Creating a strategy backtesting program for the PH stock market using Python, Part 1

Table of Contents¶

1. Introduction
2. Getting the data
3. Plotting the data
4. Knowing the data
5. Applying the data
6. Conclusion

Introduction¶

Disclaimer: This post assumes that you already have knowledge of Python or general programming. There are too many tutorials out there already that will help you get up to speed or set it up if otherwise.

While getting stock price information has become very easy the past few years thanks to the Internet, there is relatively no way for local retail investors to get long-term data for the Philippine market without having to resort to some paid solution. Unless you have access to the Bloomberg terminal or to the much cheaper eSignal, recording daily prices is a chore. Some would resort to getting the data from the PSE or Bloomberg websites and manually inserting it to their database or will painstakingly record it in a spreadsheet. While this is a commendable effort, it's too tedious to do this when you have a lot of tickers to track.

Enter Python. With Python and a few select libraries, we can get the data we want online. While we cannot match the abundance and completeness of the abovementioned tools, we can use a few tricks to maximize our process.

In addition to getting our data, we want to maintain a respectable script that allows us to create or display charts for our viewing pleasure, as well as add some really awesome technical indicators to it.

Note: Click on the input boxes to see their respective output. I've hidden most of them because the length of some of the results are very cumbersome to look at.

Getting the data¶

For this to work, we're going to scrape data first from the Bloomberg website. Our target company for now is my favorite company to watch, Energy Development Corporation (EDC:PM). I will be using the amazing Requests library, written by Kenneth Reitz.

The process is fairly simple: when you use the Bloomberg website to access a company's stock data, the URL format is simply morphed to the base URL appended with /quote/{ticker}. The exact chart data is in /quote/{ticker}/chart. This can be checked by clicking on the Stock Chart tab near beside News & Press Releases.

Programatically, we just change {ticker} to whatever ticker we need. For the time being, we'll use EDC's ticker, EDC:PM. Let's check if we can make a GET request and if we're returned a 200 response. This basically means our request went through to the site without errors.

In [1]:

import requests as rq

url = "http://www.bloomberg.com/quote/EDC:PM/chart"
r = rq.get(url)

r

Out[1]:

<Response [200]>

We are successfully returned a 200 response, which means we can access the URL from Python. What remains is to discover where the data for the graph displayed in this page is getting its data. For this, I'll be using the Firebug extension for Mozilla Firefox and inspect the GET requests that happen when we request for a specific time data.

On further checking, the real data is coming from this rather insanely long URL. We'll try to make a GET request to this URL and check if the data returned is similar to the one viewed in the browser.

In [2]:

url = "http://www.bloomberg.com/apps/data?pid=webpxta&Securities=EDC:PM"
url += "&TimePeriod=5Y&Outfields=HDATE,PR005-H,PR006-H,PR007-H,PR008-H,PR013-H"
url += ",BOLMA,BOLUP,BOLLO,BOLBW,BOLPB,RSI,MACD,MACDS,MACD2,ROCP,ROCN,TASK,TASD,TASDS,TASDSS"
r = rq.get(url)

r.text # Warning: Crapload of data coming up.

Out[2]:

