diff --git a/basic_animation.mp4 b/basic_animation.mp4 new file mode 100644 index 0000000..e69de29 diff --git a/biljeznica.ipynb b/biljeznica.ipynb index b4e39c5..8da639f 100644 --- a/biljeznica.ipynb +++ b/biljeznica.ipynb @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 145, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -27,7 +27,7 @@ "" ] }, - "execution_count": 145, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -72,7 +72,7 @@ }, { "cell_type": "code", - "execution_count": 146, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +98,7 @@ }, { "cell_type": "code", - "execution_count": 147, + "execution_count": 3, "metadata": { "scrolled": true }, @@ -109,7 +109,7 @@ "'6e3f7192eb7e4267e58ebebedaa950654c45ac025e14dc596d5aa35d6d9b7929'" ] }, - "execution_count": 147, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -136,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 148, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -145,7 +145,7 @@ "'cbef0b2cc7ecdd53ccafad1a84f7e837870192a1ba6ca7ae45bb2a8a5eee7ef8'" ] }, - "execution_count": 148, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -174,7 +174,7 @@ }, { "cell_type": "code", - "execution_count": 149, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -192,7 +192,7 @@ }, { "cell_type": "code", - "execution_count": 150, + "execution_count": 6, "metadata": { "scrolled": false }, @@ -226,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": 152, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -252,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": 131, + "execution_count": 8, "metadata": { "scrolled": true }, @@ -264,7 +264,7 @@ "" ] }, - "execution_count": 131, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -283,7 +283,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -320,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 108, "metadata": {}, "outputs": [], "source": [ @@ -335,6 +335,9 @@ " self.transactions.append(transaction)\n", " self.transactions = sorted(self.transactions, key=lambda trans: trans.fee)\n", " \n", + " def getSizeOfMempool(self):\n", + " return len(self.transactions)\n", + " \n", " def printMempool(self):\n", " \n", " for trans in self.transactions:\n", @@ -342,269 +345,287 @@ ] }, { - "cell_type": "code", - "execution_count": 189, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "import rsa\n", - "\n", - "class Wallet:\n", - " \n", - " def __init__(self,name):\n", - " \n", - " self.name = name\n", - " self.privateKey =\"\"\n", - " self.publicKey =\"\"\n", - " self.listOfTransaction = []\n", - " self.generateKeys()\n", - " \n", - " \n", - " def generateKeys(self):\n", - " \n", - " (self.publicKey, self.__privateKey)=rsa.newkeys(512)" + "### Privatni i javni ključevi" ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "Da bi se korisnik uključio u mrežu, za to mu je potrebna šifra. Da bi svakome korisniku pridružili jedinstvenu šifru koristit ćemo kriptografiju s javnim ključem, tj. šifriranje i dešifriranje koriste različite ključeve. Javni ključ će biti javno dostupan svim korisnicima, dok je privatni ključ jedinstveni indetifikator svakoga računa. Uistinu je nemoguće izvesti privatni ključ iz javnog ključa." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### RSA" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "RSA je prvi i najpopularniji kriptosustav s javnim ključem . Njegova sigurnost ima temelje na težini faktorizacije velikih prirodnih brojeva." + ] }, { "cell_type": "code", - "execution_count": 191, + "execution_count": 346, "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'str' object has no attribute 'n'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mmessage\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Kako si Alice\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mcrypto\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencrypt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwallet1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpublicKey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mdecMessage\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mrsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecrypt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcrypto\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwallet1\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprivateKey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/rsa/pkcs1.py\u001b[0m