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@benjaminchodroff
Created August 24, 2017 19:47
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Momentum with Bittrex
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{
"cells": [
{
"cell_type": "code",
"execution_count": 92,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{u'C': 0.0703001, u'H': 0.0705, u'L': 0.0702001, u'O': 0.0704, u'BV': 3.35314478, u'T': u'2017-08-14T19:40:00', u'V': 47.67147669}\n"
]
}
],
"source": [
"import urllib2\n",
"import json\n",
"market=\"BTC-ETH\"\n",
"data=json.loads(urllib2.urlopen(\"https://bittrex.com/Api/v2.0/pub/market/GetTicks?marketName=\"+market+\"&tickInterval=oneMin\").read())['result']\n",
"print data[0]"
]
},
{
"cell_type": "code",
"execution_count": 93,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"df = pd.DataFrame(data).set_index(\"T\")"
]
},
{
"cell_type": "code",
"execution_count": 94,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df.index = pd.DatetimeIndex(df.index)"
]
},
{
"cell_type": "code",
"execution_count": 95,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"DatetimeIndex: 14398 entries, 2017-08-14 19:40:00 to 2017-08-24 19:37:00\n",
"Data columns (total 6 columns):\n",
"BV 14398 non-null float64\n",
"C 14398 non-null float64\n",
"H 14398 non-null float64\n",
"L 14398 non-null float64\n",
"O 14398 non-null float64\n",
"V 14398 non-null float64\n",
"dtypes: float64(6)\n",
"memory usage: 787.4 KB\n"
]
}
],
"source": [
"df.info()\n",
"# BV - ???\n",
"# C - CloseAsk\n",
"# H - HighAsk\n",
"# L - LowAsk\n",
"# O - OpenAsk\n",
"# V - Volume"
]
},
{
"cell_type": "code",
"execution_count": 96,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 97,
"metadata": {},
"outputs": [],
"source": [
"df['returns'] = np.log(df['C'] / df['C'].shift(1))"
]
},
{
"cell_type": "code",
"execution_count": 98,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"cols = []"
]
},
{
"cell_type": "code",
"execution_count": 99,
"metadata": {},
"outputs": [],
"source": [
"for momentum in [15, 30, 60, 120]: \n",
" col = 'position_%s' % momentum\n",
" df[col] = np.sign(df['returns'].rolling(momentum).mean())\n",
" cols.append(col)\n"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import seaborn as sns\n",
"sns.set() "
]
},
{
"cell_type": "code",
"execution_count": 102,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"strats = ['returns'] "
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [],
"source": [
"for col in cols:\n",
" strat = 'strategy_%s' % col.split('_')[1]\n",
" df[strat] = df[col].shift(1) * df['returns']\n",
" strats.append(strat)"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x119064690>"
]
},
"execution_count": 104,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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m9LM0yx04lWSZBgPYDcA9Wgez6/gVxg9p7zAJCcADvRuB0UhychYvPtTOMkBm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\njrk50alLCCG6myThfZRt21i2hWVbrKlez6wlrSeimcfcjUt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IVmMZIYxoNeG6DWT1nYzm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"text/plain": [
"<matplotlib.figure.Figure at 0x11805cc90>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"df[strats].dropna().cumsum().apply(np.exp).plot()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.13"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
@benjaminchodroff
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Did I make a mistake???

@saurabh-rajput
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your logic supposes making decisions every 1 min .. this is not sustainable. also returns will be random if you check returns of 1 min.. Price generally corrects after a surge.. so you will see a negative return.. try to hold position for longer term.. Also try to include transaction costs that exchange levies on your trade volume

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