1. Untitled7
In [45]:
library(readxl)
library(ggplot2)
library(tidyr)
library(lattice)
library(lubridate)
library(quantmod)
In [46]:
ss<-read.csv('C:UsersvinodDownloadsSHAREitLe
X507filestock_AAPL.csv',header=TRUE)
stock_AAPL <- as.data.frame(ss)
colnames(stock_AAPL) <-
c('Date','Symbol','Open','High','Low','Close','Volume','Macd','Mfi','Rsi','Wi
lliam_r','Stochastic_fast','Stochastic_slow','Bollinger_bands','Chaikin_money
_flow','Obv','Log_timestamp','Datasource')
stock_AAPL$Date <- as.integer(gsub('-','',stock_AAPL$Date))
ss <- stock_AAPL
ss
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13. In [58]:
stock_AAPL_filtered <- stock_AAPL %>% dplyr :: filter(Date >20170000)
ggplot(stock_AAPL_filtered,aes(x=Date)) + geom_histogram(aes(fill=
..count..),col="grey")+
ggtitle(' Histogram of dates after filtering for
2018')
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
18. TRADING DESCISION¶
Black colour in the plot indicates a day where the closing price was #
higher than the open = GAIN¶
red color in the plot indicates a day where the open was higher than
the # close = LOSS¶
let first do analysis with 2018¶
In [39]:
plot(AAPL_s[,'AAPL.Close'], main ='AAPL_s')