hmm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1475,
   "metadata": {},
   "outputs": [],
   "source": [
    "from hmmlearn import hmm\n",
    "import numpy as np\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "POLICY_DAYS = 100\n",
    "COMPONENTS = 8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1574,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "877\n"
     ]
    }
   ],
   "source": [
    "\n",
    "listX = []\n",
    "list_price = []\n",
    "f=open('./txt-files/ERIC-B.txt', 'r')\n",
    "f1 = f.readlines()\n",
    "\n",
    "last_price = None\n",
    "last_volume = None\n",
    "for line in f1:\n",
    "    info = line.split()\n",
    "    price_str = info[1]\n",
    "    price_str = price_str.replace(',', '.')\n",
    "    price = int(price_str) / 100\n",
    "    volume = int(info[2])\n",
    "    if last_price == None:\n",
    "        rate = 0\n",
    "    else:\n",
    "        rate = price / last_price - 1\n",
    "        \n",
    "    if last_volume == None:\n",
    "        volume_rate = 0\n",
    "    else:\n",
    "        volume_rate = volume / last_volume - 1\n",
    "    listX.append([rate * 100, volume_rate * 100])\n",
    "    list_price.append(price)\n",
    "    last_price = price\n",
    "    last_volume = volume\n",
    "    \n",
    "\n",
    "training_length = int(len(listX) * 7/10)\n",
    "print(training_length)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1575,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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[0.2126240174437762, -0.46826043455584293], [-0.5815281056172698, -0.011781131046878595], [-0.16990865322307255, 0.09141587768071462], [-0.011773188795660718, 0.5145992563906606], [-0.06842236370994623, -0.9031376086535123], [-0.23861694111272191, 0.917386574484554], [-1.2876624812848858, 0.06409834173454272], [-0.07016482822796735, -0.14088466120477763], [0.9118515628559124, -0.2796524225308728], [0.5281379867997907, -0.4733089407487066], [0.8409361949142066, 0.1252679003271922], [-0.34735559141695804, -0.34405909421556735], [0.23591858456768053, -0.15408020086732316], [0.3804143455043323, 0.11131341590515952], [0.11056335096685942, -0.5697936895350042], [-1.265554285619918, 0.8242493557821681], [-0.4551820683247938, -0.3719323959736221], [1.1120135607232113, -0.11056963644328832], [0.4147655145792745, -0.7867975156939814], [1.4133027675413818, 0.96718221482654], [-1.0954246972602844, -0.5368601913117931], [-0.5208588336232971, -0.5485910600331158], [0.35705018666801774, 0.18311152031269118], [1.7971641703311176, 0.7793647199180086], [-0.2048195606128639, -0.6505707125170148], [0.09587431204519269, -0.5463126934220984], [0.8261432192364678, 0.7147543101175372], [-0.46637821185887024, -0.07197310739386086], [4.126411288064103, 1.890182832429921], [0.48219900879504957, -0.9457856364664813], [0.33075447521573004, -0.28740822340365946], [0.5715756606208156, -0.4310737745827307], [-2.319289894435669, 2.142755393747993], [0.34036115051199123, -0.891295048109453], [-0.3115730297541723, -0.7087162393696712], [0.943096546199252, 0.030185933576549126], [0.4817303444709618, -0.6579988759700799], [0.23265948228658515, -0.34711634247021417], [0.4261477201442004, -0.8334024585974733], [0.8085047619530753, 4.169928110301725], [-0.20178331630726679, -0.19416779413725402], [-1.7281341481236774, 0.9714661031740451], [-2.0332197941590477, -0.4173920819707414], [-0.42215009140948984, -0.8694618895302879], [0.35016210627800615, -0.1973737504615015], [-0.21716419533584283, -0.13901685272553496], [-0.6613197024917985, 0.5416245836666868], [1.3768320360213022, -0.2534600080778895], [0.3441475412572353, -0.0589970077637631], [1.3517339519520615, -0.7430659319275695], [1.9074279690527434, 0.9412386059341845], [-0.9609569918303676, -0.6490365176929846], [0.08490947366958176, -0.46396624412687343], [-0.3012830537658001, -0.46428984256233335], [-0.30290344480108666, -0.14108906463004464], [0.4761749576049217, 0.3364546285741418], [-0.5918691635871272, 0.01600331359703546], [-0.5495250651766063, 1.086651482391091], [-0.703334265422654, -0.644694359664281], [0.6891063238975281, -0.1246140657205135], [0.580993448313194, -0.34951674188685516], [-0.8420639220417963, -0.3206871847012165], [0.43492285286151455, -0.3321198309854005], [0.03743709730301512, 0.1405580381615285], [-0.25759205650030315, 0.042849589651182504], [1.1243628656629765, 0.08695726013553073], [2.0185627229744894, 0.40580015272826164], [-0.4305007841952224, -0.49750751843385954], [-0.34009321300135625, -0.14157902503935169], [1.3570497692649068, -0.3669991923414695], [-0.7476214279652672, -0.3798657182360395], [-0.9447825593122884, -0.14250901025224127], [1.22329264002977, -0.47417554470283946], [-0.6149833059070977, -0.2154475622199969], [-0.5281702975224799, -0.20866219479833698], [-0.675596173014648, -0.16682825897078993], [-0.29994644312420005, -0.18451165994983043], [0.42289482574167403, 0.12153479274265681], [0.27560400681927166, -0.462213463479947], [0.1787589445607556, 0.2994743507525844], [0.6146881212072471, -0.6311882827920714], [0.02574233403614894, 0.4329646463296483], [0.47557711122735413, -0.4047256946449294], [-0.6803022165096496, 0.11571767801574023], [0.5337757803843665, -0.2844894858266865], [0.5467300940207493, 0.3977481277001618], [-0.12228722365324304, -0.9861029397307763], [-0.34402120274648607, 1.053385167529439], [1.6787231644091478, 