u'HistoryData"HDataPoint"HDATE,PR005-H,PR006-H,PR007-H,PR008-H,PR013-H,BOLMA,BOLUP,BOLLO,BOLBW,BOLPB,RSI,MACD,MACDS,MACD2,ROCP,ROCN,TASK,TASD,TASDS,TASDSS"BOLLODeviation=2"BOLPeriod=20"BOLUPDeviation=2"Count=1218"From=1"ListSet=Full"MACDPeriod1=12"MACDPeriod2=26"MACDSPeriod=9"PrehistoryPeriod=8M"ROCPeriod=1"RSIPeriod=14"Security=EDC:PM"TASDPeriod=5"TASDSPeriod=5"TASDSSPeriod=3"TASKPeriod=9"TimePeriod=5Y"TotalMembers=1218\n20100303"4.7"4.8"4.8"4.65"43831000"4.8125"5.11229"4.51271"12.459"0.31237"43.955"0.01858"0.04235"-0.02378"-1.05263"-0.05"14.286"15.429"35.581"46.036\n20100304"4.6"4.7"4.75"4.6"44473000"4.805"5.11789"4.49211"13.024"0.17241"39.395"-0.0008212"0.03372"-0.03454"-2.12766"-0.1"0"11.429"26.281"35.971\n20100305"4.75"4.65"4.75"4.65"11730000"4.81"5.11594"4.50406"12.721"0.40194"48.093"-0.00404"0.02617"-0.03021"3.26087"0.15"37.5"16.071"20.414"27.425\n20100308"4.75"4.75"4.8"4.7"10806000"4.815"5.1135"4.5165"12.399"0.39112"48.093"-0.00652"0.01963"-0.02615"0"0"37.5"17.857"16.671"21.122\n20100309"4.8"4.75"4.8"4.7"25034000"4.83"5.09153"4.56847"10.83"0.44265"50.821"-0.0044"0.01482"-0.01923"1.05263"0.05"50"27.857"17.729"18.271\n20100310"4.95"4.8"4.95"4.8"32395000"4.8475"5.09137"4.60363"10.062"0.71015"57.96"0.00927"0.01371"-0.00444"3.125"0.15"100"45"23.643"19.348\n20100311"4.95"4.95"4.95"4.9"6789000"4.865"5.08432"4.64568"9.01612"0.69378"57.96"0.01988"0.01495"0.00494"0"0"100"65"34.357"25.243\n20100312"4.9"4.95"4.95"4.9"12214000"4.8675"5.08722"4.64778"9.02787"0.57396"54.88"0.02398"0.01675"0.00723"-1.0101"-0.05"85.714"74.643"46.071"34.69\n20100315"4.8"4.9"4.9"4.8"10885000"4.8575"5.07028"4.64472"8.76085"0.36488"49.244"0.01894"0.01719"0.00175"-2.04082"-0.1"57.143"78.571"58.214"46.214\n20100316"4.8"4.85"4.85"4.75"10596000"4.8475"5.05115"4.64385"8.40244"0.38338"49.244"0.01478"0.01671"-0.00193"0"0"57.143"80"68.643"57.643\n20100317"4.8"4.8"4.85"4.75"35987000"4.8375"5.02953"4.64547"7.93917"0.40236"49.244"0.01135"0.01564"-0.00429"0"0"50"70"73.643"66.833\n20100318"4.75"4.75"4.8"4.75"38377000"4.83"5.02339"4.63661"8.0079"0.29316"46.276"0.00454"0.01342"-0.00888"-1.04167"-0.05"20"54"71.443"71.243\n20100319"4.7"4.7"4.75"4.7"31365000"4.8175"5.01057"4.62443"8.01525"0.1957"43.456"-0.00483"0.00977"-0.0146"-1.05263"-0.05"0"36.857"63.886"69.657\n20100322"4.75"4.75"4.75"4.7"50178000"4.8075"4.99264"4.62236"7.70193"0.34471"46.939"-0.00813"0.00619"-0.01432"1.06383"0.05"20"29.429"54.057"63.129\n20100323"4.85"4.75"4.85"4.75"17249000"4.8025"4.97707"4.62793"7.27"0.63605"53.153"-0.00265"0.00442"-0.00707"2.10526"0.1"60"30"44.057"54\n20100324"4.9"4.9"4.9"4.85"15795000"4.8025"4.97707"4.62793"7.27"0.77926"55.933"0.00567"0.00467"0.0009982"1.03093"0.05"80"36"37.257"45.124\n20100325"4.9"4.9"4.9"4.8"15131000"4.8025"4.97707"4.62793"7.27"0.77926"55.933"0.01212"0.00616"0.00596"0"0"100"52"36.857"39.39\n20100326"4.85"4.9"4.9"4.8"18818000"4.805"4.98078"4.62922"7.31671"0.628"52.332"0.01304"0.00754"0.00551"-1.02041"-0.05"75"67"42.886"39\n20100329"5.1"4.9"5.1"4.85"72937000"4.8175"5.03493"4.60007"9.0266"1.14964"64.602"0.03357"0.01274"0.02082"5.15464"0.25"100"83"53.6"44.448\n20100330"5.1"5.1"5.2"4.95"62922000"4.835"5.08218"4.58782"10.225"1.03604"64.602"0.04926"0.02005"0.02922"0"0"80"87"65"53.829\n20100331"5"5.1"5.1"5"32043000"4.85"5.099"4.601"10.268"0.80121"57.711"0.05302"0.02664"0.02638"-1.96078"-0.1"60"83"74.4"64.333\n20100405"5"5"5.1"4.95"25621000"4.87"5.09891"4.64109"9.40084"0.78395"57.711"0.05536"0.03238"0.02298"0"0"60"75"79"72.8\n20100406"5.1"5"5.2"5"41084000"4.8875"5.13014"4.64486"9.92907"0.93789"62.367"0.06454"0.03881"0.02572"2"0.1"77.778"75.556"80.711"78.037\n20100407"5"5"5.2"4.95"56034000"4.9"5.13875"4.66125"9.74476"0.70943"55.756"0.06302"0.04366"0.01936"-1.96078"-0.1"50"65.556"77.222"78.978\n20100408"5"5"5"4.95"19951000"4.91"5.14791"4.67209"9.69074"0.68915"55.756"0.06111"0.04715"0.01396"0"0"50"59.556"71.733"76.556\n20100412"5.2"5.1"5.2"5"81811000"4.9225"5.19171"4.65329"10.938"1.01539"64.487"0.07487"0.05269"0.02218"4"0.2"100"67.556"68.644"72.533\n20100413"5.2"5.2"5.2"5.1"40406000"4.935"5.23013"4.63987"11.961"0.94896"64.487"0.0848"0.05911"0.02569"0"0"100"75.556"68.756"69.711\n20100414"5.2"5.2"5.2"5.1"20659000"4.95"5.26623"4.63377"12.777"0.89528"64.487"0.09161"0.06561"0.026"0"0"100"80"69.644"69.015\n20100415"5.1"5.2"5.2"5"36634000"4.965"5.2798"4.6502"12.681"0.71442"57.412"0.08793"0.07008"0.01785"-1.92308"-0.1"60"82"72.933"70.444\n20100416"5"5.1"5.1"4.95"28080000"4.975"5.28078"4.66922"12.293"0.54088"51.345"0.07606"0.07127"0.00479"-1.96078"-0.1"20"76"76.222"72.933\n20100419"4.9"4.95"4.95"4.9"15551000"4.98"5.27732"4.68268"11.941"0.36547"46.099"0.05792"0.0686"-0.01068"-2"-0.1"0"56"73.911"74.356\n20100420"4.8"4.95"5"4.8"28279000"4.9825"5.2728"4.6922"11.653"0.18567"41.529"0.03507"0.0619"-0.02682"-2.04082"-0.1"0"36"66"72.044