Every quant knows the ritual: collect historical prices, engineer features, and run a backtest. Yet when those same backtests are applied to thinly traded equities or frontier markets, results collapse. Missing data points, illiquidity, regulatory shifts, and outright distortions creep in. The backtest looks elegant on paper, but fails instantly in production. The issue is not strategy alone — it is the dataset itself. Markets like India, Southeast Asia, or even small-cap pockets in developed economies simply do not provide the clean, high-frequency datasets that models built on U.S. equities assume. That fragility pushes developers toward a new approach: synthetic data generation. By constructing engineered datasets that mimic volatility, liquidity droughts, and regime shifts, quants can rehearse reality in controlled environments.