def epochs_rolling_avg_correlation_matrix(dates: List[str], time_step: str,
window: str) -> None:
"""Plots the rolling average correlation matrix.
:param dates: List of the interval of dates to be analyzed
(i.e. ['1980-01-01', '2020-12-31']).
:param time_step: time step of the data (i.e. '1m', '1h', '1d', '1wk',
'1mo').
:param window: window time to compute the volatility (i.e. '25').
:return: None -- The function saves the plot in a file and does not return
a value.
"""
function_name: str = epochs_rolling_avg_correlation_matrix.__name__
epochs_tools.function_header_print_plot(function_name, dates, time_step,
window, "")
try:
# Load data
avg_corr_matrix_data: pd.Series = pickle.load(
open(
f"../data/epochs/epochs_rolling_avg_correlation_matrix_data_{dates[0]}_{dates[1]}_step"
+ f"_{time_step}_win_{window}_K_.pickle",
"rb",
))
avg_corr_matrix_data = avg_corr_matrix_data.rename(f"epochs {window}")
print(avg_corr_matrix_data)
plot_avg_corr_matrix = avg_corr_matrix_data.plot(figsize=(16, 9),
legend=True)
figure_avg_corr_matrix = plot_avg_corr_matrix.get_figure()
plt.legend()
# plt.title(f"Avg Correlation Matrix Epochs - Epoch {window}", fontsize=30)
plt.xlabel("Date", fontsize=25)
plt.ylabel("Avg. Correlation", fontsize=25)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
# plt.ylim(0, 0.5)
plt.grid(True)
plt.tight_layout()
# Plotting
epochs_tools.save_plot(figure_avg_corr_matrix, function_name, dates,
time_step, window, "")
# plt.close()
del avg_corr_matrix_data
del figure_avg_corr_matrix
del plot_avg_corr_matrix
gc.collect()
except FileNotFoundError as error:
print("No data")
print(error)
print()
def epochs_rolling_volatility_plot(dates: List[str], time_step: str,
window: str) -> None:
"""Plots the normalized rolling volatility.
:param dates: List of the interval of dates to be analyzed
(i.e. ['1980-01-01', '2020-12-31']).
:param time_step: time step of the data (i.e. '1m', '1h', '1d', '1wk',
'1mo').
:param window: window time to compute the volatility (i.e. '25').
:return: None -- The function saves the plot in a file and does not return
a value.
"""
function_name: str = epochs_rolling_volatility_plot.__name__
epochs_tools.function_header_print_plot(function_name, dates, time_step,
window, "")
try:
# Load data
volatility_data: pd.DataFrame = pickle.load(
open(
f"../data/epochs/epochs_volatility_rolling_data_{dates[0]}_{dates[1]}_step"
+ f"_{time_step}_win_{window}_K_.pickle",
"rb",
))
avg_volatility = volatility_data.mean(axis=1)
plot_volatility = avg_volatility.plot(figsize=(16, 9))
figure_volatility = plot_volatility.get_figure()
plt.title(f"Avg Volatility Epochs - Epoch {window}", fontsize=30)
plt.xlabel("Date", fontsize=25)
plt.ylabel("Volatility", fontsize=25)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.grid(True)
plt.tight_layout()
# Plotting
epochs_tools.save_plot(figure_volatility, function_name, dates,
time_step, window, "")
plt.close()
del volatility_data
del figure_volatility
del plot_volatility
gc.collect()
except FileNotFoundError as error:
print("No data")
print(error)
print()
def epochs_correlation_matrix_plot(dates: List[str], time_step: str,
window: str) -> None:
"""Plots the local normalized correlation matrix.
:param dates: List of the interval of dates to be analyzed
(i.e. ['1980-01-01', '2020-12-31']).
:param time_step: time step of the data (i.e. '1m', '1h', '1d', '1wk',
'1mo').
:param window: window time to compute the volatility (i.e. '25').
:return: None -- The function saves the plot in a file and does not return
a value.
