The frontier in
simulated markets.

Upload .py / .R

Reference

Each turn, data has

close, open, high, low, volume — lists, oldest first
returns — % change bar to bar
sma20, sma50, ema12, ema26 — moving averages (None until warmed up)
book["bids"], book["asks"] — [{"price":.., "qty":..}], best first
book["mid"], book["spread"], book["imbalance"] — top-of-book, imbalance in [-1, 1]
orders: your working limit orders
account["cash"|"shares"|"avg_entry"|"realized_pnl"|"unrealized_pnl"]

To trade

market_order("buy" | "sell", qty)

limit_order("buy" | "sell", qty, price)

cancel_all(): pull all your working orders

How a game runs

Define on_tick(data). Every few sim-minutes the market pauses for a decision round: every strategy in the room gets the same snapshot, orders are placed, then the day carries on.

Each round gets up to 60 seconds. A strategy that runs out of time is restarted (top-level variables reset). One that errors 5 rounds in a row is stopped for the day.

numpy, pandas and other Pyodide packages import normally.

Price action $100.00

Order book 15 levels

PriceQtyTotal

Spread $0.00

Order entry

Quantity

Limit price ($)

Stop loss ($)

Take profit ($)

Positions & PnL

Working orders 0

No active open orders

Active SL / TP 0

No stop loss or take profit on open positions

Cash balance$10,000.00

Shares held0

Avg entry$0.00

Position value$0.00

Unrealized PnL+$0.00

Realized PnL+$0.00

Indicator & algorithmic strategy engine

Workspace inactive

Workspace reserved for script execution and technical indicator overlays

The frontier in
simulated markets.

How will you trade?

Waiting for players

Market closed

The frontier insimulated markets.

How will you trade?

Waiting for players

The frontier in
simulated markets.