Module `backtesting.backtesting`

Core framework data structures. Objects from this module can also be imported from the top-level module directly, e.g.

from backtesting import Backtest, Strategy

Classes

class Backtest (data, strategy, *, cash=10000, commission=0.0, margin=1.0, trade_on_close=False, hedging=False, exclusive_orders=False)

Backtest a particular (parameterized) strategy on particular data.

Upon initialization, call method Backtest.run() to run a backtest instance, or Backtest.optimize() to optimize it.

Initialize a backtest. Requires data and a strategy to test.

data is a pd.DataFrame with columns: Open, High, Low, Close, and (optionally) Volume. If any columns are missing, set them to what you have available, e.g.

df['Open'] = df['High'] = df['Low'] = df['Close']

The passed data frame can contain additional columns that can be used by the strategy (e.g. sentiment info). DataFrame index can be either a datetime index (timestamps) or a monotonic range index (i.e. a sequence of periods).

strategy is a Strategy subclass (not an instance).

cash is the initial cash to start with.

commission is the commission ratio. E.g. if your broker's commission is 1% of trade value, set commission to 0.01. Note, if you wish to account for bid-ask spread, you can approximate doing so by increasing the commission, e.g. set it to 0.0002 for commission-less forex trading where the average spread is roughly 0.2‰ of asking price.

margin is the required margin (ratio) of a leveraged account. No difference is made between initial and maintenance margins. To run the backtest using e.g. 50:1 leverge that your broker allows, set margin to 0.02 (1 / leverage).

If trade_on_close is True, market orders will be filled with respect to the current bar's closing price instead of the next bar's open.

If hedging is True, allow trades in both directions simultaneously. If False, the opposite-facing orders first close existing trades in a FIFO manner.

If exclusive_orders is True, each new order auto-closes the previous trade/position, making at most a single trade (long or short) in effect at each time.

Methods

def optimize(self, *, maximize='SQN', method='grid', max_tries=None, constraint=None, return_heatmap=False, return_optimization=False, random_state=None, **kwargs)

Optimize strategy parameters to an optimal combination. Returns result pd.Series of the best run.

maximize is a string key from the Backtest.run()-returned results series, or a function that accepts this series object and returns a number; the higher the better. By default, the method maximizes Van Tharp's System Quality Number.

method is the optimization method. Currently two methods are supported:

"grid" which does an exhaustive (or randomized) search over the cartesian product of parameter combinations, and
"skopt" which finds close-to-optimal strategy parameters using model-based optimization, making at most max_tries evaluations.

max_tries is the maximal number of strategy runs to perform. If method="grid", this results in randomized grid search. If max_tries is a floating value between (0, 1], this sets the number of runs to approximately that fraction of full grid space. Alternatively, if integer, it denotes the absolute maximum number of evaluations. If unspecified (default), grid search is exhaustive, whereas for method="skopt", max_tries is set to 200.

constraint is a function that accepts a dict-like object of parameters (with values) and returns True when the combination is admissible to test with. By default, any parameters combination is considered admissible.

If return_heatmap is True, besides returning the result series, an additional pd.Series is returned with a multiindex of all admissible parameter combinations, which can be further inspected or projected onto 2D to plot a heatmap (see plot_heatmaps()).

If return_optimization is True and method = 'skopt', in addition to result series (and maybe heatmap), return raw scipy.optimize.OptimizeResult for further inspection, e.g. with scikit-optimize plotting tools.

If you want reproducible optimization results, set random_state to a fixed integer random seed.

Additional keyword arguments represent strategy arguments with list-like collections of possible values. For example, the following code finds and returns the "best" of the 7 admissible (of the 9 possible) parameter combinations:

backtest.optimize(sma1=[5, 10, 15], sma2=[10, 20, 40],
                  constraint=lambda p: p.sma1 < p.sma2)

TODO

Improve multiprocessing/parallel execution on Windos with start method 'spawn'.

def plot(self, *, results=None, filename=None, plot_width=None, plot_equity=True, plot_return=False, plot_pl=True, plot_volume=True, plot_drawdown=False, smooth_equity=False, relative_equity=True, superimpose=True, resample=True, reverse_indicators=False, show_legend=True, open_browser=True)

Plot the progression of the last backtest run.

