The first financial management course for programmers Python quantitative trading system combat
To create an automatic trading platform, novice can also improve the income of financial management lecturer DeltaF, in the past 5 years, the average annual income of personal investment and financial management is over 25%. If you also want to boost your after-sleep income and make easy money, then this course is for you. The course is based on a complete and real quantitative trading business, combined with the financial management experience of the teacher and the use of programming techniques to assist investment skills, so that you can deal with a variety of complex investment situations.
Basic Knowledge of Python (getting started)
General chapter contents of environmental parameters: Chapter 1 Quantitative science: A quick stop to knowledge literacy, understanding what is quantitative, basic financial science.
There are 8 sessions (56 minutes)
1-1 Course Guide – Open the Door to Quantitative Trading (09:36) Quantified investment history 1-5 Quantified investment history 1-6 How to build quantitative trading system (07:16) 【作 文 】 : Chapter 2 Getting Stock Data This chapter explains what a stock is, and how to use Python to get stock buying and selling data
A total of 12 sections (129 minutes) fold up the list
2-1 This chapter guide & Learning Plan 2-2 What is a stock? (07:11) 2-3 Ways to obtain stock data 2-4 Use JQData to query market data (21:18) 2-5 Use resample function to transform time series (19:51) 2-6 【 homework 】 The application of resample function – Short answer 2-7 【作 文 】 Use JQData to calculate the valuation index – Short Answer questions 2-10 Real-time update of stock data (22:01) 2-11 【 practical 】 : Chapter 3 Calculating The Buy and sell Index The buy and sell index is an important criterion for us to judge whether we need to buy and sell in investment. In this chapter, you will learn to calculate the commonly used quantitative indicators, such as returns and micro risk indicators
A total of 12 sections (156 minutes) close the list
3-3 Use shift function to calculate the rise and fall (12:05) 3-4 Imitate stock buying and selling: buy and sell signals (21:24) 3-5 imitate stock buying and selling: Calculate the return on holding (21:21) 3-6 Imitate stock trading: calculate the cumulative return rate (10:21) 3-7 [Assignment] : Compare the cumulative return rate of three stocks and stop visualization 3-8 Calculate the risk index: maximum retracement (19:51) 3-9 Calculate the risk return index: Sharpe ratio (19:01) 3-10 [extra food] : apply the maximum retractable and Sharpe ratio to select funds (17:24) 3-11 [Actual situation] : compare three stocks sharpe index (20:42) 3-12 A good buying and selling strategy is the soul of quantitative buying and selling. In this chapter, you will learn how to design a timing strategy and how to use moving averages to create a timing strategy and optimize the timing of buying and selling stocks.
Wrap up the list for 6 sessions (95 minutes)
4-2 Data preparation: Localize stock data (39:46) 4-3 what is the average strategy (12:33) 4-4 double average strategy: generate buy and sell signals (23:02) 4-5 Double average strategy: calculate signal yield (19:32) 4-6 Chapter 5 Designing buying and selling Strategies: Stock selection Strategy This chapter is still A strategic design chapter, this chapter will take you to design A stock selection strategy, understand the central logic of stock selection strategy, and based on the yield of momentum stock selection strategy, and verify its effectiveness.
There are 11 sessions (142 minutes)
5-3 Momentum strategy: Select stock pool (16:30) 5-4 Momentum strategy: Calculate momentum factor (31:19) 5-5 Momentum strategy: Generate buy and sell signal (20:10) 5-6 momentum strategy: Calculate portfolio yield (equal weight) (20:30) 5-7 [Assignment] Complete reverse momentum strategy 5-8 Print strategy evaluation index (33:25) 5-9 Expand: Chapter 6 Data backtest and optimization Strategy Design is good, but also requires a large number of data to stop textual research, in order to ensure the true and effective strategy, In this chapter, we will stop the practical data testing of the two trading strategies designed in the previous two chapters, learn to use PyalgoTrade quantitative library to generate massive data and stop the backtesting, and further verify the accuracy of the strategy.
A total of 10 sections (113 minutes) close the list
6-1 What are the commonly used data backtesting frameworks in this chapter? 6-4 Initializing the PyAlgoTrade Development Environment (17:58) 6-5 Defining Data and Strategy (19:42) 6-6 PyAlgoTrade: 6-7 PyAlgoTrade: Buy and sell Signal Visualization (12:36) 6-8 Exercise: PyAlgoTrade Backtest Double Moving Average Strategy (13:56) 6-9 PyAlgoTrade Backtest MACD 6-10 Chapter 7: Complete stock Trading Use the strategy we designed to automate Python stock trading and make your strategy work for you!
Chapter 8 Advanced content sharing Use more analysis tools to advance your quantitative learning journey
This course is constantly being updated