Social trading data connects identity, public performance, wallet activity and portfolio state. Fomo API normalizes those pieces so a product can focus on analysis and user experience instead of maintaining several fragile integrations.
Trader discovery dashboards
Combine leaderboard windows to help users identify traders with persistent results rather than a single short-term spike. Useful dashboard columns include rank, PnL, total volume, trade count, followers, account age and top holdings.
Do not label a trader “best” based on 24-hour PnL alone. A sound scoring model can include:
- Positive results across several windows
- A minimum trade count and volume
- Maximum drawdown from snapshots
- Concentration across holdings
- Average position size and hold time
- Liquidity of commonly traded assets
Wallet monitoring and alerts
Resolve a handle once, store its user ID and wallets, then poll swaps or balances. A notification service can alert on new positions, position increases, exits or high-value swaps.
An alert should communicate what the system observed, not make an investment claim:
Observed: @example received 12,500 TOKEN on Solana
Time: 2026-09-15T10:42:10Z
USD estimate: $3,240
Verification: pending onchain confirmation
That wording keeps data freshness and verification status explicit.
Portfolio analytics
Balances include human-readable amounts, current prices, entry data, cost basis and PnL fields. You can create allocation charts, realized versus unrealized PnL, concentration warnings and historical equity charts.
Use decimal arithmetic for monetary calculations. Record the source timestamp and preserve raw responses so revised parsers can rebuild historical records.
Copy-trading research
Fomo API can power a research simulator that asks: “What would have happened if this strategy followed these traders under these risk constraints?” A useful simulator accounts for observation delay, quote delay, slippage, fees, liquidity and failed transactions.
A backtest that fills every historical order at the observed price is usually overstated. Model the execution conditions your system could actually obtain.
Automated agents
An agent can use normalized data to produce candidate signals. It should not possess unrestricted signing authority. Put a deterministic risk engine between agent output and execution.
| Component | Responsibility |
|---|---|
| Fomo API client | Collect social trading data |
| Signal engine | Identify an event worth evaluating |
| Validator | Verify token, chain and market state |
| Risk engine | Enforce exposure and loss limits |
| Executor | Request quotes and submit approved transactions |
| Monitor | Confirm outcomes and reconcile balances |
Community and research products
Other useful products include public trader profile pages, performance newsletters, token trend reports, internal due-diligence tools and watchlists. Public-facing products should clearly cite freshness and avoid presenting estimated values as guaranteed results.
Choosing a first project
Start with a read-only monitor. It exercises authentication, profile resolution, polling, normalization and notifications without exposing capital. Once the data pipeline is observable and reliable, add simulation. Consider execution only after independent validation and hard risk limits are tested.