spectre

Here's a concise, professional technical briefing for the spectre repo, written in the style of a senior AI engineer consultant.

The Problem

This repo addresses the performance gap in quantitative factor analysis and backtesting. Traditional Python libraries like Zipline pipeline are CPU-bound and slow for large asset universes, making iterative research and production deployment costly in compute time.

What This Does

spectre is a GPU-accelerated quantitative trading library built on PyTorch, designed for high-throughput factor computation and backtesting. The directory structure separates core concerns: spectre/factors/ implements factor definitions (e.g., sma, ema, stddev in basic.py, statistical.py, technical.py); spectre/data/ handles loading via ArrowLoader, CsvDirLoader, and Yahoo downloader; spectre/trading/ provides a blotter, portfolio, and execution engine; and spectre/parallel/ offers algorithmic utilities. The factor engine supports tocuda() for GPU offloading, and the design is explicitly compatible with alphalens and pyfolio interfaces.

How To Use It

Setup: Install via pip from source or conda with PyTorch and CUDA. The README provides the install command:

pip install --no-deps git+git://github.com/Heerozh/spectre.git

Dependencies are split between PyTorch/CUDA (for GPU acceleration) and the requirements.txt entries: pyarrow, pandas, tqdm, plotly, requests, bs4, lxml.

Configuration: No .env or secret files are documented. Data ingestion uses the YahooDownloader class with startdate and symbols parameters, writing to a local feather cache. The ArrowLoader then reads from ./prices/yahoo/yahoo.feather.

Running it: Instantiate a FactorEngine with an ArrowLoader, call tocuda() if GPU is available, add factors via engine.add(factor, name), and run:

from spectre import factors from spectre.data import ArrowLoader loader = ArrowLoader('./prices/yahoo/yahoo.feather') engine = factors.FactorEngine(loader) engine.tocuda() engine.add(factors.SMA(5), 'ma5') engine.add(factors.OHLCV.close, 'close') df = engine.run('2019-01-11', '2019-01-15')

Real-World Use

A quant researcher needing to compute 100+ factors across 3,000+ assets daily can offload the heavy lifting to GPU via spectre, then feed the resulting DataFrames into pyfolio for performance attribution. The benchmark data (3196 assets, 5 years) shows 50–77x speedup over Zipline pipeline on SMA, EMA, and combined technical factor ranks, making it viable for production-scale research loops.

Code Health & Issues

SDLC: No CI/CD pipeline detected — no automated build/test gate. The .github/ directory is absent, meaning merges and releases rely on manual verification. Dependencies: requirements.txt declares dependencies without a lockfile (no requirements.lock, conda-lock.yml, or Pipfile.lock), risking non-reproducible builds across environments. Tests: 59 test files exist under tests/, covering data loading, factors, blotter, and parallel algorithms — a reasonable coverage baseline, but no CI config means test results are not enforced on pull requests. License: LICENSE file is present, which is a positive.

The Bottom Line

spectre delivers meaningful speedups for GPU-accelerated factor analysis and is well-structured for integration with PyTorch-based ML pipelines. It’s best suited for teams with CUDA-capable hardware who need to scale factor computation beyond what Zipline or alphalens can offer. Without CI and a lockfile, adoption should include a pinned dependency strategy and a local test gate.