Tronix (TRX, a.k.a. Tron) is the native currency of the TRON blockchain. The TRX token is based on the ERC-20 Etherium Standard and is fully compatible with it. Although the original purpose of TRX was to enable payments for digital entertainment, nowadays it has gained many other use cases that power transactions on the TRON blockchain and build up its economy (mainly in the gaming and decentralised finance sectors).
One can purchase TRX or swap it for other cryptocurrencies on numerous exchanges (e.g., Binance, Huobi, Bittrex, etc.). In February 2021, TRX’s global rank among cryptocurrencies was around 25, with a total market cap exceeding $4B.
tronr
The package offers several functions that one can use to query TRX market data provided by the public CoinGecko API. At a high level, the following data can be retrieved:
get_supported_coingecko_currencies() function);All of these functions return tibbles that are directly suitable for any downstream analyses using R. Provided below are a few examples of querying the TRX market data.
Please note that the CoinGecko API has a limit of 100 calls per minute from a given IP address. Keep this in mind when developing your applications based on
tronr.
The current price of TRX against a set of base currencies can be obtained using the get_current_trx_price() function. The list of base currencies is specified with the vs_currencies argument, e.g.:
library(tronr)
#> R toolbox to explore the TRON blockchain
#> Developed by Next Game Solutions (http://nextgamesolutions.com)
library(tibble)
get_current_trx_price(vs_currencies = c("usd", "eur", "gbp"))
#> # A tibble: 3 x 3
#> trx_price vs_currency last_updated_at
#> <dbl> <chr> <dttm>
#> 1 0.0603 usd 2021-07-12 22:04:47
#> 2 0.0509 eur 2021-07-12 22:04:47
#> 3 0.0434 gbp 2021-07-12 22:04:47The last_updated_at column shows when the price values have been updated last time (using the UTC timezone, here and in all other results returned by the tronr functions).
The get_current_trx_price() function can also be used to retrieve data on the current market cap, trading volume over the last 24 hours, and price percentage change compared to 24 hours ago. This can be done by turning on the include_market_cap, include_24h_vol, and include_24h_change options, respectively:
get_current_trx_price(vs_currencies = c("usd", "eur", "gbp"),
include_market_cap = TRUE,
include_24h_vol = TRUE,
include_24h_change = TRUE)
#> # A tibble: 3 x 6
#> trx_price vs_currency market_cap vol_24h price_percent_c… last_updated_at
#> <dbl> <chr> <dbl> <dbl> <dbl> <dttm>
#> 1 0.0603 usd 4323975921. 8.05e8 -4.68 2021-07-12 22:04:47
#> 2 0.0509 eur 3645293309. 6.78e8 -4.56 2021-07-12 22:04:47
#> 3 0.0434 gbp 3113729653. 5.79e8 -4.62 2021-07-12 22:04:47An even more comprehensive set of metrics characterising the current status of TRX on the cryptocurrency market can be obtained with the get_current_trx_market_data() function:
cur_market <- get_current_trx_market_data(vs_currencies = c("usd", "eur"))
glimpse(cur_market)
#> Rows: 2
#> Columns: 34
#> $ last_updated_at <dttm> 2021-07-12 22:04:05, 2021-07-12 22:0…
#> $ total_supply <dbl> 100850743812, 100850743812
#> $ circulating_supply <dbl> 71660220128, 71660220128
#> $ vs_currency <chr> "usd", "eur"
#> $ market_cap <dbl> 4323975921, 3645293309
#> $ market_cap_rank <int> 25, 25
#> $ market_cap_change_24h <dbl> -183088612, -150209398
#> $ market_cap_percentage_change_24h <dbl> -4.06226, -3.95756
#> $ total_trading_vol_24h <dbl> 804691900, 678359295
#> $ current_price <dbl> 0.060334, 0.050862
#> $ price_high_24h <dbl> 0.063296, 0.053296
