WIP: Solution property of a optimzation model.

backtest.py

@@ -26,6 +26,8 @@
 inital_holdings = {}
 inital_cash = 10_000
 
+# TODO: Should be in a universe class maybe...
+instrument_mapping = {}
 for id, ticker in enumerate(sorted(TEST_YAHOO_STOCK_UNIVERSE_8)):
     file = f"tests/data/{ticker}.csv"
     time_series = pd.read_csv(file, index_col='Date')
@@ -34,12 +36,13 @@
     instruments.append(ins)
     initial_weights[ins] = 1 / len(TEST_YAHOO_STOCK_UNIVERSE_8)
     inital_holdings[ins] = 10
+    instrument_mapping[ticker] = ins
 
 ts = time_series_data["XOM"]
 start_date = "2020-01-02"
-end_date = "2023-05-31"
-end_date = "2020-01-05"
-pr = pd.period_range(start=start_date, end=end_date, freq='B')
+end_date = "2020-01-03" # "2023-05-31"
+us_bd = CustomBusinessDay(calendar=USFederalHolidayCalendar())
+pr = pd.date_range(start=start_date, end=end_date, freq='B')
 
 portfolio1 = Portfolio(weights=initial_weights)
 portfolio2 = Portfolio(weights=initial_weights)
@@ -49,6 +52,7 @@
 wealth = np.zeros(len(pr))
 for i, date in enumerate(pr):
 
+
     # Build universe.
     for ins in instruments:
         ins.price_history = time_series_data[ins.identifier].loc[:str(date)]
@@ -61,16 +65,19 @@
     # Run optimizer on portfolio.
     optimizer = MPCMaximumReturn(portfolio1, simulator.return_tensor, gamma=10, l1_penalty=0, l2_penalty=1, covariance_matrix=simulator.covariance_matrix)
     optimizer.solve()
-    optimizer.solution()
-    exit()
-
+    solution = optimizer.solution
+    solution = {instrument_mapping[ticker]: weight for ticker, weight in solution.items()}
+    print(solution)
+    # optimizer = MPCMaximumUtility(portfolio2, simulator.return_tensor, gamma=1)
+    # optimizer.solve()
 
+    # optimizer = SPMaximumUtility(portfolio3, simulator.price_tensor, simulator._inital_prices, gamma=-3)
+    # optimizer.solve()
 
+    exit()
 
 
     # Update portfolio weights.
 
-
-
     # Record portfolio wealth.
     wealth[i] = 0

src/abacus/models/garch.py

@@ -156,7 +156,7 @@ def _sanity_check(self):
 
         if not parameter_check or not solution_check:
             # TODO: Make more stable or catch this error when running loop!
-            #raise ParameterError("Parameters could not be asceratined succesfully.")
+            # raise ParameterError("Parameters could not be asceratined succesfully.")
             ...
 
     def _check_parameters(self) -> bool:

src/abacus/optimizer/optimizer.py

@@ -25,6 +25,7 @@ def __init__(self, portfolio: Portfolio, simulation_tensor: torch.Tensor, solver
         self._ampl = None
 
     def solve(self):
+        # TODO: Consider a verbose mode to display command line output.
         self._initiate_ampl_engine()
         self._set_ampl_data()
         self._solve_optimzation_problem()
@@ -70,6 +71,7 @@ def solve(self):
         print(self._ampl.eval("display OBJECTIVE;"))
 
     def _set_ampl_data(self):
+        # TODO: Make consistent parameters.
         assets = self._portfolio.instruments
         asset_identifiers = [instrument.identifier for instrument in assets]
         instrument_holdings = np.array(list(self._portfolio.holdings.values()))
@@ -144,19 +146,22 @@ def __init__(self, portfolio: Portfolio, simulation_tensor: torch.Tensor, gamma:
     def _return_expectation_tensor(self):
         return torch.mean(self._simulation_tensor, dim=2)
 
+    @property
     def solution(self):
         self._check_solved()
-        print(self._ampl.get_variable("weights").get_values())
-        print(type(self._ampl.get_variable("weights").get_values()))
+        # TODO: Should be general?
+        ampl_output = self._ampl.get_variable("weights").to_pandas().loc[1].to_dict()["weights.val"]
+        return ampl_output
 
     def solve(self):
+        # TODO: Should weights be a configuration variable?
         super().solve()
-        print(self._ampl.get_variable("weights").to_pandas().loc[1])
-        print(self._ampl.eval("display OBJECTIVE;"))
+        #print(self._ampl.eval("display OBJECTIVE;"))
+
 
     def _set_ampl_data(self):
-        assets = self._portfolio.instruments
         inital_weights = self._portfolio.weights
+        assets = self._portfolio.instruments
         asset_identifiers = [instrument.identifier for instrument in assets]
         inital_weights = dict(zip(asset_identifiers, inital_weights.values()))
         expected_return_tensor = np.array(self._return_expectation_tensor)