Stock = "AAPL"
start_date <- Sys.Date()
start_date
[1] "2022-02-12"Getting Signals
Stock_data <- Stock_data %>%
group_by(symbol) %>%
tq_mutate(select = close,
mutate_fun = MACD,
col_rename = c("MACD", "Signal"))%>%
tq_mutate(select = adjusted, mutate_fun = RSI) %>%
tq_mutate(select = adjusted, mutate_fun = BBands, col_rename = "Bbands") %>%
tq_mutate_xy(x = close, y =volume, mutate_fun = EVWMA, col_rename = "EVWMA") %>%
mutate(Stock_movement = case_when(
lag(close)- close > 0 ~ "Decrease",
close > lag(close) ~ "Increase",
close == lag(close) ~"No Change"
)
) %>%
select(symbol:adjusted,Stock_movement,MACD,rsi,EVWMA)
#str(Stock_data)
Stock_data <- Stock_data %>% mutate(forecast1day = lead(close,1),
forecast1week = lead(close,7),
forecast2week = lead(close,14),
forecast4week = lead(close,31),
forecast2month = lead(close,60),
forecast4month = lead(close,120))
Stock_data %>% head(10) %>% select(close, forecast1day,forecast1week)
# A tibble: 10 x 4
# Groups: symbol [1]
symbol close forecast1day forecast1week
<chr> <dbl> <dbl> <dbl>
1 AAPL 40.7 41.1 43.1
2 AAPL 41.1 41.8 43.9
3 AAPL 41.8 43.2 44.7
4 AAPL 43.2 43.1 44.6
5 AAPL 43.1 43.0 44.5
6 AAPL 43.0 42.8 43.8
7 AAPL 42.8 43.1 44.1
8 AAPL 43.1 43.9 44.2
9 AAPL 43.9 44.7 44.2
10 AAPL 44.7 44.6 43.8Stock_data <- Stock_data[complete.cases(Stock_data),]
#Stock_data %>% colnames()
current <- lm(close ~ volume + MACD+ rsi+ EVWMA , data = Stock_data)
forecast1daymodel <- lm(forecast1day ~ volume + MACD+ rsi+ EVWMA , data = Stock_data)
forecast1weekmodel <- lm(forecast1week ~ volume + MACD+ rsi+ EVWMA , data = Stock_data)
forecast2weekmodel <- lm(forecast2week ~ volume + MACD+ rsi+ EVWMA , data = Stock_data)
forecastmonthmodel <-lm(forecast4week~ volume + MACD+ rsi+ EVWMA , data = Stock_data)
forecast2monthmodel <- lm(forecast2month~ volume + MACD+ rsi+ EVWMA , data = Stock_data)
forecast4monthmodel <- lm(forecast4month~ volume + MACD+ rsi+ EVWMA , data = Stock_data)
#summary(current)
#summary(forecast1weekmodel)
#summary(forecast2weekmodel)
#summary(forecast1weekmodel)
#summary(forecastmonthmodel)
#summary(forecast2monthmodel)
#summary(forecast4monthmodel)
Stock_data$currentPrediction <- predict(current, Stock_data)
Stock_data$day1prediction <- predict(forecast1daymodel, Stock_data)
Stock_data$day7prediction <- predict(forecast1weekmodel, Stock_data)
Stock_data$day14prediction <- predict(forecast2weekmodel, Stock_data)
Stock_data$day31prediction <- predict(forecastmonthmodel, Stock_data)
correlations <- c(cor(Stock_data$currentPrediction,Stock_data$close),cor(Stock_data$day1prediction, Stock_data$forecast1day),cor(Stock_data$day7prediction, Stock_data$forecast1week),cor(Stock_data$day14prediction, Stock_data$forecast2week),cor(Stock_data$day31prediction, Stock_data$forecast4week))
Predictions_cor <- c("Current", "1dayModel","1weekModel","2weekModel","1monthModel")
cbind(Predictions_cor,round(correlations,3)) %>% knitr::kable()
| Predictions_cor | |
|---|---|
| Current | 0.999 |
| 1dayModel | 0.998 |
| 1weekModel | 0.992 |
| 2weekModel | 0.985 |
