# -*- coding: utf-8 -*-
from __future__ import division
from numpy.random import randn
import numpy as np
# -*- coding: utf-8 -*-
###通用函数
arr = np.arange(10)
np.sqrt(arr)
np.exp(arr)
x = randn(8)
y = randn(8)
x
y
np.maximum(x, y) # 元素级最大值
arr = randn(7) * 5
print arr
np.modf(arr)
###利用数组进行数据处理
#向量化
points = np.arange(-5, 5, 0.01) # 1000 equally spaced points
xs, ys = np.meshgrid(points, points)
ys
import matplotlib.pyplot as plt
z = np.sqrt(xs ** 2 + ys ** 2)
z
plt.imshow(z, cmap=plt.cm.gray); plt.colorbar()
plt.title("Image plot of $\sqrt{x^2 + y^2}$ for a grid of values")
plt.draw()
#将条件逻辑表达为数组运算
xarr = np.array([1.1, 1.2, 1.3, 1.4, 1.5])
yarr = np.array([2.1, 2.2, 2.3, 2.4, 2.5])
cond = np.array([True, False, True, True, False])
result = [(x if c else y)
for x, y, c in zip(xarr, yarr, cond)]
result
result = np.where(cond, xarr, yarr)
result
arr = randn(4, 4)
arr
np.where(arr > 0, 2, -2)
np.where(arr > 0, 2, arr) # set only positive values to 2
# Not to be executed
result = []
for i in range(n):
if cond1[i] and cond2[i]:
result.append(0)
elif cond1[i]:
result.append(1)
elif cond2[i]:
result.append(2)
else:
result.append(3)
# Not to be executed
np.where(cond1 & cond2, 0,
np.where(cond1, 1,
np.where(cond2, 2, 3)))
# Not to be executed
result = 1 * cond1 + 2 * cond2 + 3 * -(cond1 | cond2)
#数学与统计方法
arr = np.random.randn(5, 4) # 标准正态分布数据
arr.mean()
np.mean(arr)
arr.sum()
arr.mean(axis=1)
arr.sum(0)
arr = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])
arr.cumsum(0)
arr.cumprod(1)
#用于布尔型数组的方法
arr = randn(100)
(arr > 0).sum() # 正值的数量
bools = np.array([False, False, True, False])
bools.any()
bools.all()
#排序
arr = randn(8)
arr
arr.sort()
arr
arr = randn(5, 3)
arr
arr.sort(1)
arr
large_arr = randn(1000)
large_arr.sort()
large_arr[int(0.05 * len(large_arr))] # 5%分位数
#唯一化以及其他的集合逻辑
names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])
np.unique(names)
ints = np.array([3, 3, 3, 2, 2, 1, 1, 4, 4])
np.unique(ints)
sorted(set(names))
values = np.array([6, 0, 0, 3, 2, 5, 6])
np.in1d(values, [2, 3, 6])
###线性代数
x = np.array([[1., 2., 3.], [4., 5., 6.]])
y = np.array([[6., 23.], [-1, 7], [8, 9]])
x
y
x.dot(y) # 等价于np.dot(x, y)
np.dot(x, np.ones(3))
np.random.seed(12345)
from numpy.linalg import inv, qr
X = randn(5, 5)
mat = X.T.dot(X)
inv(mat)
mat.dot(inv(mat))
q, r = qr(mat)
r
###随机数生成
samples = np.random.normal(size=(4, 4))
samples
from random import normalvariate
N = 1000000
get_ipython().magic(u'timeit samples = [normalvariate(0, 1) for _ in xrange(N)]')
get_ipython().magic(u'timeit np.random.normal(size=N)')
# 范例：随机漫步
import random
position = 0
walk = [position]
steps = 1000
for i in xrange(steps):
step = 1 if random.randint(0, 1) else -1
position += step
walk.append(position)
np.random.seed(12345)
nsteps = 1000
draws = np.random.randint(0, 2, size=nsteps)
steps = np.where(draws > 0, 1, -1)
walk = steps.cumsum()
walk.min()
walk.max()
(np.abs(walk) >= 10).argmax()
# 一次模拟多个随机漫步
nwalks = 5000
nsteps = 1000
draws = np.random.randint(0, 2, size=(nwalks, nsteps)) # 0 or 1
steps = np.where(draws > 0, 1, -1)
walks = steps.cumsum(1)
walks
walks.max()
walks.min()
hits30 = (np.abs(walks) >= 30).any(1)
hits30
hits30.sum() # 到达30或-30的数量
crossing_times = (np.abs(walks[hits30]) >= 30).argmax(1)
crossing_times.mean()
steps = np.random.normal(loc=0, scale=0.25,
size=(nwalks, nsteps))
###利用NumPy进行历史股价分析
