How to compute expectancy in a dataframe across rows

Question

I have a dataframe that contains day, symbol, strategy, and pnl. I want to analyze and compare pnl in a couple of ways.

I’d like to get the win-rate & expectancy when grouped by symbol and strategy. So I’ve done this:

def stats(s):
    winrate = s['isWinner']['count'] / (s['isWinner']['count'] + s['isLoser']['count'])
    expectancy = s['isWinner']['mean'] * winrate - s['isLoser']['mean'] * (1.0 - winrate)

df["isWinner"] = df['pnl'] >= 0
df["isLoser"] = df['pnl'] < 0
df2 = df.groupby(['day', 'symbol', 'strategy', 'isWinner']).agg({'pnl': ['count', 'mean', 'std', 'min', 'max']})
df2.groupby(['day', 'symbol', 'strategy']).agg(stats)

Apparently, I can’t do s['isWinner'] in the stats function. What am I doing wrong?

Once the stats function works, how do I add winrate and expectancy to df2?

Am I going about this the right way? Is it necessary to create df2 from df, or is there a better way?

Asked By: greymatter

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Source

Answer 1

I’m sure there is a more pythonic way to do this, but it works.

    df["isWinner"] = df['pnl'] >= 0
    df["isLoser"] = df['pnl'] < 0

    grouped = df.groupby(['strategy'])
    stats = pd.DataFrame()
    for name, grp in grouped:
      wincount = grp['isWinner'].values.sum()
      loscount = grp['isLoser'].values.sum()
      winrate = wincount / (wincount+loscount)
      profits = grp[grp['isWinner']]['pnl'].sum()
      losses = grp[grp['isLoser']]['pnl'].sum()
      avgwin = profits/wincount
      avglos = losses/loscount
      expectancy = winrate * avgwin + (1.0 - winrate) * avglos
      row = {'name': name, 'wincount': wincount, 'losscount': loscount, 'winrate': winrate, 'profits': profits, 'losses': losses, 'avgwin': avgwin, 'avgloss': avglos, 'expectancy': expectancy}
      stats = stats.append(row, ignore_index=True)

    stats

Results:

index	name	wincount	losscount	winrate	profits	losses	avgwin	avgloss	expectancy
1	Follow outside candles OPTIONS Filter 15	93.0	105.0	0.4696969696969697	36898.0	-20096.0	396.752688172043	-191.3904761904762	84.85858585858587
6	Scalp outside candles OPTIONS	11.0	20.0	0.3548387096774194	3409.0	-2971.0	309.90909090909093	-148.55	14.129032258064527
0	Follow outside candles OPTIONS	650.0	980.0	0.3987730061349693	200595.0	-178813.0	308.60769230769233	-182.46224489795918	13.36319018404906
10	Scalp outside v5	535.0	886.0	0.3764954257565095	125250.0	-108215.0	234.11214953271028	-122.13882618510158	11.988036593947925
5	Outside v4	151.0	163.0	0.48089171974522293	6257.0	-5105.0	41.437086092715234	-31.319018404907975	3.6687898089171966
4	Outside v3	110.0	172.0	0.3900709219858156	7813.0	-6852.0	71.02727272727273	-39.83720930232558	3.4078014184397176
2	Outside v1	113.0	151.0	0.42803030303030304	10498.0	-9790.0	92.90265486725664	-64.83443708609272	2.68181818181818
13	Scalp outside v8	607.0	729.0	0.45434131736526945	79443.0	-79559.0	130.87808896210873	-109.13443072702331	-0.08682634730538297
15	Scalp v2	78.0	103.0	0.430939226519337	1702.0	-1748.0	21.82051282051282	-16.97087378640777	-0.25414364640883846
3	Outside v2	81.0	117.0	0.4090909090909091	7603.0	-7773.0	93.8641975308642	-66.43589743589743	-0.8585858585858475
14	Scalp v1	47.0	53.0	0.47	833.0	-1402.0	17.72340425531915	-26.452830188679247	-5.690000000000001
7	Scalp outside v2	87.0	124.0	0.41232227488151657	18476.0	-20010.0	212.367816091954	-161.3709677419355	-7.270142180094808
8	Scalp outside v3	66.0	90.0	0.4230769230769231	9255.0	-10768.0	140.22727272727272	-119.64444444444445	-9.698717948717949
12	Scalp outside v7	27.0	52.0	0.34177215189873417	4015.0	-5784.0	148.7037037037037	-111.23076923076923	-22.392405063291136
11	Scalp outside v6	29.0	60.0	0.3258426966292135	6816.0	-8878.0	235.0344827586207	-147.96666666666667	-23.168539325842687
9	Scalp outside v4	48.0	104.0	0.3157894736842105	8015.0	-11622.0	166.97916666666666	-111.75	-23.730263157894747

Answered By: greymatter

How to compute expectancy in a dataframe across rows

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