running

src/code/5001_get_dalao_relationship.py

@@ -96,8 +96,7 @@ df =df.astype(
     }
 )
 
- 
- 
+
 
 df["gmgn_from"] = df.apply(lambda ser: baseclass.makeurl(
    f"https://gmgn.ai/sol/token/{str_tokenaddress}?maker={ser['from_owner']}" , f"gmgn_{ser['from_owner'][0:6]}" ), axis=1)

src/code/dune_sql/get_tokenswap_top_BuyOrDiff_amount.sql

@@ -1,4 +1,3 @@
-
 -- -- 两个方向
 -- -- 1.获取某一个token在某一个时间段的swap记录 ，和并分析 获取 token理论持有 买卖 token，金额，次数，价格 等 ，获取某一个方面的top
 -- -- 2.指定一些trader 获取他们的swap记录并汇总
@@ -20,17 +19,19 @@ with swap_table as (
             dex_solana.trades
         where
       
-            block_time > timestamp '2025-01-08 04:45:00 +08:00'
-            and block_time < timestamp '2025-01-08 06:00:00 +08:00'
+            block_time > timestamp '2025-03-17 15:30:00 +08:00'
+            and
+            block_time < timestamp '2025-03-17 16:00:00 +08:00'
+               -- block_time < timestamp '2025-03-17 08:00:00 +08:00'
             
             and (
             (
-                    token_bought_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump'
+                    token_bought_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump'
                     and token_sold_mint_address = 'So11111111111111111111111111111111111111112'
                 )
                 or (
                     token_bought_mint_address = 'So11111111111111111111111111111111111111112'
-                    and token_sold_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump'
+                    and token_sold_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump'
                 )
             )
             
@@ -41,7 +42,7 @@ with swap_table as (
             COALESCE(
                 SUM(
                     CASE
-                        WHEN token_bought_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump' THEN token_bought_amount
+                        WHEN token_bought_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump' THEN token_bought_amount
                         ELSE 0
                     END
                 ),
@@ -50,7 +51,7 @@ with swap_table as (
             COALESCE(
                 SUM(
                     CASE
-                        WHEN token_bought_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump' THEN token_sold_amount
+                        WHEN token_bought_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump' THEN token_sold_amount
                         ELSE 0
                     END
                 ),
@@ -59,7 +60,7 @@ with swap_table as (
             COALESCE(
                 SUM(
                     CASE
-                        WHEN token_bought_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump' THEN 1
+                        WHEN token_bought_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump' THEN 1
                         ELSE 0
                     END
                 ),
@@ -68,7 +69,7 @@ with swap_table as (
             COALESCE(
                 SUM(
                     CASE
-                        WHEN token_sold_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump' THEN token_sold_amount
+                        WHEN token_sold_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump' THEN token_sold_amount
                         ELSE 0
                     END
                 ),
@@ -77,7 +78,7 @@ with swap_table as (
             COALESCE(
                 SUM(
                     CASE
-                        WHEN token_sold_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump' THEN token_bought_amount
+                        WHEN token_sold_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump' THEN token_bought_amount
                         ELSE 0
                     END
                 ),
@@ -86,7 +87,7 @@ with swap_table as (
             COALESCE(
                 SUM(
                     CASE
-                        WHEN token_sold_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump' THEN 1
+                        WHEN token_sold_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump' THEN 1
                         ELSE 0
                     END
                 ),
@@ -102,33 +103,38 @@ select
     round( (token_bought - token_sold) / 1e6, 2) as li_lun_hold,
     round(token_bought / 1e6, 2) as token_b_M,
     round(token_sold / 1e6, 2) as token_s_M,
-    round(eth_bought, 2) as eth_b,
-    round(eth_sold, 2) as eth_s,
+    round(eth_bought, 1) as eth_b,
+    round(eth_sold, 1) as eth_s,
     bought_count as b_count,
     sold_count as s_count
     ,(
         CASE
-            WHEN token_bought != 0 THEN round(eth_bought / token_bought*1e6, 3)
+            WHEN token_bought != 0 THEN round(eth_bought / token_bought*1e3*1e6, 1)
             ELSE 0
         END
-    ) as avg_b_pri
+    ) as avg_b_mc
     , (
         CASE
-            WHEN token_sold != 0 THEN round(eth_sold / token_sold*1e6, 3)
+            WHEN token_sold != 0 THEN round(eth_sold / token_sold*1e3*1e6,1 )
             ELSE 0
         END
-    ) as avg_s_pri
+    ) as avg_s_mc
 from
     swap_table_02
     where 
-    -- abs(token_bought - token_sold) >0.2*1e6
-      -- (token_bought - token_sold) >0.2*1e6
-      abs(token_bought - token_sold) > 0.1*1e6 and
+      -- abs(token_bought - token_sold) > 0.1*1e6 and
     -- (token_bought - token_sold) < 0.1*1e6 and
-     ( eth_bought > 1.9 or       eth_sold >1.9)
+    -- (token_bought - token_sold) >0  and
+     -- token_bought > 0.5 * 1e6 and 
+    -- eth_bought >1.9
+     eth_bought >1.5
+   
+    
+     -- ( eth_bought > 1.9 or       eth_sold >1.9)
+     
 order by
     --  2 diff amount
-    2
+    eth_b
     --  3 buy amount
     -- 3
     -- 7 buy count
@@ -136,8 +142,8 @@ order by
     -- 8 sell count
     -- 8
     desc
--- limit
---     200
+limit
+    75
 
 -- 第一个方向代码 
 -- 结束 结束 结束 结束 结束 结束 结束 结束 结束
@@ -152,80 +158,6 @@ order by
 -- ('5Q544fKrFoe6tsEbD7S8EmxGTJYAKtTVhAW5Q5pge4j1'),
 -- ('Cet3TXGhtFcBo6vX9kfYjbtNyANbCWqwVC6G1QioKUhc'),
 -- ('7m2XQP2NDebaEKDhegj7WYpZBmrKF5yhiVi1JZU8cwoj'),
--- ('68WLD7YzwjqBoCEVuJ3rjVbaPaAog6ATCkjUTpNqiMNF'),
--- ('4ttYJgVcZMMXFsTPBAEKVUEDUshvsXqgvfUv3W7bdv7e'),
--- ('14qLxu9XDQtc2pgnxX2SSdWPpnuVrEqccgwmATH1khcY'),
--- ('Hh6XswyZK9WPUbb6csqi7YggDqYTstKkHFvvh7PFx7kw'),
--- ('DAr4CE2rFgvtDHSFG75rEhmxmqgfEQYir91RGY458pkZ'),
--- ('CC1xmUJCvq4wPzHSNKVHfMV9T3vRjHTZnzvW9VLVJ4gs'),
--- ('GDwiC6S14topnYmWquQb6JdQxrRsvtH7N3K8UCLZmVrk'),
--- ('HjUrUJi7zUCXu6NpXBUujYsoG5TJsXkPUrYJdWMKVtts'),
--- ('5PZs2fmTJpjaMhYbdAH6uEx7Jbbhy2RdAdkNnPw83H3a'),
--- ('FH8xnCwgoQgk3nXuePEdxYB5SZDTWSZav27smVcHjN1q'),
--- ('ET3VpuSMr15ufn4z9a5kwHhyLRse2H96TY2HXVKZxCJM'),
--- ('GD9Q87EY9guqAqkxgNwiqoqUKmBs958tcFEkQ8q1HD1N'),
--- ('5pW6xYQZxZLs67eKuurGcTVm2nHqFcDsiuxesqagoW6V'),
--- ('7V7BFFLNgHhrJq7Udm9Zi26Tbgvytg9GWKLdDLRzf4z6'),
--- ('BC8yiFFQWFEKrEEj75zYsuK3ZDCfv6QEeMRif9oZZ9TW'),
--- ('6QfdT1XZauUMCJU3SaN2d2aqdoFa4TDVNmxJig52ZGZT'),
--- ('3M2dQzm6kz9ChDfaeX3xxqzn8BdeLjJEJv9JBd9WG2Jx'),
--- ('6xMPaDn8jZ1r68BzXAunvumU8BTG2vDbgemNkWwRd1Fo'),
--- ('3UFHt8qqRtHM2hZaCYRGKaF1gYfePcRYZFo2CdaadfMa'),
--- ('Aewfbg61nn3SrFikqTsX2c8nZM8DXVbG7jpUp1Qwg22H'),
--- ('3kZ7ZYjUGSFMhXbQ3twdCmqP1cgCjjBbZHRd8eXFFaja'),
--- ('E9EpirpCKqJKnUTfhpUKzDKKFzVd7Q8RjjbdRVjPxXWX'),
--- ('3Q7bWprdJZ5JUDoy2wBAcoKzgp7mZ3ChPaR8o3SUf7EN'),
--- ('27NdJihiGpbg1kgHwAjk4UDUScTjEg7BDTo6urHjB3Cr'),
--- ('BTJk52ULuadhdzYWpK2io1urPVfqEpqoH3sS4yg4b8Z2'),
--- ('2fnRmNSYMTuyGw58UdZTrEgb2NmjNXvyRzqU9FJG31Q6'),
--- ('GE5PnwZTgWw1YqVGno2QPWqie6aLjswNeGFhiF1nDg3k'),
--- ('9r7p8uQiwRMRqQwAGDHUQ1Lmtjt4auNTxK7TgWg75J7C'),
--- ('4N41X5S1GaqKQhZvqQsE7aKCpEm2xhswKTznQoxuJEmv'),
--- ('FT78Vsmv2hWj8PK9HN1rtGEdKtt4Ajd2RSsxhrUt5nVG'),
--- ('FML97BaUTVJX15T8tCU5LUbcpvJkHQ5RcZXmV354PzRt'),
--- ('CpVNBP8bbFvT7Yt3zwTnCaXt26XojQxduNXaMz57DGLf'),
--- ('77RWuJjzTgswsgUafvhSNDAbGxHcUHQPZ3ogpvNr3eB'),
--- ('3ddwpbz7aXr6ZD7ybMZcUMTm76pgeszJekSkLdS931EH'),
--- ('4KaVawPKmG1RZCv698XD9YzRsJcFPXrEoYnZbdZmfFFq'),
--- ('HRL9mVQav58fLGJpN16qT55nXuk8S7Lx33Gvw2SHRHK5'),
--- ('AeqgApsq3CoopttTpohuLiXj53EauJ78ophVvy8hrfkt'),
--- ('51pcMTBEKwiKRVrCV5EmmHazsWetGDUxKkxoc5r3248'),
--- ('5pKTV6EhcyFQPBbEDEATyDiUHM4kDZddtopRLq7o8kJj'),
--- ('66VSSN5FiwkDXCSJ5ydNCZm8RD1DwVKXuwaeFBGJ5fKg'),
--- ('3qTNSWq6o7hfb7RgSz6GzxDMNLgrjVuwKqFD2kJznuRB'),
--- ('3fZcDhWXTBvNMkgzkyMmqxMizfysnvFZw5CzR7pnuxRz'),
--- ('F8pWbg5zGXnzghmAtNW9SQPQYwP1RkyzsHs6KZTBh7Dg'),
--- ('DuDEvMussH5iKvBPaa6mSVyJY39fPQKNEPUyT5c19S2x'),
--- ('98oWTwQBBEyhhLMcUrgy2gta7zmVHZavQmcS8z98PDHe'),
--- ('EPS4j3XEBas8okzQCe2CeuwKf6w6aif5ED4GMj3mqv4r'),
--- ('5Xu8p9emHTkuNQkPN6D2ygkJypWo1nbUzbjYHUfXcNuR'),
--- ('Hk7gzrcANZpqBnYCHeBEuxFk18HLu9Tey7TrNroyPVK7'),
--- ('Goc5dPcEM94cFW97k92XNoT45vR89isxSEjxqE3VTvhv'),
--- ('A3wcLYiBQdW5Uf5TXxbvuueJYh8pfAy57cbuE5fQJBeS'),
--- ('D6DLAnP1NnimNYuHNDhJZfGkD1TmiKENWG2PrdhL8qLP'),
--- ('CGnKC5zME8G36ftGLq1wrmQxw3SUgzVojJmVNXpidC6k'),
--- ('8oTVjgucrArfQgyGFy2AiEKz6QDhW6ATTJQ1nSkG3wAr'),
--- ('4k22yKKbCsrq7QhCd89ZXFcxhSbGmszA92H2KhthpgKq'),
--- ('dR4GCDofsA6ZpBqzQpQJeob4msVspyASATbqW4sC4YM'),
--- ('DNfuF1L62WWyW3pNakVkyGGFzVVhj4Yr52jSmdTyeBHm'),
--- ('2ANPc28rr67W8StfCFzfuY3ZbKp9FCJ9tfBc17pRK7sY'),
--- ('9cQcY83s45VsMwVmFHFUR2HVG5j9Rc3K8HU4hzLaXnWs'),
--- ('HjHBsicgsMqSrU9VPSas8sKYLGtCfwMzfiYHzfMEta6f'),
--- ('5c511kvVfCGrEsLkNb6RoVqqA5BpimGHyhkE3ki4kWkX'),
--- ('7xgxtHJX21bvXozPsmyVCi88qChqxZR4e84ckXgrTeFp'),
--- ('D7hpLQ4VEKAr2ALHZqT7JjTLqgBGsRqJZrbfzxPcyVtD'),
--- ('6mh7mv8JGBqmnV86NFJoVS3fkYgdbxCmxVys1vK3dB6t'),
--- ('3v6XiVAbays5FiZy6sWuriLHfC77EAX9mtB57Hf8VmpQ'),
--- ('Gy5GSoHmrfnv49TZHku1bxNv2Dy4tFDjgm7vGbmp8S2h'),
--- ('2geGo4wz6KgBb84Az6MM8w1dDnXoW6wmyX3hkEstAXJw'),
--- ('9UPcMHdTw8pJSvv13dH1tGawwCjSCSjjxwU5cKu9PTiq'),
--- ('2k77ScTf9ULptpVRG4M4X6SRZiuUxWCTzG2RWBT7KnHR'),
--- ('9wNu1SLCfouEsR4B8PCck6p15mcSgun7k5KEKDw43Cyu'),
--- ('3nxNfz8fx4DPW37i97Qs4Ue4Z2zVinEFeDAAymwtEtYd'),
--- ('Gg6PuMT9qs53QiSrrJsnLyh51xedbzKAV5JiQ9qHPQ7a'),
--- ('7JYCpRR4ZnKnMuxgiRta6ogs1BrC1aURW7iZdiJytsPB'),
--- ('2ydwYpyTwn1NrPpRN4n6NaHHtg82G65QAN2rQxX6e9ep'),
--- ('AR6SjcFLzQoZXXLVbaKo4GbsvmKmKKobCfrXFHcqE6TN'),
 -- ('8Mnb87uUYXcqejP6RuUyDiX9Fd1unCA3YdM3zLGSVEPQ'),
 -- ('CfpFiySF5c63fvgtjjgbefjS2JSATJYeqp8qFQX76hKT'),
 -- ('CdCM27tboWquCEC3Pq3CRphSdiGV6Q48VewWmrxbUyYW')
@@ -247,12 +179,12 @@ order by
 --             and block_time < timestamp '2025-01-15 02:00:00 +08:00'
 --             and (
 --                 (
---                     token_bought_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump'
+--                     token_bought_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump'
 --                     and token_sold_mint_address = 'So11111111111111111111111111111111111111112'
 --                 )
 --                 or (
 --                     token_bought_mint_address = 'So11111111111111111111111111111111111111112'
---                     and token_sold_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump'
+--                     and token_sold_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump'
 --                 )
 --             )
 --     ),
@@ -262,7 +194,7 @@ order by
 --             COALESCE(
 --                 SUM(
 --                     CASE
---                         WHEN token_bought_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump' THEN token_bought_amount
+--                         WHEN token_bought_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump' THEN token_bought_amount
 --                         ELSE 0
 --                     END
 --                 ),
@@ -271,7 +203,7 @@ order by
 --             COALESCE(
 --                 SUM(
 --                     CASE
---                         WHEN token_bought_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump' THEN token_sold_amount
+--                         WHEN token_bought_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump' THEN token_sold_amount
 --                         ELSE 0
 --                     END
 --                 ),
@@ -280,7 +212,7 @@ order by
 --             COALESCE(
 --                 SUM(
 --                     CASE
---                         WHEN token_bought_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump' THEN 1
+--                         WHEN token_bought_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump' THEN 1
 --                         ELSE 0
 --                     END
 --                 ),
@@ -289,7 +221,7 @@ order by
 --             COALESCE(
 --                 SUM(
 --                     CASE
---                         WHEN token_sold_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump' THEN token_sold_amount
+--                         WHEN token_sold_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump' THEN token_sold_amount
 --                         ELSE 0
 --                     END
 --                 ),
@@ -298,7 +230,7 @@ order by
 --             COALESCE(
 --                 SUM(
 --                     CASE
---                         WHEN token_sold_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump' THEN token_bought_amount
+--                         WHEN token_sold_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump' THEN token_bought_amount
 --                         ELSE 0
 --                     END
 --                 ),
@@ -307,7 +239,7 @@ order by
 --             COALESCE(
 --                 SUM(
 --                     CASE
---                         WHEN token_sold_mint_address = '5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump' THEN 1
+--                         WHEN token_sold_mint_address = '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump' THEN 1
 --                         ELSE 0
 --                     END
 --                 ),