#> $ price_low_24h <dbl> 0.059608, 0.050262
#> $ price_change_24h <dbl> -0.002861234, -0.002350672
#> $ price_percentage_change_24h <dbl> -4.52762, -4.41752
#> $ price_percentage_change_7d <dbl> -10.57446, -10.60331
#> $ price_percentage_change_14d <dbl> -7.10935, -6.48760
#> $ price_percentage_change_30d <dbl> -12.91022, -11.10287
#> $ price_percentage_change_60d <dbl> -51.43301, -50.55253
#> $ price_percentage_change_200d <dbl> 141.3054, 147.9403
#> $ price_percentage_change_1y <dbl> 228.6859, 213.1047
#> $ ath <dbl> 0.231673, 0.192595
#> $ ath_change_percentage <dbl> -73.96018, -73.59314
#> $ ath_date <dttm> 2018-01-05, 2018-01-05
#> $ atl <dbl> 0.00180434, 0.00154713
#> $ atl_change_percentage <dbl> 3243.445, 3187.279
#> $ atl_date <dttm> 2017-11-12, 2017-11-12
#> $ coingecko_rank <int> 6, 6
#> $ coingecko_score <dbl> 64.699, 64.699
#> $ developer_score <dbl> 89.218, 89.218
#> $ community_score <dbl> 48.559, 48.559
#> $ liquidity_score <dbl> 76.67, 76.67
#> $ public_interest_score <dbl> 0.091, 0.091
#> $ sentiment_votes_up_percentage <dbl> 74.59, 74.59
#> $ sentiment_votes_down_percentage <dbl> 25.41, 25.41Most of the variables in the above table should be self-explanatory. Additional details can be found at the “Methodology” page on the CoinGecko website.
TRX market data for a specific historical date can be obtained with the get_trx_market_data_for_date() function. The date argumet of this function accepts character, Date, or POSIXct values in the %Y-%m-%d format, e.g.:
# `date` as a character value:
df1 <- get_trx_market_data_for_date(date = "2021-01-01")
# `date` as a Date value:
df2 <- get_trx_market_data_for_date(date = as.Date("2021-01-01"))
identical(df1, df2)
#> [1] TRUE
df1
#> # A tibble: 2 x 5
#> date vs_currency market_cap total_trading_vol price
#> <date> <chr> <dbl> <dbl> <dbl>
#> 1 2021-01-01 usd 1918734259. 1231160613. 0.0268
#> 2 2021-01-01 eur 1570737264. 1007867475. 0.0220The history of TRX market data offered by CoinGecko starts on 2017-11-09 00:00:00. Attempts to retrieve data for earlier dates will fail with the corresponding console message:
get_trx_market_data_for_date(date = "2017-11-08")
#> Error: No data are available for dates before 2017-11-09. Check the `date` argumentAttempts to request a future date, for which no history exists yet, will fail as well:
get_trx_market_data_for_date(date = "2090-01-01")
#> Error: Cannot retrieve data for a future `date`. Check the `date` argumentOne can also retrieve TRX market data for a range of historical dates. This can be done using the get_trx_market_data_for_time_range() function. In contrast to get_trx_market_data_for_date(), this function only accepts a single base currency per query (argument vs_currency). In addition, timestamps that define the start (min_timestamp) and the end (max_timestamp) of historical range are to be character values in the Unix time format.
Granularity of the data returned by get_trx_market_data_for_time_range() depends on the requested range. Hourly data will be retrieved for periods of up to 90 days, and daily data for periods above 90 days:
# Range of less than 1 day:
(min_timestamp <- as.POSIXct("2020-01-01 10:00:10") %>% to_unix_timestamp())
#> [1] "1577872810000"
(max_timestamp = as.POSIXct("2020-01-01 20:45:10") %>% to_unix_timestamp())
#> [1] "1577911510000"
get_trx_market_data_for_time_range(
vs_currency = "usd",
min_timestamp = min_timestamp,
max_timestamp = max_timestamp
)
#> # A tibble: 11 x 5
#> timestamp vs_currency price total_trading_vol market_cap
#> <dttm> <chr> <dbl> <dbl> <dbl>
#> 1 2020-01-01 10:09:54 usd 0.0132 1102088570. 874524530.
