Scheduled macroeconomic announcements are among the few market events known in advance, and they are well documented to move financial markets in systematic ways [2, 10, 12, 18, 21]. In this chapter we present a case study that implements Conformal Prediction (CP) for financial markets trading (specifically, Foreign Exchange). Our case study uses more than 17 years’ worth of publicly available 1-minute EUR/USD quotes [16], and a historical economic release calendar [26] to forecast pre-release and post-release price-movement bounds of EUR/USD around scheduled economic events. We will show how it is possible to compress millions of EUR/USD price movements using an online retracement segmentation that yields alternating up/down “intrinsic-time”’ akin to a directional-change framework and the “zig-zag” indicator used by traders [9, 13, 25]. Resulting price segments preserve localised highs and lows in price history, enabling us to align segments around economic event releases. For each of these economic release series and respective price segments, we applied walk-forward linear regression (LR) models to forecast the price movement before and after the economic event release. We then tested the effect of wrapping these simple LR models with distribution-free CP [3, 19, 28], including Conformalised Quantile Regression (CQR) [27], to produce conformal bounds that map cleanly to trading actions such as take-profit and stop-loss. Across hundreds of economic event release series, we quantify coverage-width trade-offs and show that wider, asymmetric bands improve risk-adjusted outcomes relative to point-forecast exits produced by LR.