Loading data

A Backtest accepts any pd.DataFrame (single-asset) or dict[str, pd.DataFrame] (multi-asset) as long as the index is a DatetimeIndex and the columns include Open, High, Low, Close (Volume is nice-to-have). You can build that yourself from a CSV / database / API, but for the common cases QTrade ships two loaders.

US markets — from_yfinance

Install the [data] extra:

pip install "qtrade-lib[data]"

from qtrade.data import from_yfinance

data = from_yfinance(
    ["AAPL", "MSFT", "GC=F"],          # one ticker or a list
    start="2023-01-01",
    end="2024-12-31",
    interval="1d",                      # "1h", "5m", etc. all supported
    auto_adjust=True,                   # split / dividend adjusted closes
)
# data is a dict[str, DataFrame] aligned on the intersection of indexes
# — pass straight to Backtest.

What the helper does for you:

• Loops over symbols, raises a clean ValueError if any returned empty.

• Drops NaN rows from each frame (yfinance occasionally returns them on early-listing dates).

• Aligns every frame on the intersection of their DatetimeIndex so Backtest accepts them as a multi-asset input.

Chinese markets — from_akshare_*

For A-shares and Chinese futures, install the [cn] extra:

pip install "qtrade-lib[cn]"

A-shares

from qtrade.data import from_akshare_stock_a

data = from_akshare_stock_a(
    ["600519", "000001"],                 # 6-digit codes, no exchange prefix
    start="2024-01-01",                   # accepts "YYYY-MM-DD" or "YYYYMMDD"
    end="2024-12-31",
    period="daily",                       # or "weekly" / "monthly"
    adjust="qfq",                         # 前复权 (default; recommended for backtests)
)

Futures (main-contract continuous)

from qtrade.contracts import Contract
from qtrade.data import from_akshare_futures

data = from_akshare_futures(
    ["AU0", "RB0"],                       # main-contract codes (trailing 0)
    start="2024-01-01",
    end="2024-12-31",
)

# Pair with Contract specs for the right multipliers / margins:
SHFE_AU = Contract(multiplier=1000, margin_ratio=0.08, name="SHFE Gold")
SHFE_RB = Contract(multiplier=10,   margin_ratio=0.10, name="SHFE Rebar")

bt = Backtest(data, MyStrategy, contracts={"AU0": SHFE_AU, "RB0": SHFE_RB})

Same shape as the yfinance loader — Chinese column names are translated to standard Open, High, Low, Close, Volume, the index becomes a tz-naive DatetimeIndex, and frames are aligned on intersection.

Aligning external data

If you've assembled dict[str, DataFrame] yourself (e.g. from a CSV folder) and the indexes don't perfectly match, run:

from qtrade.data import align_indexes
data = align_indexes(data)

align_indexes reindexes every frame on the intersection of their indexes — a no-op when they already match, a one-liner when they don't.

Other data sources

The two helpers here are convenience wrappers, not framework constraints. For Tushare, JoinQuant, IB, or your own pipeline, just produce a dict[str, DataFrame] (each indexed by datetime, OHLC capitalized) and hand it to Backtest. If the indexes aren't aligned, align_indexes takes care of it.