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.

Forecasting groundwater level by artificial neural networks as an alternative approach to groundwater modeling

The main purpose of this article is to apply feed forward back propagation neural network (FNN) to predict groundwater level of Aghili plain, which is located in southwestern Iran.

Rain, evaporation, relative humidity, temperature, discharge of irrigation canal, and groundwater recharge from the plain boundary were used in input layer while future groundwater level was used as output layer.

The achieved results of ANN model in contrast with results of finite difference model showed very high accuracy of artificial neural network in predicting groundwater level.

Neuro-Fuzzy Hybrid System - Soft Computing ~xRay Pixy

If you want to download PPT than you can easily download it now from: I recreated this video on NFHS with improvement in voice and stuff

NIPS 2016 Spotlight - Unsupervised Learning for Physical Interaction through Video Prediction

Honglak Lee - Learning Disentangled Representations with Action Conditional Future Prediction

FNN: Sharon Stone Visits PHX Barrow Neurological Institute to Welcome Dr. Lawton Who Saved Her Life

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