- Introduction to Quantitative Trading
- Quantitative Trading - Advantages and Disadvantages
- Types of Quantitative Trading Strategies
- Momentum Strategies
- Mean Reversion Strategies
- Market Making Strategies and Day Trading Strategies
- How to Generate Trading Ideas
- Designing A Trading Strategy For Profit
- Backtesting a Trading Strategy - Considerations
- Risk Management of a Trading Strategy
- Risk Indicators - VIX Index and TED Spread
- Plotting the VIX Index and TED Spread in R
- Introduction to Quantmod in R
- Downloading Data Using Quantmod Package in R
- Creating Charts with Quantmod
- Data Analysis with Quantmod in R
- Measuring Overall ETFs Performance
- Quantstrat Example in R - EMA Crossover Strategy
- Quantstrat - EMA Crossover Strategy - Performance and Risk Metrics
- Quantstrat Example in R - RSI Strategy
- Quantstrat Case Study - Multiple Symbol Portfolio
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
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)) colnames(tstats) tstats QQQ 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.
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, metrics=c( "Return.cumulative", "Return.annualized", "SharpeRatio.annualized", "CalmarRatio"), metricsNames=c( "Cumulative Return", "Annualized Return", "Annualized Sharpe Ratio", "Calmar Ratio")) tab.perf #displays the performance metrics QQQ.DailyEqPL Cumulative Return -0.07613984 Annualized Return -0.01429584 Annualized Sharpe Ratio -0.42604174 Calmar Ratio -0.12881185 tab.risk <- table.Arbitrary(rets, metrics=c( "StdDev.annualized", "maxDrawdown", "VaR", "ES"), metricsNames=c( "Annualized StdDev", "Max DrawDown", "Value-at-Risk", "Conditional VaR")) tab.risk #displays the risk metrics QQQ.DailyEqPL 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$summary colnames(portfolio$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") ret 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.
Please login to view this lesson.
With our free registration, you can access to all the lessons on finance, risk, data analytics and data science for finance professionals.Sign in free