README.md
In imlijunda/tswbench: Tushare Workbench
Basic usage
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
API function usage
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）
End-of-day data
tswbench wraps various end-of-day data functions to market_eod() to
simplify data acquisition. market_eod() supports most of Tushare APIs.
Query end-of-day intraday OHLC:
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
Query end-of-day index performance:
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
Fundamental data
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
Standardise A-share symbols
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
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$connect()
Third party realtime data vendors
Realtime data provided by other vendors are provided, namely by Sina and
Tencent. Data can be easily fetched calling sina_realtime_quote(),
tencent_realtime_quote() and tencent_realtime_moneyflow().
An example of querying all stocks traded from Sina:
# fetch all traded stocks from Tushare:
api <- TushareApi()
stocks <- api$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
Keeping track of Sina realtime data
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.
Fast online algorithms
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
imlijunda/tswbench documentation built on June 12, 2021, 12:45 p.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.
Basic usage
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
API function usage
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）
End-of-day data
tswbench wraps various end-of-day data functions to market_eod() to simplify data acquisition. market_eod() supports most of Tushare APIs.
Query end-of-day intraday OHLC:
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
Query end-of-day index performance:
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
Fundamental data
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
Standardise A-share symbols
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
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$connect()
Third party realtime data vendors
Realtime data provided by other vendors are provided, namely by Sina and Tencent. Data can be easily fetched calling sina_realtime_quote(), tencent_realtime_quote() and tencent_realtime_moneyflow().
An example of querying all stocks traded from Sina:
# fetch all traded stocks from Tushare:
api <- TushareApi()
stocks <- api$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
Keeping track of Sina realtime data
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.
Fast online algorithms
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
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