Rank Long Short

What We Need

In order to backtest a portfolio using RankLongShort, we'll need:

• A metric to rank assets by over time: metric_to_rank
  • In this example, we'll use forecast returns for stocks, but we could also use LTM sales, age of CEO, etc.
• Stock returns over the time periods we wish to backtest: actual_returns
• Start and end dates: start_date and end_date

In order to make this example as easy as possible, we've prepared, and will use, forecast and actual returns from 2017 - 2018 for a universe of 319 stocks. We will also exclude cost models.

Sample Code For a RankLongShort Backtest

import pandas as pd
import investos as inv

actual_returns = pd.read_parquet("https://investos.io/example_actual_returns.parquet")
forecast_returns = pd.read_parquet("https://investos.io/example_forecast_returns.parquet")

strategy = inv.portfolio.strategy.RankLongShort(
    actual_returns = actual_returns,
    metric_to_rank = forecast_returns,
    leverage=1.6,
    ratio_long=130,
    ratio_short=30,
    percent_long=0.2,
    percent_short=0.2,
    n_periods_held=60,
    cash_column_name="cash"
)

portfolio = inv.portfolio.BacktestController(
    strategy=strategy,
    start_date='2017-01-01',
    end_date='2018-01-01',
    aum=100_000_000
)

backtest_result = portfolio.generate_positions()
backtest_result.summary

That's all that's required to run your first (RankLongShort) backtest!

When backtest_result.summary is executed, it will output summary backtest results:

# Initial timestamp                         2017-01-03 00:00:00
# Final timestamp                           2017-12-29 00:00:00
# Total portfolio return (%)                             17.22%
# Annualized portfolio return (%)                        17.49%
# Annualized excess portfolio return (%)                 14.42%
# Annualized excess risk (%)                              6.09%
# Information ratio (x)                                   2.37x
# Annualized risk over risk-free (%)                      6.09%
# Sharpe ratio (x)                                        2.37x
# Max drawdown (%)                                        3.21%
# Annual turnover (x)                                     9.97x
# Portfolio hit rate (%)                                  60.0%

If you have a charting library installed, like matplotlib, check out BaseResult for the many metrics you can plot, like:

• portfolio value evolution (backtest_result.v),
• long and short leverage evolution (backtest_result.leverage),
• trades in SBUX (backtest_result.trades['SBUX']),
• holdings in AAPL (backtest_result.h['AAPL']),
• etc.

What Could Be Improved

In the above example, for simplicity, we:

• Didn't use any cost models
  • e.g. TradingCost, ShortHoldingCost
• Assumed our initial portfolio was all cash
  • You can override this by setting the initial_portfolio kwarg equal to a (Pandas) series of asset values when initializing BacktestController.

Next: Single Period Optimization

Next, let's explore adding cost and constraint models in our next guide: Single Period Optimization.