New working instrument factory for stock instrument.

src2/main.py

@@ -0,0 +1,68 @@
+# -*- coding: utf-8 -*-
+import torch
+import pandas as pd
+import numpy as np
+
+from datetime import datetime
+from matplotlib import pyplot as plt
+
+from utils.stock_factory import StockFactory
+from utils.instruments import Stock
+from utils.config import STOCK_ADMISSIBLE_MODELS
+
+from models.ar import AR
+from models.garch import GARCH
+
+
+
+start = datetime.strptime("2005-05-01", r"%Y-%m-%d")
+end = datetime.strptime("2023-06-01", r"%Y-%m-%d")
+instrument_specification = ["XOM", "GS", "T"]
+instrument_factory = StockFactory(tickers=instrument_specification,
+                                  start=start,
+                                  end=end)
+stocks = instrument_factory.build_stocks()
+
+
+
+class Simulator:
+
+    def __init__(self, instruments: list):
+        self._instruments = instruments
+        self._calibrated = False
+
+    def calibrate(self):
+        self._calibrate_instruments()
+        self._calibrate_copula()
+        self._calibrated = True
+
+    def _calibrate_instruments(self):
+        for instrument in self._instruments:
+            if isinstance(instrument, Stock):
+                self._calibrate_stock(instrument)
+
+    def _calibrate_copula(self):
+        ...
+
+    def _calibrate_stock(self, stock):
+        current_aic = np.inf
+        risk_factor = stock.risk_factors[0]
+        data = risk_factor.price_history.log_returns
+
+        for model_name in STOCK_ADMISSIBLE_MODELS:
+            if model_name == "AR":
+                model = AR(data)
+                model.calibrate()
+                if model.aic < current_aic:
+                    risk_factor.model = model
+
+            elif model_name == "GARCH":
+                model = GARCH(data)
+                model.calibrate()
+                if model.aic < current_aic:
+                    risk_factor.model = model
+
+
+    def run_simulation(time_steps: int) -> torch.Tensor:
+        # Check for succesful calibration, Throw an error otherwise.
+        ...

src2/models/garch.py

@@ -22,7 +22,7 @@ def parameters(self) -> tuple[float, 3]:
         optimal_parameters = self._optimal_parameters
         alpha = optimal_parameters[0]
         beta = optimal_parameters[1]
-        long_run_variance = self._long_run_variance
+        long_run_variance = self._long_run_variance.item()
 
         return alpha, beta, long_run_variance
 
@@ -72,7 +72,6 @@ def transform_to_true(self, uniform_sample: torch.Tensor) -> torch.Tensor:
 
         return simulated_values
 
-
     def transform_to_uniform(self):
         self._check_calibration()
 

src2/simulator/simulator.py

@@ -7,16 +7,16 @@
 
 class Simulator:
 
-    def __init__(self, assets: list[Asset]):
-        self.assets = assets
+    def __init__(self, instruments: list[Asset]):
+        self._instruments = instruments
         self.is_calibrated = False
 
 
 
     def calibrate(self):
-        # Find models for each risk factor. Use a factory design pattern.
+        # Find models for each risk factor.
+        # Use a factory design pattern.
         # If none are succesful throw an error.
-
         ...
 
 

src2/utils/config.py

@@ -7,3 +7,6 @@
 MAXIMUM_AR_ORDER = 5
 INITIAL_VARIANCE_GARCH_OBSERVATIONS = 20
 INITIAL_GARCH_PARAMETERS = (0.05, 0.90)
+
+STOCK_ADMISSIBLE_MODELS = ("AR",
+                           "GARCH")

src2/utils/stock_factory.py

@@ -0,0 +1,23 @@
+# -*- coding: utf-8 -*-
+
+from utils.instruments import Asset, Stock, RiskFactor
+from utils.data_handler import YahooDataHandler
+
+class StockFactory:
+
+    def __init__(self, tickers, start, end, interval=None):
+        self._tickers = tickers
+        self._start = start
+        self._end = end
+        self._ydr = YahooDataHandler()
+
+
+    def build_stocks(self) -> list[Stock]:
+        stocks = []
+        for ticker in self._tickers:
+            price_history = self._ydr.get_price_history(ticker, self._start, self._end)
+            identifier = f"{ticker}_RF"
+            risk_factor = RiskFactor(identifier, price_history)
+            stock = Stock(identifier=ticker, risk_factors=[risk_factor])
+            stocks.append(stock)
+        return stocks