AI News, Machine Learning for Intraday Stock Price Prediction 2: Neural Networks

Machine Learning for Intraday Stock Price Prediction 2: Neural Networks

This is the second of a series of posts on the task of applying machine learning for intraday stock price/return prediction.

In this post, we will focus on applying neural networks on the features derived from market data.

While h(x) was a linear model in the last post, it is a feed forward neural network in this case.

Some of things I learned while optimizing the above model: The ability to model multiple tasks together is a really good advantage of using a neural network.

Our hypothesis is that the feature vectors contain enough information to be able to predict multiple securities.

A popular method is to send a limit buy order if the prediction signal from the model is more than certain threshold.

If the signal falls below the threshold after some time, we can choose to keep or cancel the order.

Similarly, send a limit sell order if the prediction signal is below a certain threshold on the negative side.

For example, you’re trading AAPL stock and your model includes AAPL, MSFT, GOOGL, FB and AMZN, you might want to continuously stream each new/cancel order event as well as all the trades happening real time.

The following would be the rough python code for such a system: At this point, it’s important to note that the above function has certain parameters that affect the trading.

A relatively lower frequency trading system might be able to utilize better pipelining of feature computation as well as more complex models.

Comparing Functional Link Artificial Neural Network And Multilayer Feedforward Neural Network Model To Forecast Crude Oil Prices

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In this study, the reactor vessel water level under the condition of a severe accident, where the water level could not be measured, was predicted using a fuzzy neural network (FNN).

The developed FNN model was sufficiently accurate to be used to predict the reactor vessel water level in severe accident situations where the integrity of the reactor vessel water level sensor is compromised.

Feed-Forward Neural Network Soft-Sensor Modeling of Flotation Process Based on Particle Swarm Optimization and Gravitational Search Algorithm

Flotation is the process which is based on the differences of the surface property of solid materials to separate useful minerals and gangue by means of the buoyancy of air bubbles from ore pulp by this method to improve the concentrate grade [1].

Process control indicators of domestic flotation process are mainly based on an experienced operator to observe the information (such as foam color, size, flow rate, and texture features) which is provided by the bubble state formed on the surface of the flotation tank and to adjust the flotation level and change agents system [2, 3].

use the flotation froth video image features as auxiliary variables and establish a soft-sensor model of the flotation pulp pH value based on the sparse polynuclear least squares support vector machine (SVM) and use Schmidt orthogonalization theory to reduce the multinuclear matrix [10].

Geng and Chai utilized least squares support vector machine to establish soft-sensor model of concentrate grade and tailing grade in the flotation process based on analyzing related influencing factors of concentrate grade and tailing grade of the flotation process technology indicators [14].

This paper proposes a feed-forward neural network (FNN) soft-sensor model by using process datum in the flotation process for predicting the flotation concentrate grade and recovery rate, which is optimized by the PSO-GSA algorithm.

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