"""
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
"""
import multiprocessing as mp
import os
import sys
import warnings
from abc import ABCMeta, abstractmethod
from concurrent.futures import ProcessPoolExecutor, as_completed
from copy import copy
from functools import lru_cache, partial
from itertools import chain, compress, product, repeat
from math import copysign
from numbers import Number
from typing import Callable, Dict, List, Optional, Sequence, Tuple, Type, Union
import numpy as np
import pandas as pd
from numpy.random import default_rng
try:
from tqdm.auto import tqdm as _tqdm
_tqdm = partial(_tqdm, leave=False)
except ImportError:
def _tqdm(seq, **_):
return seq
from ._plotting import plot # noqa: I001
from ._stats import compute_stats
from ._util import _as_str, _Indicator, _Data, try_
__pdoc__ = {
'Strategy.__init__': False,
'Order.__init__': False,
'Position.__init__': False,
'Trade.__init__': False,
}
[docs]
class Strategy(metaclass=ABCMeta):
"""
A trading strategy base class. Extend this class and
override methods
`backtesting.backtesting.Strategy.init` and
`backtesting.backtesting.Strategy.next` to define
your own strategy.
"""
def __init__(self, broker, data, params):
self._indicators = []
self._broker: _Broker = broker
self._data: _Data = data
self._params = self._check_params(params)
def __repr__(self):
return '<Strategy ' + str(self) + '>'
def __str__(self):
params = ','.join(f'{i[0]}={i[1]}' for i in zip(self._params.keys(),
map(_as_str, self._params.values())))
if params:
params = '(' + params + ')'
return f'{self.__class__.__name__}{params}'
def _check_params(self, params):
for k, v in params.items():
if not hasattr(self, k):
raise AttributeError(
f"Strategy '{self.__class__.__name__}' is missing parameter '{k}'."
"Strategy class should define parameters as class variables before they "
"can be optimized or run with.")
setattr(self, k, v)
return params
[docs]
def I(self, # noqa: E743
func: Callable, *args,
name=None, plot=True, overlay=None, color=None, scatter=False,
**kwargs) -> np.ndarray:
"""
Declare an indicator. An indicator is just an array of values,
but one that is revealed gradually in
`backtesting.backtesting.Strategy.next` much like
`backtesting.backtesting.Strategy.data` is.
Returns `np.ndarray` of indicator values.
`func` is a function that returns the indicator array(s) of
same length as `backtesting.backtesting.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
`backtesting.backtesting.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)
"""
if name is None:
params = ','.join(filter(None, map(_as_str, chain(args, kwargs.values()))))
func_name = _as_str(func)
name = (f'{func_name}({params})' if params else f'{func_name}')
else:
name = name.format(*map(_as_str, args),
**dict(zip(kwargs.keys(), map(_as_str, kwargs.values()))))
try:
value = func(*args, **kwargs)
except Exception as e:
raise RuntimeError(f'Indicator "{name}" error') from e
if isinstance(value, pd.DataFrame):
value = value.values.T
if value is not None:
value = try_(lambda: np.asarray(value, order='C'), None)
is_arraylike = bool(value is not None and value.shape)
# Optionally flip the array if the user returned e.g. `df.values`
if is_arraylike and np.argmax(value.shape) == 0:
value = value.T
if not is_arraylike or not 1 <= value.ndim <= 2 or value.shape[-1] != len(self._data.Close):
raise ValueError(
'Indicators must return (optionally a tuple of) numpy.arrays of same '
f'length as `data` (data shape: {self._data.Close.shape}; indicator "{name}" '
f'shape: {getattr(value, "shape" , "")}, returned value: {value})')
if plot and overlay is None and np.issubdtype(value.dtype, np.number):
x = value / self._data.Close
# By default, overlay if strong majority of indicator values
# is within 30% of Close
with np.errstate(invalid='ignore'):
overlay = ((x < 1.4) & (x > .6)).mean() > .6
value = _Indicator(value, name=name, plot=plot, overlay=overlay,
color=color, scatter=scatter,
# _Indicator.s Series accessor uses this:
index=self.data.index)
self._indicators.append(value)
return value
[docs]
@abstractmethod
def init(self):
"""
Initialize the strategy.
Override this method.
Declare indicators (with `backtesting.backtesting.Strategy.I`).
Precompute what needs to be precomputed or can be precomputed
in a vectorized fashion before the strategy starts.
If you extend composable strategies from `backtesting.lib`,
make sure to call:
super().init()
"""
[docs]
@abstractmethod
def next(self):
"""
Main strategy runtime method, called as each new
`backtesting.backtesting.Strategy.data`
instance (row; full candlestick bar) becomes available.
This is the main method where strategy decisions
upon data precomputed in `backtesting.backtesting.Strategy.init`
take place.
If you extend composable strategies from `backtesting.lib`,
make sure to call:
super().next()
"""
class __FULL_EQUITY(float): # noqa: N801
def __repr__(self): return '.9999'
_FULL_EQUITY = __FULL_EQUITY(1 - sys.float_info.epsilon)
[docs]
def buy(self, *,
size: float = _FULL_EQUITY,
limit: Optional[float] = None,
stop: Optional[float] = None,
sl: Optional[float] = None,
tp: Optional[float] = None,
tag: object = None):
"""
Place a new long order. For explanation of parameters, see `Order` and its properties.
See `Position.close()` and `Trade.close()` for closing existing positions.
See also `Strategy.sell()`.
