Add initial Backtesting.py (squashed dev branch)

doc/examples/Strategies Library.py

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+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: light
+#       format_version: '1.3'
+#       jupytext_version: 0.8.6
+#   kernelspec:
+#     display_name: Python 3
+#     language: python
+#     name: python3
+# ---
+
+# Library of Composable Base Strategies
+# ======================
+#
+# This tutorial will show how to reuse composable base strategies that are part of this software distribution.
+# It is assumed you're already familiar with
+# [basic _backtesting.py_ usage](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.html).
+#
+# We'll extend the same moving average cross-over strategy as in
+# [Quick Start User Guide](https://kernc.github.io/backtesting.py/doc/examples/Quick Start User Guide.html),
+# but we'll rewrite it as a vectorized signal strategy and add trailing stop-loss.
+#
+# We'll again use a helper moving average function.
+# In practice, one can use functions from any indicator library, such as
+# [TA-Lib](https://github.com/mrjbq7/ta-lib),
+# [Tulipy](https://tulipindicators.org),
+# PyAlgoTrade, ...
+
+from backtesting.test import SMA
+
+# _Backtesting.py_ package includes
+# [_lib_](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html)
+# module that contains various reusable utilities for developing strategies.
+# Some of those utilities are composable base strategies one can extend and build upon.
+#
+# We import and extend two of those strategies here:
+# * [`SignalStrategy`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.SignalStrategy)
+#   which decides upon a single signal vector whether to buy into a position, akin to
+#   [vectorized backtesting](https://www.google.com/search?q=vectorized+backtesting)
+#   engines, and
+# * [`TrailingStrategy`](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html#backtesting.lib.TrailingStrategy)
+#   which automatically trails the current price with a stop-loss order some multiple of
+#   [average true range](https://en.wikipedia.org/wiki/Average_true_range)
+#   (ATR) away.
+
+# +
+import pandas as pd
+from backtesting.lib import SignalStrategy, TrailingStrategy
+
+
+class SmaCross(SignalStrategy,
+               TrailingStrategy):
+    n1 = 10
+    n2 = 20
+    
+    def init(self):
+        # In init() and in next() it is important to call the
+        # super method to properly initialize all the classes
+        super().init()
+        
+        # Precompute the two moving averages
+        sma1 = self.I(SMA, self.data.Close, self.n1)
+        sma2 = self.I(SMA, self.data.Close, self.n2)
+        
+        # Taking a first difference (`.diff()`) of a boolean
+        # series results in +1, 0, and -1 values. In our signal,
+        # as expected by SignalStrategy, +1 means buy,
+        # -1 means sell, and 0 means to hold whatever current
+        # position and wait. See the docs.
+        signal = (pd.Series(sma1) > sma2).astype(int).diff().fillna(0)
+        
+        # Set the signal vector using the method provided
+        # by SignalStrategy
+        self.set_signal(signal)
+        
+        # Set trailing stop-loss to 4x ATR
+        # using the method provided by TrailingStrategy
+        self.set_trailing_sl(4)
+# -
+
+# 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**.
+#
+# Let's see how the example strategy fares on historical Google data.
+
+# +
+from backtesting import Backtest
+from backtesting.test import GOOG
+
+bt = Backtest(GOOG, SmaCross, commission=.002)
+
+bt.run()
+bt.plot()
+# -
+
+# Notice how managing risk with a trailing stop-loss severely limits our losses.
+#
+# For other strategies of the sort, and other reusable utilities in general, see the
+# [_lib_ module reference](https://kernc.github.io/backtesting.py/doc/backtesting/lib.html).