\n20100421"5"4.85"5.1"4.85"33684000"4.9975"5.25726"4.73774"10.396"0.50481"51.817"0.03273"0.05606"-0.02334"4.16667"0.2"50"26"55.2"65.037\n20100422"5.2"5.1"5.2"5"37206000"5.02"5.26779"4.77221"9.87212"0.86321"59.492"0.04647"0.05414"-0.00768"4"0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"0.25173"0.03383"0"0"80.682"87.751"85.882"84.827\n20141007"7.99"7.97"8.05"7.94"27755500"7.54"8.21106"6.86894"17.8"0.83529"72.959"0.2842"0.25822"0.02597"0.12531"0.01"76.471"84.864"86.083"85.568\n20141008"7.88"7.95"7.95"7.73"20049600"7.57"8.24551"6.89449"17.847"0.72945"66.403"0.27112"0.2608"0.01031"-1.37672"-0.11"60.294"77.529"84.429"85.465\n20141009"7.81"7.9"8.01"7.75"23488100"7.5975"8.26503"6.92997"17.572"0.65917"62.551"0.25219"0.25908"-0.00689"-0.88832"-0.07"50"70.195"81.404"83.972\n20141010"7.71"7.76"7.76"7.69"37838700"7.625"8.26282"6.98718"16.73"0.56663"57.426"0.22652"0.25257"-0.02605"-1.28041"-0.1"32.308"59.951"76.058"80.631\n20141013"7.37"7.57"7.57"7.36"40458600"7.632"8.25524"7.00876"16.332"0.28981"44.174"0.1767"0.23739"-0.0607"-4.40986"-0.34"1.26582"44.068"67.321"74.928\n20141014"7.35"7.35"7.5"7.18"80797700"7.6405"8.24314"7.03786"15.775"0.25898"43.537"0.13405"0.21673"-0.08267"-0.27137"-0.02"17.526"32.279"56.804"66.728\n20141015"7.16"7.36"7.41"7.16"33831100"7.643"8.23718"7.04882"15.548"0.09355"37.944"0.08396"0.19017"-0.10621"-2.58503"-0.19"0"20.22"45.342"56.489\n20141016"7.24"7.1"7.27"7.05"65937400"7.648"8.22664"7.06936"15.132"0.14745"41.36"0.05014"0.16217"-0.11203"1.11732"0.08"17.273"13.674"34.038"45.395\n20141017"7.31"7.25"7.45"7.25"25796500"7.653"8.21798"7.08802"14.765"0.19645"44.252"0.02865"0.13546"-0.10681"0.96685"0.07"26"12.413"24.531"34.637\n20141020"7.6"7.36"7.64"7.36"54210000"7.6675"8.211"7.124"14.177"0.4379"54.305"0.03463"0.1153"-0.08067"3.96717"0.29"57.292"23.618"20.441"26.337\n20141021"7.7"7.65"7.8"7.65"43196300"7.6775"8.21564"7.13936"14.019"0.52091"57.173"0.04689"0.10161"-0.05473"1.31579"0.1"67.708"33.655"20.716"21.896\n20141022"7.73"7.73"7.81"7.69"63612700"7.684"8.22138"7.14662"13.987"0.5428"58.024"0.05835"0.09296"-0.03461"0.38961"0.03"89.474"51.549"26.982"22.713\n20141023"7.7"7.73"7.73"7.68"10790500"7.683"8.22018"7.14582"13.984"0.51582"56.809"0.06428"0.08723"-0.02295"-0.3881"-0.03"85.526"65.2"37.287"28.328\n20141024"7.7"7.7"7.83"7.65"11358300"7.6865"8.22317"7.14983"13.964"0.51258"56.809"0.06819"0.08342"-0.01523"0"0"83.333"76.667"50.138"38.135\n20141027"7.65"7.72"7.72"7.5"19333100"7.688"8.22408"7.15192"13.946"0.46456"54.597"0.06649"0.08003"-0.01354"-0.64935"-0.05"76.923"80.593"61.533"49.652\n20141028"7.61"7.65"7.72"7.54"6157100"7.678"8.21206"7.14394"13.912"0.43634"52.825"0.0612"0.07627"-0.01506"-0.52288"-0.04"71.795"81.41"71.084"60.918\n20141029"7.59"7.62"7.69"7.57"17316200"7.653"8.15332"7.15268"13.075"0.43704"51.918"0.05477"0.07197"-0.01719"-0.26281"-0.02"58.621"75.24"75.822"69.479\n20141030"7.57"7.61"7.61"7.55"19329000"7.6315"8.10665"7.15635"12.452"0.43528"50.975"0.04751"0.06708"-0.01956"-0.2635"-0.02"44.681"67.071"76.196"74.367\n20141031"7.7"7.6"7.7"7.58"52343200"7.6175"8.06654"7.16846"11.79"0.59186"56.504"0.05166"0.06399"-0.01234"1.71731"0.13"60.606"62.525"73.368"75.129\n20141103"7.73"7.68"7.83"7.68"16739400"7.605"8.02602"7.18398"11.072"0.64845"57.69"0.05671"0.06254"-0.00583"0.38961"0.03"69.697"61.08"69.465"73.01\n20141104"7.66"7.74"7.76"7.61"21020800"7.5885"7.97208"7.20492"10.109"0.5932"53.991"0.05443"0.06092"-0.00648"-0.90556"-0.07"48.485"56.418"64.467"69.1\n20141105"7.7"7.66"7.76"7.61"20435400"7.5795"7.94323"7.21577"9.59771"0.66565"55.738"0.05522"0.05978"-0.00456"0.52219"0.04"60.606"56.815"60.782"64.904\n20141106"7.8"7.69"7.89"7.69"18577500"7.579"7.94149"7.21651"9.56553"0.80484"59.841"0.06319"0.06046"0.00273"1.2987"0.1"76.923"63.263"60.02"61.756\n20141107"7.79"7.81"7.81"7.7"11642200"7.583"7.95287"7.21313"9.75525"0.77983"59.25"0.06791"0.06195"0.00596"-0.12821"-0.01"71.429"65.428"60.601"60.468\n20141110"7.79"7.81"7.89"7.77"14268500"7.604"7.97079"7.23721"9.64733"0.75355"59.25"0.07084"0.06373"0.00711"0"0"70.588"65.606"61.506"60.709\n20141111"7.89"7.84"8"7.84"29934200"7.631"7.99853"7.26347"9.63247"0.85236"63.442"0.0803"0.06704"0.01326"1.2837"0.1"75.556"71.02"64.427"62.178\n20141112"8.13"8"8.3"8"33323200"7.6795"8.04158"7.31742"9.42968"1.12211"71.12"0.10594"0.07482"0.03112"3.04183"0.24"76.389"74.177"67.899"64.611\n20141113"8.1"8.2"8.2"8.09"28973000"7.7225"8.06954"7.37546"8.9877"1.04389"69.164"0.12244"0.08434"0.03809"-0.369"-0.03"71.014"72.995"69.845"67.39\n20141114"8.1"8.1"8.14"8.08"21012100"7.762"8.09164"7.43236"8.49374"1.01268"69.164"0.13396"0.09427"0.03969"0"0"71.014"72.912"71.342"69.695\n20141117"8.2"8.09"8.25"8.09"15138100"7.792"8.16373"7.42027"9.54135"1.04878"72.127"0.14944"0.1053"0.04414"1.23457"0.1"85.507"75.896"73.4"71.529\n20141118"8.2"8.25"8.48"8.2"27263