Memory cycle v1.1 — v2 honest multi-cycle backtest
First execution of the
/investing:backtest-thesisv2 SOP. Replaces v1's calendar-fold methodology with multi-cycle phase-aligned windows, per-window universe construction, anchor-data-driven phase markers, and leave-one-out reporting. The conclusion is uncomfortable: the v1.1 strategy DOES NOT beat buy-and-hold of its own universe. It does beat broad-market benchmarks (SMH, SPY) on aggregate, but only because the underlying memory basket beats them — not because the rules add alpha.
Headline (honest)
Mean strategy return across 6 cycles: +246.8% (95% bootstrap CI: +15.9% to +649.5%) Mean BH-universe-equal-weight: +273.9% (95% bootstrap CI: +50.3% to +632.1%) Strategy UNDERPERFORMS BH-universe by ~27pp on the all-cycles mean.
The strategy beat BH-universe in only 2 of 6 cycles (2008 +17pp, 2024-current +96pp). It lost to BH-universe in 4 of 6 (1997 -103pp, 2001 -13pp, 2018 -149pp, 2022 -11pp).
Leave-one-out collapses the headline. Drop the 2024-current AI-supercycle window and aggregate strategy return drops from +246.8% to +51.6%. Drop the same window from BH-universe and it drops from +273.9% to +103.2%. Without the AI supercycle, both strategy and benchmark are unimpressive — and strategy is HALF of BH-universe.
The strategy DOES beat the broad-market benchmarks on aggregate: vs BH-SMH +149pp on mean, vs BH-SPY +192pp on mean. But this is the "you were just along for the AI ride" critique made visible — beating SMH is mostly a side effect of memory beating broad semis, not a v1.1 rules win. Removing the 2024-current cycle, strategy mean drops to +51.6% vs SMH +74.6% (also unavailable) — and the alpha vs broad benchmarks disappears too.
Verdict: this is a buy-the-AI-supercycle bet with extra trades. The v1.1 rules do not add alpha vs buy-and-hold of the same universe.
Per-cycle results
| Cycle | Window | Universe | Tradable | Strategy | BH-univ | BH-SMH | BH-SPY | Strat vs BH-univ | Trades |
|---|---|---|---|---|---|---|---|---|---|
| 1997 | 1996-01 → 2000-12 | MU, TXN, HXSCY | 2/3 | -16.8% | +86.7% | -50.3% | +3.0% | -103.4pp | 14 |
| 2001 | 2001-01 → 2006-12 | MU, IFNNY | 2/2 | +25.4% | +38.0% | +15.3% | +55.5% | -12.6pp | 13 |
| 2008 | 2007-01 → 2013-12 | MU, IFNNY, SPSN | 3/3 | +107.4% | +90.4% | +63.4% | +85.7% | +17.0pp | 15 |
| 2018 | 2016-01 → 2020-12 | MU, WDC, STX, MRVL | 4/4 | +138.0% | +286.6% | +307.2% | +94.8% | -148.6pp | 27 |
| 2022 | 2021-01 → 2023-12 | MU, WDC, STX, MRVL | 4/4 | +3.8% | +14.5% | +51.9% | +32.1% | -10.6pp | 26 |
| 2024-current | 2024-01 → 2026-05 | MU, WDC, SNDK, STX, MRVL, INTC | 6/6 | +1222.7% | +1127.1% | +198.8% | +55.3% | +95.6pp | 37 |
Strategy wins vs BH-universe: 2/6. Strategy wins vs BH-SMH: 4/5 (SMH inception ~2000). Strategy wins vs BH-SPY: 3/5.
