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.

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,
    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 (%)                             52.06%
# Annualized portfolio return (%)                        52.99%
# Annualized excess portfolio return (%)                  49.9%
# Annualized excess risk (%)                               8.0%
# Information ratio (x)                                   6.24x
# Annualized risk over risk-free (%)                       8.0%
# Sharpe ratio (x)                                        6.24x
# Max drawdown (%)                                        2.11%
# Annual turnover (x)                                   609.25x
# Portfolio hit rate (%)                                  61.2%

If you have a charting library installed, like matplotlib, check out BaseResult for the many metrics you can plot, like portfolio value (backtest_result.v):

long and short leverage (backtest_result.leverage):

trades in SBUX (backtest_result.trades['SBUX']) or 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.