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Cognitive Bias on Cryptocurrency listing
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{
"cells": [
{
"cell_type": "code",
"execution_count": 131,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"import ccxt\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import time\n",
"import pandas as pd\n",
"import seaborn as sns\n",
"sns.set_style(\"darkgrid\")\n",
"from tqdm import tqdm_notebook\n",
"%pylab inline\n",
"BINANCE_API_KEY = \"YOUR_API_KEY\"\n",
"binance = ccxt.binance({\n",
" 'apiKey': BINANCE_API_KEY,\n",
" 'secret': 'NOT_NECESSARY',\n",
"})\n",
"vowels = [\"A\", \"E\", \"I\", \"O\", \"U\", \"Y\"]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Initialize"
]
},
{
"cell_type": "code",
"execution_count": 132,
"metadata": {},
"outputs": [],
"source": [
"markets = binance.load_markets()"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [],
"source": [
"# select only 3-letter tickers and pairs against BTC\n",
"tickers = np.unique([v[\"base\"] for k,v in markets.items() if (len(v[\"base\"])==3 and v[\"quote\"]==\"BTC\")])"
]
},
{
"cell_type": "code",
"execution_count": 134,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([59., 0., 0., 0., 0., 45., 0., 0., 0., 10.]),\n",
" array([0. , 0.2, 0.4, 0.6, 0.8, 1. , 1.2, 1.4, 1.6, 1.8, 2. ]),\n",
" <a list of 10 Patch objects>)"
]
},
"execution_count": 134,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# simple vowel distribution\n",
"vowel_counts = [np.sum([1 for x in tick if x in vowels]) for tick in tickers]\n",
"plt.hist(vowel_counts)"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [],
"source": [
"def has_vowel_middle(tick):\n",
" return tick[0] not in vowels and tick[1] in vowels and tick[2] not in vowels"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"WITH VOWEL:\n",
" ADA, ADX, AGI, AMB, ARK, ARN, AST, BAT, EDO, ELF, ENG, ENJ, EOS, ETC, ETH, EVX, FET, FUN, GAS, GTO, HOT, ICN, ICX, INS, KEY, LUN, MCO, MDA, MOD, NAS, NAV, NEO, OAX, OMG, ONG, ONT, OST, PAX, POA, POE, REN, REP, REQ, SKY, SUB, SYS, VEN, VET, VIA, VIB, WAN, XEM, ZEC, ZEN, ZIL\n",
"\n",
"WITHOUT VOWEL:\n",
" BCC, BCD, BCH, BCN, BLZ, BNB, BNT, BQX, BRD, BSV, BTG, BTS, BTT, CDT, CMT, CND, CVC, DCR, DGD, DLT, DNT, GNT, GRS, GVT, GXS, HSR, KMD, KNC, LRC, LSK, LTC, MFT, MTH, MTL, NXS, PHB, PHX, PPT, QKC, QLC, QSP, RCN, RDN, RLC, RPX, RVN, SNM, SNT, TNB, TNT, TRX, WPR, WTC, XLM, XMR, XRP, XVG, XZC, ZRX\n"
]
}
],
"source": [
"tickers_with_vowel = [tick for tick in tickers if np.sum([1 for x in tick if x in vowels])>0]\n",
"tickers_without_vowel = [tick for tick in tickers if np.sum([1 for x in tick if x in vowels])==0.0]\n",