in \u001b[0;36mdecrypt\u001b[0;34m(crypto, priv_key)\u001b[0m\n\u001b[1;32m 228\u001b[0m \"\"\"\n\u001b[1;32m 229\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 230\u001b[0;31m \u001b[0mblocksize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcommon\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbyte_size\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpriv_key\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 231\u001b[0m \u001b[0mencrypted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtransform\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbytes2int\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcrypto\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 232\u001b[0m \u001b[0mdecrypted\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpriv_key\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mblinded_decrypt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mencrypted\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'n'" - ] + "data": { + "text/plain": [ + "Mod(x**2, y)" + ] + }, + "execution_count": 346, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "wallet1 = Wallet(\"Alice\")\n", - "wallet2 = Wallet(\"Bob\")\n", - "wallet1.publicKey\n", - "message=\"Kako si Alice\".encode()\n", - "crypto = rsa.encrypt(message, wallet1.publicKey)\n", - "decMessage =rsa.decrypt(crypto, wallet1.privateKey)" + "from sympy.core.mod import Mod\n", + "from sympy.abc import x, y\n", + "p=x**2 % y\n", + "Mod(x**2, y)\n", + "Mod(x**2, y).subs({x: 5, y: 6})\n", + "\n", + "Mod(x**2, y)" ] }, { "cell_type": "code", - "execution_count": 170, + "execution_count": 351, "metadata": {}, "outputs": [ { "ename": "TypeError", - "evalue": "unsupported operand type(s) for pow(): 'str', 'str', 'str'", + "evalue": "unsupported operand type(s) for ** or pow(): 'list' and 'Symbol'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mcode\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"ante\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"65537\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"5551201688147\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# encode using a public key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mplaintext\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcode\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m109182490673\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m5551201688147\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# decode using a private key\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for pow(): 'str', 'str', 'str'" + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSymbol\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'x'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msympy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabc\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mz\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mE\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0moo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for ** or pow(): 'list' and 'Symbol'" ] } ], "source": [ - "code = pow(\"ante\", \"65537\", \"5551201688147\") # encode using a public key\n", - "plaintext = pow(code, 109182490673, 5551201688147) # decode using a private key" + "import sympy as sp\n", + "x = sp.Symbol('x')\n", + "from sympy.abc import z\n", + "(t**z * sp.E**-t, (t, 0, sp.oo))\n", + "b" ] }, { "cell_type": "code", - "execution_count": 105, + "execution_count": 109, "metadata": {}, "outputs": [], "source": [ - "import hashlib\n", - "\n", - "class Block:\n", - "\n", - " def __init__(self, index, timpestamp, previousHash, transactions):\n", - " \n", - " self.index = index\n", - " self.timestamp = timpestamp\n", - " self.nounce = 0\n", - " self.transactions = transactions\n", - " self.previousHash = previousHash\n", - " self.encoded = \"\"\n", - " self.hash = \"\"\n", - " self.dificulty = \"0\"*5+\"F\"*60\n", - " self.mineIt()\n", - " \n", - " def