Working sp utility model.

run.py

@@ -8,7 +8,7 @@
 from src.abacus.utils.portfolio import Portfolio
 from src.abacus.simulator.simulator import Simulator
 from src.abacus.assessor.risk_assessor import RiskAssessor
-from src.abacus.optimizer.optimizer import SPMaximumUtility
+from src.abacus.optimizer.optimizer import SPMaximumUtility, SPMaximumGain
 
 
 
@@ -44,13 +44,16 @@
 
 
 # Mock prices...
-# price_tensor = torch.tensor([ [[1000]], [[0]], [[0]], [[0]]])
-# inital_prices = torch.tensor([ [[10]], [[10]], [[10]], [[10]]])
+price_tensor = torch.tensor([ [[1000]], [[0]], [[0]], [[0]]])
+inital_prices = torch.tensor([ [[10]], [[10]], [[10]], [[10]]])
 
 # Create optimizer with different optimization models...
-optimizer = SPMaximumUtility(portfolio, simulator._price_tensor, simulator._inital_prices)
+optimizer = SPMaximumGain(portfolio, price_tensor, inital_prices)
 optimizer.solve()
 
+print()
+optimizer = SPMaximumUtility(portfolio, price_tensor, inital_prices)
+optimizer.solve()
 
 
 print("OK!")

src/abacus/optimizer/optimization_models/sp_maximize_gain.mod

@@ -0,0 +1,35 @@
+problem spMaximizeGain;
+
+set assets;
+
+param dt > 0;
+param risk_free_rate;
+param inital_cash > 0;
+param number_of_scenarios > 0 integer;
+param number_of_assets > 0 integer;
+param inital_holdings {assets};
+param inital_prices {assets};
+param prices {1..number_of_scenarios, assets};
+
+var x_buy {assets};
+var x_sell {assets};
+
+maximize Objective:
+    sum{i in 1..number_of_scenarios} 1/number_of_scenarios * ((inital_cash + sum{j in assets} (inital_prices[j] * (x_sell[j] - x_buy[j]))) * exp(risk_free_rate * dt) +  sum{j in assets} (prices[i, j] * (inital_holdings[j] + x_buy[j] - x_sell[j])));
+
+
+subject to SHORTING_CONSTRAINT {j in assets}:
+    inital_holdings[j] + x_buy[j] - x_sell[j] >= 0
+;
+
+subject to LEVERAGE_CONSTRAINT:
+    inital_cash + sum{j in assets} ( (x_sell[j] - x_buy[j]) *  inital_prices[j]) >= 0
+;
+
+subject to FEASIBLE_CONSTRAINT_BUY {j in assets}:
+    x_buy[j] >= 0
+;
+
+subject to FEASIBLE_CONSTRAINT_SELL {j in assets}:
+    x_sell[j] >= 0
+;

src/abacus/optimizer/optimization_models/sp_maximize_utility.mod

@@ -4,6 +4,7 @@ set assets;
 
 param dt > 0;
 param risk_free_rate;
+param gamma;
 param inital_cash > 0;
 param number_of_scenarios > 0 integer;
 param number_of_assets > 0 integer;
@@ -15,7 +16,7 @@ var x_buy {assets};
 var x_sell {assets};
 
 maximize Objective:
-    sum{i in 1..number_of_scenarios} 1/number_of_scenarios * ((inital_cash + sum{j in assets} (inital_prices[j] * (x_sell[j] - x_buy[j]))) * exp(risk_free_rate * dt) +  sum{j in assets} (prices[i, j] * (inital_holdings[j] + x_buy[j] - x_sell[j])));
+    sum{i in 1..number_of_scenarios} 1/number_of_scenarios * ((inital_cash + sum{j in assets} (inital_prices[j] * (x_sell[j] - x_buy[j]))) * exp(risk_free_rate * dt) +  sum{j in assets} (prices[i, j] * (inital_holdings[j] + x_buy[j] - x_sell[j]))) ** gamma / gamma;
 
 
 subject to SHORTING_CONSTRAINT {j in assets}:

src/abacus/optimizer/optimizer.py

@@ -52,9 +52,42 @@ def _check_initialization(self):
         if not self._ampl:
             raise ValueError("AMPL has not been initalized.")
 
-    def _check_valid_model_specification(self):
-        # TODO: Implement when approriate.
-        ...
+
+
+class SPMaximumGain(OptimizationModel):
+
+    _model_specification = OptimizationSpecifications.SP_MAXIMIZE_GAIN
+
+    def __init__(self, portfolio: Portfolio, price_tensor: torch.Tensor, inital_prices: torch.Tensor):
+        super().__init__(portfolio, price_tensor)
+        self._inital_prices = inital_prices
+
+    def solve(self):
+        super().solve()
+        print(self._ampl.get_variable("x_buy").get_values())
+        print(self._ampl.get_variable("x_sell").get_values())
+        print(self._ampl.eval("display Objective;"))
+
+    def _set_ampl_data(self):
+        assets = self._portfolio.instruments
+        asset_identifiers = [instrument.identifier for instrument in assets]
+        instrument_holdings = np.array(list(self._portfolio.holdings.values()))
+        price_tensor = np.array(self._simulation_tensor[:,-1,:])
+        inital_prices =  dict(zip(asset_identifiers, np.array(self._inital_prices.reshape(4))))
+        tensor_size = price_tensor.shape
+        number_of_assets = tensor_size[0]
+        number_of_scenarios = tensor_size[1]
+        price_dict = {(j+1, asset.identifier): price_tensor[asset.id][j] for asset in assets for j in range(number_of_scenarios)}
+
+        self._ampl.get_set("assets").set_values(asset_identifiers)
+        self._ampl.param["risk_free_rate"] = 0.05
+        self._ampl.param["dt"] = 10/365
+        self._ampl.param["number_of_assets"] = number_of_assets
+        self._ampl.param["number_of_scenarios"] = number_of_scenarios
+        self._ampl.param["inital_cash"] = self._portfolio._cash
+        self._ampl.param["inital_holdings"] = instrument_holdings
+        self._ampl.param["inital_prices"] = inital_prices
+        self._ampl.param["prices"] = price_dict
 
 
 
@@ -86,6 +119,7 @@ def _set_ampl_data(self):
         self._ampl.get_set("assets").set_values(asset_identifiers)
         self._ampl.param["risk_free_rate"] = 0.05
         self._ampl.param["dt"] = 10/365
+        self._ampl.param["gamma"] = -2
         self._ampl.param["number_of_assets"] = number_of_assets
         self._ampl.param["number_of_scenarios"] = number_of_scenarios
         self._ampl.param["inital_cash"] = self._portfolio._cash
@@ -94,6 +128,7 @@ def _set_ampl_data(self):
         self._ampl.param["prices"] = price_dict
 
 
+
 class MPCMaximumUtility(OptimizationModel):
 
     _model_specification = OptimizationSpecifications.MPC_MAXIMIZE_UTILITY

src/abacus/utils/enumerations.py

@@ -4,4 +4,5 @@
 
 class OptimizationSpecifications(Enum):
     SP_MAXIMIZE_UTILITY = "sp_maximize_utility.mod"
+    SP_MAXIMIZE_GAIN = "sp_maximize_gain.mod"
     MPC_MAXIMIZE_UTILITY = "mpc_maximize_utility.mod"