Disliked{quote} Curious to hear how you are planning to use it. From what I see in the marketplace, it is a valuable skill to have for sure!Ignored
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Disliked{quote} Curious to hear how you are planning to use it. From what I see in the marketplace, it is a valuable skill to have for sure!Ignored
I like this visualization a lot because instead of just showing the average monthly return, it also shows the distribution of returns.
I decided to run it on GLD and SLV to see how they look. I'm still getting used to this software so bare with me on the graphics, but here are the plots:
library(quantmod) library(PerformanceAnalytics) btc <- getSymbols("BTC-USD", auto.assign = FALSE) btc_ret <- periodReturn(btc, period = "monthly") chart.Drawdown(btc_ret, main = "BTC Drawdowns") table.Drawdowns(btc_ret)
library(quantmod) library(forecast) UNRATE <- getSymbols("UNRATE", src = "FRED", auto.assign = FALSE) forecast <- ses(UNRATE["2000/"], h = 12) autoplot(forecast)
library(quantmod) library(forecast) NHS <- getSymbols("HSN1F", src = "FRED", auto.assign = FALSE) forecast <- holt(NHS["2000/"], h = 12, damped = TRUE) autoplot(forecast)
library(quantmod) library(forecast) ICSA <- getSymbols("ICSA", src = "FRED", auto.assign = FALSE) ets <- ets(ICSA) autoplot(forecast(ets, h = 12)) forecast(ets, h = 1)