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Inquiry for peer review

I am looking to learn more about this field.
Can someone help explain the where this algorithm went wrong and were it did well if at all.

Thank you.

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Total Returns
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Alpha
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Beta
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Sharpe
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Sortino
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Max Drawdown
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Benchmark Returns
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Volatility
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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 is a sample mean-reversion algorithm on Quantopian for you to test and adapt.
This example uses a dynamic stock selector, pipeline, to select stocks to trade. 
It orders stocks from the top 1% of the previous day's dollar-volume (liquid
stocks).

Algorithm investment thesis:
Top-performing stocks from last week will do worse this week, and vice-versa.

Every Monday, we rank high dollar-volume stocks based on their previous 5 day returns.
We long the bottom 10% of stocks with the WORST returns over the past 5 days.
We short the top 10% of stocks with the BEST returns over the past 5 days.

This type of algorithm may be used in live trading and in the Quantopian Open.
"""

# Import the libraries we will use here.
from quantopian.algorithm import attach_pipeline, pipeline_output
from quantopian.pipeline import Pipeline
from quantopian.pipeline.data.builtin import USEquityPricing
from quantopian.pipeline.factors import Returns
from quantopian.pipeline.filters.morningstar import Q1500US


def initialize(context):
    """
    Called once at the start of the program. Any one-time
    startup logic goes here.
    """
    # Define context variables that can be accessed in other methods of
    # the algorithm.
    context.long_leverage = 0.7
    context.short_leverage = -0.7
    context.returns_lookback = 4

    # Rebalance on the first trading day of each week at 12:22AM.
    schedule_function(rebalance,
                      date_rules.week_start(days_offset=0),
                      time_rules.market_open(hours=2, minutes=22))

    # Record tracking variables at the end of each day.
    schedule_function(record_vars,
                      date_rules.every_day(),
                      time_rules.market_close(minutes=1))

    # Create and attach our pipeline (dynamic stock selector), defined below.
    attach_pipeline(make_pipeline(context), 'mean_reversion_example')


def make_pipeline(context):
    """
    A function to create our pipeline (dynamic stock selector). The pipeline is 
    used to rank stocks based on different factors, including builtin factors, 
    or custom factors that you can define. Documentation on pipeline can be 
    found here:
    https://www.quantopian.com/help#pipeline-title
    """

    # Filter for stocks in the Q1500US universe. For more detail, see this post:
    # https://www.quantopian.com/posts/the-q500us-and-q1500us
    universe = Q1500US()
    
    # Create a Returns factor with a 5-day lookback window for all 
    # securities in our Q1500US Filter.
    recent_returns = Returns(window_length=context.returns_lookback, 
                            mask=universe)

    # Define high and low returns filters to be the bottom 10% and top 10% of
    # securities in the high dollar-volume group.
    low_returns = recent_returns.percentile_between(3,14)
    high_returns = recent_returns.percentile_between(95,98)

    # Add a filter to the pipeline such that only high-return and low-return
    # securities are kept.
    securities_to_trade = (low_returns | high_returns)

    # Create a pipeline object with the defined columns and screen.
    pipe = Pipeline(
        columns={
            'low_returns': low_returns,
            'high_returns': high_returns,
        },
        screen = securities_to_trade
    )

    return pipe

def before_trading_start(context, data):
    """
    Called every day before market open. This is where we get the securities
    that made it through the pipeline.
    """

    # Pipeline_output returns a pandas DataFrame with the results of our factors
    # and filters.
    context.output = pipeline_output('mean_reversion_example')

    # Sets the list of securities we want to long as the securities with a 'True'
    # value in the low_returns column.
    context.long_secs = context.output[context.output['low_returns']]

    # Sets the list of securities we want to short as the securities with a 'True'
    # value in the high_returns column.
    context.short_secs = context.output[context.output['high_returns']]

    # A list of the securities that we want to order today.
    context.security_list = context.long_secs.index.union(context.short_secs.index).tolist()

    # A set of the same securities, sets have faster lookup.
    context.security_set = set(context.security_list)

def compute_weights(context):
    """
    Compute weights to our long and short target positions.
    """

    # Set the allocations to even weights for each long position, and even weights
    # for each short position.
    long_weight = context.long_leverage / len(context.long_secs)
    short_weight = context.short_leverage / len(context.short_secs)
    
    return long_weight, short_weight

def rebalance(context,data):
    """
    This rebalancing function is called according to our schedule_function settings.
    """

    long_weight, short_weight = compute_weights(context)

    # For each security in our universe, order long or short positions according
    # to our context.long_secs and context.short_secs lists.
    for stock in context.security_list:
        if data.can_trade(stock):
            if stock in context.long_secs.index:
                order_target_percent(stock, long_weight)
            elif stock in context.short_secs.index:
                order_target_percent(stock, short_weight)

    # Sell all previously held positions not in our new context.security_list.
    for stock in context.portfolio.positions:
        if stock not in context.security_set and data.can_trade(stock):
            order_target_percent(stock, 0)

    # Log the long and short orders each week.
    log.info("This week's longs: "+", ".join([long_.symbol for long_ in context.long_secs.index]))
    log.info("This week's shorts: "  +", ".join([short_.symbol for short_ in context.short_secs.index]))

def record_vars(context, data):
    """
    This function is called at the end of each day and plots certain variables.
    """

    # Check how many long and short positions we have.
    longs = shorts = 0
    for position in context.portfolio.positions.itervalues():
        if position.amount > 0:
            longs += 1
        if position.amount < 0:
            shorts += 1

    # Record and plot the leverage of our portfolio over time as well as the
    # number of long and short positions. Even in minute mode, only the end-of-day
    # leverage is plotted.
    record(leverage = context.account.leverage, long_count=longs, short_count=shorts)
There was a runtime error.
2 responses

First, I'd set leverage to 0.5 long and short, so you don't go over 1.0 (don't spend more money than you have in your account)

I think where the algo goes wrong is it makes a really rigid assumption about the market and market dynamics are always changing. Even if stocks mean revert weekly Monday-to-Monday for a while, that'd be really strange if they always did so consistently. For every simple rule that's easy to exploit you can betcha somebody is already exploiting it and has already arbitraged all the alpha out of it.

Tearsheets are a great way to evaluate an algo.

You've got a good structure on this algorithm. It's holding a constant leverage (which is good), it's carefully avoiding exposure to a single stock, things like that.

Unfortunately, the basic thesis doesn't look like it holds up. Take a look at our lectures, in particular the ones about long-short equity and factor analysis, and see if that sparks some ideas for you to find a different thesis to test.

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