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functions.py
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functions.py
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"""
# -- --------------------------------------------------------------------------------------------------- -- #
# -- project: A SHORT DESCRIPTION OF THE PROJECT -- #
# -- script: functions.py : python script with general functions -- #
# -- author: YOUR GITHUB USER NAME -- #
# -- license: GPL-3.0 License -- #
# -- repository: YOUR REPOSITORY URL -- #
# -- --------------------------------------------------------------------------------------------------- -- #
"""
import pandas as pd
import numpy as np
from sklearn.linear_model import ElasticNet, LogisticRegression
from sklearn.metrics import confusion_matrix, accuracy_score, roc_auc_score, roc_curve
from sklearn.preprocessing import StandardScaler, RobustScaler, MaxAbsScaler # estandarizacion de variables
from gplearn.genetic import SymbolicTransformer # variables simbolicas
from sklearn.svm import SVC
# -------------------------------------------------------------------------- Data Scaling/Transformation -- #
# -------------------------------------------------------------------------- --------------------------- -- #
def data_scaler(p_data, p_trans):
"""
Estandarizar (a cada dato se le resta la media y se divide entre la desviacion estandar) se aplica a
todas excepto la primera columna del dataframe que se use a la entrada
Parameters
----------
p_trans: str
Standard: Para estandarizacion (restar media y dividir entre desviacion estandar)
Robust: Para estandarizacion robusta (restar mediana y dividir entre rango intercuartilico)
p_data: pd.DataFrame
Con datos numericos de entrada
Returns
-------
p_datos: pd.DataFrame
Con los datos originales estandarizados
"""
if p_trans == 'Standard':
# estandarizacion de todas las variables independientes
lista = p_data[list(p_data.columns[1:])]
# armar objeto de salida
p_data[list(p_data.columns[1:])] = StandardScaler().fit_transform(lista)
elif p_trans == 'Robust':
# estandarizacion de todas las variables independientes
lista = p_data[list(p_data.columns[1:])]
# armar objeto de salida
p_data[list(p_data.columns[1:])] = RobustScaler().fit_transform(lista)
elif p_trans == 'Scale':
# estandarizacion de todas las variables independientes
lista = p_data[list(p_data.columns[1:])]
p_data[list(p_data.columns[1:])] = MaxAbsScaler().fit_transform(lista)
return p_data
# ------------------------------------------------------------------------------ Autoregressive Features -- #
# --------------------------------------------------------------------------------------------------------- #
def f_autoregressive_features(p_data, p_nmax):
"""
Creacion de variables de naturaleza autoregresiva (resagos, promedios, diferencias)
Parameters
----------
p_data: pd.DataFrame
Con columnas OHLCV para construir los features
p_nmax: int
Para considerar n calculos de features (resagos y promedios moviles)
Returns
-------
r_features: pd.DataFrame
Con dataframe de features (timestamp + co + co_d + features)
"""
# reasignar datos
data = p_data.copy()
# pips descontados al cierre
data['co'] = (data['close'] - data['open']) * 10000
# pips descontados alcistas
data['ho'] = (data['high'] - data['open']) * 10000
# pips descontados bajistas
data['ol'] = (data['open'] - data['low']) * 10000
# pips descontados en total (medida de volatilidad)
data['hl'] = (data['high'] - data['low']) * 10000
# clase a predecir
data['co_d'] = [1 if i > 0 else -1 for i in list(data['co'])]
# ciclo para calcular N features con logica de "Ventanas de tamaño n"
for n in range(0, p_nmax):
# rezago n de Open Interest
data['lag_vol_' + str(n + 1)] = data['volume'].shift(n + 1)
# rezago n de Open - Low
data['lag_ol_' + str(n + 1)] = data['ol'].shift(n + 1)
# rezago n de High - Open
data['lag_ho_' + str(n + 1)] = data['ho'].shift(n + 1)
# rezago n de High - Low
data['lag_hl_' + str(n + 1)] = data['hl'].shift(n + 1)
# promedio movil de open-high de ventana n
data['ma_vol_' + str(n + 1)] = data['volume'].rolling(n + 1).mean()
# promedio movil de open-high de ventana n
data['ma_ol_' + str(n + 1)] = data['ol'].rolling(n + 1).mean()
# promedio movil de ventana n
data['ma_ho_' + str(n + 1)] = data['ho'].rolling(n + 1).mean()
