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However, wrong and welcoming this new with you in this video, we're going to create a trading strategy
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using machine learning prediction.
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So we have already done our prediction, so we needed to create a trading strategy and this trading
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strategy will be very simple.
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When we have a positive reach on prediction, we're going to take a bad contract.
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So we are going to bits to the increase of stock when we have a negative return prediction.
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We're going to take a set contract and then predict the decrease of the stock.
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So the really important thing here is that we want the sign of the prediction.
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So one or minus one.
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And that's really the value.
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So to have the same?
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We are going to use the same function from Mumbai, and in this function, we put the prediction to
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have just the sign of the prediction.
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So I will pluck you the result here to a better comprehension.
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Then we needed to compute the return of this strategy.
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So we need to use the return of the assets, multiply by the position, but here we need to.
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Poots also as shift white, because it is exactly the same thing has for the moving average because
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if we take, for example, a day in the market open at eight a.m. and close at eight p.m. If you do
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your prediction at eight p.m., you cannot compute the return of your strategy by the return from eight
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a.m. to eight p.m. of the same day because you do your prediction after this variation.
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So it is predict the past by the future because
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you will not have all these data when you do a correct prediction.
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So you do.
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You need Zoe to put a shift to make a prediction at 8:00 p.m. and computes the URL of the strategy by
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multiplying this position, this signal by the region of tomorrow.
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So then we are going to pluck the cumulative return of our algorithm to see if.
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This strategy is profitable on that.
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And we need to take only the test, it's because here in the train set, it is logic that we have good
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results because the algorithm train its coefficient on this period.
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So here we have very bad results, but.
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Is not really important that we have bad results, because in the next chapter, we're going to see
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some
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customization of all approach and we're going to have very good results here.
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The main point is to understand.
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All the process to create a machine learning algorithm, to create a trading strategy, because if you
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don't understand all the process, you cannot understand the next chapter.
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And in the next chapter, we need to have some specific algorithm to increase the profitability of our
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strategy.
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So we need to understand what we have done in this chapter and the process that we have used to create.
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That trend sets the test set, etc. Because in the next chapter, we are going to go deeper into the
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algorithmic trading thing and the future of engineering.
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