By designing profitable trading algorithms on Algorier and enabling the copy feature, you can earn tokens from users' trades and generate passive income. This guide explains the quality criteria your backtest must meet before your algorithm becomes eligible for publishing.
When you publish an algorithm on Algorier, other users can copy it and run it on their own accounts. Every time a trade is executed using your algorithm, you earn a share of the tokens consumed.
At Algorier, you can earn income by enabling others to copy your algorithm. For every trade executed using your published algorithm, you receive a percentage of the tokens paid by copy traders. You always have the option to redeem your earned tokens and withdraw the equivalent amount to your bank account.
For full details on how your earnings are calculated, including examples and an interactive income estimator, please visit our Pricing Page.
To ensure that only reliable and well-performing algorithms are made available for copying, your backtest must meet the following minimum criteria.
| Metric | Threshold |
|---|---|
| Sortino Ratio | > 1 |
| Profit to Max Drawdown Ratio | > 1 |
| Total Number of Trades | > 100 |
Meeting these three criteria is required for your algorithm to be considered for publishing — but it doesn't guarantee approval. Every submission still goes through our internal review process. Our team evaluates additional factors such as overall strategy quality, robustness, and consistency before granting final approval to publish.
Below, we explain each criterion in plain language so you know exactly what to aim for when designing your algorithm.
The Sortino Ratio measures how well your algorithm generates returns relative to the downside risk it takes. Unlike the Sharpe Ratio, which penalizes all volatility equally, the Sortino Ratio focuses only on negative fluctuations — the kind that actually hurt your account.
The formula: Sortino Ratio = Average Return ÷ Downside Deviation
Why it matters: A Sortino Ratio above 1 means your algorithm produces meaningfully more return than the downside risk it creates. This protects copy traders from strategies that look profitable on paper but carry hidden drawdown dangers.
In simple terms: If two algorithms both make 10% profit, but one has wild losing streaks while the other loses small and recovers quickly, the second will have a much higher Sortino Ratio. We require this metric to ensure your algorithm earns its profits in a stable, controlled manner.
This ratio compares the total profit your algorithm generated to the largest peak-to-trough decline (maximum drawdown) it experienced during the backtest period.
Why it matters: Max Drawdown represents the worst-case scenario a copy trader would have faced — the biggest dip from a high point to a low point. A ratio above 1 means your algorithm's total profit is greater than the worst drawdown it endured. This gives copy traders confidence that the strategy can recover from its worst moments and still deliver positive returns.
In simple terms: If your algorithm's worst dip was $1,000, it must have earned more than $1,000 in total profit. The higher this ratio, the more resilient your strategy is.
The total number of trades executed by your algorithm during the backtest must exceed 100. This ensures that your backtest results are statistically meaningful rather than based on a handful of lucky trades.
Why it matters: An algorithm that has only produced 20 or 30 trades might look great by chance. With 100+ trades, the performance metrics become far more reliable, giving both you and the users who copy your algorithm a realistic picture of how the strategy behaves across different market conditions.
If your backtest doesn't pass one or more criteria, here are practical steps to improve your algorithm.
Focus on reducing the frequency and size of losing trades. Tighten your stop-loss logic, add filters that skip low-confidence setups, or avoid trading during highly volatile periods. The goal is to keep your returns steady while minimizing painful drawdowns.
Either increase your total profit by refining entry/exit conditions, or reduce your maximum drawdown by implementing better risk management — such as position sizing rules, daily loss limits, or avoiding correlated trades that can amplify losses.
If your algorithm hasn't generated 100 trades yet, consider lowering the minimum signal threshold or testing across multiple markets. The key is to reach statistical significance without sacrificing strategy quality.
Build your strategy in AlgoBuild, run a backtest, and check that it meets the criteria above. Once it passes, you can publish it and start earning.
