running

src/code/tamper_Dune_get_history_token_holders.js

@@ -140,7 +140,7 @@ async function clickButton_get_data() {
 
   let query_id = window.location.pathname.slice(1).split('/').pop();
 
-  funcDownload(res_data_get_dune_data, `${page_name}_${query_id}_${input_value}.csv`);
+  funcDownload(res_data_get_dune_data, `${page_name}_${input_value}.csv`);
 
 
 }
@@ -158,7 +158,7 @@ var ele_input;
   var mydivbox = document.createElement("div");
 
   ele_input = document.createElement("input") //创建一个input文本框
-  ele_input.value = "xx_xx";
+  ele_input.value = "xx_xx_xx";
   ele_input.className = "tam-myinput"
   ele_input.type ="text";
   ele_input.style.width = "90px";

src/code/tamper_Dune_get_tokenswap_top_BuyOrDiff_amount.js

@@ -140,7 +140,7 @@ async function clickButton_get_data() {
 
   let query_id = window.location.pathname.slice(1).split('/').pop();
 
-  funcDownload(res_data_get_dune_data, `${page_name}_${query_id}_${input_value}.csv`);
+  funcDownload(res_data_get_dune_data, `${page_name}_${input_value}.csv`);
 
 
 }
@@ -148,7 +148,7 @@ async function clickButton_get_data() {
 
 var res_data_get_dune_data = [];
 var page_name = 'dune_swap_total';
-var input_value="xx_xx";
+var input_value="xx_xx_xx";
 var ele_input;
 (async function () {
   console.log("enter load");
@@ -158,7 +158,7 @@ var ele_input;
   var mydivbox = document.createElement("div");
 
   ele_input = document.createElement("input") //创建一个input文本框
-  ele_input.value = "xx_xx";
+  ele_input.value = "xx_xx_xx";
   ele_input.className = "tam-myinput"
   ele_input.type ="text";
   ele_input.style.width = "90px";

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓/dune_holders_4283404_00_0109日0100.csv

