Ranked Universe & Long-Short Equity Strategy

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This algorithm is a good example of a long-short equity strategy that dynamically selects a portfolio by ranking securities by multiple custom factors. This strategy rebalances monthly, holds long and short positions simultaneously (is hedged), and continuously re-ranks the universe of stocks based on two custom factors: daily share turnover and 3-month price momentum.

Algorithms like these that dynamically select a hedged portfolio are good candidates for the Quantopian Open. Feel free to clone and improve this algo!

For background on the theory behind this strategy, refer to these lectures in the Quantopian Lecture Series:
- Lesson 17: Long-Short Equity
- Lesson 18: Ranking Universes by Factors

Note: This algorithm was originally created as a response to this community post and was not designed to yield positive returns, but instead to demonstrate a type of strategy that would be competitive in the contest. Try cloning the algo to see if you can improve it!

Clone Algorithm

219

Backtest from to with initial capital

Cumulative performance:

Total Returns

Alpha

Beta

Sharpe

Sortino

Max Drawdown

Benchmark Returns

Volatility

Returns	1 Month	3 Month	6 Month	12 Month

Alpha	1 Month	3 Month	6 Month	12 Month

Beta	1 Month	3 Month	6 Month	12 Month

Sharpe	1 Month	3 Month	6 Month	12 Month

Sortino	1 Month	3 Month	6 Month	12 Month

Volatility	1 Month	3 Month	6 Month	12 Month

Max Drawdown	1 Month	3 Month	6 Month	12 Month

# Backtest ID: 563a189666590310fd397ded

"""
This example comes from a request in the forums. 
The post can be found here: https://www.quantopian.com/posts/ranking-system-based-on-trading-volume-slash-shares-outstanding

The request was: 

I am stuck trying to build a stock ranking system with two signals:
1. Trading Volume/Shares Outstanding.
2. Price of current day / Price of 60 days ago.
Then rank Russell 2000 stocks every month, long the top 5%, short the bottom 5%.

"""

from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline import CustomFactor
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.data import morningstar


# Create custom factor #1 Trading Volume/Shares Outstanding
class Liquidity(CustomFactor):   
    
    # Pre-declare inputs and window_length
    inputs = [USEquityPricing.volume, morningstar.valuation.shares_outstanding] 
    window_length = 1
    
    # Compute factor1 value
    def compute(self, today, assets, out, volume, shares):       
        out[:] = volume[-1]/shares[-1]

# Create custom factor #2 Price of current day / Price of 60 days ago.        
class Momentum(CustomFactor):   
    
    # Pre-declare inputs and window_length
    inputs = [USEquityPricing.close] 
    window_length = 60
    
    # Compute factor2 value
    def compute(self, today, assets, out, close):       
        out[:] = close[-1]/close[0]
        
# Create custom factor to calculate a market cap based on yesterday's close
# We'll use this to get the top 2000 stocks by market cap
class MarketCap(CustomFactor):   
    
    # Pre-declare inputs and window_length
    inputs = [USEquityPricing.close, morningstar.valuation.shares_outstanding] 
    window_length = 1
    
    # Compute market cap value
    def compute(self, today, assets, out, close, shares):       
        out[:] = close[-1] * shares[-1]
        

def initialize(context):
    pipe = Pipeline()
    attach_pipeline(pipe, 'ranked_2000')
       
    # Add the two factors defined to the pipeline
    liquidity = Liquidity()
    pipe.add(liquidity, 'liquidity')
    
    momentum = Momentum()
    pipe.add(momentum, 'momentum')
    
    # Create and apply a filter representing the top 2000 equities by MarketCap every day
    # This is an approximation of the Russell 2000
    mkt_cap = MarketCap()
    top_2000 = mkt_cap.top(2000)
    
    # Rank factor 1 and add the rank to our pipeline
    liquidity_rank = liquidity.rank(mask=top_2000)
    pipe.add(liquidity_rank, 'liq_rank')
    
    # Rank factor 2 and add the rank to our pipeline
    momentum_rank = momentum.rank(mask=top_2000)
    pipe.add(momentum_rank, 'mom_rank')
    
    # Take the average of the two factor rankings, add this to the pipeline
    combo_raw = (liquidity_rank+momentum_rank)/2
    pipe.add(combo_raw, 'combo_raw') 
    
    # Rank the combo_raw and add that to the pipeline
    pipe.add(combo_raw.rank(mask=top_2000), 'combo_rank')
    
    # Set a screen to ensure that only the top 2000 companies by market cap 
    # with a momentum factor greater than 0 are returned
    pipe.set_screen(top_2000 & (momentum>0))
            
    # Scedule my rebalance function
    schedule_function(func=rebalance, 
                      date_rule=date_rules.month_start(days_offset=0), 
                      time_rule=time_rules.market_open(hours=0,minutes=30), 
                      half_days=True)
    
    # set my leverage
    context.long_leverage = 0.50
    context.short_leverage = -0.50
    
            
def before_trading_start(context, data):
    # Call pipelive_output to get the output
    context.output = pipeline_output('ranked_2000')
      
    # Narrow down the securities to only the top 200 & update my universe
    context.long_list = context.output.sort(['combo_rank'], ascending=False).iloc[:100]
    context.short_list = context.output.sort(['combo_rank'], ascending=False).iloc[-100:]   
    
    update_universe(context.long_list.index.union(context.short_list.index)) 


def handle_data(context, data):  
    
     # Record and plot the leverage of our portfolio over time. 
    record(leverage = context.account.leverage)
    
    print "Long List"
    log.info("\n" + str(context.long_list.sort(['combo_rank'], ascending=True).head(10)))
    
    print "Short List" 
    log.info("\n" + str(context.short_list.sort(['combo_rank'], ascending=True).head(10)))

# This rebalancing is called according to our schedule_function settings.     
def rebalance(context,data):
    
    long_weight = context.long_leverage / float(len(context.long_list))
    short_weight = context.short_leverage / float(len(context.short_list))

    
    for long_stock in context.long_list.index:
        if long_stock in data:
            log.info("ordering longs")
            log.info("weight is %s" % (long_weight))
            order_target_percent(long_stock, long_weight)
        
    for short_stock in context.short_list.index:
        if short_stock in data:
            log.info("ordering shorts")
            log.info("weight is %s" % (short_weight))
            order_target_percent(short_stock, short_weight)
        
    for stock in context.portfolio.positions.iterkeys():
        if stock not in context.long_list.index and stock not in context.short_list.index:
            order_target(stock, 0)

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2 responses

Karen Rubin

I was presenting this algo at a meetup recently, and an attendee pointed out I was calculating the Russell 2000 incorrectly. Instead of being the top 2000 by market cap, it's companies ranked 1000-3000 when ranked by market cap.

Here is an updated version of this algorithm that correctly filters to the Russell 2000.