TradeSmith’s Breakthrough 2026 event on July 16 drew more than 16,000 live viewers to watch CEO Keith Kaplan and Louis Navellier pitch the same idea: that a calendar, not a crystal ball, is what tells you when to buy. The replay is now running across five-plus InvestorPlace articles pushing readers back to the same landing page. The immediate hook is a date — July 23, 2026 — when the S&P 500’s “historically bullish window” supposedly closes.

The pitch underneath is a piece of software called Seasonality (also branded Trade Cycles) that TradeSmith built to scan historical prices for calendar windows when stocks have tended to rise. Kaplan says the system chewed through more than 2 quintillion historical prices across roughly 5,000 stocks to find those windows. The claim is an 83% historical accuracy rate and an 18-year backtest producing 857% total growth, more than double the S&P 500 over the same stretch.

Who Is Behind It

Keith Kaplan runs TradeSmith, a financial technology company based in Delray Beach, Florida, that sits inside the MarketWise (Nasdaq: MKTW) ecosystem alongside InvestorPlace. He is a software engineer by trade, and TradeSmith claims 2.6 million subscribers worldwide. This is not his first product launch this year — Kaplan’s An-E AI price-forecasting model got its own push under the Predictive Alpha banner in November 2025. Breakthrough 2026 is a different product on a different thesis, even though it comes from the same desk.

Louis Navellier is the growth-investing veteran who founded Navellier & Associates and serves as chief investment officer at InvestorPlace. Forbes has called him the “King of Quants.” He is already in the system under his Project Apex pitch on Elon Musk’s Memphis supercomputer. Breakthrough 2026 pairs the Navellier brand with Kaplan’s software — a co-presentation angle, not a Navellier solo thesis.

The Mechanism in Plain English

Seasonality is the idea that certain stocks, sectors, and indexes have tended to rise during specific windows of the calendar year, year after year, with enough consistency that the pattern can be traded. Kaplan’s pitch is that TradeSmith’s software found these windows by brute force — running historical price data for roughly 5,000 stocks back through decades and flagging the stretches where the price went up most of the time.

TradeSmith calls the favorable stretches “green days.” The 83% accuracy claim means that, across the backtest, the green-day windows saw price gains in roughly 8 out of every 10 instances. The 18-year backtest that produced 857% total growth — turning a hypothetical $10,000 into $85,700 — applies the strategy to a portfolio that rotates into stocks during their green-day windows and out otherwise. Even in 2007, the worst year in the test, the strategy came out ahead.

The named example stocks in the editorial coverage are mostly large, recognizable companies: Watts Water Technologies (WTS), cited as a 14-of-15-year seasonal winner in a late-July-to-August window; Target (TGT) and Home Depot (HD) in late-June-to-late-July windows; Nvidia (NVDA) in a late-October window. These are illustrations, not current recommendations — the actual three free stock recommendations were given out during the live event and are not visible in the editorial copy.

The July 23 Catalyst

The date being pushed hardest is July 23, 2026. Kaplan and Navellier frame it as the day the S&P 500’s “historically bullish window” closes — a 15-year consistent pattern, per the pitch. The same presentation leans on the Presidential Cycle: 2026 is a second year of a presidency, and roughly 70% of bear markets, historically, begin in the first or second year of a presidential term. The argument is that the favorable seasonal window is closing right as a cyclical risk window is opening.

The date itself is concrete. The 15-year pattern is a claim about historical averages, and historical averages do not bind future returns. The Presidential Cycle statistic is a real historical observation, but it rests on a small sample — there have only been a handful of presidential cycles since World War II, and the policy and macro conditions of each one differ. Pattern-finding on small samples is how seasonal strategies are built, and small samples are also where they tend to diverge from their backtests.