0.8690133813272926], [0.5999595202372889, -0.2614175919096706], [0.041575808544138164, -0.6700253713175411], [-1.1487190337421505, -0.04513992263048626], [-0.19339076004671626, -0.14170253943728342], [-0.5585349870398991, -0.4562035829409127], [-1.7431597348977705, 0.3940724466505728], [-0.5452631621936322, -0.598757918930939], [0.8930076413657636, 0.22957021638799494], [0.6694017135786136, 0.09204353544412418], [-4.774392385782004, 4.184852900534126], [1.571300319222224, -1.0302756570158766], [1.6442558137831846, -0.46676224018793205], [-2.138791763031047, 0.046305122227927924], [-1.4835231444778427, 0.37828434364649044], [2.353500466696325, -0.698986377661764], [-0.8651332473500227, -0.3925750311923858], [0.21016940099072626, 0.062362169160167395], [0.7114982699246365, -0.5623265322644294], [0.30527228110942023, -0.0869165806622379], [0.22457034353134842, -0.3777324150585328], [0.6548379162982906, -0.34966549749133485], [0.31731487629817456, -0.3810703834507001], [-0.5888657080963484, -0.49899725066812406], [1.213717522347582, 0.13377427215392018], [0.06424474676341597, -0.20735536549259365], [-0.22051744142948543, 0.8146464888888888], [1.2647921046923287, -0.29272015121824607], [0.6391150965888844, 0.08452208048897346], [0.024960760643819812, -0.5161797695075767], [-0.8560577797236399, 0.2708081183772045], [-1.4296930965714625, -0.22051868400186062], [-0.45290566126798293, -0.10197998648686736], [1.709818199884467, -0.3329236887750393], [0.1754668126669004, -0.2637591408599384], [-0.2356552794970961, 0.22669858738120188], [1.0187887644992153, -0.5000132543536351], [0.2822022115931823, -0.5822979804086669], [-0.19447523447989398, 0.5655599727191524], [-0.5984805264768938, -0.3298560133413633], [-0.17866711095491633, -0.13711902399010864], [0.30448562054164957, 0.3670925722346456], [-0.4555579515938271, -0.06918916804574071], [-0.9256306100609167, -0.08487600545795468], [-0.42941406213092875, -0.8508676885234198], [0.4283854813091788, -0.23540702073286926], [-1.0554944519647957, 0.35273806688175224], [0.2012454249308165, -0.3615839723476175], [-1.5161080970980467, 0.2563508779102057], [1.2990355087285506, -0.3926361790437343], [0.24006984333913092, 0.02438190030611006], [-1.0142914116005877, -0.6716731458890454], [-0.7784382015869566, 0.9586864462177457], [-0.6302900451805289, -0.33537516267785034], [1.6036079418348286, -0.3648066504556931], [-0.3055341398854628, -0.25725713125098126], [-0.18903083804091755, -0.5421712462006886], [0.48229284849823606, -0.2725694701604725], [-0.26627019341813735, 0.4636533206872992], [0.6571070161265213, -0.46422204212276014], [-0.5556221908032983, 0.19303823106616008], [0.3611665001551861, 0.07478354992786593], [1.3914113796770764, 0.23376306390525256], [-0.6190291964577067, -1.0041838434071748], [1.3130897850082612, 2.9516268513121715], [9.088090882285224, 4.345551985413468], [-0.23487791112661147, -1.0213113435991157], [0.5964053808780212, -0.45267450615084415], [0.8266725038849866, 0.2090037414495639], [-0.836918186560991, -0.5430676082183046], [-0.059232378947398086, -0.4625382731912037], [1.1757901081518671, -0.6214479989592193], [-0.38331084884067834, 0.36461019455996935], [-0.5107206459304533, -0.5349171389322728], [1.609768293604183, 0.10162176507929206], [1.0721963858587038, -0.26378726368766087], [-0.04168421375616338, -0.11873954421772288], [0.6466479809043661, -0.4142151378832508], [-1.578142187394525, 1.0869487870907106], [-0.6059722584634308, -0.4355687056361303], [-0.18130444700879453, -0.5572184211458981], [-0.3828690680320603, -0.34281484462095274], [0.4554298567699313, -0.6369869298758233], [0.18887919734140005, 0.543242674329149], [-0.027148675761752096, -0.7612261208395862], [-0.9345751829938058, 0.8155058485800986], [-0.9199266504348819, -0.0682839485092666], [0.17945943034515421, -0.358001295964778], [-0.21818299993177265, -0.6677350269345192], [-0.378414704377937, -0.5608158872052438], [0.020335004047737935, 1.7736147443244863], [-0.5251873519670576, -0.48142729338393403], [-0.20622280526781947, 1.9884470162403904], [0.16714113155122565, -0.9396770400505322], [-0.0928191102763476, -0.26852740599717306], [0.05316472931457895, -0.005779666232793644], [-0.5468430109303082, 0.27680896731193266], [-0.07730424064611915, -0.4998097955730674], [-0.1922109044908074, -0.24133810358140972], [0.35035845453935405, 0.3305982231162071], [2.4402352527333404, 0.3317043848771698], [1.1902604172323303, -0.04910881101146152], [-0.10332250954597498, -0.20981822499097422], [-0.05762855677065782, -0.531350280688076], [1.487490545268333, 0.692479225990405], [0.3897169069648265, -0.5313018195680694], [-1.4136032252775566, -0.46241105998724563], [-0.8003429162381331, -0.09483289853333593], [-0.31973809351029914, -0.4284011869175914], [0.48390540471287985, -0.18192505363590955], [-0.7789600974431798, -0.08796613438961709], [1.903759298023247, 0.6002844580141219], [-1.123274822658308, -0.7603782140006234], [-0.4568728936592622, -0.12381927755384858], [-0.6619640938744372, -0.438491969338895], [0.1763471454043387, -0.371191759305551], [0.7379965035474328, 0.1252676562029108], [0.15760750879738897, -0.3560480842164494], [0.5407643758626055, 0.02770899854269665], [-0.7863071720719762, -0.2948412353502961], [0.6511363291269745, -0.281077392441945], [0.4753467166774614, 0.4480030468934357], [-0.9468114286306089, 0.0215701455129695], [-0.1346318186304454, -0.04188600095199798], [4.421609010972002, 