600"7.817"8.226"7.408"10.464"0.96821"72.127"0.15987"0.11622"0.04365"0"0"64.557"73.696"73.935"72.893\n20141119"8.25"8.3"8.33"8.23"27697000"7.843"8.29084"7.39516"11.42"0.9544"73.598"0.1702"0.12701"0.04319"0.60976"0.05"70.513"72.521"73.604"73.647\n20141120"8.16"8.25"8.25"8.12"11637500"7.866"8.32909"7.40291"11.775"0.81743"66.767"0.16918"0.13545"0.03373"-1.09091"-0.09"54.93"69.304"72.866"73.469\n20141121"8.15"8.25"8.3"8.14"31539800"7.8885"8.36078"7.41622"11.974"0.77685"66.034"0.16565"0.14149"0.02417"-0.12255"-0.01"48.438"64.789"71.241"72.571\n20141124"8.19"8.23"8.3"8.19"17013600"7.9155"8.39188"7.43912"12.037"0.78811"67.569"0.16419"0.14603"0.01816"0.4908"0.04"39.583"55.604"67.183"70.43\n20141125"8.2"8.2"8.21"8.1"38497500"7.945"8.41509"7.47491"11.833"0.77123"67.958"0.16198"0.14922"0.01276"0.1221"0.01"30"48.693"62.182"66.869\n20141126"8.18"8.3"8.3"8.16"26446200"7.9745"8.42543"7.52357"11.309"0.72786"66.244"0.1568"0.15073"0.00606"-0.2439"-0.02"25"39.59"55.596"61.654\n20141127"7.78"8.19"8.25"7.74"58261100"7.985"8.40659"7.56341"10.56"0.25687"42.921"0.11904"0.1444"-0.02535"-4.88998"-0.4"5.40541"29.685"47.672"55.15\n20141128"8.03"7.87"8.07"7.85"26769400"8.0015"8.40251"7.60049"10.023"0.53554"53.856"0.10805"0.13713"-0.02907"3.21337"0.25"39.189"27.836"40.282"47.85\n20141201"8.02"8.01"8.09"7.9"22622800"8.016"8.39718"7.63482"9.51041"0.50525"53.415"0.09741"0.12918"-0.03177"-0.12453"-0.01"47.458"29.41"35.043"40.999\n20141202"8.03"8.02"8.05"8"19607200"8.0345"8.37891"7.69009"8.5733"0.49347"53.822"0.08876"0.1211"-0.03234"0.12469"0.01"51.786"33.768"32.058"35.794\n20141203"8.11"8.04"8.15"8.02"16634700"8.055"8.36435"7.74565"7.68105"0.58889"57.055"0.08736"0.11435"-0.027"0.99626"0.08"66.071"41.982"32.536"33.212\n20141204"8.02"8.1"8.1"7.96"42527300"8.066"8.35315"7.77885"7.12005"0.4199"52.594"0.07808"0.1071"-0.02902"-1.10974"-0.09"50"50.901"36.779"33.791\n20141205"7.97"7.95"8.02"7.86"46813500"8.075"8.33718"7.81282"6.4937"0.29976"50.244"0.06593"0.09886"-0.03293"-0.62344"-0.05"41.071"51.277"41.468"36.928\n20141209"8.09"7.97"8.16"7.97"42429400"8.09"8.31724"7.86276"5.61791"0.5"55.396"0.06524"0.09214"-0.0269"1.50565"0.12"62.5"54.286"46.443"41.563\n20141210"8.23"8.08"8.28"8.08"45379700"8.107"8.32242"7.89158"5.31432"0.78549"60.53"0.07512"0.08873"-0.01362"1.73053"0.14"90.741"62.077"52.104"46.672\n20141211"8.3"8.21"8.32"8.15"49598800"8.1155"8.34671"7.88429"5.69804"0.89898"62.834"0.08759"0.0885"-0.0009187"0.85055"0.07"95.745"68.011"57.31"51.952\n20141212"8.47"8.3"8.52"8.3"45852600"8.134"8.41181"7.85619"6.83073"1.10474"67.756"0.10992"0.09279"0.01713"2.04819"0.17"92.424"76.496"62.429"57.281\n20141215"8.47"8.2"8.52"8.19"30475100"8.1525"8.4658"7.8392"7.68591"1.00671"67.756"0.12616"0.09946"0.0267"0"0"92.424"86.767"69.527"63.089\n20141216"8.3"8.47"8.47"8.3"27970900"8.1575"8.4768"7.8382"7.82848"0.72314"58.735"0.12389"0.10435"0.01954"-2.00708"-0.17"66.667"87.6"76.19"69.382\n20141217"8.2"8.31"8.33"8.12"27949500"8.1575"8.4768"7.8382"7.82848"0.56655"54.166"0.11272"0.10602"0.0067"-1.20482"-0.1"51.515"79.755"79.726"75.148\n20141218"8.25"8.25"8.3"7.95"40434800"8.1575"8.4768"7.8382"7.82848"0.64485"56.009"0.10667"0.10615"0.0005211"0.60976"0.05"59.091"72.424"80.608"78.842\n20141219"8.16"8.4"8.54"8.16"44206200"8.1575"8.4768"7.8382"7.82848"0.50391"51.96"0.09354"0.10363"-0.01009"-1.09091"-0.09"35.593"61.058"77.521"79.285\n20141222"8.21"8.27"8.28"8.19"19109500"8.1605"8.48059"7.84041"7.84492"0.57732"53.951"0.08617"0.10014"-0.01396"0.61275"0.05"44.068"51.387"70.445"76.191\n20141223"8.19"8.21"8.27"8.17"35899100"8.1605"8.48059"7.84041"7.84492"0.54608"53.005"0.07783"0.09568"-0.01785"-0.24361"-0.02"40.678"46.189"62.163"70.043\n20141229"8.2"8.19"8.35"8.19"8982600"8.1605"8.48059"7.84041"7.84492"0.5617"53.445"0.07119"0.09078"-0.01959"0.1221"0.01"42.373"44.361"55.084"62.564\n20150105"8.5"8.34"8.64"8.34"34675100"8.1765"8.52922"7.82378"8.62764"0.95858"64.253"0.08912"0.09045"-0.00133"3.65854"0.3"79.71"48.484"50.296"55.847\n20150106"8.62"8.45"8.75"8.35"62822300"8.2185"8.57241"7.86459"8.61248"1.06724"67.504"0.11172"0.0947"0.01702"1.41176"0.12"83.75"58.116"49.707"51.696\n20150107"8.72"8.62"8.86"8.58"43129800"8.253"8.65758"7.84842"9.80441"1.07714"69.955"0.13613"0.10299"0.03314"1.16009"0.1"84.615"66.225"52.675"50.893\n20150108"8.71"8.7"8.8"8.68"40647600"8.2875"8.7232"7.8518"10.515"0.98485"69.391"0.15291"0.11297"0.03994"-0.11468"-0.01"83.516"74.793"58.396"53.593\n20150109"8.81"8.8"8.85"8.75"31974500"8.3265"8.80094"7.85206"11.396"1.00955"71.836"0.17229"0.12484"0.04745"1.14811"0.1"92.857"84.89"66.502"59.191\n20150112"8.71"8.81"8.81"8.7"8929100"8.3565"8.84796"7.86504"11.762"0.85964"66.147"0.17753"0.13538"0.04216"-1.13507"-0.1"78.261"84.6"73.725"66.207\n20150113"8.82"8.7"8.93"8.67"25089800"8.3965"8.90192"7.89108"12.039"0.91896"69.05"0.18839"0.14598"0.04