The possibility to render historical data into comprehensive patterns has added soundness to many areas, and when we consider trading, it has left a giant mark and keeps growing. It is as if you’ve numerous eyes and decades of hard-earned experience to execute wonders and gains.
However, mining the data alone isn’t enough to produce greens. It needs to get applied to real-time raw data, and it should constantly derive meaning from the new inputs. And this is where data mining meets machine learning and moves from techniques and tools for unfolding patterns into training machines over historical data to find deviance, next moves, to predict breaking out from the manually written instructions into something original.
ML can be of different types, i.e
- Supervised learning: In this method, the machine is trained to get the desired results/solutions; however, humans collect, label the data, and then input it into systems, supervising the flow.
- Unsupervised learning: As the name states, no supervision is required. The machine will learn to identify patterns and trends from not labeled training data.
- Semi-supervised learning: A combination of both supervised and unsupervised learning, however, very little labeled data is added to the machine, and it’ll contain a bigger volume of unlabeled data.
Further, this is empowered with Classification (where algo assigns labels), clustering (splitting data according to similarity), Regression (algo which finds the relationship between variables), and packing with the above-mentioned types of ML, it is possible to determine the recurring data trends and patterns.
When it comes to the stock market, the chart patterns below are popular as the historical data pattern suggests them to recur most of the time when not much manipulation by SmartMoney is witnessed.
- Head and shoulders
- Double top
- Double bottom
- Ascending triangle
- Descending triangle
- Symmetrical triangle
- Rounding bottom
- Cup and handle
- Pennant or flags
Now moving to an important question, though many indicators can determine the above chart patterns, they will still fail to make the gains, as SmartMoney too know the anomaly and they will try to infiltrate to make the chart pattern invalid or tumble it down to make investors think the opposite. This is where ML comes into the picture. Even whales can’t totally break the TA without impacting their business and goals, hence all the trickery or the manual deviation can be tackled with historical data patterns variable.
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 testing sets, it is best to consult experts because of the complexity involved. To get the accurate and flawless historical data of longer time check Nasdaq repository, etc.
And what should be liked about communities such as MQL5 is that if you have decided to go with the trading robot, you can utilize the power of the strategy tester to feed historical data, get a complete grasp and check the accuracy that matters. Also, using the power of backtesting with bad historical data, Forward Testing, intricate money management rules, analyzing external parameters, stress testing, all this and more can be an integral part of a wholesome trading system that could have a higher ratio of predictive capability.
While testing historical data patterns with ML, important principles come down to
a) More the data, the deeper system’s capacity to predict
b) Max cases per variable
c) Don’t let a few crucial dimensions dominate the model
d) Over-fitting data i.e the model produces a great result on the data that you have fed, but fails or decreases gain capacity drastically with the fresh data
To make the trend a friend, use historical data patterns smartly; you can even sharpen the accuracy of existing logic. Use both brokers provided, and also use external data; the more the better model quality. And also test with neat historical data i.e with no gaps, spikes, or missing bars for optimal predictions. With uncluttered data ML or neural networks will bring about winning strategies by understanding variables, and hidden paths directing lofty profits.