2.880901432997016], [0.030780892802076295, -0.8864387691428885], [-0.2243696700203337, -0.12760719326799266], [-0.49563618792404274, -0.9523208542382318], [0.11751083541220765, 0.09769127437773617], [0.13151957265421385, -0.13169114954457384], [-0.6833989588770283, 0.3167885854808231], [-0.8948601959719482, -0.4070903304004581], [-0.5124864565799994, -0.4196375823562159], [-0.8147533803166122, 0.20388884796484572], [0.8491067552110729, -0.699989022364222], [-0.353490202604553, -0.020459986371106433], [0.8257355100982176, -0.3708347373014646], [0.20900470766572152, -0.16106607914916218], [0.10547735480829046, -0.26143197220273334], [0.47079751387736657, 0.6949992273468464], [-0.026262122613569566, -0.7320286227525845], [-0.35960448936719797, -0.07762827492650391], [-0.08472588501767861, 0.40989585008858037], [0.06128196907835457, -0.5788181198669734], [-0.36194613066136855, 0.4481995121345253], [0.6639406012890327, -0.15768558943046332], [0.11873627639997479, 0.10231252191227407], [0.6102114367524424, -0.20842036300363787], [0.8315631871240342, -0.020551269637569942], [0.31173986294715905, -0.3385927044065303], [0.8688938796260094, 0.48450713669557066], [1.72023641035709, 0.4413637792073151], [0.2276833082154482, -0.9429344235615715], [0.08092205225427128, 0.0346087102802646], [0.027882673267590134, -0.5637854348902741], [-0.7910776763456445, 0.7601017754240763], [-0.30678117872570065, -0.2306664914705032], [0.4737206682504521, -0.5985479313676255], [-0.15874673014405846, 0.0896125716220656], [-0.8827141229557854, 0.5492476487440975], [1.7188895294648303, -0.15857210115262638], [-0.24916540566012382, -0.67632842481634], [-0.5947258087338345, -0.3654022201077957], [-0.011773188795660718, 0.4356345017980801], [-0.7755213925976181, -0.530974331691315], [0.7769551248162314, 0.18228037784150816], [0.36330474769067794, -0.7214298898137795], [-0.8230571613818055, 0.17260730705835303], [0.4881138447194487, 0.31845597148414306], [0.06851794432444269, -0.27281073744426215], [-0.5462224300625668, 0.06333140763896765], [0.3662251572662242, 0.30302799939750313], [0.7789768941653729, -0.7826143980852003], [0.8331449416620345, 0.24983805501992012], [-0.3885071797503946, -0.5244475696588139], [0.1583396517784128, -0.17039019995074028], [-0.5856628542613821, 0.15606021082813815], [0.4761997014257331, -0.25050049248757716], [-0.1032343560511548, -0.09437909827901211], [0.5248715441329636, 0.15782554407680305], [0.19551058667155008, -0.2903899806211714], [-1.1860202215352995, -0.37019494395001323], [-0.5926544034853254, -0.6256720909936809], [1.1230569330050775, 1.1021639659554787], [-1.4359542217740398, -0.19474865202722796], [-1.9327917719829417, 0.6445656916452223], [-0.5139318926376745, -0.5103967668164724], [-0.025858017500314672, -0.20275691632646334], [2.143789291747185, 0.06369546018832455], [0.7322684190968165, -0.329515997020429], [3.202503900927092, 2.5238659350837116], [1.3196971988082997, -0.6603350962450362], [-0.7956152971081923, -0.7328716875717498]]\n"
     ]
    }
   ],
   "source": [
    "# normalization\n",
    "import statistics as stats\n",
    "\n",
    "price_rate_list = [item[0] for item in listX]\n",
    "volume_rate_list = [item[1] for item in listX]\n",
    "\n",
    "mean = stats.mean(price_rate_list)\n",
    "stddev = stats.stdev(price_rate_list)\n",
    "norm_price_rate_list = [(i - mean) / stddev for i in price_rate_list]\n",
    "\n",
    "mean = stats.mean(volume_rate_list)\n",
    "stddev = stats.stdev(volume_rate_list)\n",
    "norm_volume_rate_list = [(i - mean) / stddev for i in volume_rate_list]\n",
    "\n",
    "listX = []\n",
    "listX = [[norm_price_rate_list[i], norm_volume_rate_list[i]] for i in range(len(norm_price_rate_list))]\n",
    "#listX = [[norm_price_rate_list[i]] for i in range(len(norm_price_rate_list))]\n",
    "print(listX)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1576,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remodel = hmm.GaussianHMM(n_components=COMPONENTS,  n_iter=1000, random_state = 140,  covariance_type=\"full\")\n",
    "remodel = hmm.GaussianHMM(n_components=COMPONENTS,  n_iter=1000, random_state = 140)\n",
    "remodel.fit(listX[:training_length])\n",
    "Z1=remodel.predict(listX)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1577,
   "metadata": {},
   "outputs": [],
   "source": [
    "import plotly.plotly as py\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "\n",
    "def my_func(idx, cluster_no):\n",
    "    if(Z1[idx] == cluster_no):\n",
    "        return list_price[idx]\n",
    "    else:\n",
    "        return None\n",
    "\n",
    "\n",
    "data0_y = [my_func(n, 0) for n in range(len(list_price))]\n",
    "data1_y = [my_func(n, 1) for n in range(len(list_price))]\n",
    "data2_y = [my_func(n, 2) for n in range(len(list_price))]\n",
    "data3_y = [my_func(n, 3) for n in range(len(list_price))]\n",
    "data4_y = [my_func(n, 4) for n in range(len(list_price))]\n",
    "data5_y = [my_func(n, 5) for n in range(len(list_price))]\n",
    "\n",
    "trace0 = go.Scatter(\n",
    "    x = [n for n in range(len(list_price))],\n",
    "    y = data0_y,\n",
    "    connectgaps=False,\n",
    "    name='0'\n",
    ")\n",
    "\n",
    "trace1 = go.Scatter(\n",
    "    x = [n for n in range(len(list_price))],\n",
    "    y = data1_y,\n",
    "    connectgaps=False,\n",
    "    name='1'\n",
    ")\n",
    "\n",
    "trace2 = go.Scatter(\n",
