Quantlandian

Debt to Total Assets with FactSet Data - Template Fundamental Algo

Copyright reserved by Maxwell Margenot posted Oct 16, 2018 in Quantopian

Many of our funded authors have relied upon price driven strategies. As we continue to evaluate and add algorithms to our portfolio, we will be especially interested in new strategies that take advantage of a broader range of fundamental factors.

Debt to Total Assets

The ratio of a company’s debt to its total assets is a measure of the amount of leverage taken on by a company. Higher values (above 1) indicate that a company has more liabilities than assets (aka you owe more than you own), while lower values (below 1) indicate that a company has more equity than debt. In this template, we’ve taken advantage of this measure to test the idea that companies with relatively lower levels of Debt to Total Assets are likely to outperform companies with relatively higher levels of Debt to Total Assets. Read more here.

As we look to expand the set of algorithms receiving allocations over the next few months we expect to give preference to new ideas that take advantage of a broader range of fundamental factors.

To get started, clone this algorithm, improve it with your own ideas, and submit it to the Quantopian Daily Contest.

N.B. As implemented here, this algo doesn’t fully meet all of the criteria for entry in the daily contest so we’re leaving that as an “exercise for the reader”.

Fundamental Sample Strategies Library

To see all of our fundamental sample strategies, please visit our new library post. We will be adding more templates in the future, so keep an eye on the “Algo Template” tag in the Quantopian forums: https://www.quantopian.com/posts/tag/Algo-Template/newest.

import numpy as np
import quantopian.algorithm as algo
import quantopian.optimize as opt
from quantopian.pipeline import Pipeline
from quantopian.pipeline.factors import CustomFactor
from quantopian.pipeline.filters import QTradableStocksUS
from quantopian.pipeline.data import Fundamentals

ZSCORE_FILTER = 3 # Maximum number of standard deviations to include before counting as outliers
ZERO_FILTER = 0.001 # Minimum weight we allow before dropping security

class DebtToTotalAssets(CustomFactor):
    inputs = [Fundamentals.long_term_debt,
              Fundamentals.current_debt,
              Fundamentals.cash_and_cash_equivalents,
              Fundamentals.total_assets]
    window_length = 1

    def compute(self, today, assets, out, ltd, std, cce, ta):
        std_part = np.maximum(std - cce, np.zeros(std.shape))

        out[:] = np.divide(ltd + std_part, ta)
        

def initialize(context):
    
    algo.attach_pipeline(make_pipeline(), 'alpha_factor_template')

    # Schedule our rebalance function
    algo.schedule_function(func=rebalance,
                           date_rule=algo.date_rules.week_start(),
                           time_rule=algo.time_rules.market_open(),
                           half_days=True)

    # Record our portfolio variables at the end of day
    algo.schedule_function(func=record_vars,
                           date_rule=algo.date_rules.every_day(),
                           time_rule=algo.time_rules.market_close(),
                           half_days=True)


def make_pipeline():
    # Setting up the variables
    alpha_factor = -DebtToTotalAssets(mask=QTradableStocksUS())
    
    # Standardized logic for each input factor after this point
    alpha_w = alpha_factor.winsorize(min_percentile=0.02,
                                     max_percentile=0.98,
                                     mask=QTradableStocksUS())
    
    alpha_z = alpha_w.zscore()
    alpha_weight = alpha_z / 100.0
    
    outlier_filter = alpha_z.abs() < ZSCORE_FILTER
    zero_filter = alpha_weight.abs() > ZERO_FILTER
    finite_filter = alpha_weight.isfinite()

    universe = QTradableStocksUS() & \
               outlier_filter & \
               zero_filter & \
               finite_filter

    pipe = Pipeline(
        columns={
            'alpha_weight': alpha_weight
        },
        screen=universe
    )
    return pipe


def before_trading_start(context, data):
    context.pipeline_data = algo.pipeline_output('alpha_factor_template')


def record_vars(context, data):
    # Plot the number of positions over time.
    algo.record(num_positions=len(context.portfolio.positions))
    algo.record(leverage=context.account.leverage)

    
def rebalance(context, data):
    # Retrieve pipeline output
    pipeline_data = context.pipeline_data
    
    alpha_weight = pipeline_data['alpha_weight']
    alpha_weight_norm = alpha_weight / alpha_weight.abs().sum()

    objective = opt.TargetWeights(alpha_weight_norm)

    # No constraints
    constraints = []
    
    algo.order_optimal_portfolio(
        objective=objective,
        constraints=constraints
    )

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