"""
function_name: str = epochs_correlation_matrix_plot.__name__
epochs_tools.function_header_print_plot(function_name, dates, time_step,
window)
try:
figure: plt.figure = plt.figure(figsize=(16, 9))
# Load data
correlations: pd.DataFrame = pickle.load(
open(
f"../data/epochs/epochs_correlation_matrix_data_{dates[0]}" +
f"_{dates[1]}_step_{time_step}_win_{window}.pickle",
"rb",
))
sns.heatmap(correlations, cmap="Blues") # , vmin=-1, vmax=1)
plt.title(
f"Local norm. corr. matrix {dates[0]} to" +
f" {dates[1]} - {time_step} - window {window}",
fontsize=30,
)
plt.yticks(rotation=45)
plt.xticks(rotation=45)
figure.tight_layout()
# Plotting
epochs_tools.save_plot(figure, function_name, dates, time_step, window)
plt.close()
del correlations
del figure
gc.collect()
except FileNotFoundError as error:
print("No data")
print(error)
print()
def epochs_normalized_returns_plot(dates: List[str], time_step: str,
window: str) -> None:
"""Plots the local normalized returns of five stocks.
:param dates: List of the interval of dates to be analyzed
(i.e. ['1980-01-01', '2020-12-31']).
:param time_step: time step of the data (i.e. '1m', '1h', '1d', '1wk',
'1mo').
:param window: window time to compute the volatility (i.e. '25').
:return: None -- The function saves the plot in a file and does not return
a value.
"""
function_name: str = epochs_normalized_returns_plot.__name__
epochs_tools.function_header_print_plot(function_name, dates, time_step,
window)
try:
# Load data
norm_returns_data: pd.DataFrame = pickle.load(
open(
f"../data/epochs/epochs_normalized_returns_data_{dates[0]}" +
f"_{dates[1]}_step_{time_step}_win_{window}.pickle",
"rb",
)).iloc[:, :5]
plot_ret: np.ndarray = norm_returns_data.plot(subplots=True,
sharex=True,
figsize=(16, 16),
grid=True,
sort_columns=True)
_ = [ax.set_ylabel("Norm. Returns", fontsize=20) for ax in plot_ret]
_ = [plot.legend(loc=1, fontsize=20) for plot in plt.gcf().axes]
plt.xlabel(f"Date - {time_step} - time window {window}", fontsize=20)
plt.tight_layout(pad=0.5)
figure_ret: plt.Figure = plot_ret[0].get_figure()
# Plotting
epochs_tools.save_plot(figure_ret, function_name, dates, time_step,
window)
plt.close()
del norm_returns_data
del figure_ret
del plot_ret
gc.collect()
except FileNotFoundError as error:
print("No data")
print(error)
print()
def epochs_volatility_one_stock_plot(dates: List[str], time_step: str,
window: str) -> None:
"""plots the local normalized volatility of one stocks.
:param dates: List of the interval of dates to be analyzed
(i.e. ['1980-01-01', '2020-12-31']).
:param time_step: time step of the data (i.e. '1m', '1h', '1d', '1wk',
'1mo').
:param window: window time to compute the volatility (i.e. '25').
:return: None -- The function saves the plot in a file and does not return
a value.
"""
function_name: str = epochs_volatility_one_stock_plot.__name__
epochs_tools.function_header_print_plot(function_name, dates, time_step,
window)
try:
# Load data
volatility_data: pd.DataFrame = pickle.load(
open(
f"../data/epochs/epochs_volatility_data_{dates[0]}" +
f"_{dates[1]}_step_{time_step}_win_{window}.pickle",
"rb",
))
figure_vol: plt.Figure = plt.figure()
plot_vol: np.ndarray = volatility_data.plot(figsize=(16, 9), grid=True)
plt.legend(loc=1, fontsize=20)
plt.xlabel(f"Date - {time_step} - window {window}", fontsize=20)
plt.ylabel(f"Volatility", fontsize=20)
plt.grid(True)
plt.tight_layout()
# Plotting
epochs_tools.save_plot(figure_vol, function_name, dates, time_step,
window)
plt.close()
del volatility_data
del figure_vol
del plot_vol
gc.collect()
except FileNotFoundError as error:
print("No data")
print(error)
print()
def epochs_log_log_all_empirical_dist_returns_market_plot() -> None:
"""Plots all the local normalized aggregated distributions of returns for a
market in different time steps.
