# 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

**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.Posn()*```
# Chart Performance of the Strategy
chart.Posn(portfolioName, Symbol = symbolstring)
```

**QQQ EMA Crossover Prices, Strategy P&L and Drawdowns**

**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))
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.

### Portfolio Returns

** The PortfReturns(account)** show the daily returns for each symbol of the strategy. We would pass the output of the

**to the**

*PortReturns()***function of the**

*table.Arbitrary***package to condense and create fundamental metrics with the returns of the strategy.**

*PerformanceAnalytics*```
# 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*

*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*

*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*

*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.

#### Course Downloads

- 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

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