e*n
发帖数: 1511 | 1 【 以下文字转载自 Stock 讨论区 】
发信人: mitbbs2020 (bbc), 信区: Stock
标 题: Re: 【Stragety论坛】Trading with correlations.
发信站: BBS 未名空间站 (Sun Jul 25 19:52:04 2010, 美东)
用option当然可以，但不是必须，
用option可以得到更高的leverage，
但是缺点是散户量小，不容易实现market neutral。
比如1.37的hedge ratio，你要long 137和short 100个才能实现。
13和10就不那么好，而且不容易adjust，看附图就知道不怎么neutral的情况。
说stock不便于对冲，这点我不懂你什么意思。
这本身就是market neutral的strategy。
我测试过DISCA和AXP（paper account)，追踪了至少2个月，涨跌幅从来没超过3%的，
但是这反过来说明这不是一对好的pair。
别的pair，没有任何leverage，一年可以做到100%以上的也有，如果我实际做stock到
这个%，
已经很满意了。
这是我之前测试的code：
from numpy import *
def conv2list(results):
temp = []
for result in results:
temp.append(result[0])
return temp
def getrank(num, list):
r = 1
for a in list:
if a > num:
r = r + 1
return r
def pairtrade(symbol_a, symbol_b, length, enter_threshold,
exit_threshold):
import pyodbc, os, string
cnxn = pyodbc.connect('DSN=stock')
cursor = cnxn.cursor()
adjclose_aq = "select top " + str(length) + " AdjClose\
from dbo.all_daily\
where symbol = '" + symbol_a + "' order by Date Desc"
cursor.execute(adjclose_aq)
adjclose_a = conv2list(cursor.fetchall())
adjclose_a.reverse()
adjclose_bq = "select top " + str(length) + " AdjClose\
from dbo.all_daily\
where symbol = '" + symbol_b + "' order by Date Desc"
cursor.execute(adjclose_bq)
adjclose_b = conv2list(cursor.fetchall())
adjclose_b.reverse()
# check if open price is adjusted is done seperately in databse
# only need Openprice from lookback + 2 to total length day
lookback = 260
openlength = length - lookback
adjopen_aq = "select top " + str(openlength) + " OpenPrice\
from dbo.all_daily\
where symbol = '" + symbol_a + "' order by Date Desc"
cursor.execute(adjopen_aq)
adjopen_a = conv2list(cursor.fetchall())
adjopen_a.reverse()
adjopen_bq = "select top " + str(openlength) + " OpenPrice\
from dbo.all_daily\
where symbol = '" + symbol_b + "' order by Date Desc"
cursor.execute(adjopen_bq)
adjopen_b = conv2list(cursor.fetchall())
adjopen_b.reverse()
# for pair trading, a,b share the same date, only openprice range is
needed
date_abq = "select top " + str(length) + " Date\
from dbo.all_daily\
where symbol = '" + symbol_a + "' order by Date Desc"
cursor.execute(date_abq)
date_ab = conv2list(cursor.fetchall())
date_ab.reverse()
# 0 hold, 1 long a short b, -1 long b short a
status = 0
# total investment is $10000 short $5000, long $5000
investment = 10000
long, short = investment/2, -investment/2
trades, winner, loser = 0, 0, 0
stats = []
#print "%s, %s" % (symbol_a, symbol_b)
#print "Enter Date, Enter PriceA, Enter PriceB, ShareA, ShareB, Exit
Date, Exit PriceA, Exit PriceB, Gain, %Gain"
# count from the 251th days
for i in range(lookback, length):
rank_a = getrank(adjclose_a[i], adjclose_a[i-lookback:i])
rank_b = getrank(adjclose_b[i], adjclose_b[i-lookback:i])
diff = rank_a - rank_b
# rank a is too high and rank b is too low, means a is
underpriced and b is overpriced,
# so long a short b
if(status == 0 and diff >= enter_threshold):
status = 1
# i = lookback, then buy at the first adjopen date, already
shifted
enterprice_a, enterprice_b = adjclose_a[i], adjclose_b[i]
share_a, share_b = long/enterprice_a, short/enterprice_b
trade = "%s, %f, %f, %f, %f" % (date_ab[i], adjclose_a[i],
adjclose_b[i], share_a, share_b)
elif(status == 0 and diff <= - enter_threshold):
status = -1
enterprice_a, enterprice_b = adjclose_a[i], adjclose_b[i]
share_a, share_b = short/enterprice_a, long/enterprice_b
trade = "%s, %f, %f, %f, %f" % (date_ab[i], adjclose_a[i],
adjclose_b[i], share_a, share_b)
# assume it will not go from long_a_short_b signal to
long_b_short_a signal in a day
