from base_class import BaseVariableFunction from base_class import * baseclass = BaseVariableFunction(__file__) baseclass.makedirpath(baseclass.dalao_profit_st_fm_path) baseclass.makedirpath(baseclass.dalao_ana_fm_path) baseclass.makedirpath(baseclass.dalao_total_ana_fm_path) baseclass.open_base_library() decimal.getcontext().prec = 100 old_print = print def timestamped_print(*args, **kwargs): old_print(datetime.datetime.utcnow().replace( microsecond=0), *args, **kwargs) print = timestamped_print print('\n'*5) print(f"{'{:<6}'.format('ENTER')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------") onedalao_ana_columns = ['TokenFirstTime', 'swap_tokenaddress', "issellfir", 'buy_counts', 'sell_counts', 'outtoken_per', 'cost_main', 'earn_main', 'earn_percent' , 'diffdays','fir_cost_main','fir_earn_percent' , 'fir_noadd_earn_percent'] onetxhash_onerow_ana_cols= [ "sign","swap_tokenaddress","dexurl","platform","datetime", "dalaofirsttimestamp","diffdays", "fir_cost_main","fir_earn_percent","issellfir", "fir_noadd_earn_percent","token_price", "buy_sell_diftime", "swap_type", "outtoken_per", "buy_counts","sell_counts", "timestamp","TokenFirstTime", "swap_eth_amount","swap_token_amount", "cost_main","earn_main","earn_percent", "intoken_amount","outtoken_amount", ] TotalAnalysis_columns = [ "add", # "whichcontract", "url", "beizhu", "03d_cost", "03d_earn", "03d_earnper", "03d_difcost", "03d_difearn", "03d_difper", "03d_costmedian", "03d_costmean", "03d_cost_noaddouttoken", "03d_earn_noaddouttoken", "03d_earnper_noaddouttoken", # "03d_difcost_noaddouttoken", # "03d_difearn_noaddouttoken", # "03d_difper_noaddouttoken", # "03d_costmedian_noaddouttoken", # "03d_costmean_noaddouttoken", "03d_mine_fir_earnper", "03d_mine_fir_earnper_dropmax", "03d_mine_fir_earnper_droptwomax", "03d_mine_noadd_fir_earnper" , "03d_mine_noadd_fir_earnper_dropmax" , "03d_mine_noadd_fir_earnper_droptwomax" , "03d_trans", "07d_cost", "07d_earn", "07d_earnper", "07d_difcost", "07d_difearn", "07d_difper", "07d_costmedian", "07d_costmean", "07d_cost_noaddouttoken", "07d_earn_noaddouttoken", "07d_earnper_noaddouttoken", # "07d_difcost_noaddouttoken", # "07d_difearn_noaddouttoken", # "07d_difper_noaddouttoken", # "07d_costmedian_noaddouttoken", # "07d_costmean_noaddouttoken", "07d_mine_fir_earnper", "07d_mine_fir_earnper_dropmax", "07d_mine_fir_earnper_droptwomax", "07d_mine_noadd_fir_earnper" , "07d_mine_noadd_fir_earnper_dropmax" , "07d_mine_noadd_fir_earnper_droptwomax" , "07d_trans", # "11d_cost", # "11d_earn", # "11d_earnper", # "11d_difcost", # "11d_difearn", # "11d_difper", # "11d_costmedian", # "11d_costmean", # "11d_cost_noaddouttoken", # "11d_earn_noaddouttoken", # "11d_earnper_noaddouttoken", # # "11d_difcost_noaddouttoken", # # "11d_difearn_noaddouttoken", # # "11d_difper_noaddouttoken", # # "11d_costmedian_noaddouttoken", # # "11d_costmean_noaddouttoken", # "11d_mine_fir_earnper", # "11d_mine_fir_earnper_dropmax", # "11d_mine_fir_earnper_droptwomax", # "11d_mine_noadd_fir_earnper" , # "11d_mine_noadd_fir_earnper_dropmax" , # "11d_mine_noadd_fir_earnper_droptwomax" , # "11d_trans", "15d_cost", "15d_earn", "15d_earnper", "15d_difcost", "15d_difearn", "15d_difper", "15d_costmedian", "15d_costmean", "15d_cost_noaddouttoken", "15d_earn_noaddouttoken", "15d_earnper_noaddouttoken", # "15d_difcost_noaddouttoken", # "15d_difearn_noaddouttoken", # "15d_difper_noaddouttoken", # "15d_costmedian_noaddouttoken", # "15d_costmean_noaddouttoken", "15d_mine_fir_earnper", "15d_mine_fir_earnper_dropmax", "15d_mine_fir_earnper_droptwomax", "15d_mine_noadd_fir_earnper" , "15d_mine_noadd_fir_earnper_dropmax" , "15d_mine_noadd_fir_earnper_droptwomax" , "15d_trans", "30d_cost", "30d_earn", "30d_earnper", "30d_difcost", "30d_difearn", "30d_difper", "30d_costmedian", "30d_costmean", "30d_cost_noaddouttoken", "30d_earn_noaddouttoken", "30d_earnper_noaddouttoken", # "30d_difcost_noaddouttoken", # "30d_difearn_noaddouttoken", # "30d_difper_noaddouttoken", # "30d_costmedian_noaddouttoken", # "30d_costmean_noaddouttoken", "30d_mine_fir_earnper", "30d_mine_fir_earnper_dropmax", "30d_mine_fir_earnper_droptwomax", "30d_mine_noadd_fir_earnper" , "30d_mine_noadd_fir_earnper_dropmax" , "30d_mine_noadd_fir_earnper_droptwomax" , "30d_trans", "30dearn_dif", "30d_mine_fir_earnper_dif", ] earn_kuisun_columns=[ "add", "url", "beizhu", "03d_bigbig_earn_counts" , "03d_big_earn_counts" , "03d_nor_earn_counts" , "03d_nor_kuisun_counts" , "03d_big_kuisun_counts" , "03d_bigbig_kuisun_counts" , "03d_bigbig_earn_per" , "03d_big_earn_per" , "03d_nor_earn_per" , "03d_nor_kuisun_per" , "03d_big_kuisun_per" , "03d_bigbig_kuisun_per" , "03d_total_counts", "07d_bigbig_earn_counts" , "07d_big_earn_counts" , "07d_nor_earn_counts" , "07d_nor_kuisun_counts" , "07d_big_kuisun_counts" , "07d_bigbig_kuisun_counts" , "07d_bigbig_earn_per" , "07d_big_earn_per" , "07d_nor_earn_per" , "07d_nor_kuisun_per" , "07d_big_kuisun_per" , "07d_bigbig_kuisun_per" , "07d_total_counts", # "11d_bigbig_earn_counts" , "11d_big_earn_counts" , "11d_nor_earn_counts" , # "11d_nor_kuisun_counts" , "11d_big_kuisun_counts" , "11d_bigbig_kuisun_counts" , # "11d_bigbig_earn_per" , "11d_big_earn_per" , "11d_nor_earn_per" , # "11d_nor_kuisun_per" , "11d_big_kuisun_per" , "11d_bigbig_kuisun_per" , # "11d_total_counts", "15d_bigbig_earn_counts" , "15d_big_earn_counts" , "15d_nor_earn_counts" , "15d_nor_kuisun_counts" , "15d_big_kuisun_counts" , "15d_bigbig_kuisun_counts" , "15d_bigbig_earn_per" , "15d_big_earn_per" , "15d_nor_earn_per" , "15d_nor_kuisun_per" , "15d_big_kuisun_per" , "15d_bigbig_kuisun_per" , "15d_total_counts", "30d_bigbig_earn_counts" , "30d_big_earn_counts" , "30d_nor_earn_counts" , "30d_nor_kuisun_counts" , "30d_big_kuisun_counts" , "30d_bigbig_kuisun_counts" , "30d_bigbig_earn_per" , "30d_big_earn_per" , "30d_nor_earn_per" , "30d_nor_kuisun_per" , "30d_big_kuisun_per" , "30d_bigbig_kuisun_per" , "30d_total_counts", ] def get_txhash_tokenswap_amount(df): def get_txhash_cost(gdf): token_amount_list = gdf['swap_token_amount'].to_list() arr_token_amount = [(decimal.Decimal( token_amount)) for token_amount in token_amount_list] token_amount = sum(arr_token_amount) token_amount_str = '{0:.4f}'.format(token_amount) gdf[['swap_token_amount']] = [token_amount_str] # gdf['swap_token_amount'] = token_amount_str return gdf df = df.groupby(["sign"], group_keys=False).apply( lambda gdf: get_txhash_cost(gdf)) df = df.reset_index(drop=True) return df def get_tokenswap_amount(df): def get_cost(gdf): arr_token_amount = gdf['swap_token_amount'].to_list() isfirstsell = False while len(arr_token_amount) > 0: if (decimal.Decimal(arr_token_amount[0]) < 0): arr_token_amount.pop(0) isfirstsell = True else: break if len(arr_token_amount) == 0: return gdf arr_out_token_amount = [abs(decimal.Decimal( token_amount)) for token_amount in arr_token_amount if decimal.Decimal(token_amount) < 0] arr_in_token_amount = [decimal.Decimal( token_amount) for token_amount in arr_token_amount if decimal.Decimal(token_amount) > 0] buy_counts = len(arr_in_token_amount) sell_counts = len(arr_out_token_amount) out_token_amount = sum(arr_out_token_amount) in_token_amount = sum(arr_in_token_amount) out_token_amount_str = '{0:.2f}'.format(out_token_amount) in_token_amount_str = '{0:.2f}'.format(in_token_amount) if isfirstsell: outtoken_per = "1.01" else: outtoken_per = '{0:.2f}'.format( out_token_amount/in_token_amount) if in_token_amount != 0 else '-10' gdf[['buy_counts', 'sell_counts', 'intoken_amount', 'outtoken_amount', 'outtoken_per']] = [buy_counts, sell_counts, in_token_amount_str, out_token_amount_str, outtoken_per] return gdf df[['buy_counts', 'sell_counts', 'intoken_amount', 'outtoken_amount', 'outtoken_per']] = [0, 0, '0', '0', '0'] # 如果全是sell token 那么 在get_cost（）将会在中途返回gdf 不会赋值 如上变量全为0 df = df.groupby(["swap_tokenaddress"], group_keys=False).apply( lambda gdf: get_cost(gdf)) df = df.reset_index(drop=True) return df def get_sol_profit(df): # 計算利潤 df['cost_main'] = 0 df['earn_main'] = 0 df['earn_percent'] = -10 df['fir_cost_main'] = 0 df['fir_earn_percent']=-10 df["issellfir"] = 0 df["fir_noadd_earn_percent"] = -10 df["token_price"]=-10 df["buy_sell_diftime"]=-1 df = df.astype({'swap_eth_amount': float}) df = df.astype({'swap_token_amount': float}) df = df.astype({'outtoken_per': float}) df = df.astype({'timestamp': int}) df["token_price"] = np.where( df['swap_token_amount'] != 0, -df['swap_eth_amount'] / df['swap_token_amount'], -10, ) def get_one_sol_profit(gdf): issellfir = False arr_eth_amount = gdf['swap_eth_amount'].to_list() arr_token_amount = gdf['swap_token_amount'].to_list() arr_timestamp = gdf["timestamp"].to_list() while len(arr_eth_amount) > 0: # 如果 arr_eth_amount[0]>0 则此交易为卖出 pass if (arr_eth_amount[0] >= 0): gdf["issellfir"] = 1 arr_eth_amount.pop(0) arr_token_amount.pop(0) arr_timestamp.pop(0) continue else: break if len(arr_eth_amount) == 0: return gdf number_cost_main = sum( [abs(eth_amount) for eth_amount in arr_eth_amount if eth_amount < 0]) number_earn_main = sum(arr_eth_amount) number_earn_percent = number_earn_main / \ number_cost_main if number_cost_main != 0 else -10 gdf["cost_main"] = number_cost_main gdf["earn_main"] = number_earn_main gdf["earn_percent"] = number_earn_percent arr_firswap_token_amount = arr_token_amount.copy() arr_firswap_eth_amount = arr_eth_amount.copy() # swap_tokenaddress small_sol_counts = 0 # 获取第一笔cost_eth # 如果 arr_eth_amount[0]>0 则此交易为卖出 pass # 如果 0>arr_eth_amount[0]>-0.04 此交易金额较小 pass while len(arr_firswap_eth_amount) > 0: if arr_firswap_eth_amount[0]>=0: arr_firswap_eth_amount.pop(0) arr_firswap_token_amount.pop(0) arr_timestamp.pop(0) continue elif arr_firswap_eth_amount[0]<0 and arr_firswap_eth_amount[0]>-0.04: arr_firswap_eth_amount.pop(0) arr_firswap_token_amount.pop(0) arr_timestamp.pop(0) small_sol_counts+=1 