FactorLens

FactorLens is a Rust CLI that explains why metrics changed.

Dashboards show that metrics moved. FactorLens decomposes those changes into driver contributions using deterministic math, then optionally generates narrative explanations.

Typical flow:

metric change -> driver contributions -> closure check -> residual segments

Quick Start

pip install factorlens
factorlens --help

factorlens analyze \
  --input data/factorlens_demo_sales_100.csv \
  --group-by region,channel,product_line \
  --metrics revenue_usd,cost_usd,orders \
  --rank-by revenue_usd \

factorlens analyze-drivers \
  --input data/demo_revenue.csv \
  --metric revenue_usd \
  --date-column date \
  --time-grain month \
  --period last \
  --anchor-date 2026-04-15

Example

factorlens analyze-drivers \
  --input data/demo_revenue_residual.csv \
  --metric revenue_usd \
  --date-column date \
  --time-grain month \
  --period last \
  --anchor-date 2026-04-15

Output:

revenue_usd change: -16.4%

Window: 2026-03-01..2026-03-31 vs 2026-02-01..2026-02-28

Inferred identity
- revenue_usd ≈ orders * avg_price_usd
- fit MAPE: 1.18% across 56 rows

Driver contributions
- orders: -15.9%
- avg_price_usd: -2.0%

Closure check
- explained: -17.9%
- residual: +1.5% (+77,765.73)

Residual segments
- campaign = spring_launch: mean residual +5,151.67 (16 rows)
- channel = Marketplace: mean residual +5,151.67 (16 rows)
- device_type = mobile: mean residual +5,151.67 (16 rows)

Real Use Cases

• Revenue debugging: decompose changes into orders, price, or mix effects.
• Growth analytics: explain movement in conversion, CAC, or AOV.
• Data pipeline sanity checks: large residuals often reveal joins, missing data, or definition drift.
• CI metric monitoring: run FactorLens in pipelines to catch unusual metric behavior.

Workflow

Typical workflow:

• analyze - explore segments and concentration.
• analyze-drivers - explain metric changes automatically.
• analyze-compare - compare two snapshots.
• explain-analyze - add optional narrative explanation.

Command	Purpose
analyze	factor/segment attribution from CSV or Postgres
analyze-investigate	metric change decomposition into top driver contributions
analyze-drivers	automatic metric identity detection and driver decomposition
analyze-suggest	infer likely dimensions/metrics/date and generate starter profile TOML
analyze-compare	snapshot delta analysis (biggest movers)
explain-analyze	executive narrative and actions from computed JSON
factors fit / factors regress	statistical factors (PCA) or known-factor regression

When To Use Which

• analyze answers: which groups changed?
• analyze-investigate answers: which numeric drivers account for the metric change?
• analyze-drivers answers: what metric identity or formula explains the change?

Practical rule:

• Start with analyze for wide business tables.
• Use analyze-investigate when you have a curated dataset with a few meaningful numeric drivers.
• Use analyze-drivers when the metric likely has a formula such as revenue ≈ orders * avg_price.

Design Principles

FactorLens follows a few simple design rules:

• Math-first, AI-second – deterministic factor attribution produces the artifacts, AI only explains them.
• CLI-first workflows – designed to run locally, in scripts, or inside pipelines.
• Structured outputs – results can be exported as Markdown, JSON, or HTML for humans and automation.
• Composable commands – analysis, comparison, and explanation steps can be combined in workflows.

Demo Workflow

# 1) baseline snapshot (100 rows)
factorlens analyze \
  --input data/factorlens_demo_sales_100.csv \
  --group-by region,channel,product_line,plan_tier \
  --metrics revenue_usd,cost_usd,orders \
  --rank-by revenue_usd

# 2) new snapshot (150 rows)
factorlens analyze \
  --input data/factorlens_demo_sales_150.csv \
  --group-by region,channel,product_line,plan_tier \
  --metrics revenue_usd,cost_usd,orders \
  --rank-by revenue_usd

# 3) compare + explain
factorlens analyze-compare \
  --base artifacts/demo_sales_100.json \
  --new artifacts/demo_sales_150.json

factorlens explain-analyze \
  --backend bedrock \
  --model anthropic.claude-3-haiku-20240307-v1:0 \
  --analysis-json artifacts/analysis_compare.json \
  --question "What are the top concentration risks and what 3 actions should we take in the next 30 days?"

