Rebalancing Positions

PyBroker can be used to simulate rebalancing a portfolio. This means that PyBroker can simulate adjusting the asset allocation of a portfolio to match a desired target allocation. Additionally, our portfolio can be rebalanced using portfolio optimization, as this notebook will demonstrate.

import pybroker as pyb
from datetime import datetime
from pybroker import ExecContext, Strategy, YFinance

pyb.enable_data_source_cache('rebalancing')

<diskcache.core.Cache at 0x7fd854443a60>

Equal Position Sizing

Let’s assume that we want to rebalance a long-only portfolio at the beginning of every month to make sure that each asset in our portfolio has a roughly equal allocation.

We first start by writing a helper function to detect when the current bar’s date is the start of a new month:

def start_of_month(dt: datetime) -> bool:
    if dt.month != pyb.param('current_month'):
        pyb.param('current_month', dt.month)
        return True
    return False

Next, we implement a function that will either buy or sell enough shares in an asset to reach a target allocation.

def set_target_shares(
    ctxs: dict[str, ExecContext],
    targets: dict[str, float]
):
    for symbol, target in targets.items():
        ctx = ctxs[symbol]
        target_shares = ctx.calc_target_shares(target)
        pos = ctx.long_pos()
        if pos is None:
            ctx.buy_shares = target_shares
        elif pos.shares < target_shares:
            ctx.buy_shares = target_shares - pos.shares
        elif pos.shares > target_shares:
            ctx.sell_shares = pos.shares - target_shares

If the current allocation is above the target level, the function will sell some shares of the asset, while if the current allocation is below the target level, the function will buy some shares of the asset.

Following that, we write a rebalance function to set each asset to an equal target allocation at the beginning of each month:

def rebalance(ctxs: dict[str, ExecContext]):
    dt = tuple(ctxs.values())[0].dt
    if start_of_month(dt):
        target = 1 / len(ctxs)
        set_target_shares(ctxs, {symbol: target for symbol in ctxs.keys()})

Now that we have implemented the rebalance function, the next step is to backtest our rebalancing strategy using five different stocks in our portfolio. To process all stocks at once on each bar of data, we will use the Strategy#set_after_exec method:

strategy = Strategy(YFinance(), start_date='1/1/2018', end_date='1/1/2023')
strategy.add_execution(None, ['TSLA', 'NFLX', 'AAPL', 'NVDA', 'AMZN'])
strategy.set_after_exec(rebalance)
result = strategy.backtest()

Backtesting: 2018-01-01 00:00:00 to 2023-01-01 00:00:00

Loaded cached bar data.

Test split: 2018-01-02 00:00:00 to 2022-12-30 00:00:00

100% (1259 of 1259) |####################| Elapsed Time: 0:00:00 Time:  0:00:00

Finished backtest: 0:00:02

result.orders

	type	symbol	date	shares	limit_price	fill_price	fees
id
1	buy	NFLX	2018-01-03	99	NaN	203.86	0.0
2	buy	AAPL	2018-01-03	464	NaN	43.31	0.0
3	buy	TSLA	2018-01-03	935	NaN	21.36	0.0
4	buy	AMZN	2018-01-03	336	NaN	59.84	0.0
5	buy	NVDA	2018-01-03	376	NaN	52.18	0.0
...	...	...	...	...	...	...	...
292	sell	NFLX	2022-12-02	15	NaN	315.99	0.0
293	sell	NVDA	2022-12-02	97	NaN	166.89	0.0
294	buy	AAPL	2022-12-02	27	NaN	146.82	0.0
295	buy	TSLA	2022-12-02	70	NaN	193.68	0.0
296	buy	AMZN	2022-12-02	41	NaN	94.57	0.0

296 rows × 7 columns

Portfolio Optimization

Portfolio optimization can guide our rebalancing in order to meet some objective for our portfolio. For instance, we can use portfolio optimization with the goal of allocating assets in a way to minimize risk.

Riskfolio-Lib is a popular Python library for performing portfolio optimization. Below shows how to use it to construct a minimum risk portfolio by minimizing the portfolio’s Conditional Value at Risk (CVar) based on the past year of returns:

import pandas as pd
import riskfolio as rp

pyb.param('lookback', 252)  # Use past year of returns.

def calculate_returns(ctxs: dict[str, ExecContext], lookback: int):
    prices = {}
    for ctx in ctxs.values():
        prices[ctx.symbol] = ctx.adj_close[-lookback:]
    df = pd.DataFrame(prices)
    return df.pct_change().dropna()

def optimization(ctxs: dict[str, ExecContext]):
    lookback = pyb.param('lookback')
    first_ctx = tuple(ctxs.values())[0]
    if start_of_month(first_ctx.dt):
        Y = calculate_returns(ctxs, lookback)
        port = rp.Portfolio(returns=Y)
        port.assets_stats(method_mu='hist', method_cov='hist', d=0.94)
        w = port.optimization(
            model='Classic',
            rm='CVaR',
            obj='MinRisk',
            rf=0,      # Risk free rate.
            l=0,       # Risk aversion factor.
            hist=True  # Use historical scenarios.
        )
        targets = {
            symbol: w.T[symbol].values[0]
            for symbol in ctxs.keys()
        }
        set_target_shares(ctxs, targets)

You can find more information and examples of using Riskfolio-Lib on the official documentation. Now, let’s move on to backtesting the strategy!

strategy.set_after_exec(optimization)
result = strategy.backtest(warmup=pyb.param('lookback'))

Backtesting: 2018-01-01 00:00:00 to 2023-01-01 00:00:00

Loaded cached bar data.

Test split: 2018-01-02 00:00:00 to 2022-12-30 00:00:00

100% (1259 of 1259) |####################| Elapsed Time: 0:00:01 Time:  0:00:01

Finished backtest: 0:00:01

result.orders.head()

	type	symbol	date	shares	limit_price	fill_price	fees
id
1	buy	AAPL	2019-01-04	1420	NaN	36.54	0.0
2	buy	TSLA	2019-01-04	1168	NaN	20.69	0.0
3	buy	AMZN	2019-01-04	307	NaN	77.81	0.0
4	sell	AAPL	2019-02-04	105	NaN	42.37	0.0
5	buy	TSLA	2019-02-04	51	NaN	20.57	0.0

Above, we can observe that the portfolio optimization resulted in allocating the entire portfolio to 3 of the 5 stocks during the first month of the backtest.