EOS Price in INR (EOS to INR)

EOS Price in India

EOS is an original AngelShares-style ICO model that attempts to address some of the issues that have recently been raised by the community: the overpowering power of big investors versus the small ones, and the ramping up of the blockchain of Ethereum, due to these fundraisers.

EOS price in September 2017

EOS started from 82.86 INR on 1st September 2017 and fell down to settle towards 47.05 INR on 30th September 2017.

EOS price in October 2017

EOS started from 46.08 INR on 1st October 2017 and ascended up towards 50.06 INR on 31st October 2017.

EOS price in November 2017

And at 67.26 INR on 1st November 2017, EOS finally reached 184.70 INR on 30th November 2017 .

EOS price in December 2017

Initially started off at 208.57 INR on 1st December 2017 and moved up high to 572.83 INR on 31st December 2017 ending the year with a spectacular performance in the cryptocurrency markets.

EOS price in January 2018

EOS was INR 490.11 on 1 January 2018 and spiked up to INR 725.26 on 31 January 2018.

EOS price in February 2018

EOS was INR 774.88 on 1 February 2018 and dipped down to INR 499.38 on 26 February 2018.

About EOS

Like Ethereum, EOS is a new blockchain architecture for the development of decentralized applications. It is based on the DPoS (proof of delegated stake), whose inventor is none other than Daniel Larimer, BlockOne CTO and leader of this new project. Dan Larimer is also the creator of BitShares and Steemit. The phenomenal craze surrounding this new venture, beyond his ambition (to compete with Ethereum), comes from the public's trust in this developer, considered one of the brightest brains in the blockchain ecosystem.

More Features about EOS

The EOS project aims to solve the problem of scalability of blockchains and their maintenance costs before Ethereum. The objectives are:

• Blockchain's ability to support millions of users simultaneously,
• Free use,
• Easy forks and flexibility against bugs,
• Low latency,
• Ability of the blockchain to optimize the computational load in order to sequence and parallelize tasks.