tswbench provides quick access to Tushare Pro (https://tushare.pro/) data. In order to query data from Tushare, simply create an tsapi object and access Tushare api functions using “$”.
# set token first
# SetToken("YOUR TUSHARE TOKEN")
api <- TushareApi()
# argument formats are same as official documents
eod <- api$daily(trade_date = "20200701")
eod[]## ts_code trade_date open high low close pre_close change
## 1: 000001.SZ 2020-07-01 12.79 13.15 12.74 13.12 12.80 0.32
## 2: 000002.SZ 2020-07-01 26.18 28.50 26.17 28.09 26.14 1.95
## 3: 000004.SZ 2020-07-01 29.80 30.82 29.60 30.20 29.84 0.36
## 4: 000005.SZ 2020-07-01 2.63 2.69 2.62 2.67 2.63 0.04
## 5: 000006.SZ 2020-07-01 6.69 7.49 6.66 7.49 6.81 0.68
## ---
## 3842: 688558.SH 2020-07-01 44.37 45.14 42.88 44.66 44.30 0.36
## 3843: 688566.SH 2020-07-01 45.30 46.25 44.11 44.93 45.50 -0.57
## 3844: 688588.SH 2020-07-01 45.60 45.86 43.52 44.26 44.97 -0.71
## 3845: 688598.SH 2020-07-01 92.56 94.50 88.91 90.65 92.35 -1.70
## 3846: 688599.SH 2020-07-01 17.53 17.70 16.52 16.84 16.80 0.04
## pct_chg vol amount
## 1: 2.5000 1697390.01 2202800.84
## 2: 7.4598 2602748.54 7124087.37
## 3: 1.2064 56386.06 170215.40
## 4: 1.5209 69514.04 18485.44
## 5: 9.9853 1057800.82 777350.08
## ---
## 3842: 0.8126 136725.92 599341.90
## 3843: -1.2527 46786.19 211711.52
## 3844: -1.5788 58240.48 260164.85
## 3845: -1.8408 26758.46 242859.04
## 3846: 0.2381 473675.89 806802.19
You can get help from API usage functions. To query all supported functions:
tsapi_func()## [1] "adj_factor" "anns" "balancesheet"
## [4] "block_trade" "bo_cinema" "bo_daily"
## [7] "bo_monthly" "bo_weekly" "cashflow"
## [10] "cb_basic" "cb_daily" "cb_issue"
## [13] "cctv_news" "cn_cpi" "cn_gdp"
## [16] "cn_m" "concept_detail" "concept"
## [19] "daily_basic" "daily_info" "daily"
## [22] "disclosure_date" "dividend" "eco_cal"
## [25] "express" "film_record" "fina_audit"
## [28] "fina_indicator" "fina_mainbz" "forecast"
## [31] "ft_tick" "fund_adj" "fund_basic"
## [34] "fund_company" "fund_daily" "fund_div"
## [37] "fund_manager" "fund_nav" "fund_portfolio"
## [40] "fund_share" "fut_basic" "fut_daily"
## [43] "fut_holding" "fut_mapping" "fut_settle"
## [46] "fut_weekly_detail" "fut_wsr" "fx_daily"
## [49] "fx_obasic" "ggt_daily" "ggt_monthly"
## [52] "ggt_top10" "gz_index" "hibor"
## [55] "hk_basic" "hk_daily" "hk_hold"
## [58] "hs_const" "hsgt_top10" "income"
## [61] "index_basic" "index_classify" "index_daily"
## [64] "index_dailybasic" "index_global" "index_member"
## [67] "index_monthly" "index_weekly" "index_weight"
## [70] "libor" "limit_list" "major_news"
## [73] "margin_detail" "margin" "moneyflow_hsgt"
## [76] "moneyflow" "monthly" "namechange"
## [79] "ncov_global" "ncov_num" "new_share"
## [82] "news" "opt_basic" "opt_daily"
## [85] "pledge_detail" "pledge_stat" "repurchase"
## [88] "share_float" "shibor_lpr" "shibor_quote"
## [91] "stk_account" "stk_holdernumber" "stk_holdertrade"
## [94] "stk_limit" "stk_managers" "stk_rewards"
## [97] "stock_basic" "stock_company" "suspend_d"
## [100] "teleplay_record" "tmt_twincome" "tmt_twincomedetail"
## [103] "top_inst" "top_list" "top10_floatholders"
## [106] "top10_holders" "trade_cal" "us_tbr"
## [109] "us_tltr" "us_trltr" "us_trycr"
## [112] "us_tycr" "weekly" "wz_index"
Find specific funcion usage:
tsapi_usage("daily", what = "arg")## arg dtype mandatory desc
## 1: ts_code str FALSE 股票代码（支持多个股票同时提取，逗号分隔）
## 2: trade_date str FALSE 交易日期（YYYYMMDD）
## 3: start_date str FALSE 开始日期(YYYYMMDD)
## 4: end_date str FALSE 结束日期(YYYYMMDD)
tsapi_usage("daily", what = "field")## field dtype default
