Gnosis

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Gnosis conditional token framework (CTF): tokenizing potential outcomes in prediction markets

Imagine a scenario where price forecasts are made are forecast accurately, predictions about property markets and rental rates are predicted accurately, and the news is fact-checked thoroughly, making fake news a terrible memory. This scenario could seem too good to be true, but it could be something that could become a reality, thanks to the emergence of Decentralized Prediction Markets (DPMs). So What Is A Prediction Market? To understand what a prediction market is, we need to look at the definition of a market. A market is simply a group of people that buy or sell things. These can be physical things like a grocery store or a wholesale market, financial assets like banks and stock markets, or cab aggregator services like Uber. Similarly, a prediction market is a marketplace to buy and sell predictions. A prediction market operates like a stock market. Just like how you get shares in a stock market, you get shares in the outcome of an event. This could be any event, the weather, financial prediction, or predictions about the price of utilities increasing or decreasing. Understanding How A Prediction Market Works A prediction market has two types of shares, YES shares, and NO shares. YES shares are long shares, while NO shares are short shares. The payout depends on if the event in question occurs or doesn't occur. For example, a YES share will pay out a Dollar if the event occurs, and if it doesn't, then it won't pay anything. The same applies to a NO share. If the event in question does not occur, then the NO share will pay out a Dollar, and if the event occurs, then it won't payout. The share price depends on how much buyers pay and how much the sellers are willing to accept. The price in a prediction market is equal to the probability of the event occurring. If a YES share costs 60 cents, then the market believes a 60% chance of the event or outcome occurs. Similarly, if the NO share costs 60 cents, then the market thinks a 60% chance of the outcome is not happening. What Are Some Existing Prediction Markets? Prediction markets have proven to be a useful prognosis tool as they can represent various opinions. Some examples of prediction markets are Intrade, BetFair, and Iowa Electric Market. The Iowa Electronic Market is one of the pioneers of prediction markets. It was established in 1988 and was used to predict who would win the presidential elections. Augur is another example of a prediction market. It is a decentralized prediction market based on the Ethereum blockchain. Rise Of Prediction Markets Blockchain technology has made ownerless, peer-to-peer prediction markets possible. Ethereum, in particular, has enabled prediction markets to realize their full potential through the power of smart contracts. Smart contracts are lines of code that are executed automatically when certain predetermined conditions are met. Decentralized prediction markets are smart contracts that stipulate who gets paid how much when certain predefined conditions are met. Decentralized prediction markets are dApps that substitute centralized control with code and cryptography. Decentralized prediction markets are in a very nascent stage of their evolution. They have the potential to revolutionize trading and investing. What Are The Problems Faced By Prediction Markets? Prediction markets let traders and individuals leverage their knowledge to forecast outcomes for specific events or real-world scenarios. The currency generation of prediction markets suffers from some fundamental problems. There is an apparent lack of markets and a lack of liquidity in prediction markets. There is also a lack of traders and the presence of duplicate markets. They also face legal issues and a lack of decentralization, severely limiting their user base resulting in a drop in prediction quality due to a less diverse crowd. The total prediction market volume averaged only $1 million per day through 2020. This figure was lesser in previous years. At such low volumes, traders cannot hedge against outcomes and take large positions in prediction markets. The low volumes also contribute to low liquidity, leading to low fees and less active traders. This is the problem that plagues existing prediction markets, and they end up with hardly any daily users or daily volume. Which Are The Best Decentralized Prediction Market Platforms? Augur Developed in 2014 by the Forecast Foundation, aims to incentivize a network of computers to maintain a prediction market platform on Ethereum. Augur forecasts the outcome of any event by using the "wisdom of the crowd" principle. This method collects information from the crowd and averages it into the most realistic possibility and predicting the most probable outcome. Augur Gnosis was founded in 2015 by Stefan George and Martin Koppelmann. Gnosis was one of the first projects that were backed by the Ethereum focused ConsenSys. The Gnosis platform uses insights from capital markets and data science to enable users to forecast events. Users can also build their own decentralized prediction applications. Gnosis also offers users of the platform a multisignature wallet. Gnosis had an ICO on 24th April 2017 and raised $12.5 million of Gnosis (GNO). Gnosis Polkamarkets is a DeFi powered prediction market which can be used for trading and cross-chain information exchange. Users can predict and take positions on the outcomes of real-world events and scenarios. Polkamarkets is based on a decentralized and interoperable platform on Polkadot. Polkamarkets Conclusion Prediction markets represent a variety of opinions and have proven to be a useful prognostic tool. Companies like Google also utilize prediction markets. The current economic, cultural and political environment has increased the demand for prediction markets. Prediction markets have slowly moved from the private domain to the public domain. The availability of data from multiple sources should improve estimation methods and bring about the problem of data manipulation. However, as prediction markets become more mainstream, the markets' effectiveness will improve, and ethical and human biases will be adjusted.