"""
assert 0 < size < 1 or round(size) == size, \
"size must be a positive fraction of equity, or a positive whole number of units"
return self._broker.new_order(size, limit, stop, sl, tp, tag)
[docs]
def sell(self, *,
size: float = _FULL_EQUITY,
limit: Optional[float] = None,
stop: Optional[float] = None,
sl: Optional[float] = None,
tp: Optional[float] = None,
tag: object = None):
"""
Place a new short order. For explanation of parameters, see `Order` and its properties.
See also `Strategy.buy()`.
.. note::
If you merely want to close an existing long position,
use `Position.close()` or `Trade.close()`.
"""
assert 0 < size < 1 or round(size) == size, \
"size must be a positive fraction of equity, or a positive whole number of units"
return self._broker.new_order(-size, limit, stop, sl, tp, tag)
@property
def equity(self) -> float:
"""Current account equity (cash plus assets)."""
return self._broker.equity
@property
def data(self) -> _Data:
"""
Price data, roughly as passed into
`backtesting.backtesting.Backtest.__init__`,
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 `backtesting.backtesting.Strategy.init`, `data` arrays
are available in full length, as passed into
`backtesting.backtesting.Backtest.__init__`
(for precomputing indicators and such). However, within
`backtesting.backtesting.Strategy.next`, `data` arrays are
only as long as the current iteration, simulating gradual
price point revelation. In each call of
`backtesting.backtesting.Strategy.next` (iteratively called by
`backtesting.backtesting.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`).
"""
return self._data
@property
def position(self) -> 'Position':
"""Instance of `backtesting.backtesting.Position`."""
return self._broker.position
@property
def orders(self) -> 'Tuple[Order, ...]':
"""List of orders (see `Order`) waiting for execution."""
return _Orders(self._broker.orders)
@property
def trades(self) -> 'Tuple[Trade, ...]':
"""List of active trades (see `Trade`)."""
return tuple(self._broker.trades)
@property
def closed_trades(self) -> 'Tuple[Trade, ...]':
"""List of settled trades (see `Trade`)."""
return tuple(self._broker.closed_trades)
class _Orders(tuple):
"""
"""
# TODO: remove this class. Only for deprecation.
def cancel(self):
"""Cancel all non-contingent (i.e. SL/TP) orders."""
for order in self:
if not order.is_contingent:
order.cancel()
def __getattr__(self, item):
# TODO: Warn on deprecations from the previous version. Remove in the next.
removed_attrs = ('entry', 'set_entry', 'is_long', 'is_short',
'sl', 'tp', 'set_sl', 'set_tp')
if item in removed_attrs:
raise AttributeError(f'Strategy.orders.{"/.".join(removed_attrs)} were removed in'
'Backtesting 0.2.0. '
'Use `Order` API instead. See docs.')
raise AttributeError(f"'tuple' object has no attribute {item!r}")
[docs]
class Position:
"""
Currently held asset position, available as
`backtesting.backtesting.Strategy.position` within
`backtesting.backtesting.Strategy.next`.
Can be used in boolean contexts, e.g.
if self.position:
... # we have a position, either long or short
"""
def __init__(self, broker: '_Broker'):
self.__broker = broker
def __bool__(self):
return self.size != 0
@property
def size(self) -> float:
"""Position size in units of asset. Negative if position is short."""
return sum(trade.size for trade in self.__broker.trades)
@property
def pl(self) -> float:
"""Profit (positive) or loss (negative) of the current position in cash units."""
return sum(trade.pl for trade in self.__broker.trades)
@property
def pl_pct(self) -> float:
"""Profit (positive) or loss (negative) of the current position in percent."""
weights = np.abs([trade.size for trade in self.__broker.trades])
weights = weights / weights.sum()
pl_pcts = np.array([trade.pl_pct for trade in self.__broker.trades])
return (pl_pcts * weights).sum()
@property
def is_long(self) -> bool:
"""True if the position is long (position size is positive)."""
return self.size > 0
@property
def is_short(self) -> bool:
"""True if the position is short (position size is negative)."""
return self.size < 0
[docs]
def close(self, portion: float = 1.):
"""
Close portion of position by closing `portion` of each active trade. See `Trade.close`.
"""
for trade in self.__broker.trades:
trade.close(portion)
def __repr__(self):
return f'<Position: {self.size} ({len(self.__broker.trades)} trades)>'
class _OutOfMoneyError(Exception):
pass
[docs]
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].
`filled<https://www.investopedia.com/terms/f/fill.asp>`__
`Good 'Til Canceled<https://www.investopedia.com/terms/g/gtc.asp>`__
"""
def __init__(self, broker: '_Broker',
size: float,
limit_price: Optional[float] = None,
stop_price: Optional[float] = None,
sl_price: Optional[float] = None,
tp_price: Optional[float] = None,
parent_trade: Optional['Trade'] = None,
tag: object = None):
self.__broker = broker
assert size != 0
self.__size = size
self.__limit_price = limit_price
self.__stop_price = stop_price
self.__sl_price = sl_price
self.__tp_price = tp_price
self.__parent_trade = parent_trade
self.__tag = tag
def _replace(self, **kwargs):
for k, v in kwargs.items():
setattr(self, f'_{self.__class__.__qualname__}__{k}', v)
return self
def __repr__(self):
return '<Order {}>'.format(', '.join(f'{param}={round(value, 5)}'
for param, value in (
('size', self.__size),
('limit', self.__limit_price),
('stop', self.__stop_price),
('sl', self.__sl_price),
('tp', self.__tp_price),
('contingent', self.is_contingent),
('tag', self.__tag),
) if value is not None))
[docs]
def cancel(self):
"""Cancel the order."""
self.__broker.orders.remove(self)
trade = self.__parent_trade
if trade:
if self is trade._sl_order:
trade._replace(sl_order=None)
elif self is trade._tp_order:
trade._replace(tp_order=None)
else:
# XXX: https://github.com/kernc/backtesting.py/issues/251#issuecomment-835634984 ???
assert False
# Fields getters
@property
def size(self) -> float:
"""
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.
