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Reamer
Quantitative Research Infrastructure
REAMER is local-first quantitative research infrastructure for systematic trading. Write a strategy in Python; a C++ engine replays it with deterministic synthetic ticks, verified against a 282-check execution spec. Monte Carlo robustness, tick-level replay. Multi-asset (FX, futures, equities), plus direct Databento loading (no CSV timezone bugs). Free GUI; licensed SDK, free test license on request. Proves a strategy is real before it risks anything.
About Reamer on Product Hunt
“Quantitative Research Infrastructure”
Reamer was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #134 on the daily leaderboard. REAMER is local-first quantitative research infrastructure for systematic trading. Write a strategy in Python; a C++ engine replays it with deterministic synthetic ticks, verified against a 282-check execution spec. Monte Carlo robustness, tick-level replay. Multi-asset (FX, futures, equities), plus direct Databento loading (no CSV timezone bugs). Free GUI; licensed SDK, free test license on request. Proves a strategy is real before it risks anything.
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For a complete overview of Reamer including community comment highlights and product details, visit the product overview.
Hey everyone.
I built REAMER because I kept not trusting my own backtests. A strategy would look great in a notebook, and I'd have no real way to know if that was a genuine edge or curve-fit noise, or whether the fills were even realistic. Existing tools either made that hard to check or didn't try.
REAMER is local-first quantitative research infrastructure: write a strategy in Python, a C++ engine replays it with deterministic synthetic ticks, and every fill is checked against a published execution spec (282 conformance checks). Monte Carlo testing catches overfitting before you risk anything, and tick-level replay in the GUI shows exactly which tick filled a stop or triggered a bracket on the same seeded run. Multi-asset (FX, futures, equities), 10.3-15.6x faster than Backtrader and QuantConnect LEAN on identical workloads.
One thing worth saying up front: it's research-only today — it proves a strategy is real; it doesn't place trades or connect to a broker. That's the step before deployment, not instead of it.
If you're already pulling data from Databento, there's a direct loader — mostly just saves you a CSV export/import step and a specific class of DST bug, nothing dramatic.
The GUI's free, no signup. The Python SDK needs a license to run — happy to send a free, time-limited one to anyone here who wants to actually try it on their own data.
Would genuinely love feedback, especially from anyone who's been burned by a backtest that lied to them.