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Stock版 - free back test tool in python - for quant algos
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话题: strategy话题: average话题: moving话题: day话题: aapl
1 (共1页)
g***e
发帖数: 577
1
Hi,
Our team has made a free back test tool in below github:
https://github.com/geome-mitbbs/QTS_Research
It is simple to use yet has great flexibility:
To use:
1. System requirements: you need to have python3 + numpy + pandas + (
matplotlib,optional)
2. Download files into any folder.
3. Modify Back_Test.py and run. Currently Back_Test.py contains a moving
average trend following algo: if 10 day average > 25 day average, buy. if 10
day average < 25 day average, sell. you can either test this algo with your
own parameters ( ticker, average period etc ) or you can write your own
strategy. The strategy is simply a part of python code consists of the
functions in Quant_Indicators.py
Anyone interested in more algo research. Please private email. Thanks.
C*****5
发帖数: 8812
2
多谢!

10

【在 g***e 的大作中提到】
: Hi,
: Our team has made a free back test tool in below github:
: https://github.com/geome-mitbbs/QTS_Research
: It is simple to use yet has great flexibility:
: To use:
: 1. System requirements: you need to have python3 + numpy + pandas + (
: matplotlib,optional)
: 2. Download files into any folder.
: 3. Modify Back_Test.py and run. Currently Back_Test.py contains a moving
: average trend following algo: if 10 day average > 25 day average, buy. if 10

g***e
发帖数: 577
3
no problem, any feedback welcome.We want to improve it.

【在 C*****5 的大作中提到】
: 多谢!
:
: 10

E***r
发帖数: 1037
4
how does it compare to quantopian zipline?

10

【在 g***e 的大作中提到】
: Hi,
: Our team has made a free back test tool in below github:
: https://github.com/geome-mitbbs/QTS_Research
: It is simple to use yet has great flexibility:
: To use:
: 1. System requirements: you need to have python3 + numpy + pandas + (
: matplotlib,optional)
: 2. Download files into any folder.
: 3. Modify Back_Test.py and run. Currently Back_Test.py contains a moving
: average trend following algo: if 10 day average > 25 day average, buy. if 10

g***e
发帖数: 577
5
Hi, Thanks for the question.
A quick answer for the comparison: to test a moving average strategy, you
need 4 lines of code in our platform vs 35 lines of code in zipline to do
the same thing. You can check zipline code in this link: http://www.zipline.io/beginner-tutorial.html
See our code:
from Back_Test import *
algo_str = """portfolio.buy("AAPL") if average("AAPL",-25) 10) else portfolio.sell("AAPL")"""
t = back_test_single(algo_str,-2500,-1)
print(t.back_test_summary())
t.back_test_plot()
Also our code is easily understood: buy AAPL if its 10 day average above 25
day average and sell otherwise. Just as concise as it should be for moving
average strategy.
We are at early stage - our focus will be on Strategy Research itself than
tools - but our tools will be very suitable/specialized for strategy
research.
I will update when our first research report is out. Stay tuned.

【在 E***r 的大作中提到】
: how does it compare to quantopian zipline?
:
: 10

Y****n
发帖数: 1309
6
不错,多谢
D*********e
发帖数: 646
7
看style是C++程序员吗

10

【在 g***e 的大作中提到】
: Hi,
: Our team has made a free back test tool in below github:
: https://github.com/geome-mitbbs/QTS_Research
: It is simple to use yet has great flexibility:
: To use:
: 1. System requirements: you need to have python3 + numpy + pandas + (
: matplotlib,optional)
: 2. Download files into any folder.
: 3. Modify Back_Test.py and run. Currently Back_Test.py contains a moving
: average trend following algo: if 10 day average > 25 day average, buy. if 10

c********2
发帖数: 353
8
Great job and nice tool. But here is not the proper place to post. You
should repost to job hunting. Here are just stupid gamblers looking for
Daniu.
g***e
发帖数: 577
9
haha, your comment is making my day by being just brutal fact! I realized
that
being a lucky winner is what some people here care about :) Not all.
I repost it following your advice! Thanks for it very much!

【在 c********2 的大作中提到】
: Great job and nice tool. But here is not the proper place to post. You
: should repost to job hunting. Here are just stupid gamblers looking for
: Daniu.

