Modify function to use percentage risk instead of fixed amount-CodePudding

How do I modify this function to use a percentage risk instead of current fixed risk_per_trade of 50?

I would like to start with 1000 and use 5 percent.

Function to modify:

def tradingStats(win_loss_series,risk_per_trade):
    df = pd.DataFrame(win_loss_series, columns=["win"])
    df.loc[df["win"] == True, "win_loss_amount"] = risk_per_trade
    df.loc[df["win"] == False, "win_loss_amount"] = -risk_per_trade
    df["Cumulative"] = df.win_loss_amount.cumsum()
    
    return df

Args the updated function should accept:

def tradingStats(win_loss_series,risk_percent_per_trade=5,starting_amount=1000):

Example data

np.random.seed(9)
win_loss_series = pd.Series(
    np.random.choice([True,False], 20),
    index=pd.date_range("2022-01-01", "2022-01-30", freq="B")
)

tradingStats(win_loss_series,50).head(8)

    win win_loss_amount Cumulative
2022-01-03  True    50.0    50.0
2022-01-04  True    50.0    100.0
2022-01-05  True    50.0    150.0
2022-01-06  False   -50.0   100.0
2022-01-07  True    50.0    150.0
2022-01-10  True    50.0    200.0
2022-01-11  False   -50.0   150.0
2022-01-12  True    50.0    200.0

Ideally the Cumulative gains would be compounded but I can ask in another question if it's too difficult.

CodePudding user response：

Just change the column to a multiplier rather than an additive amount, and use cumprod().

e.g.

def tradingStats(win_loss_series, risk_percent_per_trade=5, starting_amount=1000):
    df = pd.DataFrame(win_loss_series, columns=["win"])
    df.loc[df["win"] == True, "win_loss_mult"] = 1   risk_percent_per_trade / 100
    df.loc[df["win"] == False, "win_loss_mult"] = 1 - risk_percent_per_trade / 100
    df["Cumulative"] = df["win_loss_mult"].cumprod() * starting_amount
    
    return df

Output:

              win  win_loss_mult   Cumulative
2022-01-03   True           1.05  1050.000000
2022-01-04   True           1.05  1102.500000
2022-01-05   True           1.05  1157.625000
2022-01-06  False           0.95  1099.743750
2022-01-07   True           1.05  1154.730938
2022-01-10   True           1.05  1212.467484
2022-01-11  False           0.95  1151.844110
2022-01-12   True           1.05  1209.436316