hello world translated and working

examples/paper_examples/hello_world.py

@@ -11,6 +11,83 @@
 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 # See the License for the specific language governing permissions and
 # limitations under the License.
-""""""
+"""This is a simple example of back-tests with Cvxportfolio.
+"""
 
-import cvxportfolio as cvx
+import cvxportfolio as cvx
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+# Download market data.
+market_data = cvx.DownloadedMarketData(
+    universe = ['AMZN', 'GOOGL', 'TSLA', 'NKE'],
+    cash_key = 'USDOLLAR')
+
+print('Historical open-to-open total returns:')
+print(market_data.returns)
+
+# Define transaction and holding cost models.
+HALF_SPREAD = 10E-4
+BORROW_FEE = 1E-4 * (252 / 100) # in annualized percentage
+tcost_model = cvx.TcostModel(a=HALF_SPREAD)
+hcost_model = cvx.HcostModel(short_fees=BORROW_FEE)
+
+# As returns forecast, we simply take the historical means
+# computed at each point in the back-test (looking only
+# at past returns). That's the default behavior; we
+# may as well pass a dataframe here with different predictions.
+r_hat = cvx.ReturnsForecast()
+
+# As risk model, we choose the full historical covariance.
+# It is computed every day using the full past historical 
+# returns at that point. (We may as well provide it as a 
+# dataframe.)
+risk_model = cvx.FullSigma()
+
+# Constraint.
+leverage_limit = cvx.LeverageLimit(3)
+
+# Define a single-period optimization policy; its
+# objective function is maximized.
+gamma_risk, gamma_trade, gamma_hold = 5., 1., 1.
+spo_policy = cvx.SinglePeriodOpt(
+    objective = r_hat
+        - gamma_risk * risk_model
+        - gamma_trade * tcost_model 
+        - gamma_hold * hcost_model,
+    constraints=[leverage_limit])
+
+# Define the market simulator.
+market_sim = cvx.MarketSimulator(
+    market_data = market_data,
+    costs = [
+        cvx.TcostModel(a=HALF_SPREAD),
+        cvx.HcostModel(short_fees=BORROW_FEE)]) 
+
+# Initial portfolio, uniform on non-cash assets.
+init_portfolio = pd.Series(
+    index=market_data.returns.columns, data=250000.)
+init_portfolio.USDOLLAR = 0
+
+# Run two back-tests.
+results = market_sim.run_multiple_backtest(
+    h=[init_portfolio]*2,
+    start_time='2013-01-03',  end_time='2016-12-31',
+    policies=[spo_policy, cvx.Hold()])
+
+print('Back-test result, single-period optimization policy:')
+print(results[0])
+
+print('Back-test result, Hold policy:')
+print(results[1])
+
+results[0].v.plot(label='SPO')
+results[1].v.plot(label='Hold policy')
+plt.title('Portfolio total value in time (USD)')
+plt.legend()
+plt.show()
+
+results[0].w.plot()
+plt.title('SPO weights in time')
+plt.show()