If results is provided, it should be a particular result pd.Series such as returned by Backtest.run() or Backtest.optimize(), otherwise the last run's results are used.

filename is the path to save the interactive HTML plot to. By default, a strategy/parameter-dependent file is created in the current working directory.

plot_width is the width of the plot in pixels. If None (default), the plot is made to span 100% of browser width. The height is currently non-adjustable.

If plot_equity is True, the resulting plot will contain an equity (initial cash plus assets) graph section. This is the same as plot_return plus initial 100%.

If plot_return is True, the resulting plot will contain a cumulative return graph section. This is the same as plot_equity minus initial 100%.

If plot_pl is True, the resulting plot will contain a profit/loss (P/L) indicator section.

If plot_volume is True, the resulting plot will contain a trade volume section.

If plot_drawdown is True, the resulting plot will contain a separate drawdown graph section.

If smooth_equity is True, the equity graph will be interpolated between fixed points at trade closing times, unaffected by any interim asset volatility.

If relative_equity is True, scale and label equity graph axis with return percent, not absolute cash-equivalent values.

If superimpose is True, superimpose larger-timeframe candlesticks over the original candlestick chart. Default downsampling rule is: monthly for daily data, daily for hourly data, hourly for minute data, and minute for (sub-)second data. superimpose can also be a valid Pandas offset string, such as '5T' or '5min', in which case this frequency will be used to superimpose. Note, this only works for data with a datetime index.

If resample is True, the OHLC data is resampled in a way that makes the upper number of candles for Bokeh to plot limited to 10_000. This may, in situations of overabundant data, improve plot's interactive performance and avoid browser's Javascript Error: Maximum call stack size exceeded or similar. Equity & dropdown curves and individual trades data is, likewise, reasonably aggregated. resample can also be a Pandas offset string, such as '5T' or '5min', in which case this frequency will be used to resample, overriding above numeric limitation. Note, all this only works for data with a datetime index.

If reverse_indicators is True, the indicators below the OHLC chart are plotted in reverse order of declaration.

If show_legend is True, the resulting plot graphs will contain labeled legends.

If open_browser is True, the resulting filename will be opened in the default web browser.

def run(self, **kwargs)

Run the backtest. Returns pd.Series with results and statistics.

Keyword arguments are interpreted as strategy parameters.

>>> Backtest(GOOG, SmaCross).run()
Start                     2004-08-19 00:00:00
End                       2013-03-01 00:00:00
Duration                   3116 days 00:00:00
Exposure Time [%]                     93.9944
Equity Final [$]                      51959.9
Equity Peak [$]                       75787.4
Return [%]                            419.599
Buy & Hold Return [%]                 703.458
Return (Ann.) [%]                      21.328
Volatility (Ann.) [%]                 36.5383
Sharpe Ratio                         0.583718
Sortino Ratio                         1.09239
Calmar Ratio                         0.444518
Max. Drawdown [%]                    -47.9801
Avg. Drawdown [%]                    -5.92585
Max. Drawdown Duration      584 days 00:00:00
Avg. Drawdown Duration       41 days 00:00:00
# Trades                                   65
Win Rate [%]                          46.1538
Best Trade [%]                         53.596
Worst Trade [%]                      -18.3989
Avg. Trade [%]                        2.35371
Max. Trade Duration         183 days 00:00:00
Avg. Trade Duration          46 days 00:00:00
Profit Factor                         2.08802
Expectancy [%]                        8.79171
SQN                                  0.916893
_strategy                            SmaCross
_equity_curve                           Eq...
_trades                       Size  EntryB...
dtype: object

class Order

Place new orders through Strategy.buy() and Strategy.sell(). Query existing orders through Strategy.orders.

When an order is executed or filled, it results in a Trade.

If you wish to modify aspects of a placed but not yet filled order, cancel it and place a new one instead.

All placed orders are Good 'Til Canceled.

Instance variables

var is_contingent: True for contingent orders, i.e. OCO stop-loss and take-profit bracket orders placed upon an active trade. Remaining contingent orders are canceled when their parent Trade is closed.

You can modify contingent orders through Trade.sl and Trade.tp.
var is_long: True if the order is long (order size is positive).
var is_short: True if the order is short (order size is negative).
var limit: Order limit price for limit orders, or None for market orders, which are filled at next available price.
var size: Order size (negative for short orders).

