def get_first_round_to_reach_new_fundamental(trade_prices, trade_rounds): trade_prices = np.array(trade_prices) fas = get_fundamental_after_shock() try: first_round = np.min(trade_rounds[np.where(trade_prices == fas)]) except ValueError: first_round = 10**6 return first_round
def make_tradeprice_plot(rounds, prices, fit, par, filename):
fig = plt.figure(figsize=(10, 8), dpi=100)
ax = fig.add_axes([0.1, 0.3, 0.8, 0.6])
ax.set_xlabel("Round")
ax.set_ylabel("Traded price")
caption = "\n".join(textwrap.wrap(repr([(k,par[k]) for k in settings.parameters_in_genes]), 100))
caption += '\nFitness: %s'%str(settings.fitness_weights.keys())
caption += '\nFitness: %s'%str(fit)
fig.text(0.1,0.1, caption)
ax.plot(rounds, prices, lw=2)
fas = get_fundamental_after_shock()
ax.hlines([fas - settings.stability_margin, fas + settings.stability_margin], 0, settings.n_simulation_rounds)
print "Saving plot to %s"%filename
fig.savefig(filename)
plt.close()
gc.collect()
def __evaluate_simulation_results(parameters, logdata_folder, graph_folder, generation_number, plot_name = None): assert logdata_folder, "Please specify where the logdata is located" data = empty_data_matrix(1) #ob_round_based = np.genfromtxt(logdata_folder + 'columnLog_roundBased_orderbook(0,0).csv', names=True, dtype=int, delimiter=',', usecols=(1,2)) #stock_round_based = np.genfromtxt(logdata_folder + 'columnLog_roundBased_stock0.csv', names=True, dtype=int, delimiter=',', usecols=(0,1)) trades = IO.load_trade_log_data(logdata_folder) fas = get_fundamental_after_shock() within_margin = get_number_of_rounds_within_stability_margin(trades['price'], trades['round'], fas) #data['n_simulation_rounds_within_stability_margin'] = within_margin['total_number_of_rounds'] #data['n_seperate_intervals_within_stability_margin'] = within_margin['n_intervals'] if 'longest_interval_within_margin' in data_for_failed_simulation.keys(): data['longest_interval_within_margin'] = within_margin['longest_interval'] if 'stdev' in data_for_failed_simulation.keys(): data['stdev'] = get_tp_std_after_entering_margin(trades['price'], trades['round']) if 'overshoot' in data_for_failed_simulation.keys(): data['overshoot'] = calculate_overshoot(fas, trades['price'], trades['round']) if 'time_to_reach_new_fundamental' in data_for_failed_simulation.keys(): data['time_to_reach_new_fundamental'] = get_first_round_to_reach_new_fundamental(trades['price'], trades['round']) if 'round_stable' in data_for_failed_simulation.keys(): data['round_stable'] = calculate_round_stable(trades['price'], trades['round'], fas) if np.random.random() <= PLOT_SAVE_PROB: data_id = 'gen%s_%s_%s'%(generation_number, get_epoch_time(), str(abs(hash(np.random.random())))) print graph_folder + data_id + '.npz' IO.save_tradeprice_data(trades['round'], trades['price'], data, parameters, graph_folder + data_id + '.npz') plotting.make_pretty_tradeprice_plot(trades['round'], trades['price'], graph_folder + data_id + '.png') #plotting.make_tradeprice_plot(trades['round'], trades['price'], data, parameters, graph_folder + data_id + '.png') else: data_id = None if not KEEP_SIMULATION_DATA: IO.delete_simulation_data(logdata_folder) return data, data_id
def make_pretty_tradeprice_plot(rounds, prices, filename, format = 'png', **figargs):
from settings import default_parameters as dp
cmap = brewer2mpl.get_map('Set1', 'qualitative', 9)
p = Ppl(cmap, alpha=1)
fig = plt.figure(num=None, **figargs)
ax = fig.add_subplot(1,1,1)
#fig, ax = plt.subplots(1)
"""
if kwargs.has_key('dpi'):
fig.set_dpi(kwargs['dpi'])
if kwargs.has_key('figwidth'):
fig.set_figwidth(kwargs['figwidth'])
if kwargs.has_key('figheight'):
fig.set_figheight(kwargs['figheight'])
"""
p.plot(ax, rounds, prices, zorder=1)
#ax.yaxis.get_major_formatter().set_powerlimits((0, 1))
### Plotting statbility margins
fas = get_fundamental_after_shock()
ax.hlines(y = [fas - settings.stability_margin, fas + settings.stability_margin], xmin=0, xmax=settings.n_simulation_rounds, linestyles = 'dashed')
### Plotting fundamental step function
y = [dp['fundamental_initial_value'], dp['fundamental_initial_value'] + dp['fundamental_shock_size'], ]
xmin = [0, dp['fundamental_shock_round']]
xmax = [dp['fundamental_shock_round'], settings.n_simulation_rounds]
ax.hlines(y, xmin, xmax, linestyles = 'solid', colors='black', linewidth=2, zorder=2)
ax.vlines(x = dp['fundamental_shock_round'], ymin=dp['fundamental_initial_value'] + dp['fundamental_shock_size'], ymax = dp['fundamental_initial_value'], colors='black', linewidth=2, zorder=3)
ax.set_ylabel('Traded price (ticks)')
ax.set_xlabel('Time (rounds)')
ax.set_xlim([0, max(rounds)])
ax.set_ylim([min(fas - settings.stability_margin-2, min(prices)), max(fas + settings.stability_margin, max(prices))])
fig.savefig(filename)
plt.close()
gc.collect()