| Overall Statistics |
|
Total Orders 423 Average Win 1.01% Average Loss -0.71% Compounding Annual Return 4.821% Drawdown 24.000% Expectancy 0.066 Start Equity 100000 End Equity 114105.43 Net Profit 14.105% Sharpe Ratio 0.087 Sortino Ratio 0.101 Probabilistic Sharpe Ratio 7.800% Loss Rate 56% Win Rate 44% Profit-Loss Ratio 1.43 Alpha 0.01 Beta 0.121 Annual Standard Deviation 0.148 Annual Variance 0.022 Information Ratio -0.054 Tracking Error 0.196 Treynor Ratio 0.107 Total Fees $722.46 Estimated Strategy Capacity $780000000.00 Lowest Capacity Asset SPY R735QTJ8XC9X Portfolio Turnover 36.86% |
#region imports
from AlgorithmImports import *
from tensorflow.keras.models import Sequential, load_model
from tensorflow.keras.layers import Dense, Flatten
from tensorflow.keras.optimizers import Adam
#endregion
class KerasExampleAlgorithm(QCAlgorithm):
def initialize(self):
self.set_start_date(2021, 7, 5) # Set Start Date
self.set_cash(100000) # Set Strategy Cash
self.symbol = self.add_equity("SPY", Resolution.DAILY).symbol
model_key = "model.keras"
if self.object_store.contains_key(model_key):
file_name = self.object_store.get_file_path(model_key)
self.model = load_model(file_name)
else:
self.model = Sequential([Dense(10, input_shape=(5,5), activation='relu'),
Dense(10, activation='relu'),
Flatten(),
Dense(1)])
self.model.compile(loss='mse',
optimizer=Adam(),
metrics=['mae', 'mse'])
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)
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) -> Tuple[float, float]:
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)
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) -> None:
features, labels = self.get_features_and_labels()
self.model.fit(features, labels, epochs=5)
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()
features = features[-1].reshape(1, 5, 5)
prediction = float(self.model.predict(features)[-1])
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.keras"
file_name = self.object_store.get_file_path(model_key)
self.model.save(file_name)