[python-arrayfire] 165/250: Adding monte carlo option pricing example

[Ghislain Vaillant]

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ghisvail-guest pushed a commit to branch debian/master
in repository python-arrayfire.

commit a4b4e760e61c409ef5ed86b4d1ef257169c13ad4
Author: Pavan Yalamanchili <pavan at arrayfire.com>
Date:   Wed Dec 9 17:20:14 2015 -0500

    Adding monte carlo option pricing example
---
 examples/financial/monte_carlo_options.py | 61 +++++++++++++++++++++++++++++++
 1 file changed, 61 insertions(+)

diff --git a/examples/financial/monte_carlo_options.py b/examples/financial/monte_carlo_options.py
new file mode 100644
index 0000000..2c9bcc8
--- /dev/null
+++ b/examples/financial/monte_carlo_options.py
@@ -0,0 +1,61 @@
+#!/usr/bin/python
+
+#######################################################
+# Copyright (c) 2015, ArrayFire
+# All rights reserved.
+#
+# This file is distributed under 3-clause BSD license.
+# The complete license agreement can be obtained at:
+# http://arrayfire.com/licenses/BSD-3-Clause
+########################################################
+
+import arrayfire as af
+from time import time
+import math
+import sys
+
+def monte_carlo_options(N, K, t, vol, r, strike, steps, use_barrier = True, B = None, ty = af.Dtype.f32):
+    payoff = af.constant(0, N, 1, dtype = ty)
+
+    dt = t / float(steps - 1)
+    s = af.constant(strike, N, 1, dtype = ty)
+
+    randmat = af.randn(N, steps - 1, dtype = ty)
+    randmat = af.exp((r - (vol * vol * 0.5)) * dt + vol * math.sqrt(dt) * randmat);
+
+    S = af.product(af.join(1, s, randmat), 1)
+
+    if (use_barrier):
+        S = S * af.all_true(S < B, 1)
+
+    payoff = af.maxof(0, S - K)
+    return af.mean(payoff) * math.exp(-r * t)
+
+def monte_carlo_simulate(N, use_barrier, num_iter = 10):
+    steps = 180
+    stock_price = 100.0
+    maturity = 0.5
+    volatility = 0.3
+    rate = 0.01
+    strike = 100
+    barrier = 115.0
+
+    start = time()
+    for i in range(num_iter):
+        monte_carlo_options(N, stock_price, maturity, volatility, rate, strike, steps,
+                            use_barrier, barrier)
+
+    return (time() - start) / num_iter
+
+if __name__ == "__main__":
+    if (len(sys.argv) > 1):
+        af.set_device(int(sys.argv[1]))
+    af.info()
+
+    monte_carlo_simulate(1000, use_barrier = False)
+    monte_carlo_simulate(1000, use_barrier = True )
+    af.sync()
+
+    for n in range(10000, 100001, 10000):
+        print("Time for %7d paths - vanilla method: %4.3f ms, barrier method: % 4.3f ms\n" %
+              (n, 1000 * monte_carlo_simulate(n, False, 100), 1000 * monte_carlo_simulate(n, True, 100)))

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