Optimization Analysis

Period Recommendation Report
June 13, 2026
OPT-20260613-010927
Portfolio Composition — 2 Strategies
#STRATEGYSYMBOLTFTRADES
1OrderFlow Structure SMC EA MT5GBPUSDH194
2OrderFlow Structure SMC EA MT5USDCADH1231
Portfolio (2 Tests)
MULTI • MIXED
325 trades
2,317 days (6.3 years) analyzed
Portfolio
01 Equity Projection 1,000 simulations · 154 trades forward · ~36.7 months
MONTE CARLO EQUITY PROJECTION — Portfolio (2 Tests) Starting: $51,003.66
Historical equity
Simulation paths
Median (+111.1%)
Best case ($255,248)
Worst path (−29.5% DD)
5TH PCT
$70,162
+37.6%
25TH PCT
$90,308
+77.1%
MEDIAN
$107,647
+111.1%
75TH PCT
$128,916
+152.8%
95TH PCT
$167,725
+228.8%
95% DD CONFIDENCE
−17.9%
Worst DD in 95% of simulated paths
AVG RECOVERY
6 trades
Mean trades to recover from a DD episode
EST. DURATION
~36.7 mo
At current trade frequency (7.2 days/trade)
SIMULATIONS
1,000
Percentage-based resampling (exact method)
STATISTICAL OBSERVATIONS
DISTRIBUTION SHAPE
The outcome distribution is right-skewed — the upside range ($70,162–$167,725) spans $97,563. Adverse scenarios are bounded closer to the starting balance than favourable ones.
DRAWDOWN PROFILE
The 95% confidence drawdown of −17.9% is the stress-test reference — the worst expected drawdown in 19 out of 20 simulated scenarios. The median path experiences materially lower drawdowns.
TRADE FREQUENCY CONTEXT
At 7.2 days per trade, the 154-trade projection spans approximately 36.7 months. Actual duration varies with market conditions — lower-frequency periods extend the timeline without changing the statistical distribution.
RESAMPLING METHOD
Simulations use percentage-based resampling of historical trade returns. This preserves the return distribution but assumes trade independence. Structural regime changes may produce outcomes outside the simulated range.
Statistical context: Monte Carlo projections are probabilistic estimates based on historical trade return distributions. They do not predict future outcomes. Results should be interpreted as a statistical characterisation of the strategy's historical behaviour under resampling, not a forecast. Past simulation results are not indicative of future performance.
BACKTEST WINDOW ANALYSIS
Recommended Period
1428 days
3.9 years
Statistical + Academic + Bootstrap

What This Means

Based on analysis of your strategy's characteristics on MULTI MIXED trading systems, we recommend using 1,428 days (3.9 years) of historical data for optimization and backtesting.

This period balances having enough data for statistical significance while avoiding outdated market conditions. The recommendation is synthesised from three independent methods: rolling-variance statistical analysis (769 days (2.1 years)), literature-informed period benchmarks (2,518 days (6.9 years)), and Politis-Romano block bootstrap spectral analysis (998 days (2.7 years)).

02Analysis Metrics
Statistical Period
769 days (2.1 years)
Academic Period
2,518 days (6.9 years)
Bootstrap Period
998 days (2.7 years)
Sharpe Ratio
3.79
Max Drawdown
-8.7%
Recovery Factor
10.1
Volatility Ratio
0.74x
Confidence Level
MEDIUM
Re-opt Frequency
Annual (4.2 trades/mo)
Regime Changes
2
Profit Factor
1.89
Win Rate
58.2%
03Three-Method Synthesis

📈 Statistical Analysis

Rolling-variance stability test finds the lookback window where your strategy's return distribution is most stable and least regime-dependent.

Optimal period found769 days (2.1 years)
Regime changes detected2
Volatility adjustment0.74× current/historical

📚 Literature Benchmarks

Period benchmarks for MULTI MIXED strategies, informed by asset class volatility profiles and FX market microstructure research. These are calibrated heuristics, not direct citations.

Base period (MULTI MIXED)2,518 days (6.9 years)
Volatility adjustment0% (normal)
Adjusted period2,518 days (6.9 years)

🔬 Block Bootstrap — Politis-Romano (1994)

Spectral autocorrelation analysis estimates the memory structure in your returns using block resampling. Accounts for serial dependence that rolling-variance methods miss.

