running

src/code/handle_dune_py/001_concat_top20_holders_add_dune_nouse.py

@@ -0,0 +1,87 @@
+
+# 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 map_exclude_df(df1,ex_df):
+  
+    ex_df.rename(columns={'ex_add': 'holder_owner', 'info': 'domains_owned'}, inplace=True)
+    # 使用merge创建一个临时DataFrame，只包含匹配的行
+    temp_df = df1[['holder_owner']].merge(ex_df, on='holder_owner', how='left')
+    # 更新df1的'b1'列
+    df1.set_index('holder_owner', inplace=True)
+    temp_df.set_index('holder_owner', inplace=True)
+    df1.update(temp_df)
+    # 重置索引
+    df1.reset_index(inplace=True)
+
+    return df1
+
+def merge_df_fun():
+ 
+    
+    global arr_file , merged_df , domains_df,token_add
+    for index, file in enumerate(arr_file):
+        date_range = file.replace(".csv","").split("_")
+        date_range = date_range[-1]
+               
+        cur_df =   pd.read_csv(file ,dtype=object)
+        cur_df=cur_df.dropna(subset=['holder_owner'])
+
+        cur_df =cur_df.astype({
+            "amount_M":float,
+        })
+        cur_df =cur_df.round(1)
+        cur_df["belong_date"] =date_range
+
+
+        cur_df=cur_df.sort_values(by=['amount_M'],ascending = False)
+        cur_df =cur_df.head(22)
+        cur_df=cur_df[['holder_owner','domains_owned','amount_M','belong_date']]
+
+
+        if index==0 :
+            merged_df = cur_df
+            continue
+        merged_df = pd.concat([merged_df, cur_df])
+  
+    
+    # merged_df =merged_df.drop_duplicates(subset=['holder_owner'])
+
+    ex_df = pd.read_csv('exclude_add.csv',dtype=object)
+    merged_df=map_exclude_df(merged_df ,ex_df)
+    merged_df['is_dup'] = merged_df.duplicated(subset='holder_owner', keep=False).astype(int)
+
+
+    merged_df["gmgn"] = merged_df.apply(lambda ser: makeurl(
+    f"https://gmgn.ai/sol/token/{token_add}?tab=activity&maker={ser['holder_owner']}" , "gmgn" ), axis=1)
+    merged_df["defined"] = merged_df.apply(lambda ser: makeurl(
+     f"https://www.defined.fi/sol/{token_add}?maker={ser['holder_owner']}" , "defined" ), axis=1)
+    merged_df["beizhu"] = None
+
+    merged_df.to_excel(f'001_top20_holders_dune_{int(time.time())}.xlsx', index=False)
+
+ 
+    
+token_add = '8kKdLb7tWxuZHCyt9mx83Ei8pV9YTWJULMLcFWuqpump'
+if __name__ == "__main__":
+ 
+ 
+    arr_file = os.listdir(os.getcwd())
+  
+    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)
+    
+    merge_df_fun()
+ 
+    

