def main(random):
"""
Buy on the dips
run <num_runs> simulations
tracking output over 20 years
plot a return distribution
"""
# parameters specific to this continuous purchase model
title = ("Random" if random else "Real sequence") + " simulations of "
monthly = True # monthly simulations
# mappings from parameters into point formats
colors = { 0.10:"r", 0.15:"y", 0.20:"g", 0.25:"c" }
symbols = [ "x", ".", "o", "+", "*" ]
legends = []
simulator = Market(monthly=monthly)
# for a range of plausible dip thresholds
for max_dip in (0.10, 0.15, 0.20, 0.25):
# are we willing to buy part on smaller dips
for buy_points in [1, 2, 3]:
results = []
# a statistically interesting number of runs
for runs in range(num_runs * 2 if random else num_runs):
sequence = simulator.rates(length=num_years*12, random=random)
results.append( strat_dips(sequence, max_dip, buy_points, monthly) )
# summarize the results
mean = sum(results) / len(results)
sigma = statistics.stdev(results)
report = "{}({}%/{}) over {} years: mean={:.2f}, sigma={:.2f}"
print(report.format(my_name, int(100*max_dip), buy_points, num_years, mean, sigma))
# bucketize and display the results
granularity = bucketwidth(results)
buckets = bucketize(results, granularity)
(x_values, y_values) = distribution(buckets, granularity)
plt.plot(x_values, y_values, colors[max_dip] + symbols[buy_points])
legends.append(str(int(max_dip*100)) + "% dip/" + str(buy_points))
print("") # blank line between changes in threshold
# put up the title, axes, and data
plt.title(title + my_name)
plt.xlabel(str(num_years) + "-year return")
plt.ylabel("probability")
plt.legend(legends)
if output is None:
plt.show()
else:
print("saving distribution plot as " + output)
plt.savefig(output)
plt.close()
def main(random):
"""
For all-in and all-out
run <num_runs> simulations
tracking output over 20 years
plot a return distribution
"""
# parameters specific to this continuous purchase model
title = ("Random" if random else "Real sequence") + " simulations of "
monthly = False # annual simulations
legends = []
simulator = Market(monthly=monthly)
# purchases spread out over 1-5 years
for in_cds in [True, False]:
results = []
# a statistically interesting number of runs
for runs in range(num_runs * 2 if random else num_runs):
sequence = simulator.rates(length=num_years, random=random)
results.append(strat_all(sequence, in_cds, monthly))
# summarize the results
mean = sum(results) / len(results)
sigma = statistics.stdev(results)
report = "{} {}, {} years: mean={:.2f}, sigma={:.2f}"
print(
report.format(my_name, "CDs" if in_cds else "market", num_years,
mean, sigma))
# bucketize and display the results
granularity = bucketwidth(results)
buckets = bucketize(results, granularity)
(x_values, y_values) = distribution(buckets, granularity)
plt.plot(x_values, y_values, "go" if in_cds else "b*")
legends.append("CDs" if in_cds else "market")
# put up the title, axes, and data
plt.title(title + my_name)
plt.xlabel(str(num_years) + "-year return")
plt.ylabel("probability")
plt.legend(legends)
if output is None:
plt.show()
else:
print("saving distribution plot as " + output)
plt.savefig(output)
plt.close()
def main(random):
"""
Only buy in at lows
run <num_runs> simulations
tracking output over 20 years
plot a return distribution
"""
# parameters specific to this continuous purchase model
title = ("Random" if random else "Real sequence") + " simulations of "
monthly = True # monthly simulations
formats = ["w.", "r.", "y+", "g*", "co"]
legends = []
simulator = Market(monthly=monthly)
for fractions in [1, 2, 3, 4]:
results = []
# a statistically interesting number of runs
for runs in range(num_runs * 2 if random else num_runs):
sequence = simulator.rates(length=num_years * 12, random=random)
results.append(strat_bottom(sequence, fractions, monthly))
# summarize the results
mean = sum(results) / len(results)
sigma = statistics.stdev(results)
report = "{} over {} years in {} pieces: mean={:.2f}, sigma={:.2f}"
print(report.format(my_name, num_years, fractions, mean, sigma))
# bucketize and display the results
granularity = bucketwidth(results)
buckets = bucketize(results, granularity)
(x_values, y_values) = distribution(buckets, granularity)
plt.plot(x_values, y_values, formats[fractions])
legends.append("fractions=" + str(fractions))
# put up the title, axes, and data
plt.title(title + my_name)
plt.xlabel(str(num_years) + "-year return")
plt.ylabel("probability")
plt.legend(legends)
if output is None:
plt.show()
else:
print("saving distribution plot as " + output)
plt.savefig(output)
plt.close()
