Sharpe ratios; features and targets

Machine Learning for Finance in Python

Nathan George

Data Science Professor

best_portfolios

best portfolios with Sharpe point

Sharpe ratio equation

Getting our Sharpe ratios

# 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])

Create features

# 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()

Calculate features and targets

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()

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Machine Learning for Finance in Python