def get_Residuals_LinearModel(X_data, price, lag = 20):
# This functions gets the residuals when we train a linear model with
# the X_data into predictiong the price return with a given lag.
## Prediction of the thingies
Y_data = bMA.diff(price, lag = lag, cval = np.NaN)
Y_data = bMA.shift(Y_data, lag = -lag, cval = np.NaN)
## Eliminate the Nans !! Even if they appear in just one dim
mask_X = np.sum(np.isnan(X_data), axis = 1) == 0
mask_Y = np.isnan(Y_data) == 0
mask = mask_X & mask_Y[:,0]
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(X_data[mask,:], Y_data[mask,:])
# coeffs = np.array([regr.intercept_, regr.coef_])[0]
coeffs = np.append(regr.intercept_, regr.coef_)
params = np.array(coeffs)
residual = regr.residues_
residual = regr.score(X_data[mask,:], Y_data[mask,:])
return residual
def get_Residuals_LinearModel(X_data, price, lag=20):
# This functions gets the residuals when we train a linear model with
# the X_data into predictiong the price return with a given lag.
## Prediction of the thingies
Y_data = bMA.diff(price, lag=lag, cval=np.NaN)
Y_data = bMA.shift(Y_data, lag=-lag, cval=np.NaN)
## Eliminate the Nans !! Even if they appear in just one dim
mask_X = np.sum(np.isnan(X_data), axis=1) == 0
mask_Y = np.isnan(Y_data) == 0
mask = mask_X & mask_Y[:, 0]
# Create linear regression object
regr = linear_model.LinearRegression()
# Train the model using the training sets
regr.fit(X_data[mask, :], Y_data[mask, :])
# coeffs = np.array([regr.intercept_, regr.coef_])[0]
coeffs = np.append(regr.intercept_, regr.coef_)
params = np.array(coeffs)
residual = regr.residues_
residual = regr.score(X_data[mask, :], Y_data[mask, :])
return residual
# easier for algorithms to learn from it
day_of_week = np.array(data_df.index.dayofweek)
week_of_year = np.array(data_df.index.weekofyear)
year = np.array(data_df.index.year)
## Add the lagged value to the database
Nlag_time_information = 1
tut.add_lagged_values(data_df, day_of_week, "day", Nlag_time_information)
tut.add_lagged_values(data_df, week_of_year, "week", Nlag_time_information)
tut.add_lagged_values(data_df, year, "year", Nlag_time_information)
################ OCHL variables ##################
# Variables trivially obtained from daily OHCL
Target = Target # Increase in Close price
Range_HL = H - L # measure of volatility
Daily_gap = O - bMl.shift(C, lag=1).flatten() # measure of price movement
## Add the lagged value to the database
Nlag_OCHL_information = 3
tut.add_lagged_values(data_df, Target, "Target", Nlag_OCHL_information)
tut.add_lagged_values(data_df, Range_HL, "Range_HL", Nlag_OCHL_information)
tut.add_lagged_values(data_df, Daily_gap, "Daily_gap",
Nlag_OCHL_information)
################## Daily Trading Indicators ####################
# Hulls_average !! ACDC, Volatility, ATR, Short
nHMA = 20
## Hulls Average, reactive but smoothing MA
HMA = indl.get_HMA(timeData_daily.get_timeSeries(["Close"]), nHMA)
## Volatility
## ATR Parameter !
ATR = Cartera.ATR(n=14)
ATR_vel = bMA.diff(ATR, n=1)
RSI_vel = indl.get_SMA(ATR_vel, L=nsmooth_vel)
###########################################################
################# PREPARE THE DATA ########################
###########################################################
X_data = np.concatenate((MACD[:, [indx]], MACD_vel[:, [indx]]), axis=1)
X_data = np.concatenate((X_data, RSI[:, [indx]], ATR[:, [indx]]), axis=1)
X_data = np.concatenate((X_data, RSI_vel[:, [indx]], ATR_vel[:, [indx]]),
axis=1)
Y_data = bMA.diff(prices[:, indx], lag=lag, cval=np.NaN)
Y_data = bMA.shift(Y_data, lag=-lag, cval=np.NaN)
### Returns
lag_ret = 20
return_Ydata = bMA.get_return(prices[:, [indx]], lag=lag_ret)
reconstruct_Ydata = bMA.reconstruc_return(prices[:, [indx]],
return_Ydata,
lag=lag_ret)
gl.plot([], prices[:, [indx]], legend=["price"])
gl.plot([], reconstruct_Ydata, nf=0, legend=["reconstruction"])
gl.plot([], return_Ydata, nf=0, na=1, legend=["return"])
Y_data = return_Ydata
Y_data = bMA.shift(Y_data, lag=-lag, cval=np.NaN)
# we read it with this one.
######## PANDAS FORMAT
folder_dataFeatures = "./data/"
data = pd.read_csv(folder_dataFeatures + "dataPandas.csv", sep = ',', index_col = 0,
dtype = {"Date":dt.datetime})
data.index = ul.str_to_datetime (data.index.tolist())
######## NUMPY ARRAYS FORMAT
X_data = np.loadtxt(folder_dataFeatures + "Xdata.csv", delimiter=",")
price = np.loadtxt(folder_dataFeatures + "price.csv", delimiter=",")
price = price.reshape(Y_data.size,1) # TODO: Just to put it in the sahpe as it was before writing it to disk
dates = np.loadtxt(folder_dataFeatures + "dates.csv", delimiter=",")
## Generate the Y variable to estimate
lag = 20
Y_data = bMA.get_return(price, lag = lag)
Y_data = bMA.shift(Y_data, lag = -lag, cval = np.NaN)
if (model_OLS):
