Yes. Low correlation says that a strategy’s returns move differently from another strategy’s; it does not say either strategy makes money. Add a bot with a negative standalone edge to a portfolio and it can still lose money—just in a pattern that differs from the other bots. Correlation is a diversification measure, not evidence of profitability.
What does low correlation actually tell you?
Correlation describes how two return series move in relation to one another over a chosen period. A low reading can indicate that their observed movements were less alike in that sample. It does not establish that either series has positive expected returns, that the relationship will persist, or that one strategy will offset losses in another.
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That distinction matters when a crypto portfolio is made up of bots that all respond, in different ways, to whether crypto prices are rising or falling. A strategy can produce a return pattern that looks distinct from the rest of the fleet and still have poor results on its own. In that case, the portfolio has gained a different source of risk, not necessarily a useful hedge.
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In a personal account published by Dry Run on DEV Community on September 22, 2026, the author compared daily P&L changes across five bots running in paper mode over two months. The snapshots included both realized results and mark-to-market changes on open positions. These are the author’s observations, not audited or independently verified results.
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| Strategies compared | Reported correlation | What the author reported |
|---|---|---|
| Two similar dip-buying bots on different exchanges | 0.93 | Their daily P&L changes moved closely together. |
| A more conservative dip-buying sibling and the other dip-buyers | 0.51 | The author reported a lower correlation than between the two similar bots. |
| A breakout bot and the dip-buying bots | Approximately 0.05 | Their daily P&L changes were nearly uncorrelated in this sample. |
The account also reports that the breakout bot lost 445 units during the paper period. Its backtest over a market decline described as roughly 41% was down 42% and had a negative Sharpe ratio. A Supertrend bot made 501 units in the paper period, while its backtest over the same cycle was down 52%, with a Sharpe ratio of -5.8. Those figures are claims from the author’s account; the page does not provide an independent dataset or audit. They should not be treated as evidence that either strategy will perform similarly in live trading.
Why the breakout bot was not a useful hedge
The breakout bot’s reported correlation with the dip-buyers was near zero, but its reported loss and backtest results gave the author reason to question its standalone case. Low co-movement did not turn those losses into protection for the rest of the portfolio. The author’s conclusion was: “Decorrelation only helps a portfolio when it’s multiplied by an expectancy that isn’t negative. A decorrelated bot with negative edge is not a hedge. It’s a diversified way to lose money.”
That is a conclusion drawn from a small personal paper-trading sample, not a general finding about all bots or breakout strategies. The useful principle is narrower: assess whether a strategy has a credible positive case separately from whether its returns have been different from those of other strategies.
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Correlation can help describe how strategies behaved together during the period measured. It cannot substitute for examining each strategy’s own outcomes and assumptions. When considering a new bot, keep those questions separate:
- Standalone case: What do its P&L and relevant backtest results show, and what assumptions or market conditions produced them?
- Portfolio behavior: How did its returns move relative to the existing strategies during the same period?
- Evidence limits: How long is the sample, was it paper or live trading, and does it cover more than one market regime?
This is a way to organize the questions, not a validated evaluation protocol. The account does not establish that any named bot or strategy is suitable for a reader.
Where might broader diversification come from?
The author’s reader-facing question was where to look for meaningful diversification if multiple crypto strategies ultimately depend on whether crypto is up or down. In their own setup, the author described a dual-momentum ETF bot at a stock broker that rebalances monthly. It differs from the crypto bots in asset class, instruments, and timescale, illustrating one way a portfolio might vary its exposures.
That example is not a recommendation or a guarantee of independence. Different assets and schedules can still lose value or move together, and this account does not establish how the ETF bot performed.
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Keep the sample-size caveat in view
The reported correlations and results came from two months of paper trading across one market regime. That is a small sample, and paper results do not establish what would happen with live capital. As the author cautioned, “two months of paper data across a single market regime is a small, ugly sample, and every number above will move.” The figures are best read as an illustration of the difference between co-movement and profitability, not as proof of persistent strategy expectancy.
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