Package 'pedquant' reference manual

Title:	Public Economic Data and Quantitative Analysis
Description:	Provides an interface to access public economic and financial data for economic research and quantitative analysis. The data sources including NBS, FRED, Sina, Eastmoney and etc. It also provides quantitative functions for trading strategies based on the 'data.table', 'TTR', 'PerformanceAnalytics' and etc packages.
Authors:	Shichen Xie [aut, cre]
Maintainer:	Shichen Xie <[email protected]>
License:	GPL-3
Version:	0.2.4.999
Built:	2025-02-18 14:27:58 UTC
Source:	https://github.com/shichenxie/pedquant

crossover operators

Description

Binary operators which create the upwards or downwards crossover signals.

Usage

x %x>% y

x %x<% y
x %x>% y

x %x<% y

Arguments

x, y

numeric vectors

Examples

library(data.table)
library(pedquant)

data("dt_banks")
boc = md_stock_adjust(setDT(dt_banks)[symbol=='601988.SH'])
bocti = pq_addti(boc, x='close_adj', sma=list(n=200), sma=list(n=50))

dtorders = copy(bocti[[1]])[,.(symbol, name, date, close_adj, sma_50, sma_200)
][sma_50 %x>% sma_200, `:=`(
    side = 'buy', prices = close_adj
)][sma_50 %x<% sma_200, `:=`(
    side = 'sell', prices = close_adj
)][, (c('side', 'prices')) := lapply(.SD, shift), .SDcols = c('side', 'prices')]
orders = dtorders[!is.na(side)]
head(orders)

e = pq_plot(boc,  y='close_adj', addti = list(sma=list(n=200), sma=list(n=50)), orders = orders)
e[[1]]

library(data.table)
library(pedquant)

data("dt_banks")
boc = md_stock_adjust(setDT(dt_banks)[symbol=='601988.SH'])
bocti = pq_addti(boc, x='close_adj', sma=list(n=200), sma=list(n=50))

dtorders = copy(bocti[[1]])[,.(symbol, name, date, close_adj, sma_50, sma_200)
][sma_50 %x>% sma_200, `:=`(
    side = 'buy', prices = close_adj
)][sma_50 %x<% sma_200, `:=`(
    side = 'sell', prices = close_adj
)][, (c('side', 'prices')) := lapply(.SD, shift), .SDcols = c('side', 'prices')]
orders = dtorders[!is.na(side)]
head(orders)

e = pq_plot(boc,  y='close_adj', addti = list(sma=list(n=200), sma=list(n=50)), orders = orders)
e[[1]]

dataset of bank stocks in sse

Description

The daily historical data of bank stocks

Usage

dt_banks
dt_banks

Format

A data frame with 7506 rows and 15 variables:

symbol: stock ticker symbol
name: stock ticker name
date: trade date
open: stock price at the open of trading
high: stock price at the highest point during trading
low: stock price at the lowest point during trading
close: stock price at the close of trading
volume: number of shares traded
amount: monetary value of shares traded
turnover: rate of shares traded over total
close_adj: adjusted stock price at the close of trading

dataset of shanghai composite index

Description

The daily historical Shanghai Composite Index

Usage

dt_ssec
dt_ssec

Format

A data frame with 7506 rows and 15 variables:

symbol: stock ticker symbol
name: stock ticker name
date: trade date
open: stock price at the open of trading
high: stock price at the highest point during trading
low: stock price at the lowest point during trading
close: stock price at the close of trading
volume: number of shares traded
amount: monetary value of shares traded
turnover: rate of shares traded over total
close_adj: adjusted stock price at the close of trading

code list by category

Description

ed_code get the code list of country, currency, stock exchange, commodity exchange and administrative district of mainland of China.

Usage

ed_code(cate = NULL)
ed_code(cate = NULL)

Arguments

cate

The available category values including 'country', 'currency', 'stock_exchange', 'commodity_exchange', 'china_district'.

Examples

## Not run: 
# specify the categories
code_list1 = ed_code(cate = c('country', 'currency'))

# interactivly return code list
code_list2 = ed_code()


## End(Not run)

## Not run: 
# specify the categories
code_list1 = ed_code(cate = c('country', 'currency'))

# interactivly return code list
code_list2 = ed_code()


## End(Not run)

query FRED economic data

Description

ed_fred provides an interface to access the economic data provided by FRED (https://fred.stlouisfed.org)

Usage

ed_fred(symbol = NULL, date_range = "10y", from = NULL,
  to = Sys.Date(), na_rm = FALSE, print_step = 1L)
ed_fred(symbol = NULL, date_range = "10y", from = NULL,
  to = Sys.Date(), na_rm = FALSE, print_step = 1L)

Arguments

`symbol`	symbols of FRED economic indicators. It is available via function `ed_fred_symbol` or its website. Default is NULL, which calls `ed_fred_symbol` in the back.
`date_range`	date range. Available value includes '1m'-'11m', 'ytd', 'max' and '1y'-'ny'. Default is '10y'.
`from`	the start date. Default is NULL. If it is NULL, then calculate using date_range and end date.
`to`	the end date. Default is the current date.
`na_rm`	logical, whether to remove missing values. Default is FALSE
`print_step`	a non-negative integer, which will print symbol name by each print_step iteration. Default is 1L.

Value

a list of dataframes with columns of symbol, name, date, value, geo, unit. The geo column might be NA according to local internet connection.

Examples


dat = ed_fred(c("A191RL1A225NBEA", "GDPCA"))


dat = ed_fred(c("A191RL1A225NBEA", "GDPCA"))

symbol of FRED economic data

Description

ed_fred_symbol provides an interface to search symbols of economic data from FRED by category or keywords.