"""
return self.__size
@property
def limit(self) -> Optional[float]:
"""
Order limit price for `limit orders<https://www.investopedia.com/terms/l/limitorder.asp>`__, or None
for `market orders<https://www.investopedia.com/terms/m/marketorder.asp>`__, which are filled at next
available price.
"""
return self.__limit_price
@property
def stop(self) -> Optional[float]:
"""
Order stop price for
`stop-limit/stop-market order<https://www.investopedia.com/terms/s/stoporder.asp>`__,
otherwise None if no stop was set, or the stop price has already been hit.
"""
return self.__stop_price
@property
def sl(self) -> Optional[float]:
"""
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`.
"""
return self.__sl_price
@property
def tp(self) -> Optional[float]:
"""
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`.
"""
return self.__tp_price
@property
def parent_trade(self):
return self.__parent_trade
@property
def tag(self):
"""
Arbitrary value (such as a string) which, if set, enables tracking
of this order and the associated `Trade` (see `Trade.tag`).
"""
return self.__tag
__pdoc__['Order.parent_trade'] = False
# Extra properties
@property
def is_long(self):
"""True if the order is long (order size is positive)."""
return self.__size > 0
@property
def is_short(self):
"""True if the order is short (order size is negative)."""
return self.__size < 0
@property
def is_contingent(self):
"""
True for `contingent<https://www.investopedia.com/terms/c/contingentorder.asp>`__ orders, i.e.
`OCO<https://www.investopedia.com/terms/o/oco.asp>`__ 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`.
"""
return bool(self.__parent_trade)
[docs]
class Trade:
"""
When an `Order` is filled, it results in an active `Trade`.
Find active trades in `Strategy.trades` and closed, settled trades in `Strategy.closed_trades`.
"""
def __init__(self, broker: '_Broker', size: int, entry_price: float, entry_bar, tag):
self.__broker = broker
self.__size = size
self.__entry_price = entry_price
self.__exit_price: Optional[float] = None
self.__entry_bar: int = entry_bar
self.__exit_bar: Optional[int] = None
self.__sl_order: Optional[Order] = None
self.__tp_order: Optional[Order] = None
self.__tag = tag
def __repr__(self):
return f'<Trade size={self.__size} time={self.__entry_bar}-{self.__exit_bar or ""} ' \
f'price={self.__entry_price}-{self.__exit_price or ""} pl={self.pl:.0f}' \
f'{" tag="+str(self.__tag) if self.__tag is not None else ""}>'
def _replace(self, **kwargs):
for k, v in kwargs.items():
setattr(self, f'_{self.__class__.__qualname__}__{k}', v)
return self
def _copy(self, **kwargs):
return copy(self)._replace(**kwargs)
[docs]
def close(self, portion: float = 1.):
"""Place new `Order` to close `portion` of the trade at next market price."""
assert 0 < portion <= 1, "portion must be a fraction between 0 and 1"
size = copysign(max(1, round(abs(self.__size) * portion)), -self.__size)
order = Order(self.__broker, size, parent_trade=self, tag=self.__tag)
self.__broker.orders.insert(0, order)
# Fields getters
@property
def size(self):
"""Trade size (volume; negative for short trades)."""
return self.__size
@property
def entry_price(self) -> float:
"""Trade entry price."""
return self.__entry_price
@property
def exit_price(self) -> Optional[float]:
"""Trade exit price (or None if the trade is still active)."""
return self.__exit_price
@property
def entry_bar(self) -> int:
"""Candlestick bar index of when the trade was entered."""
return self.__entry_bar
@property
def exit_bar(self) -> Optional[int]:
"""
Candlestick bar index of when the trade was exited
(or None if the trade is still active).
"""
return self.__exit_bar
@property
def tag(self):
"""
A tag value inherited from the `Order` that opened
this trade.
This can be used to track trades and apply conditional
logic / subgroup analysis.
See also `Order.tag`.
"""
return self.__tag
@property
def _sl_order(self):
return self.__sl_order
@property
def _tp_order(self):
return self.__tp_order
# Extra properties
@property
def entry_time(self) -> Union[pd.Timestamp, int]:
"""Datetime of when the trade was entered."""
return self.__broker._data.index[self.__entry_bar]
@property
def exit_time(self) -> Optional[Union[pd.Timestamp, int]]:
"""Datetime of when the trade was exited."""
if self.__exit_bar is None:
return None
return self.__broker._data.index[self.__exit_bar]
@property
def is_long(self):
"""True if the trade is long (trade size is positive)."""
return self.__size > 0
@property
def is_short(self):
"""True if the trade is short (trade size is negative)."""
return not self.is_long
@property
def pl(self):
"""Trade profit (positive) or loss (negative) in cash units."""
price = self.__exit_price or self.__broker.last_price
return self.__size * (price - self.__entry_price)
@property
def pl_pct(self):
"""Trade profit (positive) or loss (negative) in percent."""
price = self.__exit_price or self.__broker.last_price
return copysign(1, self.__size) * (price / self.__entry_price - 1)
@property
def value(self):
"""Trade total value in cash (volume × price)."""
price = self.__exit_price or self.__broker.last_price
return abs(self.__size) * price
# SL/TP management API
@property
def sl(self):
"""
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.
