I built BinancyXBot, a Python bot that trades crypto on Binance by itself, and a Unity city where you can watch it happen. One important note first: every dollar in it is virtual. The bot is still in testing, so it paper-trades or uses the Binance testnet, and no real money is at stake.
The bot runs six kinds of strategy, each with its own account: EMA crossover, breakout, momentum, RSI dip buying, Bollinger dip buying and a gradient-boosted ML classifier. Buy-and-hold runs alongside them as the benchmark. Every strategy is checked with a walk-forward test: it picks its settings from past data only, then gets scored on months it has never seen, with fees included.
Turning it into a neon city in Unity
The Python side streams live prices and every agent's trades to Unity. Each strategy becomes a skyscraper:
• Its height follows the account value.
• Its window lights follow its performance.
• Its neon trim shows the coin it holds, and turns red during a drawdown.
At the centre stands the exchange tower, with holographic rings and price boards. Each trade fires a beam of light between the exchange and a tower: buys fly in, sells fly out. Rain, haze, bloom and a cinematic camera mode for recording finish the look.
Seeking Claude's help
I designed the project and made the calls, and I used Claude as a coding partner along the way. It helped me write parts of the walk-forward lab, debug the trading logic and tune the Unity shaders. Next, I plan to rerun the lab on 3-minute candles before any money goes near it.
Top comments (1)
sohaib, this is a brilliant fusion of quantitative finance and real-time 3d visualization. mapping account equity to skyscraper height and drawdowns to red neon trim is a fantastic way to make abstract portfolio metrics instantly readable.
the commitment to walk-forward testing with fees included is what separates toy bots from serious quant systems. too many devs just do standard k-fold cross-validation and get destroyed by look-ahead bias in live markets.
you mentioned planning to rerun the lab on 3-minute candles. that’s a massive jump in data granularity and market noise. for the gradient-boosted ml classifier, how are you handling feature drift and the increased transaction cost drag at that timeframe? are you adjusting the walk-forward training window size (e.g., using a shorter anchor period) to prevent the model from overfitting to micro-structure noise, or are you relying on the fee-included scoring to naturally penalize the over-trading?
would love to hear how the 3m backtest compares to the higher timeframes!