Release/0.7.5 (#187)

* BIG: Fix invalid time series of CIR process (close #182) (#186) * DOC: Add missing dt to generate_cir (#186) * DOC: Update an example in README.md (#181) Co-authored-by: GitHub Actions <action@github.com> Co-authored-by: Masanori HIRANO <masanorihirano@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com> Co-authored-by: GitHub Actions <action@github.com>
pfnet-research · Aug 13, 2021 · 743196e · 743196e
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diff --git a/README.md b/README.md
@@ -119,17 +119,17 @@ The `hedger` is also a [`Module`](https://pytorch.org/docs/stable/generated/torc
 ```py
 hedger
 # Hedger(
-#   inputs=['log_moneyness', 'expiry_time', 'volatility', 'prev_hedge'],
+#   inputs=['log_moneyness', 'expiry_time', 'volatility', 'prev_hedge']
 #   (model): MultiLayerPerceptron(
-#     (0): LazyLinear(in_features=None, out_features=32, bias=True)
+#     (0): LazyLinear(in_features=0, out_features=32, bias=True)
 #     (1): ReLU()
-#     (2): LazyLinear(in_features=None, out_features=32, bias=True)
+#     (2): Linear(in_features=32, out_features=32, bias=True)
 #     (3): ReLU()
-#     (4): LazyLinear(in_features=None, out_features=32, bias=True)
+#     (4): Linear(in_features=32, out_features=32, bias=True)
 #     (5): ReLU()
-#     (6): LazyLinear(in_features=None, out_features=32, bias=True)
+#     (6): Linear(in_features=32, out_features=32, bias=True)
 #     (7): ReLU()
-#     (8): LazyLinear(in_features=None, out_features=1, bias=True)
+#     (8): Linear(in_features=32, out_features=1, bias=True)
 #     (9): Identity()
 #   )
 #   (criterion): EntropicRiskMeasure()

diff --git a/pfhedge/instruments/primary/heston.py b/pfhedge/instruments/primary/heston.py
@@ -45,11 +45,11 @@ class HestonStock(Primary):
         >>> stock = HestonStock()
         >>> stock.simulate(n_paths=2, time_horizon=5/250)
         >>> stock.spot
-        tensor([[1.0000, 0.9958, 0.9940, 0.9895, 0.9765],
-                [1.0000, 1.0064, 1.0117, 1.0116, 1.0117]])
+        tensor([[1.0000, 0.9953, 0.9929, 0.9880, 0.9744],
+                [1.0000, 1.0043, 0.9779, 0.9770, 0.9717]])
         >>> stock.variance
-        tensor([[0.0400, 0.0433, 0.0406, 0.0423, 0.0441],
-                [0.0400, 0.0251, 0.0047, 0.0000, 0.0000]])
+        tensor([[0.0400, 0.0445, 0.0437, 0.0458, 0.0479],
+                [0.0400, 0.0314, 0.0955, 0.0683, 0.0799]])
     """
 
     def __init__(

diff --git a/pfhedge/stochastic/cir.py b/pfhedge/stochastic/cir.py
@@ -19,7 +19,7 @@ def generate_cir(
 
     .. math ::
 
-        dX(t) = \\kappa (\\theta - X(t)) + \\sigma \\sqrt{X(t)} dW(t) \\,.
+        dX(t) = \\kappa (\\theta - X(t)) dt + \\sigma \\sqrt{X(t)} dW(t) \\,.
 
     Time-series is generated by Andersen's QE-M method (See Reference for details).
 
@@ -53,8 +53,8 @@ def generate_cir(
         >>>
         >>> _ = torch.manual_seed(42)
         >>> generate_cir(2, 5)
-        tensor([[0.0400, 0.0433, 0.0406, 0.0423, 0.0441],
-                [0.0400, 0.0251, 0.0047, 0.0000, 0.0000]])
+        tensor([[0.0400, 0.0445, 0.0437, 0.0458, 0.0479],
+                [0.0400, 0.0314, 0.0955, 0.0683, 0.0799]])
 
     References:
         - Andersen, Leif B.G., Efficient Simulation of the Heston Stochastic
@@ -83,10 +83,9 @@ def generate_cir(
         # Compute m, s, psi: Eq(17,18)
         exp = (-tensor_kappa * tensor_dt).exp()
         m = tensor_theta + (v - tensor_theta) * exp
-        s2 = sum(
-            v * (tensor_sigma ** 2) * exp * (1 - exp) / tensor_kappa,
-            tensor_theta * (tensor_sigma ** 2) * ((1 - exp) ** 2) / (2 * tensor_kappa),
-        )
+        s2 = v * (tensor_sigma ** 2) * exp * (1 - exp) / tensor_kappa + tensor_theta * (
+            tensor_sigma ** 2
+        ) * ((1 - exp) ** 2) / (2 * tensor_kappa)
         psi = s2 / (m ** 2 + EPSILON)
 
         # Compute V(t + dt) where psi <= PSI_CRIT: Eq(23, 27, 28)

diff --git a/pfhedge/stochastic/heston.py b/pfhedge/stochastic/heston.py
@@ -66,11 +66,11 @@ def generate_heston(
         >>> _ = torch.manual_seed(42)
         >>> spot, variance = generate_heston(2, 5)
         >>> spot
-        tensor([[1.0000, 0.9958, 0.9940, 0.9895, 0.9765],
-                [1.0000, 1.0064, 1.0117, 1.0116, 1.0117]])
+        tensor([[1.0000, 0.9953, 0.9929, 0.9880, 0.9744],
+                [1.0000, 1.0043, 0.9779, 0.9770, 0.9717]])
         >>> variance
-        tensor([[0.0400, 0.0433, 0.0406, 0.0423, 0.0441],
-                [0.0400, 0.0251, 0.0047, 0.0000, 0.0000]])
+        tensor([[0.0400, 0.0445, 0.0437, 0.0458, 0.0479],
+                [0.0400, 0.0314, 0.0955, 0.0683, 0.0799]])
 
     References:
         - Andersen, Leif B.G., Efficient Simulation of the Heston Stochastic
@@ -107,12 +107,12 @@ def generate_heston(
         k4 = GAMMA2 * dt * (1 - rho ** 2)
         v0 = variance[:, i_step]
         v1 = variance[:, i_step + 1]
-        log_spot[:, i_step + 1] = sum(
-            (
-                log_spot[:, i_step],
-                k0 + k1 * v0 + k2 * v1,
-                (k3 * v0 + k4 * v1).sqrt() * randn[:, i_step],
-            )
+        log_spot[:, i_step + 1] = (
+            log_spot[:, i_step]
+            + k0
+            + k1 * v0
+            + k2 * v1
+            + (k3 * v0 + k4 * v1).sqrt() * randn[:, i_step]
         )
 
     return (log_spot.exp(), variance)
diff --git a/pyproject.toml b/pyproject.toml
@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "pfhedge"
-version = "0.7.4"
+version = "0.7.5"
 description = "Deep Hedging in PyTorch"
 authors = ["Shota Imaki <shota.imaki.0801@gmail.com>"]
 license = "MIT"