##配对交易
策略思路
寻找走势相关且股价相近的一对股票，根据其价格变动买卖
策略实现
历史前五日的Pearson相关系数若大于给定的阈值则触发买卖操作
from scipy.stats.stats import pearsonr
start = datetime(2013, 1, 1)
end = datetime(2014, 12, 1)
benchmark = 'HS300'
universe = ['000559.XSHE', '600126.XSHG']
capital_base = 1e6
corlen = 5
def initialize(account):
add_history('hist', corlen)
account.cutoff = 0.9
account.prev_prc1 = 0
account.prev_prc2 = 0
account.prev_prcb = 0
def handle_data(account, data):
stk1 = universe[0]
stk2 = universe[1]
prc1 = data[stk1]['closePrice']
prc2 = data[stk2]['closePrice']
prcb = data['HS300']['return']
px1 = account.hist[stk1]['closePrice'].values
px2 = account.hist[stk2]['closePrice'].values
pxb = account.hist['HS300']['return'].values
corval, pval = pearsonr(px1, px2)
mov1, mov2 = adj(prc1, prc2, prcb, account.prev_prc1, account.prev_prc2, account.prev_prcb)
amount =1e4 / prc2
if (mov1 > 0) and (abs(corval) > account.cutoff):
order(stk2, amount)
elif (mov1 < 0) and (abs(corval) > account.cutoff):
if (account.position.stkpos.get(stk2, 0) > amount):
order(stk2, -amount)
else:
order_to(stk2, 0)
amount =1e4 / prc1
if (mov2 > 0) and (abs(corval) > account.cutoff):
order(stk1, amount)
elif (mov2 < 0) and (abs(corval) > account.cutoff):
if (account.position.stkpos.get(stk1, 0) > amount):
order(stk1, -amount)
else:
order_to(stk1, 0)
account.prev_prc1 = prc1
account.prev_prc2 = prc2
account.prev_prcb = prcb
def dmv(curr, prev):
delta = curr / prev - 1
return delta
def adj(x, y, base, prev_x, prev_y, prev_base):
dhs = dmv(base, prev_base)
dx = dmv(x, prev_x) - dhs
dy = dmv(y, prev_y) - dhs
return (dx, dy)
min(bt.cash)
232096.85369499651import pandas as pd
import numpy as np
from datetime import datetime
import quartz
import quartz.backtest as qb
import quartz.performance as qp
from quartz.api import *
from scipy.stats.stats import pearsonr
start = datetime(2013, 1, 1) # 回测起始时间
end = datetime(2014, 12, 1) # 回测结束时间
benchmark = 'HS300' # 使用沪深 300 作为参考标准
capital_base = 1e6 # 起始资金
corlen = 5
def initialize(account): # 初始化虚拟账户状态
add_history('hist', corlen)
account.cutoff = 0.9
account.prev_prc1 = 0
account.prev_prc2 = 0
account.prev_prcb = 0
def handle_data(account, data): # 每个交易日的买入卖出指令
stk1 = universe[0]
stk2 = universe[1]
prc1 = data[stk1]['closePrice']
prc2 = data[stk2]['closePrice']
prcb = data['HS300']['return']
px1 = account.hist[stk1]['closePrice'].values
px2 = account.hist[stk2]['closePrice'].values
pxb = account.hist['HS300']['return'].values
corval, pval = pearsonr(px1, px2)
mov1, mov2 = adj(prc1, prc2, prcb, account.prev_prc1, account.prev_prc2, account.prev_prcb)
#amount = int( 0.08 * capital_base / prc2)
amount =1e4 / prc2
