NYC Data Science Academy| Blog
Bootcamps
Lifetime Job Support Available Financing Available
Bootcamps
Data Science with Machine Learning Flagship ๐Ÿ† Data Analytics Bootcamp Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lesson
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories Testimonials Alumni Directory Alumni Exclusive Study Program
Courses
View Bundled Courses
Financing Available
Bootcamp Prep Popular ๐Ÿ”ฅ Data Science Mastery Data Science Launchpad with Python View AI Courses Generative AI for Everyone New ๐ŸŽ‰ Generative AI for Finance New ๐ŸŽ‰ Generative AI for Marketing New ๐ŸŽ‰
Bundle Up
Learn More and Save More
Combination of data science courses.
View Data Science Courses
Beginner
Introductory Python
Intermediate
Data Science Python: Data Analysis and Visualization Popular ๐Ÿ”ฅ Data Science R: Data Analysis and Visualization
Advanced
Data Science Python: Machine Learning Popular ๐Ÿ”ฅ Data Science R: Machine Learning Designing and Implementing Production MLOps New ๐ŸŽ‰ Natural Language Processing for Production (NLP) New ๐ŸŽ‰
Find Inspiration
Get Course Recommendation Must Try ๐Ÿ’Ž An Ultimate Guide to Become a Data Scientist
For Companies
For Companies
Corporate Offerings Hiring Partners Candidate Portfolio Hire Our Graduates
Students Work
Students Work
All Posts Capstone Data Visualization Machine Learning Python Projects R Projects
Tutorials
About
About
About Us Accreditation Contact Us Join Us FAQ Webinars Subscription An Ultimate Guide to
Become a Data Scientist
    Login
NYC Data Science Acedemy
Bootcamps
Courses
Students Work
About
Bootcamps
Bootcamps
Data Science with Machine Learning Flagship
Data Analytics Bootcamp
Artificial Intelligence Bootcamp New Release ๐ŸŽ‰
Free Lessons
Intro to Data Science New Release ๐ŸŽ‰
Find Inspiration
Find Alumni with Similar Background
Job Outlook
Occupational Outlook
Graduate Outcomes Must See ๐Ÿ”ฅ
Alumni
Success Stories
Testimonials
Alumni Directory
Alumni Exclusive Study Program
Courses
Bundles
financing available
View All Bundles
Bootcamp Prep
Data Science Mastery
Data Science Launchpad with Python NEW!
View AI Courses
Generative AI for Everyone
Generative AI for Finance
Generative AI for Marketing
View Data Science Courses
View All Professional Development Courses
Beginner
Introductory Python
Intermediate
Python: Data Analysis and Visualization
R: Data Analysis and Visualization
Advanced
Python: Machine Learning
R: Machine Learning
Designing and Implementing Production MLOps
Natural Language Processing for Production (NLP)
For Companies
Corporate Offerings
Hiring Partners
Candidate Portfolio
Hire Our Graduates
Students Work
All Posts
Capstone
Data Visualization
Machine Learning
Python Projects
R Projects
About
Accreditation
About Us
Contact Us
Join Us
FAQ
Webinars
Subscription
An Ultimate Guide to Become a Data Scientist
Tutorials
Data Analytics
  • Learn Pandas
  • Learn NumPy
  • Learn SciPy
  • Learn Matplotlib
Machine Learning
  • Boosting
  • Random Forest
  • Linear Regression
  • Decision Tree
  • PCA
Interview by Companies
  • JPMC
  • Google
  • Facebook
Artificial Intelligence
  • Learn Generative AI
  • Learn ChatGPT-3.5
  • Learn ChatGPT-4
  • Learn Google Bard
Coding
  • Learn Python
  • Learn SQL
  • Learn MySQL
  • Learn NoSQL
  • Learn PySpark
  • Learn PyTorch
Interview Questions
  • Python Hard
  • R Easy
  • R Hard
  • SQL Easy
  • SQL Hard
  • Python Easy
Data Science Blog > Capstone > Numerai Hedge Fund Competition

Numerai Hedge Fund Competition

Kamal Sandhu and Abhishek Desai
Posted on Mar 30, 2017

Numerai is a hedge fund that uses a machine learning competition to crowd source trade predictions. Competition is based on proprietary hedge fund data collected and curated by Numerai. Data is encrypted before being made public because it is highly valuable, proprietary and its quality provides a competitive edge for Numerai. This enables Numerai to obtain machine learning predictions on private data without ever making it public.