@@ -1,26 +1,47 @@
 holder_owner,domains_owned,amount_M,pct_supply
+5Q544fKrFoe6tsEbD7S8EmxGTJYAKtTVhAW5Q5pge4j1,rayidumauthority.sol,81.2,8.12
 Cet3TXGhtFcBo6vX9kfYjbtNyANbCWqwVC6G1QioKUhc,,29.72,2.97
 7m2XQP2NDebaEKDhegj7WYpZBmrKF5yhiVi1JZU8cwoj,,24.19,2.42
+68WLD7YzwjqBoCEVuJ3rjVbaPaAog6ATCkjUTpNqiMNF,solcon.sol,20,2
 4ttYJgVcZMMXFsTPBAEKVUEDUshvsXqgvfUv3W7bdv7e,,15.1,1.51
 14qLxu9XDQtc2pgnxX2SSdWPpnuVrEqccgwmATH1khcY,,14.78,1.48
 Hh6XswyZK9WPUbb6csqi7YggDqYTstKkHFvvh7PFx7kw,,14.36,1.44
+DAr4CE2rFgvtDHSFG75rEhmxmqgfEQYir91RGY458pkZ,,13.59,1.36
+CC1xmUJCvq4wPzHSNKVHfMV9T3vRjHTZnzvW9VLVJ4gs,,13.18,1.32
+GDwiC6S14topnYmWquQb6JdQxrRsvtH7N3K8UCLZmVrk,,12.34,1.23
 HjUrUJi7zUCXu6NpXBUujYsoG5TJsXkPUrYJdWMKVtts,,12.34,1.23
 5PZs2fmTJpjaMhYbdAH6uEx7Jbbhy2RdAdkNnPw83H3a,,11.81,1.18
+FH8xnCwgoQgk3nXuePEdxYB5SZDTWSZav27smVcHjN1q,,11.8,1.18
+ET3VpuSMr15ufn4z9a5kwHhyLRse2H96TY2HXVKZxCJM,,11.73,1.17
+GD9Q87EY9guqAqkxgNwiqoqUKmBs958tcFEkQ8q1HD1N,,11.43,1.14
+5pW6xYQZxZLs67eKuurGcTVm2nHqFcDsiuxesqagoW6V,,10.77,1.08
 7V7BFFLNgHhrJq7Udm9Zi26Tbgvytg9GWKLdDLRzf4z6,,10.11,1.01
 BC8yiFFQWFEKrEEj75zYsuK3ZDCfv6QEeMRif9oZZ9TW,,10,1
 6QfdT1XZauUMCJU3SaN2d2aqdoFa4TDVNmxJig52ZGZT,,9.85,0.99
+3M2dQzm6kz9ChDfaeX3xxqzn8BdeLjJEJv9JBd9WG2Jx,,9.34,0.93
 6xMPaDn8jZ1r68BzXAunvumU8BTG2vDbgemNkWwRd1Fo,,9.25,0.93
 3UFHt8qqRtHM2hZaCYRGKaF1gYfePcRYZFo2CdaadfMa,,9.15,0.92
-E9EpirpCKqJKnUTfhpUKzDKKFzVd7Q8RjjbdRVjPxXWX,,8.01,0.8
+Aewfbg61nn3SrFikqTsX2c8nZM8DXVbG7jpUp1Qwg22H,,8.37,0.84
+3kZ7ZYjUGSFMhXbQ3twdCmqP1cgCjjBbZHRd8eXFFaja,,8.36,0.84
 3Q7bWprdJZ5JUDoy2wBAcoKzgp7mZ3ChPaR8o3SUf7EN,,8.01,0.8
+E9EpirpCKqJKnUTfhpUKzDKKFzVd7Q8RjjbdRVjPxXWX,,8.01,0.8
+27NdJihiGpbg1kgHwAjk4UDUScTjEg7BDTo6urHjB3Cr,,7.6,0.76
+BTJk52ULuadhdzYWpK2io1urPVfqEpqoH3sS4yg4b8Z2,,7.43,0.74
 2fnRmNSYMTuyGw58UdZTrEgb2NmjNXvyRzqU9FJG31Q6,,7.28,0.73
 GE5PnwZTgWw1YqVGno2QPWqie6aLjswNeGFhiF1nDg3k,,6.74,0.67
 9r7p8uQiwRMRqQwAGDHUQ1Lmtjt4auNTxK7TgWg75J7C,,6.26,0.63
+4N41X5S1GaqKQhZvqQsE7aKCpEm2xhswKTznQoxuJEmv,,6.2,0.62
+FT78Vsmv2hWj8PK9HN1rtGEdKtt4Ajd2RSsxhrUt5nVG,,6.06,0.61
+FML97BaUTVJX15T8tCU5LUbcpvJkHQ5RcZXmV354PzRt,,6,0.6
+,,5.61,0.56
 CpVNBP8bbFvT7Yt3zwTnCaXt26XojQxduNXaMz57DGLf,,5.51,0.55
+77RWuJjzTgswsgUafvhSNDAbGxHcUHQPZ3ogpvNr3eB,,5.49,0.55
 3ddwpbz7aXr6ZD7ybMZcUMTm76pgeszJekSkLdS931EH,,5.35,0.53
 4KaVawPKmG1RZCv698XD9YzRsJcFPXrEoYnZbdZmfFFq,,5.25,0.53
 HRL9mVQav58fLGJpN16qT55nXuk8S7Lx33Gvw2SHRHK5,,5.21,0.52
 AeqgApsq3CoopttTpohuLiXj53EauJ78ophVvy8hrfkt,,5.2,0.52
+51pcMTBEKwiKRVrCV5EmmHazsWetGDUxKkxoc5r3248,,5.19,0.52
+5pKTV6EhcyFQPBbEDEATyDiUHM4kDZddtopRLq7o8kJj,,5.15,0.52
 66VSSN5FiwkDXCSJ5ydNCZm8RD1DwVKXuwaeFBGJ5fKg,,5.05,0.51
 3qTNSWq6o7hfb7RgSz6GzxDMNLgrjVuwKqFD2kJznuRB,,4.76,0.48
 3fZcDhWXTBvNMkgzkyMmqxMizfysnvFZw5CzR7pnuxRz,,4.5,0.45
@@ -28,12 +49,53 @@ F8pWbg5zGXnzghmAtNW9SQPQYwP1RkyzsHs6KZTBh7Dg,,4.45,0.45
 DuDEvMussH5iKvBPaa6mSVyJY39fPQKNEPUyT5c19S2x,,4.28,0.43
 98oWTwQBBEyhhLMcUrgy2gta7zmVHZavQmcS8z98PDHe,,4.22,0.42
 EPS4j3XEBas8okzQCe2CeuwKf6w6aif5ED4GMj3mqv4r,,4.22,0.42
+5Xu8p9emHTkuNQkPN6D2ygkJypWo1nbUzbjYHUfXcNuR,,4.19,0.42
+Hk7gzrcANZpqBnYCHeBEuxFk18HLu9Tey7TrNroyPVK7,,4.13,0.41
 Goc5dPcEM94cFW97k92XNoT45vR89isxSEjxqE3VTvhv,,4.12,0.41
+A3wcLYiBQdW5Uf5TXxbvuueJYh8pfAy57cbuE5fQJBeS,,4.1,0.41
+D6DLAnP1NnimNYuHNDhJZfGkD1TmiKENWG2PrdhL8qLP,,4.08,0.41
 CGnKC5zME8G36ftGLq1wrmQxw3SUgzVojJmVNXpidC6k,,4,0.4
+6pcdBbgeGz8Lu1RAzJsPdiYAZzTAxFU6iBeoqS2XUe8Y,,3.98,0.4
+D9n31F7pocFeN67Gg1VwAbYrP9Xuw8vXPAR6iay6rdd,,3.9,0.39
 Gy5GSoHmrfnv49TZHku1bxNv2Dy4tFDjgm7vGbmp8S2h,,3.85,0.39
+CfpFiySF5c63fvgtjjgbefjS2JSATJYeqp8qFQX76hKT,,3.85,0.39
+9GFHYeNnwKvg2xURguFrJLfCPF6QHrGJkqkHEzTXDXgL,,3.84,0.38
+DdrNPB8JMzqeSc9p3EqCSXoeceL6Q6YLd1soUfgAJt8B,,3.81,0.38
+FSMvnfYGYP2RkEzf5zUCBWdJEgihMRgotjyYyxFrq87u,,3.68,0.37
+658EiuPsa86Y9HyGmpFY5Fk6GBrKTRRBTiArrMXQUtgP,,3.65,0.37
+4wZygF2Q8fq6j4p3NJDoHQcg6tEg4bApXWogziCZwfLJ,,3.49,0.35
 3v6XiVAbays5FiZy6sWuriLHfC77EAX9mtB57Hf8VmpQ,,3.45,0.35
+FQHea1o569UxLtNomgp9tCzZiiLsBjRuPDtTdrhjjtmj,,3.3,0.33
+7cz18prwmspzZFsVYRzQSpWvQDtUByp5eWbb5J7Tv4LD,,3.3,0.33
+7imSeksquP3rLM1h5WHDFq4Zro7DTKLDqKZxPmSrtUuC,,3.27,0.33
+3fX12nNRyZ2P8C6NrRmJZSfHEbLEtuiUNRyYuqitmcyW,,3.25,0.33
+4ZtatmZS7ZzJTQuLYU3QXSSpXpBaSkk56HAErey9RLVY,,3.23,0.32
+61U1YZfgBECnQSxDepRGMYr4NK1ugPFoPhYMyiCqshkf,,3.13,0.31
+6xHJJhNSYbyuaspAiGbg9qZnD2tXdK5gfwAZrAQBSHzU,,3.05,0.31
+8QYivc4fK7qWfpsfYbNqTUrEhvnA5qq9Kf9yLdBxLu9C,,3,0.3
+Bkj3vRjo9YMchFqNxjmec1y3VQXhCEvFxTjYTBU6n7W1,,3,0.3
+D2wBctC1K2mEtA17i8ZfdEubkiksiAH2j8F7ri3ec71V,,2.97,0.3
+Fv4wEy1uwyRzqbwE2Kb23dBbbhMtKgMBzUqvoc2Q8gL7,,2.93,0.29
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+6aLLH8jjU4gJjZKNvigTYsdmSynbX746pKCGRbmEwx2w,,2.69,0.27
+8AB7rmHfQqexxmeQACwiEb6fXXJ5wqsrAdVgTK4kJoQx,,2.69,0.27
+9aRAaT9HbxtFgjsMirRQfu1CZd6x31uyJdgFqnhFvfRh,,2.66,0.27
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+6EAaPqnwT54YnYfCRyupN8rK25i2hbguBFcbfVQ8hGWj,serge.sol,2.64,0.26
+ByTHE5eoBJxn9hvpHqTL8VFXs8hc8oiLsDud73Y9cDtw,,2.5,0.25
+CDE5AN6988EEtiQa81us2myGkorpU9VJGwHxbAYF73QN,,2.49,0.25
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 9wNu1SLCfouEsR4B8PCck6p15mcSgun7k5KEKDw43Cyu,,2.37,0.24
-3nxNfz8fx4DPW37i97Qs4Ue4Z2zVinEFeDAAymwtEtYd,,1.81,0.18
-Gg6PuMT9qs53QiSrrJsnLyh51xedbzKAV5JiQ9qHPQ7a,,1.02,0.1
-HmgJG6XqV4HjDEXG47gTx8oobq6Y5W11oeYBD86N2RPR,,0.38,0.04
-ETXkmPKnSx8jPqGvvES5Ss3k9q811r7BuqPPwcKZn2pv,,0,0
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+5hFhbvrCbsbMcD6XmFjfmbuq4HQgngLeUVRaD1JvDzAT,,1.96,0.2

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓/dune_special_holders_4283404_00_0109日0100_special.csv

@@ -0,0 +1,39 @@
+holder_owner,domains_owned,amount_M,pct_supply
+Cet3TXGhtFcBo6vX9kfYjbtNyANbCWqwVC6G1QioKUhc,,29.72,2.97
+7m2XQP2NDebaEKDhegj7WYpZBmrKF5yhiVi1JZU8cwoj,,24.19,2.42
+4ttYJgVcZMMXFsTPBAEKVUEDUshvsXqgvfUv3W7bdv7e,,15.1,1.51
+14qLxu9XDQtc2pgnxX2SSdWPpnuVrEqccgwmATH1khcY,,14.78,1.48
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+5PZs2fmTJpjaMhYbdAH6uEx7Jbbhy2RdAdkNnPw83H3a,,11.81,1.18
+7V7BFFLNgHhrJq7Udm9Zi26Tbgvytg9GWKLdDLRzf4z6,,10.11,1.01
+BC8yiFFQWFEKrEEj75zYsuK3ZDCfv6QEeMRif9oZZ9TW,,10,1
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src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/001_merge_get_all_holders_add.py

