Introduction
Equeum is a data analytics platform that empowers users to uncover valuable insights from a vast array of on-chain and off-chain data and metrics. Crypto investors and researchers depend on high-quality data to gain a deeper understanding of the market. Equeum provides a comprehensive toolset to do so. The platform, language, and tools work together seamlessly to make it easier for users to obtain valuable insights, such as market sentiment, price trends, market tops and bottoms, and network and project health.
At the core of the Equeum platform is EQL, a domain-specific language (DSL) specifically designed to analyze financial time series data. EQL's ready-to-use functions and models assist users in making sense of complex, crypto-related metrics.
DSLs, like EQL, have several advantages over general-purpose programming languages like Python. First, they are tailored to a specific domain, making them more intuitive and user-friendly for non-programmers.
Second, unlike general-purpose programming languages, EQL is specifically designed for use with time series data. The built-in functions and data structures of DSLs make them faster and more efficient to use.
Third, they can be used across different systems and platforms relevant to the domain, making them more versatile and accessible to a wider audience.
Finally, DSLs use simpler syntax and focus on a specific domain, making them more readable, especially for people with limited programming experience.
EQL enables users to run essential functions, visualize results, and create and backtest models all through a user-friendly interface. It provides easy-to-use functions that capture the power of advanced analytics, eliminating complexity so that users can extract value from them, both individually and in aggregate.
With a single line of EQL, users can perform a sentiment analysis on social media data or to create a volatility index for a particular cryptocurrency. EQL also allows users to easily integrate metrics across categories, providing actionable insights and making data analysis more efficient
Equeum's extensive resource library includes algebraic, statistical, econometric, technical, and machine learning functions. The resource library contains an ever-growing collection of pre-built models, making it simple for users to incorporate advanced analytics and metrics into their analysis.
For example, a user can create a model that calculates market volatility using a specified lookback length, then export it as "
market_volatility_model". Another user can pull that model into their analysis with a statement like: market_volatility = equeum:market_volatility_model(length)Ensemble learning is a powerful technique in machine learning that combines the predictions of multiple models to improve the overall performance when trying to forecast another time series, whether it be price trend, volatility, or other targets.
The concept behind ensemble learning is that by combining the predictions of multiple models, we can achieve a more robust and accurate prediction than what would be possible with any single model alone.
Equeum's dynamic ensemble is a superior implementation of ensemble learning. It utilizes a combination of models trained on different subsets of the data, which are then combined in real-time to produce a prediction. This approach allows Equeum to leverage multiple models' strengths while minimizing any one model's weaknesses.
One of the key benefits of Equeum's dynamic ensemble is its ability to adapt to changes in the data. For example, if there is a sudden shift in the market, Equeum's dynamic ensemble can quickly adjust the models it uses to reflect this change, enabling it to continue producing accurate predictions even in rapidly changing conditions.
Another advantage of Equeum's dynamic ensemble is its ability to utilize a wider variety of models, resulting in more robust predictions that are less prone to errors. Additionally, the dynamic ensemble can be used to create models that are specifically tailored to different subsets of the data, allowing Equeum to produce more accurate predictions for specific subgroups.
In summary, Equeum's dynamic ensemble is an advanced version of ensemble learning that allows the platform to adapt to changes in the data, use a wider range of models, and create models specific to different subsets of the data. This results in more robust, accurate and less error-prone predictions for specific subgroups of the data.
Equeum tools enable users to capture the full potential of metrics to provide insights and answer questions. Lets look at some of these metrics in terms of specific interests.
1. What are the metrics that help users forecast price trend direction?
- Technical indicators: These include indicators such as moving averages, relative strength index (RSI), and the stochastic oscillator, which are used to identify patterns and trends in historical price data.
- Volume analysis: This involves looking at the trading volume of a particular asset to gauge the level of interest and buying or selling pressure in the market.
- On-chain metrics: These include network hashrate, transaction count, and miner activity, which can provide insight into the underlying strength and health of a particular blockchain.
- Social sentiment: This refers to the general sentiment and opinions of market participants, as measured by social media mentions, news coverage, and other forms of online activity.
- Market news: Keeping track of market news and understanding how it can impact prices.
- Exchange flows: This measures the net flow of funds entering or leaving a particular exchange, indicating buying or selling pressure.