Per-ticker color (2024-current cycle = entire AI-boom outlier)
The 2024-current cycle dominates the entire backtest. Per-ticker:
- SNDK: +4151.5% (spun from WDC Feb 2025, only 15 months of data — extreme outlier)
- WDC: +1119.8% (likely includes SNDK-spin value via yfinance back-adjust)
- STX: +961.7%
- MU: +715.0%
- INTC: +196.9%
- MRVL: +191.4%
The SNDK number especially is structurally suspect — 15 months of price data captured in a once-per-generation AI-storage repricing event. The 1222.7% strategy figure on this cycle should be read as "the AI supercycle happened" not "the rules captured it."
Aggregate metrics with CI
| Metric | Value | 95% Bootstrap CI |
|---|---|---|
| Mean strategy return | +246.8% | (+15.9%, +649.5%) |
| Mean BH-universe | +273.9% | (+50.3%, +632.1%) |
| Mean BH-SMH | +97.7% | (not computed - missing 1997 cycle) |
| Mean BH-SPY | +54.4% | (not computed - missing 1997 cycle) |
Confidence interval interpretation: the CI on strategy mean spans +16% to +650%. That's not a precise measurement — that's "we cannot statistically distinguish this from a moderate positive return OR a spectacular one." With N=6 cycles and 2024-current being a massive outlier, no further precision is honest.
Per the v2 SOP "no 2-decimal Sharpe on <10 trades" rule: Sharpe is not reported. Per-cycle trade counts: 13-37, but each "trade" is a tranche entry/exit within a single position. Effective trade count is ~24 (4 per cycle × 6 cycles) — well below the 50 threshold for 2-decimal precision.
Leave-one-out check
This is the diagnostic the founder mandated. Drop each cycle in turn and recompute the aggregate:
| Dropped cycle | Strategy mean (N=5) | Delta vs all-6 | BH-univ mean (N=5) | Note |
|---|---|---|---|---|
| 1997 | +299.5% | +52.7pp | +311.3% | Drop = better (1997 was strategy's worst loss) |
| 2001 | +291.0% | +44.3pp | +321.1% | Drop = modestly better |
| 2008 | +274.6% | +27.9pp | +310.6% | Drop = strategy loses a win |
| 2018 | +268.5% | +21.8pp | +271.3% | Drop = strategy loses big underperformance |
| 2022 | +295.4% | +48.6pp | +325.8% | Drop = modestly better |
| 2024-current | +51.6% | -195.2pp | +103.2% | Drop = aggregate COLLAPSES |
Headline-changing finding: dropping the 2024-current cycle takes strategy mean from +247% to +52%, and BH-universe from +274% to +103%. Without the AI supercycle, both strategy AND benchmark drop to "ordinary" returns, AND the strategy is HALF of BH-universe (52% vs 103%). The entire "strategy is comparable to BH" framing is propped up by one extreme outlier.
This matches v1's critique: a single dominant cycle is doing the heavy lifting, and the strategy's mechanical rules are not what made it work.
Bootstrap confidence intervals
Bootstrap (5000 iterations, with replacement) on per-cycle returns:
- Strategy: 95% CI (+15.9%, +649.5%) — span of 633pp
- BH-universe: 95% CI (+50.3%, +632.1%) — span of 582pp
Both CIs span an order of magnitude. The honest reading: with N=6 cycles and one extreme outlier, no statistical statement about "strategy > or < BH-univ" can be made with confidence. Distribution is too wide.
Sensitivity sweep
Drawdown-add threshold (key v1.1 parameter — "add 0.5R on >20% drawdown with bullish anchors"):
| Threshold | Mean strategy return |
|---|---|
| -16% | +237.8% |
| -20% (locked default) | +246.8% |
| -24% | +241.6% |
Strategy is NOT sensitive to this parameter (±5pp on mean across the sweep). Good news: not overfit to this specific number. Bad news: this is because the parameter rarely fires meaningfully — most of the action is in the entry tranches and the no-mechanical-exit hold-through, not the drawdown-add logic.
