Quantstrat - EMA Crossover Strategy - Performance and Risk Metrics

Once we have the strategy results, quantstrat provides many functions to analyze the strategy and observe important metrics of performance and risk. We would combine the quantstrat package with the PerformanceAnalytics package to show important performance and risk metrics in a trading strategy. We will look at the following:

  • Plot the Strategy Performance
  • Strategy Statistics and Stats per Trade
  • Portfolio Returns
  • Account Summary and Equity Curve
  • Portfolio Summary and Strategy Performance
  • Equity Return Distribution

Plot the Strategy Performance

We can plot the performance using the chart.Posn(). The chart.Posn() takes two parameters that are portfolio name and the symbol string and return a chart with the symbol price series, the accumulated profit loss and the Drawdown charts. The trades of the strategy are marked in green and red on the price series chart.

# Chart Performance of the Strategy

chart.Posn(portfolioName, Symbol = symbolstring)

QQQ EMA Crossover Prices, Strategy P&L and Drawdowns

Strategy Statistics

tradeStats() function calculates statistics about the strategy. These statistics are related to measures about the strategy profits, risk metrics and general features of the strategy such as number of transactions, highest profitable trade, highest loser trade, average profit per trade among others.  In our example, we have the following information:

tstats <- tradeStats(portfolioName)
tstats <- data.frame(t(tstats))
Portfolio          FirstPortfolio
Symbol                        QQQ
Num.Txns                       42
Num.Trades                     21
Net.Trading.PL              -3805
Avg.Trade.PL            -171.1905
Med.Trade.PL            -231.9994
Largest.Winner               1590
Largest.Loser           -882.0001
Gross.Profits            2127.999
Gross.Losses            -5722.998
Std.Dev.Trade.PL         485.9721
Std.Err.Trade.PL         106.0478
Percent.Positive         14.28571
Percent.Negative         85.71429
Profit.Factor           0.3718328
Avg.Win.Trade            709.3329
Med.Win.Trade            337.9996
Avg.Losing.Trade        -317.9444
Med.Losing.Trade        -243.4995
Avg.Daily.PL            -171.1905
Med.Daily.PL            -231.9994
Std.Dev.Daily.PL         485.9721
Std.Err.Daily.PL         106.0478
Ann.Sharpe              -5.592018
Max.Drawdown            -5822.998
Profit.To.Max.Draw     -0.6534434
Avg.WinLoss.Ratio        2.230997
Med.WinLoss.Ratio        1.388092
Max.Equity               512.9992
Min.Equity              -5309.999
End.Equity                  -380

The perTradeStats() function takes two arguments that are the portfolio name and the symbols strings and outputs the statistics and results by trade.  The results contain information such as: the net profit for each trade, the start and end time of each trade, the percentage of profit of the trade, among other interesting variables. We will not show the output of this function, but feel free to try it in R Studio.

Portfolio Returns

The PortfReturns(account) show the daily returns for each symbol of the strategy. We would pass  the output of the PortReturns() to the table.Arbitrary function of the PerformanceAnalytics package to condense and create fundamental metrics with the returns of the strategy.

# Store the returns of the strategy in an object called rets
rets <- PortfReturns(Account = accountName)
rownames(rets) <- NULL
tab.perf <- table.Arbitrary(rets,
                              "Cumulative Return",
                              "Annualized Return",
                              "Annualized Sharpe Ratio",
                              "Calmar Ratio"))
tab.perf  #displays the performance metrics
Cumulative Return         -0.07613984
Annualized Return         -0.01429584
Annualized Sharpe Ratio   -0.42604174
Calmar Ratio              -0.12881185
tab.risk <- table.Arbitrary(rets,
                              "Annualized StdDev",
                              "Max DrawDown",
                              "Conditional VaR"))
tab.risk  #displays the risk metrics
Annualized StdDev    0.03355503
Max DrawDown         0.11098234
Value-at-Risk       -0.00248965
Conditional VaR     -0.00248965

Account Summary and Equity Curve

The getAccount(AccountName) function returns the account summary and equity curve of the strategy.

a <- getAccount(accountName)
equity <- a$summary$End.Eq
plot(equity, main = "Equity Curve QQQ")

Equity Curve QQQ EMA Crossover Strategy

Portfolio Summary and Strategy Performance

The getPortfolio(portfolioName) function returns the portfolio summary. With this function we can view how the portfolio object is updated on a daily basis every time we have new transactions. 

portfolio <- getPortfolio(portfolioName) 
portfolioSummary <- portfolio$summary
"Long.Value"           "Short.Value"          "Net.Value"           
"Gross.Value"          "Period.Realized.PL"   "Period.Unrealized.PL"
"Gross.Trading.PL"     "Txn.Fees"             "Net.Trading.PL"     

These are the columns of the portfolioSummary xts object. Every time we have a new transaction these columns would be updated.

## Account Performance Summary
ret <- Return.calculate(equity, method = "log")
charts.PerformanceSummary(ret, colorset = bluefocus, 
                          main = "Strategy Performance")

Cumulative Return, Daily Returns and Drawdown QQQ  EMA Crossover Strategy

Equity Returns Distribution

We can also draw a boxplot with the daily equity returns distribution of the strategy.

rets <- PortfReturns(Account = accountName)

chart.Boxplot(rets, main = "QQQ Returns", colorset= rich10equal)

Daily Equity Returns Distribution

As we observed, quantstrat provides many tools for the analysis of a trading strategy from multiple perspectives. With these tools we can get accurate information about strategy results and risks.  In the next section, we will create a new strategy and with that explore some more features and functionality of quantstrat.

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