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backtrader参考手册.pdf
1 Installation
Requirements and versions
Python 2.x/3.x compatibility
Install from pypi
Install from pypi (including matplotlib )
Install from source
Run from source in your project
2 Quickstart
Using the platform
From 0 to 100: the samples
Basic Setup
Setting the Cash
Adding a Data Feed
Our First Strategy
Adding some Logic to the Strategy
Do not only buy ... but SELL
The broker says: Show me the money!
Customizing the Strategy: Parameters
Adding an indicator
Visual Inspection: Plotting
Let’s Optimize
Conclusion
3 Platform Concepts
Before Starting
Data Feeds - Passing them around
Shortcuts for Data Feeds
Omitting the Data Feeds
Almost everything is a Data Feed
Parameters
Lines
Lines declaration
Accessing lines in Data Feeds
Lines len
Inheritance of Lines and Params
Params inheritance
Lines Inheritance
Indexing: 0 and -1
Slicing
Getting a slice
Lines: DELAYED indexing
Lines Coupling
Operators, using natural constructs
Stage 1 - Operators Create Objects
Stage 2 - Operators true to nature
Some non-overriden operators/functions
4 Operating the platform
Line Iterators
Extra methods for Indicators
Minimum Period
Up and Running
Data Feeds
A Strategy (derived) class
A Cerebro
5 Exceptions
Hierarchy
Location
Exceptions
StrategySkipError
6 Cerebro
Gathering input
Execute the backtesting
Standard Observers
Returning the results
Giving access to the plotting facilities
Backtesting logic
Reference
7 Cheat On Open
Trying cheat-on-open
Conclusion
Sample usage
Sample source
8 Strategy
How to Buy/Sell/Close
Information Bits:
Member Attributes:
Member Attributes (meant for statistics/observers/analyzers):
Reference: Strategy
9 Sizers - Smart Staking
Using Sizers
From Cerebro
From Strategy
Sizer Development
Practical Sizer Applicability
bt.Sizer Reference
10 Timers
Options
Usage pattern
Adding timers - Via Strategy
Adding timers - Via Cerebro
When are timers called
If cheat=False
If cheat=True
Running with daily bars
Running with 5-minute bars
Additional scenarios
Parameters to add_timer
Sample usage scheduled.py
Sample usage scheduled-min.py
Sample source scheduled.py
Sample source scheduled-min.py
11 Target Orders
The Sample
order_target_size
order_target_value
order_target_percent
Sample Usage
Sample Code
12 Strategy with Signals
Initial FAQ
Signals technicalities
Signals indications
Signals Types
Accumulation and Order Concurrency
The sample
First run: long and short
Second run: long only
Third run: short only
Fourth run: long + longexit
Usage
The code
13 Broker
Reference
14 Slippage
How it works
Configuring slippage
Practical examples
15 Fillers
The fillers signature
Adding a Filler to the broker
The sample
Reference
16 Orders
Order creation
Order notification
Order Status values
Reference: Order and associated classes
17 Order Management and Execution
Order Management
Order Execution Logic
Market
Close
Limit
Stop
StopLimit
Some samples
Execution Type: Market
Execution Type: Close
Execution Type: Limit
Execution Type: Limit with validity
Execution Type: Stop
Execution Type: StopLimit
Test Script Execution
The full code
18 OCO orders
Sample usage
Sample Code
19 StopTrail(Limit)
20 Bracket Orders
Usage Pattern
Single Issuing of a Bracket
Manual Issuing of a Bracket
A sample of it
