套利►数据分析

friend_tech 套利到底有多卷

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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from	nonce	to	transactionIndex	gasPrice	gasFee	status	new_status
0	0xa3afA500e3d88CAfA41AA7B95a8dEF7d2CBce92b	94166	0xfF231524719dB94a0Ef08A88fC4e6939134EadE8	1	125469887	24349517231279	0	0
1	0x135557B95fc164cB152e853568b25880Ba06F94A	9487	0x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c	2	125469887	23946164139867	0	0
2	0x8ac85761268B9Ac3fDe06d381611b64B232CEa97	66293	0x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c	3	125469887	23975035551359	0	0
3	0x1BA4C73e7831b8099377c0583acfF4DBE488dFc6	16626	0x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c3	4	125469887	24287970299239	0	0
4	0xa92a6331d0174c0eDC463B097E8564a492D451FA	93609	0xfF231524719dB94a0Ef08A88fC4e6939134EadE8	5	125469887	24349517231279	0	0
5	0x9a34845CCBeA93925B15F5af6d9F4aFae4EC590a	93755	0xfF231524719dB94a0Ef08A88fC4e6939134EadE8	6	125469887	24349517231279	0	0
6	0x5ED1222A1c0BdBA479B61201231f539ac131DD69	64131	0x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c3	7	125469887	24287970299239	0	0
7	0xe8C5433d6E8Fc7a2b38eF0391FabA7470c9576e4	5142	0x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c3	8	125469887	24287970299239	0	0
8	0x38Cd1a8474A948a5a3e4d4b06c4069F2116DD184	85574	0x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c	9	125469887	24061649785835	0	0
9	0x3c2c170296446e241dE2eE39d7036254d55Df9E4	93602	0xfF231524719dB94a0Ef08A88fC4e6939134EadE8	10	125469887	24262902996803	0	0

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)

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

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

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

##############################

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


 	from	nonce	to	transactionIndex	gasPrice	gasFee	status
15	0xa9ff99346829E7c578E6d38C2269997a2FCcEEE1	73039	0x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c	16	125469887	24061649785835	0
16	0x895D2d10FB9b92D0e1002c1a1bCdb90Ed42c3588	94987	0xfF231524719dB94a0Ef08A88fC4e6939134EadE8	17	125469887	24349517231279	0
17	0xDd2B7694259a83753918BF5b6dC6961fabd2FDbC	3180	0x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c3	18	125469887	24287970299239	0
18	0xF689ac0322786Ac2D7245Fcd665fe120ff1d0408	94382	0xfF231524719dB94a0Ef08A88fC4e6939134EadE8	19	125469887	24349517231279	0
19	0xC81a00ee6839EE0dFbF465d3C08E5F0b0291b71A	2708	0x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c3	20	125469887	24287970299239	0
20	0x244Fb415A8765812B9bc5B52cA14fd83C9d7E0e5	56203	0xfF231524719dB94a0Ef08A88fC4e6939134EadE8	21	125469887	24234031585311	0
21	0x5ED1222A1c0BdBA479B61201231f539ac131DD69	64132	0x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c3	22	125469887	24287970299239	0
22	0xeFa4b454A64049ff93cC37b54102982ACEdf4Eae	6682	0xe27ddadbC8779D01fC539Bf5BA8529D728418625	23	125469887	23374811326750	1
23	0x1BA4C73e7831b8099377c0583acfF4DBE488dFc6	16627	0x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c3	24	125469887	24201356064763	0
24	0xd65eD99CB8b3b65CE378e3E5CaE545B751f4DAc6	66063	0x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c	25	125469887	24061649785835	0
25	0xDa68EEe4c5b10D8A574b9ec072335c583B1C67A2	0	0xCF205808Ed36593aa40a44F10c7f7C2F67d4A4d4	26	125469887	28895454735018	1
26	0x9D94BdAf42d7906b3D3c011053ed2cBF42A1cA3f	55712	0xfF231524719dB94a0Ef08A88fC4e6939134EadE8	27	125469887	33659956846186	1
27	0xe8C5433d6E8Fc7a2b38eF0391FabA7470c9576e4	5143	0x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c3	28	125469887	25586583629689	0
28	0xd9E4b9579a5CF5B336E258820CC95cA34411c9D6	7656	0x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c3	29	125469887	25586583629689	0
29	0x1F238B47ab9fD915e5852601E83c6c1358297870	188626	0x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c	30	125469887	25293638606288	0
30	0xDb6f56639AaeA09FfEA6080D31A32851524128d4	55742	0xfF231524719dB94a0Ef08A88fC4e6939134EadE8	31	125469887	25474803297854	0
31	0x27C2a598Db389b5276c0156f14B4dE75E490712F	8112	0x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c3	32	125469887	25586583629689	0
32	0x8ac85761268B9Ac3fDe06d381611b64B232CEa97	66294	0x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c	33	125469887	25293638606288	0
33	0x75Ff68BDe78974ab54a642fD294369447bD9Feef	74796	0x4Ff30D7222B852c67A66Dc8E8d56A8cf92B8411c	34	125469887	25207024371812	0
34	0xDd2B7694259a83753918BF5b6dC6961fabd2FDbC	3181	0x4C99B25DdD0887D88b6C1364E3Df63a3bE76A5c3	35	125469887	25586583629689	0
35	0x807db242cD37b5E2B53697842197611f51693C06	55979	0xfF231524719dB94a0Ef08A88fC4e6939134EadE8	36	125469887	25474803297854	0

代码以及相关数据下载

https://github.com/jin10086/friend-tech-bot-analyze

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