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      "body": "# 암호화폐 알고리즘 트레이딩 - 1.크롤링편 <h1>\n\n__최근 암호화폐 열기가 매우 뜨겁네요. \n암호화폐시장은 시대적 흐름으로 보이고, 이러한 흐름은 상당기간 지속될 것이라 생각합니다.\n참고로 필자는 처음 호기심으로 암호화폐를 접하였지만 현재는 알고리즘 트레이딩까지 하고 있네요.__\n\n  알고리즘 트레이딩을 하게 된 계기는 암호화폐 거래소라는 특성상 24시간 거래되고, 변동성도 심하기 때문에 항상 신경 쓰여 본업 및 일상생활에도 영향을 미쳐 변동성으로부터 자유로워지고자 룰베이스 방식으로 투자하기 위해 시작하였습니다. \n\n알고리즘은 투자성과에 직결되는 것이기에 공개할 순 없지만, 알고리즘트레이딩 시스템을 구축하며 습득한 지식 및 내용을 공유하고자 연재를 하기로 하였습니다.\n\n참고로 필자는 현재, 국내 거래소 중 한곳의 API를 활용하여 알고리즘 트레이딩을 하고 있으며, 텔레그램을 활용하여 챗봇 형태로 사전에 몇 가지 전략을 설정해 놓고 전략을 변경해가며 알고리즘을 실시간 컨트롤 하고 있습니다. 또한, 초단위 데이터를 데이터베이스에 적재 후, 향후 알고리즘 시뮬레이션도 진행할 예정입니다.\n\n트레이딩 시스템 개발환경은 **Anaconda3(Python 3.6)**을 활용하였으며, **MYSQL DB**를 사용하고 있습니다. 설치 및 설정방법은 인터넷에서 쉽게 찾을 수 있습니다. \n\n\n연재 첫번째 주제로 시세를 크롤링 해  오는 방법에 대해 작성해 보겠습니다.\n물론 각 거래소에서 제공하는 API를 활용해 시세를 받아올 수 있지만, API는 거래소별로 상이하기 때문에 크롤링을 통해 시세를 가지고 오도록 하겠습니다.\n\n_참고로, 국내거래소는 빗썸을 사용하였고, 해외거래소는 Bittrex를 사용하였습니다._\n\n__먼저, 크롤링에 필요한 패키지를 불러옵니다.__\n\n![](https://steemitimages.com/DQmegFSbesaK3JMtULanEoqC8Q5fgb15aBRycPRzdw5Cets/image.png)\n\n__다음으로 거래소별로 API를 제공하는 URL을 저장해 놓습니다.__\n\n![](https://steemitimages.com/DQmQfRPU87PnMHVNSvSYKbGv5yPRELVotmUXebjXiihW1f8/image.png)\n\n__원하는 티커의 URL접속 후, 정보를 크롤링해오는 함수를 작성합니다.__\n\n![](https://steemitimages.com/DQmeeobWSEfBiVKPkynjDaGaTfua62j5svwajb4TmVJgSs5/image.png)\n\n__상기에 작성된 함수를 활용해 거래소 별 데이터를 크롤링 해옵니다. \n예시로, 각 거래소 별로 이더리움 정보를 크롤링 해보겠습니다.__\n\n![](https://steemitimages.com/DQmYJ82DgemSwPGTGPPigQviMQSD458EqAWL9KBFyE4ueuJ/image.png)\n\n__크롤링해 온 데이터를 Pandas 데이터프레임 형태로 저장합니다.__\n\n![](https://steemitimages.com/DQmY42mhtJMNRrKwB37uYccGHW43VvVMuQ559eo5zeMNQMF/image.png)\n\n_참고로,**Pandas**를 활용하여 데이터프레임을 구성하면 간단한 투자시뮬레이션 뿐만 아니라, 머신러닝 등의 고급 방법론을 적용한 시뮬레이션도 효율적으로 진행할 수 있습니다. \n만약 Pandas를 모르신다면 인터넷에도 많은 자료들이 있고, 시중에 좋은 서적들도 많으니 꼭 공부하셨으면 좋겠습니다._\n\n__원하는 정보는 다음과 같이 데이터프레임 형태로 정상적으로 들어왔습니다.__\n\n![](https://steemitimages.com/DQmYngNdL7VEsw9zVivq9XYSenrX533oubWeWFz2FPyJ4Va/image.png)\n\n_만약 빗썸 거래소에서 크롤링해 온 정보들 중 **\"Closing_price\"**의 정보만을 알고 싶다면, \n현재 Pandas 데이터프레임으로 구성되어 있기에 다음과 같이 실행한다면 됩니다._\n\n![](https://steemitimages.com/DQmUbz716JJXcJUSkxTNutWAwSLEFUh2D6DdG7M8anawWQp/image.png)\n\n\n__이것으로 암호화폐 알고리즘 트레이딩을 위한 크롤링1편을 마치도록 하겠습니다.__\n\n_PS. 취미로 만들어 사용하고 있는 것이기 때문에, 다소 부족한 부분이 있을 수 있습니다. \n쪽지 및 댓글등으로 피드백 주신다면 이를 반영하여 좀 더 완성도 높은 내용을 공유해 드리도록 하겠습니다._\n\n### 전체코드\n\n```\nimport json \nimport requests\nimport pandas as pd\nimport datetime as dt\n\n# 거래소 별 URL\nBITHUMB = \"https://api.bithumb.com/public/ticker/\"\nBITTREX = \"https://bittrex.com/api/v1.1/public/getticker?market=\"\n\n\n# 크롤링 함수\ndef crawling(url, ticker = 'BTC'):\n    get_data = requests.get(url + ticker)\n    return json.loads(get_data.text)\n\n# 거래소 별 데이터 크롤링\nbithumb_data = crawling(BITHUMB, ticker = 'ETH')\nbittrex_data = crawling(BITTREX, ticker = 'BTC-ETH')\n\n# 빗썸 데이터프레임\nbithumb = pd.DataFrame(bithumb_data['data'], \n                       index= [dt.datetime.now().strftime('%Y-%m-%d %H:%M:%S')])\n# 비트렉스 데이터프레임\nbittrex = pd.DataFrame(bittrex_data['result'], \n                       index= [dt.datetime.now().strftime('%Y-%m-%d %H:%M:%S')])",
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