Once again, you’ve delivered high-quality content and provided a thorough analysis of the situation. Here at Govmos, we’re fortunate to have a team member with over 13 years of experience in finance, trading, and market making. Consequently, this topic falls squarely within our area of expertise. To be more precise, this particular action falls under the category of volatility arbitrage, a topic far more complex than simply buying below average and selling above.
Despite the intricacies of financial arbitrage, you’ve managed to pave the way for a fruitful community debate on the matter. As you correctly identified, within the realm of professional arbitrage, discretionary arbitrage and public strategies represent two distinct methodologies which need to be addressed separately.
Discretionary Arbitrage:
- Description: Discretionary arbitrage involves making trading decisions based on the discretion and judgment of the trader or team. This method relies heavily on qualitative analysis, intuition, and market expertise.
- Approach: Traders employing discretionary arbitrage typically delve into fundamental and technical analysis, alongside considerations of macroeconomic factors and qualitative insights. Their trades are guided by subjective judgment to identify market mispricings or inefficiencies.
- Execution: Trades in discretionary arbitrage are rooted in the trader’s interpretation of market conditions and risk-reward assessment. This approach demands substantial experience and market acumen, often entailing a degree of subjectivity in decision-making.
- Flexibility: Discretionary arbitrage offers adaptability to changing market dynamics and allows for the inclusion of qualitative insights beyond quantitative models.
Public Strategies:
- Description: Public strategies, also termed systematic or quantitative strategies, rely on predefined rules, algorithms, and quantitative models to identify and exploit market inefficiencies. These strategies are typically more automated and rule-based.
- Approach: Public strategies are grounded in quantitative analysis, statistical models, and algorithmic trading techniques. These models leverage historical data and may incorporate machine learning to identify market patterns and signals.
- Execution: Trades in public strategies are executed automatically or semi-automatically based on signals generated by quantitative models. These strategies aim to minimize human biases and emotions, relying on systematic rule execution.
- Consistency: Public strategies strive for consistency and scalability by adhering to systematic rules and processes. They offer transparency and replicability compared to discretionary approaches.
In essence, when it comes to discretionary strategies, this is exactly what the post suggests when referring to the AADAO to process arbitrage. Unfortunately we regret to say that the AADAO does not have the required personnel to properly execute on this front. This could easily be outsourced via grants allocation or considering an additional recruitment targeted to this particular skill.
Regarding public strategies, you also proposed a basic approach with a few variants. On this front, we would like to bring our financial experience to the table and say that much more efficient strategies can be offered covering the key aspects of value assessment:
-
Volume: Professionals never use simple arithmetic moving averages, on the contrary we generally use VWAP (volume weighted average price) as it better represents an efficient liquidity allocation than regular “time-based” strategies.
-
Time: Markets are cyclical by nature, but to keep it simple, public strategies generally deal with the time constraint via maximum monthly/quarterly/yearly spending caps and we recommend to include this in the final design.
-
Volatility: due to the inherent chaotic nature of speculation, element in the design should include standard deviations to manage the liquidity provisions’ release into the open-market whilst properly accounting for the volatility factor.
Once the public strategy has been designed and the parameters defined by the community (we will be happy to offer our support on this journey), we definitely think the actual implementation is where the most challenging part is. To quote the author’s on this matter:
According to our model using a combination of VWAP + standard deviations + spending caps, we recommend to allocate to the above three option based on the volatility derivation. A simple design proposition could look like this:
- Below 1 standard deviations to the (x) months VWAP or if the (y) cap has been reached: do nothing.
- Above 1 but below 2 deviations: execute daily DEX operations by depositing single-sided liquidity to the ATOM/USD pair in Neutron. Daily Amounts should be defined according to the (x) and (y) parameters.
- Above 2 deviations: switch to a daily auction system towards market makers and arbitragers or consider using the AADAO’s discressionary strategy instead, if any.
This proposes a very simple public arbitrage strategy yet very efficient to adapt to different market conditions. The only parameters to define are the (x) to set the time based variable, and the (y) which sets the capped amounts. We can also imagine multiple programs overlapping each other on different duration. For example: a long term program with a (y) set to 1-2 years, alongside a smaller but more more regular monthly program.
We are looking forward to receive both @noam and the community feedback on our proposition. If anyone needs further explanation to some of the technical words we’ve used, please let us know.
On behalf of the entire PRO Delegators’ validator team, we want to thank you for reading this long post!
Govmos
