This product was not featured by Product Hunt yet. It will not be visible on their landing page and won't be ranked (cannot win product of the day regardless of upvotes).
Product upvotes vs the next 3
Waiting for data. Loading
Product comments vs the next 3
Waiting for data. Loading
Product upvote speed vs the next 3
Waiting for data. Loading
Product upvotes and comments
Waiting for data. Loading
Product vs the next 3
Loading
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.
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.
On the analytics side, Reamer Research competes within Fintech, Investing and Developer Tools — topics that collectively have 594.7k followers on Product Hunt. The dashboard above tracks how Reamer Research performed against the three products that launched closest to it on the same day.
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.
For a complete overview of Reamer Research including community comment highlights and product details, visit the product overview.
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.