Dev

backtesting.py

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+# Preprocessing
+import numpy as np
+import pandas as pd
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.metrics import precision_score
+from utils import status_calc
+
+
+def backtest():
+    """
+    A simple backtest, which splits the dataset into a train set and test set,
+    then fits a Random Forest classifier to the train set. We print the precision and accuracy
+    of the classifier on the test set, then run a backtest comparing this strategy's performance
+    to passive investment in the S&P500.
+    Please note that there is a methodological flaw in this backtest which will give deceptively
+    good results, so the results here should not encourage you to live trade.
+    """
+    # Build the dataset, and drop any rows with missing values
+    data_df = pd.read_csv("keystats.csv", index_col='Date')
+    data_df.dropna(axis=0, how='any', inplace=True)
+
+    features = data_df.columns[6:]
+    X = data_df[features].values
+    # The labels are generated by applying the status_calc to the dataframe.
+    # '1' if a stock beats the S&P500 by more than 10%, else '0'
+    y = list(
+        map(status_calc, data_df["stock_p_change"], data_df["SP500_p_change"]))
+
+    # z is required for us to track returns
+    z = np.array(data_df[["stock_p_change", "SP500_p_change"]])
+
+    # Generate the train set and test set by randomly splitting the dataset
+    X_train, X_test, y_train, y_test, z_train, z_test = train_test_split(
+        X, y, z, test_size=0.2)
+
+    # Instantiate a RandomForestClassifier with 100 trees, then fit it to the training data
+    clf = RandomForestClassifier(n_estimators=100, random_state=0)
+    clf.fit(X_train, y_train)
+
+    # Generate the predictions, then print test set accuracy and precision
+    y_pred = clf.predict(X_test)
+    print("Classifier performance\n", "=" * 20)
+    print(f"Accuracy score: {clf.score(X_test, y_test): .2f}")
+    print(f"Precision score: {precision_score(y_test, y_pred): .2f}")
+
+    num_positive_predictions = sum(y_pred)
+    if num_positive_predictions < 0:
+        print("No stocks predicted!")
+
+    # Recall that z_test stores the change in stock price in column 0, and the
+    # change in S&P500 price in column 1.
+    # Whenever a stock is predicted to outperform (y_pred = 1), we 'buy' that stock
+    # and simultaneously `buy` the index for comparison.
+    stock_returns = 1 + z_test[y_pred, 0] / 100
+    market_returns = 1 + z_test[y_pred, 1] / 100
+
+    # Calculate the average growth for each stock we predicted 'buy'
+    # and the corresponding index growth
+    avg_predicted_stock_growth = sum(stock_returns) / num_positive_predictions
+    index_growth = sum(market_returns) / num_positive_predictions
+
+    percentage_stock_returns = 100 * (avg_predicted_stock_growth - 1)
+    percentage_market_returns = 100 * (index_growth - 1)
+    total_outperformance = percentage_stock_returns - percentage_market_returns
+
+    print("\n Stock prediction performance report \n", "=" * 40)
+    print(f"Total Trades:", num_positive_predictions)
+    print(
+        f"Average return for stock predictions: {percentage_stock_returns: .1f} %")
+    print(
+        f"Average market return in the same period: {percentage_market_returns: .1f}% ")
+    print(
+        f"Compared to the index, our strategy earns {total_outperformance: .1f} percentage points more")
+
+
+if __name__ == "__main__":
+    backtest()