"""
return self.__sl_order and self.__sl_order.stop
@sl.setter
def sl(self, price: float):
self.__set_contingent('sl', price)
@property
def tp(self):
"""
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.
"""
return self.__tp_order and self.__tp_order.limit
@tp.setter
def tp(self, price: float):
self.__set_contingent('tp', price)
def __set_contingent(self, type, price):
assert type in ('sl', 'tp')
assert price is None or 0 < price < np.inf
attr = f'_{self.__class__.__qualname__}__{type}_order'
order: Order = getattr(self, attr)
if order:
order.cancel()
if price:
kwargs = {'stop': price} if type == 'sl' else {'limit': price}
order = self.__broker.new_order(-self.size, trade=self, tag=self.tag, **kwargs)
setattr(self, attr, order)
class _Broker:
def __init__(self, *, data, cash, commission, margin,
trade_on_close, hedging, exclusive_orders, index):
assert 0 < cash, f"cash should be >0, is {cash}"
assert -.1 <= commission < .1, \
("commission should be between -10% "
f"(e.g. market-maker's rebates) and 10% (fees), is {commission}")
assert 0 < margin <= 1, f"margin should be between 0 and 1, is {margin}"
self._data: _Data = data
self._cash = cash
self._commission = commission
self._leverage = 1 / margin
self._trade_on_close = trade_on_close
self._hedging = hedging
self._exclusive_orders = exclusive_orders
self._equity = np.tile(np.nan, len(index))
self.orders: List[Order] = []
self.trades: List[Trade] = []
self.position = Position(self)
self.closed_trades: List[Trade] = []
def __repr__(self):
return f'<Broker: {self._cash:.0f}{self.position.pl:+.1f} ({len(self.trades)} trades)>'
def new_order(self,
size: float,
limit: Optional[float] = None,
stop: Optional[float] = None,
sl: Optional[float] = None,
tp: Optional[float] = None,
tag: object = None,
*,
trade: Optional[Trade] = None):
"""
Argument size indicates whether the order is long or short
"""
size = float(size)
stop = stop and float(stop)
limit = limit and float(limit)
sl = sl and float(sl)
tp = tp and float(tp)
is_long = size > 0
adjusted_price = self._adjusted_price(size)
if is_long:
if not (sl or -np.inf) < (limit or stop or adjusted_price) < (tp or np.inf):
raise ValueError(
"Long orders require: "
f"SL ({sl}) < LIMIT ({limit or stop or adjusted_price}) < TP ({tp})")
else:
if not (tp or -np.inf) < (limit or stop or adjusted_price) < (sl or np.inf):
raise ValueError(
"Short orders require: "
f"TP ({tp}) < LIMIT ({limit or stop or adjusted_price}) < SL ({sl})")
order = Order(self, size, limit, stop, sl, tp, trade, tag)
# Put the new order in the order queue,
# inserting SL/TP/trade-closing orders in-front
if trade:
self.orders.insert(0, order)
else:
# If exclusive orders (each new order auto-closes previous orders/position),
# cancel all non-contingent orders and close all open trades beforehand
if self._exclusive_orders:
for o in self.orders:
if not o.is_contingent:
o.cancel()
for t in self.trades:
t.close()
self.orders.append(order)
return order
@property
def last_price(self) -> float:
""" Price at the last (current) close. """
return self._data.Close[-1]
def _adjusted_price(self, size=None, price=None) -> float:
"""
Long/short `price`, adjusted for commisions.
In long positions, the adjusted price is a fraction higher, and vice versa.
"""
return (price or self.last_price) * (1 + copysign(self._commission, size))
@property
def equity(self) -> float:
return self._cash + sum(trade.pl for trade in self.trades)
@property
def margin_available(self) -> float:
# From https://github.com/QuantConnect/Lean/pull/3768
margin_used = sum(trade.value / self._leverage for trade in self.trades)
return max(0, self.equity - margin_used)
def next(self):
i = self._i = len(self._data) - 1
self._process_orders()
# Log account equity for the equity curve
equity = self.equity
self._equity[i] = equity
# If equity is negative, set all to 0 and stop the simulation
if equity <= 0:
assert self.margin_available <= 0
for trade in self.trades:
self._close_trade(trade, self._data.Close[-1], i)
self._cash = 0
self._equity[i:] = 0
raise _OutOfMoneyError
def _process_orders(self):
data = self._data
open, high, low = data.Open[-1], data.High[-1], data.Low[-1]
prev_close = data.Close[-2]
reprocess_orders = False
# Process orders
for order in list(self.orders): # type: Order
# Related SL/TP order was already removed
if order not in self.orders:
continue
# Check if stop condition was hit
stop_price = order.stop
if stop_price:
is_stop_hit = ((high > stop_price) if order.is_long else (low < stop_price))
if not is_stop_hit:
continue
# > When the stop price is reached, a stop order becomes a market/limit order.
# https://www.sec.gov/fast-answers/answersstopordhtm.html
order._replace(stop_price=None)
