api = Binance('', '')
# 1m, 3m, 5m, 15m, 30m
# 1h, 2h, 4h, 6h, 8h
# 12h, 1d, 3d, 1w, 1M
period = '5m'
limit = 1000
base = 'BTCUSDT'
pairs = [
'ETHUSDT', 'BNBUSDT', 'BCCUSDT', 'EOSUSDT', 'ADAUSDT', 'LTCUSDT',
'NEOUSDT', 'XLMUSDT', 'XRPUSDT'
]
symb1 = pd.DataFrame(api.candlesticks(symbol=base,
interval=period,
limit=limit),
dtype=np.float)
x = symb1[4].values
for pair in pairs:
try:
print('{}\n{} vs {}:'.format(40 * '=', base, pair))
symb2 = pd.DataFrame(api.candlesticks(symbol=pair,
interval=period,
limit=limit),
dtype=np.float)
y = symb2[4].values
fn = path + base + '-' + pair + '_' + period
calculate_cointegration_scores(x,
symbols = ['BTCUSDT', 'ETHUSDT', 'BNBETH', 'BNBUSDT', 'BCCUSDT']
period = '1m'
limit = 20
ticks = 0
save_period = 1000
path = 'E:/Projects/market-analysis-system/data/crypto/'
data = dict(zip(symbols, [dict({'data':[]}) for i in symbols]))
while True:
try:
start_time = time.time()
for symbol in symbols:
candles = pd.DataFrame(api.candlesticks(symbol=symbol, interval=period, limit=limit), dtype=np.float)
tickers = pd.DataFrame(api.tickers(symbol=symbol, limit=limit))
trades = pd.DataFrame(api.aggr_trades(symbol=symbol, limit=limit), dtype=np.float)
# data[symbol]['candles'] = np.column_stack((candles.values[:, 1:6], # o,h,l,c,v
# candles.values[:, 7:11])) # qav, nt, bv, qv
# data[symbol]['tickers'] = np.column_stack(([np.array([x[0:2] for x in tickers['bids'].values], dtype=np.float),
# np.array([x[0:2] for x in tickers['asks'].values], dtype=np.float)]))
# data[symbol]['trades'] = trades[['p', 'q']].values
data[symbol]['data'] = np.append(data[symbol]['data'], np.column_stack((np.column_stack((candles.values[:, 1:6], # o,h,l,c,v
candles.values[:, 7:11])), # qav, nt, bv, qv
np.column_stack(([np.array([x[0:2] for x in tickers['bids'].values], dtype=np.float),
np.array([x[0:2] for x in tickers['asks'].values], dtype=np.float)])),
trades[['p', 'q']].values))).reshape(len(data[symbol]['data'])+1, limit*15)
if ticks % save_period == 0:
for symbol in symbols:
# labnotebook
model_desc = {'pair_1': symb1, 'pair_2': symb2,
'period': period,
symb1: balance[symb1], symb2: balance[symb2],
'USD': balance['usd'], 'sum_balance': balance['sum'],
'stop_size': stop_size}
experiment = labnotebook.start_experiment(model_desc=model_desc)
while True:
try:
sleep(30)
# update coefficients
if tick_count % 100000 == 0:
x = pd.DataFrame(
api.candlesticks(symbol=symb1,
interval=period, limit=limit),
dtype=np.float)[4].values
y = pd.DataFrame(
api.candlesticks(symbol=symb2,
interval=period, limit=limit),
dtype=np.float)[4].values
eps, mu, std = calculate_cointegration_scores(x, y)
# Update prices
new_prices = {symb1: float(api.ticker_price(symbol=symb1)['price']),
symb2: float(api.ticker_price(symbol=symb2)['price'])}
z_score = (new_prices[symb1] - eps * new_prices[symb2] - mu) / std
past_z_score = (past_prices[symb1] - eps * past_prices[symb2] - mu) / std
# Check stop levels