Melonport and Streamr: Decentralized trading by robots?

At EDCON Paris 2017, we met Mona El Isa and were struck by the ingenuity of the Melonport vision: a platform for asset management on the blockchain which allows anyone to set up well-designed, trustworthy funds and portfolios of digital investment instruments. Quite revolutionary!

Saying so is by no means a simple platitude. As mentioned in a previous blog post, we Streamr principals are all finance professionals from the “old world”: One algorithmic trader, one trading platform architect, and one with a distinguished background in institutional and quantitative asset management.

Armed with a certain understanding of the terrain, we could immediately see the usefulness of the Melon project, and also how Streamr could complement it. In this blog post, we outline some early ideas that spring to mind.

Data, data, data…

As described in our whitepaper draft, Streamr’s DATAcoin vision is one of a decentralized data backbone for Ðapps. The Streamr stack includes a data transport layer based on a peer-to-peer network, a data market above, and a processing engine and usability layer as the cherry on top. Melon funds will thrive on high-quality, professional financial data, which we have precisely the means to provide. In concrete terms, this is about building datafeed modules as per the Melon protocol, something we know how to do and are very much excited about.

So yes, Streamr can act as a data source for Melon, but Melon can equally well act as a data source for Streamr. An important part of the Streamr roadmap is the marketplace for real-time data, and lots of highly relevant data will be generated by live asset management processes in Melon. Such data includes portfolio holdings, valuations, transactions, and the like. Streamr can be the conduit whereby such data are easily made available to relevant and authorized recipients.

Algorithmic trading

Quantitative investing based on computerized trading algorithms is an increasingly popular and successful way to put money to work. Hedge funds such as Citadel, Millennium, AQR, Renaissance Technologies, D.E. Shaw, Two Sigma, AHL, Winton, and many others have a long and enviable track record. And quantitative investing is by no means limited to CTAs and hedge funds. Many of the world’s largest asset managers and financial institutions offer quantitative funds and investment products.

Systematic strategies can be used for many purposes, ranging from long-term, rule-based investing to medium or high-frequency strategies which aim to exploit potential inefficiencies or which take advantage of various risk premia. Quantitative investment is a fascinating field with lots of clever people working on great ideas. Many of the strategies and certainly much of the actual code is, of course, proprietary. But if you want to get a flavour of what works, you could do much worse than start with Antti Ilmanen’s highly rated and very readable treatise on Expected Returns.

This is also a space where innovation is making waves. Disruptive platforms such as Quantopian and QuantConnect allow anyone to try their hand at quantitative investing. Numerai and Quantiacs offer outlets for data scientists who want to apply their skills to real-life investment decisions. For new entrants to the world of computer-driven investing, barriers are lower than ever before.

There is of course no reason to assume that investment decisions will always be done by human beings. As it is, many quantitative strategies have been automated and computerized for more than two decades by now. This is often a necessity, given that human traders are simply too slow to find and act upon many fleeting opportunities.

Computerized trading systems — or robot traders for want of a better word — have a somewhat sinister reputation by association with things like dark pools and flash crashes. But there is no reason why robot traders — or swarms of decentralized robot  traders — cannot make long-term investment decisions. And there is a very real possibility that our dedicated trading robots with clever software will make much better decisions than we humans with our all-too-emotional wetware.

All change, please

If you have the investment ideas and the market knowledge, it is easy enough to get started. You need some amount of capital, the ability to code in Python, Matlab or the like, an account with a financial broker, and a platform where you can try and out and simulate your ideas, and even build your very own trading robot. But what about the next step? Even if you can accumulate a respectable live track record, how do you scale up and set up a professional trading operation?

The unfortunate reality is that setting up a fund used to be costly and time-consuming. Typically more than half the cost of running an asset management operation is spent on support functions, including fund administration, portfolio valuation, risk management, performance and management fee calculations, compliance monitoring, and reporting.

Unless you already started rich, you would need to raise tens of millions of outside capital to cover the costs of institutional money management. Enter Melon, and things are suddenly different. The time, effort, and cost can be cut by orders of magnitude. For aspiring money managers, this is exciting indeed!

Automating the asset management process

Where does all this lead to? Would it not be great if any asset manager could easily and reliably automate their investment process? We believe that Melon provides the right elements for making such a vision a reality.

One highly useful property of the Melon protocol is its modular design. This makes it amenable for use through a web portal with an advanced user interface. Imagine building a new fund in a drag-and-drop fashion: Pick the relevant valuation and risk management modules from professional and reputable third parties, choose data modules for financial market data and other inputs, adjust parameters, run backtests and simulations, build the whole process visually in a low-code front-end.

There is, of course, one such web portal already, i.e. the Melon portal. Our good friends at Melonport tell us that the portal is open and free for anyone for customization and white labelling. The Melon portal is a great starting point, and you can add any kind of functionality on top: An off-chain processing engine, integrations to data providers, know-your-customer (KYC) processes, backtesting capabilities, algorithmic trading tools — the possibilities are endless. And as it happens, Streamr is very well placed to provide such additional functionality, given our existing tools, the technology stack, and the skills.

