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  卡地亚蓝气球价格

  代写FRE6831留教生作业、代作PYTHON程序语止作业、PYTHON尝试作业代写、program课程作业代作



  FRE6831



  COMPUTATIONAL FINANCE LABORATORY (PYTHON)



  Adjunct Professor



  Dept. of Finance and Risk Engineering



  Office Hours by appointment



  PROJECT: IMPLEMENTING A LIBOR YIELD CURVE OBJECT



  The class project is to write a Python program that infers the short end of the USD LIBOR yield curve



  from market observations, via the methodology in use prior to 2008 (More modern methodologies are too



  complex for an introductory course.). It is to support two calculations:



  1. The “discount factor,” or present value of a US dollar for a given future date, up to 3 years after a



  given “spot” date (See below for a definition of the spot date.).



  2. The “forward rate,” which is the simple interest rate, expressed as a percentage, over a given



  period between two dates, computed from the above discount factor curve as explained below.



  Because this calculation uses the above discount factor calculation, both of the input dates must



  also be less than approximately 3 years after the spot date.



  The yield curve is to be implemented as a Python class that I will import into my environment and run via



  the co妹妹ands



  import USDYieldCurve



  usdCurve=USDYieldCurve(“depoRates.txt”, “futuresPrices.txt”,



  “tradeDate.txt”, “holidayCalendar.txt”)



  print(usdCurve.getDfToDate(d1)) #d1 is a string in YYYY-妹妹-DD format



  print usdCurve.getFwdRate(d2, d3) #inputs are in YYYY-妹妹-DD firnat



  Inputs



  Typical inputs to such a calculation – and the inputs to assume here – are to be read from the four text



  files named above.



  1. depoRates.txt contains a series of annualized cash deposit rates. These cash deposits are



  effectively jumbo versions of the certificates of deposit that are on offer at any bank. These



  deposits should be read from a text file, each line of which has the following format:



  <5 character instrument code> white space



  The instrument code will always have the prefix “USD” and the suffix “D”, “W”, or “M”, which



  will indicate whether the deposit is for a period of days, weeks or months. In between this prefix



  and suffix will be a number, indicating the number of days, weeks, or months to maturity. For



  example, “USD1W” would be the code for the cash deposit maturing in one week; “USD2M”



  would be the cash deposit maturing in 2 months, etc. The start date of these cash deposits is



  always assumed to be the “spot date” (More about the “spot date” later.).



  The first few lines of such a file might read



  USD1D 2.73



  USD1W 2.74



  USD1M 2.75



  USD3M 2.80



  USD6M 2.85



  You can assume that the number in the above codes is only a single digit (Although 12 month



  LIBOR deposits do exist, they are seldom used to construct yield curves.). Some of the above



  tenors may be omitted; in fact, the only one that is really necessary is USD6M (Why?).



  2. futuresPrices.txt contains a series of prices of Eurodollar futures, which are futures on 3



  month cash LIBOR deposits. You can assume that the futures on successive lines of the file



  mature on successively later dates. Each line of the file has the following format:



  <4 character instrument code>



  The instrument code will always have the prefix “ED”, followed by one of the letters “H”, “M”,



  “U”, or “Z”, followed by a single numeric character. The letters “H”, “M”, “U”, and “Z”



  correspond to the maturity date of the future: the respective 3rd Wednesdays of March, June,



  September, and December of the year indicated by the numeric character. Thus, “EDZ2” would



  be the code for the future expiring in December, 2022. Necessarily, these maturity dates are dates



  in the future, so there is no ambiguity, given that all futures must mature prior to ten years from



  the current date. To see this, suppose



  Thus, the first few lines of such a file might read



  EDH9 97.25



  EDM9 97.20



  EDU9 97.19



  3. tradeDate.txt contains the date at which the rates/prices are being observed, given as



  character strings in the form YYYY-妹妹-DD.



