版权声明：License CC BY-NC-SA 4.0 https://blog.csdn.net/wizardforcel/article/details/72828999
# 来源：NumPy Beginner's Guide 2e ch4
交易相关偶对
import numpy as np
from matplotlib.pyplot import plot
from matplotlib.pyplot import show
# 读入 BHP 的收盘价
bhp = np.loadtxt('BHP.csv', delimiter=',', usecols=(6,), unpack=True)
# 计算 BHP 的简单收益
bhp_returns = np.diff(bhp) / bhp[ : -1]
# 读入 VALE 的收盘价
vale = np.loadtxt('VALE.csv', delimiter=',', usecols=(6,), unpack=True)
# 计算 VALE 的简单收益
vale_returns = np.diff(vale) / vale[ : -1]
# 计算协方差
# cov_x_y = ((x - x.mean()) * (y - y.mean())).mean()
# cov 函数返回协方差矩阵
# [[ var_x, cov_x_y],
# [ cov_y_x, var_y]]
covariance = np.cov(bhp_returns, vale_returns)
print "Covariance", covariance
'''
Covariance [[ 0.00028179 0.00019766]
[ 0.00019766 0.00030123]]
'''
# diagonal 获取对角线上的元素
print "Covariance diagonal", covariance.diagonal()
# Covariance diagonal [ 0.00028179 0.00030123]
# trace 计算矩阵的迹（对角线元素和）
print "Covariance trace", covariance.trace()
# Covariance trace 0.00058302354992
print covariance/ (bhp_returns.std() * vale_returns.std())
# corrcoef 计算相关系数
# rho_x_y = cov_x_y / (std_x * std_y)
print "Correlation coefficient", np.corrcoef(bhp_returns, vale_returns)
'''
[[ 1.00173366 0.70264666]
[ 0.70264666 1.0708476 ]]
'''
# 检查两个股票是否同步
# 如果差值最后一项，距离均值大于两个标准差，就认为是不同步的
difference = bhp - vale
avg = np.mean(difference)
dev = np.std(difference)
print "Out of sync", np.abs(difference[-1] - avg) > 2 * dev
# Out of sync False
# 绘制两个股票的收益
t = np.arange(len(bhp_returns))
plot(t, bhp_returns, lw=1)
plot(t, vale_returns, lw=2)
show()
多项式拟合
import numpy as np
import sys
from matplotlib.pyplot import plot
from matplotlib.pyplot import show
# 导入 BHP 和 VALE 的收盘价
bhp=np.loadtxt('BHP.csv', delimiter=',', usecols=(6,), unpack=True)
vale=np.loadtxt('VALE.csv', delimiter=',', usecols=(6,), unpack=True)
# polyfit 用于多项式拟合
# 参数为训练集x，训练集y，最高项次数
# 返回方程的系数数组，高次在前
t = np.arange(len(bhp))
poly = np.polyfit(t, bhp - vale, int(sys.argv[1]))
print "Polynomial fit", poly
# 假设最高项次数为 3：
# Polynomial fit [ 1.11655581e-03 -5.28581762e-02 5.80684638e-01 5.79791202e+01]
# polyval 使用拟合结果来预测新的值
# p[0] * x**n + p[1] * x**(n-1) + ... + p[n-1]*x + p[n]
print "Next value", np.polyval(poly, t[-1] + 1)
# Next value 57.9743076081
# 返回多项式的根
print "Roots", np.roots(poly)
# Roots [ 35.48624287+30.62717062j 35.48624287-30.62717062j -23.63210575 +0.j ]
# 多项式求导
# (x ** n)' = n * x ** (n - 1)
# (a * u(x) + b * v(x))' = a * u'(x) + b * v'(x)
der = np.polyder(poly)
print "Derivative", der
# Derivative [ 0.00334967 -0.10571635 0.58068464]
# 导数的根（可能）是极值
print "Extremas", np.roots(der)
# Extremas [ 24.47820054 7.08205278]
# 拟合函数的最大值和最小值点
vals = np.polyval(poly, t)
print np.argmax(vals)
# 7
print np.argmin(vals)
# 24
# 绘制原始函数和拟合函数
plot(t, bhp - vale)
plot(t, vals)
show()
平衡成交量
import numpy as np
# 读入收盘价和成交量
c, v=np.loadtxt('BHP.csv', delimiter=',', usecols=(6, 7), unpack=True)
# 获取绝对收益
change = np.diff(c)
print "Change", change
'''
Change [ 1.92 -1.08 -1.26 0.63 -1.54 -0.28 0.25 -0.6 2.15 0.69 -1.33 1.16
1.59 -0.26 -1.29 -0.13 -2.12 -3.91 1.28 -0.57 -2.07 -2.07 2.5 1.18
-0.88 1.31 1.24 -0.59]
'''
# 计算状态
signs = np.sign(change)
print "Signs", signs
'''
Signs [ 1. -1. -1. 1. -1. -1. 1. -1. 1. 1. -1. 1. 1. -1. -1. -1. -1. -1.
-1. -1. -1. 1. 1. 1. -1. 1. 1. -1.]
