原文链接： https://igaojin.me/2023/09/18/friend-tech-%E5%A5%97%E5%88%A9%E5%88%B0%E5%BA%95%E6%9C%89%E5%A4%9A%E5%8D%B7/
背景
最近有准备搞一下friend tech的套利…
就写个 sql看看目前有多少人盈利.这个数据不会基于没有卖出去的key
数据在这friend tech top profit
按照盈利金额排序
按照亏损金额排序
头几个盈利的肯定是机器人账号.就看看他们的数据,这一看就发现了一些问题.
数据分析
所有的数据基于block 411977,真实用户0xda, 购买 tx
数据长这样
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fromnoncetotransactionIndexgasPricegasFeestatusnew_status
00xa3afA500e3d88CAfA41AA7B95a8dEF7d2CBce92b941660xfF231524719dB94a0Ef08A88fC4e6939134EadE811254698872434951723127900
10x135557B95fc164cB152e853568b25880Ba06F94A94870x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c21254698872394616413986700
20x8ac85761268B9Ac3fDe06d381611b64B232CEa97662930x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c31254698872397503555135900
30x1BA4C73e7831b8099377c0583acfF4DBE488dFc6166260x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c341254698872428797029923900
40xa92a6331d0174c0eDC463B097E8564a492D451FA936090xfF231524719dB94a0Ef08A88fC4e6939134EadE851254698872434951723127900
50x9a34845CCBeA93925B15F5af6d9F4aFae4EC590a937550xfF231524719dB94a0Ef08A88fC4e6939134EadE861254698872434951723127900
60x5ED1222A1c0BdBA479B61201231f539ac131DD69641310x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c371254698872428797029923900
70xe8C5433d6E8Fc7a2b38eF0391FabA7470c9576e451420x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c381254698872428797029923900
80x38Cd1a8474A948a5a3e4d4b06c4069F2116DD184855740x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c91254698872406164978583500
90x3c2c170296446e241dE2eE39d7036254d55Df9E4936020xfF231524719dB94a0Ef08A88fC4e6939134EadE8101254698872426290299680300
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交易数量
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import json
import pandas as pd
from hexbytes import HexBytes
with open('4119771_da681.json') as f:
txs = json.loads(f.read())
def filter_da68(txs):
flashruntxs = []
for tx in txs:
_input = tx["input"]
if "da68ee" in _input:
flashruntxs.append(
{
"from": tx["from"],
"nonce": tx["nonce"],
"to": tx["to"],
"transactionIndex": tx["transactionIndex"],
"gasPrice": tx["gasPrice"],
"gasFee": tx["gasFee"],
"status": tx["status"],
}
)
return flashruntxs
da68 = filter_da68(txs)
len(da68),len(txs)
(506, 517)
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可以看到整个区块交易517 条,其中和da68相关的 506. 就非常离谱，97.87%的用户都是来抢跑的.
一共有多少机器人？
我们这边按照合约地址分一下类. 可以看到有几个头部机器人.
前3 占了大部分交易.
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to_count = df['to'].value_counts()
to_count
0x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c3 184
0xfF231524719dB94a0Ef08A88fC4e6939134EadE8 161
0x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c 123
0xCF205808Ed36593aa40a44F10c7f7C2F67d4A4d4 19
0xe27ddadbC8779D01fC539Bf5BA8529D728418625 9
0x27E8A5B043d3f58dde1c7ab96d2E3cF07558A1f1 8
0xAfE6795A9097BbCE00fe61462d22CCeeF706a3e8 2
Name: to, dtype: int64
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看看这几个机器人花了多少gas费,没想到发送 184 条交易才花 7.69刀.
