Code Architecture of Mid-Term Trading Strategy
Hypothesis: Mid-term investment opportunities with high momentum and low risk behavior in US Stocks for profitable returns with low initial capital. Recommended tools include Rstudio, Data.table, quantmod, Ranger, Ggplot2, Tradingview, and more. The architecture involves initializing data, downloading historical data, cleaning and pre-processing data.
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Presentation Transcript
Artemis Distributed system Hunting for Bugs with Artemis Dryad Overview Logs System Architecture Data Collectio n Data collection Database View GUI Plug-ins GUI Plug-ins Conclusio ns
Hunting for Bugs with Artemis Gabriela F. Cre u-Cioc rlie Mihai Budiu Moises Goldszmidt Microsoft Research, Silicon Valley WASL 2008 This presentation is built and should be viewed with pptPlex: http://www.officelabs.com/projects/pptPlex/Pages
Artemis Goal One-stop shop for performance analysis of distributed systems
Principles 1) Modular: Separate generic from application specific parts 2) Extensible: add new analyses via plug-ins 3) Interactive: human expert part of the analysis loop
Distributed system Distributed Logs Data collection Database Local View GUI Plug-ins
Distributed system Application- Specific Logs Data collection Generic Database View GUI Plug-ins
Dryad Application Structure Input files Channels Stage Output files sort grep awk sed perl sort grep awk sed grep sort Vertices
Dryad System Architecture data plane job schedule V V V Serv Serv Serv control plane Job manager cluster
Text Text Text Binary Binary Binary XML XML XML Perfmon Perfmon Perfmon Data 10GB-1TB Copy DryadLINQ application Persisted data Parse Filter Aggregate 100MB-1GB
Complex statistics: HiLighter plug-in Key Performance Indicator Binary search over logistic regression with L1 regularization Correlated metrics Metrics 22
Interactive Analysis KPI Selection Feature Computation Visualization Hilighter
Conclusions Automatic diagnosis Goal Statistical analyses Feature extraction Artemis today Summarization Raw data Distributed system
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