java GUI for stock price prediction using R functions
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This simple GUI java that allows user to upload indices. This software used the statistical arbitrage methodology. Steps are like below and more as shown in screenshot. 1) User may choose to upload a csv file that contains historical prices/returns from indices or choose from 2. This software will do the Augmented Dickey Fuller Test to make the time series stationary. Lag will be determined inside and R will do that at back end using (AIC ) to make the pricing model fit real data best. 3. Software will test Cointegration on every time series and find the index that has the highest cointegration with the user selected time series and software will do rest to make the pair is cointegrated 4. GARCH is used to forecast the volatility 5. Profit and Loss of the pairs will be determined 6. Backtesting
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This simple GUI java that allows user to upload indices. This software used the statistical arbitrage methodology. Steps are like below and more as shown in screenshot. User may choose to upload a csv file that contains historical prices/returns from indices or choose from 2. This software will do the Augmented Dickey Fuller Test to make the time series stationary. Lag will be determined inside and R will do that at back end using (AIC ) to make the pricing model fit real data best. 3. Software will test Cointegration on every time series and find the index that has the highest cointegration with the user selected time series and software will do rest to make the pair is cointegrated 4. GARCH is used to forecast the volatility 5. Profit and Loss of the pairs will be determined 6. Backtesting
Transcript of java GUI for stock price prediction using R functions
This simple GUI java that allows user to upload indices. This software used the statistical arbitrage methodology. Steps are like below and more as shown in screenshot.
1) User may choose to upload a csv file that contains historical prices/returns from indices or choose from
2. This software will do the Augmented Dickey Fuller Test to make the time series stationary. Lag will be determined inside and R will do that at back endusing (AIC ) to make the pricing model fit real data best.
3. Software will test Cointegration on every time series and find the index that has the highest cointegration with the user selected time series and software will do rest to make the pair is cointegrated
4. GARCH is used to forecast the volatility
5. Profit and Loss of the pairs will be determined
6. Backtesting