Decentralizing Prediction Markets: Augur Vs. Gnosis Vs. Polkamarkets

As a developer, Ethereum keeps you on your toes. On the plus side, you constantly learn something new and the thrill of the “aha” moments is undeniable. Today, I want to talk about an interesting scenario that we ran into at Roll with the . This is a story with a happy ending but also a warning to other developers to handle this scenario gracefully. old Gnosis multisig wallet Note: This is not a vulnerability in the old Gnosis multisig. Rather, developers using this contract need to put in some extra work. Transaction Failure on Ethereum Transaction failures are not uncommon on Ethereum. There could be a number of reasons for it – from hitting an assertion to trying to take actions that the smart contract explicitly forbids (say, trying to run an admin or owner function without being the admin or owner). However, for most regular users, the most common transaction failure is the gas limit. Because Ethereum is , it needs to address the , i.e. make sure every program on Ethereum finishes running. Gas is a workaround to address this. Every transaction has a gas limit. If the gas limit is hit, the transaction is reverted. Turing Complete halting problem It is not hard to find these transactions on Ethereum. Here is an example and how it looks like on Etherscan. You’ll notice two things – The “Status” is “Fail”In the “To” field has the error description – “Warning! Error encountered during contract execution [Out of gas]” If you’ve been developing on Ethereum, you’ll notice this is a common occurrence. The solution here is to resubmit the transaction with a higher gas limit and move on. Status Success but Out of Gas? What we encountered was a transaction where the “Status” is “Success” but the transaction still ran out of gas. Huh? Here’s what that looked like on Etherscan. confirmTransaction on old Gnosis Multisig Let’s dig in here and see what’s happening behind the scenes. The main call we make when confirming a transaction on the old Gnosis multisig wallet is . This is how it looks – confirmTransaction confirm public
        owner transaction not {
        confirmations = ; ;
        execute ;
    } function Transaction( ) uint transactionId Exists( . ) msg sender Exists( ) transactionId Confirmed( , . ) transactionId msg sender [ ] transactionId [ . ] msg sender true Confirmation( . , ) msg sender transactionId Transaction( ) transactionId As you’ll see, this function is doing two things – Add a confirmation to the transaction (e.g. if you set up a 3-of-5 multisig and this was the second confirmation, the status would go from 1-of-5 to 2-of-5) Execute the transaction (for the same 3-of-5 multisig, this would execute the transaction during the third confirmation i.e. when the status goes from 2-of-5 to 3-of-5) These are not handled atomically, which means . This is exactly what happened in our case. it is possible for the confirmation to succeed and the execute to fail This is important to note for a few reasons. Firstly, . If you try, it will just end up failing. The reason is that the transaction is trying to add a confirmation first. If the transaction is already confirmed, then it will simply fail. See the function you cannot get out of this situation by resubmitting the transaction with a higher gas limit isConfirmed public
        constant
        returns (bool)
    {
        uint count = ; (uint = ; <owners. ; ++) { (confirmations[transactionId][owners[ ]])
                count += ; (count == required) ;
        }
    } function isConfirmed (uint transactionId) 0 for i 0 i length i if i 1 if return true Second and most importantly, do not try to submit a new multisig transaction. This could result in a double-spend! A naive and tempting way to handle this situation would be to say, “Well, I guess the transaction didn’t go through and I cannot resubmit it. Let me send a new transaction”. Don’t do this. To understand why, here’s a hypothetical. Say you have a contract with a call that does some computation and pays out someone’s salary. This is controlled by an old Gnosis multisig wallet as 2-of-3, so both CEO and CFO need to sign off. paySalary Say you run into the above situation and the transaction doesn’t seem to go through (you check on the blockchain that indeed the Ether or DAI have not been transferred) and naively submit a new transaction with a different transaction id. In the above scenario, the older transaction will be in confirmed state but not executed state, while the new transaction will be both confirmed and executed. This means the recipient can then call on the contract and be paid twice – essentially a double-spend. So this is a bad way to handle things. executeTransaction The Schrödinger State In the scenario outlined above, the transaction ended up in a confirmed state, but not executed. Essentially, the transaction is in the following state isConfirmed: true executed: false Your application needs to be able to handle this “intermediate” state the right way. Handling this Failure the Right Way So how should this failure case be handled? You should explicitly call the function on the contract with the transaction id of the original transaction. Here is the function executeTransaction execute public
        owner confirmed(transactionId, msg.sender)
        not { (is ) {
            Transaction storage txn = transactions ;
            txn.executed = ; (external ) ; { ;
                txn.executed = ;
            }
        }
    } function Transaction( ) uint transactionId Exists( . ) msg sender Executed( ) transactionId if Confirmed( ) transactionId [ ] transactionId true if _call( . , . , . . , . ) txn destination txn value txn data length txn data Execution( ) transactionId else ExecutionFailure( ) transactionId false Calling the function above will ensure that the original transaction is executed. Be sure to provide sufficient gas for this call. Check your Data If you use the old Gnosis multisig wallet in your application, it would be wise to check the data on your contract. You can loop through all the transaction ids and look at the following values: and . Unfortunately, you cannot get the value via a Web3 call (there is no function provided). isConfirmed executed executed isExecuted If you come across any transaction that is in the state , that’s where you need to pay attention. isConfirmed: true executed: false If you find such a transaction, you can make a call to revoke a confirmation that is not yet executed by calling . This is the function revokeConfirmation revoke public
        owner confirmed(transactionId, msg.sender)
        not {
        confirmations = ; ;
    } function Confirmation( ) uint transactionId Exists( . ) msg sender Executed( ) transactionId [ ] transactionId [ . ] msg sender false Revocation( . , ) msg sender transactionId

