Backtesting Frameworks for the Quant Trading Column
The notebooks start with a transparent pandas backtester so readers can inspect signal alignment, position shifting, transaction costs, turnover, and equity construction. The same signals can then be routed to established Python backtesting and reporting packages.
| Framework | Column use | Adapter |
|---|---|---|
| pandas vectorized baseline | transparent close-to-close research checks | examples/quant_trading/backtest.py |
| vectorbt | multi-asset signal matrices and parameter grids | run_vectorbt_from_signals |
| backtesting.py | single-asset strategy class tutorials | run_backtestingpy_signal |
| bt | target-weight ETF and portfolio rotation | run_bt_target_weights |
| Backtrader | event-driven data feeds and order logic | template writer |
| Zipline-Reloaded | calendar-safe factor research skeleton | template writer |
| QuantStats | HTML reports and performance tear sheets | quantstats_html_report |
vectorbt
from examples.quant_trading.frameworks import run_vectorbt_from_signals
portfolio = run_vectorbt_from_signals(prices, entries, exits)
print(portfolio.total_return())
backtesting.py
from examples.quant_trading.data import fetch_yahoo_ohlcv
from examples.quant_trading.frameworks import run_backtestingpy_signal
ohlcv = fetch_yahoo_ohlcv("SPY", start="2018-01-01")
stats = run_backtestingpy_signal(ohlcv, signal)
bt
from examples.quant_trading.frameworks import run_bt_target_weights
result = run_bt_target_weights(prices, target_weights)
Backtrader and Zipline-Reloaded
The column provides template files for these framework-specific workflows:
from examples.quant_trading.frameworks import write_framework_templates
write_framework_templates("examples/quant_trading/templates")
These event-driven frameworks require careful calendar, bundle, and execution setup. Keep DeTime feature generation outside the event loop unless you are explicitly modeling the cost and latency of online recomputation.
The current rendered notebooks exercise the transparent pandas research backtester first, then keep these adapters available as extension points for framework-specific studies.