What is an order book and how does it work?

An order book is a real-time list of all outstanding buy (bid) and sell (ask) limit orders at various price levels. When a new market order arrives to buy, it matches against the lowest available ask; a market sell matches the highest bid. The best bid and best ask form the spread, with trades occurring at the intersection. The order book reveals supply and demand at each price and determines the immediate market impact of large orders.

What causes fat tails in financial return distributions?

Empirically, extreme price moves occur far more often than a normal (Gaussian) distribution would predict—this is called fat tails or leptokurtosis. In agent-based models, fat tails emerge from herding: when trend-followers trigger further trend-followers, cascades of correlated orders amplify small shocks into large moves. Feedback loops between price and agent behavior, combined with endogenous liquidity crises, produce the power-law tails observed in all major financial markets.

What is volatility clustering and why does it occur?

Volatility clustering means that large price changes tend to be followed by large changes (of either sign), and calm periods cluster together—captured statistically by GARCH-type models. In agent-based markets, this arises because large moves change agents' beliefs and risk appetite, increasing order flow variability. The positive feedback between recent volatility and future volatility mirrors the behavioral herding and risk-adjusting behavior of real market participants.

What do the market impact lambda and noise sigma sliders control?

Market impact lambda scales how much excess demand pushes the price on each tick, so a higher lambda produces sharper swings for the same order flow. Noise sigma scales the size of the random shock added every tick, representing idiosyncratic order flow unrelated to fundamentals or trends.

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Financial Market

Simulate a financial market with fundamentalists, trend followers and noise traders. Watch price bubbles and crashes emerge from agent interactions, explore order book dynamics and market volatility.

Society & Economics3DEasy60 FPS

About this simulation

This model recreates price discovery in a limit order book populated by 200 heterogeneous traders. Each tick, fundamentalists, trend followers and noise traders submit buy or sell orders, and the resulting excess demand feeds a log-linear price rule, P(t+1) = P(t) × exp(λ·D̃ + σ·ε), where D̃ is net demand per agent and ε is drawn from a standard normal distribution. Because growth compounds multiplicatively rather than additively, the price can never turn negative, whilst still producing the fat-tailed returns and volatility clustering seen on real exchanges. Adjust the mix of trader types to swing the market between calm mean-reversion and a full bubble-crash cycle.

🔬 What it shows

Two linked canvases: a price chart plotting the traded price against the dashed fundamental value V and a 20-tick moving average, plus a synthetic order-book depth chart showing bid and ask volume either side of the mid-price. A status panel reports the price/value ratio, realised volatility and the current market regime.

🎮 How to use

Drag Fundamental value V, Fundamentalists %, Market impact λ, Noise σ and Trend sensitivity to reshape agent behaviour; the population of 200 agents rebuilds instantly. Use ⏸ Pause / ▶ Resume to freeze the simulation and study the current price path and order book.

💡 Did you know?

The price rule is a discrete geometric random walk with a demand-driven drift term, from the same mathematical family used in Black-Scholes option pricing — which is why turning up λ alone can transform gentle noise into runaway bubbles.

Frequently asked questions

How does the price update on each tick?

Every agent casts a buy or sell order, and their net excess demand D̃ (demand divided by 200) is combined with a random noise term ε and fed through P(t+1) = P(t) × exp(λ·D̃ + σ·ε). The market impact λ controls how strongly demand moves price, whilst σ controls the size of the random shock.

What distinguishes fundamentalists, trend followers and noise traders?

Fundamentalists buy when the price sits more than 3% below the fundamental value V and sell when it sits above, acting as a stabilising anchor. Trend followers buy when the 10-tick momentum is positive and sell when it is negative, amplifying moves into bubbles or crashes. Noise traders simply buy or sell at random each tick.

How is the market regime classified as stable, bubble or crash?

The regime badge compares the current price to the fundamental value V. A price/value ratio above 1.2 is labelled a bubble, below 0.8 a crash, and anything in between is classed as stable.

What do the market impact λ and noise σ sliders control?

Market impact λ scales how much excess demand pushes the price on each tick, so a higher λ produces sharper swings for the same order flow. Noise σ scales the size of the random shock added every tick, representing idiosyncratic order flow unrelated to fundamentals or trends.

Why does volatility get measured over a 20-tick window?

Realised volatility is calculated as the standard deviation of the log returns over the most recent 20 ticks, giving a rolling measure of how turbulent recent trading has been, similar to how short-window realised volatility is estimated from real intraday price data.

⚙ Under the hood

Agent-based financial market with fundamentalists, trend followers and noise traders. Observe price bubbles, crashes, order book depth and volatility.

Canvas 2DAgent-BasedOrder BookFundamentalistsTrend FollowingBubblesVolatility

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install