def make_plots(plot_data, plot_params_dict):
""" Draw and save plot from preprocessed data.
:param plot_data: Data structure processed by __name__.prep_data
:param plot_params_dict: dict holding some plotting paramaters, see e.g. plot_configs/single_day/pov_single_day_config.example.json["PLOT_PARAMS_DICT"]
:return:
"""
fig, axes = plt.subplots(nrows=2, ncols=1)
fig.set_size_inches(h=14, w=9)
colors = list(get_plot_colors(plot_data + ['dummy']*2))
color = colors.pop(0)
linestyles = get_plot_linestyles(len(plot_data) + 2)
linestyle = linestyles.pop(0)
orderbook_df = plot_data[0]['no_execution_df']
orderbook_df["MID_PRICE"].plot(ax=axes[0], label=plot_params_dict['baseline_label'], color=color, linestyle=linestyle)
for plot_data_dict in plot_data:
color = colors.pop(0)
linestyle = linestyles.pop(0)
pov = f'{100 * plot_data_dict["pov"]} %'
orderbook_df = plot_data_dict['no_execution_df']
orderbook_with_execution_df = plot_data_dict['yes_execution_df']
# mid_price
orderbook_with_execution_df["MID_PRICE"].plot(ax=axes[0], label=
f'{plot_params_dict["execution_label"]}{pov}', color=color, linestyle=linestyle)
# normalised difference
mid_price_yes_execution, mid_price_no_execution = forward_fill_series(orderbook_with_execution_df["MID_PRICE"],
orderbook_df["MID_PRICE"])
diff = 10000 * (mid_price_yes_execution - mid_price_no_execution) / mid_price_no_execution
diff = diff.to_frame()
diff = diff.loc[~diff.index.duplicated(keep='last')]
diff = diff[diff.columns[0]] # to series for plotting
diff.plot(ax=axes[1], label=f'{plot_params_dict["execution_label"]}{pov}', color=color, linestyle=linestyle)
axes[0].axvspan(pd.Timestamp(plot_params_dict['shade_start_datetime']),
pd.Timestamp(plot_params_dict['shade_end_datetime']), alpha=0.2, color='grey')
axes[1].axvspan(pd.Timestamp(plot_params_dict['shade_start_datetime']),
pd.Timestamp(plot_params_dict['shade_end_datetime']), alpha=0.2, color='grey')
axes[-1].xaxis.set_major_formatter(mdates.DateFormatter("%H:%M"))
axes[-1].xaxis.set_minor_formatter(mdates.DateFormatter("%H:%M"))
axes[0].xaxis.set_visible(False)
axes[0].legend()
axes[-1].set_xlabel('Time', size=15)
axes[0].set_ylabel('Mid-price ($)', size=15)
axes[1].set_ylabel('Normalized Difference (bps)', size=15)
fig.tight_layout()
fig.subplots_adjust(top=0.7)
fig.savefig(plot_params_dict["output_file_path"], format='png', dpi=300, transparent=False, bbox_inches='tight',
pad_inches=0.03)
def plot_binned_trade_counts(trades_within_bins_dict, binwidth, output_dir):
""" Plot binned counts of trade volume. """
fig, ax = plt.subplots(figsize=(Constants.fig_width, Constants.fig_height))
ax.set(yscale="log")
ax.set_ylabel(Constants.binned_trade_counts_ylabel)
ax.set_xlabel(Constants.binned_trade_counts_xlabel)
symbols = list(trades_within_bins_dict.keys())
symbols.sort()
colors = get_plot_colors(symbols)
alphas = [1] * len(symbols)
x_s = []
for symbol, color, alpha in zip(symbols, colors, alphas):
binned_trades_counts = trades_within_bins_dict[symbol].copy(deep=True)
binned_trades_counts = binned_trades_counts / binned_trades_counts.sum(
)
x = binned_trades_counts.sort_values()
x_s.append(x)
plt.hist(x,
bins="sqrt",
density=True,
label=symbol,
color=color,
alpha=alpha,
histtype="step",
linewidth=Constants.binned_count_linewidth)
xlim = ax.get_xlim()
ylim = ax.get_ylim()
xx = np.linspace(*xlim, 200)
# Plot fitted curves
for x, symbol, color in zip(x_s, symbols, colors):
gamma_params = stats.gamma.fit(x.values[x.values > 0], floc=0)
plt.plot(xx,
stats.gamma.pdf(xx, *gamma_params),
linestyle="--",
color=color,
label=f"{symbol} gamma fit",
linewidth=Constants.binned_count_linewidth)
ax.set_ylim(ylim)
plt.title(
f"Order volume within time window $\\tau = ${binwidth} seconds, normalized",
size=Constants.title_font_size)
plt.legend(fontsize=Constants.legend_font_size)
fig.savefig(
f'{output_dir}/{Constants.binned_trade_counts_filename}_tau_{binwidth}.png',
format='png',
dpi=300,
transparent=False,
bbox_inches='tight',
pad_inches=0.03)
def plot_interarrival_times(interarrivals_dict, output_dir, scale='log'):
""" Plots histogram of the interarrival times for symbols. """
fig, ax = plt.subplots(figsize=(Constants.fig_width, Constants.fig_height))
if scale == 'log':
ax.set(xscale="symlog", yscale="log")
ax.set_ylabel(Constants.interarrival_times_ylabel)
ax.set_xlabel(Constants.interarrival_times_xlabel)
symbols = list(interarrivals_dict.keys())
symbols.sort()
colors = get_plot_colors(symbols)
alphas = [1] * len(symbols)
x_s = []
for symbol, color, alpha in zip(symbols, colors, alphas):
interarrival_times_series = interarrivals_dict[symbol]
x = interarrival_times_series.sort_values()
x_s.append(x)
plt.hist(x, bins="sqrt", density=True, label=symbol, color=color, alpha=alpha, histtype="step",
linewidth=Constants.interarrival_linewidth)
ylim = ax.get_ylim()
xlim = ax.get_xlim()
xx = np.linspace(*xlim, 200)
# Plot fitted curves, leave out zeroes for better fit
for x, symbol, color in zip(x_s, symbols, colors):
x = x[(x > Constants.interarrival_fit_lower_bound) & (x < Constants.interarrival_fit_upper_bound)]
weibull_params = stats.weibull_min.fit(x, floc=0)
x_left = xx[xx < x.min()][1:]
x_mid = x.to_numpy()
x_right = xx[xx > x.max()]
xxx = np.concatenate((x_left, x_mid, x_right))
plt.plot(xxx, stats.weibull_min.pdf(xxx, *weibull_params), linestyle="--", color=color,
label=f"{symbol} Weibull fit", linewidth=Constants.interarrival_linewidth)
plt.legend(fontsize=Constants.legend_font_size)
ax.set_ylim(ylim)
fig.savefig(f'{output_dir}/{Constants.interarrival_times_filename}.png', format='png', dpi=300,
transparent=False, bbox_inches='tight', pad_inches=0.03)
def get_scatter_plot_params_dict(symbols):
""" Creates dictionary of parameters used by intraday seasonality plots. """
colors = get_plot_colors(symbols)
scatter_styles_sizes = itertools.cycle(Constants.scatter_marker_styles_sizes)
scatter_plot_params_dict = dict()
for symbol, color, style_and_size in zip(symbols, colors, scatter_styles_sizes):
scatter_plot_params_dict.update({
symbol : {
'color': color,
'marker': style_and_size[0],
'marker_size': style_and_size[1]
}
})
return scatter_plot_params_dict