@@ -0,0 +1,62 @@
+
+# 将不同时间的holders 的top55 地址全部合并到一个excel中
+import pandas as pd
+import sys
+
+ 
+
+arr_file = []
+merged_df = None
+
+ 
+
+def merge_df_fun():
+ 
+    
+    global arr_file , merged_df , domains_df,token_add
+    for index, file in enumerate(arr_file):
+               
+
+        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[['holder_owner','amount_M']]
+        # cur_df = merged_df[cur_mask].reset_index(drop=True)
+        cur_df=cur_df.sort_values(by=['amount_M'],ascending = False)
+        cur_df =cur_df.head(55)
+        cur_df=cur_df[['holder_owner','domains_owned']]
+
+
+        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'])
+    merged_df.to_excel('all_holdrs.xlsx', index=False)
+
+ 
+    
+         
+ 
+
+if __name__ == "__main__":
+ 
+    if len(sys.argv)<3:
+        print("args queshao ")
+        sys.exit(1)
+    arr_file =sys.argv[1:]
+    arr_file = [file for file in arr_file if "dune_holders" in file and file.endswith(".csv")]
+ 
+    arr_file.sort()
+    print("read file: ",arr_file)
+    merge_df_fun()
+ 
+    

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/002_merge_get_special_add_hold_info.py

@@ -0,0 +1,76 @@
+
+# 将special add 在不同时间持仓的csv合并为一个excel
+import pandas as pd
+import sys
+
+ 
+
+arr_file = []
+merged_df = None
+
+domains_df= None
+
+
+
+
+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.dropna(subset=['holder_owner'])
+
+        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 =cur_df.astype({
+            "amount_M":float,
+        })
+
+        cur_df.rename(columns={'amount_M': f'amount_M_{date_range}'}, inplace=True)
+        arr_date_range.append(f'{date_range}')
+        arr_col_pct.append(f'amount_M_{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')
+
+    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.to_excel('special_add_hold_info.xlsx' ,index=False)
+
+ 
+ 
+        
+if __name__ == "__main__":
+ 
+    if len(sys.argv)<3:
+        print("args queshao ")
+        sys.exit(1)
+    token_add = sys.argv[1]
+    arr_file =sys.argv[2:]
+    arr_file = [file for file in arr_file if "dune_holders_add" in file and file.endswith(".csv")]
+ 
+    arr_file.sort()
+    print("read file: ",arr_file)
+    merge_df_fun()
+ 
+    

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

@@ -0,0 +1,110 @@
+
+#  # 将不同时间的holders 合并为一个excel 并且一个时间一个sheet
+import pandas as pd
+import sys
+
+import datetime
+import pytz
+
+arr_file = []
+merged_df = None
+domains_df = None
+token_add = ""
+arr_date_range =[]
+arr_df = []
+def get_nowdate():
+
+    timestamp = datetime.datetime.now().timestamp()
+    # print(timestamp)
+    dt = datetime.datetime.fromtimestamp(timestamp)
+    tz = pytz.timezone('Asia/Shanghai')
+    normal_time_str = dt.astimezone(tz).strftime('%Y-%m-%d %H:%M:%S')
+    time_str = dt.astimezone(tz).strftime('%m%d%H%M')
+    return time_str
+
+
+
+def merge_df_fun():
+    global arr_date_range ,arr_df
+    
+    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.dropna(subset=['holder_owner'])
+
+        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 =cur_df.astype({
+            "amount_M":float,
+        })
+        cur_df = cur_df.round(2)
+        cur_df.rename(columns={'amount_M': f'amount_M_{date_range}'}, inplace=True)
+        arr_date_range.append(f'{date_range}')
+        arr_col_pct.append(f'amount_M_{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')
+
+    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)
+    
+    
+    arr_df.append(merged_df)
+    
+    for col_pct in arr_col_pct:
+        cur_df=merged_df.sort_values(by=[col_pct],ascending = False)
+        cur_df =cur_df.head(55)
+        arr_df.append(cur_df)
+
+  
+def save_to_excel():
+    global arr_date_range ,arr_df
+    time_str = get_nowdate()
+
+
+    with pd.ExcelWriter(f'merge_holders_{time_str}.xlsx') as writer:
+        for idx ,cur_df in enumerate(arr_df):
+            if idx==0:
+                cur_df.to_excel(writer, sheet_name='Sheet1', index=False)
+            else:
+                cur_df.to_excel(writer, sheet_name=f'{arr_date_range[idx-1]}', index=False)  
+    return
+
+
+ 
+
+         
+
+def makeurl( url,url_show):
+    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
+
+if __name__ == "__main__":
+ 
+    if len(sys.argv)<3:
+        print("args queshao ")
+        sys.exit(1)
+    token_add = sys.argv[1]
+    arr_file =sys.argv[2:]
+    arr_file = [file for file in arr_file if "dune_holders" in file and file.endswith(".csv")]
+ 
+    arr_file.sort()
+    print("read file: ",arr_file)
+    merge_df_fun()
+    save_to_excel()
+ 
+    

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/004_mergespecial_add_hold_info.py

@@ -0,0 +1,91 @@
+
+# 将不同时间的add 的hold信息合并在一起 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 datetime
+import pytz
+from openpyxl.styles import *
+# from openpyxl.styles.colors import RED, GREEN
+arr_file = []
+merged_df = None
+ 
+token_add = ""
+
+
+def makeurl( url,url_show):
+    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
+
+
+
+
+def get_nowdate():
+
+    timestamp = datetime.datetime.now().timestamp()
+    # print(timestamp)
+    dt = datetime.datetime.fromtimestamp(timestamp)
+    tz = pytz.timezone('Asia/Shanghai')
+    normal_time_str = dt.astimezone(tz).strftime('%Y-%m-%d %H:%M:%S')
+    time_str = dt.astimezone(tz).strftime('%m%d%H%M')
+    return time_str
+
+ 
+ 
+arr_df = []
+
+merged_hodlers_file = ""
+merged_special_ad_hold_info_file=""
+def merge_swap_df_fun_02():
+    global merged_hodlers_file , merged_special_ad_hold_info_file
+    global arr_df
+    merged_hodlers_excel = pd.ExcelFile(merged_hodlers_file)
+    arr_sheet_names = list(merged_hodlers_excel.sheet_names)
+    arr_sheet_names.pop(0)
+
+    merged_df= pd.read_excel('special_add_hold_info.xlsx',engine='openpyxl')
+    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)
+    
+  
+    
+    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['holder_owner'].isin(holder_df['holder_owner'])].reset_index(drop=True)
+        cur_df = cur_df.set_index('holder_owner').reindex(holder_df['holder_owner']).reset_index()
+
+        arr_df.append(cur_df)   
+
+
+    time_str = get_nowdate()
+
+
+    with pd.ExcelWriter(f'merge_special_add_holders_{time_str}.xlsx') 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)
+   
+ 
+  
+
+
+def makeurl( url,url_show):
+    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
+
+if __name__ == "__main__":
+    if len(sys.argv)<3:
+        print("args queshao ")
+        sys.exit(1)
+    token_add = sys.argv[1]
+    merged_hodlers_file = sys.argv[2]
+    merged_special_ad_hold_info_file = sys.argv[3]
+    merge_swap_df_fun_02()

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/005_merge_swap_transfer_row.py