src/code/handle_dune_py/001_top20_holders_dune_merged_dune.py

@@ -0,0 +1,94 @@
+
+# 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 time
+import os
+arr_file = []
+merged_df = None
+domains_df= None
+def map_exclude_df(df1,ex_df):
+  
+    ex_df.rename(columns={'ex_add': 'holder_owner', 'info': 'domains_owned'}, inplace=True)
+    # 使用merge创建一个临时DataFrame，只包含匹配的行
+    temp_df = df1[['holder_owner']].merge(ex_df, on='holder_owner', how='left')
+    # 更新df1的'b1'列
+    df1.set_index('holder_owner', inplace=True)
+    temp_df.set_index('holder_owner', inplace=True)
+    df1.update(temp_df)
+    # 重置索引
+    df1.reset_index(inplace=True)
+
+    return df1
+
+def makeurl( url,url_show):
+    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
+
+def merge_df_fun():
+    arr_date_range =[]
+    arr_col_pct=[]
+    
+    global arr_file , merged_df , domains_df,token_add
+    for index, file in enumerate(arr_file):
+        date_range = file.replace(".csv","").split("_")
+        date_range = date_range[-2]+"_"+date_range[-1]
+
+        cur_df =   pd.read_csv(file ,dtype=object)
+
+        cur_df =cur_df.astype({
+            "amount_M":float,
+        })
+ 
+        cur_df=cur_df.sort_values(by=['amount_M'],ascending = False)
+
+
+        cur_df=cur_df.dropna(subset=['holder_owner'])
+        cur_df =cur_df.head(22)
+        cur_domains_df = cur_df[['holder_owner' , 'domains_owned']]
+        domains_df = pd.concat([domains_df, cur_domains_df  ]).dropna().drop_duplicates(subset=['holder_owner'])
+         
+        cur_df=cur_df[['holder_owner','amount_M']]
+ 
+        cur_df.rename(columns={'amount_M': f'AM_{date_range}'}, inplace=True)
+        arr_date_range.append(f'{date_range}')
+        arr_col_pct.append(f'AM_{date_range}')
+        if index==0 :
+            merged_df = cur_df
+            continue
+        merged_df = pd.merge(merged_df, cur_df, on='holder_owner', how='outer')
+  
+    
+    # merged_df=merged_df.fillna(-1)
+    merged_df = pd.merge(merged_df, domains_df, on='holder_owner', how='left')
+
+
+    ex_df = pd.read_csv('exclude_add.csv',dtype=object)
+    merged_df=map_exclude_df(merged_df ,ex_df)
+ 
+
+    merged_df["gmgn"] = merged_df.apply(lambda ser: makeurl(
+    f"https://gmgn.ai/sol/token/{token_add}?tab=activity&maker={ser['holder_owner']}" , "gmgn" ), axis=1)
+
+    merged_df["defined"] = merged_df.apply(lambda ser: makeurl(
+     f"https://www.defined.fi/sol/{token_add}?maker={ser['holder_owner']}" , "defined" ), axis=1)
+    
+  
+    merged_df["beizhu"] = None
+
+    merged_df.to_excel(f'001_top20_holders_dune_merged_{int(time.time())}.xlsx', index=False)
+
+ 
+
+token_add = '8kKdLb7tWxuZHCyt9mx83Ei8pV9YTWJULMLcFWuqpump'
+ 
+ 
+if __name__ == "__main__":
+ 
+    arr_file = os.listdir(os.getcwd())
+  
+    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)
+    merge_df_fun()
+ 

src/code/handle_dune_py/002_handle_buy_sametoken_dune.py

@@ -9,6 +9,23 @@ arr_file = []
 merged_df = None
 def makeurl( url,url_show):
     return '=HYPERLINK("{}","{}")'.format(url,  url_show)
+
+import pytz
+def utc_2_shanghaidate(df , utc_time_col ):
+# 将字符串转换为UTC时间的datetime对象
+    # df[utc_time_col] = pd.to_datetime(df[utc_time_col], utc=True)
+    # 定义UTC+8时区
+    utc_plus_8 = pytz.timezone('Asia/Shanghai')
+    # 转换为UTC+8时间
+    df[utc_time_col] = df[utc_time_col].dt.tz_localize('UTC')
+    # # 提取时间字符串
+    # df['shanghai_date'] = df['shanghai_date'].dt.strftime('%Y-%m-%d %H:%M')
+    df[utc_time_col] = df[utc_time_col].dt.tz_convert(utc_plus_8)
+    # 提取时间字符串
+    df[utc_time_col] = df[utc_time_col].dt.strftime('%Y-%m-%d %H:%M')
+    return df
+
+
 def handle_df_fun():
     
     global arr_file , merged_df 
@@ -30,6 +47,13 @@ def handle_df_fun():
     
     merged_df["beizhu"] = None
     merged_df["lasheng_time"] =None
+
+    
+    merged_df["min_block_time"] = pd.to_datetime(merged_df["min_block_time"]  )
+    merged_df = utc_2_shanghaidate(merged_df, "min_block_time")
+    merged_df["create_time"] = pd.to_datetime(merged_df["create_time"]  )
+    merged_df = utc_2_shanghaidate(merged_df, "create_time")
+
     merged_df=merged_df[['token_address', 'trader_number',  'min_block_time', 
      'create_time', 'time_diff',  'lasheng_time',	'gmgn',	'defined'	,'beizhu' ]]
 

src/code/handle_dune_py/003_handle_buy_sametoken_time_diff_row_dune.py

@@ -18,7 +18,7 @@ def handle_df_fun():
         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["early_trader_number"] =   (merged_df['buy_create_time_diff'] < 60).groupby(merged_df['token_address']).transform('sum')
     
     
 
@@ -30,7 +30,7 @@ def handle_df_fun():
      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'], ascending=[False, True])
+    merged_df =  merged_df.sort_values(by=['early_trader_number','token_address','buy_create_time_diff'], ascending=[False, True,True])
  
