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_test.py
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import inspect
import multiprocessing as mp
import os
import sys
import time
import unittest
from concurrent.futures.process import ProcessPoolExecutor
from contextlib import contextmanager
from glob import glob
from runpy import run_path
from tempfile import NamedTemporaryFile, gettempdir
from unittest import TestCase
import numpy as np
import pandas as pd
from pandas.testing import assert_frame_equal
from backtesting import Backtest, Strategy
from backtesting._stats import compute_drawdown_duration_peaks
from backtesting._util import _Array, _as_str, _Indicator, patch, try_
from backtesting.lib import (
FractionalBacktest, MultiBacktest, OHLCV_AGG,
SignalStrategy,
TrailingStrategy,
barssince,
compute_stats,
cross,
crossover,
plot_heatmaps,
quantile,
random_ohlc_data,
resample_apply,
)
from backtesting.test import BTCUSD, EURUSD, GOOG, SMA
SHORT_DATA = GOOG.iloc[:20] # Short data for fast tests with no indicator lag
@contextmanager
def _tempfile():
with NamedTemporaryFile(suffix='.html') as f:
if sys.platform.startswith('win'):
f.close()
yield f.name
@contextmanager
def chdir(path):
cwd = os.getcwd()
os.chdir(path)
try:
yield
finally:
os.chdir(cwd)
class SmaCross(Strategy):
# NOTE: These values are also used on the website!
fast = 10
slow = 30
def init(self):
self.sma1 = self.I(SMA, self.data.Close, self.fast)
self.sma2 = self.I(SMA, self.data.Close, self.slow)
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()
class _S(Strategy):
def init(self):
super().init()
class TestBacktest(TestCase):
def test_run(self):
bt = Backtest(EURUSD, SmaCross)
bt.run()
def test_run_invalid_param(self):
bt = Backtest(GOOG, SmaCross)
self.assertRaises(AttributeError, bt.run, foo=3)
def test_run_speed(self):
bt = Backtest(GOOG, SmaCross)
start = time.process_time()
bt.run()
end = time.process_time()
self.assertLess(end - start, .3)
def test_data_missing_columns(self):
df = GOOG.copy(deep=False)
del df['Open']
with self.assertRaises(ValueError):
Backtest(df, SmaCross).run()
def test_data_nan_columns(self):
df = GOOG.copy()
df['Open'] = np.nan
with self.assertRaises(ValueError):
Backtest(df, SmaCross).run()
def test_data_extra_columns(self):
df = GOOG.copy(deep=False)
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_invalid(self):
with self.assertRaises(TypeError):
Backtest(GOOG.index, SmaCross).run()
with self.assertRaises(ValueError):
Backtest(GOOG.iloc[:0], SmaCross).run()
def test_assertions(self):
class Assertive(Strategy):
def init(self):
self.sma = self.I(SMA, self.data.Close, 10)
self.remains_indicator = np.r_[2] * np.cumsum(self.sma * 5 + 1) * np.r_[2]
self.transpose_invalid = self.I(lambda: np.column_stack((self.data.Open,
self.data.Close)))
resampled = resample_apply('W', SMA, self.data.Close, 3)
resampled_ind = resample_apply('W', SMA, self.sma, 3)
assert np.unique(resampled[-5:]).size == 1
assert np.unique(resampled[-6:]).size == 2
assert resampled in self._indicators, "Strategy.I not called"
assert resampled_ind in self._indicators, "Strategy.I not called"
assert 1 == try_(lambda: self.data.X, 1, AttributeError)
assert 1 == try_(lambda: self.data['X'], 1, KeyError)
assert self.data.pip == .01
assert float(self.data.Close) == self.data.Close[-1]
def next(self, _FEW_DAYS=pd.Timedelta('3 days')): # noqa: N803
assert self.equity >= 0
assert isinstance(self.sma, _Indicator)
assert isinstance(self.remains_indicator, _Indicator)
assert self.remains_indicator.name
assert isinstance(self.remains_indicator._opts, dict)
assert not np.isnan(self.data.Open[-1])
assert not np.isnan(self.data.High[-1])
assert not np.isnan(self.data.Low[-1])
