2 posts tagged with "dinvest"

In Ethereum and other blockchains there are still a lot of proof of concept implementation and developers trying out how to cope with the new concepts. As part of the dInvest post series I was also looking into Ethereum and trying to implement a hedge fund in a blockchain. In a previous post I discussed how to get a quantitative framework in python up and running. In this post I will write how to integrate python programs with Ethereum smart contracts. For one reason or another you might be also faced with the issue, that although Ethereum offers a Turing-complete language not everything is actually doable there.

Let's say you have created one of the simple tutorial contracts in Ethereum and now want to look at something more advanced. I personally liked the Hitchhiker's Guide to Smart Contracts by Manuel Aráoz to get started with more complex code, setup testrpc, and truffle. Take a look at it.

dInvest smart contract​

dInvest is composed of one smart contract that is responsible for making investments, verifying investment criteria and distribution of returns. The contract exposes public functions to create new investments and for withdrawal which will act as main functions of a hedge fund. Users of the hedge fund are identified by their Ethereum address which is equivalent for the public key. Suggestion of investment strategies and strategy execution are done in different agents that also have Ethereum addresses. These agents are set by the contract creator only. When a user is creating an investment it is possible to specify a list of industry sectors identified by a two digit number based on the Standard Industrial Classification codes. These sectors will be identified as a black list when making the investments. Therefore user have the ability control the sectors which the hedge fund will invest on.

The contract can be found in the GitHub repo.

Interaction with smart contracts​

To interact with smart contracts, there are a couple of option including RPC or a JavaScript API. I found the easiest way to interact with Ethereum smart contracts from other programs (like python programs) was using their web3 JavaScript API. As the majority of dInvest is written in python, I wanted to stick to the language and not include JS as well. Luckily, there is a web3 implementation in python. To get it up and running for the dInvest setting I switched to the virtualenv, where I also installed zipline and then install web3 simply with pip install web3.

Using web3, there are three steps to get you up and running to interact with your smart contract:

1. Getting your ABI
2. Setup the RPC connection
3. Interact with the smart contract

In the next sections, I will go into detail how to achieve the three steps. I am using this mostly as a python module for other programs. In the end our python module structure might look like this:

contract
|-- __init__.py
|-- ContractHandler.py
|-- your-contract-name.json

Getting your ABI​

Now, to interact with any smart contract you need the Application Binary Interface(ABI) defined by the contract. The ABI is a static, strongly typed interface. Whenever you create a new contract or change an existing one, chances are your ABI changes as well. In my experience the easiest way to get the current ABI of a smart contract (which might be yours or any contract you have the source code available) is to go to https://ethereum.github.io/browser-solidity/ and copy/paste your code there. Then press the "Compile" button on the upper right side and copy the entire string in the "Interface" field into a your-contract-name.json file. Once you have that JSON, your web3 interface will know how to interact with the contract.

Setting up the RPC provider​

As a next step you will need to connect to the RPC provider. In your python file (e.g. ContractHandler.py) include those lines of code:

from web3 import Web3, TestRPCProvider

class ContractHandler:
  def __init__(self):
    self.web3 = Web3(RPCProvider(host='localhost', port='8545'))
    with open(str(path.join(dir_path, 'contract_abi.json')), 'r') as abi_definition:
      self.abi = json.load(abi_definition)
    self.contract_address = your_contract_address
    self.contract = self.web3.eth.contract(self.abi, self.contract_address)

I prefer having my configurations in a separate file. There are many ways to do it and it seems like there is no standard in python. I guess using a txt file is not the best option though and I plan to switch to yml soon. See also here https://martin-thoma.com/configuration-files-in-python/. Make sure to run your favorite Ethereum client before starting your program (e.g. geth --rpc).

Interacting with the smart contract​

Note: Before interacting with your own account you need to unlock it first. This is achieved in web3 via:

self.web3.personal.unlockAccount(your_ethereum_account, your_ethereum_password)

There are some standard web3 calls you can make, like getting the current balance of an account in wei:

wei_balance = self.web3.eth.getBalance(some_ethereum_address)

In case you want to call a function in the contract you can do this by calling the command as defined by the contract ABI. In our dInvest example there is a contract call which returns the blacklisted companies for our sustainable investment. It is callable with:

blacklist = self.contract.call().blackListCompanies()

There are some more examples in the GitHub code available.