hashBlock(self):\n", - " \n", - " sha = hashlib.sha256()\n", - " self.encoded = (str(self.index) + str(self.timestamp) + str(self.nounce)+\n", - " str(self.transactions) + str(self.previousHash)).encode('utf-8')\n", - " sha.update(self.encoded)\n", - " return sha\n", + "from Crypto.PublicKey import RSA \n", + " \n", + "class Wallet:\n", " \n", - " def mineIt(self):\n", + " def __init__(self,name):\n", " \n", - " while(not self.hashBlock().hexdigest()" ] }, - "execution_count": 85, + "execution_count": 90, "metadata": {}, "output_type": "execute_result" } ], "source": [ + "Image(filename='transaction.png')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Radi jednostavnosti implementacije nećemo koristiti javne i privatne ključeve. Račun je jednistveno određen svojim imenom(name)." + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": {}, + "outputs": [], + "source": [ + "import datetime as date\n", "\n", - "\n", - "def hashBlock(string):\n", - " \n", - " sha = hashlib.sha256()\n", + "def getMeTime():\n", " \n", - " sha.update(string.encode())\n", - " return sha\n", - "\n", - "\n", - "star=\"0\"*3+\"F\"*63\n", - "hashBlock(star)\n", - "hashBlock(star).hexdigest()\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mgenesisBlock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBlock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgetMeTime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"First block\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mgenesisBlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhashMe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mgenesisBlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhash\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, index, timpestamp, previousHash, transactions)\u001b[0m\n\u001b[1;32m 11\u001b[0m self.encoded = (str(self.index) + str(self.timestamp) + \n\u001b[1;32m 12\u001b[0m str(self.transactions) + str(self.previousHash)).encode('utf-8')\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhash\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhashMyBlock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhexdigest\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mhashBlock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'Block' object has no attribute 'hashMyBlock'" - ] - } - ], + "outputs": [], "source": [ "genesisBlock = Block(0, getMeTime(),\"\",[\"First block\"])\n", "genesisBlock.hashMe()\n", @@ -680,20 +675,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "''" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "copyOfGenesisBlock = Block(genesisBlock.index,genesisBlock.timestamp,\n", " genesisBlock.previousHash,genesisBlock.transactions)\n", @@ -721,21 +705,9 @@ }, { "cell_type": "code", - "execution_count": 86, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'genesisBlock' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mtransactions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Alice Bob 50\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Bob John 10\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0msecondBlock\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mBlock\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgetMeTime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgenesisBlock\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhash\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mtransactions\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mtransactions3\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"Bob Alice 20\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"John Mary 5\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"Alice Mary 20\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mNameError\u001b[0m: name 'genesisBlock' is not defined" - ] - } - ], + "outputs": [], "source": [ "transactions = [\"Alice Bob 50\",\"Bob John 10\"]\n", "\n", @@ -820,40 +792,35 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "import csv\n", - "lista = []\n", - "with open('data/bitcoin_price.csv') as file:\n", - " read = csv.DictReader(file)\n", - " for row in read:\n", - " for feature in row:\n", - " lista.append(row)\n", - " print (row)" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "import pandas as pd\n", - "test = pd.read_csv('data/bitcoin_price.csv')" - ] + "source": [] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 176, "metadata": {}, "outputs": [], "source": [ - "%matplotlib inline \n", - "import matplotlib.pyplot as plt" + "import pandas as pd\n", + "bitcoin = pd.read_csv('data/bitcoin_price.csv')" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 265, "metadata": { "scrolled": true }, @@ -1634,1131 +1601,228 @@ "[1760 rows x 7 columns]" ] }, - "execution_count": 3, + "execution_count": 265, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "test" + "bitcoin" ] }, { "cell_type": "code", - "execution_count": 4, - "metadata": {}, + "execution_count": 266, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "import numpy\n", - "t=numpy.array(test.Date.values)\n" + "import numpy as np" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, + "execution_count": 267, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ - "df = pd.test(data=)" + "from datetime import date, timedelta\n", + "\n", + "def getDates(lista, d1,d2):\n", + " #d1 = date(2013, 4, 28) # start date\n", + " #d2 = date(2018, 2, 20) # end date\n", + "\n", + " delta = d2 - d1 # timedelta\n", + " for i in range(delta.days + 1):\n", + " lista.append(d2 - timedelta(days=i))\n", + "btcLis=[]\n", + "\n", + "getDates(btcLis,date(2013, 4, 28),date(2018, 2, 20))" ] }, { - "cell_type": "code", - "execution_count": 5, + "cell_type": "markdown", "metadata": {}, + "source": [ + "## Graf cijene Bitcoina" + ] + }, + { + "cell_type": "code", + "execution_count": 268, + "metadata": { + "scrolled": true + }, "outputs": [ { "data": { "text/plain": [ - "['Feb 20, 2018',\n", - " 'Feb 19, 2018',\n", - " 'Feb 18, 2018',\n", - " 'Feb 17, 2018',\n", - " 'Feb 16, 2018',\n", - " 'Feb 15, 2018',\n", - " 'Feb 14, 2018',\n", - " 'Feb 13, 2018',\n", - " 'Feb 12, 2018',\n", - " 'Feb 11, 2018',\n", - " 'Feb 10, 2018',\n", - " 'Feb 09, 2018',\n", - " 'Feb 08, 2018',\n", - " 'Feb 07, 2018',\n", - " 'Feb 06, 2018',\n", - " 'Feb 05, 2018',\n", - " 'Feb 04, 2018',\n", - " 'Feb 03, 2018',\n", - " 'Feb 02, 2018',\n", - " 'Feb 01, 2018',\n", - " 'Jan 31, 2018',\n", - " 'Jan 30, 2018',\n", - " 'Jan 29, 2018',\n", - " 'Jan 28, 2018',\n", - " 'Jan 27, 2018',\n", - " 'Jan 26, 2018',\n", - " 'Jan 25, 2018',\n", - " 'Jan 24, 2018',\n", - " 'Jan 23, 2018',\n", - " 'Jan 22, 2018',\n", - " 'Jan 21, 2018',\n", - " 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srRzXuiWNqOVgZreb2X4zezElNtPM1pnZ1uB9RhA3M/uumW0zs41m9uaUY1YE5bcGa1An4+eY2QvBMd+1XKRFEZE8GemYA8DBIz1sbGoFIBKPE85BlxKMvFvpDmD5gNgq4BF3Xww8EnwGuJLE2tGLgZXALZBIJiSWGD0XWAbcmEwoQZmVKccN/FkiIpNG9whmKx0tG2fb/o7Edm+M0hwMRsMIk4O7/w5oGRC+GlgdbK8G3pcSv9MT/gBMN7O5wBXAOndvcfdDwDpgebCvxt2f9MSK23emnEtEZNL58R93ASPrVkrqjsTY09pdWN1Kg5jj7nsBgvfZQbweeD2lXFMQGyrelCEuIjIpJVsCI2k5NJ6Q6GBJdkVVDHHjXDaNR/skU4eYjyKefmKzlWa23szWNzc3j6GKIiL5N3DdhkxOm1vDrMqSvumsJQU25pDJvqBLiOB9fxBvAhaklJsP7BkmPj9DPI273+ruje7eWFdXN4aqi4jkT0lRqN/jMYYSDhnRuPclh6JCGnMYxBogOeNoBfBASvy6YNbSeUBr0O30MHC5mc0IBqIvBx4O9rWb2XnBLKXrUs4lIjLpVJcWcdlps4cvCBSFjFjc++51yMVzlWCE9zmY2U+AS4BaM2siMevoJuBeM/sEsAv4UFB8LXAVsA3oBD4O4O4tZvYV4Omg3JfdPTnI/WkSM6LKgV8GLxGRSakrEutbwGc44bARjcfpDVoOubg7GkaYHNz92kF2XZahrAPXD3Ke24HbM8TXA2eMpC4iIhOZux9Tcki2HCJ9yaHwu5VEROQY9UTjuENZyQhbDqEQkZgTiea2W0nJQUQkh5I3wI24Wyl4YETyeUy56lZSchARyaGuY00Owbf07sNdgFoOIiKTUlewClz5CLuVNu1pA+Az92wAlBxERCalZMuhbIQth1iwRKgHtwarW0lEZBI61jGHgS0FtRxERCahrt7ElNSRditdtLi23+citRxERCafYx2Qfs+b5vX7nOxmGm9KDiIiOXSsYw7hAQ/nSz6ddbwpOYiI5FD3Mc5WKhqQHM6sn5b1OmWi5CAikkPHfp9D/+SgAWkRkUnoWJODWW4GoAdSchARyZFY3Lnpl1uAka0Cl0+FXTsRkUkkuTwojGwVuHxSchARyZHDnb1jOv4bHzgzSzUZnpKDiEiO7GlNPDxv7V9eNKrjz6yfns3qDEnJQUQkRz57z/MA1E8vH9XxZcW5+8oe9U8ys1PMbEPKq83MPmNmXzKz3Snxq1KO+byZbTOzl83sipT48iC2zcxWjfWiREQKWU35iBbhTFM6whlO2TC6GgLu/jKwFMDMwsBu4H4Sa0Z/293/MbW8mS0BrgFOB+YBvzazk4Pd3wPeCTQBT5vZGnd/abR1ExEpZKOdnjrwhrjxNOrkMMBlwHZ3f22Ii74auNvde4BXzWwbsCzYt83ddwCY2d1BWSUHEZlU5k4r48KTaocvOIg5NWVZrM3QstWBdQ3wk5TPN5jZRjO73cxmBLF64PWUMk1BbLB4GjNbaWbrzWx9c3NzlqouIjK+Vj+xk4ZVD7K3tXvEz1TKtzEnBzMrAd4L/FcQugVYRKLLaS/wT8miGQ73IeLpQfdb3b3R3Rvr6urGVG8RkVy5cc2mvu1cDiqPRTa6la4EnnX3fQDJdwAz+yHwi+BjE7Ag5bj5wJ5ge7C4iMikMmVaDsC1pHQpmdnclH3vB14MttcA15hZqZktBBYDfwSeBhab2cKgFXJNUFZEZMLr7I32+zxRksOYWg5mVkFiltEnU8I3m9lSEl1DO5P73H2Tmd1LYqA5Clzv7rHgPDcADwNh4HZ334SIyCSQ+sgMKPxnKiWNKTm4eycwa0Dso0OU/xrwtQzxtcDasdRFRKQQtXWNveXw00+9Ne3R3eMtW1NZRUQkg46eSL/Po0kOjQ0zs1WdEZsY7RsRkQmqoyfW7/NEma00MWopIjJBdXT3bzmMdJGffFNyEBEZRx09E3O2kpKDiMg46uiJUZKy7rO6lUREhI6eCFVlR+f+lBap5SAiMuV1dEepLA33TUVVt5KIiNDSGaGqtJjkbQrqVhIRmcJaOyN88+EtbNh1iPrp5YRMLQcRkSnvh4/v4HuPbqetO8rC2oq+5DBRHp8xMWopIjKBvN7SyV1Pvdb3uaMnyr9cezZLF0ynsmRiPJhiYtRSRGQCuebWP3Co8+jNbz2ROO9YMod3LJmTx1odG7UcRESybPfhrn6f/+LiE/NUk9FTchARybK3ntjvYdWcNrcmTzUZPSUHEZEsa++J9E1ZPbG2Ms+1GR2NOYiIZFlHd5TLlxzHd689G3fPd3VGZcwtBzPbaWYvmNkGM1sfxGaa2Toz2xq8zwjiZmbfNbNtZrbRzN6ccp4VQfmtZrZirPUSEcmX3mickmDKqlluF+nJlmx1K13q7kvdvTH4vAp4xN0XA48EnwGuJLF29GJgJXALJJIJcCNwLrAMuDGZUEREJpre2NHkMFGNV+2vBlYH26uB96XE7/SEPwDTzWwucAWwzt1b3P0QsA5YPk51ExEZVz3ReL8nsU5E2ai9A78ys2fMbGUQm+PuewGC99lBvB54PeXYpiA2WFxEZMLpjcYnzJ3Qg8lG7S9w9zeT6DK63swuHqJsps43HyLe/2CzlWa23szWNzc3j662IiJZ0LDqQb60ZlNa3N3VrQTg7nuC9/3A/STGDPYF3UUE7/uD4k3AgpTD5wN7hogP/Fm3unujuzfW1dWNteoiIqPSG40DcMcTO9P2vbS3DXeYVl6c41pl15iSg5lVmll1chu4HHgRWAMkZxytAB4IttcA1wWzls4DWoNup4eBy81sRjAQfXkQExEpOM0dPYPue2pHCwDvedO8XFVnXIz1Poc5wP3BVK0i4Mfu/pCZPQ3ca2afAHYBHwrKrwWuArYBncDHAdy9xcy+AjwdlPuyu7eMsW4iIuPi91uPdmt/79FtXH/pSX2fmw51UVESZnZ1aT6qljVjSg7uvgN4U4b4QeCyDHEHrh/kXLcDt4+lPiIiubBtf0ff9nce2TogOXQyf0b5hL2/IUl3SIuIjNAtv91OV2+UHz7+6tFgytSZL9z/Ar96aR9vP3V2+sETjJKDiMgIdPZG+cZDW9LivbF43/ZdT+0CYFZlSc7qNV4m9lwrEZEcSV2fAfoPOB860svjKeMQFy6uzVm9xotaDiIiI3DoSG+/zzd/4Cz2t3Xz1KstLPv6rzl/USIh/P27TuO9E3ymEqjlICIyIq1d/VsO5SVhVv/ZMgAiMeexVxIth09cuHDCD0aDWg4iIiNyOOhW+vQli7jopEQroaw4nFZuMiQGUHIQERmRQ52JbqWPnd/AnJqyPNdm/KlbSURkBJLdShP9sRgjpeQgIjIChzt7KS8OZ+xKSvr0JYtyWKPxpW4lEZERONQZYXrF4K2GVVeeysqLTsxhjcaXkoOIyDBauyL89JmmIct86m2Tp9UA6lYSERnWb7bsy3cVck7JQURkGJ+953kA1v7lRXmuSe4oOYiIDOOs+dMAOG1uddq+0+fVUBSaHPc2pNKYg4hMafvbuqmrLh3y5rXaqlLOrJ+Wscx/33DheFYvb9RyEJEp67bHd7Ds64/w0ItvDFqmoyfK41ubqSjJPIU1FDJCk7DlMOrkYGYLzOxRM9tsZpvM7K+C+JfMbLeZbQheV6Uc83kz22ZmL5vZFSnx5UFsm5mtGtsliYgMLxZ3vvrgZgA27m4lsRbZUb98YS8Nqx7kjBsfJhJzygdJDpPVWFoOUeCv3f004DzgejNbEuz7trsvDV5rAYJ91wCnA8uBfzOzsJmFge8BVwJLgGtTziMiMi4OpqwDfctvt7Pw82v5xcY9R2OPbe9X/oSZFTmrWyEY9ZiDu+8F9gbb7Wa2Gagf4pCrgbvdvQd41cy2AcuCfduCJUcxs7uDsi+Ntm4iIsPZ396TFvuHtVv4zZb91FaVctLsKjY2tfbt+9PzTshl9fIuK2MOZtYAnA08FYRuMLONZna7mc0IYvXA6ymHNQWxweIiIuPmW+teSYvtPtzFz57dza2/20FlydG/ne//n+ezeE76TKXJbMzJwcyqgPuAz7h7G3ALsAhYSqJl8U/JohkO9yHimX7WSjNbb2brm5ubMxURERnSpj2tNKx6kN9s2Q8knrIKcO7Cmf3K/ccfXgPga+8/g6ULpue0joVgTFNZzayYRGK4y91/BuDu+1L2/xD4RfCxCViQcvh8INnBN1i8H3e/FbgVoLGxMWMCEREZyiOb9/dtX/OWBXzx3Uv45NtOZE51GetfO0TToU4+d+/zfWU+cu7U6k5KGstsJQN+BGx292+lxOemFHs/8GKwvQa4xsxKzWwhsBj4I/A0sNjMFppZCYlB6zWjrZeIyFDiKbOSyorDhELG3GnlhELGsoUz+ZM3z2dOTWkea1gYxtKtdAHwUeDtA6at3mxmL5jZRuBS4LMA7r4JuJfEQPNDwPXuHnP3KHAD8DCwGbg3KCsiknVb9rb3bb/91NkZy9xw6UkAnHPCjIz7p4KxzFb6PZnHC9YOcczXgK9liK8d6jgRkWxwdx7adPSGt4tPrstYrrYq0XKYKgv7ZKI7pEVkyni9pQuAJXNrhnyIXm11IjmEJsl60KOhZyuJyJTw8+d285l7NgDwjQ+cxZJ5NYOWffPxM7jh0pP4wDnzc1W9gqPkICKTnrv3JQaAk4+rGrJ8OGT8zRWnjHe1Cpq6lURk0jvUGenb/vMLF1JaNLWekzQaajmIyKT3w8d3APDjvziX8xfV5rk2E4NaDiIyqXX1xvjPJ1/jXWfNVWI4BkoOIjJhtXVHiMeHfljCzQ9vob0nynVT7MF5Y6VuJRGZcGJx5+aHt/CDxxLdRcsWzuSH1zUyrbyYWNzZvLeNzXvb+MZDL3Ogo4dlC2eybMCzk2RoSg4FYH97Nx/+/pNUlxXz8+svIDwJV5USGalfv7SPb//6FX5+/QUUh9M7N2Jx5+KbH2X34a6+2B9fbWHlnev50cfewhk3Ptyv/Im1ldz5Z8uGXAZU0ik5FIDVT+xk58FOAF7a08aZwWLmIlNNPO78xX+sxx12H+qiobYSSPwB9cFbnmRXSyfzppWxp7Wb6RXF/P27lvA3/5V4SN5Tr7b0JYbZ1aWsOL+Bq5fOo356uRLDKCg5FIDt+4/0bb/nX3/PY//rEk6Ylfif4ju/3sqmPa387fJTWFRXRUdPlOqyqXtLv0xeTYc6+chtT5F8Lt4dT+ykoiTMjuYjPPZKM12RGAB7Wru55i0L+Ic/ORMz44PnzKdh1YN957l66Ty+c83Z+biEScUGrps6UTQ2Nvr69evzXY0xc3dO/T8P0RON94vvvOld7Gvr5tyvP5J2zEWLa/nIucez/Iy5aftEJpLk98/hzggf+P4T7Gg+QnVpEe090bSyX3rPEpbMm8asqhIW1fW/ie2lPW3c8cSrFIVDfP39Z+ak7hOVmT3j7o3DllNyyB9358M/eJKndx7ikxefyA9+t6Nv3w2XnsS/Prpt0GPNYOONl6sVIVmx+omd3LhmE196zxLe/aZ5HO7s5aTZ1URicYpCNqZuGXfnlse285vN+7n01Nm8uLuVP77awhn109i0p5UDHb19Za9eOo9vf3gpP322ib/96UYAfnhdIxecNIuKEnV0ZIOSwwTwi417uOHHzwGw+cvLOe2LD6WV+eK7l3DVmXOZXV3Kkd4oP35qF4vqqvjzOxPXPru6lK+9/0y++fAWXtnXwZ+cXc+lp87mnUvmUFY8/F2g7s5vX27m43c8zYyKYpafMZcrzziO9u4o9z/XRE1ZMcdNK6M4HGLJvBpOn1dDTzRObWUp0yqUmCaDva1dvPUffjPo/tqqUmqrSvjoW0/gklNmU1NWRFEoRFHY6OiO8ujL+znSG2NmRQklRSG6IjHauyM8t+swHd1RXtnfzo7mI2nnPWl2FSfMrOD0eTVMqyhhUV0lFy+uIxQyorE4a57fw1Vnzh3Rv2MZOSWHCSD51xokupH2tnZxuDPCld95HIB///hbuPSU9OfNuztf/sVL/Pv/2znk+W/+4Fl8uHEBrx44ws+ebeKK04/jtLk1fbOhuiMxPvyDJ/ston4s3nZyHR9qnE9ZUZiz5k9Z0OUBAAAK7UlEQVRjevDlIBPDi7tb+cZDW3h86wEAzqyfxgu7+/9buOSUxCOtdzQfYVdL5zH/jOSiOR859wROrKvk5DnVVJSEmT+jYoy1l9FScpgAPnvPBu5/bjcP/uWFnD4vMUOpOxLj1P/zEB87v4Evvff0IY/f0dzBP617hQc37uX4mRX89w0XUl1WxL8/sZOv/OKljMeYJZZG7InG+cP2g+xp7aZhVgV3fHwZ4ZDxz7/eSmPDDEqLQly4uJaZFSWYGZFYnGdfO8RjrzSzqK6Kf310W8Yvi+Kwsaiuik+9bRHLzziOsuIwuw93URIOUVuVOJe788CGPbxl4Uzqp5eP/T/kBNETjbG/rYcFM/t/MR7s6OGLD2ziuV2HOGFWJbtaOnnbKXW88kY7S+bVcPbx0zl+ZiVn1if+jRzu6uWhF99g0+42qsqKOHlOFYZRWhyiJxqnoztKOGQc6Y1ypCfKy2908Mq+dubUlNITjVNREiYehz/ubAFg+enHccUZc3jvm+rZ3tzByXOq0+q+r62bL63ZxDknzOBARy8VJWGisThmxkWLa5leUUJvNE7cnbLiMBUlYWZWluiv/gKk5FDgvnD/C9z11C4uOGkWd/35eVk//7/9dhs3P/Ry3+dp5cXUVZdyuLOXnkic6rIiKkqLuOzU2Xz+qtOO+fzt3RG++MAm3rlkDgCvHjhCJBbn0Zebef71w33lFtVVsj3oUqifXs686WX0RuM8H7RWzKAkmMu+ZF4Nb2mYSU1ZEZWlRSyYUcFbGmaOqPuqqzdGe0+EmRUlFGWYGz8a8bizoekwp8ypJu5ONObMqCwZ1blauyJ86PtP8Mq+DgA+fkEDkFjPOFOSNUuspDXMzb+EbOgyIYOG2kqKQkZ5cZjykjCRmHPoSC+zqkq48T2nc0a9pk5PJRMuOZjZcuA7QBi4zd1vGqr8REwOTYc6aTnSy6sHjvBXd2+gJBzi6S+8Y1z77re80cZ9zzSx6srTcnJzXWdvlLUvvEF3JMZtj++grTvK+5bWM7OymJ89u5sdB45QXVZE/fRywsHavQtrK+iNxnnu9cO8sLuVgf8kG2ZV8JFzT+C/nnmdxXOqOXvBdLY3d9AdiVMcNh59uZnm9p6+8mfU1zC7uowjPVFOm1vD7JpS2rujFIeMtu5o31/P0ViconCImrJi4u60dkXYfbiL3micrkiMzXvbaO/uP2vGLEhy08pZNLuS42dWEjKIuROPO7E4RGJxDnf18kZrN2+0dfNGaw8Hj/SkXRfA/BnlnHPCDD7cuIDzF83CzDjSE6WyNDH4etdTr/Gbzfs5sa6SuENXJEZdVSkXLq5l6YLpGPB8UyuVpWGKwyFKwiGKwyGKw0ZlaRGlRSHN8Zd+JlRyMLMw8ArwTqAJeBq41t0z940w+uTwwIbdvLSnjdPrp2Ekns3S1hXFcaaXl7CwtpKa8iI6e2PMqixh7rRyisPGG23dVJUW0RWJ0doVoTsSp6s3xpGeKC2dvbQc6SUaiweDaU55cZi4Owc6ephVVcqWvW08tOkNuiOJKaunHlfNPZ9866RehjD5zJtQSlKKxuKEzPrFUh3o6KE4FKKls5c7n9w56F/WSdMrinnz8TM4s34a7s4zuw5xsKOXorBhGNv2d/TNj4fEc/orS8LEPbGdTAQARSGjobaSypIwJUUhjptWTtw9MQgftLaaDnXx2sEjPLnjYN/vcqCQQU15McfVlHHctLK+97c0zOStJ86iozdKTVkx7d0RqkqL9OUtOTXS5FAoc8OWAdvcfQeAmd0NXA0MmhxG6+FNb7D2hTeGLxgwg+JwiN5o5i+CkZpVWcJFi+uoqy7lkpPruGhxHeUlk7s/NlMCGK7Lp2/t3opibnzP6fzd8lP59rpXuPjkOs5fNIsDHb1EYnHKihN92sOJxOJEY05R2OiKxKjO8GUcjcUJH+N0zVjcOXikBxwqS4sIh4yQWfDOkOeqCaYfaxqyFLJCSQ71wOspn5uAc8fjB33zg2/iry8/hV0tncyfXk5NeXHf/6wHj/Sw80An7d0RKkqLONDew8v72umNxjl5TjXdkRgVJWFqyospLQpRVhymuqyIGRUlzKwsIRpzMKgoCdMdiWFmHAxaDlWlhfKfemIpKw73GxOpC9b2HalEF8vR7UxGM0YRDhmzq8uO+TiRiaJQvrEy/ZmV1t9lZiuBlQDHH3/8qH5QZWkRi+qq0u6wBJhfUpG1KXbJLyIlBRGZiAplUnoTsCDl83xgz8BC7n6ruze6e2NdXV3OKiciMtUUSnJ4GlhsZgvNrAS4BliT5zqJiExZBdHn4e5RM7sBeJjEVNbb3X1TnqslIjJlFURyAHD3tcDafNdDREQKp1tJREQKiJKDiIikUXIQEZE0Sg4iIpKmIJ6tNBpm1gy8lu965EAtcCDflcgTXfvUNZWvf7yv/QR3H/ZGsQmbHKYKM1s/kodkTUa69ql57TC1r79Qrl3dSiIikkbJQURE0ig5FL5b812BPNK1T11T+foL4to15iAiImnUchARkTRKDjlmZgvM7FEz22xmm8zsr4L4TDNbZ2Zbg/cZQfxUM3vSzHrM7G8ynC9sZs+Z2S9yfS3HKpvXbmY7zewFM9tgZgW/mHiWr326mf3UzLYE53trPq7pWGTr+s3slOB3nny1mdln8nVdI5Hl3/1ng3O8aGY/MbNxW3FK3Uo5ZmZzgbnu/qyZVQPPAO8DPga0uPtNZrYKmOHuf2dms4ETgjKH3P0fB5zvc0AjUOPu787ltRyrbF67me0EGt19QsyFz/K1rwYed/fbgkfcV7j74Vxf07HI9r/74JxhYDdwrrsX7D1P2bp2M6sHfg8scfcuM7sXWOvud4xHvdVyyDF33+vuzwbb7cBmEsukXg2sDoqtJvEPA3ff7+5PA5GB5zKz+cC7gNtyUPUxy+a1TzTZunYzqwEuBn4UlOst9MQA4/a7vwzYXsiJAbJ+7UVAuZkVARVkWBQtW5Qc8sjMGoCzgaeAOe6+FxL/mIDZIzjFPwN/C8THqYrjJgvX7sCvzOwZSywfO2GM8dpPBJqBfw+6E28zs8pxrG7WZeF3n3QN8JNs1288jeXa3X038I/ALmAv0Oruvxqvuio55ImZVQH3AZ9x97ZRHP9uYL+7P5P1yo2zsV574AJ3fzNwJXC9mV2ctQqOoyxcexHwZuAWdz8bOAKsymIVx1WWfvcE3WnvBf4rW3Ubb1n4f34GidbGQmAeUGlmf5rdWh6l5JAHZlZM4h/JXe7+syC8L+ibTPZR7h/mNBcA7w363u8G3m5m/zlOVc6aLF077r4neN8P3A8sG58aZ0+Wrr0JaHL3p4LPPyWRLApetn73gSuBZ919X/Zrmn1ZuvZ3AK+6e7O7R4CfAeePV52VHHLMzIxEf/Fmd/9Wyq41wIpgewXwwFDncffPu/t8d28g0bz+jbuP218R2ZCtazezymBgj6BL5XLgxezXOHuy+Ht/A3jdzE4JQpcBL2W5ulmXretPcS0TpEspi9e+CzjPzCqCc15GYvxifLi7Xjl8AReS6C/fCGwIXlcBs4BHgK3B+8yg/HEk/lpsAw4H2zUDznkJ8It8X1uurp1Ev/vzwWsT8IV8X1suf+/AUmB9cK6fk5jlkvdrzOH1VwAHgWn5vq48XPv/BbaQ+GPoP4DS8aq3prKKiEgadSuJiEgaJQcREUmj5CAiImmUHEREJI2Sg4iIpFFyEBGRNEoOIiKSRslBRETS/H9C/DzPluW5gwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "t=list(test.Date.values)\n", - "t" + "%matplotlib inline \n", + "import matplotlib.pyplot as plt\n", + "plt.plot(btcLis,bitcoin.Open.values)" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "scrolled": true - }, + "execution_count": 269, + "metadata": {}, "outputs": [ { - "ename": "AttributeError", - "evalue": "'numpy.str_' object has no attribute 'toordinal'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdates\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdates\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 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"\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 2574\u001b[0m \u001b[0mvargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0m_n\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_n\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2576\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_vectorize_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mufunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0motypes\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_ufunc_and_otypes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2648\u001b[0m \u001b[0;31m# Convert args to object arrays first\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/numpy/lib/function_base.py\u001b[0m in \u001b[0;36m_get_ufunc_and_otypes\u001b[0;34m(self, func, args)\u001b[0m\n\u001b[1;32m 2604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2605\u001b[0m \u001b[0minputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2606\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2607\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2608\u001b[0m \u001b[0;31m# Performance note: profiling indicates that -- for simple\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36m_to_ordinalf\u001b[0;34m(dt)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0mtzi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUTC\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0mbase\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoordinal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;31m# If it's sufficiently datetime-like, it will have a `date()` method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'numpy.str_' object has no attribute 'toordinal'" - ] + "data": { + "text/plain": [ + "array([-171.9 , -672.7 , 571.6 , ..., 5. , -10.1 , 1.09])" + ] + }, + "execution_count": 269, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "import matplotlib.dates as dates\n", - "datesNew = dates.date2num(t)" + "bitcoin.Open.values-bitcoin.Close.values" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 270, "metadata": {}, "outputs": [], "source": [ - "dates.date2num?" + "pozitivniDani = bitcoin.Open.values-test.Close.values" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 271, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "956" + ] + }, + "execution_count": 271, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "import numpy as np" + "(pozitivniDani>=0).sum()\n", + "(pozitivniDani<0).sum()\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 272, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "numpy.datetime64?" + "from matplotlib.ticker import FuncFormatter\n", + "\n", + "\n", + "x = np.arange(2)\n", + "brojDana = [(pozitivniDani>=0).sum(), (pozitivniDani<0).sum()]\n", + "barlist=plt.bar(['Pozitivni Dani', 'Negativni Dani'], brojDana)\n", + "barlist[1].set_color('r')\n", + "plt.show()" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 273, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data to plot\n", + "labels = 'Pozitivni Dani', 'Negativni Dani'\n", + "sizes = [(pozitivniDani>=0).sum(), (pozitivniDani<0).sum()]\n", + "colors = ['lightskyblue', 'lightcoral']\n", + "explode = (0.1, 0) # explode 1st slice\n", + " \n", + "# Plot\n", + "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n", + " autopct='%1.1f%%', shadow=True, startangle=140)\n", + " \n", + "plt.axis('equal')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", "metadata": {}, + "source": [ + "Graf volatilnosti - razlika izmeđuu otvarajuće i zatvarajuće cijene Bitcoina" + ] + }, + { + "cell_type": "code", + "execution_count": 274, + "metadata": { + "scrolled": false + }, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 8, + "execution_count": 274, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2766,91 +1830,122 @@ } ], "source": [ - "plt.plot(test.Date.values,test.Open.values)" + "#%matplotlib inline \n", + "volatilnost = bitcoin.High.values-bitcoin.Low.values\n", + "plt.plot(btcLis,volatilnost)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 275, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "Int64Index([2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018,\n", + " ...