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Capital Expenditure Volatility (CapEx Vol) - 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.

CapEx Vol

Cash flow volatility is a fairly well studied metric that is often considered a proxy for uncertainty at a firm level. In this template algorithm we've extended that idea to see if firms with relatively more volatile capital expenditures (e.g. spending on things like new buildings, plants, equipment, etc) are also more unpredictable and, by extension, riskier and more likely to underperform firms with lower relative capex volatility. For a bit more academic detail take a look at this SSRN paper.

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 TEM(CustomFactor):
    TEM = standard deviation of past 6 quarters' reports
    window_length = 390
    def compute(self, today, assets, out, asof_date, capex, total_assets):
        values = capex/total_assets
        for column_ix in range(asof_date.shape[1]):
            _, unique_indices = np.unique(asof_date[:, column_ix], return_index=True)
            quarterly_values = values[unique_indices, column_ix]
            if len(quarterly_values) < 6:
                quarterly_values = np.hstack([
                    np.repeat([np.nan], 6 - len(quarterly_values)),
            out[column_ix] = np.std(quarterly_values[-6:])

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
    capex_vol = TEM(
    alpha_factor = -capex_vol
    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 currently
    constraints = []
There was a runtime error.

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1 response


When I start simulating from 2007 onwards, I noticed the number of holdings are around 30 but when I used factset, the number of holdings look decent?

1. Is there a way to tell the system not to add this factor to the rest of the factors before 2011?
2. Is it possible to mix factor using factset with factor using morningstar? Although I feel this way is messy...