def initialize(context):
log.info('initializing arbitrage algorithm')
# The context contains a new "exchanges" attribute which is a dictionary
# of exchange objects by exchange name. This allow easy access to the
# exchanges.
context.buying_exchange = context.exchanges['poloniex']
context.selling_exchange = context.exchanges['bitfinex']
context.trading_pair_symbol = 'eth_btc'
context.trading_pairs = dict()
# Note the second parameter of the symbol() method
# Passing the exchange name here returns a TradingPair object including
# the exchange information. This allow all other operations using
# the TradingPair to target the correct exchange.
context.trading_pairs[context.buying_exchange] = \
symbol('eth_btc', context.buying_exchange.name)
context.trading_pairs[context.selling_exchange] = \
symbol(context.trading_pair_symbol, context.selling_exchange.name)
context.entry_points = [
dict(gap=0.03, amount=0.05),
dict(gap=0.04, amount=0.1),
dict(gap=0.05, amount=0.5),
]
context.exit_points = [
dict(gap=-0.02, amount=0.5),
]
context.SLIPPAGE_ALLOWED = 0.02
pass
def initialize(context):
"""
Initialise algorithm
"""
# set exchanges
context.bitfinex = context.exchanges['bitfinex']
context.poloniex = context.exchanges['poloniex']
# set trading pairs
context.bitfinex_trading_pair = symbol('btc_usdt', context.bitfinex.name)
context.poloniex_trading_pair = symbol('btc_usdt', context.poloniex.name)
def load_pair(data, pair):
sy = symbol(pair)
startdate = symbol(pair).start_date
enddate = symbol(pair).end_minute
min_count = tdelta(startdate, enddate)
c_data = data.history(sy, DATA_KEYS, min_count, '1T')
print("{}: reading {} first: {} last: {} minutes: {}".format(
datetime.now().strftime(t_f.DT_FORMAT), pair,
startdate.strftime(t_f.DT_FORMAT), enddate.strftime(t_f.DT_FORMAT),
min_count))
assert not c_data.empty, "{datetime.now().strftime(t_f.DT_FORMAT)}: empty dataframe from Catalyst"
return c_data
def handle_data(context, data):
context.github = get_dataset('github')
context.github.sort_index(level=0, inplace=True)
context.zec = data.history(symbol('zec_usdt'),
['price', ],
bar_count=365,
frequency="1d")
context.xmr = data.history(symbol('xmr_usdt'),
['price', ],
bar_count=365,
frequency="1d")
def handle_data(context, data):
context.github = get_dataset('github')
context.github.sort_index(level=0, inplace=True)
context.zec = data.history(symbol('zec_usdt'),
['price', ],
bar_count=365,
frequency="1d")
context.xmr = data.history(symbol('xmr_usdt'),
['price', ],
bar_count=365,
frequency="1d")
def initialize(context):
log.info('initializing pairs trading algorithm')
context.bitfinex = context.exchanges['bitfinex']
context.pair1 = symbol('btc_usd', context.bitfinex.name)
context.pair2 = symbol('xmr_usd', context.bitfinex.name)
context.swallow_errors = True
context.errors = []
context.threshold = 2.6
context.in_high = False
context.in_low = False
pass
def initialize(context):
for attempt in context.attempts:
context.attempts[attempt] = 100
context.trading_pairs = [
"btc_usdt",
"eth_usdt",
"ltc_usdt",
"eos_usdt",
"xrp_usdt",
"iota_usdt",
"bch_usdt",
"trx_usdt",
"xlm_usdt",
"ada_usdt",
]
context.trading_pairs = ["btc_usdt", "eth_usdt", "ltc_usdt"]
context.allocation = 1 / len(context.trading_pairs)
context.currently_trading = 0
atr_length = 28
atr_multiplier = 3
context.rsi_length = 14
dip_levels = 31.5
context.assets = []
context.day_timer = 1439
context.btc_trading_execution_time = 1440
context.trading_execution_time = 240
context.history_time = "4H"
context.btc_history_time = "1D"
