重新平衡仓位

PyBroker 可让你通过调整资产配置以匹配目标配置来模拟投资组合再平衡。本文档还将演示如何使用 投资组合优化 进行再平衡。

import pybroker
from pybroker import ExecContext, Strategy, YFinance

pybroker.enable_data_source_cache("rebalancing")

<pybroker.cache._L1Cache at 0x7f2c8c0cb530>

等额仓位配置

假设我们希望在每个月初对一个仅做多的投资组合进行再平衡，以便为每只股票维持相等的配置。

首先，我们编写一个辅助函数，用于检测当前 K 线是否为新月份的开始：

def start_of_month(ctxs: dict[str, ExecContext]) -> bool:
    dt = tuple(ctxs.values())[0].dt
    if dt.month != pybroker.param("current_month"):
        pybroker.param("current_month", dt.month)
        return True
    return False

接下来，我们编写一个 rebalance 函数，在每个月初为每个资产设置相等的目标配置：

def rebalance(ctxs: dict[str, ExecContext]):
    if start_of_month(ctxs):
        target = 1 / len(ctxs)
        for ctx in ctxs.values():
            ctx.set_target_shares(target, dir="long")

在完成 rebalance 函数后，我们可以使用包含五只股票的投资组合对策略进行回测。为了在每个数据条上同时处理所有股票，我们使用 Strategy.set_after_exec 方法：

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

Loading bar data...

[*********************100%***********************]  5 of 5 completed

Loaded bar data: 0:00:00

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: 0:00:01

set_after_exec 函数会在通过 add_execution 添加的所有执行完成后运行。由于我们向 add_execution 传入了 None，因此在 after_exec 之前不会运行任何执行逻辑。

result.orders

	type	symbol	date	created	order_type	intent	shares	limit_price	market_price	fill_price	fees
id
1	buy	AAPL	2018-01-03	2018-01-02	market	buy_to_open	464	NaN	43.31	43.31	0.0
2	buy	AMZN	2018-01-03	2018-01-02	market	buy_to_open	336	NaN	59.84	59.84	0.0
3	buy	NFLX	2018-01-03	2018-01-02	market	buy_to_open	994	NaN	20.39	20.39	0.0
4	buy	NVDA	2018-01-03	2018-01-02	market	buy_to_open	4013	NaN	5.22	5.22	0.0
5	buy	TSLA	2018-01-03	2018-01-02	market	buy_to_open	869	NaN	21.36	21.36	0.0
...	...	...	...	...	...	...	...	...	...	...	...
293	sell	NFLX	2022-12-02	2022-12-01	market	sell_to_close	153	NaN	31.60	31.60	0.0
294	sell	NVDA	2022-12-02	2022-12-01	market	sell_to_close	974	NaN	16.69	16.69	0.0
295	buy	AAPL	2022-12-02	2022-12-01	market	buy_to_open	27	NaN	146.82	146.82	0.0
296	buy	AMZN	2022-12-02	2022-12-01	market	buy_to_open	41	NaN	94.57	94.57	0.0
297	buy	TSLA	2022-12-02	2022-12-01	market	buy_to_open	70	NaN	193.68	193.68	0.0

297 rows × 11 columns

投资组合优化

投资组合优化 可指导再平衡以实现特定目标，例如以最小化风险的方式配置股票。

Riskfolio-Lib 是一个用于投资组合优化的流行 Python 库。你可以使用 pip install riskfolio-lib 进行安装。

以下示例演示了如何通过最小化过去一年收益的 条件风险价值（CVaR） 来构建一个最小风险投资组合：

import pandas as pd
import riskfolio as rp

pybroker.param("lookback", 252)  # Use past year of returns.


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


def optimization(ctxs: dict[str, ExecContext]):
    lookback = pybroker.param("lookback")
    if start_of_month(ctxs):
        Y = calculate_returns(ctxs, lookback)
        port = rp.Portfolio(returns=Y)
        port.assets_stats(method_mu="hist", method_cov="hist")
        w = port.optimization(
            model="Classic",
            rm="CVaR",
            obj="MinRisk",
            rf=0,  # Risk free rate.
            l=0,  # Risk aversion factor.
            hist=True,  # Use historical scenarios.
        )
        for symbol, ctx in ctxs.items():
            target = w.T[symbol].values[0]
            ctx.set_target_shares(target, dir="long")

有关更多信息和示例，请参阅 Riskfolio-Lib 官方文档。接下来，我们对策略进行回测：

strategy.set_after_exec(optimization)
result = strategy.backtest(warmup=pybroker.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	created	order_type	intent	shares	limit_price	market_price	fill_price	fees
id
1	buy	AAPL	2019-01-04	2019-01-03	market	buy_to_open	1420	NaN	36.54	36.54	0.0
2	buy	AMZN	2019-01-04	2019-01-03	market	buy_to_open	347	NaN	77.81	77.81	0.0
3	buy	TSLA	2019-01-04	2019-01-03	market	buy_to_open	1020	NaN	20.69	20.69	0.0
4	sell	AAPL	2019-02-04	2019-02-01	market	sell_to_close	103	NaN	42.37	42.37	0.0
5	buy	AMZN	2019-02-04	2019-02-01	market	buy_to_open	1	NaN	81.58	81.58	0.0

在回测的第一个月，投资组合优化将整个投资组合分配给了 AAPL、AMZN 和 TSLA。