Multi-Symbol Models

The model training used so far has trained a separate instance for every ticker symbol. Instead, a single model can be trained across symbols that behave similarly, such as stocks in a specific industry.

PyBroker v2 supports training one model across all symbols passed to Strategy.add_execution, as shown in this notebook.

import pybroker
from pybroker import Strategy, YFinance
from sklearn.linear_model import LinearRegression

pybroker.enable_data_source_cache("multi_symbol_models")

<pybroker.cache._L1Cache at 0x7f0d105fee40>

Training One Model on Multiple Symbols

This notebook repurposes the linear regression example from Training a Model. Below, one shared LinearRegression model is trained with the close_minus_ma indicator on four chip stocks:

from pybroker.indicator import close_minus_ma

cmma_20 = close_minus_ma("cmma_20", lookback=20, atr_length=14)


def train_slr(symbols, train_data, test_data):
    # Shift within symbols so returns never cross a symbol boundary.
    next_close = train_data.groupby("symbol")["close"].shift(-1)
    train_data["target"] = next_close / train_data["close"] - 1
    train_data = train_data.dropna()
    model = LinearRegression()
    model.fit(train_data[["cmma_20"]], train_data["target"])
    return model, ["cmma_20"]


model_slr = pybroker.model("slr", train_slr, indicators=[cmma_20], pooled=True)

SYMBOLS = ["MU", "TXN", "ADI", "AMAT"]

Registering the model with pooled=True will train it only once per execution. This replaces the training function’s single symbol argument with a symbols tuple and passes the combined train and test splits.

During the backtest, the trained model is shared across all symbols in the execution:

POS_SIZE = 1 / len(SYMBOLS)


def hold_long(ctx):
    pred = ctx.preds("slr")[-1]
    if not ctx.long_pos():
        if pred > 0:
            ctx.buy_shares = ctx.calc_target_shares(POS_SIZE)
    elif pred < 0:
        ctx.sell_all_shares()


strategy = Strategy(YFinance(), start_date="1/1/2021", end_date="1/1/2026")
strategy.add_execution(hold_long, SYMBOLS, models=model_slr)
result = strategy.walkforward(
    warmup=20, windows=3, train_size=0.5, lookahead=1
)
result.metrics_df.head(20)

Backtesting: 2021-01-01 00:00:00 to 2026-01-01 00:00:00

Loading bar data...

[*********************100%***********************]  4 of 4 completed

Loaded bar data: 0:00:00

Computing indicators...

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Train split: 2021-01-07 00:00:00 to 2022-04-04 00:00:00
Finished training models: 0:00:00

Test split: 2022-04-05 00:00:00 to 2023-07-05 00:00:00

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Train split: 2022-04-05 00:00:00 to 2023-07-05 00:00:00
Finished training models: 0:00:00

Test split: 2023-07-06 00:00:00 to 2024-10-01 00:00:00

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Train split: 2023-07-06 00:00:00 to 2024-10-01 00:00:00
Finished training models: 0:00:00

Test split: 2024-10-02 00:00:00 to 2025-12-31 00:00:00

100% (313 of 313) |######################| Elapsed Time: 0:00:00 Time:  0:00:00

Finished backtest: 0:00:01

	name	value
0	trade_count	170
1	initial_market_value	100000.0
2	end_market_value	173448.08
3	total_pnl	76163.08
4	unrealized_pnl	-2715.0
5	total_return_pct	76.16308
6	total_profit	150256.55
7	total_loss	-74093.47
8	total_fees	0.0
9	max_drawdown	-41802.76
10	max_drawdown_pct	-28.604164
11	max_drawdown_date	2025-04-08 00:00:00
12	win_rate	70.0
13	loss_rate	30.0
14	winning_trades	119
15	losing_trades	51
16	avg_pnl	448.018118
17	avg_return_pct	1.465824
18	avg_trade_bars	14.005882
19	avg_profit	1262.660084