Regime-change diagnostic
Pre-2014 vs post-2018 cycles, tested separately:
| Era | Cycles | Strategy mean | BH-univ mean | Strategy vs BH |
|---|---|---|---|---|
| Pre-2014 | 1997, 2001, 2008 | +38.7% | +71.7% | -33.0pp |
| Post-2018 | 2018, 2022, 2024-current | +454.9% | +476.1% | -21.2pp |
Verdict: same conclusion in both regimes. Strategy underperforms BH-universe by similar margin (~25-33pp) in BOTH the pre-hyperscaler era (1997-2008) and the post-2018 era. The "regime change" hypothesis (pre/post-hyperscaler memory cycles are structurally different) is supported by the level of returns being radically different (38% vs 455%) — but the strategy-vs-BH-univ DIFFERENTIAL is consistent across regimes. The rules don't add alpha in either era.
Founder's "Castellano broke the cycle" hypothesis — that 2024-current is structurally different from prior cycles — is supported by the magnitude difference (1222% vs 107% for the next-best 2008 cycle). But it doesn't rescue the v1.1 rules.
Thesis vs strategy separation
Did the anchor data behave as the thesis predicts? (THESIS validation)
Yes — and this is the key finding the founder should preserve. The phase-history.csv shows 6 cycles each following the demand → capacity-announce → capacity-online → down-cycle → recovery sequence in the order the v1.1 thesis predicts. The 2018 cycle (Micron FY18 GM 58.9% peak → 56% stock crash within months) and 2022 cycle (TrendForce monthly + Micron capex-cut + Samsung production-cut all dateable to month) are particularly clean confirmations. The thesis structure IS empirically supported by 6 cycles of memory-industry history.
The 2024-current cycle is the one that may NOT follow the prior pattern (Samsung "minimize oversupply risk" stance, no clear down-cycle entry yet, HBM offtakes 2+ years out). That's the Castellano "HBM broke the cycle" question and it remains live.
Did the rules capture the predicted moves? (STRATEGY validation)
No. The v1.1 rules (tranche-in at phase-1 demand-recognition, hold through cycle, exit only on anchor break) DO NOT outperform buy-and-hold of the same universe at the same entry date. The tranche-add logic creates a slightly worse average entry price (waiting for -5%/-10% drawdowns means missing some immediate rallies), and the hold-through logic is equivalent to BH by construction.
The v1.1 thesis is right. The v1.1 RULES don't add value beyond just buying the universe at the same date.
Survivorship-bias confession
What gaps exist in the universe data, by cycle:
| Cycle | Intended universe | Available via yfinance | Excluded (gap) |
|---|---|---|---|
| 1997 | MU, TI memory unit, Hyundai ADR, pre-merger Samsung, Toshiba ADR | MU, TXN | HXSCY (Hyundai Electronics ADR — yfinance shows symbol delisted/missing); Samsung/Toshiba not US-listed at the time |
| 2001 | MU, Infineon, Hynix ADR, ProMOS, Mosel Vitelic | MU, IFNNY | Hynix ADR (different ticker pre-2012); ProMOS/Mosel never US-listed |
| 2008 | MU, Qimonda, SK Hynix ADR (post-restructuring), Elpida, Powerchip, Spansion | MU, IFNNY (Qimonda parent), SPSN | Qimonda (bankrupt Jan 2009; delisted before yfinance coverage); Elpida (6665.T, not US-listed); Powerchip (TW); Hynix ADR pre-2012 |
| 2018 | MU, SK Hynix ADR, WDC, STX, MRVL | MU, WDC, STX, MRVL | Hynix ADR (HXSCL — yfinance unreliable) |
| 2022 | Same as 2018 + SNDK post-spin | MU, WDC, STX, MRVL | SNDK didn't exist yet (spun Feb 2025) |
| 2024-current | + SNDK + INTC | All 6 available | None |
What this likely does to results:
- The 2008 cycle excludes Qimonda (bankrupt — would have been a 100% loss for that name) and Elpida (also bankrupt). Including them would REDUCE the +107% cycle return, possibly significantly. The cycle's strategy outperformance vs BH-univ (+17pp) is the WEAKEST claim here.
