Understanding Optimizer Results

How to interpret the metrics in create_strategy / get_strategy_status output. Each strategy in top_strategies is a Pareto-optimal configuration -- no other strategy in the search space beats it on all metrics simultaneously.

Key Metrics

CAGR (Compound Annual Growth Rate)

What it is: The annualized return rate, expressed as a decimal. 0.22 means 22% per year.

What's good:

CAGR Assessment
< 0.10 Low. Barely beating risk-free rate after fees
0.10 - 0.20 Solid. Typical for conservative IC strategies
0.20 - 0.35 Strong. Good capital utilization
> 0.35 Very aggressive. Check max_drawdown and p_ruin carefully

Caveat: CAGR is from simulated backtesting. Real-world performance may differ due to slippage, execution gaps, and regime changes.

win_rate

What it is: Fraction of trades that are profitable. 0.81 means 81% of trades make money.

What's good:

Win rate Assessment
< 0.70 Low for IC strategies. Usually means delta ranges are too wide
0.70 - 0.80 Normal. Balanced risk-reward
0.80 - 0.90 High. Typical for conservative presets with tight ranges
> 0.90 Very high. May indicate the optimizer found a narrow niche -- check robustness

Context: IC strategies are inherently high-win-rate strategies (you collect premium and win when the underlying stays in a range). A high win rate does not mean risk-free -- losing trades can be much larger than winning trades (negative skew).

max_drawdown

What it is: The worst peak-to-trough loss as a fraction of capital, measured on a mark-to-market basis. 0.09 means the portfolio lost 9% from its highest point at some time during the simulation.

Mark-to-market means open positions contribute their estimated unrealised P&L to the drawdown calculation. A market crash that puts your open ICs deep underwater will show up as a drawdown immediately -- not just when the positions eventually close.

What's good:

Max drawdown Assessment
< 0.05 Very conservative. Capital is well protected
0.05 - 0.10 Low. Comfortable for most investors
0.10 - 0.20 Moderate. Expect some painful weeks
> 0.20 Aggressive. Can test emotional discipline

Key insight: max_drawdown is the most important risk metric for IC strategies. A strategy with 22% CAGR and 8% max drawdown is fundamentally different from one with 22% CAGR and 18% max drawdown -- the second one will be much harder to stick with.

p_ruin

What it is: Probability that portfolio capital drops below the ruin threshold at any point during the simulation. 0.03 means a 3% chance of hitting the ruin level over the simulation period.

The ruin threshold is configurable per preset:

Preset Ruin threshold Meaning (on $15K)
Conservative 10% Ruin = dropping below $13,500
Balanced 15% Ruin = dropping below $12,750
Aggressive 25% Ruin = dropping below $11,250

What's good:

p_ruin Assessment
< 0.01 Excellent. Very unlikely to hit the ruin level
0.01 - 0.05 Acceptable for most risk profiles
0.05 - 0.10 Elevated. Conservative users should avoid
> 0.10 Red flag. Strategy is too aggressive for the capital allocation

Why it matters: p_ruin is the "tail risk" metric. A strategy might have great CAGR and win rate but still have high p_ruin if its losing trades are catastrophic (e.g. wide wings with low protection). Unlike max_drawdown (which reports the median worst case), p_ruin tells you the probability of hitting a dangerous level.

capital_efficiency

What it is: Fraction of the max_capital budget that is actively deployed in positions at any given time. 0.65 means 65% of capital is working.

What's good:

Efficiency Assessment
< 0.30 Low. Most capital sits idle. Safe but not productive
0.30 - 0.60 Moderate. Reasonable balance of deployment and reserve
0.60 - 0.80 High. Good capital utilization with manageable risk
> 0.80 Very high. Little reserve for margin calls or drawdowns

Trade-off: Higher efficiency = higher CAGR but less safety margin. The optimizer finds the Pareto-optimal trade-off for your risk profile.

n_lanes

What it is: Number of concurrent IC positions the strategy runs.

Context: More lanes means more diversification across expiry dates and strike levels. A 3-lane strategy has 3 ICs open simultaneously, staggered by entry date. If one IC takes a loss, the others may still be profitable.

n_lanes Description
1 Single position. All-or-nothing each cycle
2-3 Moderate. Staggered entries reduce per-trade impact
4-6 Higher diversification. Each position is a smaller % of capital
> 6 Requires sufficient capital. Diminishing diversification returns

entry_freq_days

What it is: Average calendar days between opening new positions.

Frequency Description
7 Weekly entries
14 Biweekly entries
21-30 Monthly entries

More frequent entries combined with more lanes creates a "conveyor belt" of overlapping positions, smoothing P&L.

avg_days_held

What it is: Average holding period per position in calendar days. IC strategies typically hold 21-45 days.

Shorter holds (< 21 days) indicate more active management (early exits, tight profit targets). Longer holds (> 45 days) indicate a more passive approach.

total_trades

What it is: Total number of simulated trades. More trades means more statistical confidence in the other metrics.

Trades Confidence
< 50 Low. Results are noisy. Treat with caution
50-200 Moderate. Reasonable for decision-making
> 200 High. Metrics are statistically meaningful

Robustness (Out-of-Sample Validation)

The robustness field contains OOS validation results, which measure whether the strategy's performance holds up outside the training data.

oos_cagr_median

Median CAGR across out-of-sample seeds. This is the "realistic" CAGR -- what you might expect in production, stripped of in-sample bias.

oos_cagr_p5

5th percentile CAGR across OOS seeds. This is the worst-case scenario from the validation. If this number is positive, the strategy survived even the worst resample.

overfit_gap

In-sample CAGR minus OOS median CAGR. Measures how much of the in-sample performance is "real" versus "overfit."

Overfit gap Assessment
< 0.02 Excellent. In-sample and OOS performance are nearly identical
0.02 - 0.05 Acceptable. Modest overfitting
0.05 - 0.10 Elevated. The strategy may have overfit to training data
> 0.10 Red flag. In-sample performance is unreliable

Free vs premium OOS

Aspect Free Premium
Seeds 1 3-10 (configurable)
Mode Bootstrap resample Full GARCH Monte Carlo
Gap metric Single-seed (noisier) Median over N seeds (statistically robust)

The free tier provides a basic sanity check. The premium tier provides statistical confidence with multiple seeds and parametric simulation.

How to Compare Strategies

When reviewing top_strategies, focus on these trade-offs:

CAGR vs max_drawdown

The most important trade-off. A strategy with lower CAGR but much lower drawdown may be the better choice for most users. The Pareto front presents the optimal trade-off boundary -- no strategy below the front beats one on the front on both metrics.

win_rate vs CAGR

Higher win rate often comes at the cost of smaller per-trade profit. A 90% win-rate strategy with 8% CAGR is more predictable but slower growing than a 75% win-rate strategy with 20% CAGR.

capital_efficiency vs p_ruin

Deploying more capital increases CAGR but also increases exposure. If p_ruin is above your comfort threshold, the strategy is too aggressive for your capital allocation.

Overfit gap as a tie-breaker

When two strategies have similar CAGR and drawdown, prefer the one with a lower overfit gap. It is more likely to perform consistently in live trading.

Red Flags

  • p_ruin > 0.10 -- strategy is too aggressive
  • overfit_gap > 0.10 -- in-sample results are unreliable
  • total_trades < 50 -- insufficient sample size
  • max_drawdown > 0.25 -- hard to recover from psychologically
  • capital_efficiency > 0.90 -- no safety margin for adverse moves

Cross-references