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currency_exchange_via_btc.py
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currency_exchange_via_btc.py
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#!/usr/bin/env python
"""
A little script to see how profitable it can be to use BTC to exchange currencies
python currency_exchange_via_btc.py --cur1 EUR --cur2 USD --size 100 --nodownload
"""
import os
import argparse
import pandas as pd
import numpy as np
from src.markets import *
from src.fx_currency_api import ForeignExchancheCurrencyApi
from time import sleep
from datetime import datetime, timedelta
class ConvertCurrenciesViaBTC:
def __init__(self, args):
self.args = args
self.cur1 = args.cur1.upper() #'EUR' # sent cur
self.cur2 = args.cur2.upper() #'USD' # requ cur
def update(self):
if args.size==None:
self.size = 1
else:
self.size = float(args.size)
self.load_fx_market()
self.load_btc_market()
def load_fx_market(self):
print("Loading FX market")
self.fx_api = ForeignExchancheCurrencyApi()
self.fx_api.cur1 = self.cur1
self.fx_api.cur2 = self.cur2
self.fx_api.send_amount = self.size
self.fx_api.data_path = os.path.dirname(__file__) # args.basepath
if not (args.nodownload or args.nodownloadFX):
self.fx_api.download()
self.fx_api.write_json()
else:
self.fx_api.read_json()
if args.printjson:
self.fx_api.pretty_print_json()
#requ_amount = self.fx_api.requ_amount()
#print("You will get {requ_amount}{cur2} for {send_amount}{cur1} - {cur1}{cur2}@{rate}".format(
# requ_amount=requ_amount, send_amount=self.fx_api.send_amount,
# cur1=self.fx_api.cur1, cur2=self.fx_api.cur2, rate=requ_amount/self.fx_api.send_amount))
self.fx_rate = self.fx_api.rate()
#print(self.fx_rate)
def load_btc_market(self):
print("Loading BTC market")
self.df_markets_btc = pd.read_csv(os.path.join(self.args.basepath, 'markets_btc.csv'), sep=';')
#self.df_markets_exchangers = pd.read_csv('markets_exchangers.csv', sep=';')
# raise TypeError('unhashable type')
self.df_markets = self.df_markets_btc
#self.df_markets = pd.concat([self.df_markets_btc, self.df_markets_exchangers])
# see https://www.aurumxchange.com/data/rates.json
#self.df_markets.index = range(len(self.df_markets))
#self.df_markets.
#print(self.df_markets)
self.df_markets['active'] = self.df_markets['active'].map(bool)
self.df_markets['symbol'] = self.df_markets['cur1'] + '/' + self.df_markets['cur2']
self.df_markets['fullmarketname'] = self.df_markets['market'] + '|' + self.df_markets['symbol']
self.df_markets = self.df_markets[self.df_markets['active']==True] # select only active market
self.df_markets['bid'] = None # NaN
self.df_markets['ask'] = None
#self.ob_dict = dict() # dictionary of order books
for i in self.df_markets.index:
market = self.df_markets['market'][i]
fullmarketname = self.df_markets['fullmarketname'][i]
#print(market)
symbol = self.df_markets['symbol'][i]
market_class = "OrderBook_{market}".format(market=market)
ob = eval(market_class)(self.args, symbol)
try:
self.df_markets['bid'][i] = ob.bid(self.size)
except:
print(" Can't get bid price for {symbol} (market is probably not big enough)".format(symbol=symbol)) # NaN instead of bid/ask
self.df_markets['bid'][i] = np.nan
try:
self.df_markets['ask'][i] = ob.ask(self.size)
except:
print(" Can't get ask for price {symbol} (market is probably not big enough)".format(symbol=symbol)) # NaN instead of bid/ask
self.df_markets['ask'][i] = np.nan
"""
stats = ob.get_stats()
for key, value in stats.items():
if i==0:
self.df_markets[key] = None
self.df_markets[key][i] = value
"""
self.df_markets['spread'] = self.df_markets['ask'] - self.df_markets['bid']
#self.df_markets = self.df_markets.reindex(self.df_markets['fullmarketname'])
self.df_markets.index = self.df_markets['fullmarketname']
print(self.df_markets)
filename = os.path.join(self.args.basepath, "data_out_markets/_markets.csv")
self.df_markets.to_csv(filename)
markets_buy = self.df_markets[self.df_markets['cur2']==self.cur1]
markets_sell = self.df_markets[self.df_markets['cur2']==self.cur2]
self.df_matrix = pd.DataFrame(index=markets_buy['fullmarketname'], columns=markets_sell['fullmarketname'])
self.df_ask = self.df_matrix.copy() # pd.DataFrame(index=markets_buy['fullmarketname'], columns=markets_sell['fullmarketname']) #self.df_matrix