# Makes use of the pandas structures
##############################################################################
# Multilinear regression model, calculating fit, P-values, confidence
# intervals etc.
# Fit the model
model = ols("Y ~ MACD + RSI + ATR + MACD_vel + ATR_vel + RSI_vel", data).fit()
params = model._results.params
# Print the summary
print(model.summary())
print("OLS model Parameters")
print(params)
# Peform analysis of variance on fitted linear model
#anova_results = anova_lm(model)
## Create Pandas Data Frame for the information of the ML problem
data_df = pd.DataFrame({'Time': days_keys, 'Target_clas': Target_bin, 'Target_reg': Target}) # 'Current_diff': Target}
data_df.set_index('Time',inplace = True)
"""
#########################################################
CREATE WINDOWED VECTOR OF FEATURES !!
#########################################################
"""
Nlast_Close = 2 # The last Diff in Close and increase
for i in range(Nlast_Close):
# data_df.shift()
data_df["Diff_prevC_%i"%(i+1)] = bMl.shift(Target,lag = i+1)
# We encode it as categorical !!!
data_df["Diff_prevC_bin_%i"%(i+1)] = bMl.shift(Target_bin,lag = i+1)
# data_df["Diff_prevC_bin_%i"%(i+1)] = pd.Categorical(data_df["Diff_prevC_bin_%i"%(i+1)]).codes
Nlast_Range = 2
for i in range(Nlast_Range):
# data_df.shift()
data_df["Diff_prevRangeHL_%i"%(i+1)] = bMl.shift(H-L,lag = i+1)
data_df["Diff_prevRangeCO_%i"%(i+1)] = bMl.shift(C-O,lag = i+1)
Nlast_Price = 1
for i in range(Nlast_Range):
# data_df.shift()
data_df["prevClose_%i"%(i+1)] = bMl.shift(C,lag = i+1)
def add_lagged_values(df, feature_vector, name_feature, Nlags = 1):
# Function that adds lagged values to the dataframe
for i in range(Nlags):
signal = bMl.shift(feature_vector,lag = i+1).flatten()
# print (type(signal))
df[name_feature + "_%i"%(i+1)] =signal
## Create Pandas Data Frame for the information of the ML problem
data_df = pd.DataFrame({'Time': days_keys, 'Target_clas': Target_bin, 'Target_reg': Target}) # 'Current_diff': Target}
data_df.set_index('Time',inplace = True)
"""
#########################################################
CREATE WINDOWED VECTOR OF FEATURES !!
#########################################################
"""
Nlast_Close = 2 # The last Diff in Close and increase
for i in range(Nlast_Close):
# data_df.shift()
data_df["Diff_prevC_%i"%(i+1)] = bMl.shift(Target,lag = i+1)
# We encode it as categorical !!!
data_df["Diff_prevC_bin_%i"%(i+1)] = bMl.shift(Target_bin,lag = i+1)
# data_df["Diff_prevC_bin_%i"%(i+1)] = pd.Categorical(data_df["Diff_prevC_bin_%i"%(i+1)]).codes
Nlast_Range = 2
for i in range(Nlast_Range):
# data_df.shift()
data_df["Diff_prevRangeHL_%i"%(i+1)] = bMl.shift(H-L,lag = i+1)
data_df["Diff_prevRangeCO_%i"%(i+1)] = bMl.shift(C-O,lag = i+1)
Nlast_Price = 1
for i in range(Nlast_Range):
# data_df.shift()
data_df["prevClose_%i"%(i+1)] = bMl.shift(C,lag = i+1)
# easier for algorithms to learn from it day_of_week = np.array(data_df.index.dayofweek) week_of_year = np.array(data_df.index.weekofyear) year = np.array(data_df.index.year) ## Add the lagged value to the database Nlag_time_information = 1 tut.add_lagged_values(data_df,day_of_week,"day",Nlag_time_information) tut.add_lagged_values(data_df,week_of_year,"week",Nlag_time_information) tut.add_lagged_values(data_df,year,"year",Nlag_time_information) ################ OCHL variables ################## # Variables trivially obtained from daily OHCL Target = Target # Increase in Close price Range_HL = H-L # measure of volatility Daily_gap = O - bMl.shift(C,lag = 1).flatten() # measure of price movement ## Add the lagged value to the database Nlag_OCHL_information = 3 tut.add_lagged_values(data_df,Target,"Target",Nlag_OCHL_information) tut.add_lagged_values(data_df,Range_HL,"Range_HL",Nlag_OCHL_information) tut.add_lagged_values(data_df,Daily_gap,"Daily_gap",Nlag_OCHL_information) ################## Daily Trading Indicators #################### # Hulls_average !! ACDC, Volatility, ATR, Short nHMA = 20 ## Hulls Average, reactive but smoothing MA HMA = indl.get_HMA(timeData_daily.get_timeSeries(["Close"]), nHMA) ## Volatility
def add_lagged_values(df, feature_vector, name_feature, Nlags=1):
# Function that adds lagged values to the dataframe
for i in range(Nlags):
signal = bMl.shift(feature_vector, lag=i + 1).flatten()
# print (type(signal))
df[name_feature + "_%i" % (i + 1)] = signal
