金融科技 I (FIN515)近期课程安排

因中秋、国庆假期缺课,又因课程模块集中实践训练,现将近期课程安排统一说明:

  1. 信用管理班 2018.10.15 周一晚,补 :金融科技公司案例分析展示。
    要求: 按小组课堂展示,每组8分钟。
  2. 信用管理班 2018.10.14 周日下午1:30-6:00 ,数据抓取及分析WORKSHOP
    CFA 周三班2018.10.21 周日下午 1:30-6:00 ,数据抓取及分析WORKSHOP
    CFA 周四班2018.10.28 周日下午 1:30-6:00 ,数据抓取及分析WORKSHOP
  3. 第七周,2018.10.22-2018.10.25课程照常,内容暂定为 Case Study – How to Using Python Do Sth Useful
  4. 第九周,信用管理班、CFA周三周四班,集中为2018.11.8周四晚六点半 经世楼E102,课程内容 云计算与大数据
  5. 第十周,照常

702-800-8080

很高兴看到这篇赞赏Jupyter的文章,Jupyter 自2015年从 Ipython 项目中分离出来,已成为开源世界增长最快的项目,如其项目发起人 Fernando Perez 所言,Jupyter 的目标是成为 Open Sciences 的工具。我在自身的使用过程中,越来越感受到 Jupyter的神奇和好处,它从某种意义上讲,不仅仅是一个交互式编程的工具(这类似GitHub不仅仅是Git的一个公共代码仓库),而是一个开放的可以共享的文件格式和协作工作的方式,你可以用 Jupyter来调试代码,写网页,写文章,甚至可以用它来写一本多媒体交互式的书。而开源、开放和多人协作则让远程研究、远程学习成为更加简便的事情,让 Jupyter成为 workflow 的一部分。从这些特点可以看到它的意义将不在限于编程,而是如同Paul Romer所言,” The tie-breaker is social, not technical.”

很高兴我们也在使用 Jupyter 进行我们的学习,很高兴看到越老越多的人们喜欢 Jupyter。

今天的推荐阅读来自今年诺贝尔经济学家得主Paul Romer的博文,他63岁,他会Python,他使用Jupyter。

你呢?

原文链接:/paulromer.net/jupyter-mathematica-and-the-future-of-the-research-paper/

Jupyter, Mathematica, and the Future of the Research Paper

 

The Atlantic has a great 9316754949 on new ways to share research results. Its three parts make three points:

  1. A graphical user interface (GUI) can facilitate better technical writing.
  2. Wolfram’s proprietary notebook showcased innovative technology, but decades after its introduction, still has few users.
  3. Jupyter is a new open-source alternative that is well on the way to becoming a standard for exchanging research results.

Each is spot on. I had to learn the hard way why so many kept their distance from Mathematica. Now, I’m much more productive with Jupyter. I’m experimenting with, and excited about, its potential as a way to write up research results.

The open question

The article asks why Jupyter succeed where Mathematica failed. The obvious contrast is between the proprietary world of Wolfram and the open-source model of the software ecosystem that Jupyter mobilizes.

The Mathematica developers claim that the hierarchy afforded by the proprietary model is a better way to organize innovation. To their credit, Mathematica did open up a huge technical lead in the 1990s. (Pay no attention to the preposterous suggestion that it is still the technological leader.) There are, of course, many offsetting examples of visionaries who succeeded by mobilizing an open-source community. Still, Mathematica’s early lead offers some support for the claim that from the perspective of software engineering, the proprietary model may sometimes have its advantages.

The difference that matters

This technical engineering dimension is not the only one we should use to compare the proprietary and open models. There is an independent social dimension, where the metrics assess the interactions between people. Does it increase trust? Does it increase the importance that people attach to a reputation for integrity?

It is along this social dimension that open source unambiguously dominates the proprietary model. Moreover, at a time when trust and truth are in retreat, the social dimension is the one that matters.

Jupyter rewards transparency; Mathematica rationalizes secrecy. Jupyter encourages individual integrity; Mathematica lets individuals hide behind corporate evasion. Jupyter exemplifies the social systems that emerged from the Scientific Revolution and the Enlightenment, systems that make it possible for people to cooperate by committing to objective truth; Mathematica exemplifies the horde of new Vandals whose pursuit of private gain threatens a far greater pubic loss–the collapse of social systems that took centuries to build.

Membership in an open source community is like membership in the community of science. There is a straightforward process for finding a true answer to any question. People disagree in public conversations. They must explain clearly and listen to those who response with equal clarity. Members of the community pay more attention to those who have been right in the past, and to those who enhance their reputation for integrity by admitting in public when they are wrong. They shun those who mislead. There is no court of final appeal. The only recourse is to the facts.

