This talk, titled, “Reinforcement Learning for Trading Practical Examples and Lessons Learned” was given by Dr. Tom Starke at QuantCon 2018.
Description:
Since AlphaGo beat the world Go champion, reinforcement learning has received considerable attention and seems like an attractive choice for completely autonomous trading systems. This talk shows practical aspects and examples of deep reinforcement learning applied to trading and discusses the pros and cons of this technology.
The slides for this talk can be viewed at:
About the Speaker:
Dr. Tom Starke has a Ph.D. in Physics and works as an algorithmic trader at a proprietary trading company in Sydney. He has a keen interest in mathematical modeling and machine learning in the financial markets. He has previously lectured computer simulation at Oxford University and lead strategic research projects for Rolls-Royce Plc.
Tom is very active in the quantitative trading community, running workshops for Quantopian, teaching people quantitative analysis techniques, and organizing algorithmic trading meetup groups such as Cybertraders Syd.
To learn more about Quantopian, visit
Disclaimer
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Reinforcement Learning for Trading Practical Examples and Lessons Learned by Dr. Tom Starke
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Reinforcement Learning for Trading Practical Examples and Lessons Learned by Dr. Tom Starke
financial deepmind
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95% of the trades are made by big money they can hire and build the most advance systems and programmers .. and they fail .
so it probably will not .. will not ever work ..
"LSTMs, they're somewhat new" they've been around since 1997 lol
Thank you sir for good explanation!
Please help me to solve this error – ImportError: cannot import name 'sgd' from 'keras.optimizers' am not able to fix this error and if anyone to fix this error please help me
Why can't we give reward in every time step?
On 19:53 you mentions the Right Regime – 'does it end up choosing some training process ' It's easy to understand what is Mean Reversion process, but what does it mean 'training process' ?
Thank you for great presentation!
Thank you for putting your code on github.
I thought reinforcement learning is shit for trading
I am doing deep learning but now I'm thinking of integrating it with a reinforcement learning as an ensemble on the outside.
(with a money management system on the side)
Is there Anyone in California interested in my project?
30:14 you are updating state and applying the action. When we chose an action, first we need to apply than we need to update the state and get the reward. Let's say current price is 100.20. When agent decides to buy, it's has to buy from the price 100.20 (excluding spread/slippage and commission). In your example, it's buying with the next price. Am I wrong?
If AI works, Quant Trading will go down I guess, beware!
23:55 when you said random walk most of the time I just realized you're not a real Quant my dear one.
I wonder how he smoothes the data – perhaps "now" timestamp was already including partial info of the next data point. If it was smoothly only backwards then the next timestamp at exit might be completely off than the real exit price.
SVM has some type of geometry element which responds well to any freely traded market
Siri was triggered at 1:40, hahaha. Time to rethink about ML?
Just as with any other AI algorithm, you need to clean your data before you give it to your reinforcement learner. But you can make a neural net that cleans that data for you, with relative succes. Noise is also an issue in other domains, not just finance. Ofcourse you are creating a feedback loop. When you buy/sell with succes your competitors will adapt, and so the problem shifts to a more difficult state…adding overal noise (randomness) to the system.
Hi, I work on Reinforcement Learning too, anyone hiring reply !
3:32 guys please upload his talk too !
Hi, I work on Reinforcement Learning, anyone hiring reply !