Order Book Microprice and Weighted Price Estimation
Summary
The document compares simple price proxies for estimating short-term price movement. One proposal weights the best ask price by bid size and the best bid price by ask size; the last traded price is offered as another useful predictor. These measures are presented as alternatives to the midprice, with the caveat that predictive value alone may not cover trading fees and slippage. The prediction target described is the next change in midprice, which can matter for market-taking decisions.
A separate fixed-income example compares last trade, quoted midpoint, and a size-weighted midpoint, noting that each can jump when new orders appear at the top of the book. The author suggests measuring price-change variability and instead modeling the midpoint from weighted bid and ask data across multiple book levels. The document gives no detailed model specification, validation results, or general performance evidence, so it serves as a set of ideas rather than a complete implementation guide.
Key ideas
- A top-of-book size-weighted price can serve as a short-term alternative to the quoted midpoint.
- The last traded price is also proposed as a predictor of subsequent midprice changes.
- Predictive signals may not remain profitable after fees and slippage.
- Top-of-book quote changes can create jumps in standard price measures.
- Using weighted quotes across multiple order-book levels is suggested as a way to model the midpoint.
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Full text
# The problem of the weighted price
# The problem of the weighted price
Mid price is so noisy so I try to use the weighted price, which is much better. However I want to define a better price called weighted price
My questions are:
- How can I get a good weighted price
- How to judge the weighted-price. How can I define an indicator to judge whether the weighted price is good or bad?
## Answer by Jase (score 4, accepted)
https://quant.stackexchange.com/a/53650
Arriving at a good microprice is one of the main preoccupations in the HFT and short-term quantitative trading industry. No answer here will be competitive with these more sophisticated micropricing models.
However if you want something that gives decent predictions (but won't make money after slippage and fees), use either of these two, or some weighted combination of the two:
P = (best_bid_volume * best_ask_price + best_ask_volume * best_bid_price) / (best_bid_volume + best_ask_volume)
P = last_trade_price
I know you mentioned bid-ask bounce as a significant problem, but for most applications (and certainly for prediction), it isn't. The last traded price is highly suggestive of future deltas. If you actually measure this, you will see. Either of these will be significantly better than midprice.
Note that these predict deltas in the midprice, $ln(mid_{t+1}/mid_{t})$, which is often the most directly actionable in terms of market taking and therefore the most relevant for market taking strategies.
## Answer by Attack68 (score 0)
https://quant.stackexchange.com/a/53654
For fixed income trading I acquired a single days worth of iSwap's bid-ask swap data, which is high quality constantly streamed prices of discrete book sizes.
I derived the three standard prices as measures for the mid:
- Last traded.
- Mid of Bid and Ask.
- Weighted mid of bid and ask.
All three suffered from the problem of sporadic price jumps when new top level bid or asks were entered into the orderbook (whether it was a signifcant order by volume or not). This you could measure in terms of standard deviation of the absolute prices changes across timespans.
Instead I derived a model for the mid price dependent upon weighted bid-asks at different levels of the orderbook. You can see my answer here: definition of mid price in literatureShown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.