# Determine purchase price.
# Check if limit order can be filled.
if order.limit:
is_limit_hit = low < order.limit if order.is_long else high > order.limit
# When stop and limit are hit within the same bar, we pessimistically
# assume limit was hit before the stop (i.e. "before it counts")
is_limit_hit_before_stop = (is_limit_hit and
(order.limit < (stop_price or -np.inf)
if order.is_long
else order.limit > (stop_price or np.inf)))
if not is_limit_hit or is_limit_hit_before_stop:
continue
# stop_price, if set, was hit within this bar
price = (min(stop_price or open, order.limit)
if order.is_long else
max(stop_price or open, order.limit))
else:
# Market-if-touched / market order
price = prev_close if self._trade_on_close else open
price = (max(price, stop_price or -np.inf)
if order.is_long else
min(price, stop_price or np.inf))
# Determine entry/exit bar index
is_market_order = not order.limit and not stop_price
time_index = (self._i - 1) if is_market_order and self._trade_on_close else self._i
# If order is a SL/TP order, it should close an existing trade it was contingent upon
if order.parent_trade:
trade = order.parent_trade
_prev_size = trade.size
# If order.size is "greater" than trade.size, this order is a trade.close()
# order and part of the trade was already closed beforehand
size = copysign(min(abs(_prev_size), abs(order.size)), order.size)
# If this trade isn't already closed (e.g. on multiple `trade.close(.5)` calls)
if trade in self.trades:
self._reduce_trade(trade, price, size, time_index)
assert order.size != -_prev_size or trade not in self.trades
if order in (trade._sl_order,
trade._tp_order):
assert order.size == -trade.size
assert order not in self.orders # Removed when trade was closed
else:
# It's a trade.close() order, now done
assert abs(_prev_size) >= abs(size) >= 1
self.orders.remove(order)
continue
# Else this is a stand-alone trade
# Adjust price to include commission (or bid-ask spread).
# In long positions, the adjusted price is a fraction higher, and vice versa.
adjusted_price = self._adjusted_price(order.size, price)
# If order size was specified proportionally,
# precompute true size in units, accounting for margin and spread/commissions
size = order.size
if -1 < size < 1:
size = copysign(int((self.margin_available * self._leverage * abs(size))
// adjusted_price), size)
# Not enough cash/margin even for a single unit
if not size:
self.orders.remove(order)
continue
assert size == round(size)
need_size = int(size)
if not self._hedging:
# Fill position by FIFO closing/reducing existing opposite-facing trades.
# Existing trades are closed at unadjusted price, because the adjustment
# was already made when buying.
for trade in list(self.trades):
if trade.is_long == order.is_long:
continue
assert trade.size * order.size < 0
# Order size greater than this opposite-directed existing trade,
# so it will be closed completely
if abs(need_size) >= abs(trade.size):
self._close_trade(trade, price, time_index)
need_size += trade.size
else:
# The existing trade is larger than the new order,
# so it will only be closed partially
self._reduce_trade(trade, price, need_size, time_index)
need_size = 0
if not need_size:
break
# If we don't have enough liquidity to cover for the order, cancel it
if abs(need_size) * adjusted_price > self.margin_available * self._leverage:
self.orders.remove(order)
continue
# Open a new trade
if need_size:
self._open_trade(adjusted_price,
need_size,
order.sl,
order.tp,
time_index,
order.tag)
# We need to reprocess the SL/TP orders newly added to the queue.
# This allows e.g. SL hitting in the same bar the order was open.
# See https://github.com/kernc/backtesting.py/issues/119
if order.sl or order.tp:
if is_market_order:
reprocess_orders = True
elif (low <= (order.sl or -np.inf) <= high or
low <= (order.tp or -np.inf) <= high):
warnings.warn(
f"({data.index[-1]}) A contingent SL/TP order would execute in the "
"same bar its parent stop/limit order was turned into a trade. "
"Since we can't assert the precise intra-candle "
"price movement, the affected SL/TP order will instead be executed on "
"the next (matching) price/bar, making the result (of this trade) "
"somewhat dubious. "
"See https://github.com/kernc/backtesting.py/issues/119",
UserWarning)
# Order processed
self.orders.remove(order)
if reprocess_orders:
self._process_orders()
def _reduce_trade(self, trade: Trade, price: float, size: float, time_index: int):
assert trade.size * size < 0
assert abs(trade.size) >= abs(size)
size_left = trade.size + size
assert size_left * trade.size >= 0
if not size_left:
close_trade = trade
else:
# Reduce existing trade ...
trade._replace(size=size_left)
if trade._sl_order:
trade._sl_order._replace(size=-trade.size)
if trade._tp_order:
trade._tp_order._replace(size=-trade.size)
# ... by closing a reduced copy of it
close_trade = trade._copy(size=-size, sl_order=None, tp_order=None)
self.trades.append(close_trade)
self._close_trade(close_trade, price, time_index)
def _close_trade(self, trade: Trade, price: float, time_index: int):
self.trades.remove(trade)
if trade._sl_order:
self.orders.remove(trade._sl_order)
if trade._tp_order:
self.orders.remove(trade._tp_order)
self.closed_trades.append(trade._replace(exit_price=price, exit_bar=time_index))
self._cash += trade.pl
def _open_trade(self, price: float, size: int,
sl: Optional[float], tp: Optional[float], time_index: int, tag):
trade = Trade(self, size, price, time_index, tag)
self.trades.append(trade)
# Create SL/TP (bracket) orders.
# Make sure SL order is created first so it gets adversarially processed before TP order
# in case of an ambiguous tie (both hit within a single bar).
# Note, sl/tp orders are inserted at the front of the list, thus order reversed.
if tp:
trade.tp = tp
if sl:
trade.sl = sl
[docs]
class Backtest:
"""
Backtest a particular (parameterized) strategy
on particular data.