src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/001_01_merge_dune_hold_info_from_manydunecsv_to_一个多列的xlsx文档.py → src/code/handle_dune_py/001_01_merge_dune_hold_info_from_manydunecsv_to_一个多列的xlsx文档.py

@@ -2,7 +2,7 @@
 # 将special add 在不同时间持仓的csv合并为一个excel
 import pandas as pd
 import sys
-import os
+
 
 arr_file = []
 merged_df = None
@@ -42,7 +42,6 @@ def merge_df_fun():
   
     
     # merged_df=merged_df.fillna(-1)
-    print(merged_df)
     merged_df = pd.merge(merged_df, domains_df, on='holder_owner', how='left')
 
     merged_df["gmgn"] = merged_df.apply(lambda ser: makeurl(
@@ -58,18 +57,13 @@ def merge_df_fun():
  
 if __name__ == "__main__":
  
-    if len(sys.argv)<2:
+    if len(sys.argv)<3:
         print("args queshao ")
         sys.exit(1)
     token_add = sys.argv[1]
-    # arr_file =sys.argv[2:]
-    # print(arr_file)
-    # arr_file = [file for file in arr_file if "dune_holders_xx" in file and file.endswith(".csv")]
+    arr_file =sys.argv[2:]
+    arr_file = [file for file in arr_file if   file.startswith( "dune_holders_xx")  and file.endswith(".csv")]
  
-    arr_file = os.listdir(os.getcwd())
-  
-    arr_file = [file for file in arr_file if "dune_holders_xx" in file and file.endswith(".csv")]
-    
     arr_file.sort()
     print("read file: ",arr_file)
     merge_df_fun()

src/library/analysis_token/3DRnsQ5GA2XKk8WJ3yB46KbcRWGtA3E6cgFaqzJWpump/001_merge_top20_holders_add.py → src/code/handle_dune_py/001_merge_top20_holders_add.py

@@ -67,7 +67,7 @@ if __name__ == "__main__":
  
     arr_file = os.listdir(os.getcwd())
   
-    arr_file = [file for file in arr_file if "dune_holders_xx" in file and file.endswith(".csv")]
+    arr_file = [file for file in arr_file if    file.startswith("dune_holders_xx") and file.endswith(".csv")]
  
     arr_file.sort()
     print("read file: ",arr_file)

src/library/analysis_token/jLkPtLCKP6hrRxftWvtycTNX22ijbAqmayUuiZ9pump/exclude_add.csv → src/code/handle_dune_py/exclude_add.csv

@@ -34,4 +34,7 @@ BgYAdyHRjkQ4qaPMyt2SL53MNgfDFF6hiYy7tzwfDrtW,Raydium (WSOL-JIANKUI) Market
 8AcbTJPF2dyvabhjY8oz89SQWZArBeZzLHXnYWYWUzue,Meteora (JIANKUI-WSOL) Market
 53LCtLUHJvNxsf7C9xUFC8TCecV2yAEsZmCuCgnB8LR,Meteora (YAYDOLF-WSOL) Market
 9x13yovdBSLuTRM6RMng6vvaCezBQsL7uhLkGgyLKPhJ,Pump.fun (YAYDOLF) Bonding Curve
-C4cTbb5vL97sgTN2PCPPiUiKVGHw3X6fVdFtGZ9H7wnW,Meteora (YAYDOLF-WSOL) Market
+C4cTbb5vL97sgTN2PCPPiUiKVGHw3X6fVdFtGZ9H7wnW,Meteora (YAYDOLF-WSOL) Market
+4VxktrNN2rgHdRFxwGS6QctuPfdPASXFCi5jwFv4R8MH,Meteora (Nicky-WSOL) Market
+8W6hWKzGGBh9KuSi3jigLNmpMhd5XMdHztYJSpfS7md3,Pump.fun (Nicky) Bonding Curve
+Zs54KhsoYJVykFVBcGNnxjnvc3V35MUpLD4ZUmZVgRZ,Meteora (Nicky-WSOL) Market

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓/merge_swap_df.py → src/code/handle_dune_py/merge_swap_total_info_dune_from_manydunecsv_to_一个多列xlsx文档.py

@@ -174,7 +174,7 @@ if __name__ == "__main__":
     # token_add = sys.argv[1]
     merged_hodlers_file = sys.argv[1]
     arr_file =sys.argv[2:]
-    arr_file = [file for file in arr_file if "dune_swap_total" in file and file.endswith(".csv")]
+    arr_file = [file for file in arr_file if   file.startswith( "dune_swap_total_info_xx")  and file.endswith(".csv")]
     arr_file.sort()
     print("read file: ",arr_file)
     merge_swap_df_fun_01()

src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/001_handle_json_top_trader.py → src/code/handle_gmgn_py/001_handle_json_top_trader_gmgn.py

@@ -3,6 +3,60 @@ import json
 import os
 arr_json_data = []
  
+ 
+obj_example =    {
+        "address": "EZZk361QgZDMx52AmMyc4NjFkuaNrdegwAxrLEbeaR94",
+        "account_address": "8FzF2vcsNDBinao1wDTKDKLN7SeV6HLH1uduRfRtL3XT",
+        "addr_type": 0,
+        "amount_cur": 0,
+        "usd_value": 0,
+        "cost_cur": 0,
+        "sell_amount_cur": 34148763.167781,
+        "sell_amount_percentage": 1,
+        "sell_volume_cur": 6449.83054886525,
+        "buy_volume_cur": 1501.4988493578628,
+        "buy_amount_cur": 34148763.167781,
+        "netflow_usd": -4948.331699507387,
+        "netflow_amount": 0,
+        "buy_tx_count_cur": 9,
+        "sell_tx_count_cur": 77,
+        "wallet_tag_v2": "TOP1",
+        "native_balance": "805755934855",
+        "balance": 0,
+        "profit": 4928.853565607387,
+        "realized_profit": 4928.853565607387,
+        "profit_change": 3.240583927213684,
+        "amount_percentage": 0,
+        "unrealized_profit": 0,
+        "unrealized_pnl": None,
+        "avg_cost": 0.000043969347937453594,
+        "avg_sold": 0.00018887449941234176,
+        "accu_amount": 0,
+        "accu_cost": 0,
+        "cost": 0,
+        "total_cost": 1520.976983257863,
+        "transfer_in": False,
+        "is_new": False,
+        "is_suspicious": False,
+        "start_holding_at": 1742168061,
+        "end_holding_at": 1742172930,
+        "last_active_timestamp": 1742172930,
+        "native_transfer": {
+            "name": None,
+            "from_address": "AxiomRYAid8ZDhS1bJUAzEaNSr69aTWB9ATfdDLfUbnc",
+            "timestamp": 1741555940
+        },
+        "tags": [
+            "sandwich_bot"
+        ],
+        "maker_token_tags": [],
+        "name": None,
+        "avatar": None,
+        "twitter_username": None,
+        "twitter_name": None,
+        "created_at": 1738980654
+    }
+    
 token_add= '2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump'
 arr_store_keys = [
          "address",
@@ -12,33 +66,18 @@ arr_store_keys = [
         
         "buy_tx_count_cur",
         "sell_tx_count_cur",
-        
+ 
         "profit",
         "realized_profit",
+        "profit_change",
         "avg_cost",
         "avg_sold",
         "start_holding_at",
         "end_holding_at"
 ]
-aar =   {
-        "address": "EZZk361QgZDMx52AmMyc4NjFkuaNrdegwAxrLEbeaR94",
-        "sell_amount_cur": 34148763.167781,        
-        "buy_amount_cur": 34148763.167781,
 
-        "buy_tx_count_cur": 9,
-        "sell_tx_count_cur": 77,
-        
-        "profit": 4928.853565607387,
-        "realized_profit": 4928.853565607387,
-        "avg_cost": 0.000043969347937453594,
-        "avg_sold": 0.00018887449941234176,
-        "start_holding_at": 1742168061,
-        "end_holding_at": 1742172930,
-        "tags": [
-            "sandwich_bot"
-        ],
- 
-    } 
+def makeurl( url,url_show):
+    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
     
 def get_handle_json_top_trader():
 
@@ -93,8 +132,8 @@ def arr_json_2_df():
     df['buy_tx_count'] = df['buy_tx_count_cur']  
     df['sell_tx_count'] = df['sell_tx_count_cur']  
     # 将价格转化为是市值 xxxK的市值
-    df['avg_cost'] = df['avg_cost'] *( 1e3 * 1e6/1e3) 
-    df['avg_sold'] = df['avg_sold'] *( 1e3 * 1e6 / 1e3)
+    df['avg_cost_mc'] = df['avg_cost'] *( 1e3 * 1e6/1e3) 
+    df['avg_sold_mc'] = df['avg_sold'] *( 1e3 * 1e6 / 1e3)
     
     df['duration'] = (df['end_holding_at'] - df['start_holding_at'] )/60/60
     
@@ -104,22 +143,32 @@ def arr_json_2_df():
     df["end_datetime"] = pd.to_datetime(df["end_holding_at"], unit='s' )
     
     df = utc_2_shanghaidate(df, "end_datetime")
+    
+    
+    df["gmgn"] = df.apply(lambda ser: makeurl(
+    f"https://gmgn.ai/sol/token/{token_add}?tab=activity&maker={ser['address']}" , "gmgn" ), axis=1)
+    df["beizhu"] = None
     df =df [[       "address",
         "buy_token_amount",
         "sell_token_amount",
 
