docs

lab/README.md

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 # MOS (Money Operating System)
+
+Research-grade quote-control simulator for studying dynamic pricing and market making policies.
+The system models pricing as a closed loop of **Quote → Arrival → Execution → Position**, enabling
+controlled experimentation with demand models, inventory constraints, and reward shaping.
+
+## Core Loop
+
+1. **Quote** – the policy posts prices (one-sided or two-sided depending on the mechanism).
+2. **Arrival** – a population model generates purchase opportunities or market orders.
+3. **Execution** – an execution model decides whether an arrival converts at the quoted price.
+4. **Position** – inventory/position limits censor fills and generate holding/shortage costs.
+5. **Observation & Reward** – censored fills and aggregate metrics are exposed to the agent, while
+   objectives turn metrics into a scalar reward.
+
+Each stage is pluggable via light-weight protocols so you can swap in alternative mechanisms,
+demand models, or objectives without rewriting the rest of the simulator.
+
+## Package Layout
+
+| Module            | Purpose |
+|-------------------|---------|
+| `lab.outlet`      | Core simulation engine, domain types, pricing mechanisms, objectives. |
+| `lab.population`  | Demand arrival models, execution probability models, competitor/market dynamics. |
+| `lab.experiments` | Rollout utilities, baseline policies, and off-policy evaluation helpers. |
+| `lab.config`      | Convenience factories for preconfigured retail and market-making environments. |
+
+## Preconfigured Scenarios
+
+### Retail Dynamic Pricing
+- Mechanism: posted prices with margin and delta constraints.
+- Arrivals: browsing sessions with contamination support (scrapers).
+- Execution: elasticity model with competitor cross-effects.
+- Position: inventory tracking with holding and shortage costs.
+- Market: reactive competitor that can trigger price wars.
+- Objective: PnL minus volatility, holding cost, and lost opportunity penalties.
+
+```python
+from lab.config import make_retail_platform
+from lab.experiments import rollout, fixed_price_policy
+
+platform = make_retail_platform()
+policy = fixed_price_policy(platform.instruments.refs)
+result = rollout(platform, policy, n_steps=100)
+print(result.total_pnl)
+```
+
+### Market Making
+- Mechanism: two-sided quoting with bid/ask spreads.
+- Arrivals: Hawkes order flow for clustered demand.
+- Execution: Avellaneda–Stoikov style intensity model.
+- Position: inventory risk limits and quadratic penalty objective.
+- Market: geometric Brownian motion mid-price process.
+- Objective: PnL plus spread capture minus inventory risk.
+
+```python
+from lab.config import make_market_making_platform
+from lab.experiments import rollout
+
+platform = make_market_making_platform()
+mm_policy = lambda obs, t: (platform.instruments.refs, 1.0)
+result = rollout(platform, mm_policy, n_steps=200, seed=42)
+print(result.total_pnl)
+```
+
+## Extending the Simulator
+
+- Implement `lab.outlet.protocols.Mechanism` or `ArrivalModel` to introduce new pricing
+domains or demand processes.
+- Compose objectives with `lab.outlet.objectives.factory.make_composite` to study alternate
+reward formulations.
+- Use `lab.experiments.compare_policies` to benchmark candidate policies across multiple
+random seeds.
+
+Comprehensive API documentation lives in `lab/docs` (build with `make html`).