One-command runner:

./scripts/demo_sales.sh
# optional Bedrock:
RUN_BEDROCK=1 AWS_REGION=eu-central-1 ./scripts/demo_sales.sh

Demo Data

Public-safe demo files included:

• data/factorlens_demo_sales_100.csv
• data/factorlens_demo_sales_150.csv (use for compare)

Optional Postgres load:

psql "$DATABASE_URL" -c "
create schema if not exists demo;
drop table if exists demo.factorlens_demo_sales_100;
drop table if exists demo.factorlens_demo_sales_150;
create table demo.factorlens_demo_sales_100 (
  order_date date,
  region text,
  channel text,
  product_line text,
  plan_tier int,
  revenue_usd numeric(14,2),
  cost_usd numeric(14,2),
  orders int
);
create table demo.factorlens_demo_sales_150 (like demo.factorlens_demo_sales_100);
"
psql "$DATABASE_URL" -c "\copy demo.factorlens_demo_sales_100 from 'data/factorlens_demo_sales_100.csv' with (format csv, header true)"
psql "$DATABASE_URL" -c "\copy demo.factorlens_demo_sales_150 from 'data/factorlens_demo_sales_150.csv' with (format csv, header true)"

Generate a starter profile automatically from a new dataset:

factorlens analyze-suggest \
  --input data/factorlens_demo_sales_150.csv \
  --out artifacts/demo_suggest.md \
  --profile-name demo_exec \
  --auto-group-k 4 \
  --max-metrics 3

Large file tip:

factorlens analyze-suggest \
  --input data/factorlens_demo_sales_150.csv \
  --out artifacts/demo_suggest_random.md \
  --sample-rows 1000 \
  --sample-mode random \
  --sample-seed 42

This writes:

• artifacts/demo_suggest.md (human summary)
• artifacts/demo_suggest.json (machine-readable suggestion report)
• artifacts/demo_suggest.toml (ready profile config block)

Architecture

flowchart LR
    A["CSV/Postgres"] --> B["Factor/Segment Model (Rust)"]
    B --> C["Attribution Artifacts (JSON/CSV)"]
    C --> D["Explanation Layer (Local LLM or Bedrock)"]
    C --> E["Reports (Markdown/HTML/JSON)"]

Loading

Math engine first, explanation layer second.

Why This Exists

Many analytics workflows produce dashboards without a clear explanation of why metrics changed. FactorLens prioritizes attribution and residual math first, then translates those computed results into business language.

What This Is Not

• Not a trading bot
• Not a price prediction model
• Not a chat-first analytics toy

FactorLens computes attribution first, then uses LLMs only to explain computed artifacts.

Integrations

• Local LLMs via llama.cpp
• AWS Bedrock
• Claude Desktop / Claude Code via MCP
• CSV and Postgres data sources

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

Build Instructions

For advanced build/release details, see BUILD_INSTRUCTIONS.md.

Quick local build:

cargo build -p factor_cli
cargo build -p factor_cli --release

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

factors.csv (for known-factor regression mode)

• date (YYYY-MM-DD)
• one or more numeric factor columns (for example: MKT, SMB, HML)

Quick Start

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

# safer residual analysis: auto-pick k (< number of assets)
cargo run -p factor_cli -- factors fit \
  --prices path/to/prices.csv \
  --k-auto \
  --out artifacts/ \
  --portfolio path/to/portfolio.csv

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

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

# known-factor regression mode (MKT/SMB/HML-style)
cargo run -p factor_cli -- factors regress \
  --prices path/to/prices.csv \
  --factors path/to/factors.csv \
  --out artifacts/ \
  --portfolio path/to/portfolio.csv

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 uses AWS Bedrock via AWS CLI (aws bedrock-runtime converse).
• This project is designed for explainability of computed analytics, not market prediction.

