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Introduction to quantitative investment and Trading (Python introduction to financial analysis)
2020-11-06 01:28:00 【Elementary school students in IT field】
Recommend a course : Course connection
Course name : Dead wage one party courses : The small white Introduction to quantitative investment (python)
Course orientation : This course is a video course , Comprehensive coverage of quantitative trading basic knowledge points .
Face the crowd : Weak stock base 、Python Those with a weak programming foundation .
Refer to the following table for the course sections :
Course chapters | Course content and professional ability requirements | Class name |
One 、python introduction | preparation | The first 01 course :python brief introduction |
The first 02 course :Python Install it Anaconda | ||
The first 03 course :Python Introduction to development tools | ||
Python Data type and data structure are explained in detail | The first 04 course :python introduction - Variables and data types | |
The first 05 course :python introduction -Python Operator | ||
The first 06 course :python introduction - Detailed list | ||
The first 07 course :Python introduction - Tuples 、 aggregate | ||
The first 08 course :python introduction - Dictionaries | ||
Python Introduction to programming | The first 09 course :python introduction - Cycle control - control flow | |
The first 10 course :python introduction - Cycle control - loop | ||
The first 11 course :python introduction - Cycle control -break、continue、pass | ||
The fourth part :Python Advanced | The first 12 course :python introduction - function | |
The first 13 course :python introduction - object-oriented | ||
The first 14 course :python introduction - Underline 、 Double underline 、 Double underline the head and tail | ||
The first 15 course :python introduction - Class inheritance | ||
The first 16 course :python introduction - How to write a project exception handling | ||
Two 、 Advanced | Python High order operation | The first 17 course : One line code conversion list and Dictionary . The intersection of lists 、 and 、 Bad , Lists and dictionaries are interchangeable |
The first 18 course : Higher order function ,lambda、MAP、filter | ||
3、 ... and 、python Practical application | Scientific computing module -Numpy | The first 19 course : Scientific computing module - Numpy Basic introduction |
The first 20 course : Scientific computing module - Numpy Advance quickly | ||
Data analysis library - pandas | The first 21 course : Data analysis - pandas Data structure and data preview operation | |
The first 22 course : Data analysis - Missing values delete 、 Missing value fill 、 Data substitution 、 identification 、 Data statistics, etc. | ||
The first 23 course :pandas Stock data combat | ||
Data visualization - matplotlib、seaborn | The first 24 course : from sin(x) Learn to matplotlib | |
The first 25 course : Visualization of initial statistics -seaborn | ||
The first 26 course : Statistical data visualization style design -seaborn | ||
The first 27 course :python actual combat - Analysis of the employment situation of college students | ||
Four 、 Quantitative combat | Financial knowledge | The first 28 course :A Stock market introduction and basic concept interpretation ( Introduction to Xiaobai ) |
The first 29 course : Quantitative trading | ||
Technical indicators and income indicators | The first 30 course : Initial technical specifications -python draw K Line diagram example | |
The first 31 course : Technical indicators - Single K Line form | ||
The first 32 course : Technical indicators -K Line combination form strategy | ||
The first 33 course : Technical indicators Moving average strategy - With 520 Take the tactics of war for example | ||
The first 34 course : Common income index calculation method | ||
actual combat | The first 35 course : Data acquisition demonstration | |
The first 36 course : Write the first trading strategy | ||
The first 37 course : Portfolio optimization | ||
Using algorithms to interpret stock market data , Be the master of data | The first 38 course : Recognize algorithms | |
The first 39 course : Machine learning practice | ||
The first 40 course : Deep learning practice | ||
consulting Q:595454159
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