#this python script has the gnuplot script for plotting already integrated. Please change the all-cap variables
#according to your needs
import time
import os
from pycoingecko import CoinGeckoAPI
cg = CoinGeckoAPI()
BitcoinPrice=cg.get_coin_market_chart_by_id(id='bitcoin',vs_currency='eur',days=30)
datafile=open('PATH_TO_WHERE_THE_DATA_SHALL_STORED_AS_/data.txt', 'w')
for i in xrange(len(BitcoinPrices['prices'])):
datafile.write(time.strftime('%Y.%m.%d %H:%M:%S', time.localtime((BitcoinPrices['prices'][i][0])/1000))+' '+str(BitcoinPrices['prices'][i][1])+'\n')
datafile.close()
datafile=open('PATH_TO_WHERE_YOUR_PLOT_SCRIPT_IS_TO_BE_STORED_AS/script.gnu','w')
#you may want to put the whole script in a single line in your IDE
datafile.write(
"set terminal 'pdf'\n
set output 'PATH_TO_WHERE_THE_PLOT_SHALL_STORED_AS_/plot.pdf'\n
set title 'historic BTC Price'\n
set xlabel 'time'\n
set ylabel 'Price/EUR'\n
set xdata time\n
set timefmt '%Y.%m.%d %H:%M:%S'\n
set format x '"'%m.%d'"'\n
set xtics'"'STARTDATE_YYYY.MM.DD'"',432000,'"'ENDDATE_YYYY.MM.DD'"'\n
plot 'PATH_TO_WHERE_THE_DATA_SHALL_STORED_AS_/data.txt' u 1:3 w l notitle")
datafile.close()
os.system('gnuplot PATH_TO_WHERE_YOUR_PLOT_SCRIPT_IS_TO_BE_STORED_AS/script.gnu')
}
new_mapping = {}
dates = []
date_dummy = datetime.datetime(2013,4,27)
today = str(datetime.datetime.today().date())
while date_dummy < datetime.datetime(int(today[:4]), int(today[5:7]), int(today[8:10])):
date_dummy = date_dummy + datetime.timedelta(days=1)
dates.append(date_dummy)
dates_df = pandas.DataFrame({'date': dates})
dfs = [dates_df]
days = 'max'
#looping through all coins
for i in range (0,len(mapping['name'])):
data = cg.get_coin_market_chart_by_id(mapping['name'][i], vs_currency='usd', days= days)
coin_data = {}
coin_dates = []
coin_prices = []
coin_vols = []
coin_caps = []
for time, val in data['prices']:
dt = datetime.datetime.fromtimestamp(int(time)/1000)
coin_dates.append(dt)
coin_prices.append(val)
for time, val in data['total_volumes']:
coin_vols.append(val)
for time, val in data['market_caps']:
coin_caps.append(val)
coin_data = {'date': coin_dates, 'prices': coin_prices, 'total_volume': coin_vols, 'market_cap': coin_caps}
this_coin = pd.DataFrame(columns=['date_unix',
'date_string',
'id',
'symbol',
'name',
'price_usd',
'market_cap',
'volume']
)
coinData = None
time.sleep(0.6)
while coinData is None:
try:
coinData = cg.get_coin_market_chart_by_id(id=symbol['id'],
vs_currency='usd',
days=30000,
interval='daily'
)
except Exception as e:
print(e)
time.sleep(10)
print("Resuming...")