Upon initialization, call method
`backtesting.backtesting.Backtest.run` to run a backtest
instance, or `backtesting.backtesting.Backtest.optimize` to
optimize it.
"""
def __init__(self,
data: pd.DataFrame,
strategy: Type[Strategy],
*,
cash: float = 10_000,
commission: float = .0,
margin: float = 1.,
trade_on_close=False,
hedging=False,
exclusive_orders=False
):
"""
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 `backtesting.backtesting.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<https://www.investopedia.com/terms/n/nfa-compliance-rule-2-43b.asp>`__ 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.
"""
if not (isinstance(strategy, type) and issubclass(strategy, Strategy)):
raise TypeError('`strategy` must be a Strategy sub-type')
if not isinstance(data, pd.DataFrame):
raise TypeError("`data` must be a pandas.DataFrame with columns")
if not isinstance(commission, Number):
raise TypeError('`commission` must be a float value, percent of '
'entry order price')
data = data.copy(deep=False)
# Convert index to datetime index
if (not isinstance(data.index, pd.DatetimeIndex) and
not isinstance(data.index, pd.RangeIndex) and
# Numeric index with most large numbers
(data.index.is_numeric() and
(data.index > pd.Timestamp('1975').timestamp()).mean() > .8)):
try:
data.index = pd.to_datetime(data.index, infer_datetime_format=True)
except ValueError:
pass
if 'Volume' not in data:
data['Volume'] = np.nan
if len(data) == 0:
raise ValueError('OHLC `data` is empty')
if len(data.columns.intersection({'Open', 'High', 'Low', 'Close', 'Volume'})) != 5:
raise ValueError("`data` must be a pandas.DataFrame with columns "
"'Open', 'High', 'Low', 'Close', and (optionally) 'Volume'")
if data[['Open', 'High', 'Low', 'Close']].isnull().values.any():
raise ValueError('Some OHLC values are missing (NaN). '
'Please strip those lines with `df.dropna()` or '
'fill them in with `df.interpolate()` or whatever.')
if np.any(data['Close'] > cash):
warnings.warn('Some prices are larger than initial cash value. Note that fractional '
'trading is not supported. If you want to trade Bitcoin, '
'increase initial cash, or trade μBTC or satoshis instead (GH-134).',
stacklevel=2)
if not data.index.is_monotonic_increasing:
warnings.warn('Data index is not sorted in ascending order. Sorting.',
stacklevel=2)
data = data.sort_index()
if not isinstance(data.index, pd.DatetimeIndex):
warnings.warn('Data index is not datetime. Assuming simple periods, '
'but `pd.DateTimeIndex` is advised.',
stacklevel=2)
self._data: pd.DataFrame = data
self._broker = partial(
_Broker, cash=cash, commission=commission, margin=margin,
trade_on_close=trade_on_close, hedging=hedging,
exclusive_orders=exclusive_orders, index=data.index,
)
self._strategy = strategy
self._results: Optional[pd.Series] = None
[docs]
def run(self, **kwargs) -> pd.Series:
"""
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
Kelly Criterion 0.6134
_strategy SmaCross
_equity_curve Eq...
_trades Size EntryB...
dtype: object
.. warning::
You may obtain different results for different strategy parameters.
E.g. if you use 50- and 200-bar SMA, the trading simulation will
begin on bar 201. The actual length of delay is equal to the lookback
period of the `Strategy.I` indicator which lags the most.
Obviously, this can affect results.
"""
data = _Data(self._data.copy(deep=False))
broker: _Broker = self._broker(data=data)
strategy: Strategy = self._strategy(broker, data, kwargs)
strategy.init()
data._update() # Strategy.init might have changed/added to data.df
# Indicators used in Strategy.next()
indicator_attrs = {attr: indicator
for attr, indicator in strategy.__dict__.items()
if isinstance(indicator, _Indicator)}.items()
# Skip first few candles where indicators are still "warming up"
# +1 to have at least two entries available
start = 1 + max((np.isnan(indicator.astype(float)).argmin(axis=-1).max()
for _, indicator in indicator_attrs), default=0)
# Disable "invalid value encountered in ..." warnings. Comparison
# np.nan >= 3 is not invalid; it's False.
with np.errstate(invalid='ignore'):
for i in range(start, len(self._data)):
# Prepare data and indicators for `next` call
data._set_length(i + 1)
for attr, indicator in indicator_attrs:
# Slice indicator on the last dimension (case of 2d indicator)
setattr(strategy, attr, indicator[..., :i + 1])
# Handle orders processing and broker stuff
try:
broker.next()
except _OutOfMoneyError:
break
# Next tick, a moment before bar close
strategy.next()
else:
# Close any remaining open trades so they produce some stats
for trade in broker.trades:
trade.close()
# Re-run broker one last time to handle orders placed in the last strategy
# iteration. Use the same OHLC values as in the last broker iteration.
if start < len(self._data):
try_(broker.next, exception=_OutOfMoneyError)
# Set data back to full length
# for future `indicator._opts['data'].index` calls to work
data._set_length(len(self._data))
equity = pd.Series(broker._equity).bfill().fillna(broker._cash).values
self._results = compute_stats(
trades=broker.closed_trades,
equity=equity,
ohlc_data=self._data,
risk_free_rate=0.0,
strategy_instance=strategy,
)
return self._results
[docs]
def optimize(self, *,
maximize: Union[str, Callable[[pd.Series], float]] = 'SQN',
method: str = 'grid',
max_tries: Optional[Union[int, float]] = None,
constraint: Optional[Callable[[dict], bool]] = None,
return_heatmap: bool = False,
return_optimization: bool = False,
random_state: Optional[int] = None,
**kwargs) -> Union[pd.Series,
Tuple[pd.Series, pd.Series],
Tuple[pd.Series, pd.Series, dict]]:
"""
Optimize strategy parameters to an optimal combination.
