Python - update elements of a multidimensional dictionary - python

I have the following info stored in a dict called "info1":
[{'symbol': 'ETHUSDT', 'orderId': 480977, 'orderListId': -1, 'clientOrderId': 'S3', 'price': '0.00000000', 'origQty': '0.02000000', 'executedQty': '0.02000000', 'cummulativeQuoteQty': '25.07260000', 'status': 'FILLED', 'timeInForce': 'GTC', 'type': 'MARKET', 'side': 'BUY', 'stopPrice': '0.00000000', 'icebergQty': '0.00000000', 'time': 1672948804160, 'updateTime': 1672948804160, 'isWorking': True, 'workingTime': 1672948804160, 'origQuoteOrderQty': '0.00000000', 'selfTradePreventionMode': 'NONE'}, {'symbol': 'ETHUSDT', 'orderId': 566634, 'orderListId': -1, 'clientOrderId': 'S3', 'price': '0.00000000', 'origQty': '0.02000000', 'executedQty': '0.02000000', 'cummulativeQuoteQty': '24.98800000', 'status': 'FILLED', 'timeInForce': 'GTC', 'type': 'MARKET', 'side': 'SELL', 'stopPrice': '0.00000000', 'icebergQty': '0.00000000', 'time': 1672971274007, 'updateTime': 1672971274007, 'isWorking': True, 'workingTime': 1672971274007, 'origQuoteOrderQty': '0.00000000', 'selfTradePreventionMode': 'NONE'}]
How would I go about updating the values for both 'time' keys in this dict?
Have tried to identify the index of each, without luck.

First off, you are not dealing with a dictionary, but a list of dictionaries.
isinstance(info1, list)
#True
That is why you can simply try to fetch a key and make an update here. First, you have to iterate through the list of dictionaries and then use dict.update to update specific key values. Here is how you can do this with a simple loop -
new_times = [1672948804190, 1672971274120] #one for each dict in list
for d,t in zip(info1, new_times):
d.update({'time':t})
print(info1)
[{'symbol': 'ETHUSDT',
'orderId': 480977,
'orderListId': -1,
'clientOrderId': 'S3',
'price': '0.00000000',
'origQty': '0.02000000',
'executedQty': '0.02000000',
'cummulativeQuoteQty': '25.07260000',
'status': 'FILLED',
'timeInForce': 'GTC',
'type': 'MARKET',
'side': 'BUY',
'stopPrice': '0.00000000',
'icebergQty': '0.00000000',
'time': 1672948804190, #<---------
'updateTime': 1672948804160,
'isWorking': True,
'workingTime': 1672948804160,
'origQuoteOrderQty': '0.00000000',
'selfTradePreventionMode': 'NONE'},
{'symbol': 'ETHUSDT',
'orderId': 566634,
'orderListId': -1,
'clientOrderId': 'S3',
'price': '0.00000000',
'origQty': '0.02000000',
'executedQty': '0.02000000',
'cummulativeQuoteQty': '24.98800000',
'status': 'FILLED',
'timeInForce': 'GTC',
'type': 'MARKET',
'side': 'SELL',
'stopPrice': '0.00000000',
'icebergQty': '0.00000000',
'time': 1672971274120, #<---------
'updateTime': 1672971274007,
'isWorking': True,
'workingTime': 1672971274007,
'origQuoteOrderQty': '0.00000000',
'selfTradePreventionMode': 'NONE'}]

Related

How to group real-time data into one list or dataframe? #python #FTX websocket api

