| Overall Statistics |
|
Total Orders 168 Average Win 1.79% Average Loss -1.06% Compounding Annual Return 1.902% Drawdown 26.600% Expectancy 0.075 Start Equity 100000 End Equity 105494.14 Net Profit 5.494% Sharpe Ratio -0.065 Sortino Ratio -0.077 Probabilistic Sharpe Ratio 4.338% Loss Rate 60% Win Rate 40% Profit-Loss Ratio 1.70 Alpha -0.034 Beta 0.781 Annual Standard Deviation 0.142 Annual Variance 0.02 Information Ratio -0.456 Tracking Error 0.09 Treynor Ratio -0.012 Total Fees $338.91 Estimated Strategy Capacity $1200000000.00 Lowest Capacity Asset SPY R735QTJ8XC9X Portfolio Turnover 23.25% |
#region imports
from AlgorithmImports import *
import tensorflow as tf
#endregion
class SwimmingFluorescentYellowCormorant(QCAlgorithm):
def initialize(self):
self.set_start_date(2021, 6, 22) # Set Start Date
self.set_cash(100000) # Set Strategy Cash
self.symbol = self.add_equity("SPY", Resolution.DAILY).symbol
num_factors = 5
num_neurons_1 = 10
num_neurons_2 = 20
num_neurons_3 = 5
self.epochs = 20
self.learning_rate = 0.0001
self.model = tf.keras.Sequential([
tf.keras.layers.Dense(num_neurons_1, activation=tf.nn.relu, input_shape=(num_factors,)), # input shape required
tf.keras.layers.Dense(num_neurons_2, activation=tf.nn.relu),
tf.keras.layers.Dense(num_neurons_3, activation=tf.nn.relu),
tf.keras.layers.Dense(1)
])
training_length = 252*2
self.training_data = RollingWindow[float](training_length)
history = self.history[TradeBar](self.symbol, training_length, Resolution.DAILY)
for trade_bar in history:
self.training_data.add(trade_bar.close)
self.train(self.my_training_method)
def get_features_and_labels(self, lookback=5):
lookback_series = []
data = pd.Series(list(self.training_data)[::-1])
for i in range(1, lookback + 1):
df = data.diff(i)[lookback:-1]
df.name = f"close-{i}"
lookback_series.append(df)
X = pd.concat(lookback_series, axis=1).reset_index(drop=True).dropna()
Y = data.diff(-1)[lookback:-1].reset_index(drop=True)
return X.values, Y.values
def my_training_method(self):
features, labels = self.get_features_and_labels()
# Define the loss function, we use MSE in this example
def loss_mse(target_y, predicted_y):
return tf.reduce_mean(tf.square(target_y - predicted_y))
# Train the model
optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate)
for i in range(self.epochs):
with tf.GradientTape() as t:
loss = loss_mse(labels, self.model(features))
jac = t.gradient(loss, self.model.trainable_weights)
optimizer.apply_gradients(zip(jac, self.model.trainable_weights))
def on_data(self, data):
if data.bars.contains_key(self.symbol):
self.training_data.add(data.bars[self.symbol].close)
new_features, __ = self.get_features_and_labels()
prediction = self.model(new_features)
prediction = float(prediction.numpy()[-1])
if prediction > 0:
self.set_holdings(self.symbol, 1)
else:
self.set_holdings(self.symbol, -1)