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Stress test

The verified track record answers what did happen. This page answers what would happen: a behavioral policy derived from this trader's chain, played day-by-day against market regimes they haven't traded through. One deterministic path per regime, with no Monte Carlo, no distribution, and no fan chart. A single causal story you can audit.

Edge degrades in regimes the strategy didn't train on, slippage widens with vol, and the policy sits out under drawdown. These are the standard failures of real trading reflected back at simulation speed. Behavioral simulation, not investment advice.

Feb 19 — Aug 14, 2020. Calibrated to the published SPY record: 5-week crash of approximately -34%, including a -12% single day on Mar 16; V-shaped recovery to pre-crash levels by August.

Custom regime

Define your own scenario.

Set an annualized drift and volatility, a horizon, and any single-day shocks. The same behavioral policy is replayed against the path you describe, built on the fly, never stored.

Presets:
%

−150 to 150

%

2 to 150

20 to 504

Single-day shocks (optional)

None. Add a shock to drop (or spike) the market on a specific day, a gap-down, a flash crash, a relief rally.

Deterministic and ephemeral, nothing is stored.

Policy under 2020 COVID flash crash + recovery

130 trading days · starting equity $100,000 · cached

Simulated policy

Trader's actual record

Hover for day-by-day detail

$91K$101K$111K$121K$131Kday 0day 1300%-30%EQUITY · simulated policy · vs actual (dashed)DRAWDOWN
EntryStop hitSit outVol shiftPath eventPeakTrough

Policy return

+28.57%

Actual return

+3.43%

Max drawdown

6.7%

Days in drawdown

97

Recovery

27d

Sit-out days

0

Entries

8

Exits

7

What happened

Composed deterministically from the policy decisions. The same input always yields the same paragraph, with no LLM, cached forever.

Run against the 2020 COVID flash crash + recovery (historical-calibrated regime over 130 trading days). The behavior policy derived from this trader's chain produced a +28.6% total return with a 6.7% peak drawdown; 27 days to recover from the worst trough. The policy entered 8 times, exited 7 times, and sat out 0 days under drawdown / vol thresholds. Policy thresholds: 1.8% profit target, -1.2% stop loss, 60% of equity per position, active on ~42% of trading days. This is a single deterministic path — no Monte Carlo aggregation. It shows what the policy would do in this specific scenario, not a distribution. It is not investment advice.

Key decisions

  • Day 7Hit stop loss (-1.5% over last 10 days, vol-adjusted threshold -1.2%).
  • Day 8Feb 24: first major decline
  • Day 24Mar 16: -12% (worst day since 1987)
  • Day 28Mar 24: V-shape begins
  • Day 50Realized 10-day vol jumped from 1.92% to 3.07%.
  • Day 52Equity trough: $93,879.
Important: Single deterministic path. The policy is rules-derived from observed behavior, not the trader's actual algorithm. Past performance does not guarantee future results.

Adversarial resilience

How much of the edge survives a competitor who knows the playbook and fades the policy's fills, hardest on forced stop exits. A resilience disclosure, not a verdict on the trader.

Run resilience testRe-runs the policy against a playbook-aware competitor. Deterministic; nothing stored.

Your mirrored account

What this regime would have done to your allocation if you had mirrored Tortoise Strategies (Demo). Pick an amount; the dollar figures scale, the percentage path doesn't.

Allocation

$50,000

Ending (before fee)

$64,286

+$14,286 · +28.57%

Performance fee

−$2,143

15.0% of profit

Net to you (after fee)

$62,143

+$12,143 · +24.29%

Max drawdown

$3,372

6.7% from peak

Lowest balance

$46,940

trough of the path

Your dollars on the same simulated 2020 COVID flash crash + recovery path, a linear scaling of the policy curve, not a separate simulation. Mirroring is exactly proportional, so the percentage path is identical at every allocation; only the dollar figures change. After-fee figures apply this trader's 15% performance fee on a high-watermark basis (you never pay twice for the same gain). Display only; behavioral simulation, not investment advice.

Investor lens

The same result, read through four investor archetypes. Each weights drawdown, return, recovery, and survival differently. The spread itself is the signal. When the panel disagrees, your own tolerance is the tiebreaker.

Deterministic verdicts. Not allocation advice, just a disclosure of how different profiles would read these numbers.

3 of 4 archetypes lean favourable, 0 lean negative, 1 hold. Lean-favourable but not unanimous — read the individual rationales below.

Preservation

The Capital Preserver

Hold

Drawdown weighs heaviest here: 6.7% peak drawdown, recovered after 27 days. Upside of +28.6% total return doesn't offset the capital-at-risk reading.

Conviction
49

Growth

The Growth Seeker

Invest

Total return of +28.6% drives the reading; the 6.7% drawdown is acceptable cost-of-capture. Note: the policy stayed engaged with 8 entries.

Conviction
100

Risk-Parity

The Risk-Parity Allocator

Invest

On a risk-adjusted basis the return/drawdown ratio of +4.26 is the read. +28.6% return came at 6.7% peak DD; weight in proportion to that ratio.

Conviction
90

Skeptic

The Regime Skeptic

Consider

Survivability is the question, not upside. The 6.7% max drawdown and the policy exited 7 times against 8 entries characterise how the policy responds when the regime turns.

Conviction
65
Spread:
50/100

Policy thresholds

The rules-of-thumb derived from this trader's chain. The simulator runs these decisions day-by-day.

Base notional

60%

of equity per position

Active days

42%

of trading days

Hold target

10.6d

median position

Profit target

+1.8%

exit on gain

Stop loss

-1.2%

exit on loss

Market beta

1.10

exposure to regime