Machine learning for MPT

Machine Learning for Finance in Python

Nathan George

Data Science Professor

Make train and test sets

# make train and test features
train_size = int(0.8 * features.shape[0])
train_features = features[:train_size]
train_targets = targets[:train_size]

test_features = features[train_size:]
test_targets = targets[train_size:]

print(features.shape)

(230, 3)

Fit the model

from sklearn.ensemble import RandomForestRegressor

# fit the model and check scores on train and test
rfr = RandomForestRegressor(n_estimators=300, random_state=42)
rfr.fit(train_features, train_targets)

print(rfr.score(train_features, train_targets))
print(rfr.score(test_features, test_targets))

0.8382262317599827
0.09504859048985377

Evaluate the model's performance

# get predictions from model on train and test
test_predictions = rfr.predict(test_features)

# calculate and plot returns from our RF predictions and the QQQ returns
test_returns = np.sum(returns_monthly.iloc[train_size:] * test_predictions,
                        axis=1)

plt.plot(test_returns, label='algo')
plt.plot(returns_monthly['QQQ'].iloc[train_size:], label='QQQ')
plt.legend()
plt.show()

the model's monthly returns

cash = 1000
algo_cash = [cash]

for r in test_returns:
    cash *= 1 + r
    algo_cash.append(cash)

# calculate performance for QQQ
cash = 1000  # reset cash amount
qqq_cash = [cash]
for r in returns_monthly['QQQ'].iloc[train_size:]:
    cash *= 1 + r
    qqq_cash.append(cash)


print('algo returns:', (algo_cash[-1] - algo_cash[0]) / algo_cash[0])
print('QQQ returns:', (qqq_cash[-1] - qqq_cash[0]) / qqq_cash[0])

algo returns: 0.5009443507049591
QQQ returns: 0.5186775933696601

Plot the results

plt.plot(algo_cash, label='algo')
plt.plot(qqq_cash, label='QQQ')
plt.ylabel('$')
plt.legend()  # show the legend
plt.show()

investment simulation

Train your model!

Machine Learning for Finance in Python