@@ -0,0 +1,79 @@
+# 将dune 上 special的add 的swap和transfer合并在一起
+import pandas as pd
+import sys
+
+import datetime
+import pytz
+
+def makeurl( url,url_show):
+    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
+
+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['shanghai_date'] = df['shanghai_date'].dt.tz_convert(utc_plus_8)
+    # # 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
+ 
+swap_row_file = ''
+transfer_row_file = ''
+def main():
+    df_swap_row = pd.read_csv(swap_row_file,dtype=object)
+    df_swap_row=df_swap_row.astype({'token_swap_M':float ,
+                                    'eth_swap_amount':float ,
+                                    'block_slot':float,
+                                    'pri':float})
+
+    df_transfer_row = pd.read_csv(transfer_row_file,dtype=object)
+    df_transfer_row = df_transfer_row.astype({'token_transfer_M':float , 'block_slot':float})
+
+
+    df_swap_row.rename(columns={'token_swap_M': 'token_M' }, inplace=True)
+    
+    
+    df_swap_row = utc_2_shanghaidate(df=df_swap_row, utc_time_col='block_time')
+    
+    df_transfer_row = utc_2_shanghaidate(df=df_transfer_row, utc_time_col='block_time')
+ 
+
+    # 创建两个DataFrame，一个用于'from'列，另一个用于'to'列
+    df_from = df_transfer_row[[ 'tx_id',	'block_time',	'block_slot',	'token_transfer_M' ,'from_owner']].copy()
+    df_from.rename(columns={'from_owner': 'trader_id' , 'token_transfer_M':'token_M'}, inplace=True)
+    df_from['token_M'] = -df_from['token_M']
+
+    df_to = df_transfer_row[[ 'tx_id',	'block_time',	'block_slot',	'token_transfer_M' ,'to_owner']].copy()
+    df_to.rename(columns={'to_owner': 'trader_id','token_transfer_M':'token_M'}, inplace=True)
+    df_to['token_M'] = df_to['token_M']
+
+    # 合并两个DataFrame
+    df_transfer_row = pd.concat([df_from, df_to], ignore_index=True)
+    df_transfer_row['action_type'] = 't'
+    df_transfer_row['token_M'] = df_transfer_row['token_M'].round(4)
+
+    merged_df =  pd.concat([ df_swap_row , df_transfer_row], ignore_index=True)
+    merged_df = merged_df.sort_values(by=['trader_id' , 'block_slot'],ascending=True)
+    
+
+    merged_df["oklink"] = merged_df.apply(lambda ser: makeurl(
+     f"https://www.oklink.com/zh-hans/sol/tx/{ser['tx_id']}" , "oklink" ), axis=1)
+
+    merged_df.to_excel(  'dune_swap_transfer_row.xlsx'  ,index=False)
+    
+ 
+    return
+
+
+if __name__ == "__main__":
+    if len(sys.argv)<3:
+        print("args queshao ")
+        sys.exit(1)
+    swap_row_file = sys.argv[1]
+    transfer_row_file = sys.argv[2]
+    main()

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/dune_holders_add_00_0108日2200.csv

@@ -0,0 +1,67 @@
+holder_owner,domains_owned,amount_M,pct_supply
+5Q544fKrFoe6tsEbD7S8EmxGTJYAKtTVhAW5Q5pge4j1,buy-achipompomhat.sol,51.73,5.17
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+68WLD7YzwjqBoCEVuJ3rjVbaPaAog6ATCkjUTpNqiMNF,dukeofdonk.sol,15.5,1.55
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+CfpFiySF5c63fvgtjjgbefjS2JSATJYeqp8qFQX76hKT,,0,0

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/dune_holders_add_00_0109日0100.csv

@@ -0,0 +1,68 @@
+holder_owner,domains_owned,amount_M,pct_supply
+5Q544fKrFoe6tsEbD7S8EmxGTJYAKtTVhAW5Q5pge4j1,buy-achipompomhat.sol,81.2,8.12
+Cet3TXGhtFcBo6vX9kfYjbtNyANbCWqwVC6G1QioKUhc,,29.72,2.97
+7m2XQP2NDebaEKDhegj7WYpZBmrKF5yhiVi1JZU8cwoj,,24.19,2.42
+68WLD7YzwjqBoCEVuJ3rjVbaPaAog6ATCkjUTpNqiMNF,dukeofdonk.sol,20,2
+4ttYJgVcZMMXFsTPBAEKVUEDUshvsXqgvfUv3W7bdv7e,,15.1,1.51
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src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/dune_holders_add_00_0110日1000.csv

@@ -0,0 +1,80 @@
+holder_owner,domains_owned,amount_M,pct_supply
+5Q544fKrFoe6tsEbD7S8EmxGTJYAKtTVhAW5Q5pge4j1,bonklanatoken.sol,119.48,11.95
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src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/dune_holders_add_00_0110日_2130.csv

@@ -0,0 +1,81 @@
+holder_owner,domains_owned,amount_M,pct_supply
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src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/dune_holders_add_00_0116日0100.csv

@@ -0,0 +1,81 @@
+holder_owner,domains_owned,amount_M,pct_supply
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src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/dune_holders_xx_00_0109日0100.csv

@@ -0,0 +1,101 @@
+holder_owner,domains_owned,amount_M,pct_supply
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+2h4TpnJQxPBBhjTtPmCEsbpQAnyQRvu2tGfoRcH2TpMF,,1.98,0.2
+5hFhbvrCbsbMcD6XmFjfmbuq4HQgngLeUVRaD1JvDzAT,,1.96,0.2

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/dune_holders_xx_00_0110日1000.csv

@@ -0,0 +1,101 @@
+holder_owner,domains_owned,amount_M,pct_supply
+5Q544fKrFoe6tsEbD7S8EmxGTJYAKtTVhAW5Q5pge4j1,bonklanatoken.sol,119.48,11.95
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+,,5.61,0.56
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+Eun9JS3anodrkcwAVHfqRfPnz71vkhBG2zdPn6WcioAN,,1.79,0.18

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/dune_holders_xx_00_0110日2130.csv

@@ -0,0 +1,101 @@
+holder_owner,domains_owned,amount_M,pct_supply
+5Q544fKrFoe6tsEbD7S8EmxGTJYAKtTVhAW5Q5pge4j1,bonklanatoken.sol,129.31,12.93
+Cet3TXGhtFcBo6vX9kfYjbtNyANbCWqwVC6G1QioKUhc,,29.72,2.97
+4k22yKKbCsrq7QhCd89ZXFcxhSbGmszA92H2KhthpgKq,,29.7,2.97
+8oTVjgucrArfQgyGFy2AiEKz6QDhW6ATTJQ1nSkG3wAr,,24.76,2.48
+7m2XQP2NDebaEKDhegj7WYpZBmrKF5yhiVi1JZU8cwoj,,24.19,2.42
+dR4GCDofsA6ZpBqzQpQJeob4msVspyASATbqW4sC4YM,,21.69,2.17
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+,,5.61,0.56
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src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/dune_swap_total_chicang_0108日1300.csv

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src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/dune_swap_total_chicang_0109日0100.csv

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src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/dune_swap_total_chicang_0110日1000.csv

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src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/dune_swap_total_chicang_0113日1100.csv

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src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/dune_swap_total_chicang_0114日1500.csv

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+68WLD7YzwjqBoCEVuJ3rjVbaPaAog6ATCkjUTpNqiMNF,-8.38,43.12,51.49,67.17,127.83,77,4,1.558,2.483
+3M2dQzm6kz9ChDfaeX3xxqzn8BdeLjJEJv9JBd9WG2Jx,-9.34,0,9.34,0,23.17,0,1,0,2.482
+8MpdTFumfahxWNjifdDKDomxGX5rs9KRX7XrCrxKiovL,-11.13,0,11.13,0,101.81,0,19,0,9.146