  
  

src/code/tamper/xmlhttprequest_get_top_trader_gmgn.js

@@ -48,6 +48,7 @@ function timestampToString(timestamp) {
 }
 var arr_json_res_data = [];
 function clickButton_get_toptrader() {
+    console.log("load clickButton_get_toptrader ");
     var originalXhrOpen = XMLHttpRequest.prototype.open;
     var old_open = XMLHttpRequest.prototype.open;
 

src/library/analysis_token/8kKdLb7tWxuZHCyt9mx83Ei8pV9YTWJULMLcFWuqpump/001_handle_json_top_trader_gmgn.py

@@ -0,0 +1,190 @@
+import pandas as pd
+import json
+import os
+import time
+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= ''
+arr_store_keys = [
+         "address",
+        "sell_amount_cur",
+        "buy_amount_cur",
+
+        
+        "buy_tx_count_cur",
+        "sell_tx_count_cur",
+ 
+        "profit",
+        "realized_profit",
+        "profit_change",
+        "avg_cost",
+        "avg_sold",
+        "start_holding_at",
+        "end_holding_at",
+        "tags",
+]
+
+def makeurl( url,url_show):
+    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
+    
+def get_handle_json_top_trader():
+
+    global arr_file,token_add  , arr_json_data
+    arr_file = [file for file in os.listdir() if file.startswith("top_trader_gmgn_") and file.endswith(".json")]
+    token_add=arr_file[0].replace(".json","").split("_")[-1]
+ 
+    
+ 
+    with open(f'top_trader_gmgn_{token_add}.json', 'r',encoding='utf8') as f:
+        old_arr_json_data = json.load(f)
+ 
+    
+    for obj in old_arr_json_data:
+        filtered_obj = {}
+        # 遍历obj的每一个value 元素，如果元素是数组或者obj，就删除这个key，否则就保留这个key
+        for key, value in obj.items():
+            if key in arr_store_keys:
+                if key=='tags':
+                    value = set(value) - set(['bullx' , 'photon','trojan','pepeboost','gmgn'])
+                    filtered_obj[key] = "_".join(value)
+                
+                else:    
+                    filtered_obj[key] = value
+            else:
+                continue
+                
+                
+        arr_json_data.append(filtered_obj)
+    
+    return
+                
+import pytz
+def utc_2_shanghaidate(df , utc_time_col ):
+# 将字符串转换为UTC时间的datetime对象
+    # df[utc_time_col] = pd.to_datetime(df[utc_time_col], utc=True)
+    # 定义UTC+8时区
+    utc_plus_8 = pytz.timezone('Asia/Shanghai')
+    # 转换为UTC+8时间
+    df[utc_time_col] = df[utc_time_col].dt.tz_localize('UTC')
+    # # 提取时间字符串
+    # df['shanghai_date'] = df['shanghai_date'].dt.strftime('%Y-%m-%d %H:%M')
+    df[utc_time_col] = df[utc_time_col].dt.tz_convert(utc_plus_8)
+    # 提取时间字符串
+    df[utc_time_col] = df[utc_time_col].dt.strftime('%Y-%m-%d %H:%M')
+    return df
+
+ 
+
+def arr_json_2_df():
+    global arr_json_data
+    df  = pd.DataFrame(arr_json_data)
+    # print(df)
+    df['sell_token_amount'] = df['sell_amount_cur']  / 1e6
+    df['buy_token_amount'] = df['buy_amount_cur']  / 1e6
+    
+    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_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
+    
+    df["start_datetime"] = pd.to_datetime(df["start_holding_at"], unit='s'  )
+    df = utc_2_shanghaidate(df, "start_datetime")
+
+    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",
+        "profit_change" ,
+        "avg_cost_mc",
+        "avg_sold_mc",
+        "start_datetime",
+        "end_datetime" ,
+    
+        "duration",
+            "tags",
+        "gmgn",
+        "beizhu"
+        ]]
+    df =df.round(1)
+ 
+
+    
+    df =df.sort_values(by=['realized_profit'], ascending=False)
+    
+ 
+    df.to_excel(f'top_trader_gmgn_{token_add}_{int(time.time())}.xlsx',index=False)
+ 
+
+
+get_handle_json_top_trader()
+arr_json_2_df()

src/library/analysis_token/8kKdLb7tWxuZHCyt9mx83Ei8pV9YTWJULMLcFWuqpump/001_top20_holders_dune_merged_dune.py