Homomorphic structure preserving encryption is used to transform and encrypt the data. Additionally, competition data is scaled and normalized. This scaling and normalization leaves limited room for feature engineering and participants have to rely on strong algorithms to achieve success. This makes it an algorithm vs algorithm competition rather than competitors spending endless hours feature engineering.

Numerai argues that high quality proprietary data is expensive to collect and provides a significant competitive advantage. Hedge funds and other financial institutions are in an optimal place to collect and curate this data but they have a strong incentive to keep it private and guard it. But these institutions only employ a small percent of the world's machine learning talent pool. It further argues that this makes financial markets machine learning inefficient.

This article at Wired.com provides a good background on Numerai.

With a competition style format on encrypted data, Numerai is able to obtain machine learning predictions from a larger pool of data scientist giving it a further competitive edge. Once individual participants submit their predictions, Numerai uses them to construct a meta-model. It uses the predictions made by this meta-model for live trading. Cross-entropy between the meta-model and user predictions determine the leader-board rankings for participants. Rationale for using a meta-model comes from the literature on ensemble learning.

Meta model construction and its bias over time can also be compared to financial markets. Financial markets are based on decisions made by individuals, which, when combined determine the market direction. Similar processes are at work with the movement of this meta model. It is build on the predictions of the individual participants and the direction is determined by them. Additionally, by using a meta-model formed out of a bag of predictions, Numerai is able to take a portfolio theory approach to predictions. Meta-model is made of individual bets made by many data scientists. Averaging of these bets significantly reduces the individual systematic bias and model variance. Resulting leftover bias can be interpreted as learning deduced from the data.

Our Approach

We tried dozens of algorithms to gauge their effectiveness on the data sets. Algorithms with high time, memory complexity or poor learning ability were eliminated step by step. Our aim for model selection was to maintain a quick turnaround time so that we can quickly benchmark results, twerk knobs and resubmit.

Following table shows the time taken, accuracy, loss and f1-score of multiple models. All the algorithms were executed on Google Compute High-CPU (64 core, 60 GB memory and Ubuntu 16.04) instances.

Algorithm Time (in secs) Accuracy Cross-entropy F1-score
Stochastic Gradient Descent with log optimizer 7.0169 0.5172 0.6924 0.52039
Random Forests Classifier 2.3170 0.5130 0.6926 0.4905
Gradient Boosting Classifier 110.1550 0.5140 0.6924 0.5074
Multilevel Perceptron 101.9041 0.5117 0.6926 0.5235
XGBoost Classifier 18.5751 0.5117 0.6925 0.5035
Extra Trees Classifier 26.2257 0.5165 0.6924 0.5221
Decision Trees Classifier 5.0928 0.5147 0.6927 0.4695
Logistic Regression Classifier 20.3553 0.5143 0.6928 0.5116
Keras Classifier 75.5020 0.5078 0.6929 0.4035

Results

We optimized and calibrated individual models using bayesian hyperparameter optimization and came up with three ensemble models that consistently produced low variance and low bias. Models were ensemble by soft voting, hard voting and using predictions from previous models as additional features. Deep neural network in Keras was used as a meta classifier. These same algorithms were used for predictions on the following weekโ€™s data-set. 

Soft voting ensemble model consistently ranked among top 5 for both the weeks. Other two models consistently ranked between the 5th and the 20th position.

