Dynamic Symbol Selection

Every strategy we’ve seen has traded a fixed list of ticker symbols that were chosen beforehand. Alternatively, we may want a strategy to target whichever symbols look best at any given time. These could be the most liquid names, or the ones with the highest momentum or value.

PyBroker v2 now enables dynamic symbol selection with SymbolSelector.

Loading the Candidate Universe

Below, data is downloaded for twenty liquid large-caps from YFinance:

import pandas as pd
import numpy as np
import pybroker
from pybroker import Strategy, YFinance, highv, lowv

pybroker.enable_data_source_cache("dynamic_symbol_selection")

UNIVERSE = [
    "AAPL",
    "AMZN",
    "AVGO",
    "COST",
    "CRM",
    "GOOG",
    "JNJ",
    "JPM",
    "KO",
    "LLY",
    "META",
    "MSFT",
    "NFLX",
    "NVDA",
    "PG",
    "PLTR",
    "QCOM",
    "TSLA",
    "WMT",
    "XOM",
]
start_date = "1/1/2021"
end_date = "1/1/2026"
yfinance = YFinance()
df = yfinance.query(UNIVERSE, start_date=start_date, end_date=end_date)
df.head()

Loading bar data...

[*********************100%***********************]  20 of 20 completed

Loaded bar data: 0:00:01

	date	symbol	open	high	low	close	volume	adj_close
0	2021-01-04	AAPL	133.520004	133.610001	126.760002	129.410004	143301900	125.632523
1	2021-01-04	AMZN	163.500000	163.600006	157.201004	159.331497	88228000	159.331497
2	2021-01-04	AVGO	43.932999	44.223999	42.124001	42.521999	24171000	38.112709
3	2021-01-04	COST	377.429993	381.549988	374.809998	380.149994	3322200	358.108002
4	2021-01-04	CRM	222.639999	223.750000	215.720001	220.309998	10319900	216.563354

Selecting Symbols by Liquidity

Dynamic symbol selection is handled using a SymbolSelector, which can be any callable that takes a Pandas DataFrame and returns a sequence of symbols. It is passed to Strategy.add_execution instead of a fixed list of symbols.

The example below ranks the universe by average dollar volume and keeps the top three symbols:

TOP_N = 3


def top_dollar_volume(df: pd.DataFrame):
    dollar_volume = (df["close"] * df["volume"]).groupby(df["symbol"]).mean()
    selected = dollar_volume.nlargest(TOP_N).index
    return selected

Running the Strategy

The example below implements a simple breakout strategy. It buys when a symbol closes above its previous 20-day high, and then sells when the it closes below its previous 20-day low. The strategy splits capital equally across the top three selected stocks:

from pybroker import highest, lowest


high_20 = highest("high_20", "high", 20)
low_20 = lowest("low_20", "low", 20)

POS_SIZE = 1.0 / TOP_N


def breakout(ctx):
    highs = ctx.indicator("high_20")
    lows = ctx.indicator("low_20")
    if len(highs) < 2 or np.isnan(highs[-2]):
        return
    if not ctx.long_pos():
        if ctx.close[-1] > highs[-2]:
            ctx.buy_shares = ctx.calc_target_shares(POS_SIZE)
    elif ctx.close[-1] < lows[-2]:
        ctx.sell_all_shares()


strategy = Strategy(df, start_date=start_date, end_date=end_date)
strategy.add_execution(
    breakout, top_dollar_volume, indicators=[high_20, low_20]
)

For each walkforward window, the top_dollar_volume selector runs on the train split and selects the top three stocks to trade during the subsequent test split:

result = strategy.walkforward(windows=4, train_size=0.5)
result.metrics_df.head(10)

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

Computing indicators...

100% (6 of 6) |##########################| Elapsed Time: 0:00:00 Time:  0:00:00

Test split: 2022-01-06 00:00:00 to 2023-01-04 00:00:00

100% (250 of 250) |######################| Elapsed Time: 0:00:00 Time:  0:00:00

Computing indicators...

100% (6 of 6) |##########################| Elapsed Time: 0:00:00 Time:  0:00:00

Test split: 2023-01-05 00:00:00 to 2024-01-03 00:00:00

100% (250 of 250) |######################| Elapsed Time: 0:00:00 Time:  0:00:00

Computing indicators...

100% (6 of 6) |##########################| Elapsed Time: 0:00:00 Time:  0:00:00

Test split: 2024-01-04 00:00:00 to 2024-12-31 00:00:00

100% (250 of 250) |######################| Elapsed Time: 0:00:00 Time:  0:00:00

Computing indicators...

100% (6 of 6) |##########################| Elapsed Time: 0:00:00 Time:  0:00:00

Test split: 2025-01-02 00:00:00 to 2025-12-31 00:00:00

100% (250 of 250) |######################| Elapsed Time: 0:00:00 Time:  0:00:00

Finished backtest: 0:00:00

	name	value
0	trade_count	31
1	initial_market_value	100000.0
2	end_market_value	243986.97
3	total_pnl	136797.66
4	unrealized_pnl	7189.31
5	total_return_pct	136.79766
6	total_profit	201003.09
7	total_loss	-64205.43
8	total_fees	0.0
9	max_drawdown	-49493.9