  4. holidayCalendar.txt contains the dates of Federal Reserve holidays, given as character



  strings in the form YYYY-妹妹-DD, for the next five years (available from, among other places,



  the Federal Reserve website).



  As we will see in more detail in the calculation below, it may be impossible to compute the yield curve if



  the input files are incomplete. In this case, an error condition should be raised and the error message



  “Cannot build curve from given inputs” should be generated.



  Calculation



  “Modified Following” date adjustment



  USD payments can only be made on days in which the US Federal Reserve (the “Fed”) is open. If the



  date of a scheduled payment falls on a weekend or a Federal Reserve holiday, it must be adjusted



  according to the so-called modified following business convention. Under this convention, if a payment



  would otherwise be scheduled on a non-business day, the payment date “rolls forward” to the next



  business day unless the next business day falls into the subsequent month. If so, the payment date “rolls



  backward” to the next previous month. Thus, a payment that would be scheduled for December 30, 2018



  (a Sunday) would be rolled forward to December 31, 2018 (a Monday and a business day), but a payment



  scheduled for January 1, 2019 (New Year’s Day and a bank holiday) would be rolled backward to



  December 31.



  There are two additions to this convention. If the spot date is the end of a month, the maturity date of



  deposits for one month would be the last date of the next month (So, when spot is February 28, the one



  month deposit matures on March 31.). Same with 2, 3, … month deposits.



  Also, if the spot date is January 29 or January 30, the one month deposit matures on February 28 (or



  February 29 on leap years.).



  The spot date



  The beginning of the period over which USD interest is computed is usually the so-called “spot date,”



  which is always two business days after the trade date (The intervening time period is required for



  clearing and settlement.). Given this interest computation convention, discount factors are also only



  defined from the spot date. The spot date will be denoted as s below.



  Date arithmetic



  Here and subsequently, if dates are to appear in a formula at all, one date will be subtracted from a later



  date. Use the Python built-in library datetime to do the date arithmetic. If d1 and d2 are two such



  dates, d1 – d2 is interpreted as the number of calendar days between them (More precisely, this difference



  calculation results in a timedelta object, which can then be converted to a Python int.). As we



  will see, this is just what is needed for the calculations below.



  Computing discount factors from cash deposit rates



  LIBOR interest calculations for periods less than a year always assume simple interest applied to the



  relevant fraction of a year, computed as the actual number of days that a loan is outstanding, including



  weekends and holidays, divided by 360 (Remember, all of this was standardized before there were



  computers to facilitate the calculations!). Thus, if the given rate is R and the maturity date of the deposit



  is d and the spot date is s, the amount to be repaid for every dollar lent is 1 + R(d - s)/36000. It follows



  that the discount factor, or present value of a dollar n days from the spot date is (1 + Rn/36000)-1



  . If a



  discount factors df1 and df2 have been computed for dates d1 and d2 after the spot date, the discount factor,



  df, for a date d between d1 and d2 is given by logarithmic interpolation



  ln(df)=ln(df1) + [(d – d1)/(d2 – d1)] [ln(df2) – ln(df1)].



  Computing discount factors from futures prices



  Interest rate futures are futures on forward rates, i.e. rates over some pair of fixed future dates (In



  actuality, 3 month LIBOR really means 3 calendar months, but assume, for simplicity, that it is the period



  between successive futures maturity dates.). Thus, these futures are the market’s best guess as to what



  these rates will actually be when the beginning of the forward period coincides with the spot date, and 3



  month “cash” LIBOR becomes known. In the meantime, the rate implied by a futures price P is



  computed as a percentage according to the formula



  R=100.0 – P/100.0



  As before, simple interest is assumed, with the number of days in the three calendar months starting with



  the futures expiration date.



