For some context, a professor of mine allegedly knows a guy at a hedge fund who built a model which, under certain conditions, can do what you’re trying to do 55% of the time. Barely better than flipping a coin. For that, he makes half a billion a year. Pretty much no one can do this. Every PhD in a relevant field seems to give it a try at some point. None succeed. Just know that. More on reddit.com

I work on both algo solutions (without ML) and automated ML solutions as well. Here is my experience: ML: For ML, classification models -in my opinion - are preferable compared to regression models. You can pick the one that makes more sense to you obviously.. But intuitively, predicting whether the price will go up or down is a comparably easier problem than predicting what the future price is going to be. For people who are not familiar with the terms: Classification models try to classify things (i.e. whether the price will go up or down). Or, you can also classify the action as "Buy", "Hold" and "Sell". The algo basically has a limited number of choices to pick from. And each option is given a probability. Highest the probability for one of the options, highest the algorithmic expectation for the event to be realized. Regression models try to predict the future price. (Depending on your use case, you may though define what you will predict..) In this case, options are - sort of - unlimited. The prediction can be 20, or 50, or 50.5, or 50.55 or 50.556, or whatever.. No matter which approach you use, one of the most challenging parts is to set stop loss and sell target values. Let's say that your classification model says that the price of the stock will go up with regards to the current stock price 100 USD. But what profit target do you have? And if the stock goes down, how do you set your stop loss value? The same problem is there if you happen to use regression model too. IF the current price is 100 USD and your model says that the future price (say after X minutes or Y days) is expected to be 110 USD... But what if the price starts going down..? What is your stop loss value? It is not a viable option to use the same profit target for all the stocks. For instance, saying that the profit target is 7% for all the stocks will likely not work. Because your prediction will still be doing the prediction for a given future timeframe, e.g. 10 business days. If the short term volatility of the stock is - say - below 1%, it is probably not likely that the stock price will go up 7% in 10 business days as long as no miracle happens. Using volatility figures of each stock help when setting stop loss and sell targets. Developing viable ML solutions is hard and it is a very iterative process. I know very closely how much it costs to use the IT infrastructure and market data to develop and keep iterating on these solutions. Algo solutions (without ML): If resources are limited, it is maybe a better approach to try to develop algo solutions by using technical indicators. Most technical indicators, that are (relatively easily) accessible, happen to be lagging indicators, i.e. moving averages, MACD, Signal Line, Bollinger Band, etc. They sort of say something about what has been happening but they do not necessarily say what will be happening. Mainly because of this reason, using one single technical indicator is not enough. Technical indicators can be used to check how strong a given trend has been in the price trend of a stock. When taking decisions, if decisions will be taken in this way, it is important to use multiple indicators to check whether other types of indicators give the same signals as well. Things that apply both: No matter what approach is used, some predictions will always fail. Predicting the "future" is way different than predicting the "now" because: If you make a prediction at point A on the time line for something that will be concluded at point H later on, and if things that take place on any point between A and H (e.g. B, C, D, E, F, G) can have an impact on the result, your prediction confidence can never be 100%. Your prediction can only be as good as the quality of your input data and the quality of your input data is limited to the data you may use at point A. The method that is used should target a net profit rather than being successful in all predictions. In this regard, I do not necessarily value the prediction accuracy. I find it more valuable to check the net profit figures at the end. For instance, I prefer the scenario 2 below: Scenario 1 Prediction accuracy for all predictions: 65% Net profit at the end: 2% Scenario 2 Prediction accuracy: 45% Net profit: 25% Risk distribution is critical. Rather than running a prediction algorithm on 1 single stock, it should be run on multiple stocks to avoid big disasters. It is yet possible to narrow down the stocks i.e.: You may target more volatile stocks that give more swing trading strategies You may want to go for more liquid stocks in the market that are easier to buy and/or sell when you need to You may also set a filter like "only targeting SP 500 stocks to still remain at the safe side" or "only targeting penny stocks if high risks shall be taken for whatever reason". More on reddit.com

Survivorship bias. More on reddit.com

Instead of broadly doing stock predictions, try looking in-depth into smaller problems, such as optimal execution. Given that you want to buy or sell, what's the optimal way to do it? There's a lot that goes into this practical problem, and machine learning is one approach. More on reddit.com