Some reference
Sample usage
Sample Code
21 Futures and Spot Compensation
Putting it all together
Sample Usage
Sample Code
22 Data Feeds
Data Feeds Common parameters
CSV Data Feeds Common parameters
GenericCSVData
23 CSV Data Feed Development
Caveat Emptor
24 Binary Datafeed Development
Sample binary datafeed
Initialization
Start
Stop
Actual Loading
Other Binary Formats
VChartData Test
VChartData Full Code
25 Extending a Datafeed
6](https://github.com/mementum/backtrader/issues/6)):
6](https://github.com/mementum/backtrader/issues/6)
Plotting that extra P/E line
26 Pandas DataFeed Example
27 Trading Calendar
Trading Calendar Interface
Implementations
PandasMarketCalendar
TradingCalendar
Usage pattern
Global Trading Calendar
Per data-feed
Examples
Daily to Weekly
Minutes to Daily
Extra bonus for the strategy
Sample usage (tcal.py)
Sample usage (tcal-intra.py)
Sample Code (tcal.py)
Sample Code (tcal-intra.py)
28 Data Resampling
29 Data - Multiple Timeframes
Example 1 - Daily and Weekly
Example 2 - Daily and Daily Compression (2 bars to 1)
Example 3 - Strategy with SMA
Invocation 1:
Invocation 2:
Conclusion
30 Data - Replay
Example - Replay Daily to Weekly
Example 2 - Daily to Daily with Compression
Conclusion
31 Rolling over Futures
The RollOver Data Feed
Options for the Roll-Over
Subclassing RollOver
Let’s Roll
Futures concatenation
Futures roll-over with no checks
Changing during the Week
Adding a volume condition
Concluding
Sample Usage
Sample Code
32 Filters
Purpose
Filters at work
Filter Interface
A Sample Filter
Data Pseudo-API for Filters
Another example: Pinkfish Filter
33 Using Indicators
Indicators in action
__init__ vs next
During __init__
During next
The __init__ vs next why
Some notes
Indicator Plotting
Controlling plotting
34 TA-Lib
Requirements
Using ta-lib
Moving Averages and MA_Type
Plotting ta-lib indicators
Examples and comparisons
KAMA (Kaufman Moving Average)
SMA
EMA
Stochastic
RSI
MACD
Bollinger Bands
AROON
Ultimate Oscillator
Trix
ADXR
DEMA
TEMA
PPO
WilliamsR
ROC
Sample Usage
Sample Code
35 Mixing Timeframes in Indicators
Full coupling syntax
Conclusion
Script Code and Usage
36 Indicator Development
Important note: Idempotence
A dummy (but functional) indicator
Manual/Automatic Minimum Period
A full custom indicator
37 Observers and Statistics
Accesing the Observers
Observer Implementation
Adding Observers to the Strategy
Developing Observers
Custom OrderObserver
Saving/Keeping the statistics
38 Benchmarking
Analyzers - Benchmarking
Observers - Benchmarking
Observing TimeReturn
Observing Benchmarking
Observing Benchmarking - Another data
Concluding
The code
39 Analyzers
Nature of analyzers
Location in the ecosystem
Additional Location
Attributes
Returning the analysis
Modus operandi
Analyzer Patterns
A quick example
Forensic Analysis of an Analyzer
Reference
40 PyFolio Overview
Usage
Sample Code
Reference
41 Pyfolio Integration
No PyFolio
A PyFolio run
42 Writer
Reference
43 Commissions: Stocks vs Futures
Agnosticity
Using the broker shortcuts
The meaning of the setcommission parameters
Two examples now: stocks vs futures
Creating permanent Commission schemes
Now a “real” comparison with a SMA Crossover
Commissions for futures
Commissions for stocks
The code
Reference
44 Extending Commissions
Commissions for futures (fixed and with margin)