Transaction Failures in The Old Gnosis Multisig Wallets [How To Fix It]

Cryptocurrency to predict (and influence?) the future. If you’re into this stuff already (as in, you clicked in here to read about an ethereum-based altcoin that most people have never heard of) the idea of forecasting the future shouldn’t phase you. The draw of cryptocurrency itself is more about what’s possible than what actually exists, so enthusiasts usually have some thoughts, good or bad, about where the world is heading. Gnosis just wants to give you a better view. To some extent, the past can be used to approximate the future. If you asked 5,000 people yesterday whether or not they like some brand of chewing gum and 63% said yes, it’s a safe bet that if you ask them the same question tomorrow, you’ll get similar results. Problem is, it takes a significant amount of time and money to ask people questions. Sometimes the value of a prediction can justify that expense. Even something as mundane as chewing gum habits can have value if you’re a candy manufacturer evaluating the possibility of bumping up your gum production line. If a prediction can help inform major business decisions, it’s worth that time and money. If chewing gum is important, political elections are probably more important. Election results have lasting effects that are felt far and wide. It’s well worth the time and money then, to generate predictions of election outcomes. There are many, many organizations dedicated to using scientific polling to do just that. And plenty of research has gone into analyzing, testing and refining polling techniques. Still, it’s not the most reliable forecasting technique we have available. See, there’s something interesting about us humans. Not a rule necessarily, but a tendency at the very least. When money is involved, we pay attention. Not a flattering observation, but true. If you‘ve got money riding on the outcome of the next presidential election there’s a strong possibility that you’ll do a little more research. In fact, there’s a strong chance you’ll make a more accurate prediction of the results. Prediction Markets Betting on political races is nothing new. From the mid 19th century until the early 1940’s there was even a in place in the United States for election betting, presidential and otherwise. formal market Through this market, if you were confident in a certain political candidate, you could purchase a share in their victory. These ‘prediction markets’ were viewed almost as an extension of Wall Street. Odds were posted daily in prominent newspapers and trading volume often rivaled stock trading. The going rate for a particular candidate was strongly indicative of that candidate’s probability of success. This market was eventually pressured out of operation, but during it’s existence it maintained a more successful record for predicting election outcomes than modern polling techniques. It’s inferred that the accuracy of the market was driven by it’s inherent financial incentive. The New York Times puts it very well in an article published on October 10, 1924 (I found this NYT quote in from UNC, pg. 9): this research paper “Wall Street is always the place to which inside information comes on an election canvas … [and] it is a Wall Street habit, when risking a large amount of money, not to allow sentiment or partisanship to swerve judgments … any attempt to force odds in a direction unwarranted by the facts will always instantly attract money to the opposite side.” The efficacy of this political prediction market was largely forgotten by history. But then the internet came along. Online transactions could reduce latency, increase transparency, and offer the flexibility required to get around the localized regulations that seem to have driven historical large-scale prediction markets to a close. Developed as a research project through the University of Iowa in the late 80's, the , a digital reincarnation of the old election prediction market, was one of the first examples of an online prediction market. A study in 2008 of IEM’s success rate covering 5 presidential elections, found that predictions were . Iowa Electronic Market (IEM) more accurate than polling 74% of the time Although the concept of the prediction market is well suited to politics, it is by no means limited to political predictions. Prediction markets may be applied to the forecasting of all kinds of events. They can be used to inform business decisions, trade on , , facilitate sports betting, and the list goes on. box office success monitor climate change Online prediction markets are still forced to operate through some form of central authority. In order for them to function, someone needs to be responsible for monitoring results and overseeing transactions. That’s not a bad thing necessarily, but it is a vulnerability. Today, one of the draws of cryptocurrency is the ability to leverage the authority of a widespread network to eliminate certain traditional authorities. So the question arises, what if we outsource the heavy lifting of a prediction market (the transactions, the verifiable authority, etc.) to the crowd? By providing the right incentive, it should be possible to create a network that processes prediction transactions and verifies their corresponding real-world occurrences automatically, transparently, and in real-time. The evolution of cryptographic technology then, has the potential to take the concept of the prediction market to a new level. How exactly does a prediction market work? Gary works in accounting. He loves burritos. He eats them at least once a week. We want to form a dynamic prediction of whether or not today will be burrito day. Go around the office and ask your coworkers to pony up. Minimum bet is a dollar. For each dollar wagered, you issue two tickets. One ticket signals a vote in favor of burrito day. The other ticket is a vote against burrito day. Let everyone know that when the results are in, winning vouchers may be redeemed for one dollar. Losing vouchers will be valueless. This is a pretty boring experiment if you just take money from everyone and then give it back later. To make it interesting you need to stimulate some trading. Since Gary only eats one burrito per week the odds aren’t great that today will be the day. Most of your coworkers are going to be looking to unload their burrito day tickets. They can sell their tickets if they find the right buyer. If you’re willing to accept a little more risk in hopes of a larger payout, this is a good time to pick up some burrito day tickets on the cheap, or maybe sell some non-burrito day tickets at a premium. You started this mess, so you get to act as the middleman, collecting, matching, and processing their orders. As the day goes by, information is aggregated and sentiment shifts. With a financial incentive to predict the correct outcome, your market’s participants will be motivated to look for ways to get ahead in this market. Here there is a built-in correlation between the price of each