One of the best features of Python is its extremely extensive library. An effective set of libraries allows developers to code complex tasks without rewriting multiple lines of code.
Due to its ease of development, rich tool library, and especially strong support for scientific computing, Python is currently widely used in the field of quantification. Its main application areas are:
Pandas
Added data structures and tools for practical data analysis in finance, statistics, social sciences, and engineering. Pandas handles incomplete, messy, and unlabeled data well and provides tools for shaping, merging, transforming, and slicing datasets.
NumPy
NumPy is the foundational library for scientific computing in Python, and many libraries use NumPy arrays as their basic input and output. NumPy introduces objects for multidimensional arrays and matrices, allowing developers to write as little code as possible when performing advanced mathematical and statistical functions on these array matrices.
Crawler
In the application of web crawler technology, Python scripting language is widely used, which has improved the level of web crawler technology application. Writing a crawler in Python is much simpler than writing it in other programming languages.
A considerable number of Python learners go for crawlers, because crawlers can help you solve many problems in work and life and save you time. However, Python also has a mysterious and interesting application field, and that is-quantitative trading.
Quantitative trading uses computer technology, financial engineering modeling and other means to quantitatively analyze the rules of securities investment and find better investment strategies. With the help of high-speed computer operation, it makes investment decisions based on real-time monitoring of the financial market and automatically executes them. This decision, the transaction method of buying and selling financial assets.
There are also many quantitative platforms that support Python language on the market. You can use these platforms to implement your own trading strategies, conduct verification, and even connect to trading systems.
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Domestic platform: Jukuan joinquant.com
Rice basket ricequant.com uqer.io -
Foreign platform: quantopian.com
Taking Youkuang as an example, after completing the registration, create a notebook on the “Start Research” page and start writing strategies in Python.
Select “Create New Strategy” in the upper right corner and it will automatically fill in the basic structure code for the strategy backtest for you.
The first few variables are the basic configuration of the backtest: Initialize can do some initialization work, and Handle_data is the core of the backtest code, which is used to implement the trading instructions for each trading day (or every minute).
The document contains relevant descriptions of the meaning of variables, such as backtest time, stock pool, funds, trading frequency, etc. After modifying each variable, click “Run” in the upper right corner to see the execution of the strategy on the page.
Is it easy to use Python to create quantitative trading strategies? In fact, it is not difficult to use existing data packages to run programs. What is difficult is understanding the underlying logic-Algorithm and Data Structure.
01, Array
Array is an ordered sequence of elements used to store multiple collections of the same type of data. Array is a form of programming in which several elements of the same type are organized in an orderly manner for the convenience of processing. The collection of these ordered data elements of the same type is called an array.
02, String
String is a string in programming languages. A string is a special object and is a reference type. After the String class object is created, the string cannot be changed once it is initialized. Any changes to the String class will return a new String class object.
03, Tree & amp; Binary Tree
Tree & Binary Tree is an important type of tree structure. Many data structures abstracted from practical problems are often in this form, and its storage structure and algorithm are relatively simple. The characteristic of Binary Tree is that each node can only have two subtrees at most, and they can be divided into left and right.
04, Hash Table & amp; Dictionary
Hash table is a data structure that is directly accessed based on Key Value. It accesses records by mapping key values to a location in the table to speed up lookups. This mapping function is called a hash function, and the array storing the records is called a hash table.
The Dictionary class is the abstract parent class of any class that maps keys to corresponding values, such as a Hash Table. Each key and each value is an object. In any Dictionary object, each key is associated with at most one value. Given a Dictionary and a key, you can find the associated element.
05, Heap & Stack
Heap & Stack is a data structure in which data items are arranged in order, and data items can only be inserted and deleted at one end. In microcontroller applications, Heap & Stack is a special storage area. Its main function is to temporarily store data and addresses. It is usually used to protect breakpoints and scenes.
06, DFS & amp; BAcktrack, Recursion
DFS (Depth First Search Algorithm), is an algorithm for traversing or searching a tree or graph. DFS is a kind of attempt. It will first try a possible situation to the end, and then go back to try the next situation. As long as a situation is found, it can return.
BAcktrack (backtracking method) searches from the root in the solution space tree. Every time it reaches a node in the solution space tree, it will judge whether the subtree rooted at that node definitely does not contain the solution to the problem. If it is not included, go back to its ancestors layer by layer until it encounters a node that has not been searched before turning to that node to continue searching; otherwise, enter the subtree and continue to search in depth-first order. Strategies for searching.
Recursion (recursive method) usually transforms a large and complex problem into a smaller problem similar to the original problem to solve. The recursive strategy only needs a small number of programs to describe the multiple iterations required to solve the problem. calculation, greatly reducing the amount of code in the program.
07, Divide and Conquer
The divide-and-conquer method is to divide a complex problem into two or more identical or similar sub-problems, and then divide the sub-problems into smaller sub-problems until the final sub-problem can be solved simply and directly. The solution to the original problem is the sub-problem. The merging of solutions.
08, Greedy
The greedy algorithm is a simpler and faster design technique for certain optimal solution problems. It is characterized by proceeding step by step, often based on the current situation and making optimal choices based on a certain optimization measure, without considering various possible overall situations. Although it is necessary to ensure that the local optimal solution can be obtained at each step, the resulting global solution is sometimes not necessarily optimal, so greedy algorithms do not backtrack.
The above are the basic logic of various algorithms that must be understood. If you are curious about this field and want to know more in-depth content, why not start now and start with your first strategy!
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