🧠 Methodology

The NEAR/USD pair recently launched on gTrade, a leveraged trading platform on the Gains Network protocol. In this dashboard, we will check NEAR/USD pair trades using flipsidecrypto data

To deal with this dashboard, we use the polygon.core and arbitrum.core schemas and the polygon.fact_event_logs and arbitrum.fact_event_logs tables.

To check NEAR/USD pair trades, we consider two different sections:

1️⃣ We analyze market sentiment based on Long and Short positions around the NEAR token on Polygon and Arbitrum chains
2️⃣ We compare the number and volume of positions created around the NEAR asset compared to other assets listed in the gTrade on Polygon and Arbitrum chains.

Comparison of NEAR/USD pair with New Pairs - Pair Index 102 to 108

First, we need to check a few things about gTrade and the pairs and groups that are traded there.

There are several different asset classes on gTrade, including:

Forex (minor)
Forex (exotic)
Commodity
Stock
Crypto

Each of these classes has a group index, for example the group index for Crypto is 0. In this dashboard, we will be looking at trading on Crypto pairs. Each of the pairs has a pair index which is in the following sets for Crypto:

1 to 20
31 to 57
102 to 108

The pair index for NEAR/USD is equal to 104

Well, we need to explain how to get NEAR/USD pairs trading on Polygon and Arbitrum. So let's go!

NEAR/USD pairs trading on Polygon

To get the list of gTrade Crypto class trades on Polygon, we use the fact_event_logs table.

gTrade transactions have been recorded on Polygon in several different versions and with several different contract address and topics. These contract addresses and their topics include the following:

topics[0] = '0x15f192ad872076db7d7571711429e445b0eabf30dc598ab77f02cacf622eec83' and (contract_address = '0x1e87e8703883a46ccdec4c8868043abe40235bed' or contract_address = '0x7af43f23f33642c4ea8bf10211a26d5a028a555f')

topics[0] = '0xc74e5bdf3e3b91b5cda39fb8ee72fb93dbb735b9cbbaddc7eef34178ffbaf555' and (contract_address = '0x1e87e8703883a46ccdec4c8868043abe40235bed' or contract_address = '0x7af43f23f33642c4ea8bf10211a26d5a028a555f')

topics[0] = '0x9d894746ec0e0f1ac0599e4fdb9b9a250d21e0e23a6b9cf8c62bb8f5908bb40b' and contract_address = '0xcf63914f5160e596bc574bf4fd52c13a8d270e28'

topics[0] = '0x2343a1f0e076656e179f7fb23a0cb40efbb9cfaa668dd58344a840eb0306fec6' and contract_address = '0xcf63914f5160e596bc574bf4fd52c13a8d270e28'

 topics[0] = '0x165b0f8d6347f7ebe92729625b03ace41aeea8fd7ebf640f89f2593ab0db63d1' and contract_address = '0x170cc5a70d6f544e5456881b586eb58180998a37'

topics[0] = '0x2739a12dffae5d66bd9e126a286078ed771840f2288f0afa5709ce38c3330997' and contract_address = '0x170cc5a70d6f544e5456881b586eb58180998a37'

topics[0] = '0x165b0f8d6347f7ebe92729625b03ace41aeea8fd7ebf640f89f2593ab0db63d1' and contract_address = '0x6805dd635aa542210ed572f7b93121002c629690'

topics[0] = '0x2739a12dffae5d66bd9e126a286078ed771840f2288f0afa5709ce38c3330997' and contract_address = '0x6805dd635aa542210ed572f7b93121002c629690'

topics[0] = '0x165b0f8d6347f7ebe92729625b03ace41aeea8fd7ebf640f89f2593ab0db63d1' and contract_address = '0xb454d8a8c98035c65bb73fe2a11567b9b044e0fa'

topics[0] = '0x2739a12dffae5d66bd9e126a286078ed771840f2288f0afa5709ce38c3330997' and contract_address = '0xb454d8a8c98035c65bb73fe2a11567b9b044e0fa'

