Live Signal Log
Feb 2026 → mid-May 2026

With a standard deviation of ±3.68%, on any given signal you should expect a result somewhere between −2.85% and +4.51% roughly 68% of the time. This system has genuine movement, build that into your mindset before you start.

The average loss (−3.40%) is larger than the average gain (+2.39%). That gap will feel uncomfortable. But the 73% win rate and a profit factor of 1.91× mean the math works firmly in your favour: every $1 lost comes back as $1.91.

The maximum observed cumulative drawdown was −17.82%. On a $10,000 account that means sitting through a paper loss of roughly −$1,782 before the system recovers. Knowing this in advance is what stops you from pulling out at exactly the wrong moment.

The longest observed losing streak was 4 consecutive signals. That is the moment when most traders abandon a system, right before it turns. Trust the aggregate statistics, not the short-term noise. The edge does not disappear after a bad week.

75% of signals close above -0.39% and the median return is +1.00%. This means your day-to-day experience will typically feel positive. The outliers exist, but they are the exception, not the rule.

There are standout winners (GKOS +17.6%) and real losers (CCL −7.8%). Neither defines the system. What matters is the cumulative result across all signals. Evaluate your performance over a meaningful sample, not trade by trade.

The period opened under severe pressure. The Trump administration announced sweeping new tariffs on imports from China, Europe and several other trading partners, triggering an immediate risk-off selloff across global equity markets. The S&P 500 dropped sharply through February and into early April, at one point sitting more than 15% below its January peak. Cyclical sectors (industrials, materials, consumer discretionary) led the decline. This is the environment in which the first signals were generated.

One of the most significant macro moves of the period was the sharp depreciation of the US dollar. The EUR/USD rate rose from around 1.02 at end-January to nearly 1.13 by mid-May, a move of roughly 10%. For European investors holding USD-denominated assets, this acted as a direct drag on returns: even positions that gained in dollar terms delivered less, or even nothing, when converted back to euros. The signals in this log are expressed in USD terms; EUR-based investors should account for this currency impact.

Throughout the period, inflation data came in mixed, keeping the Federal Reserve in a holding pattern. Markets oscillated between pricing in rate cuts and fearing renewed hikes, which amplified intraday volatility. The VIX, the market's fear gauge, spiked above 40 in early April at the peak of tariff anxiety, its highest level since 2020. This environment directly explains the wider distribution of signal returns seen in this dataset: the same system that captured +17.6% on GKOS also absorbed −7.8% on CCL within the same month.

Starting in mid-April, sentiment began to shift. Partial tariff exemptions were announced, inflation readings softened slightly, and corporate earnings broadly beat lowered expectations. The S&P 500 staged a rapid recovery, reclaiming most of its losses by mid-May. This is the backdrop for the concentrated gains in the second half of the signal log, including the standout GKOS trade on April 28 (+17.6%), which landed precisely as the recovery gathered momentum. It was not luck; the system was responding to improving momentum signals.

These signals were generated and executed during one of the most turbulent macro environments since the COVID crash of 2020, not during a calm trending market. A +55.8% cumulative return in this context, against an S&P 500 that was essentially flat to slightly positive over the same window, carries more weight than the same number produced in a low-volatility bull run. Surviving and profiting through a tariff shock, a dollar collapse, and a VIX above 40 is the real stress test.

The signal universe, large-cap NYSE equities, proved to be the right hunting ground in this environment. Large caps have deeper liquidity and tend to recover faster than small caps during macro shocks, which is consistent with the win rate holding at 73% even through the February–April drawdown phase. The losses that did occur (RL, CCL, AEM, F) were concentrated in names most exposed to tariff and commodity repricing, exactly the type of event-driven risk that a momentum screener cannot always anticipate.