self.df_bid = self.df_matrix.copy() # pd.DataFrame(index=markets_buy['fullmarketname'], columns=markets_sell['fullmarketname']) #self.df_matrix
for mk_buy in markets_buy.index:
self.df_ask.ix[mk_buy] = self.df_markets.ix[mk_buy]['ask']
for mk_sell in markets_sell.index:
self.df_bid[mk_sell] = self.df_markets.ix[mk_sell]['bid'] # ToImprove: this can probably be done outside this for loop
filename = os.path.join(self.args.basepath, "data_out_markets/_markets_matrix_ask.csv")
self.df_ask.to_csv(filename)
filename = os.path.join(self.args.basepath, "data_out_markets/_markets_matrix_bid.csv")
self.df_bid.to_csv(filename)
self.df_matrix = self.df_bid/self.df_ask
print(self.df_matrix)
filename = os.path.join(self.args.basepath, "data_out_markets/_markets_matrix.csv")
self.df_matrix.to_csv(filename)
self.df_convert_opportunities_all = pd.DataFrame(self.df_matrix.unstack(), columns=['rate'])
self.df_convert_opportunities_all = self.df_convert_opportunities_all[self.df_convert_opportunities_all['rate']>-100]
self.df_convert_opportunities_all = self.df_convert_opportunities_all.sort('rate', ascending=False)
self.df_convert_opportunities_all.index.names = ['market_sell', 'market_buy']
self.df_convert_opportunities_all = self.df_convert_opportunities_all.swaplevel(0, 1, axis=0) # swap to have market_buy as first hierarchical index
#print(self.df_convert_opportunities_all)
self.df_convert_opportunities_all['rateFX'] = self.fx_rate
self.df_convert_opportunities_all['rel diff (fx-btc)'] = (self.df_convert_opportunities_all['rate'] - self.fx_rate) / self.fx_rate * 100.0
#self.df_convert_opportunities_all[self.cur1] = 'ToDo'
#self.df_convert_opportunities_all[self.cur2] = 'ToDo'
#print("{cur1}/{cur2} = {fx_rate}".format(cur1=self.cur1, cur2=self.cur2, fx_rate=self.fx_rate))
self.df_convert_opportunities_all = self.df_convert_opportunities_all.sort('rate', ascending=False)
filename = os.path.join(self.args.basepath, "data_out_markets/_currency_exch_via_btc.csv")
self.df_convert_opportunities_all.to_csv(filename)
self.df_convert_opportunities_all = self.df_convert_opportunities_all.sort('rate', ascending=True)
print(self.df_convert_opportunities_all)
#print(market_buy)
#print(market_sell)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Use the following parameters')
parser.add_argument('--send_amount', action="store", help="use this flag to set how much currency is send")
parser.add_argument('--nodownload', action="store_true", help="use this flag to avoid downloading data (will use a previously downloaded file)")
parser.add_argument('--nodownloadFX', action="store_true", help="use this flag to avoid downloading FX data (will use a previously downloaded file)")
# parser.add_argument('--nodownloadBTC', action="store_true", help="use this flag to avoid downloading BTC orderbooks (will use a previously downloaded file)")
parser.add_argument('--printjson', action="store_true", help="use this flag to pretty print JSON")
parser.add_argument('--printmk', action="store_true", help="use this flag to print market")
parser.add_argument('--nocalculate', action="store_true", help="use this flag to disable calculus with DataFrame")
parser.add_argument('--showstats', action="store_true", help="use this flag to show market stats")
parser.add_argument('--plot', action="store_true", help="use this flag to display order book depth graph")
parser.add_argument('--size', action="store", help="use this flag to set arbitrage size (in BTC)")
parser.add_argument('--cur1', action="store", help="use this flag to set currency code you send")
parser.add_argument('--cur2', action="store", help="use this flag to set currency code you request")
parser.add_argument('--loop', action="store", help="use this flag to run program in an infinite loop (LOOP parameters is pause in seconds)")
args = parser.parse_args()
args.basepath = os.path.dirname(__file__)
conv = ConvertCurrenciesViaBTC(args)
if args.loop==None:
conv.update()
else:
delay_s = float(args.loop)
while True:
conv.update()
dt_next = datetime.now() + timedelta(seconds=delay_s)
print("="*10)
print("Waiting... next update @ {dt_next}".format(dt_next=dt_next.strftime("%Y-%m-%d %H:%M")))
sleep(delay_s)