- The 1997 cycle excludes the Hyundai ADR. This is the cycle where strategy lost -103pp to BH-univ; including more names wouldn't change the strategy-vs-BH delta because both would be affected equally.
- The 2018 cycle excludes Hynix ADR which would have been a winner — would slightly improve both strategy AND BH-univ.
Honest gap acknowledgment: the surviving-name bias is structural and not fixable without paid data sources. Reported numbers should be read as "this is what the strategy did on the names that have continuous US-listed price data" — which is itself a real-world tradeable constraint and not entirely a methodology defect.
"Is this overfit?" diagnostic
Three checks:
- Parameter sensitivity (sensitivity sweep above): strategy varies ±5pp across ±20% sweep on drawdown-add threshold. NOT overfit to this parameter. Low signal here.
- Cycle-by-cycle consistency: strategy beats BH-univ in 2/6 cycles. NOT consistent — the rules do not reliably add value cycle-after-cycle. If the rules captured a structural truth, we'd expect 4/6 or 5/6 wins. 2/6 is barely better than coin-flip and is fully explained by one dominant cycle.
- Out-of-sample validation: the v1.1 rules were designed AFTER seeing v1's 2019-2025 results. The 2018-2022 cycles in this backtest are essentially in-sample. The pre-2014 cycles (1997, 2001, 2008) are the closest to true out-of-sample — and the strategy loses to BH-univ in 2 of 3 of those (1997 -103pp, 2001 -13pp, 2008 +17pp = mean -33pp). Out-of-sample evidence supports the "no alpha" verdict.
Verdict: the strategy is not overfit in the parameter-sensitivity sense, but it doesn't have a real edge either. It's not memorizing noise; it's just not doing better than buying and holding the same names.
Caveats
Hyperscaler-capex regime change — pre-2014 cycles cannot use the v1.1 thesis's #1 anchor (hyperscaler capex direction). The 1997, 2001, and 2008 cycles use PC OEM and consumer demand as substitute drivers. Per phase-history-notes: this means the strategy under test in those cycles is not quite the same strategy as in the 2018+ era. Honest read: the strategy is being tested against a different (worse for it) demand regime.
Pre-2017 DRAM spot opacity — TrendForce monthly DRAM spot data starts Feb 2021. Pre-2021 phase markers come from earnings retrospectives + bankruptcy dates + DOJ records. Phase boundary uncertainty is ±3 months pre-2010 vs ±1 month post-2017. Strategy entries snapped to phase-1 demand-recognition dates with this uncertainty baked in.
Castellano "HBM broke the cycle" hypothesis — the 2024-current cycle may NOT have a down-cycle transition coming. Samsung's "minimize oversupply risk" Q1 2026 stance, HBM4 logic-die prices +40-50%, multi-year offtakes — these are evidence the cycle is structurally different from prior. If true, the strategy under test (which assumes cyclical exits) may be testing a no-longer-applicable framework. Backtest result reflects 2024-2026 mark-to-market only; live cycle outcome unknown.
SNDK is a 15-month dataset in a once-per-generation event — the +4151% per-ticker number is real but should not be treated as a sustainable repeating signal. Removing SNDK from the 2024-current cycle would meaningfully reduce the 1222% cycle return.
Survivorship gaps — Qimonda (bankrupt 2009), Elpida (bankrupt 2012), Hyundai Electronics ADR (delisted) are NOT in the backtest. v2 SOP requires including bankruptcies as "forced exits" — the harness does this for names with continuous yfinance data, but cannot include names yfinance never had. Cycle results may be modestly inflated as a result.
N=6 cycles is small. Bootstrap CIs span an order of magnitude. No strong statistical conclusion is possible.
Mode A, not Mode B. This is fixed-rule out-of-sample replicated across 6 cycles — NOT walk-forward parameter re-estimation. The v1.1 rules were locked at thesis-spec time (2026-05-18). No parameter optimization happens within the backtest. Per v2 SOP this is honest about what walk-forward actually means.
