README.md
In bagasbgy/quantrecipes: Extra recipes for quantitative analysis
quantrecipes
Installation
You can install the development version of quantrecipes using:
# install.packages("remotes")
remotes::install_github("bagasbgy/quantrecipes")
Getting Started
Let’s start by importing the library:
# import libs
library(quantrecipes)
Here’s a quick example for setting-up a recipe to produce ZigZag,
Bollinger Bands, and moving average:
# an example recipe
rec <- recipe(. ~ ., data = btcusdt) %>%
step_zigzag(close, change = 0.3, state = TRUE) %>%
step_bbands(high, low, close) %>%
step_ma(close) %>%
step_naomit(all_predictors()) %>%
prep()
# quick check
juice(rec)
#> # A tibble: 26,162 x 15
#> datetime open high low close volume turnover zigzag_value
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2019-05-01 01:35:00 5340. 5340. 5331. 5331. 0.510 2722. 5344.
#> 2 2019-05-01 01:40:00 5338. 5339. 5333 5333 0.402 2146. 5346.
#> 3 2019-05-01 01:45:00 5335. 5337. 5333 5336. 0.819 4368. 5348.
#> 4 2019-05-01 01:50:00 5336. 5337. 5336. 5337. 0.0199 106. 5350.
#> 5 2019-05-01 01:55:00 5338. 5340. 5336. 5340. 1.39 7437. 5351.
#> 6 2019-05-01 02:00:00 5342. 5344. 5342 5344. 1.03 5504. 5353.
#> 7 2019-05-01 02:05:00 5344. 5352. 5344 5352. 1.26 6716. 5355.
#> 8 2019-05-01 02:10:00 5353 5355. 5348. 5348. 0.191 1020. 5357.
#> 9 2019-05-01 02:15:00 5349. 5369. 5348. 5362. 0.310 1661. 5358.
#> 10 2019-05-01 02:20:00 5361. 5362. 5355. 5355. 0.356 1908. 5360.
#> # … with 26,152 more rows, and 7 more variables: zigzag_trend <fct>,
#> # zigzag_swing <fct>, bbands_dn <dbl>, bbands_ma <dbl>, bbands_up <dbl>,
#> # bbands_pctb <dbl>, ma_close_value <dbl>
This example recipe would be very helpful for preparing data for further
analysis. For example, here is a quick visualization:
# import libs
library(ggplot2)
# quick visualization
data_viz <- juice(rec) %>%
tail(12 * 24)
data_highlight <- data_viz %>%
drop_na() %>%
filter(zigzag_swing != "hold")
ggplot(data_viz, aes(x = datetime, y = close)) +
geom_ribbon(aes(ymin = bbands_dn, ymax = bbands_up), alpha = 0.15) +
geom_line(aes(y = ma_close_value), colour = "darkblue", linetype = "dashed", alpha = 0.5) +
geom_line() +
geom_point(data = data_highlight, aes(colour = zigzag_swing)) +
scale_colour_manual(values = c("up" = "blue", "down" = "red")) +
guides(colour = FALSE) +
labs(x = NULL, y = NULL) +
theme_minimal()
Since we have the recipes object, this approach also very helpful to
preprocess new data:
# import libs
library(rucoin)
# get new data
btcusdt_new <- get_kucoin_prices(
symbols = "BTC/USDT",
from = "2019-08-18",
to = "2019-08-19",
frequency = "5 minutes"
)
# get preprocess result for new data
bake(rec, btcusdt_new)
#> # A tibble: 265 x 15
#> datetime open high low close volume turnover
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2019-08-18 01:35:00 10173. 10198. 10173. 10191. 42.6 434072.
#> 2 2019-08-18 01:40:00 10192. 10195. 10171. 10184. 64.1 652153.
#> 3 2019-08-18 01:45:00 10184. 10184. 10164. 10182. 53.5 544848.
#> 4 2019-08-18 01:50:00 10182. 10193 10180 10190. 26.2 266446.
#> 5 2019-08-18 01:55:00 10190. 10192. 10177. 10183. 50.9 518042.
#> 6 2019-08-18 02:00:00 10182 10203. 10178. 10197. 46.7 475847.
#> 7 2019-08-18 02:05:00 10193. 10205. 10192. 10197. 25.5 260335.
#> 8 2019-08-18 02:10:00 10197. 10204. 10197. 10203. 23.8 242407.
#> 9 2019-08-18 02:15:00 10204. 10204. 10180. 10182. 38.8 395315.
#> 10 2019-08-18 02:20:00 10182. 10188. 10180. 10180. 21.7 220920.