241"1.26292"0.11"85.526"84.955"79.093"73.106\n20150114"8.96"8.84"9"8.84"34935400"8.446"8.96828"7.92372"12.368"0.99207"72.306"0.20592"0.15797"0.04795"1.5873"0.14"95.062"87.045"83.257"78.691\n20150119"8.63"8.99"9"8.6"14888500"8.473"8.97428"7.97172"11.832"0.6566"57.068"0.19098"0.16457"0.02641"-3.68304"-0.33"43.939"79.129"84.124"82.158\n20150120"8.59"8.68"8.78"8.53"46689100"8.491"8.98183"8.00017"11.561"0.60085"55.54"0.17391"0.16644"0.00747"-0.4635"-0.04"36.923"67.942"80.734"82.705\n20150121"8.43"8.66"8.66"8.35"53143600"8.4975"8.98144"8.01356"11.39"0.43026"49.797"0.14579"0.16231"-0.01652"-1.86263"-0.16"12.308"54.752"74.765"79.874\n20150122"8.45"8.48"8.5"8.39"58730900"8.4965"8.98074"8.01226"11.399"0.45199"50.487"0.12369"0.15458"-0.03089"0.23725"0.02"15.385"40.723"65.918"73.806\n20150123"8.5"8.51"8.57"8.44"40508400"8.498"8.98209"8.01391"11.393"0.50207"52.251"0.10896"0.14546"-0.0365"0.59172"0.05"23.077"26.326"53.775"64.819\n20150126"8.5"8.6"8.6"8.37"49202300"8.508"8.9835"8.0325"11.178"0.49159"52.251"0.09617"0.1356"-0.03943"0"0"23.077"22.154"42.379"54.024\n20150127"8.65"8.6"8.69"8.6"56178400"8.5305"8.98782"8.07318"10.722"0.63065"57.52"0.09703"0.12789"-0.03086"1.76471"0.15"46.154"24"33.591"43.248\n20150128"8.7"8.65"8.79"8.61"39784900"8.553"8.99699"8.10901"10.382"0.66555"59.138"0.10058"0.12243"-0.02185"0.57803"0.05"53.846"32.308"29.102"35.024\n20150129"8.49"8.69"8.69"8.39"56896800"8.5695"8.97686"8.16214"9.50711"0.40242"50.445"0.08546"0.11503"-0.02957"-2.41379"-0.21"21.538"33.538"27.665"30.12\n20150130"8.55"8.46"8.6"8.46"28898200"8.5865"8.95934"8.21366"8.68437"0.45105"52.59"0.07743"0.10751"-0.03008"0.70671"0.06"45.455"38.014"30.003"28.923\n20150202"8.49"8.53"8.57"8.48"24590800"8.6015"8.93094"8.27206"7.66007"0.33077"50.248"0.06547"0.0991"-0.03363"-0.70175"-0.06"31.818"39.762"33.524"30.398\n20150203"8.44"8.49"8.49"8.41"33703900"8.6135"8.89798"8.32902"6.60553"0.19506"48.317"0.05136"0.08956"-0.03819"-0.58893"-0.05"16.667"33.865"35.497"33.008\n20150204"8.56"8.5"8.8"8.5"31911100"8.6165"8.89738"8.33562"6.51948"0.39942"52.986"0.0493"0.08151"-0.0322"1.4218"0.12"44.186"31.933"35.422"34.815\n20150205"8.48"8.59"8.59"8.43"39779800"8.6095"8.89659"8.32241"6.66908"0.27446"49.759"0.04074"0.07335"-0.03261"-0.93458"-0.08"25.581"32.741"35.263"35.394\n20150206"8.48"8.5"8.55"8.42"29387100"8.5975"8.88517"8.30983"6.69198"0.29577"49.759"0.03357"0.0654"-0.03183"0"0"21.951"28.041"33.268"34.651\n20150209"8.65"8.5"8.69"8.49"15974800"8.5945"8.87865"8.31035"6.61228"0.59766"56.316"0.04113"0.06054"-0.01941"2.00472"0.17"63.415"34.36"32.188"33.573\n20150210"8.58"8.7"8.75"8.56"32016900"8.583"8.84939"8.31661"6.2074"0.49437"53.235"0.041"0.05663"-0.01563"-0.80925"-0.07"46.341"40.295"33.474"32.977\n20150211"8.6"8.73"8.73"8.59"32662100"8.5775"8.83764"8.31736"6.06573"0.54325"54.009"0.04203"0.05371"-0.01169"0.2331"0.02"48.718"41.201"35.328"33.663\n20150212"8.5"8.61"8.63"8.45"25229600"8.5615"8.79834"8.32466"5.53257"0.37016"49.589"0.03437"0.04984"-0.01547"-1.16279"-0.1"23.077"40.7"36.919"35.24\n20150213"8.63"8.6"8.67"8.56"25721900"8.545"8.7005"8.3895"3.63954"0.77331"54.771"0.03836"0.04755"-0.00919"1.52941"0.13"56.41"47.592"40.83"37.692\n20150216"8.66"8.63"8.75"8.58"18675800"8.5465"8.70578"8.38722"3.72744"0.85628"55.898"0.04343"0.04672"-0.00329"0.34762"0.03"63.158"47.541"43.466"40.405\n20150217"8.7"8.73"8.77"8.68"28570500"8.552"8.724"8.38"4.02245"0.93023"57.421"0.05011"0.0474"0.00271"0.46189"0.04"80"54.273"46.262"43.519\n20150218"8.69"8.73"8.73"8.67"11695200"8.565"8.73745"8.39255"4.02692"0.86242"56.892"0.05397"0.04871"0.00525"-0.11494"-0.01"77.143"59.958"50.013"46.58\n20150220"8.7"8.74"8.84"8.69"30613900"8.5775"8.75104"8.40396"4.04632"0.85295"57.316"0.05718"0.05041"0.00677"0.11507"0.01"64.103"68.163"55.505"50.593\n20150223"8.68"8.75"8.75"8.65"33653300"8.5865"8.76169"8.41131"4.08056"0.76686"56.128"0.05744"0.05181"0.00563"-0.22989"-0.02"58.974"68.676"59.722"55.08\n20150224"8.7"8.68"8.76"8.65"19564300"8.5965"8.77362"8.41938"4.12071"0.79218"57.086"0.05859"0.05317"0.00542"0.23041"0.02"64.103"68.864"63.987"59.738\n20150225"8.79"8.72"8.8"8.72"26127800"8.6035"8.79867"8.40833"4.53697"0.97779"61.191"0.066"0.05574"0.01027"1.03448"0.09"87.179"70.3"67.192"63.634\n20150226"8.75"8.74"8.8"8.65"50471400"8.606"8.80724"8.40476"4.67665"0.85779"58.512"0.06787"0.05816"0.0097"-0.45506"-0.04"67.857"68.443"68.889"66.689\n20150227"8.85"8.79"8.85"8.66"38146100"8.624"8.84404"8.40396"5.10288"1.01355"62.886"0.07653"0.06184"0.01469"1.14286"0.1"100"75.623"70.381"68.821\n20150302"8.9"8.85"8.94"8.8"38483300"8.6415"8.88915"8.39385"5.73167"1.0219"64.88"0.08643"0.06676"0.01968"0.56497"0.05"86.207"81.069"72.86"70.71\n20150303"8.96"8.92"9.13"8.92"32443900"8.665"8.93853"8.39147"6.3135"1.03924"67.161"0.098"0.073"0.02499"0.67416"0.06"64.583"81.165"75.32"72.854\n=\n'

