factorlens 0.1.1

Python wrapper for the FactorLens Rust CLI

FactorLens

FactorLens is an offline-first factor attribution assistant in Rust.

It computes statistical factors (PCA) from price history, writes artifacts, and supports explainability through a pluggable LLM backend interface (local and bedrock).

MVP Features

• Price ingestion from CSV
• PCA factor model fitting
• Portfolio factor attribution
• Residual outlier detection
• Artifact outputs (json + csv)
• Markdown report generation
• Explain command using a local llama.cpp backend (llama-cli) with a Bedrock-ready backend contract

Workspace Layout

• crates/factor_core: Returns, PCA, attribution math
• crates/factor_io: CSV IO and artifact writing
• crates/factor_cli: CLI binary (factorlens)
• crates/llm_local: LLMClient trait + local/bedrock backends
• crates/report: Markdown report generation

Input Formats

prices.csv

• date (YYYY-MM-DD)
• ticker
• close

portfolio.csv (optional)

• ticker
• weight

holdings.csv (optional alternative to portfolio.csv)

• ticker
• either market_value or both shares and price

Quick Start

cargo run -p factor_cli -- factors fit \
  --prices data/prices.csv \
  --k 3 \
  --out artifacts/ \
  --portfolio data/portfolio.csv

# alternative: derive weights automatically from holdings
cargo run -p factor_cli -- factors fit \
  --prices data/prices.csv \
  --k 3 \
  --out artifacts/ \
  --holdings data/holdings.csv

cargo run -p factor_cli -- report \
  --artifacts artifacts/ \
  --format markdown \
  --out artifacts/report.md

cargo run -p factor_cli -- explain \
  --backend local \
  --model models/llama.gguf \
  --artifacts artifacts/ \
  --question "What drove the largest drawdown?"

Notes

• explain --backend local expects llama-cli on your PATH.
• explain --backend bedrock is scaffolded through the shared interface, but runtime invocation is intentionally left as the next implementation step.
• This project is designed for explainability of computed analytics, not market prediction.

Python (pip) Package

A Python wrapper is included so you can distribute a pip package and run FactorLens via factorlens-py.

Build/install locally:

python -m pip install --upgrade build
python -m build
python -m pip install dist/factorlens-0.1.0-py3-none-any.whl

Run:

factorlens-py factors fit --prices data/prices.csv --k 3 --out artifacts/

Binary resolution order used by factorlens-py:

1. FACTORLENS_BIN env var
2. factorlens on PATH
3. cargo run -p factor_cli -- (dev fallback)

If you want to publish to PyPI, run:

python -m pip install --upgrade twine
python -m twine upload dist/*

Explainability Notes

• factors fit excludes weekend dates by default.
• Pass --include-weekends if your dataset intentionally includes weekend trading.
• explain supports focused analysis with --focus-factors.

Examples:

cargo run -p factor_cli -- factors fit --prices data/prices.csv --k 3 --out artifacts/ --portfolio data/portfolio.csv
cargo run -p factor_cli -- factors fit --prices data/prices.csv --k 3 --out artifacts/ --portfolio data/portfolio.csv --include-weekends

cargo run -p factor_cli -- explain --backend local --model models/llama_instruct.gguf --artifacts artifacts/ --question "What drove the largest drawdown?" --focus-factors factor_1,factor_2

Custom Factor Names

By default, FactorLens auto-generates factor names from your dataset loadings (top positive and negative loading tickers per factor), so it works on any dataset.

You can still override labels with a CSV or TSV file via --factor-labels.

Example data/factor_labels.csv:

factor,label
factor_1_contrib,Broad Market Beta
factor_2_contrib,Growth vs Value Rotation
factor_3_contrib,Idiosyncratic Spread

Use in explain:

cargo run -p factor_cli -- explain --backend local --model models/llama_instruct.gguf --artifacts artifacts/ --question "What drove the largest drawdown?" --factor-labels data/factor_labels.csv

Notes:

• Factor keys may be factor_1, factor_1_contrib, or just 1.
• # comment lines are ignored.

Suggested Questions

• What was the worst modeled drawdown day, and what factors drove it?
• On the worst day, what percentage came from each factor?
• Which factor is my largest average downside contributor over the full sample?
• Which dates had the biggest positive factor-driven gains?
• Which 5 days had the largest residuals (moves not explained by factors)?
• Did my risk concentration increase in the last month?
• Is my portfolio dominated by one factor or diversified across factors?
• How stable are exposures across time windows?
• Which factor changed direction most often?
• Which factor contributed most to volatility, not just returns?
• If I remove factor_1, how much modeled downside is left?
• Compare drawdown drivers with and without weekends included.
• Using only factor_1,factor_2, what drove the drawdown?
• Which assets are most aligned with factor_1 loadings?
• Which assets increased my exposure to downside factors most?

Marketplace Analysis

Analyze generic marketplace CSVs by group columns you choose:

cargo run -p factor_cli -- marketplace analyze \
  --input data/gold_marketplace_report5000.csv \
  --group-by discipline,category,subcategory,ware_name,advantage_plan \
  --out artifacts/market_report.md

# filtered + ranked view
cargo run -p factor_cli -- marketplace analyze \
  --input data/gold_marketplace_report5000.csv \
  --where advantage_plan=1 \
  --rank-by net_gmv \
  --top 10 \
  --out artifacts/market_filtered_ranked.md

Auto-detect useful grouping columns (if --group-by is omitted):

cargo run -p factor_cli -- marketplace analyze \
  --input data/gold_marketplace_report5000.csv \
  --out artifacts/market_auto.md

Notes:

• ware_name alias maps to quote_group_ware_name.
• Outputs both markdown and JSON (<out>.json).
• Default metrics: net_gmv, customer_purchase_order_retail_total_price_usd, provider_purchase_order_wholesale_total_price_usd.
• --where accepts comma-separated column=value filters (AND semantics).
• --rank-by ranks groups by a chosen metric (default ranking is by count).
• --top controls how many groups are listed in the report.

Jupyter + pip Usage

If your data + Bedrock access are in Jupyter, run FactorLens there with pip.

Option A (recommended): clone repo + editable install

git clone https://github.com/<your-user>/factorlens.git
cd factorlens
python -m pip install -e .

Then run from notebook or terminal:

factorlens-py marketplace analyze --input data/gold_marketplace_report5000.csv --out artifacts/market_auto.md

Option B: wheel install

Build wheel on your laptop:

python -m pip install --upgrade build
python -m build

Install wheel in Jupyter:

python -m pip install factorlens-0.1.0-py3-none-any.whl

For wheel-only installs, point to a built Rust binary:

export FACTORLENS_BIN=/path/to/factorlens
factorlens-py --help

Backend choice (local model or Bedrock)

Local model:

factorlens-py explain \
  --backend local \
  --model models/llama_instruct.gguf \
  --artifacts artifacts/ \
  --question "What drove the largest drawdown?"

Bedrock:

export AWS_REGION=us-east-1
factorlens-py explain \
  --backend bedrock \
  --model anthropic.claude-3-5-sonnet-20240620-v1:0 \
  --artifacts artifacts/ \
  --question "What drove the largest drawdown?"

Notes:

• factorlens-py uses FACTORLENS_BIN first, then factorlens on PATH, then cargo run -p factor_cli -- if in a cloned repo.
• Set FACTORLENS_CWD if you want to force working directory.