Analytics Working Group - S2 Proposal

Analytics Working Group - S2 Proposal

Proposal for Inverse Finance DAO to cover operations of the Analytics Working Group (AWG) in Season 2, running for 6 months from May 1, 2024 to October 31, 2024.

1. Introduction

This proposal outlines the agenda for Season 2 of the Analytics Working Group (AWG) within the Inverse Finance DAO. Leveraging the success and lessons of Season 1, we aim to deepen our data analytics capabilities to guide strategic decision-making and stimulate growth. For transparency purposes this proposal will be provided with detailed annexes about the AWG achievements last season (Annex I) as well as the cost involved (Annex II) and available on the (this) forum.

2. Analytics Working Group Overview

2.1 Role and Importance within Inverse Finance DAO

The AWG’s primary role is to analyze, interpret, and leverage data for strategic decision-making, through the provision of analytics tools and infrastructures, thereby offering invaluable insights, identifying growth opportunities, and highlighting improvement areas.

2.2 Achievements of Season 1

In Season 1, the AWG enhanced its operational excellence. Leveraging Docker, we streamlined our infrastructure, cutting costs to $455 monthly and doubling computational capacity. Our day-to-day operations—developing data queries, producing reports, and managing data inquiries—remained strong. We improved our subgraph availability with The Graph team, quickly resolving service disruptions.

Significant achievements include :

  • Maintaining and monitoring the stability and efficiency of our systems 24/7 ( and associated services)
  • Building - and using - a blockchain simulation framework on Inverse Watch,
  • Building various dashboards relying on those systems including :
    • Analytics Weekly Highlights dashboards ;
    • 1 Inch & Curve Price Simulator Dashboards ;
    • DBR Rewards and Virtual Auction ;
    • sDOLA & DOLA Savings Account ;
    • FiRM Positions per User ;
    • DOLA health dashboard.
  • Integrating LLM bots for our documentation and machine learning pipelines,
  • Integrating discord bots to continuously monitor DOLA prices or smart contract functions,
  • Launching an advanced graph visualization mode on Inverse Watch,
  • Implementing an API documentation on inverse watch

These initiatives improved our analytics, risk modeling, and made transactional data more intuitive, reinforcing the platform’s role in strategic decision-making for the DAO.

More details are available in the Annex I.

2.3. S1 Success Metrics

  • Number of Weekly AWG Insights Reports Published: During Season 1, we successfully published 19 out of 21 planned weekly AWG Insights reports since their launch, providing consistent and valuable analysis to the DAO.
  • Effectiveness of Insights Generated: Assessing the direct impact of insights generated proved challenging due to the low use of the data request forum on discord that allows to track thematically and timely those topics.
    However our internal alert systems have allowed risk and treasury to monitor markets continuously, facilitating proactive decision-making in those channels in response to market events (alert statistics available in the Annex).
  • Infrastructure Robustness: We aimed for less than 0.1% downtime across our systems and tools, including dashboards and alert systems. While our systems maintained near 100% availability (as indicated on, there were isolated instances of downtime, particularly affecting a key product hosted on The Graph decentralized network. We acknowledge these challenges as opportunities for improvement and remain committed to enhancing our infrastructure resilience and reliability in Season 2.

3. WG Goals for Season 2

For Season 2, the AWG is set to broaden its analytics capabilities, further refine our tools, and strengthen our collaboration with other working groups, aiming to create a more cohesive and data-driven DAO.

3.1 Strategic Objectives

Our mission is to empower the DAO with robust data-driven decision-making tools. We are committed to preserving our infrastructure’s integrity, enhancing our visualization capabilities, and incorporating cutting-edge machine learning and AI technologies to support the DAO’s strategic initiatives.

3.2 Usual Objectives

The main objective of the AWG is to continue operating and maintaining its systems “as-usual” in order to ensure their availability and stability and guarantee their effectiveness.

3.3 Additional Projects for Season 2

We plan to introduce more simulation tools and enhance and advertise the use of our LLM-powered bots. Additionally, we aim to apply more advanced analytical techniques for transaction and anomaly detection to improve the DAO’s operational resilience and decision-making precision.

  • Advanced Data Connectors and Visualization Tools : Our objective to integrate more diverse data sources and improve our visualization tools has been achieved, we now aim to continuously improve them.

  • Machine Learning and AI Capabilities : We are planning to expand our application of machine learning and AI, aiming to build predictive models and conduct trend analysis that can anticipate market movements and inform strategic decisions.

  • Bots and Automation Projects : We plan to further develop and enhance our suite of AI bots and infobots, improving community engagement and operational efficiency through advanced automation and AI capabilities.

  • Alert monitoring page : We plan to build a mechanism on inverse Watch that would allow team members to collaborate on and manage the alerts built and triggered by our system.

