How TraderStat verifies public trading performance.
Learn how TraderStat verifies public trader signals using AI extraction, historical Binance and cTrader data, ROI calculation, and accuracy scoring.
TraderStat uses an AI-assisted verification workflow to convert public trader claims into structured signals, compare them with historical market data, and calculate performance metrics with a consistent methodology.
TraderStat monitors public sources such as X and Telegram for trader posts that contain actionable trading claims, market direction, or signal-like language.
The engine identifies the traded instrument, direction, entry price, stop-loss, and take-profit claims. If a trader gives multiple targets or stops, they are stored as structured lists.
Crypto signals are compared against Binance market data, including historical candles and available depth context. Forex signals are verified against cTrader data where applicable.
TraderStat determines whether the target or stop was reached first, then calculates ROI, average result per signal, accuracy, drawdown, and aggregate trader rating.
The verification path is designed to keep every signal traceable from the original public message to the final database record used on trader profile pages.
Trader post
A public tweet, Telegram post, or channel message contains a market claim.
AI extraction
The model converts natural language into pair, direction, EP, SL, and TP fields.
Market replay
Historical candles and market data are checked to determine signal outcome.
Metric calculation
ROI, accuracy, drawdown, win rate, and score inputs are calculated.
Supabase record
The normalized signal and trader statistics are stored for SSR profile pages.
All verification algorithms are developed and maintained by TraderStat Team under the supervision of our CEO.