Hey, awesome that you're posting here. I actually just asked a question as a post on the algotrading subreddit, but I'm hoping you can maybe help here. I think this would be broadly helpful, so I'm asking here instead of emailing.
Is there any authoritative literature on the execution aspect of algotrading? I'm talking specifically about the things that are hard, if not possible, to analyze via backtesting. As I've moved to shorter and shorter timeframes, I feel like there's a huge execution gap that I'm suffering from. I've got good strategies that work but can definitely be improved with better execution. For example:
How to optimize limit pricing to get better fill rates or better entries or exits
Market microstructure analysis and modeling. How to analyze book depth and order flow to predict short term (shorter than 5-10s) behavior
Applications for and optimizations of various order types like IOC, MIT, LIT, FOK, Iceberg, Basket, etc.
How to handle the million ways that algotrading can do unexpected things: market gaps, trading halts, network outages or latency spikes, news events and volume explosions, etc.
Metastrategies: creating strategies that "trade" strategies to turn them off or swap them out based on market conditions.
Statistical process control, or other ways to detect when an algorithm is no longer working as expected.
Use of metainformation to dynamically inform strategies, such as implied volatility or any of the option greeks.
Is there any authoritative literature on the execution aspect of algotrading? I'm talking specifically about the things that are hard, if not possible, to analyze via backtesting. As I've moved to shorter and shorter timeframes, I feel like there's a huge execution gap that I'm suffering from. I've got good strategies that work but can definitely be improved with better execution. For example:
Do you know of any resources to point me to?