I'm just getting started with trading so figured now is a good time to start a journal.
A bit about me: I have a PhD in signal processing and optimization and spent the last 10 years in industry as a software engineer developing signal processing and machine learning algorithms. Hopefully this will make me a little more disciplined when it comes to OOS testing and cross-validation. I'm very comfortable on the programming and algorithms side but have very little experience trading.
I've had good success with property investing but that's a very different ballgame I'm sure. It's also hard to tell with property how much was just luck because the number of transactions is so low. On the plus side though I'm well capitalized as a result.
Ideally I'd like to transition to trading as a job probably for the same reasons as many others: being independent and getting out of the corporate world. I figure there is 5% or less chance of this happening which is OK with me. If it becomes a hobby that helps me generate some pocket money that's fine to. Or maybe it just doesn't work at all. It would be nice to find a way to gradually improve through consistent effort and chart a path to success rather than just giving up though.
So on to the plan. Right now I'm reading through Ernie Chan's algorithmic trading and setting up my own back testing environment in Python (although zipline / Quantopian / IB look pretty interesting too as an off the shelf solution). I'm planning to trade only once per day or so, running the algorithms nightly and then entering trades by hand, at least initially. Low frequency should keep transaction costs low and hopefully decrease the number of opportunities more experienced traders have to eat into me with more precise timing and better market knowledge. I really lack in my ability to read price action. It's something I'll need to get a better feel for. Any references on this would be much appreciated.
Ideally I'd like to identify 100 or so algorithms, short list and implement 20, back test them over many different time periods, then forward test them with small size trades. If one ended up being moderately profitable I'd be pretty happy.
The real challenge now is identifying a pipeline of algorithms to try out. I have a preference towards correlation / stat arb and machine learning models for mean reversion but am frankly open to anything. I have a strong bias against Elliott waves, square of nines and other esoteric forms of TA though.
Anyway, I'm looking forward to getting to know some of you better and setting out on this journey.
A bit about me: I have a PhD in signal processing and optimization and spent the last 10 years in industry as a software engineer developing signal processing and machine learning algorithms. Hopefully this will make me a little more disciplined when it comes to OOS testing and cross-validation. I'm very comfortable on the programming and algorithms side but have very little experience trading.
I've had good success with property investing but that's a very different ballgame I'm sure. It's also hard to tell with property how much was just luck because the number of transactions is so low. On the plus side though I'm well capitalized as a result.
Ideally I'd like to transition to trading as a job probably for the same reasons as many others: being independent and getting out of the corporate world. I figure there is 5% or less chance of this happening which is OK with me. If it becomes a hobby that helps me generate some pocket money that's fine to. Or maybe it just doesn't work at all. It would be nice to find a way to gradually improve through consistent effort and chart a path to success rather than just giving up though.
So on to the plan. Right now I'm reading through Ernie Chan's algorithmic trading and setting up my own back testing environment in Python (although zipline / Quantopian / IB look pretty interesting too as an off the shelf solution). I'm planning to trade only once per day or so, running the algorithms nightly and then entering trades by hand, at least initially. Low frequency should keep transaction costs low and hopefully decrease the number of opportunities more experienced traders have to eat into me with more precise timing and better market knowledge. I really lack in my ability to read price action. It's something I'll need to get a better feel for. Any references on this would be much appreciated.
Ideally I'd like to identify 100 or so algorithms, short list and implement 20, back test them over many different time periods, then forward test them with small size trades. If one ended up being moderately profitable I'd be pretty happy.
The real challenge now is identifying a pipeline of algorithms to try out. I have a preference towards correlation / stat arb and machine learning models for mean reversion but am frankly open to anything. I have a strong bias against Elliott waves, square of nines and other esoteric forms of TA though.
Anyway, I'm looking forward to getting to know some of you better and setting out on this journey.
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