10K+ Bitcoinを保有する🐳の動向を追え！

趣旨

10K BTC以上を保有するアドレス数の上昇とビットコインの価格チャートを比較したら、クジラが買ってるか売ってるか判断できると思ったので、TradingViewなどを使わずに直接チャートを生成するためのコードを用意しました。

コード

Jupyter Notebookでライブラリをインストールした後に起動すれば一発でチャート画像が生成されます。おすすめです。

import requests
import matplotlib.pyplot as plt
import pandas as pd
import yfinance as yf
from datetime import datetime

# Function to get balance data from API
def get_btc_balance_data():
    url = 'https://bitcoin-data.com/v1/balance-addr-10K-BTC'
    headers = {'accept': 'application/hal+json'}
    response = requests.get(url, headers=headers)
    if response.status_code == 200:
        return response.json()
    else:
        print(f"Error fetching BTC data: {response.status_code}")
        return []

# Function to get BTCUSD data from Yahoo Finance
def get_btcusd_data(start_date, end_date):
    ticker = "BTC-USD"
    btc_data = yf.download(ticker, start=start_date, end=end_date)
    return btc_data

# Function to get DOGEUSD data from Yahoo Finance
def get_dogeusd_data(start_date, end_date):
    ticker = "DOGE-USD"
    doge_data = yf.download(ticker, start=start_date, end=end_date)
    return doge_data

# Function to get XMRUSD data from Yahoo Finance
def get_xmrusd_data(start_date, end_date):
    ticker = "XMR-USD"
    xmr_data = yf.download(ticker, start=start_date, end=end_date)
    return xmr_data

# Plot all charts in a single figure with subplots
def plot_combined_charts(btc_data, btcusd_data, doge_data, xmr_data):
    # Create a figure with 4 subplots
    fig, axs = plt.subplots(4, 1, figsize=(10, 18))

    # Plot BTC 10K+ Addresses
    dates = [datetime.strptime(item["d"], "%Y-%m-%d") for item in btc_data]
    balances = [int(item["balAddr10Kbtc"]) for item in btc_data]
    axs[0].plot(dates, balances, label="BTC 10K+ Addresses", color='blue')
    axs[0].set_title("Number of BTC Addresses Holding 10K+ BTC")
    axs[0].set_xlabel("Date")
    axs[0].set_ylabel("Number of Addresses")
    axs[0].grid(True)
    axs[0].legend()

    # Plot BTCUSD (Normalized to thousands)
    axs[1].plot(btcusd_data.index, btcusd_data['Close'] / 1000, label="BTCUSD (in thousands)", color='orange')
    axs[1].set_title("BTCUSD Price Over Time (Normalized)")
    axs[1].set_xlabel("Date")
    axs[1].set_ylabel("Price (in thousands USD)")
    axs[1].grid(True)
    axs[1].legend()

    # Plot DOGEUSD
    axs[2].plot(doge_data.index, doge_data['Close'], label="DOGEUSD", color='green')
    axs[2].set_title("DOGEUSD Price Over Time")
    axs[2].set_xlabel("Date")
    axs[2].set_ylabel("Price (USD)")
    axs[2].grid(True)
    axs[2].legend()

    # Plot XMRUSD
    axs[3].plot(xmr_data.index, xmr_data['Close'], label="XMRUSD", color='purple')
    axs[3].set_title("XMRUSD Price Over Time")
    axs[3].set_xlabel("Date")
    axs[3].set_ylabel("Price (USD)")
    axs[3].grid(True)
    axs[3].legend()

    # Adjust layout and show the combined plot
    plt.tight_layout()
    plt.show()

# Main Execution
btc_data = get_btc_balance_data()

if btc_data:
    # Get the start and end dates from the BTC data
    start_date = min([item["d"] for item in btc_data])
    end_date = max([item["d"] for item in btc_data])

    # Get BTCUSD, DOGEUSD, and XMRUSD data from Yahoo Finance
    btcusd_data = get_btcusd_data(start_date, end_date)
    doge_data = get_dogeusd_data(start_date, end_date)
    xmr_data = get_xmrusd_data(start_date, end_date)

    # Plot combined charts
    if not btcusd_data.empty and not doge_data.empty and not xmr_data.empty:
        plot_combined_charts(btc_data, btcusd_data, doge_data, xmr_data)
    else:
        print("Error: One or more datasets are empty.")

結果

🐳はBTCの大暴落時には喜んで狂ったようにBTCを買い漁っていますね。今は売却傾向が強そうなので大きく買いたいと思ってる場合は一旦様子見しても良いかも知れませんね。クジラの程度を下げてチャートを生成したらまた違った側面が見れるのかも知れませんけど。