## 1: ts_code str TRUE
## 2: trade_date str TRUE
## 3: open float TRUE
## 4: high float TRUE
## 5: low float TRUE
## 6: close float TRUE
## 7: pre_close float TRUE
## 8: change float TRUE
## 9: pct_chg float TRUE
## 10: vol float TRUE
## 11: amount float TRUE
## desc
## 1: 股票代码
## 2: 交易日期
## 3: 开盘价
## 4: 最高价
## 5: 最低价
## 6: 收盘价
## 7: 昨收价
## 8: 涨跌额
## 9: 涨跌幅 （未复权，如果是复权请用 通用行情接口 ）
## 10: 成交量 （手）
## 11: 成交额 （千元）
tswbench wraps various end-of-day data functions to market_eod() to simplify data acquisition. market_eod() supports most of Tushare APIs.
Intraday data is not officially documented. However, data can be queried by special “intraday” func.
ohlc <- market_eod(func = "intraday", date = "20200701", freq = 60)
ohlc[]## ts_code trade_time open close high low
## 1: 000001.SH 2020-07-01 09:30:00 2991.181 2991.181 2991.181 2991.181
## 2: 000001.SH 2020-07-01 10:30:00 2991.033 2997.764 2999.245 2985.122
## 3: 000001.SH 2020-07-01 11:30:00 2997.550 3011.772 3011.795 2996.562
## 4: 000001.SH 2020-07-01 14:00:00 3011.772 3004.120 3019.532 2995.487
## 5: 000001.SH 2020-07-01 15:00:00 3004.574 3025.981 3026.186 3000.736
## ---
## 26336: 980023.SZ 2020-07-01 09:30:00 2869.496 2869.496 2869.496 2869.496
## 26337: 980023.SZ 2020-07-01 10:30:00 2868.555 2862.471 2871.374 2859.839
## 26338: 980023.SZ 2020-07-01 11:30:00 2862.713 2868.214 2870.863 2859.811
## 26339: 980023.SZ 2020-07-01 14:00:00 2868.300 2851.340 2869.975 2842.801
## 26340: 980023.SZ 2020-07-01 15:00:00 2851.257 2869.548 2869.548 2849.669
## vol amount
## 1: 205423300 2651727275
## 2: 9587979700 135104030933
## 3: 5268883500 69928252328
## 4: 6585416500 87287000947
## 5: 5683611000 74912790814
## ---
## 26336: 3829659 80215949
## 26337: 349502239 5926241661
## 26338: 221219267 3727458840
## 26339: 334077494 6014484837
## 26340: 217022787 4081421046
index <- market_eod(func = "index_daily", date = "20200701")
index[]## ts_code trade_date close open high low pre_close
## 1: 000001.SH 2020-07-01 3025.981 2991.181 3026.186 2984.984 2984.674
## 2: 000005.SH 2020-07-01 3031.658 3017.223 3040.640 3000.894 3015.678
## 3: 000006.SH 2020-07-01 6499.924 6223.192 6569.703 6204.929 6215.632
## 4: 000016.SH 2020-07-01 3009.861 2950.492 3010.362 2941.380 2942.074
## 5: 000300.SH 2020-07-01 4247.783 4172.642 4247.783 4163.495 4163.964
## 6: 000905.SH 2020-07-01 5881.887 5884.540 5885.874 5814.755 5864.416
## 7: 399001.SZ 2020-07-01 12112.965 12024.840 12130.302 11945.892 11992.347
## 8: 399005.SZ 2020-07-01 8095.735 8041.640 8095.735 7965.207 8015.505
## 9: 399006.SZ 2020-07-01 2419.629 2449.865 2450.842 2381.593 2438.197
## 10: 399016.SZ 2020-07-01 3888.551 3905.802 3907.847 3826.733 3890.729
## 11: 399300.SZ 2020-07-01 4247.783 4172.642 4247.783 4163.495 4163.964
## 12: 399905.SZ 2020-07-01 5881.887 5884.540 5885.874 5814.755 5864.416
## change pct_chg vol amount
## 1: 41.3069 1.3840 273313140 369883802
## 2: 15.9797 0.5299 30638848 40450024
## 3: 284.2918 4.5738 9868412 9326447
## 4: 67.7870 2.3041 42668069 83084456
## 5: 83.8198 2.0130 169990456 305711800
## 6: 17.4711 0.2979 142832982 161437632
## 7: 120.6177 1.0058 393967637 537386447
## 8: 80.2300 1.0009 179746268 232923070
## 9: -18.5673 -0.7615 102164492 179376368
## 10: -2.1775 -0.0560 28310693 93054017
## 11: 83.8198 2.0130 169990456 305711800
## 12: 17.4711 0.2979 142832982 161437632
tswbench also wraps various fundamental reports to report_market() to query whole market per year and quarter, and report_quarter() to query quaterly reports per ts_code. These functions handle update_flag and always return latest updated data.