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ethprice = 1640
for to in to_count.index:
tteth = sum(df[df.to==to].gasFee)/1e18
ttusd = tteth*ethprice
print(tteth,ttusd)
0.004690181471662096 7.691897613525838
0.004104812550636566 6.731892583043968
0.003097893590529322 5.080545488468088
0.000457215606189089 0.749833594150106
0.000226412037571068 0.3713157416165515
0.00017632952477182 0.2891804206257848
5.0244385725274e-05 0.08240079258944936
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再看看这些机器人有多少小号
有发现几个菜鸟机器人…
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for to in to_count.index:
print('to:',to)
print(df[df.to==to]['from'].value_counts())
print()
print('#'*30)
to: 0x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c3
0x5ED1222A1c0BdBA479B61201231f539ac131DD69 27
0x1BA4C73e7831b8099377c0583acfF4DBE488dFc6 26
0xd9E4b9579a5CF5B336E258820CC95cA34411c9D6 26
0x27C2a598Db389b5276c0156f14B4dE75E490712F 26
0xe8C5433d6E8Fc7a2b38eF0391FabA7470c9576e4 24
0xDd2B7694259a83753918BF5b6dC6961fabd2FDbC 24
0xC81a00ee6839EE0dFbF465d3C08E5F0b0291b71A 20
0x3Ee2183b706cf381e7e4586b8063a779f1E7b7dA 11
Name: from, dtype: int64
##############################
to: 0xfF231524719dB94a0Ef08A88fC4e6939134EadE8
0xa3afA500e3d88CAfA41AA7B95a8dEF7d2CBce92b 9
0xa92a6331d0174c0eDC463B097E8564a492D451FA 9
0x2bE3973950D0F1951E12C21bC7bB8e2428101Fdd 9
0x63cEe818600f91C4473CD14cDfD9C10d918B587b 9
0x92c21dB4a7cC0302ccCb71d444aE674Ec3980c93 9
0xCb44B9062eB0Cd22c4C0701385e6482BF4a77759 9
0x57ae46a097751124f8c35A86f64B4c38d2C62675 9
0x8eE3847cAE34e6e0292dc3DF8DD3C0C69a266b97 9
0x807db242cD37b5E2B53697842197611f51693C06 9
0xDb6f56639AaeA09FfEA6080D31A32851524128d4 9
0x9D94BdAf42d7906b3D3c011053ed2cBF42A1cA3f 9
0x244Fb415A8765812B9bc5B52cA14fd83C9d7E0e5 9
0xF689ac0322786Ac2D7245Fcd665fe120ff1d0408 9
0x895D2d10FB9b92D0e1002c1a1bCdb90Ed42c3588 9
0xF53010525Df586e78b14114C9cd05481E79E6C8C 9
0x3c2c170296446e241dE2eE39d7036254d55Df9E4 9
0x9a34845CCBeA93925B15F5af6d9F4aFae4EC590a 9
0x36da68690F238366156890010c411394FC529cd9 8
Name: from, dtype: int64
##############################
to: 0x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c
0x8ac85761268B9Ac3fDe06d381611b64B232CEa97 13
0x135557B95fc164cB152e853568b25880Ba06F94A 12
0x38Cd1a8474A948a5a3e4d4b06c4069F2116DD184 12
0x3547DbcD816bc17D7dE0e7e67a8B8bEE74384ACF 12
0xa9ff99346829E7c578E6d38C2269997a2FCcEEE1 12
0xd65eD99CB8b3b65CE378e3E5CaE545B751f4DAc6 11
0x1F238B47ab9fD915e5852601E83c6c1358297870 11
0x75Ff68BDe78974ab54a642fD294369447bD9Feef 10
0xf1E7E7Ad07C046abb6e488F73c126B897892755c 10
0x76eF08c772Db73E05fa00398746FbA31eFC5804F 10
0x7CfE232Ed8b0acc111ec780b7Cb95C68f3CF7Aa6 10
Name: from, dtype: int64
##############################
to: 0xCF205808Ed36593aa40a44F10c7f7C2F67d4A4d4
0x188eCF8f4290C4e5d641449B88CEf94574174972 15
0xDa68EEe4c5b10D8A574b9ec072335c583B1C67A2 1
0xbEbA981C5697b1CFa7c04fCB523E94bD42F79afB 1
0x40954b9B4bAfC41Ec6387325fD0069C68674a04d 1
0x296D3324Ee4b2316E87f965F4A04F073780f5310 1
Name: from, dtype: int64
##############################
to: 0xe27ddadbC8779D01fC539Bf5BA8529D728418625
0xeFa4b454A64049ff93cC37b54102982ACEdf4Eae 9
Name: from, dtype: int64
##############################
to: 0x27E8A5B043d3f58dde1c7ab96d2E3cF07558A1f1
0x8b113ad5c2b2E3ddb102305e32259c6b23D12aa3 4
0x89B5BB48f016b809EB2eE78416550487334C1331 4
Name: from, dtype: int64
##############################
to: 0xAfE6795A9097BbCE00fe61462d22CCeeF706a3e8
0xC1322133ACbAd823614bf79Ca9eC409fA3A7E39A 2
Name: from, dtype: int64
##############################
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最后总览看看
