orderwave 0.4.10

An order-flow-driven synthetic market simulator.

orderwave

Order-flow-driven synthetic market simulation for Python, with built-in visualization.

orderwave does not random-walk price directly. It simulates a sparse aggregate limit order book, participant-conditioned limit flow, marketable flow, cancellations, latent meta-orders, exogenous shocks, and session-aware state changes, then lets price emerge from those book mechanics. The same Market object can render the path, the current book snapshot, and realism-oriented diagnostics without extra plotting glue.

English Docs | 한국어 README | 한국어 문서

Why orderwave

• Minimal public entry point: from orderwave import Market
• Price changes only as a consequence of book mechanics
• Hidden fair value, session clock, shocks, and meta-orders bias flow without directly overwriting price
• Same seed, same path
• Built-in figures for overview, current book, and diagnostics
• Thin public event history plus optional latent debug history

Installation

pip install orderwave

For local development:

pip install -e .[dev]

Quick Start

from orderwave import Market

market = Market(seed=42, config={"preset": "trend"})
market.gen(steps=1_000)

snapshot = market.get()
history = market.get_history()
events = market.get_event_history()
debug = market.get_debug_history()
overview = market.plot()
book = market.plot_book()
diagnostics = market.plot_diagnostics()

print(snapshot["session_phase"], snapshot["mid_price"], snapshot["best_bid"], snapshot["best_ask"])
print(history.tail())
print(events.tail())
print(debug.tail())

overview.savefig("orderwave-overview.png")

For lighter long runs where you only need summary history, visible book snapshots, and trade strength:

fast_market = Market(seed=7, config={"preset": "balanced", "logging_mode": "history_only"})
fast_market.gen(steps=10_000)
summary = fast_market.get_history()
figure = fast_market.plot()

Performance Measurement

Use the single performance runner when you want a quick throughput check plus a full vs history_only logging comparison.

python scripts/measure_performance.py --preset balanced --seeds 20 --steps 20000 --outdir artifacts/performance

The runner writes:

• performance_metrics.csv
• performance_summary.csv
• performance_logging_modes.csv
• performance_summary.md

Validation Sweep

The repository also includes a validation runner for preset sweeps, knob sensitivity, reproducibility checks, and soak tests.

python scripts/validate_orderwave.py --profile full --jobs 4 --outdir artifacts/validation

The runner writes:

• validation_summary.md
• run_metrics.csv
• preset_summary.csv
• sensitivity_summary.csv
• invariant_failures.csv
• acceptance_decision.md
• diagnostics_<preset>_<seed>.png

Release builds run the shorter --profile release regression and compare it against tests/golden/validation_release_baseline.json before PyPI publish.

The next engine improvement target is intentionally narrow: finer intra-step event feedback only.

API Surface

API	Purpose
step()	Run one micro-batch and return the latest snapshot
gen(steps=n)	Run n micro-batches and return the latest snapshot
get()	Return the current snapshot
get_history()	Return compact pandas.DataFrame history
get_event_history()	Return the applied event log as a pandas.DataFrame
get_debug_history()	Return event-aligned latent debug history for advanced inspection
plot()	Render price, spread, trade strength, and visible-book heatmap
plot_book()	Render the current order book on a real price axis
plot_diagnostics()	Render session, excitation, imbalance, spread/volatility, resiliency, and occupancy diagnostics

Advanced configuration is available through orderwave.config.MarketConfig.

logging_mode="history_only" keeps summary history plus overview/book plotting data, but disables get_event_history(), get_debug_history(), and plot_diagnostics(). Default liquidity_backstop="always" keeps the synthetic market two-sided and observable by default.

Built-in Visualization

All plotting methods return matplotlib.figure.Figure and leave save/show control to the caller.

• plot() renders the main overview: price, spread, execution-only trade strength, and signed visible-depth heatmap
• plot_book() renders the current order book on a real price axis
• plot_diagnostics() renders session phase profile, imbalance lead, market-flow excitation, spread-volatility coupling, depletion resiliency, and regime or shock occupancy

The overview heatmap keeps signed depth. Ask liquidity is red, bid liquidity is blue, 0 maps to a light gray midpoint, and missing levels render as blank background instead of black cells.

Presets At A Glance

balanced, trend, and volatile reuse the same public API while shifting spread behavior, flow pressure, cancellation pressure, and hidden fair-price dynamics.

Core Semantics

Market.get() returns a compact dictionary with session clock fields, prices, spread, visible depth, aggressive volume, trade strength, depth imbalance, and regime.

trade_strength is an execution-only signed imbalance. It is computed from an EWMA of realized aggressor buy and sell volume, so quote-only book changes do not move it.

Important distinction:

• mid_price can move when quotes improve, cancel, or get depleted
• last_price only changes when a trade actually executes
• day, session_step, and session_phase expose the synthetic intraday clock

Core guarantees:

• Price is never random-walked directly
• Quote improvement, best-quote depletion, and market execution are the only price-moving mechanisms
• Visible history starts at step == 0 with the seeded initial book
• Applied limit, market, and cancel events are available through get_event_history()
• Participant type, meta-order progress, burst state, and shock state are available through get_debug_history()
• Aggregate depth is modeled without exposing per-order FIFO complexity in v1

Docs

• Documentation index
• Getting started
• API reference
• Examples
• Release guide
• 한국어 문서 인덱스