot have transfer fees,\nso if you bridge a taxed token from Ethereum mainnet e.g. to Polygon it will work on Uniswap v3.

See also

swap fee
honey pot

\n","name":"Token tax","slug":"token-tax","shortDescription":"A “token” tax is a term often used to describe tokens with transfer fees that cause deflation or redistribute trade profits to the protocol development:\n\nEach time a token is transferred, some transferred amount is burned, redirected to a development fund or otherwise “taxed”"},"total-equity":{"html":"

In trading strategy, total equity refers to all assets under the management of the strategy minus liabilities.

In decentralised finance, this may also be called total value locked or total assets\ndepending on the use case.

Total equity is usually expressed in US dollars. For a trading strategy, the total equity is

All assets, which may include
\n
\n
- Cash
- Open trading positions
\n
\n
Minus liabilities
\n
\n
- Undercollateralised loans
\n
\n

If a strategy is closed and all assets can be sold without price impact and slippage,\neach investor will get:

investor shares * total equity / total shares

Equity curve shows how total equity has grown over time. Total equity can grow by trading profits\nand new deposits, and decrease by redemptions and trading losses.

See also

\n","name":"Total equity","slug":"total-equity","shortDescription":"In trading strategy, total equity refers to all assets under the management of the strategy minus liabilities"},"total-value-locked":{"html":"

In decentralised finance, total value locked (TVL) refers to the amount of the assets\nlocked in a protocol.

In traditional finance, the term used is Assets under management (AUM). However, because DeFi\nprotocols are non-custodial and assets are always under the management of the users themselves,\nAUM term cannot be used.

See also

\n","name":"Total value locked","slug":"total-value-locked","shortDescription":"In decentralised finance, total value locked (TVL) refers to the amount of the assets\nlocked in a protocol"},"trades-last-week":{"html":"

In Trading Strategy Protocol, trades last week is a metric who many successful trades\na trading strategy did last week.

This metric tells who lively the underlying algorithm has been recently.

See also

Trading Strategy Protocol

\n","name":"Trades last week","slug":"trades-last-week","shortDescription":"In Trading Strategy Protocol, trades last week is a metric who many successful trades\na trading strategy did last week"},"trading-algorithm":{"html":"

Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume. Trading algorithms can be fundamentally driven or based on quantitative signals, and can be created using Python with tools such as Trading Strategy.

\n","name":"Trading algorithm","slug":"trading-algorithm","shortDescription":"Algorithmic trading is a method of executing orders using automated pre-programmed trading instructions accounting for variables such as time, price, and volume"},"trading-pair":{"html":"

In decentralised finance, trading pair represents a market for a two or more tokens.

An example trading pair can be BNB/BUSD trading pair on PancakeSwap <https://tradingstrategy.ai/trading-view/binance/pancakeswap-v2/bnb-busd>__.

Trading pairs are named as a “ticker” based on their base token and quote token symbols.

Sometimes trading pairs are also called pools, because they are liquidity pool mechanism\nbased.

Unlike in traditional finance, in decentralised finance a trading pair can also consists\nof three or more assets. This is especially popular in stablecoin markets.

Trading pairs can be

Order book based
Liquidity pool based

See also

\n","name":"Trading pair","slug":"trading-pair","shortDescription":"In decentralised finance, trading pair represents a market for a two or more tokens"},"trading-signal":{"html":"

A signal that predicts the future price movement.

For the full description, see alpha signal.

\n","name":"Trading signal","slug":"trading-signal","shortDescription":"A signal that predicts the future price movement"},"trading-strategy":{"html":"

A trading strategy is a fixed plan that is designed to achieve a profitable return by going long or short in markets.

It involves analysis to identify specific market conditions and price levels.\nThere are many different types of trading strategies, such as

Moving-average cross strategies like EMA
Trend-following
Market-making (see AMM for decentralised finance)
Arbitrage
Scalping
Portfolio construction model
Market neutral
Yield farming

Further strategies can be classified by their activity and risk profile as

The trading strategy can be automated as algorithmic trading and thus\nbecome automated trading strategy.