I'm receiving data from FTX websocket api.
wsEndpoint ='wss://ftx.com/ws/'
msg = json.dumps({'op': 'subscribe', 'channel': 'trades', 'market': 'ETH-PERP'})
def on_open(ws):
ws.send(msg)
def on_message(ws, message):
trades = json.loads(message)['data']
print(trades)
ws = websocket.WebSocketApp(wsEndpoint, on_message=on_message, on_open=on_open)
ws.run_forever()
Seems like it generates a new list whenever it sends back data.
[{'id': 5124414968, 'price': 1277.6, 'size': 0.03, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:24.362764+00:00'}]
[{'id': 5124415008, 'price': 1277.7, 'size': 0.168, 'side': 'buy', 'liquidation': False, 'time': '2022-10-11T03:44:25.501504+00:00'}]
[{'id': 5124415161, 'price': 1277.6, 'size': 1.321, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415162, 'price': 1277.6, 'size': 0.05, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415163, 'price': 1277.6, 'size': 0.006, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415164, 'price': 1277.6, 'size': 0.2, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415165, 'price': 1277.6, 'size': 0.001, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415166, 'price': 1277.6, 'size': 0.152, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415167, 'price': 1277.6, 'size': 4.29, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415168, 'price': 1277.6, 'size': 7.087, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415169, 'price': 1277.6, 'size': 2.6, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415170, 'price': 1277.6, 'size': 3.02, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415171, 'price': 1277.6, 'size': 2.6, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415172, 'price': 1277.6, 'size': 0.133, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415173, 'price': 1277.6, 'size': 0.04, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415174, 'price': 1277.6, 'size': 0.652, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}, {'id': 5124415175, 'price': 1277.6, 'size': 9.42, 'side': 'sell', 'liquidation': False, 'time': '2022-10-11T03:44:28.612715+00:00'}]
How can I put them into one list or dataframe so I can plot them? Is it to assign each new list a variable with loop and store them in an empty list? And maybe it is a dumb question to ask, but is it possible to put a for loop in a list?

Getting active Binance futures positions

I use the binance.client package to interact with the Binance API. There is a need to get active futures positions. That is, positions that are open, but there were no deals to close them. Everything I could find in the documentation returns the history of transactions, that is, it is impossible to tell from them whether all positions were closed or only part of them.
client.get_all_orders(symbol)
client.get_all_margin_orders(symbol)
Return this:
[]
[]
client.futures_get_all_orders(symbol)
Returns a list containing both purchase and sale positions. Therefore, it is impossible to say with certainty for what amount there is an open position now. An example of such data:
[{'orderId': 4438, 'symbol': 'BTCUSDT', 'status': 'FILLED', 'clientOrderId': 'XY7J7nhdGPWi6mu', 'price': '0', 'avgPrice': '37179.70000', 'origQty': '0.001', 'executedQty': '0.001', 'cumQuote': '37.17870', 'timeInForce': 'GTC', 'type': 'MARKET', 'reduceOnly': False, 'closePosition': False, 'side': 'BUY', 'positionSide': 'LONG', 'stopPrice': '0', 'workingType': 'CONTRACT_PRICE', 'priceProtect': False, 'origType': 'MARKET', 'time': 164549023, 'updateTime': 164549023},
{'orderId': 3458, 'symbol': 'BTCUSDT', 'status': 'FILLED', 'clientOrderId': '1zpqZVxYgYyfD', 'price': '0', 'avgPrice': '37219.90000', 'origQty': '0.001', 'executedQty': '0.001', 'cumQuote': '37.21890', 'timeInForce': 'GTC', 'type': 'MARKET', 'reduceOnly': True, 'closePosition': False, 'side': 'SELL', 'positionSide': 'LONG', 'stopPrice': '0', 'workingType': 'CONTRACT_PRICE', 'priceProtect': False, 'origType': 'MARKET', 'time': 16454903, 'updateTime': 16454903},
{'orderId': 4396, 'symbol': 'BTCUSDT', 'status': 'FILLED', 'clientOrderId': 'iKlNyjk7yaKtBo', 'price': '0', 'avgPrice': '36854.30000', 'origQty': '0.001', 'executedQty': '0.001', 'cumQuote': '36.74630', 'timeInForce': 'GTC', 'type': 'MARKET', 'reduceOnly': False, 'closePosition': False, 'side': 'BUY', 'positionSide': 'LONG', 'stopPrice': '0', 'workingType': 'CONTRACT_PRICE', 'priceProtect': False, 'origType': 'MARKET', 'time': 164551553, 'updateTime': 164551553}]
.get_position
https://github.com/Binance-
docs/Binance_Futures_python/blob/master/example/trade/get_position.py

