from backtesting.test import GOOG
GOOG.tail()C:\Users\86189\anaconda3\lib\site-packages\backtesting\_plotting.py:50: UserWarning: Jupyter Notebook detected. Setting Bokeh output to notebook. This may not work in Jupyter clients without JavaScript support (e.g. PyCharm, Spyder IDE). Reset with `backtesting.set_bokeh_output(notebook=False)`.
warnings.warn('Jupyter Notebook detected. '
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| Open | High | Low | Close | Volume | |
|---|---|---|---|---|---|
| 2013-02-25 | 802.3 | 808.41 | 790.49 | 790.77 | 2303900 |
| 2013-02-26 | 795.0 | 795.95 | 784.40 | 790.13 | 2202500 |
| 2013-02-27 | 794.8 | 804.75 | 791.11 | 799.78 | 2026100 |
| 2013-02-28 | 801.1 | 806.99 | 801.03 | 801.20 | 2265800 |
| 2013-03-01 | 797.8 | 807.14 | 796.15 | 806.19 | 2175400 |
pip install backtestingRequirement already satisfied: backtesting in c:\users\86189\anaconda3\lib\site-packages (0.3.3)
Requirement already satisfied: pandas!=0.25.0,>=0.25.0 in c:\users\86189\anaconda3\lib\site-packages (from backtesting) (1.3.4)
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Note: you may need to restart the kernel to use updated packages.
from backtesting.test import GOOG
GOOG.tail()
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| Open | High | Low | Close | Volume | |
|---|---|---|---|---|---|
| 2013-02-25 | 802.3 | 808.41 | 790.49 | 790.77 | 2303900 |
| 2013-02-26 | 795.0 | 795.95 | 784.40 | 790.13 | 2202500 |
| 2013-02-27 | 794.8 | 804.75 | 791.11 | 799.78 | 2026100 |
| 2013-02-28 | 801.1 | 806.99 | 801.03 | 801.20 | 2265800 |
| 2013-03-01 | 797.8 | 807.14 | 796.15 | 806.19 | 2175400 |
pip install ta-libRequirement already satisfied: ta-lib in c:\users\86189\anaconda3\lib\site-packages (0.4.19)
Requirement already satisfied: numpy in c:\users\86189\anaconda3\lib\site-packages (from ta-lib) (1.20.3)
Note: you may need to restart the kernel to use updated packages.
pip install pandas_taRequirement already satisfied: pandas_ta in c:\users\86189\anaconda3\lib\site-packages (0.3.14b0)
Requirement already satisfied: pandas in c:\users\86189\anaconda3\lib\site-packages (from pandas_ta) (1.3.4)
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Note: you may need to restart the kernel to use updated packages.
pip install ta-libRequirement already satisfied: ta-lib in c:\users\86189\anaconda3\lib\site-packages (0.4.19)
Requirement already satisfied: numpy in c:\users\86189\anaconda3\lib\site-packages (from ta-lib) (1.20.3)
Note: you may need to restart the kernel to use updated packages.
import datetime
import pandas_ta as ta
import pandas as pd
from backtesting import Backtest
from backtesting import Strategy
from backtesting.lib import crossover
from backtesting.test import GOOG
class RsiOscillator(Strategy):
upper_bound = 70
lower_bound = 30
rsi_window = 14
# Do as much initial computation as possible
def init(self):
self.rsi = self.I(ta.rsi, pd.Series(self.data.Close), self.rsi_window)
# Step through bars one by one
# Note that multiple buys are a thing here
def next(self):
if crossover(self.rsi, self.upper_bound):
self.position.close()
elif crossover(self.lower_bound, self.rsi):
self.buy()
bt = Backtest(GOOG, RsiOscillator, cash=10_000, commission=.002)
stats = bt.run()
bt.plot()Row(
id = '1476', …)
align = 'start',
aspect_ratio = None,
background = None,
children = [GridBox(id='1473', ...), ToolbarBox(id='1475', ...)],
cols = 'auto',
css_classes = [],
disabled = False,
height = None,
height_policy = 'auto',
js_event_callbacks = {},
js_property_callbacks = {},
margin = (0, 0, 0, 0),
max_height = None,
max_width = None,
min_height = None,
min_width = None,
name = None,
sizing_mode = 'stretch_width',
spacing = 0,
subscribed_events = [],
syncable = True,
tags = [],
visible = True,
width = None,
width_policy = 'auto')
import pandas as pd
def SMA(values, n):
"""
Return simple moving average of `values`, at
each step taking into account `n` previous values.
