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Reamer Research
Deterministic research engine for mid-frequency quants
A research engine for mid-frequency strategies on OHLCV bars. Test, diagnose and sweep in Python or C++, then carry the logic live with Reamer Server. A fixed seed gives byte-identical results, slippage included. Linux and macOS. $1,800/yr; 30-day trial $225.
I built Reamer Research because strategy results should reproduce exactly. Run the same strategy with the same seed and you get byte-identical output, stochastic slippage and spread included. It links into your own process as a C library, and you write strategies in Python or C++: a working Python binding and a C++ wrapper ship in the kit. I measured it on a 64-core AMD EPYC: 2,000 runs, 0.59% throughput variation, 20 of 20 runs byte-identical by SHA-256. The method is in a public whitepaper (doi.org/10.6084/m9.figshare.33972466), and every raw output is archived (doi.org/10.6084/m9.figshare.33877588). Built for mid-frequency quants who write code and run strategies on bars, intraday to multi-day.
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About Reamer Research on Product Hunt
“Deterministic research engine for mid-frequency quants”
Reamer Research was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #110 on the daily leaderboard. A research engine for mid-frequency strategies on OHLCV bars. Test, diagnose and sweep in Python or C++, then carry the logic live with Reamer Server. A fixed seed gives byte-identical results, slippage included. Linux and macOS. $1,800/yr; 30-day trial $225.
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Who hunted Reamer Research?
Reamer Research was hunted by Chaitanya Palghadmal. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
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Chaitanya Palghadmal
I built Reamer Research because strategy results should reproduce exactly. Run the same strategy with the same seed and you get byte-identical output, stochastic slippage and spread included. It links into your own process as a C library, and you write strategies in Python or C++: a working Python binding and a C++ wrapper ship in the kit. I measured it on a 64-core AMD EPYC: 2,000 runs, 0.59% throughput variation, 20 of 20 runs byte-identical by SHA-256.
The method is in a public whitepaper (doi.org/10.6084/m9.figshare.33972466), and every raw output is archived (doi.org/10.6084/m9.figshare.33877588).
Built for mid-frequency quants who write code and run strategies on bars, intraday to multi-day.