#> # … with 255 more rows, and 8 more variables: zigzag_value <dbl>,
#> # zigzag_trend <fct>, zigzag_swing <fct>, bbands_dn <dbl>,
#> # bbands_ma <dbl>, bbands_up <dbl>, bbands_pctb <dbl>,
#> # ma_close_value <dbl>
Thus, make a reproducible analysis for new data easier too:
# quick visualization
data_viz <- bake(rec, btcusdt_new)
data_highlight <- data_viz %>%
drop_na() %>%
filter(zigzag_swing != "hold")
ggplot(data_viz, aes(x = datetime, y = close)) +
geom_ribbon(aes(ymin = bbands_dn, ymax = bbands_up), alpha = 0.15) +
geom_line(aes(y = ma_close_value), colour = "darkblue", linetype = "dashed", alpha = 0.5) +
geom_line() +
geom_point(data = data_highlight, aes(colour = zigzag_swing)) +
scale_colour_manual(values = c("up" = "blue", "down" = "red")) +
guides(colour = FALSE) +
labs(x = NULL, y = NULL) +
theme_minimal()
Beside for market descriptive or predictive analysis, this package also
provide some tools for performance analytics:
# an example recipe
rec <- recipe(. ~ ., data = actions) %>%
step_cumret(benchmark, portfolio, prices = "close") %>%
step_naomit(all_predictors()) %>%
prep()
# quick check
juice(rec)
#> # A tibble: 863 x 6
#> datetime close benchmark portfolio cumret_benchmark
#> <dttm> <dbl> <fct> <fct> <dbl>
#> 1 2019-07-27 23:55:00 9486. buy buy -0.00405
#> 2 2019-07-28 00:00:00 9457. hold hold -0.0104
#> 3 2019-07-28 00:05:00 9396. hold hold -0.00879
#> 4 2019-07-28 00:10:00 9412. hold hold -0.00559
#> 5 2019-07-28 00:15:00 9442. hold hold -0.00726
#> 6 2019-07-28 00:20:00 9426. hold hold -0.00533
#> 7 2019-07-28 00:25:00 9445. hold hold -0.00482
#> 8 2019-07-28 00:30:00 9450. hold hold -0.00458
#> 9 2019-07-28 00:35:00 9452. hold hold -0.00732
#> 10 2019-07-28 00:40:00 9426. hold hold -0.00715
#> # … with 853 more rows, and 1 more variable: cumret_portfolio <dbl>
Which, again, could also very helpful to build the related
visualizations:
# another helper libs
library(stringr)
# quick visualization
data_viz <- juice(rec) %>%
select(datetime, cumret_benchmark, cumret_portfolio) %>%
gather(key, value, -datetime) %>%
mutate(key =
str_replace_all(key, "cumret_", "") %>%
str_to_title()
)
ggplot(data_viz, aes(x = datetime, y = value)) +
geom_line() +
facet_wrap(vars(key), ncol = 1, scales = "free") +
labs(title = "Portfolio vs Benchmark Return", x = NULL, y = NULL) +
theme_minimal()
bagasbgy/quantrecipes documentation built on Dec. 25, 2019, 7:54 a.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
quantrecipes
Installation
You can install the development version of quantrecipes using:
# install.packages("remotes")
remotes::install_github("bagasbgy/quantrecipes")
Getting Started
Let’s start by importing the library:
# import libs
library(quantrecipes)
Here’s a quick example for setting-up a recipe to produce ZigZag, Bollinger Bands, and moving average:
# an example recipe
rec <- recipe(. ~ ., data = btcusdt) %>%
step_zigzag(close, change = 0.3, state = TRUE) %>%
step_bbands(high, low, close) %>%
step_ma(close) %>%
step_naomit(all_predictors()) %>%
prep()
# quick check
juice(rec)
#> # A tibble: 26,162 x 15
#> datetime open high low close volume turnover zigzag_value
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2019-05-01 01:35:00 5340. 5340. 5331. 5331. 0.510 2722. 5344.
#> 2 2019-05-01 01:40:00 5338. 5339. 5333 5333 0.402 2146. 5346.
#> 3 2019-05-01 01:45:00 5335. 5337. 5333 5336. 0.819 4368. 5348.
#> 4 2019-05-01 01:50:00 5336. 5337. 5336. 5337. 0.0199 106. 5350.
#> 5 2019-05-01 01:55:00 5338. 5340. 5336. 5340. 1.39 7437. 5351.
#> 6 2019-05-01 02:00:00 5342. 5344. 5342 5344. 1.03 5504. 5353.
#> 7 2019-05-01 02:05:00 5344. 5352. 5344 5352. 1.26 6716. 5355.
#> 8 2019-05-01 02:10:00 5353 5355. 5348. 5348. 0.191 1020. 5357.
#> 9 2019-05-01 02:15:00 5349. 5369. 5348. 5362. 0.310 1661. 5358.
#> 10 2019-05-01 02:20:00 5361. 5362. 5355. 5355. 0.356 1908. 5360.