"""
return pd.Series(values).rolling(n).mean()from backtesting import Strategy
from backtesting.lib import crossover
class SmaCross(Strategy):
# Define the two MA lags as *class variables*
# for later optimization
n1 = 10
n2 = 20
def init(self):
# Precompute the two moving averages
self.sma1 = self.I(SMA, self.data.Close, self.n1)
self.sma2 = self.I(SMA, self.data.Close, self.n2)
def next(self):
# If sma1 crosses above sma2, close any existing
# short trades, and buy the asset
if crossover(self.sma1, self.sma2):
self.position.close()
self.buy()
# Else, if sma1 crosses below sma2, close any existing
# long trades, and sell the asset
elif crossover(self.sma2, self.sma1):
self.position.close()
self.sell() def next(self):
if (self.sma1[-2] < self.sma2[-2] and
self.sma1[-1] > self.sma2[-1]):
self.position.close()
self.buy()
elif (self.sma1[-2] > self.sma2[-2] and # Ugh!
self.sma1[-1] < self.sma2[-1]):
self.position.close()
self.sell()from backtesting import Backtest
bt = Backtest(GOOG, SmaCross, cash=10_000, commission=.002)
stats = bt.run()
statsStart 2004-08-19 00:00:00
End 2013-03-01 00:00:00
Duration 3116 days 00:00:00
Exposure Time [%] 97.067039
Equity Final [$] 68221.96986
Equity Peak [$] 68991.21986
Return [%] 582.219699
Buy & Hold Return [%] 703.458242
Return (Ann.) [%] 25.266427
Volatility (Ann.) [%] 38.383008
Sharpe Ratio 0.658271
Sortino Ratio 1.288779
Calmar Ratio 0.763748
Max. Drawdown [%] -33.082172
Avg. Drawdown [%] -5.581506
Max. Drawdown Duration 688 days 00:00:00
Avg. Drawdown Duration 41 days 00:00:00
# Trades 94
Win Rate [%] 54.255319
Best Trade [%] 57.11931
Worst Trade [%] -16.629898
Avg. Trade [%] 2.074326
Max. Trade Duration 121 days 00:00:00
Avg. Trade Duration 33 days 00:00:00
Profit Factor 2.190805
Expectancy [%] 2.606294
SQN 1.990216
_strategy SmaCross
_equity_curve ...
_trades Size EntryB...
dtype: object
bt.plot()Row(
id = '2262', …)
align = 'start',
aspect_ratio = None,
background = None,
children = [GridBox(id='2259', ...), ToolbarBox(id='2261', ...)],
cols = 'auto',
css_classes = [],
disabled = False,
height = None,
height_policy = 'auto',
js_event_callbacks = {},
js_property_callbacks = {},
margin = (0, 0, 0, 0),
max_height = None,
max_width = None,
min_height = None,
min_width = None,
name = None,
sizing_mode = 'stretch_width',
spacing = 0,
subscribed_events = [],
syncable = True,
tags = [],
visible = True,
width = None,
width_policy = 'auto')
stats = bt.optimize(n1=range(5, 30, 5),
n2=range(10, 70, 5),
maximize='Equity Final [$]',
constraint=lambda param: param.n1 < param.n2)
stats 0%| | 0/9 [00:00<?, ?it/s]
Start 2004-08-19 00:00:00
End 2013-03-01 00:00:00
Duration 3116 days 00:00:00
Exposure Time [%] 99.068901
Equity Final [$] 103949.42612
Equity Peak [$] 108327.71798
Return [%] 939.494261
Buy & Hold Return [%] 703.458242
Return (Ann.) [%] 31.610936
Volatility (Ann.) [%] 44.739816
Sharpe Ratio 0.70655
Sortino Ratio 1.490961
Calmar Ratio 0.718505
Max. Drawdown [%] -43.995445
Avg. Drawdown [%] -6.138853
Max. Drawdown Duration 690 days 00:00:00
Avg. Drawdown Duration 43 days 00:00:00
# Trades 153
Win Rate [%] 51.633987
Best Trade [%] 61.562908
Worst Trade [%] -19.778312
Avg. Trade [%] 1.550283
Max. Trade Duration 83 days 00:00:00
Avg. Trade Duration 21 days 00:00:00
Profit Factor 1.984581
Expectancy [%] 1.97988
SQN 1.604158
_strategy SmaCross(n1=10,n...
_equity_curve ...