-        
         "buy_tx_count_cur",
         "sell_tx_count_cur",
         
         # "profit",
         "realized_profit",
-        "avg_cost",
-        "avg_sold",
+        "profit_change" ,
+        "avg_cost_mc",
+        "avg_sold_mc",
         "start_datetime",
         "end_datetime" ,
-        "duration"
+        "duration",
+        "gmgn",
+        "beizhu"
         ]]
+    df =df.round(1)
+ 
+
     
     df =df.sort_values(by=['realized_profit'], ascending=False)
     

src/code/tamper_Dune_get_tokenswap_top_BuyOrDiff_amount.js → src/code/tamper/tamper_Dune_get_tokenswap_top_BuyOrDiff_amount.js

@@ -110,11 +110,13 @@ async function clickButton_get_data() {
   let ul_table_footer__Ky_k2 = document.querySelector('div.visual_vizFooter__vCe59').querySelector("ul.table_footer__Ky_k2");
 
   let arr_ul_children = ul_table_footer__Ky_k2.children;
-  let page_count = arr_ul_children[4].querySelectorAll("option").length;
+  // let page_count = arr_ul_children[4].querySelectorAll("option").length;
+  let str_row_num = arr_ul_children[0].innerText.replace(/[\s,a-zA-Z]/g, '');
+  let number_row_num = parseInt(str_row_num);
+  let page_count = Math.ceil(number_row_num / 25);
 
   // 获取 页数 并且不断next 页
 
-  // console.log("first= ", res_data_get_dune_data);
 
   for (let cur_page = 1; cur_page <= page_count; cur_page++) {
     console.log('cur_page=', cur_page);
@@ -129,8 +131,11 @@ async function clickButton_get_data() {
       }
 
     }
-    let button_next = arr_ul_children[5].querySelector("button");
-    button_next.click();
+    if (arr_ul_children[5] && arr_ul_children[5].querySelector("button")) {
+      let button_next = arr_ul_children[5].querySelector("button");
+      button_next.click();
+    }
+
     await sleep(1500);
 
   }
@@ -148,7 +153,7 @@ async function clickButton_get_data() {
 
 var res_data_get_dune_data = [];
 var page_name = 'dune_swap_total_info';
-var input_value="xx_xx_xx";
+var input_value = "xx_xx_xx";
 var ele_input;
 (async function () {
   console.log("enter load");
@@ -160,7 +165,7 @@ var ele_input;
   ele_input = document.createElement("input") //创建一个input文本框
   ele_input.value = "xx_xx_xx";
   ele_input.className = "tam-myinput"
-  ele_input.type ="text";
+  ele_input.type = "text";
   ele_input.style.width = "90px";
   ele_input.style.height = "30px"; // 高度
 

src/code/tamper_Dune_get_history_token_holders.js

@@ -110,12 +110,13 @@ async function clickButton_get_data() {
   let ul_table_footer__Ky_k2 = document.querySelector('div.visual_vizFooter__vCe59').querySelector("ul.table_footer__Ky_k2");
 
   let arr_ul_children = ul_table_footer__Ky_k2.children;
-  let page_count = arr_ul_children[4].querySelectorAll("option").length;
+  // let page_count = arr_ul_children[4].querySelectorAll("option").length;
+  let str_row_num = arr_ul_children[0].innerText.replace(/[\s,a-zA-Z]/g, '');
+  let number_row_num = parseInt(str_row_num);
+  let page_count = Math.ceil(number_row_num / 25);
 
   // 获取 页数 并且不断next 页
 
-  // console.log("first= ", res_data_get_dune_data);
-
   for (let cur_page = 1; cur_page <= page_count; cur_page++) {
     console.log('cur_page=', cur_page);
     while (true) {
@@ -129,8 +130,10 @@ async function clickButton_get_data() {
       }
 
     }
-    let button_next = arr_ul_children[5].querySelector("button");
-    button_next.click();
+    if (arr_ul_children[5] && arr_ul_children[5].querySelector("button")) {
+      let button_next = arr_ul_children[5].querySelector("button");
+      button_next.click();
+    }
     await sleep(1500);
 
   }
@@ -148,7 +151,7 @@ async function clickButton_get_data() {
 
 var res_data_get_dune_data = [];
 var page_name = 'dune_holders';
-var input_value="xx_xx";
+var input_value = "xx_xx";
 var ele_input;
 (async function () {
   console.log("enter load");
@@ -160,7 +163,7 @@ var ele_input;
   ele_input = document.createElement("input") //创建一个input文本框
   ele_input.value = "xx_xx_xx";
   ele_input.className = "tam-myinput"
-  ele_input.type ="text";
+  ele_input.type = "text";
   ele_input.style.width = "90px";
   ele_input.style.height = "30px"; // 高度
 

src/code/tamper_Dune_get_token_transfer_row_OR_info.js

@@ -110,12 +110,13 @@ async function clickButton_get_data() {
   let ul_table_footer__Ky_k2 = document.querySelector('div.visual_vizFooter__vCe59').querySelector("ul.table_footer__Ky_k2");
 
   let arr_ul_children = ul_table_footer__Ky_k2.children;
-  let page_count = arr_ul_children[4].querySelectorAll("option").length;
+  // let page_count = arr_ul_children[4].querySelectorAll("option").length;
+  let str_row_num = arr_ul_children[0].innerText.replace(/[\s,a-zA-Z]/g, '');
+  let number_row_num = parseInt(str_row_num);
+  let page_count = Math.ceil(number_row_num / 25);
 
   // 获取 页数 并且不断next 页
 
-  // console.log("first= ", res_data_get_dune_data);
-
   for (let cur_page = 1; cur_page <= page_count; cur_page++) {
     console.log('cur_page=', cur_page);
     while (true) {
@@ -129,8 +130,10 @@ async function clickButton_get_data() {
       }
 
     }
-    let button_next = arr_ul_children[5].querySelector("button");
-    button_next.click();
+    if (arr_ul_children[5] && arr_ul_children[5].querySelector("button")) {
+      let button_next = arr_ul_children[5].querySelector("button");
+      button_next.click();
+    }
     await sleep(1500);
 
   }

src/code/tamper_Dune_get_tokenswap_row_special_add.js

@@ -110,16 +110,14 @@ async function clickButton_get_data() {
 
   let arr_ul_children = ul_table_footer__Ky_k2.children;
   // let page_count = arr_ul_children[4].querySelectorAll("option").length;
-  let text_rows = arr_ul_children[0].innerText.replace(/[\s,a-zA-Z]/g, '');
-  let page_count = Math.round(parseInt(text_rows) / 25);
+  let str_row_num = arr_ul_children[0].innerText.replace(/[\s,a-zA-Z]/g, '');
+  let number_row_num = parseInt(str_row_num);
+  let page_count = Math.ceil(number_row_num / 25);
 
-  let next_button_idx = arr_ul_children.length - 2;
+  // let next_button_idx = arr_ul_children.length - 2;
 
 
   // 获取 页数 并且不断next 页
-
-  // console.log("first= ", res_data_get_dune_data);
-
   for (let cur_page = 1; cur_page <= page_count; cur_page++) {
     console.log('cur_page=', cur_page);
     while (true) {
@@ -133,11 +131,14 @@ async function clickButton_get_data() {
       }
 
     }
-    let button_next = arr_ul_children[next_button_idx].querySelector("button");
-    button_next.click();
+    if (arr_ul_children[5] && arr_ul_children[5].querySelector("button")) {
+      let button_next = arr_ul_children[5].querySelector("button");
+      button_next.click();
+    }
     await sleep(1500);
 
   }
+
   res_data_get_dune_data.forEach((val, index, arr) => { arr[index] = val.join() + "\n" });
   res_data_get_dune_data = res_data_get_dune_data.join('')
   let curTime = getCurrentTime();

src/code/tamper_Dune_speadd_all_transfer_row.js

@@ -110,12 +110,13 @@ async function clickButton_get_data() {
   let ul_table_footer__Ky_k2 = document.querySelector('div.visual_vizFooter__vCe59').querySelector("ul.table_footer__Ky_k2");
 
   let arr_ul_children = ul_table_footer__Ky_k2.children;
-  let page_count = arr_ul_children[4].querySelectorAll("option").length;
+  // let page_count = arr_ul_children[4].querySelectorAll("option").length;
+  let str_row_num = arr_ul_children[0].innerText.replace(/[\s,a-zA-Z]/g, '');
+  let number_row_num = parseInt(str_row_num);
+  let page_count = Math.ceil(number_row_num / 25);
 
   // 获取 页数 并且不断next 页
 
-  // console.log("first= ", res_data_get_dune_data);
-
   for (let cur_page = 1; cur_page <= page_count; cur_page++) {
     console.log('cur_page=', cur_page);
     while (true) {
@@ -129,8 +130,10 @@ async function clickButton_get_data() {
       }
 
     }
-    let button_next = arr_ul_children[5].querySelector("button");
-    button_next.click();
+    if (arr_ul_children[5] && arr_ul_children[5].querySelector("button")) {
+      let button_next = arr_ul_children[5].querySelector("button");
+      button_next.click();
+    }
     await sleep(1500);
 
   }

src/code/tamper_dune_get_dalao_buy_same_token_And_token_dif.js

@@ -110,12 +110,13 @@ async function clickButton_get_data() {
   let ul_table_footer__Ky_k2 = document.querySelector('div.visual_vizFooter__vCe59').querySelector("ul.table_footer__Ky_k2");
 
   let arr_ul_children = ul_table_footer__Ky_k2.children;
-  let page_count = arr_ul_children[4].querySelectorAll("option").length;
+  // let page_count = arr_ul_children[4].querySelectorAll("option").length;
+  let str_row_num = arr_ul_children[0].innerText.replace(/[\s,a-zA-Z]/g, '');
+  let number_row_num = parseInt(str_row_num);
+  let page_count = Math.ceil(number_row_num / 25);
 
   // 获取 页数 并且不断next 页
 
-  // console.log("first= ", res_data_get_dune_data);
-
   for (let cur_page = 1; cur_page <= page_count; cur_page++) {
     console.log('cur_page=', cur_page);
     while (true) {
@@ -129,8 +130,10 @@ async function clickButton_get_data() {
       }
 
     }
-    let button_next = arr_ul_children[5].querySelector("button");
-    button_next.click();
+    if (arr_ul_children[5] && arr_ul_children[5].querySelector("button")) {
+      let button_next = arr_ul_children[5].querySelector("button");
+      button_next.click();
+    }
     await sleep(1500);
 
   }

src/library/analysis_token/003_merge_holders_get_excel_kuangjia.py

@@ -96,7 +96,7 @@ if __name__ == "__main__":
     
     arr_file = os.listdir(os.getcwd())
 
-    arr_file = [file for file in arr_file if "dune_holders_xx" in file and file.endswith(".csv")]
+    arr_file = [file for file in arr_file if file.startswith( "dune_holders_xx")   and file.endswith(".csv")]
 
  
     arr_file.sort()

src/library/analysis_token/001_merge_top20_holders_add.py → src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/001_merge_top20_holders_add.py

@@ -1,5 +1,5 @@
 
-# 将不同时间的holders 的top55 地址全部合并到一个excel中
+# 获取每一个时间段的top20 持仓，汇总到一个excel表格中
 import pandas as pd
 import sys
 import os
@@ -67,7 +67,7 @@ if __name__ == "__main__":
  
     arr_file = os.listdir(os.getcwd())
   
-    arr_file = [file for file in arr_file if "dune_holders_xx" in file and file.endswith(".csv")]
+    arr_file = [file for file in arr_file if    file.startswith("dune_holders_xx") and file.endswith(".csv")]
  
     arr_file.sort()
     print("read file: ",arr_file)

src/library/analysis_token/merge_swap_df.py → src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/002_merge_swap_total_info_dune_from_manydunecsv_to_一个多列xlsx文档.py

@@ -2,7 +2,7 @@
 # 将不同时间的swap 合并在一起 分析他们的变化 找出庄家地址/分析庄家变化 
 import pandas as pd
 import sys
-
+import os
 import datetime
 import pytz
 from openpyxl.styles import *
@@ -10,7 +10,10 @@ from openpyxl.styles import *
 arr_file = []
 merged_df = None
  
-token_add = ""
+def makeurl( url,url_show):
+    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
+
+
 
 def get_nowdate():
 
@@ -36,9 +39,11 @@ def utc_2_shanghaidate(df , utc_time_col ):
     print(df)
  
 
+ 
 def merge_swap_df_fun_01():    
+    global merged_swap_toal_info_file
     # style = pd.DataFrame().style
-    global arr_file , merged_df , domains_df,token_add
+    global arr_file , merged_df , token_add
     for index, file in enumerate(arr_file):
         date_range = file.replace(".csv","").split("_")
         date_range = date_range[-2]+"_"+date_range[-1]
@@ -53,8 +58,8 @@ def merge_swap_df_fun_01():
             "eth_s":float,
             "b_count":float,
             "s_count":float,
-            "avg_b_pri":float,
-            "avg_s_pri":float
+            "avg_b_mc":float,
+            "avg_s_mc":float
         })
 