Recommendation for paper deployment
DO NOT DEPLOY v1.1 AS A MECHANICAL STRATEGY. The honest answer is: buy-and-hold the same universe wins.
Specifically:
The v1.1 rules do not add alpha vs BH of the same universe on the all-cycles mean (-27pp), on 4 of 6 individual cycles, and especially without the 2024-current outlier (-52pp).
The thesis itself remains supported. 6 cycles of phase-history are consistent with the v1.1 thesis structure. The error is in trying to be tactical within the cycle.
Two paths forward, founder picks:
Path A: Deploy thesis as buy-and-hold + thesis-archival kill switch. This is the base case the founder mandated and v1.1 should have collapsed to. Buy MU + WDC + SNDK + STX + MRVL + INTC equal-weight at thesis-confirmation date. Hold. Exit only on the anchor-break HIGH-severity triggers from section 4 of the thesis (2 of 4 confirmed). This captures the thesis-is-right finding without paying the tranche-timing cost.
Path B: Iterate to v1.2 with explicit edge-source hypothesis. Before another mechanical-rule backtest, the v1.2 spec must answer: WHAT specific edge are the rules trying to capture beyond buying and holding? Tranche-timing on -5%/-10% drawdowns demonstrably does not add value. Anchor-strength adds rarely fire and don't help. If there's no testable edge hypothesis, there are no rules to backtest.
My recommendation (Ray): Path A. The honest finding is that the thesis is right, the strategy rules are not. Drukenmiller doctrine taken to its logical conclusion IS buy-and-hold + thesis-archival kill switch. The v1 backtest already showed this; the v2 multi-cycle test confirms it across regimes. Continuing to refine mechanical rules is the gold-plating trap.
If founder still wants to deploy v1.1 paper-trade, do so for INFORMATION VALUE only. Live execution will produce a 7th cycle's worth of data and reveal whether the live cycle behaves like 2024-current (AI supercycle continues) or like 1997/2018 (mean reversion to "strategy loses to BH"). But do not deploy with capital expectations that v1.1 will beat BH-universe — the evidence says it won't.
Cost / scope summary
- 6 cycles tested
- 18 unique tickers attempted (5 unavailable due to delisting/non-US-listing)
- 112 strategy trades simulated, 5 benchmark variants (BH-univ, BH-SMH, BH-SPY across cycles)
- Leave-one-out + bootstrap CI + sensitivity sweep + regime-change diagnostic complete
- Real yfinance price data (no synthetic / proxy returns)
- Runtime: ~3 minutes
- LLM spend: ~$2-3 (well under cap)
Related
- [[2026-05-18-memory-cycle-v1.1]] — the thesis under test
- [[2026-05-17-memory-cycle-v1-walk-forward.md]] — v1 backtest (calendar folds, single ticker — superseded)
- [[2026-05-17-memory-cycle-v1-real-anchor-rerun.md]] — v1 real-anchor rerun (same window — superseded)
- [[01-projects/investing/anchors/memory-cycle-v1.1/phase-history.csv]] — input phase-marker data
- [[01-projects/investing/anchors/memory-cycle-v1.1/phase-history-notes.md]] — phase methodology + caveats
- [[~/.claude/skills/investing-backtest-thesis/SKILL.md]] — v2 SOP this report follows
- [[2026-05-18-memory-cycle-v1.1-v2-driver.py]] — backtest driver source
Changelog
- 2026-05-18 (v2 first execution) — First run of v2 SOP. 6 cycles, survivorship-aware universe, leave-one-out aggregation, bootstrap CI, regime-change diagnostic. Honest finding: strategy underperforms BH-universe by 27pp mean, by 52pp without 2024-current outlier. Recommendation: deploy thesis as BH + thesis-archival kill switch (Path A), not v1.1 mechanical rules.