assert not np.isnan(self.data.Close[-1])
assert not np.isnan(self.data.Volume[-1])
assert not np.isnan(self.sma[-1])
assert self.data.index[-1]
self.position
self.position.size
self.position.pl
self.position.pl_pct
self.position.is_long
if crossover(self.sma, self.data.Close):
self.orders.cancel() # cancels only non-contingent
price = self.data.Close[-1]
sl, tp = 1.05 * price, .9 * price
n_orders = len(self.orders)
self.sell(size=.21, limit=price, stop=price, sl=sl, tp=tp)
assert len(self.orders) == n_orders + 1
order = self.orders[-1]
assert order.limit == price
assert order.stop == price
assert order.size == -.21
assert order.sl == sl
assert order.tp == tp
assert not order.is_contingent
elif self.position:
assert not self.position.is_long
assert self.position.is_short
assert self.position.pl
assert self.position.pl_pct
assert self.position.size < 0
trade = self.trades[0]
if self.data.index[-1] - self.data.index[trade.entry_bar] > _FEW_DAYS:
assert not trade.is_long
assert trade.is_short
assert trade.size < 0
assert trade.entry_bar > 0
assert isinstance(trade.entry_time, pd.Timestamp)
assert trade.exit_bar is None
assert trade.exit_time is None
assert trade.entry_price > 0
assert trade.exit_price is None
assert trade.pl / 1
assert trade.pl_pct / 1
assert trade.value > 0
assert trade.sl
assert trade.tp
# Close multiple times
self.position.close(.5)
self.position.close(.5)
self.position.close(.5)
self.position.close()
self.position.close()
bt = Backtest(GOOG, Assertive)
with self.assertWarns(UserWarning):
stats = bt.run()
self.assertEqual(stats['# Trades'], 131)
def test_broker_params(self):
bt = Backtest(GOOG.iloc[:100], SmaCross,
cash=1000, spread=.01, margin=.1, trade_on_close=True)
bt.run()
def test_spread_commission(self):
class S(Strategy):
def init(self):
self.done = False
def next(self):
if not self.position:
self.buy()
else:
self.position.close()
self.next = lambda: None # Done
SPREAD = .01
COMMISSION = .01
CASH = 10_000
ORDER_BAR = 2
stats = Backtest(SHORT_DATA, S, cash=CASH, spread=SPREAD, commission=COMMISSION).run()
trade_open_price = SHORT_DATA['Open'].iloc[ORDER_BAR]
self.assertEqual(stats['_trades']['EntryPrice'].iloc[0], trade_open_price * (1 + SPREAD))
self.assertEqual(stats['_equity_curve']['Equity'].iloc[2:4].round(2).tolist(),
[9685.31, 9749.33])
stats = Backtest(SHORT_DATA, S, cash=CASH, commission=(100, COMMISSION)).run()
self.assertEqual(stats['_equity_curve']['Equity'].iloc[2:4].round(2).tolist(),
[9784.50, 9718.69])
commission_func = lambda size, price: size * price * COMMISSION # noqa: E731
stats = Backtest(SHORT_DATA, S, cash=CASH, commission=commission_func).run()
self.assertEqual(stats['_equity_curve']['Equity'].iloc[2:4].round(2).tolist(),
[9781.28, 9846.04])
def test_dont_overwrite_data(self):
df = EURUSD.copy()
bt = Backtest(df, SmaCross)
bt.run()
bt.optimize(fast=4, slow=[6, 8])
bt.plot(plot_drawdown=True, open_browser=False)
self.assertTrue(df.equals(EURUSD))
def test_strategy_abstract(self):
class MyStrategy(Strategy):
pass
self.assertRaises(TypeError, MyStrategy, None, None)
def test_strategy_str(self):
bt = Backtest(GOOG.iloc[:100], SmaCross)
self.assertEqual(str(bt.run()._strategy), SmaCross.__name__)
self.assertEqual(str(bt.run(fast=11)._strategy), SmaCross.__name__ + '(fast=11)')
def test_compute_drawdown(self):
dd = pd.Series([0, 1, 7, 0, 4, 0, 0])
durations, peaks = compute_drawdown_duration_peaks(dd)
np.testing.assert_array_equal(durations, pd.Series([3, 2], index=[3, 5]).reindex(dd.index))
np.testing.assert_array_equal(peaks, pd.Series([7, 4], index=[3, 5]).reindex(dd.index))
def test_compute_stats(self):
stats = Backtest(GOOG, SmaCross, finalize_trades=True).run()