    "    x = [n for n in range(len(list_price))],\n",
    "    y = data2_y,\n",
    "    connectgaps=False,\n",
    "    name='2'\n",
    ")\n",
    "\n",
    "trace3 = go.Scatter(\n",
    "    x = [n for n in range(len(list_price))],\n",
    "    y = data3_y,\n",
    "    connectgaps=False,\n",
    "    name='3'\n",
    ")\n",
    "\n",
    "trace4 = go.Scatter(\n",
    "    x = [n for n in range(len(list_price))],\n",
    "    y = data4_y,\n",
    "    connectgaps=False,\n",
    "    name='4'\n",
    ")\n",
    "\n",
    "trace5 = go.Scatter(\n",
    "    x = [n for n in range(len(list_price))],\n",
    "    y = data5_y,\n",
    "    connectgaps=False,\n",
    "    name='5'\n",
    ")\n",
    "\n",
    "data = [trace0, trace1, trace2, trace3, trace4, trace5]\n",
    "layout = go.Layout(\n",
    "    title='test',\n",
    "    xaxis=dict(\n",
    "        title='Time(s)',\n",
    "    ),\n",
    "    yaxis=dict(\n",
    "        title='price',\n",
    "    )\n",
    ")\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "# py.iplot(data, filename='basic-line')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1578,
   "metadata": {},
   "outputs": [],
   "source": [
    "from itertools import combinations \n",
    "\n",
    "def findCombinations(state_list, num_buy_states, num_sell_states):\n",
    "    length = len(state_list)\n",
    "    comb_buy = combinations(state_list, num_buy_states)\n",
    "    comb_list = []\n",
    "    for buy_list in list(comb_buy):\n",
    "        tmp_list = state_list.copy()\n",
    "        for state in buy_list:\n",
    "            tmp_list.remove(state)\n",
    "        comb_sell = combinations(tmp_list, num_sell_states)\n",
    "        for sell_list in comb_sell:\n",
    "            #print(\"buy_list: {}, sell_list: {}\".format(buy_list, sell_list))\n",
    "            comb_list.append([list(buy_list), list(sell_list)])\n",
    "    return comb_list\n",
    "\n",
    "def findAllComb(num_components):\n",
    "    state_list = list(range(num_components))\n",
    "    length = len(state_list)\n",
    "    output = []\n",
    "    for num_buy in range(length):\n",
    "        for num_sell in range(length):\n",
    "            comb_list = findCombinations(state_list, num_buy, num_sell)\n",
    "            output.extend(comb_list)\n",
    "    return output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1579,
   "metadata": {},
   "outputs": [],
   "source": [
    "def trade(money, stocks, price, op):\n",
    "    if op == \"buy\":\n",
    "        stocks_inc = int(money / (price))\n",
    "        stocks += stocks_inc\n",
    "        money = money - stocks_inc * price\n",
    "    elif op == \"sell\":\n",
    "        money = money + stocks * price\n",
    "        stocks = 0\n",
    "    return money, stocks\n",
    "    \n",
    "def tradeForToday(money, stocks, state, buy_list, sell_list, price, detail = True):\n",
    "    op = None\n",
    "    if state in buy_list and money > price:\n",
    "        # we buy in\n",
    "        money, stocks = trade(money, stocks, price, \"buy\")\n",
    "        if detail == True:\n",
    "            print(\"buy in at day {}: state: {} price: {} current stocks:{}, current money:{}\".format(i, state, price, stocks, money))\n",
    "            op = \"buy\"\n",
    "    elif state in sell_list and stocks > 0:\n",
    "        # we sell out\n",
    "        money, stocks = trade(money, stocks, price, \"sell\")\n",
    "        if detail == True:\n",
    "            print(\"sell out at day {}: state: {} price: {} current stocks:{}, current money:{}\".format(i, state, price, stocks, money))\n",
    "            op = \"sell\"\n",
    "    return (money, stocks, op)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1580,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_verify_price_change_list(price_seq, today, num_days, num_segs):\n",
    "    price_change_list = []\n",
    "    start = today - num_days * num_segs\n",
    "    end = start + num_days\n",
    "    for i in range(num_segs):\n",
    "        price_change_list.append(price_seq[end] / price_seq[start] - 1)\n",
    "        start += num_days\n",
    "        end += num_days\n",
    "    return price_change_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1581,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_asset_multi_segs(buy_list, sell_list, price_seq, state_seq, today, num_days, num_segs):\n",
    "    asset_list = []\n",
    "    start = today - num_days * num_segs\n",
    "    end = start + num_days\n",
    "    for i in range(num_segs):\n",
    "        asset = check_profit(buy_list, sell_list, price_seq[start:end], \n",
    "                                   state_list[start:end])\n",
    "        asset_list.append(asset) \n",
    "        start += num_days\n",
    "        end += num_days\n",
    "        \n",
    "    return asset_list\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1582,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_asset_whole_timeline(buy_list, sell_list, price_seq, state_seq, today):\n",
    "    total_asset = check_profit(buy_list, sell_list, price_seq[:today], \n",
    "                                   state_list[:today])\n",
    "    return total_asset\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1583,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_price_change_rate_multi_segs(price_seq, today, num_days, num_segs):\n",
