Basic analysis of some bitcoin stats

bitcoin_analysis.py

from datetime import timedelta
from apscheduler.schedulers.blocking import BlockingScheduler

import requests, json, datetime, numpy, requests_cache
import matplotlib.dates as mdate
import matplotlib.pyplot as plot
import numpy as np

ALL_HISTORICAL_DATA_ENDPOINT = 'https://blockchain.info/charts/market-price?timespan=all&format=json'
NUM_UNIQUE_ADDRESSES = 'https://blockchain.info/charts/n-unique-addresses?timespan=all&format=json'
NUM_CONF_TRANSACTIONS = 'https://blockchain.info/charts/n-transactions?timespan=all&format=json'
#fees in BTC
#NUM_TRANSACTIONS_FEE = 'https://blockchain.info/charts/transaction-fees?timespan=all&format=json'
#fees in USD
NUM_TRANSACTIONS_FEE = 'https://blockchain.info/charts/transaction-fees-usd?timespan=all&format=json'
NUM_DAYS_THREE_YEARS = 365 * 3
AVG_WINDOW = 25
requests_cache.install_cache('request_cache')

def plot_moving_average(x, y_data, window, y_label, title):
    fig, ax = plot.subplots()

    for (y, symbol, label) in y_data:
        y_running_mean = np.convolve(y, np.ones((window,))/window, mode='valid')
        ax.plot_date(x[window - 1:], y_running_mean, symbol, label=label)

    handles, labels = ax.get_legend_handles_labels()
    ax.legend(handles, labels)

    date_formatter = mdate.DateFormatter('%d/%m/%y')
    ax.xaxis.set_major_formatter(date_formatter)
    fig.autofmt_xdate()
    plot.ylabel(y_label)
    plot.title(title)

def plot_data():
    data = requests.get(NUM_UNIQUE_ADDRESSES).json()['values'][-NUM_DAYS_THREE_YEARS:]
    addr_x = [p['x'] for p in data]
    addr_y = [p['y'] for p in data]

    data = requests.get(ALL_HISTORICAL_DATA_ENDPOINT).json()['values'][-NUM_DAYS_THREE_YEARS:]
    price_x = [p['x'] for p in data]
    price_y = [p['y'] for p in data]

    data = requests.get(NUM_CONF_TRANSACTIONS).json()['values'][-NUM_DAYS_THREE_YEARS:]
    trans_x = [p['x'] for p in data]
    trans_y = [p['y'] for p in data]

    data = requests.get(NUM_TRANSACTIONS_FEE).json()['values'][-NUM_DAYS_THREE_YEARS:]
    fee_x = [p['x'] for p in data]
    fee_y = [p['y'] for p in data]

    x = []
    price_addr_y = []
    price_trans_y = []
    price_fee_y = []
    price_percent_change_y = []
    addr_percent_change_y = []
    trans_percent_change_y = []
    fee_percent_change_y = []

    for i in range(1, len(addr_x)):
        if addr_x[i] != price_x[i] != trans_x[i] != fee_x[i]:
            print("Data does not match")

        avgx = (addr_x[i] + addr_x[i-1]) / 2
        x.append(mdate.epoch2num(avgx))

        price_dy = price_y[i] - price_y[i-1]
        percent_change_price = price_dy / price_y[i-1]

        addr_dy = addr_y[i] - addr_y[i-1]
        percent_change_address = addr_dy / addr_y[i-1]

        trans_dy = trans_y[i] - trans_y[i-1]
        percent_change_trans = trans_dy / trans_y[i-1]

        fee_dy = fee_y[i] - fee_y[i-1]
        percent_change_fee = fee_dy / fee_y[i-1]

        price_to_addr = percent_change_price / percent_change_address
        price_to_trans = percent_change_price / percent_change_trans
        price_to_fee = percent_change_price / percent_change_fee

        price_addr_y.append(abs(price_to_addr))
        price_trans_y.append(abs(price_to_trans))
        price_fee_y.append(abs(price_to_fee))
        
        price_percent_change_y.append(percent_change_price)
        addr_percent_change_y.append(percent_change_address)
        trans_percent_change_y.append(percent_change_trans)
        fee_percent_change_y.append(percent_change_fee)

    plot_moving_average(x, [
        (price_trans_y, 'g-', "| % Δ price / % Δ trans |"),
        (price_addr_y, 'b-', "| % Δ price / % Δ addr |"),
        (price_fee_y, 'c-', "| % Δ price / % Δ fee |")
    ], AVG_WINDOW, "", "{} day moving average ratios".format(AVG_WINDOW))

    plot_moving_average(x, [
        (price_percent_change_y, 'r-', "Price"),
        (addr_percent_change_y, 'b-', "Addresses"),
        (trans_percent_change_y, 'g-', "Transactions"),
        (fee_percent_change_y, 'c-', "Fee")
    ], AVG_WINDOW, "% change", "{} day moving average ratios % change".format(AVG_WINDOW))

    plot.show()

plot_data()