<p>I've noticed that stock trading is a prominent theme among <a href="https://stats.stackexchange.com/questions/tagged/reinforcement-learning" class="post-tag" title="show questions tagged &#39;reinforcement-learning&#39;" rel="tag">reinforcement-learning</a> and <a href="https://stats.stackexchange.com/questions/tagged/neural-networks" class="post-tag" title="show questions tagged &#39;neural-networks&#39;" rel="tag">neural-networks</a> and <a href="https://stats.stackexchange.com/questions/tagged/deep-learning" class="post-tag" title="show questions tagged &#39;deep-learning&#39;" rel="tag">deep-learning</a> questions. I think this is because (1) stock trading is an easy <em>problem</em> to understand (2) the data is readily available and (3) striking it rich on the stock market is a popular fantasy.</p>

<p>Does this warrant the creation of a canonical thread? What should that thread address?</p>

<p>I ask because I'm not aware of another substantive NN/DNN/ML topic which consistently attracts questions. The closest runner-up that I can recall are questions about using neural networks to fit sine waves. (This likely reflects my own biases, since I read most <a href="https://stats.stackexchange.com/questions/tagged/neural-networks" class="post-tag" title="show questions tagged &#39;neural-networks&#39;" rel="tag">neural-networks</a> and <a href="https://stats.stackexchange.com/questions/tagged/deep-learning" class="post-tag" title="show questions tagged &#39;deep-learning&#39;" rel="tag">deep-learning</a> questions but a much smaller fraction of other questions. Most of these threads are found in this search: <a href="https://stats.stackexchange.com/search?q=stock+trade">https://stats.stackexchange.com/search?q=stock+trade</a>)</p>

<p>Usually, the subject matter itself is of secondary importance on stats.SE and users are asking methodological questions about how to design an experiment or account for an unusual quirk of their data (dependency structure such as spatial or temporal correlation, mixed effects, random effects, etc.). </p>

<p>As a statistical topic, there are some general comments which would appear to apply to all of the shallow stock trading questions, such as how one might frame the discrete choices available to the model (buy, sell, hold, short, etc.) as a reinforcement or more conventional approach. This seems like it's on-topic.</p>

<p>But the broadest versions of these questions are perhaps not suitable because they veer into quantitative finance. The most general question, "How do I make a profitable stock trading algorithm?" must certainly require a deep understanding of QF because feature engineering (how to represent financial data in a manner that is amenable to your algorithm) and computer engineering (how to load and process data to execute trades before your decision is "stale"). I think those kinds of questions are best answered by QF.SE, since it is certainly their core topic.</p>

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<p>This question is motivated by this thread, where I opted to write a rather general answer because I had happened to spend a few dreadful weeks learning about QF. <a href="https://stats.stackexchange.com/questions/377595/how-to-train-a-stock-trading-neural-network-so-that-the-profit-parameter-is-ma/377675#377675">How to train a stock trading neural network so that the &#39;profit&#39; parameter is maximized?</a></p>
