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Multi Stock Equity Long Short Trailing Stop Loss

Hello! I am new to Quantopian and am having trouble trying to add a trailing stop loss to a multi stock equity long short portfolio, like the sample provided. I have tried a couple of things but I can't get anything to work! Any help would be much appreciated. Thank you

Clone Algorithm
16
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 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 AverageDollarVolume, Returns


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 = 0.5
    context.short_leverage = -0.5
    context.returns_lookback = 5

    # Rebalance on the first trading day of each week at 11AM.
    schedule_function(rebalance,
                      date_rules.week_start(days_offset=0),
                      time_rules.market_open(hours=1, minutes=30))

    # 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
    """
    # Create a pipeline object.

    # Create a dollar_volume factor using default inputs and window_length.
    # This is a builtin factor.
    dollar_volume = AverageDollarVolume(window_length=1)

    # Define high dollar-volume filter to be the top 5% of stocks by dollar volume.
    high_dollar_volume = dollar_volume.percentile_between(95, 100)
    
    # Create a recent_returns factor with a 5-day returns lookback for all securities
    # in our high_dollar_volume Filter. This is a custom factor defined below (see 
    # RecentReturns class).
    recent_returns = Returns(window_length=context.returns_lookback, mask=high_dollar_volume)

    # 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(0,10)
    high_returns = recent_returns.percentile_between(90,100)


    # Define a column dictionary that holds all the Factors
    pipe_columns = {
            'low_returns':low_returns,
            'high_returns':high_returns,
            'recent_returns':recent_returns,
            'dollar_volume':dollar_volume
            }

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

    # Create a pipeline object with the defined columns and screen.
    pipe = Pipeline(columns=pipe_columns,screen=pipe_screen)

    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.
3 responses
   from collections import defaultdict  
    context.stops = defaultdict(lambda:0)  
   #call whenever  
    for s in context.portfolio.positions:  
                price = data.current(s, 'price')  
                context.stops[s] = max(context.stops[s], stop * price)  

this has worked for me, I think I got it from Anthony FJ Garner.
I hope this is what you are asking

Excellent thank you! I have attached the code, and I believe it works partially. I think it stops the Long stocks, but not the short.

Also does anyone know how to record or out put to a csv file the stocks bought/sold and the price and then the stocks that have been stopped out?

Thanks

Clone Algorithm
16
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 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 AverageDollarVolume, Returns
from collections import defaultdict

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 = 0.5
    context.short_leverage = -0.5
    context.returns_lookback = 5
    context.stop_pct = 0.75
    context.stop_price = defaultdict(lambda:0)


    # Rebalance on the first trading day of each week at 11AM.
    schedule_function(rebalance,
                      date_rules.week_start(days_offset=0),
                      time_rules.market_open(hours=1, minutes=30))

    # 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
    """
    # Create a pipeline object.

    # Create a dollar_volume factor using default inputs and window_length.
    # This is a builtin factor.
    dollar_volume = AverageDollarVolume(window_length=1)

    # Define high dollar-volume filter to be the top 5% of stocks by dollar volume.
    high_dollar_volume = dollar_volume.percentile_between(95, 100)
    
    # Create a recent_returns factor with a 5-day returns lookback for all securities
    # in our high_dollar_volume Filter. This is a custom factor defined below (see 
    # RecentReturns class).
    recent_returns = Returns(window_length=context.returns_lookback, mask=high_dollar_volume)

    # 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(0,10)
    high_returns = recent_returns.percentile_between(90,100)


    # Define a column dictionary that holds all the Factors
    pipe_columns = {
            'low_returns':low_returns,
            'high_returns':high_returns,
            'recent_returns':recent_returns,
            'dollar_volume':dollar_volume
            }

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

    # Create a pipeline object with the defined columns and screen.
    pipe = Pipeline(columns=pipe_columns,screen=pipe_screen)

    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 handle_data(context, data):

    for stock in context.portfolio.positions:  
        price = data.current(stock, 'price')
        context.stop_price[stock] = max(context.stop_price[stock], context.stop_pct * price)
    for stock in context.portfolio.positions:
        if data.current(stock, 'price') < context.stop_price[stock]:
            order_target(stock, 0)
            context.stop_price[stock] = 0

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.

Does anyone know how I can edit this to close out long positions? Thanks

Clone Algorithm
16
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 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 AverageDollarVolume, Returns
from collections import defaultdict

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 = 0.5
    context.short_leverage = -0.5
    context.returns_lookback = 5
    context.long_stop_pct = 0.97
    context.short_stop_pct = 1.03
    context.stop_long_price = defaultdict(lambda:0)
    context.stop_short_price = defaultdict(lambda:0)

    # Rebalance on the first trading day of each week at 11AM.
    schedule_function(rebalance,
                      date_rules.week_start(days_offset=0),
                      time_rules.market_open(hours=1, minutes=30))

    # 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
    """
    # Create a pipeline object.

    # Create a dollar_volume factor using default inputs and window_length.
    # This is a builtin factor.
    dollar_volume = AverageDollarVolume(window_length=1)

    # Define high dollar-volume filter to be the top 5% of stocks by dollar volume.
    high_dollar_volume = dollar_volume.percentile_between(95, 100)
    
    # Create a recent_returns factor with a 5-day returns lookback for all securities
    # in our high_dollar_volume Filter. This is a custom factor defined below (see 
    # RecentReturns class).
    recent_returns = Returns(window_length=context.returns_lookback, mask=high_dollar_volume)

    # 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(0,10)
    high_returns = recent_returns.percentile_between(90,100)


    # Define a column dictionary that holds all the Factors
    pipe_columns = {
            'low_returns':low_returns,
            'high_returns':high_returns,
            'recent_returns':recent_returns,
            'dollar_volume':dollar_volume
            }

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

    # Create a pipeline object with the defined columns and screen.
    pipe = Pipeline(columns=pipe_columns,screen=pipe_screen)

    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 handle_data(context, data):

    for stock in context.portfolio.positions: 
        price = data.current(stock, 'price')
        context.stop_long_price[stock] = max(context.stop_long_price[stock], context.stop_long_pct * price)
    for stock in context.portfolio.positions:
        if data.current(stock, 'price') < context.stop_long_price[stock]:
            order_target(stock, 0)
            context.stop_long_price[stock] = 0

    for stock in context.portfolio.positions:  
        price = data.current(stock, 'price')
        context.stop_short_price[stock] = min(context.stop_short_price[stock], context.stop_long_pct * price)
    for stock in context.portfolio.positions:
        if data.current(stock, 'price') < context.stop_short_price[stock]:
            order_target(stock, 0)
            context.stop_short_price[stock] = 0
            
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.