income2020q1 <- report_market(type = "income", y = 2020, q = 1)
income2020q1[]## ts_code end_date ann_date f_ann_date report_type comp_type
## 1: 000001.SZ 2020-03-31 2020-04-21 2020-04-21 1 2
## 2: 000002.SZ 2020-03-31 2020-04-28 2020-04-28 1 1
## 3: 000004.SZ 2020-03-31 2020-04-28 2020-04-28 1 1
## 4: 000005.SZ 2020-03-31 2020-04-30 2020-04-30 1 1
## 5: 000006.SZ 2020-03-31 2020-04-30 2020-04-30 1 1
## ---
## 3785: 688396.SH 2020-03-31 2020-04-23 2020-04-23 1 1
## 3786: 688398.SH 2020-03-31 2020-04-29 2020-04-29 1 1
## 3787: 688399.SH 2020-03-31 2020-04-24 2020-04-24 1 1
## 3788: 688466.SH 2020-03-31 2020-05-07 2020-05-07 1 1
## 3789: 688588.SH 2020-03-31 2020-05-08 2020-05-08 1 1
## basic_eps diluted_eps total_revenue revenue int_income
## 1: 0.4000 0.4000 37926000000 37926000000 4.7877e+10
## 2: 0.1110 0.1110 47774342785 47774342785 NA
## 3: 0.0231 0.0231 23253019 23253019 NA
## 4: -0.0198 -0.0198 45702946 45702946 NA
## 5: 0.0948 0.0948 652209471 652209471 NA
## ---
## 3785: 0.0935 0.0935 1382423958 1382423958 NA
## 3786: 0.4000 0.4000 88765034 88765034 NA
## 3787: 1.7700 1.7700 200370471 200370471 NA
## 3788: 0.0500 NA 57357651 57357651 NA
## 3789: 0.0933 0.0933 136176287 136176287 NA
## prem_earned comm_income n_commis_income n_oth_income n_oth_b_income
## 1: NA 1.1581e+10 9.688e+09 3.468e+09 2.5e+07
## 2: NA NA NA NA NA
## 3: NA NA NA NA NA
## 4: NA NA NA NA NA
## 5: NA NA NA NA NA
## ---
## 3785: NA NA NA NA NA
## 3786: NA NA NA NA NA
## 3787: NA NA NA NA NA
## 3788: NA NA NA NA NA
## 3789: NA NA NA NA NA
## prem_income out_prem une_prem_reser reins_income n_sec_tb_income
## 1: NA NA NA NA NA
## 2: NA NA NA NA NA
## 3: NA NA NA NA NA
## 4: NA NA NA NA NA
## 5: NA NA NA NA NA
## ---
## 3785: NA NA NA NA NA
## 3786: NA NA NA NA NA
## 3787: NA NA NA NA NA
## 3788: NA NA NA NA NA
## 3789: NA NA NA NA NA
## n_sec_uw_income n_asset_mg_income oth_b_income fv_value_chg_gain
## 1: NA NA 2.5e+07 -949000000.00
## 2: NA NA NA 7139004.66
## 3: NA NA NA NA
## 4: NA NA NA NA
## 5: NA NA NA 1854345.04
## ---
## 3785: NA NA NA 28116.77
## 3786: NA NA NA -83155.00
## 3787: NA NA NA 5966314.35
## 3788: NA NA NA NA
## 3789: NA NA NA NA
## invest_income ass_invest_income forex_gain total_cogs oper_cost
## 1: 3802000000.0 NA 5.1e+08 26928000000 NA
## 2: 109588131.7 -212546872.2 NA 43070811323 32832719447
## 3: NA NA NA 19466633 3936257
## 4: -2564101.9 NA NA 66751180 31296351
## 5: 16968669.9 7779680.5 NA 478073174 340353211
## ---
## 3785: 799847.8 780911.8 NA 1284442276 1037943794
## 3786: 889136.5 NA NA 72324823 57151951
## 3787: NA NA NA 89262535 23819608
## 3788: NA NA NA 52126142 39575020
## 3789: 128547.0 -143337.7 NA 105591600 79270471
## int_exp comm_exp biz_tax_surchg sell_exp admin_exp
## 1: 2.3107e+10 1.893e+09 4.020000e+08 NA 10598000000
## 2: NA NA 4.148904e+09 1241137605 2490235508
## 3: NA NA 5.229339e+04 6316247 3463214
## 4: NA NA 6.324942e+05 2477821 18637852
## 5: NA NA 7.348830e+07 3338143 24746161
## ---
## 3785: NA NA 1.514715e+07 23682508 