📍Python Library

Functions

They are the functions available in both analytic/python mode, and terminal.

1. Get the Time-series Data

get_raw_data(table_name: str, symbol: str, start_time: str, end_time: str) -> list

get_raw_data is a function to get the data you can find in the data preview. It returns the data as a list of dictionaries.

Arguments

• table_name : (string) The name of the table you want to get. It can be seen right next to the data alias at data preview. Check the data list to see the list of available table_name.

R1's table_name is binance_um_futures_candlestick_1d.

• symbol : (string) A category in the table_name. For instance, if the table_name = binance_candlestick_1h and the symbol = BTCUSDT, then it would return 1 hour interval candlestick data of BTCUSDT in the Binance spot market.

• start_time : (string)

• end_time : (string) The start_time and end_time are the time range of the data to get. They are strings of time in yyyy-MM-dd HH:mm:ss format. You can set them by strftime('%Y-%m-%d %H:%M:%S') method of datetime.datetime.

Returns

It returns the data in a list of the dictionaries. In the dictionary, every numeric value is decimal.Decimal type, and time value is datetime.datetime.

# Basic ways to get the data
btc_price = get_raw_data("binance_candlestick_1d", 
    "BTCUSDT", 
    "2022-01-01 00:00:00",
    "2022-01-08 00:00:00")
    
# Using datetime.strftime. 2hours ago ~ now.
# Format %F %T can do the same as %Y-%m-%d %H:%M:%S
xrp_price = get_raw_data("binance_um_futures_candlestick_5min", 
    "XPRBUSDT", 
    (datetime.datetime.now() - datetime.timedelta(hours=2)).strftime('%F %T'),
    datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'))

# The example of the result
'''
[
    {
        "time" : `object of datetime.datetime type`,
        "close" : `object of decimal.Decimal type`,
        "high" : `object of decimal.Decimal type`,
        "low" : `object of decimal.Decimal type`,
        "close" : `object of decimal.Decimal type`,
        ...
    },
    
    ...
]
'''

---

2. Get the Time-series Data in DataFrame

get_raw_data_in_df(table_name: str, symbol: str, start_time: str, end_time: str) -> pandas.DataFrame

get_raw_data_in_df is a function that works same as get_raw_data, but it refines the result into pandas.DataFrame type, and converts decimal.Decimal type values in it into float.

It is useful to apply methods from pandas such as ewm to the data.

Arguments

• table_name : (string)

• symbol : (string)

• start_time : (string)

• end_time : (string)

Returns

It returns the data in pandas.DataFrame object. In each entry, every numeric value is float, and the time value is datetime.datetime.

# The list of the arguments is same as of get_raw_data.
btc_price = get_raw_data_in_df("binance_candlestick_1d", 
    "BTCUSDT", 
    "2022-01-01 00:00:00", 
    "2022-01-08 00:00:00")

---

Terminal-only Functions

Terminal-only function can be used only in the terminal, which means that they are not available in the analytics.

1. Stop Bot

shutdown(message: str|None = None) -> None

It stops the bot that the code is being run. You can call this function when you want to stop your bot in a certain condition.

Do not wrap this function by try ... except, as it is implemented by raising ShutdownException.

Arguments

• message : (string) The message to record to the log when the function is called

Returns

None

# abstract on how the shutdown() is defined.
def shutdown(message:str=None):
    print(f"Bot shutdown. {message}")
    raise ShutdownException

# Example of using `shutdown()` function

# 1. stop the bot when USDT in the balance is more than 50 or less then 30.
balance = privateAPI.get_balance('binance')
usdt_val = balance['USDT']['amount'] if 'USDT' in balance else 0

if usdt_val > 50 or usdt_val < 30:
    shutdown()
...

# 2. You shouldn't wrap it by try ... except.
try:
    shutdown() # shutdown() doesn't work in this way!
except:
    ...

---