C++ investment model construction in intelligent investment advisory platform

Question: How is C++ used to build investment models in the robo-advisory platform? Answer: Build a well-component investment model architecture, involving data acquisition, pre-processing, feature engineering, model training, model evaluation and deployment. Train predictive models using machine learning algorithms (such as linear regression, decision trees, neural networks). In the actual case, C++ is used to build a stock prediction model, and prediction and trading decisions are made based on feature engineering and linear regression algorithms.

Title: C++ Investment Model Construction in Robo-Advisory Platform

Introduction

C++ is a powerful programming language that is widely used in financial applications because of its performance, efficiency, and flexibility. In the robo-advisory platform, C++ can be used to build complex investment models to help investors make informed investment decisions.

C++ investment model architecture

A typical C++ investment model usually contains the following components:

• Data acquisition module: Obtain historical and real-time financial data from various sources (e.g. financial databases, market data providers).
• Data Preprocessing Module: Cleans, transforms and standardizes data to make it suitable for modeling.
• Feature engineering module: Extract relevant features from raw data, which can be used to build models.

• Model training module: Use machine learning algorithms to train prediction models, such as:

  • Linear regression
  • Decision tree
  • Neural Network
• Model evaluation module: Use the hold-out data set to evaluate the performance of the trained model, including accuracy, recall, and F1 score.
• Deployment module: Deploy the trained model to the production environment for real-time prediction and trading decisions.

Practical case: Stock prediction model

The following is a practical case using C++ to build a stock prediction model:

// 数据获取模块
auto df = pandas::read_csv("stock_data.csv");

// 数据预处理模块
df["ClosePrice"] = df["ClosePrice"].astype(float);
df["Volume"] = df["Volume"].astype(int);

// 特征工程模块
df["RollingMean"] = df["ClosePrice"].rolling(20).mean()
df["BollingerBands"] = (df["ClosePrice"] - df["RollingMean"]) / (2 * df["ClosePrice"].rolling(20).std())

// 模型训练模块
auto model = sklearn::LinearRegression();
model->fit(df[["RollingMean", "BollingerBands"]], df["ClosePrice"])

// 模型部署模块
auto buy_threshold = -1.0
auto sell_threshold = 1.0
for (auto row in df.itertuples()):
    if row.BollingerBands < buy_threshold:
        print("Buy at", row.ClosePrice)
    elif row.BollingerBands > sell_threshold:
        print("Sell at", row.ClosePrice)

Conclusion

C++ is a powerful language that can be used to build robust and efficient investment models. By implementing data acquisition, preprocessing, feature engineering and model training modules, investors can leverage machine learning algorithms to make informed investment decisions.

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