To get the details of each trade, we use the data field for this list of addresses and subjects, which are recorded as a 2's complement hex. The trade details we need include the following:

Pair Index: The index of the pair on which the position is opened
Group Index: The group on which the transaction is done on the pair, where we check group_index = 0 (Crypto)
Trader: The user who opened the position
Trade Type (Long/Short): The type of trade that has been opened
Position Size (In DAI): Trade volume in DAI
Leverage: The leverage with which the trader opened the position
1. Leverage is the use of borrowed money (called capital) to invest in a currency. The concept of leverage is very common in crypto trading. By borrowing money from a exchange, investors can trade larger positions in a currency.
Percent Profit: The percentage rate of profit/loss from the trade
PNL: Position Size DAI * Percent Profit
Trade Volume: Position Size DAI * Leverage

In order to achieve the above, we decode the values stored in the data field for each address and subject of contract. The recording method is different. In the following sample code, you can see how to decode the data field of a contract address:

select 
  		tx_hash,
  		block_timestamp,
  		tokenflow_eth.hextoint(substr(data, 3 + 3 * 64, 64)) as group_index,
  		tokenflow_eth.hextoint(substr(data, 3 + 2 * 64, 64)) as pair_index,
  		concat('0x',substring(data, 91, 40)) as trader,
  		case 
          	when tokenflow_eth.hextoint(substr(data, 3 + 7 * 64, 64)) = 0 then 'Short'
           	else 'Long'
       	end as order_type,
  		tokenflow_eth.hextoint(substr(data, 3 + 14 * 64, 64)) * pow(10, -18) as position_size_dai,
  		tokenflow_eth.hextoint(substr(data, 3 + 6 * 64, 64)) as open_price,
  		tokenflow_eth.hextoint(substr(data, 3 + 8 * 64, 64)) as leverage,
  		tokenflow_eth.hextoint(substr(data, 3 + 9 * 64, 64)) as tp,
  		case 
  			when substr(data, 3 + 15 * 64, 64) ilike 'f%' then ((tokenflow_eth.hextoint(substring(data, 56 + 15 * 64, 11)) - tokenflow_eth.hextoint('fffffffffff'))) * pow(10, -12)
  			else tokenflow_eth.hextoint(substr(data, 3 + 15 * 64, 64)) * pow(10, -12)
  		end as percent_profit
  		from polygon.core.fact_event_logs 
  		where topics[0] = '0x165b0f8d6347f7ebe92729625b03ace41aeea8fd7ebf640f89f2593ab0db63d1'
  		and contract_address = '0xb454d8a8c98035c65bb73fe2a11567b9b044e0fa'
  		and group_index = 0

Note: the value of Percent Profit for a position may be negative (trading loss), so this value is recorded as negative. We first convert this negative number to positive so that we can decode it. Then we multiply it by minus 1 to get the actual value of the profit percentage. The following code example demonstrates this action:
((tokenflow_eth.hextoint(substring(data, 56 + 15 * 64, 11)) - tokenflow_eth.hextoint('fffffffffff'))) * pow(10, -12) as "Percent Profit"

NEAR/USD pairs trading on Arbitrum

To get the list of gTrade Crypto class trades on Arbitrum, we use the fact_event_logs table.

gTrade transactions have been recorded on Arbitrum in several different versions and with several different contract address and topics. These contract addresses and their topics include the following:

topics[0] = '0x165b0f8d6347f7ebe92729625b03ace41aeea8fd7ebf640f89f2593ab0db63d1' and contract_address = '0x6c612c804c84e3d20e3109c8efd06cd2d8b28f46'

topics[0] = '0x2739a12dffae5d66bd9e126a286078ed771840f2288f0afa5709ce38c3330997' and contract_address = '0x6c612c804c84e3d20e3109c8efd06cd2d8b28f46'

Obtaining trade details in Arbitrum is similar to Polygon, described above

Finally, after getting the list of trades, we first look at trades related to the NEAR/USD pair and then compare it with other pairs in the next step.

NEAR - Gains Network