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A low correlation between two trading bots tells you their returns have moved differently; it does not tell you either bot is profitable. If a second strategy has no credible positive edge, adding it may diversify the pattern of losses without hedging the portfolio.
That distinction is illustrated by a personal paper-trading account reported by Dry Run on DEV Community. Its figures are a short, unaudited snapshot—not proof of how bots perform generally.
What low correlation does—and does not—tell you
Correlation measures how two return streams have moved in relation to one another over a particular sample. A low reading can indicate that the strategies’ observed daily changes did not closely track each other during that period. It cannot establish that either strategy is profitable, that the relationship will persist, or that one will offset losses in another.
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Portfolio diversification is useful only when the added exposure improves the overall risk-and-return picture. A strategy that loses money independently can still reduce correlation while adding losses. In that case, it is different from the existing strategy, but difference alone does not make it a hedge.
What one paper-trading account found
In a September 2026 DEV Community article, Dry Run describes tracking daily P&L changes for five bots in paper mode over two months. The daily snapshots included both realized results and mark-to-market changes on open positions. The author reported these correlations:
| Strategies compared | Reported correlation | What the author described |
|---|---|---|
| Two similar dip-buying bots on different exchanges | 0.93 | Closely correlated in the observed sample |
| A more conservative dip-buying sibling | 0.51 | Less correlated with the other dip-buyers |
| A breakout bot and dip-buying bots | Approximately 0.05 | Very low observed correlation |
These are the author’s reported readings for that two-month paper-trading window, not independently verified measurements. They describe one account and one market regime; they should not be treated as stable properties of those strategy types.
Why the least-correlated bot may still be a bad addition
Dry Run reports that the breakout bot lost 445 units during the paper period, despite its approximately 0.05 correlation with the dip-buying bots. The author also says a backtest for that bot over a market decline of roughly 41% was down 42% and had a negative Sharpe ratio.
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Those figures illustrate why correlation and strategy quality are separate questions. The breakout bot’s low observed co-movement did not make its reported loss a hedge. The paper gain for the Supertrend bot, meanwhile, does not erase the author’s reported backtest result. Neither short-window paper results nor a backtest alone establishes future performance.
Where to look for diversification beyond one crypto fleet
The reader question raised by the account is practical: if several crypto strategies ultimately respond to whether crypto is rising or falling, changing the entry rule may not create meaningful diversification. The author describes a dual-momentum ETF bot at a stock broker that rebalances monthly as an example of diversification across asset class, instruments, and timescale.
That is an example from the author’s setup, not a recommendation. Different assets and schedules can still share risks or lose value together, and the account does not establish that any particular combination will be independent or suitable for another investor.
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How to evaluate a proposed second strategy
Assess co-movement and standalone performance separately. A correlation figure is useful context, but it should not substitute for a reason to believe the strategy has positive expectancy.
- Define the return series. Make clear whether the comparison uses daily realized P&L, mark-to-market changes, or both. Different choices can produce different readings.
- Check the sample and market regime. Record the period, market conditions, and whether the results came from live trading, paper trading, or a backtest. A two-month sample from one regime is especially limited.
- Review correlation alongside P&L. Low co-movement answers whether results moved together in the observed sample; it does not answer whether the strategy made money.
- Examine the standalone case. Consider the reported P&L and backtest outcomes, while recognizing that neither guarantees future returns. Do not treat a favorable paper result as proof of a durable edge.
- Ask whether the diversification is economically meaningful. A different asset class or timescale may change what drives returns, but the label alone does not show that losses will be offset.
Dry Run explicitly cautions that two months of paper data across a single market regime is a small sample and that the figures can change. The account supplies no independent dataset or audit, and it does not establish a validated evaluation method. Treat its numbers as an illustration of the distinction between low correlation and positive expectancy—not as evidence that a particular bot will succeed or fail.
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