Optimize with BayesOpt¶

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Installation¶

In [ ]:

!pip install bayesian-optimization

!pip install bayesian-optimization

Sample Strategy¶

In [1]:

# import talib.abstract as ta

from lettrade import DataFeed, Strategy, indicator as i
from lettrade.exchange.backtest import ForexBackTestAccount, let_backtest
from lettrade.indicator.vendor.qtpylib import inject_indicators

inject_indicators()


class SmaCross(Strategy):
    ema1_window = 9
    ema2_window = 21

    def indicators(self, df: DataFeed):
        # df["ema1"] = ta.EMA(df, timeperiod=self.ema1_window)
        # df["ema2"] = ta.EMA(df, timeperiod=self.ema2_window)
        df["ema1"] = df.close.ema(window=self.ema1_window)
        df["ema2"] = df.close.ema(window=self.ema2_window)

        df["signal_ema_crossover"] = i.crossover(df.ema1, df.ema2)
        df["signal_ema_crossunder"] = i.crossunder(df.ema1, df.ema2)

    def next(self, df: DataFeed):
        if len(self.orders) > 0 or len(self.positions) > 0:
            return

        if df.l.signal_ema_crossover[-1]:
            price = df.l.close[-1]
            self.buy(size=0.1, sl=price - 0.001, tp=price + 0.001)
        elif df.l.signal_ema_crossunder[-1]:
            price = df.l.close[-1]
            self.sell(size=0.1, sl=price + 0.001, tp=price - 0.001)


lt = let_backtest(
    strategy=SmaCross,
    datas="example/data/data/EURUSD_5m-0_10000.csv",
    account=ForexBackTestAccount,
    # plotter=None,
)

# import talib.abstract as ta from lettrade import DataFeed, Strategy, indicator as i from lettrade.exchange.backtest import ForexBackTestAccount, let_backtest from lettrade.indicator.vendor.qtpylib import inject_indicators inject_indicators() class SmaCross(Strategy): ema1_window = 9 ema2_window = 21 def indicators(self, df: DataFeed): # df["ema1"] = ta.EMA(df, timeperiod=self.ema1_window) # df["ema2"] = ta.EMA(df, timeperiod=self.ema2_window) df["ema1"] = df.close.ema(window=self.ema1_window) df["ema2"] = df.close.ema(window=self.ema2_window) df["signal_ema_crossover"] = i.crossover(df.ema1, df.ema2) df["signal_ema_crossunder"] = i.crossunder(df.ema1, df.ema2) def next(self, df: DataFeed): if len(self.orders) > 0 or len(self.positions) > 0: return if df.l.signal_ema_crossover[-1]: price = df.l.close[-1] self.buy(size=0.1, sl=price - 0.001, tp=price + 0.001) elif df.l.signal_ema_crossunder[-1]: price = df.l.close[-1] self.sell(size=0.1, sl=price + 0.001, tp=price - 0.001) lt = let_backtest( strategy=SmaCross, datas="example/data/data/EURUSD_5m-0_10000.csv", account=ForexBackTestAccount, # plotter=None, )

Optimize¶

In [2]:

from bayes_opt import BayesianOptimization

lettrade_model = lt.optimize_model()


def train_model(**params):
    params = {
        "ema1_window": int(params["ema1_window"]),
        "ema2_window": int(params["ema2_window"]),
    }
    # Model
    result = lettrade_model(params)

    # Score
    return result["equity"]


pbounds = {"ema1_window": (5, 25), "ema2_window": (10, 50)}

optimizer = BayesianOptimization(
    f=train_model,
    pbounds=pbounds,
    random_state=1,
)

optimizer.maximize(
    init_points=2,
    n_iter=200,
)

from bayes_opt import BayesianOptimization lettrade_model = lt.optimize_model() def train_model(**params): params = { "ema1_window": int(params["ema1_window"]), "ema2_window": int(params["ema2_window"]), } # Model result = lettrade_model(params) # Score return result["equity"] pbounds = {"ema1_window": (5, 25), "ema2_window": (10, 50)} optimizer = BayesianOptimization( f=train_model, pbounds=pbounds, random_state=1, ) optimizer.maximize( init_points=2, n_iter=200, )