'''
# 状态也可以用 piecewise 来计算
# 参数为输入数组 arr、状态数组 condlist 和结果数组 reslist
# 如果满足 condlist[i]，将 arr[i] 变为 reslist[i]
pieces = np.piecewise(change, [change < 0, change > 0], [-1, 1])
print "Pieces", pieces
print "Arrays equal?", np.array_equal(signs, pieces)
# Arrays equal? True
# 平衡成交量是状态乘以成交量
print "On balance volume", v[1:] * signs
'''
[ 2620800. -2461300. -3270900. 2650200. -4667300. -5359800. 7768400.
-4799100. 3448300. 4719800. -3898900. 3727700. 3379400. -2463900.
-3590900. -3805000. -3271700. -5507800. 2996800. -3434800. -5008300.
-7809799. 3947100. 3809700. 3098200. -3500200. 4285600. 3918800.
-3632200.]
'''
使用向量化来避免循环
# 向量化就是逐元素调用函数
import numpy as np
import sys
# 获取开盘价、最高价、最低价和收盘价
o, h, l, c = np.loadtxt('BHP.csv', delimiter=',', usecols=(3, 4, 5, 6), unpack=True)
# calc_profit 用于计算利润
def calc_profit(open, high, low, close):
# 以稍低于开盘价的价格买入
buy = open * float(sys.argv[1])
if low < buy < high:
# 如果这个价格在当天的区间之内
# 就以收盘价卖掉
return (close - buy)/buy
else:
# 否则就没有收益，返回 0
return 0
# 创建向量化的 calc_profit
# 也可以使用装饰器 @np.vectorize
func = np.vectorize(calc_profit)
profits = func(o, h, l, c)
print "Profits", profits
# 获取不为零的收益
real_trades = profits[profits != 0]
# 打印概率和平均利润率
print "Number of trades", len(real_trades), round(100.0 * len(real_trades)/len(c), 2), "%"
# Number of trades 28 93.33 %
print "Average profit/loss %", round(np.mean(real_trades) * 100, 2)
# Average profit/loss % 0.02
# 选择获利的交易，计算概率和平均利润率
winning_trades = profits[profits > 0]
print "Number of winning trades", len(winning_trades), round(100.0 * len(winning_trades)/len(c), 2), "%"
Number of winning trades 16 53.33 %
print "Average profit %", round(np.mean(winning_trades) * 100, 2)
# Average profit/loss % 0.72
# 选择亏损的交易，计算概率和平均利润率
losing_trades = profits[profits < 0]
print "Number of losing trades", len(losing_trades), round(100.0 * len(losing_trades)/len(c), 2), "%"
# Number of losing trades 12 40.0 %
print "Average loss %", round(np.mean(losing_trades) * 100, 2)
# Average loss % -0.92
使用海宁函数实现平滑
import numpy as np
import sys
from matplotlib.pyplot import plot
from matplotlib.pyplot import show
# 读取长度 N
N = int(sys.argv[1])
# 使用 hanning 来生成权重
weights = np.hanning(N)
print "Weights", weights
'''
Weights [ 0. 0.1882551 0.61126047 0.95048443 0.95048443 0.61126047
0.1882551 0. ]
'''
# 读取 BHP 收盘价
bhp = np.loadtxt('BHP.csv', delimiter=',', usecols=(6,), unpack=True)
# 计算简单收益
bhp_returns = np.diff(bhp) / bhp[ : -1]
# 使用 convolve 函数来使之平滑
smooth_bhp = np.convolve(weights/weights.sum(), bhp_returns)[N-1:-N+1]
# 读取 VALE 收盘价
vale = np.loadtxt('VALE.csv', delimiter=',', usecols=(6,), unpack=True)
# 计算简单收益
vale_returns = np.diff(vale) / vale[ : -1]
# 使用 convolve 函数来使之平滑
smooth_vale = np.convolve(weights/weights.sum(), vale_returns)[N-1:-N+1]
# 读取最高项系数
K = int(sys.argv[1])
# 多项式拟合 BHP 和 VALE 的平滑收益
t = np.arange(N - 1, len(bhp_returns))
poly_bhp = np.polyfit(t, smooth_bhp, K)
poly_vale = np.polyfit(t, smooth_vale, K)
# np.polysub 用于求多项式的差
poly_sub = np.polysub(poly_bhp, poly_vale)
# 差值为 0 的点就是多项式的交点
xpoints = np.roots(poly_sub)
print "Intersection points", xpoints
'''
Intersection points [ 27.73321597+0.j 27.51284094+0.j 24.32064343+0.j
18.86423973+0.j 12.43797190+1.73218179j 12.43797190-1.73218179j
6.34613053+0.62519463j 6.34613053-0.62519463j]
'''
# 检查是否是实数值
reals = np.isreal(xpoints)
print "Real number?", reals
# Real number? [ True True True True False False False False]
# 过滤实数值，转成实数
# 其实可以直接使用 xpoints[np.isreal(xpoints)].real
xpoints = np.select([reals], [xpoints])
xpoints = xpoints.real
print "Real intersection points", xpoints
# Real intersection points [ 27.73321597 27.51284094 24.32064343 18.86423973 0. 0. 0. 0.]
# trim_zeros 去除首尾的零元素
print "Sans 0s", np.trim_zeros(xpoints)
# Sans 0s [ 27.73321597 27.51284094 24.32064343 18.86423973]
# 绘制简单收益，以及 N 天的平滑收益
plot(t, bhp_returns[N-1:], lw=1.0)
plot(t, smooth_bhp, lw=2.0)
plot(t, vale_returns[N-1:], lw=1.0)
plot(t, smooth_vale, lw=2.0)
show()
