R_scripts/PortfolioEffect.R at master · algoquant/R_scripts · GitHub

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###
# scripts for "Buy-and-Hold Strategies"
# https://www.portfolioeffect.com/blog/2015/10/14/intraday-strategy-backtesting-in-r-part-1-buy-and-hold-strategies/


# Create empty portfolio
port_folio <- portfolio_create("2014-10-01 09:30:00", "2014-10-02 16:00:00")

# Add position AAPL and GOOG to portfolio
portfolio_addPosition(port_folio, "AAPL", 100)
portfolio_addPosition(port_folio, "GOOG", 200)

# position_price() returns matrix with two columns: time in miliseconds, and price
price_AAPL <- position_price(port_folio, "AAPL")
foo <- price_AAPL[seq(from=1, to=nrow(price_AAPL), by=1000)]
head(price_AAPL)
dim(price_AAPL)
class(price_AAPL)
foo <- price_AAPL[1, ]
foo[1]%/%1000
foo[1]%%1000
sprintf(fmt="%f", foo[1])
foo <- price_AAPL
foo[ , 1] <- foo[ , 1]/1000
foo <- xts(foo[ , 2], order.by=as.POSIXct(foo[ , 1], origin="1970-01-01"))
colnames(foo) <- "AAPL"
head(foo)
chart_Series(x=foo, name="AAPL prices")

# suppress exponential notation and increase display digits
options(scipen=100, digits=6)
head(foo)
as.POSIXct("2014-10-01 09:30:00", origin="1970-01-01")
as.POSIXct(foo[1 , 1], origin="1970-01-01")

plot(port_folio)

print(port_folio)

# Plot portfolio value changes over time
util_plot2d(portfolio_value(port_folio), title="Portfolio value in USD")

# Compute portfolio and position expected return (daily)
util_plot2d(position_expectedReturn(port_folio, "AAPL"), title="Expected Return, daily", Legend="AAPL") +
  util_line2d(position_expectedReturn(port_folio, "GOOG"), Legend="GOOG") +
  util_line2d(portfolio_expectedReturn(port_folio), Legend="Portfolio")

# Compute portfolio and position variance (daily)
util_plot2d(position_variance(port_folio, "AAPL"), title="Variance, daily", Legend="AAPL") +
  util_line2d(position_variance(port_folio, "GOOG"), Legend="GOOG") +
  util_line2d(portfolio_variance(port_folio), Legend="Portfolio")

#  Compute portfolio and position Sharpe Ratio (daily)
util_plot2d(positharpeRatio(port_folio, "AAPL"), title="Sharpe Ratio, daily", Legend="AAPL") +
  util_line2d(positharpeRatio(port_folio, "GOOG"), Legend="GOOG") +
  util_line2d(portfolio_sharpeRatio(port_folio), Legend="Portfolio")


###
# scripts for "Rule-based Strategies"
# https://www.portfolioeffect.com/blog/2015/10/14/intraday-strategy-backtesting-in-r-part-2-rule-based-strategies/


# create function for moving average over a vector
mov_avg <- function(x, lookb){
  output <- x
  n_row <- NROW(x)
  cumsumv <- cumsum(x)
  output[(lookb+1):n_row] <-
    (cumsumv[-(1:lookb)]-cumsumv[-((n_row-lookb+1):n_row)])/lookb
  output[1:lookb] <- cumsum(x[1:lookb])/(1:lookb)
  return(output-0.0000000001)
}  # end mov_avg


symbol <- "GOOG"
datestart <- "2014-10-13 09:30:00"
date_end <- "2014-10-14 16:00:00"

highFrequencyPortfolio <- portfolio_create(fromTime=datestart, toTime=date_end)
lowFrequencyPortfolio <- portfolio_create(fromTime=datestart, toTime=date_end)

# add position "GOOG" to portfolios
portfolio_addPosition(highFrequencyPortfolio, symbol, 1)
price <- position_price(highFrequencyPortfolio, symbol)
printTime <- price[, 1]

# create vector of portfolio weights depending on rules
highFrequencyStrategy <- array(0, dim=NROW(price))
highFrequencyStrategy[price[, "value"] > mov_avg(price[, "value"], 150)] <- 100
lowFrequencyStrategy <- array(0, dim=NROW(price))
lowFrequencyStrategy[price[, "value"] > mov_avg(price[, "value"], 800)] <- 100

# add portfolio weights to strategies
portfolio_addPosition(
  portfolio=highFrequencyPortfolio,
  symbol=symbol,
  quantity=highFrequencyStrategy,
  time=printTime)
portfolio_addPosition(
  portfolio=lowFrequencyPortfolio,
  symbol=symbol,
  quantity=lowFrequencyStrategy,
  time=printTime)

# print the strategy summaries
print(highFrequencyPortfolio)
print(lowFrequencyPortfolio)

# ggplot the strategies
plot1 <- util_ggplot(util_plot2d(position_quantity(highFrequencyPortfolio, symbol), title="High Frequency Portfolio Strategy", line_size=0.6))
plot2 <- util_ggplot(util_plot2d(position_quantity(lowFrequencyPortfolio, symbol), title="Low Frequency Portfolio Strategy", line_size=0.6))
util_multiplot(plot1, plot2, cols=1)


# ggplot Variance
util_plot2d(portfolio_variance(highFrequencyPortfolio), title="Variance, daily", Legend="HF Portfolio") +
  util_line2d(portfolio_variance(lowFrequencyPortfolio), Legend="LF Portfolio")


# ggplot Value-at-Risk
util_plot2d(portfolio_VaR(highFrequencyPortfolio, 0.05), title="Value at Risk in %, daily (95% c.i.)", Legend="HF Portfolio")+
  util_line2d(portfolio_VaR(lowFrequencyPortfolio, 0.05), Legend="LF Portfolio")

# ggplot Sharpe Ratio
util_plot2d(portfolio_sharpeRatio(highFrequencyPortfolio), title="Sharpe Ratio, daily", Legend="HF Portfolio")+
  util_line2d(portfolio_sharpeRatio(lowFrequencyPortfolio), Legend="LF Portfolio")