There is a paper around, a guy I think he wrote it for his Phd, that used several external factors not related to price, including news, something related to wikipedia, and other external data sources to precisely find out those factors that are not related to price. I think he obtained about 90% of accuracy on next day 3-day SMA price change. However, I think it is a low score for a 3-day SMA. He used random forest for that and supervised classification. One of the features I have been thinking about is capturing experts sentiment also, by scrapping news and other data sources. I think the may be useful as well. However, I am not currently interested on applying ML to trading, I've had this project inactive for two years and I wanted to close this particular model of next day price change prediction using exclusively price and volume to generate features and models. So I did it these Christmas days I had a bit of time. Regards.
I forgot to mention. To avoid overfitting I use a main approach: First you must divide into two sets your data. One for training the model and other to test it with new data. The first set, the training set is divided again into two other subsets. You only use a randomly selected partial group of the training set to train the model. For instance, only 70% of the records. The for testing it you use new data. the test set that has not been involved within the training stage. For instance, for EURUSD I think I had over 4000 rows, used a subset of the first 3000 rows and predicted the next 1000 rows to verify the accuracy. The model always fits within your data, the problem is not to overfit it. You need to fit the model for each new security, but not to each new datum. At the end, overfitting I think it is more about not understanding how to design a statistical experiment and being negligent. It is a very basic principle. However, don't you think that training a neural network to identify car plates is overfitting, but it works like charm?
Get real, you are not a genius that has cracked Matrix. If anyone could forecast the market with 75% or better accuracy. Governments, universities, & well capitalized firms with teams of PHD mathematicians have tried for many decades to forecast the market - all with dismal results no better than a coin toss. There are some big players using AI to examine the sum of all Internet babble to try to get clues as to the probabilities of market direction - will just have to see how this works out in the years to come. The problem is that the real market movers use transparency to enter/exit positions- dark pools. At best AI could only gather from the web what some of the vocal weak hands may be up to. If anyone truly had forecasting powers at 75% or greater they would be worth many billions to big operators like the J.P Morgans of the world that could use this to capture the worlds $ supply. I am sure the SMA does not have mystical crystal ball like super powers - at least mine sure doesn't. At best it may help some traders be aligned with the short term trend at times - during chop it will be worthless. There is historical data showing seasonal influences, business cycles, and the presidential election cycle - however this is at best may improve your odds a bit over the long haul.
Should I respond to this? In fact I won't answer to any personal message. However anyone is free to write as desired. For anyone else reading this particular quote, on the previous posts there is an explanation of what is this good for and what it is not. I won't repeat it again here since it would be starting a circular conversation I am not interested in.
Yes please do respond. Why? because I want to believe you but I tend to agree with comagnum. In the mean time I will do some analysis to see if there is logic behind the scheme and get some forward testing done to see if it works. Thank you for sharing and take care.
Hi Ironchef. I won't talk about myself, because an argument based on who is someone is a fallacy. So I won't respond to the personal part of the message. The summary of what's posted by me: - A method to predict 2-bar SMA direction with average 75% cross-security accuracy. - It is not a trading system. - No application on trading has been provided for it. - It may help anyone to understand better how price reacts to certain factors. - Period. Then the fact that financial data or financial prices already contain all the market information and cannot be forecasted, that is false. Why? Because it can be and it has been forecasted using non-linear methods such as Support Vector Machines, Decision tree-based models, neural networks and so on. The difference is that non-linear approach does not assume that all the information can be reduced to a line or unique function. It is very easy to find across prestigious and trusted universitites and organizations studies about it. I have provided a very easy-to-replicate model written in Python a few post before too, that's why I was a bit surprised by the answer, since it can be easily tested just with the already implemented model provided by myself. The technique I used completely removes overfitting and uses gradient descent optimization over decision trees. This is not a religion, nor an opinion, it is something one can either prove or fail to do. Doing a bit of research anyone can find plenty of documentation, taken randomly from Google: - https://www.cs.princeton.edu/sites/default/files/uploads/saahil_madge.pdf - Failure to predict price market. - https://arxiv.org/ftp/arxiv/papers/1603/1603.00751.pdf - Success to classify security movement prediction There are tens of other studies that anyone can read, I just included a couple of them, one that fails to achieve its objective and another that succeeds at it. I have not investigated yet how to predict price itself, and I don't know if it is possible to do because I simply have not researched it enough. I hope this answer is enough. I am unsure of that doubts may still remain after all the explanations given on this and precedent posts. Regards.
Thank you gon, I appreciate you taking the time to give me this lengthy response. What I missed was this: From a structure and logic basis, it makes some sense: Like any systems with some momentum and some form of damping factor (friction), the inertia will usually carry from time period to time period, especially when the time period is short. So, the 75% success rate of prediction of the direction for the next period could be a reasonable outcome. It depends on the momentum (directionality) and damping factor.... The $64 dollar question for me is how to take advantage and make a profitable system out of your observation and algorithm. I will do some forward testing using this concept and share the outcome when I get some results. Best wishes.
You are conflating the accuracy of the median prediction with the mean prediction that takes into account the whole distribution. As an extreme example, a median expectation of a late night stroll through Mogadishu is "nothing happened", but would you be willing to take that walk without knowing the mean expectation?
Hi. Indeed, the starting point of all this was thinking in price like an object with a certain mass subject to a series of dynamics. So I started to apply velocity, acceleration, force and other concepts taken precisely from physics and calculus. But, the $64 dolar question, the information that a classificator like this one provides could be used as a predictor within a bigger model, just another feature else. In my case, I got a few lessons: - Technical indicators and price/volume history matters. - Even 2-bar SMA is too smoothed to provide an accurate estimation of actual price change. This is important, because getting quite good accuracy with the 2-daySMA is not an easy task, and I found that I could not get a direct profit from it after struggling for a few weeks. - Comparing the bars and other data to the model predictions provides a mathematical confirmation of what each type of bar and price dynamics should represent, not just the theories and opinions of authors that do not provide one sole proof about what they say or sell. But, what do we want to maximize? Profits? Then if profit is the function to maximize and loss the function to minimize, then price, volume or banana is not the target item we should be studying ultimately. If you want to maximize profit, you need to provide the market information as input data, also any prediction or classification if you want to. But the target should be profit. And to map market to profit you need a set of intermediate layers. I think that here is where strategy comes. For this, if I would have time to go ahead in the future, I would be for strategy definition and parameter optimization more than purely thinking about a stochastic approach. This reminds me to unsupervised reinforcement learning, but again, maybe more simple approaches to identify the different price areas to detect breakouts, trend reversals, gaps, supports and resistance levels, etc... could be more than sufficient. So, imo if you already have a good system, just write it and optimize it. But, for anyone not being a programmer knowledgeable at statistics I would discourage of trying it. The reason why I opened this post was to find out if any forum member was using that kind of information to trade. And to find out if traders were actually using mathematical approaches, intuition or both of them.
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