At Token Data, we will use Big Data to collect and produce datasets pertaining to the blockchain economy which can be useful to our users. We began by electing to create a Data Lake by gathering data according to the categories developed below (core, trade & alternative).
To achieve the best performance, the data chain value must be fed with high quality data. These data, if robust and deep, have a significant impact on the effectiveness of investments. Several factors are at the root of a misuse of data:
- high quality data is difficult to acquire, comes from different sources and is not aggregated
- the delivery frequency of these data varies from one source to another, which implies significant processing costs of sending data to models when they need it
- Data is provided in a variety of formats, making formal transparency mandatory for efficient organization
- Data storage can complicate their reading and readiness to use
Working with a trusted partner to acquire high quality data and then conscientiously organizing and manipulating it is essential for businesses today if they want to improve their efficiency.
Our Data Lake is continuously improved by the creation of alpha sources, the most valuable source of information in today’s data driven world. With the power of Machine Learning algorithms, we’re able to create new indicators to be used in decision models, in order for our users to get unfair advantages toward their concurrents.
Examples: scam index that determines if an ICO project is going to be a fraud, risk index to evaluates the speculative risk associated with a token...
Essential financial and economic data, from the PNL to the team conducting the project. We get those data by vectorizing the White Papers in order to transform them into exploitable measures. The datasets into this category help analyse the company on its fundamentals and are essential to understand what are its taking and ending. Core data are the principal source of information when deciding if a project is viable and if it will reach its goals. We daily collect more than 2M data to enrich our Data Lake.
Examples: middle term sales objectives, localisation of the company, sector of activity, number of resources in the tech department...
Our Tradebook offers a way to actively manage complex trading strategies in more than 40 global crypto exchanges. It consists of both historical data and on-going ones, used for a wide range of applications such as training and backtesting automated trading systems and strategies, getting investment insights... We’ll soon provide users with a single platform that uses quantitative data models and analytic-driven selection to route orders to a suite of destinations, empowering traders to optimize execution.
Examples: BTC/ETH prices on 5 mins time range over the 10 biggest exchanges, Simple Floating Averages at 10, 30 and 50 days, Commodity Channel Index...