if (mov1 > 0) and (abs(corval) > account.cutoff):
order(stk2, amount)
elif (mov1 < 0) and (abs(corval) > account.cutoff):
if (account.position.stkpos.get(stk2, 0) > amount):
order(stk2, -amount)
else:
order_to(stk2, 0)
#amount = int(0.08 * capital_base / prc1)
amount =1e4 / prc1
if (mov2 > 0) and (abs(corval) > account.cutoff):
order(stk1, amount)
elif (mov2 < 0) and (abs(corval) > account.cutoff):
if (account.position.stkpos.get(stk1, 0) > amount):
order(stk1, -amount)
else:
order_to(stk1, 0)
account.prev_prc1 = prc1
account.prev_prc2 = prc2
account.prev_prcb = prcb
def dmv(curr, prev):
delta = curr / prev - 1
return delta
def adj(x, y, base, prev_x, prev_y, prev_base):
dhs = dmv(base, prev_base)
dx = dmv(x, prev_x) - dhs
dy = dmv(y, prev_y) - dhs
return (dx, dy)
pool_raw = pd.read_csv("po.pair.2012.csv")
pool = []
for i in range(len(pool_raw)):
s1, s2 = pool_raw.loc[i].tolist()
if [s2, s1] not in pool:
pool.append([s1, s2])
outfile = []
for i, universe in enumerate(pool):
print i
try:
bt = qb.backtest(start, end, benchmark, universe, capital_base, initialize = initialize, handle_data = handle_data)
perf = qp.perf_parse(bt)
outfile.append(universe + [perf["annualized_return"], perf["sharpe"]])
except:
pass
keys = ['stock1', 'stock2', 'annualized_return', 'sharpe']
outdict = {}
outfile = zip(*sorted(outfile, key=lambda x:x[2], reverse=True))
for i,k in enumerate(keys):
outdict[k] = outfile[i]
outdict = pd.DataFrame(outdict).loc[:, keys]
outdict
['000066.XSHE', '000707.XSHE']
['000066.XSHE', '600117.XSHG']
['000066.XSHE', '600126.XSHG']
['000066.XSHE', '600819.XSHG']
['000089.XSHE', '600035.XSHG']
['000089.XSHE', '600037.XSHG']
['000089.XSHE', '600595.XSHG']
['000159.XSHE', '000967.XSHE']
['000159.XSHE', '600595.XSHG']
['000417.XSHE', '000541.XSHE']
['000417.XSHE', '000685.XSHE']
['000417.XSHE', '600875.XSHG']
['000425.XSHE', '000528.XSHE']
['000507.XSHE', '600391.XSHG']
['000541.XSHE', '000987.XSHE']
['000541.XSHE', '600330.XSHG']
['000541.XSHE', '600883.XSHG']
['000554.XSHE', '000707.XSHE']
['000559.XSHE', '600026.XSHG']
['000559.XSHE', '600126.XSHG']
['000559.XSHE', '600477.XSHG']
['000559.XSHE', '600581.XSHG']
['000559.XSHE', '601666.XSHG']
['000635.XSHE', '000707.XSHE']
['000635.XSHE', '600068.XSHG']
['000635.XSHE', '600117.XSHG']
['000635.XSHE', '600188.XSHG']
['000635.XSHE', '600295.XSHG']
['000635.XSHE', '600550.XSHG']
['000635.XSHE', '600819.XSHG']
['000635.XSHE', '601168.XSHG']
['000635.XSHE', '601233.XSHG']
['000650.XSHE', '600261.XSHG']
['000683.XSHE', '000936.XSHE']
['000683.XSHE', '600595.XSHG']
['000685.XSHE', '000988.XSHE']
['000685.XSHE', '601101.XSHG']
['000698.XSHE', '000949.XSHE']
