code: def adjust_returns_for_slippage(returns, positions, transactions,
                                  slippage_bps):
      
  
      slippage = 0.0001 * slippage_bps
      portfolio_value = positions.sum(axis=1)
      pnl = portfolio_value * returns
      traded_value = get_txn_vol(transactions).txn_volume
      slippage_dollars = traded_value * slippage
      adjusted_pnl = pnl.add(-slippage_dollars, fill_value=0)
      adjusted_returns = returns * adjusted_pnl / pnl
  
      return adjusted_returns
docs: Apply a slippage penalty for every dollar traded.
  
      Parameters
      ----------
      returns : pd.Series
          Daily returns of the strategy, noncumulative.
           - See full explanation in create_full_tear_sheet.
      positions : pd.DataFrame
          Daily net position values.
           - See full explanation in create_full_tear_sheet.
      transactions : pd.DataFrame
          Prices and amounts of executed trades. One row per trade.
           - See full explanation in create_full_tear_sheet.
      slippage_bps: int/float
          Basis points of slippage to apply.
  
      Returns
      -------
      pd.Series
          Time series of daily returns, adjusted for slippage.