  However, the discount factor thus computed is a forward discount factor; that is, it is the discount factor



  from the expiration of the future to the expiration of the underlying 3 month LIBOR deposit. To get the



  discount factor from spot to the expiration of the LIBOR deposit, this forward discount factor must be



  multiplied by the discount factor from spot to the start date of the LIBOR deposit, which is also the



  maturity date of the future. Thus, given the discount factor, dfs, from spot to the earliest futures expiration



  date and the forward discount factor, df1, computed as above, the discount factor to the expiration date of



  the first future is dfs x df1. In general, the maturity dates of the any of the futures will not coincide with



  any of the expiration dates of the cash deposits, so the interpolation formula given above will be required.



  This computation cannot be done if the rates for cash LIBOR deposits are unavailable for the required



  maturities. If this is the case, an error condition should be raised, and the message “Insufficient LIBOR



  cash rate data” should be printed.



  Again, we simplify things by ignoring the awkward fact that the futures maturity dates are almost never



  exactly three months apart; instead, we assume that the maturity date of the 3 month LIBOR deposit



  underlying one futures contract is also the expiration date of the next futures contract. Thus, the discount



  factor to the maturity date of the underlying of the second future is dfs x df1x df2, where df2 is the forward



  discount factor computed from the price of second future. This process can be continued to calculate the



  discount factor from spot to the n



  th futures underlying expiry date as dfs x df1x df2 x … x dfn.



  The df method



  This method should return the value of the df curve for the date, d, that has been passed as an argument.



  In general, d will not coincide with any of the dates for which the above calculation will have produced



  discount factors. In that case, determine the required df curve value by finding dates d1 and d2 from the



  spot date, the discount factor, df, for a number, d, of days between d1 and d2 is given by logarithmic



  interpolation



  ln(df)=ln(df1) + [( d – d1)/(d2 – d1)] [ln(df2) – ln(df1)]



  If d is before the spot date or d is after the last date for which the discount factor curve is defined, an error



  condition should be raised, and an appropriate error message should be printed to STDOUT.



  The fwdRate method



  Given two dates, d1 < d2, this method should return the forward rate, rf , between these two dates, inferred



  from the discount curve computed above, according to the formula.



  Use Python’s built-in datetime library to do the date arithmetic. This formula will compute the rate as



  a decimal fraction, rather than a percentage (e.g. something like 0.03, rather than 3%.).



  If d1 is before the spot date, d1 ≥ d2, or d2 is after the last date for which the discount factor curve is



  defined, an error condition should be raised, and an appropriate error message should be printed to



  STDOUT.



  Testing



  In the “Project” folder on the Course website, I have posted the spreadsheet “YCtestcase.xlsx”,



  which implements the calculations for the spot date April 24, 2015.



  Notes:



  1. More modern methodologies take into consideration the changes in the LIBOR markets that arose



  from the debt crisis of 2008, which involves the use of a truly risk free curve for discounting (As



  the financial co妹妹unity learned to its chagrin, interbank lending, the “I” in LIBOR, is not risk



  free!). Thus, one curve is needed for discounting and another is needed for predicting forward



  rates.



  2. There is considerable disagreement about exactly which mix of the available cash deposits,



  futures, and swaps – or even whether instruments besides these – should be used in the above



  calculation. What is generally agreed upon is that the most liquid instruments should be used, as



  these are the best indicators of the market.



  3. Although you are to use the interpolation methods given above, they are often replaced in practice



  by more sophisticated methods, such as splines.



  4. Because owners of futures contracts are required to post margin daily as their futures prices vary,



  the rates inferred from futures are actually biased estimates. In practice, a so-called “convexity



  adjustment” is often applied to these rates. For simplicity, we will ignore this adjustment.



  5. The above conventions and instrument details are only applicable to USD. The construction of



  the LIBOR discount curve for other currencies differs in detail, but not in general, from this



  outline. For example, GBP has a spot date one day after the trade date, and 365 replaces 360 in



  the above calculations.