"tickers_with_vowel_middle = [tick for tick in tickers if has_vowel_middle(tick)]\n",
"tickers_without_vowel_middle = [tick for tick in tickers if not has_vowel_middle(tick)]\n",
"print(\"WITH VOWEL:\\n\", \", \".join(tickers_with_vowel))\n",
"print(\"\\nWITHOUT VOWEL:\\n\", \", \".join(tickers_without_vowel))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Download data"
]
},
{
"cell_type": "code",
"execution_count": 236,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.jupyter.widget-view+json": {
"model_id": "f5835c08fbd242dcbcfa9348c22bcb66",
"version_major": 2,
"version_minor": 0
},
"text/plain": [
"HBox(children=(IntProgress(value=0, max=114), HTML(value='')))"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\n"
]
}
],
"source": [
"all_tickers = {k:[] for k in tickers_with_vowel+tickers_without_vowel}\n",
"for symbol in tqdm_notebook(all_tickers.keys()):\n",
" time.sleep(binance.rateLimit / 1000) # time.sleep wants seconds\n",
" all_tickers[symbol] = binance.fetch_ohlcv(\"{}/BTC\".format(symbol), '1h', since=0) # one day"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Visualize the distribution"
]
},
{
"cell_type": "code",
"execution_count": 245,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/gerard/miniconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
"The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
" alternative=\"'density'\", removal=\"3.1\")\n",
"/home/gerard/miniconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
"The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
" alternative=\"'density'\", removal=\"3.1\")\n"
]
},
{
"data": {
"text/plain": [
"(-15, 15)"
]
},
"execution_count": 245,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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z/M//fItTTz2JSy+9sNElr2r3xA0dug+33HITI0acyBlnjOTjjz9a49wFC+bzhz/8nilTJjFy5HBefvlFAF5++UXOPPNUjjvuiDV6tiZNupPTTvsmI0YM4/bbb6nz+kOH7sO4cTdw6qknM3r0Wbz66mxGjTqD4447gr///Qkg38t3wQXnAPDJJ4s599zv8q1v5d9DtRcmmDjxdk488WhGjz6r3uWx5sx5jVGjzuDUU09mzJhRLFy4sMH2aS4TN0lSizrzzO/Ro0cPJkyYxHe/O3qNY1On3s+CBe8yfvzdTJw4mYMO+lrNsWXLKrnggjEMHXowhx9+FHPnvsm0aY/w61+PZ8KESZSUlPLww38pnLuMnj135NZbJ9KzZ0+efHI6d901hYkTJzNixLfXialPn37Mnp1P3F577RX22WcwH3zwPgCzZ89c796gceNuqBnKHDlyeM3+N998gxC+tMa5PXpsV7OeanMNHjyEJ56YXrM9bdojHHDAQcyZ8xoPPvhH/u//JnLLLROYOvUBXn99DhUVX65JlubMeY1lyyrJZrPMnPkS/fr1Z/HixUyceDvXXTeO8ePvZpddvsRvf3t3k+NZtmwZu+7al4kT76F//wE1Cfdq22yzLUcccTTHHz+cCRMm1SzftXDhQsaNu42f/eyX3Hzzr4B8AvnOO+9w660TueOOScT4Gi+99EKd1xww4CuMH/8bysvLufXWX3PddeP4yU9+zm23rZvs3XHHrfTr15877pjEoEH78f7779W0x7RpD3PHHZP4yU9+VuewcDab5brrfs6Pf/xTxo//Dd/4xuH83//d1OT22RAOlUqSWo3nn/8nRx99HGVl+V9PnTt3qTl20UXncdJJ36xJ5mbMeJYYX+O0074J5HvjunXrBkBpaSmDB+fXsiwv35yOHTdh7Ngfs9deg9hrr31YW+/efXj99ciyZcvIZrOUl5ez7bY9