['000707.XSHE', '000911.XSHE']
['000707.XSHE', '000969.XSHE']
['000707.XSHE', '000987.XSHE']
['000707.XSHE', '600117.XSHG']
['000707.XSHE', '600295.XSHG']
['000707.XSHE', '600550.XSHG']
['000707.XSHE', '600831.XSHG']
['000707.XSHE', '601168.XSHG']
['000707.XSHE', '601233.XSHG']
['000708.XSHE', '600327.XSHG']
['000709.XSHE', '601107.XSHG']
['000709.XSHE', '601618.XSHG']
['000717.XSHE', '600282.XSHG']
['000717.XSHE', '600307.XSHG']
['000717.XSHE', '600808.XSHG']
['000761.XSHE', '600320.XSHG']
['000761.XSHE', '600548.XSHG']
['000822.XSHE', '600117.XSHG']
['000830.XSHE', '600068.XSHG']
['000830.XSHE', '600320.XSHG']
['000830.XSHE', '600550.XSHG']
['000877.XSHE', '601519.XSHG']
['000898.XSHE', '600022.XSHG']
['000898.XSHE', '600808.XSHG']
['000911.XSHE', '600550.XSHG']
['000916.XSHE', '600033.XSHG']
['000916.XSHE', '600035.XSHG']
['000916.XSHE', '600126.XSHG']
['000930.XSHE', '600026.XSHG']
['000932.XSHE', '600569.XSHG']
['000933.XSHE', '600348.XSHG']
['000933.XSHE', '600595.XSHG']
['000936.XSHE', '600477.XSHG']
['000937.XSHE', '600348.XSHG']
['000937.XSHE', '600508.XSHG']
['000937.XSHE', '600997.XSHG']
['000937.XSHE', '601001.XSHG']
['000939.XSHE', '600819.XSHG']
['000967.XSHE', '600879.XSHG']
['000969.XSHE', '600831.XSHG']
['000973.XSHE', '600460.XSHG']
['000987.XSHE', '600636.XSHG']
['000987.XSHE', '600827.XSHG']
['000987.XSHE', '601001.XSHG']
['600008.XSHG', '600035.XSHG']
['600012.XSHG', '600428.XSHG']
['600020.XSHG', '600033.XSHG']
['600020.XSHG', '600035.XSHG']
['600026.XSHG', '600068.XSHG']
['600026.XSHG', '600089.XSHG']
['600026.XSHG', '600126.XSHG']
['600026.XSHG', '600307.XSHG']
['600026.XSHG', '600331.XSHG']
['600026.XSHG', '600375.XSHG']
['600026.XSHG', '600581.XSHG']
['600026.XSHG', '600963.XSHG']
['600026.XSHG', '601666.XSHG']
['600026.XSHG', '601898.XSHG']
['600033.XSHG', '600035.XSHG']
['600035.XSHG', '600126.XSHG']
['600035.XSHG', '600269.XSHG']
['600035.XSHG', '600307.XSHG']
['600035.XSHG', '600586.XSHG']
['600037.XSHG', '600327.XSHG']
['600068.XSHG', '600126.XSHG']
['600068.XSHG', '600269.XSHG']
['600068.XSHG', '600320.XSHG']
['600068.XSHG', '600550.XSHG']
['600068.XSHG', '601001.XSHG']
['600068.XSHG', '601666.XSHG']
['600089.XSHG', '600581.XSHG']
['600100.XSHG', '600117.XSHG']
['600117.XSHG', '600295.XSHG']
['600117.XSHG', '600339.XSHG']
['600117.XSHG', '601168.XSHG']
['600117.XSHG', '601233.XSHG']
['600126.XSHG', '600282.XSHG']
['600126.XSHG', '600327.XSHG']
['600126.XSHG', '600569.XSHG']
['600126.XSHG', '600581.XSHG']
['600126.XSHG', '600808.XSHG']
['600126.XSHG', '600963.XSHG']
['600160.XSHG', '600449.XSHG']
['600160.XSHG', '601216.XSHG']
['600160.XSHG', '601311.XSHG']