How to create python dataframe from nested json dictionary with increasing key

I have a json file with the following structure:
{'0': {'transaction': [{'transaction_key': '406.l.657872.tr.374',
'transaction_id': '374',
'type': 'add/drop',
'status': 'successful',
'timestamp': '1639593953'},
{'players': {'0': {'player': [[{'player_key': '406.p.100006'},
{'player_id': '100006'},
{'name': {'full': 'Dallas',
'first': 'Dallas',
'last': '',
'ascii_first': 'Dallas',
'ascii_last': ''}},
{'editorial_team_abbr': 'Dal'},
{'display_position': 'DEF'},
{'position_type': 'DT'}],
{'transaction_data': [{'type': 'add',
'source_type': 'freeagents',
'destination_type': 'team',
'destination_team_key': '406.l.657872.t.10',
'destination_team_name': 'Team 1'}]}]},
'1': {'player': [[{'player_key': '406.p.24793'},
{'player_id': '24793'},
{'name': {'full': 'Julio Jones',
'first': 'Julio',
'last': 'Jones',
'ascii_first': 'Julio',
'ascii_last': 'Jones'}},
{'editorial_team_abbr': 'Ten'},
{'display_position': 'WR'},
{'position_type': 'O'}],
{'transaction_data': {'type': 'drop',
'source_type': 'team',
'source_team_key': '406.l.657872.t.10',
'source_team_name': 'Team 1',
'destination_type': 'waivers'}}]},
'count': 2}}]},
'1': {'transaction': [{'transaction_key': '406.l.657872.tr.373',
'transaction_id': '373',
'type': 'add/drop',
'status': 'successful',
'timestamp': '1639575496'},
{'players': {'0': {'player': [[{'player_key': '406.p.32722'},
{'player_id': '32722'},
{'name': {'full': 'Cam Akers',
'first': 'Cam',
'last': 'Akers',
'ascii_first': 'Cam',
'ascii_last': 'Akers'}},
{'editorial_team_abbr': 'LAR'},
{'display_position': 'RB'},
{'position_type': 'O'}],
{'transaction_data': [{'type': 'add',
'source_type': 'freeagents',
'destination_type': 'team',
'destination_team_key': '406.l.657872.t.5',
'destination_team_name': 'Team 2'}]}]},
'1': {'player': [[{'player_key': '406.p.100007'},
{'player_id': '100007'},
{'name': {'full': 'Denver',
'first': 'Denver',
'last': '',
'ascii_first': 'Denver',
'ascii_last': ''}},
{'editorial_team_abbr': 'Den'},
{'display_position': 'DEF'},
{'position_type': 'DT'}],
{'transaction_data': {'type': 'drop',
'source_type': 'team',
'source_team_key': '406.l.657872.t.5',
'source_team_name': 'Team 2',
'destination_type': 'waivers'}}]},
'count': 2}}]},
'2': {'transaction': [{'transaction_key': '406.l.657872.tr.372',
'transaction_id': '372',
'type': 'add/drop',
'status': 'successful',
'timestamp': '1639575448'},
{'players': {'0': {'player': [[{'player_key': '406.p.33413'},
{'player_id': '33413'},
{'name': {'full': 'Travis Etienne',
'first': 'Travis',
'last': 'Etienne',
'ascii_first': 'Travis',
'ascii_last': 'Etienne'}},
{'editorial_team_abbr': 'Jax'},
{'display_position': 'RB'},
{'position_type': 'O'}],
{'transaction_data': [{'type': 'add',
'source_type': 'freeagents',
'destination_type': 'team',
'destination_team_key': '406.l.657872.t.5',
'destination_team_name': 'Team 2'}]}]},
'1': {'player': [[{'player_key': '406.p.24815'},
{'player_id': '24815'},
{'name': {'full': 'Mark Ingram II',
'first': 'Mark',
'last': 'Ingram II',
'ascii_first': 'Mark',
'ascii_last': 'Ingram II'}},
{'editorial_team_abbr': 'NO'},
{'display_position': 'RB'},
{'position_type': 'O'}],
{'transaction_data': {'type': 'drop',
'source_type': 'team',
'source_team_key': '406.l.657872.t.5',
'source_team_name': 'Team 2',
'destination_type': 'waivers'}}]},
'count': 2}}]}
These are transactions for a fantasy football league and I'd like to organize each transaction into a dataframe, however I'm running into issues normalizing the data. I figure I'd need to begin a loop, but am slightly stuck in the mud and would appreciate if anyone has any suggestions. Thank You.
Ideally, I'm looking to summarize each transaction with the following dataframe structure:
transaction_id type added pos_1 dropped pos_2 timestamp
374 add/drop Dallas DEF Julio Jones WR 1639593953
373 add/drop Cam Akers RB Denver DEF 1639575496
372 add/drop Travis Etienne RB Mark Ingram II RB 1639575448