@@ -0,0 +1,94 @@
+
+# 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 time
+import os
+arr_file = []
+merged_df = None
+domains_df= None
+def map_exclude_df(df1,ex_df):
+  
+    ex_df.rename(columns={'ex_add': 'holder_owner', 'info': 'domains_owned'}, inplace=True)
+    # 使用merge创建一个临时DataFrame，只包含匹配的行
+    temp_df = df1[['holder_owner']].merge(ex_df, on='holder_owner', how='left')
+    # 更新df1的'b1'列
+    df1.set_index('holder_owner', inplace=True)
+    temp_df.set_index('holder_owner', inplace=True)
+    df1.update(temp_df)
+    # 重置索引
+    df1.reset_index(inplace=True)
+
+    return df1
+
+def makeurl( url,url_show):
+    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
+
+def merge_df_fun():
+    arr_date_range =[]
+    arr_col_pct=[]
+    
+    global arr_file , merged_df , domains_df,token_add
+    for index, file in enumerate(arr_file):
+        date_range = file.replace(".csv","").split("_")
+        date_range = date_range[-2]+"_"+date_range[-1]
+
+        cur_df =   pd.read_csv(file ,dtype=object)
+
+        cur_df =cur_df.astype({
+            "amount_M":float,
+        })
+ 
+        cur_df=cur_df.sort_values(by=['amount_M'],ascending = False)
+
+
+        cur_df=cur_df.dropna(subset=['holder_owner'])
+        cur_df =cur_df.head(22)
+        cur_domains_df = cur_df[['holder_owner' , 'domains_owned']]
+        domains_df = pd.concat([domains_df, cur_domains_df  ]).dropna().drop_duplicates(subset=['holder_owner'])
+         
+        cur_df=cur_df[['holder_owner','amount_M']]
+ 
+        cur_df.rename(columns={'amount_M': f'AM_{date_range}'}, inplace=True)
+        arr_date_range.append(f'{date_range}')
+        arr_col_pct.append(f'AM_{date_range}')
+        if index==0 :
+            merged_df = cur_df
+            continue
+        merged_df = pd.merge(merged_df, cur_df, on='holder_owner', how='outer')
+  
+    
+    # merged_df=merged_df.fillna(-1)
+    merged_df = pd.merge(merged_df, domains_df, on='holder_owner', how='left')
+
+
+    ex_df = pd.read_csv('exclude_add.csv',dtype=object)
+    merged_df=map_exclude_df(merged_df ,ex_df)
+ 
+
+    merged_df["gmgn"] = merged_df.apply(lambda ser: makeurl(
+    f"https://gmgn.ai/sol/token/{token_add}?tab=activity&maker={ser['holder_owner']}" , "gmgn" ), axis=1)
+
+    merged_df["defined"] = merged_df.apply(lambda ser: makeurl(
+     f"https://www.defined.fi/sol/{token_add}?maker={ser['holder_owner']}" , "defined" ), axis=1)
+    
+  
+    merged_df["beizhu"] = None
+
+    merged_df.to_excel(f'001_top20_holders_dune_merged_{int(time.time())}.xlsx', index=False)
+
+ 
+
+token_add = '8kKdLb7tWxuZHCyt9mx83Ei8pV9YTWJULMLcFWuqpump'
+ 
+ 
+if __name__ == "__main__":
+ 
+    arr_file = os.listdir(os.getcwd())
+  
+    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)
+    merge_df_fun()
+ 

src/library/analysis_token/8kKdLb7tWxuZHCyt9mx83Ei8pV9YTWJULMLcFWuqpump/dune_holders_xx_xx_0410日0615.csv

@@ -0,0 +1,26 @@
+holder_owner,domains_owned,amount_M,pct_supply
+GYnvxtWpHeWH6ndcjL4iz4DnMx6KXutHBKwNdzMpTTof,,271.4,27.14
+,,206.9,20.69
+AFNzbFR8WXPoAaj6E1jNGSC2Xn9r3pz2krt61Vcc5pZP,,28.4,2.84
+E81oD4zDbxANqff7fwjahHvYA3Pm6zQcAYuWxvrWfMWv,,25.2,2.52
+215nhcAHjQQGgwpQSJQ7zR26etbjjtVdW74NLzwEgQjP,globalist.sol,25.2,2.52
+RcCDLh2SC91R6d8gM9j6DeKJU2B2ShMmDjQefi31Kpf,,24.2,2.42
+9432LVFidEvBmcAUH6fUGD7FY2zjvNz87y1ib4u1ueWq,,22.1,2.21
+4Yp9uAbYMXsnFxCR1ZWwQAffMuPybQrzxNhqXYcu3J7W,,21.6,2.16
+4zhYuvSqvtgxPx2BojmanEhJCXeXtWkEC6wevHXKTYHk,,20.6,2.06
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src/library/analysis_token/8kKdLb7tWxuZHCyt9mx83Ei8pV9YTWJULMLcFWuqpump/dune_holders_xx_xx_0410日0700.csv

