import matplotlib.pyplot as plt
import boto
import schedule
import time
from logger import logging
import requests
session = requests.Session()
session.verify = False
import krakenex
from pykrakenapi import KrakenAPI
api = krakenex.API()
k = KrakenAPI(api)
api.load_key('kraken_keys.py')
df = k.get_trades_history(start=1558587601)[0]
df.index = df.index.tz_localize(tz='UTC').tz_convert('US/Central')
cols = ['ordertxid','cost', 'fee', 'margin', 'price', 'vol', 'type', 'ordertype']
df2 = df[cols].reset_index().groupby('ordertxid', as_index=False).agg({'dtime':'first', 'cost':'sum', 'fee':'sum', 'margin':'sum', 'price':'sum','vol':'sum', 'type':'first', 'ordertype':'first' })
df3 = df2.set_index('dtime').sort_values('dtime')
df3['is_closing'] = ['open' if x=='limit' else 'close' for x in df3['ordertype']]
def is_long(row):
order_type = row[0]
is_closing = row[1]
if ((order_type=='buy') & (is_closing=='open')) | ((order_type=='sell') & (is_closing=='close')):
return 'long'
else:
return 'short'
"""
import krakenex
from pykrakenapi import KrakenAPI
#import os
api = krakenex.API()
key = "ignored/new_key.key"
api.load_key(key)
api.query_private(method="Balance")
k = KrakenAPI(api)
k.get_account_balance()
k.get_trades_history()
ohlc, last = k.get_ohlc_data("BCHUSD")
print(ohlc)
print(last)
# open trade
descr = k.add_standard_order(pair="XBTEUR",
type="buy",
ordertype="market",
volume=0.002)
descr
k.get_open_orders(trades=True)
k.get_tradable_asset_pairs()
response = api.query_private('AddOrder', {
def load_data(start):
# Connect to API:
con = krakenex.API()
con.load_key(kraken_key_filepath)
api = KrakenAPI(con)
# Load past trading data:
count = 0
total_count = 1
df_list = []
unix_start = api.datetime_to_unixtime(pd.to_datetime(start))
while count < total_count:
df, all_count = api.get_trades_history(start=unix_start, ofs=count)
df = df[df['pair'] == CRYPTO + FIAT]
df_list.append(df)
total_count = all_count
count += len(df)
time.sleep(1)
df = pd.concat(df_list).reset_index()
df = df.drop_duplicates()
# Convert to Central time:
df['dtime'] = pd.to_datetime(
pd.to_datetime(df['dtime']).dt.tz_localize('UTC').dt.tz_convert(
'US/Central').dt.strftime('%Y-%m-%d %H:%M:%S'))
# Merge trades with the same order id:
df_wrangled = df.reset_index().groupby('ordertxid', as_index=False).agg({
'dtime':
'min',
'type':
'min',
'ordertype':
'min',
'price':
'min',
'cost':
'sum',
'fee':
'sum',
'vol':
'sum'
}).sort_values('dtime').reset_index(drop=True)
# Build buy/sell matching ids:
df_wrangled['trade_matchID'] = 0
buy = False
matchID = 1
for i, row in df_wrangled.iterrows():
# Skip the first trade if it's a sell
if row['type'] == 'sell':
if buy:
df_wrangled['trade_matchID'][i] = matchID
matchID += 1
buy = False
else:
continue
elif row['type'] == 'buy':
df_wrangled['trade_matchID'][i] = matchID
buy = True
# Put the buy and the sell on the same line:
df_sell = df_wrangled[df_wrangled['type'] == 'sell'].set_index(
'trade_matchID')
df_buy = df_wrangled[df_wrangled['type'] == 'buy'].set_index(
'trade_matchID')
df_trade = df_buy.merge(df_sell,
how='left',
left_index=True,
right_index=True,
suffixes=['_buy', '_sell'])
# Build new fields:
df_trade['time_held'] = df_trade['dtime_sell'] - df_trade['dtime_buy']
df_trade['time_held'] = [str(x) for x in df_trade['time_held']]
df_trade['fee_total'] = df_trade['fee_buy'] + df_trade['fee_sell']
df_trade['abs_return'] = df_trade['cost_sell'] - df_trade['cost_buy']
df_trade['feeAdjusted_abs_return'] = df_trade['cost_sell'] - df_trade[
'fee_total'] - df_trade['cost_buy']
df_trade['pct_return'] = df_trade['abs_return'] / df_trade['cost_buy']
df_trade['feeAdjusted_pct_return'] = df_trade[
'feeAdjusted_abs_return'] / df_trade['cost_buy']
df_trade['winning_trade'] = [
1 if x > 0 else 0 for x in df_trade['feeAdjusted_pct_return']
]
return df_trade
