A good Sharpe ratio in algorithmic trading depends on market, strategy type, leverage, turnover, liquidity, and whether the number comes from backtesting or live results. The metric is useful, but not sufficient.
Backtest Sharpe is easier to inflate
Overfitting, ignored costs, smooth pricing, stale marks, and short samples can all make Sharpe ratio look better than it is. A high backtest Sharpe should invite more scrutiny, not automatic allocation.
Live Sharpe needs context
A live Sharpe above 1 can be meaningful for many strategies, while higher values require examination of capacity, leverage, tail risk, and sample size. Short track records can make unstable performance look impressive.
Use Sharpe with complementary metrics
Maximum drawdown, Sortino ratio, skew, turnover, hit rate, exposure, transaction costs, and stress performance help explain what Sharpe alone hides. Investors rarely allocate based on one number.
Strategic takeaway
A good Sharpe ratio is one that survives realistic costs, live execution, and risk context. The number matters most when it is hard to fake.
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