Python quantitative learning route

Python quantitative learning route

Introduction

This article introduces the learning route of python quantification, and then the basic grammar of python is known by default, and then the follow-up articles will introduce each piece of the learning route in detail.

learning path

The following is a more detailed Python quantitative learning path and process suggestions:

Phase 1: Learning the basics of Python

1. To learn the basic syntax and data structure of Python, you can choose the following ways to learn:
   • Recommended books: "Python Programming from Introduction to Practice", "Smooth Python", etc.
   • Recommended online courses: "Python Language Programming" on the MOOC platform, "Python for Everybody" on Coursera, etc.
2. Master the commonly used standard libraries of Python, such as datetime, random, math, etc.
3. Learn Python's third-party libraries, such as: numpy, pandas, etc.

The second stage: understand the basic concepts in the field of quantitative trading

1. Learn the basic concepts of financial markets, such as stocks, futures, foreign exchange, etc.
2. Understand the basic processes and principles of quantitative trading, such as: market analysis, trading strategies and execution, risk management, etc.
3. Learn about Alpha and Beta Strategies, and the types of strategies that have proven effective that are common in the quantitative investing industry.

Phase 3: In-depth study of quantitative techniques

1. Learn commonly used technologies and tools in quantitative trading, such as machine learning, natural language processing and other related algorithms and applications.
2. Learn time series analysis techniques used in quantitative trading, such as mean reversion, trend following, momentum strategies, and more.
3. Understand the application of new technologies such as big data, cloud computing, and artificial intelligence in the field of quantitative investment.

The fourth stage: Establish a quantitative investment system

1. Master the use of quantitative investment platforms, such as: Quantopian, Ricequant, etc.
2. Use Python to write your own simple trading strategy, and use the simulated trading platform for backtesting to evaluate the pros and cons of the strategy￥.
3. Continuously optimize trading strategies, make corrections and adjustments based on actual market conditions, and confirm and test optimization results.
4. After successfully testing your quantitative trading strategy for a period of time, you can start real trading.

Phase Five: Continuous Learning and Improvement

1. Keep an eye on industry trends, including changes in the financial market, relevant policies and regulations, and the development and application of the latest technologies.
2. Learn higher-level quantitative trading strategies and models, and continuously improve your own strategies.
3. Pay attention to the robot virtual trading competition platform, which is one of the most effective ways to learn quantitative trading, not only to test and improve your own skills, but also to understand the techniques and strategies used by other robot traders.

In short, Python quantitative learning requires long-term continuous learning and practice, and needs to combine market dynamics and practical experience to continuously improve and optimize strategies.