Group: wiggly-donut
We want to build a live dashboard that tracks news-focused prediction markets across multiple platforms (starting with Kalshi and Polymarket). Prediction markets are useful because they aggregate public opinion into an implied probability, often producing surprisingly strong forecasts in practice. We want to create a cross-market “basket” signal instead of treating any one platform as ground truth. By combining probabilities across these sites, we get a more robust read of what expectations are shifting in real time.
We want to do this because of the news relevance. Prediction prices move when new information arrives, so the biggest probability changes over the last week can act like a filter for current affairs on the things that have changed massively. Concretely, we’ll ingest market data from Kalshi’s public market-data endpoints and Polymarket’s read-only Gamma Markets API, compute “top movers,” and then link each moving market to the most relevant headlines via NewsAPI.
The News API: https://www.thenewsapi.com/
Kalshi API: https://docs.kalshi.com/welcome
Polymarket API: https://docs.polymarket.com/quickstart/overview
RQ1: Which 10–20 news-related prediction markets show the largest change in implied probability over the last 7 days (or last 24h), across a combined basket of prediction markets?
RQ2: What type of news provokes the most significant movement on prediction markets?
RQ3: What are the most common volatile prediction topics/placements over a week period and how are their evolution correlated with news cycle
Notebook link:https://github.com/nav-v/adv-comp-project/blob/main/proposal.ipynb
Target dashboard layout (2-panel, interactive):
Top panel: “Top Movers (Last 7 Days) - shown above.
The top panel will have line charts showing the top 10/20 markets by probability change. Each chart will show the basket’s implied probability over time (e.g., last 7d). The x axis would be time, whilst the y exist would be implied probability.
Bottom panel: “What’s the story?”
When you click a market in the top panel, the page scrolls/jumps to a details section showing:
Market title + platform(s)
Latest probability + 7d change
Relevant headlines pulled from NewsAPI (title, source, timestamp, link), using the market’s keywords.
Through our retrospective, we found that our initial API tests were successful: both the Kalshi and Polymarket APIs worked properly, and we successfully implemented the average probability function across the two platforms. However, we have two primary known unknowns we still need to address. First, we need to finalize our matching strategy for connecting news articles to specific prediction markets. Second, there are hosting-related concerns; we are unsure whether we would need to set up cron jobs to keep the newsfeed updated, or if Streamlit is the right platform to allow for constant background computation.
A major challenge we anticipate revolves around API rate limits. We still need to incorporate and fully understand the News API's rate limits under real-world usage. Furthermore, rather than simply relying on RegEx or a basic keyword matching strategy, we anticipate that achieving true semantic matching between news topics and prediction markets will be a significant technical hurdle.