wehub-resource-sync
93 lines
4.5 KiB
Python
93 lines
4.5 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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### Demonstration algorithm showing how to easily convert an old algorithm into the framework.
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###
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### 1. When making orders, also create insights for the correct direction (up/down/flat), can also set insight prediction period/magnitude/direction
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### 2. Emit insights before placing any trades
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### 3. Profit :)
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### </summary>
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### <meta name="tag" content="indicators" />
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### <meta name="tag" content="indicator classes" />
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### <meta name="tag" content="plotting indicators" />
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class ConvertToFrameworkAlgorithm(QCAlgorithm):
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'''Demonstration algorithm showing how to easily convert an old algorithm into the framework.'''
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fast_ema_period = 12
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slow_ema_period = 26
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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(2004, 1, 1)
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self.set_end_date(2015, 1, 1)
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self._symbol = self.add_security(SecurityType.EQUITY, 'SPY', Resolution.DAILY).symbol
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# define our daily macd(12,26) with a 9 day signal
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self._macd = self.macd(self._symbol, self.fast_ema_period, self.slow_ema_period, 9, MovingAverageType.EXPONENTIAL, Resolution.DAILY)
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def on_data(self, data):
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'''on_data event is the primary entry point for your algorithm. Each new data point will be pumped in here.
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Args:
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data: Slice object with your stock data'''
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# wait for our indicator to be ready
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if not self._macd.is_ready or not data.contains_key(self._symbol) or data[self._symbol] is None: return
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holding = self.portfolio[self._symbol]
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signal_delta_percent = float(self._macd.current.value - self._macd.signal.current.value) / float(self._macd.fast.current.value)
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tolerance = 0.0025
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# if our macd is greater than our signal, then let's go long
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if holding.quantity <= 0 and signal_delta_percent > tolerance:
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# 1. Call emit_insights with insights created in correct direction, here we're going long
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# The emit_insights method can accept multiple insights separated by commas
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self.emit_insights(
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# Creates an insight for our symbol, predicting that it will move up within the fast ema period number of days
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Insight.price(self._symbol, timedelta(self.fast_ema_period), InsightDirection.UP)
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)
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# longterm says buy as well
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self.set_holdings(self._symbol, 1)
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# if our macd is less than our signal, then let's go short
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elif holding.quantity >= 0 and signal_delta_percent < -tolerance:
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# 1. Call emit_insights with insights created in correct direction, here we're going short
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# The emit_insights method can accept multiple insights separated by commas
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self.emit_insights(
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# Creates an insight for our symbol, predicting that it will move down within the fast ema period number of days
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Insight.price(self._symbol, timedelta(self.fast_ema_period), InsightDirection.DOWN)
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)
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self.set_holdings(self._symbol, -1)
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# if we wanted to liquidate our positions
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## 1. Call emit_insights with insights create in the correct direction -- Flat
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#self.emit_insights(
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# Creates an insight for our symbol, predicting that it will move down or up within the fast ema period number of days, depending on our current position
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# Insight.price(self._symbol, timedelta(self.fast_ema_period), InsightDirection.FLAT)
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#)
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# self.liquidate()
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# plot both lines
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self.plot("MACD", self._macd, self._macd.signal)
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self.plot(self._symbol.value, self._macd.fast, self._macd.slow)
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self.plot(self._symbol.value, "Open", data[self._symbol].open)
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