_trades Size Entry...
dtype: object
stats._strategy<Strategy SmaCross(n1=10,n2=15)>
bt.plot(plot_volume=False, plot_pl=False)Row(
id = '2871', …)
align = 'start',
aspect_ratio = None,
background = None,
children = [GridBox(id='2868', ...), ToolbarBox(id='2870', ...)],
cols = 'auto',
css_classes = [],
disabled = False,
height = None,
height_policy = 'auto',
js_event_callbacks = {},
js_property_callbacks = {},
margin = (0, 0, 0, 0),
max_height = None,
max_width = None,
min_height = None,
min_width = None,
name = None,
sizing_mode = 'stretch_width',
spacing = 0,
subscribed_events = [],
syncable = True,
tags = [],
visible = True,
width = None,
width_policy = 'auto')
stats.tail()Expectancy [%] 1.97988
SQN 1.604158
_strategy SmaCross(n1=10,n2=15)
_equity_curve Equity DrawdownPct Drawdown...
_trades Size EntryBar ExitBar EntryPrice Exit...
dtype: object
stats['_equity_curve']
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| Equity | DrawdownPct | DrawdownDuration | |
|---|---|---|---|
| 2004-08-19 | 10000.00000 | 0.000000 | NaT |
| 2004-08-20 | 10000.00000 | 0.000000 | NaT |
| 2004-08-23 | 10000.00000 | 0.000000 | NaT |
| 2004-08-24 | 10000.00000 | 0.000000 | NaT |
| 2004-08-25 | 10000.00000 | 0.000000 | NaT |
| ... | ... | ... | ... |
| 2013-02-25 | 103035.52612 | 0.048854 | NaT |
| 2013-02-26 | 102952.32612 | 0.049622 | NaT |
| 2013-02-27 | 104206.82612 | 0.038041 | NaT |
| 2013-02-28 | 104391.42612 | 0.036337 | NaT |
| 2013-03-01 | 103949.42612 | 0.040417 | 533 days |
2148 rows × 3 columns
stats['_trades']
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| Size | EntryBar | ExitBar | EntryPrice | ExitPrice | PnL | ReturnPct | EntryTime | ExitTime | Duration | |
|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 87 | 20 | 60 | 114.64884 | 185.23 | 6140.56092 | 0.615629 | 2004-09-17 | 2004-11-12 | 56 days |
| 1 | -87 | 60 | 69 | 184.85954 | 175.80 | 788.17998 | 0.049008 | 2004-11-12 | 2004-11-26 | 14 days |
| 2 | 96 | 69 | 71 | 176.15160 | 180.71 | 437.60640 | 0.025878 | 2004-11-26 | 2004-11-30 | 4 days |
| 3 | -96 | 71 | 75 | 180.34858 | 179.13 | 116.98368 | 0.006757 | 2004-11-30 | 2004-12-06 | 6 days |
| 4 | 97 | 75 | 82 | 179.48826 | 177.99 | -145.33122 | -0.008347 | 2004-12-06 | 2004-12-15 | 9 days |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 148 | 139 | 2085 | 2111 | 689.15556 | 735.54 | 6447.43716 | 0.067306 | 2012-11-29 | 2013-01-08 | 40 days |
| 149 | -139 | 2111 | 2113 | 734.06892 | 742.83 | -1217.79012 | -0.011935 | 2013-01-08 | 2013-01-10 | 2 days |
| 150 | 136 | 2113 | 2121 | 744.31566 | 735.99 | -1132.28976 | -0.011186 | 2013-01-10 | 2013-01-23 | 13 days |
| 151 | -136 | 2121 | 2127 | 734.51802 | 750.51 | -2174.90928 | -0.021772 | 2013-01-23 | 2013-01-31 | 8 days |
| 152 | 130 | 2127 | 2147 | 752.01102 | 797.80 | 5952.56740 | 0.060889 | 2013-01-31 | 2013-03-01 | 29 days |
153 rows × 10 columns
from backtesting.test import SMAimport pandas as pd
from backtesting.lib import SignalStrategy, TrailingStrategy
class SmaCross(SignalStrategy,
TrailingStrategy):
n1 = 10
n2 = 25
def init(self):
# In init() and in next() it is important to call the
# super method to properly initialize the parent 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)
# Where sma1 crosses sma2 upwards. Diff gives us [-1,0, *1*]
signal = (pd.Series(sma1) > sma2).astype(int).diff().fillna(0)
