Portfolio (1 Tests) demonstrates strong statistical characteristics with an Edge Matrix score of 87/100. Over 6.6 years and 263 trades, it has generated a 264.9% total return (21.9% CAGR) with a maximum drawdown of 10.8%. The Calmar ratio of 2.03 indicates strong risk-adjusted returns — every unit of drawdown risk has been well compensated. Monte Carlo simulation results: 100% of 1,000 randomized trade sequences remained profitable, indicating low sensitivity to trade ordering.
Recommendation: NORMAL MARKET: Follow calculated optimal period
Re-optimize parameters every 4y 2mo to maintain edge quality. Statistical analysis suggests the market regime this strategy exploits has a half-life of approximately 772 days before parameters begin to decay.
Module 02
Edge Matrix — 19 Validation Tests
✓
Temporal Stability
97
✓
Profit Concentration
85
✓
Drawdown Analysis
10.8% max DD
85
✓
Consecutive Loss
92
✓
Sample Adequacy
92
✓
Edge Quality
73
✓
Cliff Ratio
98
✓
Edge Decay
85
✓
MC DD Stability
82
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MC Robustness
69
✓
Capital Efficiency
74
✓
Holding Time Asymmetry
85
✓
Edge Consistency
74
✓
Ulcer Index
91
✓
Statistical Significance
97
✓
Expected Shortfall (CVaR)
97
✓
Return Autocorrelation
100
✓
DD Endurance
1.4x penance, 65% underwater
75
✓
Edge Temporal Decay
95
Deep Dive: What The Tests Mean
▲ TOP 3 STRENGTHS
✓ Return Autocorrelation100
Tests if trades are independently distributed or if wins/losses cluster. Low correlation suggests a robust, non-path-dependent strategy.
✓ Cliff Detection98
Checks for suspicious sudden drops in the equity curve that could indicate data errors or catastrophic events.
✓ Statistical Significance97
Tests whether the strategy's edge is real or could have occurred by random chance. Uses t-statistic and binomial probability testing.
▼ TOP 3 CONCERNS
⚠ MC Robustness69
How well the strategy survives when trade order is randomized. High scores mean returns aren't dependent on lucky sequencing.
⚠ Edge Quality73
Measures the quality of wins vs losses — profit factor, reward-to-risk ratio, and consistency of the trading edge.
⚠ Edge Consistency74
How stable the win rate and profitability are across rolling windows. Inconsistent edges are harder to trade with confidence.
Module 03
Monte Carlo Simulation
Method: Bootstrap (resampling with replacement, models a wider universe of possible outcomes from a strategy of this type) · 1,000 simulations
MC Score
79.1
100% profitable
Historical DD
10.8%
Actual worst
50% Conf DD
13.5%
Median scenario
95% Conf DD
22.9%
Stress-test level
99% Conf DD
26.1%
Absolute worst
Monte Carlo Assessment
Every simulated trade sequence remained profitable — this is rare and indicates the strategy's returns are not dependent on trade order.
The 95th percentile drawdown (22.9%) is 2.1x the historical maximum. Under adverse conditions, drawdowns could roughly double.
Monte Carlo Drawdown Context: The 95th–99th percentile simulated drawdown range is 23–26%, compared to the historical 10.8%. This range is commonly used as a stress-test reference when evaluating strategy robustness.
This strategy demonstrates strong statistical properties across all 19 validation tests. The combination of metrics suggests the observed returns are unlikely to be solely the result of random chance or overfitting.
Module Summary
Edge Matrix: 18/19 tests passed, 1 warnings, 0 failures. Overall score: 87/100 (ROBUST). Monte Carlo: 100% survival rate across 1,000 simulations. 95% confidence drawdown: 22.9%. Projection: Median expected balance of $33,170 after 120 trades (~36 months). Optimization: Statistical analysis suggests parameter stability of approximately 1523 days. Insights: Historical schedule analysis shows +$4,886.65 differential across analyzed time segments.
Disclaimer: This is a statistical analysis of historical data. It does not constitute financial advice and does not guarantee future performance. Past results are not indicative of future returns. All trading involves risk. Edge Matrix evaluates the statistical properties of backtests — it does not predict market behavior.