Martin Molinero
117 lines
4.8 KiB
Python
117 lines
4.8 KiB
Python
# QUANTCONNECT.COM - Democratizing Finance, Empowering Individuals.
|
|
# Lean Algorithmic Trading Engine v2.0. Copyright 2014 QuantConnect Corporation.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
from clr import AddReference
|
|
AddReference("QuantConnect.Common")
|
|
AddReference("QuantConnect.Algorithm.Framework")
|
|
AddReference("QuantConnect.Indicators")
|
|
|
|
from QuantConnect import *
|
|
from QuantConnect.Indicators import *
|
|
from QuantConnect.Algorithm.Framework.Alphas import *
|
|
from Alphas.BasePairsTradingAlphaModel import BasePairsTradingAlphaModel
|
|
from datetime import timedelta
|
|
from scipy.stats import pearsonr
|
|
import numpy as np
|
|
import pandas as pd
|
|
|
|
class PearsonCorrelationPairsTradingAlphaModel(BasePairsTradingAlphaModel):
|
|
''' This alpha model is designed to rank every pair combination by its pearson correlation
|
|
and trade the pair with the hightest correlation
|
|
This model generates alternating long ratio/short ratio insights emitted as a group'''
|
|
|
|
def __init__(self, lookback = 15,
|
|
resolution = Resolution.Minute,
|
|
threshold = 1,
|
|
minimumCorrelation = .5):
|
|
'''Initializes a new instance of the PearsonCorrelationPairsTradingAlphaModel class
|
|
Args:
|
|
lookback: lookback period of the analysis
|
|
resolution: analysis resolution
|
|
threshold: The percent [0, 100] deviation of the ratio from the mean before emitting an insight
|
|
minimumCorrelation: The minimum correlation to consider a tradable pair'''
|
|
super().__init__(lookback, resolution, threshold)
|
|
self.lookback = lookback
|
|
self.resolution = resolution
|
|
self.minimumCorrelation = minimumCorrelation
|
|
self.best_pair = ()
|
|
|
|
def OnSecuritiesChanged(self, algorithm, changes):
|
|
'''Event fired each time the we add/remove securities from the data feed.
|
|
Args:
|
|
algorithm: The algorithm instance that experienced the change in securities
|
|
changes: The security additions and removals from the algorithm'''
|
|
|
|
for security in changes.AddedSecurities:
|
|
self.Securities.append(security)
|
|
|
|
for security in changes.RemovedSecurities:
|
|
if security in self.Securities:
|
|
self.Securities.remove(security)
|
|
|
|
symbols = [ x.Symbol for x in self.Securities ]
|
|
|
|
history = algorithm.History(symbols, self.lookback, self.resolution).close.unstack(level=0)
|
|
|
|
if not history.empty:
|
|
|
|
df = self.get_price_dataframe(history)
|
|
stop = len(df.columns)
|
|
|
|
corr = dict()
|
|
|
|
for i in range(0, stop):
|
|
for j in range(i+1, stop):
|
|
if (j, i) not in corr:
|
|
corr[(i, j)] = pearsonr(df.iloc[:,i], df.iloc[:,j])[0]
|
|
|
|
corr = sorted(corr.items(), key = lambda kv: kv[1])
|
|
if corr[-1][1] >= self.minimumCorrelation:
|
|
self.best_pair = (symbols[corr[-1][0][0]], symbols[corr[-1][0][1]])
|
|
|
|
super().OnSecuritiesChanged(algorithm, changes)
|
|
|
|
def HasPassedTest(self, algorithm, asset1, asset2):
|
|
'''Check whether the assets pass a pairs trading test
|
|
Args:
|
|
algorithm: The algorithm instance that experienced the change in securities
|
|
asset1: The first asset's symbol in the pair
|
|
asset2: The second asset's symbol in the pair
|
|
Returns:
|
|
True if the statistical test for the pair is successful'''
|
|
return self.best_pair is not None and self.best_pair == (asset1, asset2)
|
|
|
|
def get_price_dataframe(self, df):
|
|
timezones = { x.Symbol.Value: x.Exchange.TimeZone for x in self.Securities }
|
|
|
|
# Use log prices
|
|
df = np.log(df)
|
|
|
|
is_single_timeZone = len(set(timezones.values())) == 1
|
|
|
|
if not is_single_timeZone:
|
|
series_dict = dict()
|
|
|
|
for column in df:
|
|
# Change the dataframe index from data time to UTC time
|
|
to_utc = lambda x: Extensions.ConvertToUtc(x, timezones[column])
|
|
if self.resolution == Resolution.Daily:
|
|
to_utc = lambda x: Extensions.ConvertToUtc(x, timezones[column]).date()
|
|
|
|
data = df[[column]]
|
|
data.index = data.index.map(to_utc)
|
|
series_dict[column] = data[column]
|
|
|
|
df = pd.DataFrame(series_dict).dropna()
|
|
|
|
return (df - df.shift(1)).dropna() |