# promedio movil de ventana n
data['ma_hl_' + str(n + 1)] = data['hl'].rolling(n + 1).mean()
# asignar timestamp como index
data.index = pd.to_datetime(data.index)
# quitar columnas no necesarias para modelos de ML
r_features = data.drop(['open', 'high', 'low', 'close', 'hl', 'ol', 'ho', 'volume'], axis=1)
# borrar columnas donde exista solo NAs
r_features = r_features.dropna(axis='columns', how='all')
# borrar renglones donde exista algun NA
r_features = r_features.dropna(axis='rows')
# convertir a numeros tipo float las columnas
r_features.iloc[:, 2:] = r_features.iloc[:, 2:].astype(float)
# reformatear columna de variable binaria a 0 y 1
r_features['co_d'] = [0 if i <= 0 else 1 for i in r_features['co_d']]
# resetear index
r_features.reset_index(inplace=True, drop=True)
return r_features
# ------------------------------------------------------------------------------------ Hadamard Features -- #
# --------------------------------------------------------------------------------------------------------- #
def f_hadamard_features(p_data, p_nmax):
"""
Creacion de variables haciendo un producto hadamard entre todas las variables
Parameters
----------
p_data: pd.DataFrame
Con columnas OHLCV para construir los features
p_nmax: int
Para considerar n calculos de features (resagos y promedios moviles)
Returns
-------
r_features: pd.DataFrame
Con dataframe de features con producto hadamard
"""
# ciclo para crear una combinacion secuencial
for n in range(p_nmax):
# lista de features previos
list_hadamard = ['lag_vol_' + str(n + 1),
'lag_ol_' + str(n + 1),
'lag_ho_' + str(n + 1),
'lag_hl_' + str(n + 1)]
# producto hadamard con los features previos
for x in list_hadamard:
p_data['h_' + x + '_' + 'ma_ol_' + str(n + 1)] = p_data[x] * p_data['ma_ol_' + str(n + 1)]
p_data['h_' + x + '_' + 'ma_ho_' + str(n + 1)] = p_data[x] * p_data['ma_ho_' + str(n + 1)]
p_data['h_' + x + '_' + 'ma_hl_' + str(n + 1)] = p_data[x] * p_data['ma_hl_' + str(n + 1)]
return p_data
# ------------------------------------------------------------------ MODEL: Symbolic Features Generation -- #
# --------------------------------------------------------------------------------------------------------- #
def symbolic_features(p_x, p_y):
"""
Funcion para crear regresores no lineales
Parameters
----------
p_x: pd.DataFrame
with regressors or predictor variables
p_x = data_features.iloc[0:30, 3:]
p_y: pd.DataFrame
with variable to predict
p_y = data_features.iloc[0:30, 1]
Returns
-------
score_gp: float
error of prediction
"""
# funcion de generacion de variables simbolicas
model = SymbolicTransformer(function_set=["sub", "add", 'inv', 'mul', 'div', 'abs', 'log'],
population_size=12000, hall_of_fame=300, n_components=30,
generations=4, tournament_size=600, stopping_criteria=.75,
const_range=None, init_method='half and half', init_depth=(4, 20),
metric='pearson', parsimony_coefficient=0.001,
p_crossover=0.4, p_subtree_mutation=0.3, p_hoist_mutation=0.1,
p_point_mutation=0.2, p_point_replace=0.2,
verbose=1, random_state=None, n_jobs=-1, feature_names=p_x.columns,
warm_start=True)
# SymbolicTransformer fit
model_fit = model.fit_transform(p_x, p_y)
# output data of the model
data = pd.DataFrame(np.round(model_fit, 6))
# parameters of the model
model_params = model.get_params()
# best programs dataframe
best_programs = {}
for p in model._best_programs:
factor_name = 'sym_' + str(model._best_programs.index(p))
best_programs[factor_name] = {'raw_fitness': p.raw_fitness_, 'reg_fitness': p.fitness_,
'expression': str(p), 'depth': p.depth_, 'length': p.length_}
# formatting, drop duplicates and sort by reg_fitness
best_programs = pd.DataFrame(best_programs).T
best_programs = best_programs.drop_duplicates(subset = ['expression'])
best_programs = best_programs.sort_values(by='reg_fitness', ascending=False)
# results
results = {'fit': model_fit, 'params': model_params, 'model': model, 'data': data,
'best_programs': best_programs, 'details': model.run_details_}
return results
# -------------------------- MODEL: Multivariate Linear Regression Model with ELASTIC NET regularization -- #
# --------------------------------------------------------------------------------------------------------- #
def ols_elastic_net(p_data, p_params):
"""
Funcion para ajustar varios modelos lineales
