| Overall Statistics |
|
Total Orders 1502 Average Win 0.10% Average Loss -0.11% Compounding Annual Return 12.457% Drawdown 13.100% Expectancy 0.296 Start Equity 100000 End Equity 125371.36 Net Profit 25.371% Sharpe Ratio 0.42 Sortino Ratio 0.558 Probabilistic Sharpe Ratio 39.685% Loss Rate 31% Win Rate 69% Profit-Loss Ratio 0.88 Alpha -0.018 Beta 0.591 Annual Standard Deviation 0.102 Annual Variance 0.01 Information Ratio -0.705 Tracking Error 0.085 Treynor Ratio 0.073 Total Fees $1540.75 Estimated Strategy Capacity $78000000.00 Lowest Capacity Asset IAU T5QHGBE8K3TX Portfolio Turnover 4.43% |
# region imports
from AlgorithmImports import *
from tslearn.barycenters import softdtw_barycenter
from tslearn.clustering import TimeSeriesKMeans
import joblib
# endregion
class TslearnExampleAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2022, 7, 4)
self.set_cash(100000)
tickers = ["SPY", "QQQ", "DIA",
"AAPL", "MSFT", "TSLA",
"IEF", "TLT", "SHV", "SHY",
"GLD", "IAU", "SLV",
"USO", "XLE", "XOM"]
self.symbols = [self.add_equity(ticker, Resolution.DAILY).symbol for ticker in tickers]
training_length = 252
self.training_data = {}
history = self.history(self.symbols, training_length, Resolution.DAILY).unstack(0).close
for symbol in self.symbols:
self.training_data[symbol] = RollingWindow[float](training_length)
for close_price in history[symbol]:
self.training_data[symbol].add(close_price)
self.model = TimeSeriesKMeans(n_clusters=6, # We have 6 main groups
metric="dtw")
self.train(self.my_training_method)
self.train(self.date_rules.every(DayOfWeek.SUNDAY), self.time_rules.at(8,0), self.my_training_method)
def get_features(self):
close_price = pd.DataFrame({symbol: list(data)[::-1] for symbol, data in self.training_data.items()})
log_price = np.log(close_price)
log_normal_price = (log_price - log_price.mean()) / log_price.std()
return log_normal_price
def my_training_method(self):
features = self.get_features()
self.model.fit(features.T.values)
def on_data(self, slice: Slice) -> None:
for symbol in self.symbols:
if symbol in slice.bars:
self.training_data[symbol].add(slice.bars[symbol].close)
features = self.get_features()
self.labels = self.model.predict(features.T.values)
for i in set(self.labels):
assets_in_cluster = features.columns[[n for n, k in enumerate(self.labels) if k == i]]
size = 1/6/len(assets_in_cluster)
self.set_holdings([PortfolioTarget(symbol, size) for symbol in assets_in_cluster])
def on_end_of_algorithm(self):
model_key = "model_test.hdf5"
file_name = self.object_store.get_file_path(model_key)
import os
os.remove(file_name)
self.model.to_hdf5(file_name)