If you've noticed, I've divided the URL into three parts. The third part is just reserved for indicators like the Bollinger Bands, Moving Averages, MACD, RSI, etc. We don't actually need them, which is why we can just use the first two lines. However, the headers for the file is a bit messed up because it's got all these extra characters, not to mention that some of them are not the matching headers for the column they are over.

To address this, we'll modify our approach and use Pandas to read the URL directly and load the prices into a frame with the proper parsing of the delimiter.

In [3]:

import pandas as pd

url = "http://www.bloomberg.com/apps/data?pid=webpxta&Securities=EDC:PM"
url += "&TimePeriod=5Y&Outfields=HDATE,PR005-H,PR006-H,PR007-H,PR008-H,PR013-H"

df = pd.read_csv(url, delimiter='"')
df.head()

Out[3]:

	HistoryData	HDataPoint	HDATE,PR005-H,PR006-H,PR007-H,PR008-H,PR013-H	Count=1218	From=1	ListSet=Full	Security=EDC:PM	TimePeriod=5Y	TotalMembers=1218
0	20100303	4.70	4.80	4.80	4.65	43831000	NaN	NaN	NaN
1	20100304	4.60	4.70	4.75	4.60	44473000	NaN	NaN	NaN
2	20100305	4.75	4.65	4.75	4.65	11730000	NaN	NaN	NaN
3	20100308	4.75	4.75	4.80	4.70	10806000	NaN	NaN	NaN
4	20100309	4.80	4.75	4.80	4.70	25034000	NaN	NaN	NaN

At this point, we've ditched Requests and are exclusively working with Pandas. The issue remaining at hand now is that the column headers are incorrect. Cross-referencing with the chart data shown in the browser, it shows that the order of the columns for the raw Bloomberg data is actually Close-Open-High-Low (COHL) as opposed to the classic Open-High-Low-Close (OHLC). We can now clean up the data with the following code.

In [4]:

df.drop(df.columns[[5, 6, 7, 8]],axis=1, inplace=True)
df.columns = ["Date","Close","Open","High","Low"]
df.head()

Out[4]:

	Date	Close	Open	High	Low
0	20100303	4.70	4.80	4.80	4.65
1	20100304	4.60	4.70	4.75	4.60
2	20100305	4.75	4.65	4.75	4.65
3	20100308	4.75	4.75	4.80	4.70
4	20100309	4.80	4.75	4.80	4.70

Let's check the tail of our frame to see if the data is properly terminated.

In [5]:

df.tail()

Out[5]:

	Date	Close	Open	High	Low
1214	20150226	8.75	8.74	8.80	8.65
1215	20150227	8.85	8.79	8.85	8.66
1216	20150302	8.90	8.85	8.94	8.80
1217	20150303	8.96	8.92	9.13	8.92
1218	=	NaN	NaN	NaN	NaN

As we can see, the frame has a null terminator line. We can remove it by using either loc or dropna. Either works and the speed difference is rather negligible for data of this size. I prefer the latter though.

In [6]:

df.dropna(inplace=True)
df.tail()

Out[6]:

	Date	Close	Open	High	Low
1213	20150225	8.79	8.72	8.80	8.72
1214	20150226	8.75	8.74	8.80	8.65
1215	20150227	8.85	8.79	8.85	8.66
1216	20150302	8.90	8.85	8.94	8.80
1217	20150303	8.96	8.92	9.13	8.92

A purely aesthetic step, let's fix the columns to follow the OHLC format.

In [7]:

df = df[["Date","Open","High","Low","Close"]]
df.head()

Out[7]:

	Date	Open	High	Low	Close
0	20100303	4.80	4.80	4.65	4.70
1	20100304	4.70	4.75	4.60	4.60
2	20100305	4.65	4.75	4.65	4.75
3	20100308	4.75	4.80	4.70	4.75
4	20100309	4.75	4.80	4.70	4.80

Finally, let's consolidate all we've done so far into a function to better deal with request for other tickers.

In [8]:

def getStockFeed(ticker):

    url = 'http://www.bloomberg.com/apps/data?pid=webpxta&Securities={0}'.format(ticker)
    url += '&TimePeriod=5Y&Outfields=HDATE,PR005-H,PR006-H,PR007-H,PR008-H,PR013-H'

    df = pd.read_csv(url, delimiter='"')
    df.drop(df.columns[[5, 6, 7, 8]],axis=1, inplace=True)
    df.columns = ["Date","Close","Open","High","Low"]
    df.dropna(inplace=True)
    df = df[["Date","Open","High","Low","Close"]]

    return df

Now we've got a proper function to use for getting stock price data from Bloomberg. To test if this works with other PH market stocks, let's see if we can pull Aboitiz' five-year data with our new function. We'll feed the function with their ticker, AP:PM.

In [9]:

aboitiz_ohlc = getStockFeed("AP:PM")
aboitiz_ohlc.tail()

Out[9]:

	Date	Open	High	Low	Close
1213	20150225	45.0	45.05	44.65	44.8
1214	20150226	44.5	44.55	43.95	44.0
1215	20150227	44.7	45.05	44.20	44.4
1216	20150302	44.4	45.00	44.40	44.7
1217	20150303	44.2	44.80	44.20	44.4

Ta-da! Now we've got a working function to help us get data for PH markets. Our next step is to try plotting some of them so that it we can have an alternate way to view the charts off-browser.

Plotting the data¶

First and foremost, let's use the IPython magic to produce plots inside the IPython notebook.

In [10]:

%pylab inline
pylab.rcParams['figure.figsize'] = 8, 6 # Change the default size of plots in IPython.

Populating the interactive namespace from numpy and matplotlib

For our plots, let's do the most basic of indicators: the Simple Moving Average. We'll be using 10 and 50 days as our periods. Later on, we'll even compare and backtest some strategies using other libraries.

To get the SMA, we'll be using Pandas' built-in rolling_mean function and feed it the Close prices. We'll be using EDC:PM as our stock again.

In [11]:

ticker = "EDC:PM"
df = getStockFeed(ticker)
df["SMA10"] = pd.rolling_mean(df["Close"], 10)
df["SMA50"] = pd.rolling_mean(df["Close"], 50)
df.head()

Out[11]:

	Date	Open	High	Low	Close	SMA10	SMA50
0	20100303	4.80	4.80	4.65	4.70	NaN	NaN
1	20100304	4.70	4.75	4.60	4.60	NaN	NaN
2	20100305	4.65	4.75	4.65	4.75	NaN	NaN
3	20100308	4.75	4.80	4.70	4.75	NaN	NaN
4	20100309	4.75	4.80	4.70	4.80	NaN	NaN

We'll discard rows with NaN to streamline the data. Afterwards, we'll truncate the data to show only the last 100 days. Finally, we'll plot both SMA series.

In [12]:

df.dropna(inplace=True)
df = df.iloc[-100:,:]
fig = plt.figure()
ax = plt.subplot(111)
ax.plot(df["Close"].values, label="Close")
ax.plot(df["SMA10"].values, label="SMA10")
ax.plot(df["SMA50"].values, label="SMA50")
plt.legend(loc="best")
plt.title("EDC:PM")
plt.show()

This is a very nice output. This shows us that in the last 100 days, the 10-day average hasn't intersected the 50-day average, which means the stock's relatively current performance is fairly better than its 50-day historical performance.