Usage

ed_fred_symbol(category = NULL, keywords = NULL, ...)
ed_fred_symbol(category = NULL, keywords = NULL, ...)

Arguments

`category`	the category id. If it is NULL, then search symbols from the top categories step by step.
`keywords`	the query text. If it is NULL, the function will search symbols by category.
`...`	ignored parameters

Examples

## Not run: 
# search symbols by category
# from top categories
symbol_dt1 = ed_fred_symbol()
# specify the initial categories
symbol_dt2 = ed_fred_symbol(category = 1)

# search symbol by keywords
symbol_dt3 = ed_fred_symbol(keywords = "gdp china")


## End(Not run)

## Not run: 
# search symbols by category
# from top categories
symbol_dt1 = ed_fred_symbol()
# specify the initial categories
symbol_dt2 = ed_fred_symbol(category = 1)

# search symbol by keywords
symbol_dt3 = ed_fred_symbol(keywords = "gdp china")


## End(Not run)

query NBS economic data

Description

ed_nbs provides an interface to query economic data from National Bureau of Statistics of China (NBS, https://www.stats.gov.cn/).

Usage

ed_nbs(symbol = NULL, freq = NULL, geo_type = NULL, subregion = NULL,
  date_range = "10y", from = NULL, to = Sys.Date(), na_rm = FALSE,
  eng = FALSE, print_step = 1L, ...)
ed_nbs(symbol = NULL, freq = NULL, geo_type = NULL, subregion = NULL,
  date_range = "10y", from = NULL, to = Sys.Date(), na_rm = FALSE,
  eng = FALSE, print_step = 1L, ...)

Arguments

`symbol`	symbols of NBS indicators. It is available via `ed_nbs_symbol`. Default is NULL.
`freq`	the frequency of NBS indicators, including 'monthly', 'quarterly', 'yearly'. Default is NULL.
`geo_type`	geography type in NBS, including 'nation', 'province', 'city'. Default is NULL.
`subregion`	codes of province or city, which is available via `ed_nbs_subregion`. Default is NULL.
`date_range`	date range. Available value includes '1m'-'11m', 'ytd', 'max' and '1y'-'ny'. Default is '10y'.
`from`	the start date. Default is NULL. If it is NULL, then calculate using date_range and end date.
`to`	the end date. Default is the current date.
`na_rm`	logical. Whether to remove missing values from datasets. Default is FALSE.
`eng`	logical. The language of the query results is in English or in Chinese Default is FALSE.
`print_step`	A non-negative integer. Print symbol name by each print_step iteration. Default is 1L.
`...`	Additional parameters.

Examples

## Not run: 
# query NBS data without setting any parameters
dt = ed_nbs()

# specify paratmeters
dt1 = ed_nbs(geo_type='nation', freq='quarterly', symbol='A010101')
# or using 'n'/'q' represents 'nation'/'quarterly'
dt2 = ed_nbs(geo_type='n', freq='q', symbol='A010101')


# query data in one province
dt3 = ed_nbs(geo_type='province', freq='quarterly', 
  symbol='A010101', subregion='110000')
  
# query data in all province
dt4 = ed_nbs(geo_type='province', freq='quarterly', 
  symbol='A010101', subregion='all')

## End(Not run)

## Not run: 
# query NBS data without setting any parameters
dt = ed_nbs()

# specify paratmeters
dt1 = ed_nbs(geo_type='nation', freq='quarterly', symbol='A010101')
# or using 'n'/'q' represents 'nation'/'quarterly'
dt2 = ed_nbs(geo_type='n', freq='q', symbol='A010101')


# query data in one province
dt3 = ed_nbs(geo_type='province', freq='quarterly', 
  symbol='A010101', subregion='110000')
  
# query data in all province
dt4 = ed_nbs(geo_type='province', freq='quarterly', 
  symbol='A010101', subregion='all')

## End(Not run)

subregion code of NBS economic data

Description

ed_nbs_subregion query province or city code from NBS

Usage

ed_nbs_subregion(geo_type = NULL, eng = FALSE)
ed_nbs_subregion(geo_type = NULL, eng = FALSE)

Arguments

`geo_type`	geography type in NBS, including 'province', 'city'. Default is NULL.
`eng`	logical. The language of the query results is in English or in Chinese. Default is FALSE.

Examples

## Not run: 
# province code 
prov1 = ed_nbs_subregion(geo_type = 'province') 
# or using 'p' represents 'province'
prov2 = ed_nbs_subregion(geo_type = 'p') 

# city code in Chinese
# city = ed_nbs_subregion(geo_type = 'c', eng = FALSE) 
# city code in English
city = ed_nbs_subregion(geo_type = 'c', eng = TRUE) 

## End(Not run)
## Not run: 
# province code 
prov1 = ed_nbs_subregion(geo_type = 'province') 
# or using 'p' represents 'province'
prov2 = ed_nbs_subregion(geo_type = 'p') 

# city code in Chinese
# city = ed_nbs_subregion(geo_type = 'c', eng = FALSE) 
# city code in English
city = ed_nbs_subregion(geo_type = 'c', eng = TRUE) 

## End(Not run)

symbol of NBS economic data

Description

ed_nbs_symbol provides an interface to query symbols of economic indicators from NBS.

Usage

ed_nbs_symbol(symbol = NULL, geo_type = NULL, freq = NULL, eng = FALSE)
ed_nbs_symbol(symbol = NULL, geo_type = NULL, freq = NULL, eng = FALSE)

Arguments

`symbol`	symbols of NBS indicators.
`geo_type`	geography type in NBS, including 'nation', 'province', 'city'. Default is NULL.
`freq`	the frequency of NBS indicators, including 'monthly', 'quarterly', 'yearly'. Default is NULL.
`eng`	logical. The language of the query results is in English or in Chinese. Default is FALSE.

