GSTOCK
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'GStock' is a distributed computing project formed in 2006 for stock market analysis. It is enabled by users that volunteer to download and run a software client and donate some idle capacity of their computer processing unit (CPU). The gathered computing power is used to search for an investment strategy that historically worked best for each individual stock. GStock scans over 1 billion investments strategies on over 4,000 US publicly traded stocks.
GStock applies Technical Analysis models in its calculations. The investment strategies found to work best for each stock produce periodic BUY and SELL trading signals. These signals are publicly available on the GStock.com website along with past statistics and charts with the BUY and SELL signals plotted on them for visual comparison by users.
★ Distributed computing
★ List of distributed computing projects
★ Parallel computing
★ Grid computing
★ GStock.com website
★ List of financial distributed computing projects
'GStock' is a distributed computing project formed in 2006 for stock market analysis. It is enabled by users that volunteer to download and run a software client and donate some idle capacity of their computer processing unit (CPU). The gathered computing power is used to search for an investment strategy that historically worked best for each individual stock. GStock scans over 1 billion investments strategies on over 4,000 US publicly traded stocks.
GStock applies Technical Analysis models in its calculations. The investment strategies found to work best for each stock produce periodic BUY and SELL trading signals. These signals are publicly available on the GStock.com website along with past statistics and charts with the BUY and SELL signals plotted on them for visual comparison by users.
| Contents |
| References |
| External links |
References
★ Distributed computing
★ List of distributed computing projects
★ Parallel computing
★ Grid computing
External links
★ GStock.com website
★ List of financial distributed computing projects
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psst.. try this: add to faves

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