Give us a shout out if you want to chat about additional details.

LinkedIn - Kamal Sandhu and Abhishek Desai

Future

In the near future, we would like to build a deep learning model using transfer learning along with full generalization and automation from week to week. Implementation using PySpark (for parallelizing) and by incorporating MongoDB (parameter tracking) will also be in the works.

Tools Used

Python, Anaconda, Jupyter Notebook, Pycharm, Pandas, JSON, Scikit-learn, Xgboost, Keras, Hyperopt, Scikit-optimize, Mlxtend, Google Compute, Linux

About Authors

Kamal Sandhu

Kamal Sandhu is a finance professional keenly interested in the potential of data science in combination with financial and management theory. He is working towards the Chartered Financial Analyst (CFA) program and the Financial Risk Manager (FRM) program....
View all posts by Kamal Sandhu >

Abhishek Desai

I'm interested in all things mechanical, but particularly the ability to use machine learning and algorithm design to to locate the areas of development where efficiency can be harnessed to advance business interests. With 10+ years of experience...
View all posts by Abhishek Desai >

Related Articles

Capstone
Catching Fraud in the Healthcare System
Capstone
The Convenience Factor: How Grocery Stores Impact Property Values
Capstone
Acquisition Due Dilligence Automation for Smaller Firms
Machine Learning
Pandemic Effects on the Ames Housing Market and Lifestyle
Machine Learning
The Ames Data Set: Sales Price Tackled With Diverse Models

Leave a Comment

Cancel reply

You must be logged in to post a comment.

No comments found.

View Posts by Categories

All Posts 2399 posts
AI 7 posts
AI Agent 2 posts
AI-based hotel recommendation 1 posts
AIForGood 1 posts
Alumni 60 posts
Animated Maps 1 posts
APIs 41 posts
Artificial Intelligence 2 posts
Artificial Intelligence 2 posts
AWS 13 posts
Banking 1 posts
Big Data 50 posts
Branch Analysis 1 posts
Capstone 206 posts
Career Education 7 posts
CLIP 1 posts
Community 72 posts
Congestion Zone 1 posts
Content Recommendation 1 posts
Cosine SImilarity 1 posts
Data Analysis 5 posts
Data Engineering 1 posts
Data Engineering 3 posts
Data Science 7 posts
Data Science News and Sharing 73 posts
Data Visualization 324 posts
Events 5 posts
Featured 37 posts
Function calling 1 posts
FutureTech 1 posts
Generative AI 5 posts
Hadoop 13 posts
Image Classification 1 posts
Innovation 2 posts
Kmeans Cluster 1 posts
LLM 6 posts
Machine Learning 364 posts
Marketing 1 posts
Meetup 144 posts
MLOPs 1 posts
Model Deployment 1 posts
Nagamas69 1 posts
NLP 1 posts
OpenAI 5 posts
OpenNYC Data 1 posts
pySpark 1 posts
Python 16 posts
Python 458 posts
Python data analysis 4 posts
Python Shiny 2 posts
R 404 posts
R Data Analysis 1 posts
R Shiny 560 posts
R Visualization 445 posts
RAG 1 posts
RoBERTa 1 posts
semantic rearch 2 posts
Spark 17 posts
SQL 1 posts
Streamlit 2 posts
Student Works 1687 posts
Tableau 12 posts
TensorFlow 3 posts
Traffic 1 posts
User Preference Modeling 1 posts
Vector database 2 posts
Web Scraping 483 posts
wukong138 1 posts

Our Recent Popular Posts

AI 4 AI: ChatGPT Unifies My Blog Posts
by Vinod Chugani
Dec 18, 2022
Meet Your Machine Learning Mentors: Kyle Gallatin
by Vivian Zhang
Nov 4, 2020
NICU Admissions and CCHD: Predicting Based on Data Analysis
by Paul Lee, Aron Berke, Bee Kim, Bettina Meier and Ira Villar
Jan 7, 2020

