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algonotes

【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

alphatrading

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.

alphaware

tools for alpha research

barra-multiple-factor-risk-model

Barra-Multiple-factor-risk-model

blog-posts

coder2gwy

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。

condor

COmplex Network Description Of Regulators, an R package for bipartite network analysis

creditcard-score

The project of credit scoring Code

da-p2

Explore data set

exercise

Python Exercise

fama-french-3-factor-model

Applying the Fama Three-factor Model to the Chinese stock market

fama-french-funds

An exercise similar to Fama, French (2010). Goal is to identify and evaluate the luck vs skill of active managers.

famafrench

Implementation of 5-factor Fama French Model

famafrench3factorsmodel

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

feature-engineering-for-ml-zh

:book: [译] 面向机器学习的特征工程

ff3factor

Fama French 3 Factor Model

gbdt

gdbt implement by scikit-learn

gplearn

Genetic Programming in Python, with a scikit-learn inspired API

gplearn_guide

A short demo of gplearn for python club at university of idaho

gplearn_stock

改进gplearn,主要使用在股票公式挖掘

jane-street-market-prediction

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.

jsmpwork

kaggle竞赛Jane Street Market Prediction实操代码

kaggle

Competition Review

kaggle-airbnb-recruiting-new-user-bookings

2nd Place Solution in Kaggle Airbnb New User Bookings competition

kaggle-avito

Winning solution to the Avito CTR competition

kaggle-jane-street-market-prediction

Tommy's solution to a kaggle competition at https://www.kaggle.com/c/jane-street-market-prediction

kaggle-titanic

Entry in the Titanic: Machine Learning from Disaster competition @ kaggle.com

kaggle_loan_default_prediction

R code for Kaggle's Loan Default Prediction - Imperial College London challenge

machine-learning-for-algorithmic-trading-second-edition

Code and resources for Machine Learning for Algorithmic Trading, 2nd edition.

machinelearningtrick

Machine Learning Trick : GBDT_Feature Blending Stacking CascadeForest