g***e
发帖数: 577
10
Hi,
Continue on the project, I would like to post the preliminary result of
backtest for all SP500 underlyers using Moving Average Stategy for the last
10 years: (ie, buy/sell signal by crossing of 10day and 25day average ).
Sorry for the messy format - you can get a cleaner download of the csv file
in-
http://www.icanbeatmarket.com/research_reports.html
ticker return volatility draw_down sharpe
Avg_SP500 0.4734 0.1546 -0.2179 0.2032
industrials 0.4338 0.1484 -0.2121 0.1884
health_care 0.6145 0.1564 -0.1495 0.2740
information_technology 0.7525 0.1701 -0.2238 0.2458
consumer_discretionary 0.4288 0.1696 -0.2484 0.1597
utilities 0.4447 0.1131 -0.1562 0.3147
financials 0.4714 0.1429 -0.2398 0.2455
materials 0.4352 0.1639 -0.2280 0.1588
consumer_staples 0.5220 0.1204 -0.1355 0.2997
real_estate 0.1534 0.1353 -0.2424 0.0762
energy 0.1474 0.1972 -0.3307 0.0132
telecommunications_services 0.5045 0.1507 -0.1806 0.1745
MMM 0.5473 0.1067 -0.0937 0.4181
AYI 1.0680 0.1921 -0.3676 0.3923
ALK 1.9419 0.2055 -0.2658 0.5544
ALLE 0.2123 0.0887 -0.0224 0.2191
AAL 1.5247 0.2788 -0.3107 0.3481
AME 0.5215 0.1430 -0.0900 0.2997
ARNC -0.2276 0.2071 -0.4452 -0.1231
BA 0.8911 0.1395 -0.2093 0.4718
CHRW -0.0680 0.1404 -0.3005 -0.0500
CAT -0.0494 0.1434 -0.1594 -0.0352
CTAS 1.0714 0.1324 -0.3007 0.5703
CSX 0.1636 0.1632 -0.3053 0.0936
CMI -0.3215 0.1741 -0.4371 -0.2184
DE -0.0136 0.1356 -0.2544 -0.0101
DAL 0.1842 0.2221 -0.2439 0.0768
DOV 0.1995 0.1540 -0.1638 0.1192
DNB 0.5816 0.1518 -0.1164 0.3090
ETN 0.3360 0.1385 -0.1553 0.2122
EMR 0.0509 0.1258 -0.1786 0.0395
EFX 1.9332 0.1304 -0.0549 0.8712
EXPD 0.1864 0.1366 -0.1674 0.1263
FAST 0.1151 0.1446 -0.1858 0.0757
FDX 0.7890 0.1401 -0.0835 0.4274
FLS -0.3146 0.1690 -0.3202 -0.2193
FLR -0.2855 0.1750 -0.3000 -0.1889
FTV 0.0542 0.0271 -0.0497 0.1952
FBHS 0.0951 0.1575 -0.0268 0.0580
GD 0.4984 0.1196 -0.1231 0.3452
GE 0.4527 0.1161 -0.1672 0.3276
GWW 0.0988 0.1328 -0.0932 0.0713
HON 0.2228 0.1248 -0.1029 0.1627
ITW 0.3061 0.1202 -0.1801 0.2250
IR 0.1889 0.1522 -0.3747 0.1147
JEC -0.0676 0.1597 -0.1694 -0.0436
JBHT 0.1067 0.1337 -0.1929 0.0762
JCI 0.0263 0.1271 -0.1290 0.0204
KSU -0.2411 0.1829 -0.3454 -0.1488
LLL 0.2961 0.1267 -0.1827 0.2074
LMT 0.7575 0.1111 -0.2214 0.5222
MAS -0.1020 0.1963 -0.4111 -0.0545
NLSN -0.1754 0.1330 -0.2160 -0.1436
NSC 0.0428 0.1497 -0.3177 0.0280
NOC 0.9187 0.1247 -0.1838 0.5398
PCAR 0.1214 0.1405 -0.1966 0.0820
PH 0.3224 0.1395 -0.1494 0.2032
PNR -0.1722 0.1557 -0.3303 -0.1203
PWR -0.3315 0.2010 -0.5243 -0.1963
RTN 0.4780 0.1180 -0.2562 0.3376
RSG 0.3639 0.1110 -0.2491 0.2840
RHI -0.1076 0.1582 -0.2381 -0.0716
ROK 1.7163 0.1598 -0.1284 0.6579
COL 0.2495 0.1188 -0.1620 0.1895
ROP 0.1387 0.1201 -0.1723 0.1088
R 0.3679 0.1638 -0.1526 0.1943
LUV 2.4900 0.1810 -0.2947 0.7354
SRCL -0.1307 0.1446 -0.1307 -0.0962
TXT 0.0025 0.1870 -0.2739 0.0013
TDG 1.0514 0.1562 -0.1519 0.4770
UNP 0.0519 0.1349 -0.1870 0.0376
UAL 0.2494 0.2446 -0.3688 0.0921
UPS 0.1209 0.1024 -0.2024 0.1121
URI 5.2550 0.2695 -0.2478 0.7465
UTX 0.3629 0.1133 -0.1415 0.2776
VRSK 0.3997 0.1274 -0.0667 0.2685
WM 1.0602 0.0955 -0.1197 0.7851
XYL 0.0542 0.1205 -0.2355 0.0439
ABT 0.3841 0.1073 -0.0400 0.3079
ABBV 0.0348 0.1161 -0.0517 0.0295
AET 0.6943 0.1635 -0.1616 0.3311
A 0.4004 0.1640 -0.2602 0.2088
AGN 2.8275 0.1643 -0.1127 0.8745
ALXN 0.7614 0.2270 -0.1161 0.2566
ABC 1.6180 0.1286 -0.0796 0.7858
AMGN 1.0134 0.1399 -0.0824 0.5180
ANTM 0.0591 0.0802 -0.1415 0.0717
BCR 0.4787 0.1171 -0.1106 0.3408
BAX -0.2137 0.2171 -0.4516 -0.1094
BDX 0.7214 0.1033 -0.0777 0.5406
BIIB 0.6261 0.1956 -0.0596 0.2547
BSX 1.0410 0.1733 -0.2697 0.4267
BMY 0.5928 0.1452 -0.0459 0.3281
CAH 0.2101 0.1326 -0.1615 0.1452
HSIC 0.3984 0.1235 -0.1134 0.2761
CELG 0.1355 0.1736 -0.1719 0.0737
CNC 1.3706 0.2261 -0.2063 0.3987
CERN 0.3700 0.1512 -0.1007 0.2115
CI 1.1692 0.1586 -0.1465 0.5076
DHR 0.1291 0.1394 -0.1663 0.0876
DVA 0.6485 0.1173 -0.0241 0.4371
XRAY -0.1847 0.1257 -0.2564 -0.1609
EW 0.1788 0.2121 -0.5543 0.0782
EVHC 1.0887 0.2466 -0.0975 0.3100
ESRX 0.0646 0.1419 -0.2037 0.0442
GILD 0.2970 0.1830 -0.1461 0.1440
HCA 1.0549 0.1599 -0.0795 0.4671
HOLX 0.4412 0.1568 -0.1443 0.2374
HUM 0.8629 0.1721 -0.1222 0.3729
IDXX 0.5753 0.1638 -0.2150 0.2839
ILMN 3.3937 0.2695 -0.0604 0.5919
INCY 1.0484 0.2900 -0.2863 0.2563
ISRG 0.0125 0.1719 -0.3695 0.0072
JNJ 0.4490 0.0810 -0.0376 0.4663
LH 0.0177 0.1207 -0.2571 0.0146
LLY 0.2736 0.1249 -0.0746 0.1961
MNK 0.0681 0.1756 -0.1968 0.0377
MCK 0.7031 0.1367 -0.0176 0.4001
MDT 0.5829 0.1212 -0.0973 0.3879
MRK 0.3380 0.1123 -0.0639 0.2630
MTD 0.5280 0.1413 -0.1689 0.3066
MYL 0.6471 0.1897 -0.0424 0.2698
PDCO -0.1231 0.1403 -0.1918 -0.0930
PKI 0.4666 0.1495 -0.1286 0.2611
PRGO -0.3326 0.1784 -0.3326 -0.2222
PFE 0.7353 0.1121 -0.0968 0.5054
DGX 0.4395 0.1257 -0.2606 0.2952
REGN 1.2603 0.2762 -0.0724 0.3076
SYK -0.0246 0.1202 -0.2102 -0.0207
COO 3.0407 0.1518 -0.0329 0.9872
TMO 1.6046 0.1333 -0.1501 0.7538
UNH 0.3488 0.1473 -0.1039 0.2062
UHS 0.5787 0.1643 -0.0745 0.2844
VAR -0.0246 0.1295 -0.1385 -0.0192
VRTX -0.0345 0.2760 -0.2148 -0.0127
WAT 0.4724 0.1410 -0.1521 0.2798
ZBH 0.3064 0.0593 -0.0340 0.4565
ZTS 0.2470 0.1151 -0.1348 0.1940
ACN 0.3637 0.1366 -0.1083 0.2305
ATVI 1.3986 0.1760 -0.2178 0.5194
ADBE 0.6420 0.1703 -0.3771 0.2985
AKAM -0.4489 0.2272 -0.5573 -0.2546
ADS 1.0882 0.1531 -0.1686 0.4990
GOOGL 0.4146 0.1524 -0.1669 0.2315
GOOG 0.1881 0.0968 -0.0180 0.1797
APH 1.0014 0.1382 -0.2124 0.5201
ADI 0.6224 0.1471 -0.1695 0.3370
AAPL 1.9101 0.1461 -0.1145 0.7714
AMAT 1.0859 0.1577 -0.2010 0.4836
ADSK 0.2560 0.2007 -0.3408 0.1149
ADP 0.8376 0.1022 -0.1261 0.6138
AVGO 3.4066 0.2120 -0.1332 0.7541
CA -0.0513 0.1287 -0.1379 -0.0408
CSCO -0.3575 0.1593 -0.4283 -0.2717
CTXS 1.2602 0.2489 -0.1251 0.3414
CTSH 0.8407 0.1596 -0.0243 0.3941
GLW -0.1533 0.1634 -0.4609 -0.1010
CSRA -0.1310 0.0589 -0.1739 -0.2368
EBAY -0.3212 0.3133 -0.4961 -0.1213
EA 0.5032 0.1972 -0.3824 0.2109
FFIV -0.0407 0.2268 -0.3772 -0.0183
FB 1.9048 0.1895 -0.3018 0.5940
FIS 0.7999 0.1394 -0.1071 0.4344
FSLR -0.4694 0.3172 -0.5126 -0.1936
FISV 0.9613 0.1152 -0.0782 0.6050
FLIR -0.3972 0.1561 -0.4773 -0.3162
GPN 1.5477 0.1641 -0.0914 0.5974
HRS 0.1444 0.1357 -0.3605 0.1001
HPE -0.0508 0.0922 -0.2574 -0.0564
HPQ 0.1318 0.2019 -0.5207 0.0617
INTC 0.5223 0.1334 -0.1585 0.3218
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INTU 0.7821 0.1446 -0.0894 0.4113
JNPR 0.4988 0.1784 -0.0948 0.2315
KLAC 0.5939 0.1697 -0.2273 0.2813
LRCX 0.0555 0.1793 -0.4023 0.0302
LLTC 0.4106 0.1566 -0.1896 0.2235
MA 1.2429 0.1657 -0.0687 0.5077
MCHP -0.1119 0.1478 -0.2848 -0.0799
MU 1.2275 0.2717 -0.3415 0.3069
MSFT 0.5372 0.1438 -0.2271 0.3055
MSI -0.2036 0.1956 -0.4699 -0.1151
NTAP 0.4323 0.1792 -0.3243 0.2042
NFLX 5.7680 0.3475 -0.1622 0.6064
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PYPL -0.2499 0.0712 -0.2735 -0.3979
QRVO -0.1744 0.1337 -0.3007 -0.1419
QCOM 0.5322 0.1461 0.0000 0.2984
RHT 0.1991 0.1905 -0.1398 0.0962
CRM 0.3848 0.2101 -0.1986 0.1575
STX 2.9106 0.2521 -0.0883 0.5796
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WDC 1.3746 0.2195 -0.0540 0.4116
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AZO 1.3249 0.1205 -0.0550 0.7306
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TSCO 0.8023 0.1558 -0.0992 0.3896
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WHR 1.5656 0.1980 -0.3201 0.4989
WYN 0.1450 0.1669 -0.3764 0.0817
WYNN -0.1587 0.2180 -0.2886 -0.0786
YUM 0.2780 0.1355 -0.0816 0.1833
AES -0.4083 0.1598 -0.4411 -0.3199
LNT 0.5106 0.1144 -0.1595 0.3681
AEE 0.6227 0.1111 -0.0951 0.4466
AEP 0.6038 0.1024 -0.0744 0.4723
AWK 1.1882 0.1163 -0.1252 0.7001
CNP 0.3189 0.1215 -0.1351 0.2310
CMS 0.8768 0.1107 -0.1088 0.5868
ED 0.4267 0.0919 -0.0573 0.3936
D 0.5132 0.1009 -0.1340 0.4191
DTE 0.5318 0.1043 -0.1302 0.4177
DUK 0.5598 0.0946 -0.1002 0.4803
EIX 1.1300 0.1108 -0.0986 0.7088
ETR -0.0931 0.1095 -0.2328 -0.0888
ES 0.5015 0.1072 -0.0439 0.3871
EXC -0.2571 0.1208 -0.3647 -0.2423
FE -0.3325 0.1299 -0.3613 -0.3049
NEE 0.9267 0.1045 -0.1525 0.6486
NI 0.8270 0.1244 -0.1213 0.4994
NRG 0.0244 0.1879 -0.3548 0.0129
PCG 0.1746 0.1121 -0.1548 0.1447
PNW 0.8284 0.1035 -0.0131 0.6013
PPL -0.0163 0.1032 -0.1120 -0.0159
PEG -0.1205 0.1182 -0.3140 -0.1079
SCG 0.4211 0.1072 -0.1740 0.3338
SRE 0.9709 0.1081 -0.0733 0.6492
SO 0.4015 0.0884 -0.0666 0.3885
WEC 0.8522 0.1043 -0.0883 0.6095
XEL 0.4695 0.0999 -0.0867 0.3928
AMG 0.0228 0.1672 -0.2770 0.0135
AFL -0.0664 0.1370 -0.1855 -0.0500
ALL 0.6558 0.1161 -0.3049 0.4453
AXP -0.0288 0.1308 -0.3088 -0.0223
AIG 0.8982 0.1716 -0.1495 0.3858
AMP 1.7390 0.1667 -0.1741 0.6359
AON 0.3212 0.1098 -0.0848 0.2571
AJG 0.5170 0.0999 -0.0985 0.4261
AIZ 0.5432 0.1488 -0.2503 0.2979
BAC 2.0323 0.1727 -0.2620 0.6793
BK 0.5976 0.1424 -0.2151 0.3367
BBT 0.2191 0.1312 -0.3189 0.1525
BLK 1.1436 0.1513 -0.0173 0.5236
HRB 0.0646 0.1779 -0.4168 0.0353
COF -0.0351 0.1657 -0.3264 -0.0215
CBOE 1.0199 0.1416 -0.1710 0.5144
SCHW 1.1580 0.1738 -0.1221 0.4600
CB 1.0491 0.1210 -0.1035 0.6146
CINF 1.0338 0.1114 -0.1453 0.6605
C -0.0569 0.1718 -0.3609 -0.0340
CFG 0.7222 0.0884 0.0000 0.6321
CME 0.1992 0.1330 -0.2542 0.1378
CMA 0.3031 0.1662 -0.4127 0.1614
DFS 0.9396 0.1588 -0.1171 0.4314
ETFC 0.1093 0.2169 -0.6341 0.0481
FITB 0.2974 0.1626 -0.4651 0.1623
BEN 0.3441 0.1454 -0.0490 0.2064
GS 0.8851 0.1466 -0.3472 0.4466
HIG 0.1718 0.1751 -0.5089 0.0912
HBAN -0.0878 0.1765 -0.4690 -0.0518
ICE 0.1932 0.1473 -0.2523 0.1210
IVZ -0.0858 0.1653 -0.3446 -0.0540
JPM 0.8239 0.1452 -0.2854 0.4267
KEY -0.1101 0.1658 -0.4531 -0.0699
LUK 0.4667 0.1585 -0.1069 0.2464
LNC 0.3835 0.1861 -0.4587 0.1773
L -0.0963 0.1018 -0.2708 -0.0989
MTB -0.0034 0.1343 -0.4050 -0.0025
MMC 0.5620 0.1065 -0.0597 0.4283
MET -0.3532 0.1578 -0.4712 -0.2701
MCO 2.6091 0.1701 -0.0478 0.8050
MS 0.3707 0.1862 -0.4304 0.1721
NDAQ 1.1531 0.1579 -0.1132 0.5049
NAVI -0.3492 0.1058 -0.3492 -0.3975
NTRS 0.3674 0.1339 -0.1801 0.2373
PBCT 0.2040 0.1129 -0.1705 0.1659
PNC 0.0403 0.1375 -0.3828 0.0288
PFG 0.6737 0.1690 -0.2033 0.3127
PGR 0.3972 0.1056 -0.1079 0.3220
PRU 0.4740 0.1628 -0.3621 0.2430
RF 0.4491 0.1899 -0.3195 0.1990
SPGI 0.1699 0.0331 -0.0252 0.4784
STT 0.4675 0.1544 0.0000 0.2532
STI -0.0841 0.1719 -0.4797 -0.0509
SYF 0.0015 0.0780 -0.2022 0.0019
TROW 0.0017 0.1467 -0.1619 0.0011
TRV 0.6795 0.1059 -0.0311 0.5027
TMK 1.0758 0.1139 -0.1469 0.6654
USB 0.2377 0.1294 -0.2263 0.1666
UNM 0.9478 0.1318 -0.1745 0.5232
WFC 0.8678 0.1464 -0.1319 0.4404
WLTW 0.0858 0.0452 0.0000 0.1827
XL 0.4880 0.1350 -0.0990 0.3003
ZION 0.3499 0.1743 -0.3456 0.1748
APD -0.0540 0.1229 -0.1397 -0.0450
ALB 0.1335 0.1777 -0.2925 0.0710
AVY 0.6060 0.1517 -0.2428 0.3199
BLL 0.1935 0.1170 -0.0267 0.1525
CF 0.2059 0.2281 -0.2122 0.0829
DOW 0.2431 0.1595 -0.1937 0.1379
DD 0.3373 0.1280 -0.1918 0.2304
EMN 0.2875 0.1642 -0.2337 0.1559
ECL 0.4069 0.1227 -0.1591 0.2830
FMC 0.6424 0.1592 -0.1068 0.3195
FCX 0.0247 0.2532 -0.3238 0.0097
IP 0.1442 0.1723 -0.3959 0.0787
IFF 0.6091 0.1385 -0.2057 0.3519
LYB 5.1356 0.1995 0.0000 0.9971
MLM 1.2231 0.1688 -0.1447 0.4927
MON 0.1071 0.1345 -0.1194 0.0760
MOS -0.3978 0.1908 -0.5071 -0.2591
NEM -0.3738 0.2187 -0.6015 -0.2091
NUE -0.0747 0.1496 -0.3352 -0.0517
PPG -0.1025 0.2596 -0.1822 -0.0414
PX -0.1226 0.1047 -0.1426 -0.1241
SEE 0.3489 0.1667 -0.2886 0.1823
SHW 0.9875 0.1353 -0.1473 0.5254
VMC 0.2767 0.1900 -0.3743 0.1301
WRK 0.0924 0.0850 -0.1319 0.1044
MO 2.0167 0.0986 -0.0579 1.1837
ADM 0.2107 0.1470 -0.1131 0.1313
CPB 0.2908 0.1029 -0.1895 0.2513
CHD 0.5553 0.1042 -0.1492 0.4333
CLX 0.6325 0.1076 -0.1494 0.4669
KO -0.0216 0.0941 -0.0541 -0.0232
CL 0.1397 0.1000 -0.1023 0.1315
CAG -0.1830 0.1489 -0.2412 -0.1344
STZ 1.9402 0.1858 -0.1016 0.6128
COST 1.5933 0.1032 -0.0697 0.9687
COTY -0.0543 0.1354 -0.1297 -0.0411
CVS 1.0224 0.1115 -0.1061 0.6546
DPS 0.5753 0.1185 -0.1120 0.3922
EL 0.3078 0.1514 -0.1408 0.1796
GIS 0.1294 0.1017 -0.0897 0.1205
HRL 0.8422 0.1269 -0.0622 0.4965
SJM 0.7261 0.1099 -0.0604 0.5106
K 0.3691 0.0986 -0.1538 0.3237
KMB 0.7080 0.0995 -0.1270 0.5526
KHC -0.1622 0.0695 -0.2901 -0.2524
KR 1.0713 0.1272 -0.2225 0.5940
MKC 0.0695 0.1044 -0.0641 0.0645
MJN 0.1170 0.1542 -0.1632 0.0722
TAP -0.2026 0.1319 -0.3728 -0.1698
MDLZ -0.0682 0.1168 -0.1321 -0.0603
MNST 1.5468 0.2090 -0.1478 0.4689
PEP 0.1780 0.0887 -0.1354 0.1862
PM 0.6762 0.1106 -0.0074 0.4793
PG 0.1888 0.0841 -0.0938 0.2075
RAI 1.7465 0.1203 -0.0331 0.8838
SYY 0.2184 0.1059 -0.1802 0.1883
HSY 1.0416 0.1268 -0.1610 0.5836
TSN 0.2645 0.1697 -0.2967 0.1400
WMT 0.2486 0.1003 -0.0496 0.2238
WBA -0.0790 0.0818 -0.1228 -0.1002
WFM 0.1361 0.1870 -0.1950 0.0687
AMT 0.2978 0.1241 0.0000 0.2129
AIV -0.3810 0.1506 -0.4666 -0.3109
AVB -0.3909 0.1345 -0.4935 -0.3598
BXP 0.2534 0.1198 -0.2171 0.1906
CBG 0.4894 0.1866 -0.1403 0.2179
CCI -0.0325 0.1282 -0.2002 -0.0257
DLR 0.3522 0.1513 -0.2587 0.2024
EQIX 0.4127 0.2059 -0.2982 0.1707
EQR -0.3750 0.1298 -0.4235 -0.3536
ESS 0.1680 0.1269 -0.2053 0.1233
EXR 1.1083 0.1426 -0.1856 0.5432
FRT 0.2177 0.1098 -0.1594 0.1812
GGP 0.0253 0.1494 -0.2504 0.0167
HCP -0.1932 0.1370 -0.3480 -0.1551
HST 0.2087 0.1649 -0.2807 0.1160
IRM 0.1416 0.1467 -0.1732 0.0908
KIM -0.0777 0.1336 -0.2747 -0.0603
MAC -0.0287 0.1396 -0.2446 -0.0208
MAA 0.0186 0.0141 -0.0201 0.1315
PLD 0.0490 0.1487 -0.3178 0.0323
PSA 0.1429 0.1203 -0.2360 0.1118
O 0.3901 0.1189 -0.1294 0.2816
REG 0.0815 0.1279 -0.2642 0.0615
SPG 0.1748 0.1237 -0.2411 0.1312
SLG 0.1499 0.1478 -0.2987 0.0952
UDR -0.3201 0.1351 -0.4379 -0.2801
VTR 0.0809 0.1396 -0.2830 0.0559
VNO -0.0433 0.1270 -0.3032 -0.0348
HCN 0.7398 0.1219 0.0000 0.4671
WY 0.9431 0.1512 -0.1200 0.4543
APC -0.2981 0.2152 -0.4506 -0.1616
APA -0.2276 0.1845 -0.5066 -0.1382
BHI -0.0134 0.1938 -0.3537 -0.0070
COG 1.1657 0.2391 -0.1541 0.3361
CHK -0.7689 0.3022 -0.8590 -0.4509
CVX 0.0945 0.1229 -0.1798 0.0738
XEC -0.1469 0.2230 -0.2859 -0.0707
CXO -0.4585 0.2204 -0.5821 -0.2699
COP 0.6366 0.1394 -0.1725 0.3623
DVN -0.1952 0.1887 -0.3322 -0.1138
EOG 0.1094 0.1895 -0.2442 0.0551
EQT 0.1203 0.1891 -0.2959 0.0604
XOM 0.1820 0.1022 -0.1335 0.1650
FTI 0.2957 0.1916 -0.1695 0.1369
HAL 0.4132 0.1921 -0.2205 0.1832
HP -0.0211 0.2122 -0.2020 -0.0100
HES 0.8751 0.1832 -0.1302 0.3541
KMI 0.1930 0.1385 -0.0786 0.1285
MRO 0.6577 0.2056 -0.1389 0.2521
MPC 0.3239 0.2033 -0.2924 0.1400
MUR -0.6730 0.2046 -0.6996 -0.5168
NOV -0.0463 0.1902 -0.2117 -0.0249
NFX -0.5937 0.2370 -0.6875 -0.3635
NBL -0.4288 0.1862 -0.4288 -0.2926
OXY -0.5228 0.1421 -0.5277 -0.5017
OKE 1.5179 0.1887 -0.1828 0.5125
PSX 0.3478 0.1446 -0.0489 0.2095
PXD 1.2701 0.2220 -0.1540 0.3848
RRC -0.4188 0.2425 -0.4572 -0.2178
SLB -0.1260 0.1500 -0.2969 -0.0892
SWN -0.3146 0.2519 -0.5738 -0.1472
TSO 1.5309 0.2449 -0.3038 0.3973
RIG -0.5907 0.2317 -0.7028 -0.3688
VLO 0.9506 0.2142 -0.2057 0.3226
WMB 0.3207 0.2146 -0.3114 0.1314
T 0.3951 0.0914 -0.0705 0.3705
CTL 0.1905 0.1559 -0.0413 0.1129
FTR -0.4234 0.1705 -0.4333 -0.3141
LVLT 2.1569 0.2457 -0.2698 0.4959
VZ 0.2033 0.0900 -0.0881 0.2075