It’s a messy process but it works, the only one in all of human history that ever has. No other has ever achieved consensus at scale without recourse to coercion.

In science, anyone can experiment. In open source, anyone can access the facts of the code. Linus Torvalds may supervise a hierarchy that decides what goes into the Linux kernel, but anyone can see what’s there. Because the communities of science and open source accept facts as the ultimate source of truth and use the same public system for resolving disagreements about the facts, they foster the same norms of trust grounded in individual integrity.

The answer to the question and the lesson we should learn

So here is my conjecture about the question the article poses. Mathematica failed, despite technical accomplishments, because the norms of its developers clashed so obviously with the norms of its intended users. Jupyter is succeeding because the norms of the community that is developing it are aligned with the norms of its users.

This answer does not give me much comfort. If Steven Wolfram’s personality had made him just a bit better at faking both sincere apologies and sincere promises to do better, things might have turned out differently. The clash might not have been apparent to users until it was too late.

The take-away lessons are not to be seduced by promises of shiny technology from some proprietary initiative, even one that seems to have no strings attached; to ignore the personality of the leader of the proprietary effort; to go with the non-proprietary alternative that is fully committed to the open model; and if it doesn’t exist, create it.

Which reminds me. If you are a Julia enthusiast, how do you suppose the investors in this new language plan to make their big score?

My experience with Mathematica

In 2015, I tried to share some research results in a Mathematica notebook. I knew that Wolfram’s proprietary business model made it difficult for anyone to check many of the assertions it made. I anticipated neither the dishonesty that this would facilitate nor the cost in wasted time that it would impose.

Then, I still clung to the belief that for a for-profit corporation, the risk of damage to its reputation would keep dishonesty in check, just as it did for a person. I interpreted examples of corporate dishonesty the same way that I interpreted instances of scientific fraud, as unrepresentative exceptions. I was slow to recognize that under the proprietary software model, dishonesty isn’t a bug; it’s a feature.

I’ve been revising my expectations, but it’s so hard to keep up. I can remember a time when “You opted-in” meant “We tricked you fair and square.” Now it’s little more than a short-hand for the Bart Simpson defense, “I didn’t do it, no one saw me do it, you can’t prove anything!” I even remember how people once accepted the common law principle that a contract is not complete if its terms and conditions are unclear.

So back in 2015, full of naive optimism, I set out to correct something that was wrong in a published paper. (Yes, I know. It captures the point I am trying to make, that the publications of science are different from content of the internet, which poses a threat that we have been too slow to appreciate.) I needed to present some symbolic calculations to prove that the steady-state approximation that the paper relied on was fatally flawed, and some numerical results, summarized in graphs, which showed that the error it caused was important.

On technical grounds, the Mathematica notebook was the perfect vehicle. It let me interleave typeset text and math with tables and figures that summarized the numerical calculations, and do so in a way that made it easy for anyone to replicate my results. My plan was to distribute a PDF of the static output from one run of the notebook and to invite anyone who wanted to replicate its results to download the notebook and run it using the required Wolfram software.

Now, in my defense, I have to explain that I had used the Mathematica REPL (read, evaluate, print loop) on code and never had any reason to write paragraphs of typeset text as notes to myself. The REPL is quick only if it prints to the screen, so I had rarely tried to print to PDF. (I did save individual graphs as PDFs and this worked just fine.)

This meant that when I embarked on the production of a document that I could share with others, I had not paid any attention to the typography of the typeset text and math in the PDFs that Mathematica generates. As I wrote, the screen version of the notebook interface lived up to its promise; the typeset text and math looked good. But when I tried to print to PDF, I discovered that the built-in article styles had typography that was bad, absurdly bad, so bad that someone must have worked at making it bad. I tried to fix a print style, but gave up. Combinatorial explosion easily overwhelms trial and error via a GUI. I extracted barely acceptable PDF output by making small changes to a screen style and cut my losses.

Wolfram made it hard to share a readable PDF version of a notebook because it wanted someone like me to distribute content in its proprietary file format, the CDF. It offered a free player, analogous to Adobe’s PDF reader, albeit one that required a 1.3 gigabyte download. To keep PDF output from leaking out of Mathematica’s walled garden, this player, like the full Mathematica application, was geared only to on-screen display. The tell that this was an intentional, hidden part of Wolfram’s strategy was that the same people who had been so responsive to other questions when I explored the possibility of using notebooks to share research results, went silent when I asked how to print a PDF with reasonable typography. They knew how. This was how they converted notebooks into articles for their in-house Mathematica Journal. It must surely be how Steven Wolfram produced his books.

