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Debt to Total Assets - Template Fundamental Algo

After trawling through the stats on the many, many backtests that everyone in the community has developed, we at Quantopian have determined that a new series of template algorithms is warranted. We have the benefit of looking at community activity cross-sectionally and we have seen that there is a lot of strong development work on technical signals (mean reversal and momentum chiefly). Unsurprisingly, there are many fewer algorithms that have tapped into fundamental signals for their sources of predictive power. This algo is a great starting point for anyone looking to incorporate fundamentals-driven signals into their repertoire.

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".

Clone Algorithm
Total Returns
Max Drawdown
Benchmark Returns
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
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 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,
    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

    # Record our portfolio variables at the end of day

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,
    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 & \

    pipe = Pipeline(
            'alpha_weight': alpha_weight
    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.

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 = []
There was a runtime error.

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3 responses

These are so cool - thank you!! I'm learning a ton from all three of them.

Regarding the Investopedia link above, it looks like it links to the FCF Yield page, rather than debt to total assets. Did you mean to link to this page instead?

Thanks, I was looking for that for a long time now.
It's really helpful.

Good catch, Joakim, thank you. I'll update the link.