86958020
## 3786: NA NA 1.144182e+06 5497871 5085657
## 3787: NA NA 9.668708e+05 40708316 10843608
## 3788: NA NA 1.107816e+05 2035366 8468971
## 3789: NA NA 5.854142e+05 7065715 10680880
## fin_exp assets_impair_loss prem_refund compens_payout
## 1: NA NA NA NA
## 2: 2259880934.5 NA NA NA
## 3: -176531.5 NA NA NA
## 4: 6369405.0 NA NA NA
## 5: 34438048.4 NA NA NA
## ---
## 3785: 7264054.9 -5602327.86 NA NA
## 3786: -1351735.5 -55368.80 NA NA
## 3787: -926052.5 -10979.72 NA NA
## 3788: 141102.8 NA NA NA
## 3789: -5079899.3 NA NA NA
## reser_insur_liab div_payt reins_exp oper_exp compens_payout_refu
## 1: NA NA NA 2.6928e+10 NA
## 2: NA NA NA NA NA
## 3: NA NA NA NA NA
## 4: NA NA NA NA NA
## 5: NA NA NA NA NA
## ---
## 3785: NA NA NA NA NA
## 3786: NA NA NA NA NA
## 3787: NA NA NA NA NA
## 3788: NA NA NA NA NA
## 3789: NA NA NA NA NA
## insur_reser_refu reins_cost_refund other_bus_cost operate_profit
## 1: NA NA NA 10998000000
## 2: NA NA NA 4823237263
## 3: NA NA NA 4435078
## 4: NA NA NA -23370995
## 5: NA NA NA 192954007
## ---
## 3785: NA NA NA 121485182
## 3786: NA NA NA 31363687
## 3787: NA NA NA 122043516
## 3788: NA NA NA 5240110
## 3789: NA NA NA 37437570
## non_oper_income non_oper_exp nca_disploss total_profit income_tax
## 1: 18000000.00 58000000.00 NA 10958000000 2.410000e+09
## 2: 187303357.69 214861413.06 NA 4795679208 2.365404e+09
## 3: 4406.93 35513.65 NA 4403971 8.286899e+05
## 4: 1079102.43 9951.81 NA -22301845 5.615115e+04
## 5: 60610.50 10070601.93 NA 182944015 4.233467e+07
## ---
## 3785: 7297343.95 207732.27 NA 128574793 2.964807e+06
## 3786: 2256.17 288453.07 NA 31077490 4.616060e+06
## 3787: 5.01 281260.25 NA 121762260 1.817131e+07
## 3788: 75216.61 200000.00 NA 5115327 1.556762e+06
## 3789: 5851.70 6172.22 NA 37437250 4.012597e+06
## n_income n_income_attr_p minority_gain oth_compr_income
## 1: 8548000000 8548000000 NA 1702000000.00
## 2: 2430274853 1249359313 1180915539.9 -586446019.93
## 3: 3575281 3816023 -240742.6 NA
## 4: -22357996 -20988032 -1369963.2 NA
## 5: 140609341 127952312 12657028.9 NA
## ---
## 3785: 125609986 113658557 11951429.0 15485071.14
## 3786: 26461430 26461430 NA NA
## 3787: 103590955 103590955 NA NA
## 3788: 3558565 3798730 -240165.8 -21912.60
## 3789: 33424653 33596201 -171548.7 77465.71
## t_compr_income compr_inc_attr_p compr_inc_attr_m_s ebit ebitda
## 1: 10250000000 10250000000 NA NA NA
## 2: 1843828833 650085687 1193743146.0 6969842963 NA
## 3: 3575281 3816023 -240742.6 4258546 NA
## 4: -22357996 -20988032 -1369963.2 -14437488 NA
## 5: 140609341 127952312 12657028.9 210283653 NA
## ---
## 3785: 141095057 129143629 11951429.0 134577469 NA
## 3786: 26461430 26461430 NA 28937696 NA
## 3787: 103590955 103590955 NA 116813180 NA
## 3788: 3536652 3776818 -240165.8 NA NA
## 3789: 33502118 33673667 -171548.7 NA NA
## insurance_exp undist_profit distable_profit update_flag
## 1: NA NA NA TRUE
## 2: NA NA NA TRUE
## 3: NA NA NA TRUE
## 4: NA NA NA TRUE
## 5: NA NA NA TRUE
## ---
## 3785: NA NA NA TRUE
## 3786: NA NA NA TRUE
## 3787: NA NA NA TRUE
## 3788: NA NA NA FALSE
## 3789: NA NA NA FALSE
Different vendors may use different coding schemes for A-share traded securities. These symbols can be standardised to code and traded exchange by parse_ashare_code(). Or convert to Tushare standard by norm_ashare_code().
