def test_statistics():
alpha = [random.random() for val in range(count)]
alpha_stats = Statistics()
for val in alpha:
alpha_stats.push(val)
assert len(alpha_stats) == count
assert error(mean(alpha), alpha_stats.mean()) < error_limit
assert error(variance(alpha), alpha_stats.variance()) < error_limit
assert error(stddev(alpha), alpha_stats.stddev()) < error_limit
assert error(skewness(alpha), alpha_stats.skewness()) < error_limit
assert error(kurtosis(alpha), alpha_stats.kurtosis()) < error_limit
assert alpha_stats.minimum() == min(alpha)
assert alpha_stats.maximum() == max(alpha)
alpha_stats.clear()
assert len(alpha_stats) == 0
alpha_stats = Statistics(alpha)
beta = [random.random() for val in range(count)]
beta_stats = Statistics()
for val in beta:
beta_stats.push(val)
gamma_stats = alpha_stats + beta_stats
assert len(beta_stats) != len(gamma_stats)
assert error(mean(alpha + beta), gamma_stats.mean()) < error_limit
assert error(variance(alpha + beta), gamma_stats.variance()) < error_limit
assert error(stddev(alpha + beta), gamma_stats.stddev()) < error_limit
assert error(skewness(alpha + beta), gamma_stats.skewness()) < error_limit
assert error(kurtosis(alpha + beta), gamma_stats.kurtosis()) < error_limit
assert gamma_stats.minimum() == min(alpha + beta)
assert gamma_stats.maximum() == max(alpha + beta)
delta_stats = beta_stats.copy()
delta_stats += alpha_stats
assert len(beta_stats) != len(delta_stats)
assert error(mean(alpha + beta), delta_stats.mean()) < error_limit
assert error(variance(alpha + beta), delta_stats.variance()) < error_limit
assert error(stddev(alpha + beta), delta_stats.stddev()) < error_limit
assert error(skewness(alpha + beta), delta_stats.skewness()) < error_limit
assert error(kurtosis(alpha + beta), delta_stats.kurtosis()) < error_limit
assert delta_stats.minimum() == min(alpha + beta)
assert delta_stats.maximum() == max(alpha + beta)
def main():
args = list(map(float, sys.argv[1:]))
print('Statistics Functions')
print('Count:', len(args))
print('Mean:', mean(args))
print('Variance:', variance(args))
print('StdDev:', stddev(args))
print('Skewness:', skewness(args))
print('Kurtosis:', kurtosis(args))
fast_stats = FastStatistics()
for arg in args:
fast_stats.push(arg)
print()
print('FastStatistics')
print('Count:', len(fast_stats))
print('Mean:', fast_stats.mean())
print('Variance:', fast_stats.variance())
print('StdDev:', fast_stats.stddev())
print('Skewness:', fast_stats.skewness())
print('Kurtosis:', fast_stats.kurtosis())
core_stats = CoreStatistics()
for arg in args:
core_stats.push(arg)
print()
print('CoreStatistics')
print('Count:', len(core_stats))
print('Mean:', core_stats.mean())
print('Variance:', core_stats.variance())
print('StdDev:', core_stats.stddev())
print('Skewness:', core_stats.skewness())
print('Kurtosis:', core_stats.kurtosis())
fast_exp_stats = FastExponentialStatistics()
for arg in args:
fast_exp_stats.push(arg)
print()
print('FastExponentialStatistics')
print('Decay Rate (default):', fast_exp_stats.get_decay())
print('Exponential Mean:', fast_exp_stats.mean())
print('Exponential Variance:', fast_exp_stats.variance())
print('Exponential StdDev:', fast_exp_stats.stddev())
core_exp_stats = CoreExponentialStatistics()
for arg in args:
core_exp_stats.push(arg)
print()
print('CoreExponentialStatistics')
print('Decay Rate (default):', core_exp_stats.get_decay())
print('Exponential Mean:', core_exp_stats.mean())
print('Exponential Variance:', core_exp_stats.variance())