#> 2 2020-01-01 11:05:39 usd 0.0133 1099017083. 879244117.
#> 3 2020-01-01 12:09:46 usd 0.0132 1087184132. 872880577.
#> 4 2020-01-01 13:09:29 usd 0.0133 1096215627. 878736567.
#> 5 2020-01-01 14:09:44 usd 0.0132 1074667283. 876375501.
#> 6 2020-01-01 15:09:52 usd 0.0133 1049571116. 879150841.
#> 7 2020-01-01 16:09:50 usd 0.0132 1031092825. 874377735.
#> 8 2020-01-01 17:09:34 usd 0.0133 1044756293. 878762444.
#> 9 2020-01-01 18:09:53 usd 0.0133 1040631289. 880559705.
#> 10 2020-01-01 19:09:42 usd 0.0133 1030222943. 881087716.
#> 11 2020-01-01 20:12:32 usd 0.0133 1022783881. 879145359.
# Range of >90 days:
(min_timestamp <- as.POSIXct("2020-01-01 00:00:00") %>% to_unix_timestamp())
#> [1] "1577836800000"
(max_timestamp = as.POSIXct("2020-05-01 00:00:00") %>% to_unix_timestamp())
#> [1] "1588291200000"
get_trx_market_data_for_time_range(
vs_currency = "usd",
min_timestamp = min_timestamp,
max_timestamp = max_timestamp
)
#> # A tibble: 122 x 5
#> timestamp vs_currency price total_trading_vol market_cap
#> <dttm> <chr> <dbl> <dbl> <dbl>
#> 1 2020-01-01 00:00:00 usd 0.0133 1134528759. 877119319.
#> 2 2020-01-02 00:00:00 usd 0.0132 1032624901. 872350811.
#> 3 2020-01-03 00:00:00 usd 0.0128 1056549454. 848482045.
#> 4 2020-01-04 00:00:00 usd 0.0134 1168793811. 885589788.
#> 5 2020-01-05 00:00:00 usd 0.0134 1033957595. 888326322.
#> 6 2020-01-06 00:00:00 usd 0.0135 1138776203. 893369823.
#> 7 2020-01-07 00:00:00 usd 0.0145 1286051294. 955193949.
#> 8 2020-01-08 00:00:00 usd 0.0144 1232245488. 950130927.
#> 9 2020-01-09 00:00:00 usd 0.0140 1149165537. 923243641.
#> 10 2020-01-10 00:00:00 usd 0.0140 1029207693. 924240156.
#> # … with 112 more rowsSimilar to get_trx_market_data_for_date(), get_trx_market_data_for_time_range() will fail if min_timestamp and/or max_timestamp define a time range for which no data exist:
# Requesting non-existing data from the past:
min_timestamp <- as.POSIXct("2017-11-08 00:00:00") %>% to_unix_timestamp()
max_timestamp = as.POSIXct("2020-01-01 00:00:00") %>% to_unix_timestamp()
get_trx_market_data_for_time_range(
vs_currency = "usd",
min_timestamp = min_timestamp,
max_timestamp = max_timestamp
)
#> Error: No data are available for dates before 2017-11-09. Check the `min_timestamp` argument
# Requesting non-existing data from the future:
min_timestamp <- as.POSIXct("2021-01-01 00:00:00") %>% to_unix_timestamp()
max_timestamp = as.POSIXct("2022-01-01 00:00:00") %>% to_unix_timestamp()
get_trx_market_data_for_time_range(
vs_currency = "usd",
min_timestamp = min_timestamp,
max_timestamp = max_timestamp
)
#> Error: Cannot retrieve data for future dates. Check the `max_timestamp` argumentAs a variant of querying a range of historical dates, one can use the get_trx_market_data_for_last_n_days() function to retrieve TRX market data for a number of recent dates (as of, and including, the current date):
get_trx_market_data_for_last_n_days(vs_currency = "usd", days = 7)
#> # A tibble: 170 x 5
#> timestamp vs_currency price total_trading_vol market_cap
#> <dttm> <chr> <dbl> <dbl> <dbl>
#> 1 2021-07-05 22:01:00 usd 0.0654 1125285199. 4680797409.
