The pwb-toolbox package is designed to provide tools and resources for systematic trading strategies. It includes datasets and strategy ideas to assist in developing and backtesting trading algorithms.
To install the pwb-toolbox package:
pip install pwb-toolboxThis package requires Python 3.10 or higher.
To use PWB datasets, you need to login to Huggingface Hub (where PWB datasets are hosted) with Access Token:
huggingface-cli loginThe pwb-toolbox package offers a range of functionalities for systematic trading analysis. Here are some examples of how to utilize the package:
The pwb_toolbox.datasets module offers to load datasets for different asset classes, such as bonds, commodities, cryptocurrencies, ETFs, forex, indices, and stocks, using the get_pricing or the load_dataset functions:
import pwb_toolbox.datasets as pwb_ds
df = pwb_ds.get_pricing(["AAPL", "MSFT", "GOOGL"])
df = pwb_ds.load_dataset("Bonds-Daily-Price")
df = pwb_ds.load_dataset("Commodities-Daily-Price")
df = pwb_ds.load_dataset("Cryptocurrencies-Daily-Price")
df = pwb_ds.load_dataset("ETFs-Daily-Price")
df = pwb_ds.load_dataset("Forex-Daily-Price")
df = pwb_ds.load_dataset("Indices-Daily-Price")
df = pwb_ds.load_dataset("Stocks-Daily-Price")For more, see docs/datasets.md.
The pwb_toolbox.backtesting module offers simple building blocks for running Backtrader simulations.
Here is a strategy example:
import numpy as np
import backtrader as bt
import pwb_toolbox.backtesting as pwb_bt
import pwb_toolbox.datasets as pwb_ds
# ────────────────────────────────────────────────────────────────
# Toy dual-momentum: each month hold SPY if its lookback return > T-bill,
# otherwise sit in BIL. Kept minimal while using pwb_bt & pwb_ds.
# ────────────────────────────────────────────────────────────────
class SimpleMomentum(bt.Indicator):
"""12-month rate of change on close."""
lines = ("roc",)
params = (("period", 252),)
def __init__(self):
self.lines.roc = bt.indicators.RateOfChange(self.data.close, period=self.p.period)
class MonthlySwitcher(pwb_bt.BaseStrategy):
params = dict(
period=252, # ~12 months
risky="SPY",
safe="BIL",
leverage=1.0,
)
def __init__(self):
super().__init__()
# Attach momentum indicators to each data feed
self.mom = {d._name: SimpleMomentum(d, period=self.p.period) for d in self.datas}
self._last_month = -1
def next(self):
super().next()
# Rebalance only on month change
today = self.datas[0].datetime.date(0)
if today.month == self._last_month:
return
self._last_month = today.month
# Use prior bar to emulate "signal at month-end, trade next month"
idx = -1
spy_m = float(self.mom[self.p.risky].roc[idx])
bil_m = float(self.mom[self.p.safe].roc[idx])
# Decide allocation
hold_risky = (not np.isnan(spy_m)) and (not np.isnan(bil_m)) and (spy_m > bil_m)
targets = {
self.p.risky: self.p.leverage if hold_risky else 0.0,
self.p.safe: 0.0 if hold_risky else self.p.leverage,
}
# Set portfolio targets
for d in self.datas:
self.order_target_percent(d, targets.get(d._name, 0.0))
def run_strategy():
# Minimal universe fetched via pwb_ds inside pwb_bt
symbols = ["SPY", "BIL"]
result = pwb_bt.run_strategy(
indicator_cls=SimpleMomentum, # kept for compatibility, but not required externally
indicator_kwargs={"period": 252},
strategy_cls=MonthlySwitcher,
strategy_kwargs={"period": 252, "risky": "SPY", "safe": "BIL", "leverage": 1.0},
symbols=symbols,
start_date="2005-01-01",
cash=100_000.0,
)
return result
if __name__ == "__main__":
run_strategy()To explore more, you can find over 140 strategy examples at https://paperswithbacktest.com/strategies).
For more about backtesting, see docs/backtesting.md.
The execution helpers in pwb_toolbox.execution can connect to brokers to run
strategies in real time. A typical session collects account information,
computes target positions and submits the necessary orders.
Two brokers are supporter today:
- Interactive Brokers
- CCXT (crypto)
For more about execution, see docs/execution.md.
After running a live trading session, you can analyze the returned equity series using the
pwb_toolbox.performance module.
from pwb_toolbox.backtesting.examples import GoldenCrossAlpha, EqualWeightPortfolio
from pwb_toolbox.backtesting import run_backtest
from pwb_toolbox.backtesting.execution_models import ImmediateExecutionModel
from pwb_toolbox.performance import total_return, cagr
from pwb_toolbox.performance.plots import plot_equity_curve
result, equity = run_backtest(
ManualUniverseSelectionModel(["SPY", "QQQ"]),
GoldenCrossAlpha(),
EqualWeightPortfolio(),
execution=ImmediateExecutionModel(),
start="2015-01-01",
)
print("Total return:", total_return(equity))
print("CAGR:", cagr(equity))
plot_equity_curve(equity)To trade PWB strategies live with Interactive Brokers, you can use pwb-toolbox/tools/ib_server.
On a ubuntu server (for instance from https://www.ovhcloud.com/), install Miniconda, IB TWS, and RDP with:
cd pwb-toolbox/tools/ib_server
./install.sh
conda activate pwbIf TWS is already started:
PWB_API_KEY="" python -m execute_meta_strategyIf TWS isn'y already started:
TWS_USERNAME="" TWS_PASSWORD="" python -m launch_tws && PWB_API_KEY="" python -m execute_meta_strategyAnd to run the strategy daily, define the environment variables in .bashrc and then set up the following cron:
30 9 * * Mon-Fri /bin/bash /path/to/run_daily.sh >> /path/to/logfile 2>&1To get logs:
python -m monitor --logs-dir $HOME/pwb-data/ib/execution_logsNB: Fix to restart the desktop environment:
ps aux | grep xfce4
sudo pkill -u ubuntuContributions to the pwb-toolbox package are welcome! If you have any improvements, new datasets, or strategy ideas to share, please follow these guidelines:
- Fork the repository and create a new branch for your feature.
- Make your changes and ensure they adhere to the package's coding style.
- Write tests to validate the functionality or provide sample usage examples.
- Submit a pull request, clearly explaining the purpose and benefits of your contribution.
To build the package, run:
python -m pip install --upgrade build
rm -r dist
python -m buildTo upload the package to PyPI, run:
twine upload dist/*