    "    price_change_rate_list = []\n",
    "    start = today - num_days * num_segs\n",
    "    end = start + num_days\n",
    "    for i in range(num_segs):\n",
    "        price_change_rate = price_seq[end] / price_seq[start] - 1\n",
    "        price_change_rate_list.append(price_change_rate) \n",
    "        start += num_days\n",
    "        end += num_days\n",
    "    return price_change_rate_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1584,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_price_change_rate_whole_timeline(price_seq, today):\n",
    "    return price_seq[today] / price_seq[0] - 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1585,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_weighted_avg(input_list):\n",
    "    n = len(input_list)\n",
    "    s = 0\n",
    "    m = 0\n",
    "    for i in range(n):\n",
    "        s += input_list[i] * (i + 1)\n",
    "        m += (i + 1)\n",
    "    return s/m\n",
    "\n",
    "#get_weighted_avg([2,4,6])\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1586,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_all_elem_bigger_than(list_input, num):\n",
    "    for item in list_input:\n",
    "        if item <= num:\n",
    "            return False\n",
    "    return True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1587,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_ema(list_input, length):\n",
    "    multiplier = (2 / (length + 1))\n",
    "    output = []\n",
    "    input_len = len(list_input)\n",
    "    for i in range(input_len):\n",
    "        if i == 0:\n",
    "            output.append(list_input[i])\n",
    "        else:\n",
    "            ema = list_input[i] * multiplier + output[-1] * (1 - multiplier)\n",
    "            output.append(ema)\n",
    "    return output\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1588,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.292759999999999, 0.2583674999999994, 0.1631287499999996]\n",
      "[0.292759999999999, 0.2755637499999992, 0.2193462499999994]\n"
     ]
    }
   ],
   "source": [
    "def get_dema(list_input, length):\n",
    "    list_ema = get_ema(list_input, length)\n",
    "    print(list_ema)\n",
    "    list_dema = get_ema(list_ema, length)\n",
    "    return list_dema\n",
    "l = get_dema([0.292759999999999, 0.2239749999999998, 0.06788999999999978], 3)\n",
    "print(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1589,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_diff_list(input_list):\n",
    "    length = len(input_list)\n",
    "    output = []\n",
    "    for i in range(length):\n",
    "        if i == 0:\n",
    "            continue\n",
    "        output.append(input_list[i] - input_list[i - 1])\n",
    "    return output\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1590,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_diff_wavg(input_list):\n",
    "    list_diff = get_diff_list(input_list)\n",
    "    list_diff_wavg = get_weighted_avg(list_diff)\n",
    "    return list_diff_wavg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1591,
   "metadata": {},
   "outputs": [],
   "source": [
    "# find out the best way \n",
    "# TODO: update the model\n",
    "def check_profit(buy_list, sell_list, price_seq, state_seq):\n",
    "    #print(\"start to check: sell_list:{}, buy_list:{}, price change:{}\".format(sell_list, buy_list, \n",
    "    #                                                                          price_seq[-1] / price_seq[0] - 1 ))\n",
    "    money = 10000\n",
    "    stocks = 0\n",
    "\n",
    "    for i in range(len(state_seq)):\n",
    "        money, stocks, op = tradeForToday(money, stocks, state_seq[i], buy_list, sell_list, price_seq[i], False)\n",
    "\n",
    "    total_asset =  stocks * price_seq[-1] + money\n",
    "    #print(\"finished: current stocks:{}, current money:{}, total asset: {}\".format(stocks, money, total_asset))\n",
    "    return total_asset\n",
    "\n",
    "def getProfit(asset, origin_asset):\n",
    "    return asset / origin_asset - 1\n",
    "\n",
    "def findPolicy(list_price, state_list, today, policy_days, policy_segs, min_avg_profit):\n",
    "    buy_list = []\n",
    "    sell_list = []\n",
    "    optimized_buy_list = None\n",
    "    optimized_sell_list = None\n",
    "    max_asset_wavg = 10000\n",
    "    max_profit_wavg = 0\n",
    "    price_rate_change_list = get_price_change_rate_multi_segs(list_price, today, POLICY_DAYS, policy_segs)\n",
    "    price_rate_change_whole_timeline = get_price_change_rate_whole_timeline(list_price, today)\n",
    "    price_rate_change_wavg = get_weighted_avg(price_rate_change_list)\n",
    "    start_day = today - policy_days * policy_segs\n",
    "    \n",
    "    price_rate_change_diff_list = get_diff_list(price_rate_change_list)\n",
    "    price_rate_change_diff_wavg = get_weighted_avg(price_rate_change_diff_list)\n",
    "    comb = findAllComb(COMPONENTS)\n",
    "    for i in comb:\n",
    "\n",
    "        buy_list = i[0]\n",
    "        sell_list = i[1]\n",
    "        # find profits for multiple time segments\n",
    "        # the last element is the profit for entire time.\n",
    "        # the date is today\n",
    "        #check_profit_multi_segs(buy_list, sell_list, price_seq, state_seq, today, num_days, num_segs):\n",
