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AlgoArena

Pit rule-based trading bots against one simulated market, then join the contest manually.

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Browser game
Opens at Michael Schwartz

AlgoArena screenshot 1

About AlgoArena

AlgoArena is Michael Schwartz's experimental trading-battle simulator, designed as a game and learning aid rather than a real brokerage tool. Several rule-based bots receive the same generated market conditions and compete on a shared leaderboard. Their approaches include support and resistance, trendlines, candlestick patterns, breakouts, and mean reversion, making it possible to compare how simple strategies behave under identical price movement.

The player can enter the same arena through a manual account, placing simulated trades and managing stop-loss and take-profit levels while the bots operate. The dashboard tracks open and realized profit, balances, win rates, average wins and losses, trade counts, and efficiency. A generated OHLC candlestick feed changes between trending, ranging, and ordinary regimes, with volatility and ATR influencing both bots and risk controls. Prop-style constraints impose daily-loss stops, lot limits, cooldowns, margin checks, and contract sizing, so reckless entries can end a session even though no real money is involved. The responsive browser project was built with kodeWeave and publishes its source under the MIT licence.

How to Play

Launch the arena and choose which built-in strategies will participate. Start the simulated feed, then watch each bot respond to the same candles and market regime. Use the leaderboard and performance panels to compare balances, open profit, win rates, trade frequency, and average outcomes rather than judging a method from one isolated trade.

To compete manually, use B to buy, S to sell, and X to close all open positions. Drag stop-loss and take-profit levels on the chart, or scale into and out of a position when the simulated setup changes. Monitor margin, maximum lot size, daily loss, and cooldown restrictions before submitting another order. The feed is generated for the game and does not represent an executable market. Try the same lineup across different regimes to observe when breakout, trend, reversal, or discretionary decisions gain or lose an advantage.

Key Features

  • Five rule-based strategies competing on identical data
  • Manual buy, sell, close, scaling, stop-loss, and take-profit controls
  • Generated OHLC feed with changing volatility and market regimes
  • Leaderboard and detailed trade-performance dashboard
  • Simulated margin, lot, cooldown, and daily-loss constraints

Source & Licence

Author / Project
Michael Schwartz
Licence
MIT
Released
2025-09-28

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AlgoArena

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