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I'm working on backtesting a number of stock trading strategies and need to estimate how much the execution price will likely deviate from the historical close price for that asset using daily data; which would be used to calculate an optimal position size and estimate probable slippage. Ideal would be a rough equation to get started with, that would justify spending the time/money on historical market depth data and more extensive research.

I intend to use Market on Close orders where possible, but would appreciate any rules of thumb, experiences or literature references that any of you may have.

E.g. would an equation like price_change_per_volume = (log(high) - log(low))/volume where volume is not more than 5% of total volume traded that day (as suggested elsewhere) be worse than nothing? How can I do better using only OHLCV daily data?

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You'll have a more transparent and reproducible result if you use volume-weighted average prices (VWAP) in your backtesting instead. Many brokers guarantee performance of their accumulation algorithms within a certain range of VWAP.

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  • $\begingroup$ Thanks for the response @brian-b, its appreciated. Unfortunately my application relies on capitalizing on pricing anomalies caused by events on a daily timescale, so I need 1) to calculate a rough size that trades would/should have been, 2) factor market impact into the portfolio position optimization stage. Any other thoughts on how to best do this with OHLCV data? Cheers $\endgroup$
    – psandersen
    Commented Jul 11, 2012 at 21:12

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