Trading Strategy can also refer to Trading Strategy Protocol\nwhich is a solution for creating trading strategies for\ndecentralised finance.

See also

\n","name":"Trading strategy","slug":"trading-strategy","shortDescription":"A trading strategy is a fixed plan that is designed to achieve a profitable return by going long or short in markets"},"trading-strategy-client":{"html":"

Trading Strategy client is a Python library to access the historical market data\non Trading Strategy protocol oracles.

The data includes

Decentralised exchanges market data in candle and\nOHLCV format
Lending protocols

You can then examine the data in Jupyter notebook.

Read the API documentation for tradingstrategy.client.Client
See the project Github README

See also

\n","name":"Trading Strategy client","slug":"trading-strategy-client","shortDescription":"Trading Strategy client is a Python library to access the historical market data\non Trading Strategy protocol oracles"},"trading-strategy-framework":{"html":"

The Trading Strategy Framework is a Python based software development library\nto develop automated trading strategies for decentralised finance\nmarkets. The core audience of the library is quants.

The framework consists of

Market data feed reader in the form of Trading Strategy Client
Backtesting framework
Live trading execution environment
Third-party capital deposits using vaults

See also

\n","name":"Trading Strategy Framework","slug":"trading-strategy-framework","shortDescription":"The Trading Strategy Framework is a Python based software development library\nto develop automated trading strategies for decentralised finance\nmarkets"},"trading-strategy-protocol":{"html":"

Trading Strategy Protocol is an algorithmic trading solutions for decentralised finance\nusing AMMs and lending protocols.

It offers non-custodial way for creating trading strategies.\nQuants can develop and backtest their strategies using Python.

Live trading strategies can be securely executed using vaults.

For introduction information, visit Trading Strategy website.

See also

\n","name":"Trading Strategy Protocol","slug":"trading-strategy-protocol","shortDescription":"Trading Strategy Protocol is an algorithmic trading solutions for decentralised finance\nusing AMMs and lending protocols"},"trading-universe":{"html":"

A trading universe describes all possible assets available for a strategy\nfor its to take different trading positions. The simple\ntrading strategies trade only a single trading pair like ETH/USD. More complex\nstrategies can have trading universe consisting of thousands of trading pairs\nand assets.

\n","name":"Trading universe","slug":"trading-universe","shortDescription":"A trading universe describes all possible assets available for a strategy\nfor its to take different trading positions"},"tradingview":{"html":"

Trading view is the world most popular trading strategy platform. It lets you discover investment ideas and showcase your talents to a large and active community of traders. Easy and intuitive for beginners, and powerful enough for advanced chartists. Trading View has all the charting tools you need to share and view trading ideas. Real-time data and browser-based charts let you do your research from anywhere, since there are no installations or complex setups. Read more.

\n","name":"TradingView","slug":"tradingview","shortDescription":"Trading view is the world most popular trading strategy platform"},"traditional-finance":{"html":"

In decentralised finance, traditional finance or TradFi, refers to the old financial system.

TradFi consists of regulated securities markets, banks, commodities and FX markets.\nTradFi markets are considered to be inefficient compared to decentralised finance,\ndue to excessive amount of middlemen, custodials and lack of automation.

See also

\n","name":"Traditional finance","slug":"traditional-finance","shortDescription":"In decentralised finance, traditional finance or TradFi, refers to the old financial system"},"trailing-stop-loss":{"html":"

A trailing stop loss is a special order type or a trigger parameter,\nwhich will keep updating the position’s stop loss dollar limit as the market price moves.

Trailing stop loss is set on a position when opening it
Trailing stop loss is usually specified as % of the market price
Trailing stop loss dynamically updates the position’s stop loss upwards if the market price moves upwards
If the market price moves downwards, the highest and the latest trailing stop loss mark is triggered
Trailing stop loss allows you to lock in profits in raising markets

See also

Stop loss
Position

For more information see Trailing stop Loss on CMC Markets.