mDfvHWbNmsmwYSev1/dx1llns//+B9Zsr77HLZfL1btgfSaTaeBY067brVs3tt22B7Nnz2L77bfn7bffol+/Cu69dzL77rs/m222GQD77bc/L7/8EkcddSwxzuGzzz6jQ4eO9Oq1C3PmvMrLL7/EOeeczyuvzGLu3Df4znfyyW42u4pdd+3b1GagQ4cO7L13/nsP4Us899w/m1Ru330HU1JSQs+eO/Dxxx8D+cTtueee4VvfOgnIJ/Lz5r1N//5rrkvaoUMH9thjLwB23HEnOnToQFlZGTvuuBPvvTd/nWu99NKLXHVVvldyr70G0alTZwBmznyRfffdn0033RSAQYP2Xafs22/P5Y03/sO5534XgOrqqpp1ZYvFxE2S1GrkR6nqzlL69q3gmWeeYujQQ8hkMuRyOb72tUM588xR65zbsWPHmvvaysrKuPXWicyY8SyPPvowv/vdFG644eY1zt90003Zbrvt+fOf/0CvXrsAsOuufXj66X+wePHHiS1E3rPnjuv0Er377jzKy8spL9+cLl26rDPUVllZyRZbdGryNYYMGcr06Y/w+c9/kX33HVxIButel7ysrIxtttmGP/95Kn379mPHHXfihReeZ/78eXzxiz1599157Lbb7vzoRz9Z7+91df2rk9GSkpKaoezGdOjQodZWPvZcLsfJJ4/kyCOPafI1M5kMHTp0bPT6DSXTDcnloGfPHbjlljsaPC9JDpVKklpUeXl5vfdJDRy4Ow88cB/ZbBaAJUs+qTl22mln0rlzF669diwAX/nKQB5/fBqLFn1cc+577y1Yp87Vw5B77jmI0aPP41//er3Oa/ftW8GUKffQp0++R6lPn37ce+899O7dt9Ff4E110EGHMHPmyzU9TytWLOf666+pedCgouLL/OMfT1JZ+RkATzzxGDvttHODD1esbd99D+DJJ5/g0UcfYsiQoTX1/u1vj7N8+XKWLVvGk09Op6Kif+HYACZNuouKigFUVAzggQd+x0479SKTybDrrn2ZNetl5s17B4Dly5fXe6/Xhiov35xlyxq+bw5g99335M9/nlrz3vnwww9q/u+bo3//ATVD7E8//Q+WLs0/XVxR8WWefHI6K1Ysp7LyM/7xj7+tU/bzn/8Cixcvqhlmz2azvPHGf5odU0PscZOkdq66rJySwd9PtD4a6Fjp0qUrfftWcMopx7PHHnvXPF0KcOihRzJv3juMHHkipaVlHH74kRxzzAk1x0ePPo+rr76CceOu56yzRnP66d/h3HNHkctVU1paxpgxF7L11tuscb3KykouvngMK1euJJfLcfbZY+qMq2/fCu69956aBxF69dqFDz/8gMMOO7IZrbGmTTbZlLFjr+WXv/wZv/jFT6murubgg79e8z3utNPOHH308XznO6eRyWTo1q0bF1546Xpdo3PnzvTs2ZM333yT3r37ABDCLnzta4dy+un5YeXDDjuypmexomIAd955B3369GOzzTajY8dNapK6bt26cckll3P55ZewatVKAE4//TuJ9UAC7L33Pvzv/17I3/72BOeeW//7cODAPZg7903OPPNbAGy2WTmXXfZjunXbslnX/9a3Tufyyy/h1FNPon//L/Pf/701kG+zAw4YysiRw9l6623o16//OmU7dOjAlVf+lOuuu4ZPP/2Uqqoqjj/+RHbYYcdmxdSQTO2nJ9KsujqX++ij5t/Yqabr2rWcxYsb/ytJybHNW97ykhJ+8dCcROr63pBedGoF4xzvvfcWW2+d3C/epJWWllBVVb2xw2hXbPPiqutnrnv3TjOA3da3rlbwESJJkqSmMHGTJElKCRM3SWqH2sptMlJrl/TPmombJLUzZWUd+eyzJSZvUpHlcjk++2wJZWUdE6vTp0olqZ3p1q07ixZ9yKefFmfR9uZaPUebWo5tXjxlZR3p1q17cvUlVpMkKRVKS8vYaqttGj9xI/Hp6ZZnm6eHQ6WSJEkpYeImSZKUEiZukiRJKWHiJkmSlBImbpIkSSlh4iZJkpQSJm6SJEkpYeImSZKUEkWdgDeEcAhwPVAK3BZjHLvW8THAaUAW+BA4Ncb4VuHYCODSwqlXxhgnFjNWSZKk1q5oPW4hhFLgJuBrQG/gxBBC77VOexHYLcbYD7gP+Fmh7JbAD4HdgYHAD0MI3YoVqyRJUhoUc6h0IPDvGOMbMcaVwGTgiNonxBinxxhXr7HxDLBd4fXBwCMxxo9jjIuAR4BDihirJElSq1fModIewDu1tueR70Grz7eBvzRQtkdDF8tk8mutqeWUlpbY5i3MNm957y9dQceOyXxUdigroWvnTROpqy3zfd7ybPP0KGbilqljX66uE0MIJwO7Afutb9magzlcILeFuShxy7PNW16upISVK7OJ1LUqW+3/XxP4Pm95tnnL69690waVK+ZQ6Txg+1rb2wHz1z4phHAgcAlweIxxxfqUlSRJak+K2eP2HLBzCKEn8C4wDBhe+4QQwgDgFuCQGOMHtQ49BPyk1gMJBwEXFzFWSZKkVq9oPW4xxiwwinwS9howJcb4SgjhihDC4YXTfg5sAdwbQngphDC1UPZj4Mfkk7/ngCsK+yRJktqtos7jFmN8EHhwrX2X1Xp9YANlxwPjixedJElSurhygiRJUkqYuEmSJKWEiZskSVJKmLhJkiSlhImbJElSSpi4SZIkpYSJmyRJUkqYuEmSJKWEiZskSVJKmLhJkiSlhImbJElSSpi4SZIkpYSJmyRJUkqYuEmSJKWEiZskSVJKlG3sACSpGLIlJSzLVje7ngy5BKKRpGSYuElqk5Zlq7lx2uvNrue7Q3olEI0kJcOhUkmSpJQwcZMkSUoJEzdJkqSUMHGTJElKCRM3SZKklDBxkyRJSgmnA5GkBnQvq+SMAZskUtdWZZWsqC5PpC5J7ZOJmyQ1oCxbyaK/Xp1IXV8YfiUrSkzcJG04h0olSZJSwsRNkiQpJUzcJEmSUsLETZIkKSVM3CRJklLCxE2SJCklTNwkSZJSwsRNkiQpJUzcJEmSUsLETZIkKSVM3CRJklLCxE2SJCklTNwkSZJSwsRNkiQpJUzcJEmSUsLETZIkKSVM3CRJklLCxE2SJCklTNwkSZJSwsRNkiQpJUzcJEmSUqKsmJWHEA4BrgdKgdtijGPXOr4vcB3QDxgWY7yv1rEqYFZh8+0Y4+HFjFWSJKm1K1riFkIoBW4ChgLzgOdCCFNjjK/WOu1tYCRwfh1VLIsx9i9WfJIkSWlTzB63gcC/Y4xvAIQQJgNHADWJW4xxbuFYdRHjkCRJahOKmbj1AN6ptT0P2H09ym8aQngeyAJjY4wPJBmcJElS2hQzccvUsS+3HuU/H2OcH0LYAXgshDArxvifei+Wga5dy9c7SG240tIS27yF2eZNt3zJcjp2TOAjLgOlJXV9nG1AVSV+TjWF7/OWZ5unRzETt3nA9rW2twPmN7VwjHF+4esbIYTHgQFAvYlbLgeLF1duWKTaIF27ltvmLcw2b7pV1bByZbb5FeWgqnp9/uZsoKpqP6eawvd5y7PNW1737p02qFwxpwN5Dtg5hNAzhNARGAZMbUrBEEK3EMImhddbAXtT6944SZKk9qhJPW4hhO7A6cAXa5eJMZ5aX5kYYzaEMAp4iPx0IONjjK+EEK4Ano8xTg0hfBW4H+gGHBZC+FGMcVfgS8AthYcWSsjf42biJkmS2rWmDpX+Afgb8ChQ1dTKY4wPAg+ute+yWq+fIz+Euna5p4C+Tb2OJElSe9DUxK08xnhhUSORJElSg5p6j9ufQghfL2okkiRJalBTe9xGAz8IIawEVhX25WKMnYsTliRJktbWpMQtxrhhz6xK0kayVVklZwzYpNn1dCxp8m29klR0TZ7HLYRwOLBvYfPxGOOfihOSJDVfWbaSRX+9utn1ZIZf1vhJktRCmnSPWwhhLPnh0lcL/0YX9kmSJKmFNLXH7etA/xhjNUAIYSLwInBRsQKTJEnSmtZn5YSutV53SToQSZIkNaypPW5XAy+GEKaTXzx+X+DiokUlSZKkdTSpxy3GeA+wB/D7wr89Y4yTixmYJEmS1tRg4hZC2KXw9cvANsA84B1g28I+SZIktZDGhkrHAGcA19ZxLAcckHhEkiRJqlODiVuM8YzCy6/FGJfXPhZC2LRoUUmSJGkdTX2q9Kkm7pMkSVKRNNjjFkLYGugBbBZCGED+iVKAzkB5kWOTJElSLY3d43YwMBLYDvhFrf1LgR8UKSZJkiTVobF73CYCE0MIx8QYf9dCMUmSJKkOTZ2At08IYde1d8YYr0g4HkmSJNWjqYnbp7VebwocCryWfDiSJEmqT5MStxjjGvO4hRCuAaYWJSJJkiTVaX0Wma+tHNghyUAkSZLUsCb1uIUQZpFfKQGgFOgOeH+bJElSC2rqPW6H1nqdBd6PMWaLEI8kSZLq0aSh0hjjW8DngCOAo4G+xQxKkiRJ62pS4hZCuAyYSD552wqYEEK4tJiBSZIkaU1NHSo9ERiweqH5EMJY4AXgymIFJkmSpDU19anSueTnb1ttE+A/iUcjSZKkejW2yPyN5J8mXQG8EkJ4pLA9FPh78cOT1J5kS0pYlq1OpK7yRGqRpNalsaHS5wtfZwD319r/eFGikdSuLctWc+O01xOp68ohWyZSjyS1Jk1ZZF6SJEmtQGNDpVNijMevNQFvjRhjv6JFJkmSpDU0NlQ6uvD10AbPkiRJUtE1NlS6IIRQCtweYzywhWKSJElSHRqdDiTGWAVUhhC6tEA8kiRJqkdTJ+BdDswqTAfy2eqdMcazixKVJEmS1tHUxO3PhX+1rfOwgiRJkoqnqYlb1xjj9bV3hBBG13eyJEmSktfUxG0EcP1a+0bWsU+SNthWZZWcMWCTROrqmKlKpB5Jak0am8ftRGA40DOEMLXWoc7AR8UMTFL7U5atZNFfr06krszwyxKpR5Jak8Z63J4CFgBbAdfW2r8UmFmsoCRJkrSuBqcDiTG+FWN8HDgQ+FuM8Qnyidx2QKb44UmSJGm1RudxK3gS2DSE0AOYBnwLmFCsoCRJkrSupiZumRhjJXA0cGOM8Sigd/HCkiRJ0tqanLiFEPYETuL/z+fW1CdSJUmSlICmJm7nABcD98cYXwkh7ABML15YkiRJWluTes0KDyU8UWt0hRN0AAASSklEQVT7DcDlriRJklpQY/O4XRdjPCeE8EfqWOIqxnh4I+UPIT9JbylwW4xx7FrH9wWuA/oBw2KM99U6NgK4tLB5ZYxxYhO+H0lqtTIZWFqdTF2blZVQVp1QZZJSo7Eet7sKX69Z34pDCKXATcBQYB7wXAhhaozx1VqnvU1+BYbz1yq7JfBDYDfyCeOMQtlF6xuHJLUW1Tm4cdrridT1vSG96NTUm10ktRkNJm4xxhmFr0+EELoXXn/YxLoHAv8uDKsSQpgMHAHUJG4xxrmFY2v/2Xgw8EiM8ePC8UeAQ4B7mnhtSZKkNqexodIM+Z6vUeQn3C0JIWTJTwlyRSN19wDeqbU9D9i9iXHVVbZHE8tKkiS1SY0NlZ4D7A18Ncb4JkDhidJfhxDOjTH+soGyda2ssM59ckmVzWSga9fyJlavJJSWltjmLaytt3l2MZSWJLcoSyJ1ZZKNqWPHZGZS6lBWQtfOmyZSV2vT1t/nrZFtnh6NfYJ8ExgaY1y4ekeM8Y0QwsnAw0BDids8YPta29sB85sY1zxg8FplH2+oQC4HixdXNrF6JaFr13LbvIW19TbfvBqqqpv6913jEqkrl2xMK1dmE6lnVba6zb4X2vr7vDWyzVte9+6dNqhcY7e2dqidtK1WuM+tQyNlnwN2DiH0DCF0BIYBU5sY10PAQSGEbiGEbsBBhX2SJEntVmOJ28oNPEaMMUv+3riHgNeAKYXJe68IIRwOEEL4aghhHnAccEsI4ZVC2Y+BH5NP/p4Drlj9oIIkSVJ71dhQaUUIYUkd+zNAozdXxBgfBB5ca99ltV4/R34YtK6y44HxjV1DkiSpvWhsOpDSlgpEkiRJDXP6RkmSpJQwcZMkSUoJEzdJkqSUMHGTJElKCRM3SZKklDBxkyRJSgkTN0mSpJQwcZMkSUoJEzdJkqSUMHGTJElKCRM3SZKklGhskXlJUkI6lpVwxoBNEqlrq7JKVlSXJ1KXpPQwcZOkFpLJLmfRX69OpK4vDL+SFSUmblJ741CpJElSSpi4SZIkpYSJmyRJUkqYuEmSJKWEiZskSVJKmLhJkiSlhImbJElSSpi4SZIkpYSJmyRJUkqYuEmSJKWEiZskSVJKmLhJkiSlhIvMS2q2LUorKclWNruekpKqBKKRpLbLxE1Ss5VkK6l+/OfNrqdsv9EJRCNJbZdDpZIkSSlh4iZJkpQSJm6SJEkpYeImSZKUEiZukiRJKWHiJkmSlBImbpIkSSlh4iZJkpQSJm6SJEkpYeImSZKUEiZukiRJKWHiJkmSlBIuMi+p2XIZWJVAPR0SqEOS2jITN0nNVl0Ns9/9pNn1DEwglvaiQ1kJnasWNrue6rJyPq0qTyAiSS3BxE2SUqgku5zsE9c3v57B34eMiZuUFt7jJkmSlBImbpIkSSlh4iZJkpQSRb3HLYRwCHA9UArcFmMcu9bxTYA7ga8AHwEnxBjnhhC+CLwGxMKpz8QYzyxmrJIkSa1d0RK3EEIpcBMwFJgHPBdCmBpjfLXWad8GFsUYdwohDAN+CpxQOPafGGP/YsUnSZKUNsUcKh0I/DvG+EaMcSUwGThirXOOACYWXt8HDAkhZIoYkyRJUmoVM3HrAbxTa3teYV+d58QYs8AnwOcKx3qGEF4MITwRQtiniHFKkiSlQjHvcaur5yzXxHMWAJ+PMX4UQvgK8EAIYdcY45J6L5aBrl2di6gllZaW2OYtLMk2L1mxmMzKTxOpq5oqSkuS6SxPqp7E6sq0wpgAMtBxk+Z/hGfKSujaqXX9HPvZ0vJs8/QoZuI2D9i+1vZ2wPx6zpkXQigDugAfxxhzwAqAGOOMEMJ/gF7A8/VdLJeDxYsrEwxfjenatdw2b2FJtnnn3BKqH/95InV13G80VdVr/122YZKqJ7G6cq0wJoAcrFyRbXY1JdlqlrSyn2M/W1qebd7yunfvtEHlipm4PQfsHELoCbwLDAOGr3XOVGAE8DRwLPBYjDEXQuhOPoGrCiHsAOwMvFHEWCVJklq9oiVuMcZsCGEU8BD56UDGxxhfCSFcATwfY5wK3A7cFUL4N/Ax+eQOYF/gihBCFqgCzowxflysWCUpdTKwKoFqOvo4mJQqRZ3HLcb4IPDgWvsuq/V6OXBcHeV+B/yumLFJUprlcjD73U+aXc9XS5JZrB5csF5qCS4yL0ntWFKL1YML1kstwSWvJEmSUsLETZIkKSVM3CRJklLCe9ykFClZsZjOuXrnoV4vZZkqViZSkySppZi4SSmSWflpYpPmMnh0MvVIklqMQ6WSJEkpYY+b1I4lMYErQIeE6pEkNczETWqvEprAFWBgIrVIkhrjUKkkSVJK2OMmSe1ZQmueguueSi3BxE2S2rGk1jwF6F+N4zhSkfkjJkmSlBImbpIkSSlh4iZJkpQSJm6SJEkpYeImSZKUEiZukiRJKeF0IFIL2KK0kpJsZfMrylU3vw5JUmqZuEktoCRbSfXjP292PZn9RycQjSQprRwqlSRJSgl73KQUqSa55Yk6JFSPJKnlmLhJaZLg8kQDE6lFktSSTNwkSYnoWFYC2YXNridb2QnYpPkBSW2QiZskKRnZ5bw06YpmVzPg5CsxcZPq5sMJkiRJKWGPm1SPxOZeA8oyVaxMpCap7etQWkLnBIZcAarLyvm0qjyRuqTWwMRNqkdSc68BMNj516SmymSXs+Lx6xOpq+P+3wdM3NR2mLhJklqVXIJPT/evxpuC1KaYuEktJIn51zomUIckKb1M3KSWkFAPwsBcArFIklLLxE1tTlIPFfhAgSSptTFxU5uT2EMFPlAgpV6HshI6VyXzhGpph45UrWr+n3M+6armMHGTJLVZJdnlZJ9I5gnVssGjqU7gadeSwd+HjImbNozP2kiSJKWEPW5SA5J4EhSgQ0L1SJLaNxM3qT4JziU1MJFaJEntnYmbWgWXl5IkqXEmbmoVXF5KUhokMpF2JoFK1G6ZuEmS1BQJ3T7x1ZLkpijJlm3O+6s2a3Y9JSuyCUSjlmDi1g5lS0pYlq1udj1bVC6ic25pAhElP7yZxF/FPlAgqRiSnKIks9/3uXHaO82uZ8zBu7BpAvGo+Ezc2qFl2WpunPZ6s+v5ydAtW+fwZlLLSyUQiiQVU4eyEs4YsEmz6+mW+YxlNL/nTsVn4tYObVVWmcgPegeqSOZxAhdPl6QNUZJdzqK/Xt3ser548pVg4pYKJm7tUFm2MpEfdIZflth0Gbu7eLqkYsi0wvkYW2FMHUpL6JxN5r47l/QqLhM3SVKblWuF8zG2xpgy2eWJLOcFLulVbCZuKZHkPGclJVWJ1CNJajuS6gV0upPiKmriFkI4BLgeKAVuizGOXev4JsCdwFeAj4ATYoxzC8cuBr4NVAFnxxgfKmastSWZJJV26EjVquY/L1laVUXl479IICIo368VznPWCocOJKm9SLIXMMnpThL7HZpQPbDxh4KLlriFEEqBm4ChwDzguRDC1Bjjq7VO+zawKMa4UwhhGPBT4IQQQm9gGLArsC3waAihV4yx3q6iTK6Kzrlk3ihlVVWsTChJKhs8OpHu57L9Rre6rvUktcahA0nS+ktyupPEfocmVA9s/KHgYva4DQT+HWN8AyCEMBk4AqiduB0BXF54fR/wqxBCprB/coxxBfBmCOHfhfqeru9iuVwVKxKamqLMmfclSVIrVMzErQdQe1bAecDu9Z0TY8yGED4BPlfY/8xaZXs0eLUEe2x2J8Gx/oTqcvhPkpQKCd76ktTv0KTqgY1/D18mlyvOPAwhhOOAg2OMpxW2TwEGxhi/V+ucVwrnzCts/4d8z9oVwNMxxt8U9t8OPBhj/F0Dl/wQeKso34wkSVKyvgB0X99CxexxmwdsX2t7O2B+PefMCyGUAV2Aj5tYdm3r/c1LkiSlSUkR634O2DmE0DOE0JH8wwZT1zpnKjCi8PpY4LEYY66wf1gIYZMQQk9gZ+DZIsYqSZLU6hUtcYsxZoFRwEPAa8CUGOMrIYQrQgiHF067Hfhc4eGDMcBFhbKvAFPIP8jwV+C7DT1RKkmS1B4U7R43SZIkJauYQ6WSJElKkImbJElSSpi4SZIkpUTqF5kvzBd3OfAl8vPEPV/Y/0XyD0XEwqnPxBjP3BgxtjX1tXnh2EZbY7a9CCFcDpxOfu5CgB/EGB/ceBG1XY2tt6zkhRDmAkvJf4ZkY4y7bdSA2qAQwnjgUOCDGGOfwr4tgd8CXwTmAsfHGBdtrBjbmnra/HI24LM89YkbMBs4GriljmP/iTH2b+F42oM623xD1pjVBvtljPGajR1EW9bE9ZZVHPvHGJNZfFp1mQD8Criz1r6LgGkxxrEhhIsK2xduhNjaqgms2+awAZ/lqR8qjTG+FmOMjZ+ppDTQ5jVrzMYY3wRWrzErpVHNessxxpXA6vWWpVSLMT5JfrL72o4AJhZeTwSObNGg2rh62nyDpD5xa0TPEMKLIYQnQgj7bOxg2oG61qdteI1ZbahRIYSZIYTxIYRuGzuYNsr388aRAx4OIcwIIZyxsYNpR/47xrgAoPD1vzZyPO3Fen+Wp2KoNITwKLB1HYcuiTH+oZ5iC4DPxxg/CiF8BXgghLBrjHFJ0QJtQzawzetaeteJAjdAQ+0P/Br4Mfm2/TFwLXBqy0XXbvh+3jj2jjHODyH8F/BICGFOobdCams26LM8FYlbjPHADSizAlhReD2jsIB9L+D5BgsK2LA2Z8PWmFUdmtr+IYRbgT8VOZz2yvfzRhBjnF/4+kEI4X7yQ9YmbsX3fghhmxjjghDCNsAHGzugti7G+P7q1+vzWd5mh0pDCN0LNxcTQtiB/Hqnb2zcqNo815htAYUP1dWOIv+wiJLXlPWWlaAQwuYhhE6rXwMH4fu7pdReO3wEUN/IihKyoZ/lqV/yKoRwFHAj0B1YDLwUYzw4hHAMcAWQJf9Y+Q9jjH/ceJG2HfW1eeHYJeS7erPAOTHGv2y0QNuoEMJdQH/y3etzgf9ZfW+KkhVC+DpwHfnpQMbHGK/ayCG1aYU/su8vbJYBk2zz5IUQ7gEGA1sB7wM/BB4gv0b454G3geNijIncTK9623wwG/BZnvrETZIkqb1os0OlkiRJbY2JmyRJUkqYuEmSJKWEiZskSVJKmLhJkiSlRCom4JWk1UIIVcAs8p9fbwKnxBgXN3B+V2B4jHFcC4W4+roZYBr5NR83IT/NRVfg0hjjA4Vz/gB8Z/WksyGEa4AHY4yPtWSsktLDHjdJabMsxtg/xtiH/KLN323k/K7AWet7kdUTeDfD14GXC8vsnUh+4e49ge8X6j8MeGF10lZwI3BRM68rqQ2zx01Smj0N9Fu9EUL4PnA8hR6uGOMPgbHAjiGEl4BHgD8D58cYDy2U+RXwfIxxQghhLjCe/Iz9vwohnAn8E9iffAL47Rjj30IIuwJ3AB3J/wF8TIzxX2vFdhLwf4XXq4DNCnFVhxDKgHOAw2oXiDG+FUL4XAhh6xjje81uHUltjj1uklKp0CM2hMIyVCGEg8gvszaQ/GzkXwkh7Eu+B+s/hV667zeh6uUxxkExxsmF7bIY40DyidYPC/vOBK6PMfYHdiO/runa9gZmFF5PAg4G/gpcTr4H8M4YY2Ud5V4olJWkddjjJiltNiv0nn2RfGL0SGH/QYV/Lxa2tyCfyL29nvX/dq3t3xe+zihcE/I9fZeEELYDfl9HbxvAljHGpQAxxk+AbwCEELoBFwJHFxaW7gZcG2N8ulDuA2Db9YxZUjthj5uktFlW6On6AvmhytX3uGWAqws9a/1jjDvFGG+vo3yWNT/7Nl3r+Gdrba8ofK2i8MdujHEScDiwDHgohHBAXdcJIdT1GXsZcBX5+95mkF/b9ydrxbOsjnKSZOImKZ0KvVhnA+eHEDoADwGnhhC2AAgh9Agh/BewFOhUq+hbQO8QwiYhhC7kh1vXS2Ex9DdijDeQH6rtV8dpEdhhrXI7A9vGGJ8AyoFq8gtM104eewGz1zcmSe2DiZuk1Ioxvgi8DAyLMT5M/l6yp0MIs4D7gE4xxo+Af4QQZocQfh5jfAeYAswE7ub/D62ujxOA2YUh212AO+s458/A4LX2XQVcWnh9DzASeAa4BqCQgO4EPL8BMUlqBzK5XG5jxyBJbU4IYRvyDyAMXY8yRwFfjjH+b/Eik5Rm9rhJUhHEGBcAt4YQOq9HsTLg2iKFJKkNsMdNkiQpJexxkyRJSgkTN0mSpJQwcZMkSUoJEzdJkqSUMHGTJElKif8HWKqHCcr2zSEAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"all_returns = {}\n",
"for k,v in all_tickers.items():\n",
" df = pd.DataFrame(v, columns=[\"time\", \"open\", \"high\", \"low\", \"close\", \"volume\"])\n",
" df = df.iloc[:2*24] # get only first 2 days\n",
" all_returns[k]=(((df.close-df.shift(1).close)/df.close)*100).values[1:]\n",
"with_vowel_dist = np.concatenate([v for k,v in all_returns.items() if k in tickers_with_vowel_middle])\n",
"without_vowel_dist = np.concatenate([v for k,v in all_returns.items() if k in tickers_without_vowel_middle])\n",
"plt.figure(figsize=(10, 5))\n",
"plt.hist(with_vowel_dist, bins=np.arange(-15, 15, 1), normed=True, label=\"tickers WITH vowel in the middle\", alpha=0.6 )\n",
"plt.hist(without_vowel_dist, bins=np.arange(-15, 15, 1), normed=True, label=\"tickers WITHOUT vowel in the middle\", alpha=0.6)\n",
"plt.legend()\n",
"plt.xlabel(\"Returns (%)\")\n",
"plt.ylabel(\"Distribution\")\n",
"plt.xlim([-15,15])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comparison for tickers with vowel IN THE MIDDLE VS no vowel IN THE MIDDLE"
]
},
{
"cell_type": "code",
"execution_count": 262,
"metadata": {},
"outputs": [],
"source": [
"import scipy\n",
"mean_with = []\n",
"mean_without = []\n",
"pvals = []\n",
"for i in range(1,8):\n",
" zz = {}\n",
" for k,v in all_tickers.items():\n",
" df = pd.DataFrame(v, columns=[\"time\", \"open\", \"high\", \"low\", \"close\", \"volume\"])\n",
" df = df.iloc[:i*24] # get only first x days\n",
" zz[k]=(((df.close-df.shift(1).close)/df.close)*100).values[1:]\n",
" with_vowel_dist = np.concatenate([v for k,v in zz.items() if k in tickers_with_vowel_middle])\n",
" without_vowel_dist = np.concatenate([v for k,v in zz.items() if k in tickers_without_vowel_middle])\n",
" stats = scipy.stats.ttest_ind(with_vowel_dist, without_vowel_dist)\n",
" pvals.append(stats.pvalue)\n",
" mean_with.append(with_vowel_dist.mean())\n",
" mean_without.append(without_vowel_dist.mean())"
]
},
{
"cell_type": "code",
"execution_count": 263,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f0a36d80dd8>"
]
},
