wehub-resource-sync
100 lines
4.5 KiB
Python
100 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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from Selection.FundamentalUniverseSelectionModel import FundamentalUniverseSelectionModel
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class SykesShortMicroCapAlpha(QCAlgorithm):
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''' Alpha Streams: Benchmark Alpha: Identify "pumped" penny stocks and predict that the price of a "pumped" penny stock reverts to mean
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This alpha is part of the Benchmark Alpha Series created by QuantConnect which are open
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sourced so the community and client funds can see an example of an alpha.'''
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def initialize(self):
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self.set_start_date(2018, 1, 1)
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self.set_cash(100000)
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# Set zero transaction fees
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self.set_security_initializer(lambda security: security.set_fee_model(ConstantFeeModel(0)))
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# select stocks using PennyStockUniverseSelectionModel
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self.universe_settings.resolution = Resolution.DAILY
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self.universe_settings.schedule.on(self.date_rules.month_start())
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self.set_universe_selection(PennyStockUniverseSelectionModel())
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# Use SykesShortMicroCapAlphaModel to establish insights
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self.set_alpha(SykesShortMicroCapAlphaModel())
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# Equally weigh securities in portfolio, based on insights
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self.set_portfolio_construction(EqualWeightingPortfolioConstructionModel())
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# Set Immediate Execution Model
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self.set_execution(ImmediateExecutionModel())
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# Set Null Risk Management Model
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self.set_risk_management(NullRiskManagementModel())
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class SykesShortMicroCapAlphaModel(AlphaModel):
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'''Uses ranking of intraday percentage difference between open price and close price to create magnitude and direction prediction for insights'''
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def __init__(self, *args, **kwargs):
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lookback = kwargs['lookback'] if 'lookback' in kwargs else 1
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resolution = kwargs['resolution'] if 'resolution' in kwargs else Resolution.DAILY
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self.prediction_interval = Time.multiply(Extensions.to_time_span(resolution), lookback)
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self.number_of_stocks = kwargs['number_of_stocks'] if 'number_of_stocks' in kwargs else 10
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def update(self, algorithm, data):
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insights = []
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symbols_ret = dict()
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for security in algorithm.active_securities.values:
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if security.has_data:
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open_ = security.open
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if open_ != 0:
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# Intraday price change for penny stocks
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symbols_ret[security.symbol] = security.close / open_ - 1
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# Rank penny stocks on one day price change and retrieve list of ten "pumped" penny stocks
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pumped_stocks = dict(sorted(symbols_ret.items(),
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key = lambda kv: (-round(kv[1], 6), kv[0]))[:self.number_of_stocks])
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# Emit "down" insight for "pumped" penny stocks
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for symbol, value in pumped_stocks.items():
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insights.append(Insight.price(symbol, self.prediction_interval, InsightDirection.DOWN, abs(value), None))
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return insights
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class PennyStockUniverseSelectionModel(FundamentalUniverseSelectionModel):
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'''Defines a universe of penny stocks, as a universe selection model for the framework algorithm:
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The stocks must have fundamental data
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The stock must have positive previous-day close price
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The stock must have volume between $1000000 and $10000 on the previous trading day
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The stock must cost less than $5'''
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def __init__(self):
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super().__init__()
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# Number of stocks in Coarse Universe
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self.number_of_symbols_coarse = 500
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def select(self, algorithm, fundamental):
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# sort the stocks by dollar volume and take the top 500
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top = sorted([x for x in fundamental if x.has_fundamental_data
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and 5 > x.price > 0
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and 1000000 > x.volume > 10000],
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key=lambda x: x.dollar_volume, reverse=True)[:self.number_of_symbols_coarse]
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return [x.symbol for x in top]
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