release: v0.3.0

2026-08-24 14:38:04 +00:00 · 2026-03-21 11:50:25 +00:00
parent 53a32b361d
commit 327107e2a7
11 changed files with 705 additions and 6 deletions
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+"""Stochastic Simulation -- synthetic price paths via SDE expression DSL.
+
+Demonstrates:
+  - Built-in presets (GBM, Heston, Merton, GARCH-JD)
+  - Custom SDE model via string expressions
+  - Stochastic fan chart visualization
+  - CUDA GPU acceleration (device="cuda")
+  - All expressions compile to native Rust — full Rayon / CUDA parallelism
+
+Usage:
+    python examples/13_stochastic_simulation.py
+"""
+import time
+import manifoldbt as mbt
+
+N = 10_000_000  # 10M paths
+DEVICE = "cuda"  # "cpu" or "cuda"
+
+if __name__ == "__main__":
+    # ── 1. Geometric Brownian Motion (preset) ───────────────────────────────
+    print(f"1. GBM preset ({N:,} paths, 252 steps) [{DEVICE}]")
+    t0 = time.perf_counter()
+    result = mbt.run_stochastic(
+        "gbm",
+        s0=100.0,
+        n_paths=N,
+        n_steps=252,
+        dt=1 / 252,
+        params={"mu": 0.05, "sigma": 0.20},
+        seed=42,
+        device=DEVICE,
+    )
+    elapsed = time.perf_counter() - t0
+    print(f"   Mean final price: {result['final_price']['mean']:.2f}")
+    print(f"   Median max DD:    {result['max_drawdown']['percentiles'][3][1]:.2%}")
+    print(f"   Elapsed: {elapsed:.3f}s\n")
+
+    # ── 2. Heston stochastic volatility (preset) ────────────────────────────
+    print(f"2. Heston preset ({N:,} paths) [{DEVICE}]")
+    t0 = time.perf_counter()
+    result = mbt.run_stochastic(
+        "heston",
+        s0=100.0,
+        n_paths=N,
+        n_steps=252,
+        dt=1 / 252,
+        params={"mu": 0.05, "kappa": 2.0, "theta": 0.04, "xi": 0.3},
+        seed=42,
+        device=DEVICE,
+    )
+    elapsed = time.perf_counter() - t0
+    print(f"   Mean final price: {result['final_price']['mean']:.2f}")
+    print(f"   Ann. vol (mean):  {result['annualized_vol']['mean']:.2%}")
+    print(f"   Elapsed: {elapsed:.3f}s\n")
+
+    # ── 3. Merton Jump Diffusion (preset) ───────────────────────────────────
+    print(f"3. Merton Jump Diffusion ({N:,} paths) [{DEVICE}]")
+    t0 = time.perf_counter()
+    result = mbt.run_stochastic(
+        "merton",
+        s0=100.0,
+        n_paths=N,
+        n_steps=252,
+        dt=1 / 252,
+        params={
+            "mu": 0.05,
+            "sigma": 0.20,
+            "lambda": 1.0,     # 1 jump/year on average
+            "mu_j": -0.05,     # mean jump = -5%
+            "sigma_j": 0.08,   # jump vol = 8%
+        },
+        seed=42,
+        device=DEVICE,
+    )
+    elapsed = time.perf_counter() - t0
+    print(f"   Mean final price: {result['final_price']['mean']:.2f}")
+    print(f"   Elapsed: {elapsed:.3f}s\n")
+
+    # ── 4. Custom GARCH(1,1) Jump Diffusion ─────────────────────────────────
+    print(f"4. Custom GARCH(1,1) Jump Diffusion ({N:,} paths) [{DEVICE}]")
+    model = mbt.StochasticModel(
+        name="my_garch_jd",
+        drift="mu",
+        diffusion="sqrt(h)",
+        jump_intensity="lambda",
+        jump_size="normal(mu_j, sigma_j)",
+        state_vars={"h": 1e-4},
+        state_update={"h": "omega + alpha * (ret - mu) ** 2 + beta * h"},
+        params={
+            "mu": 0.08,
+            "omega": 1e-6,
+            "alpha": 0.10,
+            "beta": 0.85,
+            "lambda": 5.0,
+            "mu_j": -0.02,
+            "sigma_j": 0.04,
+        },
+    )
+    t0 = time.perf_counter()
+    result = mbt.run_stochastic(
+        model,
+        s0=100.0,
+        n_paths=N,
+        n_steps=252,
+        dt=1 / 252,
+        seed=42,
+        device=DEVICE,
+    )
+    elapsed = time.perf_counter() - t0
+    print(f"   Mean final price: {result['final_price']['mean']:.2f}")
+    print(f"   Max DD (P5):      {result['max_drawdown']['percentiles'][0][1]:.2%}")
+    print(f"   Elapsed: {elapsed:.3f}s\n")
+
+    # ── 5. Custom mean-reverting model (CPU — store_paths needs RAM) ────────
+    N_PLOT = 10_000
+    print(f"5. Custom mean-reverting model ({N_PLOT:,} paths) [cpu, store_paths]")
+    mean_rev = mbt.StochasticModel(
+        name="mean_reverting",
+        # Drift pulls price back toward 100
+        drift="kappa * (log(100.0) - log(S))",
+        diffusion="sigma",
+        params={"kappa": 2.0, "sigma": 0.25},
+    )
+    t0 = time.perf_counter()
+    result = mbt.run_stochastic(
+        mean_rev,
+        s0=80.0,   # start below mean
+        n_paths=N_PLOT,
+        n_steps=252,
+        dt=1 / 252,
+        seed=42,
+        store_paths=True,
+        device="cpu",
+    )
+    elapsed = time.perf_counter() - t0
+    print(f"   Mean final price: {result['final_price']['mean']:.2f} (target: 100)")
+    print(f"   Elapsed: {elapsed:.3f}s\n")
+
+    # ── 6. Fan chart visualization ──────────────────────────────────────────
+    print("6. Plotting fan chart...")
+    mbt.plot.stochastic_paths(
+        result,
+        title=f"Mean-reverting model (S0=80, target=100, {N_PLOT:,} paths)",
+        show=True,
+    )