Fast Momentum 6-1 (High Risk)
Global core
Holds the 5 strongest names by 6-month return (skipping the last month), refreshed monthly. The most aggressive core strategy: its simulated drawdowns are among the deepest on the core shelf (see the simulated stats beside this text) and its wins cluster in rebound markets. High risk, high variance.
This strategy's universe (121) →
Worst stretch in the backtest: -52.4% — a simulated $1,000 would have dipped to ~$476 before recovering.
Concentration check: 2026 (to 2026-09-30) alone returned +130.6% — more than all the other years of this backtest together (+49.3%). The headline CAGR leans on that one period; see the chart data, year by year.
Backtest window: 2021-04-30 → 2026-09-30 · as of 2026-09-30 (updated quarterly)
This window does not include the COVID crash (Feb–Mar 2020) and the 2008 financial crisis.
Full methodology & limitations
Track records are simulated backtests, not live results. Methodology and current limitations: (1) Universe membership is real point-in-time index constituents - Nasdaq-100 (US) and WIG20 (PL), with dated add/drop history - so backtests see each index as it actually was, with no survivorship from later additions. Prices are adjusted close (total-return). When the backtest runs on the licensed, delisted-capable feed (EODHD), index members that later delisted or renamed are still priced and INCLUDED, closing the survivorship gap on the price side; when it runs on the keyless fallback feed, some delisted names are unpriced and dropped, which can flatter drawdowns. Smaller residuals remain: index changes after the last sourced revision are not yet reflected (Nasdaq-100 membership as pulled on 2026-06-30, WIG20 as of its September 2025 revision), and a few long-delisted tickers may still be unresolved. A name with no real quote for 14 days is treated as untradable - never picked and never held on a frozen price - and a renamed listing is held under its new code with its full history. (2) Costs are charged per market on each rebalance's turnover - 5 bps for US names, 10 bps for EU, 20 bps for Warsaw (GPW) - the conservative end of typical half-spread-plus-fees for liquid names; market impact and FX conversion are not modelled (negligible at retail size, understated for large money). (3) Prices are total-return (dividends reinvested) where the data feed supports it. (4) Sharpe and Sortino use a flat 3%/yr cash (risk-free) rate, approximately the average US T-bill yield over the backtest window; actual cash rates varied 0-5% within it and Polish rates were mostly higher, so ratios are approximate. (5) Cross-market calendar gaps are bridged by carrying the last known price forward, which can modestly understate day-to-day volatility. (6) Rebalances are simulated at the same closing price the signal is computed from (close-to-close execution); a live follower trades at the next session's prices, so simulated results may differ from what next-bar execution would have produced. Past simulated performance does not predict future results.
Hover any metric above — I'll explain what it means.
How you'd carry it out in your broker
Every model portfolio update publishes as a plain list of tickers and weights — the exact same file for every subscriber. Here's the general shape of loading that into four popular brokers; the actual tickers and weights only appear once you unlock this strategy.
This strategy has averaged 5.0 changes in the model portfolio per month, over its last publications.
XTB, Trading 212, IBKR, Revolut
XTB
- Each published ticker is searchable in the XTB app/web platform.
- XTB has no basket/pie import — each ticker's published weight is its share of the model portfolio; whether and how much to trade is your own decision.
Trading 212
- Portfolio → Pies → Create Pie.
- Each published ticker goes in with its published weight % — the Pie then tracks that allocation for you.
Interactive Brokers (IBKR)
- Once unlocked, the model portfolio downloads as a CSV.
- Classic TWS: File → Import Watchlist reads that file — each ticker's published weight is its share of the model portfolio.
Revolut
- Each published ticker is searchable in the Invest tab.
- Revolut has no basket import — each ticker's published weight is its share of the model portfolio; whether and how much to trade is your own decision.
General information about loading a published, identical-for-everyone list into a broker — not a personal recommendation. Whether and how much to trade is your own decision, in your own account; we never trade on your behalf.
Track record
Chart data (year by year)
| Year | Simulated return |
|---|---|
| 2021 (from Apr) | -4.7% |
| 2022 | -28.5% |
| 2023 | +30.9% |
| 2024 | -9.8% |
| 2025 | +85.7% |
| 2026 | +130.6% (to 2026-09-30) |
Dashed line = Equal-weight universe (same window and costs): CAGR 11.2%, max drawdown -32.3%.
Simulated backtest — methodology & limitations above, next to the stats.
This month's model portfolio
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How it works — in plain words
Same recipe as classic momentum — rank by past return, skip the last month, buy the top 5 equally — but on a 6-month window instead of 12. A shorter memory: it latches onto new trends faster and abandons dying ones sooner, at the price of more trading.
Picture a stock that was flat all year, then exploded +60% over the last four months. The 12-month model, still weighed down by the flat first half, might rank it only mid-pack. The 6-month model sees mostly the explosion and buys it sooner — it has a shorter memory. The trade-off: that shorter memory also DUMPS names faster when they cool, so it trades more often, and every trade costs roughly 5-20 bps, which the backtest subtracts.
Shorten the lookback and you react faster but trade more (and costs eat more); lengthen it and you're calmer but slower to catch turns. There is no magic window — only trade-offs. That's why this page charges realistic per-market costs in the backtest: a fast signal that looks great cost-free can die once trading friction is priced in.
Turnover is the tax you pay for reactivity: a monthly top-5 swap can replace the whole portfolio a dozen times a year. At 20 bps a trade, that's real percentage points of CAGR gone.
Live since 2026-07-13 through 2026-11-06: -26.4% (3 mo) — the result of the published portfolios, entering at the next session's close. This is not a backtest.
Update history
| As of | What changed |
|---|---|
| 2026-11-01 | Model update: rotated the basket (2 added, 2 removed) |
| 2026-10-01 | Model update: rotated the basket (3 added, 3 removed) |
| 2026-09-03 | Model update: rotated the basket (3 added, 3 removed) |
| 2026-08-03 | Model update: rotated the basket (2 added, 2 removed) |
| 2026-07-21 | Model update: rotated the basket (2 added, 2 removed) |
Grounded in published research
The peer-reviewed findings this strategy is built on:
- Jegadeesh, N. & Titman, S. (1993). Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency. Journal of Finance, 48(1). (documents 3-12 month formation windows)
- Daniel, K. & Moskowitz, T. (2016). Momentum Crashes. Journal of Financial Economics, 122(2). (the risk citation: momentum crashes in whipsaws)
A documented, peer-reviewed effect — cited so you can read the source yourself, not take our word for it. Implementation and results are ours (see methodology); the underlying finding is the literature's.
Understand this strategy
Plain-language explainers behind the mechanics above.
This strategy's rules and every model-portfolio update are written and run by HedgeYourOwn's founder.