Returns result `pd.Series` of the best run.
`maximize` is a string key from the
`backtesting.backtesting.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<https://google.com/search?q=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<https://scikit-optimize.github.io/stable/auto_examples/bayesian-optimization.html>`__
, 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 `backtesting.lib.plot_heatmaps()`).
If `return_optimization` is True and `method = 'skopt'`,
in addition to result series (and maybe heatmap), return raw
[`scipy.optimize.OptimizeResult`]
`OptimizeResult<https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.OptimizeResult.html>`__ for
further inspection, e.g. with `scikit-optimize<https://scikit-optimize.github.io>`__\
`plotting tools<https://scikit-optimize.github.io/stable/modules/plots.html>`__.
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'.
if not kwargs:
raise ValueError('Need some strategy parameters to optimize')
maximize_key = None
if isinstance(maximize, str):
maximize_key = str(maximize)
stats = self._results if self._results is not None else self.run()
if maximize not in stats:
raise ValueError('`maximize`, if str, must match a key in pd.Series '
'result of backtest.run()')
def maximize(stats: pd.Series, _key=maximize):
return stats[_key]
elif not callable(maximize):
raise TypeError('`maximize` must be str (a field of backtest.run() result '
'Series) or a function that accepts result Series '
'and returns a number; the higher the better')
assert callable(maximize), maximize
have_constraint = bool(constraint)
if constraint is None:
def constraint(_):
return True
elif not callable(constraint):
raise TypeError("`constraint` must be a function that accepts a dict "
"of strategy parameters and returns a bool whether "
"the combination of parameters is admissible or not")
assert callable(constraint), constraint
if return_optimization and method != 'skopt':
raise ValueError("return_optimization=True only valid if method='skopt'")
def _tuple(x):
return x if isinstance(x, Sequence) and not isinstance(x, str) else (x,)
for k, v in kwargs.items():
if len(_tuple(v)) == 0:
raise ValueError(f"Optimization variable '{k}' is passed no "
f"optimization values: {k}={v}")
class AttrDict(dict):
def __getattr__(self, item):
return self[item]
def _grid_size():
size = int(np.prod([len(_tuple(v)) for v in kwargs.values()]))
if size < 10_000 and have_constraint:
size = sum(1 for p in product(*(zip(repeat(k), _tuple(v))
for k, v in kwargs.items()))
if constraint(AttrDict(p)))
return size
def _optimize_grid() -> Union[pd.Series, Tuple[pd.Series, pd.Series]]:
rand = default_rng(random_state).random
grid_frac = (1 if max_tries is None else
max_tries if 0 < max_tries <= 1 else
max_tries / _grid_size())
param_combos = [dict(params) # back to dict so it pickles
for params in (AttrDict(params)
for params in product(*(zip(repeat(k), _tuple(v))
for k, v in kwargs.items())))
if constraint(params) # type: ignore
and rand() <= grid_frac]
if not param_combos:
raise ValueError('No admissible parameter combinations to test')
if len(param_combos) > 300:
warnings.warn(f'Searching for best of {len(param_combos)} configurations.',
stacklevel=2)
heatmap = pd.Series(np.nan,
name=maximize_key,
index=pd.MultiIndex.from_tuples(
[p.values() for p in param_combos],
names=next(iter(param_combos)).keys()))
def _batch(seq):
n = np.clip(int(len(seq) // (os.cpu_count() or 1)), 1, 300)
for i in range(0, len(seq), n):
yield seq[i:i + n]
# Save necessary objects into "global" state; pass into concurrent executor
# (and thus pickle) nothing but two numbers; receive nothing but numbers.
# With start method "fork", children processes will inherit parent address space
# in a copy-on-write manner, achieving better performance/RAM benefit.
backtest_uuid = np.random.random()
param_batches = list(_batch(param_combos))
Backtest._mp_backtests[backtest_uuid] = (self, param_batches, maximize) # type: ignore
try:
# If multiprocessing start method is 'fork' (i.e. on POSIX), use
# a pool of processes to compute results in parallel.
# Otherwise (i.e. on Windos), sequential computation will be "faster".
if mp.get_start_method(allow_none=False) == 'fork':
with ProcessPoolExecutor() as executor:
futures = [executor.submit(Backtest._mp_task, backtest_uuid, i)
for i in range(len(param_batches))]
for future in _tqdm(as_completed(futures), total=len(futures),
desc='Backtest.optimize'):
batch_index, values = future.result()
for value, params in zip(values, param_batches[batch_index]):
heatmap[tuple(params.values())] = value
else:
if os.name == 'posix':
warnings.warn("For multiprocessing support in `Backtest.optimize()` "
"set multiprocessing start method to 'fork'.")