signal = signal.replace(-1, 0) # Upwards/long only
# Use 95% of available liquidity (at the time) on each order.
# (Leaving a value of 1. would instead buy a single share.)
entry_size = signal * .95
# Set order entry sizes using the method provided by
# `SignalStrategy`. See the docs.
self.set_signal(entry_size=entry_size)
# Set trailing stop-loss to 2x ATR using
# the method provided by `TrailingStrategy`
self.set_trailing_sl(2)from backtesting import Backtest
from backtesting.test import GOOG
bt = Backtest(GOOG, SmaCross, commission=.002)
bt.run()
bt.plot()Row(
id = '3580', …)
align = 'start',
aspect_ratio = None,
background = None,
children = [GridBox(id='3577', ...), ToolbarBox(id='3579', ...)],
cols = 'auto',
css_classes = [],
disabled = False,
height = None,
height_policy = 'auto',
js_event_callbacks = {},
js_property_callbacks = {},
margin = (0, 0, 0, 0),
max_height = None,
max_width = None,
min_height = None,
min_width = None,
name = None,
sizing_mode = 'stretch_width',
spacing = 0,
subscribed_events = [],
syncable = True,
tags = [],
visible = True,
width = None,
width_policy = 'auto')
import pandas as pd
def SMA(array, n):
"""Simple moving average"""
return pd.Series(array).rolling(n).mean()
def RSI(array, n):
"""Relative strength index"""
# Approximate; good enough
gain = pd.Series(array).diff()
loss = gain.copy()
gain[gain < 0] = 0
loss[loss > 0] = 0
rs = gain.ewm(n).mean() / loss.abs().ewm(n).mean()
return 100 - 100 / (1 + rs)from backtesting import Strategy, Backtest
from backtesting.lib import resample_apply
class System(Strategy):
d_rsi = 30 # Daily RSI lookback periods
w_rsi = 30 # Weekly
level = 70
def init(self):
# Compute moving averages the strategy demands
self.ma10 = self.I(SMA, self.data.Close, 10)
self.ma20 = self.I(SMA, self.data.Close, 20)
self.ma50 = self.I(SMA, self.data.Close, 50)
self.ma100 = self.I(SMA, self.data.Close, 100)
# Compute daily RSI(30)
self.daily_rsi = self.I(RSI, self.data.Close, self.d_rsi)
# To construct weekly RSI, we can use `resample_apply()`
# helper function from the library
self.weekly_rsi = resample_apply(
'W-FRI', RSI, self.data.Close, self.w_rsi)
def next(self):
price = self.data.Close[-1]
# If we don't already have a position, and
# if all conditions are satisfied, enter long.
if (not self.position and
self.daily_rsi[-1] > self.level and
self.weekly_rsi[-1] > self.level and
self.weekly_rsi[-1] > self.daily_rsi[-1] and
self.ma10[-1] > self.ma20[-1] > self.ma50[-1] > self.ma100[-1] and
price > self.ma10[-1]):
# Buy at market price on next open, but do
# set 8% fixed stop loss.
self.buy(sl=.92 * price)
# If the price closes 2% or more below 10-day MA
# close the position, if any.
elif price < .98 * self.ma10[-1]:
self.position.close()from backtesting.test import GOOG
backtest = Backtest(GOOG, System, commission=.002)
backtest.run()Start 2004-08-19 00:00:00
End 2013-03-01 00:00:00
Duration 3116 days 00:00:00
Exposure Time [%] 2.793296
Equity Final [$] 10017.44422
Equity Peak [$] 10978.3801
Return [%] 0.174442
Buy & Hold Return [%] 703.458242
Return (Ann.) [%] 0.02045
Volatility (Ann.) [%] 4.941212
Sharpe Ratio 0.004139
Sortino Ratio 0.00536
Calmar Ratio 0.002043
Max. Drawdown [%] -10.00745
Avg. Drawdown [%] -9.340092
Max. Drawdown Duration 2653 days 00:00:00
Avg. Drawdown Duration 1410 days 00:00:00
# Trades 4
Win Rate [%] 25.0
Best Trade [%] 9.687579
Worst Trade [%] -4.456159
Avg. Trade [%] 0.081712
Max. Trade Duration 35 days 00:00:00
Avg. Trade Duration 21 days 00:00:00
Profit Factor 1.10514
Expectancy [%] 0.230413
SQN 0.014232
_strategy System
_equity_curve ...