outcome and the probability that the outcome will occur. If, for instance, the going rate for a pro-burrito ticket is 32 cents, you know that roughly 32% of your market is willing to entertain the possibility that Gary will eat a burrito today. You are forecasting a 32% chance of burritos. Any information that your coworkers come across, say an open menu on Gary’s desktop, or a comment regarding lunch options, is likely to quickly be reflected in share pricing. Where does cryptocurrency fit in? There are multiple organizations working towards some form of a decentralized prediction market. Most notably, Gnosis(GNO) and Augur(REP). Neither is the clear winner at this point by any means. I focus on Gnosis here, but I provide a brief description of Augur below with some suggestions on where you can learn more. Gnosis is working on a framework for hosting and interfacing with decentralized prediction markets. You would use the Gnosis framework to create a market and link it to an event. Through this market, users may purchase something akin to a share in the event. For each share, you contribute what Gnosis refers to as a ‘collateral token’. In return, you receive a set of ‘outcome tokens’, one for each possible outcome of the event. Those who contribute collateral tokens are able to trade outcome tokens, limited only by market liquidity really, up until the moment that the corresponding event has occurred. To launch a prediction, you simply register an event and activate the market. Now, one of the key difficulties of smart contracts implementations is incorporating reliable data into the system. It’s difficult to verify data, even something as simple as the current time, across a widespread network. So one of the challenges of an automated prediction market is providing reliable verification of event outcomes. In some cases, Gnosis intends to allow references to data from external platforms to settle the outcome of an event. It’s safe to say we’ll see some services being developed in the future to provide verifiable data to smart contracts tools. But, for cases where a verifiable source cannot be found, Gnosis proposes a solution referred to as the “Ultimate Oracle”. Sounds magical, but it’s a pretty simple protocol. Basically, wagers on an event outcome would be collected during a short period of time after an event has occurred. To dispute an outcome, you would simply place money on an alternate outcome. If an outcome is a clear frontrunner over a specific period of time, the Ultimate Oracle will settle the outcome and distribute funds accordingly. This would allow users to leverage the platform itself to verify event, and in turn, this would likely generate some useful data for other applications. The Platform Itself Gnosis describes their platform as consisting of three separate layers. The first layer, the , will provide the system’s central smart contracts. The second layer, the layer, will provide some basic packaged services for developers. The third layer is represented by applications built on the Gnosis platform. core layer services But the system is still in it’s early stages. A timeline representing current and projected achievements is available on . The timeline predicts release of a trading interface in the first quarter of 2018. You can fool around with a currently. Keep an eye on if you’re interested in continued development. the Gnosis website rough beta interface their github The Token Gnosis will incorporate the use of 2 ERC20 compliant tokens, . The GNO token has a fixed supply of 10 million GNO tokens. They plan to charge for the use of certain services that operate on the core platform. Fees will be payable in ETH, BTC, or WIZ. Gnosis (GNO) and Gnosis Wizard (WIZ) WIZ is generated by holders of GNO. There will be a mechanism in place, similar to a Proof of Stake setup, by which GNO may be tied up in a time-locked contract for a set period of time, in return for a preset value of WIZ. Each Wiz token will be redeemable for 1$ worth of fees on the Gnosis platform. Additionally, the rate at which WIZ tokens are awarded is proportional to the use of WIZ tokens on the platform. This should effectively peg the value of the WIZ token at or very near 1 USD. It’s important that the WIZ token maintain low volatility in order for it to serve as a vehicle of collateral on the platform. Sales of the Gnosis token originally brought in over 12 million dollars worth of ether. The amount of GNO sold in the Gnosis ICO represents only 5 percent of the tokens created for the project, implying a total market value . That’s a lot of money for a project that‘s still this early in the making. In contrast, Augur raised $5 million for 80% of their tokens during their October, 2015 ICO. of nearly $300 million Augur It’s not really fair to talk about Gnosis without mentioning Augur. They’re very similar. A full evaluation of Augur is beyond what I’m willing to call the scope of this article right now. As always, the best place to learn is anyhow. But they also have one of the I’ve seen. You should also check out . the whitepaper best promo videos their website Augur was released before Gnosis. that their idea is unique, and that there is room in the marketplace for both ideas. describing the differences. Gnosis seems to believe Here’s a decent post Futarchy It’s not uncommon to ignore the wider ramifications of cryptocurrency. Many still see it as a financial vehicle or a technological oddity. At the heart of these concepts, and their value, is the suspicion, maybe even hope, that the systems that emerge from this evolution may have potential beyond financial gain. The weight of this concept really comes home when you realize the role that cryptocurrency may play in future markets. When you’re talking about decentralized, programatic currency, the value of creating dynamic monetary systems is most obvious. If I can hold something of value and orchestrate it’s behavior in a specific and reliable fashion, the applications are endless. Maybe a little less obvious, but prediction markets look very much like the way to inform that behavior, to provide the data that makes money move. If you can predict the future, you can plan for it, maybe even influence it. It’s one thing to envision the ability to control the flow of value, but the ability for the flow of value to control itself based on real-time data is more disruptive still. What’s more, the real-time data in this case describes public sentiment. All in all, the coolest thing about these platforms, both Augur and Gnosis, is just that they represent the kind of idea that you just really want to see exist. What happens when the wisdom of the crowd is given economic power? Gnosis Team and are college buddies that initially took an interest in developing a decentralized prediction market. Martin Köppelmann Stefan George is an Ethereum co-founder and early partner. Joseph Lubin is a co-founder for the Augur platform who seems to have jumped over to the Gnosis team. Matt Liston

What is Gnosis (GNO)?