@@ -0,0 +1,26 @@
+holder_owner,domains_owned,amount_M,pct_supply
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src/library/analysis_token/8kKdLb7tWxuZHCyt9mx83Ei8pV9YTWJULMLcFWuqpump/dune_holders_xx_xx_0410日1230.csv

@@ -0,0 +1,26 @@
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src/library/analysis_token/8kKdLb7tWxuZHCyt9mx83Ei8pV9YTWJULMLcFWuqpump/exclude_add.csv

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src/library/analysis_token/8kKdLb7tWxuZHCyt9mx83Ei8pV9YTWJULMLcFWuqpump/top_trader_gmgn_8kKdLb7tWxuZHCyt9mx83Ei8pV9YTWJULMLcFWuqpump.json

@@ -0,0 +1,5172 @@
+[
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+        "account_address": "E4eDK8No4EyGS6cszTFFSi5SnufS4C7PXqx93fYUWUdo",
+        "addr_type": 0,
+        "amount_cur": 0,
+        "usd_value": 0,
+        "cost_cur": 0,
+        "sell_amount_cur": 7798296.48646,
+        "sell_amount_percentage": 1,
+        "sell_volume_cur": 1416.52358141452,
+        "buy_volume_cur": 68.5933346809172,
+        "buy_amount_cur": 7798296.48646,
+        "netflow_usd": -1347.9302467336029,
+        "netflow_amount": 0,
+        "buy_tx_count_cur": 1,
+        "sell_tx_count_cur": 10,
+        "wallet_tag_v2": "TOP100",
+        "native_balance": "1057555981",
+        "balance": 0,
+        "profit": 1346.3428879780954,
+        "realized_profit": 1346.3428879780954,
+        "profit_change": 19.183949631357297,
+        "amount_percentage": 0,
+        "unrealized_profit": 0,
+        "unrealized_pnl": null,
+        "avg_cost": 0.00000879593829242248,
+        "avg_sold": 0.00018164525853486038,
+        "accu_amount": 0,
+        "accu_cost": 0,
+        "cost": 0,
+        "total_cost": 70.1806934364245,
+        "transfer_in": false,
+        "is_new": false,
+        "is_suspicious": false,
+        "start_holding_at": 1744233265,
+        "end_holding_at": 1744239092,
+        "last_active_timestamp": 1744239092,
+        "native_transfer": {
+            "name": null,
+            "from_address": "EZT2kab6RcWxb3iX2xkzP7nrtGNrdUE9qxYwXQwUfY9t",
+            "timestamp": 1744041014
+        },
+        "tags": [],
+        "maker_token_tags": [],
+        "name": null,
+        "avatar": null,
+        "twitter_username": null,
+        "twitter_name": null,
+        "created_at": 1744045337
+    }
+]

src/library/analysis_token/exclude_add.csv

@@ -31,4 +31,9 @@ j1oeQoPeuEDmjvyMwBmCWexzCQup77kbKKxV59CnYbd,jupiter limit order taker account
 53owRWHqDocPYVtVxAQJSTE7rY6QWyWfDNAnWg5VpF9z,Pump.fun (JIANKUI) Bonding Curve
 H3oWNGDfr5pSPDM3eDYpLiT7WsUiUF8ee8WmepovDAGt,Meteora (JIANKUI-WSOL) Market
 BgYAdyHRjkQ4qaPMyt2SL53MNgfDFF6hiYy7tzwfDrtW,Raydium (WSOL-JIANKUI) Market
-8AcbTJPF2dyvabhjY8oz89SQWZArBeZzLHXnYWYWUzue,Meteora (JIANKUI-WSOL) Market
+8AcbTJPF2dyvabhjY8oz89SQWZArBeZzLHXnYWYWUzue,Meteora (JIANKUI-WSOL) Market
+GYnvxtWpHeWH6ndcjL4iz4DnMx6KXutHBKwNdzMpTTof,Pump.fun AMM (TRUMPCOIN-WSOL)
+8SpTECa5sR7JwjVYq37YXUoVFMb6cogg6vdvvUeyscet,Raydium (WSOL-TRUMPCOIN) Market
+GF4NHQJ2GvN3ZdXiSVSXiC4jGhMmCXVFVhkYvfWkr6W5,Meteora (TRUMPCOIN-WSOL) Market
+6DAy1wRthSkpo5k9PAAD4NxKVTdvD2AAYjKynymR4q4H,Pump.fun (TRUMPCOIN) Bonding Curve
+72wBmn2sfZJ5FJMyirq6tMAUf1Nkw7XpokkY4JN9XgMa,Meteora (TRUMPCOIN-WSOL) Market