AZIMUTH QUANT

Quantitative Strategies & Market Research

Azimuth Dynamic Strategy

Adaptive Multi-Asset Strategy · Fully Funded · Long-Only

Strategy Factsheet

Investor-focused factsheet for a systematic adaptive allocation strategy. Public material presents performance, risk and validation evidence while withholding the production portfolio-construction and decision logic.

Backtest Period:

August 2014 – August 2026

Data Frequency:

Daily engine · monthly public curve

Operating Model:

Fully funded · long-only · manual alerts

Copy Trading:

Not offered

Last Updated:

August 2026

Executive Summary (Net Base Case)

Net CAGR

33.19%

Full historical evaluation

Max Drawdown

-14.50%

Audited daily-engine result

Calmar

2.29

CAGR / Max DD

Final Multiple

31.59×

$10,000 → $315,950

Base case period: 2014-08-01 – 2026-08-31 · Initial capital: $10,000 · Fully funded historical simulation with modeled turnover costs.

Return & risk

CAGR 33.19% · MDD -14.50% · Volatility 18.35%

Long-horizon stability

Worst 1Y -3.39% · Worst 3Y annualized 13.99%

Benchmark Context

The strategy is compared with the same broad U.S. Equity Benchmark used in the Tactical Allocation materials. The benchmark is shown for context only; Dynamic is not designed to track it.

Equity Curve

Azimuth Dynamic versus U.S. Equity Benchmark

Linear-scale comparison with the U.S. Equity Benchmark.

Log-scale comparison with the U.S. Equity Benchmark.

Data & Methodology

Historical scope: all headline figures, charts and studies in this factsheet use the full validated period from August 2014 through August 2026.

Portfolio structure: the strategy is a fully funded, long-only adaptive allocation process designed to balance participation and risk through changing market conditions.

Public disclosure: performance, risk characteristics and validation evidence are public. Production portfolio construction and decision logic remain confidential; approved subscribers receive only the implementation information required to follow the current target.

Key Metrics

Metric	Result	Interpretation
Net CAGR	33.19%	Annualized compounded growth over the complete evaluation period.
Maximum drawdown	-14.50%	Worst audited daily-engine peak-to-trough decline.
Calmar ratio	2.29	CAGR divided by absolute maximum drawdown.
Annual volatility	18.35%	Audited annualized variability.
Final multiple	31.59×	Growth of one unit of starting capital.
Worst rolling 1Y	-3.39%	Weakest audited one-year window.
Worst rolling 3Y	13.99%	Weakest audited three-year annualized window.
Maximum time below high-water mark	265 sessions	Longest audited recovery duration.

Daily Tail Risk Diagnostics

Diagnostic	Result	Method
One-day VaR 95%	-1.78%	Parametric normal approximation using audited annual volatility.
One-day expected shortfall 95%	-2.26%	Parametric normal approximation; diagnostic only.
One-day VaR 99%	-2.57%	Parametric normal approximation using audited annual volatility.
One-day expected shortfall 99%	-2.96%	Parametric normal approximation; diagnostic only.
Negative monthly observations	28.47%	Frequency in the public monthly observation curve.

Parametric tail estimates are diagnostics derived from audited annual volatility. They are not a guarantee and should not be interpreted as a loss limit.

Historical Drawdown & Recovery

Dynamic and U.S. Equity Benchmark drawdowns

The public curve is shown at monthly observation dates; the headline maximum drawdown and recovery-duration statistics come from the audited daily engine.

Time-Based Holdout / Out-of-Sample

Fold	OOS period	Frozen CAGR	Frozen MDD	Frozen Calmar
F1	2018-01-01 – 2019-12-31	18.82%	-8.57%	2.20
F2	2020-01-01 – 2021-12-31	57.71%	-14.50%	3.98
F3	2022-01-01 – 2023-12-31	24.27%	-14.04%	1.73
F4	2024-01-01 – 2026-08-31	48.94%	-14.09%	3.47

All four out-of-sample windows were positive. The frozen final configuration remained close to the training-selected alternatives.

Rolling Stability

Rolling 1-year and 3-year annualized returns for the strategy and benchmark.

Twelve-month volatility and drawdown for both strategy and benchmark.

Month-end rolling-window summary

Window / diagnostic	Observations	Minimum	10th percentile	Median	Positive
Rolling 1Y return	133	-2.96%	9.50%	29.15%	99.25%
Rolling 2Y annualized return	121	5.27%	18.92%	30.64%	100.00%
Rolling 3Y annualized return	109	15.19%	19.24%	30.95%	100.00%
Rolling 12M volatility	133	6.53%	9.32%	15.90%	—

The table uses month-end observation windows from the public curve. The audited daily engine can begin on any session and records a weaker worst 1Y window of -3.39%, reported in Key Metrics.

