basic hl ratios

gistfile1.txt

# -*- coding: utf-8 -*-
"""
CODE BY STALEQUANT BASED ON CODE FROM GAUTIER https://x.com/phoenixstealthy
"""

import matplotlib.pyplot as plt

import ccxt
import pandas as pd
from datetime import datetime, timedelta

# Initialize exchanges
binance = ccxt.binance({
    'options': {
        'defaultType': 'future',  # Set to 'future' for Binance Futures
    }
})

hyperliquid = ccxt.hyperliquid()

# Helper function to fetch daily volume since a specific date
def fetch_daily_volumes_since(exchange, markets, since_timestamp):
    daily_volumes = {}

    for market in markets:
        try:
            if ((exchange.id == 'binance' and market.endswith('USDT:USDT')) 
                or (exchange.id == 'hyperliquid'  and market.endswith('USDC:USDC'))):
                # Fetch daily OHLCV data since the specified timestamp
                ohlcv = exchange.fetch_ohlcv(market, timeframe='1d', since=since_timestamp)
                for candle in ohlcv:
                    timestamp = candle[0]
                    date_str = exchange.iso8601(timestamp).split('T')[0]  # Convert to YYYY-MM-DD
                    close_price = candle[4]
                    volume = candle[5]
                    if date_str not in daily_volumes:
                        daily_volumes[date_str] = 0
                    daily_volumes[date_str] += close_price * volume
        except Exception as e:
            print(f"Error processing market {market} on {exchange.id}: {e}")

    return daily_volumes

# Calculate the timestamp for 180 days ago
n_days = 180
since_date = datetime.now() - timedelta(days=n_days)
since_timestamp = int(since_date.timestamp() * 1000)

# Fetch all markets for Binance Futures and Hyperliquid
binance_markets = binance.load_markets()
hyperliquid_markets = hyperliquid.load_markets()

# Fetch daily volumes since 180 days ago for Binance Futures
binance_volumes_since = fetch_daily_volumes_since(binance, binance_markets, since_timestamp)

# Fetch daily volumes since 180 days ago for Hyperliquid
hyperliquid_volumes_since = fetch_daily_volumes_since(hyperliquid, hyperliquid_markets, since_timestamp)

# Convert the results to DataFrames
binance_df = pd.DataFrame.from_dict(binance_volumes_since, orient='index', columns=['Daily Volume (USD)'])
hyperliquid_df = pd.DataFrame.from_dict(hyperliquid_volumes_since, orient='index', columns=['Daily Volume (USD)'])

# Merge the DataFrames based on index (dates)
merged_df = pd.merge(binance_df, hyperliquid_df, left_index=True, right_index=True, how='outer', suffixes=('_Binance', '_Hyperliquid'))


# STALEQUANT CODE STARTS
#%%
bnb_ohlcv = pd.DataFrame(binance.fetch_ohlcv('BNB/USDT', timeframe='1d', since=since_timestamp)).set_index(0)[4]
hype_ohlcv = pd.DataFrame(hyperliquid.fetch_ohlcv('HYPE/USDC', timeframe='1d', since=since_timestamp)).set_index(0)[4]
#%%
merged_mc = pd.concat({'BNB':bnb_ohlcv, 
                       'HYPE':hype_ohlcv},axis=1)#
merged_mc.index = pd.to_datetime(merged_mc.index*1000000).astype('str')

#%%
ratios = {
    'HYPE Lin. Perp Volume / Binance': merged_df['Daily Volume (USD)_Hyperliquid'] / merged_df['Daily Volume (USD)_Binance'] ,
    'HYPE Circ. MC / BNB Circ. MC': (merged_mc['HYPE'] * 333_928_180) / (merged_mc['BNB'] * 145_887_575 ), 
    'HYPE MC / BNB MC ': (merged_mc['HYPE'] * 1_000_000_000 * .6 ) / (merged_mc['BNB'] * 145_887_575 ), 
    'HYPE MC / Binance EV (5 x BNB MC)': (merged_mc['HYPE'] * 1_000_000_000 * .6 ) / (merged_mc['BNB'] * 145_887_575 / .2 ), 
    }
pd.concat(ratios,axis=1).iloc[-30:].plot()


plt.show()