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 path/to/prices.csv --k 3 --out artifacts/ --portfolio path/to/portfolio.csv
cargo run -p factor_cli -- factors fit --prices path/to/prices.csv --k 3 --out artifacts/ --portfolio path/to/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 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 path/to/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?

Analyze

Use analyze when you want to see which groups changed or where concentration lives.

Recommended demo file:

• data/factorlens_demo_sales_100.csv

cargo run -p factor_cli -- analyze \
  --input data/factorlens_demo_sales_100.csv \
  --group-by region,channel,product_line \
  --metrics revenue_usd,cost_usd,orders \
  --rank-by revenue_usd \

Generic patterns:

cargo run -p factor_cli -- analyze \
  --input data/your_file.csv \
  --group-by region,product_line,channel \
  --metrics revenue_usd

# profile-based quick starts
cargo run -p factor_cli -- analyze \
  --input data/your_file.csv \
  --profile exec

cargo run -p factor_cli -- analyze \
  --input data/your_file.csv \
  --profile segment

cargo run -p factor_cli -- analyze \
  --input data/your_file.csv \
  --profile supplier

# custom profile config (recommended for private/domain fields)
cargo run -p factor_cli -- analyze \
  --input data/your_file.csv \
  --profile exec_custom \
  --profile-config profiles/profiles.example.toml

# filtered + ranked view
cargo run -p factor_cli -- analyze \
  --input data/your_file.csv \
  --where region=US \
  --rank-by revenue_usd \
  --agg median \
  --percentiles p50,p90 \
  --alert-top5-share 60 \
  --alert-blank-share 10 \
  --top 10 \
  --min-records 20

# text normalization for name/title grouping + JSON-only output
cargo run -p factor_cli -- analyze \
  --input data/your_file.csv \
  --group-by title \
  --metrics revenue_usd \
  --normalize-text-groups \
  --word-freq \
  --output-format html

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

cargo run -p factor_cli -- analyze \
  --input data/your_file.csv

Analyze Compare

Create two analysis snapshots, then compare them:

Recommended demo files:

• data/factorlens_demo_sales_100.csv
• data/factorlens_demo_sales_150.csv

# base snapshot
cargo run -p factor_cli -- analyze \
  --input data/factorlens_demo_sales_100.csv \
  --group-by region,channel,product_line \
  --metrics revenue_usd,cost_usd,orders \
  --rank-by revenue_usd

# new snapshot
cargo run -p factor_cli -- analyze \
  --input data/factorlens_demo_sales_150.csv \
  --group-by region,channel,product_line \
  --metrics revenue_usd,cost_usd,orders \
  --rank-by revenue_usd

# compare (default: both markdown + json)
cargo run -p factor_cli -- analyze-compare \
  --base artifacts/analyze_factorlens_demo_sales_100.json \
  --new artifacts/analyze_factorlens_demo_sales_150.json

# compare (html)
cargo run -p factor_cli -- analyze-compare \
  --base artifacts/analyze_factorlens_demo_sales_100.json \
  --new artifacts/analyze_factorlens_demo_sales_150.json \
  --output-format html \
  --out artifacts/compare.html

# compare (json)
cargo run -p factor_cli -- analyze-compare \
  --base artifacts/analyze_factorlens_demo_sales_100.json \
  --new artifacts/analyze_factorlens_demo_sales_150.json \
  --output-format json \
  --out artifacts/compare.json

# compare (both markdown + json)
cargo run -p factor_cli -- analyze-compare \
  --base artifacts/analyze_factorlens_demo_sales_100.json \
  --new artifacts/analyze_factorlens_demo_sales_150.json \
  --output-format both \
  --out artifacts/compare.md

Notes:

• analyze defaults to artifacts/<input_stem>.md + .json (--output-format both).
• analyze now prefixes default outputs as artifacts/analyze_<input_stem>.md + .json.
• analyze-investigate now prefixes default outputs as artifacts/investigate_<input_stem>.md + .json.
• analyze-compare defaults to artifacts/analysis_compare.md + .json (--output-format both).
• analyze-compare supports --output-format md|html|json|both.
• --top-movers controls how many largest movers are shown (default: 10).

