def predict(self, X_test, pred_index=None):
predicted_values = X_test * self.weights
if self.global_hyperparams['output_type'] == 'C':
predicted_values = to_class(predicted_values,
self.global_hyperparams['threshold'])
if pred_index is not None:
self._store_predicted_values(pred_index, predicted_values)
return predicted_values
def __init__(self,
global_hyperparams,
pred_val,
asset_data,
threshold=None):
self.pred_val = pred_val
self.output_type = global_hyperparams['output_type']
self.threshold = threshold if threshold is not None else global_hyperparams[
'threshold']
self.pred_val_class = pred_val if self.output_type == 'C' else to_class(
pred_val, threshold)
self.pred_val_class = self.pred_val_class.squeeze()
self.asset_data = asset_data[self.pred_val_class.index]
def predict(self, X_test, pred_index=None):
""" Predict function used in main and in the cross validation process
It can accept as an X_test input either an array or a dataframe and gives a corresponding output
This version of the function only works for ML algorithm and it has to be recoded for TA algorithms
If a pred_index is provided, the prediction will be stored in predicted_values with this index
"""
if self.algo_type=="ML":
predicted_values=self.model.predict(X_test)
if self.global_hyperparams["output_type"]=="C" and self.model._estimator_type!='classifier': # If we use a regression model and we still need to output a class
predicted_values=to_class(predicted_values, self.global_hyperparams["threshold"])
else:
predicted_values=np.nan # Not integrated yet
if pred_index is not None:
self._store_predicted_values(pred_index, predicted_values)
return predicted_values
def predict(self, X_test, pred_index=None):
w = self.window_size
if self.mean_type == "arithmetic":
predicted_values = X_test.iloc[:, :w].mean(axis=1, skipna=None)
elif self.mean_type == "geometric":
# Let us note that the geometric mean should be optimized using numpy vectorized operations
predicted_values = 1
for col in X_test.iloc[:, :w].columns:
predicted_values = predicted_values * (1 + X_test.iloc[:, col])
predicted_values = np.power(predicted_values, 1 / w) - 1
# The output will be different in case of a regression or classification, no need to change the output for a regression
if self.global_hyperparams["output_type"] == "C":
threshold = self.global_hyperparams["threshold"]
predicted_values = to_class(predicted_values, threshold)
if pred_index is not None:
self._store_predicted_values(pred_index, predicted_values)
return predicted_values # here we have a redundency in the return and the side effect of the method, this is used to simplify coding
def predict(self, X_test, pred_index=None):
w = self.window_size
if self.mean_type == 'arithmetic':
predicted_values = X_test.iloc[:, :w].mean(axis=1, skipna=None)
elif self.mean_type == 'geometric':
# Let us note that the geometric mean should be optimized using numpy vectorized operations
predicted_values = 1
for col in X_test.iloc[:, :
w].columns: # We stop at the column number w
predicted_values = predicted_values * (1 + X_test[col].values)
predicted_values = np.power(predicted_values, 1 / w) - 1
# We classify the predictions in case of a classification output
if self.global_hyperparams['output_type'] == 'C':
threshold = self.global_hyperparams['threshold']
predicted_values = to_class(predicted_values, threshold)
if pred_index is not None:
self._store_predicted_values(pred_index, predicted_values)
return predicted_values
dataset=data.dataset_building('quandl', asset_ids, start_date, end_date, n_max=None) # please recode the dataset_building functio to make it support local and quandl data
dataset = data.add_returns(dataset, [0]) # creates some NANs as a result of the returns computation
dataset.dropna(inplace=True)
# We select an asset returns time series to predict from the dataset
Y_0=dataset[dataset.columns[1]] # need to find a reliable way to find the index of the column
# X: include all the lags of Y and additional data
lags=range(1,rolling_window_size+1)
X=data.lagged(dataset,lags=lags) # In X please always include all the lags of Y that you want to use for the HM as first colunms
#max_lags=max(lags)