 
@@ -67,11 +72,21 @@ def merge_swap_df_fun_01():
             merged_df = cur_df
             continue
         merged_df = pd.merge(merged_df, cur_df, on='swap_trader_id', how='outer')
-
-
-    # merged_df.to_excel( f"merge_swap_{get_nowdate()}.xlsx",index=False)
+        
+    merged_df["gmgn"] = merged_df.apply(lambda ser: makeurl(
+    f"https://gmgn.ai/sol/token/{token_add}?tab=activity&maker={ser['swap_trader_id']}" , "gmgn" ), axis=1)
+    merged_df["defined"] = merged_df.apply(lambda ser: makeurl(
+     f"https://www.defined.fi/sol/{token_add}?maker={ser['swap_trader_id']}" , "defined" ), axis=1)
+    merged_df["beizhu"] = None
+    
+ 
+    
+    merged_swap_toal_info_file = f"merge_swap_total_info_{get_nowdate()}.xlsx"
+    merged_df.to_excel(merged_swap_toal_info_file ,index=False)
+    
+    
+    
 # https://www.w3schools.com/colors/colors_hex.asp    
-
 yellow_fill = PatternFill(start_color='FFFF00', end_color='FFFF00', fill_type='solid')  # 黄色填充
 
 
@@ -87,38 +102,25 @@ Orange_fill = PatternFill(start_color='FFA500', end_color='FFA500', fill_type='s
 
 
 
-merged_hodlers_file = ""
+merged_swap_toal_info_file = ""
 def merge_swap_df_fun_02():
-    global merged_hodlers_file
-    merged_hodlers_excel = pd.ExcelFile(merged_hodlers_file)
-    arr_sheet_names = list(merged_hodlers_excel.sheet_names)
-    arr_sheet_names.pop(0)
+    global merged_swap_toal_info_file
+    # merged_hodlers_excel = pd.ExcelFile(merged_swap_toal_info_file)
+    # arr_sheet_names = list(merged_hodlers_excel.sheet_names)
+    # arr_sheet_names.pop(0)
     
     
     arr_df = []
     arr_df.append(merged_df)
     
-    for sheet_name in arr_sheet_names:
-        holder_df = merged_hodlers_excel.parse(sheet_name=sheet_name)
-        cur_df = merged_df[  merged_df['swap_trader_id'].isin(holder_df['holder_owner'])].reset_index(drop=True)
-
-        # https://geek-docs.com/pandas/pandas-questions/214_pandas_coloring_cells_in_excel_with_pandas.html
-        # 上色
-# https://geek-docs.com/pandas/pandas-questions/214_pandas_coloring_cells_in_excel_with_pandas.html#google_vignette
-        cur_df = cur_df.set_index('swap_trader_id').reindex(holder_df['holder_owner']).reset_index()
 
-        arr_df.append(cur_df)   
-    time_str = get_nowdate()
-
-
-    with pd.ExcelWriter(f'merge_swap_{time_str}.xlsx') as writer:
+    with pd.ExcelWriter(merged_swap_toal_info_file) as writer:
         for idx ,cur_df in enumerate(arr_df):
             sheet_name = ''
 
             if idx==0:
                 sheet_name = 'Sheet1'
-            else:
-                sheet_name = arr_sheet_names[idx-1]
+
             cur_df.to_excel(writer, sheet_name=sheet_name, index=False)
             worksheet = writer.sheets[sheet_name]
 
@@ -126,14 +128,14 @@ def merge_swap_df_fun_02():
 # pandas 和 openpyxl 如何获取某一列的名字 ，然后对指定列填充颜色
                 # 应用填充颜色到指定单元格范围（例如：A2:A4）
             # target_column = 'swap_trader_id'
-            arr_col_buy_pri = [col for col in cur_df.columns if 'avg_b_pri' in col  ]
-            arr_col_sell_pri = [col for col in cur_df.columns if 'avg_s_pri' in col  ]
+            arr_col_buy_mc = [col for col in cur_df.columns if 'avg_b_mc' in col  ]
+            arr_col_sell_mc = [col for col in cur_df.columns if 'avg_s_mc' in col  ]
             arr_col_li_lun_hold = [col for col in cur_df.columns if 'li_lun_hold' in col  ]
             arr_col_token_b = [col for col in cur_df.columns if 'token_b_M' in col  ]
             arr_col_token_s = [col for col in cur_df.columns if 'token_s_M' in col  ]
 
 
-            for target_column  in arr_col_buy_pri:
+            for target_column  in arr_col_buy_mc:
                 column_index = cur_df.columns.get_loc(target_column) + 1  # +1 转换为 Excel 列索引
                 for row in range(2, len(cur_df) + 2):  # 从第2行到最后一行
                     cell =  worksheet.cell(row=row, column=column_index)
@@ -144,7 +146,7 @@ def merge_swap_df_fun_02():
                     cell =  worksheet.cell(row=row, column=column_index)
                     cell.fill = ForestGreen_fill
  
-            for target_column  in arr_col_sell_pri:
+            for target_column  in arr_col_sell_mc:
                 column_index = cur_df.columns.get_loc(target_column) + 1  # +1 转换为 Excel 列索引
                 for row in range(2, len(cur_df) + 2):  # 从第2行到最后一行
                     cell =  worksheet.cell(row=row, column=column_index)
@@ -164,17 +166,15 @@ def merge_swap_df_fun_02():
 
 
 
-def makeurl( url,url_show):
-    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
+
+
+token_add = "2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump"
 
 if __name__ == "__main__":
-    if len(sys.argv)<3:
-        print("args queshao ")
-        sys.exit(1)
-    # token_add = sys.argv[1]
-    merged_hodlers_file = sys.argv[1]
-    arr_file =sys.argv[2:]
-    arr_file = [file for file in arr_file if "dune_swap_total" in file and file.endswith(".csv")]
+ 
+    arr_file = os.listdir()
+    
+    arr_file = [file for file in arr_file if   file.startswith( "dune_swap_total_info_xx")  and file.endswith(".csv")]
     arr_file.sort()
     print("read file: ",arr_file)
     merge_swap_df_fun_01()

src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/Fcs5TXLXeQDZntDinYozaA6LDDjjpUZLajdDARmYRxuL_0745-0750.csv

