SPY COT backtest posts +62.47% returns

This is a market-structure story disguised as a trading flex. An X post from sopersone pushed a simple SPY strategy that mixes CFTC positioning data with a 200-day EMA filter and shows a backtest return of +62.47%. That gets attention fast — especially because the rules look clean and the drawdown appears contained. But the interesting part is not the percentage. It’s what this kind of setup is actually measuring, and where it can quietly break. ### What is the strategy really using? The core input is COT data — the Commitments of Traders reports published by the CFTC. For financial futures, the relevant version is the Traders in Financial Futures report, which splits large traders into buckets like Dealer/Intermediary, Asset Manager/Institutional, Leveraged Funds, and Other Reportables. The post appears to treat those buckets as a proxy for “institutional” versus “retail” behavior, then trades SPY when those groups diverge, with a 200-day EMA deciding whether longs are allowed. (cftc.gov) ### Why do people like COT so much? Because it is one of the few public datasets that tries to show who is positioned where in futures markets. That makes it feel like a peek into market plumbing instead of just another price indicator. If you think trend indicators are downstream and positioning is upstream, a COT-based signal feels smarter (cftc.gov)is why these posts spread. (financialresearch.gov) ### So what’s the first catch? Timing. COT is not live flow data. The CFTC releases the report on Friday afternoon, but the positions are a snapshot from the previous Tuesday. In other words, every signal starts with a built-in delay of about three trading days, and holidays can stretch that further. A backtest that uses the dat(financialresearch.gov)ne could have actually taken. (cftc.gov) ### Does “institutional versus retail” map cleanly here? Not really. The official categories are not “smart money” and “dumb money.” Asset managers are one bucket. Leveraged funds are another. Nonreportables often get treated as retail, but that group is really just traders below reporting thresholds. Dealers may be hedging cl(cftc.gov) a simple institutional-retail fight is making a strong inference, not reading a label straight off the dataset. (cftc.gov) ### Why add the 200-day EMA? Basically, it keeps the strategy from fighting the tape. A lot of positioning signals work better when they only fire in the direction of the broader trend. If SPY is above its 200-day EMA, the strategy can ignore bearish-looking noise and focus on long setup(cftc.gov)the COT logic itself. (quantifiedstrategies.substack.com) ### What about the 3% stop and 6% take-profit? Those rules make the backtest easier to understand and easier to sell. They also create a very specific payoff shape — capped upside, tightly defined downside, and lots of dependence on fill assumptions. On daily SPY data, small changes in entry timing, slip(quantifiedstrategies.substack.com)p, but they are not the same thing as a reproducible test. (marketcharts.com) ### Is +62.47% impressive? Maybe — but only if you know the test window, trade count, benchmark, exposure time, and whether the strategy beats just holding SPY over the same period. SPY itself has been a very strong asset over long stretches. A headline return without those comparisons can sound huge while still underperforming buy-and-hold after friction and tax(marketcharts.com)eturns have already been substantial, so the bar is higher than the headline suggests. (finance.yahoo.com) ### What should you take from it? Treat this as a neat prototype, not a finished edge. The post is useful because it shows how traders are trying to combine public positioning data with trend filters and explicit risk rules. But the whole idea stands or falls on data alignment, category interpretation, and whether the COT signal adds anything once you strip out the 200-day filter. That’s the real test.