|   iter    |  target   | ema1_w... | ema2_w... |
-------------------------------------------------
| 1         | 9.917e+03 | 13.34     | 38.81     |
| 2         | 9.868e+03 | 5.002     | 22.09     |
| 3         | 9.916e+03 | 13.3      | 39.0      |
| 4         | 9.949e+03 | 20.15     | 34.13     |
| 5         | 9.967e+03 | 25.0      | 26.44     |
| 6         | 1e+04     | 25.0      | 13.15     |
| 7         | 1.004e+04 | 18.64     | 10.0      |
| 8         | 1.013e+04 | 12.01     | 10.03     |
| 9         | 9.983e+03 | 6.235     | 10.0      |
| 10        | 1e+04     | 13.69     | 13.61     |
| 11        | 1.018e+04 | 14.33     | 10.0      |
| 12        | 1e+04     | 25.0      | 50.0      |
| 13        | 9.901e+03 | 5.0       | 50.0      |
| 14        | 1.011e+04 | 15.78     | 10.1      |
| 15        | 9.925e+03 | 17.32     | 50.0      |
| 16        | 9.985e+03 | 25.0      | 42.48     |
| 17        | 1.015e+04 | 13.69     | 10.84     |
| 18        | 9.865e+03 | 5.0       | 34.04     |
| 19        | 1.003e+04 | 20.96     | 19.6      |
| 20        | 9.955e+03 | 16.25     | 24.79     |
| 21        | 9.954e+03 | 24.96     | 19.59     |
| 22        | 1.015e+04 | 13.31     | 10.13     |
| 23        | 9.895e+03 | 9.931     | 11.94     |
| 24        | 1.016e+04 | 19.26     | 14.34     |
| 25        | 1.016e+04 | 18.31     | 16.36     |
| 26        | 1.014e+04 | 20.69     | 15.96     |
| 27        | 9.806e+03 | 16.24     | 18.66     |
| 28        | 1.005e+04 | 17.23     | 14.24     |
| 29        | 1.009e+04 | 21.23     | 12.96     |
| 30        | 9.907e+03 | 5.0       | 42.3      |
| 31        | 1.016e+04 | 19.26     | 15.78     |
| 32        | 9.995e+03 | 12.4      | 30.25     |
| 33        | 9.909e+03 | 19.23     | 42.99     |
| 34        | 9.914e+03 | 24.94     | 36.65     |
| 35        | 9.868e+03 | 11.44     | 46.76     |
| 36        | 1.003e+04 | 22.93     | 10.0      |
| 37        | 9.866e+03 | 9.878     | 26.32     |
| 38        | 9.897e+03 | 19.49     | 28.76     |
| 39        | 1.012e+04 | 19.53     | 17.49     |
| 40        | 1.009e+04 | 23.06     | 15.94     |
| 41        | 9.936e+03 | 5.0       | 15.73     |
| 42        | 9.977e+03 | 14.91     | 33.4      |
| 43        | 1.001e+04 | 24.98     | 46.12     |
| 44        | 9.881e+03 | 25.0      | 31.18     |
| 45        | 1e+04     | 19.39     | 12.65     |
| 46        | 1.014e+04 | 21.42     | 14.65     |
| 47        | 9.965e+03 | 10.51     | 33.99     |
| 48        | 9.984e+03 | 21.67     | 48.07     |
| 49        | 1.014e+04 | 20.15     | 14.62     |
| 50        | 9.976e+03 | 21.58     | 23.64     |
| 51        | 9.836e+03 | 5.0       | 28.46     |
| 52        | 1.016e+04 | 18.5      | 15.23     |
| 53        | 9.911e+03 | 18.6      | 38.04     |
| 54        | 9.883e+03 | 9.621     | 18.93     |
| 55        | 1.014e+04 | 22.76     | 13.63     |
| 56        | 9.945e+03 | 8.623     | 38.71     |
| 57        | 1.016e+04 | 14.99     | 11.6      |
| 58        | 1.018e+04 | 14.65     | 10.65     |
| 59        | 9.966e+03 | 15.23     | 29.0      |
| 60        | 1.008e+04 | 21.67     | 17.3      |
| 61        | 1.016e+04 | 14.47     | 11.02     |
| 62        | 1.007e+04 | 15.97     | 11.83     |
| 63        | 1.012e+04 | 24.97     | 23.21     |
| 64        | 1.003e+04 | 23.76     | 22.16     |
| 65        | 9.973e+03 | 15.12     | 44.06     |
| 66        | 9.966e+03 | 9.791     | 42.62     |