_trades Size EntryBa...
dtype: object
backtest.optimize(d_rsi=range(10, 35, 5),
w_rsi=range(10, 35, 5),
level=range(30, 80, 10)) 0%| | 0/9 [00:00<?, ?it/s]
Start 2004-08-19 00:00:00
End 2013-03-01 00:00:00
Duration 3116 days 00:00:00
Exposure Time [%] 22.486034
Equity Final [$] 22587.83224
Equity Peak [$] 23395.59144
Return [%] 125.878322
Buy & Hold Return [%] 703.458242
Return (Ann.) [%] 10.03124
Volatility (Ann.) [%] 13.124247
Sharpe Ratio 0.764329
Sortino Ratio 1.28711
Calmar Ratio 0.530172
Max. Drawdown [%] -18.920719
Avg. Drawdown [%] -3.795058
Max. Drawdown Duration 778 days 00:00:00
Avg. Drawdown Duration 97 days 00:00:00
# Trades 23
Win Rate [%] 65.217391
Best Trade [%] 25.034669
Worst Trade [%] -6.297769
Avg. Trade [%] 3.658322
Max. Trade Duration 63 days 00:00:00
Avg. Trade Duration 29 days 00:00:00
Profit Factor 4.97576
Expectancy [%] 3.923473
SQN 2.608508
_strategy System(d_rsi=30,...
_equity_curve ...
_trades Size EntryB...
dtype: object
backtest.plot()Row(
id = '4543', …)
align = 'start',
aspect_ratio = None,
background = None,
children = [GridBox(id='4540', ...), ToolbarBox(id='4542', ...)],
cols = 'auto',
css_classes = [],
disabled = False,
height = None,
height_policy = 'auto',
js_event_callbacks = {},
js_property_callbacks = {},
margin = (0, 0, 0, 0),
max_height = None,
max_width = None,
min_height = None,
min_width = None,
name = None,
sizing_mode = 'stretch_width',
spacing = 0,
subscribed_events = [],
syncable = True,
tags = [],
visible = True,
width = None,
width_policy = 'auto')
from backtesting.test import SMAfrom backtesting import Strategy
from backtesting.lib import crossover
class Sma4Cross(Strategy):
n1 = 50
n2 = 100
n_enter = 20
n_exit = 10
def init(self):
self.sma1 = self.I(SMA, self.data.Close, self.n1)
self.sma2 = self.I(SMA, self.data.Close, self.n2)
self.sma_enter = self.I(SMA, self.data.Close, self.n_enter)
self.sma_exit = self.I(SMA, self.data.Close, self.n_exit)
def next(self):
if not self.position:
# On upwards trend, if price closes above
# "entry" MA, go long
# Here, even though the operands are arrays, this
# works by implicitly comparing the two last values
if self.sma1 > self.sma2:
if crossover(self.data.Close, self.sma_enter):
self.buy()
# On downwards trend, if price closes below
# "entry" MA, go short
else:
if crossover(self.sma_enter, self.data.Close):
self.sell()
# But if we already hold a position and the price
# closes back below (above) "exit" MA, close the position
else:
if (self.position.is_long and
crossover(self.sma_exit, self.data.Close)
or
self.position.is_short and
crossover(self.data.Close, self.sma_exit)):
self.position.close()from backtesting import Backtest
from backtesting.test import GOOG
backtest = Backtest(GOOG, Sma4Cross, commission=.002)
stats, heatmap = backtest.optimize(
n1=range(10, 110, 10),
n2=range(20, 210, 20),
n_enter=range(15, 35, 5),
n_exit=range(10, 25, 5),
constraint=lambda p: p.n_exit < p.n_enter < p.n1 < p.n2,
maximize='Equity Final [$]',
max_tries=200,
random_state=0,
return_heatmap=True) 0%| | 0/9 [00:00<?, ?it/s]
heatmapn1 n2 n_enter n_exit
20 60 15 10 10102.86700
80 15 10 9864.21924
100 15 10 11003.21764
30 40 20 15 11771.28610
25 15 16178.54842
...
