Trade Execution: To Trade or Not To Trade
Article 2 of 7
Our portfolio rebalancing mechanism is based on an innovative and proprietary system we have designed, The Cascading Waterfall Round Robin Mechanism. This algorithmic approach recommends an ideal trade size for each asset during the periodic rebalancing process, factoring in the gas fee and slippage.
- In the hyper-volatile crypto market, our approach to daily rebalancing will benefit from volatility. Price movements will cause our algorithm to buy assets that drop in prices and sell as they soar. In fact, the buying and selling happen only when certain boundaries are crossed in order to weed out any market noise and ensure sound trade execution.
- Careful orchestration among mathematical optimization for portfolio construction, trade automation of the investment apparatus, and human oversight will allow us to watch out for exceptional situations and ultimately lead to a better outcome.
To Trade Or Not To Trade, that is the Question,
Whether an Optimiser can Yield the Answer,
Against the Spikes and Crashes of Markets Gone Wild,
To Quench One’s Thirst before Liquidity Runs Dry,
Or Wait till the Tide of Momentum turns Mild.
We continue with the second of the 7-article series for the upcoming Eiffel Release. These articles will describe the main components of Formation’s Risk Parity. In this article, we will take a closer look at the trade execution innovations we have brought to the DeFi space in order to rebalance our portfolios on a daily basis or even at an intraday frequency.
“Cascading Waterfall Round Robin Mechanism” are the words we use to summarize our rebalancing algorithm. To describe how it works, we first assign a certain capacity to hold funds to each asset in our portfolio. This capacity is the result of several calculations that depend upon:
- the risk and return properties of each asset,
- how the asset prices vary in comparison to other assets in the portfolio, and
- the amount of funds collected for investment (or the total requests for redemption).
Once the capacity is determined, we check how much of that capacity is utilized. This gives us an idea of how much money we can put into each individual asset when we invest money across our assets. Likewise, it also tells us how much to pull out of each asset if a withdrawal is needed. Next, we distribute funds across the assets, or redeem funds from the assets, in a circular manner, or round robin fashion, till the full capacity of each asset is reached. As the capacity on one asset reaches its full limit, the funds start trickling down to the next asset, similar to a waterfall. The reverse happens when redemptions are to be fulfilled. Hence the name, “Cascading Waterfall Round Robin Mechanism”.
After the trade execution schedule is decided, we must consider the transaction costs of completing the trade orders. There are two main implicit costs at this stage. First, there are gas fees for each transaction we execute. Second, there is slippage or market impact. The gas fees depend on a number of factors, such as the time of execution and the network on which a trade happens. The slippage depends on the size of our trades relative to the sources of liquidity (e.g. DEX, swap, liquidity pool, etc.). Here is a quick summary:
- The larger the number of trades, the greater the total gas costs.
- The larger the trade sizes, the greater the slippage.
- The smaller the trading volume (or liquidity) at the exchange, the greater the slippage.
The quintessential trading conundrum in traditional finance is timing (when to enter a trade) and trade size. The problem is compounded in crypto since we must factor in the gas fees, which are constantly variable based on network congestion and type of blockchain. We will discuss market timing in a later article as it’s a topic particularly insightful to future front-runners but generally our portfolio will rebalance daily. The trade size is then determined by the dual objectives of minimizing both gas fees and slippage. We perform asset level calculations which are coupled with our “Cascading Waterfall Round Robin Mechanism” to arrive at recommended minimum and maximum trade sizes. Basically, our proprietary algorithm will generate (recommends) a set of min-max values for each trade.
These trade size recommendations ensure that our fund managers adhere to our security guidelines, when funds need to be moved into and out of assets from our secure safehouse. Our first article has a detailed discussion of our security plan. [LINK]. The goal of strengthening security is achieved without creating bottlenecks for trading since fund movements correspond to trade size restrictions.
The calculation of asset capacities and the rebalancing methodology are among the most central elements of any investment process. It is no different in our case. If anything, it is more important for us given that we adhere to strict risk metrics and we had to build several new techniques geared towards overcoming the additional challenges in the decentralized space. These two components were some of the earliest pieces we built, and tested, to understand how well they would work in a blockchain environment. Now that these initial efforts have proved satisfactory, bringing in a set of improvements is quite natural. Till now, these pieces could be invoked and utilized on an on-demand basis. The next set of enhancements are to be able to connect them to data updates, and completely automate them, so that these calculations can run on a daily basis or even several times during a 24-hour period.
To govern a system with many moving parts, such as ours, several parameters must be monitored and tweaked on a regular basis. Our portfolio management team will observe these parameters continuously and update them, as necessary, using specialized internal tools. The bulk of the configurations that decide how our system will run are related to asset capacities and trade executions. In addition, trade executions can be error prone wherein failures need to be monitored and intelligent customizations to retry need to be incorporated into the process. Hence trade execution related parameters and operational procedures will garner significant focus and a big chunk of time from the investment team.
Our internal tools are designed such that the flow of funds happens automatically, for the most part, with human intervention to complement the decision making. Significant automation of our investment apparatus will allow us to take advantage of market opportunities seamlessly and human oversight will enable us to watch out for exceptional situations and fine-tune the decisions. This coupling of “Man-and-Machine” will lead to a better final outcome for all our participants.
An illustration of this pairing is that our approach to investing will benefit from volatility, which is seen as the bane of crypto markets by most players. Volatility, which is the up and down movement of asset prices, will cause our rebalancing algorithm to buy assets that drop in prices and sell assets as they start soaring again. But to filter out the noise and react only to real signals, the buying and selling happens only when certain boundaries or range thresholds are crossed. This spectrum over which transactions happen are automatically calculated based on asset properties, but fine tuned by our investment specialists. Suffice it to say, while mathematical optimization techniques offer powerful venues to garner profits, they might fall short of conquering the extreme scenarios that markets present. Hence mixing mathematical models with human intuition, that takes care of exceptional cases, is the ideal recipe for wealth creation.
In our next article, the third one, we will go into greater detail regarding the use of risk and return characteristics to arrive at the capacity for each asset.
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