['600188.XSHG', '600295.XSHG']
['600188.XSHG', '601001.XSHG']
['600231.XSHG', '600282.XSHG']
['600269.XSHG', '601618.XSHG']
['600282.XSHG', '600307.XSHG']
['600282.XSHG', '600569.XSHG']
['600282.XSHG', '600808.XSHG']
['600282.XSHG', '600963.XSHG']
['600307.XSHG', '600581.XSHG']
['600307.XSHG', '600808.XSHG']
['600307.XSHG', '600963.XSHG']
['600320.XSHG', '600548.XSHG']
['600320.XSHG', '601600.XSHG']
['600330.XSHG', '600883.XSHG']
['600330.XSHG', '601268.XSHG']
['600331.XSHG', '600581.XSHG']
['600348.XSHG', '600508.XSHG']
['600348.XSHG', '600997.XSHG']
['600348.XSHG', '601001.XSHG']
['600368.XSHG', '600527.XSHG']
['600375.XSHG', '600581.XSHG']
['600391.XSHG', '601100.XSHG']
['600449.XSHG', '601311.XSHG']
['600449.XSHG', '601519.XSHG']
['600460.XSHG', '601908.XSHG']
['600477.XSHG', '600581.XSHG']
['600508.XSHG', '600546.XSHG']
['600508.XSHG', '600997.XSHG']
['600522.XSHG', '600973.XSHG']
['600550.XSHG', '600831.XSHG']
['600569.XSHG', '600808.XSHG']
['600569.XSHG', '600963.XSHG']
['600581.XSHG', '600963.XSHG']
['600581.XSHG', '601001.XSHG']
['600581.XSHG', '601168.XSHG']
['600581.XSHG', '601666.XSHG']
['600586.XSHG', '601268.XSHG']
['600595.XSHG', '601001.XSHG']
['600595.XSHG', '601168.XSHG']
['600595.XSHG', '601666.XSHG']
['600688.XSHG', '600871.XSHG']
['600785.XSHG', '600827.XSHG']
['600808.XSHG', '600963.XSHG']
['600827.XSHG', '601001.XSHG']
['600875.XSHG', '601001.XSHG']
['600883.XSHG', '601268.XSHG']
['601001.XSHG', '601101.XSHG']
['601001.XSHG', '601168.XSHG']
['601001.XSHG', '601666.XSHG']
['601101.XSHG', '601666.XSHG']
['601168.XSHG', '601666.XSHG']| stock1 | stock2 | annualized_return | sharpe | |
|---|---|---|---|---|
| 0 | 000761.XSHE | 600548.XSHG | 0.489473 | 2.411514 |
| 1 | 000708.XSHE | 600327.XSHG | 0.447337 | 2.021270 |
| 2 | 600126.XSHG | 600327.XSHG | 0.438380 | 1.946916 |
| 3 | 000554.XSHE | 000707.XSHE | 0.431123 | 1.331038 |
| 4 | 000939.XSHE | 600819.XSHG | 0.409471 | 1.919758 |
| 5 | 600026.XSHG | 600963.XSHG | 0.408791 | 1.681338 |
| 6 | 600037.XSHG | 600327.XSHG | 0.395624 | 1.691877 |
| 7 | 600808.XSHG | 600963.XSHG | 0.391988 | 1.724114 |
| 8 | 000559.XSHE | 600126.XSHG | 0.389043 | 1.413595 |
| 9 | 000761.XSHE | 600320.XSHG | 0.384325 | 1.807262 |
| 10 | 600126.XSHG | 600963.XSHG | 0.378064 | 1.662569 |
| 11 | 600126.XSHG | 600808.XSHG | 0.375825 | 1.513791 |
| 12 | 000936.XSHE | 600477.XSHG | 0.375135 | 1.707097 |
| 13 | 000930.XSHE | 600026.XSHG | 0.372924 | 1.524350 |
| 14 | 600320.XSHG | 600548.XSHG | 0.372499 | 2.083496 |
| 15 | 000507.XSHE | 600391.XSHG | 0.365637 | 1.813873 |
| 16 | 000559.XSHE | 601666.XSHG | 0.350235 | 0.925901 |