Commissions for stocks (perc and withoout margin)
Commissions for futures (perc and with margin)
The code for the sample
45 User Defined Commissions
Defining a Commission Scheme
How to apply this to the platform
A practical example
Explaining pseudoexec
CommInfoBase docstring and params
46 Commissions: Credit
Parameters
The formula
Overriding the formula
47 Position
Reference: Position
48 Trade
Reference: Trade
49 Plotting
How to plot
Plotted Elements
Plotting Options
Object-wide plotting options
The meaning of the options
Line specific plotting options
Passing options to a not yet known line
Some plotlines examples
Methods controlling plotting
System-wide plotting options
PlotScheme
Colors in PlotScheme
50 Plotting Date Ranges
Sample Usage
Sample Code
51 Plotting on the same axis
Sample Usage
Sample Code
52 Optimization improvements
Data Feed Management
Results management
Some test runs
Single Core Run
Multiple Core Runs
Both optdata and optreturn active
optreturn deactivated
optdatas deactivated
Both deactivated
Concluding
Sample Usage
53 Automating BackTesting
Applying a User Defined Strategy
Using a built-in Strategy
Using no Strategy
Adding Analyzers
Adding Indicators and Observers
Plotting Control
Usage of the script
54 Saving Memory
Script Code and Usage
55 DateTime Management
Operation with timezones
Datetime Input
Datetime output
56 Live Data Feeds and Live Trading
57 Data Feeds Reference
57 AbstractDataBase
57 BacktraderCSVData
57 CSVDataBase
57 Chainer
57 DataClone
57 DataFiller
57 DataFilter
57 GenericCSVData
57 IBData
57 InfluxDB
57 MT4CSVData
57 OandaData
57 PandasData
57 PandasDirectData
57 Quandl
57 QuandlCSV
57 RollOver
57 SierraChartCSVData
57 VCData
57 VChartCSVData
57 VChartData
57 VChartFile
57 YahooFinanceCSVData
57 YahooFinanceData
57 YahooLegacyCSV
58 Yahoo Data Feed Notes
Using the v7 API/format
Using the legacy API/format
59 Indicator Reference
59 AccelerationDecelerationOscillator
59 Accum
59 AdaptiveMovingAverage
59 AdaptiveMovingAverageEnvelope
59 AdaptiveMovingAverageOscillator
59 AllN
59 AnyN
59 ApplyN
59 AroonDown
59 AroonOscillator
59 AroonUp
59 AroonUpDown
59 AroonUpDownOscillator
59 Average
59 AverageDirectionalMovementIndex
59 AverageDirectionalMovementIndexRating
59 AverageTrueRange
59 AwesomeOscillator
59 BaseApplyN
59 BollingerBands
59 BollingerBandsPct
59 CointN
59 CommodityChannelIndex
59 CrossDown
59 CrossOver
59 CrossUp
59 DV2
59 DemarkPivotPoint
59 DetrendedPriceOscillator
59 DicksonMovingAverage
59 DicksonMovingAverageEnvelope
59 DicksonMovingAverageOscillator
59 DirectionalIndicator
59 DirectionalMovement
59 DirectionalMovementIndex
59 DoubleExponentialMovingAverage
59 DoubleExponentialMovingAverageEnvelope
59 DoubleExponentialMovingAverageOscillator
59 DownDay
59 DownDayBool
59 DownMove
59 Envelope
59 ExponentialMovingAverage
59 ExponentialMovingAverageEnvelope
59 ExponentialMovingAverageOscillator
59 ExponentialSmoothing
59 ExponentialSmoothingDynamic
59 FibonacciPivotPoint
59 FindFirstIndex
59 FindFirstIndexHighest
59 FindFirstIndexLowest
59 FindLastIndex
59 FindLastIndexHighest
59 FindLastIndexLowest
59 Fractal
59 HeikinAshi
59 Highest
59 HullMovingAverage
59 HullMovingAverageEnvelope
59 HullMovingAverageOscillator
59 HurstExponent
59 Ichimoku
59 KnowSureThing
59 LaguerreFilter
59 LaguerreRSI
59 LinePlotterIndicator
59 Lowest
59 MACD
59 MACDHisto
59 MeanDeviation
59 MinusDirectionalIndicator
59 Momentum
59 MomentumOscillator
59 MovingAverageBase
59 MovingAverageSimple
59 MovingAverageSimpleEnvelope
59 MovingAverageSimpleOscillator
59 OLS_BetaN
59 OLS_Slope_InterceptN
59 OLS_TransformationN
59 OperationN
59 Oscillator
59 OscillatorMixIn
59 ParabolicSAR
59 PercentChange
59 PercentRank
59 PercentagePriceOscillator
59 PercentagePriceOscillatorShort
59 PeriodN
59 PivotPoint
59 PlusDirectionalIndicator
59 PrettyGoodOscillator
59 PriceOscillator
59 RSI_EMA
59 RSI_SMA
59 RSI_Safe
59 RateOfChange
59 RateOfChange100
59 ReduceN
59 RelativeMomentumIndex
59 RelativeStrengthIndex
59 Signal
59 SmoothedMovingAverage
59 SmoothedMovingAverageEnvelope
59 SmoothedMovingAverageOscillator
59 StandardDeviation
59 Stochastic
59 StochasticFast
59 StochasticFull
59 SumN
59 TripleExponentialMovingAverage
59 TripleExponentialMovingAverageEnvelope
59 TripleExponentialMovingAverageOscillator
59 Trix
59 TrixSignal
59 TrueHigh
59 TrueLow
59 TrueRange
59 TrueStrengthIndicator
59 UltimateOscillator
59 Buying Pressure = Close - TrueLow BP = Close - Minimum(Low or Prior Close)
59 TrueRange = TrueHigh - TrueLow TR = Maximum(High or Prior Close) -
59 UpDay
59 UpDayBool
59 UpMove
59 Vortex
59 WeightedAverage
59 WeightedMovingAverage
59 WeightedMovingAverageEnvelope
59 WeightedMovingAverageOscillator
59 WilliamsAD
59 WilliamsR
59 ZeroLagExponentialMovingAverage
59 ZeroLagExponentialMovingAverageEnvelope
59 ZeroLagExponentialMovingAverageOscillator
59 ZeroLagIndicator
59 ZeroLagIndicatorEnvelope
59 ZeroLagIndicatorOscillator
59 haDelta
60 TA-Lib Indicator Reference
TA-Lib Indicator Reference
61 Strategies Reference
61 MA_CrossOver
61 SignalStrategy
62 Analyzers Reference
AnnualReturn
Calmar
DrawDown
TimeDrawDown
GrossLeverage
PositionsValue
PyFolio
LogReturnsRolling
PeriodStats
Returns
SharpeRatio
SharpeRatio_A
SQN
TimeReturn
TradeAnalyzer
Transactions
VWR
63 Observers Reference
Benchmark
Broker
Broker - Cash
Broker - Value
BuySell
DrawDown
TimeReturn
Trades
LogReturns
LogReturns2
FundValue
FundShares
64 Sizers Reference
FixedSize
FixedReverser
PercentSizer
AllInSizer
65 Filters Reference
SessionFilter
SessionFilterSimple
SessionFilller
CalendarDays
BarReplayer_Open
DaySplitter_Close
HeikinAshi
Renko
1 Installation
Requirements and versions
backtrader is self-contained with no external dependencies (except if you want to plot)
Basic requirements are:
Python 2.7
Python 3.2/3.3/3.4/3.5
pypy/pypy3
Additional requirements if plotting is wished:
Matplotlib >= 1.4.1
It may work with previous versions, but this the one used for development
Note
At the time of writing Matplotlib is not supported under pypy/pypy3
Python 2.x/3.x compatibility
Development takes place under Python 2.7 and sometimes under 3.4. Tests are run locally with both
versions.
Compatibility with 3.2/3.3/3.5 and pypy/pyp3 is checked with continuous integration under Travis
Install from pypi
For example using pip:
pip install backtrader
easy_install with the same syntax can also be applied
Install from pypi (including matplotlib )
Use this if plotting capabilities are wished:
pip install backtrader[plotting]
This pulls in matplotlib which will in turn pull in other dependencies.
