| Overall Statistics |
|
Total Orders 266 Average Win 1.00% Average Loss -0.90% Compounding Annual Return -1.828% Drawdown 24.200% Expectancy -0.005 Start Equity 100000 End Equity 96724.45 Net Profit -3.276% Sharpe Ratio -0.349 Sortino Ratio -0.402 Probabilistic Sharpe Ratio 4.987% Loss Rate 53% Win Rate 47% Profit-Loss Ratio 1.10 Alpha -0.092 Beta 0.504 Annual Standard Deviation 0.142 Annual Variance 0.02 Information Ratio -0.948 Tracking Error 0.141 Treynor Ratio -0.098 Total Fees $517.55 Estimated Strategy Capacity $740000000.00 Lowest Capacity Asset SPY R735QTJ8XC9X Portfolio Turnover 60.49% |
# region imports
from AlgorithmImports import *
from sklearn.svm import SVR
from sklearn.model_selection import GridSearchCV
import joblib
# endregion
class ScikitLearnExampleAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2022, 7, 4)
self.set_cash(100000)
self.symbol = self.add_equity("SPY", Resolution.DAILY).symbol
training_length = 252*2
self.training_data = RollingWindow[TradeBar](training_length)
history = self.history[TradeBar](self.symbol, training_length, Resolution.DAILY)
for trade_bar in history:
self.training_data.add(trade_bar)
if self.object_store.contains_key("model"):
file_name = self.object_store.get_file_path("model")
self.model = joblib.load(file_name)
else:
param_grid = {'C': [.05, .1, .5, 1, 5, 10],
'epsilon': [0.001, 0.005, 0.01, 0.05, 0.1],
'gamma': ['auto', 'scale']}
self.model = GridSearchCV(SVR(), param_grid, scoring='neg_mean_squared_error', cv=5)
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_and_labels(self, n_steps=5):
training_df = self.pandas_converter.get_data_frame[TradeBar](list(self.training_data)[::-1])
daily_pct_change = training_df.pct_change().dropna()
features = []
labels = []
for i in range(len(daily_pct_change)-n_steps):
features.append(daily_pct_change.iloc[i:i+n_steps].values.flatten())
labels.append(daily_pct_change['close'].iloc[i+n_steps])
features = np.array(features)
labels = np.array(labels)
return features, labels
def my_training_method(self):
features, labels = self.get_features_and_labels()
if isinstance(self.model, GridSearchCV):
self.model = self.model.fit(features, labels).best_estimator_
else:
self.model = self.model.fit(features, labels)
def on_data(self, slice: Slice) -> None:
if self.symbol in slice.bars:
self.training_data.add(slice.bars[self.symbol])
features, _ = self.get_features_and_labels()
prediction = self.model.predict(features[-1].reshape(1, -1))
prediction = float(prediction)
if prediction > 0:
self.set_holdings(self.symbol, 1)
elif prediction < 0:
self.set_holdings(self.symbol, -1)
def on_end_of_algorithm(self):
model_key = "model"
file_name = self.object_store.get_file_path(model_key)
joblib.dump(self.model, file_name)
self.object_store.save(model_key)