:return: None -- The function saves the plot in a file and does not return
a value.
"""
function_name: str = epochs_log_log_all_empirical_dist_returns_market_plot.__name__
epochs_tools.function_header_print_plot(function_name, ["", ""], "", "")
try:
dates_1m = ["2021-07-19", "2021-08-07"]
dates_1h = ["2021-06-01", "2021-07-31"]
dates = ["1990-01-01", "2020-12-31"]
window = "25"
# Load data
agg_returns_min: pd.Series = pickle.load(
open(
"../data/epochs/epochs_aggregated_dist_returns_market_data" +
f"_{dates_1m[0]}_{dates_1m[1]}_step_1m_win_{window}.pickle",
"rb",
))[::5]
# agg_returns_hour: pd.Series = pickle.load(open(
# '../data/epochs/epochs_aggregated_dist_returns_market_data'
# + f'_{dates_1h[0]}_{dates_1h[1]}_step_1h_win_{window}.pickle',
# 'rb'))[::5]
agg_returns_day: pd.Series = pickle.load(
open(
"../data/epochs/epochs_aggregated_dist_returns_market_data" +
f"_{dates[0]}_{dates[1]}_step_1d_win_{window}.pickle",
"rb",
))[::5]
agg_returns_week: pd.Series = pickle.load(
open(
"../data/epochs/epochs_aggregated_dist_returns_market_data" +
f"_{dates[0]}_{dates[1]}_step_1wk_win_{window}.pickle",
"rb",
))[::5]
agg_returns_month: pd.Series = pickle.load(
open(
"../data/epochs/epochs_aggregated_dist_returns_market_data" +
f"_{dates[0]}_{dates[1]}_step_1mo_win_{window}.pickle",
"rb",
))[::5]
agg_returns_min = agg_returns_min.rename("Minute")
agg_returns_hour = agg_returns_hour.rename("Hour")
agg_returns_day = agg_returns_day.rename("Day")
agg_returns_week = agg_returns_week.rename("Week")
agg_returns_month = agg_returns_month.rename("Month")
x_values: np.ndarray = np.arange(-10, 10, 0.1)
gaussian: np.ndarray = epochs_tools.gaussian_distribution(
0, 1, x_values)
figure_log: plt.Figure = plt.figure(figsize=(16, 9))
# Log plot
plt.loglog(x_values, gaussian, "-", lw=10, label="Gaussian")
plot_log_m = agg_returns_min.plot(kind="density",
style="-",
loglog=True,
figsize=(16, 9),
legend=True,
lw=2)
# plot_log_h = agg_returns_hour.plot(kind='density', style='-',
# loglog=True, figsize=(16, 9),
# legend=True, lw=2)
plot_log_d = agg_returns_day.plot(kind="density",
style="-",
loglog=True,
figsize=(16, 9),
legend=True,
lw=2)
plot_log_wk = agg_returns_week.plot(kind="density",
style="-",
loglog=True,
figsize=(16, 9),
legend=True,
lw=2)
plot_log_mo = agg_returns_month.plot(kind="density",
style="-",
loglog=True,
figsize=(16, 9),
legend=True,
lw=2)
plt.legend(fontsize=20)
plt.title(f"Rotated Epochs", fontsize=30)
plt.xlabel(f"Aggregated returns - window {window}", fontsize=25)
plt.ylabel("PDF", fontsize=25)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.xlim(2.5, 5)
plt.ylim(10**-5, 10**-1)
plt.grid(True)
plt.tight_layout()
figure_log = plot_log_m.get_figure()
# figure_log = plot_log_h.get_figure()
figure_log = plot_log_d.get_figure()
figure_log = plot_log_wk.get_figure()
figure_log = plot_log_mo.get_figure()
# Plotting
epochs_tools.save_plot(figure_log, function_name + "_loglog", ["", ""],
"", window)
plt.close()
gc.collect()
except FileNotFoundError as error:
print("No data")
print(error)
print()
def epochs_log_log_agg_dist_returns_market_plot(dates: List[str],
time_step: str, window: str,
K_value: str,
l_values: List[int]) -> None:
"""Plots the aggregated distribution of returns for a market in a log-log
figure for diferent l values.
