Equal Weighted Equity Strategy

Understand this code: Read our step-by-step explanation of this notebook: guided walkthrough.

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Environment

import sigtech.framework as sig

from uuid import uuid4
import pandas as pd
import datetime as dtm

env = sig.init(env_date=dtm.date(2024, 2, 22))

Define the investment universe

equity_filter = sig.EquityUniverseFilter('DAX INDEX', use_add_order=True)

Create the strategy

factor_definition = sig.FactorExposures()

factor_definition.add_raw_factor_timeseries('SIZE', fundamental_field='Market Cap', fundamental_freq='Quarterly')

def include_highest_by_mcap(factors, proportion = 0.3, **kwargs):
    """
    :param factors: frame where index is each factor, columns are stocks
    :param proportion: proportion of stocks to include ranked by MCAP
    :returns: series where index is stocks, value is signal
    """

    factors = factors.sum(axis=0)

    n = int(len(factors) * proportion)

    long = pd.Series(1, index=factors.nlargest(n).index)

    return long

equal_weighted_strategy = sig.EquityFactorBasket(

    # Base currency of the strategy
    currency='USD',

    # Start date of the strategy
    start_date=dtm.date(2021, 1, 4),

    # End date of the strategy
    end_date=dtm.date(2021, 5, 31),

    # Equity universe filter object or universe time series
    universe_filter=equity_filter,

    # Rebalance frequency
    rebalance_frequency='EOM',

    # Factor exposure object
    factor_exposure_generator=factor_definition,

    # Function to convert factor values to signals
    factors_to_weight_function=include_highest_by_mcap,

    # Allocation function to convert signals to weights
    allocation_function=sig.EquityFactorBasket.AVAILABLE_ALLOCATION_FUNCTIONS.NORMALIZE_ZERO_FILLED,

    # Name of the strategy
    ticker=f'EQUAL WEIGHTED {str(uuid4())[:4]}'
)

equal_weighted_strategy.build()

Generate the performance report

sig.PerformanceReport(equal_weighted_strategy.history()).report()