Analyze Investigate

Use analyze-investigate when you want a compact “metric change + top drivers” output from a curated numeric driver set.

Recommended demo file:

• data/demo_revenue_residual.csv

It works best when your input already contains a small number of meaningful numeric drivers such as:

• net_gmv
• orders
• traffic
• avg_price_usd
• distinct-count style entity columns via explicit --drivers

# numeric driver accounting
cargo run -p factor_cli -- analyze-investigate \
  --input data/demo_revenue_residual.csv \
  --metric revenue_usd \
  --driver-preset amount \
  --driver-contrib both \
  --date-column date \
  --time-grain month \
  --period last \
  --anchor-date 2026-04-15

# entity-volume drivers
cargo run -p factor_cli -- analyze-investigate \
  --input data/your_file.csv \
  --metric revenue_usd \
  --driver-preset id \
  --driver-contrib both \
  --date-column date \
  --time-grain month \
  --period last

# mixed exploratory scan
cargo run -p factor_cli -- analyze-investigate \
  --input data/your_file.csv \
  --metric revenue_usd \
  --driver-preset mixed \
  --driver-contrib both \
  --date-column date \
  --time-grain month \
  --period last

# explicit drivers (manual override)
cargo run -p factor_cli -- analyze-investigate \
  --input data/your_file.csv \
  --metric revenue_usd \
  --drivers 'count_distinct(order_id),count_distinct(customer_id),count_distinct(account_id)' \
  --driver-contrib both \
  --date-column date \
  --time-grain month \
  --period last

Notes:

• Driver presets: id|amount|category|mixed.
• Driver contribution view: --driver-contrib percent|amount|both.
• Manual driver expressions: sum(col), avg(col), count(col), count(*), count_distinct(col).
• analyze-investigate is best for numeric driver accounting, not first-pass discovery.
• For wide business tables, start with analyze and use analyze-investigate only after curating a smaller set of useful drivers.
• amount is usually the best first preset for spend, GMV, order, or traffic-style measures.
• mixed is exploratory and may be noisier than amount.
• analyze-investigate reports decomposition_mode: regression when numeric drivers support a fitted model, otherwise heuristic.
• Demo commands use --anchor-date 2026-04-15 so --period last --time-grain month resolves to March 2026 vs February 2026 regardless of today’s date.

Example output:

revenue_usd change: -16.4%

Window: 2026-03-01..2026-03-31 vs 2026-02-01..2026-02-28

Decomposition mode: regression

Driver contributions
- sum(orders): -13.0% | delta=-696,191.18
- sum(traffic): -2.2% | delta=-116,243.57
- avg(avg_price_usd): -1.1% | delta=-61,590.98

Closure check
- explained: -16.3% (99%)
- residual: -0.1% (-6,146.70)

Analyze Drivers

Use analyze-drivers when you want FactorLens to infer the metric identity automatically instead of passing drivers.

Recommended demo files:

• data/demo_revenue.csv for a clean identity example
• data/demo_revenue_residual.csv for residual analysis

This is best for metrics that likely come from a formula, such as:

• revenue ≈ orders * avg_price
• conversion ≈ purchases / visits
• aov ≈ revenue / orders

# one-file period compare
cargo run -p factor_cli -- analyze-drivers \
  --input data/demo_revenue.csv \
  --metric revenue_usd \
  --date-column date \
  --time-grain month \
  --period last \
  --anchor-date 2026-04-15

# two-file compare
cargo run -p factor_cli -- analyze-drivers \
  --input data/day1.csv \
  --input-new data/day2.csv \
  --metric revenue_usd

