Algorithm Lab

class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># QuantAlgo Labs - Momentum Alpha Strategy
class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># Algorithm ID: algo_28451
class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># Last Modified: 2024-01-12 14:32:15 UTC
 
from quantalgo import Algorithm, Resolution, Universe
from quantalgo.indicators import RSI, MACD, SMA
from quantalgo.risk import PositionSizer, StopLoss
 
class MomentumAlpha(Algorithm):
    class="text-[#a5d6a7]">"""
    Momentum-based strategy that identifies breakout opportunities
    using RSI divergence and MACD confirmation signals.
    class="text-[#a5d6a7]">"""
    
    def initialize(self):
        class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># Strategy Configuration
        self.set_start_date(2023, 1, 1)
        self.set_end_date(2024, 1, 1)
        self.set_cash(1_000_000)
        
        class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># Universe Selection
        self.universe = Universe.top_dollar_volume(500)
        self.add_universe(self.universe)
        
        class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># Technical Indicators
        self.rsi_period = 14
        self.rsi_overbought = 70
        self.rsi_oversold = 30
        
        self.macd_fast = 12
        self.macd_slow = 26
        self.macd_signal = 9
        
        class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># Risk Parameters
        self.position_size = 0.10  class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># 10% per position
        self.max_positions = 10
        self.stop_loss = 0.02     class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># 2% stop loss
        self.take_profit = 0.06   class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># 6% take profit
        
        class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># Initialize indicators dict
        self.indicators = {}
        
        self.log(class="text-[#a5d6a7]">"Momentum Alpha initialized successfully")
    
    def on_securities_changed(self, changes):
        for security in changes.added_securities:
            symbol = security.symbol
            self.indicators[symbol] = {
                class="text-[#a5d6a7]">'rsi': RSI(symbol, self.rsi_period),
                class="text-[#a5d6a7]">'macd': MACD(symbol, self.macd_fast, self.macd_slow, self.macd_signal),
                class="text-[#a5d6a7]">'sma_50': SMA(symbol, 50),
                class="text-[#a5d6a7]">'sma_200': SMA(symbol, 200),
            }
    
    def on_data(self, data):
        for symbol in self.active_securities:
            if not self._indicators_ready(symbol):
                continue
                
            indicators = self.indicators[symbol]
            
            class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># Entry Logic
            if self._check_entry_signal(symbol, indicators):
                self._execute_entry(symbol, data[symbol].close)
            
            class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># Exit Logic
            if self.portfolio[symbol].invested:
                self._check_exit_conditions(symbol, indicators, data[symbol].close)
    
    def _check_entry_signal(self, symbol, indicators):
        rsi = indicators[class="text-[#a5d6a7]">'rsi'].current.value
        macd = indicators[class="text-[#a5d6a7]">'macd']
        sma_50 = indicators[class="text-[#a5d6a7]">'sma_50'].current.value
        sma_200 = indicators[class="text-[#a5d6a7]">'sma_200'].current.value
        
        class=class="text-[#a5d6a7]">"text-muted-foreground/60 italic"># Bullish conditions
        trend_up = sma_50 > sma_200
        rsi_signal = rsi < self.rsi_oversold
        macd_crossover = macd.signal.current.value > macd.current.value
        
        return trend_up and rsi_signal and macd_crossover
    
    def _execute_entry(self, symbol, price):
        if len(self.portfolio.invested) >= self.max_positions:
            return
            
        quantity = PositionSizer.fixed_percent(
            self.portfolio.cash,
            price,
            self.position_size
        )
        
        self.market_order(symbol, quantity)
        self.log(fclass="text-[#a5d6a7]">"ENTRY: {symbol} @ {price:.2f}, qty: {quantity}")