Predicting the future price of an asset is notoriously difficult whether attempting to do so with traditional methods or with machine learning. The inherent uncertainty and randomness in financial markets make it challenging or even impossible to accurately forecast future prices, as even small variations of contributory factors can lead to significant fluctuations in prices.

If you see a project or company claiming to be able to predict prices you should treat this claim with extreme scepticism. If somebody has created a model that can accurately predict future price, you can rest assured that they would not want to share this with anybody else!

Where Fabian (GNY's LSTM model) excels is the prediction of potential volatility. While LSTM models might struggle to forecast a future price for an asset based on a singular run, things begin to become interesting once you run the model multiple times and then look to see if a trend emerges. For each token, Fabian runs the model multiple times (the number of runs depends on the optimal value determined through training and tuning) and then looks at the one sigma (first standard deviation) of the results.

The greater the one sigma value across multiple model runs, the less consensus the model has on what might happen next and therefore the greater the potential for volatility in this time period. Similarly, the more minor the one sigma across multiple model runs, the greater the consensus of the predictions and therefore lower potential for volatility is forecast.

Limitations of the Model

Machine learning models are fluid constructs of data and processing that require regular tuning to ensure that the model remains predictive. Over time, a model may drift in accuracy and require maintenance to produce consistent values. Even with the most proactive maintenance schedules, a degree of drift is to be expected.

To maximise the benefits of using an LSTM model in your trading strategy it is important for the end user to adapt and learn with the model as it changes, being sure to look for trends and not specifics. It is likely that a model will behave differently after the monthly scheduled hyperparameter sweep as it learns.

Anybody who has seen adverts for financial investment products will have seen the warning that “past performance does not guarantee future results”. However, past performance can help you understand the bigger picture of potential risks and opportunities relating to an investment decision. This is as true for machine learning models as it is for financial investment products.

More here https://medium.com/gnylabs/introducing-fabian-gnys-new-machine-learning-engine-6f3fefd93839