python search nested dictionary for value and key

I have this tuple with nested dictionaries:
Orders = ({'ret_code': 0, 'ret_msg': 'OK', 'ext_code': '', 'ext_info': '', 'result': [{'user_id': 121212, 'stop_order_id': 'ssdf3215641d215415d', 'symbol': 'XRPUSDT', 'side': 'Sell', 'order_type': 'Market', 'price': 0, 'qty': 2, 'time_in_force': 'ImmediateOrCancel', 'order_status': 'Untriggered', 'trigger_price': 0.8839, 'base_price': '0.8860', 'order_link_id': '', 'created_time': '2021-06-14T14:21:50.000Z', 'updated_time': '2021-06-14T14:21:50.000Z', 'take_profit': 0, 'stop_loss': 0, 'tp_trigger_by': 'UNKNOWN', 'sl_trigger_by': 'UNKNOWN', 'trigger_by': 'LastPrice', 'reduce_only': False, 'close_on_trigger': False}, {'user_id': 121212, 'stop_order_id': 's65d4654sd234165sd4', 'symbol': 'XRPUSDT', 'side': 'Buy', 'order_type': 'Market', 'price': 0, 'qty': 2, 'time_in_force': 'ImmediateOrCancel', 'order_status': 'Untriggered', 'trigger_price': 0.8929, 'base_price': '0.8820', 'order_link_id': '', 'created_time': '2021-06-14T14:03:20.000Z', 'updated_time': '2021-06-14T14:18:59.000Z', 'take_profit': 0, 'stop_loss': 0, 'tp_trigger_by': 'UNKNOWN', 'sl_trigger_by': 'UNKNOWN', 'trigger_by': 'LastPrice', 'reduce_only': True, 'close_on_trigger': True}, {'user_id': 121212, 'stop_order_id': 'sd654s6d5465416ds546s5d4', 'symbol': 'XRPUSDT', 'side': 'Sell', 'order_type': 'Market', 'price': 0, 'qty': 3, 'time_in_force': 'ImmediateOrCancel', 'order_status': 'Untriggered', 'trigger_price': 0.88, 'base_price': '0.8817', 'order_link_id': '', 'created_time': '2021-06-14T14:03:04.000Z', 'updated_time': '2021-06-14T14:18:59.000Z', 'take_profit': 0, 'stop_loss': 0, 'tp_trigger_by': 'UNKNOWN', 'sl_trigger_by': 'UNKNOWN', 'trigger_by': 'LastPrice', 'reduce_only': True, 'close_on_trigger': True}, {'user_id': 121212, 'stop_order_id': 's65d46s5d46s5d46s5d46sd5', 'symbol': 'XRPUSDT', 'side': 'Buy', 'order_type': 'Market', 'price': 0, 'qty': 2, 'time_in_force': 'ImmediateOrCancel', 'order_status': 'Untriggered', 'trigger_price': 0.88, 'base_price': '0.8839', 'order_link_id': '', 'created_time': '2021-06-14T13:58:30.000Z', 'updated_time': '2021-06-14T14:18:59.000Z', 'take_profit': 0, 'stop_loss': 0, 'tp_trigger_by': 'UNKNOWN', 'sl_trigger_by': 'UNKNOWN', 'trigger_by': 'LastPrice', 'reduce_only': True, 'close_on_trigger': True}, {'user_id': 121212, 'stop_order_id': 'sd654s6d54s6d51s6d54s65d4', 'symbol': 'XRPUSDT', 'side': 'Sell', 'order_type': 'Market', 'price': 0, 'qty': 3, 'time_in_force': 'ImmediateOrCancel', 'order_status': 'Untriggered', 'trigger_price': 0.8929, 'base_price': '0.8855', 'order_link_id': '', 'created_time': '2021-06-14T13:49:20.000Z', 'updated_time': '2021-06-14T14:18:59.000Z', 'take_profit': 0, 'stop_loss': 0, 'tp_trigger_by': 'UNKNOWN', 'sl_trigger_by': 'UNKNOWN', 'trigger_by': 'LastPrice', 'reduce_only': True, 'close_on_trigger': True}], 'time_now': '1623682473.336069', 'rate_limit_status': 598, 'rate_limit_reset_ms': 1623682473333, 'rate_limit': 600}, <bravado.requests_client.RequestsResponseAdapter object at 0x00789C86BBFA0>)
I want to search every dictionary for:
'reduce_only': False, 'close_on_trigger': False
If available print (True) and get the 'side' for exactly this dictionary:
True
Sell
The problem is that the dictionary changes depending on the orders
It means that there can be two or three dictionaries in the list
I tried to use For Loop but it only prints variables and keys
for x in Orders:
for y in x:
print(x)
Your data doesn't look like a proper Python data.
