Other candidates: 交易所差价index; 跑路指数( lCO contract In/out token,time domain purchased/expiry)bitdb.network; coinbase bulletin; https://dappradar.com; Bitcointalk之后是谁- 用correlation来检测.

如果资金进出(quantity*price)最大,则经常tipping point. 值钱数据:历史流入流出数据-bitfinix

Correlation between forks: ETC => ETH

GraphQL - Etherium Name service analysis - etherium nodes, or masternode, or Privacy exposed-

我们要寻找的是相关性,这个相关性本身如何衡量(比如是事件发生的日内价格,还是事件发生1个月后均价),至关重要,主观性尺度应该也很大。 更容易的目标估计是预测热点或热点早期检测?

只能找局部规律和隐藏模式(场外交易) 因全局都是政治或货币政策(BTC main jumps since 2015)的影响

而这一程序可能就是自然选择所赋予我们的。生物学的经典规则不是"汝当......",而是"如果......那么将...(so our rules should be Flexible and simple)

老大比如CZ长鹏点名,过几天会涨ZEL. https://github.com/bokkypoobah/BokkyPooBahsDateTimeLibrary

Bokky poobah很小心,free fee exchange for 2 years, otherwise tax problematic. Not using Truffle, built own JS dev. want to build gasless contract orderPricekey = contract.getnextbestPrice(e.pairkey, buySell, orderPriceKey) Incubed

ethervendingmachine.io three hypotheses high correlation with national fiat collapse (2 single buyer of 20k coins; 3 price auto-action trigger 历史惊人的巧合,每当一个国家货币出现问题,就会引发比特币需求量的增加,从而成为上涨的导火索。2018年年初,委内瑞拉货币玻璃瓦尔闪崩,刺激了比特币2月份的反弹。去年7月,土耳其里拉因为土耳其政府财政问题闪崩,

the two strangers met to conduct a 20ドルk trade. (off the record market)

fileitem in Dic[in]: 如何克服重名(用dict). in D也是错的

MD5存下来? Python应该启用Logger方便调错

模糊匹配,

规则存储改为https://github.com/UKHomeOffice/repo-security-scanner\rules\gitrob.json 格式 https://github.com/MiSecurity/x-patrol

1. online API to store result
2. 新文件源:iCrawler(QR-pirate)+ OCR to detect new sources
3. Linux partitions
4. 如都是机械硬盘或混有Flash Disk,何时适合Multithreading(读机械盘不适合4+线程并行)

1. Push rules that based on blockchain file price trend/ indicated by future hot topic?

TO check other Forensic BItbucket and found 2 Git proj (QRcod)

WARNING:root:[Errno 2] No such file or directory: 'd:\\\xbc\xd2\xca\xc2\5円.pdf'

搜索逻辑:精确匹配,

hawkeye的 https://github.com/0xbug/Hawkeye 爬虫 报警功能 https://github.com/FeeiCN/GSIL/blob/master/gsil/engine.py 扫描邮件地址并保存-考虑并行,磁盘,冲突及无趣公用邮件库: public_mail_services = [ 'msg.com', '126.com', '139.com', '163.com', 'qq.com',

Token recommendation using BigQuery https://medium.com/google-cloud/building-token-recommender-in-google-cloud-platform-1be5a54698eb #standardSQL by Evgenny with top_tokens as ( select token_address, count(1) as transfer_count from bigquery-public-data.ethereum_blockchain.token_transfers as token_transfers group by token_address order by transfer_count desc limit 1000 ), token_balances as ( with double_entry_book as ( select token_address, to_address as address, cast(value as float64) as value, block_timestamp from bigquery-public-data.ethereum_blockchain.token_transfers union all select token_address, from_address as address, -cast(value as float64) as value, block_timestamp from bigquery-public-data.ethereum_blockchain.token_transfers ) select double_entry_book.token_address, address, sum(value) as balance from double_entry_book join top_tokens on top_tokens.token_address = double_entry_book.token_address where address != '0x0000000000000000000000000000000000000000' group by token_address, address having balance > 0 ), token_supplies as ( select token_address, sum(balance) as supply from token_balances group by token_address ) select token_balances.token_address, token_balances.address as user_address, balance/supply * 100 as rating from token_balances join token_supplies on token_supplies.token_address = token_balances.token_address where balance/supply * 100 > 0.001