if small_sol_counts==2: break continue else : break if small_sol_counts==2: return gdf if len(arr_firswap_eth_amount) == 0: return gdf fir_swap_tokenamount = arr_firswap_token_amount[0] fir_swap_costmain = abs(arr_firswap_eth_amount[0]) fir_swap_earntmain = -fir_swap_costmain fir_buy_timestamp =int(arr_timestamp[0]) fir_sell_timestamp = 0 while len(arr_firswap_eth_amount) > 0: if arr_firswap_eth_amount[0]<=0: arr_firswap_eth_amount.pop(0) arr_firswap_token_amount.pop(0) arr_timestamp.pop(0) continue # 现在 swap_token 为负 ， eth为正 代表 sell token cur_swap_tokenamount = abs(arr_firswap_token_amount[0]) cur_swap_eth = arr_firswap_eth_amount[0] fir_sell_timestamp = int(arr_timestamp[0]) arr_firswap_eth_amount.pop(0) arr_firswap_token_amount.pop(0) arr_timestamp.pop(0) if fir_swap_tokenamount>=cur_swap_tokenamount: fir_swap_earntmain+=cur_swap_eth fir_swap_tokenamount-=cur_swap_tokenamount else: fir_swap_earntmain = fir_swap_earntmain+ fir_swap_tokenamount/cur_swap_tokenamount * cur_swap_eth fir_swap_tokenamount=0 if fir_swap_tokenamount==0: break gdf['fir_cost_main'] = fir_swap_costmain gdf["fir_earn_percent"] = fir_swap_earntmain / fir_swap_costmain if fir_swap_costmain != 0 else -10 gdf["fir_noadd_earn_percent"] = fir_swap_earntmain / fir_swap_costmain if fir_swap_costmain != 0 else -10 if issellfir==1 or gdf["outtoken_per"].tolist()[0]>1: gdf["fir_noadd_earn_percent"] = -10 gdf["buy_sell_diftime"] = fir_sell_timestamp - fir_buy_timestamp return gdf df = df.groupby(["swap_tokenaddress"], group_keys=False).apply( lambda gdf: get_one_sol_profit(gdf)) return df def analysis_lastdays( df, now_unix_time, intervaldays=30): if len(df) == 0: return 0, 0, 0, 0, 0, 0, 0, 0, 0,0, 0,0 # need consider df may null if use iloc lastdf = df[df["TokenFirstTime"] >= now_unix_time - 3600*24*intervaldays] # 去除那些earnper为-10的 lastdf = lastdf[lastdf['cost_main'] != 0] # 去除 cost_main 过小的 <0.04 sol 理论上 同时fir_cost_main!=0 lastdf = lastdf[lastdf['cost_main'] >= 0.04] lastdf = lastdf[lastdf['fir_cost_main'] != 0] lastdf = lastdf[lastdf['issellfir'] ==0] lastdf = lastdf.reset_index(drop=True) if len(lastdf) == 0: return 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0 last_mine_fir_earnper = lastdf['fir_earn_percent'].sum() last_mine_fir_earnper_dropmax = last_mine_fir_earnper- lastdf['fir_earn_percent'].max() last_mine_fir_earnper_droptwomax = last_mine_fir_earnper - sum( lastdf['fir_earn_percent'].nlargest(2).tolist()) last_cost_sum = lastdf['cost_main'].sum() last_earn_sum = lastdf['earn_main'].sum() last_cost_median = lastdf['cost_main'].median() last_cost_mean = lastdf['cost_main'].mean() last_earnper = last_earn_sum / last_cost_sum if last_cost_sum != 0 else -10 last_trans_amount = len(lastdf) lastdf['earn_percent'] = np.where( lastdf['cost_main'] != 0, lastdf['earn_main'] / lastdf['cost_main'], -10, ) earnmain_max = lastdf['earn_main'].max() last_earn_difmax_sum = last_earn_sum - earnmain_max last_cost_difmax_sum = last_cost_sum - \ lastdf[lastdf['earn_main'] == earnmain_max]['cost_main'].to_list()[0] last_dif_earnper = ( last_earn_difmax_sum / last_cost_difmax_sum if last_cost_difmax_sum != 0 else -10 ) return ( last_cost_sum, last_earn_sum, last_earnper, last_cost_difmax_sum, last_earn_difmax_sum, last_dif_earnper, last_cost_median, last_cost_mean, # last_earncount_per, last_trans_amount, last_mine_fir_earnper, last_mine_fir_earnper_dropmax, last_mine_fir_earnper_droptwomax, ) def analysis_lastdays_noaddouttoken( df, now_unix_time, intervaldays=30): if len(df) == 0: return 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0 # need consider df may null if use iloc lastdf = df[df["TokenFirstTime"] >= now_unix_time - 3600*24*intervaldays] # 去除那些earnper为-10的 lastdf = lastdf[lastdf['cost_main'] != 0] # 去除 cost_main 过小的 <0.04 sol 理论上 同时fir_cost_main!=0 lastdf = lastdf[lastdf['cost_main'] >= 0.04] lastdf = lastdf[lastdf['fir_cost_main'] != 0] # fir_noadd_earn_percent # 去除 第一笔交易是sell lastdf = lastdf[lastdf['fir_noadd_earn_percent'] !=-10] lastdf = lastdf[lastdf['issellfir'] ==0] lastdf = lastdf.reset_index(drop=True) if len(lastdf) == 0: return 0, 0, 0, 0, 0, 0, 0, 0, 0,0,0,0 last_mine_noadd_fir_earnper = lastdf['fir_noadd_earn_percent'].sum() last_mine_noadd_fir_earnper_dropmax = last_mine_noadd_fir_earnper- lastdf['fir_noadd_earn_percent'].max() last_mine_noadd_fir_earnper_droptwomax = last_mine_noadd_fir_earnper - sum( lastdf['fir_noadd_earn_percent'].nlargest(2).tolist()) last_cost_sum = lastdf['cost_main'].sum() last_earn_sum = lastdf['earn_main'].sum() # last_earn_sum = lastdf["earn_main_noaddouttoken"].sum() last_cost_median = lastdf['cost_main'].median() last_cost_mean = lastdf['cost_main'].mean() last_earnper = last_earn_sum / last_cost_sum if last_cost_sum != 0 else -10 last_trans_amount = len(lastdf) lastdf['earn_percent'] = np.where( lastdf['cost_main'] != 0, lastdf["earn_main"] / lastdf['cost_main'], -10, ) earnmain_max = lastdf["earn_main"].max() last_earn_difmax_sum = last_earn_sum - earnmain_max last_cost_difmax_sum = last_cost_sum - \ lastdf[lastdf['earn_main'] == earnmain_max]['cost_main'].to_list()[ 0] last_dif_earnper = ( last_earn_difmax_sum / last_cost_difmax_sum if last_cost_difmax_sum != 0 else -10 ) return ( last_cost_sum, last_earn_sum, last_earnper, last_cost_difmax_sum, last_earn_difmax_sum, last_dif_earnper, last_cost_median, last_cost_mean, # last_earncount_per, last_trans_amount, last_mine_noadd_fir_earnper , last_mine_noadd_fir_earnper_dropmax , last_mine_noadd_fir_earnper_droptwomax ) def analysis_lastdays_earn_kuisun_counts(df ,now_unix_time,intervaldays ): if len(df) == 0: return 0, 0, 0, 0, 0, 0, 0 ,0,0,0,0,0,0 # need consider df may null if use iloc lastdf = df[df["TokenFirstTime"] >= now_unix_time - 3600*24*intervaldays] # 去除那些earnper为-10的 lastdf = lastdf[lastdf['cost_main'] != 0] # 去除 cost_main 过小的 <0.04 sol 理论上 同时fir_cost_main!=0 lastdf = lastdf[lastdf['cost_main'] >= 0.04] lastdf = lastdf[lastdf['fir_cost_main'] != 0] # fir_noadd_earn_percent # 去除 第一笔交易是sell lastdf = lastdf[lastdf['fir_noadd_earn_percent'] !=-10] lastdf = lastdf[lastdf['issellfir'] ==0] lastdf = lastdf.reset_index(drop=True) if len(lastdf) == 0: return 0, 0, 0, 0, 0, 0, 0 ,0,0,0,0,0,0 last_bigbig_earn_counts = len( lastdf[lastdf['fir_noadd_earn_percent']>=1]) last_big_earn_counts = len( lastdf[(lastdf['fir_noadd_earn_percent']>=0.5 )& (lastdf['fir_noadd_earn_percent']<1 ) ]) last_nor_earn_counts = len( lastdf[(lastdf['fir_noadd_earn_percent']>=0.05 )& (lastdf['fir_noadd_earn_percent']<0.5 ) ]) last_nor_kuisun_counts = len( lastdf[(lastdf['fir_noadd_earn_percent']>=-0.2 )& (lastdf['fir_noadd_earn_percent']<0.05 ) ]) last_big_kuisun_counts = len( lastdf[(lastdf['fir_noadd_earn_percent']>=-0.5 )& (lastdf['fir_noadd_earn_percent']<-0.2 ) ]) last_bigbig_kuisun_counts = len( lastdf[ (lastdf['fir_noadd_earn_percent']<-0.5 ) ]) last_total_counts = len( lastdf) last_bigbig_earn_per = last_bigbig_earn_counts /last_total_counts if last_total_counts!=0 else -10 last_big_earn_per = last_big_earn_counts / last_total_counts if last_total_counts!=0 else -10 last_nor_earn_per = last_nor_earn_counts / last_total_counts if last_total_counts!=0 else -10 last_nor_kuisun_per = last_nor_kuisun_counts /last_total_counts if last_total_counts!=0 else -10 last_big_kuisun_per= last_big_kuisun_counts / last_total_counts if last_total_counts!=0 else -10 last_bigbig_kuisun_per = last_bigbig_kuisun_counts / last_total_counts if last_total_counts!=0 else -10 return ( last_bigbig_earn_counts, last_big_earn_counts, last_nor_earn_counts, last_nor_kuisun_counts, last_big_kuisun_counts, last_bigbig_kuisun_counts, last_total_counts, last_bigbig_earn_per , last_big_earn_per , last_nor_earn_per , last_nor_kuisun_per , last_big_kuisun_per, last_bigbig_kuisun_per , ) return def get_analyres(df, dalao_address, now_unix_time): (last_03d_cost, last_03d_earn, last_03d_earnper, last_03d_difcost, last_03d_difearn, last_03d_difper, last_03d_costmedian, last_03d_costmean, last_03d_trans , last_03d_mine_fir_earnper ,last_03d_mine_fir_earnper_dropmax, last_03d_mine_fir_earnper_droptwomax ) = analysis_lastdays( df, now_unix_time=now_unix_time, intervaldays=3 ) (last_07d_cost, last_07d_earn, last_07d_earnper, last_07d_difcost, last_07d_difearn, last_07d_difper, last_07d_costmedian, last_07d_costmean, last_07d_trans , last_07d_mine_fir_earnper ,last_07d_mine_fir_earnper_dropmax, last_07d_mine_fir_earnper_droptwomax ) = analysis_lastdays( df, now_unix_time=now_unix_time, intervaldays=7 ) # (last_11d_cost, last_11d_earn, last_11d_earnper, last_11d_difcost, last_11d_difearn, last_11d_difper, last_11d_costmedian, last_11d_costmean, last_11d_trans , last_11d_mine_fir_earnper ,last_11d_mine_fir_earnper_dropmax , last_11d_mine_fir_earnper_droptwomax ) = analysis_lastdays( # df, now_unix_time=now_unix_time, intervaldays=11 # ) (last_15d_cost, last_15d_earn, last_15d_earnper, last_15d_difcost, last_15d_difearn, last_15d_difper, last_15d_costmedian, last_15d_costmean, last_15d_trans , last_15d_mine_fir_earnper ,last_15d_mine_fir_earnper_dropmax , last_15d_mine_fir_earnper_droptwomax ) = analysis_lastdays( df, now_unix_time=now_unix_time, intervaldays=15 ) (last_30d_cost, last_30d_earn, last_30d_earnper, last_30d_difcost, last_30d_difearn, last_30d_difper, last_30d_costmedian, last_30d_costmean, last_30d_trans , last_30d_mine_fir_earnper ,last_30d_mine_fir_earnper_dropmax , last_30d_mine_fir_earnper_droptwomax ) = analysis_lastdays( df, now_unix_time=now_unix_time, intervaldays=30 ) # # 去除所有daxin # df = df[df["isdaxin"] == "0"].reset_index(drop=True) (last_03d_cost_noaddouttoken, last_03d_earn_noaddouttoken, last_03d_earnper_noaddouttoken, last_03d_difcost_noaddouttoken, last_03d_difearn_noaddouttoken, last_03d_difper_noaddouttoken, last_03d_costmedian_noaddouttoken, last_03d_costmean_noaddouttoken, last_03d_trans , last_03d_mine_noadd_fir_earnper , last_03d_mine_noadd_fir_earnper_dropmax , last_03d_mine_noadd_fir_earnper_droptwomax ) = analysis_lastdays_noaddouttoken( df, now_unix_time=now_unix_time, intervaldays=3 ) (last_07d_cost_noaddouttoken, last_07d_earn_noaddouttoken, last_07d_earnper_noaddouttoken, last_07d_difcost_noaddouttoken, last_07d_difearn_noaddouttoken, last_07d_difper_noaddouttoken, last_07d_costmedian_noaddouttoken, last_07d_costmean_noaddouttoken, last_07d_trans, last_07d_mine_noadd_fir_earnper , last_07d_mine_noadd_fir_earnper_dropmax , last_07d_mine_noadd_fir_earnper_droptwomax) = analysis_lastdays_noaddouttoken( df, now_unix_time=now_unix_time, intervaldays=7 ) # (last_11d_cost_noaddouttoken, last_11d_earn_noaddouttoken, last_11d_earnper_noaddouttoken, last_11d_difcost_noaddouttoken, last_11d_difearn_noaddouttoken, last_11d_difper_noaddouttoken, last_11d_costmedian_noaddouttoken, last_11d_costmean_noaddouttoken, last_11d_trans, # last_11d_mine_noadd_fir_earnper , last_11d_mine_noadd_fir_earnper_dropmax , last_11d_mine_noadd_fir_earnper_droptwomax) = analysis_lastdays_noaddouttoken( # df, now_unix_time=now_unix_time, intervaldays=11 # ) (last_15d_cost_noaddouttoken, last_15d_earn_noaddouttoken, last_15d_earnper_noaddouttoken, last_15d_difcost_noaddouttoken, last_15d_difearn_noaddouttoken, last_15d_difper_noaddouttoken, last_15d_costmedian_noaddouttoken, last_15d_costmean_noaddouttoken, last_15d_trans, last_15d_mine_noadd_fir_earnper , last_15d_mine_noadd_fir_earnper_dropmax , last_15d_mine_noadd_fir_earnper_droptwomax) = analysis_lastdays_noaddouttoken( df, now_unix_time=now_unix_time, intervaldays=15 ) (last_30d_cost_noaddouttoken, last_30d_earn_noaddouttoken, last_30d_earnper_noaddouttoken, last_30d_difcost_noaddouttoken, last_30d_difearn_noaddouttoken, last_30d_difper_noaddouttoken, last_30d_costmedian_noaddouttoken, last_30d_costmean_noaddouttoken, last_30d_trans, last_30d_mine_noadd_fir_earnper , last_30d_mine_noadd_fir_earnper_dropmax , last_30d_mine_noadd_fir_earnper_droptwomax) = analysis_lastdays_noaddouttoken( df, now_unix_time=now_unix_time, intervaldays=30 ) (last_03d_bigbig_earn_counts, last_03d_big_earn_counts, last_03d_nor_earn_counts, last_03d_nor_kuisun_counts,last_03d_big_kuisun_counts,last_03d_bigbig_kuisun_counts ,last_03d_total_counts, last_03d_bigbig_earn_per , last_03d_big_earn_per , last_03d_nor_earn_per , last_03d_nor_kuisun_per , last_03d_big_kuisun_per, last_03d_bigbig_kuisun_per ) = analysis_lastdays_earn_kuisun_counts( df, now_unix_time=now_unix_time, intervaldays=3 ) (last_07d_bigbig_earn_counts, last_07d_big_earn_counts, last_07d_nor_earn_counts, last_07d_nor_kuisun_counts,last_07d_big_kuisun_counts,last_07d_bigbig_kuisun_counts ,last_07d_total_counts, last_07d_bigbig_earn_per , last_07d_big_earn_per , last_07d_nor_earn_per , last_07d_nor_kuisun_per , last_07d_big_kuisun_per, last_07d_bigbig_kuisun_per ) = analysis_lastdays_earn_kuisun_counts( df, now_unix_time=now_unix_time, intervaldays=7 ) # (last_11d_bigbig_earn_counts, last_11d_big_earn_counts, last_11d_nor_earn_counts, last_11d_nor_kuisun_counts,last_11d_big_kuisun_counts,last_11d_bigbig_kuisun_counts ,last_11d_total_counts, # last_11d_bigbig_earn_per , last_11d_big_earn_per , last_11d_nor_earn_per , last_11d_nor_kuisun_per , last_11d_big_kuisun_per, last_11d_bigbig_kuisun_per # ) = analysis_lastdays_earn_kuisun_counts( # df, now_unix_time=now_unix_time, intervaldays=11 # ) (last_15d_bigbig_earn_counts, last_15d_big_earn_counts, last_15d_nor_earn_counts, last_15d_nor_kuisun_counts,last_15d_big_kuisun_counts,last_15d_bigbig_kuisun_counts ,last_15d_total_counts, last_15d_bigbig_earn_per , last_15d_big_earn_per , last_15d_nor_earn_per , last_15d_nor_kuisun_per , last_15d_big_kuisun_per, last_15d_bigbig_kuisun_per ) = analysis_lastdays_earn_kuisun_counts( df, now_unix_time=now_unix_time, intervaldays=15 ) (last_30d_bigbig_earn_counts, last_30d_big_earn_counts, last_30d_nor_earn_counts, last_30d_nor_kuisun_counts,last_30d_big_kuisun_counts,last_30d_bigbig_kuisun_counts ,last_30d_total_counts, last_30d_bigbig_earn_per , last_30d_big_earn_per , last_30d_nor_earn_per , last_30d_nor_kuisun_per , last_30d_big_kuisun_per, last_30d_bigbig_kuisun_per ) = analysis_lastdays_earn_kuisun_counts( df, now_unix_time=now_unix_time, intervaldays=30 ) dalao_rul = f"https://solscan.io/account/{dalao_address}" analist = [ dalao_address, dalao_rul, "", last_03d_cost, last_03d_earn, last_03d_earnper, last_03d_difcost, last_03d_difearn, last_03d_difper, last_03d_costmedian, last_03d_costmean, last_03d_cost_noaddouttoken, last_03d_earn_noaddouttoken, last_03d_earnper_noaddouttoken, # last_03d_difcost_noaddouttoken, # last_03d_difearn_noaddouttoken, # last_03d_difper_noaddouttoken, # last_03d_costmedian_noaddouttoken, # last_03d_costmean_noaddouttoken, last_03d_mine_fir_earnper, last_03d_mine_fir_earnper_dropmax, last_03d_mine_fir_earnper_droptwomax, last_03d_mine_noadd_fir_earnper , last_03d_mine_noadd_fir_earnper_dropmax , last_03d_mine_noadd_fir_earnper_droptwomax, last_03d_trans, last_07d_cost, last_07d_earn, last_07d_earnper, last_07d_difcost, last_07d_difearn, last_07d_difper, last_07d_costmedian, last_07d_costmean, last_07d_cost_noaddouttoken, last_07d_earn_noaddouttoken, last_07d_earnper_noaddouttoken, # last_07d_difcost_noaddouttoken, # last_07d_difearn_noaddouttoken, # last_07d_difper_noaddouttoken, # last_07d_costmedian_noaddouttoken, # last_07d_costmean_noaddouttoken, last_07d_mine_fir_earnper, last_07d_mine_fir_earnper_dropmax, last_07d_mine_fir_earnper_droptwomax, last_07d_mine_noadd_fir_earnper , last_07d_mine_noadd_fir_earnper_dropmax , last_07d_mine_noadd_fir_earnper_droptwomax, last_07d_trans, # last_11d_cost, # last_11d_earn, # last_11d_earnper, # last_11d_difcost, # last_11d_difearn, # last_11d_difper, # last_11d_costmedian, # last_11d_costmean, # last_11d_cost_noaddouttoken, # last_11d_earn_noaddouttoken, # last_11d_earnper_noaddouttoken, # # last_11d_difcost_noaddouttoken, # # last_11d_difearn_noaddouttoken, # # last_11d_difper_noaddouttoken, # # last_11d_costmedian_noaddouttoken, # # last_11d_costmean_noaddouttoken, # last_11d_mine_fir_earnper, # last_11d_mine_fir_earnper_dropmax, # last_11d_mine_fir_earnper_droptwomax, # last_11d_mine_noadd_fir_earnper , # last_11d_mine_noadd_fir_earnper_dropmax , # last_11d_mine_noadd_fir_earnper_droptwomax , # last_11d_trans, last_15d_cost, last_15d_earn, last_15d_earnper, last_15d_difcost, last_15d_difearn, last_15d_difper, last_15d_costmedian, last_15d_costmean, last_15d_cost_noaddouttoken, last_15d_earn_noaddouttoken, last_15d_earnper_noaddouttoken, # last_15d_difcost_noaddouttoken, # last_15d_difearn_noaddouttoken, # last_15d_difper_noaddouttoken, # last_15d_costmedian_noaddouttoken, # last_15d_costmean_noaddouttoken, last_15d_mine_fir_earnper, last_15d_mine_fir_earnper_dropmax, last_15d_mine_fir_earnper_droptwomax, last_15d_mine_noadd_fir_earnper , last_15d_mine_noadd_fir_earnper_dropmax , last_15d_mine_noadd_fir_earnper_droptwomax, last_15d_trans, last_30d_cost, last_30d_earn, last_30d_earnper, last_30d_difcost, last_30d_difearn, last_30d_difper, last_30d_costmedian, last_30d_costmean, last_30d_cost_noaddouttoken, last_30d_earn_noaddouttoken, last_30d_earnper_noaddouttoken, # last_30d_difcost_noaddouttoken, # last_30d_difearn_noaddouttoken, # last_30d_difper_noaddouttoken, # last_30d_costmedian_noaddouttoken, # last_30d_costmean_noaddouttoken, last_30d_mine_fir_earnper, last_30d_mine_fir_earnper_dropmax, last_30d_mine_fir_earnper_droptwomax, last_30d_mine_noadd_fir_earnper , last_30d_mine_noadd_fir_earnper_dropmax , last_30d_mine_noadd_fir_earnper_droptwomax , last_30d_trans, last_30d_earn-last_30d_earn_noaddouttoken, last_30d_mine_fir_earnper - last_30d_mine_noadd_fir_earnper, ] arr_earn_kuisun_counts=[ dalao_address, dalao_rul, "", last_03d_bigbig_earn_counts, last_03d_big_earn_counts, last_03d_nor_earn_counts, last_03d_nor_kuisun_counts,last_03d_big_kuisun_counts,last_03d_bigbig_kuisun_counts , last_03d_bigbig_earn_per, last_03d_big_earn_per , last_03d_nor_earn_per , last_03d_nor_kuisun_per , last_03d_big_kuisun_per, last_03d_bigbig_kuisun_per, last_03d_total_counts , last_07d_bigbig_earn_counts, last_07d_big_earn_counts, last_07d_nor_earn_counts, last_07d_nor_kuisun_counts,last_07d_big_kuisun_counts,last_07d_bigbig_kuisun_counts , last_07d_bigbig_earn_per , last_07d_big_earn_per , last_07d_nor_earn_per , last_07d_nor_kuisun_per , last_07d_big_kuisun_per, last_07d_bigbig_kuisun_per, last_07d_total_counts , # last_11d_bigbig_earn_counts, last_11d_big_earn_counts, last_11d_nor_earn_counts, # last_11d_nor_kuisun_counts,last_11d_big_kuisun_counts,last_11d_bigbig_kuisun_counts , # last_11d_bigbig_earn_per , last_11d_big_earn_per , last_11d_nor_earn_per , # last_11d_nor_kuisun_per , last_11d_big_kuisun_per, last_11d_bigbig_kuisun_per, # last_11d_total_counts , last_15d_bigbig_earn_counts, last_15d_big_earn_counts, last_15d_nor_earn_counts, last_15d_nor_kuisun_counts,last_15d_big_kuisun_counts,last_15d_bigbig_kuisun_counts , last_15d_bigbig_earn_per , last_15d_big_earn_per , last_15d_nor_earn_per , last_15d_nor_kuisun_per , last_15d_big_kuisun_per, last_15d_bigbig_kuisun_per, last_15d_total_counts , last_30d_bigbig_earn_counts, last_30d_big_earn_counts, last_30d_nor_earn_counts, last_30d_nor_kuisun_counts,last_30d_big_kuisun_counts,last_30d_bigbig_kuisun_counts , last_30d_bigbig_earn_per , last_30d_big_earn_per , last_30d_nor_earn_per , last_30d_nor_kuisun_per , last_30d_big_kuisun_per, last_30d_bigbig_kuisun_per, last_30d_total_counts , ] global TotalAnalysis_list global total_arr_earn_kuisun TotalAnalysis_list.append(analist) total_arr_earn_kuisun.append(arr_earn_kuisun_counts) def calute_days(df, now_unix_time): df["diffdays"] = 0 # intervaldays_list = [15, 11, 7, 3] intervaldays_list = [30, 15, 7, 3] for intervaldays in intervaldays_list: df.loc[df["TokenFirstTime"] >= now_unix_time - 3600*24*intervaldays, "diffdays"] = intervaldays return df def calucate_earnmain_dropoutoken(row): earnmain_amount_dropoutoken = decimal.Decimal(row['earn_main']) outtoken_per = decimal.Decimal(row["outtoken_per"]) issellfir = row["issellfir"] # if outtoken_per >= 1.1 or outtoken_per == -10 if outtoken_per > 1 or issellfir==0: earnmain_amount_dropoutoken = decimal.Decimal(0) earnmain_amount_dropoutoken_str = '{0:.4f}'.format( earnmain_amount_dropoutoken) return float(earnmain_amount_dropoutoken_str) def makeDexurl(row, dalao_address): tokenAddress = row['swap_tokenaddress'] urlname = f"https://dexscreener.com/solana/{tokenAddress}?maker={dalao_address}" return '=HYPERLINK("{}","{}")'.format(urlname, "DEX/"+tokenAddress) def get_ana_df(dalao_address, df, success_address_list, onetxhash_onerow_ana_df): onedalao_ana_df = df[onedalao_ana_columns].drop_duplicates( ).reset_index(drop=True) onedalao_ana_df.insert( loc=2, column="swap_tokenadd_dexurl", value="") onedalao_ana_df['swap_tokenadd_dexurl'] = onedalao_ana_df.apply( lambda row: makeDexurl(row, dalao_address), axis=1) onedalao_ana_df["FirstDateTime"] = pd.to_datetime( onedalao_ana_df['TokenFirstTime'], unit='s') # 如果outoken超了，earn_main_noaddouttoken 的值有两种处理方式 # 一种设置为0 即为不亏不赚 # 一种为全负 即为全亏了 # onedalao_ana_df["earn_main_noaddouttoken"] = onedalao_ana_df.apply( # lambda row: calucate_earnmain_dropoutoken(row), axis=1) get_analyres(df=onedalao_ana_df, dalao_address=dalao_address, now_unix_time=now_unix_time) onedalao_ana_df = calute_days( df=onedalao_ana_df, now_unix_time=now_unix_time) handle_onedalao_ana_df = onedalao_ana_df[onedalao_ana_df['cost_main'] != 0].reset_index( drop=True) baseclass.makedirpath(baseclass.dalao_ana_fm_path / dalao_address) onedalao_ana_df.to_csv(baseclass.dalao_ana_fm_path / dalao_address/f"analysisprofit_{dalao_address}.csv", index=False) handle_onedalao_ana_df.to_csv(baseclass.dalao_ana_fm_path / dalao_address/f"analysisprofit_handle_{dalao_address}.csv", index=False) onetxhash_onerow_ana_df.to_csv(baseclass.dalao_ana_fm_path / dalao_address/f"onetxhash_onerow_profit_{dalao_address}.csv", index=False) success_address_list.remove(dalao_address) def get_profit_st(str_dalao_address): if not (baseclass.dalao_configtoken_st_solanafm_path / f"configtoken_{str_dalao_address}.csv").exists(): return None # print(f"enter get_profit_st dalao_address= {str_dalao_address} \n", end='') df = pd.read_csv(baseclass.dalao_configtoken_st_solanafm_path / f"configtoken_{str_dalao_address}.csv", dtype=object) # timestamp sign source destination token amount swap_tokenadd swap_eth_amount swap_token_amount token_idx dalaofirsttimestamp df["timestamp"] = df["timestamp"].astype(int) df['dalaofirsttimestamp'] = pd.to_datetime( df['dalaofirsttimestamp'] ) df["dexurl"] = df.apply(lambda ser: (f'https://dexscreener.com/solana/{ser["swap_tokenaddress"]}?maker={str_dalao_address}' ), axis=1) df["TokenFirstTime"] = df.groupby("token_idx")[ "timestamp"].transform("min") df = calute_days( df=df, now_unix_time=now_unix_time) df = df.sort_values(by=["dalaofirsttimestamp", 'swap_tokenaddress', 'timestamp'], ascending=[ False, True, True]).reset_index(drop=True) onetxhash_onerow_ana_df = df.copy() onetxhash_onerow_ana_df = onetxhash_onerow_ana_df[["timestamp", "TokenFirstTime", "sign", "swap_tokenaddress","dexurl","platform","swap_type", "swap_eth_amount", "swap_token_amount", "dalaofirsttimestamp","diffdays"]] onetxhash_onerow_ana_df = onetxhash_onerow_ana_df.astype( {'swap_eth_amount': float}) # 计算一个txhash的 tokenamount swap 数目 onetxhash_onerow_ana_df = get_txhash_tokenswap_amount( df=onetxhash_onerow_ana_df) # 计算一个txhash的sol swap 数目 earnMain_df = ( onetxhash_onerow_ana_df .groupby('sign')["swap_eth_amount"] .sum() ) onetxhash_onerow_ana_df['swap_eth_amount'] = onetxhash_onerow_ana_df["sign"].map( earnMain_df) onetxhash_onerow_ana_df = onetxhash_onerow_ana_df.drop_duplicates().reset_index(drop=True) onetxhash_onerow_ana_df = get_tokenswap_amount(df=onetxhash_onerow_ana_df) onetxhash_onerow_ana_df = onetxhash_onerow_ana_df.sort_values(by=["dalaofirsttimestamp", 'swap_tokenaddress', 'timestamp'], ascending=[ False, True, True]).reset_index(drop=True) onetxhash_onerow_ana_df = get_sol_profit(df=onetxhash_onerow_ana_df) onetxhash_onerow_ana_df["datetime"] = pd.to_datetime( onetxhash_onerow_ana_df["timestamp"], unit='s') onetxhash_onerow_ana_df=onetxhash_onerow_ana_df[onetxhash_onerow_ana_cols] onetxhash_onerow_ana_df.to_csv( baseclass.dalao_profit_st_fm_path / f"onetxhash_onerow_profit_{str_dalao_address}.csv", index=False) # onetxhash_onerow_ana_df=pd.DataFrame() def set_platform(gdf): if "unknown" in gdf["platform"]: gdf["platform"] = "unknown" print("unknow in plat") return gdf onetxhash_onerow_ana_df.groupby(by=["swap_tokenaddress"]).apply(lambda gdf : set_platform(gdf)) onetxhash_onerow_ana_dropdup_df = onetxhash_onerow_ana_df.drop_duplicates(subset=["swap_tokenaddress"]).reset_index(drop=True) onetxhash_onerow_ana_dropdup_df.to_csv( baseclass.dalao_profit_st_fm_path / f"onetxhash_onerow_profit_dropdup_{str_dalao_address}.csv", index=False) return onetxhash_onerow_ana_df def get_dalaoaddress_list(): df = pd.read_csv(baseclass.dalao_merge_path / "filter_dalao.csv", dtype=object) arr_dalao_address = df["dalaoAddress"].tolist() return arr_dalao_address def save_earn_kuisun_df(): global total_arr_earn_kuisun totalana_earn_kuisun_df = pd.DataFrame( data=total_arr_earn_kuisun, columns=earn_kuisun_columns ) totalana_earn_kuisun_df = totalana_earn_kuisun_df.round(4) totalana_earn_kuisun_df = totalana_earn_kuisun_df.sort_values(by=["add"], ascending=[ True]).reset_index(drop=True) totalana_earn_kuisun_df.to_excel( baseclass.dalao_total_ana_fm_path/f"totalana_earn_kuisun_df.xlsx", index=False) return def save_total_ana_df(): global TotalAnalysis_list TotalAnalysis_df = pd.DataFrame( data=TotalAnalysis_list, columns=TotalAnalysis_columns ) TotalAnalysis_df = TotalAnalysis_df.round(4) TotalAnalysis_df = TotalAnalysis_df.sort_values(by=["add"], ascending=[ True]).reset_index(drop=True) TotalAnalysis_df.to_csv( baseclass.dalao_total_ana_fm_path/f"totalana.csv", index=False) TotalAnalysis_df.to_excel( baseclass.dalao_total_ana_fm_path/f"totalana.xlsx", index=False) # mine_fir_earnper_filter_mask = ( # (TotalAnalysis_df["15d_mine_fir_earnper"] >= # 0.8*TotalAnalysis_df["11d_mine_fir_earnper"]) # & (TotalAnalysis_df["11d_mine_fir_earnper"] >= 0.8*TotalAnalysis_df["07d_mine_fir_earnper"]) # ) # mine_fir_earnper_dropmax_filter_mask = ( # (TotalAnalysis_df["15d_mine_fir_earnper_dropmax"] >= # 0.8*TotalAnalysis_df["11d_mine_fir_earnper_dropmax"]) # & (TotalAnalysis_df["11d_mine_fir_earnper_dropmax"] >= 0.8*TotalAnalysis_df["07d_mine_fir_earnper_dropmax"]) # ) mine_noadd_fir_earnper_filter_mask = ( (TotalAnalysis_df["30d_mine_noadd_fir_earnper"] >= 0.8*TotalAnalysis_df["15d_mine_noadd_fir_earnper"]) & (TotalAnalysis_df["15d_mine_noadd_fir_earnper"] >= 0.8*TotalAnalysis_df["07d_mine_noadd_fir_earnper"]) ) mine_noadd_fir_earnper_dropmax_filter_mask = ( (TotalAnalysis_df["30d_mine_noadd_fir_earnper_dropmax"] >= 0.8*TotalAnalysis_df["15d_mine_noadd_fir_earnper_dropmax"]) & (TotalAnalysis_df["15d_mine_noadd_fir_earnper_dropmax"] >= 0.8*TotalAnalysis_df["07d_mine_noadd_fir_earnper_dropmax"]) ) filter_mask = ( # mine_fir_earnper_filter_mask # | mine_fir_earnper_dropmax_filter_mask mine_noadd_fir_earnper_filter_mask | mine_noadd_fir_earnper_dropmax_filter_mask ) increase_TotalAnalysis_df = TotalAnalysis_df[filter_mask].reset_index( drop=True) increase_TotalAnalysis_df.to_excel( baseclass.dalao_total_ana_fm_path/f"increase_totalana.xlsx", index=False) # last_07d_has_mask = (increase_TotalAnalysis_df["07d_trans"]== increase_TotalAnalysis_df["11d_trans"]) # last_07d_increase_TotalAnalysis_df = increase_TotalAnalysis_df[last_07d_has_mask] increase_TotalAnalysis_df.to_excel( baseclass.dalao_total_ana_fm_path/f"increase_totalana_07d.xlsx", index=False) def get_mul_profit_st(): global arr_str_dalaoaddress success_address_list = arr_str_dalaoaddress.copy() for idx in range(0, len(arr_str_dalaoaddress), batchSize): thread_list = [] for cur_idx in range(idx, min(idx+batchSize, len(arr_str_dalaoaddress))): thread = threading.Thread( target=get_one_profit_st, args=(arr_str_dalaoaddress[cur_idx], success_address_list, cur_idx) ) thread_list.append(thread) for thread in thread_list: thread.start() for thread in thread_list: thread.join() def get_one_profit_st(dalao_address, success_address_list, cur_idx): try: df = None onetxhash_onerow_ana_df = None print(f"get_one_profit_st cur_idx={cur_idx} dalao_address={dalao_address}\n", end='') onetxhash_onerow_ana_df = get_profit_st( str_dalao_address=dalao_address) if onetxhash_onerow_ana_df is None: print(f"get_one_profit_st cur_idx={cur_idx} is None \n", end='') return get_ana_df(dalao_address=dalao_address, df=onetxhash_onerow_ana_df, success_address_list=success_address_list, onetxhash_onerow_ana_df=onetxhash_onerow_ana_df) print(f"cur_idx={cur_idx} end \n", end='') return except Exception as e: print(f"error_dalao_address={dalao_address}") raise batchSize = 5 TotalAnalysis_list = [] total_arr_earn_kuisun =[] now_unix_time = int(time.time()) arr_str_dalaoaddress = get_dalaoaddress_list() arr_str_dalaoaddress = arr_str_dalaoaddress[0:450] arr_str_dalaoaddress.append(baseclass.mywalletaddress) # arr_str_dalaoaddress = ['H5P5xig8WjcDQrzyR4HaKcrg4hd2vh5DLPnFs4ypNE1X'] print(f"arr_str_dalaoaddress len= {len(arr_str_dalaoaddress)}") get_mul_profit_st() save_total_ana_df() save_earn_kuisun_df() print(f"arr_str_dalaoaddress len= {len(arr_str_dalaoaddress)} \n", end='') print(f"{'{:<6}'.format('END')} {baseclass.scriptfilename} ----------------NOTE-----------NOTE---------------")