If size is a value between 0 and 1, it is interpreted as a fraction of current available liquidity (cash plus Position.pl minus used margin). A value greater than or equal to 1 indicates an absolute number of units.
var sl: A stop-loss price at which, if set, a new contingent stop-market order will be placed upon the Trade following this order's execution. See also Trade.sl.
var stop: Order stop price for stop-limit/stop-market order, otherwise None if no stop was set, or the stop price has already been hit.
var tp: A take-profit price at which, if set, a new contingent limit order will be placed upon the Trade following this order's execution. See also Trade.tp.

Methods

def cancel(self): Cancel the order.

class Position

Currently held asset position, available as Strategy.position within Strategy.next(). Can be used in boolean contexts, e.g.

if self.position:
    ...  # we have a position, either long or short

Instance variables

var is_long: True if the position is long (position size is positive).
var is_short: True if the position is short (position size is negative).
var pl: Profit (positive) or loss (negative) of the current position in cash units.
var pl_pct: Profit (positive) or loss (negative) of the current position in percent.
var size: Position size in units of asset. Negative if position is short.

Methods

def close(self, portion=1.0): Close portion of position by closing portion of each active trade. See Trade.close().

class Strategy

A trading strategy base class. Extend this class and override methods Strategy.init() and Strategy.next() to define your own strategy.

Subclasses

Instance variables

var closed_trades

List of settled trades (see Trade).

var data

Price data, roughly as passed into Backtest, but with two significant exceptions:

data is not a DataFrame, but a custom structure that serves customized numpy arrays for reasons of performance and convenience. Besides OHLCV columns, .index and length, it offers .pip property, the smallest price unit of change.
Within Strategy.init(), data arrays are available in full length, as passed into Backtest (for precomputing indicators and such). However, within Strategy.next(), data arrays are only as long as the current iteration, simulating gradual price point revelation. In each call of Strategy.next() (iteratively called by Backtest internally), the last array value (e.g. data.Close[-1]) is always the most recent value.
If you need data arrays (e.g. data.Close) to be indexed Pandas series, you can call their .s accessor (e.g. data.Close.s). If you need the whole of data as a DataFrame, use .df accessor (i.e. data.df).

var equity

Current account equity (cash plus assets).

var orders

List of orders (see Order) waiting for execution.

var position

Instance of Position.

var trades

List of active trades (see Trade).

Methods

def I(self, func, *args, name=None, plot=True, overlay=None, color=None, scatter=False, **kwargs)

Declare indicator. An indicator is just an array of values, but one that is revealed gradually in Strategy.next() much like Strategy.data is. Returns np.ndarray of indicator values.

func is a function that returns the indicator array(s) of same length as Strategy.data.

In the plot legend, the indicator is labeled with function name, unless name overrides it.

If plot is True, the indicator is plotted on the resulting Backtest.plot().

If overlay is True, the indicator is plotted overlaying the price candlestick chart (suitable e.g. for moving averages). If False, the indicator is plotted standalone below the candlestick chart. By default, a heuristic is used which decides correctly most of the time.

color can be string hex RGB triplet or X11 color name. By default, the next available color is assigned.

If scatter is True, the plotted indicator marker will be a circle instead of a connected line segment (default).

Additional *args and **kwargs are passed to func and can be used for parameters.

For example, using simple moving average function from TA-Lib:

def init():
    self.sma = self.I(ta.SMA, self.data.Close, self.n_sma)

def buy(self, *, size=.9999, limit=None, stop=None, sl=None, tp=None)

Place a new long order. For explanation of parameters, see Order and its properties.

Instance variables

var entry_bar: Candlestick bar index of when the trade was entered.
var entry_price: Trade entry price.
var entry_time: Datetime of when the trade was entered.
var exit_bar: Candlestick bar index of when the trade was exited (or None if the trade is still active).
var exit_price: Trade exit price (or None if the trade is still active).
var exit_time: Datetime of when the trade was exited.
var is_long: True if the trade is long (trade size is positive).
var is_short: True if the trade is short (trade size is negative).
var pl: Trade profit (positive) or loss (negative) in cash units.
var pl_pct: Trade profit (positive) or loss (negative) in percent.
var size: Trade size (volume; negative for short trades).
var sl: Stop-loss price at which to close the trade.

This variable is writable. By assigning it a new price value, you create or modify the existing SL order. By assigning it None, you cancel it.
var tp: Take-profit price at which to close the trade.

This property is writable. By assigning it a new price value, you create or modify the existing TP order. By assigning it None, you cancel it.
var value: Trade total value in cash (volume × price).

Methods

def close(self, portion=1.0): Place new Order to close portion of the trade at next market price.