Examples

# query symbol interactively
## Not run: 
sym = ed_nbs_symbol()
## End(Not run)

# query symbol interactively
## Not run: 
sym = ed_nbs_symbol()
## End(Not run)

query bond data

Description

md_bond query bond market data from FRED and ChinaBond.

Usage

md_bond(symbol = NULL, type = "history", date_range = "3y",
  from = NULL, to = Sys.Date(), print_step = 1L, ...)
md_bond(symbol = NULL, type = "history", date_range = "3y",
  from = NULL, to = Sys.Date(), print_step = 1L, ...)

Arguments

`symbol`	bond symbols. Default is NULL.
`type`	the data type. Default is history.
`date_range`	date range. Available value includes '1m'-'11m', 'ytd', 'max' and '1y'-'ny'. Default is 3y.
`from`	the start date. Default is NULL. If it is NULL, then calculate using date_range and end date.
`to`	the end date. Default is the current date.
`print_step`	a non-negative integer, which will print symbol name by each print_step iteration. Default is 1L.
`...`	Additional parameters.

query forex data

Description

md_forex query forex market data from FRED (history data) or sina (real data).

Usage

md_forex(symbol, type = "history", date_range = "3y", from = NULL,
  to = Sys.Date(), print_step = 1L, ...)
md_forex(symbol, type = "history", date_range = "3y", from = NULL,
  to = Sys.Date(), print_step = 1L, ...)

Arguments

`symbol`	forex symbols. Default is NULL.
`type`	the data type, available values including history and real. Default is history.
`date_range`	date range. Available value includes '1m'-'11m', 'ytd', 'max' and '1y'-'ny'. Default is 3y.
`from`	the start date. Default is NULL. If it is NULL, then calculate using date_range and end date.
`to`	the end date. Default is the current date.
`print_step`	a non-negative integer, which will print symbol name by each print_step iteration. Default is 1L.
`...`	Additional parameters.

Examples

## Not run: 
# history data
dtfx_hist1 = md_forex(c('usdcny', 'usdjpy'))

# real data
dtfx_real = md_forex(c('eurusd', 'usdcny', 'usdjpy'), type = 'real')


## End(Not run)


## Not run: 
# history data
dtfx_hist1 = md_forex(c('usdcny', 'usdjpy'))

# real data
dtfx_real = md_forex(c('eurusd', 'usdcny', 'usdjpy'), type = 'real')


## End(Not run)

query future market data

Description

md_future query future market data from sina finance, https://finance.sina.com.cn/futuremarket/.

Usage

md_future(symbol, type = "history", date_range = "max", from = NULL,
  to = Sys.Date(), freq = "daily", print_step = 1L, ...)
md_future(symbol, type = "history", date_range = "max", from = NULL,
  to = Sys.Date(), freq = "daily", print_step = 1L, ...)

Arguments

`symbol`	future symbols It is available via function `md_future_symbol` or its website.
`type`	the data type, including history, real and info. Default is history.
`date_range`	date range. Available value includes '1m'-'11m', 'ytd', 'max' and '1y'-'ny'. Default is max.
`from`	the start date. Default is NULL. If it is NULL, then calculate using date_range and end date.
`to`	the end date. Default is the current date.
`freq`	data frequency, default is daily.
`print_step`	a non-negative integer, which will print symbol name by each print_step iteration. Default is 1L.
`...`	Additional parameters.

Examples

## Not run: 
# history data
df_hist = md_future(symbol = c('IF0', 'A0', 'CU0', 'CF0', 'XAU'))

# real data
df_real = md_future(symbol = c('IF0', 'A0', 'CU0', 'CF0', 'XAU'), 
                    type = 'real')

## End(Not run)


## Not run: 
# history data
df_hist = md_future(symbol = c('IF0', 'A0', 'CU0', 'CF0', 'XAU'))

# real data
df_real = md_future(symbol = c('IF0', 'A0', 'CU0', 'CF0', 'XAU'), 
                    type = 'real')

## End(Not run)

symbol of future market data

Description

md_future_symbol returns all future symbols that provided by sina finance, see details on http://vip.stock.finance.sina.com.cn/quotes_service/view/qihuohangqing.html or http://vip.stock.finance.sina.com.cn/mkt/#global_qh)

Usage

md_future_symbol(...)
md_future_symbol(...)

Arguments

...

ignored parameters

Examples

## Not run: 
sybs = md_future_symbol()

## End(Not run)

## Not run: 
sybs = md_future_symbol()

## End(Not run)

query interbank offered rate

Description

md_money query libor from FRED or shibor from chinamoney.

Usage

md_money(symbol = NULL, date_range = "3y", from = NULL,
  to = Sys.Date(), print_step = 1L)
md_money(symbol = NULL, date_range = "3y", from = NULL,
  to = Sys.Date(), print_step = 1L)

Arguments

`symbol`	ibor symbols. Default is NULL.
`date_range`	date range. Available value includes '1m'-'11m', 'ytd', 'max' and '1y'-'ny'. Default is 3y.
`from`	the start date. Default is NULL. If it is NULL, then calculate using date_range and end date.
`to`	the end date. Default is the current date.
`print_step`	a non-negative integer, which will print symbol name by each print_step iteration. Default is 1L.

query chinese benchmark rates

Description

md_moneycn query benchmark rates from chinamoney.com.cn.