Parameters
----------
p_data: dict
Diccionario con datos de entrada como los siguientes:
p_x: pd.DataFrame
with regressors or predictor variables
p_x = data_features.iloc[0:30, 3:]
p_y: pd.DataFrame
with variable to predict
p_y = data_features.iloc[0:30, 1]
p_params: dict
Diccionario con parametros de entrada para modelos, como los siguientes
p_alpha: float
alpha for the models
p_alpha = alphas[1e-3]
p_iter: int
Number of iterations until stop the model fit process
p_iter = 1e6
p_intercept: bool
Si se incluye o no el intercepto en el ajuste
p_intercept = True
Returns
-------
r_models: dict
Diccionario con modelos ajustados
"""
x_train = p_data['train_x']
y_train = p_data['train_y']
x_test = p_data['test_x']
y_test = p_data['test_y']
# p_params = {'alpha': .1, 'ratio': 95}
# Fit model
en_model = ElasticNet(alpha=p_params['alpha'], l1_ratio=p_params['ratio'],
max_iter=200000, fit_intercept=False,
tol=1e-2, warm_start=False, random_state=123)
# model fit
en_model.fit(x_train, y_train)
# fitted train values
p_y_train = en_model.predict(x_train)
p_y_train_d = [1 if i > 0 else -1 for i in p_y_train]
p_y_result_train = pd.DataFrame({'y_train': y_train, 'y_train_pred': p_y_train_d})
# Confussion matrix
cm_train = confusion_matrix(p_y_result_train['y_train'], p_y_result_train['y_train_pred'])
# Probabilities of class in train data
probs_train = [0]*len(x_train)
# Accuracy rate
acc_train = accuracy_score(list(y_train), p_y_train_d)
# False Positive Rate, True Positive Rate, Thresholds
fpr_train, tpr_train, thresholds_train = roc_curve(list(y_train), probs_train, pos_label=1)
# Area Under the Curve (ROC) for train data
auc_train = roc_auc_score(list(y_train), probs_train)
# fitted test values
p_y_test = en_model.predict(x_test)
p_y_test_d = [1 if i > 0 else -1 for i in p_y_test]
p_y_result_test = pd.DataFrame({'y_test': y_test, 'y_test_pred': p_y_test_d})
cm_test = confusion_matrix(p_y_result_test['y_test'], p_y_result_test['y_test_pred'])
# Probabilities of class in test data
probs_test = [0]*len(x_test)
# Accuracy rate
acc_test = accuracy_score(list(y_test), p_y_test_d)
# False Positive Rate, True Positive Rate, Thresholds
fpr_test, tpr_test, thresholds_test = roc_curve(list(y_test), probs_test, pos_label=1)
# Area Under the Curve (ROC) for train data
auc_test = roc_auc_score(list(y_test), probs_test)
# Return the result of the model
r_models = {'results': {'data': {'train': p_y_result_train, 'test': p_y_result_test},
'matrix': {'train': cm_train, 'test': cm_test}},
'model': en_model, 'intercept': en_model.intercept_, 'coef': en_model.coef_,
'metrics': {'train': {'acc': acc_train, 'tpr': tpr_train, 'fpr': fpr_train,
'probs': probs_train, 'auc': auc_train},
'test': {'acc': acc_test, 'tpr': tpr_test, 'fpr': fpr_test,
'probs': probs_test, 'auc': auc_test}},
'params': p_params}
return r_models
# --------------------------------------------------------- MODEL: Least Squares Support Vector Machines -- #
# --------------------------------------------------------------------------------------------------------- #
def l1_svm(p_data, p_params):
"""
L1 Support Vector Machines
Parameters
----------
p_data
p_params
Returns
-------
References
----------
https://scikit-learn.org/stable/modules/svm.html#
"""
x_train = p_data['train_x']
y_train = p_data['train_y']
x_test = p_data['test_x']
y_test = p_data['test_y']
# ------------------------------------------------------------------------------ FUNCTION PARAMETERS -- #
# model hyperparameters
# C, kernel, degree (if kernel = poly), gamma (if kernel = {rbf, poly, sigmoid},
# coef0 (if kernel = {poly, sigmoid})
# computations parameters
# shrinking, probability, tol, cache_size, class_weight, verbose, max_iter, decision_function_shape,
# break_ties, random_state
# model function
svm_model = SVC(C=p_params['c'], kernel=p_params['kernel'], gamma=p_params['gamma'],
degree=p_params['degree'], coef0=p_params['coef0'],
shrinking=True, probability=True, tol=1e-5, cache_size=4000,
class_weight=None, verbose=False, max_iter=100000, decision_function_shape='ovr',
break_ties=False, random_state=None)
# save adjusted model
model = svm_model
# model fit
svm_model.fit(x_train, y_train)
# fitted train values
p_y_train_d = svm_model.predict(x_train)
p_y_result_train = pd.DataFrame({'y_train': y_train, 'y_train_pred': p_y_train_d})
