_Cl-SMA20-Crossover.Rmd

```{r libraries}
library(dplyr)
library(ggplot2)
library(lattice)
library(tidyr)
library(TTR)
```

# Close/SMA(20) Crossover

$$ Signal = 
    \begin{cases} 
        Cl >= SMA(20), BTO \\ 
        Cl < SMA(20), STC 
    \end{cases}
$$

```{r 2-1-set-symbols}
symbols <- basic_symbols()
```

```{r 2-1-settings}
strategy_title <- "Close > SMA(20)"
```

```{r 2-1-initialize-portfolio}
Cl.SMA20.Crossover <- "Cl.Sma20.Crossover"
rm.strat(Cl.SMA20.Crossover)
initPortf(Cl.SMA20.Crossover, symbols = basic_symbols(), initDate = pv$init_date)
initAcct(Cl.SMA20.Crossover, portfolios = Cl.SMA20.Crossover, 
         initDate = pv$init_date, initEq = pv$account_equity)
initOrders(portfolio = Cl.SMA20.Crossover, initDate = pv$init_date)
strategy(Cl.SMA20.Crossover, store = TRUE)
strat <- getStrategy(Cl.SMA20.Crossover)
```

```{r 2-1-add-indicators}
add.indicator(strategy = Cl.SMA20.Crossover, name = "SMA", 
              arguments = list(x = quote(Cl(mktdata)), n = 20), label = "SMA20")
```

```{r 2-1-add-signals}
add.signal(Cl.SMA20.Crossover, name="sigCrossover", 
           arguments = list(columns = c("Close", "SMA20"), relationship = "gte"),
           label="Cl.gte.SMA")
add.signal(Cl.SMA20.Crossover, name = "sigCrossover",
           arguments = list(columns = c("Close", "SMA20"), relationship = "lt"),
           label = "Cl.lt.SMA")
```

```{r 2-1-add-rules}
# BTO when Cl crosses above SMA(20)
add.rule(Cl.SMA20.Crossover, name = 'ruleSignal',
         arguments = list(sigcol = "Cl.gte.SMA", sigval = TRUE, orderqty = 100,
                          ordertype = 'market', orderside = 'long'),
         type = 'enter')

# STC when Cl crosses under SMA(20)
add.rule(Cl.SMA20.Crossover, name = 'ruleSignal',
         arguments = list(sigcol = "Cl.lt.SMA", sigval = TRUE, orderqty = 'all',
                          ordertype = 'market', orderside = 'long'),
         type = 'exit')
```

```{r 2-1-apply-strategy, include = FALSE}
applyStrategy(strategy = Cl.SMA20.Crossover, portfolios = Cl.SMA20.Crossover)
```

```{r 2-1-update-portfolio}
updatePortf(Cl.SMA20.Crossover)
updateAcct(Cl.SMA20.Crossover)
updateEndEq(Cl.SMA20.Crossover)
checkBlotterUpdate(Cl.SMA20.Crossover, Cl.SMA20.Crossover)
```

```{r 2-1-account-summary}
a <- getAccount(Cl.SMA20.Crossover)
p <- getPortfolio(Cl.SMA20.Crossover)
```

### Per Trade Stats

```{r 2-1-trade-stats, include = TRUE}
knitr::kable(t(tradeStats(Cl.SMA20.Crossover))[-c(1:2),], 
             caption = "Trade Stats per Symbol")
```

### Maximum Adverse Excursion

```{r 2-1-mae, include = TRUE, fig.caption = "Maximum Adverse Excursion by Symbol"}
par(mfrow = c(2,2))
for(symbol in symbols) {
    chart.ME(Portfolio = Cl.SMA20.Crossover, Symbol = symbol, type = "MAE", 
             scale = "percent")
}
par(mfrow = c(1,1))
```

### Maximum Favorable Excursion

```{r 2-1-mfe, include = TRUE, fig.caption = "Maximum Favorable Excursion by Symbol"}
par(mfrow = c(2,2))
for(symbol in symbols) {
    chart.ME(Portfolio = Cl.SMA20.Crossover, Symbol = symbol, type = "MFE", 
             scale = "percent", legend.loc = "none")
}
par(mfrow = c(1,1))
```

```{r 2-1-individual-asset-returns}
rets.multi <- PortfReturns(Cl.SMA20.Crossover)
colnames(rets.multi) <- symbols
rets.multi <- na.omit(cbind(rets.multi, Return.calculate(a$summary$End.Eq)))
names(rets.multi)[length(names(rets.multi))] <- "TOTAL"
rets.multi <- rets.multi[,c("TOTAL", symbols)]
```

### Cumulative Returns

```{r 2-1-return-distribution-analysis, include = TRUE, fig.cap = "Return Distribution Analysis"}
as.data.frame(rets.multi) %>% 
    mutate(Date = index(rets.multi)) %>% 
    gather(key, value, 1:ncol(rets.multi)) %>% 
    filter(value != 0) %>% 
    ggplot(aes(x = key, y = value, fill = key)) + 
    geom_boxplot() + 
    coord_flip() + 
    theme(legend.title = element_blank()) + 
    theme_bw() + 
    scale_x_discrete(name = NULL) + 
    scale_y_continuous(name = NULL) + 
    scale_fill_discrete(name = "Symbol") + 
    ggtitle("Return Distribution Analysis")
```

### Annualized Returns

```{r 2-1-annualized-risk-return, include = TRUE}
ar.tab <- table.AnnualizedReturns(rets.multi)
max.risk <- max(ar.tab["Annualized Std Dev",])
max.return <- max(ar.tab["Annualized Return",])
knitr::kable(data.frame("Max Risk" = max.risk, 
                        "Max Return" = max.return, 
                        "Ratio" = max.risk/max.return), 
             booktabs = TRUE, caption = "Max Risk, Max Reward, Ratio")

knitr::kable(t(ar.tab), booktabs = TRUE, caption = "Annualized Risk and Return")
```

```{r 2-1-annualized-risk-return-chart, include = TRUE, fig.cap = "Annualized Risk and Return"}
chart.RiskReturnScatter(rets.multi, main = "Performance", 
                        colorset = rich10equal, xlim = c(0, max.risk * 1.1), 
                        ylim = c(0, max.return))
```

```{r 2-1-consolidated-equity-curve, include = TRUE, fig.cap = "Consolidated Equity Curve"}
equity <- a$summary$End.Eq
plot(equity, main = "Consolidated Equity Curve")
```

```{r 2-1-asummary, include = TRUE, fig.cap = "Account Summary"}
a <- getAccount(Cl.SMA20.Crossover)
xyplot(a$summary, type = "h", col = 4)
```

```{r 2-1-performance-summary, include = TRUE}
ret <- Return.calculate(equity, method = "log")
charts.PerformanceSummary(ret, colorset = bluefocus, 
                          main = strategy_title)
```