Usage

md_moneycn(symbol = NULL, date_range = "3y", from = NULL,
  to = Sys.Date(), print_step = 1L)
md_moneycn(symbol = NULL, date_range = "3y", from = NULL,
  to = Sys.Date(), print_step = 1L)

Arguments

`symbol`	benchmarks, available values including 'rmbx', 'shibor', 'lpr', 'pr', 'yb'. Default is NULL,
`date_range`	date range. Available value includes '1m'-'11m', 'ytd', 'max' and '1y'-'ny'. Default is 3y.
`from`	the start date. Default is NULL. If it is NULL, then calculate using date_range and end date.
`to`	the end date. Default is the current date.
`print_step`	a non-negative integer, which will print symbol name by each print_step iteration. Default is 1L.

query stock market data

Description

md_stock provides an interface to query stock or fund data.

Usage

md_stock(symbol, type = "history", date_range = "3y", from = NULL,
  to = Sys.Date(), forward = NULL, print_step = 1L, ...)
md_stock(symbol, type = "history", date_range = "3y", from = NULL,
  to = Sys.Date(), forward = NULL, print_step = 1L, ...)

Arguments

`symbol`	symbols of stock shares.
`type`	the data type, including history, real. Defaults to history.
`date_range`	date range. Available value including '1m'-'11m', 'ytd', 'max' and '1y'-. Default is '3y'.
`from`	the start date. Default is NULL.
`to`	the end date. Default is current system date.
`forward`	whether to forward adjust the OHLC prices. If it is NULL, return the original data from source, defaults to NULL.
`print_step`	A non-negative integer. Print symbol name by each print_step iteration. Default is 1L.
`...`	Additional parameters.

Examples

## Not run: 
# Example I: query history data
# us
FAANG = md_stock(c('META', 'AMZN', 'AAPL', 'NFLX', 'GOOG'))

# hkex
TMX = md_stock(c('00700.hk', '03690.hk', '01810.hk'))

# sse/szse
## the symbol without suffix
dt_cn1 = md_stock(c("000001", "^000001", "512510"))
## the symbol with suffix
dt_cn2 = md_stock(c("000001.sz", "000001.ss", '512510.ss'))

             
# Example III: query real prices
# real price for equities
dt_real1 = md_stock(c('META', 'AMZN', 'AAPL', 'NFLX', 'GOOG',
                     '00700.hk', '03690.hk', '01810.hk',
                     "000001", "^000001", "512510"), type = 'real')
               
               
# query company information 
dt_info1 = md_stock('600036', type = 'info')

## End(Not run)

## Not run: 
# Example I: query history data
# us
FAANG = md_stock(c('META', 'AMZN', 'AAPL', 'NFLX', 'GOOG'))

# hkex
TMX = md_stock(c('00700.hk', '03690.hk', '01810.hk'))

# sse/szse
## the symbol without suffix
dt_cn1 = md_stock(c("000001", "^000001", "512510"))
## the symbol with suffix
dt_cn2 = md_stock(c("000001.sz", "000001.ss", '512510.ss'))

             
# Example III: query real prices
# real price for equities
dt_real1 = md_stock(c('META', 'AMZN', 'AAPL', 'NFLX', 'GOOG',
                     '00700.hk', '03690.hk', '01810.hk',
                     "000001", "^000001", "512510"), type = 'real')
               
               
# query company information 
dt_info1 = md_stock('600036', type = 'info')

## End(Not run)

adjust stock prices

Description

md_stock_adjust adjusts the open, high, low and close stock prices.

Usage

md_stock_adjust(dt, forward = FALSE, ...)
md_stock_adjust(dt, forward = FALSE, ...)

Arguments

`dt`	a list/dataframe of time series datasets that didnt adjust for split or dividend.
`forward`	forward adjust or backward adjust, defaults to FALSE.
`...`	Additional parameters.

Examples


data("dt_banks")

dtadj1 = md_stock_adjust(dt_banks, adjust = FALSE)
dtadj2 = md_stock_adjust(dt_banks, adjust = TRUE)

data("dt_banks")

dtadj1 = md_stock_adjust(dt_banks, adjust = FALSE)
dtadj2 = md_stock_adjust(dt_banks, adjust = TRUE)

query financial statements

Description

md_stock_financials provides an interface to query financial statements for all listed companies in SSE and SZSE by specified report date.

Usage

md_stock_financials(type = NULL, date_range = "1q", from = NULL,
  to = Sys.Date(), print_step = 1L, ...)
md_stock_financials(type = NULL, date_range = "1q", from = NULL,
  to = Sys.Date(), print_step = 1L, ...)

Arguments

`type`	the type of financial statements.
`date_range`	date range. Available value including '1m'-'11m', 'ytd', 'max' and '1y'-. Default is '3y'.
`from`	the start date. Default is NULL.
`to`	the end date. Default is current system date.
`print_step`	A non-negative integer. Print financial statements name by each print_step iteration. Default is 1L.
`...`	Additional parameters.

Examples

## Not run: 
# interactively specify type of financial table 
dtfs1 = md_stock_financials(type="fs0_summary", to = '2022-12-31')
dtfs2 = md_stock_financials(type="fs0_summary", to = c('2022-12-31', '2023-03-31'))
dtfs3 = md_stock_financials(type="fs0_summary", from = '2022-12-31', to = Sys.Date())

# all statements
dtfs4 = md_stock_financials(type = "fs", to = '2022-12-31')

# setting column names to Chinese
dtfs5 = md_stock_financials(type="fs0_summary", to = '2022-12-31', colnam_chn = TRUE)


## End(Not run)

## Not run: 
# interactively specify type of financial table 
dtfs1 = md_stock_financials(type="fs0_summary", to = '2022-12-31')
dtfs2 = md_stock_financials(type="fs0_summary", to = c('2022-12-31', '2023-03-31'))
dtfs3 = md_stock_financials(type="fs0_summary", from = '2022-12-31', to = Sys.Date())

# all statements
dtfs4 = md_stock_financials(type = "fs", to = '2022-12-31')

# setting column names to Chinese
dtfs5 = md_stock_financials(type="fs0_summary", to = '2022-12-31', colnam_chn = TRUE)


## End(Not run)

symbol components of exchange

Description

md_stock_symbol returns all stock symbols by exchange

Usage

md_stock_symbol(exchange = NULL, ...)
md_stock_symbol(exchange = NULL, ...)

