
import cbpro
import datetime
import pandas as pd
import seaborn as sns
import numpy as np
import matplotlib as plt


public_client = cbpro.PublicClient()

products = public_client.get_products()
pairs = [product['id'] for product in products]


####====================    create dataset of daily close
dataList = []
for pair in pairs:

    data = public_client.get_product_historic_rates(
        pair,
        granularity=86400,
        end=datetime.datetime.now().isoformat())

    data = pd.DataFrame(data, columns = ['unix', 'low', 'high', 'open', 'close', 'volume'])

    data['date'] = pd.to_datetime(data['unix'], unit='s')
    data = data[['date', 'close']]
    data.set_index('date', inplace=True)
    data.rename({'close':pair}, axis=1, inplace=True)

    dataList.append(data)

dataset=pd.concat(dataList, axis=1, copy=False)
dataset.plot(legend=False)
dataset=dataset.query('date>"2021-04"').dropna(axis=1)
dataset.plot(legend=False)

# dataset.to_csv('coinbase_daily_close.csv')

<AxesSubplot:xlabel='date'>


###====================    basic corr of pct change
datasetPctChange=dataset.pct_change()
datasetPctChange.plot(legend=False)

corr = datasetPctChange.corr()
sns.heatmap(corr)

<AxesSubplot:>


#####======================= long rolling corr dataset
tempList = []

dates = datasetPctChange.index
n = 30
# for date in dates[n:-n]:
for i, date in enumerate(dates):
    if i > 30:
        # 30 day lag
        date = date - datetime.timedelta(days=n)
        temp = datasetPctChange.iloc[i-30:i,:]
        corr = temp.corr()
        corrLong=pd.DataFrame(np.tril(corr), columns=corr.columns, index=corr.index).melt(ignore_index=False).query('value not in [0,1]')
        corrLong['lag'] = date
        tempList.append(corrLong)


corrAgg = pd.concat(tempList)
corrAgg.to_csv('coinbase_daily_close_rolling_30_corr_long.csv')


### ================================== wide rolling dataset
rollingCorr = (corrAgg
    .reset_index()
    .pivot_table(columns=['index', 'variable'], values='value', index='lag')
)

# rollingCorr.to_csv('coinbase_daily_close_rolling_30_corr_wide.csv')
rollingCorr.plot(legend=False)
rollingCorr.iloc[:,1:10].plot(legend=False)

<AxesSubplot:xlabel='lag'>


### ======================== individual coin's corr plot
plt.rcParams.update({'figure.max_open_warning': 0})

products = public_client.get_products()

pairs = [product['id'] for product in products]

coins = [pair.split('-') for pair in pairs]

allcoins = []
for coin in coins:
    allcoins.append(coin[0])
    allcoins.append(coin[1])

allcoins = pd.Series(allcoins).drop_duplicates()


for coin in allcoins:
    try:
        # get coin-usd and *-usd
        coins = [col for col in rollingCorr.columns if col[0] == f"{coin}-USD" and col[1].split('-')[1] == 'USD']

        rollingCorr[coins].plot(legend=False, title=coin)
    except:
        pass
        # print(coin)