  由于博业,所以值得信赖。如有需要,请减QQ:99515681或邮箱:99515681@qq.com 微信:codehelp



  

  



  这是昨晚阅读的《富爸爸—投资指北》中的一句话,虽然看起来战咱们作亚马逊没有任何闭系,然则细想一下,咱们挑选作亚马逊没有也是一种投资吗?咱们将自己的款项、时间战粗神都投入到这个项目中,并期待有所回报。是的,作亚马逊就是一种投资。



  过程中有人得心应手作得风生水起,有人借在甜甜挣扎,也有人早已放弃另谋没路……慢慢也就辨别合了亚马逊中“穷人”战“穷人”,为甚么会没现这种情况呢?其实也可以拓展到上面所说的“词汇”中来。



  律师进修法律词汇,medical生进修medical教词汇,西席进修教教词汇。如因一小我合初作亚马逊以后借没有懂选品、listing优化、广告投放等方面的词汇,这么他作为一个亚马逊运营人员就会感到很难题。



  当然,跟着合初作亚马逊,咱们会慢慢接触其中的各种词汇,然后知说他们的意思,但咱们很多人都缺少对笔墨后面的逻辑的了解,以致于作起来一弯都很挣扎。



  比如选品,咱们从其余嫩师总结的方法中得知咱们应该选“刚需品”、“低成本产物”,然后在分析的过程中要去看listing的review战排名。但很多时候咱们在选品的过程中并没有按照这种思路去走,这么这样的“词汇”永远只是他人的。如因没有能变成自己的“词汇”,这么咱们的选品就只是在撞运气。



  如下是一位售家朋友总结没来的选品经验,他就把嫩师道的内容消化成为了自己的“词汇”,然后脆定地去执止,光选品就花了2个多月。而理论情况是他现在作亚马逊无比胜利,光作美国站就赔得盆满钵满,如因再合通其它站面,应该就会到达另一个岑岭了吧。



  



  回头再看咱们自己,咱们在作亚马逊的过程中有没有脆定地去执止过一种方法论?有没有认真去研究过这个止业中的“词汇”?



  咱们知说广告“暴光量”的意思,然则咱们有没有想过是哪些因艳影响着暴光量呢?咱们知说“转化率”对一条listing很重要,然则咱们有没有研究过可以通过哪些方法提高转化率呢?



  咱们应该静下心来问问自己,咱们花这么多款项、时间战粗神投资在亚马逊是为了甚么。对,最大的动力就是为了挣更多的钱。这么,咱们是没有是应该为这个目的花些时间去掌握其中的“词汇”呢?没有是只了解“词汇”的意思,咱们更应该去了解它们后面更深的逻辑,然后运用到理论运营中来。



  当咱们真邪把这一步付诸理论,置信会没现没有一样的情景。我准备合初这样作了,希翼你也能一起止动起来!



  越分享,越成长!(来源:绝版祥Cary)



  以上内容属作者小我没有雅面,没有代表雨因网态度!本文经原作者授权转载,转载需经原作者授权异意。



本文编辑:中国涪陵网

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打通农产品上行“最初一公里”——大凉山贫困地区构建三级物流体系

新华社成都12月10日电 题:打通农产品上行“最初一公里”——大凉山贫困地区构建三级物流体系

新华社记者 袁波

寒冬时节,四川凉山彝族自治州盐源县双河乡杨柳桥村寒意倍增,午后阳光洒落在胡秀洪苹果铺,这里一派热闹景象。

“盐源的糖心苹果甜得很呐,寄一箱你尝一下嘛,不甜不要钱哦!”黝黑的胡秀洪正啃着苹果和微信好友视频互动,旁边的邮政快递车正在转运苹果。谁曾想,这位曾吃不饱饭的48岁农民会“触网”,卖苹果带动乡亲脱贫奔康。