print('Exponential StdDev:', core_exp_stats.stddev())
fast_regr = FastRegression()
for index, arg in enumerate(args, 1):
fast_regr.push(index, arg)
print()
print('FastRegression')
print('Count:', len(fast_regr))
print('Slope:', fast_regr.slope())
print('Intercept:', fast_regr.intercept())
print('Correlation:', fast_regr.correlation())
core_regr = CoreRegression()
for index, arg in enumerate(args, 1):
core_regr.push(index, arg)
print()
print('CoreRegression')
print('Count:', len(core_regr))
print('Slope:', core_regr.slope())
print('Intercept:', core_regr.intercept())
print('Correlation:', core_regr.correlation())
series.append(r_t)
#print (series)
stats = Statistics()
for x in series:
stats.push(x)
# Test kurtosis and skewness
# Skewness < 0 indicates large decreases after many small gains, skewness > 0 indicates large gains after many small losses
# Optimally want -0.8 < x < 0, or x > 1
print('skewness: ', stats.skewness())
# Kurtosis we want to be leptokurtic (> average)
print('kurtosis: ', stats.kurtosis())
# TODO Jarque-Bera about normal distribution - pref want not a normal distribution (ie reject H0 / p-value ~= 0)
# TODO time series tests
# Evaluate trading over time inputdata (not the log series)
# TODO, shorting??
start = 1000
isBought = False
isInitial = True
initialTradePrice = 1
current = start
mintick = 0.00000001 # Minimum tick
totalTrades = -1
def main():
args = list(map(float, sys.argv[1:]))
print('Statistics Functions')
print('Count:', len(args))
print('Mean:', mean(args))
print('Variance:', variance(args))
print('StdDev:', stddev(args))
print('Skewness:', skewness(args))
print('Kurtosis:', kurtosis(args))
fast_stats = FastStatistics()
for arg in args:
fast_stats.push(arg)
print()
print('FastStatistics')
print('Count:', len(fast_stats))
print('Mean:', fast_stats.mean())
print('Variance:', fast_stats.variance())
print('StdDev:', fast_stats.stddev())
print('Skewness:', fast_stats.skewness())
print('Kurtosis:', fast_stats.kurtosis())
core_stats = CoreStatistics()
for arg in args:
core_stats.push(arg)
print()
print('CoreStatistics')
print('Count:', len(core_stats))
print('Mean:', core_stats.mean())
print('Variance:', core_stats.variance())
print('StdDev:', core_stats.stddev())
print('Skewness:', core_stats.skewness())
print('Kurtosis:', core_stats.kurtosis())
fast_regr = FastRegression()
for index, arg in enumerate(args, 1):
fast_regr.push(index, arg)
print()
print('FastRegression')
print('Count:', len(fast_regr))
print('Slope:', fast_regr.slope())
print('Intercept:', fast_regr.intercept())
print('Correlation:', fast_regr.correlation())
core_regr = CoreRegression()
for index, arg in enumerate(args, 1):
core_regr.push(index, arg)
print()
print('CoreRegression')
print('Count:', len(core_regr))
print('Slope:', core_regr.slope())
print('Intercept:', core_regr.intercept())
print('Correlation:', core_regr.correlation())
def main():
args = list(map(float, sys.argv[1:]))
print('Statistics Functions')
print('Count:', len(args))
print('Mean:', mean(args))
print('Variance:', variance(args))
print('StdDev:', stddev(args))
print('Skewness:', skewness(args))
print('Kurtosis:', kurtosis(args))
exp_mean, exp_var = exp_mean_var(0.9, args)
exp_cov, exp_cor = exp_cov_cor(0.9, enumerate(args, 1))
print('Exponential Moving Mean (decay=0.9):', exp_mean)
print('Exponential Moving Variance (decay=0.9):', exp_var)
print('Exponential Moving StdDev (decay=0.9):', exp_var**0.5)
