The Hush-Hush Finazon Master Plan

Background: My everyday job is shaping Finazon’s business and product strategy. Before founding Finazon, I engineered mathematical models and transformed them into trading strategies at a quantitative finance fund.

08 December 2022

Our goal at Finazon goes beyond building a financial data marketplace. In fact, we want to create an all finance ecosystem that will include analytical tools, a trading API, access to DeFi, and more. I believe that no one wants to waste time and focus on reinventing the wheel every time they build new financial solutions. The strenuous process of searching, integrating, and verifying data quality should never distract from core product development. Instead, there must be a single solution, as this will help build better products quicker and drive innovation.

Step one towards building an ecosystem is to set up a next generation financial data marketplace. To achieve this, we’re working on creating a platform that features great data diversity, has numerous tools to access said data, and is affordable. But does the world really need yet another data provider? Will it indeed help the FinTech industry become more innovative and competitive?

Yes, and yes. Currently, users that seek financial data face the dilemma of either working with a well-established data provider through a bureaucratic process of paying premium prices to confidently access reliable yet partial data, or going with a smaller firm, a smaller fee, and lower stability and legality of data.

Below are the main challenges that we face:

Data diversity

To truly give value to users, Finazon should have an extensive collection of data that includes the most popular datasets and very specific ones. Therefor, we’re actively working on introducing more financial datasets across equities, forex, crypto, funds, indices, macroeconomics, and a multitude of other categories. These data can be added in two ways:

The first option is to add datasets from reliable public data sources, for instance from the SEC or corporate reports. This is done by algorithmically collecting data, standardizing it, and cleaning it in-house. The adaptation process is time-consuming and requires extensive and specialized expertise. However, this approach is the most effective since it allows us to control the quality and costs of all data offered. As Finazon grows, we will shift more towards creating proprietary datasets.

The second option is to add the many wonderful datasets owned or collected by other vendors, including exchanges and financial aggregators. This process is usually much faster to implement. But it is severely limited by vendor redistribution options and high purchasing costs for end users. For instance, National Stock Exchange of India (NSE) does not allow data to be redistributed by third parties. Moreover, the lowest price for real-time data hovers at $20,000 per annum, draining unnecessary resources for any startup to access. At Finazon, we’re in constant contact negotiating terms and conditions with worldwide vendors; however, we will have more leverage when our user base is significant enough for NSE and others to change its policy. Thus, by subscribing to any of our datasets, you’re actually making the world of data more user-friendly and contemporary.

Ideally, there should be a few alternative datasets from various providers for each type of data. This should create more direct and transparent competition, drive prices to the equilibrium, and thus benefit the users.


Our pricing philosophy is based on which data is used and how intensively it is used. We do not impose limits on if data is used for personal or commercial usage. Actually, we welcome redistributing our data as we believe this will make data more accessible for a larger audience.

Here is the formula we use to price datasets:
p = 0.5 * i + 0.3 * c + 0.25 * a + 0.05 * m - s
i - cost of usage of servers
c - cost of obtaining the data
a - cost of added features
m - cost of miscellaneous factors
s - direct subsidy from Finazon

The critical factor in determining the price of a dataset is the intensity of server usage (i). For instance, consumption will be low for a simple portfolio-tracking application and high for a major investment app. We also consider the cost of data sourcing (c). The transition from purchased to proprietary datasets will be critical to adjusting this variable in the long term. The added features parameter (a) represents the cost of “customizing” a dataset to match your needs, such as adding historical data or Level 2 prices. The miscellaneous factor (m) includes anything that might affect cost, such as unforeseen events, ESG responsibility or other factors. Finazon provides a direct subsidy (s) for select datasets to make costs reasonable, allowing more people to use them. This factor also realizes our commitment to providing affordable data for educational institutions and students.

Once economies of scale kick in, variable costs will start to nosedive for all datasets. Generally, we strive for user-friendly pricing with low-profit margins so that more customers, from garage startups to Fortune 500 companies, can use the data.


Having convenient ways of accessing data is essential to our users’ business and success. For developers, this means the development cycle can be significantly reduced. For business users, it opens up the opportunity to access the world’s data from one familiar program, no coding skills required. To stay in touch and listen to the pulse of our users, we have made our roadmap publicly accessible and allow everyone to vote for what future add-ons, SDKs, and packages we should prioritize.

Make no mistake: There are hundreds of libraries and tools that will need to be covered. Although our team wants to develop all of them ASAP, we realize this is practically impossible. That’s why we encourage third-party developers to contribute open-source software. Once their package meets Finazon’s quality standards, we promote it and provide cool perks to the developers!

In a nutshell

Add and sell the most popular datasets.
Use the generated profit to create more proprietary datasets. Leverage large user base to add new datasets from intractable external providers. Along the way, create many packages and tools to facilitate easy access to data.


08 December 2022

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