Modelo de Markowitz com dados da Binance

portifolio.py

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import scipy.optimize as sco
from quotes import tickers

df = pd.read_pickle("quotes.pkl")

logrets = np.log(df / df.shift(1))
rmean = logrets.mean() * 365
cov = logrets.cov() * 365

def statistics(weights):
    RE = np.sum(rmean * weights)
    DP = np.sqrt(np.dot(weights.T, np.dot(cov, weights)))
    return DP, RE

n = len(tickers)
cons = ({'type': 'eq', 'fun': lambda x: np.sum(x)-1})
bnds = tuple((0, 1) for x in range(n))
chute = [0.4, 0.4, 0.0, 0.2, 0.0, 0.0, 0.0, 0.0]
def funcao_objetivo(weights):
    DP, RE = statistics(weights)
    return DP/RE # Invertido (minimize)

opts = sco.minimize(funcao_objetivo, chute, method='SLSQP', bounds=bnds, constraints=cons)
plt.pie(opts.x.round(2), explode=n*[1/n], labels=tickers)
plt.legend(tickers, loc='best')
plt.show()

ponderacao = n * [1/n]
plt.pie(ponderacao, explode=n*[1/n], labels=tickers)
plt.legend(tickers, loc='best')
plt.show()

DP1, RE1 = statistics(opts.x)
DP2, RE2 = statistics(np.array(ponderacao))

quotes.py

import os
from datetime import datetime
import pandas as pd
from decouple import config
from binance.client import Client
import matplotlib.pyplot as plt

def download_hquotes_binance(binance_tickers):

    client = Client(config('BINANCE_APIKEY'), config('BINANCE_SECRETKEY'))

    df_buffer = pd.DataFrame()
    for idx, ticker in enumerate(binance_tickers):
        klines = client.get_historical_klines(
            ticker, Client.KLINE_INTERVAL_1DAY, "26 Jun, 2022")

        ts_list, q_list = [], []
        for k in klines:
            ts_list.append(datetime.fromtimestamp(int(k[0])/1000))
            q_list.append(float(k[4]))

        df_buffer[ticker] = pd.Series(data=q_list, index=ts_list)

    return df_buffer

tickers = [ 'BTCUSDT',
            'ETHUSDT',
            'BNBUSDT',
            'XRPUSDT',
            'ADAUSDT',
            'DOGEUSDT',
            'SOLUSDT',
            'DOTUSDT',
            #'LTCUSDT',
    ]

if __name__ == '__main__':
    df = download_hquotes_binance(tickers)
    df.to_pickle("quotes.pkl")
    (df / df.iloc[0]).plot()
    plt.show()