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Python金融大数据分析

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Python金融大数据分析

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金融行业已经以惊人的速度采用Python，一些大的投资银行和对冲基金使用Python来构建核心的交易和风险管理系统。本书可以帮助开发人员和量化分析人员入门Python，并指导他们掌握Python在计量金融学上的重要应用。
本书通过大量的实用示例并以一个大型的真实案例研究为基础，讲解如何为基于蒙特卡洛模拟的衍生品和风险分析开发一个成熟的框架。本书大部分内容使用了交互式的IPython Notebooks，并包含了如下主题。
基础知识：Python数据结构，NumPy数组处理、用pandas进行时间序列分析，用matplotlib可视化，用PyTables进行高性能I/O操作，日期/时间信息处理和精选的实践。
金融主题：使用了NumPy、SciPy和SymPy的数学技术，例如回归和优化；用于蒙特卡洛模拟、风险价值、风险信用价值计算的推断统计学；用于正态性检验、均方差投资组合优化、主成分分析（PCA）和贝叶斯回归的统计学。
特殊主题：用于金融算法的高性能Python，如向量化和并行化；Python与Excel的集成；以及构建基于Web技术的金融应用程序。
Content Description

Python凭借其简单、易读、可扩展性以及拥有巨大而活跃的科学计算社区，在需要分析、处理大量数据的金融行业得到了广泛而迅速的应用，并且成为该行业开发核心应用的编程语言。《Python金融大数据分析》提供了使用Python进行数据分析，以及开发相关应用程序的技巧和工具。
《Python金融大数据分析》总计分为3部分，共19章，第1部分介绍了Python在金融学中的应用，其内容涵盖了Python用于金融行业的原因、Python的基础架构和工具，以及Python在计量金融学中的一些具体入门实例；第2部分介绍了金融分析和应用程序开发中重要的Python库、技术和方法，其内容涵盖了Python的数据类型和结构、用matplotlib进行数据可视化、金融时间序列数据处理、高性能输入/输出操作、高性能的Python技术和库、金融学中需要的多种数学工具、随机数生成和随机过程模拟、Python统计学应用、Python和Excel的集成、Python面向对象编程和GUI的开发、Python与Web技术的集成，以及基于Web应用和Web服务的开发；第3部分关注的是蒙特卡洛模拟期权与衍生品定价实际应用的开发工作，其内容涵盖了估值框架的介绍、金融模型的模拟、衍生品的估值、投资组合的估值、波动率期权等知识。
《Python金融大数据分析》适合对使用Python进行大数据分析、处理感兴趣的金融行业开发人员阅读。
Author Description

Yves Hilpsch，是Python Quants（德国）股份有限公司的创始人和任事股东，也是Python Quants（纽约）有限责任公司的共同创办人。该集团提供基于Python的金融和衍生品分析软件（参见https://pythonquants.com，https://quant-platfrom.com和https://dx-analytics.com），以及和Python及金融相关的咨询、开发和培训服务。
Yves还是Derivatives Analytics with Python（Wiley Finance，2015）的作者。作为获得数理金融学博士学位的商业管理专业研究生，他在萨尔州大学讲授计算金融学中的数值化方法课程。
Catalogue

