llm-backtest-bench

Benchmarking LLMs on financial signal generation. Can Claude pick stocks from anonymized fundamentals?

M7: Pairwise Stock Comparison

The first experiment. Two anonymized stocks, 15 financial metrics each, one question: which delivers higher 6-month return?

Results: All four Claude models (Haiku through Opus) cluster at 52.5-52.7% accuracy. A weighted heuristic using the same 15 metrics gets 52.0%. Extended thinking and model scale make no difference.

See RESULTS.md for the full write-up.

Quick Numbers

Model	Accuracy	95% CI
Haiku	52.5%	50.8-54.2%
Sonnet	52.6%	50.9-54.3%
Sonnet + extended thinking	52.7%	51.0-54.4%
Opus (effort=high)	52.7%	51.0-54.4%
kitchen_sink baseline	52.0%	-
random	50.3%	-

3,300 pairs, 22 rebalance dates, 10 years of US equities (2015-2025), 14 heuristic baselines, McNemar's paired tests.

Setup

Requirements

• Python 3.10+
• Claude CLI (included with Claude subscription)
• TradingStudio API key (for fetching financial data)

pip install -r requirements.txt
export TS_API_KEY=your_key_here

The Claude CLI must be installed and authenticated separately. All LLM calls go through claude --print, so there's no need for an Anthropic API key.

Runner Directory

The CLI is invoked from ~/.m7-runner/ to avoid picking up any parent project's CLAUDE.md. Create it with the quant analyst persona:

mkdir -p ~/.m7-runner
cat > ~/.m7-runner/CLAUDE.md << 'EOF'
You are a quantitative financial analyst participating in a research study on stock selection.
Your task is to compare two anonymized stocks based on their financial metrics and predict
which one will deliver a higher total return over the next 6 months.

This is an academic benchmarking exercise, not investment advice. Always provide your best
analytical judgment.

Respond in exactly this format:
PICK: X or Y
CONFIDENCE: 0.50 to 1.00
REASONING: Brief explanation (1-3 sentences)
EOF

Usage

Generate pairs only (no LLM calls)

python3 -m m7.run --exchange us --pairs-only --pairs 10 --start-year 2024 --end-year 2025 --verbose

Quick test with Haiku

python3 -m m7.run --exchange us --model claude-haiku --prompt v1_direct --pairs 5 --start-year 2024 --end-year 2025 --verbose

Full run

python3 -m m7.run --exchange us --model claude-sonnet --prompt v1_direct

Resume a previous run

python3 -m m7.run --resume outputs/runs/<run_dir>

Reuse pairs from another run (skip data loading)

python3 -m m7.run --exchange us --model claude-opus --prompt v1_direct \
  --pairs-file outputs/runs/<existing_run>/pairs.json --effort high

Evaluate an existing run

python3 -m m7.run --evaluate outputs/runs/<run_dir>

Compare multiple runs

python3 -m m7.analyze outputs/runs/<run1> outputs/runs/<run2> outputs/runs/<run3>

CLI Flags

Flag	Description
--exchange	Exchange preset: us, india, hongkong, australia, germany, canada
--model	claude-haiku, claude-sonnet, claude-opus
--prompt	v1_direct, v2_cot, v3_expert, v4_structured
--pairs	Pairs per rebalance date (default: 150)
--start-year	Start year (default: 2015)
--end-year	End year (default: 2025)
--thinking-tokens	Enable extended thinking with N tokens (e.g., 10000)
--effort	Effort level for Claude CLI: low, medium, high
--pairs-file	Skip data loading, reuse existing pairs.json
--pairs-only	Generate pairs and exit (no LLM calls)
--resume	Resume a previous run from output directory
--evaluate	Re-evaluate an existing run (no new LLM calls)

Project Structure

llm-backtest-bench/
├── m7/                  # M7 experiment
│   ├── config.py        # Constants, metrics, model definitions
│   ├── data.py          # Fetch from API, cache to parquet, load DuckDB
│   ├── pairs.py         # Pair generation, stratification, anonymization
│   ├── prompts.py       # 4 prompt variants + response parser
│   ├── llm.py           # Claude CLI runner with logging and resume
│   ├── baselines.py     # 14 heuristic baselines
│   ├── evaluate.py      # Accuracy, calibration, long-short, McNemar's
│   ├── run.py           # CLI entry point
│   └── analyze.py       # Cross-run comparison
├── lib/                 # Shared utilities (API client, metrics)
├── RESULTS.md           # Full experiment results
├── RESEARCH.md          # Literature review
├── data/                # Cached parquet files (gitignored)
└── outputs/             # Run results (gitignored)
    └── runs/{run_id}/
        ├── config.json
        ├── pairs.json
        ├── llm_calls.jsonl
        └── summary.json

Baselines

14 heuristic strategies, from single-factor to multi-factor:

Baseline	Description
random	Seeded coin flip
lower_pe	Pick lower P/E
higher_roe	Pick higher ROE
higher_piotroski	Pick higher Piotroski F-Score
lower_pb	Pick lower P/B
momentum	Pick higher trailing 6mo return
mean_reversion	Pick lower trailing return (contrarian)
low_vol	Pick less volatile stock
composite	Equal-weight: value + quality + momentum + Piotroski
greenblatt	Magic Formula: earnings yield + ROE
garp	Growth at Reasonable Price: revenue growth / PE
quality	ROE + margins + Piotroski + low leverage
value	Low P/E + P/B + EV/EBITDA + high dividend yield
kitchen_sink	All 15 factors, literature-based weights

Data

Financial data is fetched from the TradingStudio API (FMP warehouse) and cached locally as parquet files. The US dataset is approximately 38M price rows and 265K metric rows covering 22K symbols.

Price data is fetched in yearly chunks to stay within API row limits. Derived features (trailing return, trailing volatility, revenue growth YoY) are computed in DuckDB after loading.

License

MIT