Alternate Start-Date Validation

Diagnostic	Result
Complete restart dates	4
Positive CAGR rate	100.00%
Minimum CAGR	33.19%
Median CAGR	37.00%
Worst maximum drawdown	-14.50%
Minimum available 3Y annualized return	13.99%

Each test begins the strategy from a new date rather than carrying inherited positions into the window.

Methodology & Investment Discipline

Investor summary: Dynamic seeks adaptive long-term compounding through a proprietary portfolio-allocation process. It manages risk but does not eliminate the possibility of loss or extended recovery periods.

Research & validation approach

The model was evaluated through full-period metrics, nearby-configuration tests, alternate starts, walk-forward out-of-sample windows, implementation stress, anonymized component perturbations, dependence tests and 2,000-path block-bootstrap simulations.

Fees & Implementation Friction

Degradation remains gradual as modeled cost and implementation stress increase.

Scenario	Modeled turnover cost	CAGR	MDD	Calmar	Final multiple
Standard timing · 0.10% turnover cost	0.10%	33.19%	-14.50%	2.29	31.59×
Moderate timing stress · 0.20% turnover cost	0.20%	29.80%	-15.47%	1.93	23.16×
Severe timing stress · 0.50% turnover cost	0.50%	21.52%	-17.84%	1.21	10.46×

Implementation Scenarios

Observed path, synthetic return transformations and severe combined implementation stress.

Supplementary monthly block-bootstrap visualization; official validation statistics are shown in the Monte Carlo section.

Cost Robustness Frontier

CAGR across modeled turnover costs and progressively more demanding timing assumptions.

Cost Sensitivity

Scenario	Modeled turnover cost	CAGR	MDD	Calmar	Final multiple
Standard timing · 0.10% turnover cost	0.10%	33.19%	-14.50%	2.29	31.59×
Moderate timing stress · 0.20% turnover cost	0.20%	29.80%	-15.47%	1.93	23.16×
Severe timing stress · 0.50% turnover cost	0.50%	21.52%	-17.84%	1.21	10.46×

The purpose of this study is to determine whether the historical edge survives realistic and deliberately severe friction rather than relying on an idealized zero-cost result.

Execution & Structural Robustness

Base-cost timing case	CAGR	MDD	Calmar	Worst 1Y
Standard timing · 0.10% turnover cost	33.19%	-14.50%	2.29	-3.39%
Moderate timing stress · 0.10% turnover cost	31.43%	-15.00%	2.10	-7.28%
Severe timing stress · 0.10% turnover cost	27.22%	-16.61%	1.64	-8.67%

Anonymized component evidence: disabling individual internal components one at a time changed return and/or risk materially. Functional identities are not disclosed publicly.

Dependence evidence: 57 anonymized dependence and exclusion scenarios were evaluated. The weakest grouped perturbation retained a 23.88% CAGR.

These studies address delay, friction and component dependence, but cannot reproduce every future liquidity event, outage or structural market break.

Probability of Severe Impairment & Recovery

Diagnostic	Result	Interpretation
Historical maximum drawdown	-14.50%	Worst audited daily-engine decline.
Maximum days below high-water mark	265	Longest audited recovery interval.
Monte Carlo probability of positive CAGR	100.00%	Across 2,000 paths in the 21-session block study.
Monte Carlo paths with MDD worse than −25%	13.90%	Historical MDD understates the range that should be budgeted prudently.
Prudent drawdown planning range	25%–35%	Risk-budgeting range indicated by the tail simulations, not a forecast or limit.

Synthetic Stress

Scenario	CAGR	MDD	Calmar
Observed monthly path	32.93%	-11.69%	2.82
Volatility ×1.5	30.51%	-21.95%	1.39
Volatility ×2.0	27.19%	-36.02%	0.75
Single-period −20% shock	30.48%	-29.35%	1.04
Annual drag 1%	31.60%	-11.76%	2.69
Annual drag 2%	30.27%	-11.84%	2.56
Annual drag 3%	28.94%	-11.91%	2.43
Severe combined implementation stress	21.52%	-17.84%	1.21

Stress Comparison

Synthetic scenarios are transformations of the full historical observation curve. They illustrate sensitivity; they do not predict a specific future path.

Robustness & Risk

21 parameter-neighborhood scenarios. Median CAGR 33.01%; 10th-percentile CAGR 30.75%; weakest CAGR 28.23%.

4 complete alternate restarts; all were profitable.