Arguments

`exchange`	the available stock exchanges are sse, szse, hkex, amex, nasdaq, nyse.
`...`	ignored parameters

Examples

## Not run: 
# get stock symbols in a stock exchange
## specify the exchanges
ex_syb1 = md_stock_symbol(exchange = c('sse', 'szse'))

## choose exchanges interactivly
ex_syb2 = md_stock_symbol()

# constituent stock symbol of index
dtidx = md_stock_symbol(index = c('930050', '000300', '000905', '000852'))

## End(Not run)

## Not run: 
# get stock symbols in a stock exchange
## specify the exchanges
ex_syb1 = md_stock_symbol(exchange = c('sse', 'szse'))

## choose exchanges interactivly
ex_syb2 = md_stock_symbol()

# constituent stock symbol of index
dtidx = md_stock_symbol(index = c('930050', '000300', '000905', '000852'))

## End(Not run)

symbol of market data

Description

md_symbol returns all symbols by market category, including forex, money, bond, stock, future.

Usage

md_symbol(market = NULL, ...)
md_symbol(market = NULL, ...)

Arguments

`market`	the market category, including forex, money, bond, stock, future. Default is NULL.
`...`	ignored parameters

Examples

## Not run: 
syblst = md_symbol()

## End(Not run)

## Not run: 
syblst = md_symbol()

## End(Not run)

adding technical indicators

Description

pq_addti creates technical indicators using the functions provided in TTR package.

Usage

pq_addti(dt, ...)
pq_addti(dt, ...)

Arguments

dt

a list/dataframe of time series datasets.

...

list of technical indicator parameters: sma = list(n=50), macd = list().

There are four types of parameters.
- set by default and do not required, such as 'OHLC', 'HLC', 'HL' and 'volume'.
- set by default and can be modified, such as 'price', 'prices', 'x'. Its default value is 'close' or 'value' column.
- always required, such as 'y', 'w'.
- numeric parameters, such as 'n', 'sd', 'v', 'nFast', 'nSlow', 'nSig', 'accel'. These parameters should be provided, otherwise using default values in corresponding function.
TTR functions are summarized in below. See TTR package's help document for more detailed parameters.
- moving averages: SMA, EMA, DEMA, WMA, EVWMA, ZLEMA, VWAP, VMA, HMA, ALMA, GMMA
- rolling functions: runMin, runMax, runMean, runMedian; runCov, runCor; runVar, runSD, runMAD; runSum, wilderSum
- bands / channels: BBands, PBands, DonchianChannel
- SAR, ZigZag
- trend direction/strength: aroon, CCI, ADX, TDI, VHF, EMV
- volatility measures: ATR, chaikinVolatility, volatility, SNR
- money flowing into/out: OBV, chaikinAD, CLV, CMF, MFI, williamsAD
- rate of change / momentum: ROC, momentum, KST, TRIX
- oscillator: MACD, DPO, DVI, ultimateOscillator; RSI, CMO; stoch, SMI, WPR

Examples


# load data
data('dt_ssec')

# add technical indicators
dt_ti1 = pq_addti(dt_ssec, sma=list(n=20), sma=list(n=50), macd = list())

# specify the price column x
dt_ti11 = pq_addti(dt_ssec, sma=list(n=20, x='open'), sma=list(n=50, x='open'))
dt_ti12 = pq_addti(dt_ssec, x='open', sma=list(n=20), sma=list(n=50))

# only technical indicators
dt_ti2 = pq_addti(
  dt_ssec, sma=list(n=20), sma=list(n=50), macd = list(), 
  col_kp = c('symbol', 'name')
)

dt_ti3 = pq_addti(
  dt_ssec, sma=list(n=20), sma=list(n=50), macd = list(), 
  col_kp = NULL
)

# self-defined technical indicators
bias = function(x, n=50, maType='SMA') {
    library(TTR)
    (x/do.call(maType, list(x=x, n=n))-1)*100
}

dt_ti3 = pq_addti(dt_ssec, bias = list(n = 200))


# load data
data('dt_ssec')

# add technical indicators
dt_ti1 = pq_addti(dt_ssec, sma=list(n=20), sma=list(n=50), macd = list())

# specify the price column x
dt_ti11 = pq_addti(dt_ssec, sma=list(n=20, x='open'), sma=list(n=50, x='open'))
dt_ti12 = pq_addti(dt_ssec, x='open', sma=list(n=20), sma=list(n=50))

# only technical indicators
dt_ti2 = pq_addti(
  dt_ssec, sma=list(n=20), sma=list(n=50), macd = list(), 
  col_kp = c('symbol', 'name')
)

dt_ti3 = pq_addti(
  dt_ssec, sma=list(n=20), sma=list(n=50), macd = list(), 
  col_kp = NULL
)

# self-defined technical indicators
bias = function(x, n=50, maType='SMA') {
    library(TTR)
    (x/do.call(maType, list(x=x, n=n))-1)*100
}

dt_ti3 = pq_addti(dt_ssec, bias = list(n = 200))

technical functions

Description

Technical functions provided in TTR package.

Usage

pq_addti_funs()
pq_addti_funs()

converting frequency of daily data

Description

pq_freq convert a daily OHLC dataframe into a specified frequency.