Wolfram knew how to do what I wanted to do. It did not want me to be able to do it. It pretended, dishonestly, that I would be able to, and refused, dishonestly, to admit that they did not want me to be able to do it.

I’m happy with Jupyter

I stopped using Mathematica and gave up on notebooks, so it was only recently that I discovered how easy it is to use the Jupyter notebook to as a front end for Python libraries. It offers the best REPL I’ve ever used. It does a better job of delivering what Theodore Gray had in mind when he designed the Mathematica notebook. It lets me get quick feedback, via text or graphics, about what happens when I select a line of code and run it.

Python libraries let me replicate everything I wanted to do with Mathematica: Matplotlib for graphics, SymPy for symbolic math, NumPy and SciPy for numerical calculations, Pandas for data, and NLTK for natural language processing. Jupyter makes it easy to use Latex to display typeset math. With Matplotlib, Latex works even in the label text for graphs. (I have not yet tried the major update, JupyterLab, which is still in beta testing.)

I’m more productive. I’m having fun. On both counts, it helps to be able to get an honest answer when I have a question.

I’m frightened by the Vandals

In the larger contest between open and proprietary models, Mathematica versus Jupyter would be a draw if the only concern were their technical accomplishments. In the 1990s, Mathematica opened up an undeniable lead. Now, Jupyter is the unambiguous technical leader.

The tie-breaker is social, not technical. The more I learn about the open source community, the more I trust its members. The more I learn about proprietary software, the more I worry that objective truth might perish from the earth.

推荐阅读:Monzo: When is a bank not a bank?

原文地址:(508) 493-2649

原文需要注册,所以我把原文抄录如下,方便阅读学习。

金融科技作为一种金融创新,对于金融业会带来什么样的冲击或者机会?银行会如何改变,本文给出了一个案例参考,供大家思考。

Monzo: When is a bank not a bank?

This is the first of two posts on the emerging overlap between banking and social media.

Monzo is frequently hailed as one of the most exciting challenges the tech industry poses to high street banks.

Wikipedia calls it a “digital, mobile-only bank”. Mashable summed it up as the “bank that’s apparently so cool it’s become a chat up line in London’s bars”. Its own stated ambition is to “build a better bank”. In April 2017, it acquired a banking licence.

But does Monzo actually behave like a bank?

Banks, under one interpretation, are economic inventions which create liquidity from an array of otherwise illiquid assets. Douglas Diamond, the US economist, 832-890-6297 a bank as “a lender financed with demand deposits”. Depositors sacrifice higher returns for the privilege of immediately being able to withdraw their capital whenever they want.

The relationship between borrowing and lending is more than a semantic exercise – it drives to the core of what banks do, why they exist in the first place, and how the current conception of a deposit might change in future.

In common with a bank, Monzo does provide demand deposits (it previously offered prepaid cards). Last year, it began offering customers current accounts and, according to its reports as of the end of February, had £71.3m of deposits on the liabilities side of its balance sheet. The assets side of the balance sheet, however, tells a very different story.

Monzo’s deposits are held in overnight accounts at the Bank of England. In fact, as of its latest report, Monzo held £97m at the Bank of England, much more than the sum of its total deposits. This implies that a large portion of equity funding the company has received is currently sitting at the central bank.

The company is expanding – it says that deposits began climbing more quickly in March, and that it is closing in on 1m customers. It adds that its deposits now exceed £200m. This year, it began to lend directly, providing a small number of overdrafts to its customers.

If Monzo’s ambition were to build up a funding base and then eventually expand in to the business of credit provision, it would represent an early-stage bank. But this is not what its ambition appears to be.

“We don’t do much of maturity transformation,” said Tom Blomfield, chief executive, referring to the traditional banking method of borrowing short term by taking deposits and lending long term, for instance as 25 year mortgages..

Mr Blomfield, who previously set up the payments business GoCardless, sees Monzo as “bringing together different business models”.

“Historically, where you put your cash, and credit, have been mushed together by banks,” he told us.

Instead, the plan is to use the company’s growing base of users to construct a kind of financial marketplace. This, in theory, allows Monzo to provide depositors with access to a wide range of other banking services, including loans. Monzo would then receive commission from these other providers.