import sys
#读入文件
c,v=np.loadtxt('data.csv', delimiter=',', usecols=(6,7), unpack=True)
#计算成交量加权平均价格
vwap = np.average(c, weights=v)
print "VWAP =", vwap
#算术平均值函数
print "mean =", np.mean(c)
#时间加权平均价格
t = np.arange(len(c))
print "twap =", np.average(c, weights=t)
#寻找最大值和最小值
h,l=np.loadtxt('data.csv', delimiter=',', usecols=(4,5), unpack=True)
print "highest =", np.max(h)
print "lowest =", np.min(l)
print (np.max(h) + np.min(l)) /2
print "Spread high price", np.ptp(h)
print "Spread low price", np.ptp(l)
#统计分析
c=np.loadtxt('data.csv', delimiter=',', usecols=(6,), unpack=True)
print "median =", np.median(c)
sorted = np.msort(c)
print "sorted =", sorted
N = len(c)
print "middle =", sorted[(N - 1)/2]
print "average middle =", (sorted[N /2] + sorted[(N - 1) / 2]) / 2
print "variance =", np.var(c)
print "variance from definition =", np.mean((c - c.mean())**2)
#股票收益率
c=np.loadtxt('data.csv', delimiter=',', usecols=(6,), unpack=True)
returns = np.diff( c ) / c[ : -1]
print "Standard deviation =", np.std(returns)
logreturns = np.diff( np.log(c) )
posretindices = np.where(returns > 0)
print "Indices with positive returns", posretindices
annual_volatility = np.std(logreturns)/np.mean(logreturns)
annual_volatility = annual_volatility / np.sqrt(1./252.)
print "Annual volatility", annual_volatility
print "Monthly volatility", annual_volatility * np.sqrt(1./12.)
#日期分析
from datetime import datetime
# Monday 0
# Tuesday 1
# Wednesday 2
# Thursday 3
# Friday 4
# Saturday 5
# Sunday 6
def datestr2num(s):
return datetime.strptime(s, "%d-%m-%Y").date().weekday()
dates, close=np.loadtxt('data.csv', delimiter=',', usecols=(1,6),
converters={1: datestr2num}, unpack=True)
print "Dates =", dates
averages = np.zeros(5)
for i in range(5):
indices = np.where(dates == i)
prices = np.take(close, indices)
avg = np.mean(prices)
print "Day", i, "prices", prices, "Average", avg
averages[i] = avg
top = np.max(averages)
print "Highest average", top
print "Top day of the week", np.argmax(averages)
bottom = np.min(averages)
print "Lowest average", bottom
print "Bottom day of the week", np.argmin(averages)
#周汇总
def datestr2num(s):
return datetime.strptime(s, "%d-%m-%Y").date().weekday()
dates, open, high, low, close=np.loadtxt('data.csv', delimiter=',',
usecols=(1, 3, 4, 5, 6), converters={1: datestr2num}, unpack=True)
close = close[:16]
dates = dates[:16]
# get first Monday
first_monday = np.ravel(np.where(dates == 0))[0]
print "The first Monday index is", first_monday
# get last Friday
last_friday = np.ravel(np.where(dates == 4))[-1]
print "The last Friday index is", last_friday
weeks_indices = np.arange(first_monday, last_friday + 1)
print "Weeks indices initial", weeks_indices
weeks_indices = np.split(weeks_indices, 3)
print "Weeks indices after split", weeks_indices
def summarize(a, o, h, l, c):
monday_open = o[a[0]]
week_high = np.max( np.take(h, a) )
week_low = np.min( np.take(l, a) )
friday_close = c[a[-1]]
return("APPL", monday_open, week_high, week_low, friday_close)
weeksummary = np.apply_along_axis(summarize, 1, weeks_indices, open, high, low, close)
print "Week summary", weeksummary
np.savetxt("weeksummary.csv", weeksummary, delimiter=",", fmt="%s")
#真实波动幅度均值
h, l, c = np.loadtxt('data.csv', delimiter=',', usecols=(4, 5, 6), unpack=True)
N =20
h = h[-N:]
l = l[-N:]
print "len(h)", len(h), "len(l)", len(l)