moving
10

【在 g***e 的大作中提到】
: Hi,
: Our team has made a free back test tool in below github:
: https://github.com/geome-mitbbs/QTS_Research
: It is simple to use yet has great flexibility:
: To use:
: 1. System requirements: you need to have python3 + numpy + pandas + (
: matplotlib,optional)
: 2. Download files into any folder.
: 3. Modify Back_Test.py and run. Currently Back_Test.py contains a moving
: average trend following algo: if 10 day average > 25 day average, buy. if 10

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s******6
发帖数: 140
11
Thanks for sharing!
c********2
发帖数: 353
12
Thanks!

last
file

【在 g***e 的大作中提到】
: Hi,
: Continue on the project, I would like to post the preliminary result of
: backtest for all SP500 underlyers using Moving Average Stategy for the last
: 10 years: (ie, buy/sell signal by crossing of 10day and 25day average ).
: Sorry for the messy format - you can get a cleaner download of the csv file
: in-
: http://www.icanbeatmarket.com/research_reports.html
: ticker return volatility draw_down sharpe
: Avg_SP500 0.4734 0.1546 -0.2179 0.2032
: industrials 0.4338 0.1484 -0.2121 0.1884

b***d
发帖数: 288
13
How about Python 2.7?

moving
10

【在 g***e 的大作中提到】
: Hi,
: Continue on the project, I would like to post the preliminary result of
: backtest for all SP500 underlyers using Moving Average Stategy for the last
: 10 years: (ie, buy/sell signal by crossing of 10day and 25day average ).
: Sorry for the messy format - you can get a cleaner download of the csv file
: in-
: http://www.icanbeatmarket.com/research_reports.html
: ticker return volatility draw_down sharpe
: Avg_SP500 0.4734 0.1546 -0.2179 0.2032
: industrials 0.4338 0.1484 -0.2121 0.1884

g***e
发帖数: 577
14
Hi - I think you can modify a few functions and it should still work, like
some module names etc.
I started from Python 3 personally so might not understand all the
constraints...