elif((status == 1 and diff <= exit_threshold) or (status == -1
and diff >= -exit_threshold)):
status = 0
trades = trades + 1
exitprice_a, exitprice_b = adjclose_a[i], adjclose_b[i]
# to be checked the close date
gain = share_a*(exitprice_a-
enterprice_a)+share_b*(exitprice_b-enterprice_b)
stats.append(gain)
if gain > 0:
winner = winner + 1
else:
loser = loser + 1
#print "%s, %s, %f, %f, %f, %f" % (trade, date_ab[i],
exitprice_a, exitprice_b, gain, gain/(long-short))
#print "total gain: %f%%" % totalgain
#print "total trades: %d" % trades
#print "winner trades: %d" % winner
#print "loser trades: %d" % loser
#print "maximum gain: %f%%" % maxgain
#print "maximum loss: %f%%" % maxloss
if trades >= 2:
if max(stats) > 0.0:
maxgain = max(stats)/investment
else:
maxgain = 0.0
if min(stats) < 0.0:
maxloss = min(stats)/investment
else:
maxloss = 0.0
totalgain = sum(stats)/investment
winpercent = winner/trades
arraystats = array(stats)
sharpe = average(stats)/arraystats.std()
#print "winner percentage: %f%%\n" % winpercent
return "%d|%d|%d|%f|%f|%f|%f" %(trades, winner, loser, maxgain,
maxloss, totalgain, sharpe)
else:
return "0|0|0|0|0|0|0"
def conv2array(results):
list = []
for result in results:
list.append(result[0])
return array(list)
import pyodbc, os, string
import time, datetime
from scipy.stats import *
t0 = time.clock()
cnxn = pyodbc.connect('DSN=stock')
cursor = cnxn.cursor()
today = (datetime.date.today() - datetime.timedelta(2)).strftime("%Y-%m-
%d")
print today
minlen = 500
result = open("nasdaq_correlation.csv","w")
result.writelines("sector|industry|symbola|symbolb|correlation|trades|wi
nner|loser|maxgain|maxloss\n")
getsector = "select distinct sector from dbo.nasdaq_sector where
isnull(sector,'')<>''"
cursor.execute(getsector)
sectors = cursor.fetchall()
sectorsize = len(sectors)
for sector in sectors:
getindustry ="select distinct industry from dbo.nasdaq_sector
where sector = '" + sector[0] + "'"
cursor.execute(getindustry)
industries = cursor.fetchall()
for industry in industries:
print "processing ", sector[0], industry[0]
getsymbol = "select b.symbol \
from dbo.nasdaq_sector a\
join dbo.nasdaq b on a.symbol = b.symbol \
where a.sector = '" + sector[0] + "'" \
+ " and a.industry = '" + industry[0] + "'"\
+ " and b.volume > 0 \
group by b.symbol \
having max(b.date) = '" + today + "'"\
" and count(b.date) > " + str(minlen)
cursor.execute(getsymbol)
symbols = cursor.fetchall()
symbolsize = len(symbols)
for i in range(symbolsize-1):
for j in range(i+1, symbolsize):
symbola = symbols[i][0]
symbolb = symbols[j][0]
print "processing %s with %s" %
(symbola, symbolb)
getadjclosea = "select top " +
str(minlen) + " adjclose \
from dbo.nasdaq \
where symbol = '" + symbola +
"'"\
+ " order by date desc"
cursor.execute(getadjclosea)
adjclose_a = cursor.fetchall()
adjclosea = conv2array(adjclose_a)
getadjcloseb = "select top " +
str(minlen) + " adjclose \
from dbo.nasdaq \
where symbol = '" + symbolb +
"'"\
+ " order by date desc"
cursor.execute(getadjcloseb)
adjclose_b = cursor.fetchall()
adjcloseb = conv2array(adjclose_b)
correlation = spearmanr(adjclosea,
adjcloseb)[0]
traderesult = pairtrade(symbola,
symbolb, minlen, 10, 0)
result.writelines(str(sector[0]) + '|' +
str(industry[0]) + '|' + str(symbola) + '|' + str(symbolb)+ '|' +
str(correlation) + '|' + traderesult + '\n')
print str(time.clock() - t0) + ' took'
result.close() | t********s
发帖数: 4503 | | e*n
发帖数: 1511 | 3 转的
python 写的
【在 t********s 的大作中提到】
: 楼主啥专业啊？太厉害了。程序是啥写的啊？
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