| Overall Statistics |
|
Total Orders 1059 Average Win 1.02% Average Loss -0.76% Compounding Annual Return 2.798% Drawdown 31.800% Expectancy 0.058 Start Equity 100000 End Equity 129403.64 Net Profit 29.404% Sharpe Ratio 0.075 Sortino Ratio 0.079 Probabilistic Sharpe Ratio 0.170% Loss Rate 55% Win Rate 45% Profit-Loss Ratio 1.35 Alpha -0.038 Beta 0.676 Annual Standard Deviation 0.152 Annual Variance 0.023 Information Ratio -0.503 Tracking Error 0.124 Treynor Ratio 0.017 Total Fees $3338.86 Estimated Strategy Capacity $860000000.00 Lowest Capacity Asset SPY R735QTJ8XC9X Portfolio Turnover 45.05% |
# 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.SetStartDate(2015, 1, 1)
self.SetEndDate(2024, 5, 1)
self.SetCash(100000)
self.symbol = self.AddEquity("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.ObjectStore.ContainsKey("model"):
file_name = self.ObjectStore.GetFilePath("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.DateRules.Every(DayOfWeek.Sunday), self.TimeRules.At(8,0), self.my_training_method)
def get_features_and_labels(self, n_steps=5):
training_df = self.PandasConverter.GetDataFrame[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 OnData(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.SetHoldings(self.symbol, 1)
elif prediction < 0:
self.SetHoldings(self.symbol, -1)
def OnEndOfAlgorithm(self):
#model_key = "model"
#file_name = self.ObjectStore.GetFilePath(model_key)
#joblib.dump(self.model, file_name)
#self.ObjectStore.Save(model_key)
pass