【在 b***d 的大作中提到】
: How about Python 2.7?
:
: moving
: 10

s*********4
发帖数: 3362
15
Thx
g***e
发帖数: 577
16
No problem,
just want to update on the first research report (preliminary results ) is
available on our website:
http://www.icanbeatmarket.com/research_reports.html
The main results copied here:
In this test, we compared 3 strategies for each of the current SP500
underlyers. Their definitions are below and the comparison of results is
here: 10 years backtest for Moving Average Strategy (25,10) on SP500
underlyers results. You can also find the results in our GitHub project:
Tool: QTS_Research. ( in folder Algo_Research )
Basic moving average strategy:
1. Buy stock when its 10 day average cross the 25 day average going upwards.
2. Sell stock when its 10 day average cross the 25 day average going
downwards.
Moving average strategy with a support price signal:
1. Buy stock when 10 day average cross 25 day average going upwards and the
price is greater than the 200 day average.
2. Sell stock when 10 day average cross 25 day average going downwards and
the price is lower than the 200 day average.
Bench Mark:
1. Just buy the stock and hold.
Strategy Idea: The moving average strategy's idea is trend following: when
10 day average cross 25 day average going upwards, it means the stock price
recently went up significantly, this implies a trend and the price might
continue to go higher, so the strategy buys the stock to try to realize the
trend profit. This is what we do in the first strategy. However, there is a
point where the first strategy missed: if a stock already went up
significantly there might be some resistance for it to continue go up. This
is what the 2nd strategy is addressing: it introduces a 200 day average
level as a "resistance" or "support" level where the trend will encounter.
And as long as the trend has gone through that barrier, the strategy think
the trend will have no more resistance (support) in nearby range and
therefore the trend will continue. That is the meaning of the 2nd strategy.
Backtest Shows:
1.The support price signal improves the performance of the basic moving
average strategy in general. In another word, the 2nd strategy is much
better than the 1st.
2.The moving average strategy underperformed compared to the buy and hold
strategy ( Bench Mark ) for most stocks. This is because the moving average
strategy has a lag in identifying the trend and therefore in a bull market,
it will realize less profit than buy and hold. However, in a bear market it
should prevent the large loss of buy and hold. So far we have been looking
at the period from 2011-2017 where is a bull market in general, we will add
the analysis of bear market in the following step.
Here is some charts of the moving average strategies (1st strategy is
portfolio, 2nd strategy is portfolio1, 3rd strategy - bench mark is the
portfolio2 ).
AAPL: Apple.
To be continued;

【在 s*********4 的大作中提到】
: Thx
w*******e
发帖数: 734
17
Drawdown太高了。longest stagnation-days长达两年(2013 high - 2015 high)。一
般的人早就放弃了。玩Algo有一段时间,发现还是我自己靠谱。呵呵
w*******e
发帖数: 734
18
Algo最大的问题是curvefit,backtest很好,forwardtest也许好也许不好。drawdown
来了你不知道是正常的或是strategies fail了。我见有人用到30多个strategies组成
portfolio。刚开始一年两年可以,时间一长也不行了。
g***e
发帖数: 577
19
Thanks for sharing the experience! Such discussion is nice and useful!
This is in a middle of research - I am still exploring this very common
strategy.
I am not sure in this particular case it has overfitted though. The next
step would be test the time frame from any starting point randomly to see
the return/sharpe, in order to avoid the bias of starting time.
On the other hand, it is probably true 90% of research might not end up
finding a profitable strategy - but the outcome is not wasted, it will show
that some ideas will not work and that could save money invisibly.
Thanks again for the discussion!

drawdown

【在 w*******e 的大作中提到】
: Algo最大的问题是curvefit,backtest很好,forwardtest也许好也许不好。drawdown
: 来了你不知道是正常的或是strategies fail了。我见有人用到30多个strategies组成
: portfolio。刚开始一年两年可以,时间一长也不行了。

E**********e
发帖数: 1736
20
should randomly select upper-performance, under-performance, so-so-
performance stocks from S&P lists and run back testing ? it was bull market
in past 8-9 years. theoretically, the return should be positive using your
strategy.
I am learning and figuring out your code. thanks.

show

【在 g***e 的大作中提到】
: Thanks for sharing the experience! Such discussion is nice and useful!
: This is in a middle of research - I am still exploring this very common
: strategy.
: I am not sure in this particular case it has overfitted though. The next
: step would be test the time frame from any starting point randomly to see
: the return/sharpe, in order to avoid the bias of starting time.
: On the other hand, it is probably true 90% of research might not end up
: finding a profitable strategy - but the outcome is not wasted, it will show
: that some ideas will not work and that could save money invisibly.
: Thanks again for the discussion!

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E**********e
发帖数: 1736
21
should randomly select upper-performance, under-performance, so-so-
performance stocks from S&P lists and run back testing ? it was bull market
in past 8-9 years. theoretically, the return should be positive using your
strategy.
I am learning and figuring out your code. thanks.

show

【在 g***e 的大作中提到】
: Thanks for sharing the experience! Such discussion is nice and useful!
: This is in a middle of research - I am still exploring this very common
: strategy.
: I am not sure in this particular case it has overfitted though. The next
: step would be test the time frame from any starting point randomly to see
: the return/sharpe, in order to avoid the bias of starting time.
: On the other hand, it is probably true 90% of research might not end up
: finding a profitable strategy - but the outcome is not wasted, it will show
: that some ideas will not work and that could save money invisibly.
: Thanks again for the discussion!

g***e
发帖数: 577
22
Hi,
Our team has made a free back test tool in below github:
https://github.com/geome-mitbbs/QTS_Research
It is simple to use yet has great flexibility:
To use:
1. System requirements: you need to have python3 + numpy + pandas + (
matplotlib,optional)
2. Download files into any folder.
3. Modify Sample_Test.py and run. Currently Sample_Test.py contains a moving
average trend following algo: if 10 day average > 25 day average, buy. if 10
day average < 25 day average, sell. you can either test this algo with your
own parameters ( ticker, average period etc ) or you can write your own
strategy. The strategy is simply a part of python code consists of the
functions in Quant_Indicators.py
Anyone interested in quant trading strategy research project/product. Please
email me in private. Thanks.
C*****5
发帖数: 8812
23
多谢!

10

【在 g***e 的大作中提到】
: Hi,
: Our team has made a free back test tool in below github:
: https://github.com/geome-mitbbs/QTS_Research
: It is simple to use yet has great flexibility:
: To use:
: 1. System requirements: you need to have python3 + numpy + pandas + (
: matplotlib,optional)
: 2. Download files into any folder.
: 3. Modify Sample_Test.py and run. Currently Sample_Test.py contains a moving
: average trend following algo: if 10 day average > 25 day average, buy. if 10

g***e
发帖数: 577
24
no problem, any feedback welcome.We want to improve it.

【在 C*****5 的大作中提到】
: 多谢!
:
: 10

E***r
发帖数: 1037
25
how does it compare to quantopian zipline?

10

【在 g***e 的大作中提到】
: Hi,
: Our team has made a free back test tool in below github:
: https://github.com/geome-mitbbs/QTS_Research
: It is simple to use yet has great flexibility:
: To use:
: 1. System requirements: you need to have python3 + numpy + pandas + (
: matplotlib,optional)
: 2. Download files into any folder.
: 3. Modify Sample_Test.py and run. Currently Sample_Test.py contains a moving
: average trend following algo: if 10 day average > 25 day average, buy. if 10

g***e
发帖数: 577
26
Hi, Thanks for the question.
A quick answer for the comparison: to test a moving average strategy, you
need 4 lines of code in our platform vs 35 lines of code in zipline to do
the same thing. You can check zipline code in this link: http://www.zipline.io/beginner-tutorial.html
See our code:
from Back_Test import *
algo_str = """portfolio.buy("AAPL") if average("AAPL",-25) 10) else portfolio.sell("AAPL")"""
t = back_test_single(algo_str,-2500,-1)
print(t.back_test_summary())
t.back_test_plot()
Also our code is easily understood: buy AAPL if its 10 day average above 25
day average and sell otherwise. Just as concise as it should be for moving
average strategy.
We are at early stage - our focus will be on Strategy Research itself than
tools - but our tools will be very suitable/specialized for strategy
research.
I will update when our first research report is out. Stay tuned.

【在 E***r 的大作中提到】
: how does it compare to quantopian zipline?
:
: 10

Y****n
发帖数: 1309
27
不错,多谢
D*********e
发帖数: 646
28
看style是C++程序员吗

10

【在 g***e 的大作中提到】
: Hi,
: Our team has made a free back test tool in below github:
: https://github.com/geome-mitbbs/QTS_Research
: It is simple to use yet has great flexibility:
: To use:
: 1. System requirements: you need to have python3 + numpy + pandas + (
: matplotlib,optional)
: 2. Download files into any folder.
: 3. Modify Sample_Test.py and run. Currently Sample_Test.py contains a moving
: average trend following algo: if 10 day average > 25 day average, buy. if 10

c********2
发帖数: 353
29
Great job and nice tool. But here is not the proper place to post. You
should repost to job hunting. Here are just stupid gamblers looking for
Daniu.
g***e
发帖数: 577
30
haha, your comment is making my day by being just brutal fact! I realized
that
being a lucky winner is what some people here care about :) Not all.
I repost it following your advice! Thanks for it very much!

