Forecasting stock charts in R.

example-chart-sma-SPY.png

example-predict-SPY.png

forecast.R

library(quantmod)
library(forecast)

# Set the ticker symbol.
ticker = 'SPY'

# Set the date range.
from <- as.Date('01/02/10', format='%m/%d/%y')
to <- Sys.Date() #as.Date('08/14/15', format='%m/%d/%y')

# Get the data.
data <- get(getSymbols('JNK', src='google', from=from, to=to))

# Display a 20-day moving average of the data.
chartSeries(data, TA="addSMA(20)")

# Convert to monthly open prices.
monthly <- to.monthly(data)
openPrices <- Op(monthly)

# Create a time-series.
timeSeries <- ts(openPrices, frequency=12)

# Plot the time series.
plot(timeSeries, xlab='Years', ylab = 'Stock')

# Decompose a time series and plot the chart.
plot(decompose(timeSeries), xlab='Years')

# Create a training and test set.
training <- window(timeSeries, start=1, end=4)
testing <- window(timeSeries, start=4)

# Plot the moving average of the time-series.
plot(training)
lines(ma(training, order=3), col='red')

# Predict with exponential smoothing.
fit <- ets(training, model='MMM')
fcast <- forecast(fit)

# Plot the training and forecast of the testing data (red).
plot(fcast)
lines(testing, col='red')

# Check accuracy.
accuracy(fcast, testing)