A recurrent neural network for predicting stock market performance
Project description
AlphaNetV3
A Recurrent Neural Network For Predicting Stock Prices
Requirement: tensorflow 2.
- The model and data utility are in
./alphanet/
folder. - CSI500 and CSI800 stock market data are included in
./data/
folder. - Model Configuration is in
config.py
. - To run the model, execute
python main.py
. - The loss plot pictures and models will be stored by each epoch and training period in
./models/
folder. - Tests of layers and time series data tools are in
./tests/
folder. To run the test, executepython -m unittest tests.tests
.
Installation
Either clone this repository or just use pypi:
pip install alphanet
.
The pypi project is here: https://pypi.org/project/alphanet/.
API Overview
Modules
alphanet
: 复现华泰金工 alpha net V3 版本.alphanet.data
: 多维多时间序列神经网络滚动训练数据工具箱.alphanet.metrics
: 训练的计量信息.
Classes
alphanet.AlphaNetV3
: alpha net v3版本模型.alphanet.Correlation
: 每个stride各个时间序列的相关系数.alphanet.Covariance
: 每个stride各个时间序列的covariance.alphanet.FeatureExpansion
: 时间序列特征扩张层.alphanet.LinearDecay
: 每个序列各个stride的线性衰减加权平均.alphanet.Return
: 每个序列各个stride的回报率.alphanet.Std
: 每个序列各个stride的标准差.alphanet.ZScore
: 每个序列各个stride的均值除以其标准差.data.TimeSeriesData
: 单个时间序列信息.data.TrainValData
: 该类用于生成不同训练阶段的tensorflow dataset.metrics.UpDownAccuracy
: 通过对return的预测来计算涨跌准确率.
Functions
alphanet.load_model
: 包装tf.kreas
的load_model
,添加UpDownAccuracy
.
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