Transparency

Why We Publish Our Trading Results

April 4, 2026 · 6 min read

The algo trading vendor space has a transparency problem. Most vendors show screenshots of their best months, post curated equity curves with no trade-level data, and go silent when their strategies underperform. When you ask for audited results or trade logs, the answer is usually vague or nonexistent.

We built HuntersAlgo differently. Every backtest result we publish includes trade-by-trade data that anyone can download, verify, and scrutinize. This article explains why we made that decision and why we think it should be the industry standard.

The Problem with the Algo Trading Industry

The barrier to selling a trading algorithm is effectively zero. Anyone can backtest a strategy, optimize it until the equity curve points up and to the right, screenshot the results, and start selling access. The buyer has no way to verify whether those results are realistic, whether the strategy has been forward-tested, or whether the vendor will even acknowledge it when the strategy stops working.

Common tactics you will encounter when evaluating algo vendors include:

• Cherry-picked date ranges — showing only the period where the strategy performed best while omitting the months of drawdown that preceded or followed it.
• No slippage or commission — backtest results that do not account for real-world execution costs. A strategy that nets $10,000 in a backtest might net $6,000 after realistic slippage and commissions.
• Optimized-for-the-past parameters — strategy parameters tuned to fit historical data perfectly, with no out-of-sample validation. These results look amazing and fall apart immediately in live trading.
• Equity curve without trade data — a smooth upward curve with no underlying trade-level detail. You cannot verify the number of trades, the win rate, or whether the results are even mathematically consistent without the trade log.
• Testimonials instead of data — screenshots of Discord messages or customer reviews replace actual performance data. Social proof is not the same as auditable results.

What We Publish and How

Our Results page shows every backtest result for every strategy we offer. Each result card displays:

• Strategy name and version
• Instrument (ES, NQ, YM, etc.)
• Date range of the test
• Total net profit
• Profit factor
• Total number of trades
• Maximum drawdown
• Win rate
• A link to download the full trade-by-trade CSV file

The CSV files contain every trade: entry time, exit time, direction (long or short), entry price, exit price, and profit or loss. You can open them in Excel, Google Sheets, or any analysis tool to verify our numbers and conduct your own analysis.

We also include the backtest settings: bar type, slippage assumption, commission per trade, and the NinjaTrader account type (simulated). This lets you replicate the test in your own environment if you have the strategy and the data.

Why Transparency Is Hard (And Why Vendors Avoid It)

Publishing full results is uncomfortable. Every strategy has losing periods. Every equity curve has drawdowns. When you publish everything, you expose your strategy's weaknesses alongside its strengths. A competitor or a skeptic can point to a losing month and say "this strategy doesn't work" without acknowledging the profitable months that came before and after.

This is why most vendors avoid transparency. Selective disclosure lets them control the narrative. They show the wins and hide the losses, creating an impression of consistency that no strategy can actually deliver.

The discomfort of full transparency is a feature, not a bug. It forces us to build strategies that we are willing to stand behind in all market conditions, not just favorable ones. If a strategy has a month of drawdown, we do not delete the results or change the parameters and retest. We publish the drawdown, explain the market conditions that caused it, and let customers decide for themselves whether the risk profile is acceptable.

How This Benefits Our Customers

Transparency creates informed customers, and informed customers make better decisions. Specifically:

• Realistic expectations — when you can see the full equity curve including drawdowns, you know what to expect. A customer who knows the strategy has a historical maximum drawdown of $3,000 will not panic when a $2,000 drawdown occurs. A customer who was sold a cherry-picked equity curve will think the strategy is broken.
• Strategy selection — different strategies suit different risk tolerances and account sizes. Full results data lets you compare strategies on the metrics that matter to you: drawdown, frequency, win rate, average trade size, and time in the market.
• Trust but verify — you do not have to take our word for any claim. Download the data, run the numbers, and see for yourself. This shifts the relationship from "trust the vendor" to "verify the data."
• Better risk management — knowing the historical drawdown characteristics of your strategy lets you set appropriate position sizes and daily loss limits. You can plan for the worst case rather than being surprised by it.

What We Think the Standard Should Be

We believe every algo trading vendor should publish:

1. Trade-level data for every result they claim. Summary statistics are not enough.
2. Full date ranges including losing periods. No cherry-picking.
3. Backtest methodology including slippage, commission, and bar type.
4. Clear labeling of whether results are from a backtest, forward test, or live account.
5. Regular updates so results stay current with market conditions.

This is not a high bar. It is the minimum that customers deserve when they are making decisions about their money. Any vendor who refuses to meet this standard is asking you to trust them without evidence, and in the trading world, trust without evidence is how people lose money.

An Invitation

Browse our Results page. Download the trade data. Run your own analysis. If you find an error or an inconsistency, tell us — we will fix it. If you have questions about a specific result, ask in our Discord community. We would rather have a difficult conversation about a real drawdown than an easy conversation about a fake equity curve.

Transparency is not a marketing strategy for us. It is a design constraint. Every decision we make — from strategy development to parameter selection to how we present results — is informed by the fact that everything we publish will be scrutinized. That constraint makes our strategies better and our claims more honest.