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https://github.com/Mihirkansara/nexus-quant-terminal.git
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45 lines
1.6 KiB
Python
45 lines
1.6 KiB
Python
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"""montecarlo.py — GBM Monte Carlo for forex options (uses GK cost basis)."""
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import numpy as np
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from .garman_kohlhagen import gk_price
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def run_montecarlo(options, S0, sigma, r_d, r_f, T, n_paths=1000, n_steps=100):
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"""Simulate GBM price paths and compute P&L distribution at expiry."""
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dt = T / n_steps
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Z = np.random.standard_normal((n_paths, n_steps))
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paths = np.zeros((n_paths, n_steps + 1)); paths[:,0] = S0
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# GBM: dS = (r_d - r_f)·S·dt + σ·S·dW (Garman-Kohlhagen drift)
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for t in range(1, n_steps + 1):
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paths[:,t] = paths[:,t-1] * np.exp(
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(r_d - r_f - 0.5*sigma**2)*dt + sigma*np.sqrt(dt)*Z[:,t-1]
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)
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terminal = paths[:,-1]
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pnl = np.zeros(n_paths)
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for opt in options:
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K, qty = float(opt["K"]), float(opt["qty"])
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payoff = (np.maximum(terminal - K, 0) if opt["type"]=="call"
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else np.maximum(K - terminal, 0))
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pnl += payoff * qty
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# Subtract initial GK cost
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cost = sum(
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gk_price(S0, float(o["K"]), float(o.get("T",T)), r_d, r_f, sigma, o["type"])
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* float(o["qty"]) for o in options
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)
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pnl -= cost
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idx = np.random.choice(n_paths, size=min(60, n_paths), replace=False)
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return {
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"time_axis": [round(i*dt, 4) for i in range(n_steps+1)],
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"sample_paths": paths[idx].tolist(),
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"pnl": pnl.tolist(),
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"pnl_mean": round(float(pnl.mean()), 5),
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"pnl_std": round(float(pnl.std()), 5),
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"pnl_5pct": round(float(np.percentile(pnl, 5)), 5),
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"pnl_95pct": round(float(np.percentile(pnl, 95)), 5),
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"prob_profit": round(float((pnl > 0).mean()), 4),
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}
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