Week 5 (Interim assessment and preparation for presentation) #

Project status & final overview #

Product strategy: #

Based on our experiences in developing and validating hypotheses, along with insights from potential users, we recognize the need to refine and refocus our project strategy. Specifically, our exploration into using advanced AI techniques and traditional trading algorithms for predicting cryptocurrency market behavior has highlighted significant challenges at this stage. As we move forward with our open-source scientific educational platform aimed at exploring AI and trade algorithms for market predictions, we will prioritize building a robust foundation of empirical data and insights. This process will involve rigorously testing hypotheses, interpreting results effectively, and rapidly iterating to refine our approach. By leveraging these learnings, we aim to establish a platform that not only educates but also contributes valuable, actionable knowledge to the broader community interested in AI-driven market analysis.

Product value: #

Our primary aim is to share the knowledge and practical insights we’ve accumulated through weeks of researching, validating, and applying AI and trading algorithms to forecast cryptocurrency prices.

In integrating this principle into our product strategy for the open-source scientific educational platform focused on AI and trade algorithms for market predictions, we prioritize transparency and accessibility. We plan to disseminate the empirical data and practical experiences gathered during our research process. This will involve detailing methodologies, presenting validated models, and offering real-world applications of AI in predicting market trends. By openly sharing our insights and fostering collaborative learning, we aim to empower users with the tools and understanding needed to navigate and leverage AI effectively in financial markets. This approach not only enriches our platform’s content but also cultivates a community of informed practitioners and enthusiasts committed to advancing the field of AI-driven market analysis.

Product Features and Benefits: #

Open Source Code:
- Interpretation: Our platform provides open-source code encompassing the methodologies and techniques utilized in predicting cryptocurrency market behavior.
- Benefits:
  - Transparency and Trust: Users can examine and verify the algorithms and techniques used, fostering transparency and building trust in our approach.
  - Learning and Adaptation: Researchers and developers can study, modify, and adapt our methods for their own research or commercial applications.
Interactive Web Dashboard:
- Interpretation: We offer an interactive web dashboard presenting statistics on the trade algorithms and AI models employed.
- Benefits:
  - Data Visualization: Users can visualize and analyze the performance metrics of different algorithms, aiding in scientific research.
  - Decision Support: Traders and investors can use these insights to make informed decisions, enhancing their trade and investment strategies.
Support for Setting Up Autonomous Systems:
- Interpretation: We provide assistance and support for reusing our code to establish autonomous systems capable of executing trades autonomously on real exchanges, with consideration for associated risks.
- Benefits:
  - Efficiency and Automation: Users can automate trading operations, potentially improving efficiency and reducing manual workload.
  - Risk Management: Guidance on risk assessment helps users implement safeguards and manage risks associated with autonomous trading systems effectively.

Usage of Knowledge in Product Strategy Development: #

In developing our product strategy, these features and benefits will guide us in creating a comprehensive and user-friendly platform. By emphasizing transparency through open-source code, providing actionable insights via interactive dashboards, and supporting the implementation of autonomous trading systems with risk management in mind, we aim to empower both researchers and traders alike. This approach not only enhances accessibility to advanced AI and trading algorithms but also fosters a community of learning and innovation in the field of cryptocurrency market prediction.

Feedback #

In week 5, we conducted a second round of user surveys using the Google forms service to get feedback on our product and adjust our current development strategy. As a result, we gathered 15 feedback responses and had several informal, friendly conversations about our project. Below you can see the statistical results we have obtained.

Google form statistics #

feedback

Results interpretation: #

One key finding is that most respondents positively recognize the project’s importance and support its further development.
Sixty percent believe it is possible to predict market behavior using AI and trade algorithms, indicating that people tend to trust this technology and actively use it for their investment decisions.
In both the initial and current surveys, most respondents believe that markets cannot be predicted and behave erratically.
The primary educational resources for trading, investing, trading algorithms, and AI financial desicion making systems are YouTube and scientific literature. This information can be used in future marketing strategies to draw attention to our project.
The overwhelming majority are interested in this topic primarily for financial reasons. Secondary interests include understanding market structures and predictive tools, followed by a desire to learn something new and improve financial literacy.
Finally, the initial non-commercial business model based on donations is unsustainable and necessitates adopting a new capitalization strategy.