If you want to do anything interesting in Ethereum, you will have to interact with smart contracts. Whether you want to send ERC20 tokens like LINK or DAI, trade non fungible tokens like digital art, or earn interest on your crypto and interact with other DeFi products, a smart contract is always involved. However, smart contracts are becoming increasingly complex. From proxy contracts to allow for upgradeability, to multi-send contracts that allow for the batching of transactions, what we are seeing is a rapid evolution of features that allow for the movement of one's assets. You've also got DeFi transactions, they are often not generated by you or your code so you need to verify it's doing what you think it is. Additionally, understanding a method call is useful for: Seeing and knowing that the contract methods calls are what you expect Seeing the parameter types and values that allow you to understand how the contracts work Analysing a contract to produce stats on method calls Tracking interactions with key addresses Writing your own rules to decide on which transactions to sign (more on that later) Having fun, if you like that sort of thing In part one of our new DeFi blog series, we will demonstrate and introduce what goes on inside an Ethereum transaction and how you can use Trustology’s custodial wallet platform to interact with smart contracts in a much more secure way. This knowledge will provide you with a good foundation in subsequent posts when we talk about our Firewall, Webhooks, and other DeFi services that we offer. Let's dig in. We’re going to start with a Gnosis Safe contract and a transaction a user would send that the Gnosis safe Dapp would generate. Gnosis Safe is a popular wallet contract implemented by . A multi-user organisation that wants to use Gnosis Safe will first have to define a list of accounts and a required threshold of signatures needed to send a transaction. Users will submit the transaction to the Safe, which will authorise the execution of the transaction only when the threshold required is reached. This way, the users have tighter control of their funds. Gnosis The Gnosis safe has lots of features including offline-signing (which saves on gas fees) but for now, we’re going to take a look at the basic method for executing a transaction. First port of call: Start with Etherscan Let's start with a transaction as seen by Etherscan and see if we can get the output they do. The transaction above is from a user that has interacted with the Gnosis Safe smart contract on the Ethereum mainnet and we can see the details directly on Etherscan. Let’s start by looking at the “data” field. This is the field that Ethereum uses to decide what method to call on the “to” (or “Interacted with” in Etherscan) contract. Here’s the full data payload below. As you can see, a lot of hex values. This looks difficult to understand but with a little skill and knowledge we can break this up into the important parts. Start at the beginning All “data” fields on an Ethereum transaction contain the name of the method that is to be called. This is the first thing we should look at. Etherscan shows this for us in the “Input Data” section: In this section we can see what Etherscan has given us: Function: execTransaction(address to, uint256 value, bytes data, uint8 operation, uint256 safeTxGas, uint256 dataGas, uint256 gasPrice, address gasToken, address refundReceiver, bytes signatures) So, how did they get this? Well, the first thing we need is to use the first 4 bytes (first 8 hex characters) of the input data, which is: 6a761202 This hex value is derived from taking the method name and its argument types, removing any whitespace, taking the `keccak hash of the result, and then taking the first 4 bytes of it and displaying it in hex. With me so far? Note: The parameter names are not included in the hash, only the types. This means that different contracts can have the same methodId's but call their parameters differently and potentially have different logic. Thus it is sensible to keep track of the contract which the method belongs to. There is a handy website that tracks these. Check out and enter the hash 6a761202. At the time of writing, there is only 1 registered method. https://www.4byte.directory/ We have the source of the Gnosis contract so we know what the method name and the parameters are. We grab them from here. In JavaScript, the following will output the keccak hash of our method plus parameters: // import a keccak decoder or write your own import { keccak } from "../decoder/keccak"; const method = `execTransaction(address to, uint256 value, bytes data, uint8 operation, uint256 safeTxGas, uint256 dataGas, uint256 gasPrice, address gasToken, address refundReceiver, bytes signatures)` // regex pattern to remove the word before a comma or closing bracket export const removeArgsFromMethod = (method: string) => { return method.replace(/\s\w+(,|\))/g, (_, commaOrBracket) => commaOrBracket); }; // remove the argument names and remove any spaces const preparedMethod = removeArgsFromMethod(method).replace(/s/g, "") // keccak hash of the method const keccakHashOfMethod = keccak(Buffer.from(preparedMethod)) // first 4 bytes const methodId = keccakHashOfMethod.slice(0,4).toString("hex") // 6a761202 Note: If you don’t have the source code of the smart contract then you won’t be able to generate the hash to check and it will be more difficult calculate the exact parameter types. The output from that Javascript is 6a761202 which matches the first 4 bytes (8 hex characters) of the data payload. Great. We’ve identified that the method being called is in fact “execTransaction” with the 10 parameters. Now, if you go back to Etherscan and click on the `Decode Input Data button you can see how Etherscan has taken the data payload and broken it up into the 10 parameters including the types. Again, how have they done this? Get a handle on parameters Our next job is to break up all the parameters that have been passed to the execTransaction and see what they are. Before we do that a quick note on bytes to hex conversion. Very simply 1 byte of data = 2 characters of Hex. So, whenever you see a string of hex characters, you can divide the number of characters by 2 to get the size in bytes. Any "0x" at the beginning is ignored in the actual calculation. Now, on to decoding the method parameters. Let's have a look at the data again but this time we split it into 32 byte (64 Hex characters) strings. Why? Because ethereum uses 32 bytes blocks