Anonymized internal perturbation	CAGR	MDD	Calmar
Internal variant A	28.03%	-16.61%	1.69
Internal variant B	28.77%	-20.38%	1.41
Internal variant C	28.22%	-21.31%	1.32
Internal variant D	31.95%	-17.00%	1.88
Internal variant E	31.35%	-17.20%	1.82
Internal variant F	22.81%	-14.50%	1.57
Internal variant G	28.14%	-19.67%	1.43

Internal Component Robustness

Internal components were disabled one at a time using anonymized labels. Each perturbation changed return and/or risk, supporting the conclusion that the production framework is not dependent on a decorative or redundant layer. Functional identities remain confidential.

Diversification & Dependence Testing

Across 57 anonymized exclusion and dependence perturbations, the 10th-percentile CAGR was 24.10%. The weakest tested grouped perturbation still produced a 23.88% CAGR. Component identities remain confidential.

Monte Carlo

For each dependence horizon, the top panel shows the 5th–95th percentile CAGR range and median; the bottom panel shows the adverse drawdown tail through the median across 2,000 paths.

Supplementary drawdown distribution summary.

Block length	CAGR p5	CAGR median	CAGR p95	MDD p1	MDD p5	MDD median	P(MDD worse than −25%)
5 sessions	22.29%	33.26%	45.36%	-38.02%	-32.59%	-22.24%	30.20%
21 sessions	22.96%	32.98%	44.01%	-33.29%	-28.88%	-19.97%	13.90%
63 sessions	22.96%	33.69%	46.06%	-28.19%	-24.84%	-17.80%	4.60%

The moving-block bootstrap preserves short-run dependence better than independent daily shuffling. It does not capture every possible structural break, instrument failure or execution disruption.

Historical Environment Review

Calendar-year returns for the strategy and benchmark.

Historical environment	Calendar year	Return
Weakest calendar year	2015	1.02%
Second-weakest calendar year	2014	8.67%
Strongest calendar year	2020	84.34%
Second-strongest calendar year	2025	77.31%

Investor Fit

Potentially suitable for:

Long-horizon investors seeking a systematic adaptive portfolio process.
Investors who value explicit risk management and disciplined implementation.
Investors able to tolerate material drawdowns and follow manual allocation alerts.
Fully funded brokerage accounts that can hold all required instruments.

Not designed for:

Guaranteed capital or guaranteed returns.
Investors unable to tolerate losses or recovery periods.
Leverage, short selling or very short holding horizons.
Generic copy trading or public disclosure of proprietary rules.

Strategy Profile

Dimension	Profile
Primary objective	Adaptive long-term capital growth through a proprietary systematic risk-management framework.
Portfolio form	Fully funded, long-only, multi-instrument allocation.
Decision structure	Proprietary adaptive allocation framework; internal rule structure remains confidential.
Signal delivery	Private manual target-allocation alerts.
Copy trading	Not offered.
Expected investor behavior	Follow the process through negative periods without discretionary overrides.

Capacity & Implementation Notes

The strategy is implemented through liquid exchange-traded instruments, but practical capacity depends on account size, local access, lot size, spreads, distributions, taxes and execution quality. Subscribers must use an eligible broker and maintain enough liquidity to implement the consolidated target without leverage.

Because follower accounts differ, manual implementation is the supported path. Exact instruments and target weights are delivered privately.

Review Protocol

The strategy should be reviewed by reproducing the frozen baseline, auditing data quality, testing parameter neighborhoods, checking alternate starts and out-of-sample windows, and confirming that costs and operational constraints remain within the validated range.

Changes should require structural evidence and commercial logic, not merely a higher full-sample CAGR or better recent performance.

Ongoing Review

Monitor data coverage, instrument availability and implementation quality.
Compare realized account behavior with the intended consolidated target.
Repeat sensitivity, out-of-sample and stress studies after material structural changes.
Preserve a frozen reference version so every proposed modification can be evaluated honestly.

Institutional Notes (Methodology & Interpretations)

Daily versus monthly evidence: headline drawdown, recovery and tail-risk statistics come from the audited daily engine. Some public charts and rolling tables use month-end observation dates for readability and are labeled accordingly.

Out-of-sample interpretation: the four time-based folds use frozen final rules for the reported fixed results. They reduce, but do not eliminate, selection and model risk.

Monte Carlo interpretation: bootstrap percentiles describe resampled historical dependence. They are not confidence limits for future returns and do not model all structural breaks.

Risk budgeting: the historical -14.50% maximum drawdown should not be used as a loss cap; the simulated tail supports planning for materially larger declines.

Important Notes

Historical simulation: all results are hypothetical and may differ from live investor outcomes because of taxes, spreads, fees, rounding, cash flows, distribution treatment and execution differences.

Risk: historical maximum drawdown is not a future loss limit. Monte Carlo evidence supports budgeting for materially larger drawdowns than those observed historically.

No forecast: past simulated performance does not guarantee future results.