View Posts by Tags

#python #trainwithnycdsa 2019 2020 Revenue 3-points agriculture air quality airbnb airline alcohol Alex Baransky algorithm alumni Alumni Interview Alumni Reviews Alumni Spotlight alumni story Alumnus ames dataset ames housing dataset apartment rent API Application artist aws bank loans beautiful soup Best Bootcamp Best Data Science 2019 Best Data Science Bootcamp Best Data Science Bootcamp 2020 Best Ranked Big Data Book Launch Book-Signing bootcamp Bootcamp Alumni Bootcamp Prep boston safety Bundles cake recipe California Cancer Research capstone car price Career Career Day ChatGPT citibike classic cars classpass clustering Coding Course Demo Course Report covid 19 credit credit card crime frequency crops D3.js data data analysis Data Analyst data analytics data for tripadvisor reviews data science Data Science Academy Data Science Bootcamp Data science jobs Data Science Reviews Data Scientist Data Scientist Jobs data visualization database Deep Learning Demo Day Discount disney dplyr drug data e-commerce economy employee employee burnout employer networking environment feature engineering Finance Financial Data Science fitness studio Flask flight delay football gbm Get Hired ggplot2 googleVis H20 Hadoop hallmark holiday movie happiness healthcare frauds higgs boson Hiring hiring partner events Hiring Partners hotels housing housing data housing predictions housing price hy-vee Income industry Industry Experts Injuries Instructor Blog Instructor Interview insurance italki Job Job Placement Jobs Jon Krohn JP Morgan Chase Kaggle Kickstarter las vegas airport lasso regression Lead Data Scienctist Lead Data Scientist leaflet league linear regression Logistic Regression machine learning Maps market matplotlib Medical Research Meet the team meetup methal health miami beach movie music Napoli NBA netflix Networking neural network Neural networks New Courses NHL nlp NYC NYC Data Science nyc data science academy NYC Open Data nyc property NYCDSA NYCDSA Alumni Online Online Bootcamp Online Training Open Data painter pandas Part-time performance phoenix pollutants Portfolio Development precision measurement prediction Prework Programming public safety PwC python Python Data Analysis python machine learning python scrapy python web scraping python webscraping Python Workshop R R Data Analysis R language R Programming R Shiny r studio R Visualization R Workshop R-bloggers random forest Ranking recommendation recommendation system regression Remote remote data science bootcamp Scrapy scrapy visualization seaborn seafood type Selenium sentiment analysis sentiment classification Shiny Shiny Dashboard Spark Special Special Summer Sports statistics streaming Student Interview Student Showcase SVM Switchup Tableau teachers team team performance TensorFlow Testimonial tf-idf Top Data Science Bootcamp Top manufacturing companies Transfers tweets twitter videos visualization wallstreet wallstreetbets web scraping Weekend Course What to expect whiskey whiskeyadvocate wildfire word cloud word2vec XGBoost yelp youtube trending ZORI

NYC Data Science Academy

NYC Data Science Academy teaches data science, trains companies and their employees to better profit from data, excels at big data project consulting, and connects trained Data Scientists to our industry.

NYC Data Science Academy is licensed by New York State Education Department.

Get detailed curriculum information about our
amazing bootcamp!

Please enter a valid email address
Sign up completed. Thank you!

Offerings

  • HOME
  • DATA SCIENCE BOOTCAMP
  • ONLINE DATA SCIENCE BOOTCAMP
  • Professional Development Courses
  • CORPORATE OFFERINGS
  • HIRING PARTNERS
  • About

  • About Us
  • Alumni
  • Blog
  • FAQ
  • Contact Us
  • Refund Policy
  • Join Us
  • SOCIAL MEDIA

    ยฉ 2025 NYC Data Science Academy
    All rights reserved. | Site Map
    Privacy Policy | Terms of Service
    Bootcamp Application