expected = pd.Series({
# NOTE: These values are also used on the website! # noqa: E126
'# Trades': 66,
'Avg. Drawdown Duration': pd.Timedelta('41 days 00:00:00'),
'Avg. Drawdown [%]': -5.925851581948801,
'Avg. Trade Duration': pd.Timedelta('46 days 00:00:00'),
'Avg. Trade [%]': 2.531715975158555,
'Best Trade [%]': 53.59595229490424,
'Buy & Hold Return [%]': 522.0601851851852,
'Calmar Ratio': 0.4414380935608377,
'Duration': pd.Timedelta('3116 days 00:00:00'),
'End': pd.Timestamp('2013-03-01 00:00:00'),
'Equity Final [$]': 51422.98999999996,
'Equity Peak [$]': 75787.44,
'Expectancy [%]': 3.2748078066748834,
'Exposure Time [%]': 96.74115456238361,
'Max. Drawdown Duration': pd.Timedelta('584 days 00:00:00'),
'Max. Drawdown [%]': -47.98012705007589,
'Max. Trade Duration': pd.Timedelta('183 days 00:00:00'),
'Profit Factor': 2.167945974262033,
'Return (Ann.) [%]': 21.180255813792282,
'Return [%]': 414.2298999999996,
'Volatility (Ann.) [%]': 36.49390889140787,
'CAGR [%]': 14.159843619607383,
'SQN': 1.0766187356697705,
'Kelly Criterion': 0.1518705127029717,
'Sharpe Ratio': 0.5803778344714113,
'Sortino Ratio': 1.0847880675854096,
'Start': pd.Timestamp('2004-08-19 00:00:00'),
'Win Rate [%]': 46.96969696969697,
'Worst Trade [%]': -18.39887353835481,
'Alpha [%]': 394.37391142027462,
'Beta': 0.03803390709192,
})
def almost_equal(a, b):
try:
return np.isclose(a, b, rtol=1.e-8)
except TypeError:
return a == b
diff = {key: print(key) or value # noqa: T201
for key, value in stats.filter(regex='^[^_]').items()
if not almost_equal(value, expected[key])}
self.assertDictEqual(diff, {})
self.assertSequenceEqual(
sorted(stats['_equity_curve'].columns),
sorted(['Equity', 'DrawdownPct', 'DrawdownDuration']))
self.assertEqual(len(stats['_trades']), 66)
indicator_columns = [
f'{entry}_SMA(C,{n})'
for entry in ('Entry', 'Exit')
for n in (SmaCross.fast, SmaCross.slow)]
self.assertSequenceEqual(
sorted(stats['_trades'].columns),
sorted(['Size', 'EntryBar', 'ExitBar', 'EntryPrice', 'ExitPrice', 'SL', 'TP',
'PnL', 'ReturnPct', 'EntryTime', 'ExitTime', 'Duration', 'Tag',
*indicator_columns]))
def test_compute_stats_bordercase(self):
class SingleTrade(Strategy):
def init(self):
self._done = False
def next(self):
if not self._done:
self.buy()
self._done = True
if self.position:
self.position.close()
class SinglePosition(_S):
def next(self):
if not self.position:
self.buy()
class NoTrade(_S):
def next(self):
pass
for strategy in (SmaCross,
SingleTrade,
SinglePosition,
NoTrade):
with self.subTest(strategy=strategy.__name__):
stats = Backtest(GOOG.iloc[:100], strategy).run()
self.assertFalse(np.isnan(stats['Equity Final [$]']))
self.assertFalse(stats['_equity_curve']['Equity'].isnull().any())
self.assertEqual(stats['_strategy'].__class__, strategy)
def test_trade_enter_hit_sl_on_same_day(self):
the_day = pd.Timestamp("2012-10-17 00:00:00")
class S(_S):
def next(self):
if self.data.index[-1] == the_day:
self.buy(sl=720)
self.assertEqual(Backtest(GOOG, S).run()._trades.iloc[0].ExitPrice, 720)
class S(_S):
def next(self):
if self.data.index[-1] == the_day:
self.buy(stop=758, sl=720)
with self.assertWarns(UserWarning):
self.assertEqual(Backtest(GOOG, S).run()._trades.iloc[0].ExitPrice, 705.58)
def test_stop_price_between_sl_tp(self):
class S(_S):
def next(self):
if self.data.index[-1] == pd.Timestamp("2004-09-09 00:00:00"):
self.buy(stop=104, sl=103, tp=110)
with self.assertWarns(UserWarning):
self.assertEqual(Backtest(GOOG, S).run()._trades.iloc[0].EntryPrice, 104)
def test_position_close_portion(self):
class SmaCross(Strategy):
def init(self):
self.sma1 = self.I(SMA, self.data.Close, 10)
self.sma2 = self.I(SMA, self.data.Close, 20)
def next(self):
