Now that the L1 Equity Quote Data is available, I wonder if we could create better and more realistic Market Impact Models. 

I've already written something about this in the L1 Equity Quote Data Thread

According to the Documentation, you can build your own Market Impact Models (is called Slippage there). 

Consider the following situation: 

A market participant wants to buy 1,000 shares of XYZ and the current bid price is $ 100.00, but as only 100 shares are offered at that price level, the order will not be executed in full at $ 100.00. Instead it will reach the next level in the order book and execute the next orders at $ 100.01 and so on. 

As a result, the average execution price wil be higher than current backtesting suggests. 

If you want to display it 100% realistically, you would need the complete history with the highest granularity (Tick) of the entire order book (Full DOM or at least Level 2 Data). You can imagine that this would pose completely new challenges to the hardware performance as the data volumes would be enormous. Therefore it makes sense to work with models that are very close to reality. 

A good and scientifically accepted approach would be the square-root formula for market impact, which we can use now where L1 Quote Data and thus the Spreads are available. 

Theoretically, you could simply build this into your Algo. The only problem is that the backtest durations would significantly increase then, because of the additional computations for the market impact model for each security at tick resolution. This brings me to my questions: 

1.) @Community

How do you handle it? Have you tried to extend the existing slippage model since the L1 Quote Data is available?

2.) @Quantconnect

Would you consider extending and improving the Constant Slippage Model currently used for Alpha Streams Brokerage by including L1 Quote Data?