Usage

pq_freq(dt, freq = "monthly", date_type = "eop")
pq_freq(dt, freq = "monthly", date_type = "eop")

Arguments

`dt`	a list/dataframe of time series dataset.
`freq`	the frequency that the input daily data will converted to. It supports weekly, monthly, quarterly and yearly.
`date_type`	the available date type are eop (end of period) and bop (bebinning of period), defaults to the eop.

Examples

## Not run: 
data(dt_ssec)
dat1_weekly = pq_freq(dt_ssec, "weekly")

data(dt_banks)
dat2_weekly = pq_freq(dt_banks, "monthly")

## End(Not run)

## Not run: 
data(dt_ssec)
dat1_weekly = pq_freq(dt_ssec, "weekly")

data(dt_banks)
dat2_weekly = pq_freq(dt_banks, "monthly")

## End(Not run)

dataframe operation

Description

It performs arithmetic operation on numeric columns on multiple series.

Usage

pq_opr(dt, opr, x = "close", rm_na = FALSE, ...)
pq_opr(dt, opr, x = "close", rm_na = FALSE, ...)

Arguments

`dt`	a list/dataframe of time series datasets.
`opr`	operation string.
`x`	the numeric column names, defaults to close.
`rm_na`	weather to remove NA values when perform arithmetic.
`...`	additional parameters.

Examples

data("dt_banks")

dt1 = pq_opr(dt_banks, '601288.SH/601988.SH')
print(dt1)

dt2 = pq_opr(dt_banks, c('(601288.SH+601988.SH)/2', '(601288.SH*601988.SH)^0.5'))
print(dt2)

data("dt_banks")

dt1 = pq_opr(dt_banks, '601288.SH/601988.SH')
print(dt1)

dt2 = pq_opr(dt_banks, c('(601288.SH+601988.SH)/2', '(601288.SH*601988.SH)^0.5'))
print(dt2)

calculating performance metrics

Description

pq_performance calculates performance metrics based on returns of market price or portfolio. The performance analysis functions are calling from PerformanceAnalytics package, which includes many widely used performance metrics.

Usage

pq_performance(dt, Ra, Rb = NULL, perf_fun, ...)
pq_performance(dt, Ra, Rb = NULL, perf_fun, ...)

Arguments

`dt`	a list/dataframe of time series datasets.
`Ra`	the column name of asset returns.
`Rb`	the column name of baseline returns, defaults to NULL.
`perf_fun`	performance function from `PerformanceAnalytics` package, see `pq_perf_funs`.
`...`	additional parameters, the arguments used in `PerformanceAnalytics` functions.

Examples

 
## Not run: 
library(pedquant) 
library(data.table)

# load data
data(dt_banks)
data(dt_ssec)

# calculate returns
datret1 = pq_return(dt_banks, 'close', freq = 'monthly', rcol_name = 'Ra')
datret2 = pq_return(dt_ssec, 'close', freq = 'monthly', rcol_name = 'Rb')

# merge returns of assets and baseline
datRaRb = merge(
    rbindlist(datret1)[, .(date, symbol, Ra)], 
    rbindlist(datret2)[, .(date, Rb)],
    by = 'date', all.x = TRUE
)

# claculate table.CAPM metrics
perf_capm = pq_performance(datRaRb, Ra = 'Ra', Rb = 'Rb', perf_fun = 'table.CAPM')
rbindlist(perf_capm, idcol = 'symbol')

## End(Not run)

## Not run: 
library(pedquant) 
library(data.table)

# load data
data(dt_banks)
data(dt_ssec)

# calculate returns
datret1 = pq_return(dt_banks, 'close', freq = 'monthly', rcol_name = 'Ra')
datret2 = pq_return(dt_ssec, 'close', freq = 'monthly', rcol_name = 'Rb')

# merge returns of assets and baseline
datRaRb = merge(
    rbindlist(datret1)[, .(date, symbol, Ra)], 
    rbindlist(datret2)[, .(date, Rb)],
    by = 'date', all.x = TRUE
)

# claculate table.CAPM metrics
perf_capm = pq_performance(datRaRb, Ra = 'Ra', Rb = 'Rb', perf_fun = 'table.CAPM')
rbindlist(perf_capm, idcol = 'symbol')

## End(Not run)

performance functions

Description

A complete list of performance functions from PerformanceAnalytics package.

Usage

pq_performance_funs()
pq_performance_funs()

creating charts for time series

Description

pq_plot provides an easy way to create interactive charts for time series dataset based on predefined formats.

Usage

pq_plot(dt, chart_type = "line", x = "date", y = "close", yb = NULL,
  date_range = "max", yaxis_log = FALSE, title = NULL, addti = NULL,
  nsd_lm = NULL, markline = TRUE, orders = NULL, arrange = list(rows =
  NULL, cols = NULL), theme = "default", ...)
pq_plot(dt, chart_type = "line", x = "date", y = "close", yb = NULL,
  date_range = "max", yaxis_log = FALSE, title = NULL, addti = NULL,
  nsd_lm = NULL, markline = TRUE, orders = NULL, arrange = list(rows =
  NULL, cols = NULL), theme = "default", ...)

