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A decentralized prediction market with AI - a deeper integration of profitability and emotion

Why do we trade an asset? For honor, for wealth, for fame, or for a feeling of winning with minimum cost? It is complicated, however, one thing is certain, it is definitely not for waiting.

In traditional trading, an asset must fluctuate beyond a certain price range for a retail trader to profit. However, there’s no assurance that the asset will reach such levels within a specific timeframe, leading to uncertainty in profit timing. Additionally, many clients engage in trading based on various time horizons, opinions, and news, rather than solely on asset prices. Yet, they often find themselves compelled to participate in wagering on asset prices, which can be influenced by numerous factors. Rarely do financial products exist that allow trading on opinions and events instead of market prices.

Let’s go deeper, most of the exchanges deliver the same combination of asset class and sentimental values to the clients which attract traders that share similar personalities. How can a financial product be designed that delivers a unique combination of profit and sentimental value, lets more people feel attached and engaged?

Current prediction markets offer some relevance but are not the perfect solution. They present a variety of events for traders to speculate on, evoking different emotional responses. However, in the competition for user attention, these platforms distribute focus evenly across various events without creating ongoing engagement. This approach fails to generate significant attraction towards any single topic. Consequently, users do not reach a level of engagement where they are motivated to make transactions. This lack of focused engagement undermines the potential effectiveness of these markets.

Sentimental values

  • Relevancy: is crucial for an event to resonate strongly with users’ emotions, prompting them to express their opinions through transactions. The topics offered should not only align with users’ interests but also be highly debatable, carrying significant stakes for winning and sparking extensive conversations. The platform must time the events right since many topics are very timely and sensitive there should be very little transition gap between the user first understanding the events, making a judgment, and making a transaction in the platform. Implementing an AI-based web scraping tool, combined with a personalized recommendation system similar to those used in e-commerce websites, could help establish a dynamic and highly engaging event market. This approach would tailor event selections to individual user preferences and trends, fostering a more interactive and emotionally invested trading environment.
  • Sustainability: Event creators supply the platform with topics and are compensated with tokens to keep the content current and appealing. Their rewards are tied to the engagement levels of users. The trading process, requiring constant attention, piques users’ interest, traders to explore the events and related products further. This scenario presents a perfect opportunity to integrate product marketing. Additionally, care is taken to steer clear of topics that might provoke political or humanitarian biases, guaranteeing a trading experience that is enjoyable and safe for everyone.


  • Depth of information: For users to realize profits, the market requires substantial liquidity. One method to enhance order book depth is by positioning the platform as a rich source of event-related information. This approach enables users to gain and consolidate extensive knowledge through their interactions with the product. Providing users with superior knowledge through the platform will attract higher-quality traders, encouraging more active participation in daily trading activities.
  • Timing: Using a trading bot via direct messaging apps, such as Telegram bots, offers an efficient method for users to execute orders. This technology reduces the time users spend on making bets during the day and helps increase trading volume following market trends. Currently, many trading bots operate with fixed settings and lack machine-learning capabilities. The focus is on decreasing the time it takes for users to develop machine learning-enabled bots. This involves the ability to convert abstract, vaguely defined user requests into well-defined models with solid mathematical interpretations.
  • Sustainability: Ensuring data fairness and reliability is vital for the longevity of the prediction market. It’s essential to go beyond just token incentives and include direct revenue sharing with data vendors to secure high-quality data. One viable approach is the implementation of a commission fee-sharing model with these top-tier data providers. This strategy emphasizes the importance of maintaining a sustainable and reliable data ecosystem for the prediction market.

Focusing on an intent-centric approach, which prioritizes user intent with minimal distractions and delays, is essential. Our goal is to provide a seamless event trading experience, enabling users to quickly profit from their knowledge and insights.

However, for the system to function effectively, alignment of intent across various parties is necessary. Data creators, product marketers, and traders should all direct their focus towards the product. The process begins with profitability but ultimately revolves around capturing attention, creating a cyclical loop. This attention is then channeled into the product, provide sentimental values and form up new bondings between users and events. In this precise and coherent prediction market, all parties can efficiently achieve their objectives, and this is the future of finance that we are fighting for.


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