Historical Data¶
4195.81 ┤
4161.76 ┤ ╭─╮
4127.72 ┤ │ ╰╮
4093.67 ┤ │ │
4059.62 ┤ ╭╮ │ ╰╮
4025.58 ┤ ╭─╯╰╮╭╮ │ ╰─╮
3991.53 ┤ ╭╯ ╰╯│ ╭╮╭╮ │ │
3957.48 ┤ │ ╰╮ ╭╯╰╯│╭╮│ │
3923.44 ┤ │ │ │ ╰╯╰╯ │
3889.39 ┤ │ │ ╭╮╭╯ │ ╭───╮
3855.34 ┤ │ │ ╭╯││ │ │ ╰─╮
3821.30 ┤ ╭╮ │ ╰──╯ ╰╯ │ ╭╮ ╭╮ ╭╯ ╰╮ ╭╮ ╭╮ ╭
3787.25 ┤ ││ │ │╭╮ ╭╯╰╮ ╭╯│ │ │ │╰─╯╰─╮ │
3753.21 ┤ ╭╮│╰─╮ │ ││╰╮╭╯ ╰╮│ │╭──╮ │ │ ╭╯ ╰─╮ │
3719.16 ┤ │╰╯ │╭─╯ ╰╯ ╰╯ ╰╯ ╰╯ │ │ ╰─╯ │ ╭─╮╭╯
3685.11 ┤ ╭╯ ╰╯ │ │ ╰──╯ ╰╯
3651.07 ┤ │ │ ╭╯
3617.02 ┤ │ ╰╮╭─╯
3582.97 ┤ │ ╰╯
3548.93 ┤╮ ╭╯
3514.88 ┼╰╮╭╯
3480.83 ┤ ╰╯
Importing Data¶
If you have your own data that has/hasn’t been processed, you should conform to the following structure. Basically, load your data into a Pandas dataframe object and be sure to convert the dates to datetime format and include the following lowercase column titles.
date high low open close
0 2017-07-08 11:00:00 2480.186778 2468.319314 2477.279567 2471.314030
1 2017-07-08 11:30:00 2471.314030 2455.014057 2471.202796 2458.073602
2 2017-07-08 12:00:00 2480.000000 2456.000000 2458.073602 2480.000000
3 2017-07-08 12:30:00 2489.004639 2476.334333 2479.402768 2481.481258
4 2017-07-08 13:00:00 2499.000000 2476.621873 2481.458643 2491.990000
5 2017-07-08 13:30:00 2503.503479 2490.314610 2492.440289 2496.005562
6 2017-07-08 14:00:00 2525.000000 2491.062741 2494.449524 2520.775500
7 2017-07-08 14:30:00 2521.500036 2510.000000 2520.775500 2518.450645
8 2017-07-08 15:00:00 2519.817394 2506.054360 2518.451000 2514.484009
Downloading Data¶
If you don’t have your own data, we’ve included useful functions for grabbing low and high timeframe historical data from crypto exchanges. These helper functions will automatically resample your datasets to any desired timeframe and return a Gemini-compatible dataframe.
import data
# Higher timeframes (>= daily)
df = data.get_htf_candles("BTC_USD", "Bitfinex", "3-DAY", "2019-01-12 00:00:00", "2019-02-01 00:00:00")
# Lower timeframes (< daily)
df = data.get_ltf_candles("USDC_BTC", "30-MIN", "2019-01-12 00:00:00", "2019-02-01 00:00:00")