Nan as result

[savioz]

Expected Behavior

I passed 3 buy orders and the result shows only NAN

<Order size=0.1, sl=35376.898, tp=37611.2284, contingent=0>
<Order size=0.1, sl=34925.7335, tp=37131.5693, contingent=0>
<Order size=0.1, sl=35910.0, tp=38178.0, contingent=0>

Start                     2021-07-27 00:00:00
End                       2021-07-28 08:25:00
Duration                      1 days 08:25:00
Exposure Time [%]                         0.0
Equity Final [$]                     100000.0
Equity Peak [$]                      100000.0
Return [%]                                0.0
Buy & Hold Return [%]                6.744267
Return (Ann.) [%]                         0.0
Volatility (Ann.) [%]                     NaN
Sharpe Ratio                              NaN
Sortino Ratio                             NaN
Calmar Ratio                              NaN
Max. Drawdown [%]                        -0.0
Avg. Drawdown [%]                         NaN
Max. Drawdown Duration                    NaN
Avg. Drawdown Duration                    NaN
# Trades                                    0
Win Rate [%]                              NaN
Best Trade [%]                            NaN
Worst Trade [%]                           NaN
Avg. Trade [%]                            NaN
Max. Trade Duration                       NaN
Avg. Trade Duration                       NaN
Profit Factor                             NaN
Expectancy [%]                            NaN
SQN                                       NaN
_strategy                              System

• Backtesting version: 0.?.?

[kernc]

Can you provide sample code in order for someone to be able to reproduce the issue?

[savioz]

I am using anaconda python

my main function. :

df2 = pd.read_excel("test.xlsx")

df2 = df2.rename(columns={"open": "Open", "high": "High",
"low": "Low", "close": "Close", "v": "Volume"})

bt = Backtest(df2, Strat1,
cash=100000, commission=.003,
exclusive_orders=True)

output = bt.run()

---- and the strategy -----

class Strat1(Strategy):
def next(self):
psar = self.data.psar[-1]
ppsar = self.data.psar[-2]
price = self.data.Close[-1]
pprice = self.data.Close[-2]

  tpPercentage = 5 #3 
  slPercentage = 5 #5
  if ppsar > pprice and psar < price:
                     tp = price + (price * tpPercentage/100)  
                      sl = price - (price * slPercentage/100) 
                      p = self.buy(size=0.1, sl=sl, tp=tp)

[kernc]

cash=100000
self.buy(size=0.1, ...) → 10_000
price ~= 35k

Can't buy any whole units of bitcoin with just $10k.