n_days = len(coinData['prices'])
if n_days < 31:
coin = {'date_unix': 0,
'date_string': '1970-01-01',
'id': symbol['id'],
'symbol': symbol['symbol'],
'name': symbol['name'],
'price_usd': 0,
def get_fund_ROI(address, file_name):
sent = requests.get(
'https://api.bloxy.info/widget/address_value_daily?address=' +
address +
'¤cy=ETH&key=ACCunOMWYpmCp&format=table&price_currency=USD'
).json()
df1 = pd.DataFrame(sent)
df1.rename(columns={0: 'Date-USD', 11: 'ROI'}, inplace=True)
df1['ROI-USD'] = df1.ROI.cumsum()
sent = requests.get(
'https://api.bloxy.info/widget/address_value_daily?address=' +
address +
'¤cy=ETH&key=ACCunOMWYpmCp&format=table&price_currency=ETH'
).json()
df2 = pd.DataFrame(sent)
df2.rename(columns={0: 'Date', 11: 'ROI'}, inplace=True)
df2['ROI-ETH'] = df2.ROI.cumsum()
df = pd.concat([df1, df2], axis=1,
sort=False)[['Date', 'ROI-USD', 'ROI-ETH']]
df = df.set_index('Date')
df.index = pd.to_datetime(df.index)
cmc = yf.Ticker("^CMC200")
hist = cmc.history(period="max")[['Open']]
hist['Open'] = (np.where(hist['Open'] < 100, hist['Open'] * 10,
hist['Open']))
df = pd.concat([df, hist], axis=1, sort=False)
df = df.interpolate(axis=0).dropna(subset=['ROI-USD'])
start_cmc200 = df.head(1)['Open'][0]
df['CMC200-USD'] = (((df['Open'] - start_cmc200) / start_cmc200) * 100)
df = df[['ROI-USD', 'ROI-ETH', 'CMC200-USD']]
cg = CoinGeckoAPI()
data = cg.get_coin_market_chart_by_id('bitcoin', 'eth', 500)
dates = [
datetime.fromtimestamp(data['prices'][i][0] /
1000).strftime('%Y-%m-%d')
for i in range(len(data['prices']))
]
price = [data['prices'][i][1] for i in range(len(data['prices']))]
df_data = {'BTC-ETH': price, 'Date': dates}
df_btc = pd.DataFrame(df_data)
df_btc = df_btc.set_index('Date')
df_btc.index = pd.to_datetime(df_btc.index)
df = pd.concat([df, df_btc], axis=1, sort=False)
df = df.dropna(subset=['ROI-USD'])
start_btc = df.head(1)['BTC-ETH'][0]
df['BTC-ETH'] = (((df['BTC-ETH'] - start_btc) / start_btc) * 100)
df.drop(df.tail(1).index, inplace=True)
df.to_csv(file_name, index=True)
def Malta(file_name):
# Get the NAV, and sharePrice of the fund on each priceUpdate
jsonData = '{"query": "{fundCalculationsHistories(where: {fund: \\"0x26491fc7da30b35d818de45982fb1de4f65ed8f5\\"}) { nav, timestamp, totalSupply, source}}"}'
data = requests.post(
'https://api.thegraph.com/subgraphs/name/melonproject/melon',
data=jsonData).json()['data']
roi = []
nav = []
timestamp = []
for i in range(len(data['fundCalculationsHistories'])):
if (data['fundCalculationsHistories'][i]['source'] == 'priceUpdate'):
roi.append(
int(data['fundCalculationsHistories'][i]['nav']) /
int(data['fundCalculationsHistories'][i]['totalSupply']))
nav.append(int(data['fundCalculationsHistories'][i]['nav']) / 1e18)
timestamp.append(
(int(data['fundCalculationsHistories'][i]['timestamp'])))
while (len(data['fundCalculationsHistories']) == 100):
jsonData = '{"query": "{fundCalculationsHistories(where: {fund: \\"0x26491fc7da30b35d818de45982fb1de4f65ed8f5\\", timestamp_gt:\\"' + str(
timestamp[-1] +
1) + '\\"}) { nav, timestamp, totalSupply, source}}"}'
data = requests.post(
'https://api.thegraph.com/subgraphs/name/melonproject/melon',
data=jsonData).json()['data']
for i in range(len(data['fundCalculationsHistories'])):
if (data['fundCalculationsHistories'][i]['source'] == 'priceUpdate'
):
roi.append(
int(data['fundCalculationsHistories'][i]['nav']) /
int(data['fundCalculationsHistories'][i]['totalSupply']))
nav.append(
int(data['fundCalculationsHistories'][i]['nav']) / 1e18)
timestamp.append(
(int(data['fundCalculationsHistories'][i]['timestamp'])))