| 67        | 9.918e+03 | 9.419     | 50.0      |
| 68        | 1.012e+04 | 25.0      | 10.0      |
| 69        | 1.015e+04 | 23.23     | 12.06     |
| 70        | 1.014e+04 | 24.41     | 11.25     |
| 71        | 9.856e+03 | 5.374     | 46.14     |
| 72        | 9.926e+03 | 5.0       | 38.36     |
| 73        | 1e+04     | 24.28     | 24.27     |
| 74        | 9.934e+03 | 22.49     | 40.17     |
| 75        | 9.964e+03 | 22.47     | 44.84     |
| 76        | 1.016e+04 | 19.17     | 16.67     |
| 77        | 1.016e+04 | 19.19     | 14.94     |
| 78        | 1.018e+04 | 14.81     | 10.12     |
| 79        | 1.015e+04 | 25.0      | 22.11     |
| 80        | 9.967e+03 | 13.68     | 49.86     |
| 81        | 9.806e+03 | 12.62     | 22.98     |
| 82        | 9.965e+03 | 18.92     | 22.54     |
| 83        | 1.016e+04 | 18.71     | 16.05     |
| 84        | 9.985e+03 | 17.73     | 46.57     |
| 85        | 1.011e+04 | 17.65     | 15.86     |
| 86        | 1e+04     | 5.0       | 12.38     |
| 87        | 1.016e+04 | 18.48     | 17.14     |
| 88        | 1.011e+04 | 15.09     | 10.82     |
| 89        | 1.005e+04 | 15.0      | 12.66     |
| 90        | 9.906e+03 | 8.988     | 30.96     |
| 91        | 9.913e+03 | 11.58     | 15.76     |
| 92        | 1.002e+04 | 25.0      | 16.26     |
| 93        | 9.957e+03 | 17.55     | 32.0      |
| 94        | 9.967e+03 | 15.59     | 36.06     |
| 95        | 9.97e+03  | 16.1      | 41.05     |
| 96        | 9.994e+03 | 20.59     | 50.0      |
| 97        | 1.009e+04 | 22.07     | 11.7      |
| 98        | 9.896e+03 | 12.69     | 42.6      |
| 99        | 1.015e+04 | 22.56     | 14.74     |
| 100       | 9.996e+03 | 19.65     | 25.56     |
| 101       | 9.875e+03 | 5.056     | 18.74     |
| 102       | 1.016e+04 | 21.75     | 15.65     |
| 103       | 9.936e+03 | 11.08     | 36.56     |
| 104       | 9.928e+03 | 15.1      | 47.03     |
| 105       | 9.937e+03 | 22.47     | 27.76     |
| 106       | 1.015e+04 | 12.15     | 11.33     |
| 107       | 1e+04     | 12.87     | 12.14     |
| 108       | 1.017e+04 | 11.56     | 10.86     |
| 109       | 1e+04     | 10.74     | 10.01     |
| 110       | 1.013e+04 | 12.21     | 10.64     |
| 111       | 9.935e+03 | 13.48     | 27.04     |
| 112       | 9.9e+03   | 23.03     | 34.18     |
| 113       | 1e+04     | 19.11     | 19.18     |
| 114       | 1.002e+04 | 8.382     | 22.81     |
| 115       | 9.886e+03 | 6.562     | 24.92     |
| 116       | 9.825e+03 | 7.853     | 15.11     |
| 117       | 1e+04     | 22.88     | 49.91     |
| 118       | 9.995e+03 | 25.0      | 39.75     |
| 119       | 1e+04     | 11.48     | 11.48     |
| 120       | 1.01e+04  | 23.88     | 14.56     |
| 121       | 1.001e+04 | 20.62     | 10.0      |
| 122       | 1.012e+04 | 23.5      | 11.04     |
| 123       | 1.007e+04 | 17.05     | 10.03     |
| 124       | 1e+04     | 15.01     | 15.67     |
| 125       | 1.016e+04 | 22.51     | 12.73     |
| 126       | 9.885e+03 | 8.067     | 35.43     |
| 127       | 1e+04     | 12.62     | 32.63     |
| 128       | 9.906e+03 | 8.381     | 44.9      |
| 129       | 1.015e+04 | 23.46     | 12.91     |
| 130       | 1.004e+04 | 24.94     | 47.97     |
| 131       | 1.001e+04 | 22.79     | 18.66     |
| 132       | 9.925e+03 | 20.04     | 46.19     |
| 133       | 9.948e+03 | 21.31     | 31.29     |
| 134       | 1.002e+04 | 18.45     | 13.93     |
| 135       | 1.013e+04 | 20.43     | 16.91     |