Conclusions: #

There is a persistent contradiction where people believe that AI and algorithms can predict the market, yet also acknowledge that financial markets are volatile and unpredictable. Given this insight, our product strategy for developing an open-source scientific educational platform should address both perspectives. By exploring AI and trade algorithms for market predictions, we can educate users on the capabilities and limitations of these technologies. This approach will build trust in our platform, providing balanced, research-based content that helps users make informed decisions about leveraging AI for market analysis while understanding the inherent unpredictability of financial markets.
The primary platforms for promoting our project will be YouTube and popular science communities. With this in mind, our product strategy for developing an open-source scientific educational platform should focus on leveraging YouTube and popular science communities to reach and engage our audience. By utilizing these channels, we can effectively showcase our exploration of AI and trade algorithms for market predictions, attract a wider audience, and build a community of learners and contributors interested in the intersection of AI and financial markets.
We need to consider a new capitalization model since a non-commercial educational platform is not viable in the early stages of our project. Offering private research services for the design, validation, and interpretation of AI and trade algorithms to individuals and companies could sustain the project until it gains credibility and trust from larger audiences and corporations. To incorporate this into our product strategy, we will initially focus on providing specialized private research services, which will generate revenue and support the development of our open-source scientific educational platform. As we build a reputation for delivering high-quality insights into AI and trade algorithms for market predictions, we can gradually transition to a more community-driven, non-commercial model once we have established trust and a solid user base.

Progress Report #

ML component: #

Done:

The training environment for the model was completed.
The final predictive model was validated and pre-trained.
The data collection and preprocessing pipelines was finalized.
An API interface was designed and initiated to facilitate communication between the model and an autonomous decision-making engine.

Key tasks for next week:

Utilize an API interface to integrate with a backend component that interprets the model’s output data, enabling the execution of transactions on the stock exchange based on the predictions.

Backend component: #

The recordered video with first test and intepretation of trade algorithms results: https://www.youtube.com/watch?v=Gkx4g3Dc-IU

Done:

We have financed the development of trade algorithms and prepared a system for making trade decisions on their predictions.
An API interface was designed and initiated to facilitate communication between the model and an autonomous decision-making engine.
Began planning for deploying the project on a remote server and initiating documentation efforts.

Key tasks for next week:

Utilize an API interface to integrate with a ML component that provides the prediction insights based on data from cryptocurrency exchange.

Frontend & Design components: #

Done:

The design has been finalized and adjusted based on the suggestions provided by the TA since the last review session.
Prepared the environment and set up a repository for frontend development.
Started actively developing main design pages using Vue.js framework.

Link for final design: https://www.figma.com/design/01rc3K1AUBfTan3Pw3I8eI/ATS-Mockup?node-id=0-1&t=tQI0KO4DD47yE9Yt-0

Key tasks for next week:

Finish developing the first version of the site based on the design and deploy it using GitHub action or natively using remote server.

Challenges & Solutions #

We encountered challenges in scaling the feedback collection process. To address this, we employed personalized and individualized approaches to each respondent to enhance motivation for survey participation.
Because our system is multi-component, we are facing challenges in integrating and synchronizing its components. To address this, we intend to dedicate the entire sixth week to resolving these issues through intensive design efforts and seeking advice from other teams.

General conclusion #

In conclusion, our journey through Week 5 has provided valuable insights and progress across multiple fronts in our project. We have refined our product strategy based on feedback and user insights, emphasizing transparency, accessibility, and the practical application of AI and trading algorithms in predicting cryptocurrency markets. Key achievements include finalizing the design, completing the initial version of our website, and advancing our ML and backend components to enable predictive analytics and autonomous decision-making.

Looking ahead to Week 6, our focus will be on achieving seamless integration of our system components and preparing for the next phase of deployment. We will continue to prioritize rigorous testing and refinement of our models, enhance user engagement through educational content and interactive tools, and explore new avenues for community outreach and support. By addressing challenges with scalability and component integration head-on, we aim to strengthen our platform’s capabilities and readiness for broader adoption and impact in the realm of AI-driven market analysis.

Week 6 Objectives:

Resolve integration challenges through intensive design efforts and collaboration with other teams.
Finalize deployment on a remote server and document the process comprehensively.
Further develop and refine user interfaces and interactive features based on ongoing feedback and usability testing.
Begin preparations for marketing and community engagement strategies, leveraging insights from user surveys and platform analytics.

With these objectives in mind, we are committed to advancing our mission of democratizing AI knowledge and empowering users to make informed decisions in financial markets.

Week 6 Objectives:

Resolve integration challenges through intensive design efforts and collaboration with other teams.
Finalize deployment on a remote server and document the process comprehensively.
Further develop and refine user interfaces and interactive features based on ongoing feedback and usability testing.
Begin preparations for marketing and community engagement strategies, leveraging insights from user surveys and platform analytics.

With these objectives in mind, we are committed to advancing our mission of democratizing AI knowledge and empowering users to make informed decisions in financial markets.