3.4. Success Metrics for S2

Success in Season 2 will be quantified through metrics such as infrastructure uptime, the frequency of insight report publication, and the completion of the above listed projects.

3.5. Responsibilities and Focus Areas

  • Data Strategy Development and Governance : Our focus remains on refining our data strategy to ensure it aligns perfectly with the DAO’s goals. This involves comprehensive data management and governance, ensuring data integrity, security, accessibility and affordability.

  • Tooling, Infrastructure Maintenance and Improvement : The AWG is dedicated 24/7 to maintaining and improving the robustness of its platform and tools, ensuring they operate efficiently and are continuously improved to meet the DAO’s evolving needs.

  • Data Analysis, Interpretation, and Insight Provision : We will continue to utilize and improve our tools to delve deeper into data, offering clear, actionable insights that can significantly influence the DAO’s strategic directions.

  • Dashboard & Report Generation : The generation of detailed reports on various topics remains a priority. These reports play a crucial role in guiding risk decision processes in the DAO.

  • Alerting System and Automated Socials Enhancement : We aim to further refine our sophisticated alerting system, ensuring we remain vigilant and responsive to any operational or financial risks that may arise.

  • Collaboration, Consultation, and Data Literacy Advocacy: By enhancing our collaboration with other working groups, we seek to provide essential data support and promote a culture of data literacy across the DAO, empowering all members with the tools & knowledge to make data-informed decisions.

  • Other AWG Activities : Like other team members @naoufel is on standby 24/7 on the following Multi Sigs :

    • Fed Chair
    • Treasury Working Group
    • Policy Committee

4. Decision-making Power and Governance

The AWG will maintain its decision-making authority over analytics infrastructure and tooling. This autonomy ensures we can efficiently allocate resources in alignment with our strategic goals, driving forward our analytics capabilities.

5. SecOps Responsibilities and Enhancements

Our ongoing collaboration with the Treasury and Risk working groups highlights our vital role in providing the technical expertise and analytical support necessary for the DAO’s operational and risk management strategies.

6. Budget for Season 2

  1. S1 Budget summary and remainder

The budget for S1 originally included $5,048 with an additional $3,800, totaling $8,848. However, the total cost for season 1 amounted to only $4,527.08, which is notably lower than anticipated. This surplus allows for coverage of expenses for several months beyond what was initially planned for season 1 (namely March and April). Such fiscal efficiency provides flexibility for potential future expenses or investments and reflects effective budget management during season 1.

For The Graph network cost, an optimization in the platform queries has led to lower our GRT consumption considerably a few epochs ago which led us to spare the budget allocation reserved for GRT tokens market purchases.

Detailed budget considerations for tooling, infrastructure maintenance, and potential project expansions will be provided separately, ensuring transparency and accountability in the allocation of DAO resources.

  1. Contributors
Name FTE Band Pro-Rata Monthly Salary Total for Season 2
Naoufel 1 B 12,000 72,000
  1. Ad-hoc & Tooling
  • Quicknode : 49 $ / month
  • Digital Ocean : ~500$/month
  • GRT Tokens: $1,200 for S2 (unused in S1)
  • OpenAI : ~ 0 to 50 $/month
  1. Flexible Budget

AWG requests a flexible allowance of 500 DOLAs to cover tooling unforeseen expenditure that arises during the Season.

  1. Summary
A S2 $DOLA allowance S2 $INV allowance
Contributors 72,000 0
Ad Hoc & Tooling 3,594 0
Flexible Budget 1,700 0
Total 77,294 0

10. Conclusion

The AWG’s achievements in Season 1 have laid a robust technical foundation for future advancements in data analytics within Inverse Finance DAO.

With Season 2, we aim to build upon this success, introducing innovative strategies to enhance decision-making and operational efficiency across the DAO.

1 Like

AnnexI : S1 Achievements

  1. Operational Efficiency and Infrastructure Updates

In Season 1, the AWG successfully maintained and enhanced the operational excellence of the Inverse Finance DAO. By maximizing the use of Docker’s containerization technology, we streamlined our data infrastructure, reducing our monthly costs while doubling our computational capacity.

Alongside these infrastructure improvements, our daily operations remained robust. We were diligent in creating data queries and alerts, generating analytical reports, responding to data inquiries, and enhancing our alerting systems performance and responsiveness.

An integral component of our infrastructure is the subgraph, which necessitates close coordination with Indexers and The Graph team. Recently, we navigated disruptions in this service with proactive measures, collaborating effectively to minimize downtime and restore service promptly.

These concerted efforts have reinforced the resilience and efficiency of the Inverse Watch platform, securing its vital contribution to the DAO’s decision-making process.

