def dump_history():
frequency = '1m'
days_before = 365 * 3
key = 'VvD5-fMBfiZ9dlMtXP2pffHj'
secret = 'jROi3UZ5q_hkVW2RnK3xbCQEnTzpLXcnbOdUCJTnQFrrdUgj'
symbol = 'XBTUSD'
file_name = "%s_%s_%sdays.txt" % (symbol, frequency, days_before)
file_name = 'temp.txt'
client = RestClient(False, key, secret, symbol)
fp = open(file_name, 'w')
start_time = datetime.utcnow() - timedelta(days=days_before)
start_time = datetime(2020, 1, 29, 8, 57, tzinfo=tzutc())
wanted_ones = ['open', 'close', 'low', 'high', 'volume', 'timestamp']
while True:
start_time = start_time.strftime("%Y-%m-%d %H:%M")
filter = '{"symbol": "%s", "startTime": "%s"}' % (symbol, start_time)
temp = client.trade_bucket(binSize=frequency,
count=1000,
filter=filter)
ohlc = []
for t in temp:
d = {k: t[k] for k in wanted_ones}
ohlc.append(str(d))
sleep(2)
fp.write('\n'.join(ohlc))
fp.write('\n')
if len(temp) < 1000:
break
start_time = temp[-1]['timestamp'] + timedelta(minutes=1)
pass
fp.close()
def resbrk_parentchild(key, secret, product, frequency, child_frequency='5m'):
client = RestClient(False, key, secret, product)
symbol = BitMEXSymbol(product, client=client, frequency=frequency)
fp = open('XBTUSD_1h_365days.txt', 'r')
data = [
eval(d.replace('datetime.datetime', 'datetime'))
for d in fp.readlines()
]
fp.close()
parent_dataframe = symbol.fetch_data(datetime.utcnow(),
data=data,
frequency=frequency)
fp = open('XBTUSD_5m_100days.txt', 'r')
data = [
eval(d.replace('datetime.datetime', 'datetime'))
for d in fp.readlines()
]
fp.close()
child_dataframe = symbol.fetch_data(datetime.utcnow(),
data=data,
frequency=child_frequency)
# parent_dataframe = import_data(symbol, "XBTUSD_1h_365days.txt")
bt = ResistanceBreakoutParentChildBackTest(parent_dataframe.iloc[-1 * 24 *
30:],
child_dataframe,
frequency,
child_frequency=child_frequency)
bt.rolling_period = 15
bt.min_profit = 50
bt.min_loss = -100
bt.volume_factor = 0.1
bt.max_loss = -150
# macd, macd_sig = bt.MACD(12,26,9)
# bt.dataframe["macd"] = macd
# bt.dataframe["macd_sig"] = macd_sig
# bt.child_dataframe["rsi"] = bt.RSI(DF=bt.child_dataframe, n=10 * 14).copy()
bt.setup()
bt.run()
sum = 0
trades = []
time_series = bt.dataframe.index.tolist()
for r in bt.returns:
signal, entry_p, exit_p, net, best_price, oi, ci = r
# open_frame = bt.dataframe.iloc[oi]
open_time = time_series[oi]
# close_frame = bt.dataframe.iloc[ci]
close_time = time_series[ci]
frame = bt.dataframe.iloc[oi:ci + 1]
child_frame = bt.child_dataframe[open_time:close_time]
max_loss = -3 * int(entry_p / 150)
sum += net - 14.5
trades.append((net, signal, entry_p, exit_p, best_price, oi, ci, frame,
child_frame))
return bt
def sample(key, secret):
db = '/Users/jganesan/workspace/algotrading/symbols.sqlite3'
conn = sqlite3.connect(db)
key = "MjLgBPoZey_vp3rQ7uM0riqA"
secret = "1AiC5B6cqs4hRSgBDPU_KyU3rSCyMiIrCoD1mkkT-LjP3ymJ"
symbol = 'XBTUSD'
frequency = '5m'
client = client = RestClient(False, key, secret, symbol)
xbt = BitMEXSymbol(symbol,
conn=conn,
client=client,
frequency=frequency)
df1 = xbt.fetch_data(datetime.utcnow() - timedelta(days=3), count=740)
xbt.write_to_db(df1)
conn.close()
s = ResistanceBreakoutBackTest(df)
s.weighted = False
s.rolling_period = 10
s.min_profit = 13.5
s.min_loss = -20
s.setup()
s.run()
sum = 0
for r in s.returns:
sum += r[-1] - 13.5
def resbrk_backtest(key, secret, product, frequency):
client = RestClient(False, key, secret, product)
symbol = BitMEXSymbol(product, client=client, frequency=frequency)
# dataframe = pd.DataFrame(symbol.fetch_data(datetime.utcnow() - timedelta(days=3)))
# dataframe = import_data(symbol, 'XBTUSD_1h_365days.txt')
fp = open('XBTUSD_1h_365days.txt', 'r')
data = [
eval(d.replace('datetime.datetime', 'datetime'))
for d in fp.readlines()
]
fp.close()
dataframe = symbol.fetch_data(datetime.utcnow(),
data=data,