| 136       | 9.919e+03 | 21.52     | 37.3      |
| 137       | 9.987e+03 | 12.79     | 19.04     |
| 138       | 9.876e+03 | 7.708     | 41.31     |
| 139       | 1.001e+04 | 17.17     | 27.0      |
| 140       | 9.996e+03 | 13.15     | 35.0      |
| 141       | 1.012e+04 | 25.0      | 10.88     |
| 142       | 9.936e+03 | 17.67     | 34.78     |
| 143       | 1.011e+04 | 24.29     | 10.34     |
| 144       | 9.976e+03 | 14.56     | 31.19     |
| 145       | 1.012e+04 | 20.1      | 13.82     |
| 146       | 1.004e+04 | 25.0      | 21.26     |
| 147       | 9.904e+03 | 16.2      | 22.08     |
| 148       | 1.015e+04 | 21.91     | 13.84     |
| 149       | 1.016e+04 | 24.05     | 12.11     |
| 150       | 1e+04     | 21.41     | 21.37     |
| 151       | 9.993e+03 | 23.54     | 47.39     |
| 152       | 9.906e+03 | 7.181     | 12.03     |
| 153       | 1.007e+04 | 21.42     | 16.27     |
| 154       | 1.007e+04 | 20.82     | 18.08     |
| 155       | 1.016e+04 | 19.94     | 16.25     |
| 156       | 1.008e+04 | 22.03     | 15.09     |
| 157       | 1.014e+04 | 20.67     | 15.36     |
| 158       | 1.01e+04  | 23.05     | 14.53     |
| 159       | 1.016e+04 | 17.44     | 16.96     |
| 160       | 1e+04     | 16.59     | 16.37     |
| 161       | 1e+04     | 17.85     | 17.68     |
| 162       | 1.014e+04 | 21.02     | 14.0      |
| 163       | 9.826e+03 | 5.958     | 31.33     |
| 164       | 9.863e+03 | 7.755     | 20.98     |
| 165       | 1.005e+04 | 17.71     | 11.35     |
| 166       | 1.007e+04 | 25.0      | 14.83     |
| 167       | 9.926e+03 | 7.685     | 48.04     |
| 168       | 9.946e+03 | 10.83     | 40.3      |
| 169       | 9.975e+03 | 22.34     | 42.59     |
| 170       | 9.929e+03 | 19.65     | 40.36     |
| 171       | 9.967e+03 | 15.87     | 38.54     |
| 172       | 1.016e+04 | 14.38     | 11.84     |
| 173       | 9.845e+03 | 10.82     | 28.92     |
| 174       | 9.973e+03 | 24.89     | 44.26     |
| 175       | 9.919e+03 | 25.0      | 28.61     |
| 176       | 1.002e+04 | 23.05     | 17.24     |
| 177       | 9.956e+03 | 9.801     | 23.78     |
| 178       | 9.898e+03 | 17.17     | 44.44     |
| 179       | 9.934e+03 | 19.25     | 48.53     |
| 180       | 1.016e+04 | 17.82     | 16.76     |
| 181       | 9.986e+03 | 13.54     | 16.94     |
| 182       | 9.926e+03 | 21.92     | 25.56     |
| 183       | 1.018e+04 | 14.04     | 10.42     |
| 184       | 9.95e+03  | 11.69     | 50.0      |
| 185       | 9.956e+03 | 17.13     | 29.51     |
| 186       | 1.014e+04 | 24.79     | 11.81     |
| 187       | 1.003e+04 | 18.21     | 24.85     |
| 188       | 1.004e+04 | 24.59     | 22.62     |
| 189       | 9.954e+03 | 22.9      | 21.09     |
| 190       | 9.954e+03 | 7.6       | 28.03     |
| 191       | 9.945e+03 | 19.62     | 20.75     |
| 192       | 1.016e+04 | 19.84     | 15.49     |
| 193       | 9.962e+03 | 20.57     | 11.62     |
| 194       | 1.003e+04 | 11.15     | 20.7      |
| 195       | 9.975e+03 | 11.17     | 31.96     |
| 196       | 1e+04     | 15.72     | 14.23     |
| 197       | 1.008e+04 | 9.859     | 21.83     |
| 198       | 1.015e+04 | 22.35     | 14.24     |
| 199       | 9.981e+03 | 13.34     | 45.09     |
| 200       | 1.014e+04 | 22.14     | 13.12     |
| 201       | 9.847e+03 | 13.78     | 20.61     |
| 202       | 9.916e+03 | 8.272     | 10.01     |
=================================================