  1. Alerting Systems

  • Objective: Enhance real-time monitoring capabilities and improve the existing alerting system for the Inverse Finance DAO to ensure financial stability and operational efficiency.
  • Implementation: We significantly expanded our SQL-based alerting system to include continuous monitoring and the development of additional alerts focused on critical financial metrics and operational events. This expansion aimed to safeguard the DAO against market volatility and operational risks.
  • Key Developments:
    • Peg Monitoring for Various Pools across the Curve Ecosystem: Implemented alerts for significant price variations in key liquidity pools, such as yETH-wETH, cvxCRV-CRV, yCRV-CRV, and cvxFXS-FXS. These alerts ensure real-time monitoring of the financial health of our pools and enable swift action to maintain peg stability.
    • High-Value Transaction Alerts: Developed an alert for Twitter to monitor and announce sDOLA deposits enhancing transparency and stakeholder awareness of significant financial movements within our ecosystem.
    • Operational Event Monitoring: Introduced several new alerts for Discord, including sDOLA Auction, DBR Virtual Auction, sDOLA operations, DBR RewardRate Update, and ALE Events. These alerts facilitate real-time operational monitoring and community engagement, ensuring that stakeholders are promptly informed of key events and auctions.
  • Achievements:
    • Improved Response Capabilities: The enhanced alerting system has significantly improved the DAO’s ability to respond swiftly to financial and operational anomalies, contributing to a more stable and responsive ecosystem.
    • Increased Transparency and Engagement: With real-time alerts across Discord and Twitter, we’ve fostered a more engaged and informed community, leading to increased transparency and trust in DAO operations.
    • Operational Efficiency: The automation and specificity of alerts have reduced manual monitoring efforts, allowing team members to focus on strategic initiatives and improvements.

Alert Statistics

Month Total Alerts Risk Alerts
Apr-24 598 6
Mar-24 765 31
Feb-24 335 22
Jan-24 344 9
Dec-23 306 26
Nov-23 433 26
Oct-23 642 26
  1. Bots and Socials

  • Objective: Develop advanced bots to support real-time market information tracking and enhance community interaction with sophisticated AI capabilities.
  • Implementation: Created the Inverse Flaskbot, a self-hosted Discord chatbot equipped with advanced data processing and AI functionalities (API and/or self-hosted). This bot interfaces seamlessly with a Flask API and employs a variety of machine learning technologies, including Ollama, Torch, Diffusers, Transformers, MistralAI models, and the Stablediffusion pipeline for independent text and image generation.
  • Key Features and Technologies:
    • Real-time Market Monitoring: Developed and maintains Discord bots that track and report market metrics in real time, crucial for maintaining awareness of asset stability and market conditions.
    • Advanced AI Integration: Additional bots utilizes a range of AI technologies to offer dynamic interactions, such as independent image and text generation, catering to user queries and data requests.
    • Diverse Command Set: Includes commands for documentation retrieval (/doc), image generation (/imagine)
  • Achievements:
    • Operational Efficiency: By automating data retrieval and documentation processes, we’ve streamlined the flow of information and reduced the workload on team members.
    • Documentation and Designer Bot Development: In addition to market monitoring bots, we’ve crafted documentation and design bots that assist with internal and external communication, providing easily accessible and up-to-date information to the community.
  1. Report Publication

  • Objective: Deliver actionable intelligence to Inverse Finance stakeholders through regular and comprehensive analytics reporting.
  • Implementation: Initiated the “Analytics Weekly Insight” series, a weekly publication dedicated to analyzing the DAO’s operational metrics, market movements, and governance activities.
  • Key Features of the Reports:
    • Supply & Market Capitalisation Analysis: Provides a weekly overview of the market capitalisation trends, highlighting significant changes in the INV and DOLA ecosystems. For example, Week 12 of 2024 noted a stabilization in market capitalisation above USD 30 million despite a week-over-week decrease.
    • DOLA Feds and FiRM Review: Details activities and changes within the DOLA Feds, such as expansions in borrowing capacity, and reports on the Total Value Locked (TVL) and borrowing trends within the FiRM protocol.
    • Significant Transactions and Market Movements: Identifies and analyzes large deposits and withdrawals, as well as significant price changes in ecosystem tokens such as INV, DOLA, and DBR.
    • Liquidity Pools and Governance Updates: Offers insights into DOLA liquidity pool dynamics and provides updates on governance proposals and their outcomes.
  • Achievements:
    • Successfully published the “Analytics Weekly Insight” since November 2023, creating a consistent and reliable source of information for the DAO community.
    • Enhanced stakeholder understanding of market dynamics, operational performance, and governance activities, supporting informed decision-making across the DAO.
    • Fostered a culture of transparency and data literacy within the DAO by making complex data accessible and understandable to a broad audience.
  • Methodology & Tools: Utilized Python scripts and SQL queries, relying on our in-house data analytics platform, Inverse Watch, to aggregate and analyze data from multiple sources. This approach underscores our commitment to accuracy, leveraging the best tools and methodologies for data processing.
  1. Simulation Capabilities