and nearly all primitive types are 32 bytes. There are a couple of exceptions though, such as the "bytes" type which we'll see later. : MethodID 0x6a761202 So, once we strip off the methodId (0x6a761202) and then break the remaining data into 32 bytes (or 64 characters) chunks we can start to see something more interesting. : MethodID 0x6a761202 These are the values of the input parameters of the execTransaction function. It's not quite straightforward so let's work through each 32 byte value in turn. Column 3 of the table above shows where each of the values maps to the parameters of the execTransaction function. For example, the first parameter (the `"to" parameter): 000000000000000000000000dac17f958d2ee523a2206206994597c13d831ec7 is of type address. Ethereum addresses are 20 byte values so it has been zero-padded to fit into a 32 byte value. To get the actual address, we just need to extract the last 20 bytes and prefix 0x. In this case, it becomes: `0xdac17f958d2ee523a2206206994597c13d831ec7` (Hmm...this is the Tether contract) In fact, removing the zero padding is how to decode most of the simple parameters like uint8, unit256, etc... The next parameter is the “value”. This, we assume, is how much value needs to be transferred from the Gnosis safe.  Its value is zero.  (This is the value of Ether remember) The next parameter is interesting, it's of types bytes. bytes is a variable-length so how long is it?  The value is: `0000000000000000000000000000000000000000000000000000000000000140` This value represents the offset in hex of bytes from the `methodId` of where the actual data begins. Think of it as a pointer, redirecting you to where you should look for this information. So we need to convert 0x140 bytes into hex characters. 0x140 (bytes in hex) = 320 bytes in decimal 320 bytes in decimal x 2 = 640 characters in hex so the data begins 320 bytes from the start, and thus it is 640 hex characters along, or 10 rows along. In JavaScript, it will look as follows: const numberOfBytesInHex = 140 const numberOfHexCharacters = parseInt(numberOfBytesInHex, 16) * 2; // = 640 Going to the 10th row the first 64 characters represent the length of the data (this requires the same conversion done above). `0000000000000000000000000000000000000000000000000000000000000044` is decoded into a length of 136 hex characters. So finally on the 11th row the data begins and ends after 136 characters. Which means our data field is the following: `a9059cbb000000000000000000000000ba339b8271afea713008314487dd98f8d941720f00000000000000000000000000000000000000000000000000000002540be400` So in summary, for a bytes field, the first 32 bytes is a pointer to where the data starts, once there, the next 32 bytes tells you how long the data is. Then you read that many bytes. If the data doesn’t end in a 32 byte block, it will be padded so whatever is next, will start in the next 32 byte block. Looking at the value again: `a9059cbb000000000000000000000000ba339b8271afea713008314487dd98f8d941720f00000000000000000000000000000000000000000000000000000002540be400` It looks a little familiar... It has a 4 byte methodId and then 2 x 32 byte chunks (2 lots of 64 hex characters). It’s another method call! In fact, we can recognise immediately that this transaction is in itself an ERC20 transfer transaction as it has the famous a9059cbb method signature. (Tip: go to https://emn178.github.io/online-tools/keccak_256.html and type in transfer(address,uint256) and you will see the first 8 characters are in fact a9059cbb`) So, we can see that this Gnosis transaction is to call another contract of address 0xdAC17F958D2ee523a2206206994597C13D831ec7 with the “data” of: `a9059cbb000000000000000000000000ba339b8271afea713008314487dd98f8d941720f00000000000000000000000000000000000000000000000000000002540be400` Which is an ERC20 “transfer” call. We could then use what we learnt above to recursively calculate the details of this data call by extracting the methodId bytes and breaking up the data and looking at the parameters. Take a look and see if you can work out the recipient and value of tokens sent to the Tether contract. Conclusion There was a lot of information here and we didn't go through every single parameter, but hopefully, you can see it was very useful to be able to break the transaction data down and by working through a data payload methodically you can always understand what's happening. We saw how for a Gnosis contract transaction we could break it down into the constituent parts and we saw the methodId and the parameter types and how to deconstruct them into useful arguments. We also saw how that, in this case, there was effectively a transaction within a transaction. The Gnosis safe transaction was just wrapping an ERC20 transfer. This was effectively moving USDT tokens from the Gnosis safe to somewhere else. In our next blog, we’ll see how we can take our understanding of Ethereum transactions to break down a Defi contract call to apply rules to decide if we want to sign/send the transaction. This is something that Trustology uses in its new Defi firewall to protect customers from signing/sending transactions that don’t meet certain criteria. The DeFi Firewall is the first in a suite of services Trustology aims to launch for institutional users looking to support new tokens in DeFi or explore yield-bearing opportunities. Notifications and flows are next on our list to complement our current firewall solution. Currently, we are the only custodial wallet to integrate with , which lets institutions tap DeFi innovation from the security of an insured custodial wallet platform. MetaMask To access the code demonstrated in this blog and more, please click here. To enquire about our DeFi services chat to us. About Trustology Backed by ConsenSys and Two Sigma Ventures, and founded in 2017, Trustology was established on the premise of enabling greater freedom to transact in a fair and efficient manner. By bringing the world of blockchain technology and cryptoassets to new market participants, we believe this will help make this happen. That’s why we built TrustVault — a fast, user-friendly, insured and highly-secure custodial wallet service for institutions and individuals designed to address the security and ownership shortcomings of existing solutions today. Learn more about us at . trustology.io Recommended reading: If you want to know more about Ethereum transactions there are some good articles, you start with: Understanding an Ethereum Transaction The transaction payload