100 200 15 10 13118.24766
20 10 11308.46180
15 16350.94380
25 10 8991.55294
30 10 9953.07010
Name: Equity Final [$], Length: 177, dtype: float64
heatmap.sort_values().iloc[-3:]n1 n2 n_enter n_exit
100 120 15 10 18159.06414
160 20 15 19216.54456
50 160 20 15 19565.69222
Name: Equity Final [$], dtype: float64
hm = heatmap.groupby(['n1', 'n2']).mean().unstack()
hm
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text-align: right;
}
| n2 | 40 | 60 | 80 | 100 | 120 | 140 | 160 | 180 | 200 |
|---|---|---|---|---|---|---|---|---|---|
| n1 | |||||||||
| 20 | NaN | 10102.867000 | 9864.219240 | 11003.217640 | NaN | NaN | NaN | NaN | NaN |
| 30 | 13974.91726 | 11696.318673 | 11757.991340 | 15092.994270 | 13152.243360 | 11518.686895 | 11271.353850 | 11384.550965 | 10649.052688 |
| 40 | NaN | 13666.448095 | NaN | 7549.099980 | 10629.479030 | 12860.993870 | 11405.291153 | 10863.807380 | 10658.139865 |
| 50 | NaN | 8383.464960 | 10180.502548 | 10563.790150 | 9081.947013 | 14272.265500 | 13575.860855 | 11383.464993 | 10053.468620 |
| 60 | NaN | NaN | 9232.415117 | 8046.485900 | 10838.454280 | 12876.589427 | 10312.954633 | 9427.545100 | 9555.402033 |
| 70 | NaN | NaN | 14712.143280 | 7192.892540 | 10403.014630 | 10065.279860 | 8293.733687 | 9895.782090 | 9360.478292 |
| 80 | NaN | NaN | NaN | 10863.108515 | 7721.243967 | 9139.946300 | 8813.949990 | 10414.656200 | 8908.486500 |
| 90 | NaN | NaN | NaN | 8958.143200 | 9538.050067 | 9884.415550 | 9685.919510 | 11343.643830 | 8806.572300 |
| 100 | NaN | NaN | NaN | NaN | 11253.156553 | 7101.260447 | 11323.427430 | 10163.321700 | 11944.455260 |
import seaborn as sns
sns.heatmap(hm[::-1], cmap='viridis')<AxesSubplot:xlabel='n2', ylabel='n1'>
from backtesting.lib import plot_heatmaps
plot_heatmaps(heatmap, agg='mean')Column(
id = '5204', …)
align = 'start',
aspect_ratio = None,
background = None,
children = [ToolbarBox(id='5203', ...), GridBox(id='5201', ...)],
css_classes = [],
disabled = False,
height = None,
height_policy = 'auto',
js_event_callbacks = {},
js_property_callbacks = {},
margin = (0, 0, 0, 0),
max_height = None,
max_width = None,
min_height = None,
min_width = None,
name = None,
rows = 'auto',
sizing_mode = None,
spacing = 0,
subscribed_events = [],
syncable = True,
tags = [],
visible = True,
width = None,
width_policy = 'auto')
pip install scikit-optimizeRequirement already satisfied: scikit-optimize in c:\users\86189\anaconda3\lib\site-packages (0.10.2)
Requirement already satisfied: joblib>=0.11 in c:\users\86189\anaconda3\lib\site-packages (from scikit-optimize) (1.1.0)
Requirement already satisfied: numpy>=1.20.3 in c:\users\86189\anaconda3\lib\site-packages (from scikit-optimize) (1.20.3)
Requirement already satisfied: pyaml>=16.9 in c:\users\86189\anaconda3\lib\site-packages (from scikit-optimize) (23.5.8)
Requirement already satisfied: scikit-learn>=1.0.0 in c:\users\86189\anaconda3\lib\site-packages (from scikit-optimize) (1.0.2)
Requirement already satisfied: scipy>=1.1.0 in c:\users\86189\anaconda3\lib\site-packages (from scikit-optimize) (1.7.1)
Requirement already satisfied: packaging>=21.3 in c:\users\86189\anaconda3\lib\site-packages (from scikit-optimize) (24.0)
Requirement already satisfied: PyYAML in c:\users\86189\anaconda3\lib\site-packages (from pyaml>=16.9->scikit-optimize) (6.0)
Requirement already satisfied: threadpoolctl>=2.0.0 in c:\users\86189\anaconda3\lib\site-packages (from scikit-learn>=1.0.0->scikit-optimize) (2.2.0)
Note: you may need to restart the kernel to use updated packages.