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

@@ -0,0 +1,64 @@
+import pandas as pd
+import sys
+import matplotlib.pyplot as plt
+import math
+chicang_file = ''
+df_chouma = None
+def draw_chouma():
+    global df_chouma ,chicang_file
+    df_grouped = df_chouma.groupby('avg_b_pri').sum()['chicang'].reset_index()
+   
+    max_pri = df_chouma['avg_b_pri'].max()
+    min_pri = df_chouma['avg_b_pri'].min()
+
+ 
+    # 自定义价格区间
+    price_range = [pri*0.5 for pri in range(math.floor(min_pri),math.ceil(max_pri)*2)]
+  
+ 
+    # # 根据价格区间计算每个区间的总持仓量
+    df_grouped['区间'] = pd.cut(df_grouped['avg_b_pri'], bins=price_range)
+    grouped_by_interval = df_grouped.groupby('区间').sum()
+
+    # 绘制筹码分布图
+    fig, ax = plt.subplots(figsize=(10, 6))
+    grouped_by_interval['chicang'].plot(kind='bar', ax=ax, color='skyblue')
+    # 设置图表标题和标签
+    ax.set_title('chouma-fenbu')
+    ax.set_xlabel('chichang-pri')
+    ax.set_ylabel('chicang-amount')
+    plt.xticks(rotation=45)
+
+    plt.ylim(0, 400)
+    # plt.yticks(np.arange(0, 401, 50))
+ 
+    # 保存图像到文件
+    
+    output_image_path = 'drop_neg'+ chicang_file.split('_')[-1].replace('.csv','') + f'.png'  # 替换为你希望保存图像的路径和文件名
+    plt.savefig(output_image_path, bbox_inches='tight', dpi=300)  # 使用高分辨率保存图像
+    
+    
+
+def get_chouma_chicang():
+    global chicang_file,df_chouma
+    df = pd.read_csv(chicang_file)
+    df_neg_hold = df[df['li_lun_hold']<0  ].reset_index(drop=True)
+    df_pos_hold = df[df['li_lun_hold']>0].reset_index(drop=True)
+    sum_neg_hold = abs(df_neg_hold['li_lun_hold'].sum())
+    sum_pos_hold = abs(df_pos_hold['li_lun_hold'].sum())
+    df_pos_hold['chicang'] = df_pos_hold['li_lun_hold']*(1-sum_neg_hold/sum_pos_hold) 
+
+    df_chouma = df_pos_hold
+
+
+# swap_trader_id,li_lun_hold,token_b_M,token_s_M,eth_b,eth_s,b_count,s_count,avg_b_pri,avg_s_pri
+
+if __name__ == "__main__":
+ 
+    if len(sys.argv)<2:
+        print("args queshao ")
+        sys.exit(1)
+    chicang_file = sys.argv[1]
+    get_chouma_chicang()
+    draw_chouma()
+ 

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

@@ -0,0 +1,100 @@
+import pandas as pd
+import sys
+import matplotlib.pyplot as plt
+import math
+chicang_file = ''
+df_chouma = None
+sum_neg_hold = 0
+sum_pos_hold = 0
+def set_text(ax):
+    variable_values = {
+         'sum_pos': round(sum_pos_hold,1),
+    'sum_neg': round(sum_neg_hold,1),
+   
+    'total': round(sum_pos_hold - sum_neg_hold,1)
+    }
+
+    # 设置文本的位置（例如右上角）
+    text_position = (0.8, 0.9)  # 根据你的图表调整这个位置
+
+    # 在指定位置添加文本
+    offset = 0
+    for key, value in variable_values.items():
+        text = f'{key}: {value}'
+        ax.text(text_position[0], text_position[1] - offset, text,transform=ax.transAxes, fontsize=10, color='black', 
+                bbox=dict(facecolor='lightgrey', edgecolor='black', boxstyle='round,pad=0.5'))
+        offset += 0.07  # 每次向下移动一行
+
+
+def calculate_midpoint(interval):
+    return (interval.left + interval.right) / 2
+
+def draw_chouma():
+    global df_chouma ,chicang_file
+    df_grouped = df_chouma.groupby('avg_b_pri').sum()['chicang'].reset_index()
+   
+    max_pri = df_chouma['avg_b_pri'].max()
+    min_pri = df_chouma['avg_b_pri'].min()
+
+ 
+    # 自定义价格区间
+    price_range = [pri*0.5 for pri in range(math.floor(min_pri),math.ceil(max_pri)*2)]
+ 
+    # # 根据价格区间计算每个区间的总持仓量
+    df_grouped['区间'] = pd.cut(df_grouped['avg_b_pri'], bins=price_range)
+ 
+    grouped_by_interval = df_grouped.groupby('区间').sum()
+  
+    grouped_by_interval = grouped_by_interval.reset_index()
+    grouped_by_interval['mid_pri'] = grouped_by_interval['区间'].apply(calculate_midpoint)
+   
+    grouped_by_interval.set_index(['区间'],inplace=True)
+   
+   
+    # 绘制筹码分布图
+    fig, ax = plt.subplots(figsize=(10, 6))
+    # ax.bar(grouped_by_interval['mid_pri'], grouped_by_interval['chicang'],   align='center', color='skyblue')
+    grouped_by_interval['chicang'].plot(kind='bar', ax=ax, color='skyblue')
+    # 设置图表标题和标签
+    ax.set_title('chouma-fenbu')
+    ax.set_xlabel('chichang-pri')
+    ax.set_ylabel('chicang-amount')
+    plt.xticks(rotation=45)
+   
+
+    plt.ylim(0, 400)
+    set_text(ax=ax)
+    # plt.yticks(np.arange(0, 401, 50))
+ 
+    # 保存图像到文件
+    
+    output_image_path =  chicang_file.split('_')[-1].replace('.csv','') + f'.png'  # 替换为你希望保存图像的路径和文件名
+    plt.savefig(output_image_path, bbox_inches='tight', dpi=300)  # 使用高分辨率保存图像
+    
+ 
+
+
+def get_chouma_chicang():
+    global chicang_file,df_chouma
+    global sum_neg_hold ,sum_pos_hold
+    df = pd.read_csv(chicang_file)
+    df_neg_hold = df[df['li_lun_hold']<0  ].reset_index(drop=True)
+    df_pos_hold = df[df['li_lun_hold']>0].reset_index(drop=True)
+    sum_neg_hold = abs(df_neg_hold['li_lun_hold'].sum())
+    sum_pos_hold = abs(df_pos_hold['li_lun_hold'].sum())
+    df_pos_hold['chicang'] = df_pos_hold['li_lun_hold'] 
+
+    df_chouma = df_pos_hold
+
+
+# swap_trader_id,li_lun_hold,token_b_M,token_s_M,eth_b,eth_s,b_count,s_count,avg_b_pri,avg_s_pri
+
+if __name__ == "__main__":
+ 
+    if len(sys.argv)<2:
+        print("args queshao ")
+        sys.exit(1)
+    chicang_file = sys.argv[1]
+    get_chouma_chicang()
+    draw_chouma()
+ 

