The Problem: Fragmented Backtesting Data

Backtesting is the foundation of systematic trading algorithms, but the ecosystem has never converged on a single, verifiable format. Every research lab, hedge fund, or hobbyist tends to ship their own JSON or CSV schema, making it easy to read someone else’s output but impossible to prove that the numbers were generated correctly. The result is a patchwork of proprietary logs, hidden assumptions, and a lack of auditability that can erode confidence in published results.

“You can usually read another person’s backtest output, but you can’t reliably verify it.” – Anonymous contributor on Hacker News

This uncertainty is not just a nuisance; it hampers reproducibility, fuels misinformation, and creates a barrier for newcomers who want to benchmark their strategies against the community.

Why It Matters for Developers

For a developer building a trading engine, reproducibility is the first step toward trust. Without a standard, a single line of code can produce wildly different results depending on hidden environment variables, random seeds, or even the version of a library. In a world where algorithmic trading can move markets in milliseconds, a mis‑specified backtest can translate into real‑world financial loss.

A unified protocol would:

  • Enable deterministic execution – every run produces the same output given the same inputs.
  • Facilitate cryptographic verification – hashes and signatures can prove that data has not been tampered with.
  • Lower the barrier to entry – new developers could adopt the same tooling without reinventing the wheel.

Key Ideas for a No‑Trust Backtesting Protocol

The discussion on Hacker News outlines several compelling features for such a protocol:

Feature Purpose
Fixed, open schemas for inputs and outputs Standardised JSON/CSV structures that everyone can validate against a schema file
Cryptographic consistency checks SHA‑256 or similar hashes of raw data and intermediate states to guarantee integrity
Required metadata for full reproducibility Time‑zone, library versions, random seeds, and environment details
Deterministic execution guidelines Explicit rules on how to handle floating‑point operations, concurrency, and external data feeds
Fully open‑source reference tools A reference implementation (e.g., in Python or Rust) that developers can fork and adapt
Complete auditability, zero‑trust assumptions No single point of control; anyone can audit the code and data
Decentralised, peer‑to‑peer distribution Use IPFS or a blockchain‑based storage layer to keep data publicly verifiable without a central authority

A Minimal JSON Schema Example

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "BacktestResult",
  "type": "object",
  "properties": {
    "meta": {
      "type": "object",
      "properties": {
        "strategy_id": {"type": "string"},
        "backtest_start": {"type": "string", "format": "date-time"},
        "backtest_end": {"type": "string", "format": "date-time"},
        "seed": {"type": "integer"},
        "environment": {"type": "string"}
      },
      "required": ["strategy_id", "backtest_start", "backtest_end", "seed"]
    },
    "results": {
      "type": "array",
      "items": {"type": "number"}
    },
    "hash": {"type": "string"}
  },
  "required": ["meta", "results", "hash"]
}

The hash field would contain a cryptographic digest of the entire payload, ensuring anyone can verify authenticity.

Existing Efforts and Gaps

While there are libraries like zipline or Backtrader that export CSV logs, none of them enforce a standard schema or cryptographic checks. Some research papers publish supplemental data, but the format is often bespoke. The lack of a neutral, non‑commercial protocol means that any standard that does emerge could become entangled with a commercial product, undermining trust.

What This Means for You

If you’re building a trading platform, consider contributing to or adopting a reference implementation early. Even if you only use the protocol for internal testing, the benefits of deterministic execution and verifiable outputs will pay off when you need to audit performance or share results with clients.

Moreover, a community‑driven standard could open new avenues for collaboration: shared backtest repositories, public leaderboards, and even automated compliance checks.

A Call to Action

The proposal on Hacker News is just the spark. It invites the community to design, implement, and adopt a no‑trust backtesting protocol that anyone can use. Whether you’re a seasoned quant, a startup founder, or a hobbyist, your input matters. Contribute a schema tweak, write a reference tool, or simply start using the standard in your own projects.

“It’s not a product or a platform, just an open protocol anyone can implement.” – The original poster

In a field where milliseconds can mean millions, the next step is to make sure those milliseconds are trustworthy.

Source: https://news.ycombinator.com/item?id=45942930