Now that we have both data and charts, our next step is to do some backtesting. Before that, let's have a history lesson first.

Knowing the data¶

Energy Development Corporation is a Lopez-owned company that was originally under the Philippine National Oil Company. It's the largest geothermal energy generation company in the world, mostly by virtue of steam. They have many plants in the country, one of which is in Ormoc, Leyte, in Eastern Visayas.

If you're a Filipino and had enough awareness of the major events in the last quarter of 2013, you'll no doubt remember Yolanda, the strongest typhoon to ever make landfall in mankind's recorded history. Internationally, its name was Haiyan. It was so powerful that the name has been permanently retired into history books (and permanently etched into the hearts of millions). Yolanda's initial landfall was in Eastern Visayas. In a matter of hours, it destroyed so much life and livelihood in the place that most of the region is still reeling from the damage two years since.

Needless to say, one particular power plant of EDC was hit by the deluge. It caused massive operational damages that their stocks, while not particularly soaring at the time, dropped dangerously low relative to their prior position. From 5.80 in 7th Nov 2013, their stocks dropped to 4.80 by 22nd Nov 2013, a 22% drawdown. That's a historic low, given that the last time the prices were at a similar level was three years prior way back in August 2010.

Pragmatically, when a stock falls like this, it's a good time to buy the stock value-wise. Buffetian wisdom declares that buying a valuable stock at a low price is already a win, and the same wisdom says that anything that people uses on a daily basis out of necessity is valuable no matter what. Assuming that we opportunistically took the chance to buy shares during it's lowest day/s, what could be our returns now?

Applying the data¶

For the next few steps, we'll be resorting to this really nifty library I found: bt. Written by Philippe Morissette, the library was made especially for quants. It has its limitations and it's not the library I base my code on at work, but it's a nifty tool when you want to quickly execute a backtest and get or crunch some numbers.

FYI, bt is an extension of another library, ffn, which is an awesome library for financial functions. It goes without saying that the names of the libraries are acronyms of what they do.

Both libraries are very easy to use. Additionally, ffn has some very thin wrappers around very useful pandas functions. In the following example, bt.get functions very similarly to our getStockFeed function, but with the additional perks of the original pandas.io.web library.

In [13]:

import bt
data = bt.get("AAPL", start="2010-01-01")
data.head()

Out[13]:

	aapl
Date
2010-01-04	28.84
2010-01-05	28.89
2010-01-06	28.43
2010-01-07	28.38
2010-01-08	28.56

The optional parameter start is a nifty idea. Let's rewrite our getStockFeed function to mirror this.

In [14]:

from datetime import datetime

def getStockFeed(ticker, start=None, end=None):

    url = 'http://www.bloomberg.com/apps/data?pid=webpxta&Securities={0}'.format(ticker)
    url += '&TimePeriod=5Y&Outfields=HDATE,PR005-H,PR006-H,PR007-H,PR008-H,PR013-H'

    df = pd.read_csv(url, delimiter='"')
    df.drop(df.columns[[5, 6, 7, 8]],axis=1, inplace=True)
    df.columns = ["Date","Close","Open","High","Low"]
    df.dropna(inplace=True)
    df = df[["Date","Open","High","Low","Close"]]
    
    # Use a lambda function and apply to parse the Date column to datetime format.
    # Don't forget the import.
    parse_to_date = lambda x:datetime.strptime(str(x), "%Y%m%d")
    df.loc[:, "Date"] = df.loc[:, "Date"].apply(parse_to_date)
    
    # Do some checks on the stat and end parameters.
    # Let's pass in raw datetime instead of strings to minimize conversions.
    if start is not None:
        if start >= df["Date"].min():
            df = df[df["Date"] >= start]
            df.reset_index(drop=True, inplace=True)
            
    if end is not None:
        if end <= df["Date"].max():
            df = df[df["Date"] <= end]
            df.reset_index(drop=True, inplace=True)

    return df

Function is not perfect but it looks nice. Compared to bt.get, our function takes in a raw datetime.datetime type. I personally prefer this to avoid lots of conversion during my tests. Your mileage may vary, of course.

Let's test the new function if it holds up.

In [15]:

start = datetime(2012,1,1)
end = datetime(2013,1,1)
df = getStockFeed("EDC:PM", start=start)
df.head()

Out[15]:

	Date	Open	High	Low	Close
0	2012-01-02	6.29	6.29	6.25	6.29
1	2012-01-03	6.31	6.36	6.30	6.31
2	2012-01-04	6.34	6.40	6.34	6.35
3	2012-01-05	6.38	6.38	6.30	6.31
4	2012-01-06	6.32	6.36	6.31	6.33

In [16]:

df = getStockFeed("EDC:PM", end=end)
df.tail()

Out[16]:

	Date	Open	High	Low	Close
690	2012-12-20	6.85	6.86	6.75	6.80
691	2012-12-21	6.80	6.89	6.75	6.75
692	2012-12-26	6.80	6.81	6.63	6.69
693	2012-12-27	6.70	6.75	6.67	6.67
694	2012-12-28	6.75	6.76	6.69	6.75

In [17]:

df = getStockFeed("EDC:PM", start=start, end=end)
df.head()

Out[17]:

	Date	Open	High	Low	Close
0	2012-01-02	6.29	6.29	6.25	6.29
1	2012-01-03	6.31	6.36	6.30	6.31
2	2012-01-04	6.34	6.40	6.34	6.35
3	2012-01-05	6.38	6.38	6.30	6.31
4	2012-01-06	6.32	6.36	6.31	6.33

In [18]:

df.tail()

Out[18]:

	Date	Open	High	Low	Close
239	2012-12-20	6.85	6.86	6.75	6.80
240	2012-12-21	6.80	6.89	6.75	6.75
241	2012-12-26	6.80	6.81	6.63	6.69
242	2012-12-27	6.70	6.75	6.67	6.67
243	2012-12-28	6.75	6.76	6.69	6.75

We've finished exhaustive checks to make sure our function works the way we want it to and returns what we need it to.

Now, let's go to backtesting. Recall the first example in this part. bt.get returns a DataFrame with the dates set as the index. To make this possible on our end, we need to isolate our Date and Close columns, then set Date as our index.

For the data, we'll be using our target date of 22nd November 2013 as an argument to our function, with no explicit end date provided so it defaults to yesterday.

In [19]:

start = datetime(2013,11,22)
edc_prices = getStockFeed("EDC:PM", start=start)
c = edc_prices[["Date","Close"]]
c.set_index("Date", inplace=True)
c.columns = ["EDC"]
c.head()

Out[19]:

	EDC
Date
2013-11-22	4.50
2013-11-25	4.59
2013-11-26	4.85
2013-11-27	4.97
2013-11-28	5.27

Really nice output. Now we'll create a strategy for our backtesting. I won't be delving much into the API but it's here, in any case.