Ethereum Transactions: Under The Hood

Why we invested in Multis We are excited to announce our investment in along with Y Combinator, Coinbase, eFounders and the CEO of Compound. Multis Multis is a small efficient team that was a part of YC’19 batch. We were extremely happy to meet them before they join, being one of the very few investors to back them while still in YC. Briefly, . Multis is a self custodian bank for the digital era In the last few years, we’ve seen tons of lending services, multi-sig wallets, exchanges and many more built in the crypto space. These are amazing steps forward building the new form of banking but the problem is fragmentation and complexity. One company does the lending, another builds multi-sig wallets which are not connected to each other so as to form a simple and complete banking experience for the user. Users and companies need to go into 10 different places to do one thing. Multis puts all these pieces together without compromising two of the most important parts of this space: security & self custody. Whatever business owners or everyday people are accustomed to from their online banks that hold fiat, Multis brings to the crypto/digital currencies: Allowances (limits of how much you can spend) Verification (multi-signature up to x-y) Multi-currency (ERC20 tokens for now and more coins coming) Internal exchange ( ) Kyber Network Savings accounts with interests ( ) Compound Connect the existing web with crypto ( ) Portis And the list will keep growing… The team is committed to creating the interfaces we need for this remarkable technology to become useable. How a business can use Multis Let’s say you run a bakery with your partner and for 800 USDT. Since this is above your daily allowance of 500 USDT, you submit the payment and then your partner gets an email asking for his approval. If he agrees, he approves the payment and it goes through instantly. you want to make a payment to your suppliers instantly But in case your partner has already made the payment but forgot to update the books, then he can reject your request with a note “already paid sorry”. A scenario would be when you have a catering event and you need to request your 900 USDT payment. Using Multis, you can request a payment of an exact amount and directly send it to your client. Once you get paid, you will receive an email from Multis, notifying you that funds have arrived. If your bakery business has some savings just sitting on your business account you can also just with a tap of a button. lend those funds and get great returns In other words, your bakery business Just like regular checking and savings accounts. can store their crypto, pay suppliers, run payroll, and earn interest. The elephant in the room: Easy to use products Besides a brilliant product that we use from day 0, Multis represents something that is written in the genes of Monday Capital: It’s the first prime example of how a young team can abstract the noise of an exploding market and focus on what’s important: User Experience. Multis took a very basic and widely used concept — a secure multi-signature smart contract wallet from — and built on top of it. They built the interfaces needed to make this powerful technology useful, exposing features and use cases that exist but were unusable due to complexity or lack of the interfaces. The team believes that easy to use interfaces are as important as innovative technologies. And Monday Capital does too. Gnosis You can follow Multis progress here . MultisHQ This post was originally published on . Monday Capital’s website

