샤프 비율: 특성과 타깃

Python으로 배우는 금융 분야 Machine Learning

Nathan George

Data Science Professor

최적 포트폴리오

샤프 포인트가 표시된 최적 포트폴리오

샤프 비율 공식

샤프 비율 계산

# empty dictionaries for sharpe ratios and best sharpe indexes by date
sharpe_ratio, max_sharpe_idxs = {}, {}

# loop through dates and get sharpe ratio for each portfolio
for date in portfolio_returns.keys():
    for i, ret in enumerate(portfolio_returns[date]):
        volatility = portfolio_volatility[date][i]
        sharpe_ratio.setdefault(date,[]).append(ret / volatility)

    # get the index of the best sharpe ratio for each date
    max_sharpe_idxs[date] = np.argmax(sharpe_ratio[date])

특성 생성

# calculate exponentially-weighted moving average of daily returns
ewma_daily = returns_daily.ewm(span=30).mean()

# resample daily returns to first business day of the month
ewma_monthly = ewma_daily.resample('BMS').first()

# shift ewma 1 month forward
ewma_monthly = ewma_monthly.shift(1).dropna()

특성과 타깃 계산

targets, features = [], []


# create features from price history and targets as ideal portfolio
for date, ewma in ewma_monthly.iterrows():
    # get the index of the best sharpe ratio
    best_idx = max_sharpe_idxs[date]
    targets.append(portfolio_weights[date][best_idx])
    features.append(ewma)

targets = np.array(targets)
features = np.array(features)

# latest date
date = sorted(covariances.keys())[-1]

cur_returns = portfolio_returns[date]
cur_volatility = portfolio_volatility[date]

plt.scatter(x=cur_volatility,
            y=cur_returns,
            alpha=0.1,
            color='blue')

best_idx = max_sharpe_idxs[date]

plt.scatter(cur_volatility[best_idx],
            cur_returns[best_idx],
            marker='x',
            color='orange')
plt.xlabel('Volatility')
plt.ylabel('Returns')
plt.show()

샤프 비율이 표시된 효율적 프런티어

샤프 비율 구하기!

Python으로 배우는 금융 분야 Machine Learning