context.btc_long = False
context.first_run = True
context.buy_all = BUY_ALL
context.liquidate_all = LIQUIDATE_ALL
if LIVE:
error_handler.push_application()
# Create BTC seperatly
asset = CryptoAsset(symbol("btc_usdt"), dip_levels, 2, 12,
context.allocation)
context.assets.append(asset)
for index, pair in enumerate(context.trading_pairs[1:]):
asset = CryptoAsset(symbol(pair), dip_levels, atr_multiplier,
atr_length, context.allocation)
context.assets.append(asset)
def initialize(context):
for attempt in context.attempts:
context.attempts[attempt] = 100
context.trading_pairs = [
"btc_usdt",
"eth_usdt",
"ltc_usdt",
"eos_usdt",
"xrp_usdt",
"iota_usdt",
"bch_usdt",
"trx_usdt",
"xlm_usdt",
"ada_usdt",
]
context.allocation = 0.5 / 9
atr_length = 12
atr_multiplier = 2
context.rsi_length = 14
dip_levels = 31.5
context.assets = []
context.day_timer = 1441
context.btc_long = False
context.trading_execution_time = 1440
context.history_time = "1D"
context.buy_all = BUY_ALL
context.liquidate_all = LIQUIDATE_ALL
context.first_run = True
if LIVE:
error_handler.push_application()
# context.exchanges['bitfinex'].get_balances()
# Create BTC seperatly
asset = CryptoAsset(symbol("btc_usdt"), dip_levels, atr_multiplier, atr_length, 0.5)
context.assets.append(asset)
for index, pair in enumerate(context.trading_pairs[1:]):
asset = CryptoAsset(
symbol(pair), dip_levels, atr_multiplier, atr_length, context.allocation
)
context.assets.append(asset)
def update_ts(self, ts, context):
self.catalyst_exchanges = [
context.exchanges[self.global_exchanges[i]]
for i in range(self.global_leg_num)
]
self.catalyst_assets = [
symbol(self.global_symbols[i], self.global_exchanges[i])
for i in range(self.global_leg_num)
]
if not ts:
return False
if not self.ts or self.ts.date() != ts.date():
self.ts = ts
eod = self.ts.date()
file_prefix = self._generate_file_prefix(eod)
self.storage_log_file = '{}.comics.log'.format(file_prefix)
self.storage_trade_file = '{}.trade.csv'.format(file_prefix)
self.storage_snapshot_file = '{}.snapshot.csv'.format(file_prefix)
self.storage_plot_file = '{}.png'.format(file_prefix)
logging.basicConfig(
filename = os.path.join(self.storage_log_folder, self.storage_log_file),\
format = '%(asctime)s - %(levelname)s - %(message)s',
level = self._get_log_level(self.storage_log_level)
)
logging.info('[multileg_config] updated eod to {}'.format(eod))
if self.global_mode != 'live':
set_commission(maker=self.sim_maker_fee_pct,
taker=self.sim_taker_fee_pct)
set_slippage(slippage=self.sim_slippage_pct)
return True
return False
def _init_func(self, context):
"""Sets up catalyst's context object and fetches external data"""
self._context_ref = context
self.state.load_from_context(context)
self.log.info(f"Starting strategy on iteration {self.state.i}")
self.state.asset = symbol(self.trading_info["ASSET"])
if self.is_backtest:
self.log.debug("Setting benchmark")
set_benchmark(self.state.asset)
if self._datasets.items():
if self.state.DATA_FREQ == "daily":
for dataset, manager in self._datasets.items():
manager.fetch_data()
else:
raise ValueError(
'Internal Error: Value of self.state.DATA_FREQ should be "minute" if you use Google Search Volume or Quandl datasets.'
)
self._extra_init(context)
if self.in_job and not self.is_backtest:
job = get_current_job()
if job.meta.get("PAUSED"):
self.notify("Your strategy has resumed!")
self.log.info(f"Resuming on trade iteration {self.state.i}")
self._load_state_end_time(context)
else:
self.notify("Your strategy has started!")
