Pinned Repositories
A02_Data_and_Tools
A01_Data_Analysis
A03_Portfolios
A04_Portfolios_Advanced
A05_CAPM
A06_Multifactor_Models
A07_Performance_Evaluation
AssetAllocation
:exclamation: This is a read-only mirror of the CRAN R package repository. AssetAllocation — Backtesting Simple Asset Allocation Strategies. Homepage: https://github.com/rubetron/AssetAllocation Report bugs for this package: https://github.com/rubetron/AssetAllocation/issues
crypto
Cryptocurrency Historical Market Data R Package
crypto2
Cryptocurrency Market Data
007-Koeffel's Repositories
007-Koeffel/MasterLab
Is used for executing the MasterLab code.
007-Koeffel/AssetAllocation
:exclamation: This is a read-only mirror of the CRAN R package repository. AssetAllocation — Backtesting Simple Asset Allocation Strategies. Homepage: https://github.com/rubetron/AssetAllocation Report bugs for this package: https://github.com/rubetron/AssetAllocation/issues
007-Koeffel/Tokyo_Stock_Predictions
Success in any financial market requires one to identify solid investments. When a stock or derivative is undervalued, it makes sense to buy. If it's overvalued, perhaps it's time to sell. While these finance decisions were historically made manually by professionals, technology has ushered in new opportunities for retail investors. Data scientists, specifically, may be interested to explore quantitative trading, where decisions are executed programmatically based on predictions from trained models. There are plenty of existing quantitative trading efforts used to analyze financial markets and formulate investment strategies. To create and execute such a strategy requires both historical and real-time data, which is difficult to obtain especially for retail investors. This competition will provide financial data for the Japanese market, allowing retail investors to analyze the market to the fullest extent. Japan Exchange Group, Inc. (JPX) is a holding company operating one of the largest stock exchanges in the world, Tokyo Stock Exchange (TSE), and derivatives exchanges Osaka Exchange (OSE) and Tokyo Commodity Exchange (TOCOM). JPX is hosting this competition and is supported by AI technology company AlpacaJapan Co.,Ltd. This competition will compare your models against real future returns after the training phase is complete. The competition will involve building portfolios from the stocks eligible for predictions (around 2,000 stocks). Specifically, each participant ranks the stocks from highest to lowest expected returns and is evaluated on the difference in returns between the top and bottom 200 stocks. You'll have access to financial data from the Japanese market, such as stock information and historical stock prices to train and test your model. All winning models will be made public so that other participants can learn from the outstanding models. Excellent models also may increase the interest in the market among retail investors, including those who want to practice quantitative trading. At the same time, you'll gain your own insights into programmatic investment methods and portfolio analysis―and you may even discover you have an affinity for the Japanese market.
007-Koeffel/Optimal_Desicion_Making_Code
Here I store my master machine learning code
007-Koeffel/Machine-Learning-for-Finance
Machine Learning for Finance, published by Packt
007-Koeffel/A07_Performance_Evaluation
007-Koeffel/crypto2
Cryptocurrency Market Data
007-Koeffel/A06_Multifactor_Models
007-Koeffel/A05_CAPM
007-Koeffel/A04_Portfolios_Advanced
007-Koeffel/A02_Data_and_Tools
007-Koeffel/A03_Portfolios
007-Koeffel/A01_Data_Analysis
007-Koeffel/crypto
Cryptocurrency Historical Market Data R Package