@@ -0,0 +1,66 @@
+TYPE,DATE,PRICE,USD,SOL,Token,MAKER,TXN
+Buy,2024/3/17 7:49,51431,259.82,2.05975,1439000,4K3QTetUnATEhcZkhmyH5ogTT4ZWoFfjRfpUVKbrKuJf,27LNmnYgEpy3TsL7dLp8J9QxRM7FPXJdCMv1AGk3dVjWbvRkpncNEGZwx5JwNzNbiM2Hm59PCAuSrNqpnkyhSURe
+Buy,2024/3/17 7:49,51403,251.11,1.99063,1419000,7AKEzTSKDnfykDo6Hx5tWX8X9EtizkDupZGsJg1APwji,3YMDruPtSzWrhQ3rqJzSpTpY6LV8EiCndJG2f62pkTQPfyZ4XKySrrMnbQ8drZR9nKgxxHrNKP772jmaJo7B21a2
+Sell,2024/3/17 7:49,51414,274.99,2.17992,1542000,2Y5g2qSQGb7UBLdhKXRL8d6r2y1kn4KsVKoWVc8QJa94,vpNUXYHcPqVS8QUkja9HPb9yQHpTpENUcFsdBTPccej8fTTg9wy36zRY94SMo4BwHZ5DCWgbuxkZRucz182paAL
+Sell,2024/3/17 7:49,51452,251.73,1.9956,1374000,BaXYopvFq4jeHVuZYYymrjC8WLi6Xc8A9DDJRbnncBfm,MomK3ePxVgL8VzarKrBBx4wu9oT35yxfQ51diFFKc64CGDv358TngbM4zA3mFZ834ocmoLuf2tFv52kjhghXko2
+Buy,2024/3/17 7:49,51455,252.03,1.998,1373000,2Bs5ix5WhpE56vBm34enmgxdRtzqrASMkSGpZskJT9eH,2ynYhqQGEao7qPTXcgRBGQ1ZQ8TYYiiUfUruMiJWEGY1GJr1pZR6BC6tnnMBevBZHYZBGiLEdrw8WWRFd8nRqoar
+Sell,2024/3/17 7:49,51451,224.17,1.77709,1225000,JDgn2UadubA1X5x7qH4yjTQQL22BEL4MbaJVPaqqPmQj,4VRS7UkX5u4pU3oHjTNCVUax1J5MEHZ1CLjQujemXhgAQNGBgnzUM1q66y9Xjj8NTRCN5ECj6mquqzC7ZBFZobDR
+Sell,2024/3/17 7:49,51484,198.15,1.57084,1059000,F6P3dHdpwzatsziFkf1xqdGXvH6nP4oQXiUc4uhVp9Ao,5NQpmXbCYQq1gRykQyZLvwysrnzp74usVM6wkfuLPuBZtLdMVfpnUMLK8emGyeckRsxNL1b8kFY4hCcz8tuxXwM2
+Buy,2024/3/17 7:49,51480,300.53,2.38245,1610000,7q5LheXruJeXTtZafmACNRiasjoonLvmcGk7kygtUPpk,4gsPFhHQts7fRyRdiEfThqfUpqhs4mB6PzJJdDx33WyuLooK4nsYogmgJwtmPfxY1mxyyZhf8jMMf1PTb63Crj2J
+Buy,2024/3/17 7:49,51499,177.85,1.40985,940429,2SBsrbvPoute3BQ96zXv1MDjrX8PRpKZa6KyRup8LTrS,3DbeXN5AEJAyaoJWJ4q3bytMQVpcK4Kb6ryEbSgWECWmogiA4hUo4jEbQLYrWzKeXXssK9wozzsZq1eKNrn1654X
+Buy,2024/3/17 7:49,51467,250,1.98179,1351000,2PMCe7bPbqrTT2qgLLD6KM9YiJThvHbH299Sg5VwexPh,3g2U7AM6qFr4LHxiCqK17NzNFopswVwFwCnpzeiPf64vBKvCYSwXfQonz7NSKtiL43tKLAkzfYDNKujpdFcDd6Ra
+Sell,2024/3/17 7:49,51455,625.82,4.96078,3409000,A98cTYc8w7a1d9dGMPZ72ZcWq9CSsLS2qUibRcThEskH,33xGY7VjcQxhtVG4EqrNSPD8YwJFRU4iBFF2cBjHoPhMTsetAfizcRPVNiBAUtz3uMMxd1BJevtXmg4hFtQSY7HB
+Sell,2024/3/17 7:49,51515,226.73,1.79728,1186000,6AVwoyX4kYtsqzR2tMdLU7jMvnd5uqVkF8Ec3vBgLrs7,3YjZK7HWr1V9hp5eiqnAHZH23wM79aYPmm78JaopCG6BTCvaa3PVnYwvEKp4xEaJF6VQLfYm7hUyVC5qLyLkJV7B
+Sell,2024/3/17 7:49,51487,170.85,1.35463,911023,5YBwhGrCjfAphVGwpEhfJ62zJoGNNKET4iambmRxmwrG,4LVJcNy54ZFvGLkF7VTdr4iDgEMM2dVtaQ3Eu5wWttTgDApPspPQLJErnB3NkKCpSv3my2S1rZjB2kg5YvyXQEED
+Buy,2024/3/17 7:49,51494,154.66,1.22634,820902,91TszupXr5iCguoYCvXib2Y8bBAPXrBigT9hn1iQWaUz,j2yoEqCEebxNaTg4yDxFtifgS5kjbGo8RfbZieGDersAheo9gRGLXKTWmnTxrucW4FEy7jUXicU5ZrDKtC8b88J
+Buy,2024/3/17 7:49,51452,624.38,4.95,3409000,A98cTYc8w7a1d9dGMPZ72ZcWq9CSsLS2qUibRcThEskH,2qXa3qwVyNbWfwxwqxhHxdkTtvUZCDc7jbPRnpyr81oSPryNBECCMJcjf8eBosu6WSpq42tzMEfbmThGdsNFxvA
+Sell,2024/3/17 7:49,51432,155.92,1.23613,863055,EXVZpGDJ6AjKtTccEidfbcTh2cwYqsxqT936WtRd7LKe,5VNuPwk66Z4x76bFfcUrJKfzRVzqsqZaxNr89xc3NpRVJuR4edLiVsLsNNzqm8GwMMZiqRr1neVrGC8brqfCYXza
+Sell,2024/3/17 7:49,51455,166.31,1.31849,906282,Cvyi18wUPh4GSFXTFGckf8BNNgCJTLLhiW9q7vNbDxSe,c5DtqvMVV6fvjvvjZtLYuTBj3EZCCiNmaKY5L1wnBq648Sw1UHdFXKwFGDWXBtbZwV4ug5YSsnp4ZrEvLeyMSGX
+Sell,2024/3/17 7:48,51438,204.73,1.62307,1129000,BqNFKfMr2risobTTQUMSUtgaARLE83hXLV2nowN2C5kP,5ryDqk5dJkT2NWh7DTNUYxiQrubu7d3XtDMzKQGLYvc9JDExUcfxgyuD9XjASyMfomCb3o8vNYVgtjrVRX3mB9Sp
+Buy,2024/3/17 7:48,51450,165.81,1.31451,906282,Cvyi18wUPh4GSFXTFGckf8BNNgCJTLLhiW9q7vNbDxSe,4TG8p8Xz3xsqTGRW2sDHcXHKMBbhWN1dTnUm2CAEVWsiySkpiFEcyecdQSKfjZiqW6YcA7qtwanCWiPz7gCnLo6E
+Sell,2024/3/17 7:48,51431,324.69,2.57412,1798000,5svRBLmVuNZSNyaWqPe2UzmhGHrYvGTY7ZYpQjFo8Ejk,4c2xtgEeTArZXRuTSEREbYJA61tKWyeEvpvMbAN12PorSBqYxitfZ3JDUiJTgVBC6dWvtuEB9XBUxUemN2M8KsVe
+Sell,2024/3/17 7:48,51474,171.32,1.35818,921320,DFqvPbXE6379ovgpAnoMszMrje7Mz8FvLsXYNdkLSUpm,AaUkpKuiv6k8tfV38qQqBNy2deNxcFshva68s7ngQzqKS2V2rAUUUGH3sUgWR96sTmGXnneXVecSbX7RHTBq5K8
+Sell,2024/3/17 7:48,51503,178.6,1.41593,942274,2qX3B4Ecz1qdvQKX5rxESBeutBcSU4GTv23AKQMicpzo,2LFdye8Vfx9tXAqZRMBQoVgMKJcbzyeLiDATpYZfR1YCTTv69UiPqjxVw39t63xHVHfdifq7xUXn6zoXbnsvct7u
+Sell,2024/3/17 7:48,51560,424.94,3.36882,2159000,9YWizNThyWcqXFqV5CMVtaaGetwKjBT8P9CouH7quMsP,2ter9jhM5BQxAWaYwCugTwHSxZN4EHmWexcNBneNxomJypC74SN8Nv76afjxpHMDUUSZadd1Z6NvyaswZzB8c2C4
+Sell,2024/3/17 7:48,51645,459.39,3.64199,2213000,CMCimXg8GULaSW8s1L4yA4vwuR3BYGRanZfWVetDXfs9,4JWcoLcMdvt7CtU9nT1HUm37qzsriFuyobDt15pXZZz8cq2uLPbL1Ay2YGJzvWYCGUz92S8sED9VERXXSnaRUKVB
+Buy,2024/3/17 7:48,51557,2287.89,18.138,11650000,Hn1MyYYiynSCP2WLBMFxgwNJeAheVAfbXD92mFWKY39E,3ukykMC8qZh4wFwN428FNAFBxL6oE3ZWyatfSjJ2eXRL88WsQxnYpwPX9m18A3KRjy6K6BNmfn94p7Chpeb4Ufrj
+Sell,2024/3/17 7:48,51399,166.39,1.3191,943194,6h8QFJXVwMpoyQ1R6VVR16KUa4r9ov4aWoFgvQ6cxGuv,5XJzKm8rWyqAEibnHLCkAWzNv21cWcX6CXDnGSYVMVvDfgTHv1kmLinUkboE3R3FaW8B94U42pAxscdGnzBJJ6or
+Sell,2024/3/17 7:48,51434,191.9,1.5216,1061000,9Z1hod97MxghVaBA2JZgW7PSCJrLFmJYgpzPsGr9RZkz,HiijCzPXvWegHDfR6vYHQPkovKCS8QGoLRQdB7w43Gxg46URpiB7eEGrrBrTL2wX9J9LwBxp3nJTbmr4ntAqoki
+Buy,2024/3/17 7:48,51348,1387.3,11,8159000,pigkea5w3wnxFvsitH8WScRV8jPKN2HaiCtvYoQk3kB,2w4PBjGv6TqbK4BzEEeqTrJz6RCNsXPMDYvTLgYhLT9VVcFDsLy1JvozxWDnyA5tsFhQq4Djt18EHYMxX88zUNKN
+Sell,2024/3/17 7:48,51278,152.57,1.20976,946597,7WD2vxA4c1uD8zxTEfnoBtxwW1Btga6YhKyou6eLTU1L,2FUmmSRvD2WyGaxouAvkGBtXuyz3x2cnQjCnr5p5QHh6p2NJ2n6p4L5cQzicQPSBxXi3RiUgP8sWXrvw49dPbKSX
+Buy,2024/3/17 7:48,51272,163.79,1.2987,1021000,ALbBiS78JBQk7KgBmrTukQYLcnpjwhtuWmJFpjk3eh5y,426suBQGyZ2CrdT1Bz1uZTYqGDoW93N3p5agejVn3SX9yMxhP2ZuphEVMBht1992T9oHvVVVQAMxP3Pmg2g5uiSW
+Buy,2024/3/17 7:48,51222,499.45,3.96013,3241000,24QLS6967nJDthk3LZbQ5uXyUCjATCE8sXEDRZmcrsog,2PLBR98ubK7qvnBA7ibnqyvpHnbyz2aJKP2uCVzcUJtycahwbwjTYQ8E33fQEvbNQeoyKy9KbySmvaDoU6ubkXfS
+Sell,2024/3/17 7:48,51205,391.66,3.10545,2578000,APqfUeKnHFgaE9RXuoh26GejnUVJhbQFRxgyHKqPikqL,ib5SdMurkQLZrGVWB4esXBFqkd69DxmKnqT8CeSmvE8jzNroJtcQ7F3xYcaoZvjBvuYFpTSa6RaMqAJqMB5t9iY
+Sell,2024/3/17 7:48,51247,198.15,1.57109,1260000,zEQxCxxJmLia2DbN83yTnKQaZpwaaoYSiMvyenetKW7,3xWZoTkGEwoTP92nRTx6rytatfN5UEiy3Q7Hxo2zFVLZSXi1RkvAbxKxZcrbpgYeDtKy7u9VCEpr1N3N4iLMH6uq
+Sell,2024/3/17 7:48,51313,325.96,2.58458,1969000,7mJRF6LuiBr4zk2pwtmEJUHV9tQXgodc2twQTSUrpa1,jzqM67MGPc4iaYwYrivWtdjACH1q4m6nBho7fJNXuLssD7js7DsbcwYZsjSkohjZsJ1u2Q1gkLV9PgxU24X2yeX
+Buy,2024/3/17 7:48,51245,1513.41,12,9636000,pigkea5w3wnxFvsitH8WScRV8jPKN2HaiCtvYoQk3kB,2G91Rkp5Ad27K2pdgWwy2ct4AksjEJsEdVgfukTr8jKavmoB4t8JUApsrVBGv5T6dQKVv9Y2edFaHHJneAVJLDY9
+Sell,2024/3/17 7:48,51172,410.56,3.25538,2778000,FPQDzXatpJ7CxcuMWcNcsDgXQS6eAXkhAcanfMNUHUv9,2qVSPUy1cHW6W3TtfWLW5mBGQRW5eUQcAzgXA9b8BUmorEyS2PML3WAucVFh4GgoM5H8vso7ddRUhUafawDykrBb
+Sell,2024/3/17 7:48,51245,156.93,1.24463,1000000,5z1Y42QrkVX4CNUscwtH4mvTpQvzTVvXem94meTU2G5x,3yZ8tQvBoNudch795H9x56Kms3bNhBcgJeWqm9vxC8QbWrpCQPi13Y64TpEzckWsz99v8AgXkF6STcbEa4wPuTgH
+Buy,2024/3/17 7:48,51231,188.51,1.49515,1215000,35zTAKqHUEaPBH8eACToXyu9pKCfq1BUGvdRid9ST2Xb,3Mys8QAU8aTNVBbRvFSqnSwjdwYRhxJ3wjSJUrgijZ8wL2oarRkf6ffnsqTmvgGJ5hq4Gqz4hGShaLcTLRTCgPG8
+Buy,2024/3/17 7:48,51205,378.25,3,2490000,PcwNUyUFf3pErAWn8gEe5wLJbZqYTgZ9i1acVtNtVii,4whEPPwFfim7sny2bJGw1H4J4j3fpmPZNtwGzNxCN2SjsWCgaXrHYoR8N5W5jHWP8148ZikRURorFXJBbMJ2aSxq
+Sell,2024/3/17 7:48,51178,170.51,1.35238,1148000,Aq2uzvudjXmhieZdzuNWR4S4tMoMZ9WbtM8MhuEKWLVM,XLKNW7jagUj39CHhfmPAqV8LeJma2WL1RXRCciFYY6LMwC3XLBgURN1967ReJpzrqfR6txZtYFbQYyApzkgs6o2
+Buy,2024/3/17 7:48,51164,378.28,3,2578000,APqfUeKnHFgaE9RXuoh26GejnUVJhbQFRxgyHKqPikqL,5syuL2qPw2mw7VC5cdXwAXEqfSsTjRU3fGwQNWjvN9NiXkhQPVqkFHKTpx1PSCQYjrusi9hFnDk3NKw9zHeg1hFi
+Sell,2024/3/17 7:48,51137,222.99,1.76848,1555000,7e2nMupmwmwXDS3az6q4oQamDJVheYzBdDnRqGVCgK3h,4iPFXh1byyPEtUFvmRvRD3iqu7CoEFsMfFLRppaqwhMYMasDYKV8oQLBgeAbsmZd5Tn1ps5GJjAYTUj4bP766BdT
+Sell,2024/3/17 7:48,51167,268.5,2.12942,1824000,8xC9qT9ZfdZhtyPMNobqmSiVo9R1kSDZGLix2PYJSfba,5WBVU4rNNtMSFgYtD74vts1C4xDAzJUpwiaMAomtBa1zUiJdHYRCtw39AGuUAJKeUxo7AGPY1NLCBhX31StoY48k
+Buy,2024/3/17 7:48,51191,183.04,1.45161,1219000,DzNHaFZ5MLszMHA9qQLGWP5FgYCJhUubmvhFZ44EYF1D,272xRu3xDcZCjP1zp3PYwoEXwhNLQMTbG6s9iV4ZLQ43e1c2darMbN3ef2oj1nBLbEk9AnpeimfcZsdoZbGwCntf
+Sell,2024/3/17 7:48,51221,676.67,5.36662,4397000,6EJvMMeHyWDbK3T1Y5mENePUV4g9HkojwJsbxRVQFqAZ,3pbEmZdSMzuVdTREGYF4yrxWsQjHAaYbtPm2brXaCrV45PgykdBv7RMFRZhcNTgdvsG7jMEohrsmqBRP4PrZyUkb
+Buy,2024/3/17 7:48,51169,1513.07,12,10260000,pigkea5w3wnxFvsitH8WScRV8jPKN2HaiCtvYoQk3kB,3tWF2dCaYEgMfab5236LJwse2iyZ2EW1EwXAA5G3csnTsKYCxczP5cswK8zr4KgqApSHvGW9rpQy84AdP28JVfxd
+Sell,2024/3/17 7:48,51074,193.93,1.53814,1432000,3jwGMDX8qFYwJ2yzfXgpc9EvUYQePFRA6TreryU6nF32,3vJUoSdjacxRsB61uVEwpW743zsS2PTUPyakEisCoRi38bX6MicWF5B3yhSQ1vc9H3JQmWVzsWZG7HqdtVR9KCRu
+Buy,2024/3/17 7:47,51083,311.66,2.47132,2281000,GacpCNMVpWYbbT45MY9vw6i2a2Hxe6Ue2zxdKiskCQ1Y,5rSMqfJFrP8KnMRQ14EbZfnZaC496uvqXc7kEseRH9NWojpUZY12giby52iytiWQwxFtbnnLb1mgzYBkKZbQMhof
+Sell,2024/3/17 7:47,51128,381.31,3.02358,2680000,H7qiVgqwpcKb1hTq2JSNBxKFesQP1Eir9ncuwALqb8fB,3LwQEKLkcSfGYQKGxxNUGQTHo2tYH3WkB37bbCUx2XMa8qKPh6HXeAxwrFYKZSmdXdnG2tdA9sMjzMV9XMEnu3Mj
+Buy,2024/3/17 7:47,51123,242.43,1.92232,1712000,9ipT3tRG86GNTU6Lds6eqkPFH9c6izo8NpRcoEr6B4zg,5JETHr5j5Q2WLnxjkKEPoxzvua2fNtX4pSLJZh7Lbfz2XTTF8tAeq2CfiY6AMCtGxvBGDc28uiXUTCewgM39gD9n
+Buy,2024/3/17 7:47,51088,214.49,1.70077,1563000,3NNSWoD5ZoDrnsvvefzYayKuFSbXu91QLnJRYqgiR5ck,sEHPpq8frCeLjFQa9HqG1VZkiXfsGt639g9NnAqVNHCgGnzRoBdTGKueU3jABHtaP72S7p7TSibwvVfPN5cnNcp
+Sell,2024/3/17 7:47,69818,288.9,2.29025,2333000,FoN3a3HEW4j2ex8dgj1FPgs2kLzt9KKkdXkCkfZexWmS,2AQcEJ6RpYjFfypv1VyH96GEgChaoocKbJGsanXicphGn348hXzMAMgkryvqUHMfZ5ZWdbwqacGWXnpNy1cnmti7
+Sell,2024/3/17 7:47,51002,152.58,1.20956,1208000,DYiRLJPw7zpqFfvUbAqSHcRCF5fXFf4uMF1gpcvZyAkW,4f2jMCuzKZdjvYPBvDbXXQVSXtVvjdy6KopL1PxCiFK8xrxwf9niv7j5FwRAwYihL6TQtJDDFTk2KE8Qjzm4pdfU
+Sell,2024/3/17 7:47,51031,173.49,1.37534,1334000,C6rNZcrLfw3JgAkxXyR8AMVabBZn6UUDJmB4ZzUVNi6c,4gME4tTeNLHhgN3VWa21yLgRSHo7cDXQz2fGMhgpYw6KJGFP1DSrpryuChremnpZ3hVCpqfgqPbNz2rMicdcJpNB
+Sell,2024/3/17 7:47,51069,519.63,4.11954,3853000,EiA7B1jYnjGkFrFzNZwfuaeDc8fGFd27Fvu3oPevznon,NirDAEBupm5t9jzafz2ejK7UHGSPAAr3BRi7Ech3VLiifj4xBJ6eHNAfLpdcnYCKLC9ynihycsJ8dRkBcNEmnzy
+Buy,2024/3/17 7:47,51080,378.42,3,2778000,FPQDzXatpJ7CxcuMWcNcsDgXQS6eAXkhAcanfMNUHUv9,3G1mToAG1UkgfZji2dbaY5nxjHpGNyKrSDAdF52ydiTFTyM7GPNNMXiswCNqRibwE9TKeJeRZEJF8cceBjLSVgw7
+Buy,2024/3/17 7:47,51004,575.65,4.56517,4546000,8jEkfj47BrpRkikfwoma28GjLMXCbDkPQRERNZH7B2v3,2idxiq8osQnAmn9Djr45pPbGhEZQ7R4jpYVGpoiZo4svATtuK8gABLXE1kRqacCFMg9DLYBXW4yXm41m7zXSM6ak
+Buy,2024/3/17 7:46,69606,187.15,1.485,1546000,DJGjfLA2WuJP4mmtLrWazu6FP1U4MJ8ckHmyq8jJMrY6,PfTrqzxd3hQ1KiuG6mbRsCbz8xTrpDMhRzeUGJWqwpwUdRNUfDVVmGcAqqj21xWs6Wn3zQsFwDMTQzCTBqmGuva
+Buy,2024/3/17 7:46,68684,359.17,2.85,3282000,9PNrgpgVcMJhpEUA37ZLqGbttMYzXGnZX2s3AzZUYk4c,23PMfv7pLtGGR8E9keLxWUS5mXZTqGFmn3Q4Go4YD7fUqviBG8CQVQ9yv6sKMst9Rkd3L52q1i7yrCJ5MKskKnVP
+Sell,2024/3/17 7:46,68505,219.65,1.74326,2050000,FSj8vurEGyiasjJKnev5hX7885ecv8u3tTLvk3ZLndku,2mGynC4L4tUf9f7CYx2yfEv51qZAAa4LELXiXAkD5Mf4BMtjxuuMNz7z43Eqibu45SH9ge7rjTZigsqCZPPBXq8h
+Buy,2024/3/17 7:46,68437,175,1.38887,1646000,7Ws5KxtBLrmgs5o6fvgHANf9mQf83nv2H5mYvvp2ZFeW,m8GnC4aK8pE6fgzUf9pJBDV9vr4K5GRsLBNSH4f7qa39Q41meQz85zZnxpfvnSHMFnauxpVE6BTKgTunmsKTegM
+Sell,2024/3/17 7:45,69196,869.14,6.88916,7491000,BrerigYnkqPk4XJCzVYUahgEd3Q8vEPdg1UfsooZfznG,5bACEdF4JGYZFAzsGsv1pQbJTx11GGVmX4WMktFhyswqMk9KoYQ1MKzbE9vGPDNipXYX4hbrFeV9KA5Yehx75F1v
+Buy,2024/3/17 7:45,69595,250.18,1.983,2067000,6LyNGnYag24o59UZrjqzJaNaygWhEXnjugizoXLCeJQk,5idud5G5ExNS5uE18LWkquRofgF9Xq5hGFxn11CfpHxsWtyLfL4atXYs5Sq7EZ83rpLtV4Dppm4rQHtetro3vTx8
+Buy,2024/3/17 7:45,68979,848.59,6.72623,7491000,BrerigYnkqPk4XJCzVYUahgEd3Q8vEPdg1UfsooZfznG,zYboRN7yK9Hc2xUE9nRimpYgZ71FYNo5pTLVpKrZ8FK9gEks2Rsg1cPADjKa7MPcx4qdsN2aimZkcwmGxoTxpAo
+Buy,2024/3/17 7:45,67858,249.87,1.98,2520000,BzKEDExJRcbnQRDeDCi7ixMeTerrq3gn77URXDe4RYEf,4xNrjUMM11pPQ6V7KtP7BUvnheHqusQ93qigG6HzaHqjnku9M7EQAQE7XZXDs3u8eNRfHzceaaUqpV1gZG5bEfZB