#> 2 2021-07-05 23:02:15 usd 0.0654 1104212328. 4676236818.
#> 3 2021-07-06 00:02:32 usd 0.0649 1066816968. 4646707803.
#> 4 2021-07-06 01:01:55 usd 0.0656 1057772816. 4704812107.
#> 5 2021-07-06 02:01:13 usd 0.0650 1026094705. 4667097163.
#> 6 2021-07-06 03:00:57 usd 0.0650 997388918. 4660854675.
#> 7 2021-07-06 04:02:30 usd 0.0651 990944229. 4667215286.
#> 8 2021-07-06 05:03:17 usd 0.0663 1010848448. 4750421838.
#> 9 2021-07-06 06:01:30 usd 0.0662 1010462956. 4756660866.
#> 10 2021-07-06 07:00:34 usd 0.0662 995403489. 4746487508.
#> # … with 160 more rowsBesides accepting numeric values, the days argument can also accept a character value "max", which will result in retrieving the entire existing history of TRX market data:
hist_max <- get_trx_market_data_for_last_n_days(vs_currency = "usd", days = "max")
hist_max
#> # A tibble: 1,342 x 5
#> timestamp vs_currency price total_trading_vol market_cap
#> <dttm> <chr> <dbl> <dbl> <dbl>
#> 1 2017-11-09 00:00:00 usd 0.00239 1224287. 156404162.
#> 2 2017-11-10 00:00:00 usd 0.00204 990423. 133968506.
#> 3 2017-11-11 00:00:00 usd 0.00191 707643. 125470649.
#> 4 2017-11-12 00:00:00 usd 0.00180 814789. 118235246.
#> 5 2017-11-13 00:00:00 usd 0.00202 894986. 132386427.
#> 6 2017-11-14 00:00:00 usd 0.00242 1073924. 158479487.
#> 7 2017-11-15 00:00:00 usd 0.00232 1179822. 152242301.
#> 8 2017-11-16 00:00:00 usd 0.00225 1324833. 147470996.
#> 9 2017-11-17 00:00:00 usd 0.00202 1905625. 132729589.
#> 10 2017-11-18 00:00:00 usd 0.00202 2184964. 132693857.
#> # … with 1,332 more rowsNotice how the data granularity changed in the last two examples. Generally, if days = 1 the data will be presented for approximately every 3-8 minutes. If days is between 2 and 90 (inclusive), an hourly time step will be used. Daily data are used for days above 90. One can use the interval argument to control this granularity (by default, interval = NULL). At the moment the only value it accepts is "daily":
# Within-day data, with `interval = "daily"`:
get_trx_market_data_for_last_n_days(
vs_currency = "usd",
days = 1,
interval = "daily"
)
#> # A tibble: 2 x 5
#> timestamp vs_currency price total_trading_vol market_cap
#> <dttm> <chr> <dbl> <dbl> <dbl>
#> 1 2021-07-12 00:00:00 usd 0.0621 736852906. 4451204211.
#> 2 2021-07-12 22:04:47 usd 0.0603 804691779. 4323975921.
# Less than 90 days, with `interval = "daily"`:
get_trx_market_data_for_last_n_days(
vs_currency = "usd",
days = 10,
interval = "daily"
)
#> # A tibble: 11 x 5
#> timestamp vs_currency price total_trading_vol market_cap
#> <dttm> <chr> <dbl> <dbl> <dbl>
#> 1 2021-07-03 00:00:00 usd 0.0666 1141455725. 4758781824.
#> 2 2021-07-04 00:00:00 usd 0.0669 940066906. 4791306600.
#> 3 2021-07-05 00:00:00 usd 0.0675 1072230609. 4846858583.