\n","name":"Trailing stop loss","slug":"trailing-stop-loss","shortDescription":"A trailing stop loss is a special order type or a trigger parameter,\nwhich will keep updating the position’s stop loss dollar limit as the market price moves"},"training-set":{"html":"

In machine learning, the training set is a subset of data that is used to train a model. It is a set of data that is used to fit the parameters of a model.

The training set is typically a random sample of the dataset. It is used to train the model by minimizing the loss function, which is a measure of how well the model fits the training data.

The training set is used to fit the parameters of the model, while the test set is used to evaluate the performance of the model. The test set is typically a random sample of the dataset that is not used to train the model.

See also

\n","name":"Training set","slug":"training-set","shortDescription":"In machine learning, the training set is a subset of data that is used to train a model"},"transaction-cost":{"html":"

How much a user pays to the block producers to have their transaction included in a blockchain.

Also known as gas fee. See gas fee for more information.

\n","name":"Transaction cost","slug":"transaction-cost","shortDescription":"How much a user pays to the block producers to have their transaction included in a blockchain"},"trend":{"html":"

A trend is a general direction of change in a set of data or a market over time. In the financial markets, trends refer to the general direction of prices for a specific security, asset class, or market index. Trends can be either up, down, or sideways, and they can occur over various time frames, including short-term (such as minutes or hours), intermediate-term (such as days or weeks), or long-term (such as months or years).

Trends are important in investment decision-making as they provide insights into market behavior and can indicate potential opportunities for buying or selling securities. Traders and investors often use technical analysis to identify and track trends, using tools such as trendlines, moving averages, and momentum indicators.

See also

\n","name":"Trend","slug":"trend","shortDescription":"A trend is a general direction of change in a set of data or a market over time"},"trend-analysis":{"html":"

In quantitative finance, Trend analysis is the study\nof historical market data, such as price and volume, to\nidentify patterns and trends that may help predict future\nmarket movements. This can be a valuable tool for traders and\ninvestors when developing trading strategies or making\ninvestment decisions.

Trend analysis often involves the use of technical indicators,\nsuch as moving averages, trendlines, and support and resistance\nlevels, to analyze and visualize market trends. It can be\napplied to various timeframes, from intraday to long-term,\ndepending on the goals and preferences of the trader or investor.

See also:

\n","name":"Trend analysis","slug":"trend-analysis","shortDescription":"In quantitative finance, Trend analysis is the study\nof historical market data, such as price and volume, to\nidentify patterns and trends that may help predict future\nmarket movements"},"trend-following":{"html":"

Trend-following is a trading strategy that follows a trend.

Trend-following is a trading strategy that seeks to profit from the directional movement of prices in financial markets. The strategy involves analyzing the historical price data of an asset to identify trends and then making trades in the direction of the trend.

Trend-following traders typically use technical analysis tools and technical indicators, such as moving averages, trend lines, and momentum indicators, to identify trends and determine when to enter or exit trades. The goal is to buy an asset when the trend is bullish (i.e., prices are rising) and sell it when the trend is bearish (i.e., prices are falling).

The key principle of trend-following is to let profits run and cut losses quickly. This means that trend-following traders will typically use stop loss orders or other risk management techniques to limit their potential losses if the trend reverses.

Trend-following strategies can be applied to a wide range of financial instruments, including stocks, bonds, currencies, and commodities. The strategy is popular among both individual and institutional traders and has been used successfully by many well-known traders and hedge funds.

One potential disadvantage of trend-following is that it can be subject to false signals and whipsaws, particularly in volatile or choppy markets. In addition, trend-following can be slow to react to sudden market shifts or changes in investor sentiment, which can result in missed opportunities or losses. As with any trading strategy, it’s important to carefully consider the risks and benefits of trend-following and to use it in combination with other tools and techniques to manage risk and optimize performance.

