def showProgressBar(self, flag=True):
if flag and self.progress == None:
self.progress = ProgBar()
elif self.progress != None and not flag:
if self.progress.isActive():
self.progress.halt()
self.progress = None
def optimap(f_xy, X_params, Y_params, heat=True, symm=False):
Stats = ["Trades", "Mean", "R | Sharpe", "R | Sterling"
] ## Around which statistical metrics are we going to optimize.
arrays = (list, tuple, dict, range, numpy.ndarray, pandas.Series
) ## Indexable arrays for "X" & "Y" parameter arguments.
assert isinstance(X_params, arrays) and isinstance(
Y_params, arrays), "{TYPE} 'X' & 'Y' params must be indexable arrays."
X_labels, Y_labels = X_params, Y_params ## "Grid" df's index/column labels may tentatively be the parameters themselves.
## However, if "X"/"Y" params' array is a dict, keys will be column labels in Grid. Values will be arguments for "f_xy".
if isinstance(X_params, dict):
X_params, X_labels = list(X_params.values()), list(X_params.keys())
if isinstance(Y_params, dict):
Y_params, Y_labels = list(Y_params.values()), list(Y_params.keys())
Grid = pandas.DataFrame(
index=X_labels,
columns=pandas.MultiIndex.from_product(iterables=(Stats, Y_labels)))
Combs = list(itertools.product(range(len(X_params)), range(
len(Y_params)))) ## Indexes of all possible "X" & "Y" pairs.
## In cases where "X" & "Y" share exact same indicator nature (e.g.: both SMA), omit repeated/swapped cases. Example:
if symm:
Combs = [(nx, ny) for nx, ny in Combs if (X_params[nx] <= Y_params[ny])
] ## "(p1, p2) = (p2, p1)". Keep just one.
Prog = ProgBar.ProgBar(steps=len(
list(Combs))) ## Create a progress bar made with characters.
## For given parameters "x" and "y", we will run the exercise, find its stats and create a 2D grid.
for nx, ny in Combs: ## For every combination of parameters.
x_param, y_param, x_label, y_label = X_params[nx], Y_params[
ny], X_labels[nx], Y_labels[ny]
try: ## Run the exercise with a function as specified.
S = f_xy(x_param, y_param).Stats[symbol][
"Return"] ## From the backtest results, keep only the returns' ".Stats".
for stat in Stats:
Grid.loc[x_label, (stat, y_label)] = S[stat]
except:
1 ## When it's impossible to calculate stats (e.g.: no signals/trades), forget about errors.
Prog.up() ## Increase progress bar.
Figure, Axes = matplotlib.pyplot.subplots(ncols=len(Stats))
## Heatmaps will display the most optimal spots in red.
Grid.replace(to_replace=[-numpy.inf, numpy.inf],
value=numpy.nan,
inplace=True)
for n, stat in enumerate(Stats): ## Infs ⇧ when denominator is 0.
lim = max(abs(Grid[stat].min().min()), abs(
Grid[stat].max().max())) * 1.25 ## Y-axes' max span for lines.
if heat:
Axes[n].contourf(*numpy.meshgrid(X_params, Y_params),
Grid[stat].values.T)
# Heatmap, 2D.
else:
Grid[stat].plot(
ylim=[-lim * (Grid[stat] < 0).any().any(),
lim], ## When no negative numbers found...
ax=Axes[n],
legend=False,
linewidth=2.5)
## ...lowest y-axis point can be 0.
Axes[n].set_title(stat, fontweight="bold")
if not (heat):
Axes[0].legend(fontsize=13) ## Add legend just to the first line plot.
matplotlib.pyplot.pause(
1e-13) ## This line avoids a (quite loooong) Tkinter warning print.
return Figure, Grid
