Proposition

Algorithmic trading is hard. Many traders start with a clear strategy they want to automate, then discover that the strategy code is only a small part of the real work.

The difficult parts are execution, position management, backtesting, access to reliable Hyperliquid historical data, deployment, and ongoing monitoring. Even when all of that works, a traditional prop firm can still become the bottleneck. Many firms profit more from losing traders than from helping profitable traders succeed.

PropArc is a platform for turning trading ideas into evaluated, funded, and deployed algorithmic strategies on Propr.

The opportunity

Imagine a world where a trader can write a strategy, backtest it, estimate expected value, and understand its odds before buying a plan.

Then, once they are ready, they can deploy that strategy without managing servers, keys, monitoring, and execution logic themselves.

That used to feel unrealistic, especially in proprietary trading. Propr already addresses the prop firm alignment problem. PropArc focuses on the remaining workflow: deciding whether a strategy is worth funding, purchasing the right plan, and keeping the strategy running once it is live.

What PropArc does

PropArc gives traders one clear path:

1. Build a strategy.
2. Backtest it on Hyperliquid data.
3. Optimize parameters without losing sight of robustness.
4. Run simulation analysis over the trade history.
5. Estimate the probability of failing, passing, and reaching payout.
6. Measure expected value and overfitting risk.
7. Move from evaluation to Propr plan purchase.
8. Deploy and monitor the strategy after purchase.

In prop firm trading, especially algorithmic prop firm trading, the most important metric is not Sharpe or Sortino. It is expected value.

PropArc turns a backtest into a decision engine. After a strategy is tested, we analyze its historical trades, simulate likely Propr outcomes, and measure whether performance depends too heavily on a narrow set of optimized parameters. This lets traders answer the questions that matter before they spend capital:

• What is the probability of losing the challenge?
• What is the probability of passing?
• What is the probability of reaching payout?
• What is the strategy's expected value?
• How sensitive is the strategy to parameter changes?
• How likely is the backtest to be overfit?

Once the trader chooses a plan, PropArc redirects them to Propr to purchase it, then hosts the strategy for deployment. Many traders can describe a strategy, but do not want to maintain infrastructure, manage exchange connectivity, handle restarts, track live execution, or build monitoring from scratch. PropArc gives them a controlled environment where a tested strategy can run continuously with clear logs, risk limits, status checks, and performance reporting.

Future expansion

Once the core platform is working, the same evaluation layer can support a strategy marketplace.

Developers could publish strategies with transparent backtests, robustness metrics, parameter analysis, and expected value estimates. Retail traders and non-developers could then use those strategies to build their own portfolio of funded trading systems without having to write the strategy code themselves.

That matters because serious algorithmic trading is not only about finding one profitable system. It is about building a portfolio of strategies that behave differently under different market conditions. PropArc could help traders compare published strategies, analyze correlation between them, and diversify across funded systems instead of concentrating all risk in one idea.

Why this matters

Propr is building a better model for prop trading. PropArc helps more traders use that model successfully.

Today, many traders who are interested in algorithmic trading never reach live execution. Others commit capital without understanding the real probability of passing, failing, or reaching payout.

PropArc helps traders decide whether a strategy is worth funding, then gives them the infrastructure to run it without turning deployment into its own engineering project.

For Propr, this means:

• More traders can evaluate plans before buying them.
• More algorithmic traders can launch and keep strategies running on Propr.
• More volume can come from traders with measured, tested systems.
• More developers have a reason to build around the Propr ecosystem.
• More retail users can access algorithmic strategies without needing to write code.

The goal is not to promise that every strategy will work. The goal is to make the pre-purchase decision clearer and the post-purchase operation simpler.

What we will build first

The first version of PropArc will include:

1. A strategy builder and backtesting environment for Hyperliquid strategies.
2. Historical data access and standardized performance reporting.
3. Monte Carlo simulation for Propr challenges and payout outcomes.
4. Expected value analysis for each tested strategy.
5. Parameter sensitivity and overfitting checks.
6. Handoff to Propr plan purchase.
7. Hosted strategy execution after purchase.
8. Execution monitoring, logs, and status checks.
9. Risk controls for live strategies.

After that foundation is working, we will expand toward portfolio analysis, strategy correlation tools, and the strategy marketplace.

The grant request

We are applying for a Propr grant because PropArc is built around a specific gap in the current flow: traders can be interested in running algorithmic strategies on Propr, but still lack the tools to evaluate those strategies before committing to a plan and run them reliably after purchase.

The grant would help turn that gap into infrastructure. Not a separate trading venue, and not a generic backtester, but a layer built around Propr for strategy evaluation, plan selection, hosted execution, and live monitoring.

Grant funding would be used for:

• Building the backtesting and simulation engine.
• Acquiring and maintaining reliable Hyperliquid historical data.
• Building the hosted deployment, monitoring, and risk-control infrastructure.
• Designing the trader dashboard and strategy management experience.
• Preparing the foundation for future strategy distribution.

The outcome should be a working platform that makes algorithmic trading on Propr easier to assess before purchase and easier to run after deployment.

The proposition

Propr fixes the incentive problem in prop trading. PropArc fixes the tooling problem for algorithmic traders who want to use it.

Together, they can make prop trading more transparent, more data-driven, and more accessible to both developers and retail traders.

PropArc is the bridge between strategy development and funded execution on Propr.