【在 c********2 的大作中提到】
: Great job and nice tool. But here is not the proper place to post. You
: should repost to job hunting. Here are just stupid gamblers looking for
: Daniu.

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g***e
发帖数: 577
31
Hi,
Continue on the project, I would like to post the preliminary result of
backtest for all SP500 underlyers using Moving Average Stategy for the last
10 years: (ie, buy/sell signal by crossing of 10day and 25day average ).
Sorry for the messy format - you can get a cleaner download of the csv file
in-
http://www.icanbeatmarket.com/research_reports.html
ticker return volatility draw_down sharpe
Avg_SP500 0.4734 0.1546 -0.2179 0.2032
industrials 0.4338 0.1484 -0.2121 0.1884
health_care 0.6145 0.1564 -0.1495 0.2740
information_technology 0.7525 0.1701 -0.2238 0.2458
consumer_discretionary 0.4288 0.1696 -0.2484 0.1597
utilities 0.4447 0.1131 -0.1562 0.3147
financials 0.4714 0.1429 -0.2398 0.2455
materials 0.4352 0.1639 -0.2280 0.1588
consumer_staples 0.5220 0.1204 -0.1355 0.2997
real_estate 0.1534 0.1353 -0.2424 0.0762
energy 0.1474 0.1972 -0.3307 0.0132
telecommunications_services 0.5045 0.1507 -0.1806 0.1745
MMM 0.5473 0.1067 -0.0937 0.4181
AYI 1.0680 0.1921 -0.3676 0.3923
ALK 1.9419 0.2055 -0.2658 0.5544
ALLE 0.2123 0.0887 -0.0224 0.2191
AAL 1.5247 0.2788 -0.3107 0.3481
AME 0.5215 0.1430 -0.0900 0.2997
ARNC -0.2276 0.2071 -0.4452 -0.1231
BA 0.8911 0.1395 -0.2093 0.4718
CHRW -0.0680 0.1404 -0.3005 -0.0500
CAT -0.0494 0.1434 -0.1594 -0.0352
CTAS 1.0714 0.1324 -0.3007 0.5703
CSX 0.1636 0.1632 -0.3053 0.0936
CMI -0.3215 0.1741 -0.4371 -0.2184
DE -0.0136 0.1356 -0.2544 -0.0101
DAL 0.1842 0.2221 -0.2439 0.0768
DOV 0.1995 0.1540 -0.1638 0.1192
DNB 0.5816 0.1518 -0.1164 0.3090
ETN 0.3360 0.1385 -0.1553 0.2122
EMR 0.0509 0.1258 -0.1786 0.0395
EFX 1.9332 0.1304 -0.0549 0.8712
EXPD 0.1864 0.1366 -0.1674 0.1263
FAST 0.1151 0.1446 -0.1858 0.0757
FDX 0.7890 0.1401 -0.0835 0.4274
FLS -0.3146 0.1690 -0.3202 -0.2193
FLR -0.2855 0.1750 -0.3000 -0.1889
FTV 0.0542 0.0271 -0.0497 0.1952
FBHS 0.0951 0.1575 -0.0268 0.0580
GD 0.4984 0.1196 -0.1231 0.3452
GE 0.4527 0.1161 -0.1672 0.3276
GWW 0.0988 0.1328 -0.0932 0.0713
HON 0.2228 0.1248 -0.1029 0.1627
ITW 0.3061 0.1202 -0.1801 0.2250
IR 0.1889 0.1522 -0.3747 0.1147
JEC -0.0676 0.1597 -0.1694 -0.0436
JBHT 0.1067 0.1337 -0.1929 0.0762
JCI 0.0263 0.1271 -0.1290 0.0204
KSU -0.2411 0.1829 -0.3454 -0.1488
LLL 0.2961 0.1267 -0.1827 0.2074
LMT 0.7575 0.1111 -0.2214 0.5222
MAS -0.1020 0.1963 -0.4111 -0.0545
NLSN -0.1754 0.1330 -0.2160 -0.1436
NSC 0.0428 0.1497 -0.3177 0.0280
NOC 0.9187 0.1247 -0.1838 0.5398
PCAR 0.1214 0.1405 -0.1966 0.0820
PH 0.3224 0.1395 -0.1494 0.2032
PNR -0.1722 0.1557 -0.3303 -0.1203
PWR -0.3315 0.2010 -0.5243 -0.1963
RTN 0.4780 0.1180 -0.2562 0.3376
RSG 0.3639 0.1110 -0.2491 0.2840
RHI -0.1076 0.1582 -0.2381 -0.0716
ROK 1.7163 0.1598 -0.1284 0.6579
COL 0.2495 0.1188 -0.1620 0.1895
ROP 0.1387 0.1201 -0.1723 0.1088
R 0.3679 0.1638 -0.1526 0.1943
LUV 2.4900 0.1810 -0.2947 0.7354
SRCL -0.1307 0.1446 -0.1307 -0.0962
TXT 0.0025 0.1870 -0.2739 0.0013
TDG 1.0514 0.1562 -0.1519 0.4770
UNP 0.0519 0.1349 -0.1870 0.0376
UAL 0.2494 0.2446 -0.3688 0.0921
UPS 0.1209 0.1024 -0.2024 0.1121
URI 5.2550 0.2695 -0.2478 0.7465
UTX 0.3629 0.1133 -0.1415 0.2776
VRSK 0.3997 0.1274 -0.0667 0.2685
WM 1.0602 0.0955 -0.1197 0.7851
XYL 0.0542 0.1205 -0.2355 0.0439
ABT 0.3841 0.1073 -0.0400 0.3079
ABBV 0.0348 0.1161 -0.0517 0.0295
AET 0.6943 0.1635 -0.1616 0.3311
A 0.4004 0.1640 -0.2602 0.2088
AGN 2.8275 0.1643 -0.1127 0.8745
ALXN 0.7614 0.2270 -0.1161 0.2566
ABC 1.6180 0.1286 -0.0796 0.7858
AMGN 1.0134 0.1399 -0.0824 0.5180
ANTM 0.0591 0.0802 -0.1415 0.0717
BCR 0.4787 0.1171 -0.1106 0.3408
BAX -0.2137 0.2171 -0.4516 -0.1094
BDX 0.7214 0.1033 -0.0777 0.5406
BIIB 0.6261 0.1956 -0.0596 0.2547
BSX 1.0410 0.1733 -0.2697 0.4267
BMY 0.5928 0.1452 -0.0459 0.3281
CAH 0.2101 0.1326 -0.1615 0.1452
HSIC 0.3984 0.1235 -0.1134 0.2761
CELG 0.1355 0.1736 -0.1719 0.0737
CNC 1.3706 0.2261 -0.2063 0.3987
CERN 0.3700 0.1512 -0.1007 0.2115
CI 1.1692 0.1586 -0.1465 0.5076
DHR 0.1291 0.1394 -0.1663 0.0876
DVA 0.6485 0.1173 -0.0241 0.4371
XRAY -0.1847 0.1257 -0.2564 -0.1609
EW 0.1788 0.2121 -0.5543 0.0782
EVHC 1.0887 0.2466 -0.0975 0.3100
ESRX 0.0646 0.1419 -0.2037 0.0442
GILD 0.2970 0.1830 -0.1461 0.1440
HCA 1.0549 0.1599 -0.0795 0.4671
HOLX 0.4412 0.1568 -0.1443 0.2374
HUM 0.8629 0.1721 -0.1222 0.3729
IDXX 0.5753 0.1638 -0.2150 0.2839
ILMN 3.3937 0.2695 -0.0604 0.5919
INCY 1.0484 0.2900 -0.2863 0.2563
ISRG 0.0125 0.1719 -0.3695 0.0072
JNJ 0.4490 0.0810 -0.0376 0.4663
LH 0.0177 0.1207 -0.2571 0.0146
LLY 0.2736 0.1249 -0.0746 0.1961
MNK 0.0681 0.1756 -0.1968 0.0377
MCK 0.7031 0.1367 -0.0176 0.4001
MDT 0.5829 0.1212 -0.0973 0.3879
MRK 0.3380 0.1123 -0.0639 0.2630
MTD 0.5280 0.1413 -0.1689 0.3066
MYL 0.6471 0.1897 -0.0424 0.2698
PDCO -0.1231 0.1403 -0.1918 -0.0930
PKI 0.4666 0.1495 -0.1286 0.2611
PRGO -0.3326 0.1784 -0.3326 -0.2222
PFE 0.7353 0.1121 -0.0968 0.5054
DGX 0.4395 0.1257 -0.2606 0.2952
REGN 1.2603 0.2762 -0.0724 0.3076
SYK -0.0246 0.1202 -0.2102 -0.0207
COO 3.0407 0.1518 -0.0329 0.9872
TMO 1.6046 0.1333 -0.1501 0.7538
UNH 0.3488 0.1473 -0.1039 0.2062
UHS 0.5787 0.1643 -0.0745 0.2844
VAR -0.0246 0.1295 -0.1385 -0.0192
VRTX -0.0345 0.2760 -0.2148 -0.0127
WAT 0.4724 0.1410 -0.1521 0.2798
ZBH 0.3064 0.0593 -0.0340 0.4565
ZTS 0.2470 0.1151 -0.1348 0.1940
ACN 0.3637 0.1366 -0.1083 0.2305
ATVI 1.3986 0.1760 -0.2178 0.5194
ADBE 0.6420 0.1703 -0.3771 0.2985
AKAM -0.4489 0.2272 -0.5573 -0.2546
ADS 1.0882 0.1531 -0.1686 0.4990
GOOGL 0.4146 0.1524 -0.1669 0.2315
GOOG 0.1881 0.0968 -0.0180 0.1797
APH 1.0014 0.1382 -0.2124 0.5201
ADI 0.6224 0.1471 -0.1695 0.3370
AAPL 1.9101 0.1461 -0.1145 0.7714
AMAT 1.0859 0.1577 -0.2010 0.4836
ADSK 0.2560 0.2007 -0.3408 0.1149
ADP 0.8376 0.1022 -0.1261 0.6138
AVGO 3.4066 0.2120 -0.1332 0.7541
CA -0.0513 0.1287 -0.1379 -0.0408
CSCO -0.3575 0.1593 -0.4283 -0.2717
CTXS 1.2602 0.2489 -0.1251 0.3414
CTSH 0.8407 0.1596 -0.0243 0.3941
GLW -0.1533 0.1634 -0.4609 -0.1010
CSRA -0.1310 0.0589 -0.1739 -0.2368
EBAY -0.3212 0.3133 -0.4961 -0.1213
EA 0.5032 0.1972 -0.3824 0.2109
FFIV -0.0407 0.2268 -0.3772 -0.0183
FB 1.9048 0.1895 -0.3018 0.5940
FIS 0.7999 0.1394 -0.1071 0.4344
FSLR -0.4694 0.3172 -0.5126 -0.1936
FISV 0.9613 0.1152 -0.0782 0.6050
FLIR -0.3972 0.1561 -0.4773 -0.3162
GPN 1.5477 0.1641 -0.0914 0.5974
HRS 0.1444 0.1357 -0.3605 0.1001
HPE -0.0508 0.0922 -0.2574 -0.0564
HPQ 0.1318 0.2019 -0.5207 0.0617
INTC 0.5223 0.1334 -0.1585 0.3218
IBM -0.0709 0.1073 -0.2892 -0.0683
INTU 0.7821 0.1446 -0.0894 0.4113
JNPR 0.4988 0.1784 -0.0948 0.2315
KLAC 0.5939 0.1697 -0.2273 0.2813
LRCX 0.0555 0.1793 -0.4023 0.0302
LLTC 0.4106 0.1566 -0.1896 0.2235
MA 1.2429 0.1657 -0.0687 0.5077
MCHP -0.1119 0.1478 -0.2848 -0.0799
MU 1.2275 0.2717 -0.3415 0.3069
MSFT 0.5372 0.1438 -0.2271 0.3055
MSI -0.2036 0.1956 -0.4699 -0.1151
NTAP 0.4323 0.1792 -0.3243 0.2042
NFLX 5.7680 0.3475 -0.1622 0.6064
NVDA 4.2856 0.2340 -0.0480 0.7743
ORCL 0.2056 0.1330 -0.1416 0.1420
PAYX 0.7596 0.1067 -0.0924 0.5448
PYPL -0.2499 0.0712 -0.2735 -0.3979
QRVO -0.1744 0.1337 -0.3007 -0.1419
QCOM 0.5322 0.1461 0.0000 0.2984
RHT 0.1991 0.1905 -0.1398 0.0962
CRM 0.3848 0.2101 -0.1986 0.1575
STX 2.9106 0.2521 -0.0883 0.5796
SWKS 4.0891 0.2507 -0.1393 0.7048
SYMC 0.2980 0.1655 -0.1994 0.1597
TEL 0.3480 0.1469 -0.2780 0.2063
TDC -0.0496 0.1977 -0.3354 -0.0257
TXN 1.2636 0.1417 -0.1514 0.6005
TSS 0.8012 0.1367 -0.0425 0.4433
VRSN 1.3914 0.1460 -0.1401 0.6238
V 0.9271 0.1461 -0.2736 0.4642
WDC 1.3746 0.2195 -0.0540 0.4116
WU 0.3673 0.1444 -0.1520 0.2201
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YHOO 0.7401 0.1759 -0.2724 0.3238
AAP 0.8538 0.1650 -0.0553 0.3859
AMZN 1.6851 0.1896 -0.1165 0.5477
AN -0.4755 0.1706 -0.5132 -0.3664
AZO 1.3249 0.1205 -0.0550 0.7306
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KMX 0.7922 0.1864 -0.0609 0.3224
CCL -0.3387 0.1633 -0.4389 -0.2481
CBS 1.7756 0.1723 -0.2003 0.6236
CHTR 1.6173 0.1746 -0.2063 0.5786
CMG 0.8984 0.1863 -0.0725 0.3553
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CMCSA 0.6210 0.1341 -0.1998 0.3691
DHI -0.5356 0.2068 -0.6018 -0.3570
DRI 0.1170 0.1418 -0.3506 0.0784
DLPH 1.1513 0.1457 -0.0275 0.5464
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DG 0.4061 0.1561 -0.0506 0.2220
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FL 1.4705 0.1816 -0.2288 0.5212
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GM 0.1747 0.1561 -0.1176 0.1040
GPC -0.1385 0.1147 -0.2278 -0.1290
GT 0.1976 0.2142 -0.3784 0.0849
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HAR 0.3260 0.2239 -0.2253 0.1278
HAS 0.3225 0.1540 -0.3081 0.1841
HD 2.2353 0.1215 -0.0945 1.0250
IPG 0.0952 0.1821 -0.2487 0.0502