if not self.position and crossover(self.sma1, self.sma2):
self.buy(size=10)
if self.position and crossover(self.sma2, self.sma1):
self.position.close(portion=.5)
bt = Backtest(GOOG, SmaCross, spread=.002)
bt.run()
def test_close_orders_from_last_strategy_iteration(self):
class S(_S):
def next(self):
if not self.position:
self.buy()
elif len(self.data) == len(SHORT_DATA):
self.position.close()
self.assertTrue(Backtest(SHORT_DATA, S, finalize_trades=False).run()._trades.empty)
self.assertFalse(Backtest(SHORT_DATA, S, finalize_trades=True).run()._trades.empty)
def test_check_adjusted_price_when_placing_order(self):
class S(_S):
def next(self):
self.buy(tp=self.data.Close * 1.01)
self.assertRaises(ValueError, Backtest(SHORT_DATA, S, spread=.02).run)
class TestStrategy(TestCase):
@staticmethod
def _Backtest(strategy_coroutine, data=SHORT_DATA, **kwargs):
class S(Strategy):
def init(self):
self.step = strategy_coroutine(self)
def next(self):
try_(self.step.__next__, None, StopIteration)
return Backtest(data, S, **kwargs)
def test_position(self):
def coroutine(self):
yield self.buy()
assert self.position
assert self.position.is_long
assert not self.position.is_short
assert self.position.size > 0
assert self.position.pl
assert self.position.pl_pct
yield self.position.close()
assert not self.position
assert not self.position.is_long
assert not self.position.is_short
assert not self.position.size
assert not self.position.pl
assert not self.position.pl_pct
self._Backtest(coroutine).run()
def test_broker_hedging(self):
def coroutine(self):
yield self.buy(size=2)
assert len(self.trades) == 1
yield self.sell(size=1)
assert len(self.trades) == 2
self._Backtest(coroutine, hedging=True).run()
def test_broker_exclusive_orders(self):
def coroutine(self):
yield self.buy(size=2)
assert len(self.trades) == 1
yield self.sell(size=3)
assert len(self.trades) == 1
assert self.trades[0].size == -3
self._Backtest(coroutine, exclusive_orders=True).run()
def test_trade_multiple_close(self):
def coroutine(self):
yield self.buy()
assert self.trades
self.trades[-1].close(1)
self.trades[-1].close(.1)
yield
self._Backtest(coroutine).run()
def test_close_trade_leaves_needsize_0(self):
def coroutine(self):
self.buy(size=1)
self.buy(size=1)
yield
if self.position:
self.sell(size=1)
self._Backtest(coroutine).run()
def test_stop_limit_order_price_is_stop_price(self):
def coroutine(self):
self.buy(stop=112, limit=113, size=1)
self.sell(stop=107, limit=105, size=1)
yield
stats = self._Backtest(coroutine).run()
self.assertListEqual(stats._trades.filter(like='Price').stack().tolist(), [112, 107])
def test_autoclose_trades_on_finish(self):
def coroutine(self):
yield self.buy()
stats = self._Backtest(coroutine, finalize_trades=True).run()
self.assertEqual(len(stats._trades), 1)
def test_order_tag(self):
def coroutine(self):
yield self.buy(size=2, tag=1)
yield self.sell(size=1, tag='s')
yield self.sell(size=1)
yield self.buy(tag=2)
yield self.position.close()
stats = self._Backtest(coroutine).run()
self.assertEqual(list(stats._trades.Tag), [1, 1, 2])
class TestOptimize(TestCase):
def test_optimize(self):
bt = Backtest(GOOG.iloc[:100], SmaCross)
OPT_PARAMS = {'fast': range(2, 5, 2), 'slow': [2, 5, 7, 9]}
self.assertRaises(ValueError, bt.optimize)
self.assertRaises(ValueError, bt.optimize, maximize='missing key', **OPT_PARAMS)
self.assertRaises(ValueError, bt.optimize, maximize='missing key', **OPT_PARAMS)
self.assertRaises(TypeError, bt.optimize, maximize=15, **OPT_PARAMS)
self.assertRaises(TypeError, bt.optimize, constraint=15, **OPT_PARAMS)
self.assertRaises(ValueError, bt.optimize, constraint=lambda d: False, **OPT_PARAMS)
self.assertRaises(ValueError, bt.optimize, return_optimization=True, **OPT_PARAMS)
