orderwave 0.6.1

A compact aggregate order-book market simulator.

orderwave

Compact aggregate order-book simulation for Python, with readable built-in heatmaps.

orderwave keeps the runtime model small: a sparse bid/ask book, Poisson limit/market/cancel flow, bounded mean-reverting fair value, and a light liquidity-state kernel that creates sweep and refill structure without the older heuristic stack.

Install

pip install orderwave

Quick Start

from orderwave import Market

market = Market(seed=42, capture="visual")
result = market.run(steps=1_000)

snapshot = result.snapshot
history = result.history
overview = market.plot()
heatmap = market.plot_heatmap(anchor="price")
book = market.plot_book()

Public API

• Market(...): create a simulator with an initial price, tick size, visible depth, seed, optional MarketConfig, and capture="summary" | "visual".
• step(): advance one step and return the latest snapshot.
• gen(steps): run multiple steps and return the latest snapshot.
• run(steps): return SimulationResult(snapshot=..., history=...).
• get(): return the current snapshot as a dict.
• get_history(): return the summary history as a pandas.DataFrame.
• plot(): render the price path with a mid-anchored signed-depth heatmap. Requires capture="visual".
• plot_heatmap(anchor="mid" | "price"): render a standalone heatmap. Requires capture="visual".
• plot_book(): render the current order book.

capture="summary" keeps the fast path lean. capture="visual" stores a fixed signed-depth window around the moving market center so the heatmap can show sweep, void, and refill structure clearly.

Snapshot and History

Snapshot fields:

• step
• last_price
• mid_price
• best_bid
• best_ask
• spread
• bids
• asks
• bid_depth
• ask_depth
• depth_imbalance
• buy_aggr_volume
• sell_aggr_volume
• fair_price

History columns:

• step
• last_price
• mid_price
• best_bid
• best_ask
• spread
• bid_depth
• ask_depth
• depth_imbalance
• buy_aggr_volume
• sell_aggr_volume
• fair_price

Model

• Fair price follows a bounded mean-reverting Gaussian process.
• Limit, market, and cancel counts are sampled from Poisson distributions.
• Event side is driven by fair-value gap, depth imbalance, and recent signed flow.
• Limit placement mixes inside join/improve, best-level refill, and deeper wall placement.
• Aggressive flow raises side-specific stress and refill pressure so the heatmap shows asymmetric withdrawal and recovery.

Documentation Assets

Regenerate the documentation images with:

python -m scripts.render_doc_images

Render the standalone heatmap example with:

python -m examples.plot_market_heatmap --output artifacts/orderwave_heatmap.png

More docs:

• English: docs/en/README.md
• Korean: README.ko.md
• Release process: docs/en/releasing.md