    "        asset_list = get_asset_multi_segs(buy_list, sell_list, list_price, state_list, today, POLICY_DAYS, policy_segs)\n",
    "        asset_whole_timeline = get_asset_whole_timeline(buy_list, sell_list, list_price, state_list, today)\n",
    "        profit_whole_timeline = getProfit(asset_whole_timeline, 10000)\n",
    "        # the profit must beat the market.\n",
    "        profit_list = [getProfit(asset, 10000) for asset in asset_list]\n",
    "        asset_wavg = get_weighted_avg(asset_list)\n",
    "        profit_wavg = get_weighted_avg(profit_list)\n",
    "        \n",
    "        profit_diff_list = get_diff_list(profit_list)\n",
    "        profit_diff_wavg = get_weighted_avg(profit_diff_list)\n",
    "\n",
    "        #print(\"comb:{}, profit_list:{}, w_avg{}, w_price{}\".format(i, profit_list, weighted_avg, weighted_price_rate_avg))\n",
    "        if asset_wavg > max_asset_wavg and profit_wavg > min_avg_profit and profit_diff_wavg > 0.1 and price_rate_change_diff_wavg > 0 and is_all_elem_bigger_than(profit_list,0):\n",
    "            print(\"comb:{}, profit_list:{}, wavg_asset:{}, wavg_price:{}, price_rate_change_list={},asset_whole_timeline:{}, price_rate_change_whole_timeline:{}, profit_diff_wavg:{}, price_rate_change_diff_wavg={}\".format(i, \n",
    "                                                                                                  profit_list, \n",
    "                                                                                                  asset_wavg, \n",
    "                                                                                                  price_rate_change_wavg,\n",
    "                                                                                                  price_rate_change_list,\n",
    "                                                                                                  asset_whole_timeline,\n",
    "                                                                                                  price_rate_change_whole_timeline,\n",
    "                                                                                                  profit_diff_wavg,\n",
    "                                                                                                  price_rate_change_diff_wavg))\n",
    "            optimized_buy_list = buy_list.copy()\n",
    "            optimized_sell_list = sell_list.copy()\n",
    "            max_asset_wavg = asset_wavg\n",
    "            max_profit_wavg = profit_wavg\n",
    "\n",
    "    return max_profit_wavg, optimized_buy_list, optimized_sell_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1593,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "updating HMM model: day: 878\n",
      "optimized buy: None, optimized sell: None, best_avg_profit=0\n",
      "updating HMM model: day: 978\n",
      "price_rate_change_wavg = -0.08975237360825979 abnormal\n",
      "updating HMM model: day: 1078\n",
      "comb:[[0, 5, 7], [1, 6]], profit_list:[0.030084999999999917, 0.009296000000000193, 0.30514800000000086], wavg_asset:11606.868333333337, wavg_price:0.03521879903746217, price_rate_change_list=[0.18285232778597016, -0.18682101513802318, 0.1340341655716164],asset_whole_timeline:8023.460000000004, price_rate_change_whole_timeline:-0.35072100313479626, profit_diff_wavg:0.19030500000000053, price_rate_change_diff_wavg=0.09067900616509528\n",
      "comb:[[0, 5, 7], [1, 2, 6]], profit_list:[0.05410500000000029, 0.009296000000000193, 0.30537600000000076], wavg_asset:11648.041666666672, wavg_price:0.03521879903746217, price_rate_change_list=[0.18285232778597016, -0.18682101513802318, 0.1340341655716164],asset_whole_timeline:8875.139999999998, price_rate_change_whole_timeline:-0.35072100313479626, profit_diff_wavg:0.1824503333333337, price_rate_change_diff_wavg=0.09067900616509528\n",
      "comb:[[0, 4, 5, 7], [1, 2, 6]], profit_list:[0.05410500000000029, 0.014206999999999859, 0.30569600000000063], wavg_asset:11666.01166666667, wavg_price:0.03521879903746217, price_rate_change_list=[0.18285232778597016, -0.18682101513802318, 0.1340341655716164],asset_whole_timeline:8925.79, price_rate_change_whole_timeline:-0.35072100313479626, profit_diff_wavg:0.18102666666666703, price_rate_change_diff_wavg=0.09067900616509528\n",
      "optimized buy: [0, 4, 5, 7], optimized sell: [1, 2, 6], best_avg_profit=0.166601166666667\n",
      "buy in at day 1078: state: 0 price: 51.78 current stocks:193, current money:6.459999999999127\n",
      "sell out at day 1079: state: 6 price: 52.5 current stocks:0, current money:10138.96\n",
      "buy in at day 1080: state: 0 price: 52.82 current stocks:191, current money:50.33999999999833\n",
      "sell out at day 1081: state: 6 price: 53.06 current stocks:0, current money:10184.8\n",
      "buy in at day 1082: state: 0 price: 53.74 current stocks:189, current money:27.93999999999869\n",
      "sell out at day 1083: state: 6 price: 54.08 current stocks:0, current money:10249.059999999998\n",
      "buy in at day 1086: state: 0 price: 54.82 current stocks:186, current money:52.539999999997235\n",
      "sell out at day 1088: state: 6 price: 55.94 current stocks:0, current money:10457.379999999997\n",
      "buy in at day 1089: state: 0 price: 56.64 current stocks:184, current money:35.61999999999716\n",
      "sell out at day 1090: state: 6 price: 56.68 current stocks:0, current money:10464.739999999998\n",