Final note​

As a final note, I would like to point out that there are other blockchain solutions like Hyperledger Fabric or Tendermint that aim to solve issues around compatibility with other programming language, transaction throughput etc. As they are permissioned blockchains I haven't yet given them a try, but might be interesting to take a look at.

As public reputation becomes one of the most important success factors beyond financial success, investment opportunity should ensure ethical decisions and keep sustainable investment as core of their strategy. These long-term investment strategies based on criteria other than pure financial data are summarized under "Value Investment". Value investment is based on the assumption that the current price of an asset might not be the actual value of an asset (e.g. stocks and derivatives). Thus, criteria other than their current prices can be taken into account to calculate their value. The value investing approach offers thereby methods to select potential investment based on social or sustainability criteria. Furthermore, value investment includes diversification of the portfolio to achieve a desired level of risk.

In this blog post I will introduce dInvest, a project Tharidu and me are working on. With the development of blockchain technologies and the Ethereum blockchain, an autonomous and decentralized approach can be taken to create an investment opportunity. Users are able to invest directly with the cryptocurrency Ether into a company which exists in the Ethereum blockchain. Alternatively, they can invest with other cryptocurrencies (e.g. Bitcoin) or fiat currencies by utilizing currency exchanges. An autonomous organization build on Smart Contracts exists solely as code and can execute the details as specified in the contract. As an example, The DAO enabled users to act as venture capitalists by suggesting investments to other users. If a certain amount of investments was raised the contracts would be executed inside the blockchain. However, this approach failed as The DAO suffered security flaws, which led to a severe attack on its system. Furthermore, The DAO failed to give incentives to users proposing investments. Investments are voted for two weeks, while a vote can only be given by bounding a user’s capital into the vote. Thus, late voting and waiting for other’s resulted in available capital.

Problem​

Certain investors want to invest their money in order to gain returns while being socially responsible and sustainable to e.g. achieve corporate sustainability objectives. However, financial intermediaries, such as investment banks or hedge funds, do not always adhere to investors’ requirements as there might be conflicting interests. Furthermore, the investment strategy applied by the financial intermediary can change through time and is highly influenced by social factors. In addition to that, investors do not have complete transparency over the transactions.

Our work​

We decided to develop an autonomous hedge fund in the Ethereum blockchain. Users are represented in the Ropsten network of the Ethereum blockchain as addresses. Users can hold certain amounts of test Ether, which they can invest into dInvest. The investment will contain an amount of Ether and a time period defined by the user. The details of the investment will be defined in a smart contract between the user and dInvest. Selecting stocks for different investment amounts and time periods is comparably computation intensive and will require the application of investment algorithms. Thus, their executing is costly in the blockchain and will therefore be executed by an autonomous agent (invest agent) outside of the blockchain. The required information will be passed on to dInvest on demand and therefore keeping the operational cost to a minimum. The invest agent will suggest investment strategies to the hedge fund according to the current Ether value. The hedge fund will decide to approve or reject the offer based on the sustainability criteria of users’ investments. Only if the criteria is fulfilled, the Ether will be transferred to the buy agent and make the investment. The invest agent will transfer the investment return to the hedge fund where the profit/loss will be divided to investors according to their investment value.

We managed to achieve a linear cost function of user investments and their sustainability criteria in the smart contract. Based on a value investment investment strategy we managed to achieve around 360% increase of the portfolio in a back-testing simulation over around 6 years. The sustainability criteria was implemented using exclusion of assets based on industry codes (i.e. exclusion of defense, alcohol, tobacco, or coal industries). If you are interested in the academic side and the results of our implementation, you can check out our report. As the paper does not cover the implementation in detail, I will post the details in separate blog posts and link them here. You can also check out our source code on GitHub.