completed

Forecasting Cardano Native Tokens

$9,900.00 Received
$9,900.00 Requested
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Community Review Results (1 reviewers)
Addresses Challenge
Feasibility
Auditability
ソリューション

私たちは、複雑なシステムのための機械知能のツールに基づいて、ネイティブトークンの自動評価方法の開発を提案します。

Problem:

ADAに比べて流通量が少なく、基礎的な要因に複雑に依存しているため、カルダノのネイティブトークンの評価は困難です。

Yes Votes:
₳ 53,579,626
No Votes:
₳ 4,073,544
Votes Cast:
139

This proposal was approved and funded by the Cardano Community via Project F7: A.I. & SingularityNet a $5T market Catalyst funding round.

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Detailed Plan

Since March 1, 2021, the Cardano blockchain has provided support for native custom tokens, which benefit from the same infrastructure and security features as the ADA cryptocurrency. In addition, by sidestepping the requirement of smart contract coding (in contrast with non-native tokens that run on Ethereum) and by being naturally coupled to work on the Cardano blockchain, Cardano native tokens are more efficient, error-prone and offer reductions in transaction costs.

However, the valuation of Cardano native tokens remains a subtle problem, given the fact that they are complex assets which may be backed up on or stand in representation of other underlying assets, services or goods as defined by the token issuer. A further complication in the valuation or forecasting of native tokens ensues from their limited circulation relative to the ADA cryptocurrency.

It is a well known fact that financial markets are complex systems with interesting macroscopic behavior, such non-trivial time correlations, scale invariance and heavy-tailed distributions. Given the myriad non-trivial dependencies and correlations on underlying factors inherent to the market of native tokens, their valuation is not a straightforward matter and may be similarly amenable to understanding from the point of view of complex systems and time series analysis.

Given the spectacular successes of machine learning methods over the past decade for pattern detection and prediction in all kinds of practical and academic applications, we propose the development of machine intelligence systems, based on reinforcement learning methods for complex systems, with the purpose of the valuation and forecasting of Cardano native tokens. This machine learning application promises to reflect the real value of these assets based on the processing of information that is publicly available and relevant to the valuation of said assets, thereby providing a powerful tool for the community, whereby fair assessments of these tokens can be produced with a rigorous and systematic methodology.

The success of this project consists in the development of a practical, user-friendly machine intelligence system for Cardano native token valuation with an accurate performance as a value predictor of said assets.

Impact:

Nelson has met with Singularity.Net to discuss hosting the completed algorithms.

The reinforcement algorithms will provide the Catalyst community with tools to assist in the valuation of Cardano Native Assets. The F7 award will complete gathering of native asset data and correlated signals for prototyping of the machine learning algorithms. A F8 proposal will be submitted to evaluate the performance of the prototype. A F9 proposal will be submitted to demonstrate the tool with users.

Feasibility:

Team Capability: Nelson leads Photrek which has expertise in the development of machine intelligence algorithms for decision-making in complex environments. Zubillaga is an expert in modeling financial systems and is currently developing reinforcement algorithms. He will design the reinforcement algorithms for control of trading bots.

Roadmap:

A six-month plan with three 2-month phases.

  • Phase 1: Research native tokens launches. Who is planning to launch what and when?
  • Phase 2: Research price of existing native tokens and what are the primary influences of those prices.
  • Phase 3: Design the machine learning algorithm.

Total Budget: 9900 USD.

Each phase will be budgeted at 3300.

The project allocations will be: 50% Applied Research, 25% Data Analytics, 25% Reporting to Catalyst and Singularity Communities.

Auditability:

Success will be identification of a set of Native Tokens with significant potential future value and a set of features of the Cardano Ecosystem that are relevant for training forecasting algorithms.

Key Metrics:

  • Number of native tokens analyzed.
  • Number of features identified for forecasting.
  • Number of engagements with community users.

コミュニティ・アドバイザー・レビュー (1)

Comments

Monthly Reports

Please change the primary point of contact for the project to Kenric Nelson, [email protected]. Bernardo Zubillaga's availability is uncertain. Further details are in the uploaded project plan.

Disbursed to Date
$9,900
Status
Still in progress
Completion Target
8/31/2022
Attachment(s)
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Thank you for the support. The meetings this month with SingularityNET have been inspiring. This has opened up a variety of new opportunities for Photrek to develop capabilities in Machine Intelligence that will support an equitable, free, sustainable planet.

Disbursed to Date
$9,900
Status
Still in progress
Completion Target
8/31/2022
Comments 0

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Photrek's customer engagements for FCNT include: a) Six briefings to the SingularityNET community including a presentation to the founder Ben Goertzel. These discussions led to submission of two proposals to SNET's Deep Fund. A $30k proposal will support development of "Risk-aware AI Assessments" and a $115K proposal will support development of a "Risk-aware Data Generator". The conversations identified an important need to simulate gaps in time-series data so that AI training sets can be complete. We are also discussing requirements for agent-based simulations of markets.

b) Photrek met with Rick McCracken of DripDropz to discuss valuation of analysis of newly launched Cardano Tokens. These are ongoing discussions to determine how Photrek's risk analysis capabilities can be applied to supporting the DripDropz process of launching tokens for the Cardano community.

Disbursed to Date
$9,900
Status
Still in progress
Completion Target
8/30/2022
Comments 0

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The customer support via SingularityNet and Drip Dropz has allowed Photrek to increase its staffing for development of risk-aware ML methods. As such, Photrek will complete the F7 FCNT project early. The project was originally planned as a six-month project to end August 31st. We are now planning to close-out the project by July 22nd. The new schedule is:

Kenric Nelson and Kevin Chen will host a Catalyst Swarm breakout presentation on July 9th. This session will review the capabilities of the open-source NeuroProphet software for learning models of time-series data, such as crypto markets.

The July report will be submitted between July 11th and July 15th.

A close-out report and video will be completed and submitted by July 22nd.

Disbursed to Date
$9,900
Status
Launched
Completion Target
7/22/2022
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Schedule for Completion and Next Steps

  1. The FCNT Final Report and Video will be submitted by July 22nd.
  2. Photrek will continue discussions with Drip Dropz and Adosia regarding analytical support for these Cardano development teams. Analysis for Drip Dropz token airdrops will wait for an up turn in the cryptocurrency market. An advisor role for Adosia's work on decentralized IOT is being defined.
  3. Photrek won two SingularityNET DeepFunds that are now underway. "Risk-aware Assessments of AI Algorithms" is a 4-month, $30K project and "Risk-aware Data Generator for SingularityNet Applications" is a 6-month, $115K project.
  4. Photrek has submitted a F9 Dapps, Products, and Integration proposal, "Learning Dynamic Models" that will extend its Coupled VAE architecture to dynamic time-series.
  5. The capabilities initiated under the FCNT project will be applied to work in environmental sciences. In this area Photrek is proposing a Phase II effort to calibrate and integrate sound pollution sensors, and Photrek is rewriting a proposal to the National Science Foundation regarding severe weather forecasting.
Disbursed to Date
$9,900
Status
Launched
Completion Target
1. In the next month
Attachment(s)
Comments 0

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