Mr Blomfield also outlined the company’s vision in a blog post in February 2016, entitled “the bank of the future will be a marketplace”. He ties this in to scepticism around the “cross-selling” role of banks, which both lend to, and borrow from, their customers. From that post:

If you have some money left over at the end of the month, imagine being able to move it into a P2P lending platform with one click. Or being able to choose a mortgage from any one of a number of providers based on your previous spending habits and income.

This is the plan, but the bulk of income for the company over the last reporting period, to February this year, came from interchange fee income, which comes from MasterCard, the provider of Monzo cards. Interchange fees are paid by merchants to card providers – EU regulation capped them at 0.2 per cent in late 2015. Overall, the company made losses after taxation of £30.5m and net operating income of £1.8m in the year ended February 28, 2018.

If its ambition is realised, Monzo would be providing deposit services primarily as a way to group together consumers and sell other financial products. It is, however, operating in an extremely competitive market for deposits. This is because, for lenders, deposits are a cheap and extremely valuable source of funding – a use which does not appear to factor meaningfully into Monzo’s plans.

Monzo does not pay any interest on its deposit accounts. While many current accounts pay no interest, banks often seek to entice new customers with a low level of interest or additional promotional offers.

Base rates, at 75 basis points, are still close to historic lows. In fact, the conception of a deposit account as a service that does not pay any nominal interest is one unsurprising consequence of the low-rates environment that followed the financial crisis. Current accounts usually provide interest at a lower rate than the risk-free rate (certainly with reference to government bond rates). If rates do rise, Monzo will be able to pass on returns from the Bank of England reserves to customers. But this relies on maintaining a very large amount of central bank reserves, probably to the point of over-collateralisation (as it were).

Monzo’s method of grouping consumers via deposits is one alternative to the two dominant current methods for grouping online consumers (social media and search). It is possible that people will end up frequently checking their daily transactions via an app, if not quite as obsessively as they check Facebook or Twitter. The more frequent the checks, the more effective the likely sales of commission-generating financial products.

But price comparison websites and apps, like current accounts, form an intensely competitive market. Monzo is entering two completely distinct markets, each with their own competitive dynamics. It is also aligning itself with peer-to-peer lending models that are largely untested in a recession, or a rapidly rising rates environment.

Another complication ties in to the balance sheet impact of commissions. If, for example, users transfer their excess cash in to P2P lending platforms, Monzo would need to transfer reserves and deposits elsewhere. In this respect, its marketplace function appears to naturally shrink the size of its balance sheet.

For Monzo, this effect would be offset by either the expensive process of acquiring new customers, or flows from salaries. But only one-fifth of customers are paying their salaries in to their account. Traditional banks, by contrast, can also supplement the size of their balance sheets through the act of lending, by creating deposits in the borrowers account.

Monzo may not mind if its balance sheet shrinks, assuming it continues to receive commission from funds that have been transferred elsewhere and remain invested. But if its balance sheet shrinks, its interchange fees shrink. The commissions would have to be high enough to offset the revenues from interchange fees that the business currently generates.

The company also may avoid the accusation of cross-selling on a single bank balance sheet, highlighted in the blog post above, but price comparison websites are vulnerable to similar ethical quagmires, given their incentives to sell products with high commissions.

The company faces many competitive challenges, as is inevitably the case for an early-stage business. It also attracts inevitable scepticism from some adherents of traditional banking. But its conception of deposits as a grouping device is an interesting one, and provides one explanation for why it is expected to attract a valuation of up to $1.5bn at its next round of fundraising, as the FT reported in August.

It also hints at how the notion of a current account might evolve in future. We’ll speculate about where that kind of thinking might ultimately lead in the next post.

This post has been amended to clarify the timing of Monzo’s fundraising.

sober-minded The Financial Times Limited 2018. All rights reserved. You may share using our article tools. Please don’t cut articles from FT.com and redistribute by email or post to the web.

推荐阅读: 为什么说Python是Fintech与金融变革的秘密武器

原文来自:/www.jiqizhixin.com/articles/2018-09-12-4

为什么说Python是Fintech与金融变革的秘密武器

金融科技离不开技术,而这种技术又更多地通过编程力来体现,python作为一门上手快,学习曲线较低的编程语言,越来越成为Data Science偏爱的语言,逐步超越R语言成为首选,与此同时,python也应为其众多的开发贡献者和友好的社区文化,快速地与各行业的应用结合,成为行业应用的主流语言选择。

对于金融行业,python的易读和便捷,使基于python和各种python开发框架发展迅速,除了Django之外,一些小型的框架,如Flask, Tornardo等都可以让开发者快速上手开发基于Web的应用,而这种快速开放的应用对于重新构建未来的银行或者金融业务,充满了机会。

学习python,并把python用于你的生活吧。

Just Do IT


又:

机器之心

586-258-8468这个网站值得关注,有助于帮助你更好地了解人工智能的发展和热门话题。

 

本条目发布于firmness。属于fintech news分类。作者是。

970-873-1836

先说说我们这门课。

这门课我们叫做金融科技实验课程,主要是作为金融科技的入门课程,计划开设一学年。

同学们可能会问,什么是金融科技?为什么要叫做实验课程?这门课程到底教什么?怎么来学?

