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【浅梦学习笔记】文章汇总:包含 排序&CXR预估,召回匹配,用户画像&特征工程,推荐搜索综合 计算广告,大数据,图算法,NLP&CV,求职面试 等内容
An workflow in factor-based equity trading, including factor analysis and factor modeling. For well-established factor models, I implement APT model, BARRA's risk model and dynamic multi-factor model in this project.
tools for alpha research
Barra-Multiple-factor-risk-model
互联网首份程序员考公指南,由3位已经进入体制内的前大厂程序员联合献上。
COmplex Network Description Of Regulators, an R package for bipartite network analysis
The project of credit scoring Code
Explore data set
Python Exercise
Applying the Fama Three-factor Model to the Chinese stock market
An exercise similar to Fama, French (2010). Goal is to identify and evaluate the luck vs skill of active managers.
Implementation of 5-factor Fama French Model
Examine Fama French 3 Factors Model in London Stock Market/在伦敦证券市场上验证Fama French三因子模型
:book: [译] 面向机器学习的特征工程
Fama French 3 Factor Model
gdbt implement by scikit-learn
Genetic Programming in Python, with a scikit-learn inspired API
A short demo of gplearn for python club at university of idaho
改进gplearn,主要使用在股票公式挖掘
https://www.kaggle.com/c/jane-street-market-prediction/overview “Buy low, sell high.” It sounds so easy…. In reality, trading for profit has always been a difficult problem to solve, even more so in today’s fast-moving and complex financial markets. Electronic trading allows for thousands of transactions to occur within a fraction of a second, resulting in nearly unlimited opportunities to potentially find and take advantage of price differences in real time. In a perfectly efficient market, buyers and sellers would have all the agency and information needed to make rational trading decisions. As a result, products would always remain at their “fair values” and never be undervalued or overpriced. However, financial markets are not perfectly efficient in the real world. Developing trading strategies to identify and take advantage of inefficiencies is challenging. Even if a strategy is profitable now, it may not be in the future, and market volatility makes it impossible to predict the profitability of any given trade with certainty. As a result, it can be hard to distinguish good luck from having made a good trading decision. In the first three months of this challenge, you will build your own quantitative trading model to maximize returns using market data from a major global stock exchange. Next, you’ll test the predictiveness of your models against future market returns and receive feedback on the leaderboard. Your challenge will be to use the historical data, mathematical tools, and technological tools at your disposal to create a model that gets as close to certainty as possible. You will be presented with a number of potential trading opportunities, which your model must choose whether to accept or reject. In general, if one is able to generate a highly predictive model which selects the right trades to execute, they would also be playing an important role in sending the market signals that push prices closer to “fair” values. That is, a better model will mean the market will be more efficient going forward. However, developing good models will be challenging for many reasons, including a very low signal-to-noise ratio, potential redundancy, strong feature correlation, and difficulty of coming up with a proper mathematical formulation.
kaggle竞赛Jane Street Market Prediction实操代码
Competition Review
2nd Place Solution in Kaggle Airbnb New User Bookings competition
Winning solution to the Avito CTR competition
Tommy's solution to a kaggle competition at https://www.kaggle.com/c/jane-street-market-prediction
Entry in the Titanic: Machine Learning from Disaster competition @ kaggle.com
R code for Kaggle's Loan Default Prediction - Imperial College London challenge
Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.
Machine Learning Trick : GBDT_Feature Blending Stacking CascadeForest
A declarative, efficient, and flexible JavaScript library for building user interfaces.
🖖 Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. 📊📈🎉
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google ❤️ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.