      "buy in at day 1092: state: 0 price: 54.24 current stocks:192, current money:50.659999999998035\n",
      "sell out at day 1097: state: 6 price: 56.62 current stocks:0, current money:10921.699999999997\n",
      "buy in at day 1101: state: 0 price: 55.92 current stocks:195, current money:17.299999999997453\n",
      "sell out at day 1105: state: 6 price: 54.36 current stocks:0, current money:10617.499999999998\n",
      "buy in at day 1106: state: 0 price: 54.82 current stocks:193, current money:37.23999999999796\n",
      "sell out at day 1108: state: 6 price: 53.94 current stocks:0, current money:10447.659999999998\n",
      "buy in at day 1111: state: 0 price: 53.96 current stocks:193, current money:33.37999999999738\n",
      "sell out at day 1112: state: 6 price: 52.92 current stocks:0, current money:10246.939999999997\n",
      "buy in at day 1115: state: 0 price: 53.12 current stocks:192, current money:47.89999999999782\n",
      "sell out at day 1117: state: 6 price: 52.64 current stocks:0, current money:10154.779999999999\n",
      "buy in at day 1118: state: 0 price: 53.14 current stocks:191, current money:5.039999999999054\n",
      "sell out at day 1120: state: 6 price: 53.56 current stocks:0, current money:10235.0\n",
      "buy in at day 1121: state: 0 price: 53.0 current stocks:193, current money:6.0\n",
      "sell out at day 1123: state: 2 price: 54.82 current stocks:0, current money:10586.26\n",
      "buy in at day 1124: state: 5 price: 54.18 current stocks:195, current money:21.159999999999854\n",
      "sell out at day 1125: state: 1 price: 55.56 current stocks:0, current money:10855.36\n",
      "buy in at day 1126: state: 7 price: 65.28 current stocks:166, current money:18.88000000000102\n",
      "sell out at day 1128: state: 6 price: 65.76 current stocks:0, current money:10935.040000000003\n",
      "buy in at day 1129: state: 0 price: 66.82 current stocks:163, current money:43.38000000000466\n",
      "sell out at day 1130: state: 6 price: 65.76 current stocks:0, current money:10762.260000000006\n",
      "buy in at day 1137: state: 0 price: 69.52 current stocks:154, current money:56.18000000000575\n",
      "sell out at day 1138: state: 6 price: 70.4 current stocks:0, current money:10897.780000000006\n",
      "buy in at day 1142: state: 0 price: 66.8 current stocks:163, current money:9.380000000006476\n",
      "sell out at day 1143: state: 6 price: 67.4 current stocks:0, current money:10995.580000000007\n",
      "buy in at day 1147: state: 0 price: 65.28 current stocks:168, current money:28.54000000000633\n",
      "sell out at day 1148: state: 6 price: 65.52 current stocks:0, current money:11035.900000000005\n",
      "buy in at day 1151: state: 7 price: 64.84 current stocks:170, current money:13.100000000004002\n",
      "sell out at day 1153: state: 1 price: 63.96 current stocks:0, current money:10886.300000000005\n",
      "buy in at day 1154: state: 5 price: 64.18 current stocks:169, current money:39.88000000000284\n",
      "sell out at day 1155: state: 6 price: 64.08 current stocks:0, current money:10869.400000000003\n",
      "buy in at day 1156: state: 0 price: 64.16 current stocks:169, current money:26.36000000000422\n",
      "sell out at day 1158: state: 6 price: 63.42 current stocks:0, current money:10744.340000000004\n",
      "buy in at day 1159: state: 0 price: 63.2 current stocks:170, current money:0.3400000000037835\n",
      "sell out at day 1161: state: 2 price: 66.64 current stocks:0, current money:11329.140000000003\n",
      "buy in at day 1162: state: 0 price: 68.18 current stocks:166, current money:11.260000000002037\n",
      "sell out at day 1164: state: 6 price: 68.0 current stocks:0, current money:11299.260000000002\n",
      "buy in at day 1167: state: 0 price: 68.6 current stocks:164, current money:48.8600000000024\n",
      "sell out at day 1169: state: 6 price: 67.16 current stocks:0, current money:11063.100000000002\n",
      "buy in at day 1170: state: 0 price: 67.8 current stocks:163, current money:11.700000000002547\n",
      "sell out at day 1172: state: 2 price: 69.26 current stocks:0, current money:11301.080000000004\n",
      "buy in at day 1173: state: 5 price: 67.78 current stocks:166, current money:49.600000000004\n",
      "sell out at day 1175: state: 6 price: 66.36 current stocks:0, current money:11065.360000000004\n",
      "buy in at day 1177: state: 0 price: 67.56 current stocks:163, current money:53.080000000003565\n",
      "sell out at day 1192: state: 6 price: 70.64 current stocks:0, current money:11567.400000000003\n",
      "updating HMM model: day: 1193\n",
      "comb:[[4, 7], [5]], profit_list:[0.03504499999999999, 0.01150400000000018, 0.36484000000000005], wavg_asset:11920.955000000002, wavg_price:0.16748824363661793, price_rate_change_list=[-0.18614718614718617, 0.1442553191489362, 0.30085533655634045],asset_whole_timeline:12811.460000000003, price_rate_change_whole_timeline:-0.12275862068965526, profit_diff_wavg:0.22771033333333332, price_rate_change_diff_wavg=0.21453418003697697\n",
      "optimized buy: [4, 7], optimized sell: [5], best_avg_profit=0.19209550000000009\n",
      "buy in at day 1212: state: 7 price: 78.2 current stocks:147, current money:72.00000000000364\n",
      "sell out at day 1251: state: 5 price: 82.82 current stocks:0, current money:12246.540000000003\n",