# We could also turn X into classes data, is that meaningful?
# X=to_class(X,threshold)
# In case of classification, we classify Y
if output_type=='C': Y=data.to_class(Y_0, threshold)
## Creating & calibrating the different algorithms
# First define a dictionary of algorithm associated with their names
# As arguments please include the fixed hyperparams of the model as a named argument
# For the hyperparameters grid to use in cross validation please provide a dictionary using sklearn syntax
algos={'HM':HM(global_hyperparams, hp_grid={'window_size':[10,100,500]}),
#'LR':LR(global_hyperparams),
#'Lasso':LR(global_hyperparams, regularization='Lasso',hp_grid={'alpha':np.logspace(-4,1,10)}),
#'ElasticNet':LR(global_hyperparams, regularization='ElasticNet',hp_grid={'alpha':np.logspace(-3,1,20),'l1_ratio':np.linspace(0,1,20)}),
#'Tree':DT(global_hyperparams,hp_grid={'max_features':['sqrt',None],'criterion':['gini','entropy']}),
#'RF':RF(global_hyperparams, hp_grid={'max_features':['sqrt',None],'n_estimators':range(10,200,20)}),
#'ADAB':ADAB(global_hyperparams, hp_grid={'n_estimators':[1,5,10]}, base_algo=DT(global_hyperparams)),
#'MLP':MLP(global_hyperparams,hp_grid={'alpha':np.linspace(0.1,1,9),'hidden_layer_sizes':[(10,),(100,),(200,)]},activation='relu', solver='lbfgs'),
def __main__():
## Global Hyperparameters
# The window size of the rolling window used to define each training set size
# The models will never see more than this number of points at once
rolling_window_size = 500
# Output type : C for Classification, R for Regression
# Note that for a Classification, 1 means positive return, -1 means negative, and 0 means bellow threshold
output_type = "C"
# In case of 3 class Classification, please provide an absolute level for the zero return threshold
# Fix it to 0 for a binary classification
# The optimal value can also be globally optimized as a result of the PnL optimisation and will be function of the volatility of the asset
threshold = 0.001
# This dictionary of global hyperparameters will be passed an an argument of all built algorithms
global_hyperparams = {
"rolling_window_size": rolling_window_size,
"output_type": output_type,
"threshold": threshold
}
## Building the dataset
dataset = data.dataset_building(n_max=2000)
# We select an asset returns time series to predict from the dataset
asset_label = "EURUSD Curncy"
Y = dataset[[asset_label]]
Y.dropna(inplace=True)
# With lags, used as X, maybe this implementation is not optimal, think about a slicing way to do that?
lags = range(1, rolling_window_size + 1)
X = data.lagged(Y, lags=lags)
max_lags = max(lags)
# We could also turn X into classes data, is that meaningful?
# X=to_class(X,threshold)
# In case of classification, we transform Y and put the classes labels into global_hyperparams
if output_type == "C":
Y = data.to_class(
Y, threshold
) # Notice that now the values of Y is the index of the class in classes
classes = np.unique(Y)
global_hyperparams["classes"] = classes
## Creating & calibrating the different algorithms
# First define a dictionary of algorithm associated with their names
# As arguments please include the fixed hyperparams of the model as a named argument
# For the hyperparameters grid to use in cross validation please provide a dictionary using sklearn syntax
algos = {
"HM AR Full window":
HM(global_hyperparams,
window_size=10,
hp_grid={'window_size': [1, 100]}),
"HM GEO Full window":
HM(global_hyperparams,
mean_type="geometric",
hp_grid={'window_size': [1, 10, 50, 100]}),
"HM AR Short Term":
HM(global_hyperparams, window_size=10),
"LR":
LR(global_hyperparams),
"Lasso":
LR(global_hyperparams,
regularization="Lasso",
hp_grid={"alpha": np.logspace(-4, 1, 5)})
}
# Then we just allow ourselves to work/calib/fit/train only a subsets of these algos
#algos_used=algos.keys()
algos_used = ["Lasso"]
#algos_used=["HM AR Full window"]
#algos_used=["HM GEO Full window"]
for key in algos_used:
# We let each algo select the relevent data to work on
algos[key].select_data(X)
for i in range(
rolling_window_size + max_lags, len(Y.index)
): # Note that i in the numeric index in Y of the predicted value
train = range(i - rolling_window_size,
i) # should be equal to i-rolling_window_size:i-1
test = [
i
] # I am not sure of the index, we can check, it is inside [] to ;ake urer the slicing produces a dataframe
pred_index = Y.index[test] # This is the timestamp of i
# We train all the algos on the testing set, it includes the calibration of hyperparameters and the fitting
algos[key].calib(X.iloc[train],
Y.iloc[train],
pred_index,
cross_val_type="ts_cv",
n_splits=5,
calib_type="GridSearch")
# We build the predictions
algos[key].predict(X.iloc[test], pred_index)
# for debug
print(i)
# We compute the outputs
algos[key].compute_outputs(Y)
# for debug
print(algos[key].best_hp)
pass
## Core algorithm
# Hyperparameters of the Core algorithm
rolling_window_size_core = rolling_window_size
core_algo = HM(
global_hyperparams,
window_size=rolling_window_size_core) # Average of the predictions
# We first built a new dataset with all the predictions for the algos, it will be our new X
X_core = data.core_dataset(algos, algos_used)
## Trading Strategy
## Backtest/Plots/Trading Execution
return 0
