Learn and implement quantitative finance using popular Python libraries like NumPy, pandas, and Keras Key Features Understand Python data structure fundamentals and work with time series data Use popular Python libraries including TensorFlow, Keras, and SciPy to deploy key concepts in quantitative finance Explore various Python programs and learn finance paradigms Book Description Python is one of the most popular languages used for quantitative finance. With this book, you'll explore the key characteristics of Python for finance, solve problems in finance, and understand risk management. The book starts with major concepts and techniques related to quantitative finance, and an introduction to some key Python libraries. Next, you'll implement time series analysis using pandas and DataFrames. The following chapters will help you gain an understanding of how to measure the diversifiable and non-diversifiable security risk of a portfolio and optimize your portfolio by implementing Markowitz Portfolio Optimization. Sections on regression analysis methodology will help you to value assets and understand the relationship between commodity prices and business stocks. In addition to this, you'll be able to forecast stock prices using Monte Carlo simulation. The book will also highlight forecast models that will show you how to determine the price of a call option by analyzing price variation. You'll also use deep learning for financial data analysis and forecasting. In the concluding chapters, you will create neural networks with TensorFlow and Keras for forecasting and prediction. By the end of this book, you will be equipped with the skills you need to perform different financial analysis tasks using Python
2024-07-28 12:22:48 12.44MB Python Finance TensorFlow Keras
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金融机器学习
2024-03-05 14:51:16 5.04MB JupyterNotebook
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Configuring SAP S4HANA Finance.pdf
2024-01-26 09:06:20 22.41MB
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Python is widely practiced in various sectors of finance, such as banking, investment management, insurance, and even real estate, for building tools that help in financial modeling, risk management, and trading. Even big financial corporations embrace Python to build their infrastructure for position management, pricing, risk management, and trading systems.
2023-06-30 19:21:26 4.57MB python
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股票买卖最佳时机leetcode Kinito.Finance Kinito 金融知识库 点安装 install -U arrow yfinance matplotlib Flask-Moment Delorean finviz pyarrow timeseries pandas-datareader Moments times moment sklearn seaborn flask freezegun bs4 bokeh 网页抓取 中等文章链接: 绘图库 Matplotlib 教程: 技术指标 移动平均收敛散度 - MACD(滞后) 横向市场上的许多误报,与其他人一起使用 典型的由 3 个时期制成: 12 缓慢移动平均 26 快速移动平均线 9 信号 平均真实利率和布林带(波动率) 布林带由来自 M 个周期 (20) 移动平均线的 N 条线 (2) 组成。 Delta 在高波动性和低波动性期间做空 摆动运动时的 ATR 范围透视 相对强弱指数 - RSI 动量振荡 [0,100] [70,100] 超买(新兴市场使用 80) [0,30] 超卖(新兴市场使用 80) 平均方向指数
2023-05-15 19:07:52 204.85MB 系统开源
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在线交易中的欺诈检测:使用欺诈检测比率小于0.00005的Anamoly检测技术(例如过采样和欠采样)来检测在线交易中的欺诈,因此,仅应用分类算法可能会导致过度拟合
2023-04-15 16:13:06 287KB finance machine-learning query deep-learning
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用户购买消费金融场景中的预测 参加2018年招行金融预测比赛 1,从数据预处理,到特征工程,到模型预测均在py文件中; 2,单模型0.860; 3,最终通过融合进入决赛; 4,成绩不够好,望大佬们莫嘲笑。
2023-03-12 13:07:20 4KB 系统开源
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SAP Central Finance 培训资料
2023-02-23 23:08:47 59.77MB SAP centralfiance
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金融机器学习 这是发布的《 的代码库。 它包含从头到尾完成本书所必需的所有支持项目文件。 关于这本书 面向金融的机器学习探索了机器学习的新进展,并展示了如何将其应用于金融领域。 它解释了主要机器学习技术背后的概念和算法,并提供了用于自己实现模型的示例Python代码。 如何执行这段程式码 此存储库中的代码计算量很大,最好在支持GPU的计算机上运行。 数据科学平台提供免费的GPU资源以及免费的在线Jupyter笔记本。 要在Kaggle笔记本上进行编辑,请单击“叉子”以创建笔记本的新副本。 您将需要一个Kaggle帐户。 或者,您可以只在上笔记本或下载代码并在本地运行。 第1章-从零开始的神经网络 从Scratch&Intro到Keras的神经网络: , 练习excel表格: 第2章-结构化数据 信用卡欺诈检测:, 第3章-计算机视觉构建基块 MNIST数字分类:在Kaggle上运行,
2023-02-22 11:27:25 2.7MB JupyterNotebook
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协方差矩阵的估计 两种方法的实现(Python) “股票收益协方差矩阵的改进估计及其在投资组合选择中的应用/ Ledoit and Wolf 2001”( “大尺寸协方差矩阵的直接非线性收缩估计/ Ledoit and Wolf 2017”
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服务器状态检查中...