See also

\n","name":"Trend-following","slug":"trend-following","shortDescription":"Trend-following is a trading strategy that follows a trend"},"twap":{"html":"

TWAP or Time-weighted Average Price is a calculation that defines the weighted average price over a specified period.

The real-time price of decentralised exchanges is subject to quite easy manipulation, especially within the\nrange of one block. A manipulator can use flash loans to access large amount of capital and make trades\nthat a normal trader would not do.

These kind of attacks may cause very high/low price candles. Using the TWAP price mitigates\nthe risk of performing e.g. an unnecessary stop loss trigger on a manipulated price.

On the security and compromises of price oracles.

Read Uniswap v3 TWAP oracle manipulation cost.

See also

Trading strategy

\n","name":"TWAP","slug":"twap","shortDescription":"TWAP or Time-weighted Average Price is a calculation that defines the weighted average price over a specified period"},"uniswap":{"html":"

The most popular AMM based decentralised exchange. Uniswap has two major versions.\nIn version 2 (v2) the liquidity is evenly distributed across the bonding curve. In version 3, the\nliquidity providers can have liquidity on a partial curve, simulate order book and have better\ncapital efficiency. Most decentralised exchanges are Uniswap v2 clones.

See also

\n","name":"Uniswap","slug":"uniswap","shortDescription":"The most popular AMM based decentralised exchange"},"unrealised-profit-and-loss":{"html":"

Unrealized Profit and Loss refer to the theoretical gains or losses on an investment or asset that has not yet been sold or closed. These are “paper” profits or losses, meaning they exist only on paper and have not been actualized through a transaction. An “unrealized profit” occurs when the current market value of an asset is higher than its purchase price, while an “unrealized loss” occurs when the current market value is lower than the purchase price. Unlike realized profits and losses, unrealized amounts are not recorded as income or loss for tax purposes until they are realized through a sale or other transaction.

See also

Realised profit and loss

\n","name":"Unrealised profit and loss","slug":"unrealised-profit-and-loss","shortDescription":"Unrealized Profit and Loss refer to the theoretical gains or losses on an investment or asset that has not yet been sold or closed"},"unrealised-risk":{"html":"

Unrealized risk refers to the potential for loss or underperformance in a financial investment or trading position that has not yet been closed or settled. Unlike realized risk, which captures actual losses or gains, unrealized risk represents the possibility of future losses or gains as long as the position remains open. It quantifies the extent to which an asset or portfolio could be impacted by future market fluctuations.

Example: If an investor owns shares of a company that have declined in value but has not yet sold those shares, the decline represents an unrealized risk. Until the investor sells the shares, the potential for loss or gain remains.

See also

Realised risk
Market exposure

\n","name":"Unrealised risk","slug":"unrealised-risk","shortDescription":"Unrealized risk refers to the potential for loss or underperformance in a financial investment or trading position that has not yet been closed or settled"},"unsupervised-learning":{"html":"

Unsupervised learning is a type of machine learning that involves training a model using unlabeled data. It is a form of regression that uses unlabeled data to train a model.

Unsupervised learning is a common practice in machine learning and data science. It is used to train a model using unlabeled data.

Unsupervised learning can be used to solve clustering and dimensionality reduction problems. Clustering problems involve grouping similar data points together, while dimensionality reduction problems involve reducing the number of features in a dataset.

See also

\n","name":"Unsupervised learning","slug":"unsupervised-learning","shortDescription":"Unsupervised learning is a type of machine learning that involves training a model using unlabeled data"},"usdc":{"html":"

USDC (USD Coin) is a stablecoin, meaning it is a cryptocurrency that is pegged to the value of the US Dollar. It is designed to maintain a value of 1 USDC = 1 USD, and its value is backed by US dollars held in reserve. USDC is used for a variety of purposes in the cryptocurrency space, including as a unit of account for trading, for remittances and as a medium of exchange. It operates on the Ethereum blockchain and is a popular choice for traders looking for a stable store of value in the cryptocurrency markets.

