Market Warrior: S&P 500 crash avoidance signals

Market Warrior: twelve trades in seventeen years to dodge market crashes

According to a forum participant’s count, the strategy involves just twelve trades over seventeen years. Market Warrior aims to convince investors they can exit the S&P 500 before a crash and re-enter in time for the rebound. It is not a high-frequency trading robot or an intraday system; it presents itself as a risk management layer on top of index investing in benchmarks like the S&P 500 or MSCI World. While its market reading is bullish, it remains invested; when conditions deteriorate, it rotates into fixed income via fund transfers.

The experiment went public on November 29, 2024, with a reference portfolio of €10,000 and two open documents updated mid-afternoon: a viewer showing daily signals and a history tracking the system's evolution. On April 12, 2025, there was a sharp turn. The author abandoned the commercial side, cancelled paid subscriptions, and made all access free. He cited two reasons: the website did not cover costs and created tax issues, while maintenance consumed time he no longer had.

Twelve trades in seventeen years: a hard-to-audit backtest

The most common criticism targets the sample size. A seventeen-year backtest producing only twelve trades cannot distinguish real edge from pure curve fitting, where any historical series looks profitable in hindsight. The author admits this: backtesting works if short periods are used and programming parameters are adjusted for each timeframe. His defense is that serious testing begins now that it is published.

In contrast, one participant argues you cannot train a system on twenty years of data and expect it to work unchanged, because market regimes shift—interest rates, liquidity, index structure—and static models become obsolete. Another notes that those who optimize thousands of moving average and oscillator combinations know variance alone produces paper winners that fail with real money.

The response states the method has been applied to family assets since 2020, added layers were designed for backtesting not forward use, and the last five annual returns were exceptionally good, which the author concedes proves little.

How are signals executed in practice?

Here things get practical. Signals translate into transfers between index funds, and the vehicle matters as much as the model. For equities, it targets low-cost funds replicating MSCI World and S&P 500; for Europe, an indexed fund to the MSCI EMU. The sought advantage is not fees but execution: transfers between funds within the same manager on certain platforms settle at the same day’s net asset value, avoiding cash drag.

This detail—direct transfer without going through cash—is what makes entry and exit viable. Without it, each rotation would eat into returns, undermining the system. It is fine print rarely mentioned in presentations.

Black swan alerts, DeepSeek, and tariffs

The system includes two proprietary indicators: a black swan alert and an S&P 500 health check, sent by email. During the DeepSeek-driven selloff, the reaction was nil: the system remained unmoved. The author justified this by explaining that cheaper AI usage accelerates adoption and long-term global compute demand, even benefiting chipmakers.

Weeks later, amid tariff antiestéticars and recession anxiety, the reading remained bullish, viewing a 10% drop in days as a short-term correction. Then the black swan alert triggered, changing the narrative: a trinc reported exiting and waiting for the entry signal, unsure if the warning came too late or missed the rebound. Bullish reinforcement signals returned much later.

“If it works, why give it away?”

This question runs through the entire discussion and yields few convincing answers. The official version cites altruism and a family portfolio already using the method: the full system is shared free with a delay, and paid subscriptions were voluntary. The skeptical view suggests the business isn’t beating the index but accumulating subscribers, where a large base profits even if the strategy fails. The author ironically admitted hundreds pay zero euros monthly.

“No one shares a money-printing machine,” critics repeat. He replies he seeks no fortune, calling it a hobby; the thread also notes pure indexing requires thirty- or forty-year horizons for statistics to hold. The 700 free registrations suggest the audience hasn’t turned away entirely.

How many months of forward testing are needed to determine if this is a risk tool or just lucky spreadsheet?