云计算代写｜Cloud Computing (COMM034) LSA 2022-23

这是一篇来自英国的关于演示对如何批判性地解释和构建使用跨云提供商的多个服务的云本地API的理解，涉及用户可指定的扩展的云计算代写，以下是相关作业内容：

Aim

To demonstrate your understanding of how to critically explain, and construct, a Cloud native API using multiple services across Cloud providers, involving user-specifiable scaling.

You will explain, implement, evaluate, and demonstrate,

an API that supports determining risks and profitability of using certain trading signals for a trading strategy using a so-called Monte Carlo method.

Your API will need to adopt the Approach within the set of provided Requirements.

Submission

Submissions will be made via SurreyLearn and comprise two components:

A PDFdocument of a maximum of 4 pages that conforms to the template provided
A Zipfile for code you need to construct/run your system, usually KBs in size
1. includeall code used for GAE/Lambda/EC2/EMR/ECS
2. excludelibraries or Python virtual environments or similar that merely bloat the zip file – if the file is MBs in size or larger, it has been bloated by something that should be excluded.

A note on linearity of description

Some aspects are described across passages and sections – e.g. Audit. Prior to posing questions, check that you have seen/searched all such mentions within the document first.

Approach

There are no marks available for re-explaining this approach in your submission.

Note that a core of Python code is provided for this approach, within this document, and this Python code must be used for the API created.

The approach involves identifying trading signals in financial time series and capturing the risk associated to these. Such an assessment might support a subsequent evaluation of a trading strategy.

Financial time series here comprise daily data – specifically, a summary of the trading day comprising the Open/High/Low/Close values for each trading day (OHLC values can readily be produced for other time intervals – e.g. every 15 minutes).

OHLC data can be visualised using a “Japanese candlestick”, and certain resulting shapes interpreted from these to indicate something about the data that may ‘signal’ making a trade (buy or sell). The figure below is an example, using real data, of such candlesticks where:

i.Open and Close values provide the top and bottom of the ‘box’ on each candlestick – if Close is higher than Open, price movement was upwards overall and the body is green(a price rise from the start of the day to the end); Open higher than Close and the body is red (a price fall from the start of the day to the end); other charts and some pattern naming might use white for upward and black for downward or other colour/shading schemes.

ii.A line projecting from the top of the body indicates that the High was above the respective Open/Close; a line projecting from the bottom of the body indicates that the Low was below the respective Open/Close. Such lines are referred to as the wick or shadow.

iii.Resulting shapes can have various names such as a Green/Red Marabozu (Japanese for dominance) or Spinning Top, and can involve more than one candlestick – for example Harami (Japanese for pregnant) or Three Black Crows. Note how the latter name implies a specific colour/shade scheme.

Here, we’re only going to look at 2 patterns that each involve 3 candlesticks: (i) Three White Soldiers; (ii) Three Black Crows. For the above figure, the combination of 6^th , 7^th and 8^th candlesticks from the left should fit our expectations for Three Black Crows, and example code is provided that will act as a detector for such shapes in data being obtained directly from Yahoo Finance (the pattern identification code could be more sophisticated, for example it doesn’t check if the 2^nd and 3^rd have Open value between Open and Close – the real body – of the candle before, but we’re using simpler principles here).

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