\n", + " 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013],\n", + " dtype='int64', length=1760)" + ] + }, + "execution_count": 275, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "test.dtypes" + "import pandas as pd\n", + "\n", + "dates2 =pd.DatetimeIndex(btcLis)\n", + "\n", + "years = dates2.year\n", + "years" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 276, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "volatilnost = bitcoin.High.values-bitcoin.Low.values\n", + "volatilnost\n", + "plt.plot(btcLis,volatilnost)\n", + "\n", + "\n", + "x = np.arange(len(volatilnost))\n", + "barlist=plt.bar(dates, volatilnost)\n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "test.Date" + "Analiza koreliranosti cijena kriptovaluta" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 314, "metadata": {}, "outputs": [], "source": [ - "dates=[]\n", - "opens =[]\n", - "index=[]\n", - "n = len(test.Date)\n", - "for i in range(0,n):\n", - " dates.append(test.Date[i])\n", - " opens.append(test.Open[n-i-1])\n", - " index.append(i)" + "ethereum = pd.read_csv('data/ethereum_price.csv')\n", + "ethLis=[]\n", + " \n", + "getDate(ethLis,date(2015, 8, 7),date(2018, 2, 20))" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'numpy.str_' object has no attribute 'toordinal'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdatees\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdate2num\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdates\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m 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\u001b[0;34m[\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mflat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2606\u001b[0;31m \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minputs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2607\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2608\u001b[0m \u001b[0;31m# Performance note: profiling indicates that -- for simple\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/anaconda3/lib/python3.6/site-packages/matplotlib/dates.py\u001b[0m in \u001b[0;36m_to_ordinalf\u001b[0;34m(dt)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0mtzi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mUTC\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0mbase\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtoordinal\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;31m# If it's sufficiently datetime-like, it will have a `date()` method\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mAttributeError\u001b[0m: 'numpy.str_' object has no attribute 'toordinal'" - ] - } - ], + "execution_count": 325, + "metadata": { + "scrolled": false + }, + "outputs": [], "source": [ - "import matplotlib\n", - "datees = matplotlib.dates.date2num(dates)\n", - "matplotlib.pyplot.plot_date(datees, opens)" + "ripple = pd.read_csv('data/ripple_price.csv')\n", + "ripLis=[]\n", + " \n", + "getDate(ripLis,date(2013, 8, 4),date(2018, 2, 20))" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 326, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 13, + "execution_count": 326, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2858,9 +1953,41 @@ } ], "source": [ - "plt.plot(index[-1600:], opens[-1600:])" + "fig3, (lijevo, srednji, desno) = plt.subplots(3, 1 , sharex=True)\n", + "x2 = np.linspace(0, 2)\n", + "lijevo.plot(btcLis,bitcoin.Open.values)\n", + "srednji.plot(ethLis,ethereum.Open.values)\n", + "desno.plot(ripLis,ripple.Open.values)" ] }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/mykey b/mykey new file mode 100644 index 0000000..e69de29 diff --git a/mykey.pem b/mykey.pem new file mode 100644 index 0000000..e69de29 diff --git a/transaction.png b/transaction.png new file mode 100644 index 0000000..c39f6b5 Binary files /dev/null and b/transaction.png differ