There are huge potential gains here in terms of lower cost, lower barriers to entry, higher efficiency, transparency, reliability, and trust. Melonport is embarking on an exciting journey, we wish them the best of luck, and will contribute to their effort where we can.

In a future blog post, we will go into specifics and implement a Streamr DataFeed module using the Melon protocol. Who knows, we might even get carried away and show how to create an automated robot trading strategy for systematically investing in popular cryptocurrencies. Watch this space, and in the meanwhile, Aux Barricades Citoyens!

Posted by / June 30, 2017

Ethereum-based peer-to-peer package delivery with PassLfix and Streamr

We very much like working with like-minded folks in the blockchain space. An opportunity for one interesting project arose earlier this year when we met Frederic Vedrunes of PassLfix at EDCON 2017. When we met, Frederic outlined the idea of a decentralized, data-driven app where Ethereum smart contracts are used to create a security layer for peer-to-peer delivery services. There’s also a significant data angle to the project, given that parcel deliveries would be monitored by IoT sensors. We were all ears, given that such ideas link nicely with our own vision of a decentralized data backbone as described in Streamr whitepaper.

To make a long story short, Frederic asked for our help in getting out a package delivery Ðapp prototype. We were happy to oblige, and the video below shows the whole thing in action from pickup all the way to delivery. The video was filmed and produced on a shoestring budget, but it’s probably all the better for it; it will bring a smile on your face.

The idea behind peer-to-peer delivery is simple: Where there’s people who’ll be travelling anyway from A to B, there’s folks who wouldn’t mind some extra income for taking a package along the ride. There’s already companies such as Dolly, Roady and Grabr out there with tens of thousands of users. Much of the activity has been in the U.S., but similar services are emerging in Europe (Nimber and Colis-Voiturage), and even international peer-to-peer delivery is on its way (PiggyBee). People clearly like interacting with people, and that’s one of the great aspects of peer-to-peer delivery networks.

The concept is catching on. But it is not like P2P delivery networks yet pose a huge threat which keeps the CEO of UPS or Fedex awake and sweating through the night. One reason why the big boys still sleep well is that they’ve earned trust. If you go with a P2P startup, you’ll hand over that sought-after Rihanna ticket to a person you’ve never met before, and expect him or her to hand it over to your favourite niece in Philly in time for Saturday’s concert.

In the case of UPS or Fedex, you trust the courier because they are employed by a big, reputable company. In the case of newly set up P2P services, several trust-building ways have been proposed, including reputation systems, designated points of pick-up and delivery, and parcel insurance. But we all know that one of the great advantages of the blockchain is the built-in trust. Can we leverage the chain and Ðapps to come up with a better delivery service?

In our mind the answer is a definite “yes”, and the paradigm becomes one of “trust but verify”. You offload the paperwork to smart contracts, and cryptographically sign all the face-to-face transactions in the blockchain. The rewards are automatic: The courier gets back the safety deposit on delivery, and receives the agreed fee in their wallets as soon as the package is delivered to the recipient. And you can complement all of this by a community, a DAO which handles the inevitable but hopefully rare disputes. All in all, this is a great example of a decentralized app with a significant real-life use case.

Apart from a clever use of smart contracts, this project makes a definite inroad to the IoT world. The sender can add one or more connected devices (sensors) in the package. The sensors (see below how they look like) will monitor conditions such as temperature, humidity, and acceleration while in transit. The idea is that the smart contract — as well as the sender and the recipient— will immediately know if the parameters stray beyond what was agreed, and appropriate compensation can then be meted out by the contract.

To make all this work, the prototype makes use of the Streamr platform as an off-chain processing engine. With the IoT data streaming in from the sensors, there’s simply way too much data for the blockchain and smart contracts to handle. In the prototype, telemetry data (such as temperature, GPS, speed and elevation) from each parcel in transit transmitted via a Bluetooth connection to the courier’s smartphone. On the smartphone, there’s an app that leverages the API of Ethereum Android to interact with the blockchain. The mobile app also sends sensor data to Streamr platform where visualizations (charts, tables, and maps) are automatically created. The visualizations can viewed by the sender, courier, and the recipient. Any contract breaches (e.g., parcel crossing a temperature limit) are automatically detected and reported to the appropriate Ethereum smart contracts.

As we see it, the prototype is an archetypal example of a data-driven Ðapp. There is a decentralized backend implemented as smart contracts which handle the delivery process. The prototype web3 frontend (see below) handles parcel and delivery setup, and the tracking view shows the real-time location of the parcel along with related sensor metrics. As such, we hope that the prototype serves as inspiration to the coders and developers out there working on the next big decentralized thing. May the Force be with you!

Questions and comments about this post and Streamr in general are appreciated! Join us on Slack, and of course feel free to follow us on Twitter as well.

 

Posted by / June 8, 2017