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
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (John P.
If CitEc recognized a reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item.
PREDICTION OF THE REACTOR VESSEL WATER LEVEL USING FUZZY NEURAL NETWORKS IN SEVERE ACCIDENT CIRCUMSTANCES OF NPPS
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 .
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 .
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 .
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.
- On Saturday, January 19, 2019
Lecture 3 | Loss Functions and Optimization
Lecture 3 continues our discussion of linear classifiers. We introduce the idea of a loss function to quantify our unhappiness with a model's predictions, and ...
Neural networks tutorial: Fully Connected 5 [Java] - Network Tools
Download link: Again I messed up something with my sound quality. Hope you still understand what I mean :
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
FNN: Sharon Stone Visits PHX Barrow Neurological Institute to Welcome Dr. Lawton Who Saved Her Life
Brought to you by Desert Diamond:
Honglak Lee - Learning Disentangled Representations with Action Conditional Future Prediction
NIPS 2016 Spotlight - Unsupervised Learning for Physical Interaction through Video Prediction
Multi-label Classification with scikit-learn
The challenge: a Kaggle competition to correctly label two million StackOverflow posts with the labels a human would assign. The tools: scikit-learn, 16GB of ...
Time Series Data Mining Forecasting with Weka
I am sorry for my poor english. I hope it helps you. when i take the data mining course, i had searched it but i couldnt. So i decided to share this video with you.
Sharon Stone Reveals What Kept Her Fighting After Health Scare and Child Custody Loss
The 57-year-old actress opened up to ET about what kept her motivated after having a stroke and losing custody of her son.
Fully Connected or Dense Layers - Deep Learning with Python