What to do with L3 orderbook data, quote engine, and parent order information
What to do with L3 orderbook data, quote engine, and parent order information
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IGottaLearnMath · External communityPost link
External question — Quantitative Finance Stack Exchange
Author: IGottaLearnMath
Original post: https://quant.stackexchange.com/questions/81073
License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/
Adaptation: HTML converted to plain text; contact email addresses removed.
I work on a trading desk as a quant/trader at a broker dealer on the electronic/algo equities execution desk. Our clients are institutional (hedge funds, asset manager) that utilise our algos to trade their orders. They use mainly liquidity seeking, POV, and VWAP. We have devs building our own stack (proprietary algos, SOR, volume predictions), so everything is in-house.
I have extremely granular data:
Level 3 orderbook across all markets.
Our own quotes that our algo engines sent to the market. I matched L3 orderbook quotes with our engine's own quotes, so I know exactly what action was taken on the book, and if it ours or not. And it is linked back to a parent order (i.e. 100 child orders can be linked back to a single parent order)
Parent order information from our clients (and information on the orders, like ADV, time of day, spread, volatility, etc...)
I spent a long time looking at things like post-trade reversion (mid-price change 1ms to 60s after) on a child level to see if we are getting adversely selected when posting passively on the book, understanding if there was more movement in the orderbook before and after sending orders out to the market, if child impact increases overtime in a parent order etc...
I realise this is an incredibly general question, but I'm in need of inspiration. What are some studies that would be interesting to run to improve our algos capabilities?
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Quoted from Forex.com.bd-Editorial External question — Quantitative Finance Stack Exchange Author: IGottaLearnMath Source score (net votes, not local likes): 1 Original post: https://quant.stackexchange.com/questions/81073 License: CC BY-SA 4.0 — https://creativecommons.org/licenses/by-sa/4.0/ Adaptation: HTML converted to plain text; contact email addresses removed. I work on a trading desk as a quant/trader at a broker dealer on the electronic/algo equities execution desk. Our clients are institutional (hedge funds, asset manager) that utilise our algos to trade their orders. They use mainly liquidity seeking, POV, and VWAP. We have devs building our own stack (proprietary algos, SOR, volume predictions), so everything is in-house. I have extremely granular data: Level 3 orderbook across all markets. Our own quotes that our algo engines sent to the market. I matched L3 orderbook quotes with our engine's own quotes, so I know exactly what action was taken on the book, and if it ours or not. And it is linked back to a parent order (i.e. 100 child orders can be linked back to a single parent order) Parent order information from our clients (and information on the orders, like ADV, time of day, spread, volatility, etc...) I spent a long time looking at things like post-trade reversion (mid-price change 1ms to 60s after) on a child level to see if we are getting adversely selected when posting passively on the book, understanding if there was more movement in the orderbook before and after sending orders out to the market, if child impact increases overtime in a parent order etc... I realise this is an incredibly general question, but I'm in need of inspiration. What are some studies that would be interesting to run to improve our algos capabilities?
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