Why We Invested in Multis, The Self Custodian Bank for the Digital Era 

Analyzing Token Sale Models

, Asia Pacific’s largest digital asset custodian and blockchain technology provider, is commemorating its 5th anniversary by inking a new NFT project, ‘Evolution’, with , an institution-compliant version of MetaMask Wallet, and , a community/DAO-driven multi-sig smart contract wallet. Cobo MetaMask Institutional Gnosis Safe said Changhao, the CTO and Co-Founder of Cobo. “We are excited to launch Evolution, the first soulbound NFT with MetaMask Institutional and Gnosis Safe, to our exclusive community. With each organization having their speciality, it makes this project a compelling first step towards deepening relationships and producing future collaborations to distribute exclusive community benefits to users in the know” With MetaMask Institutional’s reputation for providing efficient DeFi access for crypto funds, market makers, trading desks, and DAOs, Gnosis Safe’s ability to develop flexible multi-sig solutions through smart contracts, and Cobo’s positioning as the world’s only omni-custodian provider with both HSM and MPC technology, institutions in their ecosystem can safely access all of web 3.0 without the fear of being compromised. said Jorge Selva, the Growth Lead at Gnosis Safe. “Soulbound NFTs are a great way to prove identities and certifications. It has countless possibilities as compared to a tradeable NFT at the moment and Gnosis Safe is excited to work with Cobo in creating its first Soulbound NFT” Soulbound tokens are able to form an entirely different purpose. Instead of having a token that can be traded to different wallets, Soulbound NFTs have no intrinsic monetary value but helps create a personal identity that will, in turn, allow users to be recognised for their assets and achievements. It also enables organisations to create long-term bonds with their clients. added Liz Mathew, Head of Growth & Partnerships for Metamask Institutional. “The MMI-Cobo Solution is already live and being used by customers with various NFT-related uses. We are tremendously excited about the partnership with Cobo. Evolution is a great example of harnessing the power of community and the development of a multi-faceted community engagement that transcends traditional digital and analogue channels”, Evolution is a soulbound token that will be permanently bound to the owner’s wallet address along with its exclusive utilities and access. The token includes two time-based rewards that will release a specific gift related to MetaMask Institutional and Gnosis Safe during the first three days. Owners of the NFT will also be able to receive an exclusive quarterly research report that includes DeFi strategies, trends and opportunities in the market done by award-winning researchers, and the security and investment team in Cobo, and gain exclusive access to various in-person and online masterminds and events. Citing Cobo’s growth in the past five years, the company is looking to reward its clients, staff, and VIPs who have been through countless journeys with Cobo. Each and every of the 1,000 Evolution Soulbound tokens will be distributed towards institutions, HNWIs (High Net Worth Individuals), and the partners of Cobo, MetaMask Institutional, and Gnosis Safe. Also published . here

Cobo Collaborating with MetaMask Institutional and Gnosis Safe To Release ‘Evolution’

Decentralized Betting Protocol receives Investment from Institutions Azuro, a decentralized betting protocol, raised $3.5 million in a seed round from Gnosis, Flow Ventures, , Ethereal Ventures, , Arrington Capital, and Meta Cartel Ventures. The investment comes in the light of the increase in trading of prediction instruments such as derivatives on , Binance and FTX. Azuro is a decentralized betting protocol that uses smart contracts to provide an innovative solution for liquidity supply and distribution, which uses pooled liquidity to scale prediction markets, similar to how pooled liquidity grew lending protocols like Aave or Compound and DEXs like Uniswap. Consequently, as betting becomes more transparent and trustworthy, the breadth of betting events, markets, and user experience remains unrivalled. Polymorphic Capital Delhi Digital ByBit The DAO will oversee the development of the betting protocol, its ecosystem economics, treasury administration, and dispute resolution, among other things. Decentralizing the betting sector and its administration can result in a more equitable atmosphere, removing the well-deserved stigma that betting holds in countries and communities. Understanding Peer-to-Peer Betting Earlier, the peer-to-peer architecture of prediction markets prevents effective liquidity allocation. Liquidity providers must manually launch markets, set odds, and seed liquidity for each market they create on prediction markets. The liquidity is thus market-specific, implying that the LP bears betting risk associated with that betting market. All of this is due to the enormous lift that LPs are required to provide, and as a consequence, there is little liquidity and hence little betting. Furthermore, prediction markets are inefficient for bets with three or more outcomes. Most people are stuck with YES/NO marketplaces, which are woefully inadequate, particularly in sports. Another example of a lack of product depth is an “accumulator,” one of the most common bets (and the most popular among casual players). Bettors who put numerous bets on the same slip will get a substantially higher potential payout if all of their bets are successful. Instead of employing various pools that are event and market-specific, core liquidity is provided via a single pool. Liquidity providers are not needed to build markets manually and are not subject to risk particular to betting markets. Instead, the risk is dispersed throughout all betting markets on the protocol, significantly reducing risk for the LPs. For LPs, the procedure is standard DeFi – no human effort is required, and no special knowledge is necessary to supply liquidity. The yield on the protocol for LPs, on the other hand, is completely unrelated to the status of DeFi or financial markets in general. Yield is provided by the Liquidity Pool’s profitability, which is earned by betting, allowing liquidity providers and farmers in DeFi to become comparable to the “house” in the classic bookmaking model. The Rise of Online Sports Betting Industry & Crypto In 1996, roughly 15 companies offered sports betting, which was the start of online sports betting. It is now one of the most popular pastimes on the planet, and with the advancement of technology, it has never been simpler. Despite more localized control, the sector has expanded dramatically and is worth more than $100 billion each year. Like the United States, large economies have long forbidden sports betting, but in 2018, the US chose to reconsider its position. This resulted in a massive increase in sports betting income, with the United States alone taking in $1.5 billion in 2020 — mid-pandemic, and with just a few states participating. Many states have enacted sports betting laws as of 2022. Despite strict regulation and a high barrier to entry, the number of online bookies is increasing year after year. By 2028, the betting industry is expected to be worth $140 billion. However, significant innovations have only been related to broader technological leaps in its more than 20 years online — the introduction of smartphones led to mobile betting, which fueled dramatic growth; a few new forms of betting emerged, such as eSports and crypto-based gambling, which has been growing rapidly with innovations, such as bet cashouts and bonuses paid in crypto to expand the industry. Don’t forget to like this story!

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