def test_data_extra_columns(self):
df = GOOG.copy()
df['P/E'] = np.arange(len(df))
df['MCap'] = np.arange(len(df))
class S(Strategy):
def init(self):
assert len(self.data.MCap) == len(self.data.Close)
assert len(self.data['P/E']) == len(self.data.Close)
def next(self):
assert len(self.data.MCap) == len(self.data.Close)
assert len(self.data['P/E']) == len(self.data.Close)
Backtest(df, S).run()
def test_data_nan_columns(self):
df = GOOG.copy()
df['Open'] = np.nan
with self.assertRaises(ValueError):
Backtest(df, SmaCross).run()
# As a trade-off, _backtesting.py_ is a _blazing fast_, small and lightweight backtesting library that uses state-of-the-art Python data structures and procedures. The entire API easily fits into memory banks of a single human individual. It's best suited for optimizing position entrance and exit strategies, decisions upon values of technical indicators, and it's also a versatile interactive trading strategy visualization tool. # # ### Data # # _You bring your own data._ Backtesting ingests data as a [pandas.DataFrame](https://pandas.pydata.org/pandas-docs/stable/10min.html) with columns `'Open'`, `'High'`, `'Low'`, `'Close'`, and (optionally) `'Volume'`. Such data is easily obtainable (see e.g. # [pandas-datareader](https://pandas-datareader.readthedocs.io/en/latest/), # [Quandl](https://www.quandl.com/tools/python), # [findatapy](https://github.com/cuemacro/findatapy), ...). # Your data frames can have other columns, but these are necessary. # DataFrame should ideally be indexed with a _datetime index_ (convert it with [`pd.to_datetime()`](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html)), otherwise a simple range index will do. # + # Example OHLC data for Google Inc. from backtesting.test import GOOG GOOG.tail() # - # ### Strategy # # Let's create our first strategy to backtest on these Google data. Let it be a simple [moving average (MA) cross-over strategy](https://en.wikipedia.org/wiki/Moving_average_crossover). # # _Backtesting.py_ doesn't contain its own set of technical indicators. In practice, the user should probably use functions from their favorite indicator library, such as # [TA-Lib](https://github.com/mrjbq7/ta-lib), # [Tulipy](https://tulipindicators.org), # PyAlgoTrade, ... # But for this example, we define a simple helper moving average function. # + import pandas as pd
def test_data_missing_columns(self):
df = GOOG.copy()
del df['Open']
with self.assertRaises(ValueError):
Backtest(df, SmaCross).run()
from backtesting.test import GOOG
GOOG.tail()
GOOG.head()
import pandas as pd
def SMA(values, n):
return pd.Series(values).rolling(n).mean()
from backtesting import Strategy
from backtesting.lib import crossover
class SmaCross(Strategy):
n1 = 10
n2 = 20
def init(self):
self.sma1 = self.I(SMA, self.data.Close, self.n1)
self.sma2 = self.I(SMA, self.data.Close, self.n2)
def next(self):
if crossover(self.sma1, self.sma2):
self.position.close()
self.buy()
elif crossover(self.sma2, self.sma1):
self.position.close()
self.sell()
from backtesting import Backtest