res = bt.optimize(**OPT_PARAMS)
self.assertIsInstance(res, pd.Series)
default_maximize = inspect.signature(Backtest.optimize).parameters['maximize'].default
res2 = bt.optimize(**OPT_PARAMS, maximize=lambda s: s[default_maximize])
self.assertDictEqual(res.filter(regex='^[^_]').fillna(-1).to_dict(),
res2.filter(regex='^[^_]').fillna(-1).to_dict())
res3, heatmap = bt.optimize(**OPT_PARAMS, return_heatmap=True,
constraint=lambda d: d.slow > 2 * d.fast)
self.assertIsInstance(heatmap, pd.Series)
self.assertEqual(len(heatmap), 4)
self.assertEqual(heatmap.name, default_maximize)
with _tempfile() as f:
bt.plot(filename=f, open_browser=False)
def test_method_sambo(self):
bt = Backtest(GOOG.iloc[:100], SmaCross, finalize_trades=True)
res, heatmap, sambo_results = bt.optimize(
fast=range(2, 20), slow=np.arange(2, 20, dtype=object),
constraint=lambda p: p.fast < p.slow,
max_tries=30,
method='sambo',
return_optimization=True,
return_heatmap=True,
random_state=2)
self.assertIsInstance(res, pd.Series)
self.assertIsInstance(heatmap, pd.Series)
self.assertGreater(heatmap.max(), 1.1)
self.assertGreater(heatmap.min(), -2)
self.assertEqual(-sambo_results.fun, heatmap.max())
self.assertEqual(heatmap.index.tolist(), heatmap.dropna().index.unique().tolist())
def test_max_tries(self):
bt = Backtest(GOOG.iloc[:100], SmaCross)
OPT_PARAMS = {'fast': range(2, 10, 2), 'slow': [2, 5, 7, 9]}
for method, max_tries, random_state in (('grid', 5, 0),
('grid', .3, 0),
('sambo', 6, 0),
('sambo', .42, 0)):
with self.subTest(method=method,
max_tries=max_tries,
random_state=random_state):
_, heatmap = bt.optimize(max_tries=max_tries,
method=method,
random_state=random_state,
return_heatmap=True,
**OPT_PARAMS)
self.assertEqual(len(heatmap), 6)
def test_optimize_invalid_param(self):
bt = Backtest(GOOG.iloc[:100], SmaCross)
self.assertRaises(AttributeError, bt.optimize, foo=range(3))
self.assertRaises(ValueError, bt.optimize, fast=[])
def test_optimize_no_trades(self):
bt = Backtest(GOOG, SmaCross)
stats = bt.optimize(fast=[3], slow=[3])
self.assertTrue(stats.isnull().any())
def test_optimize_speed(self):
bt = Backtest(GOOG.iloc[:100], SmaCross)
start = time.process_time()
bt.optimize(fast=range(2, 20, 2), slow=range(10, 40, 2))
end = time.process_time()
print(end - start)
handicap = 5 if 'win' in sys.platform else .1
self.assertLess(end - start, .3 + handicap)
class TestPlot(TestCase):
def test_plot_before_run(self):
bt = Backtest(GOOG, SmaCross)
self.assertRaises(RuntimeError, bt.plot)
def test_file_size(self):
bt = Backtest(GOOG, SmaCross)
bt.run()
with _tempfile() as f:
bt.plot(filename=f[:-len('.html')], open_browser=False)
self.assertLess(os.path.getsize(f), 500000)
def test_params(self):
bt = Backtest(GOOG.iloc[:100], SmaCross)
bt.run()
with _tempfile() as f:
for p in dict(plot_volume=False, # noqa: C408
plot_equity=False,
plot_return=True,
plot_pl=False,
plot_drawdown=True,
plot_trades=False,
superimpose=False,
resample='1W',
smooth_equity=False,
relative_equity=False,
reverse_indicators=True,
show_legend=False).items():
with self.subTest(param=p[0]):
bt.plot(**dict([p]), filename=f, open_browser=False)
def test_hide_legend(self):
bt = Backtest(GOOG.iloc[:100], SmaCross)
bt.run()
with _tempfile() as f:
bt.plot(filename=f, show_legend=False)
# Give browser time to open before tempfile is removed
time.sleep(5)
def test_resolutions(self):
with _tempfile() as f:
for rule in 'ms s min h D W ME'.split():
with self.subTest(rule=rule):
df = EURUSD.iloc[:2].resample(rule).agg(OHLCV_AGG).dropna().iloc[:1100]
bt = Backtest(df, SmaCross)
bt.run()
bt.plot(filename=f, open_browser=False)
def test_range_axis(self):
df = GOOG.iloc[:100].reset_index(drop=True)