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

@@ -0,0 +1,181 @@
+
+# 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 datetime
+import pytz
+from openpyxl.styles import *
+# from openpyxl.styles.colors import RED, GREEN
+arr_file = []
+merged_df = None
+ 
+token_add = ""
+
+def get_nowdate():
+
+    timestamp = datetime.datetime.now().timestamp()
+    # print(timestamp)
+    dt = datetime.datetime.fromtimestamp(timestamp)
+    tz = pytz.timezone('Asia/Shanghai')
+    normal_time_str = dt.astimezone(tz).strftime('%Y-%m-%d %H:%M:%S')
+    time_str = dt.astimezone(tz).strftime('%m%d%H%M')
+    return time_str
+
+def utc_2_shanghaidate(df , utc_time_col ):
+# 将字符串转换为UTC时间的datetime对象
+    df['shanghai_date'] = 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['shanghai_date'] = df['shanghai_date'].dt.tz_convert(utc_plus_8)
+    # 提取时间字符串
+    df['shanghai_date'] = df['shanghai_date'].dt.strftime('%Y-%m-%d %H:%M')
+
+    print(df)
+ 
+
+def merge_swap_df_fun_01():    
+    # style = pd.DataFrame().style
+    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.dropna(subset=['swap_trader_id'])
+        cur_df =cur_df.astype({
+            "li_lun_hold":float,
+            "token_b_M":float,
+            "token_s_M":float,
+            "eth_b":float,
+            "eth_s":float,
+            "b_count":float,
+            "s_count":float,
+            "avg_b_pri":float,
+            "avg_s_pri":float
+        })
+
+
+         
+        chaged_cols = [f"{col}_{date_range}" for col in cur_df.columns  ]
+        chaged_cols[0] = "swap_trader_id"
+        cur_df.columns = chaged_cols
+
+        if index==0 :
+            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)
+# https://www.w3schools.com/colors/colors_hex.asp    
+
+yellow_fill = PatternFill(start_color='FFFF00', end_color='FFFF00', fill_type='solid')  # 黄色填充
+
+
+IndianRed_fill = PatternFill(start_color='CD5C5C', end_color='CD5C5C', fill_type='solid')  # 黄色填充
+LightCoralRed_fill = PatternFill(start_color='F08080', end_color='F08080', fill_type='solid')  # 黄色填充
+
+
+MediumSpringGreen_fill = PatternFill(start_color='00FA9A', end_color='00FA9A', fill_type='solid')  #  
+ForestGreen_fill = PatternFill(start_color='228B22', end_color='228B22', fill_type='solid')  #  
+SeaGreen_fill = PatternFill(start_color='2E8B57', end_color='2E8B57', fill_type='solid')  #  
+
+Orange_fill = PatternFill(start_color='FFA500', end_color='FFA500', fill_type='solid')  #  
+
+
+
+merged_hodlers_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)
+    
+    
+    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:
+        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]
+
+
+# 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_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:
+                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)
+                    cell.fill = MediumSpringGreen_fill
+            for target_column  in arr_col_token_b:
+                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)
+                    cell.fill = ForestGreen_fill
+ 
+            for target_column  in arr_col_sell_pri:
+                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)
+                    cell.fill = IndianRed_fill
+
+            for target_column  in arr_col_token_s:
+                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)
+                    cell.fill = LightCoralRed_fill
+
+            for target_column  in arr_col_li_lun_hold:
+                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)
+                    cell.fill = Orange_fill
+
+
+
+def makeurl( url,url_show):
+    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
+
+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.sort()
+    print("read file: ",arr_file)
+    merge_swap_df_fun_01()
+    merge_swap_df_fun_02()

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

@@ -0,0 +1,42 @@
+import pandas as pd
+import matplotlib.pyplot as plt
+
+# 假设你已经有了 grouped_by_interval 数据框
+# 这里我们模拟一些数据来演示
+data = {
+    '持仓价格': [10, 20, 30, 40, 50, 60, 70, 80],
+    '持仓量': [100, 200, 300, 400, 500, 600, 700, 800]
+}
+df = pd.DataFrame(data)
+
+# 自定义价格区间
+price_range = [0, 20, 40, 60, 80, 100]
+
+# 根据价格区间计算每个区间的总持仓量
+df_grouped = df.groupby(pd.cut(df['持仓价格'], bins=price_range)).sum()
+
+# 重置索引以便更容易处理
+df_grouped = df_grouped.reset_index()
+
+# 计算区间的中间值
+def calculate_midpoint(interval):
+    return (interval.left + interval.right) / 2
+
+# 新增一列“区间中间值”
+df_grouped['区间中间值'] = df_grouped['持仓价格'].apply(calculate_midpoint)
+
+print(df_grouped)
+
+# 绘制筹码分布图
+fig, ax = plt.subplots(figsize=(10, 6))
+ax.bar(df_grouped['区间中间值'], df_grouped['持仓量'], width=20, align='center', color='skyblue')
+
+# 设置图表标题和标签
+ax.set_title('筹码分布图')
+ax.set_xlabel('持仓价格区间')
+ax.set_ylabel('持仓总量')
+plt.xticks(rotation=45)
+
+# 显示图形
+plt.tight_layout()
+plt.show()

src/library/analysis_token/5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump/分析不同时间持仓_02/脚本教程.md

@@ -0,0 +1,21 @@
+
+1.dune get_history_token_holders_by_transfer_And_specialAdd 
+获取指定时间的top100 holders
+
+2.python merge_get_all_holders.py dune_holders*.csv 
+获取指定时间段top55的add 合并到 all_holdrs.xlsx中 
+
+
+
+3.dune get_history_token_holders_by_transfer_And_specialAdd 
+获取 all_holdrs的add在指定时间的持仓量
+
+4.python  merge_get_special_add_hold_info.py  token_add  dune_holders_add*.csv
+第一个参数是token_add 后面是 dune_holders_add 文件
+合并 all_holdrs 的持仓量为 special_add_hold_info.xlsx
+
+
+5.python    004_mergespecial_add_hold_info.py  token_add merge_holders.xlsx special_add_hold_info.xlsx 
+
+
+

src/library/analysis_token/get_dropneg_chicang.py

@@ -0,0 +1,64 @@
+import pandas as pd
+import sys
+import matplotlib.pyplot as plt
+import math
+chicang_file = ''
+df_chouma = None
+def draw_chouma():
+    global df_chouma ,chicang_file
+    df_grouped = df_chouma.groupby('avg_b_pri').sum()['chicang'].reset_index()
+   
+    max_pri = df_chouma['avg_b_pri'].max()
+    min_pri = df_chouma['avg_b_pri'].min()
+
+ 
+    # 自定义价格区间
+    price_range = [pri*0.5 for pri in range(math.floor(min_pri),math.ceil(max_pri)*2)]
+  
+ 
+    # # 根据价格区间计算每个区间的总持仓量
+    df_grouped['区间'] = pd.cut(df_grouped['avg_b_pri'], bins=price_range)
+    grouped_by_interval = df_grouped.groupby('区间').sum()
+
+    # 绘制筹码分布图
+    fig, ax = plt.subplots(figsize=(10, 6))
+    grouped_by_interval['chicang'].plot(kind='bar', ax=ax, color='skyblue')
+    # 设置图表标题和标签
+    ax.set_title('chouma-fenbu')
+    ax.set_xlabel('chichang-pri')
+    ax.set_ylabel('chicang-amount')
+    plt.xticks(rotation=45)
+
+    plt.ylim(0, 400)
+    # plt.yticks(np.arange(0, 401, 50))
+ 
+    # 保存图像到文件
+    
+    output_image_path = 'drop_neg'+ chicang_file.split('_')[-1].replace('.csv','') + f'.png'  # 替换为你希望保存图像的路径和文件名
+    plt.savefig(output_image_path, bbox_inches='tight', dpi=300)  # 使用高分辨率保存图像
+    
+    
+
+def get_chouma_chicang():
+    global chicang_file,df_chouma
+    df = pd.read_csv(chicang_file)
+    df_neg_hold = df[df['li_lun_hold']<0  ].reset_index(drop=True)
+    df_pos_hold = df[df['li_lun_hold']>0].reset_index(drop=True)
+    sum_neg_hold = abs(df_neg_hold['li_lun_hold'].sum())
+    sum_pos_hold = abs(df_pos_hold['li_lun_hold'].sum())
+    df_pos_hold['chicang'] = df_pos_hold['li_lun_hold']*(1-sum_neg_hold/sum_pos_hold) 
+
+    df_chouma = df_pos_hold
+
+
+# swap_trader_id,li_lun_hold,token_b_M,token_s_M,eth_b,eth_s,b_count,s_count,avg_b_pri,avg_s_pri
+
+if __name__ == "__main__":
+ 
+    if len(sys.argv)<2:
+        print("args queshao ")
+        sys.exit(1)
+    chicang_file = sys.argv[1]
+    get_chouma_chicang()
+    draw_chouma()
+ 