See also

\n","name":"Variance","slug":"variance","shortDescription":"Variance is a statistical measure that represents the spread or dispersion of a set of data points around their mean (average)"},"vault":{"html":"

In decentralised finance, a vault refers to a smart contract\nthat manages assets, in non-custodial manner, for several stakeholders.\nUsually when you deposit to a vault you receive share or liquidity provider\ntokens as a return.

See EIP-4626 Tokenised vault standard\nfor more information.

See also

\n","name":"Vault","slug":"vault","shortDescription":"In decentralised finance, a vault refers to a smart contract\nthat manages assets, in non-custodial manner, for several stakeholders"},"visual-studio-code":{"html":"

Visual Studio Code (VS Code) is a source-code editor made by Microsoft for Windows, Linux and macOS. . VS Code is free and optimised for building and debugging modern web and cloud applications. It comes with features such as code editing, debugging, integrated Git control, syntax highlighting, intelligent code completion, snippets, and more. Visual Studio Code\nis excellent for editing Jupyter notebooks.

See also

Python
Jupyter notebook

\n","name":"Visual Studio Code","slug":"visual-studio-code","shortDescription":"Visual Studio Code (VS Code) is a source-code editor made by Microsoft for Windows, Linux and macOS"},"volatility":{"html":"

Volatility refers to the degree of variation or fluctuation in the price of a financial asset over a certain period of time. It is often measured by statistical metrics like standard deviation or historical price changes. Higher volatility indicates a greater potential for rapid price changes, while lower volatility suggests more stable pricing.

Example: If a cryptocurrency experiences rapid price swings within short periods, it is said to have high volatility.

Usage: Volatility is a crucial concept in financial markets, often used by traders and investors to assess the risk and potential returns of an asset. High volatility can offer opportunities for greater returns but also poses higher risks.

Each of these terms offers a different lens through which to analyze market conditions and can be used in combination to make more informed trading or investing decisions.

See also

\n","name":"Volatility","slug":"volatility","shortDescription":"Volatility refers to the degree of variation or fluctuation in the price of a financial asset over a certain period of time"},"volume":{"html":"

In the context of cryptocurrency, trading volume refers to the total number of tokens or coins that have been traded for a specific cryptocurrency pair within a given time frame. It serves as a key indicator of the asset’s liquidity and the level of investor interest. High trading volumes often suggest a more liquid and stable market, while low volumes may indicate less liquidity and greater potential for price manipulation.

Example: If the trading volume for the Bitcoin/USD pair is 50,000 BTC, it means that 50,000 Bitcoins have been traded against the US dollar during the specified time frame.

Usage: Trading volume is a crucial metric for traders, analysts, and investors in the cryptocurrency market. It provides insights into market sentiment, liquidity, and potential volatility. High trading volume is generally seen as a sign of market strength and can be a good indicator for entry or exit points. Conversely, low trading volume may indicate a lack of interest or uncertainty.

The trading volume in the cryptocurrency market is often used alongside other metrics and indicators to form a more comprehensive view of market conditions.

See also

\n","name":"Volume","slug":"volume","shortDescription":"In the context of cryptocurrency, trading volume refers to the total number of tokens or coins that have been traded for a specific cryptocurrency pair within a given time frame"},"wallet":{"html":"

In a blockchain, a wallet refers to an application that offers interaction\nto your non-custodial blockchain account.

Users interact with different web3 and decentralised finance protocols\nusing their wallets.

Popular wallets include

MetaMask (a web browser extension)
Avalanche Core (a web browser extension)
TrustWallet (a mobile application)
Ledger (a hardware wallet)

Different services and wallets may interoperate using WalletConnect protocol.

See also

\n","name":"Wallet","slug":"wallet","shortDescription":"In a blockchain, a wallet refers to an application that offers interaction\nto your non-custodial blockchain account"},"walletconnect":{"html":"

In Web3, WalletConnect is a protocol to allow users to use various kind of wallets with different services.

