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Quantitative research is a process with many intermediate steps, each of which has to be carefully and thoroughly validated. Asset selection, data collection, feature extraction, modeling — all these phases take time and are delivered and tested by different teams. But what at the end the investor wants to see? That “flawless” backtest on historical data with high Sharpe ratio, alpha with respect to the market, and maybe some fund-related metrics as capacity, leverage, average AUM, etc. However, such an approach doesn’t tell us anything about the future performance of the strategy and such backtests can be easily overfitted and are even dangerous when they’re used as a measure of “success” of a trading idea (see more details in my article on financial idea discovery).

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