  • Objective: Enhance the AWG and RWG’s ability to simulate complex transactions and assess risks at scale, thereby supporting strategic decision-making and risk management within the DAO.
  • Implementation:
    • Integrated simulation capabilities into the Inverse Watch platform, leveraging Ganache and Brownie for blockchain environment emulation.
    • Developed dedicated libraries for simulating transactions on major DeFi protocols like 1inch and Curve.
    • Created comprehensive simulation scripts tailored for the Risk Department to model buys, sells, and complex transaction patterns, as well as to impersonate addresses on-chain.
  • Achievements:
    • Risk Management and Strategic Planning: The simulation tools have become a cornerstone for the DAO’s risk management strategy, providing actionable insights into potential market impacts of various strategic moves.
    • Operational Efficiency: By automating and scaling the simulation of transactions, we have significantly reduced the time and resources required for risk assessment and strategic planning exercises.
    • Educational and Supportive Role: Given the technical challenges associated with learning Python, these tools also serve an educational purpose, making advanced simulations accessible to the Risk Department and other non-technical stakeholders.
  • Methodology & Tools: Utilized Python, Brownie, and web3 technologies, alongside Ganache for local blockchain emulation. Data is processed and analyzed using Python scripts and SQL, relying on APIs for ABI fetching and price data. This multi-faceted approach ensures accurate, real-time simulations that reflect current market conditions and contract interactions.
  1. Graph Visualization

  • Objective: Implement an advanced visualization mode in the Inverse Watch platform to depict transactional data more intuitively as interactive graphs with nodes and edges, enhancing the user’s ability to comprehend complex transactional relationships.
  • Implementation: Integrated a graph-based visualization system utilizing D3.js to render transactional data dynamically. This mode represents each transaction as a link between nodes, with each node representing a unique address or entity within the network. It allows users to visually trace the flow of assets, identify key participants, and understand the underlying structure of transactional relationships.
  • Technical Highlights:
    • D3.js Library Integration: Employed the D3.js library’s powerful rendering capabilities to create a graph that is both performant and scalable, capable of handling large sets of transactional data.
    • Data-Driven Visualization: Utilized various D3.js modules such as force-directed graphs, zooming, dragging, and scalable vector graphics to facilitate an interactive user experience.
    • Interactive Elements: Nodes and links in the graph are interactive, enabling users to hover over elements to see additional details, such as transaction values and address information.
    • Customizable Views: Implemented features that allow users to filter and search for specific transactions or addresses, focusing the graph visualization on relevant parts of the data.
  • Achievements:
    • Enhanced User Insight: The visualization mode has significantly improved the ability of users to analyze complex chains of transactions at a glance, which is essential for effective risk management and decision-making.
    • Intuitive Data Interaction: By representing data as an interactive graph, users can now engage with the information in a more meaningful way, leading to a deeper understanding of transaction patterns.
    • Improved Data Accessibility: This innovation has made the data accessible to a broader audience, including those who may not be as familiar with traditional data analysis methods.

Annex II - AWG Expenses

Monthly Cost History

Table of Cost

September 2023 9-2023 Digital Ocean $ 324.47 $ 324.47
September 2023 9-2023 Quicknode $ 299.00 $ 623.47
September 2023 9-2023 $ 96.00 $ 719.47
October 2023 10-2023 Quicknode $ 299.00 $ 1,018.47
October 2023 10-2023 Digital Ocean $ 278.40 $ 1,296.87
November 2023 11-2023 Quicknode $ 299.00 $ 1,595.87
November 2023 11-2023 Digital Ocean $ 324.47 $ 1,920.34
December 2023 12-2023 Quicknode $ 299.00 $ 2,219.34
December 2023 12-2023 Digital Ocean $ 362.40 $ 2,581.74
January 2024 1-2024 Digital Ocean $ 478.44 $ 3,060.18
January 2024 1-2024 Quicknode $ 299.00 $ 3,359.18
February 2024 2-2024 Google Cloud $ 391.90 $ 3,751.08
February 2024 2-2024 Digital Ocean $ 477.00 $ 4,228.08
February 2024 2-2024 Quicknode $ 299.00 $ 4,527.08
End of S1 budget
March 2024 3-2024 Digital Ocean $ 470.58 $ 4,997.66
March 2024 3-2024 Quicknode $ 49.00 $ 5,046.66
March 2024 3-2024 OpenAI $ 60.00 $ 5,106.66
April 2024 4-2024 Digital Ocean $ 475.20 $ 5,581.86
April 2024 4-2024 Quicknode $ 49.00 $ 5,630.86
April 2024 4-2024 OpenAI $ 0.58 $ 5,631.44