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LKQ 0.4834 0.1638 -0.0877 0.2455
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M 1.0081 0.1720 -0.0758 0.4199
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MAT -0.4665 0.1568 -0.4729 -0.3884
MCD 0.2674 0.0975 -0.0948 0.2459
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MHK 0.7635 0.1812 -0.1918 0.3222
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NKE 0.4589 0.1427 -0.1380 0.2697
JWN 0.3729 0.1679 -0.2664 0.1918
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OMC 0.3596 0.1158 -0.1265 0.2695
RL -0.4228 0.1880 -0.4228 -0.2844
PCLN 0.9821 0.1973 -0.2154 0.3589
PHM 0.0428 0.2358 -0.3475 0.0178
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ROST 2.0560 0.1454 -0.0968 0.8131
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SNI 0.0916 0.1442 -0.1953 0.0610
SIG 0.3837 0.1827 -0.2266 0.1806
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TGT 0.0819 0.1253 -0.1830 0.0631
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TIF 0.5865 0.1767 -0.1684 0.2673
TWX 0.5858 0.1504 -0.2838 0.3136
TJX 0.8414 0.1302 -0.0572 0.4835
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WYN 0.1450 0.1669 -0.3764 0.0817
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YUM 0.2780 0.1355 -0.0816 0.1833
AES -0.4083 0.1598 -0.4411 -0.3199
LNT 0.5106 0.1144 -0.1595 0.3681
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AEP 0.6038 0.1024 -0.0744 0.4723
AWK 1.1882 0.1163 -0.1252 0.7001
CNP 0.3189 0.1215 -0.1351 0.2310
CMS 0.8768 0.1107 -0.1088 0.5868
ED 0.4267 0.0919 -0.0573 0.3936
D 0.5132 0.1009 -0.1340 0.4191
DTE 0.5318 0.1043 -0.1302 0.4177
DUK 0.5598 0.0946 -0.1002 0.4803
EIX 1.1300 0.1108 -0.0986 0.7088
ETR -0.0931 0.1095 -0.2328 -0.0888
ES 0.5015 0.1072 -0.0439 0.3871
EXC -0.2571 0.1208 -0.3647 -0.2423
FE -0.3325 0.1299 -0.3613 -0.3049
NEE 0.9267 0.1045 -0.1525 0.6486
NI 0.8270 0.1244 -0.1213 0.4994
NRG 0.0244 0.1879 -0.3548 0.0129
PCG 0.1746 0.1121 -0.1548 0.1447
PNW 0.8284 0.1035 -0.0131 0.6013
PPL -0.0163 0.1032 -0.1120 -0.0159
PEG -0.1205 0.1182 -0.3140 -0.1079
SCG 0.4211 0.1072 -0.1740 0.3338
SRE 0.9709 0.1081 -0.0733 0.6492
SO 0.4015 0.0884 -0.0666 0.3885
WEC 0.8522 0.1043 -0.0883 0.6095
XEL 0.4695 0.0999 -0.0867 0.3928
AMG 0.0228 0.1672 -0.2770 0.0135
AFL -0.0664 0.1370 -0.1855 -0.0500
ALL 0.6558 0.1161 -0.3049 0.4453
AXP -0.0288 0.1308 -0.3088 -0.0223
AIG 0.8982 0.1716 -0.1495 0.3858
AMP 1.7390 0.1667 -0.1741 0.6359
AON 0.3212 0.1098 -0.0848 0.2571
AJG 0.5170 0.0999 -0.0985 0.4261
AIZ 0.5432 0.1488 -0.2503 0.2979
BAC 2.0323 0.1727 -0.2620 0.6793
BK 0.5976 0.1424 -0.2151 0.3367
BBT 0.2191 0.1312 -0.3189 0.1525
BLK 1.1436 0.1513 -0.0173 0.5236
HRB 0.0646 0.1779 -0.4168 0.0353
COF -0.0351 0.1657 -0.3264 -0.0215
CBOE 1.0199 0.1416 -0.1710 0.5144
SCHW 1.1580 0.1738 -0.1221 0.4600
CB 1.0491 0.1210 -0.1035 0.6146
CINF 1.0338 0.1114 -0.1453 0.6605
C -0.0569 0.1718 -0.3609 -0.0340
CFG 0.7222 0.0884 0.0000 0.6321
CME 0.1992 0.1330 -0.2542 0.1378
CMA 0.3031 0.1662 -0.4127 0.1614
DFS 0.9396 0.1588 -0.1171 0.4314
ETFC 0.1093 0.2169 -0.6341 0.0481
FITB 0.2974 0.1626 -0.4651 0.1623
BEN 0.3441 0.1454 -0.0490 0.2064
GS 0.8851 0.1466 -0.3472 0.4466
HIG 0.1718 0.1751 -0.5089 0.0912
HBAN -0.0878 0.1765 -0.4690 -0.0518
ICE 0.1932 0.1473 -0.2523 0.1210
IVZ -0.0858 0.1653 -0.3446 -0.0540
JPM 0.8239 0.1452 -0.2854 0.4267
KEY -0.1101 0.1658 -0.4531 -0.0699
LUK 0.4667 0.1585 -0.1069 0.2464
LNC 0.3835 0.1861 -0.4587 0.1773
L -0.0963 0.1018 -0.2708 -0.0989
MTB -0.0034 0.1343 -0.4050 -0.0025
MMC 0.5620 0.1065 -0.0597 0.4283
MET -0.3532 0.1578 -0.4712 -0.2701
MCO 2.6091 0.1701 -0.0478 0.8050
MS 0.3707 0.1862 -0.4304 0.1721
NDAQ 1.1531 0.1579 -0.1132 0.5049
NAVI -0.3492 0.1058 -0.3492 -0.3975
NTRS 0.3674 0.1339 -0.1801 0.2373
PBCT 0.2040 0.1129 -0.1705 0.1659
PNC 0.0403 0.1375 -0.3828 0.0288
PFG 0.6737 0.1690 -0.2033 0.3127
PGR 0.3972 0.1056 -0.1079 0.3220
PRU 0.4740 0.1628 -0.3621 0.2430
RF 0.4491 0.1899 -0.3195 0.1990
SPGI 0.1699 0.0331 -0.0252 0.4784
STT 0.4675 0.1544 0.0000 0.2532
STI -0.0841 0.1719 -0.4797 -0.0509
SYF 0.0015 0.0780 -0.2022 0.0019
TROW 0.0017 0.1467 -0.1619 0.0011
TRV 0.6795 0.1059 -0.0311 0.5027
TMK 1.0758 0.1139 -0.1469 0.6654
USB 0.2377 0.1294 -0.2263 0.1666
UNM 0.9478 0.1318 -0.1745 0.5232
WFC 0.8678 0.1464 -0.1319 0.4404
WLTW 0.0858 0.0452 0.0000 0.1827
XL 0.4880 0.1350 -0.0990 0.3003
ZION 0.3499 0.1743 -0.3456 0.1748
APD -0.0540 0.1229 -0.1397 -0.0450
ALB 0.1335 0.1777 -0.2925 0.0710
AVY 0.6060 0.1517 -0.2428 0.3199
BLL 0.1935 0.1170 -0.0267 0.1525
CF 0.2059 0.2281 -0.2122 0.0829
DOW 0.2431 0.1595 -0.1937 0.1379
DD 0.3373 0.1280 -0.1918 0.2304
EMN 0.2875 0.1642 -0.2337 0.1559
ECL 0.4069 0.1227 -0.1591 0.2830
FMC 0.6424 0.1592 -0.1068 0.3195
FCX 0.0247 0.2532 -0.3238 0.0097
IP 0.1442 0.1723 -0.3959 0.0787
IFF 0.6091 0.1385 -0.2057 0.3519
LYB 5.1356 0.1995 0.0000 0.9971
MLM 1.2231 0.1688 -0.1447 0.4927
MON 0.1071 0.1345 -0.1194 0.0760
MOS -0.3978 0.1908 -0.5071 -0.2591
NEM -0.3738 0.2187 -0.6015 -0.2091
NUE -0.0747 0.1496 -0.3352 -0.0517
PPG -0.1025 0.2596 -0.1822 -0.0414
PX -0.1226 0.1047 -0.1426 -0.1241
SEE 0.3489 0.1667 -0.2886 0.1823
SHW 0.9875 0.1353 -0.1473 0.5254
VMC 0.2767 0.1900 -0.3743 0.1301
WRK 0.0924 0.0850 -0.1319 0.1044
MO 2.0167 0.0986 -0.0579 1.1837
ADM 0.2107 0.1470 -0.1131 0.1313
CPB 0.2908 0.1029 -0.1895 0.2513
CHD 0.5553 0.1042 -0.1492 0.4333
CLX 0.6325 0.1076 -0.1494 0.4669
KO -0.0216 0.0941 -0.0541 -0.0232
CL 0.1397 0.1000 -0.1023 0.1315
CAG -0.1830 0.1489 -0.2412 -0.1344
STZ 1.9402 0.1858 -0.1016 0.6128
COST 1.5933 0.1032 -0.0697 0.9687
COTY -0.0543 0.1354 -0.1297 -0.0411
CVS 1.0224 0.1115 -0.1061 0.6546
DPS 0.5753 0.1185 -0.1120 0.3922
EL 0.3078 0.1514 -0.1408 0.1796
GIS 0.1294 0.1017 -0.0897 0.1205
HRL 0.8422 0.1269 -0.0622 0.4965
SJM 0.7261 0.1099 -0.0604 0.5106
K 0.3691 0.0986 -0.1538 0.3237
KMB 0.7080 0.0995 -0.1270 0.5526
KHC -0.1622 0.0695 -0.2901 -0.2524
KR 1.0713 0.1272 -0.2225 0.5940
MKC 0.0695 0.1044 -0.0641 0.0645
MJN 0.1170 0.1542 -0.1632 0.0722
TAP -0.2026 0.1319 -0.3728 -0.1698
MDLZ -0.0682 0.1168 -0.1321 -0.0603
MNST 1.5468 0.2090 -0.1478 0.4689
PEP 0.1780 0.0887 -0.1354 0.1862
PM 0.6762 0.1106 -0.0074 0.4793
PG 0.1888 0.0841 -0.0938 0.2075
RAI 1.7465 0.1203 -0.0331 0.8838
SYY 0.2184 0.1059 -0.1802 0.1883
HSY 1.0416 0.1268 -0.1610 0.5836
TSN 0.2645 0.1697 -0.2967 0.1400
WMT 0.2486 0.1003 -0.0496 0.2238
WBA -0.0790 0.0818 -0.1228 -0.1002
WFM 0.1361 0.1870 -0.1950 0.0687
AMT 0.2978 0.1241 0.0000 0.2129
AIV -0.3810 0.1506 -0.4666 -0.3109
AVB -0.3909 0.1345 -0.4935 -0.3598
BXP 0.2534 0.1198 -0.2171 0.1906
CBG 0.4894 0.1866 -0.1403 0.2179
CCI -0.0325 0.1282 -0.2002 -0.0257
DLR 0.3522 0.1513 -0.2587 0.2024
EQIX 0.4127 0.2059 -0.2982 0.1707
EQR -0.3750 0.1298 -0.4235 -0.3536
ESS 0.1680 0.1269 -0.2053 0.1233
EXR 1.1083 0.1426 -0.1856 0.5432
FRT 0.2177 0.1098 -0.1594 0.1812
GGP 0.0253 0.1494 -0.2504 0.0167
HCP -0.1932 0.1370 -0.3480 -0.1551
HST 0.2087 0.1649 -0.2807 0.1160
IRM 0.1416 0.1467 -0.1732 0.0908
KIM -0.0777 0.1336 -0.2747 -0.0603
MAC -0.0287 0.1396 -0.2446 -0.0208
MAA 0.0186 0.0141 -0.0201 0.1315
PLD 0.0490 0.1487 -0.3178 0.0323
PSA 0.1429 0.1203 -0.2360 0.1118
O 0.3901 0.1189 -0.1294 0.2816
REG 0.0815 0.1279 -0.2642 0.0615
SPG 0.1748 0.1237 -0.2411 0.1312
SLG 0.1499 0.1478 -0.2987 0.0952
UDR -0.3201 0.1351 -0.4379 -0.2801
VTR 0.0809 0.1396 -0.2830 0.0559
VNO -0.0433 0.1270 -0.3032 -0.0348
HCN 0.7398 0.1219 0.0000 0.4671
WY 0.9431 0.1512 -0.1200 0.4543
APC -0.2981 0.2152 -0.4506 -0.1616
APA -0.2276 0.1845 -0.5066 -0.1382
BHI -0.0134 0.1938 -0.3537 -0.0070
COG 1.1657 0.2391 -0.1541 0.3361
CHK -0.7689 0.3022 -0.8590 -0.4509
CVX 0.0945 0.1229 -0.1798 0.0738
XEC -0.1469 0.2230 -0.2859 -0.0707
CXO -0.4585 0.2204 -0.5821 -0.2699
COP 0.6366 0.1394 -0.1725 0.3623
DVN -0.1952 0.1887 -0.3322 -0.1138
EOG 0.1094 0.1895 -0.2442 0.0551
EQT 0.1203 0.1891 -0.2959 0.0604
XOM 0.1820 0.1022 -0.1335 0.1650
FTI 0.2957 0.1916 -0.1695 0.1369
HAL 0.4132 0.1921 -0.2205 0.1832
HP -0.0211 0.2122 -0.2020 -0.0100
HES 0.8751 0.1832 -0.1302 0.3541
KMI 0.1930 0.1385 -0.0786 0.1285
MRO 0.6577 0.2056 -0.1389 0.2521
MPC 0.3239 0.2033 -0.2924 0.1400
MUR -0.6730 0.2046 -0.6996 -0.5168
NOV -0.0463 0.1902 -0.2117 -0.0249
NFX -0.5937 0.2370 -0.6875 -0.3635
NBL -0.4288 0.1862 -0.4288 -0.2926
OXY -0.5228 0.1421 -0.5277 -0.5017
OKE 1.5179 0.1887 -0.1828 0.5125
PSX 0.3478 0.1446 -0.0489 0.2095
PXD 1.2701 0.2220 -0.1540 0.3848
RRC -0.4188 0.2425 -0.4572 -0.2178
SLB -0.1260 0.1500 -0.2969 -0.0892
SWN -0.3146 0.2519 -0.5738 -0.1472
TSO 1.5309 0.2449 -0.3038 0.3973
RIG -0.5907 0.2317 -0.7028 -0.3688
VLO 0.9506 0.2142 -0.2057 0.3226
WMB 0.3207 0.2146 -0.3114 0.1314
T 0.3951 0.0914 -0.0705 0.3705
CTL 0.1905 0.1559 -0.0413 0.1129
FTR -0.4234 0.1705 -0.4333 -0.3141
LVLT 2.1569 0.2457 -0.2698 0.4959
VZ 0.2033 0.0900 -0.0881 0.2075

