The purpose of the ricequant quantification community is to allow those who are interested in quantifying to collide and learn useful and practical quantitative knowledge in sharing and arguing.
Depending on your contribution to the community and the quality of the discussion, from the introduction of programming to technical indicators to multi-factor stock selection, financial data analysis, including a lot of knowledge.
I am here to organize the next community to send out the content, and classification, so that it is easier for everyone to find the corresponding knowledge points. This post is constantly updated
Thank you so much for a lot of interesting discussions, because of the length of the reasons are not listed. Its growth, depends on the contribution and efforts of everyone!
The following begins poison:
Getting Started with Python
-Python basic syntax and basic data types
Python control flow and basic data structure
[Scattered sand] Python Scientific Computing Series-Station B link
Write more Python-style code to improve coding efficiency
Data analysis teaching video of scattered sand-python
Some introductory learning materials for Python
Two basic data structures for Pandas series and Dataframe
Ricequant Platform Introductory Teaching
-[ricequant Teaching 1]-Introduction to Ricequant and strategy trading IDE
[Ricequant Teaching 2]-start writing the first quantitative trading strategy
[Ricequant Teaching 3]-backtesting The first quantitative trading strategy
[Ricequant Teaching 4]-using ta-lib to calculate technical indicators in a python strategy
[Ricequant Teaching 5]-fundamental inquiry to explore value investment & Screener functions
[Ricequant Study Teaching 1]-Introduction to the basic functions of the study platform
Technical analysis indicators and the use of TALIB
-Use ta-lib to calculate technical indicators in a python strategy
SMA starter Strategy-moving averages
Stoch (KD indicator)
Average trend index ADX and trend index DMI
RSI indicator
Trend indicator Share-Aroon indicator
Bollinger Bands Application
* * [value investment] [Financial analysis] [fundamentals] * *
Explore value investments & Screener features with fundamental queries
Graham number Graham Digital value investment law
Greenblatt Value Investment Magic formula
Strategy-Michael Hi-Profit investment
Investment growth Companies
Growth investment combined with market technical index strategy
Reverse strategy backtesting on ricequant with stock price-earnings ratio data
Fundamentals, high yield intersection strategy
Fundamental selector: Piotroski F-score Ranking System
Multi-Factor strategy (I.)
Classic strategy
The full Python version of the Turtle trading system
Trend strategy Small trial sledgehammer, the construction of turtle trading system
Pairing Trading-paper-version
Paired trades (revised Version)
The sharp weapon of the market--grid trading strategy
Wheel motion strategy, ETF
-Less Correlated ETF portfolio strategy
ETF Wheel Action strategy
ETF rotation strategy in a-share market
CSI 300ETF Arbitrage
A-Share ETF secret History-turn
Momentum, trend, reversal
-Worst-k Strategy
Dual Thrust Trading Strategy
A grid trading method that is less effective than imagined
Trend or reversal-some thoughts on MFI and RSI
Public opinion Big Data
-Snowball-Snowball roll up-in the end whether we can rely on public opinion events to make money
- Hold a card together, a-share brand concept strategy sharing
Papers, books, reading materials, etc.
[Paper sharing] classical academic papers in the field of quantitative trading
Start your journey of quantification | Quantify Investment Learning Resources
[Python tutorial] Some python introductory learning materials, continue to add ...
[Paper Share] 101 x Alpha-world Quant
[Learning materials] Python, R language, econometrics, investment books, research reports, etc. (Book+video)
Introduction to Python, quantitative strategies, quantitative knowledge summary stickers