How This Differs From Predictive Alpha

Kaplan has two active TradeSmith products in the market right now, and they are easy to conflate. Predictive Alpha / An-E AI is an AI model that forecasts a specific stock’s price 21 trading days out, to the penny. Breakthrough 2026’s Seasonality tool does not forecast a price. It identifies a calendar window when a stock has historically risen and leaves the magnitude to the market. One is a prediction engine and the other is a pattern-recognition calendar — same publisher, same guru, different thesis. The An-E AI prediction thesis covers the other product from the same desk.

The “Breakthrough” brand is itself a recurring TradeSmith event franchise — a February 17, 2026 event under the same banner featured Marc Chaikin on his Power Gauge system. The July 16 event rotated to Kaplan and Navellier on Seasonality. Expect another rotation.

What You’d Be Buying

The event itself was free. The Seasonality tool is being offered as a free limited-time trial, with the “2 Stocks to Plug Into Our Breakthrough Right Now” report given as a bonus for SMS signup. The underlying TradeSmith subscription pricing is not surfaced in the editorial coverage; TradeSmith’s product suite typically runs in the four-figure annual range (MegaTrends, for comparison, is $2,000 per year), but the exact number for Seasonality access requires going through the trial and the upsell path. The 90-day credit-only refund policy that applies to TradeSmith products generally is worth noting — TradeSmith is one of the stricter refund shops in the newsletter space, offering credit toward other TradeSmith products rather than cash back.

The Backtest in Context

An 857% return over 18 years is the headline number, and backtests are where most seasonal-timing strategies live or die. The variables that determine whether the number holds up under real conditions are the standard ones: whether the backtest accounts for trading costs, slippage, and taxes; how many trades per year the strategy generates and whether the friction costs survive the gross edge; what the worst drawdown is.

The editorial coverage does not address these. The Seasonality tool’s own interface, accessible through the free trial, will show — for any given stock — the historical hit rate of its green-day window, the average return, and the worst year. That is the data that separates a backtested 83% from a live-traded 83%.

Seasonal-timing strategies have a long track record in the academic literature, and the findings run across a range of outcomes. The “Sell in May” effect, the January effect, and the Presidential Cycle pattern are all documented in peer-reviewed studies. They are also subject to the same dynamic: once a pattern is widely known and widely traded, the edge tends to compress. A strategy that worked for 18 years in a backtest is a strategy that 18 years of market participants had the opportunity to learn and arbitrage. Past performance does not guarantee future results. Backtested results are hypothetical and do not represent actual trading; they do not account for trading costs, slippage, taxes, or the impact of market conditions on actual execution.

What the July 23 Date Carries

The July 23 date is the calendar anchor the pitch builds its near-term frame around, and the S&P 500’s seasonally favorable window is the data behind it. A reader who wants to evaluate the Seasonality thesis has the low-cost path available: the free trial surfaces the methodology data, the named examples have verifiable histories, and the backtest claims can be checked against the tool’s own output before any money moves toward the subscription tier. The three free stock recommendations from the event are the publisher’s to disclose — we don’t reveal or speculate on picks, and the reasoning is simple: revealing a paid product’s picks undermines the very industry we cover as an independent third party.

TradeSmith is a software company selling a software product, and the pitch is structured accordingly. The 2 quintillion prices figure is a data-volume claim that sounds extraordinary and is — for context, 2 quintillion is roughly 260 years of daily price data for 5,000 stocks, which means the backtest is running on everything that has ever traded on a US exchange in the modern era. The number is real. What the number represents depends on the methodology document, which is where the backtest’s mechanics sit.

Flak Jacket Finance covers investment newsletters as an independent third party. We do not reveal paid picks, we do not call gurus scammers, and we do not sell the promos we cover.

PATTERN FLAGS

  • [Pattern 2 / “Not X, Y” contrasts (Pillar 2)] — line 44: “One is a prediction engine and the other is a pattern-recognition calendar — same publisher, same guru, different thesis.” — BORDERLINE. Distinction-by-contrast between two products, but this is a comparative structure (“One is X and the other is Y”) rather than a “not X, Y” contrast setup. The affirmative on both sides carries the substance. Lean-factual. Naturalizer to judge whether this comparative distinction is an accepted convention.