In [3]:

optimizer.max

optimizer.max

Out[3]:

{'target': 10176.96,
 'params': {'ema1_window': 14.332034902880366, 'ema2_window': 10.0}}

Plot¶

In [4]:

lt.plotter.heatmap(x="ema1_window", y="ema2_window", z="equity")

lt.plotter.heatmap(x="ema1_window", y="ema2_window", z="equity")

In [5]:

lt.plotter.contour(x="ema1_window", y="ema2_window", z="equity")

lt.plotter.contour(x="ema1_window", y="ema2_window", z="equity")

Plot plotly¶

Init Plotly environment¶

In [6]:

import plotly.io as pio

pio.renderers.default = "notebook"
pio.templates.default = "plotly_dark"

import plotly.io as pio pio.renderers.default = "notebook" pio.templates.default = "plotly_dark"

In [7]:

optimizer.res

optimizer.res

Out[7]:

[{'target': 9917.18,
  'params': {'ema1_window': 13.34044009405148,
   'ema2_window': 38.81297973768632}},
 {'target': 9868.46,
  'params': {'ema1_window': 5.002287496346898,
   'ema2_window': 22.09330290527359}},
 {'target': 9916.34,
  'params': {'ema1_window': 13.3001407276959,
   'ema2_window': 39.00039537269421}},
 {'target': 9949.32,
  'params': {'ema1_window': 20.150617901155936,
   'ema2_window': 34.129567895477145}},
 {'target': 9966.86,
  'params': {'ema1_window': 25.0, 'ema2_window': 26.43865565208876}},
 {'target': 10004.82,
  'params': {'ema1_window': 25.0, 'ema2_window': 13.152876659642798}},
 {'target': 10042.6,
  'params': {'ema1_window': 18.637708287319818, 'ema2_window': 10.0}},
 {'target': 10131.88,
  'params': {'ema1_window': 12.008472233635572,
   'ema2_window': 10.0274533068913}},
 {'target': 9983.02,
  'params': {'ema1_window': 6.2345994038838395, 'ema2_window': 10.0}},
 {'target': 10000.0,
  'params': {'ema1_window': 13.688189977287218,
   'ema2_window': 13.60562993820364}},
 {'target': 10176.96,
  'params': {'ema1_window': 14.332034902880366, 'ema2_window': 10.0}},
 {'target': 10002.76, 'params': {'ema1_window': 25.0, 'ema2_window': 50.0}},
 {'target': 9900.52, 'params': {'ema1_window': 5.0, 'ema2_window': 50.0}},
 {'target': 10113.68,
  'params': {'ema1_window': 15.77634291874924,
   'ema2_window': 10.095666359342271}},
 {'target': 9925.06,
  'params': {'ema1_window': 17.317919291899347, 'ema2_window': 50.0}},
 {'target': 9984.86,
  'params': {'ema1_window': 25.0, 'ema2_window': 42.478422889764204}},
 {'target': 10146.74,
  'params': {'ema1_window': 13.686433812325918,
   'ema2_window': 10.841210837456936}},
 {'target': 9864.66,
  'params': {'ema1_window': 5.0, 'ema2_window': 34.03725595237402}},
 {'target': 10025.12,
  'params': {'ema1_window': 20.96386324637636,
   'ema2_window': 19.600444342364295}},
 {'target': 9954.84,
  'params': {'ema1_window': 16.245235934093564,
   'ema2_window': 24.785210642918983}},
 {'target': 9954.2,
  'params': {'ema1_window': 24.96002025326071,
   'ema2_window': 19.59334093052412}},
 {'target': 10146.74,
  'params': {'ema1_window': 13.306169655394562,
   'ema2_window': 10.13232677990274}},
 {'target': 9894.52,
  'params': {'ema1_window': 9.930676380039309,
   'ema2_window': 11.938672363287571}},
 {'target': 10160.68,
  'params': {'ema1_window': 19.257596012820454,
   'ema2_window': 14.336820273737521}},
 {'target': 10162.62,
  'params': {'ema1_window': 18.307901480002904,
   'ema2_window': 16.36450151429704}},
 {'target': 10142.82,
  'params': {'ema1_window': 20.6937850140083,
   'ema2_window': 15.95972188704653}},
 {'target': 9806.22,
  'params': {'ema1_window': 16.238539833310536,
   'ema2_window': 18.658641629078}},
 {'target': 10050.0,
  'params': {'ema1_window': 17.231153783951616,
   'ema2_window': 14.238758133764623}},
 {'target': 10091.24,
  'params': {'ema1_window': 21.22834513283038,
   'ema2_window': 12.964148435614263}},
 {'target': 9906.82,
  'params': {'ema1_window': 5.0, 'ema2_window': 42.304883853435676}},
 {'target': 10162.72,
  'params': {'ema1_window': 19.262294554696986,
   'ema2_window': 15.780118424220234}},
 {'target': 9995.02,
  'params': {'ema1_window': 12.395960332116548,
   'ema2_window': 30.248121994893673}},
 {'target': 9908.56,
  'params': {'ema1_window': 19.225913938565377,
   'ema2_window': 42.98755074072035}},
 {'target': 9913.82,
  'params': {'ema1_window': 24.944026515368797,
   'ema2_window': 36.64519955481913}},
 {'target': 9867.9,
  'params': {'ema1_window': 11.442254688637123,
   'ema2_window': 46.76195501211062}},
 {'target': 10030.54,
  'params': {'ema1_window': 22.928098068871368, 'ema2_window': 10.0}},
 {'target': 9866.08,
  'params': {'ema1_window': 9.878419429314752,
   'ema2_window': 26.320564289347878}},
 {'target': 9897.02,
  'params': {'ema1_window': 19.492865397009265,
   'ema2_window': 28.75987475220795}},
 {'target': 10124.06,
  'params': {'ema1_window': 19.532335468288842,
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 {'target': 9915.82,
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   'ema2_window': 10.01356944516527}}]