moving
10

【在 g***e 的大作中提到】
: Hi,
: Our team has made a free back test tool in below github:
: https://github.com/geome-mitbbs/QTS_Research
: It is simple to use yet has great flexibility:
: To use:
: 1. System requirements: you need to have python3 + numpy + pandas + (
: matplotlib,optional)
: 2. Download files into any folder.
: 3. Modify Sample_Test.py and run. Currently Sample_Test.py contains a moving
: average trend following algo: if 10 day average > 25 day average, buy. if 10

s******6
发帖数: 140
32
Thanks for sharing!
c********2
发帖数: 353
33
Thanks!

last
file

【在 g***e 的大作中提到】
: Hi,
: Continue on the project, I would like to post the preliminary result of
: backtest for all SP500 underlyers using Moving Average Stategy for the last
: 10 years: (ie, buy/sell signal by crossing of 10day and 25day average ).
: Sorry for the messy format - you can get a cleaner download of the csv file
: in-
: http://www.icanbeatmarket.com/research_reports.html
: ticker return volatility draw_down sharpe
: Avg_SP500 0.4734 0.1546 -0.2179 0.2032
: industrials 0.4338 0.1484 -0.2121 0.1884

b***d
发帖数: 288
34
How about Python 2.7?

moving
10

【在 g***e 的大作中提到】
: Hi,
: Continue on the project, I would like to post the preliminary result of
: backtest for all SP500 underlyers using Moving Average Stategy for the last
: 10 years: (ie, buy/sell signal by crossing of 10day and 25day average ).
: Sorry for the messy format - you can get a cleaner download of the csv file
: in-
: http://www.icanbeatmarket.com/research_reports.html
: ticker return volatility draw_down sharpe
: Avg_SP500 0.4734 0.1546 -0.2179 0.2032
: industrials 0.4338 0.1484 -0.2121 0.1884

g***e
发帖数: 577
35
Hi - I think you can modify a few functions and it should still work, like
some module names etc.
I started from Python 3 personally so might not understand all the
constraints...

