Quantitative investment of Python digital currency

1. The so-called quantitative trading of digital currency is irregular in China. It is recommended to choose cautiously, because firstly, there is no digital currency exchange in China. Secondly, the country does not recognize the so-called digital currency trading, there is no formal supervision, and there is no formal digital currency trading platform in China. Once you choose, you may lose all your money.

2. Look at the indivial requirements for the accuracy of quantification. What comes out of big data analysis is that the success rate must be higher. Digital currency is still very little to quantify, now it seems that jiuzhuang bcbot is doing quantification. For reference, in a bear market, high-frequency trading will surely be more reliable.

3. Python digital currency quantitative trading advanced course, has been learned, generally mastered.

4. These are very normal, but there is a solution. Need can teach you.

5. The car steering wheel has a large amount of open space, so it should be said that the free travel of the steering wheel is large. It generally needs to be adjusted
because the steering wheel moves in a circle and the wheels swing left and right to form a steering. Therefore, it is necessary to add a mechanical conversion< 1. Worm type
1. There are tie rods and tie rods below. First, if there is looseness in the worm gear, it will be reflected on the steering wheel, causing looseness. It can be adjusted
2. The horizontal and vertical pull rod has wide ball head and large free travel of steering wheel. There are adjusting screws, which can be adjusted< 2. Rack type
1. Firstly, if there is looseness in the gear and rack, it will be reflected on the steering wheel, causing looseness. It can be adjusted
2, 2, wide horizontal and vertical rod head, also large free travel of steering wheel. There are adjusting screws, which can be adjusted
generally, it can be adjusted, and few of them replace the assembly. Normal steering wheel free driving does not exceed 0.5cm

6. This paper will explain the basic process of quantitative investment process, quantitative investment is nothing more than these processes, data input --- strategy writing --- back test output
the strategy writing part also involves the choice of programming language, if you don't want to worry about data input and back test output, you should also choose the back test platform< First of all, data is the basis of quantitative investment

wind: the most complete data source is wind, but students can have a free trial if they have to pay. Later, they will share with you how to extract data from wind. Wind has many software excuses, such as Excel, MATLAB, Python and C + +<
forecaster.com: unexpectedly, we found that forecaster.com, a website that provides stock data for free, downloads CSV format
TB trading Pioneer: tradeblazer, thank you @ sun cunhao for providing data source
tushare: tushare - financial data interface package, which is based on python, and uses Python to extract
how to store data
MySQL
how to preprocess data

null value processing: use the fill. Na() function of dataframe to replace the null value (Nan) with the average, median or mode of the column
data standardization
how to classify the data
market data
financial data
macro data
Second, computing language & software

many people have asked online what language to choose? At the beginning, the author used Matlab, but finally chose Python
Python: there are many libraries, only you can't find, not you can't think of, which are closely combined with quantification:
numpy & SciPy: scientific calculation library, matrix calculation
pandas: financial data analysis artifact, a library written by the former AQR capital staff, The standard configuration of processing time series

Matplotlib: Drawing Library
scikit learn: Machine Learning Library
statsmodes: statistical analysis mole
tushare: free and open source Python financial data interface package

zip line: back testing system
Talib: technical index library
MATLAB: mainly matrix operation and scientific operation are very powerful, The main advantage is that workspace variable visualization

Python's numpy + SciPy library can completely replace Matlab's matrix operation
MATLAB's drawing function
Python has many other functions
pychar (an ide of Python) has great debugging function, Can replace Matlab's workspace variable visualization
recommended Python learning documents and books
on the basis of python, suggest Liao Xuefeng Python 2.7 tutorial, suitable for people without program foundation to see first, involving the basic data types, loop statements, conditional statements, functions, classes and objects, file reading and writing and other very important basic knowledge of Python

when it comes to data operation, there is no application for the basic course. All kinds of packages in Python have been written for you. The best learning material is its official document. In the document, there are not only APIs, but also examples
pandas document
statsmodel document
SciPy and numpy document
Matplotlib document

tushare document
Second, It is recommended to use Python for data analysis. The original intention of panda is to process financial data.
3. Backtest framework and website are two open source backtest frameworks.
pyalgotrade - algorithmic trading

zip line, a python algorithmic trading library

7. In recent years, with the rapid development of digital currency market, more and more digital currency exchanges are focusing on Grid Trading. The so-called grid trading is to first set the value center, and use the "stall" mode to mechanically operate the investment target. When it falls, it will buy by stalls, and when it rises, it will sell by stalls. In the robot Library of pioneer, there are ten quantitative trading strategies, including grid trading, infinite grid, tracking profit, lending network, reverse leverage network, reverse grid, time-sharing Commission, leverage grid, top speed fixed investment and term arbitrage.. I'm glad to answer your question

8. As a programming language, python mainly compiles quantitative strategy model in quantitative investment.