self.state.i = 0
self.state.errors = []
self.state.end = context.end
for k, v in self.trading_info.items():
if "__" not in k:
setattr(self.state, k, v)
self.log.info("Initilized Strategy")
self._check_configuration(context)
# Set self.state.BARS size to work with custom minute frequency
if self.state.DATA_FREQ == "minute":
self.state.BARS = int(
self.state.BARS * 24 * 60 / int(24 * 60 / int(self.state.MINUTE_FREQ))
)
self.date_init_reference = pd.Timestamp(
"2013-01-01 00:00:00", tz="utc"
) + pd.Timedelta(minutes=int(self.state.MINUTE_TO_OPERATE))
# Set commissions
context.set_commission(
maker=self.state.MAKER_COMMISSION, taker=self.state.TAKER_COMMISSION
)
self.state.dump_to_context(context)
def initialize(context):
log.info('Starting TALib macd')
context.ASSET_NAME = 'BTC_USD'
context.asset = symbol(context.ASSET_NAME)
context.ORDER_SIZE = 10
context.SLIPPAGE_ALLOWED = 0.05
context.errors = []
# Bars to look at per iteration should be bigger than SMA_SLOW
context.BARS = 365
context.COUNT = 0
context.macds = pd.Series()
context.macdSignals = pd.Series()
context.macdHists = pd.Series()
# Technical Analysis Settings
context.MACD_FAST = 12
context.MACD_SLOW = 26
context.MACD_SIGNAL = 9
# 买卖点
context.BTC_BUY_TIME = [pd.Timestamp('2018-03-01 23:59:00',tz='UTC'), pd.Timestamp('2018-04-01 23:59:00',tz='UTC'), pd.Timestamp('2018-05-01 23:59:00',tz='UTC'), pd.Timestamp('2018-06-01 23:59:00',tz='UTC')]
context.BTC_SELL_TIME = [pd.Timestamp('2018-07-01 23:59:00',tz='UTC'), pd.Timestamp('2018-08-01 23:59:00',tz='UTC'), pd.Timestamp('2018-09-01 23:59:00',tz='UTC'),pd.Timestamp('2018-10-01 23:59:00',tz='UTC')]
def initialize(context):
context.i = 0
context.asset = symbol('btc_usdt')
context.base_price = None
context.signal = SIGNAL_INIT
context.set_commission(maker=0.001, taker=0.001)
context.set_slippage(slippage=0.001)
def initialize(context):
log.info('Starting TALib Simple Example')
context.ASSET_NAME = 'BTC_USDT'
context.asset = symbol(context.ASSET_NAME)
context.ORDER_SIZE = 10
context.SLIPPAGE_ALLOWED = 0.05
context.errors = []
# Bars to look at per iteration should be bigger than SMA_SLOW
context.BARS = 365
context.COUNT = 0
# Technical Analysis Settings
context.SMA_FAST = 50
context.SMA_SLOW = 100
context.RSI_PERIOD = 14
context.RSI_OVER_BOUGHT = 80
context.RSI_OVER_SOLD = 20
context.RSI_AVG_PERIOD = 15
context.MACD_FAST = 12
context.MACD_SLOW = 26
context.MACD_SIGNAL = 9
context.STOCH_K = 14
context.STOCH_D = 3
context.STOCH_OVER_BOUGHT = 80
context.STOCH_OVER_SOLD = 20
pass
def initialize(context):
log.info('Starting TALib Simple Example')
context.ASSET_NAME = 'BTC_USDT'
context.asset = symbol(context.ASSET_NAME)
context.ORDER_SIZE = 10
context.SLIPPAGE_ALLOWED = 0.05
context.swallow_errors = True
context.errors = []
# Bars to look at per iteration should be bigger than SMA_SLOW
context.BARS = 365
context.COUNT = 0
# Technical Analysis Settings
context.SMA_FAST = 50
context.SMA_SLOW = 100
context.RSI_PERIOD = 14
context.RSI_OVER_BOUGHT = 80
context.RSI_OVER_SOLD = 20
context.RSI_AVG_PERIOD = 15
context.MACD_FAST = 12
context.MACD_SLOW = 26
context.MACD_SIGNAL = 9
context.STOCH_K = 14
context.STOCH_D = 3
context.STOCH_OVER_BOUGHT = 80
context.STOCH_OVER_SOLD = 20
pass
def initialize(context):
log.info('Initializing Sure Fire Hedge Algorithm')
context.bitfinex = context.exchanges['poloniex']
context.asset = symbol('btc_usdt', context.bitfinex.name)
context.ORDER_SIZE = 0.1
context.BARS = 365
context.frequency = 'minute'
context.swallow_errors = True
context.errors = []
context.upper = 150
context.lower = 300
context.multiplyBy = 3
context.distance = 150
context.level = 1
context.SMA_FAST = 50
context.SMA_SLOW = 100
context.in_long = False
context.in_short = False
context.cost_basis = 1
pass
def __init__(self, market, strategy):
super(Agent, self).__init__()
self.logger.info("Initialized agent with {}".format(
strategy.__class__.__name__))
self.market = symbol(market)
self.strategy = strategy
def initialize(context):
context.ASSET_NAME = 'USDT_ETH'
context.asset = symbol(context.ASSET_NAME)