cm_train = confusion_matrix(p_y_result_train['y_train'], p_y_result_train['y_train_pred'])
# Probabilities of class in train data
probs_train = svm_model.predict_proba(x_train)
# Accuracy rate
acc_train = accuracy_score(list(y_train), p_y_train_d)
# False Positive Rate, True Positive Rate, Thresholds
fpr_train, tpr_train, thresholds_train = roc_curve(list(y_train), probs_train[:, 1], pos_label=1)
# Area Under the Curve (ROC) for train data
auc_train = roc_auc_score(list(y_train), probs_train[:, 1])
# fitted test values
p_y_test_d = svm_model.predict(x_test)
p_y_result_test = pd.DataFrame({'y_test': y_test, 'y_test_pred': p_y_test_d})
cm_test = confusion_matrix(p_y_result_test['y_test'], p_y_result_test['y_test_pred'])
# Probabilities of class in test data
probs_test = svm_model.predict_proba(x_test)
# Accuracy rate
acc_test = accuracy_score(list(y_test), p_y_test_d)
# False Positive Rate, True Positive Rate, Thresholds
fpr_test, tpr_test, thresholds_test = roc_curve(list(y_test), probs_test[:, 1], pos_label=1)
# Area Under the Curve (ROC) for train data
auc_test = roc_auc_score(list(y_test), probs_test[:, 1])
# Return the result of the model
r_models = {'results': {'data': {'train': p_y_result_train, 'test': p_y_result_test},
'matrix': {'train': cm_train, 'test': cm_test}},
'model': model,
'metrics': {'train': {'acc': acc_train, 'tpr': tpr_train, 'fpr': fpr_train,
'probs': probs_train, 'auc': auc_train},
'test': {'acc': acc_test, 'tpr': tpr_test, 'fpr': fpr_test,
'probs': probs_test, 'auc': auc_test}},
'params': p_params}
return r_models
# --------------------------------------------------------------------------- Divide the data in T-Folds -- #
# --------------------------------------------------------------------------- ----------------------------- #
def t_folds(p_data, p_period):
"""
Function to separate in T-Folds the data, considering not having filtrations (Month and Quarter)
Parameters
----------
p_data : pd.DataFrame
DataFrame with data
p_period : str
'month': monthly data division
'quarter' quarterly data division
Returns
-------
m_data or q_data : 'period_'
References
----------
https://web.stanford.edu/~hastie/ElemStatLearn/
"""
# data scaling by standarization
p_data.iloc[:, 1:] = data_scaler(p_data=p_data.copy(), p_trans='Standard')
# For monthly separation of the data
if p_period == 'month':
# List of months in the dataset
months = list(set(time.month for time in list(p_data['timestamp'])))
# List of years in the dataset
years = list(set(time.year for time in list(p_data['timestamp'])))
m_data = {}
# New key for every month_year
for j in years:
m_data.update({'m_' + str('0') + str(i) + '_' + str(j) if i <= 9 else str(i) + '_' + str(j):
p_data[(pd.to_datetime(p_data['timestamp']).dt.month == i) &
(pd.to_datetime(p_data['timestamp']).dt.year == j)]
for i in months})
return m_data
# For quarterly separation of the data
elif p_period == 'quarter':
# List of quarters in the dataset
quarters = list(set(time.quarter for time in list(p_data['timestamp'])))
# List of years in the dataset
years = set(time.year for time in list(p_data['timestamp']))
q_data = {}
# New key for every quarter_year
for y in sorted(list(years)):
q_data.update({'q_' + str('0') + str(i) + '_' + str(y) if i <= 9 else str(i) + '_' + str(y):
p_data[(pd.to_datetime(p_data['timestamp']).dt.year == y) &
(pd.to_datetime(p_data['timestamp']).dt.quarter == i)]
for i in quarters})
return q_data
# For quarterly separation of the data
elif p_period == 'semester':
# List of years in the dataset
years = set(time.year for time in list(p_data['timestamp']))
s_data = {}
# New key for every quarter_year
for y in sorted(list(years)):
# y = sorted(list(years))[0]
s_data.update({'s_' + str('0') + str(1) + '_' + str(y):
p_data[(pd.to_datetime(p_data['timestamp']).dt.year == y) &
((pd.to_datetime(p_data['timestamp']).dt.quarter == 1) |
(pd.to_datetime(p_data['timestamp']).dt.quarter == 2))]})
s_data.update({'s_' + str('0') + str(2) + '_' + str(y):
p_data[(pd.to_datetime(p_data['timestamp']).dt.year == y) &
((pd.to_datetime(p_data['timestamp']).dt.quarter == 3) |
(pd.to_datetime(p_data['timestamp']).dt.quarter == 4))]})
return s_data
# In the case a different label has been receieved
return 'Error: verify parameters'