A 75% accuracy rate in identifying the next day's direction is good. But focusing on one single day's price action is seriously under-ambitious.
I assume your comment is related to the model, not to the trading trainer, since the trading trainer has nothing to do with one-day price action. Yes, that's why this statistical model is good to improve understanding price action trading through the days the system fails to predict only. However, I am speaking about daily chart price action, not one day price action. I am unsure if we are on the same row. More important even is that predicting the 2-days smoothed price direction is not always telling you actually where price will go, because the 2-day sma and the 1-day price can move in opposite directions. However, the 2-day moving average is quite an ambitious target, since its variablity is also very high. For the daily price I have not worked on a system for it. I wish I had time enough to write in code a price action model that takes into account all the price chart setup, the market dynamics, the traders sentiment, news, other securities information, etc. The model innacuracy, those 25% of the predictions may tell something. Why has it failed since, which factors made the most probable forecast to be wrong? Why was price at the opposite side of the balance point? How many times does it fail consecutively. This is interesting, the system never failed three consecutive bars virtually (maybe it could fail three times one time over one thousand). It usually failed only one bars and the next the prediction was always right also 70-80% of the times, I do not remember exactly. So, if you are tracking a few thousand securities, you may detect which predictions failed two times consecutively and bet that next bar's prediction is right almost without doubt. What happened those days that the model failed? For instance, I performed some basic statistical analysis and found out that the average variability of price the days that the model failed was about 5 times lower than the days the model was accurate. So, this means that for very low price change (short bars), the immediate price change is much more uncertain than for bars with regular or high price variability. It also tells that if you ignore very small bars you are likely to perform good entries. There are certain programming languages like Python or R that are especially appropriate for mathematical and statistical analysis. You can perform advanced analytics with a few lines, the analytical methods are already implemented so you only have to understand them, you can easily use GPU and distributed computing, etc. When I was fine-tuning the model I found that most of the papers published were not actually providing the details of the development done by the authors; that's also why I decided to write a short but practical article and what it is most important, with source code and the data set used. Regards.
Those who have knowledge, don't predict. Those who predict, don't have knowledge. Lao Tzu The markets have always had many Casandra's that believed they could predict the market. History has shown none to date were any more accurate than flipping a coin. Most traders cant even predict what they will do next.
Lao Tzu and many like him weren't traders in a dynamic modern age, they were vague philosophers of ancient wars and other slower, nearly cosmic events of life and people. Which all still hold true to some degree and extent, but within a different collective environment. Basically what I'm saying is the market is essentially part art, part science...and I feel like eating fish and chips...High-Five`
You’re on a productive path. The third bar contained within the high and low of the first bar defines a lateral. A lateral exists until there are two closes outside it’s boundary. The bar that Initially breaks out of the lateral (of the two bar close criteria) is the lateral definition. Ie lat32 contains 25 more bars than lat7. Laterals can contain laterals. To trade inside a lateral is like the black diamond trails, thrilling, exhilarating and requiring a heighten degree of concentration definitely not for beginners. It requires a different logic than PA not contained within a lateral. The fails of the system also have labels. There are two distinct labels in the same fail set. This set is different than the label that defines what’s true. There are 54 distinct true labels. Yet all these labels both true and false are also of the same overarching Set. This Set includes all distinct yet finite possibilities, therefore it defines all possibilities - both the true and the false. As you build your model, it’s accuracy is dependent on how coupled it is to the grandularity of market information. Back testing has a limited usefulness. Technical indicators are formulations of ohlcv Time series. Therefore they are derivatives of what has already been defined. Foreword testing has a present time usefulness. It operates on negative logic. One can know a thing by knowing what it is not. It’s logical construction by it’s very nature is distinct from back testing. Forward testing has anticipatory usefulness when the results are logged. The log will unveil an ever-repeating sequence of events in the progression of trend. Understanding this difference is the crux from backtesting to forward testing in implementing a model for successful production.
I am probably too stupid as I cannot predict. At the opening I know what I should do based on statistics about patterns. So at the last minute. I also know in long term how my chances are to be correct. And it is funny, but I beat friends who try to predict, over and over again. They predict and wait till their prediction is fullfilled or they get stopped out. To me it looks like they don't predict but gamble. And watching their stats confirm my opinion. Predictions should be confirmed with spectacular stats, if not it was a lucky shot (or even a few in a row). I follow my statistics and if wrong immediatelly act based on the changes of the patterns. And follow again my stats as in long term they are right, or at least give me the best result I can achieve.
Everyone trading is trying to predict, the only difference is that a machine is simply better than a human being doing it. In order to prepare a real automated trading system you should just be able to write in mathematical (or algorithmic) language your thoughts and how you evaluate what you see. So the difference is that implementing an automated trading system is quite complex. For us it's easy to draw in our minds all the support/resistance areas, the patterns, trends, relative movements, etc. But doing it programatically is another stuff and there is no one unique approach, and none of those approaches is easy. Regarding the idea and the comment that price movements are random and cannot be predicted is completely false. Otherwise, how could a system predict more than 50% of price changes for every security tested over years of data? If it would have been a random process, the prediction accuracy would have been 50% in average. This is more than obvious. The current state of IA offers many possible approaches. One that seems quite promising is reinforcement learning, a type of methodology that learns without instructions, given a set of allowed actions. Here is a paper about it taken randomly from Google: https://web.stanford.edu/class/cs221/2017/restricted/p-final/sk3391/final.pdf Tip: start reading the conclusion it's always the more interesting part.
Thank you for sharing. It is the first time I see it. It seems to be a strategy parser that converts features extracted from the users definitions into predictors to minimize probably one or several loss functions using deep networks. I have done similar works using Tensorflow and Keras, both of them deep learning libraries. I find those a bit slow even directing computer resources into GPU-processing. That's why I finally ended up using gradient boosting machines. Another good point I see is that the DLPAL integrates with commercial software. I have found challenging, for instance, wrapping over metatrader. I did it most often using MT4API and C# to manage all the instances, but it was very prone to failure. The strategy shown on the page, however will not be profitable because it is not taking into account: - Volatility and ROC - Relative volatility and ROC - Volume. - Support and resistance bands. - I think it is not using trailing SL/TP neither. - It uses a fixed ratio for TP levels iof taking into account factors such as the relative displacement of price within the bar against the other bars, price levels, moving averages, etc.. - Inter-bar direction of movement analysis. - etc... Now that we talk about it, have you ever tested a system trained with Japanese candlesticks? I have done it but I have never found a statistical correlation between Japanese c. patterns and price change. Regards.
Just as a few general thoughts: -- You probably want to re-test your results on a return stream that's market neutral and adjusted for dividends -- I am usually very skeptical of ML applications to trading strategies in terms of generating alpha factors because of the overfitting concerns (especially unsupervised ML) -- If you are going down the ML path with EOD data, I suggest looking for external alpha factors instead of focusing on price/volume