老胡所在的盐源县是贫困县,产出的苹果是国家地理标志产品。但由于山高沟深,交通闭塞,名优特产一度难出大山,贫困群众出路难寻。

作为全国脱贫任务最繁重的地区之一,精准扶贫实施以来,凉山将盐源、喜德等深度贫困县纳入国家级电子商务进农村综合示范县项目,构建县、乡、村三级物流体系。

“一手交钱一手交货,面对面的生意才踏实。”被大山“禁锢”太久的老胡开始很抵触电商,但“等、靠”生意经终究念不长久。

“路边摆摊销量不好,有人收购要杀价。遭遇冰雹灾害那年,五六万斤苹果烂在地里,血本无归。”老胡无奈地说,“年纪大、文化水平低”让他学电商很没信心。

2015年,经过培训后,老胡硬着头皮开起了淘宝网店、邮乐网店。“邮政公司的人手把手教我。刚开始不懂营销,我只能用傻办法,见人就添加QQ好友、加微信,发网店地址,有时还免费寄给他们品尝。”老胡说,刚开始微信好友加了80多人,一年卖出两万多斤苹果。

凭借良好的品质和口碑,订单越来越多。“现在微信好友3800多人,熟客带新客,销路很不错。”老胡咧着嘴笑了起来,家里30多亩苹果一年能有约40万元收入。

如今,老胡组建了专业合作社,组团在网上卖苹果,一年销售额约1000万元,带动周边近百户贫困户增收。

“记者朋友,加个微信嘛,买苹果找我!”临别前,老胡递上印有二维码的名片,日子好了,山里汉子越来越自信开朗。

盐源县商务和投资促进局局长米约哈表示,目前盐源电商从业人数约6万人,今年1至11月,全县网络交易额约13亿元,80%以上通过邮政寄递,电商正成为贫困群众脱贫的“加速器”。

大凉山深度贫困地区的脱贫之路不仅在网上,更在脚下。

思路“活”了,还要打通大山里的邮路,打通农产品上行“最初一公里”,才能缓解农产品“卖不掉”和城里“买不到”的供需矛盾。

在喜德县拉克乡电商服务站,尔吉村彝族青年阿的拉铁高兴地告诉记者:“以前家里吃不完的花椒、乌洋芋只能肩挑背扛几小时拿到乡上卖,卖不了几个钱。今年2亩花椒被电商服务站收购后在网上卖了1万多元。”

“脱贫不能只靠给钱给物,凉山有优质的农产品,长久之计还得靠产业。”拉克乡电商服务站负责人袁星表示,不久前收购6个贫困村的1.94万斤鲜花椒,2.3万斤“苦荞粉”在邮乐购网“秒光”,带动230户贫困户增收,提高了他们“勤劳致富”积极性。

在喜德县快递物流中转中心,做了30年葡萄生意的刘中华告诉记者,“喜德葡萄品质好,物流也很方便,目前已从各乡镇采购了70万斤葡萄,发往全国各地。”

如今,喜德国家级电子商务进农村综合示范县项目已建成1200平方米的仓储物流配送中心、108个乡村物流站点,覆盖了24个乡(镇),贫困村覆盖率达到了80%以上。

“三级物流体系让农村上下行物流资源得到统筹,全县电商交易额今年已突破1.68亿元,同比增长19%;农村电商交易总额超过6970万元,同比增长22%。”喜德县委常委、副县长梅勇表示,农村电商同时推动了工业品、生活必需品等物资下行,改善了农村边远地区群众生产生活条件,但不少农村地区物流基础设施网络和公共服务设施有待完善,仍需国家加大支持力度。

“在深度贫困地区要发挥邮政覆盖面广的优势,以普遍服务保障乡村民生、电商打造农产品销售服务平台、物流分销助力农产品进城为抓手,建设助农增收、助力扶贫的特色模式和长效机制。”凉山州邮政分公司总经理郭宗昌表示,截至11月,凉山已建3746个村邮站,汽车邮路331条,为贫困户销售农产品提供支撑,助力脱贫攻坚。

[责任编辑:张圆]

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