第1部分 Python与金融
第1章 为什么将Python用于金融 3
1．1 Python是什么 3
1．1．1 Python简史 5
1．1．2 Python生态系统 5
1．1．3 Python用户谱系 7
1．1．4 科学栈 7
1．2 金融中的科技 8
1．2．1 科技开销 9
1．2．2 作为业务引擎的科技 9
1．2．3 作为进入门槛的科技和人才 9
1．2．4 不断提高的速度、频率、数据量 10
1．2．5 实时分析的兴起 11
1．3 用于金融的Python 12
1．3．1 金融和Python语法 12
1．3．2 Python的效率和生产率 15
1．3．3 从原型化到生产 19
1．4 结语 20
1．5 延伸阅读 20
第2章 基础架构和工具 21
2．1 Python部署 22
2．1．1 Anaconda 22
2．1．2 Python Quant Platform 27
2．1．3 工具 30
2．1．4 Python 30
2．1．5 IPython 30
2．1．6 Spyder 40
2．2 结语 42
2．3 延伸阅读 43
第3章 入门示例 45
3．1 隐含波动率 46
3．2 蒙特卡洛模拟 54
3．2．1 纯Python 56
3．2．2 用NumPy向量化 57
3．2．3 利用对数欧拉方法实现全向量化 59
3．2．4 图形化分析 60
3．2．5 技术分析 62
3．3 结语 67
3．4 延伸阅读 68
第2部分 金融分析和开发
第4章 数据类型和结构 71
4．1 基本数据类型 72
4．1．1 整数 72
4．1．2 浮点数 73
4．1．3 字符串 75
4．2 基本数据结构 77
4．2．1 元组 77
4．2．2 列表 78
4．2．3 离题：控制结构 80
4．2．4 离题：函数式编程 81
4．2．5 字典 82
4．2．6 集合 84
4．3 NumPy数据结构 85
4．3．1 用Python列表形成数组 85
4．3．2 常规NumPy数组 87
4．3．3 结构数组 90
4．4 代码向量化 91
4．5 内存布局 93
4．6 结语 95
4．7 延伸阅读 95
第5章 数据可视化 97
5．1 二维绘图 97
5．1．1 一维数据集 98
5．1．2 二维数据集 103
5．1．3 其他绘图样式 109
5．2 金融学图表 116
5．3 3D绘图 119
5．4 结语 122
5．5 延伸阅读 122
第6章 金融时间序列 123
6．1 pandas基础 124
6．1．1 使用DataFrame类的第一步 124
6．1．2 使用DataFrame类的第二步 127
6．1．3 基本分析 131
6．1．4 Series类 134
6．1．5 GroupBy操作 135
6．2 金融数据 136
6．3 回归分析 142
6．4 高频数据 150
6．5 结语 154
6．6 延伸阅读 154
第7章 输入/输出操作 155
7．1 Python基本I/O 156
7．1．1 将对象写入磁盘 156
7．1．2 读写文本文件 159
7．1．3 SQL数据库 160
7．1．4 读写NumPy数组 162
7．2 Pandas的I/O 164
7．2．1 SQL数据库 165
7．2．2 从SQL到pandas 166
7．2．3 CSV文件数据 168
7．2．4 Excel文件数据 169
7．3 PyTables的快速I/O 170
7．3．1 使用表 170
7．3．2 使用压缩表 175
7．3．3 使用数组 176
7．3．4 内存外计算 177
7．4 结语 179
7．5 延伸阅读 180
第8章 高性能的Python 181
8．1 Python范型与性能 182
8．2 内存布局与性能 184
8．3 并行计算 186
8．3．1 蒙特卡洛算法 186
8．3．2 顺序化计算 187
8．3．3 并行计算 188
8．3．4 性能比较 191
8．4 多处理 191
8．5 动态编译 193
8．5．1 介绍性示例 193
8．5．2 二项式期权定价方法 195
8．6 用Cython进行静态编译 199
8．7 在GPU上生成随机数 201
8．8 结语 205
8．9 延伸阅读 205
第9章 数学工具 207
9．1 逼近法 208
9．1．1 回归 208
9．1．2 插值 218
9．2 凸优化 221
9．2．1 全局优化 222
9．2．2 局部优化 223
9．2．3 有约束优化 224
9．3 积分 226
9．3．1 数值积分 228
9．3．2 通过模拟求取积分 228
9．4 符号计算 229
9．4．1 基本知识 229
9．4．2 方程式 230
9．4．3 积分 231
9．4．4 微分 232
9．5 结语 233
9．6 延伸阅读 233
第10章 推断统计学 235
10．1 随机数 236
10．2 模拟 241
10．2．1 随机变量 241
10．2．2 随机过程 244
10．2．3 方差缩减 256
10．3 估值 259

10．3．1 欧式期权 259
10．3．2 美式期权 263
10．4 风险测度 266
10．4．1 风险价值 266
10．4．2 信用价值调整 270
10．5 结语 272
10．6 延伸阅读 273
第11章 统计学 275
11．1 正态性检验 276
11．1．1 基准案例 277
11．1．2 现实世界的数据 284
11．2 投资组合优化 289
11．2．1 数据 290
11．2．2 基本理论 291
11．2．3 投资组合优化 294
11．2．4 有效边界 296
11．2．5 资本市场线 297
11．3 主成分分析 300
11．3．1 DAX指数和30种成分股 301
11．3．2 应用PCA 301
11．3．3 构造PCA指数 302
11．4 贝叶斯回归 305
11．4．1 贝叶斯公式 305
11．4．2 PyMC3 306
11．4．3 介绍性示例 307
11．4．4 真实数据 310
11．5 结语 318
11．6 延伸阅读 318
第12章 Excel集成 321
12．1 基本电子表格交互 322
12．1．1 生成工作簿（．xls） 323
12．1．2 生成工作簿（．xslx） 324
12．1．3 从工作簿中读取 326
12．1．4 使用OpenPyxl 328
12．1．5 使用pandas读写 329
12．2 用Python编写Excel脚本 332