# Warm-up. CPython bug bpo-29620.
try:
with self.assertWarns(UserWarning):
Backtest(df, SmaCross)
except RuntimeError:
pass
with self.assertWarns(UserWarning):
bt = Backtest(df, SmaCross)
bt.run()
with _tempfile() as f:
bt.plot(filename=f, open_browser=False)
def test_preview(self):
class Strategy(SmaCross):
def init(self):
super().init()
def ok(x):
return x
self.a = self.I(SMA, self.data.Open, 5, overlay=False, name='ok')
self.b = self.I(ok, np.random.random(len(self.data.Open)))
bt = Backtest(GOOG, Strategy)
bt.run()
with _tempfile() as f:
bt.plot(filename=f, plot_drawdown=True, smooth_equity=True)
# Give browser time to open before tempfile is removed
time.sleep(5)
def test_wellknown(self):
class S(_S):
def next(self):
date = self.data.index[-1]
if date == pd.Timestamp('Thu 19 Oct 2006'):
self.buy(stop=484, limit=466, size=100)
elif date == pd.Timestamp('Thu 30 Oct 2007'):
self.position.close()
elif date == pd.Timestamp('Tue 11 Nov 2008'):
self.sell(stop=self.data.Low,
limit=324.90, # High from 14 Nov
size=200)
bt = Backtest(GOOG, S, margin=.1)
stats = bt.run()
trades = stats['_trades']
self.assertAlmostEqual(stats['Equity Peak [$]'], 46961)
self.assertEqual(stats['Equity Final [$]'], 0)
self.assertEqual(len(trades), 2)
assert trades[['EntryTime', 'ExitTime']].equals(
pd.DataFrame({'EntryTime': pd.to_datetime(['2006-11-01', '2008-11-14']),
'ExitTime': pd.to_datetime(['2007-10-31', '2009-09-21'])}))
assert trades['PnL'].round().equals(pd.Series([23469., -34420.]))
