Robinhood Social is open to every eligible U.S. user and eToro lists more than 4,000 copyable investors. What sourced trader data adds to a broker's social features, and where it fits in the app.
Centaur · Research
October 18, 2026 · 8 min read
Social trading features have become a standard part of the broker experience. eToro has run a copy trading network for years, and on 29 September 2026, Robinhood opened Robinhood Social to every eligible U.S. user, with verified market commentators and company profiles listed as coming soon.
As these features grow, practical questions arise about the data behind them.
Whose trades should appear, how are they verified, and how should they be shown to users? This guide explains what verified trader data is, how it differs from today’s social features, and where it fits inside your app.
Verified social trading data turns public trading activity into a sourceable record. A trader posts a position on X or Telegram, and that disclosure can be structured into a long or short position. From there, the position follows through its later activity, giving users a trading record rather than a collection of posts.
That distinction matters because a social post gives users a statement.
Structuring that post into a position gives them trading information they can investigate. Subsequent activity, current status, and historical performance also give users more context about the trader behind the post, including their positions and performance over time.
Two features separate it from a general social feed. Every trade can be traced to its source, so anyone can confirm what was said and when. The record also keeps every outcome, including the losing trades, so a trader's history cannot be trimmed down to their best calls.
Broker social features usually draw on one of three kinds of data, and each one answers a different question.
| In-platform verified P&L | Sourced trader data | Sentiment | |
|---|---|---|---|
| Source | User accounts on your platform | Public trades disclosed on X and Telegram | Posts and mentions across social platforms |
| Who is covered | Your users who choose to post | Traders outside your platform with a public record | Anyone posting about a ticker |
| What is sourceable | Account-level trades and P&L | Disclosed trades priced at posting and linked to the source | Volume and tone, with no trades attached |
| What is available | Grows as users post | Comes with months of existing history | Yes, with no performance behind it |
The three feed into each other. In-platform P&L shows what a broker's own community is doing, sentiment shows what the market is talking about, and verified trader data takes you one step further by showing what specific outside traders are doing.
However, sentiment only flags the buzz, signaling that an asset is capturing market attention. Positioning measures the reaction, showing traders' conviction on that particular asset. Layer on entries, position history, and performance, and suddenly you have the deep, unvarnished context behind every trade.
Most broker social trading features fall into a few familiar models. Some, including Robinhood Social, build profiles from KYC-verified customer accounts and verify posted trades live, so the trades on the feed come from real accounts.
Copy trading networks, the model eToro popularised, let users mirror a selected group of investors, and eToro counted more than 4,000 copyable investors across 70 countries in October 2025.
Alongside these, brokers are opening their doors to external market intelligence. Robinhood's new Agent Apps include 11 paid data tools, giving users access to institutional-grade data inside the brokerage.
The reality is that not every trader with a meaningful record is one of your users. Many KOLs also disclose their positions publicly rather than through an exchange account you can see.
Yet, you do not need to own every source of market intelligence to make it useful. Specialist providers bridge that gap, allowing you to retain both the user relationship and the execution.
A social feature is only as useful as the number of credible voices it can show on the asset a user is looking at. Coverage is worth measuring in two ways.
The first is depth, meaning how many experienced traders have a meaningful record on a single ticker. Between 20 July and 30 September 2026, our tracked traders opened 250 positions across ten heavily traded U.S. stocks, including Nvidia, Meta, Apple, Amazon and Robinhood.
Yet, of the 95 traders behind those positions, only 9 had five or more scored 30-day positions in those ten names by 18 October. Depth on individual tickers requires a large pool of traders and a long history to build, which is why a network made only of your own users is usually thin.
History matters for the same reason. A position can only be scored after its evaluation window, so a trader who started posting last week has nothing to show yet, no matter how confident their trades are. Records that already stretch back months let you show scored performance from the day a feature goes live, instead of waiting for your own community to catch up.
The second is breadth, that is, how far coverage reaches beyond the household names. On 18 October, some of our tracked traders held Korean and Taiwanese chip names such as SK Hynix and Unimicron, along with small-cap miners listed in Canada, the corners of the market where your own community often falls quiet and a handful of experienced voices can go a long way.
The limitation becomes clear when a social layer handles Nvidia and Bitcoin brilliantly but has nothing to say about less-followed assets, reducing its usefulness to the same tickers everyone already knows.
There are three places for it:
Where the data sits will depend on how you build your product. What matters more is that it shows up inside the research a user is already doing. A trader's record is most useful at the moment a user is weighing a position, and far less useful in a separate tab they have to search.
A positioning panel answers three questions in order. Which way are experienced traders leaning, at what prices did they enter, and how are those positions doing now?
The third quarter of 2026 offers a clear example. Between 20 July and 30 September, Centaur’s tracked traders leaned firmly long on Nvidia, with 34 long positions against 4 short, and even more so on Amazon, where all 25 positions were long. Apple was the odd one out among the ten stocks, the only one where tracked traders tilted short, with 9 short positions against 5 long.
A good panel also shows every entry on the same stock, not only the best one. One tracked trader added to an AMD long on 3 September at $456.09, and on 18 October that entry was up 15.9% at $528.75. Another posted on 21 September that it had bought AMD, priced at $603.94 when the post went out, and that long was down 12.4%. Showing only the first figure would give users a misleading picture, while showing both lets them weigh conviction against risk.
Most social features eventually include a ranking, and the ranking is where trust is either built or quietly spent. Three checks help a ranking earn that trust.
Ranking built this way helps your users find traders worth following and understand why they rank the way they do, saving them extra research that a single figure would leave behind.
Verified trader data adds information to your product without changing how the product runs. Centaur does not execute trades or hold user funds. Instead, it measures only the trades traders disclose publicly. The user relationship, order flow, and compliance decisions all stay with you. The data arrives through a REST API or MCP.
To see how the records are structured, explore the API docs or read the signals playbook.