Example output:

revenue_usd change: -14.4%

Window: 2026-03-01..2026-03-31 vs 2026-02-01..2026-02-28

Inferred identity
- revenue_usd ≈ orders * avg_price_usd
- fit MAPE: 0.00% across 56 rows

Driver contributions
- orders: -11.3%
- avg_price_usd: -3.2%

Closure check
- explained: -14.5% (100%)
- residual: +0.1% (+5,970.47)

Artifacts written
- artifacts/drivers_demo_revenue.md
- artifacts/drivers_demo_revenue.json

Residual demo:

cargo run -p factor_cli -- analyze-drivers \
  --input data/demo_revenue_residual.csv \
  --metric revenue_usd \
  --date-column date \
  --time-grain month \
  --period last \
  --anchor-date 2026-04-15

revenue_usd change: -16.4%

Window: 2026-03-01..2026-03-31 vs 2026-02-01..2026-02-28

Inferred identity
- revenue_usd ≈ orders * avg_price_usd
- fit MAPE: 1.18% across 56 rows

Driver contributions
- orders: -15.9%
- avg_price_usd: -2.0%

Closure check
- explained: -17.9% (109%)
- residual: +1.5% (+77,765.73)

Residual segments
- campaign = spring_launch: mean residual +5,151.67 (16 rows)
- channel = Marketplace: mean residual +5,151.67 (16 rows)
- device_type = mobile: mean residual +5,151.67 (16 rows)

Artifacts written
- artifacts/drivers_demo_revenue_residual.md
- artifacts/drivers_demo_revenue_residual.json

Notes:

• Current scope infers two-term identities only: metric ~= a * b or metric ~= a / b.
• Residual is computed as observed metric change minus explained identity change.
• Residual segments rank leftover numeric/categorical fields against row-level unexplained error.
• analyze-drivers is always math-first; analyze-investigate may fall back to heuristic mode when only non-numeric/count-distinct drivers are available.
• Demo commands use --anchor-date 2026-04-15 so --period last --time-grain month resolves to March 2026 vs February 2026 regardless of today’s date.
• Period mode uses one input file plus --date-column and period flags.
• Two-file mode uses --input and --input-new.
• Default output path is artifacts/drivers_<input_stem>.md + .json.

Or analyze directly from Postgres:

# option 1: inline query
factorlens analyze \
  --postgres-url "$DATABASE_URL" \
  --query "SELECT region, channel, revenue_usd, cost_usd FROM analytics.sales" \
  --postgres-ssl-mode require \
  --postgres-ca-file /path/to/rds-ca-bundle.pem \
  --profile exec_custom \
  --profile-config profiles/profiles.example.toml \
  --out artifacts/analysis.md

# option 2: query file
factorlens analyze \
  --postgres-url "$DATABASE_URL" \
  --query-file sql/sales_analysis.sql \
  --profile exec_custom \
  --profile-config profiles/profiles.example.toml \
  --out artifacts/analysis.md

# option 3: AWS RDS/Aurora TLS with explicit CA bundle (recommended in pods)
mkdir -p /path/to/certs
curl -fL "https://truststore.pki.rds.amazonaws.com/global/global-bundle.pem" \
  -o /path/to/rds-global-bundle.pem

factorlens analyze \
  --query "SELECT * FROM schema.table_a LIMIT 5000" \
  --postgres-ssl-mode require \
  --postgres-ca-file /path/to/rds-global-bundle.pem \
  --profile exec_custom \
  --profile-config profiles/profiles.example.toml \
  --out artifacts/analysis.md

Notes:

