By Timothy Washington
Clojure is a dynamic programming language with an emphasis on useful programming. Clojure is definitely suited for monetary modeling because it is a sensible programming language. Such languages aid builders paintings with high-level mathematical abstractions with no need to enforce low-level code that handles the mathematics operations.
Starting with the significance of representing information and calculations successfully, this e-book will take you all of the technique to being efficient in monetary analytics and development monetary applications.
First, we introduce the notions of computation and finance, so as to assist you comprehend Clojure's software to resolve real-world difficulties in lots of domain names, particularly finance. subsequent, we'll enable you increase the simple-moving-average functionality through the use of the extra complex partition Clojure information transformation functionality. This functionality, besides others, may be used to calculate and manage information.
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Extra resources for Clojure for Finance
Example text
Feel free to play around with the data and share your results. We've taken pains to first understand what a stream of stock price data may look like. We then applied some core functional programming features that Clojure provides to transform data into the stock price shape we desire. This is an important point to note for any programming language. It should be a tool that empowers you to shape your data as your needs develop. In the next chapter, we're going to continue this lesson, paying close attention to some useful ideas on how to fully capture and represent our data.
This is what I'll describe as fully perceiving a problem. Let's take Clojure data structures, functions, and FP approaches as a way of representing our problem and solving it. We ultimately need to gauge how close our perception and representations are to actual stock price data. However, for now, we have an infinite stream of price and time points. Apart from this stream of data, we want to calculate a moving average of prices. So, an average is just the sum of a collection of things divided by the length of the collection.
The result is then in the form of a list of maps with the :time and :price entries, for example, ({:time x :price y}). As such, we can simplify this function to just one map call over both collections and return a map with both values. core) (defn generate-prices [lower-bound upper-bound] (filter (fn [x] (>= x lower-bound)) (repeatedly (fn [] (rand upper-bound))))) (defn generate-timeseries [pricelist] (map (fn [x y] {:time x :price y}) (iterate inc 0) pricelist)) Then, in your REPL, you can require the namespace and generate a time series (see the following code).