for batch_index in _tqdm(range(len(param_batches))):
_, values = Backtest._mp_task(backtest_uuid, batch_index)
for value, params in zip(values, param_batches[batch_index]):
heatmap[tuple(params.values())] = value
finally:
del Backtest._mp_backtests[backtest_uuid]
best_params = heatmap.idxmax()
if pd.isnull(best_params):
# No trade was made in any of the runs. Just make a random
# run so we get some, if empty, results
stats = self.run(**param_combos[0])
else:
stats = self.run(**dict(zip(heatmap.index.names, best_params)))
if return_heatmap:
return stats, heatmap
return stats
def _optimize_skopt() -> Union[pd.Series,
Tuple[pd.Series, pd.Series],
Tuple[pd.Series, pd.Series, dict]]:
try:
from skopt import forest_minimize
from skopt.callbacks import DeltaXStopper
from skopt.learning import ExtraTreesRegressor
from skopt.space import Categorical, Integer, Real
from skopt.utils import use_named_args
except ImportError:
raise ImportError("Need package 'scikit-optimize' for method='skopt'. "
"pip install scikit-optimize") from None
nonlocal max_tries
max_tries = (200 if max_tries is None else
max(1, int(max_tries * _grid_size())) if 0 < max_tries <= 1 else
max_tries)
dimensions = []
for key, values in kwargs.items():
values = np.asarray(values)
if values.dtype.kind in 'mM': # timedelta, datetime64
# these dtypes are unsupported in skopt, so convert to raw int
# TODO: save dtype and convert back later
values = values.astype(int)
if values.dtype.kind in 'iumM':
dimensions.append(Integer(low=values.min(), high=values.max(), name=key))
elif values.dtype.kind == 'f':
dimensions.append(Real(low=values.min(), high=values.max(), name=key))
else:
dimensions.append(Categorical(values.tolist(), name=key, transform='onehot'))
# Avoid recomputing re-evaluations:
# "The objective has been evaluated at this point before."
# https://github.com/scikit-optimize/scikit-optimize/issues/302
memoized_run = lru_cache()(lambda tup: self.run(**dict(tup)))
# np.inf/np.nan breaks sklearn, np.finfo(float).max breaks skopt.plots.plot_objective
INVALID = 1e300
progress = iter(_tqdm(repeat(None), total=max_tries, desc='Backtest.optimize'))
@use_named_args(dimensions=dimensions)
def objective_function(**params):
next(progress)
# Check constraints
# TODO: Adjust after https://github.com/scikit-optimize/scikit-optimize/pull/971
if not constraint(AttrDict(params)):
return INVALID
res = memoized_run(tuple(params.items()))
value = -maximize(res)
if np.isnan(value):
return INVALID
return value
with warnings.catch_warnings():
warnings.filterwarnings(
'ignore', 'The objective has been evaluated at this point before.')
res = forest_minimize(
func=objective_function,
dimensions=dimensions,
n_calls=max_tries,
base_estimator=ExtraTreesRegressor(n_estimators=20, min_samples_leaf=2),
acq_func='LCB',
kappa=3,
n_initial_points=min(max_tries, 20 + 3 * len(kwargs)),
initial_point_generator='lhs', # 'sobel' requires n_initial_points ~ 2**N
callback=DeltaXStopper(9e-7),
random_state=random_state)
stats = self.run(**dict(zip(kwargs.keys(), res.x)))
output = [stats]
if return_heatmap:
heatmap = pd.Series(dict(zip(map(tuple, res.x_iters), -res.func_vals)),
name=maximize_key)
heatmap.index.names = kwargs.keys()
heatmap = heatmap[heatmap != -INVALID]
heatmap.sort_index(inplace=True)
output.append(heatmap)
if return_optimization:
valid = res.func_vals != INVALID
res.x_iters = list(compress(res.x_iters, valid))
res.func_vals = res.func_vals[valid]
output.append(res)
return stats if len(output) == 1 else tuple(output)
if method == 'grid':
output = _optimize_grid()
elif method == 'skopt':
output = _optimize_skopt()
else:
raise ValueError(f"Method should be 'grid' or 'skopt', not {method!r}")
return output
@staticmethod
def _mp_task(backtest_uuid, batch_index):
bt, param_batches, maximize_func = Backtest._mp_backtests[backtest_uuid]
return batch_index, [maximize_func(stats) if stats['# Trades'] else np.nan
for stats in (bt.run(**params)
for params in param_batches[batch_index])]
_mp_backtests: Dict[float, Tuple['Backtest', List, Callable]] = {}
[docs]
def plot(self, *, results: pd.Series = None, filename=None, plot_width=None,
plot_equity=True, plot_return=False, plot_pl=True,
plot_volume=True, plot_drawdown=False, plot_trades=True,
smooth_equity=False, relative_equity=True,
superimpose: Union[bool, str] = 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
`backtesting.backtesting.Backtest.run` or
`backtesting.backtesting.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 `plot_trades` is `True`, the stretches between trade entries
and trade exits are marked by hash-marked tractor beams.
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<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>`__
, 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_] `TRADES_AGG<lib.html#backtesting.lib.TRADES_AGG>`__.
`resample` can also be a
`Pandas offset string<https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects>`__
, 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.
"""
if results is None:
if self._results is None:
raise RuntimeError('First issue `backtest.run()` to obtain results.')
results = self._results
return plot(
results=results,
df=self._data,
indicators=results._strategy._indicators,
filename=filename,
plot_width=plot_width,
plot_equity=plot_equity,
plot_return=plot_return,
plot_pl=plot_pl,
plot_volume=plot_volume,
plot_drawdown=plot_drawdown,
plot_trades=plot_trades,
smooth_equity=smooth_equity,
relative_equity=relative_equity,
superimpose=superimpose,
resample=resample,
reverse_indicators=reverse_indicators,
show_legend=show_legend,
open_browser=open_browser)