Again you may prefer (or only have access to ...) easy_install
Install from source
First downloading a release or the latest tarball from the github site:
https://github.com/mementum/backtrader
And after unpacking run the command:
python setup.py install
Run from source in your project
Download a release or the latest tarball from the github site:
https://github.com/mementum/backtrader
And then copy the backtrader package directory to your own project. Under a Unix-like OS for example:
Again tar xzf backgrader.tgz
cd backtrader
cp -r backtrader project_directory
Remember that you would then need to manually install matplotlib for plotting.
2 Quickstart
Note
The data files used in the quickstart guide are updated from time to time, which means that the
adjusted close changes and with it the close (and the other components). That means that the actual
output may be different to what was put in the documentation at the time of writing.
Using the platform
Let’s run through a series of examples (from almost an empty one to a fully fledged strategy) but not
without before roughly explaining 2 basic concepts when working with backtrader
1. Lines
Data Feeds, Indicators and Strategies have lines.
A line is a succession of points that when joined together form this line. When talking about the
markets, a Data Feed has usually the following set of points per day:
Open, High, Low, Close, Volume, OpenInterest
The series of “Open”s along time is a Line. And therefore a Data Feed has usually 6 lines.
If we also consider “DateTime” (which is the actual reference for a single point), we could count 7
lines.
2. Index 0 Approach
When accessing the values in a line, the current value is accessed with index: 0
And the “last” output value is accessed with -1. This in line with Python conventions for iterables
(and a line can be iterated and is therefore an iterable) where index -1 is used to access the “last” item of
the iterable/array.
In our case is the last output value what’s getting accessed.
As such and being index 0 right after -1 , it is used to access the current moment in line.
With that in mind and if we imagine a Strategy featuring a Simple Moving average created during
initialization:
self.sma = SimpleMovingAverage(.....)
The easiest and simplest way to access the current value of this moving average:
av = self.sma[0]
There is no need to know how many bars/minutes/days/months have been processed, because “0”
uniquely identifies the current instant.
Following pythonic tradition, the “last” output value is accessed using -1 :
previous_value = self.sma[-1]
Of course earlier output values can be accessed with -2, -3, ...
From 0 to 100: the samples
Basic Setup
Let’s get running.
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import backtrader as bt
if __name__ == '__main__':
cerebro = bt.Cerebro()
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
After the execution the output is:
Starting Portfolio Value: 10000.00
Final Portfolio Value: 10000.00
In this example:
backtrader was imported
The Cerebro engine was instantiated
The resulting cerebro instance was told to run (loop over data)
And the resulting outcome was printed out
Although it doesn’t seem much, let’s point out something explicitly shown:
The Cerebro engine has created a broker instance in the background
The instance already has some cash to start with
This behind the scenes broker instantiation is a constant trait in the platform to simplify the life of the
user. If no broker is set by the user, a default one is put in place.
And 10K monetary units is a usual value with some brokers to begin with.
Setting the Cash
In the world of finance, for sure only “losers” start with 10k. Let’s change the cash and run the example
again.
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import backtrader as bt
if __name__ == '__main__':
cerebro = bt.Cerebro()
cerebro.broker.setcash(100000.0)
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
cerebro.run()
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
After the execution the output is:
Starting Portfolio Value: 1000000.00
Final Portfolio Value: 1000000.00
Mission accomplished. Let’s move to tempestuous waters.
Adding a Data Feed
Having cash is fun, but the purpose behind all this is to let an automated strategy multiply the cash
without moving a finger by operating on an asset which we see as a Data Feed
Ergo ... No Data Feed - > No Fun. Let’s add one to the ever growing example.
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])
# Import the backtrader platform
import backtrader as bt
if __name__ == '__main__':
# Create a cerebro entity
cerebro = bt.Cerebro()
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')
# Create a Data Feed
data = bt.feeds.YahooFinanceCSVData(
dataname=datapath,
# Do not pass values before this date
fromdate=datetime.datetime(2000, 1, 1),
# Do not pass values after this date
todate=datetime.datetime(2000, 12, 31),
reverse=False)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(100000.0)
# Print out the starting conditions
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
# Run over everything
cerebro.run()
# Print out the final result
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
After the execution the output is:
Starting Portfolio Value: 1000000.00
Final Portfolio Value: 1000000.00
The amount of boilerplate has grown slightly, because we added:
Finding out where our example script is to be able to locate the sample Data Feed file
Having datetime objects to filter on which data from the Data Feed we will be operating
Aside from that, the Data Feed is created and added to cerebro.
