Keith Kaplan of Tradesmith argues that the current market environment—new S&P highs amid war, oil shock, sticky inflation, and extreme short-term swings—creates unusually strong conditions for signal-based trading. He pitches Tradesmith’s AI-driven system as a way to identify repeatable, historically tested setups and highlights bullish short-term signals in United Airlines, DT Midstream, and Astera Labs, while also pointing to the NASDAQ’s 11-day winning streak as a historically bullish pattern with near-term volatility likely ahead.
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Keith Kaplan’s core thesis is that this is not a normal market and that traditional buy-and-hold thinking is less useful in the post-2020 regime than short-term, data-driven trading signals. He frames the moment as unusually chaotic: the S&P 500 hit an all-time high, then sold off sharply after war, oil shocks, and geopolitical turmoil, before rebounding to new highs in just 16 days. He pairs that with elevated gold, volatile oil, and inflation still above target, arguing that the combination signals fear, instability, and opportunity all at once. A major part of his case is historical and methodological. Kaplan says the market changed materially after 2020 because of remote trading, meme stocks, zero commissions, Fed stimulus, rising rates, and AI-driven speculation. …
Near term, the tape still looks tradable but choppy: he expects fast rotations, short-lived mean reversion, and frequent exit signals rather than a clean trend. The immediate risk is chasing extended names; the setup favors disciplined entries, tight sizing, and quick profit-taking.
Over the next few months, his base case is continued volatility with a constructive equity bias, especially if the NASDAQ follow-through resembles prior rare streak episodes. The view would change if the rally loses breadth or the signal engine stops finding repeatable rebounds across sectors.
Structurally, the transcript argues that post-2020 markets are more algorithmic, faster, and more reflexive than the pre-2020 era. That implies systematic signal-based trading may matter more than traditional long-horizon fundamental timing for active participants.
The NASDAQ has always closed higher on average a year after 11 consecutive up days, up 28%.
Speaker cites a signal study run on the NASDAQ after it went up 11 consecutive days, showing historical tendency to be higher six months to a year later.
The QQQ ETF closing up 11 consecutive days has only happened 9 times in history and historically points to the NASDAQ being much higher 12 months from now.
Speaker shows a signal study page analyzing historical precedents of 11 consecutive up days in QQQ, concluding the market points higher at the 12-month mark.
United Airlines has a short-term bullish mean-reversion signal with an 8% predefined target gain, backed by a 10-year 92.41% accuracy rate and average 6.66% return in about half a month.
The system's historical backtesting and current signal scoring (98.82 quality score) support this claim.
Can you remember any other time quite like this in the markets?
Keith describes this as unprecedented: all-time highs during a war, a 10% drop in 30 days followed by a rapid recovery to new highs, gold near 4780, oil over $90, and sticky inflation at 3.3%. He says he has seen volatile markets before, but not this specific combination happening all at once.
Is there any historical evidence we can look at for guidance in this kind of situation?
Keith says yes and no — he frames markets as before 2020 and after 2020, which are genuinely two different types. Before 2020 you could look back 30 years and hold quality companies through cycles. After COVID, retail traders, zero commissions, Fed stimulus, rates spiking, and AI created completely new behavior patterns. He argues the old buy-and-hold approach is now 'just hoping,' and points to Jim Simons' signal-based mathematical approach as the only framework that makes sense in a post-2020 world.
How can you look for a pattern when everything seems to happen on a whim in this kind of market?
Keith explains that volatility doesn't destroy signals — it multiplies them. Rapid price swings force market data into rare configurations that surface only a handful of times per decade, which have the highest historical accuracy. He cites the 2020 COVID crash where Apple fell 19% in a month for no rational reason, yet their backtested model kept finding winners. He says signals don't care about a crash — they just check if the thumbprint of a high-probability trade is present.
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