with _tempfile() as f:
bt.plot(filename=f, plot_drawdown=True, smooth_equity=False)
# Give browser time to open before tempfile is removed
time.sleep(1)
def test_resample(self):
class S(SmaCross):
def init(self):
self.I(lambda: ['x'] * len(self.data)) # categorical indicator, GH-309
super().init()
bt = Backtest(GOOG, S)
bt.run()
import backtesting._plotting
with _tempfile() as f, \
patch(backtesting._plotting, '_MAX_CANDLES', 10), \
self.assertWarns(UserWarning):
bt.plot(filename=f, resample=True)
# Give browser time to open before tempfile is removed
time.sleep(1)
def test_indicator_name(self):
test_self = self
class S(Strategy):
def init(self):
def _SMA():
return SMA(self.data.Close, 5), SMA(self.data.Close, 10)
test_self.assertRaises(TypeError, self.I, _SMA, name=42)
test_self.assertRaises(ValueError, self.I, _SMA, name=("SMA One", ))
test_self.assertRaises(
ValueError, self.I, _SMA, name=("SMA One", "SMA Two", "SMA Three"))
for overlay in (True, False):
self.I(SMA, self.data.Close, 5, overlay=overlay)
self.I(SMA, self.data.Close, 5, name="My SMA", overlay=overlay)
self.I(SMA, self.data.Close, 5, name=("My SMA", ), overlay=overlay)
self.I(_SMA, overlay=overlay)
self.I(_SMA, name="My SMA", overlay=overlay)
self.I(_SMA, name=("SMA One", "SMA Two"), overlay=overlay)
def next(self):
pass
bt = Backtest(GOOG, S)
bt.run()
with _tempfile() as f:
bt.plot(filename=f,
plot_drawdown=False, plot_equity=False, plot_pl=False, plot_volume=False,
open_browser=False)
def test_indicator_color(self):
class S(Strategy):
def init(self):
a = self.I(SMA, self.data.Close, 5, overlay=True, color='red')
b = self.I(SMA, self.data.Close, 10, overlay=False, color='blue')
self.I(lambda: (a, b), overlay=False, color=('green', 'orange'))
def next(self):
pass
bt = Backtest(GOOG, S)
bt.run()
with _tempfile() as f:
bt.plot(filename=f,
plot_drawdown=False, plot_equity=False, plot_pl=False, plot_volume=False,
open_browser=False)
def test_indicator_scatter(self):
class S(Strategy):
def init(self):
self.I(SMA, self.data.Close, 5, overlay=True, scatter=True)
self.I(SMA, self.data.Close, 10, overlay=False, scatter=True)
def next(self):
pass
bt = Backtest(GOOG, S)
bt.run()
with _tempfile() as f:
bt.plot(filename=f,
plot_drawdown=False, plot_equity=False, plot_pl=False, plot_volume=False,
open_browser=False)
class TestLib(TestCase):
def test_barssince(self):
self.assertEqual(barssince(np.r_[1, 0, 0]), 2)
self.assertEqual(barssince(np.r_[0, 0, 0]), np.inf)
self.assertEqual(barssince(np.r_[0, 0, 0], 0), 0)
def test_cross(self):
self.assertTrue(cross([0, 1], [1, 0]))
self.assertTrue(cross([1, 0], [0, 1]))
self.assertFalse(cross([1, 0], [1, 0]))
def test_crossover(self):
self.assertTrue(crossover([0, 1], [1, 0]))
self.assertTrue(crossover([0, 1], .5))
self.assertTrue(crossover([0, 1], pd.Series([.5, .5], index=[5, 6])))
self.assertFalse(crossover([1, 0], [1, 0]))
self.assertFalse(crossover([0], [1]))
def test_quantile(self):
self.assertEqual(quantile(np.r_[1, 3, 2], .5), 2)
self.assertEqual(quantile(np.r_[1, 3, 2]), .5)
def test_resample_apply(self):
res = resample_apply('D', SMA, EURUSD.Close, 10)
self.assertEqual(res.name, 'C[D]')
self.assertEqual(res.count() / res.size, .9634)
np.testing.assert_almost_equal(res.iloc[-48:].unique().tolist(),
[1.242643, 1.242381, 1.242275],
decimal=6)
def resets_index(*args):
return pd.Series(SMA(*args).values)
res2 = resample_apply('D', resets_index, EURUSD.Close, 10)
self.assertTrue((res.dropna() == res2.dropna()).all())
self.assertTrue((res.index == res2.index).all())
res3 = resample_apply('D', None, EURUSD)
self.assertIn('Volume', res3)
res3 = resample_apply('D', lambda df: (df.Close, df.Close), EURUSD)
self.assertIsInstance(res3, pd.DataFrame)
def test_plot_heatmaps(self):
bt = Backtest(GOOG, SmaCross)
stats, heatmap = bt.optimize(fast=range(2, 7, 2),
slow=range(7, 15, 2),
return_heatmap=True)
with _tempfile() as f:
for agg in ('mean',
lambda x: np.percentile(x, 75)):
plot_heatmaps(heatmap, agg, filename=f, open_browser=False)
# Preview
plot_heatmaps(heatmap, filename=f)
time.sleep(5)
def test_random_ohlc_data(self):
generator = random_ohlc_data(GOOG, frac=1)
new_data = next(generator)
self.assertEqual(list(new_data.index), list(GOOG.index))
self.assertEqual(new_data.shape, GOOG.shape)
self.assertEqual(list(new_data.columns), list(GOOG.columns))
def test_compute_stats(self):
stats = Backtest(GOOG, SmaCross).run()
only_long_trades = stats._trades[stats._trades.Size > 0]
long_stats = compute_stats(stats=stats, trades=only_long_trades,
data=GOOG, risk_free_rate=.02)
self.assertNotEqual(list(stats._equity_curve.Equity),
list(long_stats._equity_curve.Equity))
self.assertNotEqual(stats['Sharpe Ratio'], long_stats['Sharpe Ratio'])
self.assertEqual(long_stats['# Trades'], len(only_long_trades))
self.assertEqual(stats._strategy, long_stats._strategy)
assert_frame_equal(long_stats._trades, only_long_trades)
def test_SignalStrategy(self):
class S(SignalStrategy):
def init(self):
sma = self.data.Close.s.rolling(10).mean()
self.set_signal(self.data.Close > sma,
self.data.Close < sma)
stats = Backtest(GOOG, S).run()
self.assertIn(stats['# Trades'], (1179, 1180)) # varies on different archs?
