mimic-framework 0.1.0

Compose foundation models into corporate decision agents

Mimic

Compose foundation models into corporate decision agents.

Mimic lets you spin up an LLM-based digital twin of any public company, then simulate how it would respond to real-world events — validated against what companies actually did.

import mimic

twin = mimic.Twin.from_ticker("WMT")
result = twin.simulate("China port closes for 30 days")
print(result.pretty())

EVENT: China port closes for 30 days
────────────────────────────────────────
0-24h:  Activate Vietnam + Mexico supplier tier, alert logistics team
1-7d:   Pre-build 45-day inventory buffer on top 200 SKUs
8-30d:  Temporary pricing adjustment on affected categories (+2-4%)

Financial impact (P10/P50/P90): -$800M / -$1.4B / -$2.1B
Confidence: 73%

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How It Works

Mimic composes three things that already exist — foundation models, free public data, and textbook economic formulas — into something that doesn't: validated company behavior simulation.

SEC EDGAR (free)     →
yfinance (free)      →  CompanyContext  →  Economic Formulas  →  LLM Orchestrator  →  Decision
FRED / news (free)   →
                            ↑
                    TimesFM / Chronos (plug-and-play)

You swap any component in one line. Don't want TimesFM? Use Chronos. Don't want GPT-4? Use Claude. Don't want the default COGS formula? Override it.

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Install

pip install mimic-framework

Requires Python 3.11+. Set OPENAI_API_KEY in your env.

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Quick Start

1. Build a twin from any ticker

from mimic import Twin

# Auto-pulls from SEC EDGAR + yfinance
twin = Twin.from_ticker("AAPL")

2. Simulate an event

result = twin.simulate(
    event="Taiwan Strait closes for 6 weeks",
    severity=0.85,
)

print(result.immediate_action_0_24h)
print(result.financial_impact_mid)   # $M, P50 estimate
print(result.confidence)

3. Swap foundation models (plug-and-play)

import mimic

twin = Twin.from_ticker("JPM")
result = twin.simulate("Fed raises rates 100bps", model="claude-opus-4-5")

4. Run the benchmark

from mimic.benchmark import run_benchmark

results = run_benchmark(
    tickers=["WMT", "AAPL", "JPM", "XOM", "MSFT"],
    event_set="crisis_2015_2024",
)
print(results.summary())
# Average fidelity: 0.71 across 2,847 labeled (event, company) pairs

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Economic Formulas Library

Mimic ships 10 textbook economic primitives that feed into every simulation. Each is a pure Python function and fully overridable:

Formula	What It Computes
dcf_impact	EV change from a cash flow shock
altman_z	Bankruptcy risk score
cogs_sensitivity	Input cost shock → margin impact
fx_passthrough	Currency moves → P&L impact
inventory_burn	Days of buffer remaining
bayes_update	Probability updating from new evidence
capm_response	Stock reaction to market move
operating_leverage	Margin elasticity to revenue
supplier_hhi	Supply concentration risk (HHI)
cascade_propagate	Supply chain shock propagation

from mimic.formulas import cogs_sensitivity

result = cogs_sensitivity(
    revenue=650_000,   # $M
    cogs=490_000,
    input_shock_pct=0.15,   # 15% cost spike
    passthrough_rate=0.40,
)
# {'margin_compression': 0.031, 'annual_ebitda_impact_usdM': -4410.0}

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Benchmark

Mimic ships a benchmark of 200 historical events × 50 companies, with ~2,800 ground-truth-labeled (event, company) response pairs extracted from actual earnings calls and 8-K filings.

Metric	v0.1
Average fidelity score	0.71
Companies covered	50 (S&P 500)
Historical events	200 (2010–2024)
Labeled pairs	~2,847

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Roadmap

• SEC EDGAR ingestion
• 10 economic formulas
• LLM orchestrator (GPT-4o + Claude)
• Benchmark v1 (200 events × 50 companies)
• TimesFM + Chronos integration (v0.2)
• FinBERT2 sentiment layer (v0.2)
• Multi-company simulation (v0.3)
• REST API + hosted version

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License

MIT