src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/aaa.py

@@ -0,0 +1,3 @@
+string = 'Mar 17 07:49:46\n0.0\n51455\n$\n252.03\n1.998\n1.373\nM\n\n…kJT9eH\n\nMar 17 07:49:39\n0.0\n51480\n$\n300.53\n2.38245\n1.61\nM\n\n…gtUPpk\n\nMar 17 07:49:32\n0.0\n51467\n$\n250.00\n1.98179\n1.351\nM\n\n…VwexPh\n\nMar 17 07:49:03\n0.0\n51452\n$\n624.38\n4.950\n3.409\nM\n\n…ThEskH\n\nMar 17 07:48:50\n0.0\n51557\n$\n2,287.89\n18.138\n11.65\nM\n\n…WKY39E\n\nMar 17 07:48:45\n0.0\n51348\n$\n1,387.30\n11.00\n8.159\nM\n\n…oQk3kB\n\nMar 17 07:48:36\n0.0\n51222\n$\n499.45\n3.96013\n3.241\nM\n\n…mcrsog\n\nMar 17 07:48:26\n0.0\n51245\n$\n1,513.41\n12.00\n9.636\nM\n\n…oQk3kB\n\nMar 17 07:48:20\n0.0\n51205\n$\n378.25\n3.000\n2.49\nM\n\n…tNtVii\n\nMar 17 07:48:14\n0.0\n51164\n$\n378.28\n3.000\n2.578\nM\n\n…qPikqL\n\nMar 17 07:48:08\n0.0\n51169\n$\n1,513.07\n12.00\n10.26\nM\n\n…oQk3kB\n\nMar 17 07:47:53\n0.0\n51083\n$\n311.66\n2.47132\n2.281\nM\n\n…skCQ1Y\n\nMar 17 07:47:48\n0.0\n51123\n$\n242.43\n1.92232\n1.712\nM\n\n…r6B4zg\n\nMar 17 07:47:46\n0.0\n51088\n$\n214.49\n1.70077\n1.563\nM\n\n…giR5ck\n\nMar 17 07:47:06\n0.0\n51080\n$\n378.42\n3.000\n2.778\nM\n\n…NUHUv9\n\nMar 17 07:47:02\n0.0\n51004\n$\n575.65\n4.56517\n4.546\nM\n\n…H7B2v3\n\nMar 17 07:46:39\n0.0\n68684\n$\n359.17\n2.850\n3.282\nM\n\n…ZUYk4c\n\nMar 17 07:45:38\n0.0\n69595\n$\n250.18\n1.983\n2.067\nM\n\n…LCeJQk\n\nMar 17 07:45:38\n0.0\n68979\n$\n848.59\n6.72623\n7.491\nM\n\n…oZfznG'
+string=string.replace('$\n','$')
+print(string)

src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/aaaa.json

@@ -0,0 +1,1520 @@
+"items": [
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+        "token1ValueBase": "0.0000014744972467005098691877691663939761",
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+        },
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+        "quoteToken": "token1",
+        "data": {
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+            "priceBaseTokenTotal": "2.059753667",
+            "type": "Swap"
+        },
+        "__typename": "Event"
+    },
+    {
+        "address": "Fcs5TXLXeQDZntDinYozaA6LDDjjpUZLajdDARmYRxuL",
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+            "__typename": "LabelsForEvent"
+        },
+        "transactionIndex": 1356,
+        "quoteToken": "token1",
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+            "amount0In": null,
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+            "priceBaseToken": "0.0000014032749305237657863431918951857567",
+            "priceBaseTokenTotal": "1.990628428",
+            "type": "Swap"
+        },
+        "__typename": "Event"
+    },
+    {
+        "address": "Fcs5TXLXeQDZntDinYozaA6LDDjjpUZLajdDARmYRxuL",
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+        },
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+        "quoteToken": "token1",
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+            "__typename": "LabelsForEvent"
+        },
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+        "quoteToken": "token1",
+        "data": {
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+            "type": "Swap"
+        },
+        "__typename": "Event"
+    },
+    {
+        "address": "Fcs5TXLXeQDZntDinYozaA6LDDjjpUZLajdDARmYRxuL",
+        "baseTokenPrice": "12614981710.085",
+        "blockNumber": 327238054,
+        "eventDisplayType": "Buy",
+        "eventType": "Swap",
+        "id": "Fcs5TXLXeQDZntDinYozaA6LDDjjpUZLajdDARmYRxuL:1399811149",
+        "liquidityToken": "So11111111111111111111111111111111111111112",
+        "logIndex": 2,
+        "maker": "2PMCe7bPbqrTT2qgLLD6KM9YiJThvHbH299Sg5VwexPh",
+        "timestamp": 1742168971,
+        "token0SwapValueUsd": "126.14981710085001641337527274695663834933566782232630969305",
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+        "labels": {
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+            "__typename": "LabelsForEvent"
+        },
+        "transactionIndex": 29,
+        "quoteToken": "token1",
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+            "priceBaseToken": "0.0000014673978911668465857929082158898685",
+            "priceBaseTokenTotal": "1.981793882",
+            "type": "Swap"
+        },
+        "__typename": "Event"
+    },
+    {
+        "address": "Fcs5TXLXeQDZntDinYozaA6LDDjjpUZLajdDARmYRxuL",
+        "baseTokenPrice": "12613790644.04578",
+        "blockNumber": 327237982,
+        "eventDisplayType": "Buy",
+        "eventType": "Swap",
+        "id": "Fcs5TXLXeQDZntDinYozaA6LDDjjpUZLajdDARmYRxuL:1399811149",
+        "liquidityToken": "So11111111111111111111111111111111111111112",
+        "logIndex": 3,
+        "maker": "A98cTYc8w7a1d9dGMPZ72ZcWq9CSsLS2qUibRcThEskH",
+        "timestamp": 1742168942,
+        "token0SwapValueUsd": "126.13790644045787149770033927272727272727272838264131834838",
+        "token0ValueBase": "1",
+        "token1SwapValueUsd": "0.00018313581752748259619464780677583440648543252624588472",
+        "token1ValueBase": "0.0000014893910814294458077809333879824135",
+        "transactionHash": "2qXa3qwVyNbWfwxwqxhHxdkTtvUZCDc7jbPRnpyr81oSPryNBECCMJcjf8eBosu6WSpq42tzMEfbmThGdsNFxvA",
+        "labels": {
+            "sandwich": null,
+            "__typename": "LabelsForEvent"
+        },
+        "transactionIndex": 1453,
+        "quoteToken": "token1",
+        "data": {
+            "__typename": "SwapEventData",
+            "amount0In": null,
+            "amount0Out": null,
+            "amount1In": null,
+            "amount1Out": null,
+            "amount0": "4950000000",
+            "amount1": "-3409396617822",
+            "amountNonLiquidityToken": "3409396.617822",
+            "priceUsd": "0.00018313581752748259619464780677583440648543252624588472",
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+            "priceBaseToken": "0.0000014518698042125038633888343018480734",
+            "priceBaseTokenTotal": "4.95",
+            "type": "Swap"
+        },
+        "__typename": "Event"
+    }
+],
+"items": [
+    {
+        "address": "Fcs5TXLXeQDZntDinYozaA6LDDjjpUZLajdDARmYRxuL",
+        "baseTokenPrice": "12613790644.04578",
+        "blockNumber": 327237949,
+        "eventDisplayType": "Buy",
+        "eventType": "Swap",
+        "id": "Fcs5TXLXeQDZntDinYozaA6LDDjjpUZLajdDARmYRxuL:1399811149",
+        "liquidityToken": "So11111111111111111111111111111111111111112",
+        "logIndex": 3,
+        "maker": "Hn1MyYYiynSCP2WLBMFxgwNJeAheVAfbXD92mFWKY39E",
+        "timestamp": 1742168929,
+        "token0SwapValueUsd": "126.13790644045782695716933806036285627678092349668189180739",
+        "token0ValueBase": "1",
+        "token1SwapValueUsd": "0.00019636044088372205803544068889910625976322098275845152",
+        "token1ValueBase": "0.0000015950976692903685098374530251653066",
+        "transactionHash": "3ukykMC8qZh4wFwN428FNAFBxL6oE3ZWyatfSjJ2eXRL88WsQxnYpwPX9m18A3KRjy6K6BNmfn94p7Chpeb4Ufrj",
+        "labels": {
+            "sandwich": null,
+            "__typename": "LabelsForEvent"
+        },
+        "transactionIndex": 1533,
+        "quoteToken": "token1",
+        "data": {
+            "__typename": "SwapEventData",
+            "amount0In": null,
+            "amount0Out": null,
+            "amount1In": null,
+            "amount1Out": null,
+            "amount0": "18137997993",
+            "amount1": "-11651476659767",
+            "amountNonLiquidityToken": "11651476.659767",
+            "priceUsd": "0.00019636044088372205803544068889910625976322098275845152",
+            "priceUsdTotal": "2287.88909385824535040119539999999999997757464850752981403880399584",
+            "priceBaseToken": "0.0000015567123827000580358343277599930753",
+            "priceBaseTokenTotal": "18.137997993",
+            "type": "Swap"
+        },
+        "__typename": "Event"
+    },
+    {
+        "address": "Fcs5TXLXeQDZntDinYozaA6LDDjjpUZLajdDARmYRxuL",
+        "baseTokenPrice": "12611835505.60823",
+        "blockNumber": 327237935,
+        "eventDisplayType": "Buy",
+        "eventType": "Swap",
+        "id": "Fcs5TXLXeQDZntDinYozaA6LDDjjpUZLajdDARmYRxuL:1399811149",
+        "liquidityToken": "So11111111111111111111111111111111111111112",
+        "logIndex": 5,
+        "maker": "pigkea5w3wnxFvsitH8WScRV8jPKN2HaiCtvYoQk3kB",
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+        "__typename": "Event"
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+],

src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/dune_holders_xx_xx_0317日0655.csv