| 1monthModel | 0.968 |
Stock_data %>% ggplot()+
geom_point(aes(x = date, Stock_data$day7prediction), color ="red")+
geom_point(aes(x = date, Stock_data$forecast2week), color ="blue")
Stock_data %>% ggplot()+
geom_point(aes(x = date, Stock_data$day31prediction), color ="red")+
geom_point(aes(x = date, Stock_data$forecast4week), color ="blue")
Stock_data %>% ggplot()+
geom_point(aes(x = date, Stock_data$day7prediction), color ="red")+
geom_point(aes(x = date, Stock_data$forecast1week), color ="blue")
start_date <- Sys.Date()
start_date
[1] "2022-02-12"retrieval_date <- start_date - months(6)
new_stock_data <- tq_get(Stock, get = "stock.prices", from = retrieval_date, to = start_date)
new_stock_data <- new_stock_data %>%
group_by(symbol) %>%
tq_mutate(select = close,
mutate_fun = MACD,
col_rename = c("MACD", "Signal"))%>%
tq_mutate(select = adjusted, mutate_fun = RSI) %>%
tq_mutate(select = adjusted, mutate_fun = BBands, col_rename = "Bbands") %>%
tq_mutate_xy(x = close, y =volume, mutate_fun = EVWMA, col_rename = "EVWMA") %>%
mutate(Stock_movement = case_when(
lag(close)- close > 0 ~ "Decrease",
close > lag(close) ~ "Increase",
close == lag(close) ~"No Change"
)
) %>%
select(symbol:adjusted,Stock_movement,MACD,rsi,EVWMA)
new_stock_data$monthforecast <- predict(forecastmonthmodel, new_stock_data)
new_stock_data$week2forecast <- predict(forecast2weekmodel, new_stock_data)
new_stock_data$month2forecast <- predict(forecast2monthmodel, new_stock_data)
new_stock_data$month4forecast <- predict(forecast4monthmodel, new_stock_data)
new_stock_data$monthforecastdate <- new_stock_data$date + days(31)
new_stock_data$week2forecastdate <- new_stock_data$date +days(14)
new_stock_data$month2forecastdate <- new_stock_data$date+ days(60)
new_stock_data$month4forecastdate <- new_stock_data$date +days(120)
new_stock_data%>% ggplot()+
geom_line(aes(x = monthforecastdate, y = monthforecast),color = "red")+
geom_line(aes(x = week2forecastdate, y = week2forecast),color = "green")+
geom_line(aes(x = date, y = close), color = "blue")+
geom_line(aes(x = month2forecastdate, y = month2forecast) ,color = "orange")+
geom_line(aes(x = month4forecastdate, y = month4forecast), color = "gray")
#new_stock_data %>% select(monthforecastdate,monthforecast) %>% filter(monthforecastdate > "2022-01-01")
new_stock_data %>% ggplot()+geom_line(aes(x = month4forecastdate, y = month4forecast), color = 'red')+
geom_line(aes(x = date, y = close), color = 'blue')
x<-new_stock_data %>%
ggplot()+geom_line(data = new_stock_data %>% filter(month2forecastdate > Sys.Date()),aes(x = month2forecastdate, y = month2forecast), color = 'red')+
geom_line(aes(x = date, y = close), color = 'blue')+
geom_line(data = new_stock_data %>% filter(month4forecastdate > Sys.Date()),aes(x = month4forecastdate,y = month4forecast), color = 'green')+
geom_line(data = new_stock_data %>% filter(monthforecastdate >Sys.Date()), aes(x = monthforecastdate,y = monthforecast), color = 'orange')+
labs(title = "Price Predictions")+
xlab("Date")+
ylab("Price")
print(x)