WalletConnect is an abstraction layer that supports connecting different wallet to\na web3 and decentralised finance protocols. It supports both non-custodial\nand custodial wallets.

Wallet types may include

Mobile app based wallets
Desktop app based wallets
Website based wallets
Hardware wallets
Multisignature wallets
Professional custodial wallets like MetaMask Institutional, Fireblock, Q-redo

WalletConnect is supported across multiple blockchains, including e.g. EVM-Compatible ones,\nNEAR, Chia and Solana.

See also

\n","name":"WalletConnect","slug":"walletconnect","shortDescription":"In Web3, WalletConnect is a protocol to allow users to use various kind of wallets with different services"},"web3":{"html":"

Web3 is a social and technical movement to decentralised social media and online communities.

Web 2.0 led to the concentration of power to few Silicon Valley based social media conglomerates,\nlike Facebook and Twitter. Many creators, users and governments are dissatisfied these companies,\nas they see them executing profit sharing and censorship powers unfairly.

Web3 movement aims to address this issue with user owned content.\nThe content is usually digital content and can be pictures, posts, social media profiles or video game assets.

The content is not siloed,\ntruly owned by the user, and can be transferred across different services and protocols.\nThe profit share is made more fair the users, not the corporation owners, have more negotiation power.\nThis is achieved using open source, blockchains,\nopen protocols, public key cryptography and token based governance.

Web3 protocols are funded using decentralised finance instead of private venture capital.\nWeb3 protocols aim to fair token distribution so that users would own the largest\nshare of protocol control and revenues.

Popular Web3 protocols and projects include e.g.

Lens protocol (social media)
Farcast (social media)
Paradise Tycoon (mobile game with NFTs)
Phaver (Reddit for Web3)
Sandbox (metaverse game)

Web3 has also other meanings

Historically, the technical term web3 referred to the three original decentralised technologies that were visioned when Ethereum launched:\nEthereum, shh, and bzz.
Web3 foundation is the name of the Swiss non-profit that launched Polkadot blockchain.

See also

\n","name":"Web3","slug":"web3","shortDescription":"Web3 is a social and technical movement to decentralised social media and online communities"},"weight-allocation":{"html":"

In portfolio construction, the weight allocation determines how many percents an individual asset position\nwill make out of the total portfolio.

One of the simplest weight allocation method is “1/Nth” allocation: the asset with the strongest alpha signal\nreceives 1/1th multiplier, the second asset 1/2th. The weightings are then normalised so that\nall assets make total of 100%.

Read about 1/Nth weight allocation <https://www.r-bloggers.com/2013/06/the-fallacy-of-1n-and-static-weight-allocation>.

See also

\n","name":"Weight allocation","slug":"weight-allocation","shortDescription":"In portfolio construction, the weight allocation determines how many percents an individual asset position\nwill make out of the total portfolio"},"xy-liquidity-model":{"html":"

XY liquidity model, as known as XYK, is a bonding curve model where the price of an asset follows the equation:

\$x*y=k_{market\\_maker}\$

This model was popularised by Uniswap version 2 decentralised exchange.\nAnyone can buy or sell coins by essentially shifting the market maker’s, also known as a liquidity provider, position on the x*y=k curve.

On Trading Strategy, the available liquidity is usually expressed as the US dollar amount of one side of the pair. For example adding 100 BNB + 5000 USD to the liquidity\nis presented as 5000 USD available liquidity.

See also

\n","name":"Yield farming","slug":"yield-farming","shortDescription":"Yield farming is a passive trading strategy in decentralised finance"},"zstd":{"html":"

The Zstd compression is a modern compression algorithm developed by Facebook. It tries\nto strike a good balance with speed/compression ratio for modern multithreaded\nCPUs.

zstd is also the command line utility that can be used to compress/decompress\nfiles in a terminal. It can be combined with tar to create archives\nof directories, like blockchain snaphots.

\n","name":"zstd","slug":"zstd","shortDescription":"The Zstd compression is a modern compression algorithm developed by Facebook"}}

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