@@ -1,27 +0,0 @@
-holder_owner,domains_owned,amount_M,pct_supply
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-J4rYYPEXHwYMvyNzVwRsTyaSVpHv4SXK6kQNGgvBdvc4,,29.74,2.97
-oVHpCFMvZ1ZPinBeUGNSf93T4A5Ru9pguQ696Bp6uAE,,23.24,2.32
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-9H8wEA9ALybJbRmBxjendamofQJZa4VCW4z4fDo37zA4,,21.56,2.16
-282Si2J6j5MPnLbHxzrGDAYnTw5Gu4SnKMSt4EaVsecD,,20.75,2.08
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-2ZWG4EFE2dWbFsw5r2J4rcZVBU7HrxtcjbV83SMc2FJ1,,14.73,1.47
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-7GpiTzud8oKFMWGXAaAHRmRUt7WBFGS8Sb1Tcjd9ZPU5,,10.25,1.02
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-9sMmDCA8w1Xx2vqpS4WwZEHpQxqm2bytXM8K3GPmEkt4,,7.6,0.76
-8AZ1zezFUym8rfN4WVSVf7n2WV4j1XzNqQABDqLPQiMt,,7.35,0.74
-8iLfJvRjzWdoxwn1PLCHCzGHfcLfNH8Wf6faQLsBUHPg,,6.85,0.69
-2AAYRsTauytE6zEzW7xCcmo2ALamNE6pKMTXf4L6hWWn,,6.74,0.67
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-3tQi2NfLkAUndq7Vy8uXQeN8NBrTdEVmQBnXEXYaxsdR,,6.11,0.61
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src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/dune_holders_xx_xx_0317日0745.csv

@@ -1,9 +1,9 @@
 holder_owner,domains_owned,amount_M,pct_supply
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+215nhcAHjQQGgwpQSJQ7zR26etbjjtVdW74NLzwEgQjP,binladen.sol,18.63,1.86
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src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/dune_holders_xx_xx_0317日1000.csv

@@ -1,27 +0,0 @@
-holder_owner,domains_owned,amount_M,pct_supply
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-,,38.2,3.82
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src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/dune_holders_xx_xx_0317日1530.csv

@@ -0,0 +1,27 @@
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+,,42.86,4.29
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src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/dune_swap_total_info_xx_0317日0730_0317日0815.csv

@@ -0,0 +1,76 @@
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src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/dune_swap_total_info_xx_0317日0745_0317日0750_6KU.csv

@@ -0,0 +1,5 @@
+swap_trader_id,li_lun_hold,sol_dif,token_b_M,token_s_M,eth_b,eth_s,b_count,s_count,avg_b_mc,avg_s_mc
+pigkea5w3wnxFvsitH8WScRV8jPKN2HaiCtvYoQk3kB,28.06,35,28.06,0,35,0,3,0,1247.4,0
+Hn1MyYYiynSCP2WLBMFxgwNJeAheVAfbXD92mFWKY39E,11.65,18.1,11.65,0,18.1,0,1,0,1556.7,0
+8jEkfj47BrpRkikfwoma28GjLMXCbDkPQRERNZH7B2v3,4.55,4.6,4.55,0,4.6,0,1,0,1004.2,0
+24QLS6967nJDthk3LZbQ5uXyUCjATCE8sXEDRZmcrsog,3.24,4,3.24,0,4,0,1,0,1221.9,0

src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/dune_swap_total_info_xx_0317日1035_0317日1040_5KU.csv

@@ -0,0 +1,5 @@
+swap_trader_id,li_lun_hold,sol_dif,token_b_M,token_s_M,eth_b,eth_s,b_count,s_count,avg_b_mc,avg_s_mc
+482wsuUM6amyjuivNpwbzV8A1h74EDSf9kHbE3miXMMZ,21.96,39.6,21.96,0,39.6,0,1,0,1803.8,0
+H8QD9bFaGwnW3EYRLXmGFUG6aWsC1h7U8v7GnJwJ2Vys,18.57,30,18.57,0,30,0,1,0,1615.7,0
+B7Zpksf18iYLuKMX4g9SURACkCGRPEtk9KZbGhrFr8ym,9.16,16.2,9.16,0,16.2,0,1,0,1765.4,0
+pigkea5w3wnxFvsitH8WScRV8jPKN2HaiCtvYoQk3kB,7.89,15,7.89,0,15,0,1,0,1901.3,0

src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/dune_swap_total_info_xx_0317日1530_0317日1600.csv

@@ -0,0 +1,31 @@
+swap_trader_id,li_lun_hold,token_b_M,token_s_M,eth_b,eth_s,b_count,s_count,avg_b_mc,avg_s_mc
+H6KiqN3wwirFumem4ZqxbF3hhHNTjpyps3q7TTLX3n33,11.1,11.1,0,15.73,0,3,0,1416.464,0
+EVbP6Gkd7UfJo6ARWuTefssoaJuDmGuVfZbYm9e1PGZX,0.01,6.42,6.41,10.6,10.68,1,1,1650.681,1667.957
+4Degk564qYYcK4hDZikpNcuS4jNgcGH8PGcJuhgJdPoY,0,5.68,5.68,10,10.96,1,1,1759.82,1928.554
+GqWHWPXeaYH8USxfpxP38DTYbjeCKQZnBAf8DK9nyf5Y,5.39,5.39,0,10,0,2,0,1856.702,0
+49qx4mZz6WrgimSsFPXgfgM1weXvRimytF8K4R7LQFgP,0,5.13,5.13,10,8.92,2,1,1947.993,1737.623
+7rtiKSUDLBm59b1SBmD9oajcP8xE64vAGSMbAN5CXy1q,6.45,6.45,0,9.15,0,2,0,1418.74,0
+4vJfp62jEzcYFnQ11oBJDgj6ZFrdEwcBBpoadNTpEWys,0,5.99,5.99,8.06,8.45,4,4,1345.443,1410.442
+42LqGvRxTiHNz9tNni71PoTqYyrrmYXLc6oSVpEAMazX,5.58,5.58,0,6.64,0,1,0,1189.841,0
+AR76YMMrA9U9dY5gEjF6kdUYnGkqhpRNvA58Q36bqcPe,4.82,4.82,0,6.53,0,2,0,1353.623,0
+8minXrQHroV9C48SdP86XGzRr3ABydPVoe3aQjMMnBcJ,2.62,2.62,0,5,0,1,0,1908.912,0
+8kf4xKtrYQpZzXkkqndWbueXLQr84mEgj2f1FKyt37EM,3.19,3.19,0,5,0,1,0,1563.467,0
+Ar87pN4N5SFtry848DC339xjXB4hSKxDs7YATNtZESod,0,2.5,2.5,5,4.75,1,1,1998.265,1897.973
+AJHfZbWYi7bQnnPQ9q7rsJq7QxNBQFaeuGAe2K48EMDS,0,2.18,2.18,4.02,4.14,2,2,1844.165,1900.081
+Em3w1NMiQpCakAA4mHMxuMVZZjTVDG3UiNXC1921Mq2E,2.19,2.19,0,4,0,2,0,1827.765,0
+2mPTV3XF81ywJZnpsNS2Lt28mvnCdKvY1DT7FqNsyQ2w,2.67,2.67,0,3.96,0,4,0,1485.092,0
+5i8Ed4SDzvSDxL4YouhmWzwU8fEh2H77CxZvuEkZQDV2,0,2.45,2.45,3.9,4.3,1,1,1591.845,1753.233
+vmSb6tmwhii1fwEYzKNUHwekPj7owPN6ae4mh95VewX,0.55,1.69,1.14,3.06,2.18,6,2,1812.338,1911.039
+DYTXYi9GVQsdYwRAadSWck5if8BDjrGXDtfCgpio8V1K,0,1.82,1.82,3,3.57,1,1,1649.538,1960.751
+B4etobNBWwZSoZst7o5uNuXLYh3AwuRsKB9JExBmiYw5,1.48,1.48,0,2.97,0,1,0,2008.579,0
+H6YX2NZrdMvKuNSKTJZuKLCUj3LWJxTNekXE89TS5JBZ,1.5,1.5,0,2.75,0,1,0,1836.498,0
+Y8e95Q1YSFhRqcZt99KDUTW1WRa1Zr86weZBmkUfczH,0,1.44,1.44,2.74,2.79,1,1,1897.575,1929.772
+5svRBLmVuNZSNyaWqPe2UzmhGHrYvGTY7ZYpQjFo8Ejk,2.25,2.25,0,2.55,0,1,0,1135.541,0
+6YgXi3rGPXmd74nMuMxUpsvbgk3P63x1uPW8hDjhZYLq,1.23,1.23,0,2.5,0,1,0,2038.569,0
+FZGqWRuXECoSLapTwHVzeWaqpkWGcpGuZhGh1f6x1bK7,1.71,1.71,0,2.24,0,2,0,1308.436,0
+CN4AG1iqQeLaZrQB2qsfJaAQgUBHeSXUK6UvC31n77UJ,1.09,1.09,0,2.05,0,1,0,1889.791,0
+75texw5HGP7yXyLG5VwEvxUcwo6LJDTvqcZH9p9MZ3YP,1,1,0,1.78,0,1,0,1783.998,0
+CWherccKBYq2UdPYG4urukiCuTQ8gabbzNTXDyeAKjsZ,0,1.23,1.23,1.63,1.74,2,2,1328.462,1414.549
+4NZCxZBoHHBGcYWepQ4pU3kLfNqXLj63WK2QKSzJ51K3,1.04,1.04,0,1.59,0,1,0,1536.603,0
+FdHEV7sRsHJch6LmnEBtzLtU28zK3Dogb9tbhXLNyyGT,0.81,0.81,0,1.56,0,1,0,1936.716,0
+FmLd92BmZ2eQnVUWTqyPz7L6UZ9PptizvRG55awP2Y9v,0.89,0.89,0,1.56,0,1,0,1756.056,0