Arguments

`dt`	a list/dataframe of time series dataset
`chart_type`	chart type, including line, step, candle.
`x`	column name for x axis
`y`	column name for y axis
`yb`	column name for baseline
`date_range`	date range of x axis to display. Available value includes '1m'-'11m', 'ytd', 'max' and '1y'-'ny'. Default is max.
`yaxis_log`	whether to display y axis values in log. Default is FALSE.
`title`	chart title. It will added to the front of chart title if it is specified.
`addti`	list of technical indicators or numerical columns in dt. For technical indicator, it is calculated via `pq_addti`, which including overlays and indicators.
`nsd_lm`	number of standard deviation from linear regression fitting values.
`markline`	whether to display markline. Default is TRUE.
`orders`	a data frame of trade orders, which including columns of symbol, date, side, prices, and quantity.
`arrange`	a list. Number of rows and columns charts to connect. Default is NULL.
`theme`	name of echarts theme, see details in `e_theme`
`...`	ignored

Examples


# single serie
library(data.table)
library(pedquant)
data(dt_ssec)

# line chart (default)
e1 = pq_plot(dt_ssec, chart_type = 'line') # line chart (default)
e1[[1]]

# add technical indicators
e2 = pq_plot(dt_ssec, addti = list(
        sma = list(n = 200), 
        sma = list(n = 50), 
        volume = list(),
        macd = list()
))
e2[[1]]

# linear trend with yaxis in log
e3 = pq_plot(dt_ssec, nsd_lm = c(-0.8, 0, 0.8), markline=FALSE)
e3[[1]]

# multiple series
data(dt_banks)
setDT(dt_banks)
dt_banksadj = md_stock_adjust(dt_banks)

# linear trend
elist = pq_plot(dt_banksadj)
e4 = pq_plot(dt_banksadj, arrange = list(rows=1, cols=1))
e4[[1]]

# orders 
b2 = dt_banks[symbol %in% c('601988.SH', '601398.SH')]
b2orders = b2[sample(.N, 20), .(
    symbol, date, prices=close,
    side = sample(c(-1,  1), 20, replace=TRUE),
    term = sample(c(10, 20), 20, replace=TRUE)
)]
                
e5 = pq_plot(b2, orders=b2orders)
e5[[1]]

e6 = pq_plot(b2, orders=b2orders, arrange = list(rows=1, cols=1))
e6[[1]]


# single serie
library(data.table)
library(pedquant)
data(dt_ssec)

# line chart (default)
e1 = pq_plot(dt_ssec, chart_type = 'line') # line chart (default)
e1[[1]]

# add technical indicators
e2 = pq_plot(dt_ssec, addti = list(
        sma = list(n = 200), 
        sma = list(n = 50), 
        volume = list(),
        macd = list()
))
e2[[1]]

# linear trend with yaxis in log
e3 = pq_plot(dt_ssec, nsd_lm = c(-0.8, 0, 0.8), markline=FALSE)
e3[[1]]

# multiple series
data(dt_banks)
setDT(dt_banks)
dt_banksadj = md_stock_adjust(dt_banks)

# linear trend
elist = pq_plot(dt_banksadj)
e4 = pq_plot(dt_banksadj, arrange = list(rows=1, cols=1))
e4[[1]]

# orders 
b2 = dt_banks[symbol %in% c('601988.SH', '601398.SH')]
b2orders = b2[sample(.N, 20), .(
    symbol, date, prices=close,
    side = sample(c(-1,  1), 20, replace=TRUE),
    term = sample(c(10, 20), 20, replace=TRUE)
)]
                
e5 = pq_plot(b2, orders=b2orders)
e5[[1]]

e6 = pq_plot(b2, orders=b2orders, arrange = list(rows=1, cols=1))
e6[[1]]

calculating returns/equity of portfolio

Description

pq_portfolio calculates the weighted returns or the equity of a portfolio assets.

Usage

pq_portfolio(dt, orders, x = "close", dtb = NULL, init_fund = NULL,
  method = "arithmetic", cols_keep = NULL, ...)
pq_portfolio(dt, orders, x = "close", dtb = NULL, init_fund = NULL,
  method = "arithmetic", cols_keep = NULL, ...)

Arguments

`dt`	a list/dataframe of price by asset.
`orders`	a data frame of transaction orders, which includes symbol, date, prices, quantity and side columns.
`x`	the column name of adjusted asset price, defaults to close.
`dtb`	a list/dataframe of price base asset.
`init_fund`	initial fund value.
`method`	the method to calculate asset returns, the available values include arithmetic and log, defaults to arithmetic.
`cols_keep`	the columns keep in the return data. The columns of symbol, name and date will always kept if they are exist in the input data.
`...`	ignored

Examples

library(pedquant)

data(dt_banks)
datadj = md_stock_adjust(dt_banks)


# example I 
orders = data.frame(
    symbol = c("601288.SH","601328.SH","601398.SH","601939.SH","601988.SH"), 
    quantity = c(100, 200, 300, 300, 100)
)
dtRa = pq_portfolio(datadj, orders=orders) 

e1 = pq_plot(dtRa, y = 'cumreturns')
e1[[1]]


# example II 
data(dt_ssec)
orders = data.frame(
    symbol = rep(c("601288.SH","601328.SH","601398.SH","601939.SH","601988.SH"), 3), 
    date = rep(c('2009-03-02', '2010-01-04', '2014-09-01'), each = 5), 
    quantity = rep(c(100, 200, 300, 300, 100), 3) * rep(c(1, -1, 2), each = 5)
)
dtRab = pq_portfolio(datadj, orders=orders, dtb = dt_ssec, init_fund = 10000) 

e2 = pq_plot(dtRab, y = 'cumreturns', yb = 'cumreturns_000001.SH', addti = list(portfolio=list()))
e2[[1]]


# example III
orders = data.frame(symbol = "000001.SH", 
     date = c("2009-04-13", "2010-03-24", "2014-08-13", "2015-09-10"), 
     quantity = c(400, -400, 300, -300))
dtRa2 = pq_portfolio(dt_ssec, orders=orders, cols_keep = 'all')

e3 = pq_plot(dtRa2, y = 'close', addti = list(cumreturns=list(), portfolio=list()))
e3[[1]]

library(pedquant)

data(dt_banks)
datadj = md_stock_adjust(dt_banks)