- Moving averages and trendlines: This helps to identify the general direction of the market and can be used to identify support and resistance levels.
- Volume Profile: This measures the amount of trading activity at a certain price level, and can indicate buying or selling pressure.
- Order book data: This provides insight into the supply and demand at different price levels, which can indicate buying or selling pressure.
2. What are the metrics that can help gauge trend strength
- Moving averages: A moving average is a trend-following indicator that calculates the average price of an asset over a certain period of time. There are various types of moving averages (e.g. simple, exponential, weighted), and they can be used to identify trends, measure trend strength, and generate buy or sell signals.
- Relative Strength Index (RSI): RSI is a momentum indicator that compares the magnitude of recent gains to recent losses in order to determine overbought or oversold conditions. RSI can also be used to identify trends and measure trend strength.
- Bollinger Bands: Bollinger Bands are a volatility indicator that consists of a moving average and two standard deviation lines. The distance between the bands can be used to measure volatility and identify potential trend changes. Bollinger Bands can also be used to measure trend strength.
- Trading volume: Trading volume is the number of shares or contracts traded during a specific period of time. It can give insight into the interest level of an asset and whether there is buying or selling pressure in the market. Higher trading volume can indicate a stronger trend.
- On-chain metrics such as network hashrate, transaction count, and miner activity can provide insight into the underlying strength and health of a particular blockchain. This can also give an idea of how much a project is being used and how much it is gaining traction.
- Social sentiment: Social sentiment is the general sentiment and opinions of market participants, as measured by social media mentions, news coverage, and other forms of online activity. This can give insight in the level of interest and how market participants view the project
3. What are the metrics that help gauge the health of a network or project?
- Network hashrate: This metric measures the total computing power being used to secure the network, and can be used as an indicator of the network's security and decentralization.
- Active addresses: This metric measures the number of unique addresses that have been active on the network in a given time period, and can be used as an indicator of the network's usage and adoption.
- Daily transaction volume: This metric measures the total number of transactions that have been processed by the network in a given time period, and can be used as an indicator of the network's usage and popularity.
- Difficulty: This metric measures the level of difficulty in finding the next block in a blockchain network and can be used as an indicator of the network's security and the amount of miners’ interest in the e network.
- Block time: This metric measures the time it takes for a new block to be added to the blockchain, and can be used as an indicator of the network's speed and efficiency.
- Gas fees: This metric measures the cost of conducting transactions on the network and can be used as an indicator of the network's demand and scalability.
- Development activity: This metric measures the level of development activity on the network, and can be used as an indicator of the network's growth and innovation.
- community engagement: This metric measures the level of engagement of community members with the project, and can be used as an indicator of the network's adoption, popularity, and awareness.
- Network upgrades: This metric measures the number of upgrades and updates made to the network and can be used as an indicator of the network's innovation and development.
- Network scalability: This metric measures the ability of a network to handle an increasing amount of transactions and can be used as an indicator of the network's scalability and capacity to handle more users.
4. What metrics help users gauge risks?
- Volatility data: Measures the fluctuation of prices over a specific period of time, providing an indication of how much the market is prone to change.
- Open interest measures: Provide information on the number of outstanding contracts or positions in a market, indicating the level of market participation and liquidity.
- Sentiment trends: Track the overall sentiment of market participants, providing an indication of the level of bullish or bearish sentiment in the market.
- Mining metrics: Provide information on the mining activity and network hashrate, indicating the level of decentralization and security of the network.
- Gas fees: Provide information on the cost of conducting transactions on the blockchain, indicating the level of demand for the network.
- Unspent transaction outputs: Provide information on the distribution of cryptocurrency holdings among addresses, indicating the level of concentration of wealth in the market.
- Volume analytics and abnormal trading activity: Provide information on the trading volume and patterns of market participants, indicating the level of manipulation or abnormal activity in the market.
- Whale activity and large transactions: Provide information on the activity of large market participants, indicating the level of influence on the market.
- Transactions per second and velocity of circulation: Provide information on the rate of transactions and the speed at which cryptocurrency is circulating in the market, indicating the level of demand and adoption.
- Volatility and risk analysis: Provide a comprehensive analysis of the level of risk in the market, taking into account various factors such as volatility, trading volume, and market sentiment.
Last modified 29d ago