ts_code <- norm_ashare_code(code = c("000001.sz", "sh600000", "601989"), type = "stock")
ts_code## [1] "000001.SZ" "600000.SH" "601989.SH"
Realtime data provided by Tushare Pro is not officially documented. Experimental support is provided by tushare_realtime_websocket(). Data handling is provided by user defined callback function, which should accept at least three arguments: - topic : a character vector of length 1 - code: a character vector of length 1 - record: a character vector of various lengths depending on the topic
Here is an example simple prints received data to console:
# define a callback function to handle realtime data
# the call back function should accept at least three arguments:
#
simple_callback <- function(topic, code, record, ...) {
cat("topic: ", topic, "\n")
cat("code: ", code, "\n")
cat("record: ")
str(record)
TRUE
}
# connect to Tushare service
ws <- tushare_realtime_websocket(topic = "some_topic", code = "000001.SZ", callback = simple_callback)
ws$connect()Realtime data provided by other vendors are provided, namely by Sina and Tencent. Data can be easily fetched calling sina_realtime_quote(), tencent_realtime_quote() and tencent_realtime_moneyflow().
An example of querying all stocks traded from Sina:
# fetch all traded stocks from Tushare:
api <- TushareApi()
stocks <- api$stock_basic()
# convert ts_code to Sina format
codes <- sina_ashare_code(stocks$ts_code)
# query from Sina
quotes <- sina_realtime_quote(codes)
quotes[]## Name Open PreClose Price High Low Bid Ask Vol
## 1: 平安银行 13.57 13.43 14.25 14.32 13.56 14.25 14.26 376833363
## 2: 万 科Ａ 28.65 28.39 28.65 29.12 28.14 28.65 28.66 174435560
## 3: 国农科技 29.45 29.45 28.46 29.49 28.38 28.46 28.47 5627413
## 4: 世纪星源 2.69 2.69 2.72 2.74 2.69 2.71 2.72 9756879
## 5: 深振业Ａ 7.48 7.48 7.51 7.65 7.26 7.51 7.52 88168721
## ---
## 3866: 吉贝尔 44.30 44.34 44.95 45.24 44.11 44.94 44.95 4073785
## 3867: 凌志软件 45.90 45.81 48.07 50.20 45.15 48.06 48.07 13103432
## 3868: 金博股份 90.86 89.99 90.42 92.30 87.98 90.41 90.42 2131819
## 3869: 天合光能 17.08 17.07 16.81 17.09 16.70 16.81 16.82 39427041
## 3870: N皖仪 35.50 15.50 35.90 40.00 35.20 35.90 35.93 21816313
## Tnvr Bid_V1 Bid_P1 Bid_V2 Bid_P2 Bid_V3 Bid_P3 Bid_V4 Bid_P4
## 1: 5280918011 6224 14.25 598900 14.24 1038802 14.23 231500 14.22
## 2: 4993062505 562333 28.65 22800 28.64 334300 28.63 133500 28.62
## 3: 161436906 34900 28.46 18400 28.45 20300 28.44 30500 28.43
## 4: 26440407 217705 2.71 330100 2.70 395300 2.69 168000 2.68
## 5: 656871650 79640 7.51 363500 7.50 57300 7.49 54600 7.48
## ---
## 3866: 182096786 12322 44.94 7200 44.93 5779 44.92 3636 44.90
## 3867: 625339824 600 48.06 200 48.05 400 48.04 5910 48.03
## 3868: 192086026 800 90.41 700 