#> # … with 26,152 more rows, and 7 more variables: zigzag_trend <fct>,
#> # zigzag_swing <fct>, bbands_dn <dbl>, bbands_ma <dbl>, bbands_up <dbl>,
#> # bbands_pctb <dbl>, ma_close_value <dbl>
This example recipe would be very helpful for preparing data for further analysis. For example, here is a quick visualization:
# import libs
library(ggplot2)
# quick visualization
data_viz <- juice(rec) %>%
tail(12 * 24)
data_highlight <- data_viz %>%
drop_na() %>%
filter(zigzag_swing != "hold")
ggplot(data_viz, aes(x = datetime, y = close)) +
geom_ribbon(aes(ymin = bbands_dn, ymax = bbands_up), alpha = 0.15) +
geom_line(aes(y = ma_close_value), colour = "darkblue", linetype = "dashed", alpha = 0.5) +
geom_line() +
geom_point(data = data_highlight, aes(colour = zigzag_swing)) +
scale_colour_manual(values = c("up" = "blue", "down" = "red")) +
guides(colour = FALSE) +
labs(x = NULL, y = NULL) +
theme_minimal()
Since we have the recipes object, this approach also very helpful to preprocess new data:
# import libs
library(rucoin)
# get new data
btcusdt_new <- get_kucoin_prices(
symbols = "BTC/USDT",
from = "2019-08-18",
to = "2019-08-19",
frequency = "5 minutes"
)
# get preprocess result for new data
bake(rec, btcusdt_new)
#> # A tibble: 265 x 15
#> datetime open high low close volume turnover
#> <dttm> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2019-08-18 01:35:00 10173. 10198. 10173. 10191. 42.6 434072.
#> 2 2019-08-18 01:40:00 10192. 10195. 10171. 10184. 64.1 652153.
#> 3 2019-08-18 01:45:00 10184. 10184. 10164. 10182. 53.5 544848.
#> 4 2019-08-18 01:50:00 10182. 10193 10180 10190. 26.2 266446.
#> 5 2019-08-18 01:55:00 10190. 10192. 10177. 10183. 50.9 518042.
#> 6 2019-08-18 02:00:00 10182 10203. 10178. 10197. 46.7 475847.
#> 7 2019-08-18 02:05:00 10193. 10205. 10192. 10197. 25.5 260335.
#> 8 2019-08-18 02:10:00 10197. 10204. 10197. 10203. 23.8 242407.
#> 9 2019-08-18 02:15:00 10204. 10204. 10180. 10182. 38.8 395315.
#> 10 2019-08-18 02:20:00 10182. 10188. 10180. 10180. 21.7 220920.
#> # … with 255 more rows, and 8 more variables: zigzag_value <dbl>,
#> # zigzag_trend <fct>, zigzag_swing <fct>, bbands_dn <dbl>,
#> # bbands_ma <dbl>, bbands_up <dbl>, bbands_pctb <dbl>,
#> # ma_close_value <dbl>
Thus, make a reproducible analysis for new data easier too:
# quick visualization
data_viz <- bake(rec, btcusdt_new)
data_highlight <- data_viz %>%
drop_na() %>%
filter(zigzag_swing != "hold")
ggplot(data_viz, aes(x = datetime, y = close)) +
geom_ribbon(aes(ymin = bbands_dn, ymax = bbands_up), alpha = 0.15) +
geom_line(aes(y = ma_close_value), colour = "darkblue", linetype = "dashed", alpha = 0.5) +
geom_line() +
geom_point(data = data_highlight, aes(colour = zigzag_swing)) +
scale_colour_manual(values = c("up" = "blue", "down" = "red")) +
guides(colour = FALSE) +
labs(x = NULL, y = NULL) +
theme_minimal()
Beside for market descriptive or predictive analysis, this package also provide some tools for performance analytics:
# an example recipe
rec <- recipe(. ~ ., data = actions) %>%
step_cumret(benchmark, portfolio, prices = "close") %>%
step_naomit(all_predictors()) %>%
prep()
# quick check
juice(rec)
#> # A tibble: 863 x 6
#> datetime close benchmark portfolio cumret_benchmark
#> <dttm> <dbl> <fct> <fct> <dbl>
#> 1 2019-07-27 23:55:00 9486. buy buy -0.00405
#> 2 2019-07-28 00:00:00 9457. hold hold -0.0104
#> 3 2019-07-28 00:05:00 9396. hold hold -0.00879
#> 4 2019-07-28 00:10:00 9412. hold hold -0.00559
#> 5 2019-07-28 00:15:00 9442. hold hold -0.00726
#> 6 2019-07-28 00:20:00 9426. hold hold -0.00533
#> 7 2019-07-28 00:25:00 9445. hold hold -0.00482
#> 8 2019-07-28 00:30:00 9450. hold hold -0.00458
#> 9 2019-07-28 00:35:00 9452. hold hold -0.00732
#> 10 2019-07-28 00:40:00 9426. hold hold -0.00715
#> # … with 853 more rows, and 1 more variable: cumret_portfolio <dbl>
Which, again, could also very helpful to build the related visualizations:
# another helper libs
library(stringr)
# quick visualization
data_viz <- juice(rec) %>%
select(datetime, cumret_benchmark, cumret_portfolio) %>%
gather(key, value, -datetime) %>%
mutate(key =
str_replace_all(key, "cumret_", "") %>%
str_to_title()
)
ggplot(data_viz, aes(x = datetime, y = value)) +
geom_line() +
facet_wrap(vars(key), ncol = 1, scales = "free") +
labs(title = "Portfolio vs Benchmark Return", x = NULL, y = NULL) +
theme_minimal()
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.