As for the dictionaries, I see no problem:
orders = ({
'ret_code': 0,
'ret_msg': 'OK',
'ext_code': '',
'ext_info': '',
'result': [{
'user_id': 121212,
'stop_order_id': 'ssdf3215641d215415d',
'symbol': 'XRPUSDT',
'side': 'Sell',
'order_type': 'Market',
'price': 0,
'qty': 2,
'time_in_force': 'ImmediateOrCancel',
'order_status': 'Untriggered',
'trigger_price': 0.8839,
'base_price': '0.8860',
'order_link_id': '',
'created_time': '2021-06-14T14: 21: 50.000Z',
'updated_time': '2021-06-14T14: 21: 50.000Z',
'take_profit': 0,
'stop_loss': 0,
'tp_trigger_by': 'UNKNOWN',
'sl_trigger_by': 'UNKNOWN',
'trigger_by': 'LastPrice',
'reduce_only': False,
'close_on_trigger': False
},
{
'user_id': 121212,
'stop_order_id': 's65d4654sd234165sd4',
'symbol': 'XRPUSDT',
'side': 'Buy',
'order_type': 'Market',
'price': 0,
'qty': 2,
'time_in_force': 'ImmediateOrCancel',
'order_status': 'Untriggered',
'trigger_price': 0.8929,
'base_price': '0.8820',
'order_link_id': '',
'created_time': '2021-06-14T14: 03: 20.000Z',
'updated_time': '2021-06-14T14: 18: 59.000Z',
'take_profit': 0,
'stop_loss': 0,
'tp_trigger_by': 'UNKNOWN',
'sl_trigger_by': 'UNKNOWN',
'trigger_by': 'LastPrice',
'reduce_only': True,
'close_on_trigger': True
},
{
'user_id': 121212,
'stop_order_id': 'sd654s6d5465416ds546s5d4',
'symbol': 'XRPUSDT',
'side': 'Sell',
'order_type': 'Market',
'price': 0,
'qty': 3,
'time_in_force': 'ImmediateOrCancel',
'order_status': 'Untriggered',
'trigger_price': 0.88,
'base_price': '0.8817',
'order_link_id': '',
'created_time': '2021-06-14T14: 03: 04.000Z',
'updated_time': '2021-06-14T14: 18: 59.000Z',
'take_profit': 0,
'stop_loss': 0,
'tp_trigger_by': 'UNKNOWN',
'sl_trigger_by': 'UNKNOWN',
'trigger_by': 'LastPrice',
'reduce_only': True,
'close_on_trigger': True
},
{
'user_id': 121212,
'stop_order_id': 's65d46s5d46s5d46s5d46sd5',
'symbol': 'XRPUSDT',
'side': 'Buy',
'order_type': 'Market',
'price': 0,
'qty': 2,
'time_in_force': 'ImmediateOrCancel',
'order_status': 'Untriggered',
'trigger_price': 0.88,
'base_price': '0.8839',
'order_link_id': '',
'created_time': '2021-06-14T13: 58: 30.000Z',
'updated_time': '2021-06-14T14: 18: 59.000Z',
'take_profit': 0,
'stop_loss': 0,
'tp_trigger_by': 'UNKNOWN',
'sl_trigger_by': 'UNKNOWN',
'trigger_by': 'LastPrice',
'reduce_only': True,
'close_on_trigger': True
},
{
'user_id': 121212,
'stop_order_id': 'sd654s6d54s6d51s6d54s65d4',
'symbol': 'XRPUSDT',
'side': 'Sell',
'order_type': 'Market',
'price': 0,
'qty': 3,
'time_in_force': 'ImmediateOrCancel',
'order_status': 'Untriggered',
'trigger_price': 0.8929,
'base_price': '0.8855',
'order_link_id': '',
'created_time': '2021-06-14T13: 49: 20.000Z',
'updated_time': '2021-06-14T14: 18: 59.000Z',
'take_profit': 0,
'stop_loss': 0,
'tp_trigger_by': 'UNKNOWN',
'sl_trigger_by': 'UNKNOWN',
'trigger_by': 'LastPrice',
'reduce_only': True,
'close_on_trigger': True
}
],
'time_now': '1623682473.336069',
'rate_limit_status': 598,
'rate_limit_reset_ms': 1623682473333,
'rate_limit': 600
}, '<bravado.requests_client.RequestsResponseAdapter object at 0x00789C86BBFA0 >')
dicts = orders[0]['result']
for i, d in enumerate(dicts):
reduce = d['reduce_only']
close = d['close_on_trigger']
sell = d['side']
try:
prnt = d['print']
except:
prnt = ""
print(f'dict {i} : {reduce=} {close=} {sell=} {prnt=}')
Output:
dict 0 : reduce=False close=False sell='Sell' prnt=''
dict 1 : reduce=True close=True sell='Buy' prnt=''
dict 2 : reduce=True close=True sell='Sell' prnt=''
dict 3 : reduce=True close=True sell='Buy' prnt=''
dict 4 : reduce=True close=True sell='Sell' prnt=''

