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examples: 13 runs on the free tier

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Jimmy7892
2026-10-23 23:06:12 +02:00
parent 71b542fe4b
commit 292d879d6a
1 changed files with 7 additions and 4 deletions
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examples/13_stochastic_simulation.py
+7 -4
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@@ -4,7 +4,6 @@ Demonstrates:
- Built-in presets (GBM, Heston, Merton, GARCH-JD) - Built-in presets (GBM, Heston, Merton, GARCH-JD)
- Custom SDE model via string expressions - Custom SDE model via string expressions
- Stochastic fan chart visualization - Stochastic fan chart visualization
- CUDA GPU acceleration (device="cuda")
- All expressions compile to native Rust — full Rayon / CUDA parallelism - All expressions compile to native Rust — full Rayon / CUDA parallelism
Data: none — this file simulates price paths, it does not backtest on any Data: none — this file simulates price paths, it does not backtest on any
@@ -18,8 +17,12 @@ import manifoldbt as mbt
from _bootstrap import plots_available from _bootstrap import plots_available
N = 10_000_000 # 10M paths # The mechanics are identical at any scale, so the defaults run on the free
DEVICE = "cuda" # "cpu" or "cuda" # tier, which caps simulations at 1,000 paths. With a Pro licence the same
# file is a throughput demo: N = 10_000_000 and DEVICE = "cuda" run each
# preset below in about two seconds.
N = 1_000
DEVICE = "cpu" # Pro: "cuda"
if __name__ == "__main__": if __name__ == "__main__":
# ── 1. Geometric Brownian Motion (preset) ─────────────────────────────── # ── 1. Geometric Brownian Motion (preset) ───────────────────────────────
@@ -117,7 +120,7 @@ if __name__ == "__main__":
print(f" Elapsed: {elapsed:.3f}s\n") print(f" Elapsed: {elapsed:.3f}s\n")
# ── 5. Custom mean-reverting model (CPU — store_paths needs RAM) ──────── # ── 5. Custom mean-reverting model (CPU — store_paths needs RAM) ────────
N_PLOT = 10_000 N_PLOT = min(N, 10_000)
print(f"5. Custom mean-reverting model ({N_PLOT:,} paths) [cpu, store_paths]") print(f"5. Custom mean-reverting model ({N_PLOT:,} paths) [cpu, store_paths]")
mean_rev = mbt.StochasticModel( mean_rev = mbt.StochasticModel(
name="mean_reverting", name="mean_reverting",