# example I 
orders = data.frame(
    symbol = c("601288.SH","601328.SH","601398.SH","601939.SH","601988.SH"), 
    quantity = c(100, 200, 300, 300, 100)
)
dtRa = pq_portfolio(datadj, orders=orders) 

e1 = pq_plot(dtRa, y = 'cumreturns')
e1[[1]]


# example II 
data(dt_ssec)
orders = data.frame(
    symbol = rep(c("601288.SH","601328.SH","601398.SH","601939.SH","601988.SH"), 3), 
    date = rep(c('2009-03-02', '2010-01-04', '2014-09-01'), each = 5), 
    quantity = rep(c(100, 200, 300, 300, 100), 3) * rep(c(1, -1, 2), each = 5)
)
dtRab = pq_portfolio(datadj, orders=orders, dtb = dt_ssec, init_fund = 10000) 

e2 = pq_plot(dtRab, y = 'cumreturns', yb = 'cumreturns_000001.SH', addti = list(portfolio=list()))
e2[[1]]


# example III
orders = data.frame(symbol = "000001.SH", 
     date = c("2009-04-13", "2010-03-24", "2014-08-13", "2015-09-10"), 
     quantity = c(400, -400, 300, -300))
dtRa2 = pq_portfolio(dt_ssec, orders=orders, cols_keep = 'all')

e3 = pq_plot(dtRa2, y = 'close', addti = list(cumreturns=list(), portfolio=list()))
e3[[1]]

calculating returns by frequency

Description

pq_return calculates returns for daily series based on specified column, frequency and method type.

Usage

pq_return(dt, x, freq = "daily", n = 1, date_type = "eop",
  method = "arithmetic", cumreturns = FALSE, rcol_name = NULL,
  cols_keep = NULL, date_range = "max", from = NULL, to = Sys.Date(),
  ...)
pq_return(dt, x, freq = "daily", n = 1, date_type = "eop",
  method = "arithmetic", cumreturns = FALSE, rcol_name = NULL,
  cols_keep = NULL, date_range = "max", from = NULL, to = Sys.Date(),
  ...)

Arguments

`dt`	a list/dataframe of daily series.
`x`	the column name of adjusted asset price.
`freq`	the frequency of returns. It supports 'daily', 'weekly', 'monthly', 'quarterly', 'yearly' and 'all'. Defaults to daily.
`n`	the number of preceding periods used as the base value, defaults to 1, which means based on the previous period value.
`date_type`	the available date type are eop (end of period) and bop (beginning of period), defaults to the eop.
`method`	the method to calculate asset returns, the available methods including arithmetic and log, defaults to arithmetic.
`cumreturns`	logical, whether to return cumulative returns. Defaults to FALSE.
`rcol_name`	setting the column name of returns, defaults to NULL.
`cols_keep`	the columns keep in the return data. The columns of symbol, name and date will always kept if they are exist in the input data.
`date_range`	date range. Available value includes '1m'-'11m', 'ytd', 'max' and '1y'-'ny'. Default is max.
`from`	the start date. Default is NULL. If it is NULL, then calculate using date_range and end date.
`to`	the end date. Default is the current date.
`...`	ignored

Examples


# load data and adjust
data(dt_banks)
datadj = md_stock_adjust(dt_banks)

# set freq
dts_returns1 = pq_return(datadj, x = 'close_adj', freq = 'all')

# set method
dts_returns2 = pq_return(datadj, x = 'close_adj', method = 'log')

# set cols_keep
dts_returns3 = pq_return(datadj, x = 'close_adj', cols_keep = 'cap_total')

# cumulative returns
dts_cumreturns = pq_return(datadj, x = 'close_adj', from = '2012-01-01', cumreturns = TRUE)
e1 = pq_plot(dts_cumreturns, y = 'cumreturns.daily', title='cumreturns', 
        arrange = list(rows=1, cols=1))
e1[[1]]


# load data and adjust
data(dt_banks)
datadj = md_stock_adjust(dt_banks)

# set freq
dts_returns1 = pq_return(datadj, x = 'close_adj', freq = 'all')

# set method
dts_returns2 = pq_return(datadj, x = 'close_adj', method = 'log')

# set cols_keep
dts_returns3 = pq_return(datadj, x = 'close_adj', cols_keep = 'cap_total')

# cumulative returns
dts_cumreturns = pq_return(datadj, x = 'close_adj', from = '2012-01-01', cumreturns = TRUE)
e1 = pq_plot(dts_cumreturns, y = 'cumreturns.daily', title='cumreturns', 
        arrange = list(rows=1, cols=1))
e1[[1]]

Package 'pedquant'

Help Index

crossover operators

Description

Usage

Arguments

Examples

dataset of bank stocks in sse

Description

Usage

Format

dataset of shanghai composite index

Description

Usage

Format

code list by category

Description

Usage

Arguments

Examples

query FRED economic data

Description

Usage

Arguments

Value

Examples

symbol of FRED economic data

Description

Usage

Arguments

Examples

query NBS economic data

Description

Usage

Arguments

Examples

subregion code of NBS economic data

Description

Usage

Arguments

Examples

symbol of NBS economic data

Description

Usage

Arguments

Examples

query bond data

Description

Usage

Arguments

query forex data

Description

Usage

Arguments

Examples

query future market data

Description

Usage

Arguments

Examples

symbol of future market data

Description

Usage

Arguments

Examples

query interbank offered rate

Description

Usage

Arguments

query chinese benchmark rates

Description

Usage

Arguments

query stock market data

Description

Usage

Arguments

Examples

adjust stock prices

Description