input: Write a function to create a decision tree model for predicting stock values based on a CSV file containing three months of stock data. The function should load the data, split it into training and testing sets, train the decision tree model, and print the model's accuracy. The function should be named `train_decision_tree` and should take the path to the CSV file as an input parameter.
  
  assert train_decision_tree("stockdata.csv")=='Model accuracy: ' + str(clf.score(X_test, y_test))
output: def train_decision_tree(csv_path):
      import pandas as pd
      from sklearn.model_selection import train_test_split
      from sklearn.tree import DecisionTreeClassifier
      from sklearn.metrics import accuracy_score
  
      data = pd.read_csv(csv_path)
      X = data.drop('target', axis=1)
      y = data['target']
      X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
      clf = DecisionTreeClassifier()
      clf.fit(X_train, y_train)
      accuracy = clf.score(X_test, y_test)
      print('Model accuracy: ' + str(accuracy))