The six numbers BlaveClaw reports — what each means and which ones to trust.
The btc_sma_cross strategy (SMA45/SMA100 on BTCUSDT 1h, 2022–2026) produced these results:
The strategy more than doubled the buy-and-hold return — but it also had a brutal −42.6% drawdown at one point. Whether that's acceptable depends entirely on your risk tolerance, and the metrics below will help you think through it.
Sharpe ratio = annualized return ÷ annualized volatility (both upside and downside). It answers: how much return per unit of total risk?
| Sharpe | Interpretation |
|---|---|
| < 0 | Losing money on a risk-adjusted basis |
| 0 – 0.5 | Weak — barely compensates for risk |
| 0.5 – 1.0 | Acceptable for trend-following (our SMA example falls here) |
| 1.0 – 1.5 | Good |
| > 1.5 | Strong — treat with skepticism if backtest period is short |
Sortino ratio is like Sharpe, but only penalizes downside volatility. Upside variance doesn't count as risk. It answers: how much return per unit of bad volatility?
For the SMA Cross, Sortino (0.72) is close to Sharpe (0.77) — meaning the strategy's volatility is fairly symmetric. A strategy where Sortino is significantly higher than Sharpe has large upside swings but controlled drawdowns, which is desirable.
| Sortino vs Sharpe | What it suggests |
|---|---|
| Sortino ≈ Sharpe | Symmetric returns — upside and downside volatility are similar |
| Sortino >> Sharpe | Returns are positively skewed — large gains, small losses (ideal) |
| Sortino << Sharpe | Returns are negatively skewed — small gains, occasional large losses (dangerous) |
Max drawdown is the largest peak-to-trough decline in equity during the backtest. If equity peaked at $100,000 and dropped to $57,380 before recovering, MDD = −42.6%.
MDD matters because it measures how much pain you'd have had to endure to stay in the strategy. Most people quit well before the recovery.
Omega ratio = total returns above a threshold ÷ total returns below it. The threshold is usually 0 (break-even). Unlike Sharpe, Omega captures the full return distribution, not just mean and variance.
| Omega | Interpretation |
|---|---|
| < 1.0 | More total loss than gain — unprofitable |
| 1.0 – 1.1 | Marginally profitable, fragile under real-world costs |
| 1.1 – 1.3 | Solid |
| > 1.5 | Strong — or possibly overfitted |
The SMA Cross has Omega = 1.04 — only slightly above 1.0. After the 22% of capital paid in fees over 4 years, the real-world edge is thin. This is normal for a trend-following strategy; the Sharpe and Sortino ratios are the better primary quality metrics for this strategy type.
Lookahead bias: Using data that wasn't available at decision time. BlaveClaw enforces next-bar-open execution to prevent this, but custom indicator calculations can still introduce it — e.g., computing a Z-score using the full series' mean.
Skipping the warm-up period: Indicators like SMA(100) need 100 bars before they're meaningful. The first 100 bars should be excluded from performance measurement. BlaveClaw's WARMUP parameter handles this automatically.
Optimizing on the full dataset: If you scan parameters using all available data and then report performance on the same data, you're reporting training error, not generalization. Always hold out at least 20–30% of data for out-of-sample validation.
Ignoring fees: The btc_sma_cross paid 22% of capital in fees over 4 years. For higher-frequency strategies, fees can easily exceed alpha. Use FEE = 0.0005 (maker/taker 0.05%) as a realistic baseline on Binance.
Confusing total return with risk-adjusted return: A strategy that returns 200% with an MDD of −80% is not "better" than one returning 80% with an MDD of −15%. The first will cause most real users to panic-exit near the bottom.