src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/exclude_add.csv

@@ -0,0 +1,40 @@
+ex_add,info
+5Q544fKrFoe6tsEbD7S8EmxGTJYAKtTVhAW5Q5pge4j1,Raydium Authority V4
+BQ72nSv9f3PRyRKCBnHLVrerrv37CYTHm5h3s9VSGQDV,Jupiter Aggregator Authority 1
+2MFoS3MPtvyQ4Wh4M9pdfPjz6UhVoNbFbGJAskCPCj3h,Jupiter Aggregator Authority 2
+HU23r7UoZbqTUuh3vA7emAGztFtqwTeVips789vqxxBw,Jupiter Aggregator Authority 3
+3CgvbiM3op4vjrrjH2zcrQUwsqh5veNVRjFCB9N6sRoD,Jupiter Aggregator Authority 4
+6LXutJvKUw8Q5ue2gCgKHQdAN4suWW8awzFVC6XCguFx,Jupiter Aggregator Authority 5
+CapuXNQoDviLvU1PxFiizLgPNQCxrsag1uMeyk6zLVps,Jupiter Aggregator Authority 6
+GGztQqQ6pCPaJQnNpXBgELr5cs3WwDakRbh1iEMzjgSJ,Jupiter Aggregator Authority 7
+9nnLbotNTcUhvbrsA6Mdkx45Sm82G35zo28AqUvjExn8,Jupiter Aggregator Authority 8
+3LoAYHuSd7Gh8d7RTFnhvYtiTiefdZ5ByamU42vkzd76,Jupiter Aggregator Authority 9
+DSN3j1ykL3obAVNv7ZX49VsFCPe4LqzxHnmtLiPwY6xg,Jupiter Aggregator Authority 10
+69yhtoJR4JYPPABZcSNkzuqbaFbwHsCkja1sP1Q2aVT5,Jupiter Aggregator Authority 11
+6U91aKa8pmMxkJwBCfPTmUEfZi6dHe7DcFq2ALvB2tbB,Jupiter Aggregator Authority 12
+7iWnBRRhBCiNXXPhqiGzvvBkKrvFSWqqmxRyu9VyYBxE,Jupiter Aggregator Authority 13
+4xDsmeTWPNjgSVSS1VTfzFq3iHZhp77ffPkAmkZkdu71,Jupiter Aggregator Authority 14
+GP8StUXNYSZjPikyRsvkTbvRV1GBxMErb59cpeCJnDf1,Jupiter Aggregator Authority 15
+HFqp6ErWHY6Uzhj8rFyjYuDya2mXUpYEk8VW75K9PSiY,Jupiter Aggregator Authority 16
+45ruCyfdRkWpRNGEqWzjCiXRHkZs8WXCLQ67Pnpye7Hp,Jupiter Partner Referral Fee Vault
+ZG98FUCjb8mJ824Gbs6RsgVmr1FhXb2oNiJHa2dwmPd,CxvksNjwhdHDLr3qbCXNKVdeYACW8cs93vFqLqtgyFE5s fee account
+DGT9TPRGQwpJeMvDdZJDEaVRZa2ZMXtp16pGZgAgBoLx,pump Bonding Curve
+GE5PnwZTgWw1YqVGno2QPWqie6aLjswNeGFhiF1nDg3k,Raydium 8008-USDC Market
+25mYnjJ2MXHZH6NvTTdA63JvjgRVcuiaj6MRiEQNs1Dq,ZG98FUCjb8mJ824Gbs6RsgVmr1FhXb2oNiJHa2dwmPd
+14qLxu9XDQtc2pgnxX2SSdWPpnuVrEqccgwmATH1khcY,Raydium SOL-8008 Market
+5YET3YapxD6to6rqPqTWB3R9pSbURy6yduuUtoZkzoPX,Jupiter ApePro Keeper
+F5sw1r94VXmUGwesPrcY3TvwSrfVAZmRaWwMtD1tr3Yf,Meteora 8008-SOL Market   
+j1oAbxxiDUWvoHxEDhWE7THLjEkDQW2cSHYn2vttxTF,jupiter limit order taker account
+4KaVawPKmG1RZCv698XD9YzRsJcFPXrEoYnZbdZmfFFq,okx相关
+j1oeQoPeuEDmjvyMwBmCWexzCQup77kbKKxV59CnYbd,jupiter limit order taker account
+6KFVj2czLwNQeE4yBgFau6LHUS72vqgP11Sv1JUNxghz,fee account
+53owRWHqDocPYVtVxAQJSTE7rY6QWyWfDNAnWg5VpF9z,Pump.fun (JIANKUI) Bonding Curve
+H3oWNGDfr5pSPDM3eDYpLiT7WsUiUF8ee8WmepovDAGt,Meteora (JIANKUI-WSOL) Market
+BgYAdyHRjkQ4qaPMyt2SL53MNgfDFF6hiYy7tzwfDrtW,Raydium (WSOL-JIANKUI) Market
+8AcbTJPF2dyvabhjY8oz89SQWZArBeZzLHXnYWYWUzue,Meteora (JIANKUI-WSOL) Market
+53LCtLUHJvNxsf7C9xUFC8TCecV2yAEsZmCuCgnB8LR,Meteora (YAYDOLF-WSOL) Market
+9x13yovdBSLuTRM6RMng6vvaCezBQsL7uhLkGgyLKPhJ,Pump.fun (YAYDOLF) Bonding Curve
+C4cTbb5vL97sgTN2PCPPiUiKVGHw3X6fVdFtGZ9H7wnW,Meteora (YAYDOLF-WSOL) Market
+4VxktrNN2rgHdRFxwGS6QctuPfdPASXFCi5jwFv4R8MH,Meteora (Nicky-WSOL) Market
+8W6hWKzGGBh9KuSi3jigLNmpMhd5XMdHztYJSpfS7md3,Pump.fun (Nicky) Bonding Curve
+Zs54KhsoYJVykFVBcGNnxjnvc3V35MUpLD4ZUmZVgRZ,Meteora (Nicky-WSOL) Market

src/library/analysis_token/2ZoJVM15fbbDgo6s5cTTX2Sj8V4sJ47rimb7gQhGpump/不确定dune_holders_xx_xx_0317日0620.csv

@@ -1,27 +0,0 @@
-holder_owner,domains_owned,amount_M,pct_supply
-DRmX6MzLEB5UbuCHujn7askeohc1TCbsJf7wnn8HUuWy,,43.58,4.36
-GSAikdWfDHgY8Zsz1DnwsQNiiAwoowHTFmSv2sEZNJGS,,30.9,3.09
-9sYzSv2VN3MsBEFiR2aesN4EZGLGwM2tbFLeCM2NHBNf,,27.86,2.79
-GHqWJ3KxvTALTgPTssXwpvqMjr9SeDDfbZQxabKhYcmx,,26.81,2.68
-CVcM4qWbAgpwS5SRnm9gdRoNkyF7LocgQee3nG6rUdKW,,25.23,2.52
-HbuRPHj2rssVeBe4fXFLQaqZGEvsvwdfGDNjeAejVPu9,,24.15,2.42
-9SySwmGj8ZyzxcoeZ5EUJdQ8gjvdfAnfP8RDHrAKD7AN,,20.59,2.06
-BqwqBzh6j1jiA4qNLkwk6YL2ZjwGLbKSCuARbmMsaz6p,,20.22,2.02
-9Q4CVx77eJKQkVahxSph6H4dWSvh1PziEa2VGzVEmJjR,,19.36,1.94
-2mPTV3XF81ywJZnpsNS2Lt28mvnCdKvY1DT7FqNsyQ2w,,18.3,1.83
-2JMXseMZh2DhUwYHj9psoZ9YzQXXzHUmfgESdPKXvcQQ,,14.7,1.47
-EsBxbxj86ZiqYq9EtumvuiJNybNTeS84iAv6A35FB6fx,,11.55,1.16
-Dd47tR3SCkCD8B2gV2eayPQuGXckuvA9EV9Aetht7KRQ,,11,1.1
-3gvWGDyDT2Q9xC5hAD4ezg56UEapi5g1anYNU8rG76mH,,10.45,1.05
-965XB2QNTdpxrGfiXKhdjsxRLDUVqf3Kc7vetmyqe9zp,,9.76,0.98
-rkewuyTScR3cH9kZFX9yxSHxUoj77pQwGf84e6VNvcc,,9.22,0.92
-FgatT4UdXGwdNP557zbDFscc8D452F4ezUfG6DQXDF9v,,9.14,0.91
-HPczfmGKV7Dbqb5yiQTxkEHTm2ugAJZ8pKmzJeNZaGRX,,8.25,0.83
-2KngczUUyKypmkPe2vbr5jBtAxGFfJwVqE6X6vLHxGvs,,8.21,0.82
-3Vt9hcu3HCqZajqqNPjy1bVmdi5C6f9q73QfgzKZeV7i,,8.07,0.81
-Bsafj6bHwmnPfBQZG2vTf6M5wgoW2Kg5AsNhW54twKA2,,8.04,0.8
-7P4YNbBApaD1aijnNsbqZhFNG5uBJ67z9mxGEXxyH4bg,,7.89,0.79
-GGfXyUVdE8sQe7iAR9s29e9DVoWZ6oXXK1rJRrSisD7f,,7.1,0.71
-8LeAYA5gie2fYZfaUUdY246q5tav99EpDE9LDynYNSz9,,6.69,0.67
-ByQUUijePim3b7nj5TC5pBNf7gM2x3jphrQXLBgTFfCj,,6.53,0.65
-FTLp8RPENCoR5MMQqgJ4yG6wBEhBZShw2UiM6egRnNbC,,6.39,0.64

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/001_merge_get_all_holders_add.py

@@ -67,7 +67,7 @@ if __name__ == "__main__":
  
     arr_file = os.listdir(os.getcwd())
   
-    arr_file = [file for file in arr_file if "dune_holders_xx" in file and file.endswith(".csv")]
+    arr_file = [file for file in arr_file if file.startswith( "dune_holders_xx")   and file.endswith(".csv")]
  
     arr_file.sort()
     print("read file: ",arr_file)

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/003_merge_holders_get_excel_kuangjia.py

@@ -96,7 +96,7 @@ if __name__ == "__main__":
     
     arr_file = os.listdir(os.getcwd())
 
-    arr_file = [file for file in arr_file if "dune_holders_xx" in file and file.endswith(".csv")]
+    arr_file = [file for file in arr_file if file.startswith( "dune_holders_xx")   and file.endswith(".csv")]
 
  
     arr_file.sort()

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/merge_swap_df.py

@@ -174,7 +174,7 @@ if __name__ == "__main__":
     # token_add = sys.argv[1]
     merged_hodlers_file = sys.argv[1]
     arr_file =sys.argv[2:]
-    arr_file = [file for file in arr_file if "dune_swap_total" in file and file.endswith(".csv")]
+    arr_file = [file for file in arr_file if   file.startswith( "dune_swap_total_info_xx")  and file.endswith(".csv")]
     arr_file.sort()
     print("read file: ",arr_file)
     merge_swap_df_fun_01()

src/library/analysis_token/HeJUFDxfJSzYFUuHLxkMqCgytU31G6mjP4wKviwqpump/分析.md

@@ -1,6 +0,0 @@
-
-
-tele 西瓜 提及的
-
-
-

src/library/analysis_token/jLkPtLCKP6hrRxftWvtycTNX22ijbAqmayUuiZ9pump/001_01_merge_dune_hold_info_from_manydunecsv_to_一个多列的xlsx文档.py

@@ -62,7 +62,7 @@ if __name__ == "__main__":
         sys.exit(1)
     token_add = sys.argv[1]
     arr_file =sys.argv[2:]
-    arr_file = [file for file in arr_file if "dune_holders_xx" in file and file.endswith(".csv")]
+    arr_file = [file for file in arr_file if file.startswith( "dune_holders_xx")   and file.endswith(".csv")]
  
     arr_file.sort()
     print("read file: ",arr_file)

src/library/analysis_token/jLkPtLCKP6hrRxftWvtycTNX22ijbAqmayUuiZ9pump/002_handle_dune_buy_sametoken.py

@@ -1,55 +0,0 @@
-
-# 获取每一个时间段的top20 持仓，汇总到一个excel表格中
-import pandas as pd
-import sys
-import os
-import time
-
-arr_file = []
-merged_df = None
-def makeurl( url,url_show):
-    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
-def handle_df_fun():
-    
-    global arr_file , merged_df 
-    for index, file in enumerate(arr_file):
-        cur_df =   pd.read_csv(file )
-        cur_df =cur_df.astype({
-            "trader_number":int,
-        })
-
-        if index==0 :
-            merged_df = cur_df
-            continue
-
-    merged_df["gmgn"] = merged_df.apply(lambda ser: makeurl(
-    f"https://gmgn.ai/sol/token/{ser['token_address']}" , "gmgn" ), axis=1)
-
-    merged_df["defined"] = merged_df.apply(lambda ser: makeurl(
-     f"https://www.defined.fi/sol/{ser['token_address']}" , "defined" ), axis=1)
-    
-    merged_df["beizhu"] = None
-    merged_df["lasheng_time"] =None
-    merged_df=merged_df[['token_address', 'trader_number',  'min_block_time', 'time_diff',
-     'create_time','lasheng_time',	'gmgn',	'defined'	,'beizhu' ]]
-
- 
-
-    merged_df.to_excel(f'002_dune_buy_sametoken_{int(time.time())}.xlsx', index=False)
-    return
- 
-    
-
-if __name__ == "__main__":
- 
- 
-    arr_file = os.listdir(os.getcwd())
-  
-    arr_file = [file for file in arr_file if   file.startswith("dune_buy_sametoken_")  and file.endswith(".csv")]
- 
-    arr_file.sort()
-    print("read file: ",arr_file)
-    
-    handle_df_fun()
- 
-    

src/library/analysis_token/jLkPtLCKP6hrRxftWvtycTNX22ijbAqmayUuiZ9pump/003_handle_dune_buy_sametoken_time_diff.py

@@ -1,55 +0,0 @@
-
-# 获取每一个时间段的top20 持仓，汇总到一个excel表格中
-import pandas as pd
-import sys
-import os
-import time
-
-arr_file = []
-merged_df = None
-def makeurl( url,url_show):
-    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
-def handle_df_fun():
-    
-    global arr_file , merged_df 
-    for index, file in enumerate(arr_file):
-        cur_df =   pd.read_csv(file )
-    
-        if index==0 :
-            merged_df = cur_df
-            continue
-    merged_df["early_trader_number"] =   (merged_df['buy_create_time_diff'] < 45).groupby(merged_df['token_address']).transform('sum')
-    
-    
-
- 
-    
-    merged_df["gmgn"] = merged_df.apply(lambda ser: makeurl(
-    f"https://gmgn.ai/sol/token/{ser['token_address']}?tab=activity&maker={ser['trader_id']}" , "gmgn" ), axis=1)
-    merged_df["defined"] = merged_df.apply(lambda ser: makeurl(
-     f"https://www.defined.fi/sol/{ser['token_address']}?maker={ser['trader_id']}" , "defined" ), axis=1)
-    
-    merged_df["beizhu"] = None
-    merged_df =  merged_df.sort_values(by=['early_trader_number','token_address' , 'buy_create_time_diff'], ascending=[False, True , False])
- 
- 
- 
-
-    merged_df.to_excel(f'003_dune_sametoken_time_diff_{int(time.time())}.xlsx', index=False)
-    return
- 
-    
-
-if __name__ == "__main__":
- 
- 
-    arr_file = os.listdir(os.getcwd())
-  
-    arr_file = [file for file in arr_file if "dune_sametoken_time_diff" in file and file.endswith(".csv")]
- 
-    arr_file.sort()
-    print("read file: ",arr_file)
-    
-    handle_df_fun()
- 
-