frequency=frequency)
res_bro = ResistanceBreakoutBackTest(dataframe.iloc[-1 * 24 * 30:])
res_bro.rolling_period = 15
# res_bro.min_profit = 50
# res_bro.min_loss = -100
# res_bro.max_loss = -150
res_bro.volume_factor = 0.1
res_bro.setup()
res_bro.run()
sum = 0
trades = []
time_series = res_bro.dataframe.index.tolist()
max_loss = 0
max_loss_frame = None
loss_frames = []
for r in res_bro.returns:
signal, entry_p, exit_p, net, best_price, oi, ci = r
max_loss = int(entry_p / 150) * -3
sum += max(max_loss, net) - 14.5
open_frame = res_bro.dataframe.iloc[oi]
open_time = time_series[oi]
close_frame = res_bro.dataframe.iloc[ci]
close_time = time_series[ci]
frame = res_bro.dataframe.iloc[oi:ci + 1]
trades.append(
(net, signal, entry_p, exit_p, best_price, oi, ci, frame))
if net < max_loss:
max_loss_frame = frame
max_loss = net
if net < 0:
loss_frames.append(
(net, signal, entry_p, exit_p, best_price, oi, ci, frame))
print(sum)
return res_bro
def renkomacd_backtest(key, secret, product, frequency):
client = RestClient(False, key, secret, product)
symbol = BitMEXSymbol(product, client=client, frequency=frequency)
# dataframe = pd.DataFrame(symbol.fetch_data(datetime.utcnow() - timedelta(days=365)))
dataframe = import_data(symbol, 'XBTUSD_5m_100days.txt')
backtest = RenkoMACDBackTest(dataframe.iloc[-1 * 24 * 30:])
# backtest.atr_period = 60
# backtest.slope_period = 3
# backtest.macd_array = (6, 15, 6)
backtest.min_profit = 0
backtest.min_loss = 0
backtest.setup()
backtest.run()
sum = 0
trades = []
time_series = backtest.dataframe.index.tolist()
max_loss = 0
max_loss_frame = None
loss_frames = []
for r in backtest.returns:
sum += max(-200, r[2]) - 14.5
open_frame = backtest.dataframe.iloc[r[0]]
open_time = time_series[r[0]]
close_frame = backtest.dataframe.iloc[r[1]]
close_time = time_series[r[1]]
frame = backtest.dataframe.iloc[r[0]:r[1] + 1]
trades.append((open_time, open_frame['Adj Close'], close_time,
close_frame['Adj Close'], r[2]))
if r[2] < max_loss:
max_loss_frame = frame
max_loss = r[2]
if r[2] < 0:
loss_frames.append((r[5], r[3], r[4], r[2], frame))
print(sum)
return backtest
pd.set_option('display.max_columns', 500)
pd.set_option('display.width', 1000)
with open("config.json", 'r') as f:
data = json.load(f)
test_env = False
if test_env is False:
params = data['prod']
else:
params = data['test']
key = params['key']
secret = params['secret']
product = params['symbol']
frequency = '1h'
logging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s', level=logging.INFO, filemode='a',
filename='app.log')
if test_env is True:
endpoint = "https://testnet.bitmex.com/api/v1"
else:
endpoint = "https://www.bitmex.com/api/v1"
client = RestClient(test_env, key, secret, product)
websocket = MyWebSocket(endpoint, product, key, secret)
websocket.connect_ws()
symbol = BitMEXSymbol(product, client=client, frequency=frequency)
dataframe = pd.DataFrame(symbol.fetch_data(datetime.utcnow() - timedelta(days=4), count=200))
res_bro = run_resistance_breakout()
s.weighted = False
s.rolling_period = 10
s.min_profit = 13.5
s.min_loss = -20
s.setup()
s.run()
sum = 0
for r in s.returns:
sum += r[-1] - 13.5
key = "VvD5-fMBfiZ9dlMtXP2pffHj"
secret = "jROi3UZ5q_hkVW2RnK3xbCQEnTzpLXcnbOdUCJTnQFrrdUgj"
product = 'XBTUSD'
frequency = '5m'
client = RestClient(False, key, secret, product)
symbol = BitMEXSymbol(product, client=client, frequency=frequency)
# now = datetime.utcnow()
# data = []
# fp = open('XBTUSD_5m_100days.txt', 'w')
# wanted_ones = ['open', 'close', 'low', 'high', 'volume', 'timestamp']
# for i in range(0, 100, 3):
# temp = symbol.fetch_data(datetime.utcnow() - timedelta(days=100 - i), count=864)
# ohlc = []
# for t in temp:
# d = {k: t[k] for k in wanted_ones}
# ohlc.append(str(d))
# sleep(2)
# fp.write('\n'.join(ohlc))
# fp.write('\n')
# fp.close()