| Overall Statistics |
|
Total Orders 372 Average Win 4.80% Average Loss -4.51% Compounding Annual Return 87.544% Drawdown 53.700% Expectancy 0.436 Start Equity 1000000 End Equity 26582134.24 Net Profit 2558.213% Sharpe Ratio 1.437 Sortino Ratio 0.957 Probabilistic Sharpe Ratio 64.236% Loss Rate 30% Win Rate 70% Profit-Loss Ratio 1.07 Alpha 0.711 Beta -0.107 Annual Standard Deviation 0.489 Annual Variance 0.239 Information Ratio 1.194 Tracking Error 0.525 Treynor Ratio -6.564 Total Fees $215639.94 Estimated Strategy Capacity $2300000.00 Lowest Capacity Asset GIG XNAR4L6AIOV9 Portfolio Turnover 6.17% |
# region imports from AlgorithmImports import * from sklearn.cluster import KMeans from sklearn.preprocessing import MinMaxScaler # endregion class MuscularFluorescentPinkPanda(QCAlgorithm): def initialize(self): self.set_start_date(2020, 1, 1) self.set_cash(1_000_000) self.universe_settings.extended_market_hours = True self._universe = self.add_universe(self._select_assets) self._spy = self.add_equity('SPY') self.schedule.on(self.date_rules.every_day(self._spy.symbol), self.time_rules.before_market_open(self._spy.symbol, 30), self._rebalance) self._liquidity_filter_size = self.get_parameter('liquidity_filter_size', 100) self._roc_threshold = self.get_parameter('roc_threshold', 0.1) self._clusters = self.get_parameter('clusters', 5) self._training_data_period = timedelta(self.get_parameter('training_data_days', 365)) # Calendar days self._hold_duration = self.get_parameter('hold_duration', 3) # Trading days self._max_positions = 3 self._scaler = MinMaxScaler(feature_range=(0, 1)) self._kmeans = KMeans(n_clusters=self._clusters, random_state=42) self._trades = [] self._columns = ['end_time', 'symbol', 'price_volatility', 'volume_volatility', 'pct_above_vwap', 'vwap_deviation', 'fraction_of_vol_below_vwap'] self._factor_history = pd.DataFrame(columns=self._columns).set_index(['end_time', 'symbol']) self.set_warm_up(self._training_data_period) def _select_assets(self, fundamentals): # Select the most liquid assets. symbols = [f.symbol for f in sorted(fundamentals, key=lambda f: f.dollar_volume)[-self._liquidity_filter_size:]] # Select the subset of symbols that had >=10% growth yesterday from open to close. history = self.history(symbols, 1, Resolution.DAILY) symbols = [idx[0] for idx in history[(history.close / history.open - 1) >= self._roc_threshold].index] # Get the data we'll need to calculate the factors. vwap_by_symbol = {s: IntradayVwap('') for s in symbols} df_by_symbol = {s: pd.DataFrame(columns=['close', 'vwap', 'volume']) for s in symbols} for bars in self.history[TradeBar](symbols, self._spy.exchange.hours.get_previous_market_open(self.time, False), self.time, Resolution.MINUTE, extended_market_hours=False): for symbol, bar in bars.items(): vwap = vwap_by_symbol[symbol] if vwap.update(bar): df_by_symbol[symbol].loc[bar.end_time] = [bar.close, vwap.current.value, bar.volume] # Calculate factors for the selected assets. # The clustering process will try to minimize these. factors = pd.DataFrame(columns=self._columns) for symbol, df in df_by_symbol.items(): if df.empty: continue factors.loc[len(factors)] = [ self.time, symbol, df.close.std() / df.close.mean(), # price_volatility df.volume.std() / df.volume.mean(), # volume_volatility (df.close > df.vwap).astype(int).sum() / len(df), # pct_above_vwap np.mean(np.abs(df.close - df.vwap)) / df.vwap.mean(), # vwap_deviation df[df.close < df.vwap].volume.sum() / df.volume.sum() # fraction_of_vol_below_vwap ] if factors.empty: return [] factors.set_index(['end_time', 'symbol'], inplace=True) # Define the universe. universe = [] if not self.is_warming_up: # Fit the scaler and k-means model to the training data. self._kmeans.fit(self._scaler.fit_transform(self._factor_history)) # Drop the `time` column # Find the cluster that's closest to the origin. closest_cluster_idx = np.argmin(np.linalg.norm(self._kmeans.cluster_centers_, axis=1)) # Predict the cluster of the current universe. cluster_labels = self._kmeans.predict(self._scaler.transform(factors)) # Select the subset of asset in the universe that are in the cluster closest to the origin. universe = [factors.index.levels[1][i] for i, label in enumerate(cluster_labels) if label == closest_cluster_idx] # Append the latest factor samples to the history. self._factor_history = pd.concat([self._factor_history, factors], axis=0).reset_index() # Trim off samples that have fallen out of the lookback window (1 year). self._factor_history = self._factor_history[self._factor_history.end_time >= self.time-self._training_data_period].set_index(['end_time', 'symbol']) # Return the assets selected for the universe. return universe def _rebalance(self): # Scan for exits. closed_trades = [] for i, trade in enumerate(self._trades): trade.scan(self) if trade.closed: closed_trades.append(i) # Delete closed trades for i in closed_trades[::-1]: del self._trades[i] # Scan for entries. if self._universe.selected and len(self._trades) < self._max_positions: for symbol in self._universe.selected: self._trades.append(Trade(self, symbol, self._hold_duration, -1/self._max_positions)) self.plot('Trades', 'Open', len(self._trades)) class Trade: def __init__(self, algorithm, symbol, hold_duration, weight): self._symbol = symbol # Determine position size self.closed = True price = algorithm.securities[symbol].price if not price: return self._quantity = algorithm.calculate_order_quantity(symbol, weight) if self._quantity == 0: return self.closed = False # Enter trade algorithm.market_order(symbol, self._quantity) # Set variable for exit logic self._hold_duration = hold_duration def scan(self, algorithm): if self.closed: return self._hold_duration -= 1 if self._hold_duration <= 0: algorithm.market_order(self._symbol, -self._quantity) self.closed = True
