12 karma · joined December 11, 2022
Long story short: I created an option backtester (MesoSim) and started analyzing public domain trades.
I have a couple of listed in this blog: https://blog.deltaray.io/tags/strategies/
If you are interested I'd suggest taking a look at the Weekend Effect, it's relatively easy to understand.
Best luck to you getting back to trading!
There is indeed an inherent complexity in options. Creating a simulator helped to understand this complexity.
My personal experience is that you don't need to fully understand the Black Scholes Pricing model in order to trade profitable options.
As an example consider the public income trades, such as NetZero, Boxcar, M3, Theta Engine. Trading those doesn't require you to understand how Implied Volatility.
One can argue, however, that selling options and hedging them isn't the 'Quant way' of profiting from options.
Options Backtesting Service focusing on income and hedging trading strategies. Still under heavy development but have a handful of users who find it useful.
Put it together while I was learning about Options Trading and manual backtesting was very tedious. It made me better understand options, develop and validate strategies and now also enables users to iterate quickly on trading system development on SPX, BTCUSD and ETHUSD options.
For example trades see these posts: - https://blog.deltaray.io/boxcar-trade - https://blog.deltaray.io/boxcar-ng-an-optimized-boxcar
My decade long journey of trading: Beginning of my career at IBM I was doing processor and system simulations (Cell, PowerPC). We developed a simulation method there, which was published as: "Mesoscale Performance Simulations of MultiCore Processor Systems"
Meanwhile in an other dimension my uncle tried his luck with trading, without much success. I realised that simulators and backtesters are related and that made me interested trying out trading algos.
Learned about StatArb from Ernie Chan's books, created my own version (now open source at https://github.com/tibkiss/huba-v1) and slowly started trading it. It didn't win or lose much either. Later, I found Quantopian and extended their engine to be able to trade live, on-prem (https://zipline-live.net). Ported my StatArb to zipline-live, still limited succes. Finally moved to QuantConnect, similar result. Tried other algos (mean reversion, momentum), which seemed to work better (in bull market).
Started my sabbatical in 2020 to focus on trading and to learn more about statistics and options. I found that premium of options (theta decay) can be successfully extracted via income strategies. The tricky part there is to not to kill yourself with leverage and learn to hedge your position. Learned that majority of the folks are using manual backtesting using a Windows based software, what I also tried. I realized that backtesting one year manually takes approximately 1 hour. Very useful, but super time consuming.
I decided to create a purpose built descriptor for Options Trading to expedite the process. Bought options data, implemented the backtester using similar approach what we used at processor and cloud simulations: Model what's really important and use good enough approximations for the rest. MesoSim was (re)born.
Shared the Service with groups of similar interest, they found it useful to expedite their options trading process. In the last 6 months had a couple of thousand visitors and a solid set of paying customers now.
Went on and started creating content, here is one trade as an example: https://blog.deltaray.io/boxcar-ng-an-optimized-boxcar