src/library/analysis_token/get_neg_chicang.py

@@ -0,0 +1,100 @@
+import pandas as pd
+import sys
+import matplotlib.pyplot as plt
+import math
+chicang_file = ''
+df_chouma = None
+sum_neg_hold = 0
+sum_pos_hold = 0
+def set_text(ax):
+    variable_values = {
+         'sum_pos': round(sum_pos_hold,1),
+    'sum_neg': round(sum_neg_hold,1),
+   
+    'total': round(sum_pos_hold - sum_neg_hold,1)
+    }
+
+    # 设置文本的位置（例如右上角）
+    text_position = (0.8, 0.9)  # 根据你的图表调整这个位置
+
+    # 在指定位置添加文本
+    offset = 0
+    for key, value in variable_values.items():
+        text = f'{key}: {value}'
+        ax.text(text_position[0], text_position[1] - offset, text,transform=ax.transAxes, fontsize=10, color='black', 
+                bbox=dict(facecolor='lightgrey', edgecolor='black', boxstyle='round,pad=0.5'))
+        offset += 0.07  # 每次向下移动一行
+
+
+def calculate_midpoint(interval):
+    return (interval.left + interval.right) / 2
+
+def draw_chouma():
+    global df_chouma ,chicang_file
+    df_grouped = df_chouma.groupby('avg_b_pri').sum()['chicang'].reset_index()
+   
+    max_pri = df_chouma['avg_b_pri'].max()
+    min_pri = df_chouma['avg_b_pri'].min()
+
+ 
+    # 自定义价格区间
+    price_range = [pri*0.5 for pri in range(math.floor(min_pri),math.ceil(max_pri)*2)]
+ 
+    # # 根据价格区间计算每个区间的总持仓量
+    df_grouped['区间'] = pd.cut(df_grouped['avg_b_pri'], bins=price_range)
+ 
+    grouped_by_interval = df_grouped.groupby('区间').sum()
+  
+    grouped_by_interval = grouped_by_interval.reset_index()
+    grouped_by_interval['mid_pri'] = grouped_by_interval['区间'].apply(calculate_midpoint)
+   
+    grouped_by_interval.set_index(['区间'],inplace=True)
+   
+   
+    # 绘制筹码分布图
+    fig, ax = plt.subplots(figsize=(10, 6))
+    # ax.bar(grouped_by_interval['mid_pri'], grouped_by_interval['chicang'],   align='center', color='skyblue')
+    grouped_by_interval['chicang'].plot(kind='bar', ax=ax, color='skyblue')
+    # 设置图表标题和标签
+    ax.set_title('chouma-fenbu')
+    ax.set_xlabel('chichang-pri')
+    ax.set_ylabel('chicang-amount')
+    plt.xticks(rotation=45)
+   
+
+    plt.ylim(0, 400)
+    set_text(ax=ax)
+    # plt.yticks(np.arange(0, 401, 50))
+ 
+    # 保存图像到文件
+    
+    output_image_path =  chicang_file.split('_')[-1].replace('.csv','') + f'.png'  # 替换为你希望保存图像的路径和文件名
+    plt.savefig(output_image_path, bbox_inches='tight', dpi=300)  # 使用高分辨率保存图像
+    
+    
+
+
+def get_chouma_chicang():
+    global chicang_file,df_chouma
+    global sum_neg_hold ,sum_pos_hold
+    df = pd.read_csv(chicang_file)
+    df_neg_hold = df[df['li_lun_hold']<0  ].reset_index(drop=True)
+    df_pos_hold = df[df['li_lun_hold']>0].reset_index(drop=True)
+    sum_neg_hold = abs(df_neg_hold['li_lun_hold'].sum())
+    sum_pos_hold = abs(df_pos_hold['li_lun_hold'].sum())
+    df_pos_hold['chicang'] = df_pos_hold['li_lun_hold'] 
+
+    df_chouma = df_pos_hold
+
+
+# swap_trader_id,li_lun_hold,token_b_M,token_s_M,eth_b,eth_s,b_count,s_count,avg_b_pri,avg_s_pri
+
+if __name__ == "__main__":
+ 
+    if len(sys.argv)<2:
+        print("args queshao ")
+        sys.exit(1)
+    chicang_file = sys.argv[1]
+    get_chouma_chicang()
+    draw_chouma()
+ 

src/library/analysis_token/merge_holder_df.py → src/library/analysis_token/merge_holders_df.py

@@ -40,14 +40,14 @@ def merge_df_fun():
  
    
         
-        cur_df=cur_df[['holder_owner','pct_supply']]
+        cur_df=cur_df[['holder_owner','amount_M']]
         cur_df =cur_df.astype({
-            "pct_supply":float,
+            "amount_M":float,
         })
         cur_df = cur_df.round(2)
-        cur_df.rename(columns={'pct_supply': f'pct_supply_{date_range}'}, inplace=True)
+        cur_df.rename(columns={'amount_M': f'amount_M_{date_range}'}, inplace=True)
         arr_date_range.append(f'{date_range}')
-        arr_col_pct.append(f'pct_supply_{date_range}')
+        arr_col_pct.append(f'amount_M_{date_range}')
         if index==0 :
             merged_df = cur_df
             continue
@@ -89,10 +89,6 @@ def merge_df_fun():
                 cur_df.to_excel(writer, sheet_name=f'{arr_date_range[idx-1]}', index=False)
 