# For all trading pairs in the poloniex bundle, the default denomination
# currently supported by Catalyst is 1/1000th of a full coin. Use this
# constant to scale the price of up to that of a full coin if desired.
context.TICK_SIZE = 1.0
def initialize(context):
context.i = 0
context.asset = symbol('btc_usd')
context.base_price = None
context.stop_price = 0
context.stop_pct = 0.99
context.trailing_stop_price = 0œ
context.trailing_stop_pct = 0.98
def initialize(context):
# Select asset of interest
context.asset = symbol('BTC_USD')
# set_commission(TradingPairFeeSchedule(maker_fee=0.5, taker_fee=0.5))
# set_slippage(TradingPairFixedSlippage(spread=0.5))
# Set up a rebalance method to run every day
schedule_function(rebalance, date_rule=date_rules.every_day())
def initialize(context):
context.A = symbol('xmr_usd')
context.B = symbol('neo_usd')
context.leverage = 1.0 # 1.0 - no leverage
context.n_modelling = 72 # number of lookback bars for modelling
context.tf = str(
60
) + "T" # how many minutes in a timeframe; 1 - to get minute data (often errors happen); 60 - to get hourly data
context.z_signal_in = st.norm.ppf(
1 - 0.0001 / 2) # z-score threshold to open an order
context.z_signal_out = st.norm.ppf(
1 - 0.60 / 2) # z-score threshold to close an order
context.min_spread = 0.035 # Threshold for minimal allowed spread
context.set_commission(maker=0.001, taker=0.002)
context.set_slippage(slippage=0.0005)
def initialize(context):
# define asset
context.asset = symbol("btc_usd")
# amount of bars at specified timeframe
context.i = 0
# beginning price
context.base_price = None
def initialize(context):
context.ASSET_NAME = global_vars.TARGET_ASSET
if context.ASSET_NAME == "BCH_BTC":
context.ASSET_NAME = "BCC_BTC"
context.is_buying = True
context.asset = symbol(context.ASSET_NAME)
context.i = 0
def initialize(context):
context.asset1 = symbol('btc_usd')
context.asset2 = symbol('eth_usd')
context.asset3 = symbol('xmr_usd')
context.asset4 = symbol('xrp_usd')
context.changes1Length = 2
context.changes2Length = 2
context.emaOfChanges1Length = 24 # The length of the change indicator.
context.emaOfChanges2Length = 24 # The length of the change in change indicator.
context.bar_count = 25
context.order_size = 1
context.slippage_allowed = 0.05
context._changes1Ratio = -1.0
context._changes2Ratio = 0.0 # The influence of change in change upon fitness.
context._historyLength = 2
context._changes1Length = 2
context._changes2Length = 2
def initialize(context):
# Portfolio assets list
context.asset = symbol('btc_usd') # Bitcoin on Poloniex
# Creates a .CSV file with the same name as this script to store results
context.csvfile = open(FILE_NAME +'.csv', 'w+')
context.csvwriter = csv.writer(context.csvfile)
context.csvwriter.writerow(["Time", "price", "volume","high", "low","open","close"])
def initialize(context):
context.ASSET_NAME = 'btc_usdt'
context.TARGET_HODL_RATIO = 0.8
context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO
context.is_buying = True
context.asset = symbol(context.ASSET_NAME)
context.i = 0
def initialize(context):
context.ASSET_NAME = 'btc_usd'
context.TARGET_HODL_RATIO = 0.8
context.RESERVE_RATIO = 1.0 - context.TARGET_HODL_RATIO
context.is_buying = True
context.asset = symbol(context.ASSET_NAME)
context.i = 0
def initialize(context):
context.i = 0
context.asset = symbol('btc_usdt')
context.base_price = None
context.set_commission(maker=0.001, taker=0.001)
context.set_slippage(slippage=0.001)
context.last_prediction = 1000000
context.prediction = 0
context.num_hours = 0
context.train = pd.read_csv('../input/jan_june_btc_minute.csv')
def initialize(context):
context.ASSET_NAME = 'ltc_usdt'
context.asset = symbol(context.ASSET_NAME)
context.i = -1
context.TARGET_HODL_RATIO = 0.8
context.startParabola = 0 #begin parabolic movement
context.endParabola = 0 #end parabolic movement
context.trackingState = 0 #0=nonparabolic ,
def _handle_data(context, data):
global symbols
if symbols is None: symbols = [symbol(c + '_usdt') for c in coins]
print 'getting history for: %s' % [s.symbol for s in symbols]
history = data.history(
symbols,
['close', 'volume'],
bar_count=1, # EXCEPTION, Change to 2
frequency='5T')
def initialize(context):
"""
初始化
"""
context.i = 0 # 经历过的交易周期
context.asset = symbol('bnb_usdt') # 交易对
context.base_price = None # 初始价格
context.signal = SIGNAL_INIT # 交易信号
context.set_commission(maker=0.001, taker=0.001) # 设置手续费
context.set_slippage(slippage=0.001) # 设置滑点
def handle_data_polo_partial_candles(context, data):
history = data.history(symbol('btc_usdt'), ['volume'],
bar_count=10,
frequency='1D')
print('\nnow: %s\n%s' % (data.current_dt, history))
if not hasattr(context, 'i'):
context.i = 0
context.i += 1
if context.i > 5:
raise Exception('stop')
def initialize(context):