:param dates: List of the interval of dates to be analyzed
(i.e. ['1980-01-01', '2020-12-31']).
:param time_step: time step of the data (i.e. '1m', '1h', '1d', '1wk',
'1mo').
:param window: window time to compute the volatility (i.e. '25').
:param K_value: number of companies to be used (i.e. '80', 'all').
:param l_values: List of the values of the shape parameter l
(i.e. [2, 4, 6, 8]).
:return: None -- The function saves the plot in a file and does not return
a value.
"""
function_name: str = epochs_log_log_agg_dist_returns_market_plot.__name__
epochs_tools.function_header_print_plot(function_name, dates, time_step,
window, K_value)
try:
# Load data
agg_returns_data: pd.Series = pickle.load(
open(
"../data/epochs/epochs_aggregated_dist_returns_market_data" +
f"_{dates[0]}_{dates[1]}_step_{time_step}_win_{window}_K" +
f"_{K_value}.pickle",
"rb",
))[::10]
agg_returns_data = agg_returns_data.rename("Agg. returns")
x_values: np.ndarray = np.arange(-10, 10, 0.1)
gaussian: np.ndarray = epochs_tools.gaussian_distribution(
0, 1, x_values)
figure_log: plt.Figure = plt.figure(figsize=(16, 9))
# Log plot
for l_value in l_values:
m_value = 2 * l_value - int(K_value) - 2
algebraic: np.ndarray = epochs_tools.algebraic_distribution(
int(K_value), l_value, x_values)
plt.loglog(
x_values,
algebraic,
"-",
lw=1,
label=f"A - K={K_value} - l={l_value} - m={m_value}",
)
plt.loglog(x_values, gaussian, "-", lw=10, label="Gaussian")
plot_log = agg_returns_data.plot(kind="density",
style="-",
loglog=True,
figsize=(16, 9),
legend=True,
lw=5)
plt.legend(fontsize=20)
plt.title(f"Epochs from {dates[0]} to {dates[1]} - {time_step}",
fontsize=30)
plt.xlabel(f"Aggregated returns - window {window}", fontsize=25)
plt.ylabel("PDF", fontsize=25)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.xlim(3, 5.5)
plt.ylim(10**-4, 10**-2)
plt.grid(True)
plt.tight_layout()
figure_log = plot_log.get_figure()
# Plotting
epochs_tools.save_plot(figure_log, function_name + "_loglog", dates,
time_step, window, K_value)
plt.close()
del agg_returns_data
del figure_log
del plot_log
gc.collect()
except FileNotFoundError as error:
print("No data")
print(error)
print()
def epochs_var_time_step_all_empirical_dist_returns_market_plot() -> None:
"""Plots all the local normalized aggregated distributions of returns for a
market in different time steps.
:return: None -- The function saves the plot in a file and does not return
a value.
