Python: How can we smooth a noisy signal using mov

For an evaluation of a random forest regression, I am trying to improve a result using a moving average filter after fitting a model using a RandomForestRegressor for a dataset found in this link

import pandas as pd
import math
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import r2_score, mean_squared_error, make_scorer
from sklearn.model_selection import train_test_split
from math import sqrt
from sklearn.cross_validation import train_test_split


n_features=3000

df = pd.read_csv('cubic32.csv')

for i in range(1,n_features):
    df['X_t'+str(i)] = df['X'].shift(i)

print(df)

df.dropna(inplace=True)


X = df.drop('Y', axis=1)
y = df['Y']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.40)

X_train = X_train.drop('time', axis=1)
X_test = X_test.drop('time', axis=1)


parameters = {'n_estimators': [10]}
clf_rf = RandomForestRegressor(random_state=1)
clf = GridSearchCV(clf_rf, parameters, cv=5, scoring='neg_mean_squared_error', n_jobs=-1)
model = clf.fit(X_train, y_train)
model.cv_results_['params'][model.best_index_]
math.sqrt(model.best_score_*-1)
model.grid_scores_

#####
print()
print(model.grid_scores_)
print("The best score: ",model.best_score_)

print("RMSE:",math.sqrt(model.best_score_*-1))

clf_rf.fit(X_train,y_train)
modelPrediction = clf_rf.predict(X_test)
print(modelPrediction)

print("Number of predictions:",len(modelPrediction))

meanSquaredError=mean_squared_error(y_test, modelPrediction)
print("Mean Square Error (MSE):", meanSquaredError)
rootMeanSquaredError = sqrt(meanSquaredError)
print("Root-Mean-Square Error (RMSE):", rootMeanSquaredError)

fig, ax = plt.subplots()
index_values=range(0,len(y_test))

y_test.sort_index(inplace=True)
X_test.sort_index(inplace=True)

modelPred_test = clf_rf.predict(X_test)
ax.plot(pd.Series(index_values), y_test.values)

smoothed = np.convolve(modelPred_test, np.ones(10)/10)
PlotInOne=pd.DataFrame(pd.concat([pd.Series(smoothed), pd.Series(y_test.values)], axis=1))
plt.figure(); PlotInOne.plot(); plt.legend(loc='best')

However, the plot of the predicted values seems (as shown below) to be very coarse (the blue line) even if i am smoothing the prediction values like the following

smoothed = np.convolve(modelPred_test, np.ones(10)/10)

The orange line is a plot of the actual value.

Is there any way that we can penalize the prediction error (or smooth the noise of the signal using a moving average or other smoothing techniques) so that we get a plot closer to the actual value? I am also trying to increase the number of estimators for the random forest tree (n_estimators), but it doesn't seem to improve much. If we plot the actual value alone, it looks like the following.