# This initialize function sets any data or variables that you'll use in
# your algorithm. For instance, you'll want to define the trading pair (or
# trading pairs) you want to backtest. You'll also want to define any
# parameters or values you're going to use.
# In our example, we're looking at Ether in USD Tether.
context.eth_btc = symbol('etc_usdt')
context.base_price = None
context.current_day = None
def initialize(context):
# Portfolio assets list
context.asset = symbol(symbol_str)
# Create an empty DataFrame to store results
#context.pricing_data_1m = pd.DataFrame()
#context.pricing_data_4h = pd.DataFrame()
context.pricing_data_1h = pd.DataFrame()
context.start_time = time.time()
def initialize(context):
"""
初始化
"""
log.info('策略初始化')
context.i = 0 # 经历过的交易周期
# 设置加密货币池
context.asset_pool = [symbol('bnb_usdt')]
context.set_commission(maker=0.001, taker=0.001) # 设置手续费
context.set_slippage(slippage=0.001) # 设置滑点
def initialize(context):
log.info('initializing algo')
context.ASSET_NAME = 'btc_usdt'
context.asset = symbol(context.ASSET_NAME)
context.TARGET_POSITIONS = 30
context.PROFIT_TARGET = 0.1
context.SLIPPAGE_ALLOWED = 0.02
context.errors = []
pass
def handle_data(context, data):
prices = data.history(
symbol('xlm_eth'),
fields=['open', 'high', 'low', 'close'],
bar_count=50,
frequency='1T'
)
set_print_settings()
print(prices.tail(10))
context.data.append(prices)
context.i = context.i + 1
if context.i == 3:
context.interrupt_algorithm()
def default_initialize(context):
# FIXME: set_benchmark
# set_benchmark(symbol(context.parameters.COIN_PAIR))
context.coin_pair = symbol(context.parameters.COIN_PAIR)
context.base_price = None
context.current_day = None
context.counter = -1
context.i = 0
context.candles_sample_rate = context.parameters.CANDLES_SAMPLE_RATE
context.candles_frequency = context.parameters.CANDLES_FREQUENCY
context.candles_buffer_size = context.parameters.CANDLES_BUFFER_SIZE
context.set_commission(
commission.PerShare(cost=context.parameters.COMMISSION_FEE))
def initialize(context):
# This initialize function sets any data or variables that you'll use in
# your algorithm. For instance, you'll want to define the trading pair (or
# trading pairs) you want to backtest. You'll also want to define any
# parameters or values you're going to use.
# In our example, we're looking at Neo in Ether.
context.market = symbol('eth_btc')
context.base_price = None
context.current_day = None
context.RSI_OVERSOLD = 50
context.RSI_OVERBOUGHT = 60
context.CANDLE_SIZE = '5T'
context.start_time = time.time()
context.set_commission(maker=0.001, taker=0.002)
def initialize(context):
log.info('initializing algo')
context.asset = symbol('eth_btc')
context.base_price = None
context.MAX_HOLDINGS = 0.2
context.RSI_OVERSOLD = 30
context.RSI_OVERSOLD_BBANDS = 45
context.RSI_OVERBOUGHT_BBANDS = 55
context.SLIPPAGE_ALLOWED = 0.03
context.TARGET = 0.15
context.STOP_LOSS = 0.1
context.STOP = 0.03
context.position = None
context.last_bar = None
context.errors = []
pass
def initialize(context):
context.asset = symbol('btc_usdt')
def initialize(context):
context.assets = [symbol('eth_btc'), symbol('eth_usdt')]
def initialize(context):
log.info('initializing')
context.asset = symbol('eth_btc')
context.base_price = None
def initialize(context):
context.i = 0
context.asset = symbol('ltc_usd')
context.base_price = None
def initialize(context):
context.asset1 = symbol('fct_btc')
context.asset2 = symbol('btc_usdt')
context.coins = [context.asset1, context.asset2]
def initialize(context):
print('initializing')
context.asset = symbol('xcp_btc')
def initialize(context):
context.asset = symbol('trx_btc')