"""
function_name: str = (
epochs_var_time_step_all_empirical_dist_returns_market_plot.__name__)
epochs_tools.function_header_print_plot(function_name, ["", ""], "", "",
"")
try:
dates_1m = ["2021-07-19", "2021-08-14"]
# dates_1h = ['2021-06-01', '2021-07-31']
dates = ["1990-01-01", "2020-12-31"]
windows = ["10", "25", "40", "55"]
K_values = ["20", "50"]
dates_vals = [dates_1m, dates, dates, dates]
time_steps = ["1m", "1d", "1wk", "1mo"]
for window in windows:
for K_value in K_values:
figure_log: plt.Figure = plt.figure(figsize=(16, 9))
x_values: np.ndarray = np.arange(-10, 10, 0.1)
gaussian: np.ndarray = epochs_tools.gaussian_distribution(
0, 1, x_values)
# Log plot
plt.semilogy(x_values, gaussian, "-", lw=10, label="Gaussian")
for idx, date_val in enumerate(dates_vals):
# Load data
agg_returns: pd.Series = pickle.load(
open(
"../data/epochs/epochs_aggregated_dist_returns_market"
+ f"_data_{date_val[0]}_{date_val[1]}" +
f"_step_{time_steps[idx]}_win_{window}_K_{K_value}"
+ ".pickle",
"rb",
))[::10]
agg_returns = agg_returns.rename(f"{time_steps[idx]}")
plot_log = agg_returns.plot(
kind="density",
style="-",
logy=True,
figsize=(16, 9),
legend=True,
lw=2,
)
figure_log = plot_log.get_figure()
plt.legend(fontsize=20)
plt.title(f"Rotated Epochs - K = {K_value}", fontsize=30)
plt.xlabel(f"Aggregated returns - Win = {window}", fontsize=25)
plt.ylabel("PDF", fontsize=25)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.xlim(-6, 6)
plt.ylim(10**-5, 1)
plt.grid(True)
plt.tight_layout()
# Plotting
epochs_tools.save_plot(
figure_log,
function_name + "_log_time_step",
date_val,
"var",
window,
K_value,
)
plt.close()
gc.collect()
except FileNotFoundError as error:
print("No data")
print(error)
print()
def epochs_normalized_returns_distribution_plot(dates: List[str],
time_step: str,
window: str) -> None:
"""Plots the normalized returns distribution of five stocks.
:param dates: List of the interval of dates to be analyzed
(i.e. ['1980-01-01', '2020-12-31']).
:param time_step: time step of the data (i.e. '1m', '1h', '1d', '1wk',
'1mo').
:param window: window time to compute the volatility (i.e. '25').
:return: None -- The function saves the plot in a file and does not return
a value.
"""
function_name: str = epochs_normalized_returns_distribution_plot.__name__
epochs_tools.function_header_print_plot(function_name + "lin", dates,
time_step, window)
try:
# Load data
norm_returns_data: pd.DataFrame = pickle.load(
open(
f"../data/epochs/epochs_normalized_returns_data_{dates[0]}" +
f"_{dates[1]}_step_{time_step}_win_{window}.pickle",
"rb",
)).iloc[:, :5]
x_gauss: np.ndarray = np.arange(-6, 6, 0.001)
gaussian: np.ndarray = epochs_tools.gaussian_distribution(
0, 1, x_gauss)
# Linear plot
plot_lin = norm_returns_data.plot(kind="density", figsize=(16, 9))
plt.plot(x_gauss, gaussian, lw=5, label="Gaussian")
plt.title(
f"Local normalized returns distribution {dates[0]} to" +
f" {dates[1]}",
fontsize=30,
)
plt.legend(loc=1, fontsize=20)
plt.xlabel(f"Returns - {time_step} - window {window}", fontsize=25)
plt.ylabel("Counts", fontsize=25)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.xlim(-5, 5)
plt.grid(True)
plt.tight_layout()
figure_lin: plt.Figure = plot_lin.get_figure()
# Plotting
epochs_tools.save_plot(figure_lin, function_name + "_lin", dates,
time_step, window)
plt.close()
del figure_lin
del plot_lin
# Log plot
plot_log = norm_returns_data.plot(kind="density",
figsize=(16, 9),
logy=True)
plt.semilogy(x_gauss, gaussian, lw=5, label="Gaussian")
plt.title(
f"Local normalized returns distribution {dates[0]} to" +
f" {dates[1]}",
fontsize=30,
)
plt.legend(loc=1, fontsize=20)
plt.xlabel(f"Returns - {time_step} - window {window}", fontsize=25)
plt.ylabel("Counts", fontsize=25)
plt.xticks(fontsize=15)
plt.yticks(fontsize=15)
plt.xlim(-5, 5)
plt.ylim(10**-6, 10)
plt.grid(True)
plt.tight_layout()
figure_log: plt.Figure = plot_log.get_figure()
# Plotting
epochs_tools.save_plot(figure_log, function_name + "_log", dates,
time_step, window)
plt.close()
del norm_returns_data
del figure_log
del plot_log
gc.collect()
except FileNotFoundError as error:
print("No data")
print(error)
print()