What we want is a strategy that goes long, runs monthly, weighs equally (which doesn't really apply here since we're only testing a single instrument), and rebalances after each run. If this is not obvious, this is the very same strategy in the examples provided for the API.

In [20]:

s = bt.Strategy('EDC Monthly', [bt.algos.RunMonthly(),
                       bt.algos.SelectAll(),
                       bt.algos.WeighEqually(),
                       bt.algos.Rebalance()])

Now we'll create a backtest, run it, and print out our equity curve and statistics.

In [21]:

test = bt.Backtest(s, c)
res = bt.run(test)
res.plot(title="Equity")

In [22]:

res.display()

Stat                 EDC Monthly
-------------------  -------------
Start                2013-11-22
End                  2015-03-03

Total Return         73.66%
Daily Sharpe         2.07
CAGR                 54.13%
Max Drawdown         -11.50%

MTD                  1.24%
3m                   10.48%
6m                   24.27%
YTD                  9.27%
1Y                   58.87%
3Y (ann.)            54.13%
5Y (ann.)            -
10Y (ann.)           -
Since Incep. (ann.)  54.13%

Daily Sharpe         2.07
Daily Mean (ann.)    48.53%
Daily Vol (ann.)     23.44%
Daily Skew           -0.00
Daily Kurt           2.03
Best Day             6.17%
Worst Day            -4.89%

Monthly Sharpe       2.07
Monthly Mean (ann.)  44.12%
Monthly Vol (ann.)   21.31%
Monthly Skew         0.88
Monthly Kurt         0.78
Best Month           18.40%
Worst Month          -4.82%

Yearly Sharpe        1.00
Yearly Mean          31.56%
Yearly Vol           31.53%
Yearly Skew          -
Yearly Kurt          -
Best Year            53.85%
Worst Year           9.27%

Avg. Drawdown        -4.00%
Avg. Drawdown Days   19.05
Avg. Up Month        6.56%
Avg. Down Month      -2.67%
Win Year %           100.00%
Win 12m %            100.00%

54.13% CAGR! Following the rule of 72, our equity would have doubled in 1.33 years if we invested during the Yolanda drawdown. This is not surprising as the latest close of EDC has almost broken 9.xx as of this writing, double of the close at the designated start date. Also, notice the 2.07 Sharpe. Whew, basically risk-free investment!

This is if we followed a monthly strategy however. What if we ran our strategy weekly and annually? Let's run some comparisons.

In [23]:

s1 = bt.Strategy('EDC Weekly', [bt.algos.RunWeekly(),
                       bt.algos.SelectAll(),
                       bt.algos.WeighEqually(),
                       bt.algos.Rebalance()])
test1 = bt.Backtest(s1, c)

s2 = bt.Strategy('EDC Monthly', [bt.algos.RunMonthly(),
                       bt.algos.SelectAll(),
                       bt.algos.WeighEqually(),
                       bt.algos.Rebalance()])
test2 = bt.Backtest(s2, c)

s3 = bt.Strategy('EDC Yearly', [bt.algos.RunYearly(),
                       bt.algos.SelectAll(),
                       bt.algos.WeighEqually(),
                       bt.algos.Rebalance()])
test3 = bt.Backtest(s3, c)

res = bt.run(test1, test2, test3)
res.plot(title='Equity')

Oddly enough, the weekly and the monthly plots overlap. This could be a possible issue with the wrapper, because when you display the stats, you can see that there's quite a large disparity between the values.

In [24]:

res.display()

Stat                 EDC Weekly    EDC Monthly    EDC Yearly
-------------------  ------------  -------------  ------------
Start                2013-11-22    2013-11-22     2013-11-22
End                  2015-03-03    2015-03-03     2015-03-03

Total Return         94.83%        73.66%         63.01%
Daily Sharpe         2.36          2.07           1.96
CAGR                 68.67%        54.13%         46.67%
Max Drawdown         -11.50%       -11.50%        -11.51%

MTD                  1.24%         1.24%          1.24%
3m                   10.48%        10.48%         10.49%
6m                   24.27%        24.27%         24.30%
YTD                  9.27%         9.27%          9.27%
1Y                   58.87%        58.87%         58.96%
3Y (ann.)            68.67%        54.13%         46.67%
5Y (ann.)            -             -              -
10Y (ann.)           -             -              -
Since Incep. (ann.)  68.67%        54.13%         46.67%

Daily Sharpe         2.36          2.07           1.96
Daily Mean (ann.)    58.37%        48.53%         42.93%
Daily Vol (ann.)     24.70%        23.44%         21.89%
Daily Skew           0.25          -0.00          -0.04
Daily Kurt           2.26          2.03           2.87
Best Day             6.17%         6.17%          6.18%
Worst Day            -4.89%        -4.89%         -4.90%

Monthly Sharpe       2.06          2.07           1.77
Monthly Mean (ann.)  43.96%        44.12%         39.41%
Monthly Vol (ann.)   21.31%        21.31%         22.29%
Monthly Skew         0.88          0.88           0.85
Monthly Kurt         0.79          0.78           0.54
Best Month           18.40%        18.40%         18.43%
Worst Month          -4.82%        -4.82%         -4.83%

Yearly Sharpe        1.00          1.00           1.04
Yearly Mean          31.56%        31.56%         29.23%
Yearly Vol           31.52%        31.53%         28.22%
Yearly Skew          -             -              -
Yearly Kurt          -             -              -
Best Year            53.85%        53.85%         49.18%
Worst Year           9.27%         9.27%          9.27%

Avg. Drawdown        -4.08%        -4.00%         -3.84%
Avg. Drawdown Days   19.45         19.05          18.95
Avg. Up Month        6.54%         6.56%          6.90%
Avg. Down Month      -2.67%        -2.67%         -2.74%
Win Year %           100.00%       100.00%        100.00%
Win 12m %            100.00%       100.00%        100.00%

Overall, running the strategy on a weekly basis gives the best results both in return and risk-reward ratio. Drawdown at -11.50% is manageable, especially considering that the overall performance of our chosen stock is already proven to be solid due to its industry and size.

Conclusion¶

Admittedly, what we've done so far is the most basic of steps. When condensed for brevity, the entire code will take no more than about 70 lines (64 on my end in Sublime Text). One of the improvements we can make is make a class out of the getStockFeed function. Additionally, it can be refactored to be able to take in multiple tickers. This way, even a (very skewed) comparison between a PH and a US or EUR stock can be made.

In my next post, I will be presenting a new module which includes our function as a class method, with additional capabilities like being able to call functions from bt or ffn. Better yet, we'll do some awesome graphing of our own using matplotlib. Of course, most would be thin wrappers over pre-existing libraries, but we need to start there first before we delve into more complicated stuff.

Eventually, we'll delve into storing this data into an SQL database, possibly by using built-in sqlite3 library as a springboard.