Strength Discovering for Store Assessment

Can we actually predict the price of Google stock based on a dataset of price history? I’ll answer that question by building a Python demo that uses an underutilized technique in financial market prediction, reinforcement learning. The specific technique we’ll use in this video is a subset of RL called Q learning. Using a combination of code, animations, and theory i’ll explain how we can let our AI learn a policy for when to buy and sell google stock to maximize profit.

Code for this video:

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Introduction to Learning to Trade with Reinforcement Learning

How to develop a stock price predictive model using Reinforcement Learning and TensorFlow

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18 Antworten auf „Strength Discovering for Store Assessment“

  1. What are the features feeding in the mentioned policy – aka the financial performance indicators ? A historical stock trend is not a values predictor by itself.
    How does the company strategy influences the long term investment decision – how is this information captured and used?

  2. Isn't the reward function initially unknown (and to be determined by the model) in the case of Q-learning(model-free) as opposed to the DP approach. The github link specifies the reward function as reward = max(data[t] – bought_price, 0) beforehand. What am I missing here?

  3. These videos never go anywhere. He always gets to the end and the results "aren't that good but can be improved". I thought it was going to show you a trading strategy? Not just a chart with some plots. He just goes on and on through all this super-complex shit, then it doesn't work anyway lmao. Oh well I guess it's like they say: if you can't do, teach. He has all these people who don't know anything about machine learning like "omg he's so smart, what a genius" but he's just putting out these same style videos on new topics with no real positive end result. Reminds me of tv shows that end every episode in a cliffhanger, except he shows us the result of the cliffhanger, without actually making us watch the next episode, and it's always a disappointment. Actually if he actually reads this, that's a damn good idea I just brought should hold the results and put them in the beginning of your next video. I bet you get like 50% increased views that way. "So for today's video, first of all here's the results of the last video, it was another fucking failure…but oh it can be improved with other strategies i'm sure of it! Okay onto today's project which i'm sure will end with very positive results!" fucking genius.

  4. In your GitHub link the code is very nicely explained but how we plot graphs, what area the parameters you are setting in x-axis and y-axis. please let me know sir, how you predict the model. Please let me know sir.

  5. this video is all about show! Also if you want to show Q-learning in real-world you better show the algorithm that works with function approximation (aka neural networks) and not tabular representation, and there are so many other flaws I see in terms of applications and from identifying what state is and how you represents them. Well misleading from technical point of view but amazing videos for the purpose of marketing your channel! (nothing bad about it but it is important to make sure the level of technicality is truthful.)

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