按照我们开设这门课程的初衷,是希望让同学们通过课程的学习,可以更好地了解金融科技是什么?金融科技主要包括哪些科学技术?这些技术有什么特点?金融科技主要有哪些应用?使一些什么样的应用?它对于金融行业会有什么样的影响?产生了什么样的影响?它是金融业的一种创新?还是对于金融行业的一种颠覆呢?它与其他的农业科技、生物科技等等有什么不同呢?

这门课程作为一门入门性质的课程,无法解答所有的问题,也无法让同学们通过一个学年的学习就了解和掌握所有的金融科技的内容,特别是其中相关的科技知识。金融科技涉及到的每一个关键技术,都涉及到一个很大的知识领域,需要大量的知识积累和经验积累,这都不是我们一门课程所能够承担的。

那我们这门课程将如何来讲授呢? 

这门课程除了对于金融科技的概述性的介绍,对其中涉及到的关键技术做一些基础性介绍,我们希望能通过加入更多的实践动手,让同学们能更多地去感知和了解相关的技术和应用,由此能对于金融科技的特点、相关的技术有所了解,能更好地去了解和理解技术对于金融业务的改造、创新和推动,能更快地建立起基于技术的观点、视角来看待金融业务的进化,了解到这些科技是如何更好或者更坏地解决金融业务的痛点和难点。

要了解一个新的知识,最好的方法就是动手去做。“想,都是问题;做,才是答案”,这也是我们把这门课作为一门实验课程的出发点。我们在课程设计中增加了一些技术的练习,主要是有关编程和网络应用方面,希望同学们可以建立起学习编程的兴趣,了解业界常用的编程工作流程、要求和习惯等,建立自己阅读代码的能力。同时,我们在课程设计中也加入了一些讨论,希望能让同学更加深入去思考。

这是我们开设这门课程的目标,也是我们对于同学们的希望。我们希望通过这门课程的学习,同学们可以更加快速地了解、认识、熟悉金融科技,能更多去了解相关的技术,甚至可以用这些技术去设计一些应用,创造一些应用场景,推动金融业务的更新和革命。

 第一课的508-896-9438

619-999-2842

时间:2018年5月6日 下午16:00-18:00
地点:颐德楼i-504
课程对象: CFA2017、信用管理2017及兴趣小组旁听人员
课程内容:
1.  时间管理
2.  IT基础
3.  MarkDown语法
4.  GIT
5.  Jupyter使用
课程准备要求:
1. 笔记本电脑(装好vmware、linux虚拟机、python、jupyter、docker等)
2. USB盘(装好linux boot启动盘)
3. notebook(可选)
课件下载:/pan.baidu.com/s/14iOD96UT3k2yoG9jS_jssA

金融科技学习兴趣小组-第四周活动预告

金融科技学习兴趣小组-第四次活动预告

时间:
2018.4.10日下午14:00-15:30 (量化交易小组参加,有兴趣可旁听)
2018.4.14日 10:00-16:00 (全体参加)
地点:
颐德楼i406

活动内容:
1. 4.10日下午14:00-15:30  量化交易讲座;
2. 4.14日周六 10:00-16:00 Python基础 (一)

活动准备:
1. 熟悉 /labtest.swufe.edu.cn 平台。(使用学校账户登录)
2. 完成课前摸底测验(两道):
a. 第一题

b. 第二题

 

第三次学习兴趣小组活动

第三次学习兴趣小组活动预告
时间:2018.4.3日下午14:00-17:00
地点:  颐德楼i406
参加人员:  全体学习兴趣小组成员
活动内容:
1.两点-三点半的量化交易第三次讲座;
2.三点半-四点半的基于R的数据处理(暂定,看大毛时间)
活动准备:
1. 预装R及R-Studio
2. 熟悉 /labtest.swufe.edu.cn 平台。(使用学校账户登录)
附件:
R语言学习资料01:链接:/pan.baidu.com/s/1uF30Lj2jhwmhaUnteSa5dA 密码:tcbe