print('Exponential Moving Covariance (decay=0.9):', exp_cov)
print('Exponential Moving Correlation (decay=0.9):', exp_cor)
fast_stats = FastStatistics()
for arg in args:
fast_stats.push(arg)
print()
print('FastStatistics')
print('Count:', len(fast_stats))
print('Mean:', fast_stats.mean())
print('Variance:', fast_stats.variance())
print('StdDev:', fast_stats.stddev())
print('Skewness:', fast_stats.skewness())
print('Kurtosis:', fast_stats.kurtosis())
core_stats = CoreStatistics()
for arg in args:
core_stats.push(arg)
print()
print('CoreStatistics')
print('Count:', len(core_stats))
print('Mean:', core_stats.mean())
print('Variance:', core_stats.variance())
print('StdDev:', core_stats.stddev())
print('Skewness:', core_stats.skewness())
print('Kurtosis:', core_stats.kurtosis())
fast_exp_stats = FastExponentialStatistics()
for arg in args:
fast_exp_stats.push(arg)
print()
print('FastExponentialMovingStatistics')
print('Decay Rate (default):', fast_exp_stats.decay)
print('Exponential Mean:', fast_exp_stats.mean())
print('Exponential Variance:', fast_exp_stats.variance())
print('Exponential StdDev:', fast_exp_stats.stddev())
core_exp_stats = CoreExponentialStatistics()
for arg in args:
core_exp_stats.push(arg)
print()
print('CoreExponentialMovingStatistics')
print('Decay Rate (default):', core_exp_stats.decay)
print('Exponential Mean:', core_exp_stats.mean())
print('Exponential Variance:', core_exp_stats.variance())
print('Exponential StdDev:', core_exp_stats.stddev())
fast_regr = FastRegression()
for index, arg in enumerate(args, 1):
fast_regr.push(index, arg)
print()
print('FastRegression')
print('Count:', len(fast_regr))
print('Slope:', fast_regr.slope())
print('Intercept:', fast_regr.intercept())
print('Correlation:', fast_regr.correlation())
core_regr = CoreRegression()
for index, arg in enumerate(args, 1):
core_regr.push(index, arg)
print()
print('CoreRegression')
print('Count:', len(core_regr))
print('Slope:', core_regr.slope())
print('Intercept:', core_regr.intercept())
print('Correlation:', core_regr.correlation())
fast_exp_cov = FastExponentialCoveriance()
for index, arg in enumerate(args, 1):
fast_exp_cov.push(index, arg)
print()
print('FastExponentialCovariance')
print('Decay Rate (default):', fast_exp_cov.decay)
print('Exponential Moving Covariance:', fast_exp_cov.covariance())
print('Exponential Moving Correlation:', fast_exp_cov.correlation())
core_exp_cov = CoreExponentialCoveriance()
for index, arg in enumerate(args, 1):
core_exp_cov.push(index, arg)
print()
print('CoreExponentialCovariance')
print('Decay Rate (default):', core_exp_cov.decay)
print('Exponential Moving Covariance:', core_exp_cov.covariance())
print('Exponential Moving Correlation:', core_exp_cov.correlation())
print('Skewness:', skewness(args))
print('Kurtosis:', kurtosis(args))
fast_stats = FastStatistics()
for arg in args:
fast_stats.push(arg)
print()
print('FastStatistics')
print('Count:', len(fast_stats))
print('Mean:', fast_stats.mean())
print('Variance:', fast_stats.variance())
print('StdDev:', fast_stats.stddev())
print('Skewness:', fast_stats.skewness())
print('Kurtosis:', fast_stats.kurtosis())
core_stats = CoreStatistics()
for arg in args:
core_stats.push(arg)
print()
print('CoreStatistics')
print('Count:', len(core_stats))
print('Mean:', core_stats.mean())
print('Variance:', core_stats.variance())
print('StdDev:', core_stats.stddev())
print('Skewness:', core_stats.skewness())
print('Kurtosis:', core_stats.kurtosis())