【在 b***d 的大作中提到】
: How about Python 2.7?
:
: moving
: 10

s*********4
发帖数: 3362
36
Thx
g***e
发帖数: 577
37
No problem,
just want to update on the first research report (preliminary results ) is
available on our website:
http://www.icanbeatmarket.com/research_reports.html
The main results copied here:
In this test, we compared 3 strategies for each of the current SP500
underlyers. Their definitions are below and the comparison of results is
here: 10 years backtest for Moving Average Strategy (25,10) on SP500
underlyers results. You can also find the results in our GitHub project:
Tool: QTS_Research. ( in folder Algo_Research )
Basic moving average strategy:
1. Buy stock when its 10 day average cross the 25 day average going upwards.
2. Sell stock when its 10 day average cross the 25 day average going
downwards.
Moving average strategy with a support price signal:
1. Buy stock when 10 day average cross 25 day average going upwards and the
price is greater than the 200 day average.
2. Sell stock when 10 day average cross 25 day average going downwards and
the price is lower than the 200 day average.
Bench Mark:
1. Just buy the stock and hold.
Strategy Idea: The moving average strategy's idea is trend following: when
10 day average cross 25 day average going upwards, it means the stock price
recently went up significantly, this implies a trend and the price might
continue to go higher, so the strategy buys the stock to try to realize the
trend profit. This is what we do in the first strategy. However, there is a
point where the first strategy missed: if a stock already went up
significantly there might be some resistance for it to continue go up. This
is what the 2nd strategy is addressing: it introduces a 200 day average
level as a "resistance" or "support" level where the trend will encounter.
And as long as the trend has gone through that barrier, the strategy think
the trend will have no more resistance (support) in nearby range and
therefore the trend will continue. That is the meaning of the 2nd strategy.
Backtest Shows:
1.The support price signal improves the performance of the basic moving
average strategy in general. In another word, the 2nd strategy is much
better than the 1st.
2.The moving average strategy underperformed compared to the buy and hold
strategy ( Bench Mark ) for most stocks. This is because the moving average
strategy has a lag in identifying the trend and therefore in a bull market,
it will realize less profit than buy and hold. However, in a bear market it
should prevent the large loss of buy and hold. So far we have been looking
at the period from 2011-2017 where is a bull market in general, we will add
the analysis of bear market in the following step.
Here is some charts of the moving average strategies (1st strategy is
portfolio, 2nd strategy is portfolio1, 3rd strategy - bench mark is the
portfolio2 ).
AAPL: Apple.
To be continued;

【在 s*********4 的大作中提到】
: Thx
w*******e
发帖数: 734
38
Drawdown太高了。longest stagnation-days长达两年(2013 high - 2015 high)。一
般的人早就放弃了。玩Algo有一段时间,发现还是我自己靠谱。呵呵
w*******e
发帖数: 734
39
Algo最大的问题是curvefit,backtest很好,forwardtest也许好也许不好。drawdown
来了你不知道是正常的或是strategies fail了。我见有人用到30多个strategies组成
portfolio。刚开始一年两年可以,时间一长也不行了。
g***e
发帖数: 577
40
Thanks for sharing the experience! Such discussion is nice and useful!
This is in a middle of research - I am still exploring this very common
strategy.
I am not sure in this particular case it has overfitted though. The next
step would be test the time frame from any starting point randomly to see
the return/sharpe, in order to avoid the bias of starting time.
On the other hand, it is probably true 90% of research might not end up
finding a profitable strategy - but the outcome is not wasted, it will show
that some ideas will not work and that could save money invisibly.
Thanks again for the discussion!

drawdown

【在 w*******e 的大作中提到】
: Algo最大的问题是curvefit,backtest很好,forwardtest也许好也许不好。drawdown
: 来了你不知道是正常的或是strategies fail了。我见有人用到30多个strategies组成
: portfolio。刚开始一年两年可以,时间一长也不行了。

相关主题
2014年我看好的tech和service股前30未来两周经济事件及业绩报告Apr 26 - May 7 2010
股版和家版合并了NFLX 不错
Nasdaq-100离十年新高只有3%了没买上的别担心,这200MA肯定日后要回测,到时候再买
E**********e
发帖数: 1736
41
should randomly select upper-performance, under-performance, so-so-
performance stocks from S&P lists and run back testing ? it was bull market
in past 8-9 years. theoretically, the return should be positive using your
strategy.
I am learning and figuring out your code. thanks.

show

【在 g***e 的大作中提到】
: Thanks for sharing the experience! Such discussion is nice and useful!
: This is in a middle of research - I am still exploring this very common
: strategy.
: I am not sure in this particular case it has overfitted though. The next
: step would be test the time frame from any starting point randomly to see
: the return/sharpe, in order to avoid the bias of starting time.
: On the other hand, it is probably true 90% of research might not end up
: finding a profitable strategy - but the outcome is not wasted, it will show
: that some ideas will not work and that could save money invisibly.
: Thanks again for the discussion!

E**********e
发帖数: 1736
42
should randomly select upper-performance, under-performance, so-so-
performance stocks from S&P lists and run back testing ? it was bull market
in past 8-9 years. theoretically, the return should be positive using your
strategy.
I am learning and figuring out your code. thanks.

show

【在 g***e 的大作中提到】
: Thanks for sharing the experience! Such discussion is nice and useful!
: This is in a middle of research - I am still exploring this very common
: strategy.
: I am not sure in this particular case it has overfitted though. The next
: step would be test the time frame from any starting point randomly to see
: the return/sharpe, in order to avoid the bias of starting time.
: On the other hand, it is probably true 90% of research might not end up
: finding a profitable strategy - but the outcome is not wasted, it will show
: that some ideas will not work and that could save money invisibly.
: Thanks again for the discussion!

g***e
发帖数: 577
43
Thanks for looking.
Updates continued:
http://www.icanbeatmarket.com/research_reports.html
Copied the main updates here on which stocks are suitable for moving average
with support strategy ( a common trend-following strategy ).
AAPL trend following 2011-2017
Strategy Return Volatility Draw_Down Max_Draw_Down Sharpe
Moving Average 2.21 0.14 0 -0.28 0.85
Moving Average with Support 3.43 0.17 0 -0.22 0.94
Bench Mark: Buy and Hold 3.52 0.21 0 -0.43 0.76
ALK trend following 2003-2011
Strategy Return Volatility Draw_Down Max_Draw_Down Sharpe
Moving Average -0.3 0.29 -0.65 -0.77 -0.12
Moving Average with Support -0.27 0.28 -0.63 -0.75 -0.1
Bench Mark: Buy and Hold 0.9 0.44 -0.49 -0.76 0.14
Analysis: Why is the performance on ALK trend following is pretty bad
compared to AAPL? One explanation is the underlyer is too volatile. ALK's
volatility is 44% compared to AAPL's volatility 21%. Moving average is a
trend following strategy, so it has a lag to identify the trend in its
nature. If the underlyer has less volatility, it will have longer trends so
the strategy can realize profit even with a certain lag. If the underlyer
has large volatility, then there is very quick ups and downs and also in
large scale, this all make it difficult for trend following.
Attached AAPL and ALK charts.
g***e
发帖数: 577
44
In next, we shall explore how to add volatility as a signal/filter to
improve the strategy. A simple thought would be only run the strategy on low
volatility stocks and buy stock when trend goes up and vol goes down, and
sell stock when trend goes down or vol goes up. We implemented this strategy
and checked the back test result. Overall this simple signal didn't improve
the performance much. In below we exhibit what the problem is:
The portfolio is the simple moving average strategy, the portfolio 1 is the
moving average with support price signal and portfolio 2 is the moving
average with support price signal and vol signal. The portfolio 3 is the
bench mark ( buy and hold AMT stock ).
We see that with vol signal, the strategy had a long idle period from 2011
Summer to 2012 Summer. That is the time where the strategy with support
price signal started to overperform the vol signal. Also we see all moving
average strategy had a big shock at March 2011, that is when the stock
quickly dipped down and then bounced back the next day. Unfortunately this
quick dip down (over 10% down) knocked out the strategy and it won't capture
the next day quick bounce back. This brings us to the next realization:
Moving average strategy can perform badly in a scenario that market quickly
crashed down and next day backed up quickly. While human trader might
realize the quick dip down is abnormal in some situations which they can
reasonably expect market back to normal level quickly, computer programs don
't understand this. There had been several famous flash crash scenarios
which is fundamentally caused by algorithms reacted to absurd market moves
badly and even more magnified the absurd move.
To be continued...

average

【在 g***e 的大作中提到】
: Thanks for looking.
: Updates continued:
: http://www.icanbeatmarket.com/research_reports.html
: Copied the main updates here on which stocks are suitable for moving average
: with support strategy ( a common trend-following strategy ).
: AAPL trend following 2011-2017
: Strategy Return Volatility Draw_Down Max_Draw_Down Sharpe
: Moving Average 2.21 0.14 0 -0.28 0.85
: Moving Average with Support 3.43 0.17 0 -0.22 0.94
: Bench Mark: Buy and Hold 3.52 0.21 0 -0.43 0.76

g***e
发帖数: 577
45
+Update on the algo performance per ticker (SP500) for year 2011-2017.
https://github.com/geome-mitbbs/QTS_Research/blob/master/Algo_Research/MA_
per_ticker_comparison_11_17.csv
1 (共1页)
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话题: strategy话题: average话题: moving话题: day话题: aapl