| 17 | 600012.XSHG | 600428.XSHG | 0.327834 | 1.722317 |
| 18 | 000916.XSHE | 600033.XSHG | 0.327795 | 1.406093 |
| 19 | 600035.XSHG | 600126.XSHG | 0.326167 | 1.442674 |
| 20 | 600827.XSHG | 601001.XSHG | 0.322705 | 0.957791 |
| 21 | 000717.XSHE | 600808.XSHG | 0.320737 | 1.293439 |
| 22 | 000559.XSHE | 600477.XSHG | 0.306670 | 1.218095 |
| 23 | 000685.XSHE | 000988.XSHE | 0.302593 | 1.692933 |
| 24 | 000683.XSHE | 000936.XSHE | 0.301804 | 1.550496 |
| 25 | 000559.XSHE | 600026.XSHG | 0.295510 | 1.279449 |
| 26 | 600269.XSHG | 601618.XSHG | 0.294215 | 1.486413 |
| 27 | 600026.XSHG | 600126.XSHG | 0.293884 | 1.441490 |
| 28 | 600068.XSHG | 600126.XSHG | 0.289457 | 1.261351 |
| 29 | 000159.XSHE | 600595.XSHG | 0.288982 | 0.946365 |
| 30 | 600020.XSHG | 600033.XSHG | 0.288243 | 1.489764 |
| 31 | 600126.XSHG | 600569.XSHG | 0.287607 | 1.371374 |
| 32 | 000635.XSHE | 600819.XSHG | 0.285135 | 1.364688 |
| 33 | 600068.XSHG | 600320.XSHG | 0.273513 | 1.262845 |
| 34 | 600785.XSHG | 600827.XSHG | 0.272658 | 0.842093 |
| 35 | 000089.XSHE | 600595.XSHG | 0.269903 | 1.256524 |
| 36 | 000898.XSHE | 600808.XSHG | 0.269717 | 1.074201 |
| 37 | 000717.XSHE | 600282.XSHG | 0.267478 | 1.270872 |
| 38 | 600282.XSHG | 600808.XSHG | 0.266402 | 1.181157 |
| 39 | 000916.XSHE | 600035.XSHG | 0.264325 | 1.079520 |
| 40 | 000089.XSHE | 600037.XSHG | 0.264201 | 1.467101 |
| 41 | 600026.XSHG | 600068.XSHG | 0.263959 | 1.107977 |
| 42 | 600026.XSHG | 600331.XSHG | 0.261025 | 0.977858 |
| 43 | 600020.XSHG | 600035.XSHG | 0.260176 | 1.119975 |
| 44 | 600569.XSHG | 600963.XSHG | 0.260006 | 1.154372 |
| 45 | 600307.XSHG | 600963.XSHG | 0.258488 | 1.322409 |
| 46 | 000898.XSHE | 600022.XSHG | 0.258246 | 1.100292 |
| 47 | 600282.XSHG | 600963.XSHG | 0.257496 | 1.175741 |
| 48 | 600307.XSHG | 600808.XSHG | 0.256071 | 1.062023 |
| 49 | 600126.XSHG | 600282.XSHG | 0.255657 | 1.318676 |
| 50 | 600033.XSHG | 600035.XSHG | 0.255634 | 1.055682 |
| 51 | 000709.XSHE | 601618.XSHG | 0.253129 | 1.062565 |
| 52 | 600026.XSHG | 600307.XSHG | 0.253119 | 0.985825 |
| 53 | 600026.XSHG | 600375.XSHG | 0.250793 | 1.063874 |
| 54 | 000066.XSHE | 600126.XSHG | 0.247493 | 1.469341 |
| 55 | 000830.XSHE | 600320.XSHG | 0.247001 | 1.370327 |
| 56 | 600320.XSHG | 601600.XSHG | 0.246534 | 0.966634 |
| 57 | 000717.XSHE | 600307.XSHG | 0.245805 | 1.202750 |
| 58 | 000417.XSHE | 000685.XSHE | 0.245031 | 1.189700 |
| 59 | 600330.XSHG | 600883.XSHG | 0.243437 | 1.086147 |
| ... | ... | ... | ... |
174 rows × 4 columns
a = list(outfile[2])
'percentage of outperform HS300: %f' % (1.*len([x for x in a if x>0.117]) / len(a))
'percentage of outperform HS300: 0.741379'