Quantitative Analysis | Dmitry Pukhov

Load the data

library(curl)
library(quantmod)
library(rusquant)
library(xts)
library(utils)

# ASTSA lib is for ARIMA
library(astsa)

# Load the data
price.ohlc <- getSymbols("SBER", from = Sys.Date()-1, to = Sys.Date(), src = "Finam", period = "1min", auto.assign = FALSE)
price.cl <- Cl(price.ohlc)
# Display close price on plot
plot(price.cl)

library(curl)

library(quantmod)

library(rusquant)

library(xts)

library(utils)

# ASTSA lib is for ARIMA

library(astsa)

# Load the data

price.ohlc <- getSymbols("SBER", from = Sys.Date()-1, to = Sys.Date(), src = "Finam", period = "1min", auto.assign = FALSE)

price.cl <- Cl(price.ohlc)

# Display close price on plot

plot(price.cl)

R code template to predict the future with ARIMA auto regression. To try different models, change the line with fit <- arima(...)

# Rusquant is quantmod library with ability 
# to load Russian market with intraday data
# Use vanilla quantmod for other markets
library(rusquant)

# Load symbol from data provider
data.raw <- getSymbols("SBER", src = "Finam", period="5min", from = "2017-08-25", to = "2017-08-25", auto.assign = FALSE)

# Convert price to stationary data
data <- diff(log(Cl(data.raw)))

# Train auto regression arima model
fit <- arima(data, order=c(10,0,1))

# Predict the future
pred <- predict(fit, n.ahead=100)

# Xts index lost during prediction, so convert predicted data to xts
pred.index <- as.POSIXct(index(pred$pred), origin = min(index(data)))
pred.xts <- xts(pred$pred, order.by = pred.index)

# Draw original and predicted values on the same chart
plot(c(data, pred.xts), type = "n")
lines(data)
lines(pred.xts, col = "red")

# Rusquant is quantmod library with ability

# to load Russian market with intraday data

# Use vanilla quantmod for other markets

library(rusquant)

# Load symbol from data provider

data.raw <- getSymbols("SBER", src = "Finam", period="5min", from = "2017-08-25", to = "2017-08-25", auto.assign = FALSE)

# Convert price to stationary data

data <- diff(log(Cl(data.raw)))

# Train auto regression arima model

fit <- arima(data, order=c(10,0,1))

# Predict the future

pred <- predict(fit, n.ahead=100)

# Xts index lost during prediction, so convert predicted data to xts

pred.index <- as.POSIXct(index(pred$pred), origin = min(index(data)))

pred.xts <- xts(pred$pred, order.by = pred.index)

# Draw original and predicted values on the same chart

plot(c(data, pred.xts), type = "n")

lines(data)

lines(pred.xts, col = "red")

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Dmitry Pukhov

Software Developer, Architect

Tag Archives: Quantitative Analysis

Use ASTSA and ARIMA for time series prediction

Load the data

Look at ACF and PACF

Predict financial data with R