wehub-resource-sync
89 lines
3.9 KiB
Python
89 lines
3.9 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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### Algorithm demonstrating custom charting support in QuantConnect.
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### The entire charting system of quantconnect is adaptable. You can adjust it to draw whatever you'd like.
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### Charts can be stacked, or overlayed on each other. Series can be candles, lines or scatter plots.
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### Even the default behaviours of QuantConnect can be overridden.
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### </summary>
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### <meta name="tag" content="charting" />
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### <meta name="tag" content="adding charts" />
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### <meta name="tag" content="series types" />
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### <meta name="tag" content="plotting indicators" />
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class CustomChartingAlgorithm(QCAlgorithm):
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def initialize(self):
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self.set_start_date(2016,1,1)
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self.set_end_date(2017,1,1)
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self.set_cash(100000)
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spy = self.add_equity("SPY", Resolution.DAILY).symbol
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# In your initialize method:
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# Chart - Master Container for the Chart:
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stock_plot = Chart("Trade Plot")
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# On the Trade Plotter Chart we want 3 series: trades and price:
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stock_plot.add_series(Series("Buy", SeriesType.SCATTER, 0))
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stock_plot.add_series(Series("Sell", SeriesType.SCATTER, 0))
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stock_plot.add_series(Series("Price", SeriesType.LINE, 0))
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self.add_chart(stock_plot)
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# On the Average Cross Chart we want 2 series, slow MA and fast MA
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avg_cross = Chart("Average Cross")
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avg_cross.add_series(Series("FastMA", SeriesType.LINE, 0))
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avg_cross.add_series(Series("SlowMA", SeriesType.LINE, 0))
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self.add_chart(avg_cross)
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# There's support for candlestick charts built-in:
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weekly_spy_plot = Chart("Weekly SPY")
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spy_candlesticks = CandlestickSeries("SPY")
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weekly_spy_plot.add_series(spy_candlesticks)
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self.add_chart(weekly_spy_plot)
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self.consolidate(spy, Calendar.WEEKLY, lambda bar: self.plot("Weekly SPY", "SPY", bar))
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self.fast_ma = 0
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self.slow_ma = 0
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self.last_price = 0
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self.resample = datetime.min
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self.resample_period = (self.end_date - self.start_date) / 2000
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def on_data(self, slice):
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if slice["SPY"] is None: return
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self.last_price = slice["SPY"].close
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if self.fast_ma == 0: self.fast_ma = self.last_price
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if self.slow_ma == 0: self.slow_ma = self.last_price
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self.fast_ma = (0.01 * self.last_price) + (0.99 * self.fast_ma)
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self.slow_ma = (0.001 * self.last_price) + (0.999 * self.slow_ma)
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if self.time > self.resample:
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self.resample = self.time + self.resample_period
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self.plot("Average Cross", "FastMA", self.fast_ma)
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self.plot("Average Cross", "SlowMA", self.slow_ma)
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# On the 5th days when not invested buy:
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if not self.portfolio.invested and self.time.day % 13 == 0:
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self.order("SPY", (int)(self.portfolio.margin_remaining / self.last_price))
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self.plot("Trade Plot", "Buy", self.last_price)
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elif self.time.day % 21 == 0 and self.portfolio.invested:
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self.plot("Trade Plot", "Sell", self.last_price)
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self.liquidate()
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def on_end_of_day(self, symbol):
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#Log the end of day prices:
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self.plot("Trade Plot", "Price", self.last_price)
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