print "Close", c
previousclose = c[-N -1: -1]
print "len(previousclose)", len(previousclose)
print "Previous close", previousclose
truerange = np.maximum(h - l, h - previousclose, previousclose - l)
print "True range", truerange
atr = np.zeros(N)
atr[0] = np.mean(truerange)
for i in range(1, N):
atr[i] = (N - 1) * atr[i - 1] + truerange[i]
atr[i] /= N
print "ATR", atr
#简单移动平均线
from matplotlib.pyplot import plot
from matplotlib.pyplot import show
N = 5
weights = np.ones(N) / N
print "Weights", weights
c = np.loadtxt('data.csv', delimiter=',', usecols=(6,), unpack=True)
sma = np.convolve(weights, c)[N-1:-N+1]
t = np.arange(N - 1, len(c))
plot(t, c[N-1:], lw=1.0)
plot(t, sma, lw=2.0)
show()
#指数移动平均线
x = np.arange(5)
print "Exp", np.exp(x)
print "Linspace", np.linspace(-1, 0, 5)
N = 5
weights = np.exp(np.linspace(-1., 0., N))
weights /= weights.sum()
print "Weights", weights
c = np.loadtxt('data.csv', delimiter=',', usecols=(6,), unpack=True)
ema = np.convolve(weights, c)[N-1:-N+1]
t = np.arange(N - 1, len(c))
plot(t, c[N-1:], lw=1.0)
plot(t, ema, lw=2.0)
show()
#布林带
N = 5
weights = np.ones(N) / N
print "Weights", weights
c = np.loadtxt('data.csv', delimiter=',', usecols=(6,), unpack=True)
sma = np.convolve(weights, c)[N-1:-N+1]
deviation = []
C = len(c)
for i in range(N - 1, C):
if i + N < C:
dev = c[i: i + N]
else:
dev = c[-N:]
averages = np.zeros(N)
averages.fill(sma[i - N - 1])
dev = dev - averages
dev = dev ** 2
dev = np.sqrt(np.mean(dev))
deviation.append(dev)
deviation = 2 * np.array(deviation)
print len(deviation), len(sma)
upperBB = sma + deviation
lowerBB = sma - deviation
c_slice = c[N-1:]
between_bands = np.where((c_slice < upperBB) & (c_slice > lowerBB))
print lowerBB[between_bands]
print c[between_bands]
print upperBB[between_bands]
between_bands = len(np.ravel(between_bands))
print "Ratio between bands", float(between_bands)/len(c_slice)
t = np.arange(N - 1, C)
plot(t, c_slice, lw=1.0)
plot(t, sma, lw=2.0)
plot(t, upperBB, lw=3.0)
plot(t, lowerBB, lw=4.0)
show()
#线性模型
N = int(sys.argv[1])
c = np.loadtxt('data.csv', delimiter=',', usecols=(6,), unpack=True)
b = c[-N:]
b = b[::-1]
print "b", b
A = np.zeros((N, N), float)
print "Zeros N by N", A
for i in range(N):
A[i, ] = c[-N - 1 - i: - 1 - i]
print "A", A
(x, residuals, rank, s) = np.linalg.lstsq(A, b)
print x, residuals, rank, s
print np.dot(b, x)
#趋势线
def fit_line(t, y):
A = np.vstack([t, np.ones_like(t)]).T
return np.linalg.lstsq(A, y)[0]
h, l, c = np.loadtxt('data.csv', delimiter=',', usecols=(4, 5, 6), unpack=True)
pivots = (h + l + c) / 3
print "Pivots", pivots
t = np.arange(len(c))
sa, sb = fit_line(t, pivots - (h - l))
ra, rb = fit_line(t, pivots + (h - l))
support = sa * t + sb
resistance = ra * t + rb
condition = (c > support) & (c < resistance)
print "Condition", condition
between_bands = np.where(condition)
print support[between_bands]
print c[between_bands]
print resistance[between_bands]
between_bands = len(np.ravel(between_bands))
print "Number points between bands", between_bands
print "Ratio between bands", float(between_bands)/len(c)
print "Tomorrows support", sa * (t[-1] + 1) + sb
print "Tomorrows resistance", ra * (t[-1] + 1) + rb
a1 = c[c > support]
a2 = c[c < resistance]
print "Number of points between bands 2nd approach" ,len(np.intersect1d(a1, a2))
plot(t, c)
plot(t, support)
plot(t, resistance)
show()