from backtesting.test import EURUSD, SMA
data = EURUSD.copy()
data
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| Open | High | Low | Close | Volume | |
|---|---|---|---|---|---|
| 2017-04-19 09:00:00 | 1.07160 | 1.07220 | 1.07083 | 1.07219 | 1413 |
| 2017-04-19 10:00:00 | 1.07214 | 1.07296 | 1.07214 | 1.07260 | 1241 |
| 2017-04-19 11:00:00 | 1.07256 | 1.07299 | 1.07170 | 1.07192 | 1025 |
| 2017-04-19 12:00:00 | 1.07195 | 1.07280 | 1.07195 | 1.07202 | 1460 |
| 2017-04-19 13:00:00 | 1.07200 | 1.07230 | 1.07045 | 1.07050 | 1554 |
| ... | ... | ... | ... | ... | ... |
| 2018-02-07 11:00:00 | 1.23390 | 1.23548 | 1.23386 | 1.23501 | 2203 |
| 2018-02-07 12:00:00 | 1.23501 | 1.23508 | 1.23342 | 1.23422 | 2325 |
| 2018-02-07 13:00:00 | 1.23422 | 1.23459 | 1.23338 | 1.23372 | 2824 |
| 2018-02-07 14:00:00 | 1.23374 | 1.23452 | 1.23238 | 1.23426 | 4065 |
| 2018-02-07 15:00:00 | 1.23427 | 1.23444 | 1.22904 | 1.22904 | 6143 |
5000 rows × 5 columns
def BBANDS(data, n_lookback, n_std):
"""Bollinger bands indicator"""
hlc3 = (data.High + data.Low + data.Close) / 3
mean, std = hlc3.rolling(n_lookback).mean(), hlc3.rolling(n_lookback).std()
upper = mean + n_std*std
lower = mean - n_std*std
return upper, lower
close = data.Close.values
sma10 = SMA(data.Close, 10)
sma20 = SMA(data.Close, 20)
sma50 = SMA(data.Close, 50)
sma100 = SMA(data.Close, 100)
upper, lower = BBANDS(data, 20, 2)
# Design matrix / independent features:
# Price-derived features
data['X_SMA10'] = (close - sma10) / close
data['X_SMA20'] = (close - sma20) / close
data['X_SMA50'] = (close - sma50) / close
data['X_SMA100'] = (close - sma100) / close
data['X_DELTA_SMA10'] = (sma10 - sma20) / close
data['X_DELTA_SMA20'] = (sma20 - sma50) / close
data['X_DELTA_SMA50'] = (sma50 - sma100) / close
# Indicator features
data['X_MOM'] = data.Close.pct_change(periods=2)
data['X_BB_upper'] = (upper - close) / close
data['X_BB_lower'] = (lower - close) / close
data['X_BB_width'] = (upper - lower) / close
data['X_Sentiment'] = ~data.index.to_series().between('2017-09-27', '2017-12-14')
# Some datetime features for good measure
data['X_day'] = data.index.dayofweek
data['X_hour'] = data.index.hour
data = data.dropna().astype(float)import numpy as np
def get_X(data):
"""Return model design matrix X"""
return data.filter(like='X').values
def get_y(data):
"""Return dependent variable y"""
y = data.Close.pct_change(48).shift(-48) # Returns after roughly two days
y[y.between(-.004, .004)] = 0 # Devalue returns smaller than 0.4%
y[y > 0] = 1
y[y < 0] = -1
return y
def get_clean_Xy(df):
"""Return (X, y) cleaned of NaN values"""
X = get_X(df)
y = get_y(df).values
isnan = np.isnan(y)
X = X[~isnan]
y = y[~isnan]
return X, yimport pandas as pd
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
X, y = get_clean_Xy(data)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.5, random_state=0)
clf = KNeighborsClassifier(7) # Model the output based on 7 "nearest" examples
clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
_ = pd.DataFrame({'y_true': y_test, 'y_pred': y_pred}).plot(figsize=(15, 2), alpha=.7)
print('Classification accuracy: ', np.mean(y_test == y_pred))Classification accuracy: 0.4210960032962505
from backtesting import Backtest, Strategy
N_TRAIN = 400
class MLTrainOnceStrategy(Strategy):
price_delta = .004 # 0.4%
def init(self):
# Init our model, a kNN classifier
self.clf = KNeighborsClassifier(7)
# Train the classifier in advance on the first N_TRAIN examples
df = self.data.df.iloc[:N_TRAIN]
X, y = get_clean_Xy(df)
self.clf.fit(X, y)
# Plot y for inspection
self.I(get_y, self.data.df, name='y_true')
# Prepare empty, all-NaN forecast indicator
self.forecasts = self.I(lambda: np.repeat(np.nan, len(self.data)), name='forecast')
def next(self):
# Skip the training, in-sample data
if len(self.data) < N_TRAIN:
return
# Proceed only with out-of-sample data. Prepare some variables
high, low, close = self.data.High, self.data.Low, self.data.Close
current_time = self.data.index[-1]
# Forecast the next movement
X = get_X(self.data.df.iloc[-1:])
forecast = self.clf.predict(X)[0]
