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104 lines
3.8 KiB
Python
104 lines
3.8 KiB
Python
# ---
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# jupyter:
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# jupytext:
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# text_representation:
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# extension: .py
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# format_name: light
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# format_version: '1.5'
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# jupytext_version: 1.3.0
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# kernelspec:
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# display_name: Python 3
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# language: python
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# name: python3
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# ---
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# Library of Composable Base Strategies
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# ======================
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#
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# This tutorial will show how to reuse composable base strategies that are part of this software distribution.
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# It is assumed you're already familiar with
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# [basic _backtesting.py_ usage](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.html).
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#
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# We'll extend the same moving average cross-over strategy as in
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# [Quick Start User Guide](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.html),
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# but we'll rewrite it as a vectorized signal strategy and add trailing stop-loss.
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#
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# Again, we use our helper moving average function.
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# In practice, one can use functions from any indicator library, such as
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# [TA-Lib](https://github.com/mrjbq7/ta-lib),
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# [Tulipy](https://tulipindicators.org),
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# PyAlgoTrade, ...
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from backtesting.test import SMA
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# _Backtesting.py_ package includes
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# [_lib_](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html)
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# module that contains various reusable utilities for developing strategies.
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# Some of those utilities are composable base strategies one can extend and build upon.
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#
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# We import and extend two of those strategies here:
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# * [`SignalStrategy`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.SignalStrategy)
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# which decides upon a single signal vector whether to buy into a position, akin to
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# [vectorized backtesting](https://www.google.com/search?q=vectorized+backtesting)
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# engines, and
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# * [`TrailingStrategy`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.TrailingStrategy)
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# which automatically trails the current price with a stop-loss order some multiple of
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# [average true range](https://en.wikipedia.org/wiki/Average_true_range)
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# (ATR) away.
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# +
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import pandas as pd
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from backtesting.lib import SignalStrategy, TrailingStrategy
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class SmaCross(SignalStrategy,
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TrailingStrategy):
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n1 = 10
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n2 = 20
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def init(self):
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# In init() and in next() it is important to call the
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# super method to properly initialize all the classes
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super().init()
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# Precompute the two moving averages
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sma1 = self.I(SMA, self.data.Close, self.n1)
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sma2 = self.I(SMA, self.data.Close, self.n2)
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# Taking a first difference (`.diff()`) of a boolean
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# series results in +1, 0, and -1 values. In our signal,
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# as expected by SignalStrategy, +1 means buy,
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# -1 means sell, and 0 means to hold whatever current
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# position and wait. See the docs.
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signal = (pd.Series(sma1) > sma2).astype(int).diff().fillna(0)
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# Set the signal vector using the method provided
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# by SignalStrategy
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self.set_signal(signal)
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# Set trailing stop-loss to 4x ATR
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# using the method provided by TrailingStrategy
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self.set_trailing_sl(4)
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# -
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# Note, since the strategies in _lib_ may require their own intialization and next-tick logic, be sure to **always call `super().init()` and `super().next()` in your overridden methods**.
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#
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# Let's see how the example strategy fares on historical Google data.
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# +
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from backtesting import Backtest
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from backtesting.test import GOOG
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bt = Backtest(GOOG, SmaCross, commission=.002)
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bt.run()
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bt.plot()
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# -
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# Notice how managing risk with a trailing stop-loss severely limits our losses.
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#
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# For other strategies of the sort, and other reusable utilities in general, see the
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# [_lib_ module reference](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html).
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