"execution_count": 263,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,5))\n",
"plt.plot(mean_with, \"o-\", label=\"Mean of returns for tickers WITH middle-vowel (n={})\".format(len(tickers_with_vowel_middle)))\n",
"plt.plot(mean_without, \"o-\", label=\"Mean of returns for tickers WITHOUT middle-vowel (n={})\".format(len(tickers_without_vowel_middle)))\n",
"plt.plot(pvals, \"o-\", label=\"p-value of a t-test\")\n",
"for i,j in enumerate(pvals):\n",
" fontweight = 600 if j < 0.05 else 300\n",
" star = \"*\" if j < 0.05 else \"\"\n",
" plt.annotate(str(round(j,4))+star,xy=(i,j+0.03), fontweight=fontweight)\n",
"plt.xticks(range(7), range(1,8))\n",
"plt.xlabel(\"Days included in the analysis since listing in Binance\")\n",
"plt.ylabel(\"Mean returns/p-value\")\n",
"plt.legend()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Comparison for tickers WITH VOWEL vs NO VOWEL"
]
},
{
"cell_type": "code",
"execution_count": 258,
"metadata": {},
"outputs": [],
"source": [
"import scipy\n",
"mean_with = []\n",
"mean_without = []\n",
"pvals = []\n",
"for i in range(1,8):\n",
" zz = {}\n",
" for k,v in all_tickers.items():\n",
" df = pd.DataFrame(v, columns=[\"time\", \"open\", \"high\", \"low\", \"close\", \"volume\"])\n",
" df = df.iloc[:i*24] # get only first x days\n",
" zz[k]=(((df.close-df.shift(1).close)/df.close)*100).values[1:]\n",
" with_vowel_dist = np.concatenate([v for k,v in zz.items() if k in tickers_with_vowel])\n",
" without_vowel_dist = np.concatenate([v for k,v in zz.items() if k in tickers_without_vowel])\n",
" stats = scipy.stats.ttest_ind(with_vowel_dist, without_vowel_dist)\n",
" pvals.append(stats.pvalue)\n",
" mean_with.append(with_vowel_dist.mean())\n",
" mean_without.append(without_vowel_dist.mean())"
]
},
{
"cell_type": "code",
"execution_count": 259,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.legend.Legend at 0x7f0a36e7dc18>"
]
},
"execution_count": 259,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(10,5))\n",
"plt.plot(mean_with, \"o-\", label=\"Mean of returns for tickers WITH vowel/s (n={})\".format(len(tickers_with_vowel)))\n",
"plt.plot(mean_without, \"o-\", label=\"Mean of returns for tickers WITHOUT vowels (n={})\".format(len(tickers_without_vowel)))\n",
"plt.plot(pvals, \"o-\", label=\"p-value of a t-test\")\n",
"for i,j in enumerate(pvals):\n",
" fontweight = 600 if j < 0.05 else 300\n",
" star = \"*\" if j < 0.05 else \"\"\n",
" plt.annotate(str(round(j,4))+star,xy=(i,j+0.03), fontweight=fontweight)\n",
"plt.xticks(range(7), range(1,8))\n",
"plt.xlabel(\"Days included in the analysis since listing in Binance\")\n",
"plt.ylabel(\"Mean returns/p-value\")\n",
"plt.legend()"
]
}
],
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