PolyData Explorer Dashboard Documentation

1. Introduction

Welcome to the PolyData Explorer Dashboard! This tool allows you to filter, explore, and download historical market event data and their associated price histories.

2. How to Use

2.1. Initial Load

Upon opening the dashboard, you'll see a progress bar. This indicates that the application is initializing or checking the status of backend data sources. Once ready, the "Overall Progress" section will typically indicate "Application ready" or a similar status, allowing you to proceed.

2.2. Filtering Data

The "Data Filters" section is your primary tool for finding specific market events.

1. Enter Filter Values: Populate one or more fields with your search criteria. All filters are optional; leaving a filter blank means it won't be applied.
2. Select Comparison Types: For many fields (like ID, Ticker, Volume), you can select a comparison operator (e.g., EQ, GT, Partial).
3. Apply Filters: Once your criteria are set, click the "Apply Filters & Load Data" button.

The dashboard will then fetch the queried data and present as a JSON format.

2.3. Viewing Results

• Market Selector: If the query returns matching events, the "Select Market to Display" dropdown will be populated with their titles (or IDs). Select an event from this list.
• Event Metadata: The "Event Metadata" table below the selector will display detailed attributes for the chosen market event.
• Price History: The "Price History" table will show time-series price data (if available and fetched) for the selected market event.

2.4. Downloading Data

• After data has been successfully fetched (i.e., currentEventsData is populated), the "Download JSON" and "Download CSV" buttons becomes active.
• Clicking the JSON button will prepare and initiate a download for a JSON file. This file typically contains:
  • An array of all filtered events metadata.
  • An object (priceHistory) where keys are event titles and values are arrays of price data points for those events.
• Our data is formatted to be easy to use, especially in Python for data analytics. The Price History data is represented as a dictionary of dataframe, which you can convert as such:

  import pandas
  # sample data
  raw_data = {"event name" : timeseries_data_list} # timeseries_data_list is a list of dicts
  dict_of_df = {key: pd.DataFrame(value) for key, value in raw_data.items()}
  

3. Data Filters Explained

All filter inputs are combined to form a query. Text-based "Partial" matches are generally case-insensitive, but "Exact" matches may be case-sensitive depending on the backend field. (You would most likely use market slug field to query for similar fields)

• ID: Filter by the unique numerical ID of the event.

  • EQ (Equal): Matches events with an ID exactly equal to the provided number.
  • GT (Greater Than): Matches events with an ID greater than the provided number.
  • LT (Less Than): Matches events with an ID less than the provided number.

• Ticker: Filter by the event's ticker symbol.

  • Partial: Matches events where the ticker contains the entered text (e.g., "TKR" would match "ATKRS").
  • Exact: Matches events where the ticker is an exact match to the entered text.

• Market Slug: Filter by the URL-friendly identifier for the market.

  • Partial: Matches events where the market slug contains the entered text.
  • Exact: Matches events where the market slug is an exact match to the entered text.

• Title: Filter by the full title of the market event.

  • Partial: Matches events where the title contains the entered text.
  • Exact: Matches events where the title is an exact match to the entered text.

• Start Date (Range): Filter events based on their start date (UTC timestamps are used internally).

  • From Date: Select a date. Events that started on or after 00:00:00 UTC of this date will be included.
  • To Date: Select a date. Events that started on or before 23:59:59 UTC of this date will be included.
  • Using both defines a specific date range for the event's start time.

• Closed Status: Filter by whether the market event is marked as closed.

  • Any: Includes both open and closed events (default).
  • True: Includes only events that are marked as closed.
  • False: Includes only events that are not marked as closed (i.e., currently open or unresolved).

• Tag: Filter by a specific tag associated with the event.

  • Enter a tag name (e.g., "Politics", "Crypto").
  • This performs an partial, case-insensitive match for the tag within the event's list of tags.

• Volume 24hr: Filter by the 24-hour trading volume.

  • EQ (Equal): Matches events with a 24hr volume exactly equal to the value.
  • GT (Greater Than): Matches events with a 24hr volume greater than the value.
  • LT (Less Than): Matches events with a 24hr volume less than the value.

• Series Slug: Filter by a slug identifying a series of related markets.

  • Partial: Matches events where the series slug contains the entered text.
  • Exact: Matches events where the series slug is an exact match to the entered text.

4. Understanding the Results

4.1. Event Metadata Table

This table displays key-value pairs of attributes for the market event selected from the dropdown.

• Field: The name of the data attribute (e.g., id, title, startDate).
• Value: The corresponding value for that attribute.
  • Dates/times (like startDate, endDate, closedTime, createdAt) are typically Unix timestamps in the raw data, converted to your browser's local date and time string for display.
  • The description field can contain multi-line text, which is rendered with line breaks.
  • Complex data types like arrays (e.g., tags) or nested objects are displayed as a formatted JSON string within the cell.

4.2. Price History Table

This table shows historical price data for the selected market event, structured like a financial data frame.

• Each row represents a data point for a specific time interval (1 minute).
• Each column is a different market denoting the specific token and the side, seperated by a "_".
• Bitcoin-above-105k_Yes: The token is Bitcoin-above-105k, and the price displayed is for the Yes side.

5. Rate Limiting & Data Caps

• Data Fetch Limit: The backend API enforces a rate limit on queries. The dashboard is currently configured with an understanding of a 20 query limit. You may not query more than 20 markets at once. If you want to query a large number of markets, you may contact us for details.
• Rate Limits: We may also implement limits on how many requests you can send us within a time period.

6. Example Usage

Using partial market slug matches, you can filter for recurring markets and get all the price history at once!

• Filter for Elon Tweet
  • market slug (partial) = "elon-musk-of-tweets"
• Filter for Bitcoin weekly market
  • market slug (partial) = "bitcoin-price-on"

7. Troubleshooting

• No Data Found:
  • Your filter criteria might be too specific. Try removing some filters or making them less restrictive.
  • Check for typos in text fields.
• Error Messages:
  • Red banner messages often indicate an issue with the request (e.g., invalid date format) or a problem on the backend.
  • Pay attention to messages in the "Overall Progress Description" as they can provide clues about rate limits or data caps being hit.
• Download Button Disabled: This button is only enabled after successfully fetching event data. If no events match your filters, it will remain disabled.
• Data Not Appearing: Ensure you've selected an event from the dropdown after applying filters.

For persistent issues or questions not covered here, please refer to the Support page.