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The Position object represents the aggregate of all currently active trades. Access it via Strategy.position inside next(). 
from backtesting import Strategy

class MyStrategy(Strategy):
    def next(self):
        if self.position:
            print(self.position.size, self.position.pl)
Position evaluates to False in boolean context when there are no active trades (flat), and True when any trade is open. 
if self.position:        # True if long or short
    ...
if not self.position:   # True if flat
    ...

​
Properties

​
size
float
Net position size: the sum of all active trade sizes.
  • Positive when net long.
  • Negative when net short.
  • 0.0 when flat (no open trades).
print(self.position.size)   # e.g. 15.0 (long) or -10.0 (short)
When hedging=True is set on Backtest, long and short trades coexist. position.size is their net sum and may not reflect total exposure.
​
pl
float
Total unrealized profit (positive) or loss (negative) of the current position in cash units. Equivalent to the sum of trade.pl across all active trades.
if self.position.pl < -500:
    self.position.close()   # cut losses
​
pl_pct
float
P&L as a percentage relative to total invested value (sum of entry_price × abs(size) for each active trade).Returns 0 when there are no open trades.
​
is_long
bool
True when the net position is long (i.e. size > 0).
​
is_short
bool
True when the net position is short (i.e. size < 0).

​
Methods

​
Position.close()

def close(portion: float = 1.0) -> None
Close portion of the current position by calling Trade.close(portion) on every active trade.
​
portion
float
default:"1.0"
Fraction of each trade to close. Must satisfy 0 < portion <= 1.
  • 1.0 (default) closes all active trades entirely.
  • 0.5 closes 50% of each trade.
self.position.close()      # exit the entire position
self.position.close(0.5)   # halve the position
position.close() closes each individual trade proportionally. If you need finer control — for example, closing only long trades — iterate over self.trades and call trade.close() selectively.

​
Example

The following example uses Position to guard entries, monitor P&L, and exit on a signal or drawdown threshold: 
from backtesting import Backtest, Strategy
from backtesting.lib import crossover
from backtesting.test import GOOG
import talib

class PositionExample(Strategy):
    sma_period = 20
    max_loss = -300   # max acceptable loss in cash

    def init(self):
        self.sma = self.I(talib.SMA, self.data.Close, self.sma_period)

    def next(self):
        # Only enter when flat
        if not self.position:
            if crossover(self.data.Close, self.sma):
                self.buy()
        else:
            # Report current state
            print(
                f"Position size: {self.position.size}, "
                f"P&L: {self.position.pl:.2f} "
                f"({self.position.pl_pct:.2%})"
            )

            # Exit on signal reversal
            if crossover(self.sma, self.data.Close):
                self.position.close()

            # Emergency exit if loss exceeds threshold
            elif self.position.pl < self.max_loss:
                self.position.close()

            # Partial close: lock in half profits when up 5%
            elif self.position.pl_pct >= 0.05:
                self.position.close(0.5)

bt = Backtest(GOOG, PositionExample, cash=10_000, commission=0.002)
stats = bt.run()
print(stats[['Return [%]', 'Win Rate [%]', '# Trades']])

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