三、实践方案 此部分将主要基于one-hot编码与Embedding两种方案进行Kaggle NIFTY-50 Stock Market Data(2000-2020)示例实战。由于量化投资与机器学习已经很好的基于tensordlow对于Embedding进行了示例,因此这里不做过多的展开,仅进行列示,附录进行了链接的展示。 3.1 tensordlow+emdeding 原文中进行了emdeding层的数据展...
市场数据(Market data)由Intrinio提供。这家公司应该是一间小公司,Real-Time Financial Market Data Provider ...,进去主页就能看到我最爱的斯坦福的标志,肯定是一间厉害的小公司,公司网址:https://intrinio.com/。 新闻数据(News data)由Thomson Reuters提供。汤森路透是一家大型跨国传媒集团,总部在加拿大多伦多。平...
kaggle - "Deep Reinforcement Learning on Stock Data" 을 위해, 우선 특정 주식 데이터를 두고 RL 모델을 단계별로 적용해 보고자 합니다. (기초: SKKU RL 팀프로젝트 결과물) ...
零基础参赛Kaggle竞赛历程(1):JPX Tokyo Stock Exchange Prediction 本次kaggle官网上的金融相关的商赛是东京市场的股票交易预测,对于接下来打算进军股票量化市场市场的同学是一个比较好的练手机会。 规则还是熟悉的味道:团队规模3-5人,每天最多提交5次代码 Training Timeline April 4,2022-Start Date June 28,2022-...
You can view and download the dataset fromKaggle. Exploring the dataset SNES consists of two files: 1. sp500wiki.csv/sp500wiki.parquet:List of S&P 500 companies as of July 2022 and various metadata in tabular format. 2. data.csv/data.parquet:The main dataset contain...
S&P 500 stock data is a stock dataset available on the data competition platform, Kaggle. The dataset, sourced from Standard & Poor's, a renowned financial services company, provides information about the S&P 500 index, an essential indicator of the US stock market performance.The ...
First, we get the S&P500 intraday trading data from Kaggle, then we calculate technical indicators and finally, we train the regression Long-Short Term Memory model. Based on the price history, alongside technical analysis indicators and strategies, this model is executed, and the results are ...
In this article, the data has been collected from kaggle.com. The historical data from the year 1996 to 2017 were taken in to account for analysis. The BoxJenkins methodology(ARIMA model) is trained and predicted the stock prices on the test dataset. ...
原文档可以看这里:Stock Market Analysis + Prediction using LSTM | Kaggle In this notebook, we will discover and explore data from the stock market, particularly some technology stocks (Apple, Amazon, Google, and Microsoft). We will learn how to use yfinance to get stock information, and visual...
test_data = scaled_data[training_data_len - 60:, :] x_test = [] y_test = dataset[training_data_len:, :]foriinrange(60, len(test_data)): x_test.append(test_data[i-60:i, 0]) x_test = np.array(x_test) x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[...