#> 4 2021-07-06 00:00:00 usd 0.0649 1066816968. 4646707803.
#> 5 2021-07-07 00:00:00 usd 0.0650 905777528. 4648866288.
#> 6 2021-07-08 00:00:00 usd 0.0644 920669464. 4618022352.
#> 7 2021-07-09 00:00:00 usd 0.0614 1115885436. 4397188049.
#> 8 2021-07-10 00:00:00 usd 0.0626 888427762. 4480289556.
#> 9 2021-07-11 00:00:00 usd 0.0616 794567260. 4416356903.
#> 10 2021-07-12 00:00:00 usd 0.0621 736852906. 4451204211.
#> 11 2021-07-12 21:47:14 usd 0.0602 803682206. 4304384209.
# More than 90 days, with `interval = "daily"`:
get_trx_market_data_for_last_n_days(
vs_currency = "usd",
days = 100,
interval = "daily"
)
#> # A tibble: 101 x 5
#> timestamp vs_currency price total_trading_vol market_cap
#> <dttm> <chr> <dbl> <dbl> <dbl>
#> 1 2021-04-04 00:00:00 usd 0.102 8364754397. 7321302884.
#> 2 2021-04-05 00:00:00 usd 0.128 10746448327. 9144431642.
#> 3 2021-04-06 00:00:00 usd 0.138 13018731030. 9985858391.
#> 4 2021-04-07 00:00:00 usd 0.125 9499933826. 8954301783.
#> 5 2021-04-08 00:00:00 usd 0.111 8323619561. 8072075620.
#> 6 2021-04-09 00:00:00 usd 0.124 6225961259. 8877512598.
#> 7 2021-04-10 00:00:00 usd 0.116 3766873571. 8323623723.
#> 8 2021-04-11 00:00:00 usd 0.126 4610974849. 8984059740.
#> 9 2021-04-12 00:00:00 usd 0.122 4159674472. 8760333396.
#> 10 2021-04-13 00:00:00 usd 0.130 6655337315. 9304010446.
#> # … with 91 more rowsIf the requested number of days covers dates before 2017-11-09, the retrived data will be clipped at 2017-11-09 (the beginning of history for TRX). Compare the following result to the previously created tibble hist_max:
hist_clipped <- get_trx_market_data_for_last_n_days(
vs_currency = "usd",
days = 100000,
interval = "daily"
)
min(hist_max$timestamp)
#> [1] "2017-11-09 UTC"
min(hist_clipped$timestamp)
#> [1] "2017-11-09 UTC"The open-high-low-close (OHLC) data characterise within-date and between-date price movements of a financial asset. This type of data for TRX can be retrieved using the get_trx_ohlc_data_for_last_n_days() function, which has the same arguments as get_trx_market_data_for_last_n_days(), except for not having the interval argument. Granularity of the retrieved data (i.e. candle’s body) depends on the value of days as follows:
The only values currently accepted by the days argument are 1, 7, 14, 30, 90, 180, 365 and "max". Here are some examples:
# 30-min granularity:
get_trx_ohlc_data_for_last_n_days(vs_currency = "usd", days = 1)
#> # A tibble: 49 x 6
#> timestamp vs_currency price_open price_high price_low price_close
#> <dttm> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2021-07-11 22:30:00 usd 0.0633 0.0633 0.0631 0.0631
#> 2 2021-07-11 23:00:00 usd 0.0630 0.0630 0.0621 0.0621
#> 3 2021-07-11 23:30:00 usd 0.0622 0.0622 0.0621 0.0621
#> 4 2021-07-12 00:00:00 usd 0.0620 0.0621 0.0620 0.0621
#> 5 2021-07-12 00:30:00 usd 0.0621 0.0621 0.0618 0.0619
