Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
machine-learning
deep-learning
python
platform
research
finance
algorithmic-trading
auto-quant
fintech
investment
paper
quant
quant-dataset
quant-models
quantitative-finance
quantitative-trading
research-paper
stock-data
Обновлено 2024-09-12 18:44:27 +03:00
Microsoft Finance Time Series Forecasting Framework (FinnTS) is a forecasting package that utilizes cutting-edge time series forecasting and parallelization on the cloud to produce accurate forecasts for financial data.
microsoft
machine-learning
deep-learning
data-science
r
finance
time-series
forecasting
business
finnts
r-package
rstats
Обновлено 2024-08-05 18:19:32 +03:00
Multi-Agent Resource Optimization (MARO) platform is an instance of Reinforcement Learning as a Service (RaaS) for real-world resource optimization problems.
docker
reinforcement-learning
simulator
finance
inventory-management
logistics
maro
multi-agent
multi-agent-reinforcement-learning
operations-research
raas
resource-optimization
rl-algorithms
transportation
agent
citi-bike
Обновлено 2024-02-23 11:45:58 +03:00
Recurring Integrations Scheduler (RIS) is a solution that can be used in file-based integration scenarios for Dynamics 365 Finance and Dynamics 365 Supply Chain Management.
rest
finance
operations
dynamics
d365fo
quartzax
recurring-integrations-scheduler
ris
scheduler-service
supply-chain-management
d365
dixf
integration
package-api
Обновлено 2023-06-28 02:12:38 +03:00
Repo to showcase solution examples and learning content curated by the advanced analytics experts within Microsoft Finance
Обновлено 2022-09-02 22:38:04 +03:00
Qlib-Server is the data server system for Qlib. It enable Qlib to run in online mode. Under online mode, the data will be deployed as a shared data service. The data and their cache will be shared by all the clients. The data retrieval performance is expected to be improved due to a higher rate of cache hits. It will consume less disk space, too.
Обновлено 2022-07-08 05:15:09 +03:00