def test_TrailingStrategy(self):
class S(TrailingStrategy):
def init(self):
super().init()
self.set_atr_periods(40)
self.set_trailing_pct(.1)
self.set_trailing_sl(3)
self.sma = self.I(lambda: self.data.Close.s.rolling(10).mean())
def next(self):
super().next()
if not self.position and self.data.Close > self.sma:
self.buy()
stats = Backtest(GOOG, S).run()
self.assertEqual(stats['# Trades'], 56)
def test_FractionalBacktest(self):
ubtc_bt = FractionalBacktest(BTCUSD['2015':], SmaCross, fractional_unit=1 / 1e6, cash=100)
stats = ubtc_bt.run(fast=2, slow=3)
self.assertEqual(stats['# Trades'], 41)
def test_MultiBacktest(self):
import backtesting
assert callable(getattr(backtesting, 'Pool', None)), backtesting.__dict__
for start_method in mp.get_all_start_methods():
with self.subTest(start_method=start_method), \
patch(backtesting, 'Pool', mp.get_context(start_method).Pool):
start_time = time.monotonic()
btm = MultiBacktest([GOOG, EURUSD, BTCUSD], SmaCross, cash=100_000)
res = btm.run(fast=2)
self.assertIsInstance(res, pd.DataFrame)
self.assertEqual(res.columns.tolist(), [0, 1, 2])
heatmap = btm.optimize(fast=[2, 4], slow=[10, 20])
self.assertIsInstance(heatmap, pd.DataFrame)
self.assertEqual(heatmap.columns.tolist(), [0, 1, 2])
print(start_method, time.monotonic() - start_time)
plot_heatmaps(heatmap.mean(axis=1), open_browser=False)
class TestUtil(TestCase):
def test_as_str(self):
def func():
pass
class Class:
def __call__(self):
pass
self.assertEqual(_as_str('4'), '4')
self.assertEqual(_as_str(4), '4')
self.assertEqual(_as_str(_Indicator([1, 2], name='x')), 'x')
self.assertEqual(_as_str(func), 'func')
self.assertEqual(_as_str(Class), 'Class')
self.assertEqual(_as_str(Class()), 'Class')
self.assertEqual(_as_str(pd.Series([1, 2], name='x')), 'x')
self.assertEqual(_as_str(pd.DataFrame()), 'df')
self.assertEqual(_as_str(lambda x: x), 'λ')
for s in ('Open', 'High', 'Low', 'Close', 'Volume'):
self.assertEqual(_as_str(_Array([1], name=s)), s[0])
def test_patch(self):
class Object:
pass
o = Object()
o.attr = False
with patch(o, 'attr', True):
self.assertTrue(o.attr)
self.assertFalse(o.attr)
def test_pandas_accessors(self):
class S(Strategy):
def init(self):
close, index = self.data.Close, self.data.index
assert close.s.equals(pd.Series(close, index=index))
assert self.data.df['Close'].equals(pd.Series(close, index=index))
self.data.df['new_key'] = 2 * close
def next(self):
close, index = self.data.Close, self.data.index
assert close.s.equals(pd.Series(close, index=index))
assert self.data.df['Close'].equals(pd.Series(close, index=index))
assert self.data.df['new_key'].equals(pd.Series(self.data.new_key, index=index))
Backtest(GOOG.iloc[:20], S).run()