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Capital Expenditure Volatility (CapEx Vol) with FactSet Data - Template Fundamental Algo

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.

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".

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.

Clone Algorithm
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Backtest from to with initial capital
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
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 factset

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)),
                    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
    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
    capex_vol = TEM(
        inputs=[factset.Fundamentals.capex_qf_asof_date,
                factset.Fundamentals.capex_qf,
                factset.Fundamentals.assets],
        mask=QTradableStocksUS()
    )
    
    alpha_factor = -capex_vol
    
    # Standardized logic for each input factor after this point
    alpha_w = alpha_factor.winsorize(min_percentile=0.02,
                                     max_percentile=0.98,
                                     mask=alpha_factor.isfinite())
    
    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

    universe = QTradableStocksUS() & \
               outlier_filter & \
               zero_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, want all assets allocated to
    constraints = []
    
    algo.order_optimal_portfolio(
        objective=objective,
        constraints=constraints
    )
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