rhizopus is a Python trading simulation framework and a backtesting tool. It
can be used to construct broker simulators for backtesting with historical
data, as well as for live trading. Its main goal is to provide a simple unified
interface for both backtesting and live trading.
- Support for multiple currencies.
- Bid-ask spreads.
- Easy integration of any type to transaction costs, e.g. fixed transaction fees.
The following code runs a constant-mix strategy given by the weights in the target_alloc dict.
See example.py for details.
target_alloc = {
'USD': 0.4,
'KRW': 0.15,
'JPY': 0.25,
'HUF': 0.2,
}
assert abs(sum(target_alloc.values()) - 1.0) < 1e-8
series_store = get_series_store('EUR')
filters = [
TransactionCostFilter('EUR', 5.0, "transaction_cost", []), # 5 EUR per transaction
]
broker_simulator = BrokerSimulator(
series_store,
filters,
default_numeraire='EUR',
)
accounts = {num: (0.0, num) for num in series_store.vertices()}
accounts['EUR'] = (1.0e6, 'EUR') # start capital
initial_orders = [CreateAccountOrder(num, amount) for num, amount in accounts.items()]
broker = Broker(broker_simulator, initial_orders=initial_orders)
strategy = ConstantMixStrategy(broker, target_alloc)
# On the first day we just observe the market prices and do nothing. Trading starts on the next day.
trading_start_time = series_store.get_min_time() + datetime.timedelta(days=1)
strategy.run(trading_start_time, max_iterations=100)
df = get_observer_df(strategy.observer)
plot_normalized_asset_performance(df, target_alloc.keys(), 'EUR')
plot_account_weights(df, target_alloc.keys())
rhizopus does not depend on any other python package outside the Python standard library.
pip install rhizopus
Clone this repository and call pip install from the main directory:
git clone https://github.com/jwergieluk/rhizopus.git
cd rhizopus
pip install -e .
conda install rhizopus
rhizopus is released under GNU GENERAL PUBLIC LICENSE Version 3. See LICENSE file for details.
Copyright (c) 2016--2021 Julian Wergieluk