df1 = pd.DataFrame({'NAV': nav, 'ROI-ETH': roi}, index=timestamp)
# Get the price of USDC on each price update
jsonData = '{"query": "{assetPriceHistories(where: {asset: \\"0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48\\", , timestamp_gt: \\"' + str(
timestamp[0] - 1) + '\\"}) {price, timestamp}}"}'
data = requests.post(
'https://api.thegraph.com/subgraphs/name/melonproject/melon',
data=jsonData).json()['data']
price = []
timestamp = []
for i in range(len(data['assetPriceHistories'])):
price.append(int(data['assetPriceHistories'][i]['price']) / 1e18)
timestamp.append((int(data['assetPriceHistories'][i]['timestamp'])))
while (len(data['assetPriceHistories']) == 100):
jsonData = '{"query": "{assetPriceHistories(where: {asset: \\"0xa0b86991c6218b36c1d19d4a2e9eb0ce3606eb48\\", , timestamp_gt: \\"' + str(
timestamp[-1] + 1) + '\\"}) {price, timestamp}}"}'
data = requests.post(
'https://api.thegraph.com/subgraphs/name/melonproject/melon',
data=jsonData).json()['data']
for i in range(len(data['assetPriceHistories'])):
price.append(int(data['assetPriceHistories'][i]['price']) / 1e18)
timestamp.append(
(int(data['assetPriceHistories'][i]['timestamp'])))
df2 = pd.DataFrame(price, index=timestamp, columns=['USD-ETH'])
df = pd.concat([df1, df2], axis=1, sort=False)
df['ROI-USD'] = ((df['ROI-ETH'] / df['USD-ETH']) /
(1 / df.iloc[0]['USD-ETH']) - 1) * 100
df['ROI-ETH'] = (df['ROI-ETH'] - 1) * 100
df.index = pd.to_datetime(df.index, unit='s').strftime('%Y-%m-%d')
df = df[~df.index.duplicated(keep='first')]
df.index.name = 'Date'
df.to_csv(file_name[:-4] + '_raw.csv', index=True)
cmc = yf.Ticker("^CMC200")
hist = cmc.history(period="max")[['Open']]
hist['Open'] = (np.where(hist['Open'] < 100, hist['Open'] * 10,
hist['Open']))
hist.index = pd.to_datetime(hist.index).strftime('%Y-%m-%d')
df = pd.concat([df, hist], axis=1, sort=True)
df = df.interpolate(axis=0).dropna(subset=['ROI-USD'])
df.index = pd.to_datetime(df.index).strftime('%Y-%m-%d')
start_cmc200 = df.head(1)['Open'][0]
df['CMC200-USD'] = (((df['Open'] - start_cmc200) / start_cmc200) * 100)
df = df[['ROI-USD', 'ROI-ETH', 'CMC200-USD']]
cg = CoinGeckoAPI()
data = cg.get_coin_market_chart_by_id('bitcoin', 'eth', 500)
dates = [
datetime.fromtimestamp(data['prices'][i][0] /
1000).strftime('%Y-%m-%d')
for i in range(len(data['prices']))
]
price = [data['prices'][i][1] for i in range(len(data['prices']))]
df_btc = pd.DataFrame({'BTC-ETH': price}, index=dates)
df_btc.index = pd.to_datetime(df_btc.index).strftime('%Y-%m-%d')
df_btc.drop(df_btc.tail(1).index, inplace=True)
df = pd.concat([df, df_btc], axis=1, sort=True)
df = df.dropna(subset=['ROI-USD'])
start_btc = df.head(1)['BTC-ETH'][0]
df['BTC-ETH'] = (((df['BTC-ETH'] - start_btc) / start_btc) * 100)
df.drop(df.tail(1).index, inplace=True)
df.to_csv(file_name, index=True)
#!/usr/bin/env python
# coding: utf-8
# In[1]:CODE TO OBTAIN CANDLE STICK PLOT TO GET PRICE FLUCTUATION ON BITCOIN FOR PAST 30 DAYS
get_ipython().system('pip install pycoingecko') #INSTALL pycoingecko
from pycoingecko import CoinGeckoAPI #Import CoinGecko API
cg = CoinGeckoAPI()
bitcoin_data = cg.get_coin_market_chart_by_id(id='bitcoin',
vs_currency='usd',
days=30)
bitcoin_price_data = bitcoin_data['prices']
import pandas as pd
data = pd.DataFrame(bitcoin_price_data, columns=['TimeStamp', 'Price'])
print(data)
data['Date'] = pd.to_datetime(data['TimeStamp'], unit='ms')
print(data)
candlestick_data = data.groupby(data.Date.dt.date).agg(
{'Price': ['min', 'max', 'first', 'last']})
candlestick_data