      "finished: current stocks:0, current money:12246.540000000003, cur price:84.94 total asset: 12246.540000000003\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "\n",
    "money = 10000\n",
    "stocks = 0\n",
    "exp_profit = 0\n",
    "verify_profit = 0\n",
    "update_now = False\n",
    "delayed_update = False\n",
    "optimized_buy_list = None\n",
    "optimized_sell_list = None\n",
    "for i in range(training_length + 1, len(list_price)):\n",
    "    state_list = remodel.predict(listX[:i])\n",
    "    price_list = list_price[:i]\n",
    "    if (i - training_length - 1) % 100 == 0 or update_now:\n",
    "        if stocks > 0:\n",
    "\n",
    "            delayed_update = True\n",
    "            continue\n",
    "        update_now = False\n",
    "        # rebuilt the HMM\n",
    "        if i != training_length:\n",
    "            print(\"updating HMM model: day: {}\".format(i))\n",
    "            remodel = hmm.GaussianHMM(n_components=COMPONENTS,  n_iter=1000, random_state = 140)\n",
    "            #remodel = hmm.GaussianHMM(n_components=COMPONENTS,  n_iter=1000, random_state = 140,  covariance_type=\"full\")\n",
    "            remodel.fit(listX[:i])\n",
    "            state_list = remodel.predict(listX[:i])\n",
    "            optimized_buy_list = None\n",
    "            optimized_sell_list = None\n",
    "        # we are out of date, find the policy\n",
    "        \n",
    "\n",
    "        price_rate_change_list = get_price_change_rate_multi_segs(list_price, i - 1, POLICY_DAYS, 3)\n",
    "        #print(\"WARNING: diff_wavg is too low\")    \n",
    "        price_rate_change_diff_list = get_diff_list(price_rate_change_list)\n",
    "        price_rate_change_diff_wavg = get_weighted_avg(price_rate_change_diff_list)\n",
    "        if price_rate_change_diff_wavg < 0 or price_rate_change_diff_wavg > 0.4:\n",
    "            print(\"price_rate_change_wavg = {} abnormal\".format(price_rate_change_diff_wavg))\n",
    "            continue\n",
    "            \n",
    "        \n",
    "        best_avg_profit, optimized_buy_list, optimized_sell_list = findPolicy(price_list, \n",
    "                                                                              state_list, \n",
    "                                                                              i - 1, \n",
    "                                                                              POLICY_DAYS, \n",
    "                                                                              3, \n",
    "                                                                              0.15\n",
    "                                                                              )\n",
    "\n",
    "        print(\"optimized buy: {}, optimized sell: {}, best_avg_profit={}\".format(optimized_buy_list, \n",
    "                                                            optimized_sell_list, \n",
    "                                                            best_avg_profit))\n",
    "            \n",
    "    if optimized_buy_list == None:\n",
    "        if stocks > 0:\n",
    "            money, stocks = trade(money, stocks, list_price[i - 1], \"sell\")\n",
    "            print(\"no idea about the stock, sell it at price: {}!\")\n",
    "            print(\"sell out at day {}: state: {} price: {} current stocks:{}, current money:{}\".format(i,\n",
    "                                                                                                       state_seq[i - days_back], \n",
    "                                                                                                       price_list[i], \n",
    "                                                                                                       stocks, \n",
    "                                                                                                       money))\n",
    "            if delayed_update:\n",
    "                delayed_update = False\n",
    "                update_now = True\n",
    "        continue\n",
    "    \n",
    "    # if we lost 10%, sell it\n",
    "    if stocks > 0 and price_list[i - 1] / trade_price < 0.90:\n",
    "        print(\"day: {} lost 10%, trade price:{}, sell it at price: {}.\".format(i, trade_price, price_list[i - 1]))\n",
    "        money, stocks = trade(money, stocks, price_list[i - 1], \"sell\")\n",
    "        # seems the model is bad\n",
    "        update_now = True\n",
    "        continue\n",
    "    # trace back if we need sell or buy\n",
    "    for days_back in range(1, 2):\n",
    "        money, stocks, op = tradeForToday(money, stocks, state_list[i - days_back], \n",
    "                                      optimized_buy_list, optimized_sell_list, price_list[i - 1])\n",
    "        if op == \"sell\":\n",
    "            if delayed_update:\n",
    "                delayed_update = False\n",
    "                update_now = True\n",
    "            break\n",
    "        elif op == \"buy\":\n",
    "            trade_price = price_list[i - 1]\n",
    "            break\n",
    "            \n",
    "        \n",
    "total_asset =  stocks * price_list[-1] + money\n",
    "print(\"finished: current stocks:{}, current money:{}, cur price:{} total asset: {}\".format(stocks, money, price_list[-1], total_asset))\n"
   ]
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