12．2．1 安装DataNitro 333
12．2．2 使用DataNitro 333
12．3 xlwings 342
12．4 结语 342
12．5 延伸阅读 343
第13章 面向对象和图形用户界面 345
13．1 面向对象 345
13．1．1 Python类基础知识 346
13．1．2 简单的短期利率类 350
13．1．3 现金流序列类 354
13．2 图形用户界面 356
13．2．1 带GUI的短期利率类 356
13．2．2 值的更新 358
13．2．3 带GUI的现金流序列类 360
13．3 结语 362
13．4 延伸阅读 362
第14章 Web集成 365
14．1 Web基础知识 366
14．1．1 ftplib 366
14．1．2 httplib 368
14．1．3 urllib 369
14．2 Web图表绘制 372
14．2．1 静态图表绘制 372
14．2．2 交互式图表绘制 374
14．2．3 实时图表绘制 375
14．3 快速Web应用 383
14．3．1 交易者的聊天室 384
14．3．2 数据建模 384
14．3．3 Python代码 385
14．3．4 模板 391
14．3．5 样式化 396
14．4 Web服务 397
14．4．1 金融模型 399
14．4．2 实现 400
14．5 结语 406
14．6 延伸阅读 406

第3部分 衍生品分析库
第15章 估值框架 409
15．1 资产定价基本定理 409
15．1．1 简单示例 409
15．1．2 一般结果 410
15．2 风险中立折现 412
15．2．1 日期建模和处理 412
15．2．2 固定短期利率 413
15．3 市场环境 415
15．4 结语 418
15．5 延伸阅读 419
第16章 金融模型的模拟 421
16．1 随机数生成 422
16．2 泛型模拟类 423
16．3 几何布朗运动 427
16．3．1 模拟类 427
16．3．2 用例 429
16．4 跳跃扩散 431
16．4．1 模拟类 431
16．4．2 用例 434
16．5 平方根扩散 435
16．5．1 模拟类 435
16．5．2 用例 437
16．6 结语 438
16．7 延伸阅读 440
第17章 衍生品估值 441
17．1 泛型估值类 441
17．2 欧式行权 445
17．3 估值类 445
17．4 美式行权 451
17．4．1 最小二乘蒙特卡洛方法 451
17．4．2 估值类 453
17．4．3 用例 454
17．5 结语 457
17．6 延伸阅读 458
第18章 投资组合估值 459
18．1 衍生品头寸 460
18．1．1 类 460
18．1．2 用例 462
18．2 衍生品投资组合 463
18．2．1 类 463
18．2．2 用例 467
18．3 结语 472
18．4 延伸阅读 474
第19章 波动率期权 475
19．1 VSTOXX数据 476
19．1．1 VSTOXX指数数据 476
19．1．2 VSTOXX期货数据 477
19．1．3 VSTOXX期权数据 479
19．2 模型检验 480
19．2．1 相关市场数据 480
19．2．2 期权建模 481
19．2．3 检验过程 483
19．3 基于VSTOXX的美式期权 487
19．3．1 期权头寸建模 487
19．3．2 期权投资组合 488
19．4 结语 489
19．5 延伸阅读 490
附录A 精选的最佳实践 491
附录B 看涨期权类 499
附录C 日期和时间 503

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Seller will ship the orders within 1-2 business days. The logistics time limit is expected to be 7-15 working days. In case of customs clearance, the delivery time will be extended by 3-7 days. The final receipt date is subject to the information of the postal company.

Yami Points information

All items are excluding from any promotion or points events on Yamibuy.com

Return Policy

You may return product within 30 days upon receiving the product. Items returned must be new in it's original packing, including the original invoice for the purchase. Customer return product at their own expense.

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About the brand

Jingdong book

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About the brand

Jingdong book