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1.5 ETH (~300 USD)
Create a useful visualization of the `TradingEnvironment` in the `render` method.
* Implement a useful visualization of the `TradingEnvironment` and the underlying the learning agent's interactions with the environment's `InstrumentExchange`.
* Display the exchange balance, and all exchange observations/agent trades during each episode in a useful manner.
* E.g. [a candlestick chart with highlighted trades](https://towardsdatascience.com/visualizing-stock-trading-agents-using-matplotlib-and-gym-584c992bc6d4)
* Display the total P/L, sharpe ratio, largest drawdown %, and any metrics you see as useful.
* The rendered visualization should use a performant, well-designed charting application such as [Bokeh](https://docs.bokeh.org/en/latest/index.html) or [Dash](https://dash.plot.ly/getting-started)
_Use the [RLTrader Github](https://github.com/notadamking/RLTrader) and the corresponding [Medium article](https://towardsdatascience.com/creating-bitcoin-trading-bots-that-dont-lose-money-2e7165fb0b29) as a reference._