90.40 2725 90.38 700 90.36
## 3869: 664759874 223678 16.81 378826 16.80 71982 16.79 86283 16.78
## 3870: 797248900 69870 35.90 1000 35.89 8556 35.88 200 35.87
## Bid_V5 Bid_P5 Ask_V1 Ask_P1 Ask_V2 Ask_P2 Ask_V3 Ask_P3 Ask_V4
## 1: 238500 14.21 390914 14.26 221833 14.27 693567 14.28 390920
## 2: 448000 28.61 16200 28.66 600 28.67 7800 28.68 100
## 3: 4600 28.42 4300 28.47 1800 28.49 4800 28.50 2500
## 4: 75900 2.67 416238 2.72 359200 2.73 479400 2.74 467530
## 5: 14000 7.47 72982 7.52 81400 7.53 91500 7.54 143835
## ---
## 3866: 1892 44.89 25496 44.95 12336 44.96 3966 44.97 9783
## 3867: 5130 48.02 46506 48.07 2974 48.08 12012 48.09 21606
## 3868: 200 90.35 450 90.42 3000 90.45 1764 90.51 900
## 3869: 23359 16.77 47759 16.82 24499 16.83 41278 16.84 40038
## 3870: 300 35.86 500 35.93 2650 35.94 6707 35.95 2100
## Ask_P4 Ask_V5 Ask_P5 Time sina_code
## 1: 14.29 3707952 14.30 2020-07-03 15:00:03 sz000001
## 2: 28.69 36200 28.70 2020-07-03 15:00:03 sz000002
## 3: 28.51 300 28.53 2020-07-03 15:00:03 sz000004
## 4: 2.75 248300 2.76 2020-07-03 15:00:03 sz000005
## 5: 7.55 160554 7.56 2020-07-03 15:00:03 sz000006
## ---
## 3866: 44.98 1923 44.99 2020-07-03 15:29:59 sh688566
## 3867: 48.10 4356 48.11 2020-07-03 15:29:59 sh688588
## 3868: 90.54 200 90.57 2020-07-03 15:29:59 sh688598
## 3869: 16.85 23301 16.86 2020-07-03 15:29:59 sh688599
## 3870: 35.96 7885 35.97 2020-07-03 15:29:59 sh688600
A simple incremental model is created to record and track Sina realtime data. Due to the single-thread nature of R, we need to query Sina data in a separate R process:
# define an sqlite database file
db <- "/path/to/data.db"
# run data query loop
sina_realtime_loop(db)On our working R process, a incremental data loader can be created and used to fetch newly arrived data:
db <- "/path/to/data.db"
ld <- sina_realtime_loader(db)
# get new data
quotes <- ld()
# consume data
quotes[, spread := Bid - Ask]
summary(quotes$spread)
# do other thins with quotes
# get updated data
quotes <- ld()Please notice that PRAGMA WAL is used on the sqlite database, so data R process and working R process must be on same machine.
tswbench provides a set of stateful online functions for realtime data processing. All online functions are created by calling corresponding make_*() functions. Please refer to manual to find full list of supported algorithms.
# calculate triple exponential moving average
tema <- make_tema(period = 12)
data <- runif(10, max = 10)
value <- tema(data)
# the generated function tema() is stateful, thus suitable for incremental calculation
new_data <- runif(2, max = 10)
new_value <- tema(new_data)# supported algorithm are usually pretty fast
n = 10000
w = 120
# moving quantile
x <- runif(n)
probs <- seq(0, 1, 0.25)
f <- make_moving_quantile(window = w, probs = probs)
t1 <- system.time({y1 <- f(x)})
t2 <- system.time({y2 <- zoo::rollapply(x, 120, quantile, probs = probs, type = 3, fill = NA, align = "right")})
#speed-up
t2 / t1## user system elapsed
## 21.8169 34.0000 137.4444