Speed Meds for Data Pipelines
A service for quant analysts and biotech researchers that scans their Python notebooks and pipelines, then rewrites the slow parts using optimized native backends, with plain-English explanations and side-by-side benchmarks.
quant analysts at small funds and computational biology researchers who need faster runs but don't have a systems engineer to hand-tune Cython or Rust
- Notebook upload that profiles hot loops (like the bucketize step in scikit-learn) and identifies branch-heavy, cache-unfriendly code
- Auto-rewrite to vectorized/Numba/Polars equivalents with a before/after wallclock benchmark on the user's own data
- Plain-English diff explaining what changed (loop replaced with vectorized op, etc.) and why it matters
- Drop-in backtest mode that re-runs a strategy and reports a Sharpe or drawdown delta, not just a speed delta
The Hacker News article shows that even well-engineered libraries like scikit-learn leave 6x performance on the table from mechanical sympathy tweaks, which is exactly the kind of optimization that small quant teams and biotech labs never have time to chase themselves.
Direct pain point is real and well-documented: quantoptimize.com markets 5–615× speedups and quant teams publicly complain about slow backtests; Python perf rewriting is a frequent blog/tutorial topic (PyO3, Cython, Numba) showing active practitioner demand.Quant Optimize - Performance Engineering for Quantitative Finance ↗
Whitespace is very narrow: quantoptimize.com (FrenchQuant) is a near-identical productized service for the same quant audience with published case studies and €3,500 fixed-fee pricing; Polars/pola-rs, Numba, and mature PyO3 tutorials also let teams DIY; the biotech angle is less crowded but bio teams increasingly self-serve with Nextflow/Snakemake.Quant Optimize - Performance Engineering for Quantitative Finance ↗Polars — DataFrames for the new era ↗
Strong willingness to pay signals: quantoptimize.com charges €3,500 for a 5-day audit and separate fixed SOW for Phase 2 (likely €10k–€50k+), and quant funds routinely spend on latency wins; biotech grant-funded labs are a weaker payer but pharma/biotech consult rates are also high.Quant Optimize - Performance Engineering for Quantitative Finance ↗
Python's dominance in quant and biotech is durable, hardware ceilings keep pushing mechanical-sympathy gains, and the optimization problem doesn't self-resolve — though LLMs and faster library defaults (Polars, DuckDB) will erode the gap over time, keeping the need meaningful but not permanent.Polars — DataFrames for the new era ↗
Technically hard to build well: requires profiling, numerical-parity guarantees, safe Rust/Cython codegen, and convincing benchmarks — beyond a weekend project; competitor demonstrates the depth needed (custom data structures, FFT plan caching, parity tests).Quant Optimize - Performance Engineering for Quantitative Finance ↗