About Binance API

I'm trying to learn my open position information (in Futures) with Python but I can't.
How can I find out my position information using the Binance API?
Here is that i try;
from binance.client import Client
import MyAPI
Login = Client(MyAPI.KEY, MyAPI.SECRET)
myPosition = Login.futures_position_information(symbol='BTCUSDT')
print(myPosition)
and server response;
[{'symbol': 'XRPUSD_210625', 'positionAmt': '0', 'entryPrice': '0.00000000', 'markPrice': '0.00000000', 'unRealizedProfit': '0.00000000', 'liquidationPrice': '0', 'leverage': '20', 'maxQty': '500000', 'marginType': 'cross', 'isolatedMargin': '0.00000000', 'isAutoAddMargin': 'false', 'positionSide': 'BOTH', 'notionalValue': '0', 'isolatedWallet': '0'}, {'symbol': 'LTCUSD_210625', 'positionAmt': '0', 'entryPrice': '0.00000000', 'markPrice': '0.00000000', 'unRealizedProfit': '0.00000000', 'liquidationPrice': '0', 'leverage': '20', 'maxQty': '2500', 'marginType': 'cross', 'isolatedMargin': '0.00000000', 'isAutoAddMargin': 'false', 'positionSide': 'BOTH', 'notionalValue': '0', 'isolatedWallet': '0'}, {'symbol': 'EOSUSD_PERP', 'positionAmt': '0', 'entryPrice': '0.00000000', 'markPrice': '0.00000000', 'unRealizedProfit': '0.00000000', 'liquidationPrice': '0', 'leverage': '20', 'maxQty': '10000', 'marginType': 'cross',
'isolatedMargin': '0.00000000', 'isAutoAddMargin': 'false', 'positionSide': 'BOTH', 'notionalValue': '0', 'isolatedWallet': '0'}, {'symbol': 'LTCUSD_210924', 'positionAmt': '0', 'entryPrice': '0.00000000', 'markPrice': '0.00000000', 'unRealizedProfit': '0.00000000', 'liquidationPrice': '0', 'leverage': '20', 'maxQty': '2500', 'marginType': 'cross', 'isolatedMargin': '0.00000000', 'isAutoAddMargin': 'false', 'positionSide': 'BOTH', 'notionalValue': '0', 'isolatedWallet': '0'}, {'symbol': 'FILUSD_PERP', 'positionAmt': '0', 'entryPrice': '0.00000000',
'markPrice': '0.00000000', 'unRealizedProfit': '0.00000000', 'liquidationPrice': '0', 'leverage': '20', 'maxQty': '5000', 'marginType': 'cross', 'isolatedMargin': '0.00000000', 'isAutoAddMargin': 'false', 'positionSide': 'BOTH', 'notionalValue': '0', 'isolatedWallet': '0'}, {'symbol': 'ADAUSD_210625', 'positionAmt': '0', 'entryPrice': '0.00000000', 'markPrice': '0.00000000', 'unRealizedProfit': '0.00000000', 'liquidationPrice': '0', 'leverage': '20', 'maxQty': '500000', 'marginType': 'cross', 'isolatedMargin': '0.00000000', 'isAutoAddMargin': 'false', 'positionSide': 'BOTH', 'notionalValue': '0', 'isolatedWallet': '0'}
PS: I have one open position now. It's Long Position.

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