Bootstrap spectral period998 days (2.7 years)
MethodPolitis-Romano stationary bootstrap
Combined (all three)1,428 days (3.9 years)
04Why This Period Works

Interpreting the Optimal Range

Too short (under 928 days (2.5 years)): Not enough data to capture full market cycles. Results are statistically unreliable and prone to overfitting to a single regime.

Above range (over 2,070 days (5.7 years)): The window extends beyond the calculated optimal range. Older data points contribute proportionally less signal in recent-regime models.

The sweet spot (928 days (2.5 years) – 2,070 days (5.7 years) for MULTI MIXED): Provides enough trades for statistical confidence while focusing on recent, relevant market conditions.

The three-method synthesis gives 1,428 days (3.9 years) — averaged from rolling-variance statistical analysis (769 days (2.1 years)), literature-informed period benchmarks (2,518 days (6.9 years)), and Politis-Romano block bootstrap spectral analysis (998 days (2.7 years)).
05Your Backtest Period Diagnostic
◉
Period Assessment
ABOVE RANGE
The backtest window is moderately longer than the calculated optimal range.
Period Coverage
162%
2,317 days (6.3 years) of 1,428 days (3.9 years) optimal
Trade Coverage
162%
325 of 200 estimated minimum
Detailed Findings
Backtest window above calculated range — 2,317 days (6.3 years) vs 1,428 days (3.9 years) optimal
The backtest window differs from the calculated optimal by 889 days (2.4 years).
⚠️ Sharpe Ratio 3.79 — Exceptionally High
López de Prado (2014) shows Sharpe ratios above 3.0 are extremely rare in professional trading. Top-tier hedge funds rarely sustain above 2.5. With 325 trades, the standard error of your Sharpe estimate is ±0.06, meaning the true Sharpe could realistically be 3.7–3.9 at 95% confidence. Verify backtest conditions match live: spreads, slippage, rollover costs.
✅ 2 Regime Changes Detected — Low structural risk
Minimal regime shifts in the analysis window indicate parameter stability. The strategy has operated in a consistent market environment.
References

Bailey, D. et al. (2014) "The Deflated Sharpe Ratio" — Minimum track record length, multiple testing correction
López de Prado, M. (2014) "The Deflated Sharpe Ratio" — MinTRL formula, Sharpe standard error at finite samples
Politis, D. & Romano, J. (1994) "The Stationary Bootstrap" — Block resampling for serially dependent data
Pardo, R. (2008) "The Evaluation and Optimization of Trading Strategies" — Walk-forward, regime-based optimization

06Acceptable Period Ranges
Range TypeDaysUse Case
Minimum Acceptable928 days (2.5 years)Quick tests, high-frequency strategies
Recommended1,428 days (3.9 years)Calculated optimal — three-method average
Maximum Useful2,070 days (5.7 years)Conservative analysis, lower-frequency strategies
07 Re-optimisation Frequency
Recommended
Annual
(4.2 trades/mo avg)
Derived from optimal period
The re-optimisation frequency is derived from the recommended period and your timeframe's typical regime change rate. Re-running optimisation annual using a rolling 1,428 days (3.9 years) window keeps strategy parameters aligned with current market conditions.
REGIME CHANGES DETECTED
2
Low structural risk — stable parameters
VOLATILITY REGIME
0.74×
Normal range
08How to Apply These Results

💡 Practical Recommendations

1
Set Your Backtest Period
When running backtests, use approximately 1,428 days (3.9 years) of historical data. This gives enough trades for statistical validity while focusing on current market conditions.
2
Re-optimise Annual (4.2 trades/mo)
Markets change over time. Re-run optimisation annual (4.2 trades/mo) using the most recent 1,428 days (3.9 years) window to ensure parameters stay relevant to current market conditions.
3
Use Walk-Forward Testing
For robust validation: optimise on 1,428 days (3.9 years), test on the next 471 days (1.3 years), then roll forward and repeat. This prevents overfitting and validates parameter stability across time.
4
Use 95% DD (17.9%) as Your Risk Budget
Monte Carlo simulation projects that in the worst 5% of scenarios, maximum drawdown reaches 17.9%. Size your position so this drawdown is within your risk tolerance before allocating capital.