26是用户的交易.可以看到所有的 gasPrice都和用户的一样…
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# 筛选出 transactionIndex 在 16-40 范围内的数据
filtered_df = df[(df['transactionIndex'] >= 16) & (df['transactionIndex'] <= 36)]
# 按照 transactionIndex 从小到大排序
sorted_df = filtered_df.sort_values(by='transactionIndex')
def highlight_row(row):
if row['transactionIndex'] == 26:
return ['background-color: yellow'] * len(row)
else:
return [''] * len(row)
highlighted_df = filtered_df.style.apply(highlight_row, axis=1)
highlighted_df
fromnoncetotransactionIndexgasPricegasFeestatus
150xa9ff99346829E7c578E6d38C2269997a2FCcEEE1730390x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c16125469887240616497858350
160x895D2d10FB9b92D0e1002c1a1bCdb90Ed42c3588949870xfF231524719dB94a0Ef08A88fC4e6939134EadE817125469887243495172312790
170xDd2B7694259a83753918BF5b6dC6961fabd2FDbC31800x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c318125469887242879702992390
180xF689ac0322786Ac2D7245Fcd665fe120ff1d0408943820xfF231524719dB94a0Ef08A88fC4e6939134EadE819125469887243495172312790
190xC81a00ee6839EE0dFbF465d3C08E5F0b0291b71A27080x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c320125469887242879702992390
200x244Fb415A8765812B9bc5B52cA14fd83C9d7E0e5562030xfF231524719dB94a0Ef08A88fC4e6939134EadE821125469887242340315853110
210x5ED1222A1c0BdBA479B61201231f539ac131DD69641320x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c322125469887242879702992390
220xeFa4b454A64049ff93cC37b54102982ACEdf4Eae66820xe27ddadbC8779D01fC539Bf5BA8529D72841862523125469887233748113267501
230x1BA4C73e7831b8099377c0583acfF4DBE488dFc6166270x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c324125469887242013560647630
240xd65eD99CB8b3b65CE378e3E5CaE545B751f4DAc6660630x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c25125469887240616497858350
250xDa68EEe4c5b10D8A574b9ec072335c583B1C67A200xCF205808Ed36593aa40a44F10c7f7C2F67d4A4d426125469887288954547350181
260x9D94BdAf42d7906b3D3c011053ed2cBF42A1cA3f557120xfF231524719dB94a0Ef08A88fC4e6939134EadE827125469887336599568461861
270xe8C5433d6E8Fc7a2b38eF0391FabA7470c9576e451430x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c328125469887255865836296890
280xd9E4b9579a5CF5B336E258820CC95cA34411c9D676560x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c329125469887255865836296890
290x1F238B47ab9fD915e5852601E83c6c13582978701886260x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c30125469887252936386062880
300xDb6f56639AaeA09FfEA6080D31A32851524128d4557420xfF231524719dB94a0Ef08A88fC4e6939134EadE831125469887254748032978540
310x27C2a598Db389b5276c0156f14B4dE75E490712F81120x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c332125469887255865836296890
320x8ac85761268B9Ac3fDe06d381611b64B232CEa97662940x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c33125469887252936386062880
330x75Ff68BDe78974ab54a642fD294369447bD9Feef747960x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c34125469887252070243718120
340xDd2B7694259a83753918BF5b6dC6961fabd2FDbC31810x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c335125469887255865836296890
350x807db242cD37b5E2B53697842197611f51693C06559790xfF231524719dB94a0Ef08A88fC4e6939134EadE836125469887254748032978540
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代码以及相关数据下载
https://github.com/jin10086/friend-tech-bot-analyze
本文作者：高金
本文地址： https://igaojin.me/2023/09/18/friend-tech-套利到底有多卷/
版权声明：转载请注明出处！
本文转自： https://igaojin.me/2023/09/18/friend-tech-%E5%A5%97%E5%88%A9%E5%88%B0%E5%BA%95%E6%9C%89%E5%A4%9A%E5%8D%B7/
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