• Outputs both markdown and JSON (<out>.json).
• If --metrics is omitted, numeric metrics are auto-detected from the input file.
• --profile built-ins (exec, segment, supplier) are generic (no hardcoded domain columns).
• Use --profile-config <path.toml> for your own private, file-specific profile mappings.
• Input source is exclusive: use either --input <csv> or --postgres-url + (--query or --query-file).
• --postgres-url can be omitted if DATABASE_URL env var is set.
• --postgres-ssl-mode supports prefer (default), require, or disable.
• --postgres-ca-file optionally adds PEM CA certificates for DB TLS verification.
• For AWS RDS/Aurora in containers/pods, pass explicit RDS CA bundle via --postgres-ca-file if TLS handshake fails with system certs.
• Recommended layout: commit profiles/profiles.example.toml, keep private variants as profiles/*.local.toml or profiles/*.private.toml (gitignored).
• --where accepts comma-separated column=value filters (AND semantics).
• --rank-by ranks groups by a chosen metric (default ranking is by count).
• --agg controls metric aggregation: sum (default), mean, or median.
• --percentiles adds optional metric columns (p50, p90) per metric.
• --count-only disables numeric metric aggregation and reports concentration using records only.
• --exclude-blank-groups drops (blank) segment keys before ranking/reporting.
• --alert-top5-share and --alert-blank-share add threshold-based alerts to report output.
• --alert-rule adds custom rules (for example: top5_record_share_pct>60, blank_share_pct>10, segments<50). Quote rules containing < or > in shell commands, for example: --alert-rule 'segments<50,top5_record_share_pct>60'.
• --top controls how many groups are listed in the report.
• --top-insights adds deterministic Top Risks and Top Opportunities bullets to the report.
• --opportunity-min-records sets minimum records required for Top Opportunities candidates (default: 2).
• --normalize-text-groups normalizes group values for columns like name/title (lowercase + punctuation cleanup).
• --word-freq adds a Top Words section/counts for name/title-style grouping columns.
• --output-format supports md, json, both (default), or html.
• --min-records drops tiny segments before ranking (useful to avoid one-record outliers).
• analyze-suggest --out-profile <path.toml> writes a ready profile file directly.

Example --profile-config file:

[profiles.exec_custom]
group_by = ["region", "channel"]
metrics = ["revenue_usd"]
rank_by = "revenue_usd"
top = 12
min_records = 20
auto_group_k = 3

pip Package Usage

Install from PyPI:

For packaging/build/publish details, see BUILD_INSTRUCTIONS.md.

pip install factorlens
factorlens --help

Local model:

factorlens explain \
  --backend local \
  --model /path/to/model.gguf \
  --artifacts /path/to/artifacts \
  --question "What drove the largest drawdown?"

Bedrock:

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

Explain from generic table analysis output (analysis.json):

Local model

factorlens explain-analyze \
  --backend local \
  --model /path/to/model.gguf \
  --analysis-json /path/to/analysis.json \
  --question "What are the top concentration risks and 3 actions?"

Bedrock

factorlens explain-analyze \
  --backend bedrock \
  --model anthropic.claude-3-haiku-20240307-v1:0 \
  --analysis-json /path/to/analysis.json \
  --question "What are the top concentration risks and 3 actions?"

MCP Server (Optional)

If you want to call FactorLens as tools from an MCP client, use:

• scripts/mcp/factorlens_mcp_server.py
• scripts/mcp/README.md

Quick start:

pip install mcp
python scripts/mcp/factorlens_mcp_server.py

What Bedrock Step Is Doing

factorlens explain --backend bedrock does not compute analytics. It only explains already-computed artifacts.

Step-by-step:

1. You run analytics first (factors fit or analyze) to produce artifacts.
2. explain loads artifact context (for factor mode: factors.json, attribution.csv, outliers.csv).
3. FactorLens builds a constrained prompt from that context.
4. FactorLens calls AWS Bedrock through AWS CLI (aws bedrock-runtime converse).
5. Bedrock returns plain-text explanation grounded in the provided artifact context.

Important:

• analyze command = pure Rust analytics, no LLM used.
• explain command = LLM narrative layer over artifacts.
• For table-analysis markdown (analysis.md), you can optionally call Bedrock directly with AWS CLI by passing report text as prompt.