#> 6 2021-07-12 01:00:00 usd 0.0620 0.0622 0.0620 0.0621
#> 7 2021-07-12 01:30:00 usd 0.0620 0.0620 0.0619 0.0619
#> 8 2021-07-12 02:00:00 usd 0.0620 0.0621 0.0620 0.0621
#> 9 2021-07-12 02:30:00 usd 0.0622 0.0622 0.0621 0.0622
#> 10 2021-07-12 03:00:00 usd 0.0625 0.0625 0.0624 0.0624
#> # … with 39 more rows
# 4-hours granularity:
get_trx_ohlc_data_for_last_n_days(vs_currency = "usd", days = 7)
#> # A tibble: 43 x 6
#> timestamp vs_currency price_open price_high price_low price_close
#> <dttm> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2021-07-06 00:00:00 usd 0.0654 0.0654 0.0654 0.0654
#> 2 2021-07-06 04:00:00 usd 0.0649 0.0656 0.0649 0.065
#> 3 2021-07-06 08:00:00 usd 0.0651 0.0663 0.0651 0.0662
#> 4 2021-07-06 12:00:00 usd 0.0664 0.0664 0.0649 0.0649
#> 5 2021-07-06 16:00:00 usd 0.0656 0.0658 0.0648 0.0648
#> 6 2021-07-06 20:00:00 usd 0.0649 0.0649 0.0646 0.0648
#> 7 2021-07-07 00:00:00 usd 0.0648 0.0648 0.0642 0.0647
#> 8 2021-07-07 04:00:00 usd 0.0650 0.0651 0.0648 0.0650
#> 9 2021-07-07 08:00:00 usd 0.0654 0.0657 0.0654 0.0656
#> 10 2021-07-07 12:00:00 usd 0.0656 0.0658 0.0656 0.0657
#> # … with 33 more rows
# 4-days granularity:
get_trx_ohlc_data_for_last_n_days(vs_currency = "usd", days = 90)
#> # A tibble: 25 x 6
#> timestamp vs_currency price_open price_high price_low price_close
#> <dttm> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2021-04-15 00:00:00 usd 0.147 0.147 0.141 0.141
#> 2 2021-04-19 00:00:00 usd 0.166 0.166 0.144 0.144
#> 3 2021-04-23 00:00:00 usd 0.132 0.134 0.111 0.111
#> 4 2021-04-27 00:00:00 usd 0.109 0.117 0.102 0.117
#> 5 2021-04-30 00:00:00 usd 0.125 0.125 0.121 0.121
#> 6 2021-05-03 00:00:00 usd 0.133 0.133 0.128 0.128
#> 7 2021-05-07 00:00:00 usd 0.132 0.153 0.120 0.153
#> 8 2021-05-11 00:00:00 usd 0.149 0.149 0.129 0.129
#> 9 2021-05-15 00:00:00 usd 0.139 0.139 0.122 0.125
#> 10 2021-05-19 00:00:00 usd 0.121 0.121 0.113 0.117
#> # … with 15 more rows
# 4-days granularity:
get_trx_ohlc_data_for_last_n_days(vs_currency = "usd", days = "max")
#> # A tibble: 351 x 6
#> timestamp vs_currency price_open price_high price_low price_close
#> <dttm> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 2017-11-11 00:00:00 usd 0.00239 0.00239 0.00192 0.00192
#> 2 2017-11-15 00:00:00 usd 0.00180 0.00242 0.00180 0.00232
#> 3 2017-11-19 00:00:00 usd 0.00225 0.00225 0.00198 0.00198
#> 4 2017-11-23 00:00:00 usd 0.00213 0.00233 0.00213 0.00214
#> 5 2017-11-27 00:00:00 usd 0.00207 0.00211 0.00202 0.00209
#> 6 2017-11-30 00:00:00 usd 0.00239 0.00239 0.00205 0.00231
#> 7 2017-12-03 00:00:00 usd 0.00214 0.00214 0.0021 0.0021
#> 8 2017-12-07 00:00:00 usd 0.00215 0.00393 0.00214 0.00393
#> 9 2017-12-11 00:00:00 usd 0.00424 0.00514 0.00424 0.00514
#> 10 2017-12-15 00:00:00 usd 0.00754 0.0176 0.00754 0.0176
#> # … with 341 more rows