wehub-resource-sync
109 lines
4.6 KiB
Python
109 lines
4.6 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 Alphas.BasePairsTradingAlphaModel import BasePairsTradingAlphaModel
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from scipy.stats import pearsonr
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class PearsonCorrelationPairsTradingAlphaModel(BasePairsTradingAlphaModel):
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''' This alpha model is designed to rank every pair combination by its pearson correlation
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and trade the pair with the hightest correlation
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This model generates alternating long ratio/short ratio insights emitted as a group'''
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def __init__(self, lookback = 15,
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resolution = Resolution.MINUTE,
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threshold = 1,
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minimum_correlation = .5):
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'''Initializes a new instance of the PearsonCorrelationPairsTradingAlphaModel class
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Args:
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lookback: lookback period of the analysis
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resolution: analysis resolution
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threshold: The percent [0, 100] deviation of the ratio from the mean before emitting an insight
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minimum_correlation: The minimum correlation to consider a tradable pair'''
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super().__init__(lookback, resolution, threshold)
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self.lookback = lookback
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self.resolution = resolution
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self.minimum_correlation = minimum_correlation
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self.best_pair = ()
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def on_securities_changed(self, algorithm, changes):
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'''Event fired each time the we add/remove securities from the data feed.
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Args:
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algorithm: The algorithm instance that experienced the change in securities
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changes: The security additions and removals from the algorithm'''
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for security in changes.added_securities:
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self.securities.add(security)
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for security in changes.removed_securities:
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if security in self.securities:
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self.securities.remove(security)
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symbols = sorted([ x.symbol for x in self.securities ])
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history = algorithm.history(symbols, self.lookback, self.resolution)
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if not history.empty:
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history = history.close.unstack(level=0)
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df = self.get_price_dataframe(history)
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stop = len(df.columns)
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corr = dict()
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for i in range(0, stop):
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for j in range(i+1, stop):
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if (j, i) not in corr:
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corr[(i, j)] = pearsonr(df.iloc[:,i], df.iloc[:,j])[0]
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corr = sorted(corr.items(), key = lambda kv: kv[1])
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if corr[-1][1] >= self.minimum_correlation:
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self.best_pair = (symbols[corr[-1][0][0]], symbols[corr[-1][0][1]])
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super().on_securities_changed(algorithm, changes)
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def has_passed_test(self, algorithm, asset1, asset2):
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'''Check whether the assets pass a pairs trading test
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Args:
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algorithm: The algorithm instance that experienced the change in securities
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asset1: The first asset's symbol in the pair
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asset2: The second asset's symbol in the pair
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Returns:
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True if the statistical test for the pair is successful'''
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return self.best_pair is not None and self.best_pair[0] == asset1 and self.best_pair[1] == asset2
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def get_price_dataframe(self, df):
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timezones = { x.symbol.value: x.exchange.time_zone for x in self.securities }
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# Use log prices
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df = np.log(df)
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is_single_timeZone = len(set(timezones.values())) == 1
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if not is_single_timeZone:
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series_dict = dict()
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for column in df:
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# Change the dataframe index from data time to UTC time
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to_utc = lambda x: Extensions.convert_to_utc(x, timezones[column])
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if self.resolution == Resolution.DAILY:
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to_utc = lambda x: Extensions.convert_to_utc(x, timezones[column]).date()
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data = df[[column]]
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data.index = data.index.map(to_utc)
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series_dict[column] = data[column]
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df = pd.DataFrame(series_dict).dropna()
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return (df - df.shift(1)).dropna()
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