mirror of
git://git.acid.vegas/random.git
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100 lines
3.1 KiB
Python
100 lines
3.1 KiB
Python
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#!/usr/bin/env python
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# CoinMarketCap Standard Deviation - Developed by acidvegas in Python (https://acid.vegas/random)
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'''
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The script will calculate the mean, median, mode, high, low & std for the entire cryptocurrency market over the last 7 days.
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API Documentation:
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https://coinmarketcap.com/api/
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'''
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import datetime
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import http.client
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import json
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import math
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import time
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import statistics
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class CoinMarketCap(object):
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def __init__(self):
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self.cache = {'ticker':{'BTC':{'last_updated':0}}}
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def _ticker(self):
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conn = http.client.HTTPSConnection('api.coinmarketcap.com')
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conn.request('GET', '/v1/ticker/?limit=0')
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data = json.loads(conn.getresponse().read().replace(b': null', b': "0"'))
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conn.close()
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return data
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def _markets():
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conn = http.client.HTTPSConnection('s2.coinmarketcap.com')
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conn.request('GET', '/generated/search/quick_search.json')
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data = json.loads(conn.getresponse().read())
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conn.close()
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results = dict()
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for item in data:
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results[item['id']] = item['name']
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return results
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def _graph(self, name, start_time, end_time):
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conn = http.client.HTTPSConnection('graphs2.coinmarketcap.com', timeout=60)
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conn.request('GET', f'/currencies/{name}/{start_time}/{end_time}/')
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return json.loads(conn.getresponse().read())
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def generate_table(data):
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matrix = dict()
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keys = data[0].keys()
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for item in keys:
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matrix[item] = list()
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del keys
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for item in data:
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for subitem in item:
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matrix[subitem].append(item[subitem])
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for item in matrix:
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matrix[item] = len(max(matrix[item], key=len))
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columns = [item.ljust(matrix[item]) for item in matrix.keys()]
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print(' '.join(columns))
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del columns
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for item in data:
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row_columns = [item[subitem].ljust(matrix[subitem]) for subitem in item]
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print(' | '.join(row_columns))
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def stddev(data):
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n = len(data)
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if n <= 1:
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return 0.0
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mean = avg_calc(data)
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sd = 0.0
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for el in data:
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sd += (float(el)-mean)**2
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sd = math.sqrt(sd/float(n-1))
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return sd
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def avg_calc(ls):
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n = len(ls)
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mean = 0.0
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if n <= 1:
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return ls[0]
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for el in ls:
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mean = mean+float(el)
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mean = mean/float(n)
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return mean
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def get_data(coin, start_time, end_time):
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try:
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time.sleep(4)
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data = [item[1] for item in CMC._graph(coin, start_time, end_time)['price_usd']]
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return {'name':coin,'mean':f'{sum(data)/len(data):.2f}','median':f'{statistics.median(data):.2f}','mode':f'{max(set(data),key=data.count):.2f}','high':f'{max(data):.2f}','low':f'{min(data):.2f}','std':f'{stddev(data):.2f}'}
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except:
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return {'name':'none','mean':'none','median':'none','mode':'none','high':'none','low':'none','std':'0'}
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CMC = CoinMarketCap()
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ticker_data = CMC._ticker()
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start_time = int((datetime.datetime.now()-datetime.timedelta(days=7)).timestamp()*1000)
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end_time = int(datetime.datetime.now().timestamp()*1000)
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coins = [item['id'] for item in ticker_data] #[:10]
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data = [get_data(coin, start_time, end_time) for coin in coins]
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data = sorted(data, key=lambda k: float(k['std']), reverse=True)
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generate_table(data)
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size=len(CMC._graph('bitcoin', start_time, end_time)['price_usd'])
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print('Spread acrosss 7 days - ' + str(size) + ' points')
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