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This talk details statistical model optimization using past order book data, a key metric, and converting model output into high-frequency trades.
How to do some statistical model optimization on past order book data for trading.
What is a good metric for optimizing a model of indicators and why.
How to convert to reward of such model into high frequency trades.
I will share one order book imbalance indicator.
I will present a python bot that is connected in live to a public binance API, computing the imbalance indicator and outputting some simulated orders. I will also illustrate the metric to optimize the model
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