          
-        
-
-    # merged_df.to_excel( f"merge_holders_{get_nowdate()}.xlsx",index=False)
- 
 
 def makeurl( url,url_show):
     return '=HYPERLINK("{}","{}")'.format(url,  url_show)

src/library/analysis_token/merge_swap_df.py

@@ -0,0 +1,181 @@
+
+# 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 datetime
+import pytz
+from openpyxl.styles import *
+# from openpyxl.styles.colors import RED, GREEN
+arr_file = []
+merged_df = None
+ 
+token_add = ""
+
+def get_nowdate():
+
+    timestamp = datetime.datetime.now().timestamp()
+    # print(timestamp)
+    dt = datetime.datetime.fromtimestamp(timestamp)
+    tz = pytz.timezone('Asia/Shanghai')
+    normal_time_str = dt.astimezone(tz).strftime('%Y-%m-%d %H:%M:%S')
+    time_str = dt.astimezone(tz).strftime('%m%d%H%M')
+    return time_str
+
+def utc_2_shanghaidate(df , utc_time_col ):
+# 将字符串转换为UTC时间的datetime对象
+    df['shanghai_date'] = 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['shanghai_date'] = df['shanghai_date'].dt.tz_convert(utc_plus_8)
+    # 提取时间字符串
+    df['shanghai_date'] = df['shanghai_date'].dt.strftime('%Y-%m-%d %H:%M')
+
+    print(df)
+ 
+
+def merge_swap_df_fun_01():    
+    # style = pd.DataFrame().style
+    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.dropna(subset=['swap_trader_id'])
+        cur_df =cur_df.astype({
+            "li_lun_hold":float,
+            "token_b_M":float,
+            "token_s_M":float,
+            "eth_b":float,
+            "eth_s":float,
+            "b_count":float,
+            "s_count":float,
+            "avg_b_pri":float,
+            "avg_s_pri":float
+        })
+
+
+         
+        chaged_cols = [f"{col}_{date_range}" for col in cur_df.columns  ]
+        chaged_cols[0] = "swap_trader_id"
+        cur_df.columns = chaged_cols
+
+        if index==0 :
+            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)
+# https://www.w3schools.com/colors/colors_hex.asp    
+
+yellow_fill = PatternFill(start_color='FFFF00', end_color='FFFF00', fill_type='solid')  # 黄色填充
+
+
+IndianRed_fill = PatternFill(start_color='CD5C5C', end_color='CD5C5C', fill_type='solid')  # 黄色填充
+LightCoralRed_fill = PatternFill(start_color='F08080', end_color='F08080', fill_type='solid')  # 黄色填充
+
+
+MediumSpringGreen_fill = PatternFill(start_color='00FA9A', end_color='00FA9A', fill_type='solid')  #  
+ForestGreen_fill = PatternFill(start_color='228B22', end_color='228B22', fill_type='solid')  #  
+SeaGreen_fill = PatternFill(start_color='2E8B57', end_color='2E8B57', fill_type='solid')  #  
+
+Orange_fill = PatternFill(start_color='FFA500', end_color='FFA500', fill_type='solid')  #  
+
+
+
+merged_hodlers_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)
+    
+    
+    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:
+        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]
+
+
+# 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_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:
+                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)
+                    cell.fill = MediumSpringGreen_fill
+            for target_column  in arr_col_token_b:
+                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)
+                    cell.fill = ForestGreen_fill
+ 
+            for target_column  in arr_col_sell_pri:
+                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)
+                    cell.fill = IndianRed_fill
+
+            for target_column  in arr_col_token_s:
+                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)
+                    cell.fill = LightCoralRed_fill
+
+            for target_column  in arr_col_li_lun_hold:
+                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)
+                    cell.fill = Orange_fill
+
+
+
+def makeurl( url,url_show):
+    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
+
+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.sort()
+    print("read file: ",arr_file)
+    merge_swap_df_fun_01()
+    merge_swap_df_fun_02()

src/library/analysis_token/merge_swap_transfer_row.py

@@ -0,0 +1,78 @@
+ 
+import pandas as pd
+import sys
+
+import datetime
+import pytz
+
+def makeurl( url,url_show):
+    return '=HYPERLINK("{}","{}")'.format(url,  url_show)
+
+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['shanghai_date'] = df['shanghai_date'].dt.tz_convert(utc_plus_8)
+    # # 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
+ 
+swap_row_file = ''
+transfer_row_file = ''
+def main():
+    df_swap_row = pd.read_csv(swap_row_file,dtype=object)
+    df_swap_row=df_swap_row.astype({'token_swap_M':float ,'eth_swap_amount':float ,
+                                    'block_slot':float,
+                                    'pri':float})
+
+    df_transfer_row = pd.read_csv(transfer_row_file,dtype=object)
+    df_transfer_row = df_transfer_row.astype({'token_transfer_M':float , 'block_slot':float})
+
+
+    df_swap_row.rename(columns={'token_swap_M': 'token_M' }, inplace=True)
+    
+    
+    df_swap_row = utc_2_shanghaidate(df=df_swap_row, utc_time_col='block_time')
+    
+    df_transfer_row = utc_2_shanghaidate(df=df_transfer_row, utc_time_col='block_time')
+ 
+
+    # 创建两个DataFrame，一个用于'from'列，另一个用于'to'列
+    df_from = df_transfer_row[[ 'tx_id',	'block_time',	'block_slot',	'token_transfer_M' ,'from_owner']].copy()
+    df_from.rename(columns={'from_owner': 'trader_id' , 'token_transfer_M':'token_M'}, inplace=True)
+    df_from['token_M'] = -df_from['token_M']
+
+    df_to = df_transfer_row[[ 'tx_id',	'block_time',	'block_slot',	'token_transfer_M' ,'to_owner']].copy()
+    df_to.rename(columns={'to_owner': 'trader_id','token_transfer_M':'token_M'}, inplace=True)
+    df_to['token_M'] = df_to['token_M']
+
+    # 合并两个DataFrame
+    df_transfer_row = pd.concat([df_from, df_to], ignore_index=True)
+    df_transfer_row['action_type'] = 't'
+    df_transfer_row['token_M'] = df_transfer_row['token_M'].round(4)
+
+    merged_df =  pd.concat([ df_swap_row , df_transfer_row], ignore_index=True)
+    merged_df = merged_df.sort_values(by=['trader_id' , 'block_slot'],ascending=True)
+    
+
+    merged_df["oklink"] = merged_df.apply(lambda ser: makeurl(
+     f"https://www.oklink.com/zh-hans/sol/tx/{ser['tx_id']}" , "oklink" ), axis=1)
+
+    merged_df.to_excel(  'dune_swap_transfer_row.xlsx'  ,index=False)
+    
+ 
+    return
+
+
+if __name__ == "__main__":
+    if len(sys.argv)<3:
+        print("args queshao ")
+        sys.exit(1)
+    swap_row_file = sys.argv[1]
+    transfer_row_file = sys.argv[2]
+    main()

src/library/analysis_token/笔记.md

@@ -66,13 +66,16 @@ Cda7Qx8zkwFTqUafKtRCr5PBZ6JENtHLz6TUDFucpump Rug=100
 B8wdWmHnpGmhsTKi6mkDR6oAttMN76s2mkKJqSinpump
 DAYv2KWkurCHoh1K26QCNmRjDeogH1FGbQ6QgYg1pump
 
-
-
+2025.2.1
 分析思路 以5puhwnyz2Tv8jSmmBD5DSqCwFVXwwPGZacymM7DQpump为例子
 有一轮高点 然后低谷 然后第二轮高点的经典模式
 获取 第二轮高点前的持仓 top
 然后分析他们在低谷 高点的买卖 来分析那些是庄家地址 ，以及分析什么是庄家介入的特征
 要涉及三个时间点
-刚进入低谷a 高点前b 高点出盘后c
+刚进入低谷a 拉升前b 高点出盘后c
 以为b时间的top 地址为基准 获取 a时候的持仓 ， a--b的持仓变化(swap记录,transfer记录） 这些是分析庄家特征的重点（未来分析token只有这些信息，不可能马后炮获取第二轮高点）
-b--c的持仓变化更像是做验证，用来分析已有第二轮高点的token 是与回溯的。
+b--c的持仓变化更像是做验证，用来分析已有第二轮高点的token 是与回溯的。
+
+2025.2.5
+刚进入低点的top holder h1 低点时间一半的topholder h2 拉升前的top holder h3
+只针对这三个h1-h3 的各个时间的holder 变化以及swap 和transfer