How to Explore Historical Data Patterns with Machine Learning

The ability to render historical data into comprehensive models has added solidity to many areas, and when we consider trading, it has left a giant mark and continues to grow. It’s as if you have many eyes and decades of hard-earned experience to achieve wonders and gains.

However, mining data alone is not enough to produce greens. It must be applied to raw data in real time and must constantly derive meaning from new inputs. And this is where data mining meets machine learning and moves from model deployment techniques and tools to training machines on historical data to find deviance, next moves, to predict breakup hand-written instructions into something original.

The ML can be of different types, i.e.

  1. Supervised teaching: In this method, the machine is trained to achieve the desired results/solutions; however, humans collect, label the data, and then enter it into systems, overseeing the flow.
  2. Unsupervised learning: As the name suggests, no monitoring is required. The machine will learn to identify patterns and trends from unlabeled training data.
  3. Semi-supervised learning: A combination of supervised and unsupervised learning, however, very little labeled data is added to the machine, and it will contain a larger volume of unlabeled data.

Additionally, this is empowered with Classification (where algo assigns labels)grouping (distribution of data according to similarity)Regression (algo that finds the relationship between variables), and packing with the types of ML mentioned above, it is possible to determine trends and recurring data patterns.

When it comes to the stock market, the chart patterns below are popular because the historical data pattern suggests that they reoccur most of the time when there isn’t much manipulation by SmartMoney.

  1. head and shoulders
  2. Double top
  3. Double bottom
  4. Ascending triangle
  5. descending triangle
  6. Symmetric triangle
  7. Corners
  8. Rounded bottom
  9. Cup and handle
  10. pennant or flags

Now let’s move on to an important question, although many indicators can determine the above chart patterns, they still will fail to make any gains, because SmartMoney also knows about the anomaly and they will try to infiltrate to make the pattern invalid chart or drop it to make investors think otherwise. This is where ML comes into play. Even whales can’t totally break the TA without affecting their activity and goals, therefore any trickery or manual deviations can be handled with variable historical data models.

Validating with an insufficient amount of historical data or not using historical quotes for the required pairs may produce inaccurate calculations or even errors. And to develop such ML algo or train a model from scratch, to create training, validation and test sets, it is best to consult experts due to the complexity involved. To get the accurate and flawless historical data of longer time verification Nasdaq repositoryetc

And what to love communities such as MQL5 is that if you have decided to use the trading robot, you can use the power of the strategy tester to feed the historical data, get a complete understanding and verify the accuracy that matters. Also, using the power of backtesting with bad historical data, advanced testing, complex money management rules, external parameter analysis, stress testing, all of this and more can be an integral part of a healthy trading system that could have a higher ratio of predictive ability. .

When testing historical data models with ML, important principles boil down to

a) The more data, the deeper the system’s ability to predict

b) Max case per variable

c) Don’t let a few crucial dimensions dominate the model

d) Over-fitting the data, i.e. the model produces a great result on the data you fed, but fails or drastically decreases earning power with the new data

Conclusion

To make the trend a friend, make smart use of historical data patterns; you can even fine-tune the accuracy of existing logic. Use the two provided brokers and also use external data; the more there are, the better the quality of the model. And also test with clean historical data i.e. no gaps, peaks or missing bars for optimal predictions. With clean data, ML or neural networks will bring winning strategies by understanding the hidden variables and paths leading to big profits.

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