In [8]:

import pandas as pd

df = pd.DataFrame(columns=["ema1_window", "ema2_window", "score"])
for i, trial in enumerate(optimizer.res):
    df.loc[i] = [
        int(trial["params"]["ema1_window"]),
        int(trial["params"]["ema2_window"]),
        trial["target"],
    ]

df

import pandas as pd df = pd.DataFrame(columns=["ema1_window", "ema2_window", "score"]) for i, trial in enumerate(optimizer.res): df.loc[i] = [ int(trial["params"]["ema1_window"]), int(trial["params"]["ema2_window"]), trial["target"], ] df

Out[8]:

	ema1_window	ema2_window	score
0	13.0	38.0	9917.18
1	5.0	22.0	9868.46
2	13.0	39.0	9916.34
3	20.0	34.0	9949.32
4	25.0	26.0	9966.86
...	...	...	...
197	22.0	14.0	10153.36
198	13.0	45.0	9980.68
199	22.0	13.0	10142.84
200	13.0	20.0	9847.48
201	8.0	10.0	9915.82

202 rows × 3 columns

Type 1¶

In [9]:

from plotly import express as px

fig = px.scatter(df, x=df.index, y="score")
fig.show()

from plotly import express as px fig = px.scatter(df, x=df.index, y="score") fig.show()

Type 2¶

In [10]:

import plotly.express as px


fig = px.density_contour(
    df,
    x="ema1_window",
    y="ema2_window",
    z="score",
    histfunc="max",
)
fig.update_traces(contours_coloring="fill", contours_showlabels=True)
fig.show()

import plotly.express as px fig = px.density_contour( df, x="ema1_window", y="ema2_window", z="score", histfunc="max", ) fig.update_traces(contours_coloring="fill", contours_showlabels=True) fig.show()

Type 3¶

In [11]:

import plotly.express as px

fig = px.density_heatmap(
    df,
    x="ema1_window",
    y="ema2_window",
    z="score",
    nbinsx=20,
    nbinsy=40,
    histfunc="max",
    color_continuous_scale="Viridis",
)
fig.show()

import plotly.express as px fig = px.density_heatmap( df, x="ema1_window", y="ema2_window", z="score", nbinsx=20, nbinsy=40, histfunc="max", color_continuous_scale="Viridis", ) fig.show()