wehub-resource-sync
146 lines
6.8 KiB
Python
146 lines
6.8 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
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# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from AlgorithmImports import *
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### <summary>
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### The demonstration algorithm shows some of the most common order methods when working with Crypto assets.
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### </summary>
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### <meta name="tag" content="using data" />
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### <meta name="tag" content="using quantconnect" />
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### <meta name="tag" content="trading and orders" />
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class BasicTemplateCryptoAlgorithm(QCAlgorithm):
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def initialize(self):
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'''Initialise the data and resolution required, as well as the cash and start-end dates for your algorithm. All algorithms must initialized.'''
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self.set_start_date(2018, 4, 4) #Set Start Date
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self.set_end_date(2018, 4, 4) #Set End Date
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# Although typically real brokerages as GDAX only support a single account currency,
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# here we add both USD and EUR to demonstrate how to handle non-USD account currencies.
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# Set Strategy Cash (USD)
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self.set_cash(10000)
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# Set Strategy Cash (EUR)
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# EUR/USD conversion rate will be updated dynamically
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self.set_cash("EUR", 10000)
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# Add some coins as initial holdings
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# When connected to a real brokerage, the amount specified in SetCash
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# will be replaced with the amount in your actual account.
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self.set_cash("BTC", 1)
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self.set_cash("ETH", 5)
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self.set_brokerage_model(BrokerageName.GDAX, AccountType.CASH)
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# You can uncomment the following lines when live trading with GDAX,
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# to ensure limit orders will only be posted to the order book and never executed as a taker (incurring fees).
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# Please note this statement has no effect in backtesting or paper trading.
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# self.default_order_properties = GDAXOrderProperties()
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# self.default_order_properties.post_only = True
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# Find more symbols here: http://quantconnect.com/data
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self.add_crypto("BTCUSD", Resolution.MINUTE)
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self.add_crypto("ETHUSD", Resolution.MINUTE)
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self.add_crypto("BTCEUR", Resolution.MINUTE)
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symbol = self.add_crypto("LTCUSD", Resolution.MINUTE).symbol
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# create two moving averages
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self.fast = self.ema(symbol, 30, Resolution.MINUTE)
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self.slow = self.ema(symbol, 60, Resolution.MINUTE)
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def on_data(self, data):
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'''OnData event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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Arguments:
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data: Slice object keyed by symbol containing the stock data
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'''
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# Note: all limit orders in this algorithm will be paying taker fees,
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# they shouldn't, but they do (for now) because of this issue:
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# https://github.com/QuantConnect/Lean/issues/1852
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if self.time.hour == 1 and self.time.minute == 0:
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# Sell all ETH holdings with a limit order at 1% above the current price
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limit_price = round(self.securities["ETHUSD"].price * 1.01, 2)
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quantity = self.portfolio.cash_book["ETH"].amount
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self.limit_order("ETHUSD", -quantity, limit_price)
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elif self.time.hour == 2 and self.time.minute == 0:
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# Submit a buy limit order for BTC at 5% below the current price
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usd_total = self.portfolio.cash_book["USD"].amount
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limit_price = round(self.securities["BTCUSD"].price * 0.95, 2)
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# use only half of our total USD
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quantity = usd_total * 0.5 / limit_price
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self.limit_order("BTCUSD", quantity, limit_price)
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elif self.time.hour == 2 and self.time.minute == 1:
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# Get current USD available, subtracting amount reserved for buy open orders
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usd_total = self.portfolio.cash_book["USD"].amount
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usd_reserved = sum(x.quantity * x.limit_price for x
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in [x for x in self.transactions.get_open_orders()
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if x.direction == OrderDirection.BUY
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and x.type == OrderType.LIMIT
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and (x.symbol.value == "BTCUSD" or x.symbol.value == "ETHUSD")])
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usd_available = usd_total - usd_reserved
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self.debug("usd_available: {}".format(usd_available))
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# Submit a marketable buy limit order for ETH at 1% above the current price
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limit_price = round(self.securities["ETHUSD"].price * 1.01, 2)
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# use all of our available USD
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quantity = usd_available / limit_price
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# this order will be rejected (for now) because of this issue:
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# https://github.com/QuantConnect/Lean/issues/1852
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self.limit_order("ETHUSD", quantity, limit_price)
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# use only half of our available USD
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quantity = usd_available * 0.5 / limit_price
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self.limit_order("ETHUSD", quantity, limit_price)
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elif self.time.hour == 11 and self.time.minute == 0:
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# Liquidate our BTC holdings (including the initial holding)
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self.set_holdings("BTCUSD", 0)
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elif self.time.hour == 12 and self.time.minute == 0:
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# Submit a market buy order for 1 BTC using EUR
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self.buy("BTCEUR", 1)
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# Submit a sell limit order at 10% above market price
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limit_price = round(self.securities["BTCEUR"].price * 1.1, 2)
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self.limit_order("BTCEUR", -1, limit_price)
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elif self.time.hour == 13 and self.time.minute == 0:
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# Cancel the limit order if not filled
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self.transactions.cancel_open_orders("BTCEUR")
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elif self.time.hour > 13:
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# To include any initial holdings, we read the LTC amount from the cashbook
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# instead of using Portfolio["LTCUSD"].quantity
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if self.fast > self.slow:
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if self.portfolio.cash_book["LTC"].amount == 0:
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self.buy("LTCUSD", 10)
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else:
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if self.portfolio.cash_book["LTC"].amount > 0:
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self.liquidate("LTCUSD")
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def on_order_event(self, order_event):
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self.debug("{} {}".format(self.time, order_event.to_string()))
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def on_end_of_algorithm(self):
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self.log("{} - TotalPortfolioValue: {}".format(self.time, self.portfolio.total_portfolio_value))
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self.log("{} - CashBook: {}".format(self.time, self.portfolio.cash_book))
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