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Re: Today's webinar / Market neutral and statistical arb starts

So,

I have some rankings and systems I've been trading on some other sites that work completely market neutral and that I've tested buying and selling 50+ stocks every week, month, quarter, etc. from a universe with no selection bias that's rebuilt each rebalance period based on PIT data and existing stocks.

I would like to rebuild them on here and put them into the contest. The problem is, I can't.

To do this, I need:
a) Better Rules interface for constructing a PIT universe of stocks based on criteria I set up. The current get fundamentals is extremely limited - for example I have to impose 'values' as opposed to ranks or ranks against historical averages - or technicals. I general I like to set up Universe rules that are checked against all publicly available stocks at the time - and returns several hundred to several thousand stocks.
b) I then like to use a real, multifactor ranking 'module' that I apply to this entire universe to rank stocks. So, maybe I have - say - 10 core factors - 3 value, 3 quality and 3 technical. And I want to combine them into one multi-factor ranking system. Maybe the 'fundamental factors' require me to look back at various historical values and compare the stock with all stocks, as well as with its sector and industry. Maybe I need to rank all the stocks in the core universe before returning my buy and sell list. These are the types of systems I have found have both a core philosophy (for example replicating fundamental bottom up analysis with a bunch of 'expert nodes') and solid backtesting results that aren't based on selection bias or highly tuned parameters. Many market neutral funds are doing similar things. However, I see no way to rebuild these easily on Quantopian? The multifactor ranking and ability to scan the entire universe of 'possible stocks' is key. Both are very limited here (or seem that way to me after 2 or so weeks). I then would need to impose sector and beta neutrality (or targets that vary based on a market rank). These can be done here (although I don't know how to aim for sector neutrality - but it seems like I could probably program it?)

So... I'd just like to vote for the ability to do this here.

Other programs I have used have had prebuilt options simplifying this. If you could expand the ranking options and - expand the number of stocks that can be 'ranked' as well as the number of user defined 'multifactor' rankings that they could be ranked against, and have more examples of these types of systems that would help.

Best,
Tom

4 responses

Also really need something like 'slippage' for calculating cost to borrow. Many stocks can have borrowing costs over 20% per year. So, not having these (as well as short availability lists and negative 'dividends' of shorts that are held at ex-div date) will really distort systems in unpredictable ways.

I'm happy to say that your a) and b) requests are quite close to being fulfilled. It's not done yet, but I believe you will find the new API to be quite powerful.

The cost-to-borrow for both margin and shorted stocks is another highly requested feature. It is no where near as far along in terms of planning and development, but we completely agree that it's important and will be built at some point.

Disclaimer

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.

Dan,
Thanks. If you could allow for 'at least two' different user defined stock / etf universes in a system (for example one long and one short), that would be very helpful. I frequently have one uni on the long side and one on the short side.

Best,
Tom

Hi Tom,
The pipeline API was launched today and should help with these types of strategies. You can read all about it here.

I general I like to set up Universe rules that are checked against all publicly available stocks at the time - and returns several hundred to several thousand stocks

You can now create a custom factor, using any fundamental or pricing data, and have it calculated for every security in the universe (8000+).

I then like to use a real, multifactor ranking 'module' that I apply to this entire universe to rank stocks.

This is also possible now. Take a look at the attached example. This calculates two factors (one momentum based, the other liquidity) and then ranks the universe by those factors. It longs 5% and shorts 5% of the resulting universe respectively.

I'd love your feedback on this, so don't hesitate to let me know if you have any questions.

Clone Algorithm
939
Loading...
Backtest from to with initial capital
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
"""
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)
We have migrated this algorithm to work with a new version of the Quantopian API. The code is different than the original version, but the investment rationale of the algorithm has not changed. We've put everything you need to know here on one page.
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Disclaimer

The material on this website is provided for informational purposes only and does not constitute an offer to sell, a solicitation to buy, or a recommendation or endorsement for any security or strategy, nor does it constitute an offer to provide investment advisory services by Quantopian. In addition, the material offers no opinion with respect to the suitability of any security or specific investment. No information contained herein should be regarded as a suggestion to engage in or refrain from any investment-related course of action as none of Quantopian nor any of its affiliates is undertaking to provide investment advice, act as an adviser to any plan or entity subject to the Employee Retirement Income Security Act of 1974, as amended, individual retirement account or individual retirement annuity, or give advice in a fiduciary capacity with respect to the materials presented herein. If you are an individual retirement or other investor, contact your financial advisor or other fiduciary unrelated to Quantopian about whether any given investment idea, strategy, product or service described herein may be appropriate for your circumstances. All investments involve risk, including loss of principal. Quantopian makes no guarantees as to the accuracy or completeness of the views expressed in the website. The views are subject to change, and may have become unreliable for various reasons, including changes in market conditions or economic circumstances.