# Update the plotted "forecast" indicator
self.forecasts[-1] = forecast
# If our forecast is upwards and we don't already hold a long position
# place a long order for 20% of available account equity. Vice versa for short.
# Also set target take-profit and stop-loss prices to be one price_delta
# away from the current closing price.
upper, lower = close[-1] * (1 + np.r_[1, -1]*self.price_delta)
if forecast == 1 and not self.position.is_long:
self.buy(size=.2, tp=upper, sl=lower)
elif forecast == -1 and not self.position.is_short:
self.sell(size=.2, tp=lower, sl=upper)
# Additionally, set aggressive stop-loss on trades that have been open
# for more than two days
for trade in self.trades:
if current_time - trade.entry_time > pd.Timedelta('2 days'):
if trade.is_long:
trade.sl = max(trade.sl, low)
else:
trade.sl = min(trade.sl, high)
bt = Backtest(data, MLTrainOnceStrategy, commission=.0002, margin=.05)
bt.run()Start 2017-04-25 12:00:00
End 2018-02-07 15:00:00
Duration 288 days 03:00:00
Exposure Time [%] 79.412365
Equity Final [$] 14165.286009
Equity Peak [$] 14979.134698
Return [%] 41.65286
Buy & Hold Return [%] 12.869869
Return (Ann.) [%] 42.861028
Volatility (Ann.) [%] 26.903138
Sharpe Ratio 1.593161
Sortino Ratio 3.591481
Calmar Ratio 4.5411
Max. Drawdown [%] -9.438468
Avg. Drawdown [%] -1.106726
Max. Drawdown Duration 41 days 23:00:00
Avg. Drawdown Duration 2 days 15:00:00
# Trades 354
Win Rate [%] 52.542373
Best Trade [%] 0.578258
Worst Trade [%] -0.519427
Avg. Trade [%] 0.023596
Max. Trade Duration 3 days 09:00:00
Avg. Trade Duration 0 days 19:00:00
Profit Factor 1.215787
Expectancy [%] 0.024023
SQN 1.845402
_strategy MLTrainOnceStrategy
_equity_curve ...
_trades Size Entr...
dtype: object
bt.plot()Row(
id = '6142', …)
align = 'start',
aspect_ratio = None,
background = None,
children = [GridBox(id='6139', ...), ToolbarBox(id='6141', ...)],
cols = 'auto',
css_classes = [],
disabled = False,
height = None,
height_policy = 'auto',
js_event_callbacks = {},
js_property_callbacks = {},
margin = (0, 0, 0, 0),
max_height = None,
max_width = None,
min_height = None,
min_width = None,
name = None,
sizing_mode = 'stretch_width',
spacing = 0,
subscribed_events = [],
syncable = True,
tags = [],
visible = True,
width = None,
width_policy = 'auto')
class MLWalkForwardStrategy(MLTrainOnceStrategy):
def next(self):
# Skip the cold start period with too few values available
if len(self.data) < N_TRAIN:
return
# Re-train the model only every 20 iterations.
# Since 20 << N_TRAIN, we don't lose much in terms of
# "recent training examples", but the speed-up is significant!
if len(self.data) % 20:
return super().next()
# Retrain on last N_TRAIN values
df = self.data.df[-N_TRAIN:]
X, y = get_clean_Xy(df)
self.clf.fit(X, y)
# Now that the model is fitted,
# proceed the same as in MLTrainOnceStrategy
super().next()
bt = Backtest(data, MLWalkForwardStrategy, commission=.0002, margin=.05)
bt.run()Start 2017-04-25 12:00:00
End 2018-02-07 15:00:00
Duration 288 days 03:00:00
Exposure Time [%] 71.720057
Equity Final [$] 5885.371145
Equity Peak [$] 10052.843539
Return [%] -41.146289
Buy & Hold Return [%] 12.869869
Return (Ann.) [%] -41.902348
Volatility (Ann.) [%] 10.794943
Sharpe Ratio 0.0
Sortino Ratio 0.0
Calmar Ratio 0.0
Max. Drawdown [%] -41.471113
Avg. Drawdown [%] -14.356287
Max. Drawdown Duration 261 days 18:00:00
Avg. Drawdown Duration 88 days 05:00:00
# Trades 324
Win Rate [%] 41.666667
Best Trade [%] 0.384255
Worst Trade [%] -0.506643
Avg. Trade [%] -0.048462
Max. Trade Duration 3 days 07:00:00
Avg. Trade Duration 0 days 18:00:00
Profit Factor 0.673567
Expectancy [%] -0.04804
SQN -2.792099
_strategy MLWalkForwardStr...
_equity_curve ...
_trades Size Entr...
dtype: object
bt.plot()Row(
id = '7075', …)
align = 'start',
aspect_ratio = None,
background = None,
children = [GridBox(id='7072', ...), ToolbarBox(id='7074', ...)],
cols = 'auto',
css_classes = [],
disabled = False,
height = None,
height_policy = 'auto',
js_event_callbacks = {},
js_property_callbacks = {},
margin = (0, 0, 0, 0),
max_height = None,
max_width = None,
min_height = None,
min_width = None,
name = None,
sizing_mode = 'stretch_width',
spacing = 0,
subscribed_events = [],
syncable = True,
tags = [],
visible = True,
width = None,
width_policy = 'auto')