The output has not changed and it would be a miracle if it had.
Note
Yahoo Online sends the CSV data in date descending order, which is not the standard convention. The
reversed=True prameter takes into account that the CSV data in the file has already been reversed and has the
standard expected date ascending order.
Our First Strategy
The cash is in the broker and the Data Feed is there. It seems like risky business is just around the corner.
Let’s put a Strategy into the equation and print the “Close” price of each day (bar).
DataSeries (the underlying class in Data Feeds ) objects have aliases to access the well known OHLC
(Open High Low Close) daily values. This should ease up the creation of our printing logic.
from __future__ import (absolute_import, division, print_function,
unicode_literals)
import datetime # For datetime objects
import os.path # To manage paths
import sys # To find out the script name (in argv[0])
# Import the backtrader platform
import backtrader as bt
# Create a Stratey
class TestStrategy(bt.Strategy):
def log(self, txt, dt=None):
''' Logging function for this strategy'''
dt = dt or self.datas[0].datetime.date(0)
print('%s, %s' % (dt.isoformat(), txt))
def __init__(self):
# Keep a reference to the "close" line in the data[0] dataseries
self.dataclose = self.datas[0].close
def next(self):
# Simply log the closing price of the series from the reference
self.log('Close, %.2f' % self.dataclose[0])
if __name__ == '__main__':
# Create a cerebro entity
cerebro = bt.Cerebro()
# Add a strategy
cerebro.addstrategy(TestStrategy)
# Datas are in a subfolder of the samples. Need to find where the script is
# because it could have been called from anywhere
modpath = os.path.dirname(os.path.abspath(sys.argv[0]))
datapath = os.path.join(modpath, '../../datas/orcl-1995-2014.txt')
# Create a Data Feed
data = bt.feeds.YahooFinanceCSVData(
dataname=datapath,
# Do not pass values before this date
fromdate=datetime.datetime(2000, 1, 1),
# Do not pass values before this date
todate=datetime.datetime(2000, 12, 31),
# Do not pass values after this date
reverse=False)
# Add the Data Feed to Cerebro
cerebro.adddata(data)
# Set our desired cash start
cerebro.broker.setcash(100000.0)
# Print out the starting conditions
print('Starting Portfolio Value: %.2f' % cerebro.broker.getvalue())
# Run over everything
cerebro.run()
# Print out the final result
print('Final Portfolio Value: %.2f' % cerebro.broker.getvalue())
After the execution the output is:
Starting Portfolio Value: 100000.00
2000-01-03T00:00:00, Close, 27.85
2000-01-04T00:00:00, Close, 25.39
2000-01-05T00:00:00, Close, 24.05
...
...
...
2000-12-26T00:00:00, Close, 29.17
2000-12-27T00:00:00, Close, 28.94
2000-12-28T00:00:00, Close, 29.29
2000-12-29T00:00:00, Close, 27.41
Final Portfolio Value: 100000.00
Someone said the stockmarket was risky business, but it doesn’t seem so.
Let’s explain some of the magic:
Upon init being called the strategy already has a list of datas that are present in the platform
This is a standard Python list and datas can be accessed in the order they were inserted.
The first data in the list self.datas[0] is the default data for trading operations and to keep all
strategy elements synchronized ( it’s the system clock )
self.dataclose = self.datas[0].close keeps a reference to the close line. Only one level of
indirection is later needed to access the close values.
The strategy next method will be called on each bar of the system clock (self.datas[0]). This is
true until other things come into play like indicators , which need some bars to start producing
an output. More on that later.
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