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Apply machine learning algorithms in the financial market. Ensemble Model, including XGBoost, LightGBM, CNN, ResNet and LSTM.
使用BP神经网络和LSTM预测股票价格
(陆续更新)重新整理过的基于机器学习的股票价格预测算法,里面包含了基本的回测系统以及各种不同的机器学习算法的股票价格预测,包含:LSTM算法、Prophet算法、AutoARIMA、朴素贝叶斯、SVM等
This project accelerates CNN computation with the help of FPGA, for more than 50x speed-up compared with CPU.
AI模型序列化总结
Use unsupervised and supervised learning to predict stocks
基于Keras的LSTM多变量时间序列预测
resources for graph convolutional networks (图卷积神经网络相关资源)
Quant/Algorithm trading resources with an emphasis on Machine Learning
深度学习近年来关于神经网络模型解释性的相关高引用/顶会论文(附带代码)
基于历史数据预测期货的波动率,用于期货的辅助分析,具备模型的核心代码
A python program to implement the discrete binomial option pricing model
基于C++,使用BP神经网络识别手写字体
基于BP神经网络的高频金融时间序列分析 (毕设)
基于BP神经网络的高频金融时间序列分析
BP神经网络+Kalman滤波器融合预测模型(预测点坐标)
开放式的缠论python实现框架,支持形态学/动力学买卖点分析计算,多级别K线联立,区间套策略,可视化绘图,多种数据接入,策略开发,交易系统对接;
基于缠中说禅所讲缠论理论,以便量化分析市场行情的工具
Channel break out strategy for High Frequency Trading.
经典BP神经网络预测上证指数(**股市)
A minimalist, low-latency, HFT CME MDP 3.0 C++ market data feed handler implementing all required features
该项目利用卷积神经网络算法识别股票技术图线,步骤包括图像生成、图像标注、模型学习
使用卷积神经网络-长短期记忆网络(bi-LSTM)-注意力机制对股票收盘价进行回归预测。The convolution neural network, short-term memory network and attention mechanism are used to predict the closing price.
基于真实业务上手数据挖掘(银行流失预警):数据的处理、LightGBM、skLearning包(里面含有:GridSearchCV寻找最优参、StratifiedKFold分层5折切分、_test_Split单次数据切分等)、叠层模型融合、画AUC图、画混淆矩阵图,并输出预测名单。
Keras implementation of CNN, DeepConvLSTM, and SDAE and LightGBM for sensor-based Human Activity Recognition (HAR).
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
本平台利用Google推出的深度学习框架TensorFlow基于CNN(卷积神经网络)、RNN(循环神经网络)、LSTM(长短期记忆模型)的若干算法和API构建文本分类、文本相似度计算和文本内容摘要这三个具体应用。本平台将数据特征处理、深度学习模型构建和预测的流程进行了良好的封装,用户仅需几步简单的页面操作即可完成一项复杂、艰巨的深度学习任务,在NLP领域需要针对性的语料库,在构建具体的智能应用时需要使用者提供相应的预料数据。
深度学习以进行时间序列预测
神经网络股价预测
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